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[
{"id":"01YourFirst","accessed":{"date-parts":[[2016,12,2]]},"citation-key":"01YourFirst","title":"01_Your first component with the IDE |","type":"webpage","URL":"http://self-star.imag.fr/?page_id=196"},
{"id":"02UsingComponent","accessed":{"date-parts":[[2016,12,2]]},"citation-key":"02UsingComponent","title":"02_Using component properties to configure instances |","type":"webpage","URL":"http://self-star.imag.fr/?page_id=198"},
{"id":"03ProvidingUsing","accessed":{"date-parts":[[2016,12,2]]},"citation-key":"03ProvidingUsing","title":"03_Providing and using services |","type":"webpage","URL":"http://self-star.imag.fr/?page_id=204"},
{"id":"04BuildingApplication","accessed":{"date-parts":[[2016,12,2]]},"citation-key":"04BuildingApplication","title":"04_Building an application from multiple bundles |","type":"webpage","URL":"http://self-star.imag.fr/?page_id=206"},
{"id":"05ICasaICasa","accessed":{"date-parts":[[2016,12,2]]},"citation-key":"05ICasaICasa","title":"05_iCasa iCasa Architecture","type":"webpage","URL":"http://adeleresearchgroup.github.io/iCasa/snapshot/architecture.html"},
{"id":"06TutorialFollow","accessed":{"date-parts":[[2016,12,2]]},"citation-key":"06TutorialFollow","title":"06_Tutorial Follow me","type":"webpage","URL":"http://self-star.imag.fr/?page_id=61"},
{"id":"10.1007/11871637_49","abstract":"Multinomial naive Bayes (MNB) is a popular method for document classification due to its computational efficiency and relatively good predictive performance. It has recently been established that predictive performance can be improved further by appropriate data transformations [1,2]. In this paper we present another transformation that is designed to combat a potential problem with the application of MNB to unbalanced datasets. We propose an appropriate correction by adjusting attribute priors. This correction can be implemented as another data normalization step, and we show that it can significantly improve the area under the ROC curve. We also show that the modified version of MNB is very closely related to the simple centroid-based classifier and compare the two methods empirically.","author":[{"family":"Frank","given":"Eibe"},{"family":"Bouckaert","given":"Remco R."}],"citation-key":"10.1007/11871637_49","container-title":"Knowledge discovery in databases: PKDD 2006","editor":[{"family":"Fürnkranz","given":"Johannes"},{"family":"Scheffer","given":"Tobias"},{"family":"Spiliopoulou","given":"Myra"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-540-46048-0","issued":{"date-parts":[[2006]]},"page":"503-510","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","title":"Naive bayes for text classification with unbalanced classes","type":"paper-conference"},
{"id":"10.1007/978-3-030-20948-3_19","abstract":"IoT-technologies allow for the connection of miscellaneous devices, thereby creating a platform that sustains rich data sources. Given the circumstances, it is essential to have decent machinery in order to exploit the existing infrastructure and provide users with personalized services. Among others, recommender systems have been widely used to suggest users additional items that best match their needs and expectation. The use of recommender systems has gained considerable momentum in recent years. Nevertheless, the selection of a proper recommendation technique depends much on the input data as well as the domain of applications. In this work, we present an evaluation of two well-known collaborative-filtering (CF) techniques to build an information system for managing and recommending books in the IoT context. To validate the performance, we conduct a series of experiments on two considerably large datasets. The experimental results lead us to some interesting conclusions. In contrast to many existing studies which state that the item-based CF technique outperforms the user-based CF technique, we found out that there is no distinct winner between them. Furthermore, we confirm that the performance of a CF recommender system may be good with regards to some quality metrics, but not to some others.","author":[{"family":"Nguyen","given":"Phuong T."},{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"}],"citation-key":"10.1007/978-3-030-20948-3_19","container-title":"Advanced information systems engineering workshops","editor":[{"family":"Proper","given":"Henderik A."},{"family":"Stirna","given":"Janis"}],"event-place":"Cham","ISBN":"978-3-030-20948-3","issued":{"date-parts":[[2019]]},"page":"214-226","publisher":"Springer International Publishing","publisher-place":"Cham","title":"Building information systems using collaborative-filtering recommendation techniques","type":"paper-conference"},
{"id":"10.1007/978-3-030-61527-7_21","abstract":"Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem class, e.g., choosing solvers for SAT problems. Benchmark suites for AS usually comprise candidate sets consisting of at most tens of algorithms, whereas in algorithm configuration (AC) and combined algorithm selection and hyperparameter optimization (CASH) problems the number of candidates becomes intractable, impeding to learn effective meta-models and thus requiring costly online performance evaluations. In this paper, we propose the setting of extreme algorithm selection (XAS), which, despite assuming limited time resources and hence excluding online evaluations at prediction time, allows for considering thousands of candidate algorithms and thereby facilitates meta learning. We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic representation, in which both problem instances and algorithms are described in terms of feature vectors. We find this approach to significantly improve over the current state of the art in various metrics.","author":[{"family":"Tornede","given":"Alexander"},{"family":"Wever","given":"Marcel"},{"family":"Hüllermeier","given":"Eyke"}],"citation-key":"10.1007/978-3-030-61527-7_21","container-title":"Discovery science","editor":[{"family":"Appice","given":"Annalisa"},{"family":"Tsoumakas","given":"Grigorios"},{"family":"Manolopoulos","given":"Yannis"},{"family":"Matwin","given":"Stan"}],"event-place":"Cham","ISBN":"978-3-030-61527-7","issued":{"date-parts":[[2020]]},"note":"00011","page":"309–324","publisher":"Springer International Publishing","publisher-place":"Cham","title":"Extreme algorithm selection with dyadic feature representation","type":"paper-conference"},
{"id":"10.1007/978-3-319-19069-3_17","author":[{"family":"Khelladi","given":"Djamel Eddine"},{"family":"Hebig","given":"Regina"},{"family":"Bendraou","given":"Reda"},{"family":"Robin","given":"Jacques"},{"family":"Gervais","given":"Marie-Pierre"}],"citation-key":"10.1007/978-3-319-19069-3_17","container-title":"Advanced information systems engineering","editor":[{"family":"Zdravkovic","given":"Jelena"},{"family":"Kirikova","given":"Marite"},{"family":"Johannesson","given":"Paul"}],"event-place":"Cham","ISBN":"978-3-319-19069-3","issued":{"date-parts":[[2015]]},"page":"263-278","publisher":"Springer International Publishing","publisher-place":"Cham","title":"Detecting complex changes during metamodel evolution","type":"paper-conference"},
{"id":"10.1007/978-3-319-60438-1_47","abstract":"Document clustering plays an important role in several applications. K-Medoids and CLARA are among the most notable algorithms for clustering. These algorithms together with their relatives have been employed widely in clustering problems. In this paper we present a solution to improve the original K-Medoids and CLARA by making change in the way they assign objects to clusters. Experimental results on various document datasets using three distance measures have shown that the approach helps enhance the clustering outcomes substantially as demonstrated by three quality metrics, i.e. Entropy, Purity and F-Measure.","author":[{"family":"Nguyen","given":"Phuong T."},{"family":"Eckert","given":"Kai"},{"family":"Ragone","given":"Azzurra"},{"family":"Di Noia","given":"Tommaso"}],"citation-key":"10.1007/978-3-319-60438-1_47","container-title":"Foundations of intelligent systems","editor":[{"family":"Kryszkiewicz","given":"Marzena"},{"family":"Appice","given":"Annalisa"},{"family":"Ślęzak","given":"Dominik"},{"family":"Rybinski","given":"Henryk"},{"family":"Skowron","given":"Andrzej"},{"family":"Raś","given":"Zbigniew W."}],"event-place":"Cham","ISBN":"978-3-319-60438-1","issued":{"date-parts":[[2017]]},"page":"481-491","publisher":"Springer International Publishing","publisher-place":"Cham","title":"Modification to k-medoids and CLARA for effective document clustering","type":"paper-conference"},
{"id":"10.1007/978-3-319-74730-9_13","author":[{"family":"Cabot","given":"Jordi"},{"family":"Clarisó","given":"Robert"},{"family":"Brambilla","given":"Marco"},{"family":"Gérard","given":"Sébastien"}],"citation-key":"10.1007/978-3-319-74730-9_13","container-title":"Software technologies: Applications and foundations","editor":[{"family":"Seidl","given":"Martina"},{"family":"Zschaler","given":"Steffen"}],"event-place":"Cham","issued":{"date-parts":[[2018]]},"page":"154-160","publisher":"Springer International Publishing","publisher-place":"Cham","title":"Cognifying model-driven software engineering","type":"paper-conference"},
{"id":"10.1007/978-3-319-74730-9_33","abstract":"Deciding if an OSS project meets the required standards for adoption is hard, and keeping up-to-date with a rapidly evolving project is even harder. Making decisions about quality and adoption involves analysing code, documentation, online discussions, and issue trackers. There is too much information to process manually and it is common that uninformed decisions have to be made with detrimental effects. CROSSMINER aims to remedy this by automatically extracting the required knowledge and injecting it into the developers' Integrated Development Environments (IDE), at the time they need it to make design decisions. This allows them to reduce their effort in knowledge acquisition and to increase the quality of their code. CROSSMINER uniquely combines advanced software project analyses with online IDE monitoring. Developers will be monitored to infer which information is timely, based on readily available knowledge stored earlier by a set of advanced offline deep analyses of related OSS projects.","author":[{"family":"Bagnato et. al.","given":"Alessandra"}],"citation-key":"10.1007/978-3-319-74730-9_33","container-title":"Software technologies: Applications and foundations","ISBN":"978-3-319-74730-9","issued":{"date-parts":[[2018]]},"page":"375-384","publisher":"Springer International Publishing","title":"Developer-centric knowledge mining from large open-source software repositories (CROSSMINER)","type":"paper-conference"},
{"id":"10.1007/978-3-540-30104-2_12","abstract":"Recommendation agents employ prediction algorithms to provide users with items that match their interests. In this paper, we describe and evaluate several prediction algorithms, some of which are novel in that they combine user-based and item-based similarity measures derived from either explicit or implicit ratings. We compare both statistical and decision-support accuracy metrics of the algorithms against different levels of data sparsity and different operational thresholds. The first metric evaluates the accuracy in terms of average absolute deviation, while the second evaluates how effectively predictions help users to select high-quality items. Our experimental results indicate better performance of item-based predictions derived from explicit ratings in relation to both metrics. Category-boosted predictions can lead to slightly better predictions when combined with explicit ratings, while implicit ratings (in the sense that we have defined them here) perform much worse than explicit ratings.","author":[{"family":"Papagelis","given":"Manos"},{"family":"Plexousakis","given":"Dimitris"}],"citation-key":"10.1007/978-3-540-30104-2_12","container-title":"Cooperative information agents VIII","editor":[{"family":"Klusch","given":"Matthias"},{"family":"Ossowski","given":"Sascha"},{"family":"Kashyap","given":"Vipul"},{"family":"Unland","given":"Rainer"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-540-30104-2","issued":{"date-parts":[[2004]]},"page":"152-166","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","title":"Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents","type":"paper-conference"},
{"id":"10.1007/978-3-540-30549-1_43","abstract":"This paper presents empirical results for several versions of the multinomial naive Bayes classifier on four text categorization problems, and a way of improving it using locally weighted learning. More specifically, it compares standard multinomial naive Bayes to the recently proposed transformed weight-normalized complement naive Bayes classifier (TWCNB) [1], and shows that some of the modifications included in TWCNB may not be necessary to achieve optimum performance on some datasets. However, it does show that TFIDF conversion and document length normalization are important. It also shows that support vector machines can, in fact, sometimes very significantly outperform both methods. Finally, it shows how the performance of multinomial naive Bayes can be improved using locally weighted learning. However, the overall conclusion of our paper is that support vector machines are still the method of choice if the aim is to maximize accuracy.","author":[{"family":"Kibriya","given":"Ashraf M."},{"family":"Frank","given":"Eibe"},{"family":"Pfahringer","given":"Bernhard"},{"family":"Holmes","given":"Geoffrey"}],"citation-key":"10.1007/978-3-540-30549-1_43","container-title":"AI 2004: Advances in artificial intelligence","editor":[{"family":"Webb","given":"Geoffrey I."},{"family":"Yu","given":"Xinghuo"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-540-30549-1","issued":{"date-parts":[[2005]]},"page":"488-499","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","title":"Multinomial naive bayes for text categorization revisited","type":"paper-conference"},
{"id":"10.1007/978-3-642-03013-0_15","author":[{"family":"Zhong","given":"Hao"},{"family":"Xie","given":"Tao"},{"family":"Zhang","given":"Lu"},{"family":"Pei","given":"Jian"},{"family":"Mei","given":"Hong"}],"citation-key":"10.1007/978-3-642-03013-0_15","container-title":"23rd european conference on object-oriented programming","event-place":"Berlin, Heidelberg","ISBN":"978-3-642-03012-3","issued":{"date-parts":[[2009]]},"page":"318-343","publisher":"Springer","publisher-place":"Berlin, Heidelberg","title":"MAPO: Mining and recommending API usage patterns","type":"paper-conference"},
{"id":"10.1007/978-3-642-37456-2_14","abstract":"We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.","author":[{"family":"Campello","given":"Ricardo J. G. B."},{"family":"Moulavi","given":"Davoud"},{"family":"Sander","given":"Joerg"}],"citation-key":"10.1007/978-3-642-37456-2_14","container-title":"Advances in knowledge discovery and data mining","editor":[{"family":"Pei","given":"Jian"},{"family":"Tseng","given":"Vincent S."},{"family":"Cao","given":"Longbing"},{"family":"Motoda","given":"Hiroshi"},{"family":"Xu","given":"Guandong"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-642-37456-2","issued":{"date-parts":[[2013]]},"page":"160-172","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","title":"Density-based clustering based on hierarchical density estimates","type":"paper-conference"},
{"id":"10.1109/SANER.2017.7884605","author":[{"family":"Zhang","given":"Yun"},{"family":"Lo","given":"David"},{"family":"Kochhar","given":"Pavneet Singh"},{"family":"Xia","given":"Xin"},{"family":"Li","given":"Quanlai"},{"family":"Sun","given":"Jianling"}],"citation-key":"10.1109/SANER.2017.7884605","container-title":"2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)","issued":{"date-parts":[[2017]]},"page":"13-23","title":"Detecting similar repositories on GitHub","type":"article-journal","volume":"00"},
{"id":"11697_100182","author":[{"family":"Di Ruscio","given":"Davide"},{"family":"Etzlstorfer","given":"Juergen"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Schwinger","given":"Wieland"}],"citation-key":"11697_100182","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"Modelling foundations and applications, 12th european conference, ECMFA 2016, held as part of STAF 2016, vienna, austria, july 6-7, 2016, proceedings","DOI":"10.1007/978-3-319-42061-5_15","ISBN":"978-3-319-42060-8","issued":{"date-parts":[[2016]]},"page":"231-246","publisher":"Springer Verlag","title":"Supporting variability exploration and resolution during model migration","type":"paper-conference","URL":"http://springerlink.com/content/0302-9743/copyright/2005/","volume":"9764"},
{"id":"11697_100188","author":[{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Iovino","given":"Ludovico"}],"citation-key":"11697_100188","container-title":"DSM 2015 - proceedings of the workshop on domain-specific modeling","DOI":"10.1145/2846696.2846703","ISBN":"978-1-4503-3903-2","issued":{"date-parts":[[2015]]},"page":"47-54","publisher":"Association for Computing Machinery, Inc","title":"Supporting users to manage breaking and unresolvable changes in coupled evolution","type":"paper-conference"},
{"id":"11697_100190","author":[{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_100190","container-title":"IEEE SOFTWARE","DOI":"10.1109/MS.2015.61","issued":{"date-parts":[[2015]]},"page":"28-34","title":"Collaborative repositories in model-driven engineering","type":"article-journal","URL":"http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=52","volume":"32"},
{"id":"11697_10461","author":[{"family":"Ruscio","given":"Davide Di"},{"literal":"Pelliccione"},{"literal":"P"},{"family":"Alfonso","given":"Pierantonio"}],"citation-key":"11697_10461","container-title":"ERCIM NEWS","issued":{"date-parts":[[2012]]},"page":"319-342","title":"Managing the evolution of FOSS systems","type":"article-journal","URL":"http://ercim-news.ercim.eu/en88/special/managing-the-evolution-of-foss-systems","volume":"88"},
{"id":"11697_106776","abstract":"Robots are meant to replace humans for a broad variety of everyday tasks, such as environmental monitoring or patrolling large public areas for security assurance. The main focus of researchers and practitioners has been on providing tailored software and hardware solutions for very specific and often complex tasks. On one hand, these solutions show great potential and provide advanced capabilities for solving the specific task. On the other hand, the polarized attention to task-specific solutions makes them hard to reuse, customize, and combine. In this paper we propose a family of domain-specific modeling languages for the specification of civilian missions of mobile multi-robot systems. These missions are meant to be described in terms of models that are: 1) closer to the general problem domain; 2) independent from the underlying technologies; 3) ready to be analyzed, simulated, and executed; and 4) extensible to new application domains, thus opening up the use of robots to even non-technical operators. Moreover, we show the applicability of the proposed family of languages in two real-world application domains: unmanned multicopters and autonomous underwater vehicles.","author":[{"literal":"Ciccozzi"},{"literal":"Federico"},{"family":"Davide","given":"Di Ruscio"},{"family":"Malavolta","given":"Ivano"},{"family":"Pelliccione","given":"Patrizio"}],"citation-key":"11697_106776","container-title":"IEEE ACCESS","DOI":"10.1109/ACCESS.2016.2613642","issued":{"date-parts":[[2016]]},"page":"6451-6466","title":"Adopting MDE for specifying and executing civilian missions of mobile multi-robot systems","type":"article-journal","volume":"4"},
{"id":"11697_106777","author":[{"family":"Di Ruscio","given":"Davide"},{"family":"Pelliccione","given":"Patrizio"}],"citation-key":"11697_106777","container-title":"JOURNAL OF SOFTWARE","DOI":"10.1002/smr.1716","issued":{"date-parts":[[2015]]},"page":"294-318","title":"A model-driven approach to detect faults in FOSS systems","type":"article-journal","URL":"http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2047-7481","volume":"27"},
{"id":"11697_106779","author":[{"family":"Ciccozzi","given":"Federico"},{"family":"Crnkovic","given":"Ivica"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Malavolta","given":"Ivano"},{"family":"Pelliccione","given":"Patrizio"},{"family":"Spalazzese","given":"Romina"}],"citation-key":"11697_106779","container-title":"IEEE SOFTWARE","DOI":"10.1109/MS.2017.1","issued":{"date-parts":[[2017]]},"page":"46-53","title":"Model-driven engineering for mission-critical IoT systems","type":"article-journal","volume":"34"},
{"id":"11697_107894","author":[{"family":"Basciani","given":"Francesco"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_107894","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"17th international conference, MODELS 2014","issued":{"date-parts":[[2014]]},"page":"602-618","publisher":"Springer Verlag","title":"Automated chaining of model transformations with incompatible metamodels","type":"paper-conference","URL":"http://springerlink.com/content/0302-9743/copyright/2005/","volume":"8767"},
{"id":"11697_107911","author":[{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_107911","container-title":"6th international workshop on modeling in software engineering, MiSE 2014 - proceedings","DOI":"10.1145/2593770.2593774","ISBN":"978-1-4503-2849-4","issued":{"date-parts":[[2014]]},"page":"55-60","publisher":"Association for Computing Machinery, Inc","title":"Mining metrics for understanding metamodel characteristics","type":"paper-conference"},
{"id":"11697_107914","author":[{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_107914","container-title":"Proceedings - 7th international workshop on modeling in software engineering, MiSE 2015","DOI":"10.1109/MiSE.2015.17","ISBN":"978-1-4799-1934-5","issued":{"date-parts":[[2015]]},"page":"54-59","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Mining correlations of ATL model transformation and metamodel metrics","type":"paper-conference"},
{"id":"11697_107917","author":[{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_107917","container-title":"Proceedings of the 2nd workshop on graphical modeling language development, GMLD 2013 - in conjunction with european conference on modelling foundations and applications, ECMFA 2013","DOI":"10.1145/2489820.2489824","ISBN":"978-1-4503-2044-3","issued":{"date-parts":[[2013]]},"page":"51-62","title":"Traceability visualization in metamodel change impact detection","type":"paper-conference"},
{"id":"11697_110553","author":[{"family":"Autili","given":"Marco"},{"family":"Bertolino","given":"Antonia"},{"family":"DE ANGELIS","given":"Guglielmo"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Di Sandro","given":"Alessio"}],"citation-key":"11697_110553","container-title":"IEEE TRANSACTIONS ON SOFTWARE ENGINEERING","DOI":"10.1109/TSE.2015.2449319","issued":{"date-parts":[[2016]]},"page":"2-25","title":"A tool-supported methodology for validation and refinement of early-stage domain models","type":"article-journal","URL":"http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=32","volume":"42"},
{"id":"11697_110621","author":[{"family":"Autili","given":"Marco"},{"family":"Di Ruscio","given":"Davide"},{"family":"Di Salle","given":"Amleto"},{"family":"Perucci","given":"Alexander"}],"citation-key":"11697_110621","container-title":"Proceedings of the ACM SIGSOFT symposium on the foundations of software engineering","DOI":"10.1145/2635868.2661667","ISBN":"978-1-4503-3056-5","issued":{"date-parts":[[2014]]},"page":"723-726","publisher":"Association for Computing Machinery","title":"CHOReOSynt: Enforcing choreography realizability in the future internet","type":"paper-conference"},
{"id":"11697_110934","author":[{"family":"Di Ruscio","given":"Davide"},{"family":"Lara","given":"Juan","non-dropping-particle":"de"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_110934","container-title":"COMPUTER LANGUAGES, SYSTEMS & STRUCTURES","DOI":"10.1016/j.cl.2016.12.003","issued":{"date-parts":[[2017]]},"title":"Special issue on flexible model driven engineering","type":"article-journal","URL":"www.elsevier.com/locate/complang"},
{"id":"11697_111411","author":[{"family":"Addazi","given":"Lorenzo"},{"family":"Cicchetti","given":"Antonio"},{"family":"DI ROCCO","given":"Juri"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_111411","collection-title":"CEUR WORKSHOP PROCEEDINGS","container-title":"Proceedings of the 10th workshop on models and evolution co-located with ACM/IEEE 19th international conference on model driven engineering languages and systems (MODELS 2016), saint-malo, france, october 2, 2016.","issued":{"date-parts":[[2016]]},"page":"40-49","publisher":"CEUR-WS","title":"Semantic-based model matching with EMFcompare","type":"paper-conference","URL":"http://ceur-ws.org/","volume":"1706"},
{"id":"11697_111412","author":[{"family":"Di Ruscio","given":"Davide"},{"family":"Kolovos","given":"Dimitrios S."},{"family":"Korkontzelos","given":"Yannis"},{"family":"Matragkas","given":"Nicholas"},{"family":"Vinju","given":"Jurgen"}],"citation-key":"11697_111412","container-title":"Proceedings - 2016 10th international conference on the quality of information and communications technology, QUATIC 2016","DOI":"10.1109/QUATIC.2016.026","ISBN":"978-1-5090-3581-6","issued":{"date-parts":[[2016]]},"page":"94-99","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Supporting custom quality models to analyse and compare open-source software","type":"paper-conference"},
{"id":"11697_111413","author":[{"family":"Basciani","given":"Francesco"},{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_111413","container-title":"Proceedings - 2016 10th international conference on the quality of information and communications technology, QUATIC 2016","DOI":"10.1109/QUATIC.2016.025","ISBN":"978-1-5090-3581-6","issued":{"date-parts":[[2016]]},"page":"88-93","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A customizable approach for the automated quality assessment of modelling artifacts","type":"paper-conference"},
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{"id":"11697_121412","author":[{"family":"DI ROCCO","given":"Juri"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Heinz","given":"Marcel"},{"family":"Iovino","given":"Ludovico"},{"family":"Laemmel","given":"Ralf"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_121412","container-title":"Proceedings of MODELS 2017 satellite event: Workshops (ModComp,ME, EXE, COMMitMDE, MRT, MULTI, GEMOC, MoDeVVa, MDETools, FlexMDE,MDEbug), posters, doctoral symposium, educator symposium, ACM StudentResearch competition, and tools and demonstrations co-located withACM/IEEE 20th international conference on model driven EngineeringLanguages and systems (MODELS 2017), austin, TX, USA, September,17, 2017.","issued":{"date-parts":[[2017]]},"page":"116-122","publisher":"CEUR-WS.org","title":"Consistency recovery in interactive modeling","type":"paper-conference","URL":"http://ceur-ws.org/Vol-2019/exe_6.pdf","volume":"2019"},
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{"id":"11697_126147","author":[{"family":"Basciani","given":"Francesco"},{"family":"Demidio","given":"Mattia"},{"family":"Di Ruscio","given":"Davide"},{"family":"Frigioni","given":"Daniele"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_126147","container-title":"IEEE TRANSACTIONS ON SOFTWARE ENGINEERING","DOI":"10.1109/TSE.2018.2846223","issued":{"date-parts":[[2018]]},"page":"1-1","title":"Automated selection of optimal model transformation chains via shortest-path algorithms","type":"article-journal","URL":"http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=32"},
{"id":"11697_128217","abstract":"Nowadays, collaborative modeling performed by multiple stakeholders is gaining a growing interest in both academia and practice. However, it poses a set of research challenges, such as large and complex models management, support for multi-user modeling environments, and synchronization mechanisms like models migration and merging, conflicts management, models versioning and rollback support. A body of knowledge in the scientific literature about collaborative model-driven software engineering (MDSE) exists. Still, those studies are scattered across different independent research areas, such as software engineering, model-driven engineering languages and systems, model integrated computing, etc., and a study classifying and comparing the various approaches and methods for collaborative MDSE is still missing. Under this perspective, a systematic mapping study (SMS) can help researchers and practitioners in (i) having a complete, comprehensive and valid picture of the state of the art about collaborative MDSE, and (ii) identifying potential gaps in current research and future research directions.","author":[{"family":"Franzago","given":"MIRCO GIOVANNI UMBERTO"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Malavolta","given":"Ivano"},{"family":"Muccini","given":"Henry"}],"citation-key":"11697_128217","issued":{"date-parts":[[2016]]},"publisher":"arXiv","title":"Protocol for a systematic mapping study on collaborative model-driven software engineering","type":"book","URL":"http://arxiv.org/abs/1611.02619v1"},
{"id":"11697_128309","author":[{"family":"Afzal","given":"Wasif"},{"family":"Bruneliere","given":"Hugo"},{"family":"Di Ruscio","given":"Davide"},{"family":"Sadovykh","given":"Andrey"},{"family":"Mazzini","given":"Silvia"},{"family":"Cariou","given":"Eric"},{"family":"Truscan","given":"Dragos"},{"family":"Cabot","given":"Jordi"},{"family":"Gómez","given":"Abel"},{"family":"Gorroñogoitia","given":"Jesús"},{"family":"Pomante","given":"Luigi"},{"family":"Smrz","given":"Pavel"}],"citation-key":"11697_128309","container-title":"MICROPROCESSORS AND MICROSYSTEMS","DOI":"10.1016/j.micpro.2018.05.010","issued":{"date-parts":[[2018]]},"page":"86-95","title":"The MegaM@Rt2 ECSEL project: MegaModelling at Runtime – Scalable model-based framework for continuous development and runtime validation of complex systems","type":"article-journal","volume":"61"},
{"id":"11697_128310","author":[{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Narayanankutty","given":"Hrishikesh"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_128310","collection-title":"CEUR WORKSHOP PROCEEDINGS","container-title":"CEUR workshop proceedings","issued":{"date-parts":[[2018]]},"publisher":"CEUR-WS","title":"Resilience in sirius editors: Understanding the impact of meta-model changes","type":"paper-conference","URL":"http://ceur-ws.org/","volume":"2192"},
{"id":"11697_128311","author":[{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Härtel","given":"Johannes"},{"family":"Iovino","given":"Ludovico"},{"family":"Lämmel","given":"Ralf"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_128311","collection-title":"LECTURE NOTES IN ARTIFICIAL INTELLIGENCE","container-title":"Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics)","DOI":"10.1007/978-3-319-93317-7_5","ISBN":"978-3-319-93316-0","issued":{"date-parts":[[2018]]},"page":"110-126","publisher":"Springer Verlag","title":"Systematic recovery of MDE technology usage","type":"paper-conference","URL":"https://www.springer.com/series/558","volume":"10888"},
{"id":"11697_128312","author":[{"family":"Nguyen","given":"Phuong T."},{"family":"Di Rocco","given":"Juri"},{"family":"Rubei","given":"Riccardo"},{"family":"Di Ruscio","given":"Davide"}],"citation-key":"11697_128312","container-title":"44th euromicro conference on software engineering and advanced applications","DOI":"10.1109/SEAA.2018.00069","ISBN":"978-1-5386-7383-6","issued":{"date-parts":[[2018]]},"page":"388-395","title":"CrossSim: Exploiting mutual relationships to detect similar OSS projects","type":"paper-conference"},
{"id":"11697_128313","author":[{"family":"Basciani","given":"Francesco"},{"family":"Di Ruscio","given":"Davide"},{"family":"D'Emidio","given":"Mattia"},{"family":"Frigioni","given":"Daniele"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Iovino","given":"Ludovico"}],"citation-key":"11697_128313","container-title":"Proceedings of the 21st ACM/IEEE international conference on model driven engineering languages and systems: Companion proceedings","DOI":"10.1145/3270112.3270123","ISBN":"978-1-4503-5965-8","issued":{"date-parts":[[2018]]},"page":"2-6","title":"A tool for automatically selecting optimal model transformation chains","type":"paper-conference"},
{"id":"11697_132211","author":[{"family":"Bettini","given":"Lorenzo"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_132211","container-title":"IEEE ACCESS","DOI":"10.1109/ACCESS.2019.2891357","issued":{"date-parts":[[2019]]},"page":"16364-16376","title":"Quality-driven detection and resolution of metamodel smells","type":"article-journal","URL":"http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639","volume":"7"},
{"id":"11697_132481","author":[{"family":"Bozhinoski","given":"Darko"},{"family":"Di Ruscio","given":"Davide"},{"family":"Malavolta","given":"Ivano"},{"family":"Pelliccione","given":"Patrizio"},{"family":"Crnkovic","given":"Ivica"}],"citation-key":"11697_132481","container-title":"THE JOURNAL OF SYSTEMS AND SOFTWARE","DOI":"10.1016/j.jss.2019.02.021","issued":{"date-parts":[[2019]]},"page":"150-179","title":"Safety for mobile robotic system: A systematic mapping study from a software engineering perspective","type":"article-journal","volume":"151"},
{"id":"11697_134269","author":[{"family":"Di Ruscio","given":"Davide"},{"family":"Franzago","given":"Mirco"},{"family":"Muccini","given":"Henry"},{"family":"Malavolta","given":"Ivano"}],"citation-key":"11697_134269","container-title":"40th international conference on software engineering","DOI":"10.1145/3180155.3182543","ISBN":"978-1-4503-5638-1","issued":{"date-parts":[[2018]]},"page":"535-535","title":"Collaborative model-driven software engineering","type":"paper-conference"},
{"id":"11697_134405","author":[{"family":"Basciani","given":"Francesco"},{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_134405","container-title":"JOURNAL OF COMPUTER LANGUAGES","DOI":"10.1016/j.cola.2019.02.003","issued":{"date-parts":[[2019]]},"page":"173-192","title":"A tool-supported approach for assessing the quality of modeling artifacts","type":"article-journal","URL":"https://www.journals.elsevier.com/journal-of-computer-languages","volume":"51"},
{"id":"11697_135647","author":[{"family":"DE LARA","given":"Juan"},{"family":"Guerra","given":"Esther"},{"family":"DI RUSCIO","given":"Davide"},{"family":"DI ROCCO","given":"Juri"},{"family":"SANCHEZ CUADRADO","given":"Jesus"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_135647","container-title":"ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY","issued":{"date-parts":[[9999]]},"title":"Automated reuse of model transformations through typing requirements models","type":"article-journal"},
{"id":"11697_136218","author":[{"family":"DI ROCCO","given":"Juri"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Härtel","given":"Johannes"},{"family":"Iovino","given":"Ludovico"},{"family":"Lämmel","given":"Ralf"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_136218","container-title":"SOFTWARE AND SYSTEMS MODELING","DOI":"10.1007/s10270-019-00748-7","issued":{"date-parts":[[2019]]},"page":"1-23","title":"Understanding MDE projects: megamodels to the rescue for architecture recovery","type":"article-journal","URL":"http://link.springer.com/article/10.1007/s10270-019-00748-7"},
{"id":"11697_13879","author":[{"family":"Di Ruscio","given":"D"},{"family":"Pelliccione","given":"P"}],"citation-key":"11697_13879","container-title":"INFORMATION AND SOFTWARE TECHNOLOGY","issued":{"date-parts":[[2014]]},"page":"438-462","title":"Simulating upgrades of complex systems: The case of Free and Open Source Software","type":"article-journal","volume":"56"},
{"id":"11697_19479","abstract":"The adoption of Model-Driven Engineering (MDE) in the development of Web Applications permitted to decouple the functional description of applications from the underlying implementation platform. This is of paramount relevance for preserving the intellectual property encoded in models and making applications, languages and processes resilient to technological changes. This paper proposes a model-driven approach for supporting the migration and evolution of data-intensive Web applications. In particular, model differencing techniques are considered to realize a migration facility capable of detecting the modifications a model underwent during its lifecycle and to automatically derive from them the programs that are capable of migrating/adapting also those aspects which are not directly derivable from the source models, as for instance the data persistently stored in a database and the page layout usually written using graphic templates. The approach is validated by considering applications described with the beContent and WebML modeling languages.","author":[{"family":"Cicchetti","given":"A"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Iovino","given":"L"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_19479","container-title":"SOFTWARE AND SYSTEMS MODELING","DOI":"10.1007/s10270-011-0193-0","issued":{"date-parts":[[2012]]},"page":"1-31","title":"Managing the evolution of data-intensive Web applications by model-driven techniques","type":"article-journal","URL":"http://www.springerlink.com/content/3560j06140344197/","volume":"12"},
{"id":"11697_26169","author":[{"literal":"ANTONIO"},{"literal":"BUCCHIARONE"},{"family":"RUSCIO","given":"DAVIDE DI"},{"family":"HENRY","given":"MUCCINI"},{"family":"PELLICCIONE","given":"PATRIZIO"}],"citation-key":"11697_26169","container-title":"Model-driven software development: Integrating quality assurance","DOI":"abs/0910.0493","ISBN":"978-1-60566-006-6","issued":{"date-parts":[[2008]]},"title":"From requirements to Java code: An architecture-centric approach for producing quality systems","type":"chapter","URL":"journals/corr/abs-0910-0493","volume":"abs/0910.0493"},
{"id":"11697_26888","author":[{"literal":"ANTONIO"},{"literal":"CICCHETTI"},{"family":"RUSCIO","given":"DAVIDE DI"},{"family":"PELLICCIONE","given":"P"},{"family":"ALFONSO","given":"PIERANTONIO"},{"family":"STEFANO","given":"ZACCHIROLI"}],"citation-key":"11697_26888","container-title":"COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE","DOI":"10.1007/978-3-642-14819-4","event-place":"HEIDELBERG","ISBN":"978-3-642-14818-7","issued":{"date-parts":[[2010]]},"page":"262-276","publisher":"SPRINGER","publisher-place":"HEIDELBERG","title":"A model driven approach to upgrade package based software systems","type":"chapter","volume":"69"},
{"id":"11697_287","author":[{"family":"Balzerani","given":"L"},{"family":"Di Ruscio","given":"D"},{"family":"Pierantonio","given":"A"},{"family":"De Angelis","given":"G"}],"citation-key":"11697_287","container-title":"JOURNAL OF WEB ENGINEERING","issued":{"date-parts":[[2006]]},"page":"25-42","title":"Supporting Web applications development with a product line architecture","type":"article-journal","volume":"5"},
{"id":"11697_29795","author":[{"family":"Ruscio","given":"Davide Di"},{"literal":"Ivano"},{"literal":"Malavolta"},{"family":"Muccini","given":"H"},{"family":"Patrizio","given":"Pelliccione"},{"family":"Alfonso","given":"Pierantonio"}],"citation-key":"11697_29795","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"4th european conference on software architecture, ECSA 2010","event-place":"BERLIN HEIDELBERG","ISBN":"978-3-642-15113-2","issued":{"date-parts":[[2010]]},"page":"527-531","publisher":"Springer","publisher-place":"BERLIN HEIDELBERG","title":"ByADL: An MDE framework for building extensible architecture description languages","type":"paper-conference","volume":"Lecture Notes in Computer Science 6285"},
{"id":"11697_30086","author":[{"family":"Cicchetti","given":"A"},{"family":"Di Ruscio","given":"D"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_30086","container-title":"Proceedings of the ACM symposium on applied computing","issued":{"date-parts":[[2006]]},"title":"Weaving concerns in model based development of data-intensive Web applications","type":"paper-conference"},
{"id":"11697_30601","author":[{"literal":"ANTONIO"},{"literal":"CICCHETTI"},{"family":"RUSCIO","given":"DAVIDE DI"},{"family":"PIERANTONIO","given":"A"}],"citation-key":"11697_30601","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"Proc. of the ACM/IEEE 11th international conference on model driven engineering languages and systems (MODELS 2008)","DOI":"10.1007/978-3-540-87875-9_23","issued":{"date-parts":[[2008]]},"page":"311-325","title":"Managing model conflicts in distributed development","type":"paper-conference","volume":"5301"},
{"id":"11697_30610","author":[{"family":"Balzerani","given":"L"},{"family":"Di Ruscio","given":"D"},{"family":"Pierantonio","given":"A"},{"family":"De Angelis","given":"G"}],"citation-key":"11697_30610","container-title":"Proc. ACM symposium on applied computing (SAC 2005), special track on web technologies and applications, ACM press","DOI":"10.1145/1066677.1067059","issued":{"date-parts":[[2005]]},"title":"A product line architecture for web applications","type":"paper-conference"},
{"id":"11697_30657","author":[{"family":"CICCHETTI","given":"A."},{"family":"DI RUSCIO","given":"D."},{"family":"PIERANTONIO","given":"A"}],"citation-key":"11697_30657","container-title":"1st european workshop on composition of model transformations - CMT 2006","issued":{"date-parts":[[2006]]},"title":"Composition of model differences","type":"paper-conference"},
{"id":"11697_31176","author":[{"family":"Cicchetti","given":"A"},{"family":"Di Ruscio","given":"D"},{"family":"Pelliccione","given":"P"},{"family":"Pierantonio","given":"A"},{"family":"Zacchiroli","given":"S"}],"citation-key":"11697_31176","container-title":"ENASE 2009 - 4th international conference on evaluation of novel approaches to software engineering, proceedings","ISBN":"978-989-8111-98-2","issued":{"date-parts":[[2009]]},"page":"121-133","publisher":"Elsevier B.V.","title":"Towards a model driven approach to upgrade complex software systems","type":"paper-conference"},
{"id":"11697_32865","abstract":"The current practice of software architecture modeling and analysis would benefit of using different architectural languages, each specialized on a particular view and each enabling specific analysis. Thus, it is fundamental to pursue architectural language interoperability. An approach for enabling interoperability consists in defining a transformation from each single notation to a pivot language, and vice versa. When the pivot assumes the form of a small and abstract kernel, extension mechanisms are required to compensate the loss of information. The aim of this paper is to enhance architectural languages interoperability by means of hierarchies of pivot languages obtained by systematically extending a root pivot language. Model-driven techniques are employed to support the creation and the management of such hierarchies and to realize the interoperability by means of model transformations. Even though the approach is applied to the software architecture domain, it is completely general.","author":[{"family":"Ruscio","given":"Davide Di"},{"literal":"Ivano"},{"literal":"Malavolta"},{"family":"Muccini","given":"H"},{"family":"Patrizio","given":"Pelliccione"},{"family":"Alfonso","given":"Pierantonio"}],"citation-key":"11697_32865","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"15th international conference on fundamental approaches to software engineering (FASE)","DOI":"10.1007/978-3-642-28872-2_2","event-place":"BERLIN HEIDELBERG","ISBN":"978-3-642-28871-5","issued":{"date-parts":[[2012]]},"page":"26-42","publisher":"Springer-Verlag","publisher-place":"BERLIN HEIDELBERG","title":"Model-driven techniques to enhance architectural languages interoperability","type":"paper-conference","URL":"http://link.springer.com/chapter/10.1007/978-3-642-28872-2_2","volume":"7212"},
{"id":"11697_34038","author":[{"family":"Cicchetti","given":"A"},{"family":"Di Ruscio","given":"D"},{"family":"Kolovos","given":"D"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_34038","container-title":"Emerging technologies for the evolution and maintenance of software models","DOI":"10.4018/978-1-61350-438-3.ch012","event-place":"NEY YORK","issued":{"date-parts":[[2012]]},"page":"319-342","publisher":"IGI Global","publisher-place":"NEY YORK","title":"A test-driven approach for metamodel development","type":"chapter"},
{"id":"11697_35267","author":[{"family":"Caporuscio","given":"M"},{"family":"DI RUSCIO","given":"D"},{"family":"Inverardi","given":"P"},{"family":"Pelliccione","given":"P"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_35267","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"Software architecture, 2nd european workshop, EWSA 2005","ISBN":"3-540-26275","issued":{"date-parts":[[2005]]},"page":"130-145","publisher":"Springer - LNCS series","title":"Engineering MDA into compositional reasoning for analyzing middleware-based applications","type":"paper-conference","volume":"3527"},
{"id":"11697_37088","abstract":"Increasingly, recording the various kinds of design-level structural evolution that a system undergoes throughout its entire life-cycle is gaining relevance in software modeling and development. In this respect, an interesting and useful operation between subsequent system versions is model difference consisting in calculation, representation, and visualization. This work shows how to generalize the application of differences, represented as first-class artefacts, in order to abstract from persistent identifiers and enable more flexibility. Then, modifications can be applied as model patches to arbitrary models according to weaving specifications.","author":[{"family":"Cicchetti","given":"A"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_37088","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"Models in software engineering, workshops and symposia at MODELS 2009","ISBN":"978-3-642-12260-6","issued":{"date-parts":[[2010]]},"page":"190-204","title":"Model patches in model-driven engineering","type":"paper-conference","volume":"6002"},
{"id":"11697_37099","abstract":"Despite the flourishing of languages to describe software architectures, existing Architecture Description Languages (ADLs) are still far away from what it is actually needed. In fact, while they support a traditional perception of a Software Architecture (SA) as a set of constituting elements (such as components, connectors and interfaces), they mostly fail to capture multiple stakeholders concerns and their design decisions that represent a broader view of SA being accepted today. Next generation ADLs must cope with various and ever evolving stakeholder concerns by employing semantic extension mechanisms. In this paper we present a framework, called BYADL – Build Your ADL, for developing a new generation of ADLs. BYADL ex- ploits model-driven techniques that provide the needed technologies to allow a software architect, starting from existing ADLs, to define its own new generation ADL by: i) adding domain specificities, new architectural views, or analysis aspects, ii) integrating ADLs with development processes and methodologies, and iii) customizing ADLs by fine tuning them. The framework is put in practice in different scenarios showing the incremental extension and customization of the Darwin ADL.","author":[{"family":"Di Ruscio","given":"D"},{"family":"Malavolta","given":"I"},{"family":"Muccini","given":"H"},{"family":"Pelliccione","given":"P"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_37099","collection-title":"PROCEEDINGS - INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING","container-title":"Proceedings - international conference on software engineering","DOI":"10.1145/1806799.1806816","event-place":"NEW YORK, NY, USA","ISBN":"978-1-60558-719-6","issued":{"date-parts":[[2010]]},"page":"85-94","publisher":"Association for Computing Machinery, Inc. (ACM)","publisher-place":"NEW YORK, NY, USA","title":"Developing next generation ADLs through MDE techniques","type":"paper-conference"},
{"id":"11697_37101","author":[{"family":"Cicchetti","given":"A"},{"family":"Di Ruscio","given":"D"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_37101","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"Theory and practice of model transformations, second international conference, ICMT 2009","DOI":"10.1007/978-3-642-02408-5_4","ISBN":"978-3-642-02407-8","issued":{"date-parts":[[2009]]},"page":"35-51","title":"Managing dependent changes in coupled evolution","type":"paper-conference","volume":"5563"},
{"id":"11697_37103","author":[{"family":"Cicchetti","given":"A"},{"family":"Di Ruscio","given":"D"},{"family":"Eramo","given":"R"},{"family":"Maccarrone","given":"F"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_37103","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"Web engineering, 9th international conference, ICWE 2009","DOI":"10.1007/978-3-642-02818-2_52","issued":{"date-parts":[[2009]]},"page":"518-522","title":"BeContent: A model-driven platform for designing and maintaining web applications","type":"paper-conference","volume":"5648"},
{"id":"11697_37139","author":[{"family":"Cicchetti","given":"A"},{"family":"Di Ruscio","given":"D"},{"family":"Eramo","given":"R"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_37139","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"Third international conference, SLE 2010, eindhoven, the netherlands","ISBN":"978-3-642-19439-9","issued":{"date-parts":[[2010]]},"page":"183-202","title":"JTL: A bidirectional and change propagating transformation language","type":"paper-conference","volume":"6563"},
{"id":"11697_37543","author":[{"family":"Di Ruscio","given":"D"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_37543","container-title":"Proc. 17th conference on advanced information systems engineering (CAiSE'05), springer LNCS","issued":{"date-parts":[[2005]]},"page":"475-490","title":"Model transformations in the development of data-intensive web applications","type":"paper-conference","volume":"3520"},
{"id":"11697_37559","author":[{"family":"Di Ruscio","given":"D"},{"family":"Muccini","given":"H"},{"family":"Pierantonio","given":"A"},{"family":"Pelliccione","given":"P"}],"citation-key":"11697_37559","container-title":"Joint meeting of the fourth workshop on model-based development of computer-based systems and third international workshop on model-based methodologies for pervasive and embedded software, proceedings","DOI":"10.1109/MBD-MOMPES.2006.24","event-place":"NEW YORK","ISBN":"0-7695-2538-5","issued":{"date-parts":[[2006]]},"publisher":"IEEE Computer Society","publisher-place":"NEW YORK","title":"Towards weaving software architecture models","type":"paper-conference"},
{"id":"11697_38176","author":[{"family":"Di Ruscio","given":"D"},{"family":"Pelliccione","given":"P"},{"family":"Pierantonio","given":"A"},{"family":"Zacchiroli","given":"S"}],"citation-key":"11697_38176","container-title":"IWOCE 2009: INTERNATIONAL WORKSHOP ON OPEN COMPONENT ECOSYSTEM","DOI":"10.1145/1595800.1595803","event-place":"NEW YORK, NY, USA","ISBN":"978-1-60558-677-9","issued":{"date-parts":[[2009]]},"page":"11-20","publisher":"Association for Computing Machinery, Inc. (ACM)","publisher-place":"NEW YORK, NY, USA","title":"Towards maintainer script modernization in FOSS distributions","type":"paper-conference"},
{"id":"11697_39261","author":[{"family":"Ruscio","given":"Davide Di"},{"literal":"Pelliccione"},{"literal":"P"}],"citation-key":"11697_39261","container-title":"V conferenza italiana sul software libero - milano 23-24 giugno 2011","issued":{"date-parts":[[2011]]},"title":"Managing the evolution of free and open source software complex systems","type":"paper-conference"},
{"id":"11697_40934","abstract":"Choreographies are an emergent Service Engineering approach to compose together and coordinate distributed services. They represent a global specication of the interactions between the participant services. BPMN2 provides a dedicated notation, called Choreography Diagrams, to dene choreographies. This paper presents a model transformation to automatically transform a BPMN2 choreography speci cation into an automata-based representation called Choreography LTS (CLTS). The latter is a LTS suitably extended to, on one side model the complex interactions that can be specied by choreography diagrams, on the other provide modelers with a means to precisely extract the not-easy-to-grasp coordination logic \" into BPMN2 Choreography Diagrams. Dedicated Eclipse plugins, within the CHOReOSynt tool, have been developed to support the presented transformation.","author":[{"family":"Autili","given":"Marco"},{"family":"DI RUSCIO","given":"Davide"},{"family":"DI SALLE","given":"Amleto"},{"family":"Inverardi","given":"Paola"}],"citation-key":"11697_40934","container-title":"1st international workshop on model-driven engineering for component-based software systems","event-place":"AACHEN","issued":{"date-parts":[[2014]]},"page":"67-77","publisher":"CEUR-WS.org","publisher-place":"AACHEN","title":"Synthesizing an automata-based representation of BPMN2 choreography diagrams","type":"paper-conference","URL":"http://ceur-ws.org/Vol-1281/7.pdf","volume":"1281"},
{"id":"11697_4393","author":[{"family":"DI RUSCIO","given":"Davide"},{"family":"Paige","given":"R"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_4393","container-title":"SCIENCE OF COMPUTER PROGRAMMING","DOI":"10.1016/j.scico.2013.12.006","issued":{"date-parts":[[2014]]},"title":"Guest editorial to the special issue on Success Stories in Model Driven Engineering","type":"article-journal"},
{"id":"11697_89156","abstract":"This paper overviews Mancoosi, an European project in the 7th Research Framework Programme (FP7) of the European Commission, on managing software complexity. The focus of the project has been on managing the evolution of Free and Open Source Software distributions. Evolution of these distributions is realized through the upgrade, the addition, and the removal of software packages. The project has two main objectives: (i) develop a model-based approach to safely support the upgrade of FOSS systems, (ii) develop better algorithms and tools to plan upgrade paths based on various information sources about software packages and on optimization criteria. The paper focuses on the first objective of the project. The main result of this objective is an approach that promotes the simulation of upgrades to predict failures before affecting the real system. Both fine-grained static aspects (e.g., configuration incoherences) and dynamic aspects (e.g., the execution of configuration scripts) are taken into account, improving over the state of the art of package managers.","author":[{"family":"Di Ruscio","given":"D"},{"family":"Pelliccione","given":"P"}],"citation-key":"11697_89156","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"Software engineering for resilient systems - 5th international workshop proceedings","DOI":"10.1007/978-3-642-40894-6_5","event-place":"BERLIN HEIDELBERG","ISBN":"978-3-642-40893-9","issued":{"date-parts":[[2013]]},"page":"56-63","publisher":"Springer-Verlag","publisher-place":"BERLIN HEIDELBERG","title":"Supporting the evolution of free and open source software distributions","type":"paper-conference","URL":"http://link.springer.com/chapter/10.1007/978-3-642-40894-6_5"},
{"id":"11697_89159","abstract":"In the context of Model Driven Engineering (MDE) the definition of a Domain Specific Modeling Language (DSML) consists of a set of coordinated artifacts specifying the abstract and concrete syntax of the language, and possibly further aspects related to semantics. Concerning the specification of concrete syntaxes a number of tools are available. They typically permit to associate syntactic elements to metamodel (abstract syntax) of the modeling language being developed and to generate a number of supporting tools (e.g., parsers, pretty printers, and editors). Currently, tools for the specification of textual concrete syntaxes lack support for propagating metamodel changes to the corresponding concrete syntax specifications. In this paper, we analyze such a co-evolution problem, and provide an approach able to automate the propagation of metamodel changes to textual concrete specifications given by means of the TCS tool. The approach relies on model-to-model transformations which are applied according to difference models which represent the occurred metamodel changes","author":[{"family":"Di Ruscio","given":"D"},{"family":"Iovino","given":"L"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_89159","container-title":"39th euromicro conference series on software engineering and advanced applications, SEAA 2013","DOI":"10.1109/SEAA.2013.22","ISBN":"978-0-7695-5091-6","issued":{"date-parts":[[2013]]},"page":"114-121","publisher":"IEEE","title":"Managing the coupled evolution of metamodels and textual concrete syntax specifications","type":"paper-conference","URL":"http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6619498&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6619498"},
{"id":"11697_89171","abstract":"Identifying and removing the causes of poor performance in software systems are complex problems, and these issues are usually tackled after software deployment only with human-based means. Performance antipatterns can be used to harness these problems since they capture design patterns that are known leading to performance problems, and they suggest refactoring actions that can solve the problems. This paper introduces an approach to automate software model refactoring based on performance antipatterns. A Role-Based Modeling Language is used to model antipattern problems as Source Role Models (SRMs), and antipattern solutions as Target Role Models (TRMs). Each (SRM, TRM) pair is represented by a difference model that encodes refactoring actions to be operated on a software model to remove the corresponding antipattern. Differences are applied to software models through a model transformation automatically generated by a higher-order transformation. The approach is shown at work on an example in the e-commerce domain.","author":[{"family":"Arcelli","given":"D"},{"family":"Cortellessa","given":"Vittorio"},{"family":"DI RUSCIO","given":"Davide"}],"citation-key":"11697_89171","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"Computer performance engineering - 10th european workshop, EPEW 2013, venice, italy, september 16-17, 2013. Proceedings. Lecture notes in computer science","DOI":"10.1007/978-3-642-40725-3_24","ISBN":"978-3-642-40724-6","issued":{"date-parts":[[2013]]},"page":"312-324","publisher":"Springer","title":"Applying model differences to automate performance-driven refactoring of software models","type":"paper-conference","URL":"http://link.springer.com/chapter/10.1007/978-3-642-40725-3_24","volume":"8168"},
{"id":"11697_89209","author":[{"family":"DI RUSCIO","given":"Davide"},{"family":"Malavolta","given":"Ivano"},{"family":"Pelliccione","given":"Patrizio"}],"citation-key":"11697_89209","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"Software engineering for resilient systems, 5th international workshop, SERENE 2013, kiev, ukraine, october 3-4, 2013. Proceedings. Lecture notes in computer science","DOI":"10.1007/978-3-642-40894-6_3","event-place":"BERLIN HEIDELBERG","ISBN":"978-3-642-40893-9","issued":{"date-parts":[[2013]]},"page":"33-47","publisher":"Springer-Verlag","publisher-place":"BERLIN HEIDELBERG","title":"Engineering a platform for mission planning of autonomous and resilient quadrotors","type":"paper-conference","volume":"8166"},
{"id":"11697_89217","abstract":"Model-Driven Engineering is a software discipline that relies on (meta) models as first class entities and that aims to develop, maintain and evolve software by exploiting model transformations. Analogously to software, metamodels are subject to evolutionary pressures which might compromise a wide range of artefacts including transformations. In contrast with the problem of metamodel/model co-evolution, the problem of adapting model transformations according to the changes operated on the corresponding metamodels is to a great extent unexplored. This is largely due to its intricacy but also to the difficulty in having a mature process which on one hand is able to evaluate the cost and benefits of adaptations, and on the other hand ensures that consistent methods are used to maintain quality and design integrity during the adaptation. This paper proposes a methodological approach to the coupled evolution of ATL transformations aiming at evaluating its sustainability prior to any adaptation step based on the assessment of change impact significance.","author":[{"family":"Di Ruscio","given":"D"},{"family":"Iovino","given":"L"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_89217","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"6th international conference on theory and practice of model transformations, ICMT 2013;","DOI":"10.1007/978-3-642-38883-5_9","event-place":"BERLIN HEIDELBERG","ISBN":"978-3-642-38882-8","issued":{"date-parts":[[2013]]},"page":"60-75","publisher":"Springer-Verlag","publisher-place":"BERLIN HEIDELBERG","title":"A methodological approach for the coupled evolution of metamodels and ATL transformations","type":"paper-conference","URL":"http://link.springer.com/chapter/10.1007/978-3-642-38883-5_9","volume":"7909"},
{"id":"11697_89278","abstract":"The Eclipse Graphical Modeling (GMF) Framework provides the major approach for implementing visual languages on top of the Eclipse platform. GMF relies on a family of modeling languages to describe abstract syntax, concrete syntax as well as other aspects of the visual language and its implementation in an editor. GMF uses a model-driven approach to map the different GMF models to Java code. The framework, as it stands, lacks support for evolution. In particular, there is no support for propagating changes from the domain model (i.e., the abstract syntax of the visual language) to other editor models. We analyze the resulting co-evolution challenge, and we provide a solution by means of GMF model adapters, which automate the propagation of domain-model changes. These GMF model adapters are special model-to-model transformations that are driven by difference models for domain-model changes.","author":[{"family":"DI RUSCIO","given":"Davide"},{"family":"Lammel","given":"R"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"11697_89278","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"Software language engineering - third international conference, SLE 2010, eindhoven, the netherlands, october 12-13, 2010, revised selected papers","DOI":"10.1007/978-3-642-19440-5_9","event-place":"BERLIN HEIDELBERG","ISBN":"978-3-642-19439-9","issued":{"date-parts":[[2011]]},"page":"143-162","publisher":"Springer-Verlag","publisher-place":"BERLIN HEIDELBERG","title":"Automated Co-evolution of GMF editor models","type":"paper-conference","URL":"http://link.springer.com/chapter/10.1007/978-3-642-19440-5_9","volume":"6563"},
{"id":"11697_89297","author":[{"family":"Di Ruscio","given":"D"},{"family":"Iovino","given":"L"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_89297","container-title":"Proceedings of the 2nd international workshop on model comparison in practice","DOI":"10.1145/2000410.2000416","event-place":"Zurich, Switzerland","ISBN":"978-1-4503-0668-3","issued":{"date-parts":[[2011]]},"page":"30-38","publisher-place":"Zurich, Switzerland","title":"What is needed for managing co-evolution in MDE?","type":"paper-conference"},
{"id":"11697_89302","abstract":"In this paper we briefly describe a case study, i.e. the Mobile eHealth (MeH), developed in the context of the IST PLASTIC project aimed at supporting self-adapting and context-aware services. The goal of the case study is to show how to model a service-based application and to demonstrate that model-based solutions are suitable to generate Quality of Service (QoS) models and adaptable code from service models.","author":[{"family":"Autili","given":"Marco"},{"family":"Berardinelli","given":"Luca"},{"family":"Di Ruscio","given":"Davide"},{"family":"Trubiani","given":"Catia"}],"citation-key":"11697_89302","container-title":"ICSE workshop on principles of engineering service oriented systems","DOI":"10.1109/PESOS.2012.6225946","event-place":"NEW YORK","ISBN":"978-1-4673-1754-2","issued":{"date-parts":[[2012]]},"page":"69-70","publisher":"IEEE Computer Society","publisher-place":"NEW YORK","title":"Providing lightweight and adaptable service technology for information and communication (PLASTIC) in the mobile eHealth case study","type":"paper-conference","URL":"http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6225946"},
{"id":"11697_89303","abstract":"Model-to-model transformations are often employed to establish translational semantics of Domain-Specific Languages (DSLs) by mapping high-level models into more concrete ones. Such semantics are also executable when there exists a target platform able to execute the target models. Conceiving a transformation that targets a low-level language still remains arduous due to the large semantic gap between the DSL and the corresponding target language. In this respect, depending on the domain of the DSL, this task can be made easier by reusing an existing platform and bytecode language for that domain, as for instance the EMF Transformation Virtual Machine (EMFTVM) for the domain of model transformation. This paper defines executable semantics for EMFMigrate, a model transformation language specifically designed for managing the coupled evolution in model-driven development. To this end, the approach considers EMFTVM as the runtime engine targeted by the proposed semantic mappings.","author":[{"family":"Wagelaar","given":"D"},{"family":"Iovino","given":"L"},{"family":"Di Ruscio","given":"D"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_89303","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"Theory and practice of model transformations","DOI":"10.1007/978-3-642-30476-7_13","ISBN":"978-3-642-30475-0","issued":{"date-parts":[[2012]]},"page":"192-207","title":"Translational semantics of a co-evolution specific language with the EMF transformation virtual machine","type":"paper-conference","URL":"http://link.springer.com/chapter/10.1007%2F978-3-642-30476-7_13","volume":"7307"},
{"id":"11697_89304","author":[{"family":"Di Ruscio","given":"D"},{"family":"Eramo","given":"R"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_89304","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"Formal methods for model-driven engineering","DOI":"10.1007/978-3-642-30982-3_4","event-place":"BERLIN HEIDELBERG","ISBN":"978-3-642-30981-6","issued":{"date-parts":[[2012]]},"page":"91-136","publisher":"Springer-Verlag","publisher-place":"BERLIN HEIDELBERG","title":"Model transformations","type":"paper-conference","volume":"7320"},
{"id":"11697_89344","abstract":"Software systems increasingly require to deal with continuous evolution. In this paper we present the EVOSS tool that has been defined to support the upgrade of free and open source software systems. EVOSS is composed of a simulator and of a fault detector component. The simulator is able to predict failures before they can affect the real system. The fault detector component has been defined to discover inconsistencies in the system configuration model. EVOSS improves the state of the art of current tools, which are able to predict a very limited set of upgrade faults, while they leave a wide range of faults unpredicted.","author":[{"family":"Di Ruscio","given":"D"},{"family":"Pelliccione","given":"P"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_89344","container-title":"Proceedings of the 34th international conference on software engineering (ICSE 2012)","DOI":"10.1109/ICSE.2012.6227234","event-place":"NEW YORK","ISBN":"978-1-4673-1066-6","issued":{"date-parts":[[2012]]},"page":"1415-1418","publisher":"IEEE Computer Society","publisher-place":"NEW YORK","title":"EVOSS: A tool for managing the evolution of free and open source software systems","type":"paper-conference","URL":"http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6227234"},
{"id":"11697_89346","abstract":"In Model-Driven Engineering (MDE) metamodels are cornerstones for defining a wide range of related artifacts interlaced with explicit or implicit correspondences. According to this view, models, transformations, editors, and supporting tools can be regarded as a whole pursuing a common scope and therefore constituting an ecosystem. Analogously to software, metamodels are subject to evolutionary pressures too. However, changing a metamodel might compromise the validity of the artifacts in the ecosystem which therefore require to co-evolve as well in order to restore their validity. Different approaches have been proposed to support at different extent the adaptation of artifacts according to the changes operated on the corresponding metamodels. Each technique is specialized in the adaptation of specific kind of artifact (e.g., models, or transformations) by forcing modelers to learn different technologies and languages. This paper discusses the different relations occurring in a typical metamodeling ecosystem among the metamodel and the related artifacts, and identifies the commonalities which can be leveraged to define a unifying and comprehensive adaptation process. A language and corresponding supporting tools are also proposed for the management of metamodel evolution and the corresponding togetherness with the related artifacts.","author":[{"family":"Di Ruscio","given":"D"},{"family":"Iovino","given":"L"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_89346","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"Procs. International conference on graph transformations (ICGT2012)","DOI":"10.1007/978-3-642-33654-6_2","event-place":"BERLIN HEIDELBERG","ISBN":"978-3-642-33653-9","issued":{"date-parts":[[2012]]},"page":"20-37","publisher":"Springer-Verlag","publisher-place":"BERLIN HEIDELBERG","title":"Evolutionary togetherness: how to manage coupled evolution in metamodeling ecosystems","type":"paper-conference","URL":"http://link.springer.com/chapter/10.1007%2F978-3-642-33654-6_2","volume":"7562"},
{"id":"11697_89593","author":[{"family":"Di Cosmo","given":"R"},{"family":"Di Ruscio","given":"D"},{"family":"Pelliccione","given":"P"},{"family":"Pierantonio","given":"A"},{"family":"Zacchiroli","given":"S"}],"citation-key":"11697_89593","container-title":"SCIENCE OF COMPUTER PROGRAMMING","DOI":"10.1016/j.scico.2010.11.001","issued":{"date-parts":[[2011]]},"page":"1144-1160","title":"Supporting software evolution in component-based FOSS systems","type":"article-journal","volume":"76"},
{"id":"11697_89601","abstract":"Model-driven engineering bases a wide range of artifacts on metamodels. When such metamodels evolve, such as a new version of Unified Modeling Language or Business Process Execution Notation or a company-specific metamodel, underlying artifacts often become invalid. In this article, the authors provide an overview of coupled evolution methods and tools to handle such dependencies. I look forward to hearing from both readers and prospective authors about this column and the technologies you want to know more about.","author":[{"family":"Di Ruscio","given":"D"},{"family":"Iovino","given":"L"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_89601","container-title":"IEEE SOFTWARE","DOI":"10.1109/MS.2012.153","issued":{"date-parts":[[2012]]},"page":"78-84","title":"Coupled evolution in model-driven engineering","type":"article-journal","URL":"http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6336727","volume":"29"},
{"id":"11697_9327","author":[{"family":"Cicchetti","given":"A"},{"family":"Di Ruscio","given":"D"},{"family":"Pierantonio","given":"A"}],"citation-key":"11697_9327","container-title":"JOURNAL OF OBJECT TECHNOLOGY","issued":{"date-parts":[[2007]]},"page":"165-185","title":"A metamodel independent approach to difference representation","type":"article-journal","volume":"6"},
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{"id":"ab.rahimSurveyApproachesVerifying2013","abstract":"As with other software development artifacts, model transformations are not bug-free and so must be systematically verified. Their nature, however, means that transformations require specialist verification techniques. This paper brings together current research on model transformation verification by classifying existing approaches along two dimensions. Firstly, we present a coarse-grained classification based on the technical details of the approach (e.g., testing, theorem proving, model checking). Secondly, we present a finer-grained classification which categorizes approaches according to criteria such as level of formality, transformation language, properties verified. The purpose of the survey is to bring together research in model transformation verification to act as a resource for the community. Furthermore, based on the survey, we identify a number of trends in current and past research on model transformation verification.","accessed":{"date-parts":[[2015,3,24]]},"author":[{"family":"Ab. Rahim","given":"Lukman"},{"family":"Whittle","given":"Jon"}],"citation-key":"ab.rahimSurveyApproachesVerifying2013","container-title":"Software & Systems Modeling","container-title-short":"Softw Syst Model","DOI":"10.1007/s10270-013-0358-0","ISSN":"1619-1366, 1619-1374","issued":{"date-parts":[[2013,6,13]]},"page":"1-26","source":"link.springer.com","title":"A survey of approaches for verifying model transformations","type":"article-journal","URL":"http://link.springer.com/article/10.1007/s10270-013-0358-0"},
{"id":"Abbar09context-awarerecommender","author":[{"family":"Abbar","given":"Sofiane"},{"family":"Bouzeghoub","given":"Mokrane"},{"family":"Lopez","given":"Stéphane"}],"citation-key":"Abbar09context-awarerecommender","container-title":"VLDB PersDB workshop","issued":{"date-parts":[[2009]]},"title":"Context-aware recommender systems: A service oriented approach","type":"paper-conference"},
{"id":"Abdalhadi20221356","abstract":"A magnetic levitation system (MLS) is a complex nonlinear system that requires an electromagnetic force to levitate an object in the air. The electromagnetic field is extremely sensitive to noise which can cause the acceleration on the spherical object, leading it to move into the unbalanced region. This paper presents a comparative assessment of controllers for the magnetic levitation system using proportional integral derivative (PID) controller based optimal tuning. The analysis was started by deriving the mathematical model followed by the implementation of radial basis function neural network (RBFNN) based metamodel. The optimal tuning of the PID controller has offered better transient responses with the improvement of overshoot and the rise time as compared to the standard optimization methods. It is more robust and tolerant as compared to gradient descent method. The simulation output using the radial basis based metamodel approach showed an overshoot of 9.34% and rise time of 9.84 ms, which are better than the gradient descent (GD) and conventional PID methods. For the verification purpose, a Simscape model has been developed which mimic the real model. It was found that the model has produced about similar performance as what has been obtained from the Matlab simulation. © 2022 Institute of Advanced Engineering and Science. All rights reserved.","author":[{"family":"Abdalhadi","given":"A."},{"family":"Wahid","given":"H."},{"family":"Burhanuddin","given":"D.H."}],"citation-key":"Abdalhadi20221356","container-title":"Indonesian Journal of Electrical Engineering and Computer Science","DOI":"10.11591/ijeecs.v25.i3.pp1356-1366","ISSN":"25024752","issue":"3","issued":{"date-parts":[[2022]]},"page":"1356-1366","publisher":"Institute of Advanced Engineering and Science","title":"An optimal proportional integral derivative tuning for a magnetic levitation system using metamodeling approach","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124672634&doi=10.11591%2fijeecs.v25.i3.pp1356-1366&partnerID=40&md5=410c4d7e7746202fc32c1808aa16f981","volume":"25"},
{"id":"abdalhadiOptimalProportionalIntegral2022a","abstract":"A magnetic levitation system (MLS) is a complex nonlinear system that requires an electromagnetic force to levitate an object in the air. The electromagnetic field is extremely sensitive to noise which can cause the acceleration on the spherical object, leading it to move into the unbalanced region. This paper presents a comparative assessment of controllers for the magnetic levitation system using proportional integral derivative (PID) controller based optimal tuning. The analysis was started by deriving the mathematical model followed by the implementation of radial basis function neural network (RBFNN) based metamodel. The optimal tuning of the PID controller has offered better transient responses with the improvement of overshoot and the rise time as compared to the standard optimization methods. It is more robust and tolerant as compared to gradient descent method. The simulation output using the radial basis based metamodel approach showed an overshoot of 9.34% and rise time of 9.84 ms, which are better than the gradient descent (GD) and conventional PID methods. For the verification purpose, a Simscape model has been developed which mimic the real model. It was found that the model has produced about similar performance as what has been obtained from the Matlab simulation. © 2022 Institute of Advanced Engineering and Science. All rights reserved.","author":[{"family":"Abdalhadi","given":"A."},{"family":"Wahid","given":"H."},{"family":"Burhanuddin","given":"D.H."}],"citation-key":"abdalhadiOptimalProportionalIntegral2022a","container-title":"Indonesian Journal of Electrical Engineering and Computer Science","DOI":"10.11591/ijeecs.v25.i3.pp1356-1366","ISSN":"25024752","issue":"3","issued":{"date-parts":[[2022]]},"page":"1356-1366","publisher":"Institute of Advanced Engineering and Science","title":"An optimal proportional integral derivative tuning for a magnetic levitation system using metamodeling approach","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124672634&doi=10.11591%2fijeecs.v25.i3.pp1356-1366&partnerID=40&md5=410c4d7e7746202fc32c1808aa16f981","volume":"25"},
{"id":"abdalkareemCodeReuseStackOverflow2017","abstract":"Context: Source code reuse has been widely accepted as a fundamental activity in software development. Recent studies showed that StackOverflow has emerged as one of the most popular resources for code reuse. Therefore, a plethora of work proposed ways to optimally ask questions, search for answers and find relevant code on StackOverflow. However, little work studies the impact of code reuse from StackOverflow. Objective: To better understand the impact of code reuse from StackOverflow, we perform an exploratory study focusing on code reuse from StackOverflow in the context of mobile apps. Specifically, we investigate how much, why, when, and who reuses code. Moreover, to understand the potential implications of code reuse, we examine the percentage of bugs in files that reuse StackOverflow code. Method: We perform our study on 22 open source Android apps. For each project, we mine their source code and use clone detection techniques to identify code that is reused from StackOverflow. We then apply different quantitative and qualitative methods to answer our research questions. Results: Our findings indicate that 1) the amount of reused StackOverflow code varies for different mobile apps, 2) feature additions and enhancements in apps are the main reasons for code reuse from StackOverflow, 3) mid-age and older apps reuse StackOverflow code mostly later on in their project lifetime and 4) that in smaller teams/apps, more experienced developers reuse code, whereas in larger teams/apps, the less experienced developers reuse code the most. Additionally, we found that the percentage of bugs is higher in files after reusing code from StackOverflow. Conclusion: Our results provide insights on the potential impact of code reuse from StackOverflow on mobile apps. Furthermore, these results can benefit the research community in developing new techniques and tools to facilitate and improve code reuse from StackOverflow.","author":[{"family":"Abdalkareem","given":"Rabe"},{"family":"Shihab","given":"Emad"},{"family":"Rilling","given":"Juergen"}],"citation-key":"abdalkareemCodeReuseStackOverflow2017","container-title":"Information and Software Technology","ISSN":"0950-5849","issued":{"date-parts":[[2017]]},"page":"148 - 158","title":"On code reuse from StackOverflow: An exploratory study on Android apps","type":"article-journal","URL":"http://www.sciencedirect.com/science/article/pii/S0950584917303610","volume":"88"},
{"id":"abdelhediLogicalUnifiedModeling2017","abstract":"Big Data, NoSQL, UML Conceptual Model, MDA, QVT.","accessed":{"date-parts":[[2018,5,7]]},"author":[{"family":"Abdelhedi","given":"Fatma"},{"family":"Brahim","given":"Amal Ait"},{"family":"Atigui","given":"Faten"},{"family":"Zurfluh","given":"Gilles"}],"citation-key":"abdelhediLogicalUnifiedModeling2017","DOI":"10.5220/0006311702490256","ISBN":"978-989-758-247-9 978-989-758-248-6 978-989-758-249-3","issued":{"date-parts":[[2017]]},"page":"249-256","publisher":"SCITEPRESS - Science and Technology Publications","source":"Crossref","title":"Logical Unified Modeling for NoSQL Databases:","title-short":"Logical Unified Modeling for NoSQL Databases","type":"paper-conference","URL":"http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006311702490256"},
{"id":"abeywickramaSimSOTAEngineeringSimulating2013","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Abeywickrama","given":"Dhaminda B."},{"family":"Hoch","given":"Nicklas"},{"family":"Zambonelli","given":"Franco"}],"citation-key":"abeywickramaSimSOTAEngineeringSimulating2013","container-title":"Proceedings of the International C* Conference on Computer Science and Software Engineering","issued":{"date-parts":[[2013]]},"page":"67–76","publisher":"ACM","source":"Google Scholar","title":"SimSOTA: engineering and simulating feedback loops for self-adaptive systems","title-short":"SimSOTA","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=2494446"},
{"id":"Abid2019355","abstract":"Learning how to build software systems using new tools can be a daunting task to anyone new to the job. This is especially true of tools that provide a large number of functionalities and views on the system under development, such as IDES for Model-Driven Development (MDD). Applying Machine Learning (ML) techniques can help in this state of affairs by pointing out to appropriate next actions to rookie or even intermediate developers. AutoFOCUS3 (AF3) is a mature MDD tool we are building in-house and for which we provide regular tutorials to new users. These users come from both the academia (e.g, students/professors) and the industry (e.g. managers/software engineers). Nonetheless, AF3 remains a complex tool and we have found there is a need to speedup the learning curve of the tool for students that attend our tutorials - or alternatively and more importantly for others that simply download the tool and attempt using it without human supervision. In this paper, we describe a machine learning-based recommendation system named MAGNET for aiding beginner and intermediate users of AF3 in learning the tool. We describe how we have gathered data and trained an ML model to suggest new commands, how a recommender system was integrated in the AF3, experiments we have run thus far, and the future directions of our work. © 2019 Knowledge Systems Institute Graduate School. All rights reserved.","author":[{"family":"Abid","given":"S.B."},{"family":"Mahajan","given":"V."},{"family":"Lucio","given":"L."}],"citation-key":"Abid2019355","collection-title":"Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE","DOI":"10.18293/SEKE2019-050","ISBN":"1-891706-48-9","ISSN":"23259000","issued":{"date-parts":[[2019]]},"page":"355-360","publisher":"Knowledge Systems Institute Graduate School","title":"Towards machine learning for learnability of MDD tools","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071359227&doi=10.18293%2fSEKE2019-050&partnerID=40&md5=65e2722e4df5eee642b59c9ee77a4792","volume":"2019-July"},
{"id":"abrahaoModelDrivenEngineeringLanguages2014","abstract":"This book constitutes the refereed proceedings of the 17th International Conference on Model Driven Engineering Languages and Systems, MODELS 2014, held in Valencia, Spain, in September/October 2014. The 41 full papers presented in this volume were carefully reviewed and selected from a total of 126 submissions. The scope of the conference series is broad, encompassing modeling languages, methods, tools, and applications considered from theoretical and practical angles and in academic and industrial settings. The papers report on the use of modeling in a wide range of cloud, mobile, and web computing, model transformation behavioral modeling, MDE: past, present, future, formal semantics, specification, and verification, models at runtime, feature and variability modeling, composition and adaptation, practices and experience, modeling for analysis, pragmatics, model extraction, manipulation and persistence, querying, and reasoning","call-number":"005.13","citation-key":"abrahaoModelDrivenEngineeringLanguages2014","collection-number":"8767","collection-title":"Programming and Software Engineering","DOI":"10.1007/978-3-319-11653-2","edition":"1st ed. 2014","editor":[{"family":"Abrahao","given":"Silvia"},{"family":"Dingel","given":"Juergen"},{"family":"Insfran","given":"Emilio"},{"family":"Ramos","given":"Isidro"},{"family":"Schulte","given":"Wolfram"}],"event-place":"Cham","ISBN":"978-3-319-11653-2","issued":{"date-parts":[[2014]]},"note":"00006","number-of-pages":"1","publisher":"Springer International Publishing : Imprint: Springer","publisher-place":"Cham","source":"Library of Congress ISBN","title":"Model-Driven Engineering Languages and Systems: 17th International Conference, MODELS 2014, Valencia, Spain, September 283- October 4, 2014. Proceedings","title-short":"Model-Driven Engineering Languages and Systems","type":"book"},
{"id":"abu-elkheirDataManagementInternet2013","accessed":{"date-parts":[[2021,1,5]]},"author":[{"family":"Abu-Elkheir","given":"Mervat"},{"family":"Hayajneh","given":"Mohammad"},{"family":"Ali","given":"Najah"}],"citation-key":"abu-elkheirDataManagementInternet2013","container-title":"Sensors","container-title-short":"Sensors","DOI":"10.3390/s131115582","ISSN":"1424-8220","issue":"11","issued":{"date-parts":[[2013,11,14]]},"note":"00188","page":"15582-15612","source":"DOI.org (Crossref)","title":"Data Management for the Internet of Things: Design Primitives and Solution","title-short":"Data Management for the Internet of Things","type":"article-journal","URL":"http://www.mdpi.com/1424-8220/13/11/15582","volume":"13"},
{"id":"ACMInternationalConference2010","citation-key":"ACMInternationalConference2010","issued":{"date-parts":[[2010]]},"note":"00000","title":"ACM International Conference Proceeding Series: Foreword","type":"book"},
{"id":"acmsigchisymposiumonengineeringinteractivecomputingsystemsEICS13Proceedings2013","accessed":{"date-parts":[[2016,9,24]]},"author":[{"literal":"ACM SIGCHI Symposium on Engineering Interactive Computing Systems"},{"family":"Forbrig","given":"Peter"},{"family":"Dewan","given":"Prasun"},{"literal":"SIGCHI (Group : U.S.)"},{"family":"City University (London","given":"England)"},{"literal":"Springer (Firm)"},{"literal":"IFIP Working Group 2.7/13.4"},{"literal":"Association for Computing Machinery"},{"literal":"ACM Digital Library"}],"citation-key":"acmsigchisymposiumonengineeringinteractivecomputingsystemsEICS13Proceedings2013","issued":{"date-parts":[[2013]]},"note":"OCLC: 858090417","source":"Open WorldCat","title":"EICS '13: proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems : June 24-27, 2013, London, United Kingdom","title-short":"EICS '13","type":"book","URL":"http://dl.acm.org/citation.cfm?id=2494603"},
{"id":"ACMStudentResearch2017","citation-key":"ACMStudentResearch2017","issued":{"date-parts":[[2017]]},"note":"00000","number-of-pages":"547–548","publisher":"CEUR-WS","title":"ACM student research competition at MoDELS 2017","type":"book","volume":"2019"},
{"id":"acretoaieHypersonicModelAnalysis2014","author":[{"family":"Acretoaie","given":"Vlad"},{"family":"Störrle","given":"Harald"}],"citation-key":"acretoaieHypersonicModelAnalysis2014","issued":{"date-parts":[[2014]]},"title":"Hypersonic: Model Analysis and Checking in the Cloud","type":"article-journal"},
{"id":"Adamopoulos_TIST","author":[{"family":"Adamopoulos","given":"Panagiotis"},{"family":"Tuzhilin","given":"Alexander"}],"citation-key":"Adamopoulos_TIST","container-title":"ACM Transactions on Intelligent Systems and Technology","container-title-short":"ACM Trans. Intell. Syst. Technol.","ISSN":"2157-6904","issue":"4","issued":{"date-parts":[[2014,12]]},"page":"54:1-54:32","title":"On unexpectedness in recommender systems: Or how to better expect the unexpected","type":"article-journal","URL":"http://doi.acm.org/10.1145/2559952","volume":"5"},
{"id":"addaziSemanticbasedModelMatching","abstract":"In MDE resolving pragmatic issues related to the management of models is key to success. Model comparison is one of the most challenging operations playing a central role in a wide range of modelling activities including model versioning, evolution and even collaborative and distributed specification of models. Over the last decade, several syntactic methods have been proposed to compare models even though they struggle in achieving higher levels of accuracy especially when the semantics of the application domain has to be considered. Existing methods improve comparison precision at the price of high performance costs.","author":[{"family":"Addazi","given":"Lorenzo"},{"family":"Cicchetti","given":"Antonio"},{"family":"Rocco","given":"Juri Di"}],"citation-key":"addaziSemanticbasedModelMatching","page":"10","source":"Zotero","title":"Semantic-based Model Matching with EMFCompare","type":"article-journal"},
{"id":"addaziSemanticbasedModelMatching2016","author":[{"family":"Addazi","given":"Lorenzo"},{"family":"Cicchetti","given":"Antonio"},{"family":"DI ROCCO","given":"Juri"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"addaziSemanticbasedModelMatching2016","container-title":"Proceedings of the 10th Workshop on Models and Evolution co-located with ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems (MODELS 2016), Saint-Malo, France, October 2, 2016.","issued":{"date-parts":[[2016]]},"note":"00000","page":"40–49","publisher":"CEUR-WS","title":"Semantic-based model matching with EMFcompare","type":"paper-conference","URL":"http://ceur-ws.org/","volume":"1706"},
{"id":"addaziSemanticbasedModelMatching2016a","author":[{"family":"Addazi","given":"Lorenzo"},{"family":"Cicchetti","given":"Antonio"},{"family":"Rocco","given":"Juri Di"},{"family":"Ruscio","given":"Davide Di"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"addaziSemanticbasedModelMatching2016a","collection-title":"CEUR Workshop Proceedings","container-title":"Proceedings of the 10th Workshop on Models and Evolution co-located with ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems (MODELS 2016), Saint-Malo, France, October 2, 2016","editor":[{"family":"Mayerhofer","given":"Tanja"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Schätz","given":"Bernhard"},{"family":"Tamzalit","given":"Dalila"}],"issued":{"date-parts":[[2016]]},"note":"00000","page":"40–49","publisher":"CEUR-WS.org","title":"Semantic-based Model Matching with EMFCompare","type":"paper-conference","URL":"http://ceur-ws.org/Vol-1706/paper6.pdf","volume":"1706"},
{"id":"addaziSemanticbasedModelMatching2016b","author":[{"family":"Addazi","given":"Lorenzo"},{"family":"Cicchetti","given":"Antonio"},{"family":"Rocco","given":"Juri Di"},{"family":"Ruscio","given":"Davide Di"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"addaziSemanticbasedModelMatching2016b","collection-title":"CEUR Workshop Proceedings","container-title":"Proceedings of the 10th Workshop on Models and Evolution co-located with ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems (MODELS 2016), Saint-Malo, France, October 2, 2016","editor":[{"family":"Mayerhofer","given":"Tanja"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Schätz","given":"Bernhard"},{"family":"Tamzalit","given":"Dalila"}],"issued":{"date-parts":[[2016]]},"note":"00000","page":"40–49","publisher":"CEUR-WS.org","title":"Semantic-based Model Matching with EMFCompare","type":"paper-conference","URL":"http://ceur-ws.org/Vol-1706/paper6.pdf","volume":"1706"},
{"id":"Adomavicius:2008:CRS:1454008.1454068","author":[{"family":"Adomavicius","given":"Gediminas"},{"family":"Tuzhilin","given":"Alexander"}],"citation-key":"Adomavicius:2008:CRS:1454008.1454068","collection-title":"RecSys '08","container-title":"Proceedings of the 2008 ACM conference on recommender systems","event-place":"New York, NY, USA","ISBN":"978-1-60558-093-7","issued":{"date-parts":[[2008]]},"page":"335-336","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Context-aware recommender systems","type":"paper-conference","URL":"http://doi.acm.org/10.1145/1454008.1454068"},
{"id":"Adomavicius:2012:aggrDiv","author":[{"family":"Adomavicius","given":"Gediminas"},{"family":"Kwon","given":"YoungOk"}],"citation-key":"Adomavicius:2012:aggrDiv","container-title":"IEEE Trans. on Knowl. and Data Eng.","ISSN":"1041-4347","issue":"5","issued":{"date-parts":[[2012,5]]},"page":"896-911","title":"Improving aggregate recommendation diversity using ranking-based techniques","type":"article-journal","URL":"http://dx.doi.org/10.1109/TKDE.2011.15","volume":"24"},
{"id":"AdversarialMachineLearning","citation-key":"AdversarialMachineLearning","container-title":"Machine Learning","note":"00000","page":"26","source":"Zotero","title":"Adversarial Machine Learning —An Introduction","type":"article-journal"},
{"id":"aggarwalNeighborhoodbasedCollaborativeFiltering2016","abstract":"Neighborhood-based collaborative filtering algorithms, also referred to as memory-based algorithms, were among the earliest algorithms developed for collaborative filtering. These algorithms are based on the fact that similar users display similar patterns of rating behavior and similar items receive similar ratings. There are two primary types of neighborhood-based algorithms:","author":[{"family":"Aggarwal","given":"Charu"}],"citation-key":"aggarwalNeighborhoodbasedCollaborativeFiltering2016","container-title":"Recommender systems: The textbook","DOI":"10.1007/978-3-319-29659-3₂","event-place":"Cham","ISBN":"978-3-319-29659-3","issued":{"date-parts":[[2016]]},"page":"29-70","publisher":"Springer International Publishing","publisher-place":"Cham","title":"Neighborhood-based collaborative filtering","type":"chapter","URL":"https://doi.org/10.1007/978-3-319-29659-3₂"},
{"id":"AGIRRE10.534","author":[{"family":"Agirre","given":"Eneko"},{"family":"Cuadros","given":"Montse"},{"family":"Rigau","given":"German"},{"family":"Soroa","given":"Aitor"}],"citation-key":"AGIRRE10.534","container-title":"Proceedings of the seventh international conference on language resources and evaluation (LREC'10)","editor":[{"family":"Chair)","given":"Nicoletta Calzolari (Conference"},{"family":"Choukri","given":"Khalid"},{"family":"Maegaard","given":"Bente"},{"family":"Mariani","given":"Joseph"},{"family":"Odijk","given":"Jan"},{"family":"Piperidis","given":"Stelios"},{"family":"Rosner","given":"Mike"},{"family":"Tapias","given":"Daniel"}],"event-place":"Valletta, Malta","ISBN":"2-9517408-6-7","issued":{"literal":"19-21, 2010-05"},"publisher":"European Language Resources Association (ELRA)","publisher-place":"Valletta, Malta","title":"Exploring knowledge bases for similarity","type":"paper-conference"},
{"id":"agirrePersonalizingPageRankWord2009","author":[{"family":"Agirre","given":"Eneko"},{"family":"Soroa","given":"Aitor"}],"citation-key":"agirrePersonalizingPageRankWord2009","collection-title":"EACL '09","container-title":"Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics","event-place":"Stroudsburg, PA, USA","issued":{"date-parts":[[2009]]},"page":"33-41","publisher":"Association for Computational Linguistics","publisher-place":"Stroudsburg, PA, USA","title":"Personalizing PageRank for word sense disambiguation","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=1609067.1609070"},
{"id":"agt-rickauerSupportingDomainModeling","author":[{"family":"Agt-Rickauer","given":"Henning"}],"citation-key":"agt-rickauerSupportingDomainModeling","page":"196","source":"Zotero","title":"Supporting Domain Modeling with Automated Knowledge Acquisition and Modeling Recommendations","type":"article-journal"},
{"id":"Aha:1991:ILA:104713.104717","author":[{"family":"Aha","given":"David W."},{"family":"Kibler","given":"Dennis"},{"family":"Albert","given":"Marc K."}],"citation-key":"Aha:1991:ILA:104713.104717","container-title":"Machine Learning","container-title-short":"Mach. Learn.","ISSN":"0885-6125","issue":"1","issued":{"date-parts":[[1991,1]]},"page":"37-66","title":"Instance-based learning algorithms","type":"article-journal","URL":"https://doi.org/10.1023/A:1022689900470","volume":"6"},
{"id":"AhmedOuameur2021435","abstract":"Deep learning (DL) is attracting considerable attention in the design of communication systems. This paper derives a deep unfolded conjugate gradient (CG) architecture for large-scale multiple-input multiple-output detection. The proposed technique combines the advantages of a model-driven approach in readily incorporating domain knowledge and deep learning in effective parameters learning. The parameters are trained via backpropagation over a data flow graph inspired from the iterative conjugate gradient method. We derive the closed-form expressions for the gradients for parameters training and discuss early results on the performance in a statistically identical and independent distributed channel where the training overhead is considerably low. It is worth noting that the loss function is based on the residual error that is not an explicit function of the desired signal, which makes the proposed algorithm blind. As an initial framework, we will point to the inherent issues and future directions. © 2021 The Authors. IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology","author":[{"family":"Ahmed Ouameur","given":"M."},{"family":"Massicotte","given":"D."}],"citation-key":"AhmedOuameur2021435","container-title":"IET Communications","DOI":"10.1049/cmu2.12076","ISSN":"17518628","issue":"3","issued":{"date-parts":[[2021]]},"page":"435-444","publisher":"John Wiley and Sons Inc","title":"Early results on deep unfolded conjugate gradient-based large-scale MIMO detection","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101022881&doi=10.1049%2fcmu2.12076&partnerID=40&md5=bf153b840b5a194b66542a4202dd8691","volume":"15"},
{"id":"ahmedouameurEarlyResultsDeep2021a","abstract":"Deep learning (DL) is attracting considerable attention in the design of communication systems. This paper derives a deep unfolded conjugate gradient (CG) architecture for large-scale multiple-input multiple-output detection. The proposed technique combines the advantages of a model-driven approach in readily incorporating domain knowledge and deep learning in effective parameters learning. The parameters are trained via backpropagation over a data flow graph inspired from the iterative conjugate gradient method. We derive the closed-form expressions for the gradients for parameters training and discuss early results on the performance in a statistically identical and independent distributed channel where the training overhead is considerably low. It is worth noting that the loss function is based on the residual error that is not an explicit function of the desired signal, which makes the proposed algorithm blind. As an initial framework, we will point to the inherent issues and future directions. © 2021 The Authors. IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology","author":[{"family":"Ahmed Ouameur","given":"M."},{"family":"Massicotte","given":"D."}],"citation-key":"ahmedouameurEarlyResultsDeep2021a","container-title":"IET Communications","DOI":"10.1049/cmu2.12076","ISSN":"17518628","issue":"3","issued":{"date-parts":[[2021]]},"page":"435-444","publisher":"John Wiley and Sons Inc","title":"Early results on deep unfolded conjugate gradient-based large-scale MIMO detection","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101022881&doi=10.1049%2fcmu2.12076&partnerID=40&md5=bf153b840b5a194b66542a4202dd8691","volume":"15"},
{"id":"Ahsan2020","abstract":"The model-driven algorithms have been investigated in wireless communications for decades. Presently, the model-free methods based on machine learning techniques are rapidly being developed in the field of non-orthogonal multiple access (NOMA) to dynamically optimize multiples parameters (e.g., number of resource blocks and QoS). With the aid of SARSA Q-learning and Deep reinforcement Learning (DRL), in this paper, we proposed a user clustering-based resource allocation with uplink NOMA techniques in multi-cell systems. It performs user grouping based on network traffic to efficiently utilise the available resources, we apply SARSA Q-learning to light and DRL to heavy network traffic. To characterize the performance of the proposed optimization algorithms, achieved the capacity for all the users is used to define the reward function. The proposed SARSA Q-learning and DRL algorithms are capable of assisting base-stations to efficiently assign available resources to IoT users considering different traffic conditions. As a result, simulation outcomes show that both the algorithms, SARSA Q-learning and DRL performed better than orthogonal multiple access (OMA) in all the experiments and converged with maximum sum-rate. © 2020 IEEE.","author":[{"family":"Ahsan","given":"W."},{"family":"Yi","given":"W."},{"family":"Liu","given":"Y."},{"family":"Qin","given":"Z."},{"family":"Nallanathan","given":"A."}],"citation-key":"Ahsan2020","collection-title":"2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings","DOI":"10.1109/ICCWorkshops49005.2020.9145187","ISBN":"978-1-72817-440-2","issued":{"date-parts":[[2020]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Reinforcement learning for user clustering in NOMA-enabled uplink IoT","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090283282&doi=10.1109%2fICCWorkshops49005.2020.9145187&partnerID=40&md5=bb070065997bd29dd55becc5a592b99a"},
{"id":"ahsanReinforcementLearningUser2020a","abstract":"The model-driven algorithms have been investigated in wireless communications for decades. Presently, the model-free methods based on machine learning techniques are rapidly being developed in the field of non-orthogonal multiple access (NOMA) to dynamically optimize multiples parameters (e.g., number of resource blocks and QoS). With the aid of SARSA Q-learning and Deep reinforcement Learning (DRL), in this paper, we proposed a user clustering-based resource allocation with uplink NOMA techniques in multi-cell systems. It performs user grouping based on network traffic to efficiently utilise the available resources, we apply SARSA Q-learning to light and DRL to heavy network traffic. To characterize the performance of the proposed optimization algorithms, achieved the capacity for all the users is used to define the reward function. The proposed SARSA Q-learning and DRL algorithms are capable of assisting base-stations to efficiently assign available resources to IoT users considering different traffic conditions. As a result, simulation outcomes show that both the algorithms, SARSA Q-learning and DRL performed better than orthogonal multiple access (OMA) in all the experiments and converged with maximum sum-rate. © 2020 IEEE.","author":[{"family":"Ahsan","given":"W."},{"family":"Yi","given":"W."},{"family":"Liu","given":"Y."},{"family":"Qin","given":"Z."},{"family":"Nallanathan","given":"A."}],"citation-key":"ahsanReinforcementLearningUser2020a","container-title":"2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings","DOI":"10.1109/ICCWorkshops49005.2020.9145187","ISBN":"978-1-72817-440-2","issued":{"date-parts":[[2020]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Reinforcement learning for user clustering in NOMA-enabled uplink IoT","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090283282&doi=10.1109%2fICCWorkshops49005.2020.9145187&partnerID=40&md5=bb070065997bd29dd55becc5a592b99a"},
{"id":"Al-Azzoni202087","abstract":"This paper presents new meta-models for addressing machine learning problems using artificial neural networks. Models conforming to these meta-models can capture the main elements of learning problems and neural networks. This serves as the foundation step for the use of Model-Driven Engineering (MDE) based approach to machine learning using neural networks. The aim is to reap the same benefits which MDE brings to solving software engineering problems. This includes solutions to tool interoperability and standardization challenges, in addition to helping users to develop solutions with less dependence on a particular set of tools and technologies. The presented framework is implemented using Eclipse Modeling Framework (EMF), and several features are demonstrated, including model validation, model transformation, and code generation. © 2020 IEEE.","author":[{"family":"Al-Azzoni","given":"I."}],"citation-key":"Al-Azzoni202087","collection-title":"2020 International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2020","DOI":"10.1109/IDSTA50958.2020.9264067","editor":[{"family":"Alsmirat M., Jararweh Y.","given":"Lloret Mauri J.","suffix":"Aloqaily M."}],"ISBN":"978-1-72818-376-3","issued":{"date-parts":[[2020]]},"page":"87-94","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Model driven approach for neural networks","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098666755&doi=10.1109%2fIDSTA50958.2020.9264067&partnerID=40&md5=d136d588ab28a1628f4a304bfe5d26fe"},
{"id":"al-garadiSurveyMachineDeep2018","abstract":"The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT system effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML /DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.","accessed":{"date-parts":[[2021,1,10]]},"author":[{"family":"Al-Garadi","given":"Mohammed Ali"},{"family":"Mohamed","given":"Amr"},{"family":"Al-Ali","given":"Abdulla"},{"family":"Du","given":"Xiaojiang"},{"family":"Guizani","given":"Mohsen"}],"citation-key":"al-garadiSurveyMachineDeep2018","container-title":"arXiv:1807.11023 [cs]","issued":{"date-parts":[[2018,7,29]]},"note":"00106","source":"arXiv.org","title":"A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security","type":"article-journal","URL":"http://arxiv.org/abs/1807.11023"},
{"id":"Al-Jamimi201454","abstract":"Model transformation is defined as a central concept in model driven engineering. Identifying the transformation rules is nontrivial task, where it might be much easier for the experts to provide examples of the transformations rather than specifying complete and consistent rules. The examples provided by expert represent their knowledge in the domain. Thus, it is much beneficial to utilize a set of examples, i.e. pairs of transformation source and target models, in order to learn transformation rules. Machine learning (ML) techniques proved their ability of learning relations and concepts in various domains. In this paper, we aim to apply Inductive Logic Programming (ILP) for learning the transformation rules between the requirements analysis and software design based on a set of pairs of transformation analysis and design models. ALEPH and GILPS systems have been employed, individually, to induce the intended transformation rules; however the resultant rules don't accommodate the desire transformations. Thus, in this paper we focus on identifying the problem of analysis-design transformation and discussing the derived rules as well as the limitations of the current ILP systems. © 2014 IEEE.","author":[{"family":"Al-Jamimi","given":"H.A."},{"family":"Ahmed","given":"M.A."}],"citation-key":"Al-Jamimi201454","collection-title":"Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS","DOI":"10.1109/ICSESS.2014.6933513","editor":[{"family":"Prasad Babu M.S., Wenzheng L.","given":"Tsui E."}],"ISBN":"978-1-4799-3278-8","ISSN":"23270586","issued":{"date-parts":[[2014]]},"page":"54-57","publisher":"IEEE Computer Society","title":"Learning requirements analysis to software design transformation rules by examples: Limitations of current ILP systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910031359&doi=10.1109%2fICSESS.2014.6933513&partnerID=40&md5=0adb75c340297c72da2767ecac95adfb"},
{"id":"Al-Jamimi2015","abstract":"Model transformation by example is a novel trend in model-driven software engineering. The rationale behind this is to utilize existing knowledge represented by source and target models of previously developed systems; such as requirements analysis and software design models, respectively. Such knowledge can be utilized to derive transformation rules to be applied in future system developments. To achieve this goal, machine learning techniques can assist in discovering and formalizing desired transformation rules. Inductive Logic Programming (ILP) represents a highly applicable machine learning technique in this context. Given a set of examples and background knowledge encoded as a set of first-order logic descriptions, an ILP system attempts to derive rules describing different transformation steps in a purely declarative way. The induced rules follow the same logical description as the given examples and background knowledge. The objective of this work is to introduce initial setup of an ILP system that can be utilized to derive analysis-design transformation rules from a set of examples that represent pairs of analysis-design models. © 2014 IEEE.","author":[{"family":"Al-Jamimi","given":"H.A."},{"family":"Ahmed","given":"M.A."}],"citation-key":"Al-Jamimi2015","collection-title":"IEEE Region 10 Annual International Conference, Proceedings/TENCON","DOI":"10.1109/TENCON.2014.7022470","ISBN":"978-1-4799-4075-2","ISSN":"21593442","issued":{"date-parts":[[2015]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Knowledge acquisition in model driven development transformations: An inductive logic programming approach","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940511677&doi=10.1109%2fTENCON.2014.7022470&partnerID=40&md5=d8bd4ebdf40966d79f15887cb187fb0f","volume":"2015-January"},
{"id":"Al-Subaihin:2016:CMA:2961111.2962600","author":[{"family":"Al-Subaihin","given":"A. A."},{"family":"Sarro","given":"F."},{"family":"Black","given":"S."},{"family":"Capra","given":"L."},{"family":"Harman","given":"M."},{"family":"Jia","given":"Y."},{"family":"Zhang","given":"Y."}],"citation-key":"Al-Subaihin:2016:CMA:2961111.2962600","collection-title":"ESEM '16","container-title":"Proceedings of the 10th ACM/IEEE international symposium on empirical software engineering and measurement","event-place":"New York, NY, USA","ISBN":"978-1-4503-4427-2","issued":{"date-parts":[[2016]]},"page":"38:1-38:10","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Clustering mobile apps based on mined textual features","type":"paper-conference","URL":"http://doi.acm.org/10.1145/2961111.2962600"},
{"id":"Alaa2019","abstract":"Understanding the predictions of a machine learning model can be as crucial as the model's accuracy in many application domains. However, the black-box nature of most highly-accurate (complex) models is a major hindrance to their interpretability. To address this issue, we introduce the symbolic metamodeling framework - a general methodology for interpreting predictions by converting “black-box” models into “white-box” functions that are understandable to human subjects. A symbolic metamodel is a model of a model, i.e., a surrogate model of a trained (machine learning) model expressed through a succinct symbolic expression that comprises familiar mathematical functions and can be subjected to symbolic manipulation. We parameterize metamodels using Meijer G-functions - a class of complex-valued contour integrals that depend on real-valued parameters, and whose solutions reduce to familiar algebraic, analytic and closed-form functions for different parameter settings. This parameterization enables efficient optimization of metamodels via gradient descent, and allows discovering the functional forms learned by a model with minimal a priori assumptions. We show that symbolic metamodeling provides a generalized framework for model interpretation - many common forms of model explanation can be analytically derived from a symbolic metamodel. © 2019 Neural information processing systems foundation. All rights reserved.","author":[{"family":"Alaa","given":"A.M."},{"family":"Schaar","given":"M.","non-dropping-particle":"van der"}],"citation-key":"Alaa2019","collection-title":"Advances in Neural Information Processing Systems","ISSN":"10495258","issued":{"date-parts":[[2019]]},"publisher":"Neural information processing systems foundation","title":"Demystifying black-box models with symbolic metamodels","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078483653&partnerID=40&md5=e624a02d5e67fdb84876eed77ec60513","volume":"32"},
{"id":"alaaDemystifyingBlackboxModels2019a","abstract":"Understanding the predictions of a machine learning model can be as crucial as the model's accuracy in many application domains. However, the black-box nature of most highly-accurate (complex) models is a major hindrance to their interpretability. To address this issue, we introduce the symbolic metamodeling framework - a general methodology for interpreting predictions by converting “black-box” models into “white-box” functions that are understandable to human subjects. A symbolic metamodel is a model of a model, i.e., a surrogate model of a trained (machine learning) model expressed through a succinct symbolic expression that comprises familiar mathematical functions and can be subjected to symbolic manipulation. We parameterize metamodels using Meijer G-functions - a class of complex-valued contour integrals that depend on real-valued parameters, and whose solutions reduce to familiar algebraic, analytic and closed-form functions for different parameter settings. This parameterization enables efficient optimization of metamodels via gradient descent, and allows discovering the functional forms learned by a model with minimal a priori assumptions. We show that symbolic metamodeling provides a generalized framework for model interpretation - many common forms of model explanation can be analytically derived from a symbolic metamodel. © 2019 Neural information processing systems foundation. All rights reserved.","author":[{"family":"Alaa","given":"A.M."},{"family":"Schaar","given":"M.","non-dropping-particle":"van der"}],"citation-key":"alaaDemystifyingBlackboxModels2019a","container-title":"Advances in Neural Information Processing Systems","ISSN":"10495258","issued":{"date-parts":[[2019]]},"publisher":"Neural information processing systems foundation","title":"Demystifying black-box models with symbolic metamodels","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078483653&partnerID=40&md5=e624a02d5e67fdb84876eed77ec60513","volume":"32"},
{"id":"alaminEmpiricalStudyDeveloper2021","abstract":"Low-code software development (LCSD) is an emerging paradigm that combines minimal source code with interactive graphical interfaces to promote rapid application development. LCSD aims to democratize application development to software practitioners with diverse backgrounds. Given that LCSD is relatively a new paradigm, it is vital to learn about the challenges developers face during their adoption of LCSD platforms. The online developer forum, Stack Overflow (SO), is popular among software developers to ask for solutions to their technical problems. We observe a growing body of posts in SO with discussions of LCSD platforms. In this paper, we present an empirical study of around 5K SO posts (questions + accepted answers) that contain discussions of nine popular LCSD platforms. We apply topic modeling on the posts to determine the types of topics discussed. We find 13 topics related to LCSD in SO. The 13 topics are grouped into four categories: Customization, Platform Adoption, Database Management, and Third-Party Integration. More than 40% of the questions are about customization, i.e., developers frequently face challenges with customizing user interfaces or services offered by LCSD platforms. The topic \"Dynamic Event Handling\" under the \"Customization\" category is the most popular (in terms of average view counts per question of the topic) as well as the most difficult. It means that developers frequently search for customization solutions such as how to attach dynamic events to a form in low-code UI, yet most (75.9%) of their questions remain without an accepted answer. We manually label 900 questions from the posts to determine the prevalence of the topics' challenges across LCSD phases. We find that most of the questions are related to the development phase, and low-code developers also face challenges with automated testing.","accessed":{"date-parts":[[2022,3,22]]},"author":[{"family":"Alamin","given":"Md Abdullah Al"},{"family":"Malakar","given":"Sanjay"},{"family":"Uddin","given":"Gias"},{"family":"Afroz","given":"Sadia"},{"family":"Haider","given":"Tameem Bin"},{"family":"Iqbal","given":"Anindya"}],"citation-key":"alaminEmpiricalStudyDeveloper2021","container-title":"2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR)","DOI":"10.1109/MSR52588.2021.00018","issued":{"date-parts":[[2021,5]]},"page":"46-57","source":"arXiv.org","title":"An Empirical Study of Developer Discussions on Low-Code Software Development Challenges","type":"article-journal","URL":"http://arxiv.org/abs/2103.11429"},
{"id":"aldallalEmpiricalEvaluationImpact2018","accessed":{"date-parts":[[2018,1,23]]},"author":[{"family":"Al Dallal","given":"Jehad"},{"family":"Abdin","given":"Anas"}],"citation-key":"aldallalEmpiricalEvaluationImpact2018","container-title":"IEEE Transactions on Software Engineering","DOI":"10.1109/TSE.2017.2658573","ISSN":"0098-5589, 1939-3520","issue":"1","issued":{"date-parts":[[2018,1,1]]},"page":"44-69","source":"CrossRef","title":"Empirical Evaluation of the Impact of Object-Oriented Code Refactoring on Quality Attributes: A Systematic Literature Review","title-short":"Empirical Evaluation of the Impact of Object-Oriented Code Refactoring on Quality Attributes","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7833023/","volume":"44"},
{"id":"AlessioTonioniAutonomousFlightROSSimple","accessed":{"date-parts":[[2016,9,11]]},"citation-key":"AlessioTonioniAutonomousFlightROSSimple","title":"AlessioTonioni/Autonomous-Flight-ROS: A simple autopilot for a quadrotor realized using MoveIt!. The system use a simulated RGBD sensor to reconstruct the map, then ompl for path generation.","type":"webpage","URL":"https://github.com/AlessioTonioni/Autonomous-Flight-ROS"},
{"id":"alexanderCertificationAutonomousSystems2007","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Alexander","given":"Robert"},{"family":"Hall-May","given":"Martin"},{"family":"Kelly","given":"Tim"}],"citation-key":"alexanderCertificationAutonomousSystems2007","container-title":"Proceedings of the 2nd Systems Engineering for Autonomous Systems (SEAS) Defence Technology Centre (DTC) Annual Technical Conference","issued":{"date-parts":[[2007]]},"publisher":"Citeseer","source":"Google Scholar","title":"Certification of autonomous systems","type":"paper-conference","URL":"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.126.7288&rep=rep1&type=pdf"},
{"id":"alfonsoSelfadaptiveArchitecturesIoT2021","abstract":"Over the past few years, the relevance of the Internet of Things (IoT) has grown significantly and is now a key component of many industrial processes and even a transparent participant in various activities performed in our daily life. IoT systems are subjected to changes in the dynamic environments they operate in. These changes (e.g. variations in the bandith consumption or new devices joining/leaving) may impact the Quality of Service (QoS) of the IoT system. A number of self-adaptation strategies for IoT architectures to better deal with these changes have been proposed in the literature. Nevertheless, they focus on isolated types of changes. We lack a comprehensive view of the trade-offs of each proposal and how they could be combined to cope with dynamic situations involving simultaneous types of events. In this paper, we identify, analyze, and interpret relevant studies related to IoT adaptation and develop a comprehensive and holistic view of the interplay of different dynamic events, their consequences on the architecture QoS, and the alternatives for the adaptation. To do so, we have conducted a systematic literature review of existing scientific proposals and defined a research agenda for the near future based on the findings and weaknesses identified in the literature.","accessed":{"date-parts":[[2021,10,4]]},"author":[{"family":"Alfonso","given":"Iván"},{"family":"Garcés","given":"Kelly"},{"family":"Castro","given":"Harold"},{"family":"Cabot","given":"Jordi"}],"citation-key":"alfonsoSelfadaptiveArchitecturesIoT2021","container-title":"arXiv:2109.03312 [cs]","issued":{"date-parts":[[2021,9,7]]},"note":"00000","source":"arXiv.org","title":"Self-adaptive Architectures in IoT Systems: A Systematic Literature Review","title-short":"Self-adaptive Architectures in IoT Systems","type":"article-journal","URL":"http://arxiv.org/abs/2109.03312"},
{"id":"Ali20192979","abstract":"The work reported in this paper presents a novel hierarchical modular neural network architecture (HMNNA) for automated screening of cervical cancer. HMNNA consists of three neural networks trained specifically on different areas of problem space under consideration, and the trained networks are then arranged in a tree structure forming hierarchical modular neural network architecture. The three specialized neural networks are trained by Levenberg–Maarquardt neural network algorithm. As compared to the standard back propagation algorithm, Levenberg–Maarquardt is fast and stable for convergence with only one drawback, i.e., storage requirement for estimated Hessian Matrix. For training and testing of HMNNA, a huge primary database is created which contains 8091 cervical cell images pertaining to 200 clinical cases collected from two health care institutions of northern India. The raw cases of cervical cancer in the form of Pap smear slides were photographed under a multi-headed digital microscope. Individual cells were manually cropped off from these slide images which were then passed through a feature extraction module for morphological profiling. Each cell was calibrated on the basis of 40 features from both cytoplasm and nucleus. After profiling, these cells were vigilantly assigned cell classes as per the latest 2001-Bethesda system of cervical cancer cell classification, by trained cytotechnicians and histopathologists. HMNNA is also trained and tested on the Herlev Benchmark dataset created by the Denmark University, which consists of 1417 cervical cancer cells. Both the datasets have seven classes of diagnosis, i.e., superficial squamous, intermediate squamous, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ, corresponding to the level of abnormality in cervical cells. These datasets are available in public domain at http://digitalpapsmeardb.in/ and http://mde-lab.aegean.gr/index.php/downloads. The screening potential of the HMNNA is compared with 25 well-known machine learning algorithms available in MatlabR2016 (Machine learning and statistics toolbox 10.2) and monolithic neural network algorithms available in Matlab neural network pattern recognition toolbox. The HMNNA outperformed in all the 25 algorithms for both the datasets. For the Novel Benchmark database, it produced a classification accuracy of 95.32% with an F-value of 0.949310 and classification accuracy of 88.41% with an F-value of 0.89145 for the Herlev dataset. The screening potential of HMNNA was also evaluated and compared with the other diagnostic systems available in the recently published literature and was found to be performing much better than the counterparts on multiple parameters of performance evaluation. © 2017, The Natural Computing Applications Forum.","author":[{"family":"Ali","given":"M."},{"family":"Sarwar","given":"A."},{"family":"Sharma","given":"V."},{"family":"Suri","given":"J."}],"citation-key":"Ali20192979","container-title":"Neural Computing and Applications","DOI":"10.1007/s00521-017-3246-7","ISSN":"09410643","issue":"7","issued":{"date-parts":[[2019]]},"page":"2979-2993","publisher":"Springer London","title":"Artificial neural network based screening of cervical cancer using a hierarchical modular neural network architecture (HMNNA) and novel benchmark uterine cervix cancer database","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032804156&doi=10.1007%2fs00521-017-3246-7&partnerID=40&md5=19b285387ba1c18e57778635149e63a8","volume":"31"},
{"id":"Ali2019421","abstract":"Artificial Intelligence (AI) has been around for many years and plays a vital role in developing automatic systems that require decision using a data- or model-driven approach. Smart homes are one such system; in them, AI is used to recognize user activities, which is a fundamental task in smart home system design. There are many approaches to this challenge, but data-driven activity recognition approaches are currently perceived the most promising to address the sensor selection uncertainty problem. However, a smart home using a data-driven approach exclusively cannot immediately provide its new occupant with the expected functionality, which has reduced the popularity of the data-driven approach. This paper proposes an approach to develop an integrated personalized system using a user-centric approach comprising survey, simulation, activity recognition and transfer learning. This system will optimize the behaviour of the house using information from the user’s experience and provide required services. The proposed approach has been implemented in a smart home and validated with actual users. The validation results indicate that users benefited from smart features as soon as they move into the new home. © 2019, Springer Nature Switzerland AG.","author":[{"family":"Ali","given":"S.M.M."},{"family":"Augusto","given":"J.C."},{"family":"Windridge","given":"D."}],"citation-key":"Ali2019421","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-34885-4_32","editor":[{"family":"Bramer M.","given":"Petridis M."}],"ISBN":"9783030348847","ISSN":"03029743","issued":{"date-parts":[[2019]]},"page":"421-434","publisher":"Springer","title":"Improving the adaptation process for a new smart home user","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076985697&doi=10.1007%2f978-3-030-34885-4_32&partnerID=40&md5=73df214e2970e32a374c51f12bb1171e","volume":"11927 LNAI"},
{"id":"Ali20221034","abstract":"In this paper, we propose to use commercial off-the-shelf (COTS) monostatic RFID devices (i.e. which use a single antenna at a time for both transmitting and receiving RFID signals to and from the tags) to monitor browsing activity of customers in front of display items in places such as retail stores. To this end, we propose TagSee, a multi-person imaging system based on monostatic RFID imaging. TagSee is based on the insight that when customers are browsing the items on a shelf, they stand between the tags deployed along the boundaries of the shelf and the reader, which changes the multi-paths that the RFID signals travel along, and both the RSS and phase values of the RFID signals that the reader receives change. Based on these variations observed by the reader, TagSee constructs a coarse grained image of the customers. Afterwards, TagSee identifies the items that are being browsed by the customers by analyzing the constructed images. The key novelty of this paper is on achieving browsing behavior monitoring of multiple customers in front of display items by constructing coarse grained images via robust, analytical model-driven deep learning based, RFID imaging. To achieve this, we first mathematically formulate the problem of imaging humans using monostatic RFID devices and derive an approximate analytical imaging model that correlates the variations caused by human obstructions in the RFID signals. Based on this model, we then develop a deep learning framework to robustly image customers with high accuracy. We implement TagSee scheme using a Impinj Speedway R420 reader and SMARTRAC DogBone RFID tags. TagSee can achieve a TPR of more than ∼90%∼90% and a FPR of less than ∼10%∼10% in multi-person scenarios using training data from just 3-4 users. © 2002-2012 IEEE.","author":[{"family":"Ali","given":"K."},{"family":"Liu","given":"A.X."},{"family":"Chai","given":"E."},{"family":"Sundaresan","given":"K."}],"citation-key":"Ali20221034","container-title":"IEEE Transactions on Mobile Computing","DOI":"10.1109/TMC.2020.3019652","ISSN":"15361233","issue":"3","issued":{"date-parts":[[2022]]},"page":"1034-1048","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Monitoring browsing behavior of customers in retail stores via RFID imaging","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124595301&doi=10.1109%2fTMC.2020.3019652&partnerID=40&md5=37f014b9c29285dfa3a36bd993566bf4","volume":"21"},
{"id":"AliNaqvi2021521","abstract":"Testing and code reviews are known techniques to improve the quality and robustness of software. Unfortunately, the complexity of modern software systems makes it impossible to anticipate all possible problems that can occur at runtime, which limits what issues can be found using testing and reviews. Thus, it is of interest to consider autonomous self-healing software systems, which can automatically detect, diagnose, and contain unanticipated problems at runtime. Most research in this area has adopted a model-driven approach, where actual behavior is checked against a model specifying the intended behavior, and a controller takes action when the system behaves outside of the specification. However, it is not easy to develop these specifications, nor to keep them up-to-date as the system evolves. We pose that, with the recent advances in machine learning, such models may be learned by observing the system. Moreover, we argue that artificial immune systems (AISs) are particularly well-suited for building self-healing systems, because of their anomaly detection and diagnosis capabilities. We present the state-of-the-art in self-healing systems and in AISs, surveying some of the research directions that have been considered up to now. To help advance the state-of-the-art, we develop a research agenda for building self-healing software systems using AISs, identifying required foundations, and promising research directions. © 2021 IEEE.","author":[{"family":"Ali Naqvi","given":"M."},{"family":"Astekin","given":"M."},{"family":"Malik","given":"S."},{"family":"Moonen","given":"L."}],"citation-key":"AliNaqvi2021521","collection-title":"Proceedings - 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2021","DOI":"10.1109/SANER50967.2021.00058","ISBN":"978-1-72819-630-5","issued":{"date-parts":[[2021]]},"page":"521-525","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Adaptive immunity for software: Towards autonomous self-healing systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106590420&doi=10.1109%2fSANER50967.2021.00058&partnerID=40&md5=863c0555388bb547463814d901c2bdb7"},
{"id":"allenEngineeringAcademicSoftware2017","accessed":{"date-parts":[[2017,5,30]]},"author":[{"family":"Allen","given":"Alice"},{"family":"Aragon","given":"Cecilia"},{"family":"Becker","given":"Christoph"},{"family":"Carver","given":"Jeffrey"},{"family":"Chis","given":"Andrei"},{"family":"Combemale","given":"Benoit"},{"family":"Croucher","given":"Mike"},{"family":"Crowston","given":"Kevin"},{"family":"Garijo","given":"Daniel"},{"family":"Gehani","given":"Ashish"},{"literal":"others"}],"citation-key":"allenEngineeringAcademicSoftware2017","collection-number":"1","container-title":"Dagstuhl Manifestos","issued":{"date-parts":[[2017]]},"publisher":"Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik","source":"Google Scholar","title":"Engineering Academic Software (Dagstuhl Perspectives Workshop 16252)","type":"chapter","URL":"http://drops.dagstuhl.de/opus/volltexte/2017/7146/","volume":"6"},
{"id":"allhoffInternetThingsFoundational2018","abstract":"This paper surveys foundational ethical issues that attach to the Internet of Things (IoT). In Section 1, we provide an overview of the technology, indicating both current and future applications. Subsequent sections consider particular ethical issues, including: informed consent (Section 2), privacy (Section 3), information security (Section 4), physical safety (Section 5), and trust (Section 6). Section 7 emphasizes that these ethical issues do not exist in isolation, but converge and intersect in myriad ways. And that these issues are not comprehensive, but rather are foundational starting points that stand to be expanded and further elucidated through future research.","accessed":{"date-parts":[[2018,11,7]]},"author":[{"family":"Allhoff","given":"Fritz"},{"family":"Henschke","given":"Adam"}],"citation-key":"allhoffInternetThingsFoundational2018","container-title":"Internet of Things","DOI":"10.1016/j.iot.2018.08.005","ISSN":"25426605","issued":{"date-parts":[[2018,9]]},"page":"55-66","source":"Crossref","title":"The Internet of Things: Foundational ethical issues","title-short":"The Internet of Things","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2542660518300532","volume":"1-2"},
{"id":"almonteAutomatingConstructionRecommender2020","abstract":"Low-code development platforms allow users with a low technical background to build complete software solutions, typically by means of graphical user interfaces, diagrams or declarative languages. In these platforms, recommender systems play an important role as they can provide users with relevant, personalised suggestions generated according to previously developed software solutions. However, developing recommender systems requires a high investment of time as it implies the selection and implementation of a suitable recommendation method, its configuration for the problem and domain at hand, and its evaluation to assess the accuracy of its recommendations.","author":[{"family":"Almonte","given":"Lissette"},{"family":"Cantador","given":"Iván"},{"family":"Guerra","given":"Esther"}],"citation-key":"almonteAutomatingConstructionRecommender2020","issued":{"date-parts":[[2020]]},"page":"10","source":"Zotero","title":"Towards automating the construction of recommender systems for low-code development platforms","type":"article-journal"},
{"id":"almonteRecommenderSystemsModeldriven2021","abstract":"Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and modelbased development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research.","accessed":{"date-parts":[[2021,11,17]]},"author":[{"family":"Almonte","given":"Lissette"},{"family":"Guerra","given":"Esther"},{"family":"Cantador","given":"Iván"},{"family":"Lara","given":"Juan","non-dropping-particle":"de"}],"citation-key":"almonteRecommenderSystemsModeldriven2021","container-title":"Software and Systems Modeling","container-title-short":"Softw Syst Model","DOI":"10.1007/s10270-021-00905-x","ISSN":"1619-1366, 1619-1374","issued":{"date-parts":[[2021,7,26]]},"note":"00000","source":"DOI.org (Crossref)","title":"Recommender systems in model-driven engineering: A systematic mapping review","title-short":"Recommender systems in model-driven engineering","type":"article-journal","URL":"https://link.springer.com/10.1007/s10270-021-00905-x"},
{"id":"almorsySuiteDomainspecificVisual2013","accessed":{"date-parts":[[2017,2,23]]},"author":[{"family":"Almorsy","given":"Mohamed"},{"family":"Grundy","given":"John"},{"family":"Sadus","given":"Richard"},{"family":"Straten","given":"Willem","non-dropping-particle":"van"},{"family":"Barnes","given":"David G."},{"family":"Kaluza","given":"Owen"}],"citation-key":"almorsySuiteDomainspecificVisual2013","container-title":"Visual Languages and Human-Centric Computing (VL/HCC), 2013 IEEE Symposium on","issued":{"date-parts":[[2013]]},"page":"91–94","publisher":"IEEE","source":"Google Scholar","title":"A suite of domain-specific visual languages for scientific software application modelling","type":"paper-conference","URL":"http://ieeexplore.ieee.org/abstract/document/6645249/"},
{"id":"alomranChoosingNLPLibrary2017","accessed":{"date-parts":[[2018,1,31]]},"author":[{"family":"Al Omran","given":"Fouad Nasser A"},{"family":"Treude","given":"Christoph"}],"citation-key":"alomranChoosingNLPLibrary2017","DOI":"10.1109/MSR.2017.42","ISBN":"978-1-5386-1544-7","issued":{"date-parts":[[2017,5]]},"page":"187-197","publisher":"IEEE","source":"CrossRef","title":"Choosing an NLP Library for Analyzing Software Documentation: A Systematic Literature Review and a Series of Experiments","title-short":"Choosing an NLP Library for Analyzing Software Documentation","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/7962368/"},
{"id":"Alreshedy2018SCCAC","author":[{"family":"Alreshedy","given":"Kamel"},{"family":"Dharmaretnam","given":"Dhanush"},{"family":"German","given":"Daniel M."},{"family":"Srinivasan","given":"Venkatesh"},{"family":"Gulliver","given":"T. Aaron"}],"citation-key":"Alreshedy2018SCCAC","container-title":"CoRR","issued":{"date-parts":[[2018]]},"title":"SCC: Automatic classification of code snippets","type":"article-journal","volume":"abs/1809.07945"},
{"id":"alrubayeUseInformationRetrieval2019","abstract":"The migration process between different third-party libraries is hard, complex and error-prone. Typically, during a library migration, developers need to find methods in the new library that are most adequate in replacing the old methods of the retired library. This process is subjective and time-consuming as developers need to fully understand the documentation of both libraries' Application Programming Interfaces, and find the right matching between their methods, if it exists. In this context, several studies rely on mining existing library migrations to provide developers with by-example approaches for similar scenarios. In this paper, we introduce a novel mining approach that extracts existing instances of library method replacements that are manually performed by developers for a given library migration to automatically generate migration patterns in the method level. Thereafter, our approach combines the mined method-change patterns with method-related lexical similarity to accurately detect mappings between replacing/replaced methods. We conduct a large scale empirical study to evaluate our approach on a benchmark of 57,447 open-source Java projects leading to 9 popular library migrations. Our qualitative results indicate that our approach significantly increases the accuracy of mining method-level mappings by an average accuracy of 12%, as well as increasing the number of discovered method mappings, in comparison with existing state-of-the-art studies. Finally, we provide the community with an open source mining tool along with a dataset of all mined migrations at the method level.","author":[{"family":"Alrubaye","given":"Hussein"},{"family":"Mkaouer","given":"Mohamed Wiem"},{"family":"Ouni","given":"Ali"}],"citation-key":"alrubayeUseInformationRetrieval2019","container-title":"2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC)","DOI":"10.1109/ICPC.2019.00053","event":"2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC)","ISSN":"2643-7147","issued":{"date-parts":[[2019,5]]},"note":"00000","page":"347-357","source":"IEEE Xplore","title":"On the Use of Information Retrieval to Automate the Detection of Third-Party Java Library Migration at the Method Level","type":"paper-conference"},
{"id":"alsrehinIntelligentTransportationControl2019","abstract":"Traffic congestion is becoming the issues of the entire globe. This study aims to explore and review the data mining and machine learning technologies adopted in research and industry to attempt to overcome the direct and indirect traffic issues on humanity and societies. The study’s methodology is to comprehensively review around 165 studies, criticize, and categorize all these studies into a chronological and understandable category. The study is focusing on the traffic management approaches that were depended on data mining and machine learning technologies to detect and predict the traffic only. This study has found that there is no standard traffic management approach that the community of traffic management has agreed on. This study is important to the traffic research communities, traffic software companies, and traffic government officials. It has a direct impact on drawing a clear path for new traffic management propositions. This study is one of the largest studies with respect to the size of its reviewed articles that were focused on data mining and machine learning. Additionally, this study will draw general attention to a new traffic management proposition approach.","accessed":{"date-parts":[[2022,2,3]]},"author":[{"family":"Alsrehin","given":"Nawaf O."},{"family":"Klaib","given":"Ahmad F."},{"family":"Magableh","given":"Aws"}],"citation-key":"alsrehinIntelligentTransportationControl2019","container-title":"IEEE Access","container-title-short":"IEEE Access","DOI":"10.1109/ACCESS.2019.2909114","ISSN":"2169-3536","issued":{"date-parts":[[2019]]},"page":"49830-49857","source":"DOI.org (Crossref)","title":"Intelligent Transportation and Control Systems Using Data Mining and Machine Learning Techniques: A Comprehensive Study","title-short":"Intelligent Transportation and Control Systems Using Data Mining and Machine Learning Techniques","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/8681028/","volume":"7"},
{"id":"altulyanRecommenderSystemsInternet2020","abstract":"Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things (IoT). Traditional recommender systems fail to exploit ever-growing, dynamic, and heterogeneous IoT data. This paper presents a comprehensive review of the state-of-the-art recommender systems, as well as related techniques and application in the vibrant field of IoT. We discuss several limitations of applying recommendation systems to IoT and propose a reference framework for comparing existing studies to guide future research and practices.","accessed":{"date-parts":[[2020,12,14]]},"author":[{"family":"Altulyan","given":"May"},{"family":"Yao","given":"Lina"},{"family":"Wang","given":"Xianzhi"},{"family":"Huang","given":"Chaoran"},{"family":"Kanhere","given":"Salil S."},{"family":"Sheng","given":"Quan Z."}],"citation-key":"altulyanRecommenderSystemsInternet2020","container-title":"arXiv:2007.06758 [cs, stat]","issued":{"date-parts":[[2020,7,13]]},"note":"00000","source":"arXiv.org","title":"Recommender Systems for the Internet of Things: A Survey","title-short":"Recommender Systems for the Internet of Things","type":"article-journal","URL":"http://arxiv.org/abs/2007.06758"},
{"id":"alurSystemsComputingChallenges2016","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Alur","given":"Rajeev"},{"family":"Berger","given":"Emery"},{"family":"Drobnis","given":"Ann W."},{"family":"Fix","given":"Limor"},{"family":"Fu","given":"Kevin"},{"family":"Hager","given":"Gregory D."},{"family":"Lopresti","given":"Daniel"},{"family":"Nahrstedt","given":"Klara"},{"family":"Mynatt","given":"Elizabeth"},{"family":"Patel","given":"Shwetak"},{"literal":"others"}],"citation-key":"alurSystemsComputingChallenges2016","container-title":"arXiv preprint arXiv:1604.02980","issued":{"date-parts":[[2016]]},"source":"Google Scholar","title":"Systems Computing Challenges in the Internet of Things","type":"article-journal","URL":"http://arxiv.org/abs/1604.02980"},
{"id":"alvarezMTCFlowTool2013","author":[{"family":"Alvarez","given":"Camilo"},{"family":"Casallas","given":"Rubby"}],"citation-key":"alvarezMTCFlowTool2013","container-title":"Proceedings of the workshop on ACadeMics Tooling with Eclipse - ACME '13","DOI":"10.1145/2491279.2491286","issued":{"date-parts":[[2013]]},"page":"1–9","title":"MTC Flow: a tool to design, develop and deploy model transformation chains","type":"article-journal"},
{"id":"alvinoLessonsLearnedLarge","author":[{"family":"Alvino","given":"Chris"}],"citation-key":"alvinoLessonsLearnedLarge","page":"22","source":"Zotero","title":"Lessons Learned from Large Scale Real World Recommender Systems","type":"article-journal"},
{"id":"AmbitiousPlan","abstract":"A plan for projects and related publications Project Workshops/ Doctoral Symposium Conference Journal Leading role Other members of the group mainly involved Work to be done Note 1 CrossSim SEAA 2018 SQJ Phuong Riccardo Wait for the notification from SQJ Response to revie...","accessed":{"date-parts":[[2020,2,11]]},"citation-key":"AmbitiousPlan","container-title":"Google Docs","title":"An ambitious plan :-)","title-short":"An ambitious plan","type":"webpage","URL":"https://docs.google.com/document/d/1uWyVw2JEI6A6KcB1kMYx_9sXqTSgy8r5CfWc1xQSHRM/edit?ts=5be56d68&usp=embed_facebook"},
{"id":"amershiSoftwareEngineeringMachine2019","abstract":"Recent advances in machine learning have stimulated widespread interest within the Information Technology sector on integrating AI capabilities into software and services. This goal has forced organizations to evolve their development processes. We report on a study that we conducted on observing software teams at Microsoft as they develop AI-based applications. We consider a nine-stage workflow process informed by prior experiences developing AI applications (e.g., search and NLP) and data science tools (e.g. application diagnostics and bug reporting). We found that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace. We collected some best practices from Microsoft teams to address these challenges. In addition, we have identified three aspects of the AI domain that make it fundamentally different from prior software application domains: 1) discovering, managing, and versioning the data needed for machine learning applications is much more complex and difficult than other types of software engineering, 2) model customization and model reuse require very different skills than are typically found in software teams, and 3) AI components are more difficult to handle as distinct modules than traditional software components — models may be “entangled” in complex ways and experience non-monotonic error behavior. We believe that the lessons learned by Microsoft teams will be valuable to other organizations.","accessed":{"date-parts":[[2020,7,9]]},"author":[{"family":"Amershi","given":"Saleema"},{"family":"Begel","given":"Andrew"},{"family":"Bird","given":"Christian"},{"family":"DeLine","given":"Robert"},{"family":"Gall","given":"Harald"},{"family":"Kamar","given":"Ece"},{"family":"Nagappan","given":"Nachiappan"},{"family":"Nushi","given":"Besmira"},{"family":"Zimmermann","given":"Thomas"}],"citation-key":"amershiSoftwareEngineeringMachine2019","container-title":"2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","DOI":"10.1109/ICSE-SEIP.2019.00042","event":"2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","event-place":"Montreal, QC, Canada","ISBN":"978-1-72811-760-7","issued":{"date-parts":[[2019,5]]},"note":"00000","page":"291-300","publisher":"IEEE","publisher-place":"Montreal, QC, Canada","source":"DOI.org (Crossref)","title":"Software Engineering for Machine Learning: A Case Study","title-short":"Software Engineering for Machine Learning","type":"paper-conference","URL":"https://ieeexplore.ieee.org/document/8804457/"},
{"id":"amine_benelallam_2018_1489120","author":[{"family":"Benelallam","given":"Amine"},{"family":"Harrand","given":"Nicolas"},{"family":"Valero","given":"César Soto"},{"family":"Baudry","given":"Benoit"},{"family":"Barais","given":"Olivier"}],"citation-key":"amine_benelallam_2018_1489120","DOI":"10.5281/zenodo.1489120","issued":{"date-parts":[[2018,11]]},"title":"Maven central dependency graph","type":"article-journal","URL":"https://doi.org/10.5281/zenodo.1489120"},
{"id":"Ammar2018247","abstract":"Automatic planning is a separate discipline of Artificial Intelligence (AI). It aims to formalize the planning problems described by the concept of state space. The Planning Domain Definition Language (PDDL) is a de facto standard language in the field of automatic planning. PDDL-related dynamic analysis tools, namely planners and validators, are insufficient for verifying and validating PDDL descriptions. Such tools make it possible to detect errors a posteriori by means of a test activity. In this article, we recommend a rigorous approach coupling Event-B and PDDL for automatic planning. Event-B is used for formal modeling by stepwise refinement with mathematical proofs of planning problems. A refinement strategy appropriate to planning problems is, then, proposed. The ultimate Event-B model, correct by construction, supposed to be translatable into PDDL, is automatically translated into PDDL using our MDE Event-B2PDDL tool. The obtained PDDL description is submitted to efficient planners for generation of solution plans. © Springer Nature Switzerland AG 2018.","author":[{"family":"Ammar","given":"S."},{"family":"Bhiri","given":"M.T."}],"citation-key":"Ammar2018247","container-title":"Communications in Computer and Information Science","DOI":"10.1007/978-3-030-02852-7_21","editor":[{"family":"Golfarelli M., Bellatreche L.","given":"Jean S.","suffix":"Nakamatsu K., Ordonez C., Mery D., Benslimane D., Abdelwahed E.H."}],"ISBN":"9783030028510","ISSN":"18650929","issued":{"date-parts":[[2018]]},"page":"247-254","publisher":"Springer Verlag","title":"Automatic planning: From event-B to PDDL","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055804834&doi=10.1007%2f978-3-030-02852-7_21&partnerID=40&md5=9fb935147bb979c69c4f03ba984df34b","volume":"929"},
{"id":"Ammar2021261","abstract":"In artificial intelligence, the goal of automatic planning is to structure actions in the form of a plan to achieve an expressed goal. The PDDL (Planning Domain Definition Language) was designed to allow the common representation of planning problems during ICAPS (International Conference on Automated Planning and Scheduling) competitions. PDDL has many verification and validation tools allowing the description, resolution and validation of planning problems. But they only allow the reliability of PDDL descriptions a posteriori. In this article, we recommend a rigorous approach coupling Event-B and PDDL favoring obtaining PDDL descriptions deemed correct, a priori, from an ultimate Event-B model. The formal Event-B method allows us to obtain, by successive refinements with mathematical proofs, correct by construction formal models of planning problems. A refinement strategy appropriate to planning problems is, then, proposed. The ultimate Event-B model, correct by construction, is automatically translated into PDDL using our MDE Event-B2PDDL tool. The obtained PDDL description is submitted to efficient planners for generation of correct and efficient plan-solutions. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved","author":[{"family":"Ammar","given":"S."},{"family":"Bhiri","given":"M.T."}],"citation-key":"Ammar2021261","collection-title":"Proceedings of the 16th International Conference on Software Technologies, ICSOFT 2021","DOI":"10.5220/0010577102610268","editor":[{"family":"Fill H.-G., van Sinderen M.","given":"Maciaszek L.","suffix":"Maciaszek L."}],"ISBN":"978-989-758-523-4","issued":{"date-parts":[[2021]]},"page":"261-268","publisher":"SciTePress","title":"A formal approach combining event-b and pddl for planning problems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111720896&doi=10.5220%2f0010577102610268&partnerID=40&md5=3bf2d59aadd4eb4e842e8c47733c6c8d"},
{"id":"amraniTridimensionalApproachStudying2012","abstract":"In Model Driven Engineering (MDE), models are first-class citizens, and model transformation is MDE's \"heart and soul\". Since model transformations are executed for a family of conforming models, their validity becomes a crucial issue. This paper proposes to explore the question of the formal verification of model transformation properties through a tri-dimensional approach: the transformation involved, the properties of interest addressed, and the formal verification techniques used to establish the properties. This work allows a better understanding of the expected properties for a particular transformation, and facilitates the identification of the suitable tools and techniques for enabling their verification.","author":[{"family":"Amrani","given":"M."},{"family":"Lucio","given":"L."},{"family":"Selim","given":"G."},{"family":"Combemale","given":"B."},{"family":"Dingel","given":"J."},{"family":"Vangheluwe","given":"H."},{"family":"Le Traon","given":"Y."},{"family":"Cordy","given":"J.R."}],"citation-key":"amraniTridimensionalApproachStudying2012","container-title":"2012 IEEE Fifth International Conference on Software Testing, Verification and Validation (ICST)","DOI":"10.1109/ICST.2012.197","event":"2012 IEEE Fifth International Conference on Software Testing, Verification and Validation (ICST)","issued":{"date-parts":[[2012,4]]},"page":"921-928","source":"IEEE Xplore","title":"A Tridimensional Approach for Studying the Formal Verification of Model Transformations","type":"paper-conference"},
{"id":"An20211999","abstract":"With the development of technology, new complex systems such as human cyber-physical systems (hCPS) have become indistinguishable from social life. The cyberspace where the software system located is increasingly integrated with the physical space of people's daily life. The uncertain factors such as the dynamic environment in the physical space, the explosive growth of the spatio- temporal data, as well as the unpredictable human behavior are all compromise the security of the system. As a result of the increasing security requirements, the scale and complexity of the system are also increasing. This situation leads to a series of problems that remain unresolved. Therefore, developing intelligent and safe human cyber-physical systems under uncertain environment is becoming the inevitable challenge for the software industry. It is difficult for the human cyber-physical systems to perceive the runtime environment accurately under uncertain surroundings. The uncertain perception will lead to the system's misinterpretation, thus affecting the security of the system. It is difficult for the system designers to construct formal specifications for the human cyber-physical systems under uncertain environment. For safety-critical systems, formal specifications are the prerequisites to ensure system security. To cope with the uncertainty of the specifications, a combination of data-driven and model-driven modeling methodology is proposed, that is, the machine learning-based algorithms are used to model the environment based on spatio-temporal data. An approach is introduced to integrate machine learning method and runtime verification technology as a unified framework to ensure the safety of the human cyber-physical systems. The proposed approach is illustrated by modeling and analyzing a scenario of the interaction of an autonomous vehicle and a human-driven motorbike. © Copyright 2021, Institute of Software, the Chinese Academy of Sciences. All rights reserved.","author":[{"family":"An","given":"D.-D."},{"family":"Liu","given":"J."},{"family":"Chen","given":"X.-H."},{"family":"Sun","given":"H.-Y."}],"citation-key":"An20211999","container-title":"Ruan Jian Xue Bao/Journal of Software","DOI":"10.13328/j.cnki.jos.006272","ISSN":"10009825","issue":"7","issued":{"date-parts":[[2021]]},"page":"1999-2015","publisher":"Chinese Academy of Sciences","title":"Formal modeling and dynamic verification for human cyber physical systems under uncertain environment [不确定环境下hCPS系统的形式化建模与动态验证]","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109022509&doi=10.13328%2fj.cnki.jos.006272&partnerID=40&md5=47abe1df92ab3cbdd6093fbee9006bec","volume":"32"},
{"id":"AnalisiSperimentazioneDi","citation-key":"AnalisiSperimentazioneDi","note":"00000","title":"Analisi e sperimentazione di Algoritmi di Outlier Detection in Sistemi GDO","type":"thesis"},
{"id":"AnalysisLicenseInconsistency","citation-key":"AnalysisLicenseInconsistency","title":"Analysis of license inconsistency in large collections of open source projects","type":"article-newspaper","URL":"http://rdcu.be/tez8"},
{"id":"AnalysisMetamodelingPractices","accessed":{"date-parts":[[2015,6,12]]},"citation-key":"AnalysisMetamodelingPractices","title":"An analysis of metamodeling practices for MOF and OCL","type":"webpage","URL":"http://www.sciencedirect.com/science/article/pii/S1477842415000068"},
{"id":"AnalyzeUnderstandText","accessed":{"date-parts":[[2021,2,1]]},"citation-key":"AnalyzeUnderstandText","note":"00000","title":"Analyze and Understand Text: Guide to Natural Language Processing - Strumenta","type":"webpage","URL":"https://tomassetti.me/guide-natural-language-processing/?utm_source=newsletter&utm_medium=email&utm_campaign=onboardingsequence"},
{"id":"Anavangot20216314","abstract":"Classical quantizer design approaches using the Lloyd-Max algorithm (or k-means) have served signal processing applications for more than three decades. With the advent of distributed signal processing and machine learning at edge devices, novel alternatives for quantizers design will be desired to address the energy, communication and hardware constraints. To address these resource challenges, we propose a model-driven approach, termed Approximate Lloyd-Max (ALM) design, based on piecewise linear approximation of the signal-source probability density. From the principles of the ALM design, we develop a data-driven quantizer, or Learning ALM (LALM), using statistical learning methods. By mathematical analysis, we show convergence of the ALM quantizer near the limit of the Lloyd-Max quantizer. Both ALM and LALM quantizers satisfy asymptotic optimality and exponential convergence rate. Simulation performed over smooth signal source distributions validate our mathematical analysis. Experiments for LALM quantizer are implemented on an Android-based edge device, and the proposed quantizer demonstrate improved performance over k-means, in terms of algorithm speedup, energy usage and memory utilization. © 1991-2012 IEEE.","author":[{"family":"Anavangot","given":"V."},{"family":"Kumar","given":"A."}],"citation-key":"Anavangot20216314","container-title":"IEEE Transactions on Signal Processing","DOI":"10.1109/TSP.2021.3125602","ISSN":"1053587X","issued":{"date-parts":[[2021]]},"page":"6314-6328","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Signal source distribution approximation to speedup scalar quantizer design","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121055537&doi=10.1109%2fTSP.2021.3125602&partnerID=40&md5=27c6e507babfb0388659ecaf10a2451a","volume":"69"},
{"id":"Anderson:2006:LTW:1197299","author":[{"family":"Anderson","given":"Chris"}],"citation-key":"Anderson:2006:LTW:1197299","ISBN":"1-4013-0237-8","issued":{"date-parts":[[2006]]},"publisher":"Hyperion","title":"The long tail: Why the future of business is selling less of more","type":"book"},
{"id":"andSwingSWTBack2010","author":[{"literal":"and"}],"citation-key":"andSwingSWTBack2010","container-title":"2010 IEEE international conference on software maintenance","DOI":"10.1109/ICSM.2010.5610429","ISSN":"1063-6773","issued":{"date-parts":[[2010,9]]},"page":"1-10","title":"Swing to SWT and back: Patterns for API migration by wrapping","type":"paper-conference"},
{"id":"anelliElliotComprehensiveRigorous2021","abstract":"Recommender Systems have shown to be an effective way to alleviate the over-choice problem and provide accurate and tailored recommendations. However, the impressive number of proposed recommendation algorithms, splitting strategies, evaluation protocols, metrics, and tasks, has made rigorous experimental evaluation particularly challenging. Puzzled and frustrated by the continuous recreation of appropriate evaluation benchmarks, experimental pipelines, hyperparameter optimization, and evaluation procedures, we have developed an exhaustive framework to address such needs. Elliot is a comprehensive recommendation framework that aims to run and reproduce an entire experimental pipeline by processing a simple configuration file. The framework loads, filters, and splits the data considering a vast set of strategies (13 splitting methods and 8 filtering approaches, from temporal training-test splitting to nested K-folds Cross-Validation). Elliot optimizes hyperparameters (51 strategies) for several recommendation algorithms (50), selects the best models, compares them with the baselines providing intra-model statistics, computes metrics (36) spanning from accuracy to beyond-accuracy, bias, and fairness, and conducts statistical analysis (Wilcoxon and Paired t-test). The aim is to provide the researchers with a tool to ease (and make them reproducible) all the experimental evaluation phases, from data reading to results collection. Elliot is available on GitHub (https://github.com/sisinflab/elliot).","accessed":{"date-parts":[[2021,3,9]]},"author":[{"family":"Anelli","given":"Vito Walter"},{"family":"Bellogín","given":"Alejandro"},{"family":"Ferrara","given":"Antonio"},{"family":"Malitesta","given":"Daniele"},{"family":"Merra","given":"Felice Antonio"},{"family":"Pomo","given":"Claudio"},{"family":"Donini","given":"Francesco Maria"},{"family":"Di Noia","given":"Tommaso"}],"citation-key":"anelliElliotComprehensiveRigorous2021","container-title":"arXiv:2103.02590 [cs]","issued":{"date-parts":[[2021,3,3]]},"note":"00000","source":"arXiv.org","title":"Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation","title-short":"Elliot","type":"article-journal","URL":"http://arxiv.org/abs/2103.02590"},
{"id":"anelliSemanticInterpretationTopN2020","abstract":"Over the years, model-based approaches have shown their effectiveness in computing recommendation lists in different domains and settings. By relying on the computation of latent factors, they can recommend items with a very high level of accuracy. Unfortunately, when moving to the latent space, even if the model embeds content-based information, we miss references to the actual semantics of the recommended item. It makes the interpretation of the recommendation process non-trivial. In this paper, we show how to initialize latent factors in Factorization Machines by using semantic features coming from knowledge graphs to train an interpretable model, which is, in turn, able to provide recommendations with a high level of accuracy. In the presented approach, semantic features are injected into the learning process to retain the original informativeness of the items available in the dataset. By relying on the information encoded in the original knowledge graph, we also propose two metrics to evaluate the semantic accuracy and robustness of knowledge-aware interpretability. An extensive experimental evaluation on six different datasets shows the effectiveness of the interpretable model in terms of both accuracy and diversity of recommendation results and interpretability robustness.","accessed":{"date-parts":[[2020,7,21]]},"author":[{"family":"Anelli","given":"Vito Walter"},{"family":"Di Noia","given":"Tommaso"},{"family":"Di Sciascio","given":"Eugenio"},{"family":"Ragone","given":"Azzurra"},{"family":"Trotta","given":"Joseph"}],"citation-key":"anelliSemanticInterpretationTopN2020","container-title":"IEEE Transactions on Knowledge and Data Engineering","container-title-short":"IEEE Trans. Knowl. Data Eng.","DOI":"10.1109/TKDE.2020.3010215","ISSN":"1041-4347, 1558-2191, 2326-3865","issued":{"date-parts":[[2020]]},"note":"00000","page":"1-1","source":"DOI.org (Crossref)","title":"Semantic Interpretation of Top-N Recommendations","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/9143460/"},
{"id":"anFormalModelingDynamic2021a","abstract":"With the development of technology, new complex systems such as human cyber-physical systems (hCPS) have become indistinguishable from social life. The cyberspace where the software system located is increasingly integrated with the physical space of people's daily life. The uncertain factors such as the dynamic environment in the physical space, the explosive growth of the spatio- temporal data, as well as the unpredictable human behavior are all compromise the security of the system. As a result of the increasing security requirements, the scale and complexity of the system are also increasing. This situation leads to a series of problems that remain unresolved. Therefore, developing intelligent and safe human cyber-physical systems under uncertain environment is becoming the inevitable challenge for the software industry. It is difficult for the human cyber-physical systems to perceive the runtime environment accurately under uncertain surroundings. The uncertain perception will lead to the system's misinterpretation, thus affecting the security of the system. It is difficult for the system designers to construct formal specifications for the human cyber-physical systems under uncertain environment. For safety-critical systems, formal specifications are the prerequisites to ensure system security. To cope with the uncertainty of the specifications, a combination of data-driven and model-driven modeling methodology is proposed, that is, the machine learning-based algorithms are used to model the environment based on spatio-temporal data. An approach is introduced to integrate machine learning method and runtime verification technology as a unified framework to ensure the safety of the human cyber-physical systems. The proposed approach is illustrated by modeling and analyzing a scenario of the interaction of an autonomous vehicle and a human-driven motorbike. © Copyright 2021, Institute of Software, the Chinese Academy of Sciences. All rights reserved.","author":[{"family":"An","given":"D.-D."},{"family":"Liu","given":"J."},{"family":"Chen","given":"X.-H."},{"family":"Sun","given":"H.-Y."}],"citation-key":"anFormalModelingDynamic2021a","container-title":"Ruan Jian Xue Bao/Journal of Software","DOI":"10.13328/j.cnki.jos.006272","ISSN":"10009825","issue":"7","issued":{"date-parts":[[2021]]},"page":"1999-2015","publisher":"Chinese Academy of Sciences","title":"Formal Modeling and Dynamic Verification for Human Cyber Physical Systems under Uncertain Environment [不确定环境下hCPS系统的形式化建模与动态验证]","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109022509&doi=10.13328%2fj.cnki.jos.006272&partnerID=40&md5=47abe1df92ab3cbdd6093fbee9006bec","volume":"32"},
{"id":"aNoSQLImplementationConceptual2018","accessed":{"date-parts":[[2021,3,24]]},"author":[{"family":"A","given":"Benmakhlouf"}],"citation-key":"aNoSQLImplementationConceptual2018","container-title":"International Journal of Database Management Systems","container-title-short":"IJDMS","DOI":"10.5121/ijdms.2018.10201","ISSN":"09755985, 09755705","issue":"2","issued":{"date-parts":[[2018,4,30]]},"note":"00003","page":"01-10","source":"DOI.org (Crossref)","title":"NoSQL Implementation of a Conceptual Data Model : UML Class Diagram to a Document Oriented Model","title-short":"NoSQL Implementation of a Conceptual Data Model","type":"article-journal","URL":"http://aircconline.com/ijdms/V10N2/10218ijdms01.pdf","volume":"10"},
{"id":"antonioModelDrivenApproach2010","author":[{"family":"ANTONIO","given":"CICCHETTI"},{"family":"DAVIDE DI RUSCIO","given":""},{"family":"PELLICCIONE","given":"P"},{"family":"ALFONSO","given":"PIERANTONIO"},{"family":"STEFANO","given":"ZACCHIROLI"}],"citation-key":"antonioModelDrivenApproach2010","container-title":"Evaluation of Novel Approaches to Software Engineering","DOI":"10.1007/978-3-642-14819-4","event-place":"HEIDELBERG","ISBN":"978-3-642-14818-7","issued":{"date-parts":[[2010]]},"note":"00000","page":"262–276","publisher":"SPRINGER","publisher-place":"HEIDELBERG","title":"A Model Driven Approach to Upgrade Package Based Software Systems","type":"chapter","volume":"69"},
{"id":"ApplicationofAIandMLinIoTPdf","citation-key":"ApplicationofAIandMLinIoTPdf","note":"00000","title":"ApplicationofAIandMLinIoT.pdf","type":"article-newspaper"},
{"id":"aranegaUsingFeatureModel2012","author":[{"family":"Aranega","given":"Vincent"},{"family":"Etien","given":"Anne"},{"family":"Mosser","given":"Sebastien"}],"citation-key":"aranegaUsingFeatureModel2012","container-title":"Model Driven Engineering Languages and Systems","DOI":"10.1007/978-3-642-33666-9_36","issued":{"date-parts":[[2012]]},"page":"562–578","title":"Using Feature Model to Build Model Transformation Chains","type":"article-journal","volume":"7590"},
{"id":"arcainiModelingAnalyzingMAPEK2015","abstract":"The MAPE-K (Monitor-Analyze-Plan-Execute over a shared Knowledge) feedback loop is the most influential reference control model for autonomic and self-adaptive systems. This paper presents a conceptual and methodological framework for formal modeling, validating, and verifying distributed self-adaptive systems. We show how MAPE-K loops for selfadaptation can be naturally specified in an abstract stateful language like Abstract State Machines. In particular, we exploit the concept of multi-agent Abstract State Machines to specify decentralized adaptation control by using MAPE computations. We support techniques for validating and verifying adaptation scenarios, and getting feedback of the correctness of the adaptation logic as implemented by the MAPE-K loops. In particular, a verification technique based on meta-properties is proposed to allow discovering unwanted interferences between MAPE-K loops at the early stages of the system design. As a proof-ofconcepts, we model and analyze a traffic monitoring system.","accessed":{"date-parts":[[2021,12,7]]},"author":[{"family":"Arcaini","given":"Paolo"},{"family":"Riccobene","given":"Elvinia"},{"family":"Scandurra","given":"Patrizia"}],"citation-key":"arcainiModelingAnalyzingMAPEK2015","container-title":"2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","DOI":"10.1109/SEAMS.2015.10","event":"2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","event-place":"Florence, Italy","ISBN":"978-0-7695-5567-6","issued":{"date-parts":[[2015,5]]},"note":"00165","page":"13-23","publisher":"IEEE","publisher-place":"Florence, Italy","source":"DOI.org (Crossref)","title":"Modeling and Analyzing MAPE-K Feedback Loops for Self-Adaptation","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/7194653/"},
{"id":"ArchivaDocumentationInstalling","accessed":{"date-parts":[[2015,4,16]]},"citation-key":"ArchivaDocumentationInstalling","title":"Archiva Documentation - Installing Apache Archiva","type":"webpage","URL":"http://archiva.apache.org/docs/2.2.0/adminguide/installing.html"},
{"id":"Areferencearchitecturefortheinternetofthings","citation-key":"Areferencearchitecturefortheinternetofthings","title":"a-reference-architecture-for-the-internet-of-things","type":"article-journal"},
{"id":"arendtEMFMetricsSpecification","author":[{"family":"Arendt","given":"Thorsten"},{"family":"Stepien","given":"Pawel"},{"family":"Taentzer","given":"Gabriele"}],"citation-key":"arendtEMFMetricsSpecification","title":"EMF Metrics: Specification and Calculation of Model Metrics within the Eclipse Modeling Framework","type":"article-journal"},
{"id":"arendtIntegrationSmellsRefactorings2012","author":[{"family":"Arendt","given":"Thorsten"},{"family":"Taentzer","given":"Gabriele"}],"citation-key":"arendtIntegrationSmellsRefactorings2012","container-title":"Proceedings of the Fifth Workshop on Refactoring Tools - WRT '12","DOI":"10.1145/2328876.2328878","issued":{"date-parts":[[2012]]},"page":"8–15","title":"Integration of smells and refactorings within the Eclipse modeling framework","type":"article-journal"},
{"id":"arendtToolEnvironmentQuality2012","author":[{"family":"Arendt","given":"Thorsten"},{"family":"Taentzer","given":"Gabriele"}],"citation-key":"arendtToolEnvironmentQuality2012","container-title":"Automated Software Engineering","DOI":"10.1007/s10515-012-0114-7","issue":"2","issued":{"date-parts":[[2012]]},"page":"141–184","title":"A tool environment for quality assurance based on the Eclipse Modeling Framework","type":"article-journal","volume":"20"},
{"id":"arhippainenUseIntegrationThridparty2003","author":[{"family":"Arhippainen","given":"Leena"}],"citation-key":"arhippainenUseIntegrationThridparty2003","collection-number":"489","collection-title":"VTT publications","event-place":"Espoo","ISBN":"978-951-38-6032-5 978-951-38-6033-2","issued":{"date-parts":[[2003]]},"note":"OCLC: 249286656","number-of-pages":"68","publisher":"VTT","publisher-place":"Espoo","source":"Gemeinsamer Bibliotheksverbund ISBN","title":"Use and integration of thrid-party components in software development","type":"book"},
{"id":"ariasOrccadRobotController2010","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Arias","given":"Soraya"},{"family":"Boudin","given":"Florine"},{"family":"Pissard-Gibollet","given":"Roger"},{"family":"Simon","given":"Daniel"}],"citation-key":"ariasOrccadRobotController2010","container-title":"5th National Conference on Control Architecture of Robots","issued":{"date-parts":[[2010]]},"source":"Google Scholar","title":"Orccad, robot controller model and its support using eclipse modeling tools","type":"paper-conference","URL":"https://hal.archives-ouvertes.fr/inria-00482559/"},
{"id":"Arif2020102","abstract":"The pervasiveness of ubiquitously connected smart devices are the main factors in shaping the computing. With the advent of Internet of things (IoTs), massive amount of data is being generated from different sources. The centralized architecture of cloud has become inefficient for the services provision to IoT enabled applications. For better support and services, fog layer is introduced in order to manage the IoT applications demands like latency, responsiveness, deadlines, resource availability and access time etc. of the fog nodes. However, there are some issues related to resource management and fog nodes allocation to the requesting application based on user expectations in the fog layer that need to be addressed. In this paper, we have proposed a Framework, based on Model Driven Software Engineering (MDSE) that practices Machine Learning algorithms and places fog enabled IoT applications at a most suitable fog node. MDSE is meant to develop software by exploiting the problem at domain model level. It is the abstract representation of knowledge that enhances productivity by maximization of compatibility between the systems. The proposed framework is a meta-model that prioritizes the placement requests of applications based on their required expectations and calculates the abilities of the fog nodes for different application placement requests. Rules based machine learning methods are used to create rules based on user’s requirements metrics and then results are optimized to get requesting device placement in the fog layer. At the end, a case study is conducted that uses fuzzy logic for application mapping and shows how the actual application placement will be done by the framework. The proposed meta-model reduces complexity and provides flexibility to make further enhancements according to the user’s requirement to use any of the Machine Learning approaches. © 2020, Springer Nature Switzerland AG.","author":[{"family":"Arif","given":"M."},{"family":"Azam","given":"F."},{"family":"Anwar","given":"M.W."},{"family":"Rasheed","given":"Y."}],"citation-key":"Arif2020102","container-title":"Communications in Computer and Information Science","DOI":"10.1007/978-3-030-59506-7_9","editor":[{"family":"Lopata A., Butkiene R.","given":"Gudoniene D.","suffix":"Sukacke V."}],"ISBN":"9783030595050","ISSN":"18650929","issued":{"date-parts":[[2020]]},"page":"102-112","publisher":"Springer Science and Business Media Deutschland GmbH","title":"A model-driven framework for optimum application placement in fog computing using a machine learning based approach","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093094169&doi=10.1007%2f978-3-030-59506-7_9&partnerID=40&md5=be1f85b4b745ef0cb6242c1bfd72b8f6","volume":"1283 CCIS"},
{"id":"arkinModelDrivenTransformationsMapping2013","accessed":{"date-parts":[[2017,2,23]]},"author":[{"family":"Arkin","given":"Ethem"},{"family":"Tekinerdogan","given":"Bedir"}],"citation-key":"arkinModelDrivenTransformationsMapping2013","container-title":"MDHPCL@ MoDELS","issued":{"date-parts":[[2013]]},"page":"63–72","source":"Google Scholar","title":"Model-Driven Transformations for Mapping Parallel Algorithms on Parallel Computing Platforms.","type":"article-journal","URL":"http://ceur-ws.org/Vol-1118/08-paper.pdf","volume":"2013"},
{"id":"arlot2010","author":[{"family":"Arlot","given":"Sylvain"},{"family":"Celisse","given":"Alain"}],"citation-key":"arlot2010","container-title":"Statist. Surv.","issued":{"date-parts":[[2010]]},"page":"40-79","title":"A survey of cross-validation procedures for model selection","type":"article-journal","volume":"4"},
{"id":"assmannReferenceArchitectureRoadmap2014","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"A\\s smann","given":"Uwe"},{"family":"Götz","given":"Sebastian"},{"family":"Jézéquel","given":"Jean-Marc"},{"family":"Morin","given":"Brice"},{"family":"Trapp","given":"Mario"}],"citation-key":"assmannReferenceArchitectureRoadmap2014","container-title":"Models@ run. time","issued":{"date-parts":[[2014]]},"page":"1–18","publisher":"Springer","source":"Google Scholar","title":"A reference architecture and roadmap for Models@ run. time systems","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-319-08915-7_1"},
{"id":"atkinsonUnifyingApproachConnections","accessed":{"date-parts":[[2015,9,24]]},"author":[{"family":"Atkinson","given":"Colin"},{"family":"Gerbig","given":"Ralph"},{"family":"Kühne","given":"Thomas"}],"citation-key":"atkinsonUnifyingApproachConnections","source":"Google Scholar","title":"A Unifying Approach to Connections for Multi-Level Modeling","type":"article-journal","URL":"http://homepages.ecs.vuw.ac.nz/~tk/publications/papers/deep-connections.pdf"},
{"id":"atouaniArtifactReferenceModels2021","abstract":"Machine learning is a discipline which has become ubiquitous in the last few years. While the research of machine learning algorithms is very active and continues to reveal astonishing possibilities on a regular basis, the wide usage of these algorithms is shifting the research focus to the integration, maintenance, and evolution of AI-driven systems. Although there is a variety of machine learning frameworks on the market, there is little support for process automation and DevOps in machine learning-driven projects. In this paper, we discuss how metamodels can support the development of deep learning frameworks and help deal with the steadily increasing variety of learning algorithms. In particular, we present a deep learning-oriented artifact model which serves as a foundation for build automation and data management in iterative, machine learning-driven development processes. Furthermore, we show how schema and reference models can be used to structure and maintain a versatile deep learning framework. Feasibility is demonstrated on several state-of-the-art examples from the domains of image and natural language processing as well as decision making and autonomous driving.","author":[{"family":"Atouani","given":"Abdallah"},{"family":"Kirchhof","given":"Jörg Christian"},{"family":"Kusmenko","given":"Evgeny"},{"family":"Rumpe","given":"Bernhard"}],"citation-key":"atouaniArtifactReferenceModels2021","issued":{"date-parts":[[2021]]},"note":"00000","page":"14","source":"Zotero","title":"Artifact and Reference Models for Generative Machine Learning Frameworks and Build Systems","type":"article-journal"},
{"id":"Atoui2022","abstract":"Service providers in network function virtualization usually design manually or with static automation the deployment descriptors for virtual network functions. The descriptors are semi-structured files that contain information about the resource requirements and the operational behavior of virtual network functions. Designing the descriptors manually and without formal strategies is certainly a cumbersome and error-prone task for service providers. In this work, we propose a model-driven approach that assists service providers in designing the deployment descriptors. This approach uses a configurable model iteratively to give service providers insights on which best configuration to choose. Concretely, we propose (1) to use a configurable deployment descriptor model, (2) a learning approach based on machine learning to automatically construct the configurable model, and (3) an approach that learns configuration guidelines from a catalog of deployment descriptors to assist service providers with the selection of the configuration to use. The configurable deployment descriptor model captures the relation and also the variability between the virtualized network function (VNF) elements from different deployment descriptors. We propose a learning approach to build the configurable deployment descriptor model by finding and federating similar VNF elements from different deployment descriptors. With our machine learning approach, we construct automatically the configurable model from a set of deployment descriptors. We use afterward the configurable model to learn configuration guidelines from the deployment descriptors and recommend them for service providers. The results of our experiments highlight the effectiveness of our approach to learning configurable deployment descriptor models. © 2021 John Wiley & Sons, Ltd.","author":[{"family":"Atoui","given":"W.S."},{"family":"Assy","given":"N."},{"family":"Gaaloul","given":"W."},{"family":"Ben Yahia","given":"I.G."}],"citation-key":"Atoui2022","container-title":"International Journal of Network Management","DOI":"10.1002/nem.2165","ISSN":"10557148","issue":"1","issued":{"date-parts":[[2022]]},"publisher":"John Wiley and Sons Ltd","title":"A model-driven approach for deployment descriptor design in network function virtualization","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122490012&doi=10.1002%2fnem.2165&partnerID=40&md5=54c2cd1d6e97827b9d5198c4e26f0304","volume":"32"},
{"id":"atzeniDataModelDescriptions2011","accessed":{"date-parts":[[2015,3,23]]},"author":[{"family":"Atzeni","given":"Paolo"},{"family":"Gianforme","given":"Giorgio"},{"family":"Cappellari","given":"Paolo"}],"citation-key":"atzeniDataModelDescriptions2011","container-title":"Annals of Mathematics and Artificial Intelligence","DOI":"10.1007/s10472-012-9277-y","ISSN":"1012-2443, 1573-7470","issue":"3-4","issued":{"date-parts":[[2011,12]]},"page":"287-315","source":"CrossRef","title":"Data model descriptions and translation signatures in a multi-model framework","type":"article-journal","URL":"http://link.springer.com/10.1007/s10472-012-9277-y","volume":"63"},
{"id":"atzeniModelindependentSchemaTranslation2008","accessed":{"date-parts":[[2015,3,23]]},"author":[{"family":"Atzeni","given":"Paolo"},{"family":"Cappellari","given":"Paolo"},{"family":"Torlone","given":"Riccardo"},{"family":"Bernstein","given":"Philip A."},{"family":"Gianforme","given":"Giorgio"}],"citation-key":"atzeniModelindependentSchemaTranslation2008","container-title":"The VLDB Journal","DOI":"10.1007/s00778-008-0105-2","ISSN":"1066-8888, 0949-877X","issue":"6","issued":{"date-parts":[[2008,11]]},"page":"1347-1370","source":"CrossRef","title":"Model-independent schema translation","type":"article-journal","URL":"http://link.springer.com/10.1007/s00778-008-0105-2","volume":"17"},
{"id":"atzeniModelsNoSQLDatabases2015","accessed":{"date-parts":[[2018,5,9]]},"author":[{"family":"Atzeni","given":"Paolo"}],"citation-key":"atzeniModelsNoSQLDatabases2015","container-title":"Advances in Conceptual Modeling","DOI":"10.1007/978-3-319-25747-1_13","editor":[{"family":"Jeusfeld","given":"Manfred A."},{"family":"Karlapalem","given":"Kamalakar"}],"event-place":"Cham","ISBN":"978-3-319-25746-4 978-3-319-25747-1","issued":{"date-parts":[[2015]]},"page":"133-133","publisher":"Springer International Publishing","publisher-place":"Cham","source":"Crossref","title":"Models for NoSQL Databases: A Contradiction?","title-short":"Models for NoSQL Databases","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-319-25747-1_13","volume":"9382"},
{"id":"atzeniRuntimeApproachModelgeneric2012","accessed":{"date-parts":[[2015,3,23]]},"author":[{"family":"Atzeni","given":"Paolo"},{"family":"Bellomarini","given":"Luigi"},{"family":"Bugiotti","given":"Francesca"},{"family":"Celli","given":"Fabrizio"},{"family":"Gianforme","given":"Giorgio"}],"citation-key":"atzeniRuntimeApproachModelgeneric2012","container-title":"Information Systems","DOI":"10.1016/j.is.2011.11.003","ISSN":"03064379","issue":"3","issued":{"date-parts":[[2012,5]]},"page":"269-287","source":"CrossRef","title":"A runtime approach to model-generic translation of schema and data","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0306437911001542","volume":"37"},
{"id":"atzeniUniformAccessNoSQL2014","accessed":{"date-parts":[[2015,3,23]]},"author":[{"family":"Atzeni","given":"Paolo"},{"family":"Bugiotti","given":"Francesca"},{"family":"Rossi","given":"Luca"}],"citation-key":"atzeniUniformAccessNoSQL2014","container-title":"Information Systems","DOI":"10.1016/j.is.2013.05.002","ISSN":"03064379","issued":{"date-parts":[[2014,7]]},"page":"117-133","source":"CrossRef","title":"Uniform access to NoSQL systems","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0306437913000719","volume":"43"},
{"id":"atzeniUniversalMetamodelIts2009","abstract":"We discuss a universal metamodel aimed at the representation of schemas in a way that is at the same time model-independent (in the sense that it allows for a uniform representation of different data models) and model-aware (in the sense that it is possible to say to whether a schema is allowed for a data model). This metamodel can be the basis for the definition of a complete model-management system. Here we illustrate the details of the metamodel and the structure of a dictionary for its representation. Exemplifications of a concrete use of the dictionary are provided, by means of the representations of the main data models, such as relational, object-relational or XSD-based. Moreover, we demonstrate how set operators can be redefined with respect to our dictionary and easily applied on it. Finally, we show how such a dictionary can be exploited to automatically produce detailed descriptions of schema and data models, in a textual (i.e. XML) or visual (i.e. UML class diagram) way.","accessed":{"date-parts":[[2018,4,16]]},"author":[{"family":"Atzeni","given":"Paolo"},{"family":"Gianforme","given":"Giorgio"},{"family":"Cappellari","given":"Paolo"}],"citation-key":"atzeniUniversalMetamodelIts2009","container-title":"Transactions on Large-Scale Data- and Knowledge-Centered Systems I","DOI":"10.1007/978-3-642-03722-1_2","editor":[{"family":"Hameurlain","given":"Abdelkader"},{"family":"Küng","given":"Josef"},{"family":"Wagner","given":"Roland"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-642-03721-4 978-3-642-03722-1","issued":{"date-parts":[[2009]]},"page":"38-62","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","source":"CrossRef","title":"A Universal Metamodel and Its Dictionary","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-642-03722-1_2","volume":"5740"},
{"id":"atzoriInternetThingsSurvey2010a","abstract":"This paper addresses the Internet of Things. Main enabling factor of this promising paradigm is the integration of several technologies and communications solutions. Identification and tracking technologies, wired and wireless sensor and actuator networks, enhanced communication protocols (shared with the Next Generation Internet), and distributed intelligence for smart objects are just the most relevant. As one can easily imagine, any serious contribution to the advance of the Internet of Things must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. In such a complex scenario, this survey is directed to those who want to approach this complex discipline and contribute to its development. Different visions of this Internet of Things paradigm are reported and enabling technologies reviewed. What emerges is that still major issues shall be faced by the research community. The most relevant among them are addressed in details.","accessed":{"date-parts":[[2019,9,3]]},"author":[{"family":"Atzori","given":"Luigi"},{"family":"Iera","given":"Antonio"},{"family":"Morabito","given":"Giacomo"}],"citation-key":"atzoriInternetThingsSurvey2010a","container-title":"Computer Networks","container-title-short":"Computer Networks","DOI":"10.1016/j.comnet.2010.05.010","ISSN":"13891286","issue":"15","issued":{"date-parts":[[2010,10]]},"page":"2787-2805","source":"DOI.org (Crossref)","title":"The Internet of Things: A survey","title-short":"The Internet of Things","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1389128610001568","volume":"54"},
{"id":"augusteijnNeuralNetworkClassification2002","author":[{"family":"Augusteijn","given":"M. F."},{"literal":"B. A. Folkert"}],"citation-key":"augusteijnNeuralNetworkClassification2002","container-title":"International Journal of Remote Sensing","issue":"14","issued":{"date-parts":[[2002]]},"page":"2891-2902","title":"Neural network classification and novelty detection","type":"article-journal","volume":"23"},
{"id":"authorTopFilterApproachRecommend2020","author":[{"family":"Author","given":"Anonymous"}],"citation-key":"authorTopFilterApproachRecommend2020","issued":{"date-parts":[[2020]]},"note":"00000","page":"11","source":"Zotero","title":"TopFilter: An Approach to Recommend Relevant GitHub Topics","type":"article-journal"},
{"id":"autiliDevelopmentProcessContextAware2008","author":[{"family":"Autili","given":"Marco"},{"family":"DI BENEDETTO","given":"P"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Inverardi","given":"Paola"},{"family":"Tivoli","given":"Massimo"}],"citation-key":"autiliDevelopmentProcessContextAware2008","container-title":"23rd IEEE/ACM International Conference on Automated Software Engineering - Workshop Proceedings (ASE Workshops 2008)","DOI":"10.1109/ASEW.2008.4686288","event-place":"NEW YORK","ISBN":"978-1-4244-2776-5","issued":{"date-parts":[[2008]]},"note":"00000","page":"9–16","publisher":"IEEE Computer Society","publisher-place":"NEW YORK","title":"A Development Process for Context-Aware Adaptive Services","type":"paper-conference","URL":"https://doi.org/10.1109/ASEW.2008.4686288"},
{"id":"autiliDevelopmentProcessRequirements2011","abstract":"The Future Internet envisions a ubiquitous world where available services can be easily discovered and coordinated so to fit users' requirements and needs. Service choreographies will play a central role in this vision as an effective means to allow heterogeneous services to suitably collaborate. This paper describes our experience of choreography development within the CHOReOS project.","author":[{"family":"Autili","given":"M"},{"family":"Di Ruscio","given":"D"},{"family":"Inverardi","given":"P"},{"family":"Lockerbie","given":"J"},{"family":"Tivoli","given":"M"}],"citation-key":"autiliDevelopmentProcessRequirements2011","container-title":"Workshop on Requirements Engineering for Systems, Services and Systems-of-Systems (RESS)","DOI":"10.1109/RESS.2011.6043925","event-place":"NEW YORK","ISBN":"978-1-4577-0939-5","issued":{"date-parts":[[2011]]},"note":"00000","page":"59–62","publisher":"IEEE Computer Society","publisher-place":"NEW YORK","title":"A Development Process for Requirements Based Service Choreography","type":"paper-conference","URL":"http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6043925"},
{"id":"autiliDevelopmentProcessSelfadapting2007","author":[{"family":"Autili","given":"Marco"},{"family":"Cortellessa","given":"Vittorio"},{"family":"DI MARCO","given":"Antinisca"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Inverardi","given":"Paola"},{"family":"Tivoli","given":"Massimo"}],"citation-key":"autiliDevelopmentProcessSelfadapting2007","container-title":"Proceedings of the International Conference on Service-oriented Computing (ICSOC)","DOI":"10.1007/978-3-540-74974-5_41","event-place":"BERLIN HEIDELBERG","ISBN":"978-3-540-74973-8","issued":{"date-parts":[[2007]]},"note":"00000","page":"442–448","publisher":"Springer-Verlag","publisher-place":"BERLIN HEIDELBERG","title":"A Development Process for Self-adapting Service Oriented Applications","type":"paper-conference","URL":"https://doi.org/10.1007/978-3-540-74974-5_41","volume":"4749"},
{"id":"autiliEAGLEEngineeringSoftwAre2011","abstract":"In the next future we will be surrounded by a virtually infinite number of software applications that provide computational software resources in the open Globe. This will radically change the way software will be produced and used. Users will be keen on producing their own piece of software, by also reusing existing software, to better satisfy their needs, therefore with a goal oriented, opportunistic use in mind. The produced software will need to be able to evolve, react and adapt to a continuously changing environment, while guaranteeing dependability. The strongest adversary to this view is the lack of knowledge on the software's structure, behavior, and execution context. Despite the possibility to extract observational models from existing software, a producer will always operate with software artifacts that exhibit a degree of uncertainty in terms of their functional and non functional characteristics. We believe that uncertainty can only be controlled by making it explicit and by using it to drive the production process itself. In this paper, we introduce a novel paradigm of software production process that explores available software and assesses its degree of uncertainty in relation to the opportunistic goal G, assists the producer in creating the appropriate integration means towards G, and validates the quality of the integrated system with respect to G and the current context.","author":[{"family":"Autili","given":"M"},{"family":"Cortellessa","given":"V"},{"family":"Di Ruscio","given":"D"},{"family":"Inverardi","given":"P"},{"family":"Pelliccione","given":"P"},{"family":"Tivoli","given":"M"}],"citation-key":"autiliEAGLEEngineeringSoftwAre2011","container-title":"Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering","DOI":"10.1145/2025113.2025199","event-place":"NEW YORK, NY, USA","ISBN":"978-1-4503-0443-6","issued":{"date-parts":[[2011]]},"note":"00000","page":"488–491","publisher":"Association for Computing Machinery, Inc. (ACM)","publisher-place":"NEW YORK, NY, USA","title":"EAGLE: Engineering softwAre in the ubiquitous Globe by Leveraging uncErtainty","type":"paper-conference","URL":"http://dx.doi.org/10.1145/2025113.2025199"},
{"id":"autiliIntegrationArchitectureSynthesis2012","abstract":"\"\\\"\\\"The abundance of software that will be more and more available will promote the production of appropriate integration means (architectures, connectors, integration patterns). The produced software will need to be able to evolve, react and adapt quickly to a continuously changing environment, while guaranteeing dependability through (on-the-fly) validation. The strongest adversary to this view is the lack of information about the software, notably about its structure, behavior, and execution context. Despite the possibility to extract observational models from existing software, a producer will always operate with software artifacts that exhibit a degree of uncertainty in terms of their functional and non functional characteristics. Uncertainty can only be controlled by making it explicit and by using it to drive the production process itself. This calls for a production process that explores available software and assesses its degree of uncertainty in relation to the opportunistic goal G, assists the producer in creating the appropriate integration means towards G, and validates the quality of the integrated system with respect to the goal G and the current context. In this paper we discuss how goal-oriented software systems can be opportunistically created by integrating under uncertainty existing pieces of software. © 2012 Springer-Verlag.\\\"\\\"\"","author":[{"family":"Autili","given":"Marco"},{"family":"Cortellessa","given":"Vittorio"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Inverardi","given":"Paola"},{"family":"Pelliccione","given":"Patrizio"},{"family":"Tivoli","given":"Massimo"}],"citation-key":"autiliIntegrationArchitectureSynthesis2012","container-title":"Large-Scale Complex IT Systems. Development, Operation and Management - 17th Monterey Workshop 2012, Oxford, UK, March 19-21, 2012, Revised Selected Papers. Lecture Notes in Computer Science","DOI":"10.1007/978-3-642-34059-8_6","event-place":"BERLIN HEIDELBERG","ISBN":"978-3-642-34058-1","issued":{"date-parts":[[2012]]},"note":"00000","page":"118–131","publisher":"Springer-Verlag","publisher-place":"BERLIN HEIDELBERG","title":"Integration Architecture Synthesis for Taming Uncertainty in the Digital Space","type":"paper-conference","URL":"http://dx.doi.org/10.1007/978-3-642-34059-8_6","volume":"7539"},
{"id":"autiliModelbasedSynthesisProcess2013","abstract":"\"\"The near future in service-oriented system development envisions a ubiquitous world of available services that collaborate to fit users’ needs. Modern service-oriented applications are often built by reusing and assembling distributed services. This can be done by considering a global specification of the interactions between the participant services, namely the choreography. In this paper, we propose a synthesis approach to automatically synthesize a choreography out of a specification of it and a set of services discovered as suitable participants. The synthesis is model-based in the sense that it works by assuming a finite state model of the services’s protocol and a BPMN model for the choreography specification. The result of the synthesis is a set of distributed components, called coordination delegates, that coordinate the services’ interaction in order to realize the specified choreography. The work advances the state-of-the-art in two directions: (i) we provide a solution to the problem of choreography realizability enforcement, and (ii) we provide a model-based tool chain to support the development of choreography-based systems.\"\"","author":[{"family":"Autili","given":"Marco"},{"family":"DI RUSCIO","given":"Davide"},{"family":"DI SALLE","given":"Amleto"},{"family":"Inverardi","given":"Paola"},{"family":"Tivoli","given":"Massimo"}],"citation-key":"autiliModelbasedSynthesisProcess2013","container-title":"Fundamental Approaches to Software Engineering (FASE 2013)","DOI":"10.1007/978-3-642-37057-1_4","ISBN":"978-3-642-37056-4","issued":{"date-parts":[[2013]]},"note":"00000","page":"37–52","publisher":"Springer Berlin Heidelberg","title":"A model-based synthesis process for choreography realizability enforcement","type":"paper-conference","URL":"http://link.springer.com/chapter/10.1007/978-3-642-37057-1_4#page-1","volume":"7793"},
{"id":"autiliModelLANDWhereModels2011","author":[{"family":"Autili","given":"Marco"},{"family":"Ruscio","given":"Davide Di"},{"family":"Inverardi","given":"Paola"},{"family":"Pelliccione","given":"Patrizio"},{"family":"Tivoli","given":"Massimo"}],"citation-key":"autiliModelLANDWhereModels2011","collection-title":"Lecture Notes in Computer Science","container-title":"Models@run.time - Foundations, Applications, and Roadmaps [Dagstuhl Seminar 11481, November 27 - December 2, 2011]","DOI":"10.1007/978-3-319-08915-7_6","editor":[{"family":"Bencomo","given":"Nelly"},{"family":"France","given":"Robert B."},{"family":"Cheng","given":"Betty H. C."},{"family":"Aßmann","given":"Uwe"}],"issued":{"date-parts":[[2011]]},"page":"162–187","publisher":"Springer","title":"ModelLAND: Where Do Models Come from?","type":"paper-conference","URL":"https://doi.org/10.1007/978-3-319-08915-7_6","volume":"8378"},
{"id":"autiliModelLANDWhereModels2014","author":[{"family":"Autili","given":"Marco"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Inverardi","given":"Paola"},{"family":"Pelliccione","given":"Patrizio"},{"family":"Tivoli","given":"Massimo"}],"citation-key":"autiliModelLANDWhereModels2014","container-title":"Models@run.time - Foundations, Applications, and Roadmaps [Dagstuhl Seminar 11481, November 27 - December 2, 2011]","DOI":"10.1007/978-3-319-08915-7_6","ISBN":"978-3-319-08914-0","issued":{"date-parts":[[2014]]},"note":"00000","page":"162–187","publisher":"Springer Verlag","title":"ModelLAND: Where do models come from?","type":"chapter","URL":"http://springerlink.com/content/0302-9743/copyright/2005/","volume":"LNCS 8378"},
{"id":"autiliSoftwareExoskeletonProtect2019","author":[{"family":"Autili","given":"Marco"},{"family":"Di Ruscio","given":"Davide"},{"family":"Inverardi","given":"Paola"},{"family":"Pelliccione","given":"Patrizio"},{"family":"Tivoli","given":"Massimo"}],"citation-key":"autiliSoftwareExoskeletonProtect2019","container-title":"IEEE Access","issued":{"date-parts":[[2019]]},"title":"A software exoskeleton to protect and support citizen's ethics and privacy in the digital world","type":"article-magazine","URL":"https://ieeexplore.ieee.org/document/8712524/"},
{"id":"autiliSoftwareExoskeletonProtect2019a","author":[{"family":"Autili","given":"Marco"},{"family":"Di Ruscio","given":"Davide"},{"family":"Inverardi","given":"Paola"},{"family":"Pelliccione","given":"Patrizio"},{"family":"Tivoli","given":"Massimo"}],"citation-key":"autiliSoftwareExoskeletonProtect2019a","container-title":"IEEE ACCESS","DOI":"10.1109/ACCESS.2019.2916203","issued":{"date-parts":[[2019]]},"note":"00000","page":"62011–62021","title":"A software exoskeleton to protect and support citizen's ethics and privacy in the digital world","type":"article-journal","URL":"https://doi.org/10.1109/ACCESS.2019.2916203","volume":"7"},
{"id":"AutonomousSemiAutonomousSoftware","accessed":{"date-parts":[[2016,8,24]]},"citation-key":"AutonomousSemiAutonomousSoftware","title":"Autonomous and Semi-Autonomous Software Systems","type":"webpage","URL":"http://aosgrp.com/featured-research/autonomy_and_agents/autonomous_systems/autonomous_and_semi-autonom.html"},
{"id":"AutonomousSystems","accessed":{"date-parts":[[2016,8,21]]},"citation-key":"AutonomousSystems","title":"Autonomous Systems","type":"webpage","URL":"https://www.cranfield.ac.uk/Academic%20disciplines/Autonomous-Systems"},
{"id":"AutonomousSystemsFormerly","accessed":{"date-parts":[[2016,8,26]]},"citation-key":"AutonomousSystemsFormerly","title":"Autonomous Systems (formerly Unmanned Systems)","type":"webpage","URL":"http://www.northropgrumman.com/Capabilities/AutonomousSystems/Pages/default.aspx"},
{"id":"Autonomy","accessed":{"date-parts":[[2016,8,24]]},"citation-key":"Autonomy","title":"Autonomy","type":"webpage","URL":"http://aosgrp.com/featured-research/autonomy_and_agents/autonomous_systems/autonomy.html"},
{"id":"AutoTaskLearningGenerate2021","citation-key":"AutoTaskLearningGenerate2021","issued":{"date-parts":[[2021]]},"note":"00000","page":"11","source":"Zotero","title":"AutoTask: Learning to Generate Machine Learning Pipelines","type":"article-journal"},
{"id":"avgeriouOverviewComparisonTechnical2021","abstract":"Different tools adopt different terms, metrics, and ways to identify and measure technical debt. We attempt to clarify the situation by comparing the features and popularity of technical debt measurement tools and analyzing the existing empirical evidence on their validity.","accessed":{"date-parts":[[2021,5,10]]},"author":[{"family":"Avgeriou","given":"Paris C."},{"family":"Taibi","given":"Davide"},{"family":"Ampatzoglou","given":"Apostolos"},{"family":"Fontana","given":"Francesca Arcelli"},{"family":"Besker","given":"Terese"},{"family":"Chatzigeorgiou","given":"Alexander"},{"family":"Lenarduzzi","given":"Valentina"},{"family":"Martini","given":"Antonio"},{"family":"Moschou","given":"Athanasia"},{"family":"Pigazzini","given":"Ilaria"},{"family":"Saarimaki","given":"Nyyti"},{"family":"Sas","given":"Darius Daniel"},{"family":"Toledo","given":"Saulo Soares","dropping-particle":"de"},{"family":"Tsintzira","given":"Angeliki Agathi"}],"citation-key":"avgeriouOverviewComparisonTechnical2021","container-title":"IEEE Software","DOI":"10.1109/MS.2020.3024958","ISSN":"0740-7459","issue":"03","issued":{"date-parts":[[2021,5,1]]},"note":"00010","page":"61-71","publisher":"IEEE Computer Society","source":"www.computer.org","title":"An Overview and Comparison of Technical Debt Measurement Tools","type":"article-journal","URL":"https://www.computer.org/csdl/magazine/so/2021/03/09200792/1ndVmuTsh7W","volume":"38"},
{"id":"avitabileDefeatingMassSurveillance","abstract":"Mass surveillance can be more easily achieved leveraging fear and desire of the population to feel protected while affected by devastating events. In such cases governments are more legitimate to adopt exceptional measures that limit civil rights, usually receiving large support from their citizens.","author":[{"family":"Avitabile","given":"Gennaro"},{"family":"Botta","given":"Vincenzo"},{"family":"Iovino","given":"Vincenzo"},{"family":"Visconti","given":"Ivan"}],"citation-key":"avitabileDefeatingMassSurveillance","note":"00000","page":"25","source":"Zotero","title":"Towards Defeating Mass Surveillance and SARS-CoV-2: The Pronto-C2 Fully Decentralized Automatic Contact Tracing System","type":"article-journal"},
{"id":"azzaraPyoTMacroprogrammingFramework2014","accessed":{"date-parts":[[2016,5,30]]},"author":[{"family":"Azzara","given":"Andrea"},{"family":"Alessandrelli","given":"Daniele"},{"family":"Bocchino","given":"Stefano"},{"family":"Petracca","given":"Matteo"},{"family":"Pagano","given":"Paolo"}],"citation-key":"azzaraPyoTMacroprogrammingFramework2014","container-title":"Industrial Embedded Systems (SIES), 2014 9th IEEE International Symposium on","issued":{"date-parts":[[2014]]},"page":"96–103","publisher":"IEEE","source":"Google Scholar","title":"PyoT, a macroprogramming framework for the Internet of Things","type":"paper-conference","URL":"http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6871193"},
{"id":"baba-cheikhPreliminaryStudyOpensource2020","abstract":"The Internet of Things (IoT) market is growing fast with an increasing number of connected devices. This led many software companies to shift their focus to develop and provide IoT solutions. IoT development has its own challenges as typical IoT solutions are composed of heterogeneous devices, protocols and software. To cope with these challenges, many frameworks are available to help developers to build IoT applications. Some of these frameworks are open source and might be of great interest for small and mediumsized companies wishing to build IoT solutions at a lower cost. In this paper, we present the results of a preliminary study of four open source IoT development frameworks. In particular, we used these frameworks to implement a sample of three IoT applications and we analyze them against a minimal set of IoT requirements. We focus in our study on the IoT development for Raspberry PI as it is a very low-cost and popular platform.","author":[{"family":"Baba-Cheikh","given":"Zeineb"},{"family":"El-Boussaidi","given":"Ghizlane"},{"family":"Gascon-Samson","given":"Julien"},{"family":"Mili","given":"Hafedh"},{"family":"Guéhéneuc","given":"Yann-Gael"}],"citation-key":"baba-cheikhPreliminaryStudyOpensource2020","issued":{"date-parts":[[2020]]},"note":"00000","page":"8","source":"Zotero","title":"A preliminary study of open-source IoT development frameworks","type":"article-journal"},
{"id":"Babur2016888","abstract":"Many applications in Model-Driven Engineering involve processing multiple models, e.g. for comparing and merging of model variants into a common domain model. Despite many sophisticated techniques for model comparison, little attention has been given to the initial data analysis and filtering activities. These are hard to ignore especially in the case of a large dataset, possibly with outliers and sub-groupings. We would like to develop a generic approach for model comparison and analysis for large datasets; using techniques from information retrieval, natural language processing and machine learning. We are implementing our approach as an open framework and have so far evaluated it on public datasets involving domain analysis, repository management and model searching scenarios. © 2016 ACM.","author":[{"family":"Babur","given":"Ö."}],"citation-key":"Babur2016888","collection-title":"ASE 2016 - Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering","DOI":"10.1145/2970276.2975938","editor":[{"family":"Khurshid S., Lo D.","given":"Apel S."}],"ISBN":"978-1-4503-3845-5","issued":{"date-parts":[[2016]]},"page":"888-891","publisher":"Association for Computing Machinery, Inc","title":"Statistical analysis of large sets of models","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84989186712&doi=10.1145%2f2970276.2975938&partnerID=40&md5=ee323ce7dd7037a23d0b04461878995c"},
{"id":"Babur2018129","abstract":"With increased adoption of Model-Driven Engineering, the number of related artefacts in use, such as models, metamodels and transformations, greatly increases. To confirm this, we present quantitative evidence from both academia — in terms of repositories and datasets — and industry — in terms of large domain-specific language ecosystems. To be able to tackle this dimension of scalability in MDE, we propose to treat the artefacts as data, and apply various techniques — ranging from information retrieval to machine learning — to analyse and manage those artefacts in a holistic, scalable and efficient way. © Springer International Publishing AG 2018.","author":[{"family":"Babur","given":"Ö."},{"family":"Cleophas","given":"L."},{"family":"Brand","given":"M.","non-dropping-particle":"van den"},{"family":"Tekinerdogan","given":"B."},{"family":"Aksit","given":"M."}],"citation-key":"Babur2018129","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-319-74730-9_10","editor":[{"family":"Zschaler S.","given":"Seidl M."}],"ISBN":"9783319747293","ISSN":"03029743","issued":{"date-parts":[[2018]]},"page":"129-135","publisher":"Springer Verlag","title":"Models, more models, and then a lot more","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042682091&doi=10.1007%2f978-3-319-74730-9_10&partnerID=40&md5=e230890e386f827d4afb4bb913f0ad02","volume":"10748 LNCS"},
{"id":"Babur2018778","abstract":"Model-based approaches promote the use of models and related artifacts (such as metamodels and model transformations) as central elements to tackle the complexity of building systems. Both in academia and in industry there is a growing need to efficiently i) store; ii) analyze; and ii) search & navigate, and iii) curate large collections of models. Such collections include for example large sets of software models such as the Lindholmen UML dataset [1], or of heterogeneous models in large MDE ecosystems and systems-of-systems, including e.g. software, hardware, and business models. The workshop Analytics and Mining of Model Repositories (AMMoRe) aims to gather modelling researchers and practitioners to discuss the emerging problems and propose solutions. The scope ranges from industrial reports and empirical analyses in the problem domain to novel cross-disciplinary approaches for large-scale analytics and management, e.g. exploiting techniques from data analytics, repository mining and machine learning. © 2018 CEUR-WS. All rights reserved.","author":[{"family":"Babur","given":"Ö."},{"family":"Chaudron","given":"M.R.V."},{"family":"Cleophas","given":"L."},{"family":"Ruscio","given":"D.D."},{"family":"Kolovos","given":"D."}],"citation-key":"Babur2018778","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Hebig R.","given":"Berger T."}],"ISSN":"16130073","issued":{"date-parts":[[2018]]},"page":"778-779","publisher":"CEUR-WS","title":"AMMoRe 2018: First international workshop on analytics and mining of model repositories","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063109105&partnerID=40&md5=9b6af321cedc951abe47939224d38e2b","volume":"2245"},
{"id":"Babur2018778","abstract":"Model-based approaches promote the use of models and related artifacts (such as metamodels and model transformations) as central elements to tackle the complexity of building systems. Both in academia and in industry there is a growing need to efficiently i) store; ii) analyze; and ii) search & navigate, and iii) curate large collections of models. Such collections include for example large sets of software models such as the Lindholmen UML dataset [1], or of heterogeneous models in large MDE ecosystems and systems-of-systems, including e.g. software, hardware, and business models. The workshop Analytics and Mining of Model Repositories (AMMoRe) aims to gather modelling researchers and practitioners to discuss the emerging problems and propose solutions. The scope ranges from industrial reports and empirical analyses in the problem domain to novel cross-disciplinary approaches for large-scale analytics and management, e.g. exploiting techniques from data analytics, repository mining and machine learning. © 2018 CEUR-WS. All rights reserved.","author":[{"family":"Babur","given":"Ö."},{"family":"Chaudron","given":"M.R.V."},{"family":"Cleophas","given":"L."},{"family":"Ruscio","given":"D.D."},{"family":"Kolovos","given":"D."}],"citation-key":"Babur2018778","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Hebig R.","given":"Berger T."}],"ISSN":"16130073","issued":{"date-parts":[[2018]]},"page":"778-779","publisher":"CEUR-WS","title":"AMMoRe 2018: First international workshop on analytics and mining of model repositories","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063109105&partnerID=40&md5=9b6af321cedc951abe47939224d38e2b","volume":"2245"},
{"id":"baburAMMoRe2018First2018","author":[{"family":"Babur","given":"O."},{"family":"Chaudron","given":"M. R. V."},{"family":"Cleophas","given":"L."},{"family":"Di Ruscio","given":"D."},{"family":"Kolovos","given":"D."}],"citation-key":"baburAMMoRe2018First2018","container-title":"CEUR Workshop Proceedings","issued":{"date-parts":[[2018]]},"note":"00000","page":"778–779","publisher":"CEUR-WS","title":"AMMoRe 2018: First international workshop on analytics and mining of model repositories","type":"chapter","volume":"2245"},
{"id":"baburLabeledEcoreMetamodel2019","abstract":"Manually labeled 555 metamodels mined from GitHub in April 2017. \n\nDomains: (1) bibliography, (2) conference management, (3) bug/issue tracker, (4) build systems, (5) document/office products, (6) requirement/use case, (7) database/sql, (8) state machines, (9) petri nets\n\nProcedure for constructing the dataset: fully manual, by searching for certain keywords and regexes (e.g. \"state\" and \"transition\" for state machines) in the metamodels and inspecting the results for inclusion. \n\nFormat for the file names: ABSINDEX_CLUSTER_ITEMINDEX_name_hash.ecore","accessed":{"date-parts":[[2021,5,10]]},"author":[{"family":"Babur","given":"Önder"}],"citation-key":"baburLabeledEcoreMetamodel2019","DOI":"10.5281/ZENODO.2585456","issued":{"date-parts":[[2019,3,6]]},"note":"00003","publisher":"Zenodo","source":"DOI.org (Datacite)","title":"A labeled Ecore metamodel dataset for domain clustering","type":"dataset","URL":"https://zenodo.org/record/2585456","version":"0.1.1"},
{"id":"baburStatisticalAnalysisLarge2016a","abstract":"Many applications in Model-Driven Engineering involve processing multiple models, e.g. for comparing and merging of model variants into a common domain model. Despite many sophisticated techniques for model comparison, little attention has been given to the initial data analysis and filtering activities. These are hard to ignore especially in the case of a large dataset, possibly with outliers and sub-groupings. We would like to develop a generic approach for model comparison and analysis for large datasets; using techniques from information retrieval, natural language processing and machine learning. We are implementing our approach as an open framework and have so far evaluated it on public datasets involving domain analysis, repository management and model searching scenarios. © 2016 ACM.","author":[{"family":"Babur","given":"Ö."}],"citation-key":"baburStatisticalAnalysisLarge2016a","container-title":"ASE 2016 - Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering","DOI":"10.1145/2970276.2975938","editor":[{"family":"Khurshid S.","given":"Apel S.","suffix":"Lo D."}],"ISBN":"978-1-4503-3845-5","issued":{"date-parts":[[2016]]},"page":"888-891","publisher":"Association for Computing Machinery, Inc","title":"Statistical analysis of large sets of models","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84989186712&doi=10.1145%2f2970276.2975938&partnerID=40&md5=ee323ce7dd7037a23d0b04461878995c"},
{"id":"Bachinger2020263","abstract":"With the increasing number of created and deployed prediction models and the complexity of machine learning workflows we require so called model management systems to support data scientists in their tasks. In this work we describe our technological concept for such a model management system. This concept includes versioned storage of data, support for different machine learning algorithms, fine tuning of models, subsequent deployment of models and monitoring of model performance after deployment. We describe this concept with a close focus on model lifecycle requirements stemming from our industry application cases, but generalize key features that are relevant for all applications of machine learning. © 2020, Springer Nature Switzerland AG.","author":[{"family":"Bachinger","given":"F."},{"family":"Kronberger","given":"G."}],"citation-key":"Bachinger2020263","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-45093-9_32","editor":[{"family":"Moreno-Diaz R., Quesada-Arencibia A.","given":"Pichler F."}],"ISBN":"9783030450922","ISSN":"03029743","issued":{"date-parts":[[2020]]},"page":"263-270","publisher":"Springer","title":"Concept for a technical infrastructure for management of predictive models in industrial applications","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084008107&doi=10.1007%2f978-3-030-45093-9_32&partnerID=40&md5=7beb438ba1254153f53b8d509c9a2352","volume":"12013 LNCS"},
{"id":"bagnatoDeveloperCentricKnowledgeMining2017","author":[{"family":"Bagnato","given":"Alessandra"},{"family":"Barmpis","given":"Konstantinos"},{"family":"Bessis","given":"Nik"},{"family":"Cabrera-Diego","given":"Luis Adrián"},{"family":"Rocco","given":"Juri Di"},{"family":"Ruscio","given":"Davide Di"},{"family":"Gergely","given":"Tamás"},{"family":"Hansen","given":"Scott"},{"family":"Kolovos","given":"Dimitris S."},{"family":"Krief","given":"Philippe"},{"family":"Korkontzelos","given":"Ioannis"},{"family":"Laurière","given":"Stéphane"},{"family":"Fuente","given":"Jose Manrique Lopez","dropping-particle":"de la"},{"family":"Maló","given":"Pedro"},{"family":"Paige","given":"Richard F."},{"family":"Spinellis","given":"Diomidis"},{"family":"Thomas","given":"Cedric"},{"family":"Vinju","given":"Jurgen J."}],"citation-key":"bagnatoDeveloperCentricKnowledgeMining2017","collection-title":"Lecture Notes in Computer Science","container-title":"Software Technologies: Applications and Foundations - STAF 2017 Collocated Workshops, Marburg, Germany, July 17-21, 2017, Revised Selected Papers","DOI":"10.1007/978-3-319-74730-9_33","editor":[{"family":"Seidl","given":"Martina"},{"family":"Zschaler","given":"Steffen"}],"issued":{"date-parts":[[2017]]},"note":"00000","page":"375–384","publisher":"Springer","title":"Developer-Centric Knowledge Mining from Large Open-Source Software Repositories (CROSSMINER)","type":"paper-conference","URL":"https://doi.org/10.1007/978-3-319-74730-9_33","volume":"10748"},
{"id":"Bai201261","abstract":"Different from classical probability theory, evidence theory has been proposed to handle uncertainties with incomplete or imprecise information. Evidence theory has a flexible framework to represent different types of uncertainties, and has been introduced to perform reliability analysis and design. However, its application for reliability analysis is still a challenging problem due to excessive computational cost. The coupling of the discontinuous nature of uncertainty representation in evidence theory with practical complex problem makes the computational cost extremely prohibitive. To improve its practical utility, metamodels are always used to replace the actual limit-state function to reduce the computational cost. In this paper, we systematically compare three selected metamodeling techniques - quadratic polynomial without cross terms (termed as polynomial approach), radial basis function (RBF), high-dimensional model representation combined with moving least square (HDMR-MLS) - to test the average analysis accuracy and robustness using six representative reliability problems. The objective of this research is to study applicability of different metamodeling techniques for reliability analysis using evidence theory, conclude their overall performances under different test cases, and further investigate their advantages and disadvantages for predicting low failure-probability problems. © 2012 Elsevier Ltd. All rights reserved.","author":[{"family":"Bai","given":"Y.C."},{"family":"Han","given":"X."},{"family":"Jiang","given":"C."},{"family":"Liu","given":"J."}],"citation-key":"Bai201261","container-title":"Advances in Engineering Software","DOI":"10.1016/j.advengsoft.2012.07.007","ISSN":"09659978","issued":{"date-parts":[[2012]]},"page":"61-71","publisher":"Elsevier Ltd","title":"Comparative study of metamodeling techniques for reliability analysis using evidence theory","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84865497733&doi=10.1016%2fj.advengsoft.2012.07.007&partnerID=40&md5=eb8fce4acd824bbbefaf79fc9a0f1322","volume":"53"},
{"id":"Bai2022112","abstract":"In massive machine-type communications (mMTC), the conflict between millions of potential access devices and limited channel freedom leads to a sharp decrease in spectrum efficiency. The nature of sporadic activity in mMTC provides a solution to enhance spectrum efficiency by employing compressive sensing (CS) to perform multiuser detection (MUD). However, CS-MUD suffers from high computation complexity and fails to meet the strict latency requirement in some critical applications. To address this problem, in this paper, we propose a novel deep learning (DL) based framework for grant-free non-orthogonal multiple access (GF-NOMA), where we utilize the information distilled from the initial data recovery phase to further enhance channel estimation, which in turn improves data recovery performance. Besides, we design an interpretable and structured Model-driven Prior Information Aided Network (M-PIAN) and provide theoretical analysis that demonstrates the proposed M-PIAN can converge faster and support more users. Experiments show that the proposed method outperforms existing CS algorithms and DL methods in both computation complexity and reconstruction accuracy. © 1983-2012 IEEE.","author":[{"family":"Bai","given":"Y."},{"family":"Chen","given":"W."},{"family":"Ai","given":"B."},{"family":"Zhong","given":"Z."},{"family":"Wassell","given":"I.J."}],"citation-key":"Bai2022112","container-title":"IEEE Journal on Selected Areas in Communications","DOI":"10.1109/JSAC.2021.3126071","ISSN":"07338716","issue":"1","issued":{"date-parts":[[2022]]},"page":"112-126","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Prior information aided deep learning method for grant-free NOMA in mMTC","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121862914&doi=10.1109%2fJSAC.2021.3126071&partnerID=40&md5=6eb639919a7f5f082f7f309de0f8d6bc","volume":"40"},
{"id":"bakerSingularValueDecomposition2005","author":[{"family":"Baker","given":"Kirk"},{"family":"Baker","given":"Kirk"}],"citation-key":"bakerSingularValueDecomposition2005","issued":{"date-parts":[[2005]]},"title":"Singular value decomposition tutorial","type":"manuscript"},
{"id":"balabanPatternbasedApproachImproving2015","accessed":{"date-parts":[[2015,9,21]]},"author":[{"family":"Balaban","given":"Mira"},{"family":"Maraee","given":"Azzam"},{"family":"Sturm","given":"Arnon"},{"family":"Jelnov","given":"Pavel"}],"citation-key":"balabanPatternbasedApproachImproving2015","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-013-0390-0","ISSN":"1619-1366, 1619-1374","issue":"4","issued":{"date-parts":[[2015,10]]},"page":"1527-1555","source":"CrossRef","title":"A pattern-based approach for improving model quality","type":"article-journal","URL":"http://link.springer.com/10.1007/s10270-013-0390-0","volume":"14"},
{"id":"Balasubramanian20153","abstract":"Complex sensing, processing and control applications running on distributed platforms are difficult to design, develop, analyze, integrate, deploy and operate, especially if resource constraints, fault tolerance and security issues are to be addressed. While technology exists today for engineering distributed, real-time component-based applications, many problems remain unsolved by existing tools. Model-driven development techniques are powerful, but there are very few existing and complete tool chains that offer an end-to-end solution to developers, from design to deployment. There is a need for an integrated model-driven development environment that addresses all phases of application lifecycle including design, development, verification, analysis, integration, deployment, operation and maintenance, with supporting automation in every phase. Arguably, a centerpiece of such a model-driven environment is the modeling language. To that end, this paper presents a wide-spectrum architecture design language called DREMS ML that itself is an integrated collection of individual domain-specific sub-languages. We claim that the language promotes \"correct-by-construction\" software development and integration by supporting each individual phase of the application lifecycle. Using a case study, we demonstrate how the design of DREMS ML impacts the development of embedded systems. © 2015 Elsevier B.V. All rights reserved.","author":[{"family":"Balasubramanian","given":"D."},{"family":"Dubey","given":"A."},{"family":"Otte","given":"W."},{"family":"Levendovszky","given":"T."},{"family":"Gokhale","given":"A."},{"family":"Kumar","given":"P."},{"family":"Emfinger","given":"W."},{"family":"Karsai","given":"G."}],"citation-key":"Balasubramanian20153","container-title":"Science of Computer Programming","DOI":"10.1016/j.scico.2015.04.002","ISSN":"01676423","issued":{"date-parts":[[2015]]},"page":"3-29","publisher":"Elsevier B.V.","title":"DREMS ML: A wide spectrum architecture design language for distributed computing platforms","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930180339&doi=10.1016%2fj.scico.2015.04.002&partnerID=40&md5=9c6311f351378a9ecbce6ceeef85c640","volume":"106"},
{"id":"Balayesu2020995","abstract":"In recent days, Face sketch synthesis (FSS) attracts various researchers for sketching the images to retrieve faces and in multimedia applications. The intention of FSS is to create a sketch for the image provided from a collection of sketch and photo images as the training set. Presently, the rise of deep learning (DL) models becomes useful in FSS because of its diverse benefits. As the FSS is employed in various applications, detailed experimentation to analyze the state of the art approaches methods is nontrivial. Though numerous FSS approaches are available, there is no review paper exist regarding the hierarchical classification of DL based FSS. Keeping this in mind, in this paper, we provide an extensive review of the available DL as well as conventional FSS techniques. We made a clear classification of the FSS techniques, and these are categorized into data-driven and model-driven methods. A comparative analysis of the reviewed techniques is made based on various aspects such as the objective, algorithms used, benefits, and performance measures. © 2019, Bharati Vidyapeeth's Institute of Computer Applications and Management.","author":[{"family":"Balayesu","given":"N."},{"family":"Kalluri","given":"H.K."}],"citation-key":"Balayesu2020995","container-title":"International Journal of Information Technology (Singapore)","DOI":"10.1007/s41870-019-00386-8","ISSN":"25112104","issue":"3","issued":{"date-parts":[[2020]]},"page":"995-1004","publisher":"Springer Science and Business Media B.V.","title":"An extensive survey on traditional and deep learning-based face sketch synthesis models","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091992278&doi=10.1007%2fs41870-019-00386-8&partnerID=40&md5=698f58daaf8af87413bf950d07f600f0","volume":"12"},
{"id":"balogDeepCoderLearningWrite2016","accessed":{"date-parts":[[2017,2,25]]},"author":[{"family":"Balog","given":"Matej"},{"family":"Gaunt","given":"Alexander L."},{"family":"Brockschmidt","given":"Marc"},{"family":"Nowozin","given":"Sebastian"},{"family":"Tarlow","given":"Daniel"}],"citation-key":"balogDeepCoderLearningWrite2016","container-title":"arXiv preprint arXiv:1611.01989","issued":{"date-parts":[[2016]]},"source":"Google Scholar","title":"DeepCoder: Learning to Write Programs","title-short":"DeepCoder","type":"article-journal","URL":"https://arxiv.org/abs/1611.01989"},
{"id":"baltesSOTorrentReconstructingAnalyzing2018","author":[{"family":"Baltes","given":"Sebastian"},{"family":"Dumani","given":"Lorik"},{"family":"Treude","given":"Christoph"},{"family":"Diehl","given":"Stephan"}],"citation-key":"baltesSOTorrentReconstructingAnalyzing2018","collection-title":"MSR '18","container-title":"Proceedings of the 15th international conference on mining software repositories","event-place":"New York, NY, USA","ISBN":"978-1-4503-5716-6","issued":{"date-parts":[[2018]]},"page":"319-330","publisher":"ACM","publisher-place":"New York, NY, USA","title":"SOTorrent: Reconstructing and analyzing the evolution of stack overflow posts","type":"paper-conference","URL":"http://doi.acm.org/10.1145/3196398.3196430"},
{"id":"balzeraniSupportingWebApplications2006","author":[{"family":"Balzerani","given":"Luca"},{"family":"Angelis","given":"Guglielmo De"},{"family":"Ruscio","given":"Davide Di"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"balzeraniSupportingWebApplications2006","container-title":"J. Web Eng.","issue":"1","issued":{"date-parts":[[2006]]},"page":"25–42","title":"Supporting Web Applications development with a PLA","type":"article-journal","URL":"http://www.rintonpress.com/xjwe5/jwe-5-1/025-042.pdf","volume":"5"},
{"id":"banchsInformationRetrievalTechnology2013","accessed":{"date-parts":[[2017,9,25]]},"citation-key":"banchsInformationRetrievalTechnology2013","collection-editor":[{"family":"Hutchison","given":"David"},{"family":"Kanade","given":"Takeo"},{"family":"Kittler","given":"Josef"},{"family":"Kleinberg","given":"Jon M."},{"family":"Mattern","given":"Friedemann"},{"family":"Mitchell","given":"John C."},{"family":"Naor","given":"Moni"},{"family":"Nierstrasz","given":"Oscar"},{"family":"Pandu Rangan","given":"C."},{"family":"Steffen","given":"Bernhard"},{"family":"Sudan","given":"Madhu"},{"family":"Terzopoulos","given":"Demetri"},{"family":"Tygar","given":"Doug"},{"family":"Vardi","given":"Moshe Y."},{"family":"Weikum","given":"Gerhard"}],"collection-title":"Lecture Notes in Computer Science","DOI":"10.1007/978-3-642-45068-6","editor":[{"family":"Banchs","given":"Rafael E."},{"family":"Silvestri","given":"Fabrizio"},{"family":"Liu","given":"Tie-Yan"},{"family":"Zhang","given":"Min"},{"family":"Gao","given":"Sheng"},{"family":"Lang","given":"Jun"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-642-45067-9 978-3-642-45068-6","issued":{"date-parts":[[2013]]},"publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","source":"CrossRef","title":"Information Retrieval Technology","type":"book","URL":"http://link.springer.com/10.1007/978-3-642-45068-6","volume":"8281"},
{"id":"bansiyaHierarchicalModelObjectoriented2002","author":[{"family":"Bansiya","given":"J."},{"family":"Davis","given":"C.G."}],"citation-key":"bansiyaHierarchicalModelObjectoriented2002","container-title":"IEEE Transactions on Software Engineering","DOI":"10.1109/32.979986","issue":"1","issued":{"date-parts":[[2002]]},"page":"4–17","title":"A hierarchical model for object-oriented design quality assessment","type":"article-journal","volume":"28"},
{"id":"Bao2021706","abstract":"Model-driven development has been gradually adopted as an important approach of designing and developing safety-critical cyber-physical systems(SC-CPSs). The requirement of SC-CPSs is often described in natural language. How to link natural language requirements and the model-driven design and development process of SC-CPSs automatically or semi-automatically is a main existing challenge. In this paper, a method named RNL2SysML is proposed for the automatic generation of SysML models from restricted natural language requirements in Chinese. Firstly, in view of the problem that glossaries need to be manually extracted, a method for extracting and recommending terms of SC-CPSs based on artificial intelligence is proposed. Secondly, in order to reduce the ambiguity of natural language requirements, a restricted natural language requirement template is proposed for requirement specification. Then, the method of transformation from natural language requirement specification to SysML model is given. Finally, based on the open source tool Papyrus, the plugin for the method proposed in this paper is implemented, and the effectiveness and practicality of the method is evaluated and proved by an industry case of the airplane air compressor system in the aviation field. © 2021, Science Press. All right reserved.","author":[{"family":"Bao","given":"Y."},{"family":"Yang","given":"Z."},{"family":"Yang","given":"Y."},{"family":"Xie","given":"J."},{"family":"Zhou","given":"Y."},{"family":"Yue","given":"T."},{"family":"Huang","given":"Z."},{"family":"Guo","given":"P."}],"citation-key":"Bao2021706","container-title":"Jisuanji Yanjiu yu Fazhan/Computer Research and Development","DOI":"10.7544/issn1000-1239.2021.20200757","ISSN":"10001239","issue":"4","issued":{"date-parts":[[2021]]},"page":"706-730","publisher":"Science Press","title":"An automated approach to generate SysML models from restricted natural language requirements in chinese [基于限定中文自然语言需求的SysML模型自动生成方法]","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104131599&doi=10.7544%2fissn1000-1239.2021.20200757&partnerID=40&md5=5a5cbf39de1bdd3ecd21c4207d9750b3","volume":"58"},
{"id":"baresiBuildingSoftwareInternet2015","accessed":{"date-parts":[[2019,8,22]]},"author":[{"family":"Baresi","given":"Luciano"},{"family":"Mottola","given":"Luca"},{"family":"Dustdar","given":"Schahram"}],"citation-key":"baresiBuildingSoftwareInternet2015","container-title":"IEEE Internet Computing","container-title-short":"IEEE Internet Comput.","DOI":"10.1109/MIC.2015.31","ISSN":"1089-7801","issue":"2","issued":{"date-parts":[[2015,3]]},"page":"6-8","source":"DOI.org (Crossref)","title":"Building Software for the Internet of Things","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7061810/","volume":"19"},
{"id":"Barmpis20141","abstract":"Scalability in Model-Driven Engineering (MDE) is often a bot-tleneck for industrial applications. Industrial scale models need to be per-sisted in a way that allows for their seamless and ecient manipulation, often by multiple stakeholders simultaneously. This paper compares the conventional and commonly used persistence mechanisms in MDE with novel approaches such as the use of graph-based NoSQL databases; Pro-totype integrations of Neo4J and OrientDB with EMF are used to compare with relational database, XMI and document-based NoSQL database per-sistence mechanisms. It also compares and benchmarks two approaches for querying models persisted in graph databases to measure and compare their relative performance in terms of memory usage and execution time. © JOT 2014.","author":[{"family":"Barmpis","given":"K."},{"family":"Kolovos","given":"D.S."}],"citation-key":"Barmpis20141","container-title":"Journal of Object Technology","DOI":"10.5381/jot.2014.13.3.a3","ISSN":"16601769","issue":"3","issued":{"date-parts":[[2014]]},"page":"1-26","publisher":"Association Internationale pour les Technologies Objets","title":"Evaluation of contemporary graph databases for ecient persistence of large-scale models","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904428418&doi=10.5381%2fjot.2014.13.3.a3&partnerID=40&md5=520e4d3e1cb2d06320a5ecb1f5faa7a6","volume":"13"},
{"id":"barmpisEvaluationContemporaryGraph2014a","abstract":"Scalability in Model-Driven Engineering (MDE) is often a bot-tleneck for industrial applications. Industrial scale models need to be per-sisted in a way that allows for their seamless and ecient manipulation, often by multiple stakeholders simultaneously. This paper compares the conventional and commonly used persistence mechanisms in MDE with novel approaches such as the use of graph-based NoSQL databases; Pro-totype integrations of Neo4J and OrientDB with EMF are used to compare with relational database, XMI and document-based NoSQL database per-sistence mechanisms. It also compares and benchmarks two approaches for querying models persisted in graph databases to measure and compare their relative performance in terms of memory usage and execution time. © JOT 2014.","author":[{"family":"Barmpis","given":"K."},{"family":"Kolovos","given":"D.S."}],"citation-key":"barmpisEvaluationContemporaryGraph2014a","container-title":"Journal of Object Technology","DOI":"10.5381/jot.2014.13.3.a3","ISSN":"16601769","issue":"3","issued":{"date-parts":[[2014]]},"page":"1-26","publisher":"Association Internationale pour les Technologies Objets","title":"Evaluation of contemporary graph databases for ecient persistence of large-scale models","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904428418&doi=10.5381%2fjot.2014.13.3.a3&partnerID=40&md5=520e4d3e1cb2d06320a5ecb1f5faa7a6","volume":"13"},
{"id":"Barriga20221135","abstract":"Artificial intelligence has already proven to be a powerful tool to automate and improve how we deal with software development processes. The application of artificial intelligence to model-driven engineering projects is becoming more and more popular; however, within the model repair field, the use of this technique remains mostly an open challenge. In this paper, we explore some existing approaches in the field of AI-powered model repair. From the existing approaches in this field, we identify a series of challenges which the community needs to overcome. In addition, we present a number of research opportunities by taking inspiration from other fields which have successfully used artificial intelligence, such as code repair. Moreover, we discuss the connection between the existing approaches and the opportunities with the identified challenges. Finally, we present the outcomes of our experience of applying artificial intelligence to model repair. © 2022, The Author(s).","author":[{"family":"Barriga","given":"A."},{"family":"Rutle","given":"A."},{"family":"Heldal","given":"R."}],"citation-key":"Barriga20221135","container-title":"Software and Systems Modeling","DOI":"10.1007/s10270-022-00983-5","ISSN":"16191366","issue":"3","issued":{"date-parts":[[2022]]},"page":"1135-1157","publisher":"Springer Science and Business Media Deutschland GmbH","title":"AI-powered model repair: an experience report—lessons learned, challenges, and opportunities","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124985776&doi=10.1007%2fs10270-022-00983-5&partnerID=40&md5=f3ddec9ca15934c82f63a722471ece60","volume":"21"},
{"id":"barrigaExtensibleToolchainAnalyzing2020","author":[{"family":"Barriga","given":"Angela"},{"family":"Di Ruscio","given":"D."},{"family":"Iovino","given":"L."},{"family":"Nguyen","given":"Thanh Phuong"},{"family":"Pierantonio","given":"A."}],"citation-key":"barrigaExtensibleToolchainAnalyzing2020","container-title":"Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings","DOI":"10.1145/3417990.3419626","ISBN":"978-1-4503-8135-2","issued":{"date-parts":[[2020]]},"note":"00000","page":"316–323","publisher":"Association for Computing Machinery, Inc","title":"An extensible tool-chain for analyzing datasets of metamodels","type":"paper-conference"},
{"id":"Barzdins202076","abstract":"A new Domain Specific Language (DSL) based approach to Deep Learning (DL) lifecycle data management (LDM) is presented: a very simple but universal DL LDM tool, still usable in practice (called Core tool); and an advanced extension mechanism, that converts the Core tool into a DSL tool building framework for DL LDM tasks. The method used is based on the metamodel specialisation approach for DSL modeling tools introduced by authors. © 2020 Owner/Author.","author":[{"family":"Barzdins","given":"P."},{"family":"Celms","given":"E."},{"family":"Barzdins","given":"J."},{"family":"Kalnins","given":"A."},{"family":"Sprogis","given":"A."},{"family":"Grasmanis","given":"M."},{"family":"Rikacovs","given":"S."}],"citation-key":"Barzdins202076","collection-title":"Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings","DOI":"10.1145/3417990.3420050","ISBN":"978-1-4503-8135-2","issued":{"date-parts":[[2020]]},"page":"76-77","publisher":"Association for Computing Machinery, Inc","title":"Metamodel specialization based DSL for DL lifecycle data management","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096796058&doi=10.1145%2f3417990.3420050&partnerID=40&md5=c5bb510ce564c6f9fac559605b598171"},
{"id":"Barzdins202217","abstract":"This paper outlines our Deep Learning Lifecycle Data Management system. It consists of two major parts: the LDM Core Tool - a simple data logging tool; and an Extension Mechanism - this mechanism allows the user to extend the simple LDM Core Tool to match their specific requirements. Current extensions support adding new visualisations for data stored on the server. Our approach allows the Core Tool to be a complete black box; we need only a metamodel denoting the logical structure of the stored data. By then specialising this metamodel we can define an Extension Metamodel which, when communicated to the tool through configuration, allows us to define and thus add the extensions. © 2022 University of Latvia All Rights Reserved.","author":[{"family":"Barzdins","given":"P."},{"family":"Kalnins","given":"A."},{"family":"Celms","given":"E."},{"family":"Barzdins","given":"J."},{"family":"Sprogis","given":"A."},{"family":"Grasmanis","given":"M."},{"family":"Rikacovs","given":"S."},{"family":"Barzdins","given":"G."}],"citation-key":"Barzdins202217","container-title":"Baltic Journal of Modern Computing","DOI":"10.22364/BJMC.2022.10.1.02","ISSN":"22558942","issue":"1","issued":{"date-parts":[[2022]]},"page":"17-35","publisher":"University of Latvia","title":"Metamodel specialisation based tool extension","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128597279&doi=10.22364%2fBJMC.2022.10.1.02&partnerID=40&md5=2cb370319de0e9367ad238fb9d6954cc","volume":"10"},
{"id":"basciani2015model","author":[{"family":"Basciani","given":"Francesco"},{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"basciani2015model","container-title":"CloudMDE@ MoDELS","issued":{"date-parts":[[2015]]},"page":"37-42","title":"Model repositories: Will they become reality?","type":"paper-conference"},
{"id":"BASCIANI2019173","abstract":"Context: Software quality engineering is increasingly gaining interests also in the Model-Driven Engineering community as testified by a large corpus of research that has been produced over the last few years. Quality models are presented as convenient artifacts to specify and organize quality attributes that are of interest for considered stakeholders. Motivation: Existing approaches enabling the specification of quality models are affected by relevant limitations including limited extensibility, artifact specificity, and manual assessment, which might lead to informal, subjective, and non-reproducible assessment processes. Goal: This paper presents an approach and related tools supporting the definition of quality models underpinning the quality assessment of modeling artifacts. Quality models are defined in terms of sets of high-level quality attributes, which are top-down decomposed into sets of subordinate attributes. An operative environment is also provided to apply the defined quality models on actual modeling artifacts enabling automated quality assessment. A set of dedicated experiments is conducted to validate the approach. The experimental results show that the proposed techniques permit modelers to define quality models taken from the literature, and apply them to assess the quality of metamodels and transformations retrieved from public repositories. The validation permitted also to analyse the performance in terms of various population structures and size.","archive":"pre_print: http://vps.diruscio.org/pubs/comlan_pre_print.pdf","author":[{"family":"Basciani","given":"Francesco"},{"family":"Juri","given":"Di Rocco"},{"family":"Davide","given":"Di Ruscio"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"BASCIANI2019173","container-title":"Journal of Computer Languages","ISSN":"2590-1184","issued":{"date-parts":[[2019]]},"page":"173 - 192","title":"A tool-supported approach for assessing the quality of modeling artifacts","type":"article-magazine","URL":"https://doi.org/10.1016/j.cola.2019.02.003","volume":"51"},
{"id":"bascianiAutomatedClusteringMetamodel2016","accessed":{"date-parts":[[2020,2,20]]},"author":[{"family":"Basciani","given":"Francesco"},{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"bascianiAutomatedClusteringMetamodel2016","container-title":"Advanced Information Systems Engineering","DOI":"10.1007/978-3-319-39696-5_21","editor":[{"family":"Nurcan","given":"Selmin"},{"family":"Soffer","given":"Pnina"},{"family":"Bajec","given":"Marko"},{"family":"Eder","given":"Johann"}],"event-place":"Cham","ISBN":"978-3-319-39695-8 978-3-319-39696-5","issued":{"date-parts":[[2016]]},"page":"342-358","publisher":"Springer International Publishing","publisher-place":"Cham","source":"DOI.org (Crossref)","title":"Automated Clustering of Metamodel Repositories","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-319-39696-5_21","volume":"9694"},
{"id":"bascianiAutomatedClusteringMetamodel2016a","abstract":"Over the last years, several model repositories have been proposed in response to the need of the MDE community for advanced systems supporting the reuse of modeling artifacts. Modelers can interact with MDE repositories with different intents ranging from merely repository browsing, to searching specific artifacts satisfying precise requirements. The organization and browsing facilities provided by current repositories is limited since they do not produce structured overviews of the contained artifacts, and the ategorization mechanisms (if any) are based on manual activities. When dealing with large numbers of modeling artifacts, such limitations increase the effort for managing and reusing artifacts stored in model repositories. By focusing on metamodel repositories, in this paper we propose the application of clustering techniques to automatically organize stored metamodels and to provide users with overviews of the application domains covered by the available metamodels. The approach has been implemented in the MDEForge repository.","accessed":{"date-parts":[[2021,4,30]]},"author":[{"family":"Basciani","given":"Francesco"},{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"bascianiAutomatedClusteringMetamodel2016a","container-title":"Advanced Information Systems Engineering","DOI":"10.1007/978-3-319-39696-5_21","editor":[{"family":"Nurcan","given":"Selmin"},{"family":"Soffer","given":"Pnina"},{"family":"Bajec","given":"Marko"},{"family":"Eder","given":"Johann"}],"event-place":"Cham","ISBN":"978-3-319-39695-8 978-3-319-39696-5","issued":{"date-parts":[[2016]]},"note":"00000","page":"342-358","publisher":"Springer International Publishing","publisher-place":"Cham","source":"DOI.org (Crossref)","title":"Automated Clustering of Metamodel Repositories","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-319-39696-5_21","volume":"9694"},
{"id":"bascianiAutomatedClusteringMetamodel2016b","author":[{"family":"Basciani","given":"Francesco"},{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"bascianiAutomatedClusteringMetamodel2016b","collection-title":"LECTURE NOTES IN COMPUTER SCIENCE","container-title":"Advanced information systems engineering, 28th international conference, CAiSE 2016, ljubljana, slovenia, june 13-17, 2016. Proceedings","DOI":"10.1007/978-3-319-39696-5_21","ISBN":"978-3-319-39695-8","issued":{"date-parts":[[2016]]},"page":"342-358","publisher":"Springer Verlag","title":"Automated clustering of metamodel repositories","type":"paper-conference","URL":"http://springerlink.com/content/0302-9743/copyright/2005/","volume":"9694"},
{"id":"bascianiAutomatedClusteringMetamodel2016c","abstract":"Over the last years, several model repositories have been proposed in response to the need of the MDE community for advanced systems supporting the reuse of modeling artifacts. Modelers can interact with MDE repositories with different intents ranging from merely repository browsing, to searching specific artifacts satisfying precise requirements. The organization and browsing facilities provided by current repositories is limited since they do not produce structured overviews of the contained artifacts, and the categorization mechanisms (if any) are based on manual activities. When dealing with large numbers of modeling artifacts, such limitations increase the effort related to both managing and reusing artifacts stored in model repositories.","accessed":{"date-parts":[[2022,5,10]]},"author":[{"family":"Basciani","given":"Francesco"},{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"bascianiAutomatedClusteringMetamodel2016c","container-title":"Advanced Information Systems Engineering","DOI":"10.1007/978-3-319-39696-5_21","editor":[{"family":"Nurcan","given":"Selmin"},{"family":"Soffer","given":"Pnina"},{"family":"Bajec","given":"Marko"},{"family":"Eder","given":"Johann"}],"event-place":"Cham","ISBN":"978-3-319-39695-8 978-3-319-39696-5","issued":{"date-parts":[[2016]]},"page":"342-358","publisher":"Springer International Publishing","publisher-place":"Cham","source":"DOI.org (Crossref)","title":"Automated Clustering of Metamodel Repositories","type":"chapter","URL":"https://link.springer.com/10.1007/978-3-319-39696-5_21","volume":"9694"},
{"id":"bascianiAutomatedSelectionOptimal2018","author":[{"family":"Basciani","given":"Francesco"},{"family":"Demidio","given":"Mattia"},{"family":"Di Ruscio","given":"Davide"},{"family":"Frigioni","given":"Daniele"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"bascianiAutomatedSelectionOptimal2018","container-title":"IEEE TRANSACTIONS ON SOFTWARE ENGINEERING","issued":{"date-parts":[[2018]]},"page":"1–1","title":"Automated Selection of Optimal Model Transformation Chains via Shortest-Path Algorithms","type":"article-magazine","URL":"http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=32"},
{"id":"bascianiAutomatedSelectionOptimal2020","author":[{"family":"Basciani","given":"Francesco"},{"family":"DEmidio","given":"Mattia"},{"family":"Di Ruscio","given":"Davide"},{"family":"Frigioni","given":"Daniele"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"bascianiAutomatedSelectionOptimal2020","container-title":"IEEE TRANSACTIONS ON SOFTWARE ENGINEERING","DOI":"10.1109/TSE.2018.2846223","issued":{"date-parts":[[2020]]},"note":"00000","page":"251–279","title":"Automated Selection of Optimal Model Transformation Chains via Shortest-Path Algorithms","type":"article-journal","URL":"http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=32","volume":"46"},
{"id":"bascianiExploringModelRepositories","abstract":"Great strides have been made in the development of tools and techniques for advance model management over the last decade. Despite the use of model repositories is gaining traction in industry, their use is still hampered by the limited understanding of the underlying platform semantics. Consequently, the all-important goal of reusing artefacts has led to an enduring quest for ways to search and retrieve artifacts more efficiently and accurately. Arguably, a contributory factor limiting the use of current search engines is the poor alignment between the query languages and the lattice of relations among the different and heterogeneous artifacts in the repository.","author":[{"family":"Basciani","given":"Francesco"},{"family":"Ruscio","given":"Davide Di"},{"family":"Rocco","given":"Juri Di"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"bascianiExploringModelRepositories","page":"6","source":"Zotero","title":"Exploring model repositories by means of megamodel-aware search operators","type":"article-journal"},
{"id":"bascianiExploringModelRepositories2018","author":[{"family":"Basciani","given":"F."},{"family":"Di Ruscio","given":"D."},{"family":"Di Rocco","given":"J."},{"family":"Iovino","given":"L."},{"family":"Pierantonio","given":"A."}],"citation-key":"bascianiExploringModelRepositories2018","container-title":"CEUR Workshop Proceedings","issued":{"date-parts":[[2018]]},"page":"793–798","publisher":"CEUR-WS","title":"Exploring model repositories by means of megamodel-aware search operators","type":"paper-conference","volume":"2245"},
{"id":"bascianiExploringModelRepositoriesa","abstract":"Great strides have been made in the development of tools and techniques for advance model management over the last decade. Despite the use of model repositories is gaining traction in industry, their use is still hampered by the limited understanding of the underlying platform semantics. Consequently, the all-important goal of reusing artefacts has led to an enduring quest for ways to search and retrieve artifacts more efficiently and accurately. Arguably, a contributory factor limiting the use of current search engines is the poor alignment between the query languages and the lattice of relations among the different and heterogeneous artifacts in the repository.","author":[{"family":"Basciani","given":"Francesco"},{"family":"Ruscio","given":"Davide Di"},{"family":"Rocco","given":"Juri Di"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"bascianiExploringModelRepositoriesa","note":"00003","page":"6","source":"Zotero","title":"Exploring model repositories by means of megamodel-aware search operators","type":"article-journal"},
{"id":"bascianiMDEForgeExtensibleWebbased","abstract":"Model-Driven Engineering (MDE) refers to the systematic use of models as first class entities throughout the software development life cycle. Over the last few years, many MDE technologies have been conceived for developing domain specific modeling languages, and for supporting a wide range of model management activities. However, existing modeling platforms neglect a number of important features that if missed reduce the acceptance and the relevance of MDE in industrial contexts, e.g., the possibility to search and reuse already developed modeling artifacts, and to adopt model management tools as a service.","author":[{"family":"Basciani","given":"Francesco"},{"family":"Rocco","given":"Juri Di"},{"family":"Ruscio","given":"Davide Di"},{"family":"Salle","given":"Amleto Di"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"bascianiMDEForgeExtensibleWebbased","page":"10","source":"Zotero","title":"MDEForge: an extensible Web-based modeling platform","type":"article-journal"},
{"id":"bascianiMDEForgeExtensibleWebbased2014","author":[{"family":"Basciani","given":"Francesco"},{"family":"DI ROCCO","given":"Juri"},{"family":"DI RUSCIO","given":"Davide"},{"family":"DI SALLE","given":"Amleto"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"bascianiMDEForgeExtensibleWebbased2014","collection-title":"CEUR WORKSHOP PROCEEDINGS","container-title":"Proceedings of the 2nd international workshop on model-driven engineering on and for the cloud co-located with the 17th international conference on model driven engineering languages and systems, CloudMDE@MoDELS 2014, valencia, spain, september 30, 2014","issued":{"date-parts":[[2014]]},"page":"66-75","publisher":"CEUR-WS","title":"MDEForge: An extensible Web-based modeling platform","type":"paper-conference","URL":"http://ceur-ws.org/","volume":"1242"},
{"id":"bascianiToolsupportedApproachAssessing2019","author":[{"family":"Basciani","given":"Francesco"},{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"bascianiToolsupportedApproachAssessing2019","container-title":"JOURNAL OF COMPUTER LANGUAGES","DOI":"10.1016/j.cola.2019.02.003","issued":{"date-parts":[[2019]]},"note":"00000","page":"173–192","title":"A tool-supported approach for assessing the quality of modeling artifacts","type":"article-journal","URL":"https://www.journals.elsevier.com/journal-of-computer-languages","volume":"51"},
{"id":"bascianiToolsupportedApproachAssessing2019a","accessed":{"date-parts":[[2021,4,19]]},"author":[{"family":"Basciani","given":"Francesco"},{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"bascianiToolsupportedApproachAssessing2019a","container-title":"Journal of Computer Languages","container-title-short":"Journal of Computer Languages","DOI":"10.1016/j.cola.2019.02.003","ISSN":"25901184","issued":{"date-parts":[[2019,4]]},"note":"00006","page":"173-192","source":"DOI.org (Crossref)","title":"A tool-supported approach for assessing the quality of modeling artifacts","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1045926X18302106","volume":"51"},
{"id":"bascianiTyphonMLModelingEnvironment2020","author":[{"family":"Basciani","given":"F."},{"family":"Di Rocco","given":"J."},{"family":"Di Ruscio","given":"D."},{"family":"Pierantonio","given":"A."},{"family":"Iovino","given":"L."}],"citation-key":"bascianiTyphonMLModelingEnvironment2020","container-title":"Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings","DOI":"10.1145/3417990.3421999","ISBN":"978-1-4503-8135-2","issued":{"date-parts":[[2020]]},"note":"00000","page":"6–10","publisher":"Association for Computing Machinery, Inc","title":"TyphonML: A modeling environment to develop hybrid polystores","type":"paper-conference"},
{"id":"basiliSoftwareEngineeringResearch2018","abstract":"Software engineering is not only an increasingly challenging endeavor that goes beyond the intellectual capabilities of any single individual engineer but also an intensely human one. Tools and methods to develop software are employed by engineers of varied backgrounds within a large variety of organizations and application domains. As a result, the variation in challenges and practices in system requirements, architecture, and quality assurance is staggering. Human, domain, and organizational factors define the context within which software engineering methodologies and technologies are to be applied and therefore the context that research needs to account for, if it is to be impactful. This article provides an assessment of the current challenges faced by software engineering research in achieving its potential, a description of the root causes of such challenges, and a proposal for the field to move forward and become more impactful through collaborative research and innovation between public research and industry. This article is part of a theme issue on software engineering’s 50th anniversary.","author":[{"family":"Basili","given":"V."},{"family":"Briand","given":"L."},{"family":"Bianculli","given":"D."},{"family":"Nejati","given":"S."},{"family":"Pastore","given":"F."},{"family":"Sabetzadeh","given":"M."}],"citation-key":"basiliSoftwareEngineeringResearch2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.290110216","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"44-49","source":"IEEE Xplore","title":"Software Engineering Research and Industry: A Symbiotic Relationship to Foster Impact","title-short":"Software Engineering Research and Industry","type":"article-journal","volume":"35"},
{"id":"Basmer201986","abstract":"Nowadays software is often highly configurable, and the required adaptation is a complex and tedious task when performed manually. Moreover, hand-crafted configurations are often far from optimal. In this paper, we study the software configuration problem in the context of the model comparison tool SiDiff, which needs to be carefully adapted to domain-specific modeling languages used in model-driven engineering. To tackle the configuration challenge, we propose to draw from the field of automated algorithm configuration, a research area which has studied the optimization of parameterizable algorithms for many years and which has gained particular momentum through its applications to hyper-parameter tuning in machine learning. Specifically, we report on ongoing work encoding the adaptability of SiDiff as an algorithm configuration problem which is amenable to a sequential model-based optimization tool known as SMAC. While empirical evaluation results are left for future work, the main goal of this paper is to foster active discussions at the workshop and to collect early feedback on our ongoing research. © 2019 IEEE.","author":[{"family":"Basmer","given":"M."},{"family":"Kehrer","given":"T."}],"citation-key":"Basmer201986","collection-title":"Proceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering Workshops, ASEW 2019","DOI":"10.1109/ASEW.2019.00035","ISBN":"978-1-72814-136-7","issued":{"date-parts":[[2019]]},"page":"86-89","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Encoding adaptability of software engineering tools as algorithm configuration problem: A case study","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079290299&doi=10.1109%2fASEW.2019.00035&partnerID=40&md5=2286845dc17b1563c35124ebf70d2e9d"},
{"id":"basmerEncodingAdaptabilitySoftware2019a","abstract":"Nowadays software is often highly configurable, and the required adaptation is a complex and tedious task when performed manually. Moreover, hand-crafted configurations are often far from optimal. In this paper, we study the software configuration problem in the context of the model comparison tool SiDiff, which needs to be carefully adapted to domain-specific modeling languages used in model-driven engineering. To tackle the configuration challenge, we propose to draw from the field of automated algorithm configuration, a research area which has studied the optimization of parameterizable algorithms for many years and which has gained particular momentum through its applications to hyper-parameter tuning in machine learning. Specifically, we report on ongoing work encoding the adaptability of SiDiff as an algorithm configuration problem which is amenable to a sequential model-based optimization tool known as SMAC. While empirical evaluation results are left for future work, the main goal of this paper is to foster active discussions at the workshop and to collect early feedback on our ongoing research. © 2019 IEEE.","author":[{"family":"Basmer","given":"M."},{"family":"Kehrer","given":"T."}],"citation-key":"basmerEncodingAdaptabilitySoftware2019a","container-title":"Proceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering Workshops, ASEW 2019","DOI":"10.1109/ASEW.2019.00035","ISBN":"978-1-72814-136-7","issued":{"date-parts":[[2019]]},"page":"86-89","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Encoding adaptability of software engineering tools as algorithm configuration problem: A case study","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079290299&doi=10.1109%2fASEW.2019.00035&partnerID=40&md5=2286845dc17b1563c35124ebf70d2e9d"},
{"id":"batotPromotingSocialDiversity2022","accessed":{"date-parts":[[2022,5,24]]},"author":[{"family":"Batot","given":"Edouard R."},{"family":"Sahraoui","given":"Houari"}],"citation-key":"batotPromotingSocialDiversity2022","container-title":"Software and Systems Modeling","container-title-short":"Softw Syst Model","DOI":"10.1007/s10270-021-00969-9","ISSN":"1619-1366, 1619-1374","issue":"3","issued":{"date-parts":[[2022,6]]},"page":"1159-1178","source":"DOI.org (Crossref)","title":"Promoting social diversity for the automated learning of complex MDE artifacts","type":"article-journal","URL":"https://link.springer.com/10.1007/s10270-021-00969-9","volume":"21"},
{"id":"Bauer:2012:SAA:2473496.2473600","author":[{"family":"Bauer","given":"Veronika"},{"family":"Heinemann","given":"Lars"},{"family":"Deissenboeck","given":"Florian"}],"citation-key":"Bauer:2012:SAA:2473496.2473600","collection-title":"ICSM '12","container-title":"Proceedings of the 2012 IEEE international conference on software maintenance","event-place":"Washington, DC, USA","ISBN":"978-1-4673-2313-0","issued":{"date-parts":[[2012]]},"page":"483-492","publisher":"IEEE Computer Society","publisher-place":"Washington, DC, USA","title":"A structured approach to assess third-party library usage","type":"paper-conference","URL":"http://dx.doi.org/10.1109/ICSM.2012.6405311"},
{"id":"bauerIoTReferenceModel2013","accessed":{"date-parts":[[2016,5,30]]},"author":[{"family":"Bauer","given":"Martin"},{"family":"Bui","given":"Nicola"},{"family":"De Loof","given":"Jourik"},{"family":"Magerkurth","given":"Carsten"},{"family":"Nettsträter","given":"Andreas"},{"family":"Stefa","given":"Julinda"},{"family":"Walewski","given":"Joachim W."}],"citation-key":"bauerIoTReferenceModel2013","container-title":"Enabling Things to Talk","editor":[{"family":"Bassi","given":"Alessandro"},{"family":"Bauer","given":"Martin"},{"family":"Fiedler","given":"Martin"},{"family":"Kramp","given":"Thorsten"},{"family":"Kranenburg","given":"Rob","non-dropping-particle":"van"},{"family":"Lange","given":"Sebastian"},{"family":"Meissner","given":"Stefan"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-642-40402-3 978-3-642-40403-0","issued":{"date-parts":[[2013]]},"page":"113-162","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","source":"CrossRef","title":"IoT Reference Model","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-642-40403-0_7"},
{"id":"bauerTestSuiteQuality2011","author":[{"family":"Bauer","given":"Eduard"},{"family":"Küster","given":"Jochen M."},{"family":"Engels","given":"Gregor"}],"citation-key":"bauerTestSuiteQuality2011","container-title":"Objects, Models, Components, Patterns","DOI":"10.1007/978-3-642-21952-8_3","issued":{"date-parts":[[2011]]},"page":"3–19","title":"Test Suite Quality for Model Transformation Chains","type":"article-journal","volume":"6705"},
{"id":"BeautifulMonitoringGrafana","accessed":{"date-parts":[[2021,1,5]]},"citation-key":"BeautifulMonitoringGrafana","note":"00000","title":"Beautiful Monitoring With Grafana and InfluxDB","type":"webpage","URL":"https://www2.slideshare.net/leesjensen/beautiful-monitoring-with-grafana-and-influxdb?qid=2eb80839-115d-421d-afaa-e6dcbd79c280&v=&b=&from_search=4"},
{"id":"beckerSymbolicInvariantVerification2006","accessed":{"date-parts":[[2015,4,7]]},"author":[{"family":"Becker","given":"Basil"},{"family":"Beyer","given":"Dirk"},{"family":"Giese","given":"Holger"},{"family":"Klein","given":"Florian"},{"family":"Schilling","given":"Daniela"}],"citation-key":"beckerSymbolicInvariantVerification2006","container-title":"Proceedings of the 28th international conference on Software engineering","issued":{"date-parts":[[2006]]},"page":"72–81","publisher":"ACM","source":"Google Scholar","title":"Symbolic invariant verification for systems with dynamic structural adaptation","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=1134297"},
{"id":"beechamHowBestTeach2017","author":[{"family":"Beecham","given":"Sarah"},{"family":"Clear","given":"Tony"},{"family":"Damian","given":"Daniela"},{"family":"Barr","given":"John"},{"family":"Noll","given":"John"},{"family":"Scacchi","given":"Walt"},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""}],"citation-key":"beechamHowBestTeach2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"16-19","source":"IEEE Computer Society","title":"How Best to Teach Global Software Engineering? Educators Are Divided","title-short":"How Best to Teach Global Software Engineering?","type":"article-magazine","volume":"34"},
{"id":"beechamHowBestTeach2017a","accessed":{"date-parts":[[2019,8,22]]},"author":[{"family":"Beecham","given":"Sarah"},{"family":"Clear","given":"Tony"},{"family":"Damian","given":"Daniela"},{"family":"Barr","given":"John"},{"family":"Noll","given":"John"},{"family":"Scacchi","given":"Walt"}],"citation-key":"beechamHowBestTeach2017a","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2017.12","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017,1]]},"page":"16-19","source":"DOI.org (Crossref)","title":"How Best to Teach Global Software Engineering? Educators Are Divided","title-short":"How Best to Teach Global Software Engineering?","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7819407/","volume":"34"},
{"id":"beechamPreparingTomorrowSoftware2017","abstract":"Global software engineering (GSE) is becoming common. It's thus important to educate university software engineering students in GSE. The authors discuss challenges to and recommendations for implementing such instruction.","author":[{"family":"Beecham","given":"Sarah"},{"family":"Clear","given":"Tony"},{"family":"Barr","given":"John"},{"family":"Daniels","given":"Mats"},{"family":"Oudshoorn","given":"Michael"},{"family":"Noll","given":"John"},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""}],"citation-key":"beechamPreparingTomorrowSoftware2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"9-12","source":"IEEE Computer Society","title":"Preparing Tomorrow's Software Engineers for Work in a Global Environment","type":"article-magazine","volume":"34"},
{"id":"beechamPreparingTomorrowSoftware2017a","accessed":{"date-parts":[[2019,8,22]]},"author":[{"family":"Beecham","given":"Sarah"},{"family":"Clear","given":"Tony"},{"family":"Barr","given":"John"},{"family":"Daniels","given":"Mats"},{"family":"Oudshoorn","given":"Michael"},{"family":"Noll","given":"John"}],"citation-key":"beechamPreparingTomorrowSoftware2017a","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2017.16","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017,1]]},"page":"9-12","source":"DOI.org (Crossref)","title":"Preparing Tomorrow's Software Engineers for Work in a Global Environment","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7819397/","volume":"34"},
{"id":"begelAnalyzeThis1452014","accessed":{"date-parts":[[2016,1,22]]},"author":[{"family":"Begel","given":"Andrew"},{"family":"Zimmermann","given":"Thomas"}],"citation-key":"begelAnalyzeThis1452014","container-title":"Proceedings of the 36th International Conference on Software Engineering","issued":{"date-parts":[[2014]]},"page":"12–23","publisher":"ACM","source":"Google Scholar","title":"Analyze this! 145 questions for data scientists in software engineering","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=2568233"},
{"id":"begoliHeterogeneousPolystorelikeData2016","accessed":{"date-parts":[[2018,4,17]]},"author":[{"family":"Begoli","given":"Edmon"},{"family":"Kistler","given":"Derek"},{"family":"Bates","given":"Jack"}],"citation-key":"begoliHeterogeneousPolystorelikeData2016","DOI":"10.1109/BigData.2016.7840896","ISBN":"978-1-4673-9005-7","issued":{"date-parts":[[2016,12]]},"page":"2550-2554","publisher":"IEEE","source":"CrossRef","title":"Towards a heterogeneous, polystore-like data architecture for the US Department of Veteran Affairs (VA) enterprise analytics","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/7840896/"},
{"id":"Behnaz201717","abstract":"Over the past decade, several sophisticated analytic techniques such as machine learning, neural networks, and predictive modelling have evolved to enable scientists to derive insights from data. Data Science is characterised by a cycle of model selection, customization and testing, as scientists often do not know the exact goal or expected results beforehand. Existing research efforts which explore maximising automation, reproducibility and interoperability are quite mature and fail to address a third criterion, usability. The main contribution of this paper is to explore the development of more complex semantic data models linked with existing ontologies (e.g. FIBO) that enable the standardisation of data formats as well as meaning and interpretation of data in automated data analysis. A model-driven architecture with the reference model that capture statistical learning requirement is proposed together with a prototype based around a case study in commodity pricing. © Springer International Publishing AG 2017.","author":[{"family":"Behnaz","given":"A."},{"family":"Natarajan","given":"A."},{"family":"Rabhi","given":"F.A."},{"family":"Peat","given":"M."}],"citation-key":"Behnaz201717","container-title":"Lecture Notes in Business Information Processing","DOI":"10.1007/978-3-319-52764-2_2","editor":[{"family":"Feuerriegel S.","given":"Neumann D."}],"ISBN":"9783319527635","ISSN":"18651348","issued":{"date-parts":[[2017]]},"page":"17-31","publisher":"Springer Verlag","title":"A semantic-based analytics architecture and its application to commodity pricing","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011298633&doi=10.1007%2f978-3-319-52764-2_2&partnerID=40&md5=dd45b61348add207e00a75bb41454fec","volume":"276"},
{"id":"BellogiN:2013:CSH:2397740.2398191","author":[{"family":"Bellogín","given":"Alejandro"},{"family":"Cantador","given":"IváN"},{"family":"Castells","given":"Pablo"}],"citation-key":"BellogiN:2013:CSH:2397740.2398191","container-title":"Inf. Sci.","ISSN":"0020-0255","issued":{"date-parts":[[2013,2]]},"page":"142-169","title":"A comparative study of heterogeneous item recommendations in social systems","type":"article-journal","URL":"http://dx.doi.org/10.1016/j.ins.2012.09.039","volume":"221"},
{"id":"Bellogin2011","author":[{"family":"Bellogin","given":"A."},{"family":"Castells","given":"P."},{"family":"Cantador","given":"I."}],"citation-key":"Bellogin2011","container-title":"ACM RecSys '11","issued":{"date-parts":[[2011]]},"page":"333-336","title":"Precision-oriented evaluation of recommender systems: An algorithmic comparison","type":"paper-conference"},
{"id":"Belloir2019260","abstract":"Digitalization of the whole society changes the way Systems-of-Systems have to be considered. Remaining independently operated and managed, SoS increase their collaboration skills using shared or cooperated information systems. People can be seen as particular digital sub-systems due to smart equipments they can use. Military operations, which are considered as typical SoS, are no exception to this fact. New operational doctrines have to be created to take advantage of those new capabilities. In this paper, we propose to develop methods and tools inspired by software engineering to create new automated capabilities in battlefield engineering. More precisely, we explain the direction which should be considered in the area of battlefield engineering in order to deal with those new capabilities. Inspired from Model-Based Engineering, we realized a proof-of-concept showing how to change textual operation orders with graphical ones. The latter can be exported in a common standardized format, that enables digital interpretation. We present the OPORD-ML language which is based on a metamodel inspired from a NATO operation order standard. It is supported by an automatically generated tool. © 2019 IEEE.","author":[{"family":"Belloir","given":"N."},{"family":"Buisson","given":"J."},{"family":"Bartheye","given":"O."}],"citation-key":"Belloir2019260","collection-title":"2019 14th Annual Conference System of Systems Engineering, SoSE 2019","DOI":"10.1109/SYSOSE.2019.8753885","ISBN":"978-1-72810-457-7","issued":{"date-parts":[[2019]]},"page":"260-265","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Metamodeling NATO operation orders: A proof-of-concept to deal with digitalization of the battlefield","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069758322&doi=10.1109%2fSYSOSE.2019.8753885&partnerID=40&md5=2e71042289a6ef84ab768150616d6155"},
{"id":"Benaben20191549","abstract":"Artificial Intelligence (AI) is currently on top of the hype regarding simultaneously research publications and industrial development. However, the current status of AI makes it quite far and different from the current understanding of Human intelligence. One suggestion that is made in this article is that Model-Driven approaches could be considered as an interesting avenue to complement classical visions of AI and to provide some missing features. Specifically, the use of Model-Driven Engineering tools (such as metamodel and model transformation) could benefit to the domain of AI by introducing a way to extend the apprehension of unknown situations. To support that proposal, an illustrative example is provided regarding the domain of risk and crisis management. © 2019 IEEE.","author":[{"family":"Benaben","given":"F."},{"family":"Lauras","given":"M."},{"family":"Fertier","given":"A."},{"family":"Salatge","given":"N."}],"citation-key":"Benaben20191549","collection-title":"Proceedings - Winter Simulation Conference","DOI":"10.1109/WSC40007.2019.9004828","ISBN":"978-1-72813-283-9","ISSN":"08917736","issued":{"date-parts":[[2019]]},"page":"1549-1563","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Integrating model-driven engineering as the next challenge for artificial intelligence - application to risk and crisis management","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081126404&doi=10.1109%2fWSC40007.2019.9004828&partnerID=40&md5=83878e109535ed424301db260b378bb9","volume":"2019-December"},
{"id":"Bencomo20121","abstract":"The Models@run.time (MRT) workshop series offers a discussion forum for the rising need to leverage modeling techniques for the software of the future. The main goals are to explore the benefits of models@run.time and to foster collaboration and cross-fertilization between different research communities like for example like model-driven engineering (e.g. MODELS), self-adaptive/autonomous systems communities (e.g., SEAMS and ICAC), the control theory community and the artificial intelligence community. © 2012 Authors.","author":[{"family":"Bencomo","given":"N."},{"family":"Blair","given":"G."},{"family":"Götz","given":"S."},{"family":"Morin","given":"B."},{"family":"Rumpe","given":"B."}],"citation-key":"Bencomo20121","collection-title":"Proceedings of the 7th Workshop on Models@run.time, MRT 2012 - Being Part of the ACM/IEEE 15th International Conference on Model Driven Engineering Languages and Systems, MODELS 2012","DOI":"10.1145/2422518.2422519","ISBN":"978-1-4503-1799-3","issued":{"date-parts":[[2012]]},"page":"1-2","title":"Summary of the 7th International Workshop on Models@run.time","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84873822170&doi=10.1145%2f2422518.2422519&partnerID=40&md5=2011d3c170ab4694bd9188491717ef75"},
{"id":"bencomoModelsRunTime2014","accessed":{"date-parts":[[2016,8,21]]},"citation-key":"bencomoModelsRunTime2014","collection-editor":[{"family":"Hutchison","given":"David"},{"family":"Kanade","given":"Takeo"},{"family":"Kittler","given":"Josef"},{"family":"Kleinberg","given":"Jon M."},{"family":"Kobsa","given":"Alfred"},{"family":"Mattern","given":"Friedemann"},{"family":"Mitchell","given":"John C."},{"family":"Naor","given":"Moni"},{"family":"Nierstrasz","given":"Oscar"},{"family":"Pandu Rangan","given":"C."},{"family":"Steffen","given":"Bernhard"},{"family":"Terzopoulos","given":"Demetri"},{"family":"Tygar","given":"Doug"},{"family":"Weikum","given":"Gerhard"}],"collection-title":"Lecture Notes in Computer Science","editor":[{"family":"Bencomo","given":"Nelly"},{"family":"France","given":"Robert"},{"family":"Cheng","given":"Betty H. C."},{"family":"Aßmann","given":"Uwe"}],"event-place":"Cham","ISBN":"978-3-319-08914-0 978-3-319-08915-7","issued":{"date-parts":[[2014]]},"publisher":"Springer International Publishing","publisher-place":"Cham","source":"CrossRef","title":"Models@run.time","type":"book","URL":"http://link.springer.com/10.1007/978-3-319-08915-7","volume":"8378"},
{"id":"bendraouComparisonSixUMLBased2010","abstract":"Describing and managing activities, resources, and constraints of software development processes is a challenging goal for many organizations. A first generation of Software Process Modeling Languages (SPMLs) appeared in the 1990s but failed to gain broad industrial support. Recently, however, a second generation of SPMLs has appeared, leveraging the strong industrial interest for modeling languages such as UML. In this paper, we propose a comparison of these UML-based SPMLs. While not exhaustive, this comparison concentrates on SPMLs most representative of the various alternative approaches, ranging from UML-based framework specializations to full-blown executable metamodeling approaches. To support the comparison of these various approaches, we propose a frame gathering a set of requirements for process modeling, such as semantic richness, modularity, executability, conformity to the UML standard, and formality. Beyond discussing the relative merits of these approaches, we also evaluate the overall suitability of these UML-based SPMLs for software process modeling. Finally, we discuss the impact of these approaches on the current state of the practice, and conclude with lessons we have learned in doing this comparison.","accessed":{"date-parts":[[2020,12,21]]},"author":[{"family":"Bendraou","given":"Reda"},{"family":"Jezequel","given":"Jean-Marc"},{"family":"Gervais","given":"Marie-Pierre"},{"family":"Blanc","given":"Xavier"}],"citation-key":"bendraouComparisonSixUMLBased2010","container-title":"IEEE Transactions on Software Engineering","container-title-short":"IIEEE Trans. Software Eng.","DOI":"10.1109/TSE.2009.85","ISSN":"0098-5589","issue":"5","issued":{"date-parts":[[2010,9]]},"note":"00119","page":"662-675","source":"DOI.org (Crossref)","title":"A Comparison of Six UML-Based Languages for Software Process Modeling","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/5593045/","volume":"36"},
{"id":"benelallamMavenDependencyGraph2019","author":[{"family":"Benelallam","given":"Amine"},{"family":"Harrand","given":"Nicolas"},{"family":"Soto-Valero","given":"César"},{"family":"Baudry","given":"Benoit"},{"family":"Barais","given":"Olivier"}],"citation-key":"benelallamMavenDependencyGraph2019","container-title":"CoRR","issued":{"date-parts":[[2019]]},"title":"The maven dependency graph: a temporal graph-based representation of maven central","type":"article-journal","URL":"http://arxiv.org/abs/1901.05392","volume":"abs/1901.05392"},
{"id":"bengioPracticalRecommendationsGradientbased2012","abstract":"Learning algorithms related to artificial neural networks and in particular for Deep Learning may seem to involve many bells and whistles, called hyper-parameters. This chapter is meant as a practical guide with recommendations for some of the most commonly used hyperparameters, in particular in the context of learning algorithms based on back-propagated gradient and gradient-based optimization. It also discusses how to deal with the fact that more interesting results can be obtained when allowing one to adjust many hyper-parameters. Overall, it describes elements of the practice used to successfully and efficiently train and debug large-scale and often deep multi-layer neural networks. It closes with open questions about the training difficulties observed with deeper architectures.","author":[{"family":"Bengio","given":"Yoshua"}],"citation-key":"bengioPracticalRecommendationsGradientbased2012","container-title":"Neural networks: Tricks of the trade: Second edition","DOI":"10.1007/978-3-642-35289-8₂6","editor":[{"family":"Montavon","given":"Grégoire"},{"family":"Orr","given":"Geneviève B."},{"family":"Müller","given":"Klaus-Robert"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-642-35289-8","issued":{"date-parts":[[2012]]},"page":"437-478","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","title":"Practical recommendations for gradient-based training of deep architectures","type":"chapter"},
{"id":"benoitGlobalizingModelingLanguages","author":[{"family":"Benoit","given":"Comemale"},{"family":"Julien","given":"DeAntoni"},{"family":"Benoit","given":"Baudry"},{"family":"Robert B.","given":"France"},{"family":"Jean-Marc","given":"Jezequel"},{"family":"Jeff","given":"Gray"}],"citation-key":"benoitGlobalizingModelingLanguages","DOI":"10.1109/MC.2014.147","title":"Globalizing Modeling Languages","type":"article-journal"},
{"id":"Benzaid2020124","abstract":"The ETSI's Zero touch network and Service Management (ZSM) framework is a prominent initiative to tame the envisioned complexity in operating and managing 5G and beyond networks. To this end, the ZSM framework promotes the shift toward full Automation of Network and Service Management and Operation (ANSMO) by leveraging the flexibility of SDN/NFV technologies along with Artificial Intelligence, combined with the portability and reusability of model-driven, open interfaces. Besides its benefits, each leveraged enabler will bring its own security threats, which should be carefully tackled to make the ANSMO vision a reality. This paper introduces the ZSM's potential attack surface and recommends possible mitigation measures along with some research directions to safeguard ZSM system security. © 1986-2012 IEEE.","author":[{"family":"Benzaid","given":"C."},{"family":"Taleb","given":"T."}],"citation-key":"Benzaid2020124","container-title":"IEEE Network","DOI":"10.1109/MNET.001.1900273","ISSN":"08908044","issue":"3","issued":{"date-parts":[[2020]]},"page":"124-133","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"ZSM security: Threat surface and best practices","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079624393&doi=10.1109%2fMNET.001.1900273&partnerID=40&md5=1fe03b2d4c3d62ac119d9b45318dffd7","volume":"34"},
{"id":"benzaidZSMSecurityThreat2020","abstract":"The ETSI's Zero touch network and Service Management (ZSM) framework is a prominent initiative to tame the envisioned complexity in operating and managing 5G and beyond networks. To this end, the ZSM framework promotes the shift toward full Automation of Network and Service Management and Operation (ANSMO) by leveraging the flexibility of SDN/NFV technologies along with Artificial Intelligence, combined with the portability and reusability of model-driven, open interfaces. Besides its benefits, each leveraged enabler will bring its own security threats, which should be carefully tackled to make the ANSMO vision a reality. This paper introduces the ZSM's potential attack surface and recommends possible mitigation measures along with some research directions to safeguard ZSM system security. © 1986-2012 IEEE.","author":[{"family":"Benzaid","given":"C."},{"family":"Taleb","given":"T."}],"citation-key":"benzaidZSMSecurityThreat2020","container-title":"IEEE Network","DOI":"10.1109/MNET.001.1900273","ISSN":"08908044","issue":"3","issued":{"date-parts":[[2020]]},"page":"124-133","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"ZSM Security: Threat Surface and Best Practices","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079624393&doi=10.1109%2fMNET.001.1900273&partnerID=40&md5=1fe03b2d4c3d62ac119d9b45318dffd7","volume":"34"},
{"id":"bergmayrOutplaceTransformationEvolution2014","accessed":{"date-parts":[[2015,6,17]]},"author":[{"family":"Bergmayr","given":"Alexander"},{"family":"Troya","given":"Javier"},{"family":"Wimmer","given":"Manuel"}],"citation-key":"bergmayrOutplaceTransformationEvolution2014","DOI":"10.1145/2642937.2642946","ISBN":"978-1-4503-3013-8","issued":{"date-parts":[[2014]]},"page":"647-652","publisher":"ACM Press","source":"CrossRef","title":"From out-place transformation evolution to in-place model patching","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?doid=2642937.2642946"},
{"id":"Berkhin2006","author":[{"family":"Berkhin","given":"P."}],"citation-key":"Berkhin2006","container-title":"Grouping multidimensional data: Recent advances in clustering","DOI":"10.1007/3-540-28349-8_2","editor":[{"family":"Kogan","given":"Jacob"},{"family":"Nicholas","given":"Charles"},{"family":"Teboulle","given":"Marc"}],"event-place":"Berlin, Heidelberg","issued":{"date-parts":[[2006]]},"note":"00000","page":"25-71","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","title":"A survey of clustering data mining techniques","type":"chapter"},
{"id":"berkhinSurveyClusteringData2006","accessed":{"date-parts":[[2017,9,25]]},"author":[{"family":"Berkhin","given":"Pavel"},{"literal":"others"}],"citation-key":"berkhinSurveyClusteringData2006","container-title":"Grouping multidimensional data","issued":{"date-parts":[[2006]]},"page":"71","source":"Google Scholar","title":"A survey of clustering data mining techniques.","type":"article-journal","URL":"http://link.springer.com/content/pdf/10.1007/3-540-28349-8.pdf#page=34","volume":"25"},
{"id":"bermejo-alonsoOntologicalFrameworkAutonomous2010","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Bermejo-Alonso","given":"Julita"},{"family":"Sanz","given":"Ricardo"},{"family":"Rodríguez","given":"Manuel"},{"family":"Hernández","given":"Carlos"}],"citation-key":"bermejo-alonsoOntologicalFrameworkAutonomous2010","container-title":"International Journal on Advances in Intelligent Systems","issue":"3","issued":{"date-parts":[[2010]]},"source":"Google Scholar","title":"An ontological framework for autonomous systems modelling","type":"article-journal","URL":"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.641.7853&rep=rep1&type=pdf#page=57","volume":"3"},
{"id":"bermejoalonsoEngineeringOntologyAutonomous2011","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Bermejo Alonso","given":"Julita"},{"family":"Sanz Bravo","given":"Ricardo"},{"family":"Rodríguez","given":"Manuel"},{"family":"Hernández Corbato","given":"Carlos"}],"citation-key":"bermejoalonsoEngineeringOntologyAutonomous2011","issued":{"date-parts":[[2011]]},"source":"Google Scholar","title":"Engineering an Ontology for Autonomous Systems-The OASys Ontology","type":"article-journal","URL":"http://oa.upm.es/11957/"},
{"id":"Berry:1997:DMT:560675","author":[{"family":"Berry","given":"Michael J."},{"family":"Linoff","given":"Gordon"}],"citation-key":"Berry:1997:DMT:560675","event-place":"New York, NY, USA","ISBN":"0-471-17980-9","issued":{"date-parts":[[1997]]},"publisher":"John Wiley & Sons, Inc.","publisher-place":"New York, NY, USA","title":"Data mining techniques: For marketing, sales, and customer support","type":"book"},
{"id":"Bertoa2010QualityAF","author":[{"family":"Bertoa","given":"Manuel F."},{"family":"Vallecillo","given":"Antonio"}],"citation-key":"Bertoa2010QualityAF","issued":{"date-parts":[[2010]]},"title":"Quality attributes for software metamodels","type":"paper-conference"},
{"id":"bertoaQualityAttributesSoftware2010","accessed":{"date-parts":[[2015,9,15]]},"author":[{"family":"Bertoa","given":"Manuel"},{"family":"Vallecillo","given":"Antonio"}],"citation-key":"bertoaQualityAttributesSoftware2010","container-title":"Málaga, Spain","issued":{"date-parts":[[2010]]},"source":"Google Scholar","title":"Quality attributes for software metamodels","type":"article-journal","URL":"http://www.lcc.uma.es/~av/Publicaciones/10/qaoose10.pdf"},
{"id":"BestDataPipeline","accessed":{"date-parts":[[2021,3,18]]},"citation-key":"BestDataPipeline","note":"00000","title":"The Best Data Pipeline Tools List for 2021 | Hevo Blog","type":"webpage","URL":"https://hevodata.com/blog/data-pipeline-tools-list/"},
{"id":"bettiniDetectingMetamodelEvolutions2020","abstract":"Model-Driven Engineering [Sch06] (MDE) is a discipline that leverages abstraction and automation in software development. Projects are typically composed of inherently different artifacts, including models, metamodels, model transformations, code generators, and concrete syntax definitions. Despite the increasing availability of reusable projects (e.g., through GitHub), their reuse possibilities depend on the availability of accurate, high-level metadata describing architectural information about the project at hand.","accessed":{"date-parts":[[2020,7,25]]},"author":[{"family":"Bettini","given":"Lorenzo"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"bettiniDetectingMetamodelEvolutions2020","container-title":"The Journal of Object Technology","container-title-short":"JOT","DOI":"10.5381/jot.2020.19.2.a14","ISSN":"1660-1769","issue":"2","issued":{"date-parts":[[2020]]},"note":"00000","page":"14:1","source":"DOI.org (Crossref)","title":"Detecting Metamodel Evolutions in Repositories of Model-Driven Projects.","type":"article-journal","URL":"http://www.jot.fm/contents/issue_2020_02/article14.html","volume":"19"},
{"id":"bettiniEdeltaApproachDefining2017","author":[{"family":"Bettini","given":"Lorenzo"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"bettiniEdeltaApproachDefining2017","container-title":"Proceedings of MODELS 2017 satellite event: Workshops (ModComp,ME, EXE, COMMitMDE, MRT, MULTI, GEMOC, MoDeVVa, MDETools, FlexMDE,MDEbug), posters, doctoral symposium, educator symposium, ACM StudentResearch competition, and tools and demonstrations co-located withACM/IEEE 20th international conference on model driven EngineeringLanguages and systems (MODELS 2017), austin, TX, USA, September,17, 2017.","issued":{"date-parts":[[2017]]},"page":"71-80","publisher":"CEUR-WS.org","title":"Edelta: An approach for defining and applying reusable metamodel refactorings","type":"paper-conference","URL":"http://ceur-ws.org/Vol-2019/me_4.pdf","volume":"2019"},
{"id":"bettiniEdeltaSupportingLive2020","author":[{"family":"Bettini","given":"Lorenzo"},{"family":"Di Ruscio","given":"D."},{"family":"Iovino","given":"L."},{"family":"Pierantonio","given":"A."}],"citation-key":"bettiniEdeltaSupportingLive2020","container-title":"Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings","DOI":"10.1145/3417990.3419501","ISBN":"978-1-4503-8135-2","issued":{"date-parts":[[2020]]},"note":"00000","page":"324–333","publisher":"Association for Computing Machinery, Inc","title":"Edelta 2.0: Supporting live metamodel evolutions","type":"paper-conference"},
{"id":"bettiniQualityDrivenDetectionResolution2019","author":[{"family":"Bettini","given":"Lorenzo"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"bettiniQualityDrivenDetectionResolution2019","container-title":"IEEE ACCESS","DOI":"10.1109/ACCESS.2019.2891357","issued":{"date-parts":[[2019]]},"note":"00000","page":"16364–16376","title":"Quality-Driven Detection and Resolution of Metamodel Smells","type":"article-journal","URL":"http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639","volume":"7"},
{"id":"bettiniQualitydrivenDetectionResolution2019","abstract":"In Model Driven Engineering (MDE), analogously to any software development practice, metamodel design must be accurate and performed by considering relevant quality factors including maintainability, reusability, and understandability. The quality of metamodels might be compromised by the introduction of smells that can be the result of inappropriate design decisions. Detecting and resolving metamodel smells is a complex task. Existing approaches deal with this problem by supporting the identification and resolution of smells without providing the means to explicitly trace them with the quality attributes that can be potentially affected. In this paper, we present an approach to defining extensible catalogues of metamodel smells. Each smell can be linked to corresponding quality attributes. Such links are exploited to automatically select only those smells that have to be necessarily resolved for enhancing the quality factors that are of interest for the modeler. The implementation of the approach is based on the Edelta language and it has been validated on a corpus of metamodels retrieved from a publicly available repository.","author":[{"family":"Bettini","given":"Lorenzo"},{"family":"Di Ruscio","given":"Davide"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"bettiniQualitydrivenDetectionResolution2019","container-title":"IEEE Access","ISSN":"2169-3536","issued":{"date-parts":[[2019]]},"note":"00000","source":"Crossref","title":"Quality-driven Detection and Resolution of Metamodel Smells","type":"article-magazine","URL":"https://ieeexplore.ieee.org/document/8632659/"},
{"id":"Beyer:2018:ACP:3196321.3196333","author":[{"family":"Beyer","given":"Stefanie"},{"family":"Macho","given":"Christian"},{"family":"Pinzger","given":"Martin"},{"family":"Di Penta","given":"Massimiliano"}],"citation-key":"Beyer:2018:ACP:3196321.3196333","collection-title":"ICPC '18","container-title":"Proceedings of the 26th conference on program comprehension","event-place":"New York, NY, USA","ISBN":"978-1-4503-5714-2","issued":{"date-parts":[[2018]]},"page":"211-221","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Automatically classifying posts into question categories on stack overflow","type":"paper-conference","URL":"http://doi.acm.org/10.1145/3196321.3196333"},
{"id":"beyhlSmartModelSearch","author":[{"family":"Beyhl","given":"Thomas"},{"family":"Giese","given":"Holger"}],"citation-key":"beyhlSmartModelSearch","title":"Smart Model Search in Model Repositories by Modular Search Index Generation and Querying (Submitted to SLE2014 - confidential)","type":"article-journal"},
{"id":"beyhlSmartModelSearcha","abstract":"Model search engines (MSEs) retrieve knowledge embodied by model repositories. Existing MSEs perform text-based search and exploit meta-models to enable queries that require meta-information. However, model repositories embody relevant hidden knowledge as well. Existing MSEs do not retrieve such hidden knowledge, because their general-purpose search index does not support to derive hidden knowledge effectively. In this paper, we present a smart model search approach, which exploits low-level knowledge to derive high-level knowledge by supporting modules that allow the integration of existing querying and mining techniques. Our approach permits the pre-computation of results for time-consuming modules in terms of a search index to guarantee reasonable response times, while less time-consuming modules are computed on demand. Our approach guides the systematic integration of modules by means of well-formedness checks to guarantee reasonable search results. We evaluate our approach by a case study using multiple data sets derived from an open source project.","author":[{"family":"Beyhl","given":"Thomas"},{"family":"Giese","given":"Holger"}],"citation-key":"beyhlSmartModelSearcha","page":"20","source":"Zotero","title":"Smart Model Search in Model Repositories by Modular Search Index Generation and Querying","type":"article-journal"},
{"id":"BezivinJRV05","author":[{"family":"Bézivin","given":"Jean"},{"family":"Jouault","given":"Frédéric"},{"family":"Rosenthal","given":"Peter"},{"family":"Valduriez","given":"Patrick"}],"citation-key":"BezivinJRV05","collection-title":"LNCS","container-title":"European MDA workshops MDAFA 2003 and MDAFA 2004, revised selected papers","issued":{"date-parts":[[2005]]},"page":"33-46","publisher":"Springer","title":"Modeling in the Large and Modeling in the Small","type":"paper-conference","volume":"3599"},
{"id":"BezivinJV04","author":[{"family":"Bézivin","given":"J."},{"family":"Jouault","given":"F."},{"family":"Valduriez","given":"P."}],"citation-key":"BezivinJV04","container-title":"Proc. of the OOPSLA/GPCE: Best practices for model-driven software development workshop","issued":{"date-parts":[[2004]]},"title":"On the need for Megamodels","type":"paper-conference"},
{"id":"bezivinUnificationPowerModels2005","accessed":{"date-parts":[[2021,1,30]]},"author":[{"family":"Bézivin","given":"Jean"}],"citation-key":"bezivinUnificationPowerModels2005","container-title":"Software & Systems Modeling","container-title-short":"Softw Syst Model","DOI":"10.1007/s10270-005-0079-0","ISSN":"1619-1366, 1619-1374","issue":"2","issued":{"date-parts":[[2005,5]]},"note":"01268","page":"171-188","source":"DOI.org (Crossref)","title":"On the unification power of models","type":"article-journal","URL":"http://link.springer.com/10.1007/s10270-005-0079-0","volume":"4"},
{"id":"bhandariSerendipitousRecommendationMobile2013","author":[{"family":"Bhandari","given":"Upasna"},{"family":"Sugiyama","given":"Kazunari"},{"family":"Datta","given":"Anindya"},{"family":"Jindal","given":"Rajni"}],"citation-key":"bhandariSerendipitousRecommendationMobile2013","collection-title":"Lecture notes in computer science","container-title":"AIRS","editor":[{"family":"Banchs","given":"Rafael E."},{"family":"Silvestri","given":"Fabrizio"},{"family":"Liu","given":"Tie-Yan"},{"family":"Zhang","given":"Min"},{"family":"Gao","given":"Sheng"},{"family":"Lang","given":"Jun"}],"ISBN":"978-3-642-45067-9","issued":{"date-parts":[[2013]]},"page":"440-451","publisher":"Springer","title":"Serendipitous recommendation for mobile apps using item-item similarity graph.","type":"paper-conference","URL":"http://dblp.uni-trier.de/db/conf/airs/airs2013.html#BhandariSDJ13","volume":"8281"},
{"id":"Bhattacharjee20191607","abstract":"Smart Internet of Things (IoT) applications require real-time and robust predictive analytics, which are based on Machine Learning (ML) models. Building ML models from Big Data is not only time-consuming, but developers often lack the needed expertise for feature engineering, parameter tuning, and model selection. The proliferation of ML libraries and frameworks, data ingestion tools, stream and batch processing engines, visualization techniques, and the range of available hardware platforms further exacerbates the system design, rapid development, and deployment problems. Finally, resource constraints of IoT require that the execution of the analytics engine be distributed across the cloud-edge spectrum. To overcome these daunting challenges, we present Stratum, which is an event-driven Big Data-as-a-Service offering for IoT analytics lifecycle management. Stratum provides users with an intuitive, declarative mechanism based on the principles of model-driven engineering to specify the application and infrastructure requirements. It automates the deployment via generative programming principles. This paper highlights the problems that Stratum resolves, demonstrating its capabilities using real-world case studies. © 2019 IEEE.","author":[{"family":"Bhattacharjee","given":"A."},{"family":"Barve","given":"Y."},{"family":"Khare","given":"S."},{"family":"Bao","given":"S."},{"family":"Kang","given":"Z."},{"family":"Gokhale","given":"A."},{"family":"Damiano","given":"T."}],"citation-key":"Bhattacharjee20191607","collection-title":"Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019","DOI":"10.1109/BigData47090.2019.9006518","editor":[{"family":"Baru C., Huan J.","given":"Khan L.","suffix":"Hu X.T., Ak R., Tian Y., Barga R., Zaniolo C., Lee K., Ye Y.F."}],"ISBN":"978-1-72810-858-2","issued":{"date-parts":[[2019]]},"page":"1607-1612","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"STRATUM: A BigData-as-a-Service for lifecycle management of IoT analytics applications","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081297051&doi=10.1109%2fBigData47090.2019.9006518&partnerID=40&md5=31dc18e566c3234a36d03923e197a652"},
{"id":"bhattacharjeeSTRATUMBigDataasaServiceLifecycle2019a","abstract":"Smart Internet of Things (IoT) applications require real-time and robust predictive analytics, which are based on Machine Learning (ML) models. Building ML models from Big Data is not only time-consuming, but developers often lack the needed expertise for feature engineering, parameter tuning, and model selection. The proliferation of ML libraries and frameworks, data ingestion tools, stream and batch processing engines, visualization techniques, and the range of available hardware platforms further exacerbates the system design, rapid development, and deployment problems. Finally, resource constraints of IoT require that the execution of the analytics engine be distributed across the cloud-edge spectrum. To overcome these daunting challenges, we present Stratum, which is an event-driven Big Data-as-a-Service offering for IoT analytics lifecycle management. Stratum provides users with an intuitive, declarative mechanism based on the principles of model-driven engineering to specify the application and infrastructure requirements. It automates the deployment via generative programming principles. This paper highlights the problems that Stratum resolves, demonstrating its capabilities using real-world case studies. © 2019 IEEE.","author":[{"family":"Bhattacharjee","given":"A."},{"family":"Barve","given":"Y."},{"family":"Khare","given":"S."},{"family":"Bao","given":"S."},{"family":"Kang","given":"Z."},{"family":"Gokhale","given":"A."},{"family":"Damiano","given":"T."}],"citation-key":"bhattacharjeeSTRATUMBigDataasaServiceLifecycle2019a","container-title":"Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019","DOI":"10.1109/BigData47090.2019.9006518","editor":[{"family":"Baru C.","given":"Ye Y.F.","suffix":"Huan J., Khan L., Hu X.T., Ak R., Tian Y., Barga R., Zaniolo C., Lee K."}],"ISBN":"978-1-72810-858-2","issued":{"date-parts":[[2019]]},"page":"1607-1612","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"STRATUM: A BigData-as-a-Service for Lifecycle Management of IoT Analytics Applications","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081297051&doi=10.1109%2fBigData47090.2019.9006518&partnerID=40&md5=31dc18e566c3234a36d03923e197a652"},
{"id":"BigDAWGPolystoreSystem","accessed":{"date-parts":[[2018,4,16]]},"citation-key":"BigDAWGPolystoreSystem","title":"The BigDAWG polystore system and architecture — Northwestern Scholars","type":"webpage","URL":"https://www.scholars.northwestern.edu/en/publications/the-bigdawg-polystore-system-and-architecture"},
{"id":"BinTang2007","author":[{"family":"Tang","given":"B."},{"family":"Spiteri","given":"R."},{"family":"Milios","given":"E."},{"family":"Zhang","given":"R."},{"family":"Wang","given":"S."},{"family":"Tougas","given":"J."},{"family":"Shafiei","given":"M."}],"citation-key":"BinTang2007","container-title":"2013 IEEE 29th int. Conf. on data engineering workshops (ICDEW)","event-place":"Los Alamitos, CA, USA","issued":{"date-parts":[[2007,4]]},"page":"770-779","publisher":"IEEE Computer Society","publisher-place":"Los Alamitos, CA, USA","title":"Document representation and dimension reduction for text clustering","type":"paper-conference"},
{"id":"Bishop:1995:NNP:525960","author":[{"family":"Bishop","given":"Christopher M."}],"citation-key":"Bishop:1995:NNP:525960","event-place":"New York, NY, USA","ISBN":"0-19-853864-2","issued":{"date-parts":[[1995]]},"publisher":"Oxford University Press, Inc.","publisher-place":"New York, NY, USA","title":"Neural networks for pattern recognition","type":"book"},
{"id":"bislimovskaTextualContentBasedSearch2014","author":[{"family":"Bislimovska","given":"Bojana"},{"family":"Bozzon","given":"Alessandro"},{"family":"Brambilla","given":"Marco"},{"family":"Fraternali","given":"Piero"}],"citation-key":"bislimovskaTextualContentBasedSearch2014","container-title":"ACM Transactions on the Web","DOI":"10.1145/2579991","issue":"2","issued":{"date-parts":[[2014]]},"page":"1–47","title":"Textual and Content-Based Search in Repositories of Web Application Models","type":"article-journal","volume":"8"},
{"id":"bizer_linked_2009","author":[{"family":"Bizer","given":"C."},{"family":"Heath","given":"T."},{"family":"Berners-Lee","given":"T."}],"citation-key":"bizer_linked_2009","container-title":"Int. J. Semantic Web Inf. Syst.","issue":"3","issued":{"date-parts":[[2009]]},"page":"1-22","title":"Linked data - the story so far","type":"article-journal","volume":"5"},
{"id":"bjarnasonAligningRequirementsTesting2017","abstract":"The proper alignment of requirements engineering and testing (RET) can be key to software's success. Three practices can provide effective RET alignment: using test cases as requirements, harvesting trace links, and reducing distances between requirements engineers and testers. The Web extra https://youtu.be/M65ZKxfxqME is an audio podcast of author Elizabeth Bjarnason reading the the Requirements column she cowrote with Markus Borg.","author":[{"family":"Bjarnason","given":"Elizabeth"},{"family":"Borg","given":"Markus"},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""}],"citation-key":"bjarnasonAligningRequirementsTesting2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"20-23","source":"IEEE Computer Society","title":"Aligning Requirements and Testing: Working Together toward the Same Goal","title-short":"Aligning Requirements and Testing","type":"article-magazine","volume":"34"},
{"id":"bjarnasonChallengesPracticesAligning2014","accessed":{"date-parts":[[2020,3,31]]},"author":[{"family":"Bjarnason","given":"Elizabeth"},{"family":"Runeson","given":"Per"},{"family":"Borg","given":"Markus"},{"family":"Unterkalmsteiner","given":"Michael"},{"family":"Engström","given":"Emelie"},{"family":"Regnell","given":"Björn"},{"family":"Sabaliauskaite","given":"Giedre"},{"family":"Loconsole","given":"Annabella"},{"family":"Gorschek","given":"Tony"},{"family":"Feldt","given":"Robert"}],"citation-key":"bjarnasonChallengesPracticesAligning2014","container-title":"Empirical Software Engineering","container-title-short":"Empir Software Eng","DOI":"10.1007/s10664-013-9263-y","ISSN":"1382-3256, 1573-7616","issue":"6","issued":{"date-parts":[[2014,12]]},"page":"1809-1855","source":"DOI.org (Crossref)","title":"Challenges and practices in aligning requirements with verification and validation: a case study of six companies","title-short":"Challenges and practices in aligning requirements with verification and validation","type":"article-journal","URL":"http://link.springer.com/10.1007/s10664-013-9263-y","volume":"19"},
{"id":"Blondel:2004:MSG:1035533.1035557","author":[{"family":"Blondel","given":"Vincent D."},{"family":"Gajardo","given":"Anahí"},{"family":"Heymans","given":"Maureen"},{"family":"Senellart","given":"Pierre"},{"family":"Dooren","given":"Paul Van"}],"citation-key":"Blondel:2004:MSG:1035533.1035557","container-title":"SIAM Review","container-title-short":"SIAM Rev.","ISSN":"0036-1445","issue":"4","issued":{"date-parts":[[2004,4]]},"page":"647-666","title":"A measure of similarity between graph vertices: Applications to synonym extraction and web searching","type":"article-journal","URL":"http://dx.doi.org/10.1137/S0036144502415960","volume":"46"},
{"id":"blouinKomprenModelingGenerating2012","author":[{"family":"Blouin","given":"Arnaud"},{"family":"Combemale","given":"Benoît"},{"family":"Baudry","given":"Benoit"},{"family":"Beaudoux","given":"Olivier"}],"citation-key":"blouinKomprenModelingGenerating2012","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-012-0300-x","issued":{"date-parts":[[2012]]},"title":"Kompren: modeling and generating model slicers","type":"article-journal"},
{"id":"blouinSlicingbasedTechniquesVisualizing","abstract":"In model-driven engineering, a model describes an aspect of a system. A model conforms to a metamodel that defines the concepts and relationships of a given domain. Metamodels are thus corner-stones of various meta-modeling activities that require a good understanding of the metamodels or parts of them. Current metamodel editing tools are based on standard visualization and navigation features, such as physical zooms. However, as soon as metamodels become larger, navigating through large metamodels becomes a tedious task that hinders their understanding. In this work, we promote the use of model slicing techniques to build visualization techniques dedicated to metamodels. We propose an approach based on model slicing, inspired from program slicing, to build interactive visualization techniques dedicated to metamodels. These techniques permit users to focus on metamodel elements of interest, which aims at improving the understandability. This approach is implemented in a metamodel visualizer, called Explen.","author":[{"family":"Blouin","given":"Arnaud"},{"family":"Moha","given":"Naouel"},{"family":"Baudry","given":"Benoit"},{"family":"Saharaoui","given":"Houaru"}],"citation-key":"blouinSlicingbasedTechniquesVisualizing","title":"Slicing-based Techniques for Visualizing Large Metamodels","type":"article-journal"},
{"id":"Blum:1992:NNC:129269","author":[{"family":"Blum","given":"Adam"}],"citation-key":"Blum:1992:NNC:129269","event-place":"New York, NY, USA","ISBN":"0-471-53847-7","issued":{"date-parts":[[1992]]},"publisher":"John Wiley & Sons, Inc.","publisher-place":"New York, NY, USA","title":"Neural networks in C++: An object-oriented framework for building connectionist systems","type":"book"},
{"id":"boardmanSystemSystemstheMeaning2006","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Boardman","given":"John"},{"family":"Sauser","given":"Brian"}],"citation-key":"boardmanSystemSystemstheMeaning2006","container-title":"2006 IEEE/SMC International Conference on System of Systems Engineering","issued":{"date-parts":[[2006]]},"page":"6–pp","publisher":"IEEE","source":"Google Scholar","title":"System of Systems-the meaning of of","type":"paper-conference","URL":"http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1652284"},
{"id":"bononiIoTSensorData","author":[{"family":"Bononi","given":"L"},{"family":"Felice","given":"M Di"}],"citation-key":"bononiIoTSensorData","note":"00000","page":"28","source":"Zotero","title":"IoT Sensor Data Management","type":"article-journal"},
{"id":"bononiIoTSensorDataa","author":[{"family":"Bononi","given":"L"},{"family":"Felice","given":"M Di"}],"citation-key":"bononiIoTSensorDataa","note":"00000","page":"43","source":"Zotero","title":"IoT Sensor Data Processing","type":"article-journal"},
{"id":"boochHistorySoftwareEngineering2018","abstract":"Grady Booch, one of UML’s original authors, offers his perspective on the history of software engineering. This article is part of a theme issue on software engineering’s 50th anniversary. The Web Extra, a version of the article with an expanded bibliography, is at https://extras.computer.org/extra/mso2018050108s1.pdf.","author":[{"family":"Booch","given":"G."}],"citation-key":"boochHistorySoftwareEngineering2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.3571234","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"108-114","source":"IEEE Xplore","title":"The History of Software Engineering","type":"article-journal","volume":"35"},
{"id":"books/sp/mining2012/AggarwalZ12a","author":[{"family":"Aggarwal","given":"Charu C."},{"family":"Zhai","given":"ChengXiang"}],"citation-key":"books/sp/mining2012/AggarwalZ12a","container-title":"Mining text data","editor":[{"family":"Aggarwal","given":"Charu C."},{"family":"Zhai","given":"ChengXiang"}],"ISBN":"978-1-4419-8462-3","issued":{"date-parts":[[2012]]},"page":"77-128","publisher":"Springer","title":"A survey of text clustering algorithms.","type":"chapter","URL":"http://dblp.uni-trier.de/db/books/collections/Mining2012.html#AggarwalZ12a"},
{"id":"Borg:2014:RSD:2652524.2652556","author":[{"family":"Borg","given":"Markus"},{"family":"Runeson","given":"Per"},{"family":"Johansson","given":"Jens"},{"family":"Mäntylä","given":"Mika V."}],"citation-key":"Borg:2014:RSD:2652524.2652556","collection-title":"ESEM '14","container-title":"Proceedings of the 8th ACM/IEEE international symposium on empirical software engineering and measurement","event-place":"New York, NY, USA","ISBN":"978-1-4503-2774-9","issued":{"date-parts":[[2014]]},"page":"8:1-8:4","publisher":"ACM","publisher-place":"New York, NY, USA","title":"A replicated study on duplicate detection: Using apache lucene to search among android defects","type":"paper-conference","URL":"http://doi.acm.org/10.1145/2652524.2652556"},
{"id":"borgSupportingChangeImpact2016","accessed":{"date-parts":[[2017,5,14]]},"author":[{"family":"Borg","given":"Markus"},{"family":"Wnuk","given":"Krzysztof"},{"family":"Regnell","given":"Bjorn"},{"family":"Runeson","given":"Per"}],"citation-key":"borgSupportingChangeImpact2016","container-title":"IEEE Transactions on Software Engineering","DOI":"10.1109/TSE.2016.2620458","ISSN":"0098-5589, 1939-3520","issued":{"date-parts":[[2016]]},"page":"1-1","source":"CrossRef","title":"Supporting Change Impact Analysis Using a Recommendation System: An Industrial Case Study in a Safety-Critical Context","title-short":"Supporting Change Impact Analysis Using a Recommendation System","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7637029/"},
{"id":"Bottou91stochasticgradient","author":[{"family":"Bottou","given":"Léon"}],"citation-key":"Bottou91stochasticgradient","container-title":"In proceedings of neuro-nîmes. EC2","issued":{"date-parts":[[1991]]},"title":"Stochastic gradient learning in neural networks","type":"paper-conference"},
{"id":"Boubekeur202084","abstract":"Software models are increasingly popular. To educate the next generation of software engineers, it is important that they learn how to model software systems well, so that they can design them effectively in industry. It is also important that instructors have the tools that can help them assess students' models more effectively. In this paper, we investigate how a tool that combines a simple heuristic with machine learning techniques can be used to help assess student submissions in model-driven engineering courses. We apply our proposed technique to first identify submissions of high quality and second to predict approximate letter grades. The results are comparable to human grading and a complex rule-based technique for the former and surprisingly accurate for the latter. © 2020 ACM.","author":[{"family":"Boubekeur","given":"Y."},{"family":"Mussbacher","given":"G."},{"family":"McIntosh","given":"S."}],"citation-key":"Boubekeur202084","collection-title":"Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings","DOI":"10.1145/3417990.3418741","ISBN":"978-1-4503-8135-2","issued":{"date-parts":[[2020]]},"page":"84-93","publisher":"Association for Computing Machinery, Inc","title":"Automatic assessment of students' software models using a simple heuristic and machine learning","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096797563&doi=10.1145%2f3417990.3418741&partnerID=40&md5=ef6998e6990d69641204423ec9a31954"},
{"id":"Boubekeur202094","abstract":"The enrolment of software engineering students has increased rapidly in the past few years following industry demand. At the same time, model-driven engineering (MDE) continues to become relevant to more domains like embedded systems and machine learning. It is therefore important to teach students MDE skills in an effective manner to prepare them for future careers in academia and industry. The use of interactive online tools can help instructors deliver course material to more students in a more efficient manner, allowing them to offload repetitive or tedious tasks to these systems and focus on other teaching activities that cannot be easily automated. Interactive online tools can provide students with a more engaging learning experience than static resources like books or written exercises. Domain modeling with class diagrams is a fundamental modeling activity in MDE. While there exist multiple modeling tools that allow students to build a domain model, none of them offer an interactive learning experience. In this paper, we explore the interactions between a student modeler and an interactive domain modeling assistant with the aim of better understanding the required interaction. We illustrate desired interactions with three examples and then formalize them in a metamodel. Based on the metamodel, we explain how to form a corpus of learning material that supports the assistant interactions. © 2020 ACM.","author":[{"family":"Boubekeur","given":"Y."},{"family":"Mussbacher","given":"G."}],"citation-key":"Boubekeur202094","collection-title":"Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings","DOI":"10.1145/3417990.3418742","ISBN":"978-1-4503-8135-2","issued":{"date-parts":[[2020]]},"page":"94-103","publisher":"Association for Computing Machinery, Inc","title":"Towards a better understanding of interactions with a domain modeling assistant","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096756949&doi=10.1145%2f3417990.3418742&partnerID=40&md5=aa9e8e4bb7eea7761cce6163131eec2a"},
{"id":"boubekeurAutomaticAssessmentStudents2020a","abstract":"Software models are increasingly popular. To educate the next generation of software engineers, it is important that they learn how to model software systems well, so that they can design them effectively in industry. It is also important that instructors have the tools that can help them assess students' models more effectively. In this paper, we investigate how a tool that combines a simple heuristic with machine learning techniques can be used to help assess student submissions in model-driven engineering courses. We apply our proposed technique to first identify submissions of high quality and second to predict approximate letter grades. The results are comparable to human grading and a complex rule-based technique for the former and surprisingly accurate for the latter. © 2020 ACM.","author":[{"family":"Boubekeur","given":"Y."},{"family":"Mussbacher","given":"G."},{"family":"McIntosh","given":"S."}],"citation-key":"boubekeurAutomaticAssessmentStudents2020a","container-title":"Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings","DOI":"10.1145/3417990.3418741","ISBN":"978-1-4503-8135-2","issued":{"date-parts":[[2020]]},"page":"84-93","publisher":"Association for Computing Machinery, Inc","title":"Automatic assessment of students' software models using a simple heuristic and machine learning","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096797563&doi=10.1145%2f3417990.3418741&partnerID=40&md5=ef6998e6990d69641204423ec9a31954"},
{"id":"boudeffaIntegratingDeployingHeterogeneous2019","author":[{"family":"Boudeffa","given":"A."},{"family":"Abherve","given":"A."},{"family":"Bagnato","given":"A."},{"family":"Di Ruscio","given":"D."},{"family":"Mateus","given":"M."},{"family":"Almeida","given":"B."}],"citation-key":"boudeffaIntegratingDeployingHeterogeneous2019","container-title":"CEUR Workshop Proceedings","issued":{"date-parts":[[2019]]},"note":"00000","page":"67–72","publisher":"CEUR-WS","title":"Integrating and deploying heterogeneous components by means of a microservices architecture in the CROSSMINER project","type":"paper-conference","volume":"2405"},
{"id":"Bouquet201274","abstract":"In this paper, we propose an approach to translate the Sys ML language to VHDL-AMS code. This approach is the first step to the generation of the VHDL-AMS code from the structural diagrams Sys ML. In this step, we address the Block Definition Diagram and the Internal Block Diagram. The translation uses Model Driven Engineer (MDE) methods as the transformation of model to another model (M2M) with ATL Atlas Transformation Language and the code generation from models (M2T) using Xpand. We provide the translation rules between the two elements. Implementation and methodology are illustrated on a micro-system case study: the Smart surface system. © 2012 IEEE.","author":[{"family":"Bouquet","given":"F."},{"family":"Gauthier","given":"J.-M."},{"family":"Hammad","given":"A."},{"family":"Peureux","given":"F."}],"citation-key":"Bouquet201274","collection-title":"Proceedings - 2012 2nd Workshop on Design, Control and Software Implementation for Distributed MEMS, dMEMS 2012","DOI":"10.1109/dMEMS.2012.12","ISBN":"978-0-7695-4679-7","issued":{"date-parts":[[2012]]},"page":"74-81","title":"Transformation of SysML structure diagrams to VHDL-AMS","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862070499&doi=10.1109%2fdMEMS.2012.12&partnerID=40&md5=31faae5796f3c72005d35bfc1f9f5c81"},
{"id":"bourqueGuideSoftwareEngineering2014","author":[{"family":"Bourque","given":"Pierre"},{"family":"Fairley","given":"R. E"},{"literal":"IEEE Computer Society"}],"citation-key":"bourqueGuideSoftwareEngineering2014","ISBN":"978-0-7695-5166-1","issued":{"date-parts":[[2014]]},"note":"00032 \nOCLC: 973217192","source":"Open WorldCat","title":"Guide to the software engineering body of knowledge","type":"book"},
{"id":"bousseGenerativeApproachDefine2015","accessed":{"date-parts":[[2015,6,24]]},"author":[{"family":"Bousse","given":"Erwan"},{"family":"Mayerhofer","given":"Tanja"},{"family":"Combemale","given":"Benoit"},{"family":"Baudry","given":"Benoit"}],"citation-key":"bousseGenerativeApproachDefine2015","container-title":"11th European Conference on Modelling Foundations and Applications (ECMFA)","issued":{"date-parts":[[2015]]},"source":"Google Scholar","title":"A Generative Approach to Define Rich Domain-Specific Trace Metamodels","type":"paper-conference","URL":"https://hal.inria.fr/hal-01154225/document"},
{"id":"bozhinoskiFLYAQEnablingNonexpert2015","author":[{"family":"Bozhinoski","given":"Darko"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Malavolta","given":"Ivano"},{"family":"Pelliccione","given":"Patrizio"},{"family":"Tivoli","given":"Massimo"}],"citation-key":"bozhinoskiFLYAQEnablingNonexpert2015","container-title":"Proceedings - 2015 30th IEEE/ACM International Conference on Automated Software Engineering, ASE 2015","DOI":"10.1109/ASE.2015.104","ISBN":"978-1-5090-0024-1","issued":{"date-parts":[[2015]]},"note":"00000","page":"801–806","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"FLYAQ: Enabling non-expert users to specify and generate missions of autonomous multicopters","type":"paper-conference"},
{"id":"bozhinoskiSafetyMobileRobotic2019","abstract":"Robotic research is making huge progress. However, existing solutions are facing a number of challenges preventing them from being used in our everyday tasks: (i) robots operate in unknown environments, (ii) robots collaborate with each other and even with humans, and (iii) robots shall never injure people or create damages. Researchers are targeting those challenges from various perspectives, producing a fragmented research landscape.","author":[{"family":"Bozhinoski","given":"Darko"},{"family":"Ruscio","given":"Davide Di"},{"family":"Malavolta","given":"Ivano"},{"family":"Pelliccione","given":"Patrizio"},{"family":"Crnkovic","given":"Ivica"}],"citation-key":"bozhinoskiSafetyMobileRobotic2019","container-title":"Elsevier Journal of Systems and Software (JSS)","issue":"to appear","issued":{"date-parts":[[2019]]},"source":"Zotero","title":"Safety for Mobile Robotic System: a Systematic Mapping Study from a Software Engineering Perspective","type":"article-magazine","URL":"http://people.disim.univaq.it/diruscio/pubs/JSS_ROB_2019.pdf"},
{"id":"breuTenPrinciplesLiving2010","abstract":"The new generation of open networked IT systems poses particular challenges to software engineering due to their evolving nature and their high quality requirements. In particular, the management of service oriented systems requires the integration of perspectives from IT management, software engineering and systems operation and a systematic way to handle changes. In this paper we will present the core ideas of Living Models – a novel paradigm of model–based development, management and operation of evolving service oriented systems. A core concern of Living Models is to support the cooperation of stakeholders from IT management, software engineering and systems operation by providing appropriate model-based abstractions and the fostering of interdependencies. Based on this idea the running services together with their modelling environments constitute the basic unit of quality management and evolution. Living Models provides a coherent view of the quality status of the system (integrating the perspectives of all stakeholders) which evolves together with the running systems. This comes along with a software engineering process in which change is a first–class citizen.","accessed":{"date-parts":[[2021,4,7]]},"author":[{"family":"Breu","given":"Ruth"}],"citation-key":"breuTenPrinciplesLiving2010","container-title":"2010 International Conference on Complex, Intelligent and Software Intensive Systems","DOI":"10.1109/CISIS.2010.73","event":"2010 International Conference on Complex, Intelligent and Software Intensive Systems (CISIS)","event-place":"Krakow, TBD, Poland","ISBN":"978-1-4244-5917-9","issued":{"date-parts":[[2010,2]]},"note":"00081","page":"1-8","publisher":"IEEE","publisher-place":"Krakow, TBD, Poland","source":"DOI.org (Crossref)","title":"Ten Principles for Living Models - A Manifesto of Change-Driven Software Engineering","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/5447399/"},
{"id":"BRIGUEZ20146467","author":[{"family":"Briguez","given":"Cristian E."},{"family":"Budán","given":"Maximiliano C.D."},{"family":"Deagustini","given":"Cristhian A.D."},{"family":"Maguitman","given":"Ana G."},{"family":"Capobianco","given":"Marcela"},{"family":"Simari","given":"Guillermo R."}],"citation-key":"BRIGUEZ20146467","container-title":"Expert Systems with Applications","ISSN":"0957-4174","issue":"14","issued":{"date-parts":[[2014]]},"page":"6467 - 6482","title":"Argument-based mixed recommenders and their application to movie suggestion","type":"article-journal","URL":"http://www.sciencedirect.com/science/article/pii/S0957417414001845","volume":"41"},
{"id":"broringEnablingIoTEcosystems2017","abstract":"Today, the Internet of Things (IoT) comprises vertically oriented platforms for things. Developers who want to use them need to negotiate access individually and adapt to the platform-specific API and information models. Having to perform these actions for each platform often outweighs the possible gains from adapting applications to multiple platforms. This fragmentation of the IoT and the missing interoperability result in high entry barriers for developers and prevent the emergence of broadly accepted IoT ecosystems. The BIG IoT (Bridging the Interoperability Gap of the IoT) project aims to ignite an IoT ecosystem as part of the European Platforms Initiative. As part of the project, researchers have devised an IoT ecosystem architecture. It employs five interoperability patterns that enable cross-platform interoperability and can help establish successful IoT ecosystems.","author":[{"family":"Broring","given":"Arne"},{"family":"Schmid","given":"Stefan"},{"family":"Schindhelm","given":"Corina-Kim"},{"family":"Khelil","given":"Abdelmajid"},{"family":"Kabisch","given":"Sebastian"},{"family":"Kramer","given":"Denis"},{"family":"Phuoc","given":"Danh Le"},{"family":"Mitic","given":"Jelena"},{"family":"Anicic","given":"Darko"},{"family":"Teniente","given":"Ernest"}],"citation-key":"broringEnablingIoTEcosystems2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"54-61","source":"IEEE Computer Society","title":"Enabling IoT Ecosystems through Platform Interoperability","type":"article-magazine","volume":"34"},
{"id":"broyYesterdayTodayTomorrow2018","abstract":"In 2018, we’re now 50 years after the famous groundbreaking conference on software engineering in Garmisch, organized by its chairman F.L. Bauer and his cochairs L. Bolliet and H.J. Helms. This conference introduced the notion and discipline of software engineering. This is a moment to look back at what we’ve achieved, what we haven’t achieved, where we are today, and what challenges lie ahead. This article is part of a theme issue on software engineering’s 50th anniversary.","author":[{"family":"Broy","given":"M."}],"citation-key":"broyYesterdayTodayTomorrow2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.290111138","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018]]},"note":"00000","page":"38-43","source":"IEEE Xplore","title":"Yesterday, Today, and Tomorrow: 50 Years of Software Engineering","title-short":"Yesterday, Today, and Tomorrow","type":"article-journal","volume":"35"},
{"id":"bruchEvaluatingRecommenderSystems2008","author":[{"family":"Bruch","given":"Marcel"},{"family":"Schäfer","given":"Thorsten"},{"family":"Mezini","given":"Mira"}],"citation-key":"bruchEvaluatingRecommenderSystems2008","collection-title":"RSSE '08","container-title":"Proceedings of the 2008 international workshop on recommendation systems for software engineering","event-place":"New York, NY, USA","ISBN":"978-1-60558-228-3","issued":{"date-parts":[[2008]]},"page":"16-20","publisher":"ACM","publisher-place":"New York, NY, USA","title":"On evaluating recommender systems for API usages","type":"paper-conference","URL":"http://doi.acm.org/10.1145/1454247.1454254"},
{"id":"bruelModelTransformationReuse","abstract":"Model transformations (MTs) are essential elements of model-driven engineering (MDE) solutions. MDE promotes the creation of domain-specific metamodels, but without proper reuse mechanisms, MTs need to be developed from scratch for each new metamodel. In this paper, we classify reuse approaches for MTs across different metamodels and compare a sample of specific approaches – model types, concepts, a-posteriori typing, multilevel modeling, and design patterns for MTs – with the help of a feature model developed for this purpose, as well as a common example. We discuss strengths and weaknesses of each approach, provide a reading grid used to compare their features, and identify gaps in current reuse approaches.","author":[{"family":"Bruel","given":"Jean-Michel"},{"family":"Combemale","given":"Benoit"},{"family":"Guerra","given":"Esther"},{"family":"Jézéquel","given":"Jean-Marc"}],"citation-key":"bruelModelTransformationReuse","page":"15","source":"Zotero","title":"Model Transformation Reuse across Metamodels","type":"article-journal"},
{"id":"brun2007emf","author":[{"family":"Brun","given":"Cedric"},{"family":"Musset","given":"Jonathan"},{"family":"Toulme","given":"Antoine"}],"citation-key":"brun2007emf","issued":{"date-parts":[[2007]]},"title":"EMF compare","type":"article-journal"},
{"id":"bruneliere2014modisco","author":[{"family":"Bruneliere","given":"Hugo"},{"family":"Cabot","given":"Jordi"},{"family":"Dupé","given":"Grégoire"},{"family":"Madiot","given":"Frédéric"}],"citation-key":"bruneliere2014modisco","container-title":"Information and Software Technology","issue":"8","issued":{"date-parts":[[2014]]},"note":"00216","page":"1012–1032","publisher":"Elsevier","title":"Modisco: A model driven reverse engineering framework","type":"article-journal","volume":"56"},
{"id":"bruneliereIndustrializationResearchTools2010","accessed":{"date-parts":[[2016,10,10]]},"author":[{"family":"Bruneliere","given":"Hugo"},{"family":"Cabot","given":"Jordi"},{"family":"Jouault","given":"Frédéric"},{"family":"Tisi","given":"Massimo"},{"family":"Bézivin","given":"Jean"}],"citation-key":"bruneliereIndustrializationResearchTools2010","container-title":"Third International Workshop on Academic Software Development Tools and Techniques-WASDeTT-3 (co-located with the 25th IEEE/ACM International Conference on Automated Software Engineering-ASE'2010)","issued":{"date-parts":[[2010]]},"source":"Google Scholar","title":"Industrialization of research tools: The ATL case","title-short":"Industrialization of research tools","type":"paper-conference","URL":"https://hal.inria.fr/hal-00539173/"},
{"id":"bruneliereLightweightMetamodelExtension","accessed":{"date-parts":[[2015,6,24]]},"author":[{"family":"Bruneliere","given":"Hugo"},{"family":"Garcia","given":"Jokin"},{"family":"Desfray","given":"Philippe"},{"family":"Khelladi","given":"Djamel Eddine"},{"family":"Hebig","given":"Regina"},{"family":"Bendraou","given":"Reda"},{"family":"Cabot","given":"Jordi"}],"citation-key":"bruneliereLightweightMetamodelExtension","container-title":"11th European Conference on Modelling Foundations and Applications (ECMFA 2015)(a STAF 2015 conference)","source":"Google Scholar","title":"On Lightweight Metamodel Extension to Support Modeling Tools Agility","type":"paper-conference","URL":"https://hal.inria.fr/hal-01146802/"},
{"id":"brunEngineeringSelfadaptiveSystems2009","accessed":{"date-parts":[[2016,11,3]]},"author":[{"family":"Brun","given":"Yuriy"},{"family":"Serugendo","given":"Giovanna Di Marzo"},{"family":"Gacek","given":"Cristina"},{"family":"Giese","given":"Holger"},{"family":"Kienle","given":"Holger"},{"family":"Litoiu","given":"Marin"},{"family":"Müller","given":"Hausi"},{"family":"Pezzè","given":"Mauro"},{"family":"Shaw","given":"Mary"}],"citation-key":"brunEngineeringSelfadaptiveSystems2009","container-title":"Software engineering for self-adaptive systems","issued":{"date-parts":[[2009]]},"page":"48–70","publisher":"Springer","source":"Google Scholar","title":"Engineering self-adaptive systems through feedback loops","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-642-02161-9_3"},
{"id":"brunschwigModellingMobileDevices","abstract":"Modelling is central to many disciplines in engineering and the natural and social sciences. A wide variety of modelling languages and tools have been proposed along the years, traditionally for static environments such as desktops and laptops. However, the availability of increasingly powerful mobile devices makes it possible to profit from their embedded sensors and components (e.g., camera, microphone, GPS, accelerometer, gyroscope) for modelling. This has promoted a new range of modelling tools specially designed for their use in mobility. Such tools open the door to modelling in dynamic scenarios that go beyond the capabilities of traditional desktop tools. For example, modelling in mobility can be useful to design smart factories on-site, or to create models of hiking routes while walking along the routes, among many other scenarios.","author":[{"family":"Brunschwig","given":"Lea"},{"family":"Guerra","given":"Esther"},{"family":"Lara","given":"Juan","non-dropping-particle":"de"}],"citation-key":"brunschwigModellingMobileDevices","note":"00000","page":"27","source":"Zotero","title":"Modelling on mobile devices","type":"article-journal"},
{"id":"brynjulfsenXAITextDomainSpecificLanguage","author":[{"family":"Brynjulfsen","given":"Håvard"},{"family":"Rabbi","given":"Fazle"}],"citation-key":"brynjulfsenXAITextDomainSpecificLanguage","page":"105","source":"Zotero","title":"XAIText: A Domain-Specific Language for Developing an AI Pipeline","type":"article-journal"},
{"id":"bucchiaroneAutonomousShuttleasaServiceASaaS2020","abstract":"Providing mobility services effectively to residents and visitors is a complex socio-technical system task to city public managers. Smart mobility systems aim to support the efficient exploitation of city transport facilities and sustainable mobility within the urban environment. People need to travel quickly and conveniently between locations at different scales, ranging from a few blocks within a city to a journey across cities. At the same time, goods need to be timely delivered, considering both the users and the businesses’ needs. Several cities indicated an interest in using Autonomous Vehicles (AV) for the “last-mile” mobility services in the last few years. With them, it seems to be easier to get people and goods around using fewer vehicles. In this context, Autonomous Shuttles (AS) are beginning to be thought of as a new mobility/delivery service into the city center where narrow streets are not easily served by traditional buses. They allow them to perform critical areas with minimal new infrastructure and reduce noise and pollution. The article analyses the state-of-art on autonomous shuttles by proposing four application scenarios targeting the last-mile delivery of goods, the tourist experiences, and the shared and integrated mobility. Furthermore, we contribute with the proposition of the Autonomous Shuttles-as-a service (ASaaS) concept as the key pillar for the realization of innovative and sustainable proximity mobility. Our research proposed new research challenges for ASaaS, and we discuss social implications and governance challenges that consider user engagement and sustainability. It also recommended extending new research to focus on simulation and machine learning techniques for last-mile mobility planning and explore the journeys tracking certification via artificial intelligence and blockchain-based techniques.","accessed":{"date-parts":[[2020,10,5]]},"author":[{"family":"Bucchiarone","given":"Antonio"},{"family":"Battisti","given":"Sandro"},{"family":"Marconi","given":"Annapaola"},{"family":"Maldacea","given":"Roberto"},{"family":"Ponce","given":"Diego Cardona"}],"citation-key":"bucchiaroneAutonomousShuttleasaServiceASaaS2020","container-title":"IEEE Transactions on Intelligent Transportation Systems","container-title-short":"IEEE Trans. Intell. Transport. Syst.","DOI":"10.1109/TITS.2020.3025670","ISSN":"1524-9050, 1558-0016","issued":{"date-parts":[[2020]]},"page":"1-10","source":"DOI.org (Crossref)","title":"Autonomous Shuttle-as-a-Service (ASaaS): Challenges, Opportunities, and Social Implications","title-short":"Autonomous Shuttle-as-a-Service (ASaaS)","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/9210552/"},
{"id":"bucchiaroneRequirementsCodeArchitecturecentric2009","author":[{"family":"Bucchiarone","given":"Antonio"},{"family":"Ruscio","given":"Davide Di"},{"family":"Muccini","given":"Henry"},{"family":"Pelliccione","given":"Patrizio"}],"citation-key":"bucchiaroneRequirementsCodeArchitecturecentric2009","container-title":"CoRR","issued":{"date-parts":[[2009]]},"note":"00000 \n_eprint: 0910.0493","title":"From Requirements to code: an Architecture-centric Approach for producing Quality Systems","type":"article-journal","URL":"http://arxiv.org/abs/0910.0493","volume":"abs/0910.0493"},
{"id":"bucchiaroneWhatFutureModeling2021","accessed":{"date-parts":[[2021,3,26]]},"author":[{"family":"Bucchiarone","given":"Antonio"},{"family":"Ciccozzi","given":"Federico"},{"family":"Lambers","given":"Leen"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Tichy","given":"Matthias"},{"family":"Tisi","given":"Massimo"},{"family":"Wortmann","given":"Andreas"},{"family":"Zaytsev","given":"Vadim"}],"citation-key":"bucchiaroneWhatFutureModeling2021","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2020.3041522","ISSN":"0740-7459, 1937-4194","issue":"2","issued":{"date-parts":[[2021,3]]},"note":"00000","page":"119-127","source":"DOI.org (Crossref)","title":"What Is the Future of Modeling?","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/9354405/","volume":"38"},
{"id":"buczakSurveyDataMining2016","accessed":{"date-parts":[[2022,3,22]]},"author":[{"family":"Buczak","given":"Anna L."},{"family":"Guven","given":"Erhan"}],"citation-key":"buczakSurveyDataMining2016","container-title":"IEEE Communications Surveys & Tutorials","container-title-short":"IEEE Commun. Surv. Tutorials","DOI":"10.1109/COMST.2015.2494502","ISSN":"1553-877X, 2373-745X","issue":"2","issued":{"season":2,"date-parts":[[2016]]},"page":"1153-1176","source":"DOI.org (Crossref)","title":"A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/7307098/","volume":"18"},
{"id":"budinskyEclipseModelingFramework2003","author":[{"family":"Budinsky","given":"F."},{"family":"Steinberg","given":"D."},{"family":"Merks","given":"E."},{"family":"Ellersick","given":"R."},{"literal":"T.J. Grose"}],"citation-key":"budinskyEclipseModelingFramework2003","issued":{"date-parts":[[2003]]},"publisher":"Addison Wesley","title":"Eclipse Modeling Framework","type":"book"},
{"id":"bugiottiComparisonDataModels","abstract":"NoSQL datastore systems are a new generation of non-relational databases. More than fifty NoSQL systems have been already implemented, each with different characteristics — especially, with different data models and different APIs to access the data. In this paper we describe and compare the data models and operations offered by a number of representative NoSQL datastores, which we have directly used while developing the SOS (Save Our Systems) and ONDM (Object-NoSQL Datastore Mapper) frameworks. We discuss how these NoSQL systems can be used to manage a database consisting of collections of objects. Furthermore, we report on some experimental results concerning the use of the various systems and the implementation of the data representations described in this paper.","author":[{"family":"Bugiotti","given":"Francesca"},{"family":"Cabibbo","given":"Luca"}],"citation-key":"bugiottiComparisonDataModels","page":"12","source":"Zotero","title":"A Comparison of Data Models and APIs of NoSQL Datastores","type":"article-journal"},
{"id":"bugiottiDatabaseDesignNoSQL2014","abstract":"We propose a database design methodology for NoSQL systems. The approach is based on NoAM (NoSQL Abstract Model), a novel abstract data model for NoSQL databases, which exploits the commonalities of various NoSQL systems and is used to specify a system-independent representation of the application data. This intermediate representation can be then implemented in target NoSQL databases, taking into account their specific features. Overall, the methodology aims at supporting scalability, performance, and consistency, as needed by next-generation web applications.","accessed":{"date-parts":[[2018,5,17]]},"author":[{"family":"Bugiotti","given":"Francesca"},{"family":"Cabibbo","given":"Luca"},{"family":"Atzeni","given":"Paolo"},{"family":"Torlone","given":"Riccardo"}],"citation-key":"bugiottiDatabaseDesignNoSQL2014","container-title":"Conceptual Modeling","DOI":"10.1007/978-3-319-12206-9_18","editor":[{"family":"Yu","given":"Eric"},{"family":"Dobbie","given":"Gillian"},{"family":"Jarke","given":"Matthias"},{"family":"Purao","given":"Sandeep"}],"event-place":"Cham","ISBN":"978-3-319-12205-2 978-3-319-12206-9","issued":{"date-parts":[[2014]]},"page":"223-231","publisher":"Springer International Publishing","publisher-place":"Cham","source":"Crossref","title":"Database Design for NoSQL Systems","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-319-12206-9_18","volume":"8824"},
{"id":"BuildingAutomatedMachine","accessed":{"date-parts":[[2021,4,21]]},"citation-key":"BuildingAutomatedMachine","note":"00000","title":"Building an Automated Machine Learning Pipeline: Part One | by Ceren Iyim | Towards Data Science","type":"webpage","URL":"https://towardsdatascience.com/building-an-automated-machine-learning-pipeline-part-one-5c70ae682f35"},
{"id":"BuildingIoTOntologies","accessed":{"date-parts":[[2016,9,27]]},"citation-key":"BuildingIoTOntologies","title":"Building IoT ontologies and integrating them with Eclipse projects | EclipseCon Europe 2016","type":"webpage","URL":"https://www.eclipsecon.org/europe2016/session/building-iot-ontologies-and-integrating-them-eclipse-projects"},
{"id":"BuildingRaspberryPi","accessed":{"date-parts":[[2015,4,17]]},"citation-key":"BuildingRaspberryPi","title":"Building A Raspberry Pi VPN Part One: How And Why To Build A Server - ReadWrite","type":"webpage","URL":"http://readwrite.com/2014/04/10/raspberry-pi-vpn-tutorial-server-secure-web-browsing"},
{"id":"BuildingSmarterEclipse","accessed":{"date-parts":[[2016,9,27]]},"citation-key":"BuildingSmarterEclipse","title":"Building a Smarter Eclipse IoT Greenhouse with Eclipse Vorto, Kura, Californium and Paho | EclipseCon Europe 2016","type":"webpage","URL":"https://www.eclipsecon.org/europe2016/session/building-smarter-eclipse-iot-greenhouse-eclipse-vorto-kura-californium-and-paho"},
{"id":"buresSoftwareEngineeringSmart2015","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Bures","given":"Tomas"},{"family":"Krikava","given":"Filip"},{"family":"Mordinyi","given":"Richard"},{"family":"Pronios","given":"Nikos"},{"family":"Weyns","given":"Danny"},{"family":"Berger","given":"Christian"},{"family":"Biffl","given":"Stefan"},{"family":"Daun","given":"Marian"},{"family":"Gabor","given":"Thomas"},{"family":"Garlan","given":"David"},{"family":"Gerostathopoulos","given":"Ilias"},{"family":"Julien","given":"Christine"}],"citation-key":"buresSoftwareEngineeringSmart2015","container-title":"ACM SIGSOFT Software Engineering Notes","DOI":"10.1145/2830719.2830736","ISSN":"01635948","issue":"6","issued":{"date-parts":[[2015,11,11]]},"page":"28-32","source":"CrossRef","title":"Software Engineering for Smart Cyber-Physical Systems -- Towards a Research Agenda: Report on the First International Workshop on Software Engineering for Smart CPS","title-short":"Software Engineering for Smart Cyber-Physical Systems -- Towards a Research Agenda","type":"article-journal","URL":"http://dl.acm.org/citation.cfm?doid=2830719.2830736","volume":"40"},
{"id":"Burgueño20191","abstract":"Model transformations are the key element that brings life to model-driven engineering. Animation, simulations, VV, code-generation, etc. all depend on some kind of model transformation to work. Model transformations are typically defined via specialized model transformation languages but this is now in question due to the lack of convincing evidence that specialised languages are substantially better than generalpurpose languages for model transformation specification, and the rise of artificial intelligence. We report on the results of an open discussion with the model transformation community on the future of these languages, including whether such a future exists at all. © 2019 Association Internationale pour les Technologies Objets.","author":[{"family":"Burgueño","given":"L."},{"family":"Cabot","given":"J."},{"family":"Gérard","given":"S."}],"citation-key":"Burgueño20191","container-title":"Journal of Object Technology","DOI":"10.5381/JOT.2019.18.3.A7","ISSN":"16601769","issue":"3","issued":{"date-parts":[[2019]]},"page":"1-11","publisher":"Association Internationale pour les Technologies Objets","title":"The future of model transformation languages: An open community discussion","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079538800&doi=10.5381%2fJOT.2019.18.3.A7&partnerID=40&md5=db33da45ba29371a673b2a2e989ddb14","volume":"18"},
{"id":"Burgueno2019168","abstract":"Model-driven engineering (MDE) and Artificial Intelligence (AI) are two separate fields in computer science, which can clearly benefit from cross-fertilization and collaboration. There are at least two ways in which such integrations - which we call MDE Intelligence - can manifest: (1) MDE can benefit from integrating AI concepts and ideas to increasing the power and flexibility of model-driven techniques by means of the application of AI algorithms. (2) Conversely, AI can benefit from integrating concepts and ideas from MDE - for example, using domain-specific languages and model transformations allows domain experts to directly express and manipulate their problems while providing an auditable computation pipeline. To discuss and further stimulate such integrations, the 1st edition of the Workshop on Artificial Intelligence and Model-driven Engineering (MDE Intelligence) was held on September 16, 2019 in Munich, Germany, as part of the satellite events of the IEEE/ACM 22th International Conference on Model-Driven Engineering Languages and Systems (MODELS 2019). © 2019 IEEE.","author":[{"family":"Burgueno","given":"L."},{"family":"Burdusel","given":"A."},{"family":"Gerard","given":"S."},{"family":"Wimmer","given":"M."}],"citation-key":"Burgueno2019168","collection-title":"Proceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2019","DOI":"10.1109/MODELS-C.2019.00028","editor":[{"family":"Burgueno L., Burgueno L.","given":"Pretschner A.","suffix":"Voss S., Chaudron M., Kienzle J., Volter M., Gerard S., Zahedi M., Bousse E., Rensink A., Polack F., Engels G., Kappel G."}],"ISBN":"978-1-72815-125-0","issued":{"date-parts":[[2019]]},"page":"168-169","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Preface to MDE intelligence 2019: 1st workshop on artificial intelligence and model-driven engineering","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075941198&doi=10.1109%2fMODELS-C.2019.00028&partnerID=40&md5=40568b47d4377efde008863a02d3f78e"},
{"id":"Burgueño2019294","abstract":"Model transformations are a key element in any model-driven engineering approach. But writing them is a time-consuming and error-prone activity that requires specific knowledge of the transformation language semantics. We propose to take advantage of the advances in Artificial Intelligence and, in particular Long Short-Term Memory Neural Networks (LSTM), to automatically infer model transformations from sets of input-output model pairs. Once the transformation mappings have been learned, the LSTM system is able to autonomously transform new input models into their corresponding output models without the need of writing any transformation-specific code. We evaluate the correctness and performance of our approach and discuss its advantages and limitations. © 2019 IEEE.","author":[{"family":"Burgueño","given":"L."},{"family":"Cabot","given":"J."},{"family":"Gérard","given":"S."}],"citation-key":"Burgueño2019294","collection-title":"Proceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems, MODELS 2019","DOI":"10.1109/MODELS.2019.00013","editor":[{"family":"Kessentini M., Yue T.","given":"Yue T.","suffix":"Pretschner A., Voss S., Burgueno L., Burgueno L."}],"ISBN":"978-1-72812-535-0","issued":{"date-parts":[[2019]]},"page":"294-299","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"An LSTM-Based neural network architecture for model transformations","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076107429&doi=10.1109%2fMODELS.2019.00013&partnerID=40&md5=709367fbc4399d9c4a826c18d2bb39ea"},
{"id":"Burgueno2021148","abstract":"Model-driven engineering (MDE) and Artificial Intelligence (AI) are two separate fields in computer science, which continue to benefit from cross-fertilization and collaboration. Such integrations - which we call MDE Intelligence - can go both ways: MDE activities benefit from the integration of AI ideas and, conversely, AI can benefit from the automation and subject-matter-expert integration offered by MDE. This 3rd edition of the Workshop on Artificial Intelligence and Model-driven Engineering (MDE Intelligence), held in conjunction with the IEEE/ACM 24th International Conference on Model-Driven Engineering Languages and Systems (MODELS 2021), builds on the success of the previous two editions and provides the space for discussions of the integration of AI and MDE and for the identification of opportunities for new integrations between the two fields. © 2021 IEEE.","author":[{"family":"Burgueno","given":"L."},{"family":"Kessentini","given":"M."},{"family":"Wimmer","given":"M."},{"family":"Zschaler","given":"S."}],"citation-key":"Burgueno2021148","collection-title":"Companion Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021","DOI":"10.1109/MODELS-C53483.2021.00026","ISBN":"978-1-66542-484-4","issued":{"date-parts":[[2021]]},"page":"148-149","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"MDE intelligence 2021: 3rdWorkshop on artificial intelligence and model-driven engineering","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123983535&doi=10.1109%2fMODELS-C53483.2021.00026&partnerID=40&md5=b230e0972b74c7d0dc48f71094b656f5"},
{"id":"Burgueno2021148","abstract":"Model-driven engineering (MDE) and Artificial Intelligence (AI) are two separate fields in computer science, which continue to benefit from cross-fertilization and collaboration. Such integrations - which we call MDE Intelligence - can go both ways: MDE activities benefit from the integration of AI ideas and, conversely, AI can benefit from the automation and subject-matter-expert integration offered by MDE. This 3rd edition of the Workshop on Artificial Intelligence and Model-driven Engineering (MDE Intelligence), held in conjunction with the IEEE/ACM 24th International Conference on Model-Driven Engineering Languages and Systems (MODELS 2021), builds on the success of the previous two editions and provides the space for discussions of the integration of AI and MDE and for the identification of opportunities for new integrations between the two fields. © 2021 IEEE.","author":[{"family":"Burgueno","given":"L."},{"family":"Kessentini","given":"M."},{"family":"Wimmer","given":"M."},{"family":"Zschaler","given":"S."}],"citation-key":"Burgueno2021148","collection-title":"Companion Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021","DOI":"10.1109/MODELS-C53483.2021.00026","ISBN":"978-1-66542-484-4","issued":{"date-parts":[[2021]]},"page":"148-149","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"MDE intelligence 2021: 3rdWorkshop on artificial intelligence and model-driven engineering","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123983535&doi=10.1109%2fMODELS-C53483.2021.00026&partnerID=40&md5=b230e0972b74c7d0dc48f71094b656f5"},
{"id":"burguenoContentsModelBasedSoftware2019","abstract":"Although Model-Based Software Engineering (MBE) is a widely accepted Software Engineering (SE) discipline, no agreedupon core set of concepts and practices (i.e., a Body of Knowledge) has been defined for it yet. With the goals of characterizing the contents of the MBE discipline, promoting a global consistent view of it, clarifying its scope with regard to other SE disciplines, and defining a foundation for the development of educational curricula on MBE, this paper proposes the contents for a Body of Knowledge for MBE. We also describe the methodology that we have used to come up with the proposed list of contents, as well as the results of a survey study that we conducted to sound out the opinion of the community on the importance of the proposed topics and their level of coverage in the existing SE curricula.","accessed":{"date-parts":[[2021,7,20]]},"author":[{"family":"Burgueño","given":"Loli"},{"family":"Ciccozzi","given":"Federico"},{"family":"Famelis","given":"Michalis"},{"family":"Kappel","given":"Gerti"},{"family":"Lambers","given":"Leen"},{"family":"Mosser","given":"Sebastien"},{"family":"Paige","given":"Richard F."},{"family":"Pierantonio","given":"Alfonso"},{"family":"Rensink","given":"Arend"},{"family":"Salay","given":"Rick"},{"family":"Taentzer","given":"Gabriele"},{"family":"Vallecillo","given":"Antonio"},{"family":"Wimmer","given":"Manuel"}],"citation-key":"burguenoContentsModelBasedSoftware2019","container-title":"Software and Systems Modeling","container-title-short":"Softw Syst Model","DOI":"10.1007/s10270-019-00746-9","ISSN":"1619-1366, 1619-1374","issue":"6","issued":{"date-parts":[[2019,12]]},"note":"00012","page":"3193-3205","source":"DOI.org (Crossref)","title":"Contents for a Model-Based Software Engineering Body of Knowledge","type":"article-journal","URL":"http://link.springer.com/10.1007/s10270-019-00746-9","volume":"18"},
{"id":"burguenoGuestEditorialTheme2022","abstract":"This theme section brings together the latest research at the intersection of artificial intelligence (AI) and model-driven engineering (MDE). Over the past years, we have witnessed a substantial rise of AI successfully applied to different domains, including software development and MDE. Dedicated events at the intersection of AI and MDE have been created, too, such as the MDE Intelligence workshop series co-located with the MODELS conference. This theme section covers research contributions integrating AI components into MDE approaches—increasing the current benefits of MDE processes and tools and pushing the limits of “classic” MDE with the goal to provide software and systems engineers with the right techniques to develop the next generation of highly complex model-based systems—and applications of MDE to the development of AI components. In total, nine submissions were accepted in the theme section after a thorough peer-reviewing process.","accessed":{"date-parts":[[2022,5,24]]},"author":[{"family":"Burgueño","given":"Lola"},{"family":"Cabot","given":"Jordi"},{"family":"Wimmer","given":"Manuel"},{"family":"Zschaler","given":"Steffen"}],"citation-key":"burguenoGuestEditorialTheme2022","container-title":"Software and Systems Modeling","container-title-short":"Softw Syst Model","DOI":"10.1007/s10270-022-00988-0","ISSN":"1619-1366, 1619-1374","issue":"3","issued":{"date-parts":[[2022,6]]},"page":"963-965","source":"DOI.org (Crossref)","title":"Guest editorial to the theme section on AI-enhanced model-driven engineering","type":"article-journal","URL":"https://link.springer.com/10.1007/s10270-022-00988-0","volume":"21"},
{"id":"burguenoLSTMBasedNeuralNetwork2019a","abstract":"Model transformations are a key element in any model-driven engineering approach. But writing them is a time-consuming and error-prone activity that requires specific knowledge of the transformation language semantics. We propose to take advantage of the advances in Artificial Intelligence and, in particular Long Short-Term Memory Neural Networks (LSTM), to automatically infer model transformations from sets of input-output model pairs. Once the transformation mappings have been learned, the LSTM system is able to autonomously transform new input models into their corresponding output models without the need of writing any transformation-specific code. We evaluate the correctness and performance of our approach and discuss its advantages and limitations. © 2019 IEEE.","author":[{"family":"Burgueño","given":"L."},{"family":"Cabot","given":"J."},{"family":"Gérard","given":"S."}],"citation-key":"burguenoLSTMBasedNeuralNetwork2019a","container-title":"Proceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems, MODELS 2019","DOI":"10.1109/MODELS.2019.00013","editor":[{"family":"Kessentini M.","given":"Burgueno L.","suffix":"Yue T., Yue T., Pretschner A., Voss S., Burgueno L."}],"ISBN":"978-1-72812-535-0","issued":{"date-parts":[[2019]]},"page":"294-299","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"An LSTM-Based Neural Network Architecture for Model Transformations","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076107429&doi=10.1109%2fMODELS.2019.00013&partnerID=40&md5=709367fbc4399d9c4a826c18d2bb39ea"},
{"id":"burguenoMDEIntelligence20212021a","abstract":"Model-driven engineering (MDE) and Artificial Intelligence (AI) are two separate fields in computer science, which continue to benefit from cross-fertilization and collaboration. Such integrations - which we call MDE Intelligence - can go both ways: MDE activities benefit from the integration of AI ideas and, conversely, AI can benefit from the automation and subject-matter-expert integration offered by MDE. This 3rd edition of the Workshop on Artificial Intelligence and Model-driven Engineering (MDE Intelligence), held in conjunction with the IEEE/ACM 24th International Conference on Model-Driven Engineering Languages and Systems (MODELS 2021), builds on the success of the previous two editions and provides the space for discussions of the integration of AI and MDE and for the identification of opportunities for new integrations between the two fields. © 2021 IEEE.","author":[{"family":"Burgueno","given":"L."},{"family":"Kessentini","given":"M."},{"family":"Wimmer","given":"M."},{"family":"Zschaler","given":"S."}],"citation-key":"burguenoMDEIntelligence20212021a","container-title":"Companion Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021","DOI":"10.1109/MODELS-C53483.2021.00026","ISBN":"978-1-66542-484-4","issued":{"date-parts":[[2021]]},"page":"148-149","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"MDE Intelligence 2021: 3rdWorkshop on Artificial Intelligence and Model-Driven Engineering","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123983535&doi=10.1109%2fMODELS-C53483.2021.00026&partnerID=40&md5=b230e0972b74c7d0dc48f71094b656f5"},
{"id":"burguenoNLPbasedArchitectureAutocompletion","abstract":"Domain models capture the key concepts and relationships of a business domain. Typically, domain models are manually defined by software designers in the initial phases of a software development cycle, based on their interactions with the client and their own domain expertise. Given the key role of domain models in the quality of the final system, it is important that they properly reflect the reality of the business. To facilitate the definition of domain models and improve their quality, we propose to move towards a more assisted domain modeling building process where an NLP-based assistant will provide autocomplete suggestions for the partial model under construction based on the automatic analysis of the textual information available for the project (contextual knowledge) and/or its related business domain (general knowledge). The process will also take into account the feedback collected from the designer’s interaction with the assistant. We have developed a proof-of-concept tool and have performed a preliminary evaluation that shows promising results.","author":[{"family":"Burgueño","given":"Loli"},{"family":"Clarisó","given":"Robert"},{"family":"Li","given":"Shuai"},{"family":"Gérard","given":"Sébastien"},{"family":"Cabot","given":"Jordi"}],"citation-key":"burguenoNLPbasedArchitectureAutocompletion","note":"00000","page":"16","source":"Zotero","title":"A NLP-based architecture for the autocompletion of partial domain models","type":"article-journal"},
{"id":"burguenoPrefaceMDEIntelligence2019a","abstract":"Model-driven engineering (MDE) and Artificial Intelligence (AI) are two separate fields in computer science, which can clearly benefit from cross-fertilization and collaboration. There are at least two ways in which such integrations - which we call MDE Intelligence - can manifest: (1) MDE can benefit from integrating AI concepts and ideas to increasing the power and flexibility of model-driven techniques by means of the application of AI algorithms. (2) Conversely, AI can benefit from integrating concepts and ideas from MDE - for example, using domain-specific languages and model transformations allows domain experts to directly express and manipulate their problems while providing an auditable computation pipeline. To discuss and further stimulate such integrations, the 1st edition of the Workshop on Artificial Intelligence and Model-driven Engineering (MDE Intelligence) was held on September 16, 2019 in Munich, Germany, as part of the satellite events of the IEEE/ACM 22th International Conference on Model-Driven Engineering Languages and Systems (MODELS 2019). © 2019 IEEE.","author":[{"family":"Burgueno","given":"L."},{"family":"Burdusel","given":"A."},{"family":"Gerard","given":"S."},{"family":"Wimmer","given":"M."}],"citation-key":"burguenoPrefaceMDEIntelligence2019a","container-title":"Proceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2019","DOI":"10.1109/MODELS-C.2019.00028","editor":[{"family":"Burgueno L.","given":"Kappel G.","suffix":"Burgueno L., Pretschner A., Voss S., Chaudron M., Kienzle J., Volter M., Gerard S., Zahedi M., Bousse E., Rensink A., Polack F., Engels G."}],"ISBN":"978-1-72815-125-0","issued":{"date-parts":[[2019]]},"page":"168-169","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Preface to MDE intelligence 2019: 1st workshop on artificial intelligence and model-driven engineering","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075941198&doi=10.1109%2fMODELS-C.2019.00028&partnerID=40&md5=40568b47d4377efde008863a02d3f78e"},
{"id":"burguenoProceedingsMODELS20172017","citation-key":"burguenoProceedingsMODELS20172017","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Burgueño","given":"Loli"},{"family":"Corley","given":"Jonathan"},{"family":"Bencomo","given":"Nelly"},{"family":"Clarke","given":"Peter J."},{"family":"Collet","given":"Philippe"},{"family":"Famelis","given":"Michalis"},{"family":"Ghosh","given":"Sudipto"},{"family":"Gogolla","given":"Martin"},{"family":"Greenyer","given":"Joel"},{"family":"Guerra","given":"Esther"},{"family":"Kokaly","given":"Sahar"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Rubin","given":"Julia"},{"family":"Ruscio","given":"Davide Di"}],"issued":{"date-parts":[[2017]]},"publisher":"CEUR-WS.org","title":"Proceedings of MODELS 2017 Satellite Event: Workshops (ModComp, ME, EXE, COMMitMDE, MRT, MULTI, GEMOC, MoDeVVa, MDETools, FlexMDE, MDEbug), Posters, Doctoral Symposium, Educator Symposium, ACM Student Research Competition, and Tools and Demonstrations co-located with ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MODELS 2017), Austin, TX, USA, September, 17, 2017","type":"book","URL":"http://ceur-ws.org/Vol-2019","volume":"2019"},
{"id":"burguenoStaticFaultLocalization2015","accessed":{"date-parts":[[2015,6,17]]},"author":[{"family":"Burgueno","given":"Loli"},{"family":"Troya","given":"Javier"},{"family":"Wimmer","given":"Manuel"},{"family":"Vallecillo","given":"Antonio"}],"citation-key":"burguenoStaticFaultLocalization2015","container-title":"IEEE Transactions on Software Engineering","DOI":"10.1109/TSE.2014.2375201","ISSN":"0098-5589, 1939-3520","issue":"5","issued":{"date-parts":[[2015,5,1]]},"page":"490-506","source":"CrossRef","title":"Static Fault Localization in Model Transformations","type":"article-journal","URL":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6967841","volume":"41"},
{"id":"buttingModelingReusablePlatformIndependent2016","accessed":{"date-parts":[[2016,1,18]]},"author":[{"family":"Butting","given":"Arvid"},{"family":"Rumpe","given":"Bernhard"},{"family":"Schulze","given":"Christoph"},{"family":"Thomas","given":"Ulrike"},{"family":"Wortmann","given":"Andreas"}],"citation-key":"buttingModelingReusablePlatformIndependent2016","container-title":"arXiv preprint arXiv:1601.02452","issued":{"date-parts":[[2016]]},"source":"Google Scholar","title":"Modeling Reusable, Platform-Independent Robot Assembly Processes","type":"article-journal","URL":"http://arxiv.org/abs/1601.02452"},
{"id":"buyyaInternetThingsPrinciples2016","author":[{"family":"Buyya","given":"Rajkumar"},{"family":"Dastjerdi","given":"Amir Vahid"}],"citation-key":"buyyaInternetThingsPrinciples2016","event-place":"Amsterdam Boston Heidelberg","ISBN":"978-0-12-805395-9","issued":{"date-parts":[[2016]]},"note":"OCLC: 958384012","number-of-pages":"354","publisher":"Morgan Kaufmann","publisher-place":"Amsterdam Boston Heidelberg","source":"Gemeinsamer Bibliotheksverbund ISBN","title":"Internet of Things: principles and paradigms","title-short":"Internet of Things","type":"book"},
{"id":"CA7F13858DC9A0A2F2B68A7CEA562E672","citation-key":"CA7F13858DC9A0A2F2B68A7CEA562E672","note":"00000","title":"CA7F13858DC9A0A2F2B68A7CEA562E67-2","type":"article-journal"},
{"id":"Cabot2018154","abstract":"The limited adoption of Model-Driven Software Engineering (MDSE) is due to a variety of social and technical factors, which can be summarized in one: its (real or perceived) benefits do not outweigh its costs. In this vision paper we argue that the cognification of MDSE has the potential to reverse this situation. Cognification is the application of knowledge (inferred from large volumes of information, artificial intelligence or collective intelligence) to boost the performance and impact of a process. We discuss the opportunities and challenges of cognifying MDSE tasks and we describe some potential scenarios where cognification can bring quantifiable and perceivable advantages. And conversely, we also discuss how MDSE techniques themselves can help in the improvement of AI, Machine learning, bot generation and other cognification techniques. © Springer International Publishing AG 2018.","author":[{"family":"Cabot","given":"J."},{"family":"Clarisó","given":"R."},{"family":"Brambilla","given":"M."},{"family":"Gérard","given":"S."}],"citation-key":"Cabot2018154","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-319-74730-9_13","editor":[{"family":"Zschaler S.","given":"Seidl M."}],"ISBN":"9783319747293","ISSN":"03029743","issued":{"date-parts":[[2018]]},"page":"154-160","publisher":"Springer Verlag","title":"Cognifying model-driven software engineering","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042643413&doi=10.1007%2f978-3-319-74730-9_13&partnerID=40&md5=dd5f4fc3dcfcf5d408fed0d45b83bc51","volume":"10748 LNCS"},
{"id":"cabotCognifyingModelDrivenSoftware2018a","abstract":"The limited adoption of Model-Driven Software Engineering (MDSE) is due to a variety of social and technical factors, which can be summarized in one: its (real or perceived) benefits do not outweigh its costs. In this vision paper we argue that the cognification of MDSE has the potential to reverse this situation. Cognification is the application of knowledge (inferred from large volumes of information, artificial intelligence or collective intelligence) to boost the performance and impact of a process. We discuss the opportunities and challenges of cognifying MDSE tasks and we describe some potential scenarios where cognification can bring quantifiable and perceivable advantages. And conversely, we also discuss how MDSE techniques themselves can help in the improvement of AI, Machine learning, bot generation and other cognification techniques. © Springer International Publishing AG 2018.","author":[{"family":"Cabot","given":"J."},{"family":"Clarisó","given":"R."},{"family":"Brambilla","given":"M."},{"family":"Gérard","given":"S."}],"citation-key":"cabotCognifyingModelDrivenSoftware2018a","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-319-74730-9_13","editor":[{"family":"Zschaler S.","given":"Seidl M."}],"ISBN":"9783319747293","ISSN":"03029743","issued":{"date-parts":[[2018]]},"page":"154-160","publisher":"Springer Verlag","title":"Cognifying Model-Driven Software Engineering","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042643413&doi=10.1007%2f978-3-319-74730-9_13&partnerID=40&md5=dd5f4fc3dcfcf5d408fed0d45b83bc51","volume":"10748 LNCS"},
{"id":"Cacheda:2011:CCF:1921591.1921593","author":[{"family":"Cacheda","given":"Fidel"},{"family":"Carneiro","given":"Víctor"},{"family":"Fernández","given":"Diego"},{"family":"Formoso","given":"Vreixo"}],"citation-key":"Cacheda:2011:CCF:1921591.1921593","container-title":"ACM Transactions on the Web","container-title-short":"ACM Trans. Web","ISSN":"1559-1131","issue":"1","issued":{"date-parts":[[2011,2]]},"page":"2:1-2:33","title":"Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems","type":"article-journal","URL":"http://doi.acm.org/10.1145/1921591.1921593","volume":"5"},
{"id":"cadavidAnalysisMetamodelingPractices2015","accessed":{"date-parts":[[2015,3,20]]},"author":[{"family":"Cadavid","given":"Juan José"},{"family":"Combemale","given":"Benoit"},{"family":"Baudry","given":"Benoit"}],"citation-key":"cadavidAnalysisMetamodelingPractices2015","container-title":"Computer Languages, Systems & Structures","DOI":"10.1016/j.cl.2015.02.002","ISSN":"14778424","issued":{"date-parts":[[2015,3]]},"source":"CrossRef","title":"An analysis of metamodeling practices for MOF and OCL","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S1477842415000068"},
{"id":"Caione2020","abstract":"Commercial voice services like Google Assistant and Amazon Alexa are reaching extreme popularity. While Natural Language Processing (NLP) and Artificial Intelligence (AI) techniques - applied to the aural channel - deliver high quality voice recognition, the voice channel still lacks a good methodology to design user experiences. For instance, The Amazon Alexa team suggests gathering the information model of Alexa skills by talking with test users behind a curtain, pretending to be the machine. In our opinion, such kind of bottom-up strategy is not effective because it overfits the UX to very specific cases. A top-down approach could provide the right answer also in unseen and unpredictable situations instead. Our work aims to propose a novel model driven approach that allows authors to design from scratch the overall vocal UX as well as rethink existing visual UX before porting them to the aural channel. Our approach, which is inherently top-down, is based on Aural IDM, an UX design method thought for screen readers modelling in the early '00. In this paper we've refactored the Spotify Alexa skill to demonstrate the validity of Aural IDM for designing vocal UXs. The experience of Spotify on Alexa is quite primordial and does not reflect the richness of the desktop app. A prototype is currently under development, and the result of a comparison between the AS-IS and TO-BE voice skill will be subject of a future work. © 2020 ACM.","author":[{"family":"Caione","given":"A."},{"family":"Fiore","given":"A."},{"family":"Mainetti","given":"L."},{"family":"Manco","given":"L."},{"family":"Vergallo","given":"R."}],"citation-key":"Caione2020","collection-title":"ACM International Conference Proceeding Series","DOI":"10.1145/3447568.3448538","editor":[{"family":"Laouar M.R.","given":"Capodieci A."}],"ISBN":"978-1-4503-7655-6","issued":{"date-parts":[[2020]]},"publisher":"Association for Computing Machinery","title":"Refactoring the UX of a popular voice application","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103656579&doi=10.1145%2f3447568.3448538&partnerID=40&md5=2afc0d83b724567e2cb5a0ffd7acbe1e"},
{"id":"caioneRefactoringUXPopular2020a","abstract":"Commercial voice services like Google Assistant and Amazon Alexa are reaching extreme popularity. While Natural Language Processing (NLP) and Artificial Intelligence (AI) techniques - applied to the aural channel - deliver high quality voice recognition, the voice channel still lacks a good methodology to design user experiences. For instance, The Amazon Alexa team suggests gathering the information model of Alexa skills by talking with test users behind a curtain, pretending to be the machine. In our opinion, such kind of bottom-up strategy is not effective because it overfits the UX to very specific cases. A top-down approach could provide the right answer also in unseen and unpredictable situations instead. Our work aims to propose a novel model driven approach that allows authors to design from scratch the overall vocal UX as well as rethink existing visual UX before porting them to the aural channel. Our approach, which is inherently top-down, is based on Aural IDM, an UX design method thought for screen readers modelling in the early '00. In this paper we've refactored the Spotify Alexa skill to demonstrate the validity of Aural IDM for designing vocal UXs. The experience of Spotify on Alexa is quite primordial and does not reflect the richness of the desktop app. A prototype is currently under development, and the result of a comparison between the AS-IS and TO-BE voice skill will be subject of a future work. © 2020 ACM.","author":[{"family":"Caione","given":"A."},{"family":"Fiore","given":"A."},{"family":"Mainetti","given":"L."},{"family":"Manco","given":"L."},{"family":"Vergallo","given":"R."}],"citation-key":"caioneRefactoringUXPopular2020a","container-title":"ACM International Conference Proceeding Series","DOI":"10.1145/3447568.3448538","editor":[{"family":"Laouar M.R.","given":"Capodieci A."}],"ISBN":"978-1-4503-7655-6","issued":{"date-parts":[[2020]]},"publisher":"Association for Computing Machinery","title":"Refactoring the UX of a popular voice application","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103656579&doi=10.1145%2f3447568.3448538&partnerID=40&md5=2afc0d83b724567e2cb5a0ffd7acbe1e"},
{"id":"calegariVerificationModelTransformations2013","abstract":"Within the Model-Driven Engineering paradigm, software development is based on the definition of models providing different views of the system to be constructed and model transformations supporting a (semi)automatic development process. The verification of models and model transformations is crucial in order to improve the quality and the reliability of the products developed using this paradigm. In this context, the verification of a model transformation has three main components: the transformation itself, the properties of interest addressed, and the verification techniques used to establish the properties. In this paper we present an exhaustive review of the literature on the verification of model transformations analyzing these three components. We also take a problem-based approach exemplifying those aspects of interest that could be verified on a model transformation and show how this can be done. Finally, we conclude the need of an integrated environment for addressing the heterogeneous verification of model transformations.","accessed":{"date-parts":[[2015,4,1]]},"author":[{"family":"Calegari","given":"Daniel"},{"family":"Szasz","given":"Nora"}],"citation-key":"calegariVerificationModelTransformations2013","collection-title":"Proceedings of the XXXVIII Latin American Conference in Informatics (CLEI)","container-title":"Electronic Notes in Theoretical Computer Science","container-title-short":"Electronic Notes in Theoretical Computer Science","DOI":"10.1016/j.entcs.2013.02.002","ISSN":"1571-0661","issued":{"date-parts":[[2013,3,5]]},"page":"5-25","source":"ScienceDirect","title":"Verification of Model Transformations: A Survey of the State-of-the-Art","title-short":"Verification of Model Transformations","type":"article-journal","URL":"http://www.sciencedirect.com/science/article/pii/S1571066113000042","volume":"292"},
{"id":"callowAddressingSystemsVerification2011","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Callow","given":"Glenn"},{"family":"Kalawsky","given":"Roy"},{"family":"Watson","given":"Graham"},{"family":"Okuda","given":"Yuki"}],"citation-key":"callowAddressingSystemsVerification2011","container-title":"System of Systems Engineering (SoSE), 2011 6th International Conference on","issued":{"date-parts":[[2011]]},"page":"311–316","publisher":"IEEE","source":"Google Scholar","title":"Addressing systems verification of autonomous systems through Bi-directional model transformations: A systems model driven architecture approach","title-short":"Addressing systems verification of autonomous systems through Bi-directional model transformations","type":"paper-conference","URL":"http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5966616"},
{"id":"Çam2018372","abstract":"Recent developments in computational science and engineering allow a great deal of experimental work to be conducted through computer simulation. In a simulation experiment, a model of the phenomena to be studied is run in a computing environment under varying model and environment settings. As models are adjusted to experimental procedures and execution environments, variations arise. Models also evolve in time. Thus, models must be managed. We propose to bring Global Model Management (GMM) to bear on simulation experiment management by using techniques and tools from megamodeling. The proposed approach will facilitate model management tasks by providing an interface to query the model repository, relate models with each other, and apply model transformations from/to simulation models. Our proposed Megamodel for Simulation Experiments is based on SED-ML (Simulation Experiment Description Markup Language). Copyright © 2018 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.","author":[{"family":"Çam","given":"S."},{"family":"Dayibaş","given":"O."},{"family":"Görür","given":"B.K."},{"family":"Oǧuztüzün","given":"H."},{"family":"Yilmaz","given":"L."},{"family":"Chakladar","given":"S."},{"family":"Doud","given":"K."},{"family":"Smith","given":"A.E."},{"family":"Teran-Somohano","given":"A."}],"citation-key":"Çam2018372","collection-title":"MODELSWARD 2018 - Proceedings of the 6th International Conference on Model-Driven Engineering and Software Development","DOI":"10.5220/0006586703720378","editor":[{"family":"Hammoudi S., Pires L.F.","given":"Selic B."}],"ISBN":"978-989-758-283-7","issued":{"date-parts":[[2018]]},"page":"372-378","publisher":"SciTePress","title":"Supporting simulation experiments with megamodeling","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051067178&doi=10.5220%2f0006586703720378&partnerID=40&md5=0d01f90b7633ac4ec2fa371812b3dbf5","volume":"2018-January"},
{"id":"Cam202217","abstract":"Most of the frameworks or assistance systems for experiment specification do not provide a process explicitly based on formally specified hypotheses. This deficiency leads to inaccurate or insufficient record of an experiment, decreasing the trustworthiness and reproducibility of the experiment. Moreover, the wide variety of models, metamodels, tools, and data for experimentation requires Global Model Management (GMM) that is utilizing Model-Driven Engineering techniques, facilitates documentation, sharing, reusability, and replicability of simulation experiments. In this study, we strive to illustrate how to support simulation experimentation with hypotheses as a scientific workflow through GMM with an extension to the Simulation Experiment Description Mark-up Language (SED-ML). In particular, we present a megamodel built to serve as a repository to manage the artefacts employed in a simulation experiment. Based on the SED-ML, and enriched with hypothesis handling, our megamodel attempts to address all the phases of a simulation experiment, including specification, input data generation, execution, and output data analysis. © 2022, DAAAM International Vienna. All rights reserved.","author":[{"family":"Cam","given":"S."},{"family":"Oguztuzun","given":"H."},{"family":"Yilmaz","given":"L."}],"citation-key":"Cam202217","container-title":"International Journal of Simulation Modelling","DOI":"10.2507/IJSIMM21-1-583","ISSN":"17264529","issue":"1","issued":{"date-parts":[[2022]]},"page":"17-28","publisher":"DAAAM International Vienna","title":"A HYPOTHESIS-DRIVEN SIMULATION EXPERIMENTS WITH AN EXTENSION TO SED-ML","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126489619&doi=10.2507%2fIJSIMM21-1-583&partnerID=40&md5=8fad242bb6eb550512b3f4559f4d415a","volume":"21"},
{"id":"camaraBridgingGapControl2020","abstract":"Two of the main paradigms used to build adaptive software employ different types of properties to capture relevant aspects of the system's run-time behavior. On the one hand, control systems consider properties that concern static aspects like stability, as well as dynamic properties that capture the transient evolution of variables such as settling time. On the other hand, self-adaptive systems consider mostly non-functional properties that capture concerns such as performance, reliability, and cost. In general, it is not easy to reconcile these two types of properties or identify under which conditions they constitute a good fit to provide run-time guarantees. There is a need of identifying the key properties in the areas of control and self-adaptation, as well as of characterizing and mapping them to better understand how they relate and possibly complement each other. In this paper, we take a first step to tackle this problem by: (1) identifying a set of key properties in control theory, (2) illustrating the formalization of some of these properties employing temporal logic languages commonly used to engineer self-adaptive software systems, and (3) illustrating how to map key properties that characterize self-adaptive software systems into control properties, leveraging their formalization in temporal logics. We illustrate the different steps of the mapping on an exemplar case in the cloud computing domain and conclude with identifying open challenges in the area.","accessed":{"date-parts":[[2020,10,5]]},"author":[{"family":"Cámara","given":"Javier"},{"family":"Papadopoulos","given":"Alessandro V."},{"family":"Vogel","given":"Thomas"},{"family":"Weyns","given":"Danny"},{"family":"Garlan","given":"David"},{"family":"Huang","given":"Shihong"},{"family":"Tei","given":"Kenji"}],"citation-key":"camaraBridgingGapControl2020","container-title":"Proceedings of the IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","DOI":"10.1145/3387939.3391568","issued":{"date-parts":[[2020,6,29]]},"page":"78-84","source":"arXiv.org","title":"Towards Bridging the Gap between Control and Self-Adaptive System Properties","type":"article-journal","URL":"http://arxiv.org/abs/2004.11846"},
{"id":"candelUnifiedMetamodelNoSQL2021","abstract":"The Database field is undergoing significant changes. Although relational systems are still predominant, the interest in NoSQL systems is continuously increasing. In this scenario, polyglot persistence is envisioned as the database architecture to be prevalent in the future. Multi-model database tools normally use a generic or unified metamodel to represent schemas of the data model that they support. Such metamodels facilitate developing utilities, as they can be built on a common representation. Also, the number of mappings required to migrate databases from a data model to another is reduced, and integrability is favored. In this paper, we present the U-Schema unified metamodel able to represent logical schemas for the four most popular NoSQL paradigms (columnar, document, key-value, and graph) as well as relational schemas. We will formally define the mappings between U-Schema and the data model defined for each paradigm. How these mappings have been implemented and validated will be discussed, and some applications of U-Schema will be shown. To achieve flexibility to respond to data changes, most of NoSQL systems are \"schema-on-write,\" and the declaration of schemas is not required. Such an absence of schema declaration makes structural variability possible, i.e., stored data of the same entity type can have different structure. Moreover, data relationships supported by each data model are different. We will show how all these issues have been tackled in our approach. Our metamodel goes beyond the existing proposals by distinguishing entity types and relationship types, representing aggregation and reference relationships, and including the notion of structural variability. Our contributions also include developing schema extraction strategies for schemaless systems of each NoSQL data model, and tackling performance and scalability in the implementation for each store.","accessed":{"date-parts":[[2021,6,27]]},"author":[{"family":"Candel","given":"Carlos J. Fernández"},{"family":"Ruiz","given":"Diego Sevilla"},{"family":"García-Molina","given":"Jesús J."}],"citation-key":"candelUnifiedMetamodelNoSQL2021","container-title":"arXiv:2105.06494 [cs]","issued":{"date-parts":[[2021,5,17]]},"note":"00000","source":"arXiv.org","title":"A Unified Metamodel for NoSQL and Relational Databases","type":"article-journal","URL":"http://arxiv.org/abs/2105.06494"},
{"id":"canoHybridRecommenderSystems2017","abstract":"Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. This systematic literature review presents the state of the art in hybrid recommender systems of the last decade. It is the first quantitative review work completely focused in hybrid recommenders. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them. We also explore the hybridization classes each hybrid recommender belongs to, the application domains, the evaluation process and proposed future research directions. Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way. Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors. As most of the studies are evaluated by comparisons with similar methods using accuracy metrics, providing more credible and user oriented evaluations remains a typical challenge. Besides this, newer challenges were also identified such as responding to the variation of user context, evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid recommenders represent a good basis with which to respond accordingly by exploring newer opportunities such as contextualizing recommendations, involving parallel hybrid algorithms, processing larger datasets, etc.","accessed":{"date-parts":[[2018,5,14]]},"author":[{"family":"Çano","given":"Erion"},{"family":"Morisio","given":"Maurizio"}],"citation-key":"canoHybridRecommenderSystems2017","container-title":"Intelligent Data Analysis","DOI":"10.3233/IDA-163209","ISSN":"1088467X, 15714128","issue":"6","issued":{"date-parts":[[2017,11,15]]},"page":"1487-1524","source":"Crossref","title":"Hybrid recommender systems: A systematic literature review","title-short":"Hybrid recommender systems","type":"article-journal","URL":"http://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/IDA-163209","volume":"21"},
{"id":"CanYouTrust","accessed":{"date-parts":[[2021,5,14]]},"citation-key":"CanYouTrust","note":"00000","title":"Can you trust AutoML?. Is overfitting really significant, or… | by Ioannis Tsamardinos | Analytics Vidhya | Medium","type":"webpage","URL":"https://medium.com/analytics-vidhya/can-you-trust-automl-3a02332e66a0"},
{"id":"Cao2020","abstract":"How to predict the wireless network level performance such as the network capacity, the average user data rate, and the 5-tile user data rate is a million-dollar question. In the literature, some pioneering works have been proposed by exploiting either the information theoretic techniques on the physical layer (PHY) information or the Markov chain techniques on the multiple access control (MAC) layer information. However, since these mathematical model-driven approaches usually focus on a small part of the network structure, they cannot characterize the whole network performance. In this paper, we propose to utilize a data-driven machine learning approach to tackle this problem. More specifically, both PHY and MAC information is fed into a deep neural network (DNN) specifically designed for network-level performance prediction. Simulation results show that the network level performance can be accurately predicted at the cost of higher computational complexity. © 2020 IEEE.","author":[{"family":"Cao","given":"Q."},{"family":"Zeng","given":"S."},{"family":"Pun","given":"M.-O."},{"family":"Chen","given":"Y."}],"citation-key":"Cao2020","collection-title":"IEEE International Conference on Communications","DOI":"10.1109/ICC40277.2020.9149189","ISBN":"978-1-72815-089-5","ISSN":"15503607","issued":{"date-parts":[[2020]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Network-level system performance prediction using deep neural networks with cross-layer information","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089420643&doi=10.1109%2fICC40277.2020.9149189&partnerID=40&md5=1237bb970543ac93a3007056e6e635d1","volume":"2020-June"},
{"id":"Cao2021","abstract":"In this paper, the model-driven deep learning (DL) technology is used to solve the problem of either high complexity or poor performance in traditional massive multiple input multiple output (MIMO) signal detection and the model-driven detection network is proposed: JC-Net. The JC-Net structure is designed by unfolding the damped Jacobi detector and adding three trainable parameters to each layer, which are used to control the residual vector, adjust the relationship between the current layer and the previous layer, and for soft projection. Furthermore, the performance of JC-Net can be further improved by increasing the dimension of the residual vector and the JC-Net-Improved is proposed later. Simulation results show that the proposed model-driven massive MIMO detection networks can significantly improve the performance of the corresponding damped Jacobi detector and achieve superior detection performance with low complexity. © 2021 IEEE.","author":[{"family":"Cao","given":"Q."},{"family":"Li","given":"F."},{"family":"Li","given":"T."},{"family":"Ji","given":"W."},{"family":"Liang","given":"Y."}],"citation-key":"Cao2021","collection-title":"13th International Conference on Wireless Communications and Signal Processing, WCSP 2021","DOI":"10.1109/WCSP52459.2021.9613228","ISBN":"978-1-66540-785-4","issued":{"date-parts":[[2021]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Adaptive signal detection method based on model-driven for massive MIMO systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123365603&doi=10.1109%2fWCSP52459.2021.9613228&partnerID=40&md5=7ee60e29b3d49e5510ca2fcb27b348c4"},
{"id":"Capiluppi:2019:JSS:Clustering","author":[{"family":"Capiluppi","given":"Andrea"},{"family":"Di Ruscio","given":"Davide"},{"family":"Di Rocco","given":"Juri"},{"family":"Nguyen","given":"Phuong T."},{"family":"Ajienka","given":"Nemitari"}],"citation-key":"Capiluppi:2019:JSS:Clustering","container-title":"Journal of Systems and Software","issued":{"date-parts":[[2019]]},"title":"The Effects of Clustering on the Characteristics of Java Software - manuscript under revision","type":"article-journal"},
{"id":"capiluppiDetectingJavaSoftware2019","abstract":"Research on empirical software engineering has increasingly been conducted by analysing and measuring vast amounts of software systems. Hundreds, thousands and even millions of systems have been (and are) considered by researchers, and often within the same study, in order to test theories, demonstrate approaches or run prediction models. A much less investigated aspect is whether the collected metrics might be context-specific, or whether systems should be better analysed in clusters.","author":[{"family":"Capiluppi","given":"Andrea"},{"family":"Di Ruscio","given":"Davide"},{"family":"Di Rocco","given":"Juri"},{"family":"Nguyen","given":"Phuong T"},{"family":"Ajienka","given":"Nemitari"}],"citation-key":"capiluppiDetectingJavaSoftware2019","container-title":"Elsevier Information and Software Technology (IST) Journal","issued":{"date-parts":[[2019]]},"note":"00000","page":"40","source":"Zotero","title":"Detecting Java Software Similarities by using Different Clustering Techniques","type":"article-journal"},
{"id":"capiluppiDetectingJavaSoftware2020","author":[{"family":"Capiluppi","given":"Andrea"},{"family":"Di Ruscio","given":"Davide"},{"family":"Di Rocco","given":"Juri"},{"family":"Nguyen","given":"Phuong T."},{"family":"Ajienka","given":"Nemitari"}],"citation-key":"capiluppiDetectingJavaSoftware2020","container-title":"INFORMATION AND SOFTWARE TECHNOLOGY","DOI":"10.1016/j.infsof.2020.106279","issued":{"date-parts":[[2020]]},"note":"00000","title":"Detecting Java Software Similarities by using Different Clustering Techniques","type":"article-journal"},
{"id":"cardelliUnderstandingTypesData1985","abstract":"Our objective is to understand the notion of type in programming languages, present a model of typed, polymorphic programming languages that reflects recent research in type theory, and examine the relevance of recent research to the design of practical programming languages.","accessed":{"date-parts":[[2021,2,1]]},"author":[{"family":"Cardelli","given":"Luca"},{"family":"Wegner","given":"Peter"}],"citation-key":"cardelliUnderstandingTypesData1985","container-title":"ACM Computing Surveys","container-title-short":"ACM Comput. Surv.","DOI":"10.1145/6041.6042","ISSN":"0360-0300, 1557-7341","issue":"4","issued":{"date-parts":[[1985,12,10]]},"note":"02768","page":"471-523","source":"DOI.org (Crossref)","title":"On understanding types, data abstraction, and polymorphism","type":"article-journal","URL":"https://dl.acm.org/doi/10.1145/6041.6042","volume":"17"},
{"id":"carletonAIEffectWorking2020","accessed":{"date-parts":[[2020,7,9]]},"author":[{"family":"Carleton","given":"Anita D."},{"family":"Harper","given":"Erin"},{"family":"Menzies","given":"Tim"},{"family":"Xie","given":"Tao"},{"family":"Eldh","given":"Sigrid"},{"family":"Lyu","given":"Michael R."}],"citation-key":"carletonAIEffectWorking2020","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2020.2987666","ISSN":"0740-7459, 1937-4194","issue":"4","issued":{"date-parts":[[2020,7]]},"note":"00000","page":"26-35","source":"DOI.org (Crossref)","title":"The AI Effect: Working at the Intersection of AI and SE","title-short":"The AI Effect","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/9121618/","volume":"37"},
{"id":"carpenterHandbookBrainTheory1998","author":[{"family":"Carpenter","given":"Gail A."},{"family":"Grossberg","given":"Stephen"}],"citation-key":"carpenterHandbookBrainTheory1998","editor":[{"family":"Arbib","given":"Michael A."}],"event-place":"Cambridge, MA, USA","ISBN":"0-262-51102-9","issued":{"date-parts":[[1998]]},"page":"79-82","publisher":"MIT Press","publisher-place":"Cambridge, MA, USA","title":"The handbook of brain theory and neural networks","type":"chapter","URL":"http://dl.acm.org/citation.cfm?id=303568.303586"},
{"id":"carverExtractingRequirementsModeling2021","abstract":"Presents papers from the 2020 IEEE Conference on Requirements Engineering and the ACM/ IEEE 23rd International Conference on Model Driven Engineering Languages and Systems (MODELS 2020).","accessed":{"date-parts":[[2021,5,10]]},"author":[{"family":"Carver","given":"Jeffrey C."},{"family":"Abrahao","given":"Silvia"},{"family":"Penzenstadler","given":"Birgit"}],"citation-key":"carverExtractingRequirementsModeling2021","container-title":"IEEE Software","DOI":"10.1109/MS.2021.3056989","ISSN":"0740-7459","issue":"03","issued":{"date-parts":[[2021,5,1]]},"note":"00000","page":"121-124","publisher":"IEEE Computer Society","source":"www.computer.org","title":"Extracting Requirements and Modeling Information and Controlling Risk","type":"article-journal","URL":"https://www.computer.org/csdl/magazine/so/2021/03/09407295/1sVEJCtHmQo","volume":"38"},
{"id":"carverIndustryAcademiaCollaboration2018","abstract":"This article aims to encourage more industry–academia collaborations by highlighting examples of successful collaborations. Through these examples, the authors hope to help practitioners and researchers understand the breadth of options available for such interactions. This article is part of a theme issue on software engineering’s 50th anniversary.","author":[{"family":"Carver","given":"J. C."},{"family":"Prikladnicki","given":"R."}],"citation-key":"carverIndustryAcademiaCollaboration2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.3571250","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"120-124","source":"IEEE Xplore","title":"Industry–Academia Collaboration in Software Engineering","type":"article-journal","volume":"35"},
{"id":"carverRequirementsHumanValues2017","author":[{"family":"Carver","given":"Jeffrey C."},{"family":"Minku","given":"Leandro L."},{"family":"Penzenstadler","given":"Birgit"},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""}],"citation-key":"carverRequirementsHumanValues2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"13-15","source":"IEEE Computer Society","title":"Requirements, Human Values, and the Development Technology Landscape","type":"article-magazine","volume":"34"},
{"id":"carverRequirementsHumanValues2017a","accessed":{"date-parts":[[2019,8,22]]},"author":[{"family":"Carver","given":"Jeffrey C."},{"family":"Minku","given":"Leandro L."},{"family":"Penzenstadler","given":"Birgit"}],"citation-key":"carverRequirementsHumanValues2017a","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2017.6","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017,1]]},"page":"13-15","source":"DOI.org (Crossref)","title":"Requirements, Human Values, and the Development Technology Landscape","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7819412/","volume":"34"},
{"id":"Casalaro202219","abstract":"Mobile robots operate in various environments (e.g. aquatic, aerial, or terrestrial), they come in many diverse shapes and they are increasingly becoming parts of our lives. The successful engineering of mobile robotics systems demands the interdisciplinary collaboration of experts from different domains, such as mechanical and electrical engineering, artificial intelligence, and systems engineering. Research and industry have tried to tackle this heterogeneity by proposing a multitude of model-driven solutions to engineer the software of mobile robotics systems. However, there is no systematic study of the state of the art in model-driven engineering (MDE) for mobile robotics systems that could guide research or practitioners in finding model-driven solutions and tools to efficiently engineer mobile robotics systems. The paper is contributing to this direction by providing a map of software engineering research in MDE that investigates (1) which types of robots are supported by existing MDE approaches, (2) the types and characteristics of MRSs that are engineered using MDE approaches, (3) a description of how MDE approaches support the engineering of MRSs, (4) how existing MDE approaches are validated, and (5) how tools support existing MDE approaches. We also provide a replication package to assess, extend, and/or replicate the study. The results of this work and the highlighted challenges can guide researchers and practitioners from robotics and software engineering through the research landscape. © 2021, The Author(s).","author":[{"family":"Casalaro","given":"G.L."},{"family":"Cattivera","given":"G."},{"family":"Ciccozzi","given":"F."},{"family":"Malavolta","given":"I."},{"family":"Wortmann","given":"A."},{"family":"Pelliccione","given":"P."}],"citation-key":"Casalaro202219","container-title":"Software and Systems Modeling","DOI":"10.1007/s10270-021-00908-8","ISSN":"16191366","issue":"1","issued":{"date-parts":[[2022]]},"page":"19-49","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Model-driven engineering for mobile robotic systems: a systematic mapping study","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112060247&doi=10.1007%2fs10270-021-00908-8&partnerID=40&md5=5507b51b1aaa6177ace895004926cbfa","volume":"21"},
{"id":"Castaño2020495","abstract":"Monitoring of complex processes faces several challenges mainly due to the lack of relevant sensory information or insufficient elaborated decision-making strategies. These challenges motivate researchers to adopt complex data processing and analysis in order to improve the process representation. This paper presents the development and implementation of quality monitoring framework based on a model-driven approach using embedded artificial intelligence strategies. In this work, the strategies are applied to the supervision of a microfabrication process aiming at showing the great performance of the framework in a very complex system in the manufacturing sector. The procedure involves two methods for modelling a representative quality variable, such as surface roughness. Firstly, the hybrid incremental modelling strategy is applied. Secondly, a generalized fuzzy clustering c-means method is developed. Finally, a comparative study of the behavior of the two models for predicting a quality indicator, represented by surface roughness of manufactured components, is presented for specific manufacturing process. The manufactured part used in this study is a critical structural aerospace component. In addition, the validation and testing are performed at laboratory and industrial levels, demonstrating proper real-time operation for non-linear processes with relatively fast dynamics. The results of this study are very promising in terms of computational efficiency and transfer of knowledge to manufacturing industry. Copyright © 2020 Techno-Press, Ltd.","author":[{"family":"Castaño","given":"F."},{"family":"Haber","given":"R.E."},{"family":"Mohammed","given":"W.M."},{"family":"Nejman","given":"M."},{"family":"Villalonga","given":"A."},{"family":"Martinez Lastra","given":"J.L."}],"citation-key":"Castaño2020495","container-title":"Smart Structures and Systems","DOI":"10.12989/sss.2020.26.4.495","ISSN":"17381584","issue":"4","issued":{"date-parts":[[2020]]},"page":"495-506","publisher":"Techno-Press","title":"Quality monitoring of complex manufacturing systems on the basis of model driven approach","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098064771&doi=10.12989%2fsss.2020.26.4.495&partnerID=40&md5=e08b45c3ea369be4d192081e4da34c56","volume":"26"},
{"id":"castanoQualityMonitoringComplex2020a","abstract":"Monitoring of complex processes faces several challenges mainly due to the lack of relevant sensory information or insufficient elaborated decision-making strategies. These challenges motivate researchers to adopt complex data processing and analysis in order to improve the process representation. This paper presents the development and implementation of quality monitoring framework based on a model-driven approach using embedded artificial intelligence strategies. In this work, the strategies are applied to the supervision of a microfabrication process aiming at showing the great performance of the framework in a very complex system in the manufacturing sector. The procedure involves two methods for modelling a representative quality variable, such as surface roughness. Firstly, the hybrid incremental modelling strategy is applied. Secondly, a generalized fuzzy clustering c-means method is developed. Finally, a comparative study of the behavior of the two models for predicting a quality indicator, represented by surface roughness of manufactured components, is presented for specific manufacturing process. The manufactured part used in this study is a critical structural aerospace component. In addition, the validation and testing are performed at laboratory and industrial levels, demonstrating proper real-time operation for non-linear processes with relatively fast dynamics. The results of this study are very promising in terms of computational efficiency and transfer of knowledge to manufacturing industry. Copyright © 2020 Techno-Press, Ltd.","author":[{"family":"Castaño","given":"F."},{"family":"Haber","given":"R.E."},{"family":"Mohammed","given":"W.M."},{"family":"Nejman","given":"M."},{"family":"Villalonga","given":"A."},{"family":"Martinez Lastra","given":"J.L."}],"citation-key":"castanoQualityMonitoringComplex2020a","container-title":"Smart Structures and Systems","DOI":"10.12989/sss.2020.26.4.495","ISSN":"17381584","issue":"4","issued":{"date-parts":[[2020]]},"page":"495-506","publisher":"Techno-Press","title":"Quality monitoring of complex manufacturing systems on the basis of model driven approach","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098064771&doi=10.12989%2fsss.2020.26.4.495&partnerID=40&md5=e08b45c3ea369be4d192081e4da34c56","volume":"26"},
{"id":"Castells_noveltyand","author":[{"family":"Castells","given":"Pablo"},{"family":"Vargas","given":"Saúl"}],"citation-key":"Castells_noveltyand","container-title":"In proceedings of international workshop on diversity in document retrieval (DDR","page":"29-37","title":"Novelty and diversity metrics for recommender systems: Choice, discovery and relevance","type":"paper-conference"},
{"id":"CatedrasaesumuNoSQLDataEngineeringNoSQL","accessed":{"date-parts":[[2018,5,7]]},"citation-key":"CatedrasaesumuNoSQLDataEngineeringNoSQL","title":"catedrasaes-umu/NoSQLDataEngineering: NoSQL Data Engineering","type":"webpage","URL":"https://github.com/catedrasaes-umu/NoSQLDataEngineering#schema-models"},
{"id":"CatherineTamilarasi2018433","abstract":"Neuro Imaging and Artificial Intelligence (AI) are two big technology oceans. Machine learning and Deep learning are subfields of AI with numerous customized tools facilitating a statistically driven Neuro Image data analysis and accurate disease prediction. This paper suggests a Cognitive Ontology model driven by Machine and Deep learning based analysis on functional Magnetic Resonance Image (fMRI) data. © BEIESP.","author":[{"family":"Catherine Tamilarasi","given":"F."},{"family":"Shanmugam","given":"J."}],"citation-key":"CatherineTamilarasi2018433","container-title":"International Journal of Innovative Technology and Exploring Engineering","ISSN":"22783075","issue":"2","issued":{"date-parts":[[2018]]},"page":"433-435","publisher":"Blue Eyes Intelligence Engineering and Sciences Publication","title":"Artificial intelligence – Deep learning ased cognitive ontology model","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064173477&partnerID=40&md5=c6698c6ba782208ffe22a5354c29b558","volume":"8"},
{"id":"catherinetamilarasiArtificialIntelligenceDeep2018a","abstract":"Neuro Imaging and Artificial Intelligence (AI) are two big technology oceans. Machine learning and Deep learning are subfields of AI with numerous customized tools facilitating a statistically driven Neuro Image data analysis and accurate disease prediction. This paper suggests a Cognitive Ontology model driven by Machine and Deep learning based analysis on functional Magnetic Resonance Image (fMRI) data. © BEIESP.","author":[{"family":"Catherine Tamilarasi","given":"F."},{"family":"Shanmugam","given":"J."}],"citation-key":"catherinetamilarasiArtificialIntelligenceDeep2018a","container-title":"International Journal of Innovative Technology and Exploring Engineering","ISSN":"22783075","issue":"2","issued":{"date-parts":[[2018]]},"page":"433-435","publisher":"Blue Eyes Intelligence Engineering and Sciences Publication","title":"Artificial intelligence – Deep learning ased cognitive ontology model","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064173477&partnerID=40&md5=c6698c6ba782208ffe22a5354c29b558","volume":"8"},
{"id":"cedeno-mielesDataAnalysisModeling2020","abstract":"There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.","accessed":{"date-parts":[[2021,3,18]]},"author":[{"family":"Cedeno-Mieles","given":"Vanessa"},{"family":"Hu","given":"Zhihao"},{"family":"Ren","given":"Yihui"},{"family":"Deng","given":"Xinwei"},{"family":"Contractor","given":"Noshir"},{"family":"Ekanayake","given":"Saliya"},{"family":"Epstein","given":"Joshua M."},{"family":"Goode","given":"Brian J."},{"family":"Korkmaz","given":"Gizem"},{"family":"Kuhlman","given":"Chris J."},{"family":"Machi","given":"Dustin"},{"family":"Macy","given":"Michael"},{"family":"Marathe","given":"Madhav V."},{"family":"Ramakrishnan","given":"Naren"},{"family":"Saraf","given":"Parang"},{"family":"Self","given":"Nathan"}],"citation-key":"cedeno-mielesDataAnalysisModeling2020","container-title":"PLOS ONE","container-title-short":"PLoS ONE","DOI":"10.1371/journal.pone.0242453","editor":[{"family":"Cai","given":"Ning"}],"ISSN":"1932-6203","issue":"11","issued":{"date-parts":[[2020,11,24]]},"note":"00000","page":"e0242453","source":"DOI.org (Crossref)","title":"Data analysis and modeling pipelines for controlled networked social science experiments","type":"article-journal","URL":"https://dx.plos.org/10.1371/journal.pone.0242453","volume":"15"},
{"id":"celebiFAIRProtocolsWorkflows2020","abstract":"It is essential for the advancement of science that researchers share, reuse and reproduce each other’s workflows and protocols. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize the importance of making digital objects findable and reusable by others. The question of how to apply these principles not just to data but also to the workflows and protocols that consume and produce them is still under debate and poses a number of challenges. In this paper we describe a two-fold approach of simultaneously applying the FAIR principles to scientific workflows as well as the involved data. We apply and evaluate our approach on the case of the PREDICT workflow, a highly cited drug repurposing workflow. This includes FAIRification of the involved datasets, as well as applying semantic technologies to represent and store data about the detailed versions of the general protocol, of the concrete workflow instructions, and of their execution traces. We propose a semantic model to address these specific requirements and was evaluated by answering competency questions. This semantic model consists of classes and relations from a number of existing ontologies, including Workflow4ever, PROV, EDAM, and BPMN. This allowed us then to formulate and answer new kinds of competency questions. Our evaluation shows the high degree to which our FAIRified OpenPREDICT workflow now adheres to the FAIR principles and the practicality and usefulness of being able to answer our new competency questions.","accessed":{"date-parts":[[2022,2,27]]},"author":[{"family":"Celebi","given":"Remzi"},{"family":"Rebelo Moreira","given":"Joao"},{"family":"Hassan","given":"Ahmed A."},{"family":"Ayyar","given":"Sandeep"},{"family":"Ridder","given":"Lars"},{"family":"Kuhn","given":"Tobias"},{"family":"Dumontier","given":"Michel"}],"citation-key":"celebiFAIRProtocolsWorkflows2020","container-title":"PeerJ Computer Science","DOI":"10.7717/peerj-cs.281","ISSN":"2376-5992","issued":{"date-parts":[[2020,9,21]]},"note":"00005","page":"e281","source":"DOI.org (Crossref)","title":"Towards FAIR protocols and workflows: the OpenPREDICT use case","title-short":"Towards FAIR protocols and workflows","type":"article-journal","URL":"https://peerj.com/articles/cs-281","volume":"6"},
{"id":"celisseOptimalCrossvalidationDensity2014","accessed":{"date-parts":[[2021,5,13]]},"author":[{"family":"Celisse","given":"Alain"}],"citation-key":"celisseOptimalCrossvalidationDensity2014","container-title":"The Annals of Statistics","container-title-short":"Ann. Statist.","DOI":"10.1214/14-AOS1240","ISSN":"0090-5364","issue":"5","issued":{"date-parts":[[2014,10,1]]},"note":"00047","source":"DOI.org (Crossref)","title":"Optimal cross-validation in density estimation with the $L^{2}$-loss","type":"article-journal","URL":"https://projecteuclid.org/journals/annals-of-statistics/volume-42/issue-5/Optimal-cross-validation-in-density-estimation-with-the-L2-loss/10.1214/14-AOS1240.full","volume":"42"},
{"id":"Celms2020205","abstract":"A new method based on Domain Specific Language (DSL) approach to Deep Learning (DL) lifecycle data management tool support is presented: a very simple DL lifecycle data management tool, which however is usable in practice (it will be called Core tool) and a very advanced extension mechanism which in fact converts the Core tool into domain specific tool (DSL tool) building framework for DL lifecycle data management tasks. The extension mechanism will be based on the metamodel specialization approach to DSL modeling tools introduced by authors. The main idea of metamodel specialization is that we, at first, define the Universal Metamodel (UMM) for a domain and then for each use case define a Specialized Metamodel. But for use in our new domain the specialization concept will be extended: we add a functional specialization where invoking an additional custom program at appropriate points of Core tool is supported. © Springer Nature Switzerland AG 2020.","author":[{"family":"Celms","given":"E."},{"family":"Barzdins","given":"J."},{"family":"Kalnins","given":"A."},{"family":"Sprogis","given":"A."},{"family":"Grasmanis","given":"M."},{"family":"Rikacovs","given":"S."},{"family":"Barzdins","given":"P."}],"citation-key":"Celms2020205","container-title":"Communications in Computer and Information Science","DOI":"10.1007/978-3-030-57672-1_16","editor":[{"family":"Robal T., Haav H.-M.","given":"Penjam J.","suffix":"Matulevicius R."}],"ISBN":"9783030576714","ISSN":"18650929","issued":{"date-parts":[[2020]]},"page":"205-218","publisher":"Springer","title":"Towards dsl for dl lifecycle data management","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089720702&doi=10.1007%2f978-3-030-57672-1_16&partnerID=40&md5=f961b63c20618f7adbdcf65e832096a6","volume":"1243 CCIS"},
{"id":"Celms2021597","abstract":"A new approach to Deep Learning (DL) lifecycle data management tool support is presented: a very simple DL lifecycle data management tool, which however is usable in practice (it will be called Core tool) and a very advanced extension mechanism for this Core tool which in fact converts the Core tool into a DSL tool building framework for DL lifecycle data management tasks. The extension mechanism is based on the metamodel specialisation approach to Domain Specific Language (DSL) modelling tools introduced by the authors. The main idea of metamodel specialisation is that we first define the Universal Metamodel (UMM) for a domain and then for each use case in the domain define a Specialised Metamodel (SMM). The paper concludes with a detailed description of future research directions, concerned with defining a more general UMM and its usage. © 2020 University of Latvia. All rights reserved.","author":[{"family":"Celms","given":"E."},{"family":"Barzdins","given":"J."},{"family":"Kalnins","given":"A."},{"family":"Barzdins","given":"P."},{"family":"Sprogis","given":"A."},{"family":"Grasmanis","given":"M."},{"family":"Rikacovs","given":"S."}],"citation-key":"Celms2021597","container-title":"Baltic Journal of Modern Computing","DOI":"10.22364/BJMC.2020.8.4.09","ISSN":"22558942","issue":"4","issued":{"date-parts":[[2021]]},"page":"597-617","publisher":"University of Latvia","title":"DSL approach to deep learning lifecycle data management","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099175492&doi=10.22364%2fBJMC.2020.8.4.09&partnerID=40&md5=16097d2d7bfa82988d77eff7d5c53bc2","volume":"8"},
{"id":"celmsDSLApproachDeep2021a","abstract":"A new approach to Deep Learning (DL) lifecycle data management tool support is presented: a very simple DL lifecycle data management tool, which however is usable in practice (it will be called Core tool) and a very advanced extension mechanism for this Core tool which in fact converts the Core tool into a DSL tool building framework for DL lifecycle data management tasks. The extension mechanism is based on the metamodel specialisation approach to Domain Specific Language (DSL) modelling tools introduced by the authors. The main idea of metamodel specialisation is that we first define the Universal Metamodel (UMM) for a domain and then for each use case in the domain define a Specialised Metamodel (SMM). The paper concludes with a detailed description of future research directions, concerned with defining a more general UMM and its usage. © 2020 University of Latvia. All rights reserved.","author":[{"family":"Celms","given":"E."},{"family":"Barzdins","given":"J."},{"family":"Kalnins","given":"A."},{"family":"Barzdins","given":"P."},{"family":"Sprogis","given":"A."},{"family":"Grasmanis","given":"M."},{"family":"Rikacovs","given":"S."}],"citation-key":"celmsDSLApproachDeep2021a","container-title":"Baltic Journal of Modern Computing","DOI":"10.22364/BJMC.2020.8.4.09","ISSN":"22558942","issue":"4","issued":{"date-parts":[[2021]]},"page":"597-617","publisher":"University of Latvia","title":"DSL approach to deep learning lifecycle data management","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099175492&doi=10.22364%2fBJMC.2020.8.4.09&partnerID=40&md5=16097d2d7bfa82988d77eff7d5c53bc2","volume":"8"},
{"id":"CEURWorkshopProceedings2015","citation-key":"CEURWorkshopProceedings2015","issued":{"date-parts":[[2015]]},"note":"00000","publisher":"CEUR-WS","title":"CEUR Workshop Proceedings","type":"book","volume":"1406"},
{"id":"Chabanet2021","abstract":"Although digital simulations are becoming increasingly important in the industrial world owing to the transition toward Industry 4.0, as well as the development of digital twin technologies, they have become increasingly computationally intensive. Many authors have proposed the use of machine learning (ML) metamodels to alleviate this cost and take advantage of the enormous amount of data that are currently available in industry. In an industrial context, it is necessary to continuously train predictive models integrated into decision support systems to ensure the consistency of their prediction quality over time. This led the authors to investigate active learning (AL) concepts in the particular context of the sawmilling industry. In this paper, a method based on AL is proposed to combine simulation and an ML metamodel that is trained incrementally using only selected data (smart data). A case study based on the sawmilling industry and experiments are shown, the results of which prove the possible advantages of this approach. © 2021 Elsevier B.V.","author":[{"family":"Chabanet","given":"S."},{"family":"Bril El-Haouzi","given":"H."},{"family":"Thomas","given":"P."}],"citation-key":"Chabanet2021","container-title":"Computers in Industry","DOI":"10.1016/j.compind.2021.103529","ISSN":"01663615","issued":{"date-parts":[[2021]]},"publisher":"Elsevier B.V.","title":"Coupling digital simulation and machine learning metamodel through an active learning approach in Industry 4.0 context","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114443195&doi=10.1016%2fj.compind.2021.103529&partnerID=40&md5=f0387239499424d0fb5b30d0dbf19267","volume":"133"},
{"id":"Chabanet2021573","abstract":"Several sawmill simulators exist in the forest-product industry. They are able to simulate the sawing of a log to generate the set of lumbers that would be obtained by transforming a log at a sawmill. In particular, such simulators are able to use a 3D scan of the exterior shape of the logs as input for the simulation. However, it was observed that they can be computationally intensive. Therefore, several authors have proposed to use Artificial Intelligence metamodel, which, in general, can make predictions extremely fast once trained. Such models can approximate the results of a simulator using a vector of descriptive features representing a log, or, alternatively, the full 3D log scans. This paper proposes to use dissimilarity to representative log scans as features to train a Machine Learning classifier. The concept of class Medoids as representative elements of a class will be presented, and a Simlarity Discrimant Analysis was chosen as a good candidate ML classier. This classifier will be compared with two others models studied by the authors. © 2021, IFIP International Federation for Information Processing.","author":[{"family":"Chabanet","given":"S."},{"family":"Chazelle","given":"V."},{"family":"Thomas","given":"P."},{"family":"El-Haouzi","given":"H.B."}],"citation-key":"Chabanet2021573","container-title":"IFIP Advances in Information and Communication Technology","DOI":"10.1007/978-3-030-85906-0_62","editor":[{"family":"Dolgui A., Bernard A.","given":"Lemoine D.","suffix":"von Cieminski G., Romero D."}],"ISBN":"9783030859053","ISSN":"18684238","issued":{"date-parts":[[2021]]},"page":"573-581","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Dissimilarity to class medoids as features for 3D point cloud classification","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115331515&doi=10.1007%2f978-3-030-85906-0_62&partnerID=40&md5=8bc1f30e0ef217e5a6482fec7c89a34b","volume":"632 IFIP"},
{"id":"chabanetCouplingDigitalSimulation2021a","abstract":"Although digital simulations are becoming increasingly important in the industrial world owing to the transition toward Industry 4.0, as well as the development of digital twin technologies, they have become increasingly computationally intensive. Many authors have proposed the use of machine learning (ML) metamodels to alleviate this cost and take advantage of the enormous amount of data that are currently available in industry. In an industrial context, it is necessary to continuously train predictive models integrated into decision support systems to ensure the consistency of their prediction quality over time. This led the authors to investigate active learning (AL) concepts in the particular context of the sawmilling industry. In this paper, a method based on AL is proposed to combine simulation and an ML metamodel that is trained incrementally using only selected data (smart data). A case study based on the sawmilling industry and experiments are shown, the results of which prove the possible advantages of this approach. © 2021 Elsevier B.V.","author":[{"family":"Chabanet","given":"S."},{"family":"Bril El-Haouzi","given":"H."},{"family":"Thomas","given":"P."}],"citation-key":"chabanetCouplingDigitalSimulation2021a","container-title":"Computers in Industry","DOI":"10.1016/j.compind.2021.103529","ISSN":"01663615","issued":{"date-parts":[[2021]]},"publisher":"Elsevier B.V.","title":"Coupling digital simulation and machine learning metamodel through an active learning approach in Industry 4.0 context","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114443195&doi=10.1016%2fj.compind.2021.103529&partnerID=40&md5=f0387239499424d0fb5b30d0dbf19267","volume":"133"},
{"id":"chabanetDissimilarityClassMedoids2021","abstract":"Several sawmill simulators exist in the forest-product industry. They are able to simulate the sawing of a log to generate the set of lumbers that would be obtained by transforming a log at a sawmill. In particular, such simulators are able to use a 3D scan of the exterior shape of the logs as input for the simulation. However, it was observed that they can be computationally intensive. Therefore, several authors have proposed to use Artificial Intelligence metamodel, which, in general, can make predictions extremely fast once trained. Such models can approximate the results of a simulator using a vector of descriptive features representing a log, or, alternatively, the full 3D log scans. This paper proposes to use dissimilarity to representative log scans as features to train a Machine Learning classifier. The concept of class Medoids as representative elements of a class will be presented, and a Simlarity Discrimant Analysis was chosen as a good candidate ML classier. This classifier will be compared with two others models studied by the authors. © 2021, IFIP International Federation for Information Processing.","author":[{"family":"Chabanet","given":"S."},{"family":"Chazelle","given":"V."},{"family":"Thomas","given":"P."},{"family":"El-Haouzi","given":"H.B."}],"citation-key":"chabanetDissimilarityClassMedoids2021","container-title":"IFIP Advances in Information and Communication Technology","DOI":"10.1007/978-3-030-85906-0_62","editor":[{"family":"Dolgui A.","given":"Romero D.","suffix":"Bernard A., Lemoine D., von Cieminski G."}],"ISBN":"9783030859053","ISSN":"18684238","issued":{"date-parts":[[2021]]},"page":"573-581","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Dissimilarity to Class Medoids as Features for 3D Point Cloud Classification","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115331515&doi=10.1007%2f978-3-030-85906-0_62&partnerID=40&md5=8bc1f30e0ef217e5a6482fec7c89a34b","volume":"632 IFIP"},
{"id":"Chai2022","abstract":"Machine learning(ML) has widespread applications and has revolutionized many industries, but suffers from several challenges. First, sufficient high-quality training data is inevitable for producing a well-performed model, but the data is always human expensive to acquire.Second, a large amount of training data and complicated model structures lead to the inefficiency of training and inference. Third, given an ML task, one always needs to train lots of models, which are hard to manage in real applications. Fortunately, database techniques can benefit ML by addressing the above three challenges. In this paper, we review existing studies from the following three aspects along with the pipeline highly related to ML. (1) Data preparation(Pre-ML): it focuses on preparing high-quality training data that can improve the performance of the ML model, where we review data discovery, data cleaning and data labeling. (2) Model training & inference(In-ML): researchers in ML community focus on improving the model performance during training, while in this survey we mainly study how to accelerate the entire training process, also including feature selection and model selection. (3) Model management(Post-ML): in this part, we survey how to store, query, deploy and debug the models after training. Finally, we provide research challenges and future directions. IEEE","author":[{"family":"Chai","given":"C."},{"family":"Wang","given":"J."},{"family":"Luo","given":"Y."},{"family":"Niu","given":"Z."},{"family":"Li","given":"G."}],"citation-key":"Chai2022","container-title":"IEEE Transactions on Knowledge and Data Engineering","DOI":"10.1109/TKDE.2022.3148237","ISSN":"10414347","issued":{"date-parts":[[2022]]},"publisher":"IEEE Computer Society","title":"Data management for machine learning: A survey","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124237590&doi=10.1109%2fTKDE.2022.3148237&partnerID=40&md5=5ac26e4c5b8ef7475f01c672a4165ec5"},
{"id":"Challenger2014111","abstract":"The study of Multiagent Systems (MASs) focuses on those systems in which many intelligent agents interact with each other. The agents are considered to be autonomous entities which contain intelligence that serves for solving their selfish or common problems, and to achieve certain goals. However, the autonomous, responsive, and proactive natures of agents make the development of agent-based software systems more complex than other software systems. Furthermore, the design and implementation of a MAS may become even more complex and difficult to implement when considering new requirements and interactions for new agent environments like the Semantic Web. We believe that both domain-specific modeling and the use of a domain-specific modeling language (DSML) may provide the required abstraction, and hence support a more fruitful methodology for the development of MASs. In this paper, we first introduce a DSML for MASs called SEA-ML with both its syntax and semantics definitions and then show how the language and its graphical tools can be used during model-driven development of real MASs. In addition to the classical viewpoints of a MAS, the proposed DSML includes new viewpoints which specifically support the development of software agents working within the Semantic Web environment. The methodology proposed for the MAS development based on SEA-ML is also discussed including its example application on the development of an agent-based stock exchange system. © 2013 Elsevier Ltd. All rights reserved.","author":[{"family":"Challenger","given":"M."},{"family":"Demirkol","given":"S."},{"family":"Getir","given":"S."},{"family":"Mernik","given":"M."},{"family":"Kardas","given":"G."},{"family":"Kosar","given":"T."}],"citation-key":"Challenger2014111","container-title":"Engineering Applications of Artificial Intelligence","DOI":"10.1016/j.engappai.2013.11.012","ISSN":"09521976","issued":{"date-parts":[[2014]]},"page":"111-141","title":"On the use of a domain-specific modeling language in the development of multiagent systems","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84892845703&doi=10.1016%2fj.engappai.2013.11.012&partnerID=40&md5=1133325bf7d3b08606a359d5ec47450f","volume":"28"},
{"id":"Challenger2016755","abstract":"Multi-agent systems (MASs) include multiple interacting agents within an environment to provide a solution for complex systems that cannot be easily solved with individual agents or monolithic systems. However, the development of MASs is not trivial due to the various agent properties such as autonomy, responsiveness, and proactiveness, and the need for realization of the many different agent interactions. To support the development of MASs various domain-specific modeling languages (DSMLs) have been introduced that provide a declarative approach for modeling and supporting the generation of agent-based systems. To be effective, the proposed DSMLs need to meet the various stakeholder concerns and the related quality criteria for the corresponding MASs. Unfortunately, very often the evaluation of the DSML is completely missing or has been carried out in idiosyncratic approach. If the DSMLs are not well defined, then implicitly this will have an impact on the quality of the MASs. In this paper, we present an evaluation framework and systematic approach for assessing existing or newly defined DSMLs for MASs. The evaluation is specific for MAS DSMLs and targets both the language and the corresponding tools. To illustrate the evaluation approach, we first present SEA<sub>M</sub>L, which is a model-driven MAS DSML for supporting the modeling and generation of agent-based systems. The evaluation of SEA<sub>M</sub>L is based on a multi-case study research approach and provides both qualitative evaluation and quantitative analysis. We report on the lessons learned considering the adoption of the evaluation approach as well as the SEA<sub>M</sub>L for supporting the generation of agent-based systems. © 2015, Springer Science+Business Media New York.","author":[{"family":"Challenger","given":"M."},{"family":"Kardas","given":"G."},{"family":"Tekinerdogan","given":"B."}],"citation-key":"Challenger2016755","container-title":"Software Quality Journal","DOI":"10.1007/s11219-015-9291-5","ISSN":"09639314","issue":"3","issued":{"date-parts":[[2016]]},"page":"755-795","publisher":"Springer New York LLC","title":"A systematic approach to evaluating domain-specific modeling language environments for multi-agent systems","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941359634&doi=10.1007%2fs11219-015-9291-5&partnerID=40&md5=0932198a9d474a5bfc14ec75f563e229","volume":"24"},
{"id":"challengerUseDomainspecificModeling2014a","abstract":"The study of Multiagent Systems (MASs) focuses on those systems in which many intelligent agents interact with each other. The agents are considered to be autonomous entities which contain intelligence that serves for solving their selfish or common problems, and to achieve certain goals. However, the autonomous, responsive, and proactive natures of agents make the development of agent-based software systems more complex than other software systems. Furthermore, the design and implementation of a MAS may become even more complex and difficult to implement when considering new requirements and interactions for new agent environments like the Semantic Web. We believe that both domain-specific modeling and the use of a domain-specific modeling language (DSML) may provide the required abstraction, and hence support a more fruitful methodology for the development of MASs. In this paper, we first introduce a DSML for MASs called SEA-ML with both its syntax and semantics definitions and then show how the language and its graphical tools can be used during model-driven development of real MASs. In addition to the classical viewpoints of a MAS, the proposed DSML includes new viewpoints which specifically support the development of software agents working within the Semantic Web environment. The methodology proposed for the MAS development based on SEA-ML is also discussed including its example application on the development of an agent-based stock exchange system. © 2013 Elsevier Ltd. All rights reserved.","author":[{"family":"Challenger","given":"M."},{"family":"Demirkol","given":"S."},{"family":"Getir","given":"S."},{"family":"Mernik","given":"M."},{"family":"Kardas","given":"G."},{"family":"Kosar","given":"T."}],"citation-key":"challengerUseDomainspecificModeling2014a","container-title":"Engineering Applications of Artificial Intelligence","DOI":"10.1016/j.engappai.2013.11.012","ISSN":"09521976","issued":{"date-parts":[[2014]]},"page":"111-141","title":"On the use of a domain-specific modeling language in the development of multiagent systems","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84892845703&doi=10.1016%2fj.engappai.2013.11.012&partnerID=40&md5=1133325bf7d3b08606a359d5ec47450f","volume":"28"},
{"id":"Chander2021147","abstract":"As a result of continuous and extreme inclusion of the Internet, computer networks, and social life, there has been a complete transformation of how people learn and work. With the expansion of the Internet and its application to our lives, it opens an abysmal for cyber security attacks. The continuous increase in cyberattacks has given rise to Artificial Intelligence (AI) and Machine Learning (ML)-based techniques that have a vital measurement in detecting security risks, security breaches and alerts, progress triage events, and malware detection to defense issues. ML, AI is the set of statistical and mathematical forms to clarify higher non-linearity troubles of dissimilar themes such as data organization, prediction, and classification. Moreover, it is an undeniable fact that information is an attractive reasonable presence for each corporation and big business. For that reason, protecting security models driven by the real data sets logically turns out to be important. Hence, this chapter presents the role of ML and AI in cyber security, describes a variety of active ML techniques, how and where to add ML and AI models for network security, cyber security threats classification. This chapter presents commonly used ML techniques and network data sets. Finally, challenges and future works are discussed. © 2021, Springer Nature Singapore Pte Ltd.","author":[{"family":"Chander","given":"B."},{"family":"Kumaravelan","given":"G."}],"citation-key":"Chander2021147","container-title":"Lecture Notes in Networks and Systems","DOI":"10.1007/978-981-15-9317-8_6","ISSN":"23673370","issued":{"date-parts":[[2021]]},"page":"147-171","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Cyber security with AI—Part I","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096915113&doi=10.1007%2f978-981-15-9317-8_6&partnerID=40&md5=9c207678271b823147c08cd75c1d4348","volume":"163"},
{"id":"chanderCyberSecurityAI2021a","abstract":"As a result of continuous and extreme inclusion of the Internet, computer networks, and social life, there has been a complete transformation of how people learn and work. With the expansion of the Internet and its application to our lives, it opens an abysmal for cyber security attacks. The continuous increase in cyberattacks has given rise to Artificial Intelligence (AI) and Machine Learning (ML)-based techniques that have a vital measurement in detecting security risks, security breaches and alerts, progress triage events, and malware detection to defense issues. ML, AI is the set of statistical and mathematical forms to clarify higher non-linearity troubles of dissimilar themes such as data organization, prediction, and classification. Moreover, it is an undeniable fact that information is an attractive reasonable presence for each corporation and big business. For that reason, protecting security models driven by the real data sets logically turns out to be important. Hence, this chapter presents the role of ML and AI in cyber security, describes a variety of active ML techniques, how and where to add ML and AI models for network security, cyber security threats classification. This chapter presents commonly used ML techniques and network data sets. Finally, challenges and future works are discussed. © 2021, Springer Nature Singapore Pte Ltd.","author":[{"family":"Chander","given":"B."},{"family":"Kumaravelan","given":"G."}],"citation-key":"chanderCyberSecurityAI2021a","container-title":"Lecture Notes in Networks and Systems","DOI":"10.1007/978-981-15-9317-8_6","ISSN":"23673370","issued":{"date-parts":[[2021]]},"page":"147-171","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Cyber Security with AI—Part I","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096915113&doi=10.1007%2f978-981-15-9317-8_6&partnerID=40&md5=9c207678271b823147c08cd75c1d4348","volume":"163"},
{"id":"Changdar2021600","abstract":"The traditional model-driven methods are not much efficient to predict the viscosity of nanofluids accurately. This study presents a novel approach of using physics-guided deep learning technique for predicting viscosity of water-based nanofluids from large dataset containing both experimental and simulated data of spherical oxide nanoparticles Al2O3, CuO, SiO2, and TiO2. Further, this study introduces a novel methodology of combining deep learning methods and physics-based models to leverage their complementary strengths. To the best of the author’s knowledge, theory-guided deep learning prediction model was never used to predict viscosity before. The theory-guided deep neural networks (TGDNN) model is trained by minimizing the mean square error (MSE) and regularization terms using Adam optimization technique. The investigations reveal that the values of R2, RMSE, and AARD% are, respectively, 0.999868, 0.001143, and 2.198887 on experimental testing dataset. The TGDNN model learns non-linear relationship among the input variables from the training data. Additionally, the results show that the proposed method performed better than the other well-known existing theoretical and computer-aided models to predict the viscosity in wide range with high level of accuracy. © 2021","author":[{"family":"Changdar","given":"S."},{"family":"Bhaumik","given":"B."},{"family":"De","given":"S."}],"citation-key":"Changdar2021600","container-title":"Journal of Computational Design and Engineering","DOI":"10.1093/jcde/qwab001","ISSN":"22884300","issue":"2","issued":{"date-parts":[[2021]]},"page":"600-614","publisher":"Oxford University Press","title":"Physics-based smart model for prediction of viscosity of nanofluids containing nanoparticles using deep learning","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108658931&doi=10.1093%2fjcde%2fqwab001&partnerID=40&md5=8fd3041bbac6fd8c4f0cdbcaf807408a","volume":"8"},
{"id":"changdarPhysicsbasedSmartModel2021a","abstract":"The traditional model-driven methods are not much efficient to predict the viscosity of nanofluids accurately. This study presents a novel approach of using physics-guided deep learning technique for predicting viscosity of water-based nanofluids from large dataset containing both experimental and simulated data of spherical oxide nanoparticles Al2O3, CuO, SiO2, and TiO2. Further, this study introduces a novel methodology of combining deep learning methods and physics-based models to leverage their complementary strengths. To the best of the author’s knowledge, theory-guided deep learning prediction model was never used to predict viscosity before. The theory-guided deep neural networks (TGDNN) model is trained by minimizing the mean square error (MSE) and regularization terms using Adam optimization technique. The investigations reveal that the values of R2, RMSE, and AARD% are, respectively, 0.999868, 0.001143, and 2.198887 on experimental testing dataset. The TGDNN model learns non-linear relationship among the input variables from the training data. Additionally, the results show that the proposed method performed better than the other well-known existing theoretical and computer-aided models to predict the viscosity in wide range with high level of accuracy. © 2021","author":[{"family":"Changdar","given":"S."},{"family":"Bhaumik","given":"B."},{"family":"De","given":"S."}],"citation-key":"changdarPhysicsbasedSmartModel2021a","container-title":"Journal of Computational Design and Engineering","DOI":"10.1093/jcde/qwab001","ISSN":"22884300","issue":"2","issued":{"date-parts":[[2021]]},"page":"600-614","publisher":"Oxford University Press","title":"Physics-based smart model for prediction of viscosity of nanofluids containing nanoparticles using deep learning","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108658931&doi=10.1093%2fjcde%2fqwab001&partnerID=40&md5=8fd3041bbac6fd8c4f0cdbcaf807408a","volume":"8"},
{"id":"Chatzimparmpas20211","abstract":"Stacked generalization (also called stacking) is an ensemble method in machine learning that uses a metamodel to combine the predictive results of heterogeneous base models arranged in at least one layer. K-fold cross-validation is employed at the various stages of training in this method. Nonetheless, another validation strategy is to try out several splits of data leading to different train and test sets for the base models and then use only the latter to train the metamodel - this is known as blending. In this work, we present a modification of an existing visual analytics system, entitled StackGenVis, that now supports the process of composing robust and diverse ensembles of models with both aforementioned methods. We have built multiple ensembles using our system with the two respective methods, and we tested the performance with six small- to large-sized data sets. The results indicate that stacking is significantly more powerful than blending based on three performance metrics. However, the training times of the base models and the final ensembles are lower and more stable during various train/test splits in blending rather than stacking. © 2021 IEEE.","author":[{"family":"Chatzimparmpas","given":"A."},{"family":"Martins","given":"R.M."},{"family":"Kucher","given":"K."},{"family":"Kerren","given":"A."}],"citation-key":"Chatzimparmpas20211","collection-title":"Proceedings - 2021 23rd International Conference on Control Systems and Computer Science Technologies, CSCS 2021","DOI":"10.1109/CSCS52396.2021.00008","ISBN":"978-1-66543-939-8","issued":{"date-parts":[[2021]]},"page":"1-8","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Empirical study: Visual analytics for comparing stacking to blending ensemble learning","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112033363&doi=10.1109%2fCSCS52396.2021.00008&partnerID=40&md5=2d1172ee9dd9ac0a184a4285b48fd421"},
{"id":"Chatzimparmpas20211547","abstract":"In machine learning (ML), ensemble methods-such as bagging, boosting, and stacking-are widely-established approaches that regularly achieve top-notch predictive performance. Stacking (also called 'stacked generalization') is an ensemble method that combines heterogeneous base models, arranged in at least one layer, and then employs another metamodel to summarize the predictions of those models. Although it may be a highly-effective approach for increasing the predictive performance of ML, generating a stack of models from scratch can be a cumbersome trial-and-error process. This challenge stems from the enormous space of available solutions, with different sets of data instances and features that could be used for training, several algorithms to choose from, and instantiations of these algorithms using diverse parameters (i.e., models) that perform differently according to various metrics. In this work, we present a knowledge generation model, which supports ensemble learning with the use of visualization, and a visual analytics system for stacked generalization. Our system, StackGenVis, assists users in dynamically adapting performance metrics, managing data instances, selecting the most important features for a given data set, choosing a set of top-performant and diverse algorithms, and measuring the predictive performance. In consequence, our proposed tool helps users to decide between distinct models and to reduce the complexity of the resulting stack by removing overpromising and underperforming models. The applicability and effectiveness of StackGenVis are demonstrated with two use cases: a real-world healthcare data set and a collection of data related to sentiment/stance detection in texts. Finally, the tool has been evaluated through interviews with three ML experts. © 1995-2012 IEEE.","author":[{"family":"Chatzimparmpas","given":"A."},{"family":"Martins","given":"R.M."},{"family":"Kucher","given":"K."},{"family":"Kerren","given":"A."}],"citation-key":"Chatzimparmpas20211547","container-title":"IEEE Transactions on Visualization and Computer Graphics","DOI":"10.1109/TVCG.2020.3030352","ISSN":"10772626","issue":"2","issued":{"date-parts":[[2021]]},"page":"1547-1557","publisher":"IEEE Computer Society","title":"StackGenVis: Alignment of data, algorithms, and models for stacking ensemble learning using performance metrics","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099566430&doi=10.1109%2fTVCG.2020.3030352&partnerID=40&md5=dad346723136c4d98941e872fa49455d","volume":"27"},
{"id":"chatzimparmpasEmpiricalStudyVisual2021a","abstract":"Stacked generalization (also called stacking) is an ensemble method in machine learning that uses a metamodel to combine the predictive results of heterogeneous base models arranged in at least one layer. K-fold cross-validation is employed at the various stages of training in this method. Nonetheless, another validation strategy is to try out several splits of data leading to different train and test sets for the base models and then use only the latter to train the metamodel - this is known as blending. In this work, we present a modification of an existing visual analytics system, entitled StackGenVis, that now supports the process of composing robust and diverse ensembles of models with both aforementioned methods. We have built multiple ensembles using our system with the two respective methods, and we tested the performance with six small- to large-sized data sets. The results indicate that stacking is significantly more powerful than blending based on three performance metrics. However, the training times of the base models and the final ensembles are lower and more stable during various train/test splits in blending rather than stacking. © 2021 IEEE.","author":[{"family":"Chatzimparmpas","given":"A."},{"family":"Martins","given":"R.M."},{"family":"Kucher","given":"K."},{"family":"Kerren","given":"A."}],"citation-key":"chatzimparmpasEmpiricalStudyVisual2021a","container-title":"Proceedings - 2021 23rd International Conference on Control Systems and Computer Science Technologies, CSCS 2021","DOI":"10.1109/CSCS52396.2021.00008","ISBN":"978-1-66543-939-8","issued":{"date-parts":[[2021]]},"page":"1-8","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Empirical Study: Visual Analytics for Comparing Stacking to Blending Ensemble Learning","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112033363&doi=10.1109%2fCSCS52396.2021.00008&partnerID=40&md5=2d1172ee9dd9ac0a184a4285b48fd421"},
{"id":"chatzimparmpasStackGenVisAlignmentData2021a","abstract":"In machine learning (ML), ensemble methods-such as bagging, boosting, and stacking-are widely-established approaches that regularly achieve top-notch predictive performance. Stacking (also called 'stacked generalization') is an ensemble method that combines heterogeneous base models, arranged in at least one layer, and then employs another metamodel to summarize the predictions of those models. Although it may be a highly-effective approach for increasing the predictive performance of ML, generating a stack of models from scratch can be a cumbersome trial-and-error process. This challenge stems from the enormous space of available solutions, with different sets of data instances and features that could be used for training, several algorithms to choose from, and instantiations of these algorithms using diverse parameters (i.e., models) that perform differently according to various metrics. In this work, we present a knowledge generation model, which supports ensemble learning with the use of visualization, and a visual analytics system for stacked generalization. Our system, StackGenVis, assists users in dynamically adapting performance metrics, managing data instances, selecting the most important features for a given data set, choosing a set of top-performant and diverse algorithms, and measuring the predictive performance. In consequence, our proposed tool helps users to decide between distinct models and to reduce the complexity of the resulting stack by removing overpromising and underperforming models. The applicability and effectiveness of StackGenVis are demonstrated with two use cases: a real-world healthcare data set and a collection of data related to sentiment/stance detection in texts. Finally, the tool has been evaluated through interviews with three ML experts. © 1995-2012 IEEE.","author":[{"family":"Chatzimparmpas","given":"A."},{"family":"Martins","given":"R.M."},{"family":"Kucher","given":"K."},{"family":"Kerren","given":"A."}],"citation-key":"chatzimparmpasStackGenVisAlignmentData2021a","container-title":"IEEE Transactions on Visualization and Computer Graphics","DOI":"10.1109/TVCG.2020.3030352","ISSN":"10772626","issue":"2","issued":{"date-parts":[[2021]]},"page":"1547-1557","publisher":"IEEE Computer Society","title":"StackGenVis: Alignment of data, algorithms, and models for stacking ensemble learning using performance metrics","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099566430&doi=10.1109%2fTVCG.2020.3030352&partnerID=40&md5=dad346723136c4d98941e872fa49455d","volume":"27"},
{"id":"chaudronProceedings40thInternational2018","citation-key":"chaudronProceedings40thInternational2018","DOI":"10.1145/3180155","editor":[{"family":"Chaudron","given":"Michel"},{"family":"Crnkovic","given":"Ivica"},{"family":"Chechik","given":"Marsha"},{"family":"Harman","given":"Mark"}],"ISBN":"978-1-4503-5638-1","issued":{"date-parts":[[2018]]},"publisher":"ACM","title":"Proceedings of the 40th international conference on software engineering, ICSE 2018, gothenburg, sweden, may 27 - june 03, 2018","type":"book","URL":"https://doi.org/10.1145/3180155"},
{"id":"Chen:2005:CCF:2154509.2154540","author":[{"family":"Chen","given":"Annie"}],"citation-key":"Chen:2005:CCF:2154509.2154540","collection-title":"LoCA'05","container-title":"Proceedings of the first international conference on location- and context-awareness","event-place":"Berlin, Heidelberg","ISBN":"3-540-25896-5 978-3-540-25896-4","issued":{"date-parts":[[2005]]},"page":"244-253","publisher":"Springer-Verlag","publisher-place":"Berlin, Heidelberg","title":"Context-aware collaborative filtering system: Predicting the user's preference in the ubiquitous computing environment","type":"paper-conference","URL":"http://dx.doi.org/10.1007/11426646_23"},
{"id":"chengSoftwareEngineeringSelfadaptive2009","call-number":"QA76.76.S375 S64 2009","citation-key":"chengSoftwareEngineeringSelfadaptive2009","collection-number":"5525","collection-title":"Lecture notes in computer science","editor":[{"family":"Cheng","given":"Betty H. C."}],"event-place":"Berlin ; New York","ISBN":"978-3-642-02160-2","issued":{"date-parts":[[2009]]},"note":"OCLC: ocn401153787","number-of-pages":"260","publisher":"Springer","publisher-place":"Berlin ; New York","source":"Library of Congress ISBN","title":"Software engineering for self-adaptive systems","type":"book"},
{"id":"chenouardAutomaticallyDiscoveringHidden2009","author":[{"family":"Chenouard","given":"Raphaël"},{"family":"Jouault","given":"Frédéric"}],"citation-key":"chenouardAutomaticallyDiscoveringHidden2009","container-title":"Model Driven Engineering Languages and Systems","DOI":"10.1007/978-3-642-04425-0_8","issued":{"date-parts":[[2009]]},"page":"92–106","title":"Automatically Discovering Hidden Transformation Chaining Constraints","type":"article-journal","volume":"5795"},
{"id":"chenSimAppFrameworkDetecting2015","accessed":{"date-parts":[[2017,9,25]]},"author":[{"family":"Chen","given":"Ning"},{"family":"Hoi","given":"Steven C.H."},{"family":"Li","given":"Shaohua"},{"family":"Xiao","given":"Xiaokui"}],"citation-key":"chenSimAppFrameworkDetecting2015","DOI":"10.1145/2684822.2685305","event-place":"Shanghai, China","ISBN":"978-1-4503-3317-7","issued":{"date-parts":[[2015]]},"page":"305-314","publisher":"ACM Press","publisher-place":"Shanghai, China","source":"CrossRef","title":"SimApp: A Framework for Detecting Similar Mobile Applications by Online Kernel Learning","title-short":"SimApp","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?doid=2684822.2685305"},
{"id":"Chowdhury2017","abstract":"Wearable sensors are revolutionizing the health monitoring and medical diagnostics arena. Algorithms and software platforms that can convert the sensor data streams into useful/actionable knowledge are central to this emerging domain, with machine learning and signal processing tools dominating this space. While serving important ends, these tools are not designed to provide functional relationships between vital signs and measures of physical activity. This paper investigates the application of the metamodeling paradigm to health data to unearth important relationships between vital signs and physical activity. To this end, we leverage neural networks and a recently developed metamodeling framework that automatically selects and trains the metamodel that best represents the data set. A publicly available data set is used that provides the ECG data and the IMU data from three sensors (ankle/arm/chest) for ten volunteers, each performing various activities over one-minute time periods. We consider three activities, namely running, climbing stairs, and the baseline resting activity. For the following three extracted ECG features - heart rate, QRS time, and QR ratio in each heartbeat period - models with median error of ¡25% are obtained. Fourier amplitude sensitivity testing, facilitated by the metamodels, provides further important insights into the impact of the different physical activity parameters on the ECG features, and the variation across the ten volunteers. © 2017 ASME.","author":[{"family":"Chowdhury","given":"S."},{"family":"Mehmani","given":"A."}],"citation-key":"Chowdhury2017","collection-title":"Proceedings of the ASME Design Engineering Technical Conference","DOI":"10.1115/DETC2017-68385","ISBN":"978-0-7918-5815-8","issued":{"date-parts":[[2017]]},"publisher":"American Society of Mechanical Engineers (ASME)","title":"Optimal metamodeling to interpret activity-based health sensor data","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034637861&doi=10.1115%2fDETC2017-68385&partnerID=40&md5=5fc8b734bb4f1828ece8575c5d6993ca","volume":"3"},
{"id":"chowdhuryOptimalMetamodelingInterpret2017a","abstract":"Wearable sensors are revolutionizing the health monitoring and medical diagnostics arena. Algorithms and software platforms that can convert the sensor data streams into useful/actionable knowledge are central to this emerging domain, with machine learning and signal processing tools dominating this space. While serving important ends, these tools are not designed to provide functional relationships between vital signs and measures of physical activity. This paper investigates the application of the metamodeling paradigm to health data to unearth important relationships between vital signs and physical activity. To this end, we leverage neural networks and a recently developed metamodeling framework that automatically selects and trains the metamodel that best represents the data set. A publicly available data set is used that provides the ECG data and the IMU data from three sensors (ankle/arm/chest) for ten volunteers, each performing various activities over one-minute time periods. We consider three activities, namely running, climbing stairs, and the baseline resting activity. For the following three extracted ECG features - heart rate, QRS time, and QR ratio in each heartbeat period - models with median error of <25% are obtained. Fourier amplitude sensitivity testing, facilitated by the metamodels, provides further important insights into the impact of the different physical activity parameters on the ECG features, and the variation across the ten volunteers. © 2017 ASME.","author":[{"family":"Chowdhury","given":"S."},{"family":"Mehmani","given":"A."}],"citation-key":"chowdhuryOptimalMetamodelingInterpret2017a","container-title":"Proceedings of the ASME Design Engineering Technical Conference","DOI":"10.1115/DETC2017-68385","ISBN":"978-0-7918-5815-8","issued":{"date-parts":[[2017]]},"publisher":"American Society of Mechanical Engineers (ASME)","title":"Optimal metamodeling to interpret activity-based health sensor data","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034637861&doi=10.1115%2fDETC2017-68385&partnerID=40&md5=5fc8b734bb4f1828ece8575c5d6993ca","volume":"3"},
{"id":"chuiInternetThings2010","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Chui","given":"Michael"},{"family":"Löffler","given":"Markus"},{"family":"Roberts","given":"Roger"}],"citation-key":"chuiInternetThings2010","container-title":"McKinsey Quarterly","issue":"2010","issued":{"date-parts":[[2010]]},"page":"1–9","source":"Google Scholar","title":"The internet of things","type":"article-journal","URL":"https://realyze.in/downloads/TheInternetofThings.pdf","volume":"2"},
{"id":"cicchettiAutomatingCoevolutionModelDriven2008","author":[{"family":"Cicchetti","given":"A"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Eramo","given":"R"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"cicchettiAutomatingCoevolutionModelDriven2008","container-title":"12th International IEEE Enterprise Distributed Object Computing Conference, EDOC 2008","DOI":"10.1109/EDOC.2008.44","issued":{"date-parts":[[2008]]},"note":"00000","page":"222–231","publisher":"IEEE Computer Society","title":"Automating Co-evolution in Model-Driven Engineering","type":"paper-conference"},
{"id":"cicchettiModelDrivenApproach2009","author":[{"family":"Cicchetti","given":"Antonio"},{"family":"Ruscio","given":"Davide Di"},{"family":"Pelliccione","given":"Patrizio"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Zacchiroli","given":"Stefano"}],"citation-key":"cicchettiModelDrivenApproach2009","collection-title":"Communications in Computer and Information Science","container-title":"Evaluation of Novel Approaches to Software Engineering - 3rd and 4th International Conferences, ENASE 2008/2009, Funchal, Madeira, Portugal, May 4-7, 2008 / Milan, Italy, May 9-10, 2009. Revised Selected Papers","DOI":"10.1007/978-3-642-14819-4_19","editor":[{"family":"Maciaszek","given":"Leszek A."},{"family":"González-Pérez","given":"César"},{"family":"Jablonski","given":"Stefan"}],"issued":{"date-parts":[[2009]]},"page":"262–276","title":"A Model Driven Approach to Upgrade Package-Based Software Systems","type":"paper-conference","URL":"https://doi.org/10.1007/978-3-642-14819-4_19","volume":"69"},
{"id":"cicchettiModelDrivenApproach2009a","author":[{"family":"Cicchetti","given":"Antonio"},{"family":"Ruscio","given":"Davide Di"},{"family":"Pelliccione","given":"Patrizio"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Zacchiroli","given":"Stefano"}],"citation-key":"cicchettiModelDrivenApproach2009a","collection-title":"Communications in Computer and Information Science","container-title":"Evaluation of Novel Approaches to Software Engineering - 3rd and 4th International Conferences, ENASE 2008/2009, Funchal, Madeira, Portugal, May 4-7, 2008 / Milan, Italy, May 9-10, 2009. Revised Selected Papers","DOI":"10.1007/978-3-642-14819-4_19","editor":[{"family":"Maciaszek","given":"Leszek A."},{"family":"González-Pérez","given":"César"},{"family":"Jablonski","given":"Stefan"}],"issued":{"date-parts":[[2009]]},"page":"262–276","title":"A Model Driven Approach to Upgrade Package-Based Software Systems","type":"paper-conference","URL":"https://doi.org/10.1007/978-3-642-14819-4_19","volume":"69"},
{"id":"Ciccozzi20131459","abstract":"Ever increasing complexity of modern software systems demands new powerful development mechanisms. Model-driven engineering (MDE) can ease the development process through problem abstraction and automated code generation from models. In order for MDE solutions to be trusted, such generation should preserve the system's properties defined at modelling level, both functional and extra-functional, all the way down to the target code. The outcome of our research is an approach that aids the preservation of system's properties in MDE of embedded systems. More specifically, we provide generation of full source code from design models defined using the CHESS-ML, monitoring of selected extra-functional properties at code level, and back-propagation of observed values to design models. The approach is validated against industrial case-studies in the telecommunications applicative domain. © 2013 IEEE.","author":[{"family":"Ciccozzi","given":"F."}],"citation-key":"Ciccozzi20131459","collection-title":"Proceedings - International Conference on Software Engineering","DOI":"10.1109/ICSE.2013.6606744","ISBN":"978-1-4673-3076-3","ISSN":"02705257","issued":{"date-parts":[[2013]]},"page":"1459-1461","title":"From models to code and back: Correct-by-construction code from UML and ALF","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886429863&doi=10.1109%2fICSE.2013.6606744&partnerID=40&md5=5228c5a0bdc2ecbb1952352e851744ac"},
{"id":"Ciccozzi201581","abstract":"Modern software systems are becoming more and more complex thus demanding for new powerful development mechanisms. Model-driven engineering has been recognised as a promising paradigm for the development of complex systems especially for its capability of abstracting the problem through models and then manipulating them to reach the implementation. In this work we provide a solution for the problem of automatically generating target code from models expressed in CHESS-ML, a UML profile that leverages the Action Language for Foundational UML. The goal is to produce code that does not require any manual intervention after its automatic generation to be executed on the target platform. Focus is on the generation of complex systems targeting both single and multi process deployment configurations as well as different execution platforms. © 2015 IEEE.","author":[{"family":"Ciccozzi","given":"F."},{"family":"Cicchetti","given":"A."},{"family":"Sjodin","given":"M."}],"citation-key":"Ciccozzi201581","collection-title":"Proceedings - 12th International Conference on Information Technology: New Generations, ITNG 2015","DOI":"10.1109/ITNG.2015.19","editor":[{"family":"Latifi S., Arai K.","given":"Carneiro G.","suffix":"Debnath N., Vieira Dias L.A., Hashemi R., Minamoto T., Reddy Y., Saleem K., Shen F., Thuemmler C."}],"ISBN":"978-1-4799-8827-3","issued":{"date-parts":[[2015]]},"page":"81-88","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"On the generation of full-fledged code from UML profiles and ALF for complex systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84936804939&doi=10.1109%2fITNG.2015.19&partnerID=40&md5=044729dc741edda7c058cef26c589291"},
{"id":"ciccozziBodyKnowledgeModelbased2018","abstract":"Model-based Software Engineering (MBSE) is now accepted as a Software Engineering (SE) discipline and is being taught as part of more general SE curricula. However, an agreed core of concepts, mechanisms and practices — which constitutes the Body of Knowledge of a discipline — has not been captured anywhere, and is only partially covered by the SE Body of Knowledge (SWEBOK). With the goals of characterizing the contents of the MBSE discipline, promoting a consistent view of it worldwide, clarifying its scope with regard to other SE disciplines, and defining a foundation for a curriculum development on MBSE, this paper provides a proposal for an extension of the contents of SWEBOK with the set of fundamental concepts, terms and mechanisms that should constitute the MBSE Body of Knowledge.","accessed":{"date-parts":[[2021,7,19]]},"author":[{"family":"Ciccozzi","given":"Federico"},{"family":"Famelis","given":"Michalis"},{"family":"Kappel","given":"Gerti"},{"family":"Lambers","given":"Leen"},{"family":"Mosser","given":"Sebastien"},{"family":"Paige","given":"Richard F."},{"family":"Pierantonio","given":"Alfonso"},{"family":"Rensink","given":"Arend"},{"family":"Salay","given":"Rick"},{"family":"Taentzer","given":"Gabi"},{"family":"Vallecillo","given":"Antonio"},{"family":"Wimmer","given":"Manuel"}],"citation-key":"ciccozziBodyKnowledgeModelbased2018","container-title":"Proceedings of the 21st ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings","DOI":"10.1145/3270112.3270121","event":"MODELS '18: ACM/IEEE 21th International Conference on Model Driven Engineering Languages and Systems","event-place":"Copenhagen Denmark","ISBN":"978-1-4503-5965-8","issued":{"date-parts":[[2018,10,14]]},"note":"00007","page":"82-89","publisher":"ACM","publisher-place":"Copenhagen Denmark","source":"DOI.org (Crossref)","title":"Towards a body of knowledge for model-based software engineering","type":"paper-conference","URL":"https://dl.acm.org/doi/10.1145/3270112.3270121"},
{"id":"ciccozziGenerationFullFledgedCode2015a","abstract":"Modern software systems are becoming more and more complex thus demanding for new powerful development mechanisms. Model-driven engineering has been recognised as a promising paradigm for the development of complex systems especially for its capability of abstracting the problem through models and then manipulating them to reach the implementation. In this work we provide a solution for the problem of automatically generating target code from models expressed in CHESS-ML, a UML profile that leverages the Action Language for Foundational UML. The goal is to produce code that does not require any manual intervention after its automatic generation to be executed on the target platform. Focus is on the generation of complex systems targeting both single and multi process deployment configurations as well as different execution platforms. © 2015 IEEE.","author":[{"family":"Ciccozzi","given":"F."},{"family":"Cicchetti","given":"A."},{"family":"Sjodin","given":"M."}],"citation-key":"ciccozziGenerationFullFledgedCode2015a","container-title":"Proceedings - 12th International Conference on Information Technology: New Generations, ITNG 2015","DOI":"10.1109/ITNG.2015.19","editor":[{"family":"Latifi S.","given":"Thuemmler C.","suffix":"Arai K., Carneiro G., Debnath N., Vieira Dias L.A., Hashemi R., Minamoto T., Reddy Y., Saleem K., Shen F."}],"ISBN":"978-1-4799-8827-3","issued":{"date-parts":[[2015]]},"page":"81-88","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"On the Generation of Full-Fledged Code from UML Profiles and ALF for Complex Systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84936804939&doi=10.1109%2fITNG.2015.19&partnerID=40&md5=044729dc741edda7c058cef26c589291"},
{"id":"ciccozziProceedings1stInternational2018","citation-key":"ciccozziProceedings1stInternational2018","editor":[{"family":"Ciccozzi","given":"Federico"},{"family":"Ruscio","given":"Davide Di"},{"family":"Malavolta","given":"Ivano"},{"family":"Pelliccione","given":"Patrizio"},{"family":"Wortmann","given":"Andreas"}],"ISBN":"978-1-4503-5760-9","issued":{"date-parts":[[2018]]},"note":"00000","publisher":"ACM","title":"Proceedings of the 1st International Workshop on Robotics Software Engineering, RoSE@ICSE 2018, Gothenburg, Sweden, May 28, 2018","type":"book","URL":"http://dl.acm.org/citation.cfm?id=3196558"},
{"id":"cioffiArtificialIntelligenceMachine2020","abstract":"Adaptation and innovation are extremely important to the manufacturing industry. This development should lead to sustainable manufacturing using new technologies. To promote sustainability, smart production requires global perspectives of smart production application technology. In this regard, thanks to intensive research efforts in the field of artificial intelligence (AI), a number of AI-based techniques, such as machine learning, have already been established in the industry to achieve sustainable manufacturing. Thus, the aim of the present research was to analyze, systematically, the scientific literature relating to the application of artificial intelligence and machine learning (ML) in industry. In fact, with the introduction of the Industry 4.0, artificial intelligence and machine learning are considered the driving force of smart factory revolution. The purpose of this review was to classify the literature, including publication year, authors, scientific sector, country, institution, and keywords. The analysis was done using the Web of Science and SCOPUS database. Furthermore, UCINET and NVivo 12 software were used to complete them. A literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now. Eighty-two articles were reviewed and classified. A first interesting result is the greater number of works published by the USA and the increasing interest after the birth of Industry 4.0.","accessed":{"date-parts":[[2020,12,18]]},"author":[{"family":"Cioffi","given":"Raffaele"},{"family":"Travaglioni","given":"Marta"},{"family":"Piscitelli","given":"Giuseppina"},{"family":"Petrillo","given":"Antonella"},{"family":"De Felice","given":"Fabio"}],"citation-key":"cioffiArtificialIntelligenceMachine2020","container-title":"Sustainability","container-title-short":"Sustainability","DOI":"10.3390/su12020492","ISSN":"2071-1050","issue":"2","issued":{"date-parts":[[2020,1,8]]},"note":"00021","page":"492","source":"DOI.org (Crossref)","title":"Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions","title-short":"Artificial Intelligence and Machine Learning Applications in Smart Production","type":"article-journal","URL":"https://www.mdpi.com/2071-1050/12/2/492","volume":"12"},
{"id":"clarisoBackwardsReasoningModel2015","accessed":{"date-parts":[[2015,9,15]]},"author":[{"family":"Clarisó","given":"Robert"},{"family":"Cabot","given":"Jordi"},{"family":"Guerra","given":"Esther"},{"family":"Lara","given":"Juan","non-dropping-particle":"de"}],"citation-key":"clarisoBackwardsReasoningModel2015","container-title":"Journal of Systems and Software","DOI":"10.1016/j.jss.2015.08.017","ISSN":"01641212","issued":{"date-parts":[[2015,8]]},"source":"CrossRef","title":"Backwards reasoning for model transformations: Method and applications","title-short":"Backwards reasoning for model transformations","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0164121215001788"},
{"id":"ClosedloopSystemClosedloop","accessed":{"date-parts":[[2016,11,1]]},"citation-key":"ClosedloopSystemClosedloop","title":"Closed-loop System and Closed-loop Control Systems","type":"webpage","URL":"http://www.electronics-tutorials.ws/systems/closed-loop-system.html"},
{"id":"ClusteringIntroduction","accessed":{"date-parts":[[2015,4,23]]},"citation-key":"ClusteringIntroduction","title":"Clustering - Introduction","type":"webpage","URL":"http://home.deib.polimi.it/matteucc/Clustering/tutorial_html/"},
{"id":"ClusteringValidationTechniques","accessed":{"date-parts":[[2015,5,7]]},"citation-key":"ClusteringValidationTechniques","title":"On Clustering Validation Techniques - Springer","type":"webpage","URL":"http://link.springer.com/article/10.1023/A:1012801612483"},
{"id":"ClusterSummarizationDense","accessed":{"date-parts":[[2015,11,2]]},"citation-key":"ClusterSummarizationDense","title":"Cluster Summarization with Dense Region Detection - Springer","type":"webpage","URL":"http://link.springer.com/chapter/10.1007/978-3-319-25840-9_5?wt_mc=alerts.TOCseries"},
{"id":"Cohen:1995:FER:3091622.3091637","author":[{"family":"Cohen","given":"William W."}],"citation-key":"Cohen:1995:FER:3091622.3091637","collection-title":"ICML'95","container-title":"Proceedings of the twelfth international conference on international conference on machine learning","event-place":"San Francisco, CA, USA","ISBN":"1-55860-377-8","issued":{"date-parts":[[1995]]},"page":"115-123","publisher":"Morgan Kaufmann Publishers Inc.","publisher-place":"San Francisco, CA, USA","title":"Fast effective rule induction","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=3091622.3091637"},
{"id":"cohenFourPillarsResearch2021","accessed":{"date-parts":[[2021,1,17]]},"author":[{"family":"Cohen","given":"Jeremy"},{"family":"Katz","given":"Daniel S."},{"family":"Barker","given":"Michelle"},{"family":"Chue Hong","given":"Neil"},{"family":"Haines","given":"Robert"},{"family":"Jay","given":"Caroline"}],"citation-key":"cohenFourPillarsResearch2021","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2020.2973362","ISSN":"0740-7459, 1937-4194","issue":"1","issued":{"date-parts":[[2021,1]]},"note":"00000","page":"97-105","source":"DOI.org (Crossref)","title":"The Four Pillars of Research Software Engineering","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/8994167/","volume":"38"},
{"id":"colinaInternetThingsIoT","author":[{"family":"Colina","given":"Antonio Liñán"},{"family":"Vives","given":"Alvaro"},{"family":"Bagula","given":"Antoine"},{"family":"Zennaro","given":"Marco"},{"family":"Pietrosemoli","given":"Ermanno"}],"citation-key":"colinaInternetThingsIoT","page":"227","source":"Zotero","title":"Internet of Things (IoT) in 5 days","type":"article-journal"},
{"id":"collobertNaturalLanguageProcessing2011","author":[{"family":"Collobert","given":"Ronan"},{"family":"Weston","given":"Jason"},{"family":"Bottou","given":"Léon"},{"family":"Karlen","given":"Michael"},{"family":"Kavukcuoglu","given":"Koray"},{"family":"Kuksa","given":"Pavel"}],"citation-key":"collobertNaturalLanguageProcessing2011","container-title":"Journal of Machine Learning Research","container-title-short":"J. Mach. Learn. Res.","ISSN":"1532-4435","issued":{"date-parts":[[2011,11]]},"page":"2493-2537","title":"Natural language processing (almost) from scratch","type":"article-journal","URL":"http://dl.acm.org/citation.cfm?id=1953048.2078186","volume":"12"},
{"id":"colnagoInformingDesignPersonalized2020","accessed":{"date-parts":[[2021,3,31]]},"author":[{"family":"Colnago","given":"Jessica"},{"family":"Feng","given":"Yuanyuan"},{"family":"Palanivel","given":"Tharangini"},{"family":"Pearman","given":"Sarah"},{"family":"Ung","given":"Megan"},{"family":"Acquisti","given":"Alessandro"},{"family":"Cranor","given":"Lorrie Faith"},{"family":"Sadeh","given":"Norman"}],"citation-key":"colnagoInformingDesignPersonalized2020","container-title":"Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems","DOI":"10.1145/3313831.3376389","event":"CHI '20: CHI Conference on Human Factors in Computing Systems","event-place":"Honolulu HI USA","ISBN":"978-1-4503-6708-0","issued":{"date-parts":[[2020,4,21]]},"note":"00014","page":"1-13","publisher":"ACM","publisher-place":"Honolulu HI USA","source":"DOI.org (Crossref)","title":"Informing the Design of a Personalized Privacy Assistant for the Internet of Things","type":"paper-conference","URL":"https://dl.acm.org/doi/10.1145/3313831.3376389"},
{"id":"combemaleFormallyDefiningIterating2012","accessed":{"date-parts":[[2021,4,7]]},"author":[{"family":"Combemale","given":"Benoit"},{"family":"Thirioux","given":"Xavier"},{"family":"Baudry","given":"Benoit"}],"citation-key":"combemaleFormallyDefiningIterating2012","collection-editor":[{"family":"Hutchison","given":"David"},{"family":"Kanade","given":"Takeo"},{"family":"Kittler","given":"Josef"},{"family":"Kleinberg","given":"Jon M."},{"family":"Mattern","given":"Friedemann"},{"family":"Mitchell","given":"John C."},{"family":"Naor","given":"Moni"},{"family":"Nierstrasz","given":"Oscar"},{"family":"Pandu Rangan","given":"C."},{"family":"Steffen","given":"Bernhard"},{"family":"Sudan","given":"Madhu"},{"family":"Terzopoulos","given":"Demetri"},{"family":"Tygar","given":"Doug"},{"family":"Vardi","given":"Moshe Y."},{"family":"Weikum","given":"Gerhard"}],"container-title":"Model Driven Engineering Languages and Systems","DOI":"10.1007/978-3-642-33666-9_9","editor":[{"family":"France","given":"Robert B."},{"family":"Kazmeier","given":"Jürgen"},{"family":"Breu","given":"Ruth"},{"family":"Atkinson","given":"Colin"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-642-33665-2 978-3-642-33666-9","issued":{"date-parts":[[2012]]},"page":"119-133","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","source":"DOI.org (Crossref)","title":"Formally Defining and Iterating Infinite Models","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-642-33666-9_9","volume":"7590"},
{"id":"combemaleGlobalizingDomainSpecificLanguages2015","accessed":{"date-parts":[[2016,1,26]]},"citation-key":"combemaleGlobalizingDomainSpecificLanguages2015","collection-title":"Lecture Notes in Computer Science","editor":[{"family":"Combemale","given":"Benoit"},{"family":"Cheng","given":"Betty H.C."},{"family":"France","given":"Robert B."},{"family":"Jézéquel","given":"Jean-Marc"},{"family":"Rumpe","given":"Bernhard"}],"event-place":"Cham","ISBN":"978-3-319-26171-3 978-3-319-26172-0","issued":{"date-parts":[[2015]]},"publisher":"Springer International Publishing","publisher-place":"Cham","source":"CrossRef","title":"Globalizing Domain-Specific Languages","type":"book","URL":"http://link.springer.com/10.1007/978-3-319-26172-0","volume":"9400"},
{"id":"combemaleGlobalizingModelingLanguages2014","accessed":{"date-parts":[[2015,9,23]]},"author":[{"family":"Combemale","given":"Benoit"},{"family":"Deantoni","given":"Julien"},{"family":"Baudry","given":"Benoit"},{"family":"France","given":"Robert B."},{"family":"Jézéquel","given":"Jean-Marc"},{"family":"Gray","given":"Jordan"}],"citation-key":"combemaleGlobalizingModelingLanguages2014","container-title":"Computer","issue":"6","issued":{"date-parts":[[2014]]},"page":"68–71","source":"Google Scholar","title":"Globalizing modeling languages","type":"article-journal","URL":"http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6839148","volume":"47"},
{"id":"combemaleLanguageOrientedModeling2015","accessed":{"date-parts":[[2016,1,26]]},"author":[{"family":"Combemale","given":"Benoit"}],"citation-key":"combemaleLanguageOrientedModeling2015","issued":{"date-parts":[[2015]]},"publisher":"Université de Rennes 1","source":"Google Scholar","title":"Towards Language-Oriented Modeling","type":"thesis","URL":"https://hal.inria.fr/tel-01238817/"},
{"id":"combemaleSLEBOKSoftwareLanguage2018","abstract":"This report documents the program and the outcomes of Dagstuhl Seminar 17342 \"SLEBOK: The Software Language Engineering Body of Knowledge\". Software Language Engineering (SLE) has emerged as a scientific field, with a strong motivation to connect and integrate different research disciplines such as compiler construction, reverse engineering, software transformation, model-driven engineering, and ontologies. This seminar supported further integration of said communities with the clear objective of assembling a Body of Knowledge on SLE (SLEBoK). The BoK features artifacts, definitions, methods, techniques, best practices, open challenges, case studies, teaching material, and other components that will afterwards help students, researchers, teachers, and practitioners to learn from, to better leverage, to better contribute to, and to better disseminate the intellectual contributions and practical tools and techniques coming from the SLE field.","accessed":{"date-parts":[[2021,7,19]]},"author":[{"family":"Combemale","given":"Benoît"},{"family":"Lämmel","given":"Ralf"},{"family":"Van Wyk","given":"Eric"}],"citation-key":"combemaleSLEBOKSoftwareLanguage2018","DOI":"10.4230/DAGREP.7.8.45","issued":{"date-parts":[[2018]]},"medium":"application/pdf","note":"00006","page":"10 pages","publisher":"Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany","source":"DOI.org (Datacite)","title":"SLEBOK: The Software Language Engineering Body of Knowledge (Dagstuhl Seminar 17342)","title-short":"SLEBOK","type":"article-journal","URL":"http://drops.dagstuhl.de/opus/volltexte/2018/8429/"},
{"id":"CommunicationsACMApril","citation-key":"CommunicationsACMApril","page":"140","source":"Zotero","title":"Communications of the ACM - April 2022","type":"article-journal"},
{"id":"CommunicationsACMApril2022","accessed":{"date-parts":[[2022,4,26]]},"citation-key":"CommunicationsACMApril2022","DOI":"10.1145/3501714","edition":"1","event-place":"New York, NY, USA","ISBN":"978-1-4503-9586-1","issued":{"date-parts":[[2022,2,28]]},"publisher":"ACM","publisher-place":"New York, NY, USA","source":"DOI.org (Crossref)","title":"Communications of the ACM - April 2022","title-short":"Probabilistic and Causal Inference","type":"book","URL":"https://dl.acm.org/doi/book/10.1145/3501714"},
{"id":"CommunicationsACMFebruary","citation-key":"CommunicationsACMFebruary","note":"00000","page":"124","source":"Zotero","title":"Communications of the ACM - February 2022","type":"article-journal"},
{"id":"CommunicationsACMJuly","citation-key":"CommunicationsACMJuly","note":"00000","page":"116","source":"Zotero","title":"Communications of the ACM - July 2020","type":"article-journal"},
{"id":"CommunicationsACMJulya","citation-key":"CommunicationsACMJulya","note":"00000","page":"116","source":"Zotero","title":"Communications of the ACM - July 2021","type":"article-journal"},
{"id":"CommunicationsACMJune","citation-key":"CommunicationsACMJune","note":"00000","page":"100","source":"Zotero","title":"Communications of the ACM - June 2020","type":"article-journal"},
{"id":"CommunicationsACMJunea","citation-key":"CommunicationsACMJunea","note":"00000","page":"124","source":"Zotero","title":"Communications of the ACM - June 2021","type":"article-journal"},
{"id":"CommunicationsACMMay","citation-key":"CommunicationsACMMay","note":"00000","page":"116","source":"Zotero","title":"Communications of the ACM - May 2020","type":"article-journal"},
{"id":"CommunicationsACMOctober","citation-key":"CommunicationsACMOctober","note":"00000","page":"112","source":"Zotero","title":"Communications of the ACM - October 2020","type":"article-journal"},
{"id":"ComparisonModelMigration","accessed":{"date-parts":[[2015,3,24]]},"citation-key":"ComparisonModelMigration","title":"A Comparison of Model Migration Tools - Springer","type":"webpage","URL":"http://link.springer.com/chapter/10.1007/978-3-642-16145-2_5"},
{"id":"ComplexNatureMDE","accessed":{"date-parts":[[2015,12,8]]},"citation-key":"ComplexNatureMDE","title":"On the complex nature of MDE evolution and its impact on changeability - Online First - Springer","type":"webpage","URL":"http://link.springer.com/article/10.1007%2Fs10270-015-0464-2"},
{"id":"conf:iscis:MadylovaO09","author":[{"family":"Madylova","given":"Ainura"},{"family":"Ögüducü","given":"Sule Gündüz"}],"citation-key":"conf:iscis:MadylovaO09","container-title":"ISCIS","issued":{"literal":"2009-12-30, 2009"},"page":"129-134","publisher":"IEEE","title":"A taxonomy based semantic similarity of documents using the cosine measure.","type":"paper-conference","URL":"http://dblp.uni-trier.de/db/conf/iscis/iscis2009.html#MadylovaO09"},
{"id":"conf/stids/OlssonPSP11","author":[{"family":"Olsson","given":"Catherine"},{"family":"Petrov","given":"Plamen"},{"family":"Sherman","given":"Jeff"},{"family":"Perez-Lopez","given":"Andrew"}],"citation-key":"conf/stids/OlssonPSP11","collection-title":"CEUR workshop proceedings","container-title":"STIDS","editor":[{"family":"Costa","given":"Paulo Cesar G.","non-dropping-particle":"da"},{"family":"Laskey","given":"Kathryn B."}],"issued":{"date-parts":[[2011]]},"page":"52-59","publisher":"CEUR-WS.org","title":"Finding and explaining similarities in linked data.","type":"paper-conference","URL":"http://dblp.uni-trier.de/db/conf/stids/stids2011.html#OlssonPSP11","volume":"808"},
{"id":"connollyWhyComputingBelongs2020a","abstract":"Fully appreciating the overarching scope of CS requires weaving more than ethics into the reigning curricula.","accessed":{"date-parts":[[2022,1,29]]},"author":[{"family":"Connolly","given":"Randy"}],"citation-key":"connollyWhyComputingBelongs2020a","container-title":"Communications of the ACM","container-title-short":"Commun. ACM","DOI":"10.1145/3383444","ISSN":"0001-0782, 1557-7317","issue":"8","issued":{"date-parts":[[2020,7,22]]},"note":"00000","page":"54-59","source":"DOI.org (Crossref)","title":"Why computing belongs within the social sciences","type":"article-journal","URL":"https://dl.acm.org/doi/10.1145/3383444","volume":"63"},
{"id":"conselInternetThingsChallenge","author":[{"family":"Consel","given":"Charles"},{"family":"Kabáč","given":"Milan"}],"citation-key":"conselInternetThingsChallenge","page":"3","source":"Zotero","title":"Internet of Things: A Challenge for Software Engineering","type":"article-journal"},
{"id":"ConstructingAutonomousSystems","accessed":{"date-parts":[[2016,8,24]]},"citation-key":"ConstructingAutonomousSystems","title":"Constructing Autonomous Systems","type":"webpage","URL":"http://aosgrp.com/featured-research/autonomy_and_agents/autonomous_systems/constructing_autonomous_sys.html"},
{"id":"ContinuousDeliveryMap","accessed":{"date-parts":[[2018,4,30]]},"citation-key":"ContinuousDeliveryMap","title":"Continuous Delivery Map | Continuous Delivery Map","type":"webpage","URL":"https://assessment-tools.ca.com/tools/continuous-delivery-tools/en?embed"},
{"id":"ControlSystemsFeedback","accessed":{"date-parts":[[2016,11,1]]},"citation-key":"ControlSystemsFeedback","title":"Control Systems/Feedback Loops - Wikibooks, open books for an open world","type":"webpage","URL":"https://en.wikibooks.org/wiki/Control_Systems/Feedback_Loops"},
{"id":"ControlTheory101","accessed":{"date-parts":[[2016,9,20]]},"citation-key":"ControlTheory101","title":"Control Theory 101 for Beginners | Nuvation","type":"webpage","URL":"http://www.nuvation.com/blog/electronic-design-services/control-theory-101-beginners"},
{"id":"ControlTheory1012013","abstract":"While not as ubiquitous as electric power or microelectronics, control theory is applied everywhere in our daily lives but it is rarely noticed.","accessed":{"date-parts":[[2016,9,20]]},"citation-key":"ControlTheory1012013","container-title":"Nuvation","issued":{"date-parts":[[2013,9,24]]},"title":"Control Theory 101 for Beginners","type":"webpage","URL":"http://www.nuvation.com/blog/electronic-design-services/control-theory-101-beginners"},
{"id":"corbelliniPersistingBigdataNoSQL2017","abstract":"The growing popularity of massively accessed Web applications that store and analyze large amounts of data, being Facebook, Twitter and Google Search some prominent examples of such applications, have posed new requirements that greatly challenge traditional RDBMS. In response to this reality, a new way of creating and manipulating data stores, known as NoSQL databases, has arisen. This paper reviews implementations of NoSQL databases in order to provide an understanding of current tools and their uses. First, NoSQL databases are compared with traditional RDBMS and important concepts are explained. Only databases allowing to persist data and distribute them along different computing nodes are within the scope of this review. Moreover, NoSQL databases are divided into different types: Key-Value, Wide-Column, Document-oriented and Graphoriented. In each case, a comparison of available databases is carried out based on their most important features.","accessed":{"date-parts":[[2018,5,8]]},"author":[{"family":"Corbellini","given":"Alejandro"},{"family":"Mateos","given":"Cristian"},{"family":"Zunino","given":"Alejandro"},{"family":"Godoy","given":"Daniela"},{"family":"Schiaffino","given":"Silvia"}],"citation-key":"corbelliniPersistingBigdataNoSQL2017","container-title":"Information Systems","DOI":"10.1016/j.is.2016.07.009","ISSN":"03064379","issued":{"date-parts":[[2017,1]]},"page":"1-23","source":"Crossref","title":"Persisting big-data: The NoSQL landscape","title-short":"Persisting big-data","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0306437916303210","volume":"63"},
{"id":"Cornia2017309","abstract":"Image and video captioning are important tasks in visual data analytics, as they concern the capability of describing visual content in natural language. They are the pillars of query answering systems, improve indexing and search and allow a natural form of human-machine interaction. Even though promising deep learning strategies are becoming popular, the heterogeneity of large image archives makes this task still far from being solved. In this paper we explore how visual saliency prediction can support image captioning. Recently, some forms of unsupervised machine attention mechanisms have been spreading, but the role of human attention prediction has never been examined extensively for captioning. We propose a machine attention model driven by saliency prediction to provide captions in images, which can be exploited for many services on cloud and on multimedia data. Experimental evaluations are conducted on the SALICON dataset, which provides groundtruths for both saliency and captioning, and on the large Microsoft COCO dataset, the most widely used for image captioning. © 2017 IEEE.","author":[{"family":"Cornia","given":"M."},{"family":"Baraldi","given":"L."},{"family":"Serra","given":"G."},{"family":"Cucchiara","given":"R."}],"citation-key":"Cornia2017309","collection-title":"2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017","DOI":"10.1109/ICMEW.2017.8026277","ISBN":"978-1-5386-0560-8","issued":{"date-parts":[[2017]]},"page":"309-314","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Visual saliency for image captioning in new multimedia services","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031674363&doi=10.1109%2fICMEW.2017.8026277&partnerID=40&md5=35158d92d3054b2086f4f209a46b99e9"},
{"id":"corniaVisualSaliencyImage2017a","abstract":"Image and video captioning are important tasks in visual data analytics, as they concern the capability of describing visual content in natural language. They are the pillars of query answering systems, improve indexing and search and allow a natural form of human-machine interaction. Even though promising deep learning strategies are becoming popular, the heterogeneity of large image archives makes this task still far from being solved. In this paper we explore how visual saliency prediction can support image captioning. Recently, some forms of unsupervised machine attention mechanisms have been spreading, but the role of human attention prediction has never been examined extensively for captioning. We propose a machine attention model driven by saliency prediction to provide captions in images, which can be exploited for many services on cloud and on multimedia data. Experimental evaluations are conducted on the SALICON dataset, which provides groundtruths for both saliency and captioning, and on the large Microsoft COCO dataset, the most widely used for image captioning. © 2017 IEEE.","author":[{"family":"Cornia","given":"M."},{"family":"Baraldi","given":"L."},{"family":"Serra","given":"G."},{"family":"Cucchiara","given":"R."}],"citation-key":"corniaVisualSaliencyImage2017a","container-title":"2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017","DOI":"10.1109/ICMEW.2017.8026277","ISBN":"978-1-5386-0560-8","issued":{"date-parts":[[2017]]},"page":"309-314","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Visual saliency for image captioning in new multimedia services","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031674363&doi=10.1109%2fICMEW.2017.8026277&partnerID=40&md5=35158d92d3054b2086f4f209a46b99e9"},
{"id":"correaCoupledEvolutionMetamodels2013","author":[{"family":"Correa","given":"Chessman"},{"family":"Toacy","given":"Oliveira"},{"family":"Claudia","given":"Werner"}],"citation-key":"correaCoupledEvolutionMetamodels2013","issued":{"date-parts":[[2013]]},"title":"Towards Coupled Evolution of Metamodels, Models, Graph-Based Transformations and Traceability Links","type":"article-journal"},
{"id":"cosentinoSystematicMappingStudy2017","accessed":{"date-parts":[[2017,6,20]]},"author":[{"family":"Cosentino","given":"Valerio"},{"family":"Canovas Izquierdo","given":"Javier L."},{"family":"Cabot","given":"Jordi"}],"citation-key":"cosentinoSystematicMappingStudy2017","container-title":"IEEE Access","DOI":"10.1109/ACCESS.2017.2682323","ISSN":"2169-3536","issued":{"date-parts":[[2017]]},"page":"7173-7192","source":"CrossRef","title":"A Systematic Mapping Study of Software Development With GitHub","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7887704/","volume":"5"},
{"id":"cosmoSoftwareHeritageWhy","abstract":"Software is now a key component present in all aspects of our society. Its preservation has attracted growing attention over the past years within the digital preservation community. We claim that source code—the only representation of software that contains human readable knowledge—is a precious digital object that needs special handling: it must be a first class citizen in the preservation landscape and we need to take action immediately, given the increasingly more frequent incidents that result in permanent losses of source code collections. In this paper we present Software Heritage, an ambitious initiative to collect, preserve, and share the entire corpus of publicly accessible software source code. We discuss the archival goals of the project, its use cases and role as a participant in the broader digital preservation ecosystem, and detail its key design decisions. We also report on the project road map and the current status of the Software Heritage archive that, as of early 2017, has collected more than 3 billion unique source code files and 700 million commits coming from more than 50 million software development projects. ACM Reference Format: Roberto Di Cosmo and Stefano Zacchiroli. 2017. Software Heritage: Why and How to Preserve Software Source Code. In Proceedings of 14th International Conference on Digital Preservation (iPRES2017). ACM, New York, NY, USA, 10 pages.","author":[{"family":"Cosmo","given":"Roberto Di"},{"family":"Zacchiroli","given":"Stefano"}],"citation-key":"cosmoSoftwareHeritageWhy","page":"10","source":"Zotero","title":"Software Heritage: Why and How to Preserve Software Source Code","type":"article-journal"},
{"id":"costaModelingIoTApplications2016","accessed":{"date-parts":[[2021,1,8]]},"author":[{"family":"Costa","given":"Bruno"},{"family":"Pires","given":"Paulo F."},{"family":"Delicato","given":"Flavia C."}],"citation-key":"costaModelingIoTApplications2016","container-title":"2016 42th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","DOI":"10.1109/SEAA.2016.19","event":"2016 42th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","event-place":"Limassol, Cyprus","ISBN":"978-1-5090-2820-7","issued":{"date-parts":[[2016,8]]},"note":"00011","page":"157-164","publisher":"IEEE","publisher-place":"Limassol, Cyprus","source":"DOI.org (Crossref)","title":"Modeling IoT Applications with SysML4IoT","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/7592792/"},
{"id":"Coutinho2014AnalysisOD","author":[{"family":"Coutinho","given":"Ana Emília Victor Barbosa"},{"family":"Cartaxo","given":"Emanuela Gadelha"},{"family":"Lima Machado","given":"Patrícia Duarte","non-dropping-particle":"de"}],"citation-key":"Coutinho2014AnalysisOD","container-title":"Software Quality Journal","issued":{"date-parts":[[2014]]},"page":"407-445","title":"Analysis of distance functions for similarity-based test suite reduction in the context of model-based testing","type":"article-journal","volume":"24"},
{"id":"Covington:2016:DNN:2959100.2959190","author":[{"family":"Covington","given":"Paul"},{"family":"Adams","given":"Jay"},{"family":"Sargin","given":"Emre"}],"citation-key":"Covington:2016:DNN:2959100.2959190","collection-title":"RecSys '16","container-title":"Proceedings of the 10th ACM conference on recommender systems","event-place":"New York, NY, USA","ISBN":"978-1-4503-4035-9","issued":{"date-parts":[[2016]]},"page":"191-198","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Deep neural networks for YouTube recommendations","type":"paper-conference","URL":"http://doi.acm.org/10.1145/2959100.2959190"},
{"id":"coyleEthicalConcernsUnmanned","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Coyle","given":"Eric Joe"}],"citation-key":"coyleEthicalConcernsUnmanned","container-title":"age","page":"1","source":"Google Scholar","title":"Ethical Concerns of Unmanned and Autonomous Systems in Engineering Programs","type":"article-journal","URL":"https://www.asee.org/file_server/papers/attachment/file/0004/3811/ASEE-2014-UNMANNED-ETHICS-final.pdf","volume":"24"},
{"id":"Cremonesi:2008:EMC:1468165.1468327","author":[{"family":"Cremonesi","given":"Paolo"},{"family":"Turrin","given":"Roberto"},{"family":"Lentini","given":"Eugenio"},{"family":"Matteucci","given":"Matteo"}],"citation-key":"Cremonesi:2008:EMC:1468165.1468327","collection-title":"AXMEDIS '08","container-title":"Proceedings of the 2008 international conference on automated solutions for cross media content and multi-channel distribution","event-place":"Washington, DC, USA","ISBN":"978-0-7695-3406-0","issued":{"date-parts":[[2008]]},"page":"224-231","publisher":"IEEE Computer Society","publisher-place":"Washington, DC, USA","title":"An evaluation methodology for collaborative recommender systems","type":"paper-conference","URL":"https://doi.org/10.1109/AXMEDIS.2008.13"},
{"id":"cremonesiPerformanceRecommenderAlgorithms2010","author":[{"family":"Cremonesi","given":"Paolo"},{"family":"Koren","given":"Yehuda"},{"family":"Turrin","given":"Roberto"}],"citation-key":"cremonesiPerformanceRecommenderAlgorithms2010","collection-title":"RecSys '10","container-title":"Proceedings of the fourth ACM conference on recommender systems","event-place":"New York, NY, USA","ISBN":"978-1-60558-906-0","issued":{"date-parts":[[2010]]},"page":"39-46","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Performance of recommender algorithms on top-n recommendation tasks","type":"paper-conference","URL":"http://doi.acm.org/10.1145/1864708.1864721"},
{"id":"criadoEnablingReuseStored","accessed":{"date-parts":[[2015,5,26]]},"author":[{"family":"Criado","given":"Javier"},{"family":"Martınez","given":"Salvador"},{"family":"Iribarne","given":"Luis"},{"family":"Cabot","given":"Jordi"}],"citation-key":"criadoEnablingReuseStored","source":"Google Scholar","title":"Enabling the reuse of stored model transformations through annotations","type":"article-journal","URL":"http://modeling-languages.com/wp-content/uploads/2015/04/icmt2015.pdf"},
{"id":"CROSSMETERQuestionsBegel","accessed":{"date-parts":[[2016,1,22]]},"citation-key":"CROSSMETERQuestionsBegel","title":"CROSSMETER - Questions from Begel/Zimmermann's ICSE 2014 paper - Google Docs","type":"webpage","URL":"https://docs.google.com/document/d/1jyZJE4xIUsRLHqMqsGjpZDGt9RBGmZAZLh5S_ueTsQQ/edit"},
{"id":"CROSSREC-DATA","author":[{"family":"Di Rocco","given":"Juri"},{"family":"Nguyen","given":"Phuong T."},{"family":"Di Ruscio","given":"Davide"}],"citation-key":"CROSSREC-DATA","issued":{"date-parts":[[2018]]},"title":"CrossRec tool and evaluation data","type":"article-journal"},
{"id":"Crussell2015AnDarwinSD","author":[{"family":"Crussell","given":"Jonathan"},{"family":"Gibler","given":"Clint"},{"family":"Chen","given":"Hao"}],"citation-key":"Crussell2015AnDarwinSD","container-title":"IEEE Transactions on Mobile Computing","issued":{"date-parts":[[2015]]},"page":"2007-2019","title":"AnDarwin: Scalable detection of android application clones based on semantics","type":"article-journal","volume":"14"},
{"id":"crussellAndarwinScalableDetection2013","accessed":{"date-parts":[[2017,9,25]]},"author":[{"family":"Crussell","given":"Jonathan"},{"family":"Gibler","given":"Clint"},{"family":"Chen","given":"Hao"}],"citation-key":"crussellAndarwinScalableDetection2013","container-title":"European Symposium on Research in Computer Security","issued":{"date-parts":[[2013]]},"page":"182–199","publisher":"Springer","source":"Google Scholar","title":"Andarwin: Scalable detection of semantically similar android applications","title-short":"Andarwin","type":"paper-conference","URL":"http://link.springer.com/chapter/10.1007/978-3-642-40203-6_11"},
{"id":"CSGSSISEAI","accessed":{"date-parts":[[2021,5,7]]},"citation-key":"CSGSSISEAI","note":"00000","title":"CS@GSSI - SE-AI Course 2021","type":"webpage","URL":"https://sites.google.com/gssi.it/csgssi/ph-d-program/se-ai-course-2021"},
{"id":"cuadradoModelFindingEMF2020","abstract":"The EMF framework is the main meta-modelling framework used nowadays. It has a rich ecosystem of plug-ins and tools built with and for it, including the option of enriching meta-models with OCL constraints. However, the EMF ecosystem lacks usable model finding approaches. Given a meta-model, a model finder automatically searches for models that satisfy a given set of formulas (e.g., OCL constraints). This feature can be used for a number of purposes, including model verification and model synthesis. In this paper, we present an approach to support model finding in the EMF ecosystem that is designed to realize several scenarios including model consistency, example generation, partial solution completion and scrolling. Moreover, it allows several OCL variants to be plugged-in via an intermediate representation. This approach has been realized in a tool called EFinder. We have assessed the usability of the approach by implementing three advanced application scenarios and evaluated its verification capabilities by analyzing OCL constraints from an external OCL dataset containing about 300 valid EMF/OCL specifications. Our model finder is able to process about 65% of these EMF/OCL models.","accessed":{"date-parts":[[2021,4,29]]},"author":[{"family":"Cuadrado","given":"Jesús Sánchez"},{"family":"Gogolla","given":"Martin"}],"citation-key":"cuadradoModelFindingEMF2020","container-title":"The Journal of Object Technology","container-title-short":"JOT","DOI":"10.5381/jot.2020.19.2.a10","ISSN":"1660-1769","issue":"2","issued":{"date-parts":[[2020]]},"note":"00001","page":"10:1","source":"DOI.org (Crossref)","title":"Model Finding in the EMF Ecosystem.","type":"article-journal","URL":"http://www.jot.fm/contents/issue_2020_02/article10.html","volume":"19"},
{"id":"cuadradoVerifiedCatalogueOCL2019","abstract":"OCL is widely used by model-driven engineering tools with different purposes like writing integrity constraints for meta-models, as a navigation language in model transformation languages or to define transformation specifications. Another scenario is the automatic generation of OCL code by a repair system. These generated expressions tend to be complex and unreadable due to the nature of the generative process. However, to be useful this code should be simple and resemble manually written code as much as possible when a developer must manually maintain it. There exists refactorings approaches for manually written OCL code, but there is no tool targeted to the optimisation of OCL expressions which have been automatically synthesised. Moreover, there is no available catalogue of OCL refactorings which can be integrated seamlessly into a tool. In this work, we contribute a set of refactorings intended to optimise OCL expressions, notably covering cases likely to arise in generated OCL code. We also contribute the implementation of these refactorings, built as a generic transformation catalogue using bentō, a transformation reuse tool for ATL. This makes it possible to specialise the catalogue for any OCL variant based on Ecore. Moreover, we propose a method to verify the correctness of the implemented catalogue based on translation validation and model finding. We describe the design and implementation of the catalogue and evaluate it by optimising a large amount of OCL expressions and proving the correctness of each optimisation execution. We also derive working implementations of the catalogue for ATL, EMF/OCL and SimpleOCL made available in a tool called BeautyOCL.","accessed":{"date-parts":[[2020,1,13]]},"author":[{"family":"Cuadrado","given":"Jesús Sánchez"}],"citation-key":"cuadradoVerifiedCatalogueOCL2019","container-title":"Software & Systems Modeling","container-title-short":"Softw Syst Model","DOI":"10.1007/s10270-019-00740-1","ISSN":"1619-1374","issued":{"date-parts":[[2019,7,2]]},"source":"Springer Link","title":"A verified catalogue of OCL optimisations","type":"article-journal","URL":"https://doi.org/10.1007/s10270-019-00740-1"},
{"id":"Cui2021788","abstract":"In this article, we investigate jointly sparse signal recovery and jointly sparse support recovery in Multiple Measurement Vector (MMV) models for complex signals, which arise in many applications in communications and signal processing. Recent key applications include channel estimation and device activity detection in MIMO-based grant-free random access which is proposed to support massive machine-type communications (mMTC) for Internet of Things (IoT). Utilizing techniques in compressive sensing, optimization and deep learning, we propose two model-driven approaches, based on the standard auto-encoder structure for real numbers. One is to jointly design the common measurement matrix and jointly sparse signal recovery method, and the other aims to jointly design the common measurement matrix and jointly sparse support recovery method. The proposed model-driven approaches can effectively utilize features of sparsity patterns in designing common measurement matrices and adjusting model-driven decoders, and can greatly benefit from the underlying state-of-the-art recovery methods with theoretical guarantee. Hence, the obtained common measurement matrices and recovery methods can significantly outperform the underlying advanced recovery methods. We conduct extensive numerical results on channel estimation and device activity detection in MIMO-based grant-free random access. The numerical results show that the proposed approaches provide pilot sequences and channel estimation or device activity detection methods which can achieve higher estimation or detection accuracy with shorter computation time than existing ones. Furthermore, the numerical results explain how such gains are achieved via the proposed approaches. © 1983-2012 IEEE.","author":[{"family":"Cui","given":"Y."},{"family":"Li","given":"S."},{"family":"Zhang","given":"W."}],"citation-key":"Cui2021788","container-title":"IEEE Journal on Selected Areas in Communications","DOI":"10.1109/JSAC.2020.3018802","ISSN":"07338716","issue":"3","issued":{"date-parts":[[2021]]},"page":"788-803","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Jointly sparse signal recovery and support recovery via deep learning with applications in MIMO-Based grant-free random access","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090201428&doi=10.1109%2fJSAC.2020.3018802&partnerID=40&md5=fc284ac11b403e82e6b18cbd4516bccb","volume":"39"},
{"id":"cusumanoSelfdrivingVehicleTechnology2020","accessed":{"date-parts":[[2021,1,8]]},"author":[{"family":"Cusumano","given":"Michael A."}],"citation-key":"cusumanoSelfdrivingVehicleTechnology2020","container-title":"Communications of the ACM","container-title-short":"Commun. ACM","DOI":"10.1145/3417074","ISSN":"0001-0782, 1557-7317","issue":"10","issued":{"date-parts":[[2020,9,23]]},"note":"00000","page":"20-22","source":"DOI.org (Crossref)","title":"Self-driving vehicle technology: progress and promises","title-short":"Self-driving vehicle technology","type":"article-journal","URL":"https://dl.acm.org/doi/10.1145/3417074","volume":"63"},
{"id":"CyberPhysicalSystemsConcept","accessed":{"date-parts":[[2015,10,9]]},"citation-key":"CyberPhysicalSystemsConcept","title":"Cyber-Physical Systems - a Concept Map","type":"webpage","URL":"http://cyberphysicalsystems.org/"},
{"id":"dagenaisMovingNewSoftware2010","author":[{"family":"Dagenais","given":"Barthélémy"},{"family":"Ossher","given":"Harold"},{"family":"Bellamy","given":"Rachel K. E."},{"family":"Robillard","given":"Martin P."},{"family":"Vries","given":"Jacqueline P.","non-dropping-particle":"de"}],"citation-key":"dagenaisMovingNewSoftware2010","collection-title":"ICSE '10","container-title":"Proceedings of the 32Nd ACM/IEEE international conference on software engineering - volume 1","event-place":"New York, NY, USA","ISBN":"978-1-60558-719-6","issued":{"date-parts":[[2010]]},"page":"275-284","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Moving into a new software project landscape","type":"paper-conference","URL":"http://doi.acm.org/10.1145/1806799.1806842"},
{"id":"dalpiazNaturalLanguageProcessing2018","abstract":"As part of the growing interest in natural language processing for requirements engineering (RE), RE researchers, computational linguists, and industry practitioners met at the First Workshop on Natural Language Processing for Requirements Engineering (NLP4RE 18). This article summarizes the workshop and presents an overview of the discussion held on the field’s future. This article is part of a theme issue on software engineering’s 50th anniversary.","author":[{"family":"Dalpiaz","given":"F."},{"family":"Ferrari","given":"A."},{"family":"Franch","given":"X."},{"family":"Palomares","given":"C."}],"citation-key":"dalpiazNaturalLanguageProcessing2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.3571242","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"115-119","source":"IEEE Xplore","title":"Natural Language Processing for Requirements Engineering: The Best Is Yet to Come","title-short":"Natural Language Processing for Requirements Engineering","type":"article-journal","volume":"35"},
{"id":"Damasceno2021285","abstract":"Sharing research artifacts is known to help people to build upon existing knowledge, adopt novel contributions in practice, and increase the chances of papers receiving attention. In Model-Driven Engineering (MDE), openly providing research artifacts plays a key role, even more so as the community targets a broader use of AI techniques, which can only become feasible if large open datasets and confidence measures for their quality are available. However, the current lack of common discipline-specific guidelines for research data sharing opens the opportunity for misunderstandings about the true potential of research artifacts and subjective expectations regarding artifact quality. To address this issue, we introduce a set of guidelines for artifact sharing specifically tailored to MDE research. To design this guidelines set, we systematically analyzed general-purpose artifact sharing practices of major computer science venues and tailored them to the MDE domain. Subsequently, we conducted an online survey with 90 researchers and practitioners with expertise in MDE. We investigated our participants' experiences in developing and sharing artifacts in MDE research and the challenges encountered while doing so. We then asked them to prioritize each of our guidelines as essential, desirable, or unnecessary. Finally, we asked them to evaluate our guidelines with respect to clarity, completeness, and relevance. In each of these dimensions, our guidelines were assessed positively by more than 92% of the participants. © 2021 IEEE.","author":[{"family":"Damasceno","given":"C.D.N."},{"family":"Struber","given":"D."}],"citation-key":"Damasceno2021285","collection-title":"Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS 2021","DOI":"10.1109/MODELS50736.2021.00036","ISBN":"978-1-66543-495-9","issued":{"date-parts":[[2021]]},"page":"285-296","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Quality guidelines for research artifacts in model-driven engineering","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115854249&doi=10.1109%2fMODELS50736.2021.00036&partnerID=40&md5=fc7f2e02660a30a25c12569a8ae3a7b7"},
{"id":"damascenoQualityGuidelinesResearch2021a","abstract":"Sharing research artifacts is known to help people to build upon existing knowledge, adopt novel contributions in practice, and increase the chances of papers receiving attention. In Model-Driven Engineering (MDE), openly providing research artifacts plays a key role, even more so as the community targets a broader use of AI techniques, which can only become feasible if large open datasets and confidence measures for their quality are available. However, the current lack of common discipline-specific guidelines for research data sharing opens the opportunity for misunderstandings about the true potential of research artifacts and subjective expectations regarding artifact quality. To address this issue, we introduce a set of guidelines for artifact sharing specifically tailored to MDE research. To design this guidelines set, we systematically analyzed general-purpose artifact sharing practices of major computer science venues and tailored them to the MDE domain. Subsequently, we conducted an online survey with 90 researchers and practitioners with expertise in MDE. We investigated our participants' experiences in developing and sharing artifacts in MDE research and the challenges encountered while doing so. We then asked them to prioritize each of our guidelines as essential, desirable, or unnecessary. Finally, we asked them to evaluate our guidelines with respect to clarity, completeness, and relevance. In each of these dimensions, our guidelines were assessed positively by more than 92% of the participants. © 2021 IEEE.","author":[{"family":"Damasceno","given":"C.D.N."},{"family":"Struber","given":"D."}],"citation-key":"damascenoQualityGuidelinesResearch2021a","container-title":"Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS 2021","DOI":"10.1109/MODELS50736.2021.00036","ISBN":"978-1-66543-495-9","issued":{"date-parts":[[2021]]},"page":"285-296","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Quality Guidelines for Research Artifacts in Model-Driven Engineering","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115854249&doi=10.1109%2fMODELS50736.2021.00036&partnerID=40&md5=fc7f2e02660a30a25c12569a8ae3a7b7"},
{"id":"damevskiMiningSequencesDeveloper2017","accessed":{"date-parts":[[2017,5,26]]},"author":[{"family":"Damevski","given":"Kostadin"},{"family":"Shepherd","given":"David C."},{"family":"Schneider","given":"Johannes"},{"family":"Pollock","given":"Lori"}],"citation-key":"damevskiMiningSequencesDeveloper2017","container-title":"IEEE Transactions on Software Engineering","DOI":"10.1109/TSE.2016.2592905","ISSN":"0098-5589, 1939-3520","issue":"4","issued":{"date-parts":[[2017,4,1]]},"page":"359-371","source":"CrossRef","title":"Mining Sequences of Developer Interactions in Visual Studio for Usage Smells","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7516714/","volume":"43"},
{"id":"danielUMLtoGraphDBMappingConceptual2016","abstract":"The need to store and manipulate large volume of (unstructured) data has led to the development of several NoSQL databases for better scalability. Graph databases are a particular kind of NoSQL databases that have proven their efficiency to store and query highly interconnected data, and have become a promising solution for multiple applications. While the mapping of conceptual schemas to relational databases is a well-studied field of research, there are only few solutions that target conceptual modeling for NoSQL databases and even less focusing on graph databases. This is specially true when dealing with the mapping of business rules and constraints in the conceptual schema. In this article we describe a mapping from UML/OCL conceptual schemas to Blueprints, an abstraction layer on top of a variety of graph databases, and Gremlin, a graph traversal language, via an intermediate Graph metamodel. Tool support is fully available.","accessed":{"date-parts":[[2018,5,7]]},"author":[{"family":"Daniel","given":"Gwendal"},{"family":"Sunyé","given":"Gerson"},{"family":"Cabot","given":"Jordi"}],"citation-key":"danielUMLtoGraphDBMappingConceptual2016","container-title":"Conceptual Modeling","DOI":"10.1007/978-3-319-46397-1_33","editor":[{"family":"Comyn-Wattiau","given":"Isabelle"},{"family":"Tanaka","given":"Katsumi"},{"family":"Song","given":"Il-Yeol"},{"family":"Yamamoto","given":"Shuichiro"},{"family":"Saeki","given":"Motoshi"}],"event-place":"Cham","ISBN":"978-3-319-46396-4 978-3-319-46397-1","issued":{"date-parts":[[2016]]},"page":"430-444","publisher":"Springer International Publishing","publisher-place":"Cham","source":"Crossref","title":"UMLtoGraphDB: Mapping Conceptual Schemas to Graph Databases","title-short":"UMLtoGraphDB","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-319-46397-1_33","volume":"9974"},
{"id":"Daosabah2021324","abstract":"With the continuous development of services in ubiquitous systems, service orientation is becoming increasingly important in its structuring. As a result, the design and the development of applications are gradually migrating from a traditional model to a service-oriented model. In this regard, we propose through this work an approach for web service (WS) composition guided by the context and the intention, by which we suggest an architecture for service composition that reduces the complexity of the generated artificial intelligence (AI) planning problem and to ensure the interoperability of any system independently of the domain planners. The general idea of this approach is to conceive an intentional-contextual metamodel that will be transformed into an OWL model using OMG standards, which will be used to map the WS composition problem into AI planning problems. This article describes the architectural, conceptual, and strategic aspects to deal with the WS composition problem. © 2021 Inderscience Enterprises Ltd.","author":[{"family":"Daosabah","given":"A."},{"family":"Guermah","given":"H."},{"family":"Nassar","given":"M."}],"citation-key":"Daosabah2021324","container-title":"International Journal of Web Engineering and Technology","DOI":"10.1504/IJWET.2021.122768","ISSN":"14761289","issue":"4","issued":{"date-parts":[[2021]]},"page":"324-354","publisher":"Inderscience Publishers","title":"Dynamic composition of services: an approach driven by the user’s intention and context","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124011575&doi=10.1504%2fIJWET.2021.122768&partnerID=40&md5=f9387d7a26ae641892da27f98947542d","volume":"16"},
{"id":"daosabahDynamicCompositionServices2021a","abstract":"With the continuous development of services in ubiquitous systems, service orientation is becoming increasingly important in its structuring. As a result, the design and the development of applications are gradually migrating from a traditional model to a service-oriented model. In this regard, we propose through this work an approach for web service (WS) composition guided by the context and the intention, by which we suggest an architecture for service composition that reduces the complexity of the generated artificial intelligence (AI) planning problem and to ensure the interoperability of any system independently of the domain planners. The general idea of this approach is to conceive an intentional-contextual metamodel that will be transformed into an OWL model using OMG standards, which will be used to map the WS composition problem into AI planning problems. This article describes the architectural, conceptual, and strategic aspects to deal with the WS composition problem. © 2021 Inderscience Enterprises Ltd.","author":[{"family":"Daosabah","given":"A."},{"family":"Guermah","given":"H."},{"family":"Nassar","given":"M."}],"citation-key":"daosabahDynamicCompositionServices2021a","container-title":"International Journal of Web Engineering and Technology","DOI":"10.1504/IJWET.2021.122768","ISSN":"14761289","issue":"4","issued":{"date-parts":[[2021]]},"page":"324-354","publisher":"Inderscience Publishers","title":"Dynamic composition of services: an approach driven by the user’s intention and context","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124011575&doi=10.1504%2fIJWET.2021.122768&partnerID=40&md5=f9387d7a26ae641892da27f98947542d","volume":"16"},
{"id":"daSilva201915","abstract":"To turn big data into actionable knowledge, the adoption of machine learning (ML) methods has proven to be one of the de facto approaches. When elaborating an appropriate ML model for a given task, one typically builds many models and generates several data artifacts. Given the amount of information associated with the developed models performance, their appropriate selection is often difficult. Therefore, appropriately comparing a set of competitive ML models and choosing one according to an arbitrary set of user metrics require systematic solutions. In particular, ML model management is a promising research direction for a more systematic and comprehensive approach for machine learning model selection. Therefore, in this paper, we introduce a conceptual model for ML development. Based on this conceptualization, we introduce our vision toward a knowledge-based model management system oriented to model selection. Copyright © 2019 for this paper by its authors.","author":[{"family":"Silva","given":"D.N.R.","non-dropping-particle":"da"},{"family":"Simões","given":"A."},{"family":"Cardoso","given":"C."},{"family":"Oliveira","given":"D.E.M.","non-dropping-particle":"de"},{"family":"Rittmeyer","given":"J.N."},{"family":"Wehmuth","given":"K."},{"family":"Lustosa","given":"H."},{"family":"Pereira","given":"R.S."},{"family":"Souto","given":"Y."},{"family":"Vignoli","given":"L.E.G."},{"family":"Salles","given":"R."},{"family":"Heleno, S.C.","given":"Jr.","non-dropping-particle":"de"},{"family":"Ziviani","given":"A."},{"family":"Ogasawara","given":"E."},{"family":"Delicato","given":"F.C."},{"family":"Pires","given":"P.F.","non-dropping-particle":"de"},{"family":"Pinto","given":"H.L.C.P.","non-dropping-particle":"da"},{"family":"Maia","given":"L."},{"family":"Porto","given":"F."}],"citation-key":"daSilva201915","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Panach J.I., Guizzardi R.","given":"Claro D.B."}],"ISSN":"16130073","issued":{"date-parts":[[2019]]},"page":"15-27","publisher":"CEUR-WS","title":"A conceptual vision toward the management of machine learning models","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074090321&partnerID=40&md5=71c78c0900e41656c1b6f88703cb4f35","volume":"2469"},
{"id":"DataDistributionService","abstract":"The Data Distribution Service™ (DDS™) is a middleware protocol and API standard for data-centric connectivity from the OMG®. This presentation will cover the use cases of DDS and share example implementations of the DDS standard. DDS integrates the components of a system together, providing low-latency data connectivity, extreme reliability, and a scalable architecture required by business and mission-critical Internet of Things (IoT) applications. \n\nIn a distributed system, middleware is the software layer that lies between the operating system and applications. It enables the various components of a system to more easily communicate and share data. It simplifies the development of a distributed system by letting software developers focus on the specific purpose of their applications rather than the mechanics of passing information between applications and systems.\n\nPresenter: Dr. Gerardo Pardo-Castellote, Co-Chair OMG DDS Special Interest Group, OMG Board of Directors, and CTO, RTI","accessed":{"date-parts":[[2019,10,20]]},"citation-key":"DataDistributionService","dimensions":"50:53","source":"YouTube","title":"Data Distribution Service™ (DDS™)","type":"motion_picture","URL":"https://www.youtube.com/watch?v=6iICap5G7rw"},
{"id":"DataistsTaxonomyData","accessed":{"date-parts":[[2021,3,18]]},"citation-key":"DataistsTaxonomyData","note":"00000","title":"dataists » A Taxonomy of Data Science","type":"post-weblog","URL":"http://www.dataists.com/2010/09/a-taxonomy-of-data-science/"},
{"id":"DataMiningCluster","accessed":{"date-parts":[[2015,4,22]]},"citation-key":"DataMiningCluster","title":"Data Mining Cluster Analysis","type":"webpage","URL":"http://www.tutorialspoint.com/data_mining/dm_cluster_analysis.htm"},
{"id":"DataModelDesign","accessed":{"date-parts":[[2018,4,30]]},"citation-key":"DataModelDesign","title":"Data Model Design and Best Practices (Part 1) - Talend","type":"webpage","URL":"https://www.talend.com/blog/2017/05/05/data-model-design-best-practices-part-1/"},
{"id":"DataModelingAge","abstract":"by Jennifer Zaino Hadoop Hbase. MongoDB. Cassandra. Couchbase. Neo4J. Riak. Those are just a few of the sprawling community of NoSQL databases, a category that originally sprang up in response to the internal needs of companies such as Google, Amazon, Facebook, LinkedIn, Yahoo and more – needs for better scalability, lower latency, greater flexibility, and a better price/performance ratio in an age of Big Data and Cloud computing.","accessed":{"date-parts":[[2015,3,26]]},"citation-key":"DataModelingAge","container-title":"DATAVERSITY","title":"Data Modeling In The Age Of NoSQL And Big Data","type":"post-weblog","URL":"http://www.dataversity.net/data-modeling-age-nosql-big-data/"},
{"id":"DataModelingDead","accessed":{"date-parts":[[2019,11,11]]},"citation-key":"DataModelingDead","title":"Data Modeling is Dead...Long Live Schema Design! - DATAVERSITY","type":"webpage","URL":"https://www.dataversity.net/data-modeling-dead-long-live-schema-design/"},
{"id":"DataModelingGuidelines","accessed":{"date-parts":[[2018,5,7]]},"citation-key":"DataModelingGuidelines","title":"Data Modeling Guidelines for NoSQL JSON Document Databases | MapR","type":"webpage","URL":"https://mapr.com/blog/data-modeling-guidelines-nosql-json-document-databases/"},
{"id":"DataModelingKey","abstract":"In Key Value data stores, data is represented as a collection of key–value pairs. The key–value model is one of the simplest non-trivial data models, and richer data models are implemented on top of it. InfoQ spoke with Casey Rosenthal from Basho team about the data modeling concepts and best practices when using these NoSQL databases for data management.","accessed":{"date-parts":[[2015,3,26]]},"citation-key":"DataModelingKey","container-title":"InfoQ","title":"Data Modeling with Key Value NoSQL Data Stores – Interview with Casey Rosenthal","type":"webpage","URL":"http://www.infoq.com/articles/data-modeling-with-key-value-nosql-data-stores"},
{"id":"DataModelsInternet","accessed":{"date-parts":[[2016,9,27]]},"citation-key":"DataModelsInternet","title":"Data models for the Internet of Things","type":"webpage","URL":"http://iot-datamodels.blogspot.it/"},
{"id":"DataStreamingIoT","accessed":{"date-parts":[[2021,1,5]]},"citation-key":"DataStreamingIoT","note":"00000","title":"Data Streaming in IoT and Big Data Analytics","type":"webpage","URL":"https://www2.slideshare.net/VincenzoGulisano/data-streaming-in-iot-and-big-data-analytics?qid=9a707bc0-4c0e-41ae-a551-1c8462c82314&v=&b=&from_search=21"},
{"id":"davidediruscioManagingEvolutionFree2011","author":[{"family":"Davide Di Ruscio","given":""},{"family":"Pelliccione","given":"P"}],"citation-key":"davidediruscioManagingEvolutionFree2011","container-title":"V Conferenza Italiana sul Software Libero - Milano 23-24 Giugno 2011","issued":{"date-parts":[[2011]]},"note":"00000","title":"Managing the Evolution of Free and Open Source Software Complex Systems","type":"paper-conference"},
{"id":"davidEvaluatingCapabilitiesEnterprise2015","accessed":{"date-parts":[[2016,2,26]]},"author":[{"family":"David","given":"Naranjo"},{"family":"Sánchez","given":"Mario"},{"family":"Villalobos","given":"Jorge"}],"citation-key":"davidEvaluatingCapabilitiesEnterprise2015","container-title":"The Journal of Object Technology","DOI":"10.5381/jot.2015.14.1.a3","ISSN":"1660-1769","issue":"1","issued":{"date-parts":[[2015]]},"page":"3:1","source":"CrossRef","title":"Evaluating the capabilities of Enterprise Architecture modeling tools for Visual Analysis.","type":"article-journal","URL":"http://www.jot.fm/contents/issue_2015_01/article3.html","volume":"14"},
{"id":"davidStreamingModelTransformations2014","abstract":"Streaming model transformations represent a novel class of transformations dealing with models whose elements are continuously produced or modified by a background process [1]. Executing streaming transformations requires efficient techniques to recognize the activated transformation rules on a potentially infinite input stream. Detecting a series of events triggered by compound structural changes is especially challenging for a high volume of rapid modifications, a characteristic of an emerging class of applications built on runtime models.","accessed":{"date-parts":[[2021,4,7]]},"author":[{"family":"Dávid","given":"István"},{"family":"Ráth","given":"István"},{"family":"Varró","given":"Dániel"}],"citation-key":"davidStreamingModelTransformations2014","container-title":"Model-Driven Engineering Languages and Systems","DOI":"10.1007/978-3-319-11653-2_5","editor":[{"family":"Dingel","given":"Juergen"},{"family":"Schulte","given":"Wolfram"},{"family":"Ramos","given":"Isidro"},{"family":"Abrahão","given":"Silvia"},{"family":"Insfran","given":"Emilio"}],"event-place":"Cham","ISBN":"978-3-319-11652-5 978-3-319-11653-2","issued":{"date-parts":[[2014]]},"note":"00000","page":"68-83","publisher":"Springer International Publishing","publisher-place":"Cham","source":"DOI.org (Crossref)","title":"Streaming Model Transformations By Complex Event Processing","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-319-11653-2_5","volume":"8767"},
{"id":"davisRelationshipPrecisionrecallROC2006","author":[{"family":"Davis","given":"Jesse"},{"family":"Goadrich","given":"Mark"}],"citation-key":"davisRelationshipPrecisionrecallROC2006","collection-title":"ICML '06","container-title":"Proceedings of the 23rd international conference on machine learning","event-place":"New York, NY, USA","ISBN":"1-59593-383-2","issued":{"date-parts":[[2006]]},"page":"233-240","publisher":"ACM","publisher-place":"New York, NY, USA","title":"The relationship between precision-recall and ROC curves","type":"paper-conference","URL":"http://doi.acm.org/10.1145/1143844.1143874"},
{"id":"DBLP:conf/icse/RigbyR13","author":[{"family":"Rigby","given":"Peter C."},{"family":"Robillard","given":"Martin P."}],"citation-key":"DBLP:conf/icse/RigbyR13","container-title":"35th international conference on software engineering, ICSE '13, san francisco, CA, USA, may 18-26, 2013","DOI":"10.1109/ICSE.2013.6606629","issued":{"date-parts":[[2013]]},"page":"832-841","title":"Discovering essential code elements in informal documentation","type":"paper-conference","URL":"https://doi.org/10.1109/ICSE.2013.6606629"},
{"id":"DBLP:conf/models/Stevens18","author":[{"family":"Stevens","given":"Perdita"}],"citation-key":"DBLP:conf/models/Stevens18","container-title":"Proceedings of the 21th ACM/IEEE international conference on model driven engineering languages and systems, MODELS 2018, copenhagen, denmark, october 14-19, 2018","DOI":"10.1145/3239372.3239378","issued":{"date-parts":[[2018]]},"page":"301-311","title":"Towards sound, optimal, and flexible building from megamodels","type":"paper-conference","URL":"https://doi.org/10.1145/3239372.3239378"},
{"id":"DBLP:conf/recsys/WuSCTP14","author":[{"family":"Wu","given":"Lili"},{"family":"Shah","given":"Sam"},{"family":"Choi","given":"Sean"},{"family":"Tiwari","given":"Mitul"},{"family":"Posse","given":"Christian"}],"citation-key":"DBLP:conf/recsys/WuSCTP14","collection-title":"CEUR workshop proceedings","container-title":"RSWeb@RecSys","issued":{"date-parts":[[2014]]},"publisher":"CEUR-WS.org","title":"The browsemaps: Collaborative filtering at LinkedIn","type":"paper-conference","volume":"1271"},
{"id":"DBLP:journals/corr/abs-0911-5046","author":[{"family":"Pérez-Iglesias","given":"Joaquín"},{"family":"Pérez-Agüera","given":"José R."},{"family":"Fresno","given":"Víctor"},{"family":"Feinstein","given":"Yuval Z."}],"citation-key":"DBLP:journals/corr/abs-0911-5046","container-title":"CoRR","issued":{"date-parts":[[2009]]},"title":"Integrating the probabilistic models BM25/BM25F into lucene","type":"article-journal","URL":"http://arxiv.org/abs/0911.5046","volume":"abs/0911.5046"},
{"id":"DBLP:journals/corr/abs-1812-04894","author":[{"family":"Lamothe","given":"Maxime"},{"family":"Shang","given":"Weiyi"},{"family":"Chen","given":"Tse-Hsun"}],"citation-key":"DBLP:journals/corr/abs-1812-04894","container-title":"CoRR","issued":{"date-parts":[[2018]]},"title":"A4: Automatically assisting android API migrations using code examples","type":"article-journal","URL":"http://arxiv.org/abs/1812.04894","volume":"abs/1812.04894"},
{"id":"DBLP:journals/corr/IzmaylovaKSV13","author":[{"family":"Izmaylova","given":"Anastasia"},{"family":"Klint","given":"Paul"},{"family":"Shahi","given":"Ashim"},{"family":"Vinju","given":"Jurgen J."}],"citation-key":"DBLP:journals/corr/IzmaylovaKSV13","container-title":"CoRR","issued":{"date-parts":[[2013]]},"title":"M3: An open model for measuring code artifacts","type":"article-journal","URL":"http://arxiv.org/abs/1312.1188","volume":"abs/1312.1188"},
{"id":"DBLP:journals/ijswis/HliaoutakisVVPM06","author":[{"family":"Hliaoutakis","given":"Angelos"},{"family":"Varelas","given":"Giannis"},{"family":"Voutsakis","given":"Epimenidis"},{"family":"Petrakis","given":"Euripides G. M."},{"family":"Milios","given":"Evangelos E."}],"citation-key":"DBLP:journals/ijswis/HliaoutakisVVPM06","container-title":"Int. J. Semantic Web Inf. Syst.","DOI":"10.4018/jswis.2006070104","issue":"3","issued":{"date-parts":[[2006]]},"page":"55-73","title":"Information retrieval by semantic similarity","type":"article-journal","URL":"https://doi.org/10.4018/jswis.2006070104","volume":"2"},
{"id":"DBLP:journals/sigmobile/Shannon01","author":[{"family":"Shannon","given":"Claude E."}],"citation-key":"DBLP:journals/sigmobile/Shannon01","container-title":"Mobile Computing and Communications Review","DOI":"10.1145/584091.584093","issue":"1","issued":{"date-parts":[[2001]]},"page":"3-55","title":"A mathematical theory of communication","type":"article-journal","URL":"https://doi.org/10.1145/584091.584093","volume":"5"},
{"id":"Dean:2012:LSD:2999134.2999271","author":[{"family":"Dean","given":"Jeffrey"},{"family":"Corrado","given":"Greg S."},{"family":"Monga","given":"Rajat"},{"family":"Chen","given":"Kai"},{"family":"Devin","given":"Matthieu"},{"family":"Le","given":"Quoc V."},{"family":"Mao","given":"Mark Z."},{"family":"Ranzato","given":"Marc'Aurelio"},{"family":"Senior","given":"Andrew"},{"family":"Tucker","given":"Paul"},{"family":"Yang","given":"Ke"},{"family":"Ng","given":"Andrew Y."}],"citation-key":"Dean:2012:LSD:2999134.2999271","collection-title":"NIPS'12","container-title":"Proceedings of the 25th int. Conf. on neural information processing systems - volume 1","event-place":"USA","issued":{"date-parts":[[2012]]},"page":"1223-1231","publisher":"Curran Associates Inc.","publisher-place":"USA","title":"Large scale distributed deep networks","type":"paper-conference"},
{"id":"Deb:2002:FEM:2221359.2221582","author":[{"family":"Deb","given":"K."},{"family":"Pratap","given":"A."},{"family":"Agarwal","given":"S."},{"family":"Meyarivan","given":"T."}],"citation-key":"Deb:2002:FEM:2221359.2221582","container-title":"Trans. Evol. Comp","ISSN":"1089-778X","issue":"2","issued":{"date-parts":[[2002,4]]},"page":"182-197","title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","type":"article-journal","URL":"http://dx.doi.org/10.1109/4235.996017","volume":"6"},
{"id":"debieAutomatingDataScience2022","abstract":"Given the complexity of data science projects and related demand for human expertise, automation has the potential to transform the data science process.","accessed":{"date-parts":[[2022,2,27]]},"author":[{"family":"De Bie","given":"Tijl"},{"family":"De Raedt","given":"Luc"},{"family":"Hernández-Orallo","given":"José"},{"family":"Hoos","given":"Holger H."},{"family":"Smyth","given":"Padhraic"},{"family":"Williams","given":"Christopher K. I."}],"citation-key":"debieAutomatingDataScience2022","container-title":"Communications of the ACM","container-title-short":"Commun. ACM","DOI":"10.1145/3495256","ISSN":"0001-0782, 1557-7317","issue":"3","issued":{"date-parts":[[2022,3]]},"note":"00004","page":"76-87","source":"DOI.org (Crossref)","title":"Automating data science","type":"article-journal","URL":"https://dl.acm.org/doi/10.1145/3495256","volume":"65"},
{"id":"degyurkyAutonomousSystemFoundational2014","abstract":"This book describes-in modern computer science terms-the Level II architecture of the meaning and definition of the process referred to as \"thinking\". It applies the basis of early cognitive science research to the creation of autonomous system architectures-connecting philosophical findings of the past with cutting-edge progress in artificial intelligence. Providing an in-depth introduction to the classical, philosophical theories of cognitive scientists like Immanuel Kant, Arthur Schopenhauer, and G.W.F. Hegel, the book examines the Will System, Reason System, Imagination System, and the C.","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"De Gyurky","given":"Szabolcs Michael"},{"family":"Tarbell","given":"Mark A"}],"citation-key":"degyurkyAutonomousSystemFoundational2014","event-place":"Hoboken. N.J.","ISBN":"978-1-118-75749-9 978-1-118-75995-0 978-1-118-29424-6 978-1-299-98883-5","issued":{"date-parts":[[2014]]},"note":"OCLC: 860626514","publisher":"Wiley","publisher-place":"Hoboken. N.J.","source":"Open WorldCat","title":"The autonomous system a foundational synthesis of the sciences of the mind","type":"book","URL":"http://public.eblib.com/choice/publicfullrecord.aspx?p=1465945"},
{"id":"dehghaniFacilitatingMigrationMicroservice2022","accessed":{"date-parts":[[2022,5,24]]},"author":[{"family":"Dehghani","given":"MohammadHadi"},{"family":"Kolahdouz-Rahimi","given":"Shekoufeh"},{"family":"Tisi","given":"Massimo"},{"family":"Tamzalit","given":"Dalila"}],"citation-key":"dehghaniFacilitatingMigrationMicroservice2022","container-title":"Software and Systems Modeling","container-title-short":"Softw Syst Model","DOI":"10.1007/s10270-022-00977-3","ISSN":"1619-1366, 1619-1374","issue":"3","issued":{"date-parts":[[2022,6]]},"page":"1115-1133","source":"DOI.org (Crossref)","title":"Facilitating the migration to the microservice architecture via model-driven reverse engineering and reinforcement learning","type":"article-journal","URL":"https://link.springer.com/10.1007/s10270-022-00977-3","volume":"21"},
{"id":"dehuryTOSCAdataModelingData2022","abstract":"The serverless platform allows a customer to effectively use cloud resources and pay for the exact amount of used resources. A number of dedicated open source and commercial cloud data management tools are available to handle the massive amount of data. Such modern cloud data management tools are not enough matured to integrate the generic cloud application with the serverless platform due to the lack of mature and stable standards. One of the most popular and mature standards, TOSCA (Topology and Orchestration Specification for Cloud Applications), mainly focuses on application and service portability and automated management of the generic cloud application components. This paper proposes the extension of the TOSCA standard, TOSCAdata, that focuses on the modeling of data pipeline-based cloud applications. Keeping the requirements of modern data pipeline cloud applications, TOSCAdata provides a number of TOSCA models that are independently deployable, schedulable, scalable, and re-usable, while effectively handling the flow and transformation of data in a pipeline manner. We also demonstrate the applicability of proposed TOSCAdata models by taking a web-based cloud application in the context of tourism promotion as a use case scenario.","accessed":{"date-parts":[[2022,3,14]]},"author":[{"family":"Dehury","given":"Chinmaya Kumar"},{"family":"Jakovits","given":"Pelle"},{"family":"Srirama","given":"Satish Narayana"},{"family":"Giotis","given":"Giorgos"},{"family":"Garg","given":"Gaurav"}],"citation-key":"dehuryTOSCAdataModelingData2022","container-title":"Journal of Systems and Software","container-title-short":"Journal of Systems and Software","DOI":"10.1016/j.jss.2021.111164","ISSN":"01641212","issued":{"date-parts":[[2022,4]]},"note":"00001","page":"111164","source":"DOI.org (Crossref)","title":"TOSCAdata: Modeling data pipeline applications in TOSCA","title-short":"TOSCAdata","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0164121221002508","volume":"186"},
{"id":"delaraAutomatedReuseModel2019","author":[{"family":"De Lara","given":"Juan"},{"family":"Guerra","given":"Esther"},{"family":"Di Ruscio","given":"Davide"},{"family":"Di Rocco","given":"Juri"},{"family":"Sanchez Cuadrado","given":"Jesus"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"delaraAutomatedReuseModel2019","container-title":"ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY","issued":{"date-parts":[[2019]]},"note":"00000","title":"Automated Reuse of Model Transformations through Typing Requirements Models","type":"article-journal"},
{"id":"delaraReusableAbstractionsModeling2013","accessed":{"date-parts":[[2015,10,15]]},"author":[{"family":"Lara","given":"Juan","non-dropping-particle":"de"},{"family":"Guerra","given":"Esther"},{"family":"Sánchez Cuadrado","given":"Jesús"}],"citation-key":"delaraReusableAbstractionsModeling2013","container-title":"Information Systems","DOI":"10.1016/j.is.2013.06.001","ISSN":"03064379","issue":"8","issued":{"date-parts":[[2013,11]]},"page":"1128-1149","source":"CrossRef","title":"Reusable abstractions for modeling languages","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S030643791300080X","volume":"38"},
{"id":"delavegaLavoisierDSLIncreasing2020","accessed":{"date-parts":[[2021,10,17]]},"author":[{"family":"Vega","given":"Alfonso","non-dropping-particle":"de la"},{"family":"García-Saiz","given":"Diego"},{"family":"Zorrilla","given":"Marta"},{"family":"Sánchez","given":"Pablo"}],"citation-key":"delavegaLavoisierDSLIncreasing2020","container-title":"Journal of Computer Languages","container-title-short":"Journal of Computer Languages","DOI":"10.1016/j.cola.2020.100987","ISSN":"25901184","issued":{"date-parts":[[2020,10]]},"note":"00004","page":"100987","source":"DOI.org (Crossref)","title":"Lavoisier: A DSL for increasing the level of abstraction of data selection and formatting in data mining","title-short":"Lavoisier","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2590118420300472","volume":"60"},
{"id":"deldjooAdversarialMachineLearning","author":[{"family":"Deldjoo","given":"Yashar"},{"family":"Noia","given":"Tommaso Di"},{"family":"Merra","given":"Felice Antonio"}],"citation-key":"deldjooAdversarialMachineLearning","note":"00000","page":"35","source":"Zotero","title":"Adversarial Machine Learning in Recommender Systems: State of the art and Challenges","type":"article-journal"},
{"id":"deldjooSurveyAdversarialRecommender2021","abstract":"Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommendation accuracy. However, success has been accompanied with a major new arising challenge:\n Many applications of machine learning (ML) are adversarial in nature\n [146]. In recent years, it has been shown that these methods are vulnerable to adversarial examples, i.e., subtle but non-random perturbations designed to force recommendation models to produce erroneous outputs.\n \n The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models) and (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 76 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community working on the security of RS or on generative models using GANs to improve their quality.","accessed":{"date-parts":[[2021,4,2]]},"author":[{"family":"Deldjoo","given":"Yashar"},{"family":"Noia","given":"Tommaso Di"},{"family":"Merra","given":"Felice Antonio"}],"citation-key":"deldjooSurveyAdversarialRecommender2021","container-title":"ACM Computing Surveys","container-title-short":"ACM Comput. Surv.","DOI":"10.1145/3439729","ISSN":"0360-0300, 1557-7341","issue":"2","issued":{"date-parts":[[2021,3]]},"note":"00005","page":"1-38","source":"DOI.org (Crossref)","title":"A Survey on Adversarial Recommender Systems: From Attack/Defense Strategies to Generative Adversarial Networks","title-short":"A Survey on Adversarial Recommender Systems","type":"article-journal","URL":"https://dl.acm.org/doi/10.1145/3439729","volume":"54"},
{"id":"delemosSoftwareEngineeringSelfadaptive2013","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"De Lemos","given":"Rogério"},{"family":"Giese","given":"Holger"},{"family":"Müller","given":"Hausi A."},{"family":"Shaw","given":"Mary"},{"family":"Andersson","given":"Jesper"},{"family":"Litoiu","given":"Marin"},{"family":"Schmerl","given":"Bradley"},{"family":"Tamura","given":"Gabriel"},{"family":"Villegas","given":"Norha M."},{"family":"Vogel","given":"Thomas"},{"literal":"others"}],"citation-key":"delemosSoftwareEngineeringSelfadaptive2013","container-title":"Software Engineering for Self-Adaptive Systems II","issued":{"date-parts":[[2013]]},"page":"1–32","publisher":"Springer","source":"Google Scholar","title":"Software engineering for self-adaptive systems: A second research roadmap","title-short":"Software engineering for self-adaptive systems","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-642-35813-5_1"},
{"id":"delimaWorkloaddrivenLogicalDesign2015","accessed":{"date-parts":[[2021,3,24]]},"author":[{"family":"Lima","given":"Claudio","non-dropping-particle":"de"},{"family":"Santos Mello","given":"Ronaldo","non-dropping-particle":"dos"}],"citation-key":"delimaWorkloaddrivenLogicalDesign2015","container-title":"Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services","DOI":"10.1145/2837185.2837218","event":"iiWAS '15: The 17th International Conference on Information Integration and Web-based Application & Services","event-place":"Brussels Belgium","ISBN":"978-1-4503-3491-4","issued":{"date-parts":[[2015,12,11]]},"note":"00024","page":"1-10","publisher":"ACM","publisher-place":"Brussels Belgium","source":"DOI.org (Crossref)","title":"A workload-driven logical design approach for NoSQL document databases","type":"paper-conference","URL":"https://dl.acm.org/doi/10.1145/2837185.2837218"},
{"id":"demuthSupportingCoevolutionMetamodels2013","abstract":"Design models must abide by constraints that can come from diverse sources, like metamodels, requirements, or the problem domain. Modelers intent to live by these constraints and thus desire automated mechanism that provide instant feedback on constraint violations. However, typical approaches assume that constraints do not evolve over time, which, unfortunately, is becoming increasingly unrealistic. For example, the co-evolution of metamodels and models requires corresponding constraints to be co-evolved continuously. This demands efficient constraint adaptation mechanisms to ensure that validated constraints are up-to-date. This paper presents an approach based on constraint templates that tackles this evolution scenario by automatically updating constraints. We developed the Cross-Layer Modeler (XLM) approach which relies on incremental consistency-checking. As a case study, we performed evolutions of the UML-metamodel and 21 design models. Our approach is sound and the empirical evaluation shows that it is near instant and scales with increasing model sizes.","accessed":{"date-parts":[[2015,3,24]]},"author":[{"family":"Demuth","given":"Andreas"},{"family":"Lopez-Herrejon","given":"Roberto E."},{"family":"Egyed","given":"Alexander"}],"citation-key":"demuthSupportingCoevolutionMetamodels2013","collection-number":"8107","collection-title":"Lecture Notes in Computer Science","container-title":"Model-Driven Engineering Languages and Systems","editor":[{"family":"Moreira","given":"Ana"},{"family":"Schätz","given":"Bernhard"},{"family":"Gray","given":"Jeff"},{"family":"Vallecillo","given":"Antonio"},{"family":"Clarke","given":"Peter"}],"ISBN":"978-3-642-41532-6 978-3-642-41533-3","issued":{"date-parts":[[2013]]},"page":"287-303","publisher":"Springer Berlin Heidelberg","source":"link.springer.com","title":"Supporting the Co-evolution of Metamodels and Constraints through Incremental Constraint Management","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-642-41533-3_18"},
{"id":"derakhshanmaneshModelintegratingDevelopmentSoftware2018","accessed":{"date-parts":[[2018,8,6]]},"author":[{"family":"Derakhshanmanesh","given":"Mahdi"},{"family":"Ebert","given":"Jürgen"},{"family":"Grieger","given":"Marvin"},{"family":"Engels","given":"Gregor"}],"citation-key":"derakhshanmaneshModelintegratingDevelopmentSoftware2018","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-018-0682-5","ISSN":"1619-1366, 1619-1374","issued":{"date-parts":[[2018,6,16]]},"source":"Crossref","title":"Model-integrating development of software systems: a flexible component-based approach","title-short":"Model-integrating development of software systems","type":"article-journal","URL":"http://link.springer.com/10.1007/s10270-018-0682-5"},
{"id":"derlerModelingCyberPhysical2012","accessed":{"date-parts":[[2015,10,9]]},"author":[{"family":"Derler","given":"P."},{"family":"Lee","given":"E. A."},{"family":"Vincentelli","given":"A. S."}],"citation-key":"derlerModelingCyberPhysical2012","container-title":"Proceedings of the IEEE","DOI":"10.1109/JPROC.2011.2160929","ISSN":"0018-9219, 1558-2256","issue":"1","issued":{"date-parts":[[2012,1]]},"page":"13-28","source":"CrossRef","title":"Modeling Cyber&#x2013;Physical Systems","type":"article-journal","URL":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5995279","volume":"100"},
{"id":"deServiceModellingInternet2011","accessed":{"date-parts":[[2016,2,9]]},"author":[{"family":"De","given":"Suparna"},{"family":"Barnaghi","given":"Payam"},{"family":"Bauer","given":"Martin"},{"family":"Meissner","given":"Stefan"}],"citation-key":"deServiceModellingInternet2011","container-title":"Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on","issued":{"date-parts":[[2011]]},"page":"949–955","publisher":"IEEE","source":"Google Scholar","title":"Service modelling for the Internet of Things","type":"paper-conference","URL":"http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6078180"},
{"id":"desouzaRankingCrowdKnowledge2014","author":[{"family":"Souza","given":"Lucas B. L.","non-dropping-particle":"de"},{"family":"Campos","given":"Eduardo C."},{"family":"Maia","given":"Marcelo de A."}],"citation-key":"desouzaRankingCrowdKnowledge2014","collection-title":"ICPC 2014","container-title":"Proceedings of the 22Nd international conference on program comprehension","event-place":"New York, NY, USA","ISBN":"978-1-4503-2879-1","issued":{"date-parts":[[2014]]},"page":"72-82","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Ranking crowd knowledge to assist software development","type":"paper-conference","URL":"http://doi.acm.org/10.1145/2597008.2597146"},
{"id":"DeStefani2021","abstract":"State-of-the-art multivariate forecasting methods are restricted to low dimensional tasks, linear dependencies and short horizons. The technological advances (notably the Big data revolution) are instead shifting the focus to problems characterized by a large number of variables, non-linear dependencies and long forecasting horizons. In the last few years, the majority of the best performing techniques for multivariate forecasting have been based on deep-learning models. However, such models are characterized by high requirements in terms of data availability and computational resources and suffer from a lack of interpretability. To cope with the limitations of these methods, we propose an extension to the DFML framework, a hybrid forecasting technique inspired by the Dynamic Factor Model (DFM) approach, a successful forecasting methodology in econometrics. This extension improves the capabilities of the DFM approach, by implementing and assessing both linear and non-linear factor estimation techniques as well as model-driven and data-driven factor forecasting techniques. We assess several method integrations within the DFML, and we show that the proposed technique provides competitive results both in terms of forecasting accuracy and computational efficiency on multiple very large-scale (&gt;102 variables and &gt; 103 samples) real forecasting tasks. © Copyright © 2021 De Stefani and Bontempi.","author":[{"family":"De Stefani","given":"J."},{"family":"Bontempi","given":"G."}],"citation-key":"DeStefani2021","container-title":"Frontiers in Big Data","DOI":"10.3389/fdata.2021.690267","ISSN":"2624909X","issued":{"date-parts":[[2021]]},"publisher":"Frontiers Media S.A.","title":"Factor-based framework for multivariate and multi-step-ahead forecasting of large scale time series","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115729870&doi=10.3389%2ffdata.2021.690267&partnerID=40&md5=460bd7f8e28b2d1f95aebee6a9c8f0e5","volume":"4"},
{"id":"destefaniFactorBasedFrameworkMultivariate2021a","abstract":"State-of-the-art multivariate forecasting methods are restricted to low dimensional tasks, linear dependencies and short horizons. The technological advances (notably the Big data revolution) are instead shifting the focus to problems characterized by a large number of variables, non-linear dependencies and long forecasting horizons. In the last few years, the majority of the best performing techniques for multivariate forecasting have been based on deep-learning models. However, such models are characterized by high requirements in terms of data availability and computational resources and suffer from a lack of interpretability. To cope with the limitations of these methods, we propose an extension to the DFML framework, a hybrid forecasting technique inspired by the Dynamic Factor Model (DFM) approach, a successful forecasting methodology in econometrics. This extension improves the capabilities of the DFM approach, by implementing and assessing both linear and non-linear factor estimation techniques as well as model-driven and data-driven factor forecasting techniques. We assess several method integrations within the DFML, and we show that the proposed technique provides competitive results both in terms of forecasting accuracy and computational efficiency on multiple very large-scale (&gt;102 variables and &gt; 103 samples) real forecasting tasks. © Copyright © 2021 De Stefani and Bontempi.","author":[{"family":"De Stefani","given":"J."},{"family":"Bontempi","given":"G."}],"citation-key":"destefaniFactorBasedFrameworkMultivariate2021a","container-title":"Frontiers in Big Data","DOI":"10.3389/fdata.2021.690267","ISSN":"2624909X","issued":{"date-parts":[[2021]]},"publisher":"Frontiers Media S.A.","title":"Factor-Based Framework for Multivariate and Multi-step-ahead Forecasting of Large Scale Time Series","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115729870&doi=10.3389%2ffdata.2021.690267&partnerID=40&md5=460bd7f8e28b2d1f95aebee6a9c8f0e5","volume":"4"},
{"id":"dhouibRobotmlDomainspecificLanguage2012","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Dhouib","given":"Saadia"},{"family":"Kchir","given":"Selma"},{"family":"Stinckwich","given":"Serge"},{"family":"Ziadi","given":"Tewfik"},{"family":"Ziane","given":"Mikal"}],"citation-key":"dhouibRobotmlDomainspecificLanguage2012","container-title":"International Conference on Simulation, Modeling, and Programming for Autonomous Robots","issued":{"date-parts":[[2012]]},"page":"149–160","publisher":"Springer","source":"Google Scholar","title":"Robotml, a domain-specific language to design, simulate and deploy robotic applications","type":"paper-conference","URL":"http://link.springer.com/chapter/10.1007/978-3-642-34327-8_16"},
{"id":"diceMeasuresAmountEcologic1945","author":[{"family":"Dice","given":"Lee R"}],"citation-key":"diceMeasuresAmountEcologic1945","container-title":"Ecology","issue":"3","issued":{"date-parts":[[1945]]},"page":"297-302","title":"Measures of the amount of ecologic association between species","type":"article-journal","volume":"26"},
{"id":"Did32Waterfall2017","citation-key":"Did32Waterfall2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"7","source":"IEEE Computer Society","title":"Did 32% Waterfall Surprise You?","type":"article-magazine","volume":"34"},
{"id":"Did32Waterfall2017a","accessed":{"date-parts":[[2019,8,22]]},"citation-key":"Did32Waterfall2017a","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2017.10","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017,1]]},"page":"7-7","source":"DOI.org (Crossref)","title":"Did 32% Waterfall Surprise You?","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7819417/","volume":"34"},
{"id":"dimartinoInternetThingsReference2018","abstract":"The term Internet of Things (IoT) is used as an umbrella that covers several topics, related to the application of technological means to monitor, measure and act upon the environment. As a result, it is difficult to determine a univocal architecture to identify as a reference and several scenarios, involving different sensors, smart devices, networks or gateways, can unfold. The data exchanged within and among IoT frameworks are growing exponentially, and the pervasiveness of such systems brings them to come in possession of very sensitive information: as a consequence, Security and Privacy have become a hot topic on the IoT scenery. Furthermore, due to the great variety of technological solutions which are currently available, interoperability issues are bound to arise, especially when no standard API interface, or communication protocol, has been officially adopted.","accessed":{"date-parts":[[2018,11,7]]},"author":[{"family":"Di Martino","given":"B."},{"family":"Rak","given":"M."},{"family":"Ficco","given":"M."},{"family":"Esposito","given":"A."},{"family":"Maisto","given":"S.A."},{"family":"Nacchia","given":"S."}],"citation-key":"dimartinoInternetThingsReference2018","container-title":"Internet of Things","DOI":"10.1016/j.iot.2018.08.008","ISSN":"25426605","issued":{"date-parts":[[2018,9]]},"page":"99-112","source":"Crossref","title":"Internet of things reference architectures, security and interoperability: A survey","title-short":"Internet of things reference architectures, security and interoperability","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2542660518300428","volume":"1-2"},
{"id":"dingSwoogleSearchMetadata2004","author":[{"family":"Ding","given":"Li"},{"family":"Finin","given":"Tim"},{"family":"Joshi","given":"Anupam"},{"family":"Pan","given":"Rong"},{"family":"Cost","given":"R. Scott"},{"family":"Peng","given":"Yun"},{"family":"Reddivari","given":"Pavan"},{"family":"Doshi","given":"Vishal"},{"family":"Sachs","given":"Joel"}],"citation-key":"dingSwoogleSearchMetadata2004","collection-title":"CIKM '04","container-title":"Proceedings of the thirteenth ACM international conference on information and knowledge management","event-place":"New York, NY, USA","ISBN":"1-58113-874-1","issued":{"date-parts":[[2004]]},"page":"652-659","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Swoogle: A search and metadata engine for the semantic web","type":"paper-conference","URL":"http://doi.acm.org/10.1145/1031171.1031289"},
{"id":"DiNoia:2012:LOD:2362499.2362501","author":[{"family":"Di Noia","given":"Tommaso"},{"family":"Mirizzi","given":"Roberto"},{"family":"Ostuni","given":"Vito Claudio"},{"family":"Romito","given":"Davide"},{"family":"Zanker","given":"Markus"}],"citation-key":"DiNoia:2012:LOD:2362499.2362501","collection-title":"I-semantics '12","container-title":"Proceedings of the 8th international conference on semantic systems","event-place":"New York, NY, USA","ISBN":"978-1-4503-1112-0","issued":{"date-parts":[[2012]]},"page":"1-8","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Linked open data to support content-based recommender systems","type":"paper-conference","URL":"http://doi.acm.org/10.1145/2362499.2362501"},
{"id":"DiNoia2014","abstract":"In this chapter we present a report of the ESWC 2014 Challenge on Linked Open Data-enabled Recommender Systems, which consisted of three tasks in the context of book recommendation: rating prediction in cold-start situations, top N recommendations from binary user feedback, and diversity in content-based recommendations. Participants were requested to address the tasks by means of recommendation approaches that made use of Linked Open Data and semantic technologies. In the chapter we describe the challenge motivation, goals and tasks, summarize and compare the nine final participant recommendation approaches, and discuss their experimental results and lessons learned. Finally, we end with some conclusions and potential lines of future research.","author":[{"family":"Di Noia","given":"Tommaso"},{"family":"Cantador","given":"Iván"},{"family":"Ostuni","given":"Vito Claudio"}],"citation-key":"DiNoia2014","container-title":"Semantic web evaluation challenge: SemWebEval 2014 at ESWC 2014, anissaras, crete, greece, may 25-29, 2014, revised selected papers","DOI":"10.1007/978-3-319-12024-9₁7","editor":[{"family":"Presutti","given":"Valentina"},{"family":"Stankovic","given":"Milan"},{"family":"Cambria","given":"Erik"},{"family":"Cantador","given":"Iván"},{"family":"Di Iorio","given":"Angelo"},{"family":"Di Noia","given":"Tommaso"},{"family":"Lange","given":"Christoph"},{"family":"Reforgiato Recupero","given":"Diego"},{"family":"Tordai","given":"Anna"}],"event-place":"Cham","ISBN":"978-3-319-12024-9","issued":{"date-parts":[[2014]]},"page":"129-143","publisher":"Springer International Publishing","publisher-place":"Cham","title":"Linked open data-enabled recommender systems: ESWC 2014 challenge on book recommendation","type":"chapter","URL":"https://doi.org/10.1007/978-3-319-12024-9₁7"},
{"id":"diroccoUsingATLTransformation2016","abstract":"In the last years, the increasing complexity of Model-Driven Engineering (MDE) tools and techniques has led to higher demands in terms of computation, interoperability, and configuration management. Harnessing the softwareas-a-service (SaaS) paradigm and shifting applications from local, mono-core implementations to cloud-based architectures is key to enhance scalability and flexibility. To this end, we propose MDEForge: an extensible, collaborative modeling platform that provides remote model management facilities and prevents the user from focussing on time-consuming, and less creative procedures. This demo paper illustrates the extensibility of MDEForge by integrating ATL services for the remote execution, automated testing, and static analysis of ATL transformations. The usefulness of their employment under the SaaS paradigm is demonstrated with a case-study showing a wide range of new application possibilities.","accessed":{"date-parts":[[2020,12,17]]},"author":[{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Cuadrado","given":"Jesús Sánchez"},{"family":"Lara","given":"Juan","non-dropping-particle":"de"},{"family":"Guerra","given":"Esther"}],"citation-key":"diroccoUsingATLTransformation2016","container-title":"Theory and Practice of Model Transformations","DOI":"10.1007/978-3-319-42064-6_5","editor":[{"family":"Van Gorp","given":"Pieter"},{"family":"Engels","given":"Gregor"}],"event-place":"Cham","ISBN":"978-3-319-42063-9 978-3-319-42064-6","issued":{"date-parts":[[2016]]},"page":"70-78","publisher":"Springer International Publishing","publisher-place":"Cham","source":"DOI.org (Crossref)","title":"Using ATL Transformation Services in the MDEForge Collaborative Modeling Platform","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-319-42064-6_5","volume":"9765"},
{"id":"diroccoUsingATLTransformation2016a","author":[{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Cuadrado","given":"Jesús Sánchez"},{"family":"Lara","given":"Juan","non-dropping-particle":"de"},{"family":"Guerra","given":"Esther"}],"citation-key":"diroccoUsingATLTransformation2016a","container-title":"9th International Conference on Theory and Practice of Model Transformations, ICMT 2016 Held as Part of Conference on Software Technologies: Applications and Foundations, STAF 2016","DOI":"10.1007/978-3-319-42064-6_5","ISBN":"978-3-319-42064-6","issued":{"date-parts":[[2016]]},"note":"00000","page":"70–78","title":"Using ATL Transformation Services in the MDEForge Collaborative Modeling Platform","type":"paper-conference","volume":"9765"},
{"id":"diruscio9thWorkshopModelling2017","author":[{"family":"Di Ruscio","given":"Davide"},{"family":"Chechik","given":"Marsha"},{"family":"Rumpe","given":"Bernhard"}],"citation-key":"diruscio9thWorkshopModelling2017","container-title":"Proceedings - 2017 IEEE/ACM 9th International Workshop on Modelling in Software Engineering, MiSE 2017","DOI":"10.1109/MiSE.2017.15","ISBN":"978-1-5386-0426-7","issued":{"date-parts":[[2017]]},"note":"00000","page":"1–1","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"9th Workshop on Modelling in Software Engineering (MiSE 2017)","type":"chapter"},
{"id":"diruscioAutomaticGenerationDetailed2016","author":[{"family":"DI RUSCIO","given":"Davide"},{"family":"Malavolta","given":"Ivano"},{"family":"Pelliccione","given":"Patrizio"},{"family":"Tivoli","given":"Massimo"}],"citation-key":"diruscioAutomaticGenerationDetailed2016","container-title":"Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems (MODELS '16)","DOI":"10.1145/2976767.2976794","ISBN":"978-1-4503-4321-3","issued":{"date-parts":[[2016]]},"note":"00000","page":"45–55","publisher":"Association for Computing Machinery, Inc","title":"Automatic Generation of detailed Flight Plans from High-level Mission Descriptions","type":"paper-conference"},
{"id":"diruscioMaintainerScriptModernization2009","author":[{"family":"Di Ruscio","given":"D"},{"family":"Pelliccione","given":"P"},{"family":"Pierantonio","given":"A"},{"family":"Zacchiroli","given":"S"}],"citation-key":"diruscioMaintainerScriptModernization2009","container-title":"IWOCE 2009: INTERNATIONAL WORKSHOP ON OPEN COMPONENT ECOSYSTEM","DOI":"10.1145/1595800.1595803","event-place":"NEW YORK, NY, USA","ISBN":"978-1-60558-677-9","issued":{"date-parts":[[2009]]},"note":"00000","page":"11–20","publisher":"Association for Computing Machinery, Inc. (ACM)","publisher-place":"NEW YORK, NY, USA","title":"Towards Maintainer Script Modernization in FOSS Distributions","type":"paper-conference"},
{"id":"diruscioPreface2014","author":[{"family":"Di Ruscio","given":"Davide"},{"family":"De Lara","given":"Juan"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"diruscioPreface2014","container-title":"Proceedings of the 3rd Workshop on Extreme Modeling (XM 2014)","issued":{"date-parts":[[2014]]},"publisher":"CEUR-WS","title":"Preface","type":"paper-conference","URL":"http://ceur-ws.org/","volume":"1239"},
{"id":"diruscioPreface2014a","author":[{"family":"DI RUSCIO","given":"Davide"},{"family":"Varro","given":"Daniel"}],"citation-key":"diruscioPreface2014a","container-title":"ICMT 2014","ISBN":"978-3-319-08788-7","issued":{"date-parts":[[2014]]},"note":"00000","page":"VII–VIII","publisher":"Springer Verlag","title":"Preface","type":"paper-conference","URL":"http://springerlink.com/content/0302-9743/copyright/2005/","volume":"8568"},
{"id":"diruscioPreface2015","author":[{"family":"Di Ruscio","given":"Davide"},{"family":"Völter","given":"Markus"},{"family":"Paige","given":"Richard F."}],"citation-key":"diruscioPreface2015","container-title":"SLE 2015 - Proceedings of the 2015 ACM SIGPLAN International Conference on Software Language Engineering","ISBN":"978-1-4503-3686-4","issued":{"date-parts":[[2015]]},"page":"iii–iv","publisher":"Association for Computing Machinery, Inc","title":"Preface","type":"paper-conference"},
{"id":"disipioMultinomialNaiveBayesian2020","abstract":"GitHub has become a precious service for storing and managing software source code. Over the last year, 10M new developers have joined the GitHub community, contributing to more than 44M repositories. In order to help developers increase the reachability of their repositories, in 2017 GitHub introduced the possibility to classify them by means of topics. However, assigning wrong topics to a given repository can compromise the possibility of helping other developers reach it and eventually contribute to its development.","author":[{"family":"Di Sipio","given":"Claudio"},{"family":"Di Ruscio","given":"Davide"},{"family":"Rubei","given":"Riccardo"},{"family":"Nguyen","given":"Phuong T"}],"citation-key":"disipioMultinomialNaiveBayesian2020","container-title":"24th International Conference on Evaluation and Assessment in Software Engineering (EASE 2020)","DOI":"https://doi.org/10.1145/3383219.3383227","issued":{"date-parts":[[2020]]},"note":"00000","source":"Zotero","title":"A Multinomial Naive Bayesian (MNB) network to automatically recommend topics for GitHub repositories","type":"paper-conference"},
{"id":"diskinTraceabilityMappingsFundamental2017","accessed":{"date-parts":[[2017,3,24]]},"author":[{"family":"Diskin","given":"Zinovy"},{"family":"Gómez","given":"Abel"},{"family":"Cabot","given":"Jordi"}],"citation-key":"diskinTraceabilityMappingsFundamental2017","container-title":"Fundamental Approaches to Software Engineering","DOI":"10.1007/978-3-662-54494-5_14","editor":[{"family":"Huisman","given":"Marieke"},{"family":"Rubin","given":"Julia"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-662-54493-8 978-3-662-54494-5","issued":{"date-parts":[[2017]]},"page":"247-263","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","source":"CrossRef","title":"Traceability Mappings as a Fundamental Instrument in Model Transformations","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-662-54494-5_14","volume":"10202"},
{"id":"doAmaral20225205","abstract":"In the context of modern industry, optimization emerges as one of the most powerful tools, allowing decision-makers to allocate their resources more assertively and deal with complex manufacturing problems. Moreover, manufacturing systems usually involve activities’ interdependency and high stochastic levels, which are necessary to associate optimization and simulation techniques to solve problems. Although simulation optimization is a powerful technique, it can converge on a good solution, which often limits its use in day-to-day operations. As an alternative, metamodels may be used to replace simulation models in the optimization process. In recent years, with the development in the machine learning area, algorithms with high learning capacity have emerged, making the metamodel-based simulation optimization (MBSO) a promising study field. Based on the latest theoretical research on the theme, MBSO techniques have been widely used to solve manufacturing problems. However, there is still no consensus about the experimental design, the learning algorithms, and the importance of the hyperparameter optimization step. Then, the article evaluates the performance of six machine learning algorithms trained with and without hyperparameter optimization, two experimental designs, and five different sample sizes to build metamodels in three real manufacturing cases. Based on the results, the random forest algorithm and the random design with 40 × sample size expressed the better performance to metamodels’ development. Furthermore, the hyperparameter optimization step reduced the metamodels’ error in about 32.83%. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.","author":[{"family":"Amaral","given":"J.V.S.","non-dropping-particle":"do"},{"family":"Carvalho Miranda","given":"R.","non-dropping-particle":"de"},{"family":"Montevechi","given":"J.A.B."},{"family":"Santos","given":"C.H.","non-dropping-particle":"dos"},{"family":"Gabriel","given":"G.T."}],"citation-key":"doAmaral20225205","container-title":"International Journal of Advanced Manufacturing Technology","DOI":"10.1007/s00170-022-09072-9","ISSN":"02683768","issue":"7-8","issued":{"date-parts":[[2022]]},"page":"5205-5224","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Metamodeling-based simulation optimization in manufacturing problems: a comparative study","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127285523&doi=10.1007%2fs00170-022-09072-9&partnerID=40&md5=46d3f7e958cd5e9ff64b014f47ba46e9","volume":"120"},
{"id":"doi:10.1080/21693277.2016.1192517","author":[{"family":"Wuest","given":"Thorsten"},{"family":"Weimer","given":"Daniel"},{"family":"Irgens","given":"Christopher"},{"family":"Thoben","given":"Klaus-Dieter"}],"citation-key":"doi:10.1080/21693277.2016.1192517","container-title":"Production & Manufacturing Research","issue":"1","issued":{"date-parts":[[2016]]},"page":"23-45","title":"Machine learning in manufacturing: advantages, challenges, and applications","type":"article-journal","volume":"4"},
{"id":"Domingos:2012:FUT:2347736.2347755","author":[{"family":"Domingos","given":"Pedro"}],"citation-key":"Domingos:2012:FUT:2347736.2347755","container-title":"Communications of the ACM","container-title-short":"Commun. ACM","ISSN":"0001-0782","issue":"10","issued":{"date-parts":[[2012,10]]},"page":"78-87","title":"A few useful things to know about machine learning","type":"article-journal","volume":"55"},
{"id":"dornenburgPathDevOps2018","abstract":"IT’s role in the business world has changed dramatically over the past decades. New technologies and techniques let enterprises get much more out of IT, while increasingly sophisticated business models have pushed IT to investigate and deliver novel solutions. Agile development led the way, and now the DevOps and DesignOps movements are hitting the mainstream. IT in businesses is now entirely a team activity. While we still need experts with deep technical knowledge, we must focus on how to get people from all disciplines working together effectively. This article is part of a theme issue on software engineering’s 50th anniversary.","author":[{"family":"Dörnenburg","given":"E."}],"citation-key":"dornenburgPathDevOps2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.290110337","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"71-75","source":"IEEE Xplore","title":"The Path to DevOps","type":"article-journal","volume":"35"},
{"id":"Dorodnykh202160","abstract":"The complexity of creating artificial intelligence applications remains high. One of the factors that cause such complexity is the high qualification requirements for developers in the field of programming. Development complexity can be reduced by using methods and tools based on a paradigm known as End-user development. One of the problems that requires the application of the methods of this paradigm is the development of intelligent systems for supporting the search and troubleshooting onboard aircraft. Some tasks connected with this problem are identified, including the task of dynamic formation of task cards for troubleshooting in terms of forming a list of operations. This paper presents a solution to this problem based on some principles of End-user development: model-driven development, visual programming, and wizard form-filling. In particular, an extension of the Prototyping expert systems based on transformations technology, which implements the End-user development, is proposed in the context of the problem to be solved for Sukhoi Superjet aircraft. The main contribution of the work is as follows: expanded the main technology method by supporting event trees formalism (as a popular expert method for formalizing scenarios for the development of problem situations and their localization); created a domain-specific tool (namely, Extended event tree editor) for building standard and extended event trees, including for diagnostic tasks; developed a module for supporting transformations of XML-like event tree representation format for the knowledge base prototyping system - Personal knowledge base designer. A description of the proposed extension and the means of its implementation, as well as an illustrative example, are provided. Copyright © 2021 for this paper by its authors.","author":[{"family":"Dorodnykh","given":"N.O."},{"family":"Kotlov","given":"Y.V."},{"family":"Nikolaychuk","given":"O.A."},{"family":"Popov","given":"V.M."},{"family":"Yurin","given":"A.Y."}],"citation-key":"Dorodnykh202160","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Bychkov I., Tchernykh A.","given":"Feoktistov A."}],"ISSN":"16130073","issued":{"date-parts":[[2021]]},"page":"60-73","publisher":"CEUR-WS","title":"End-user development of knowledge bases for semi-automated formation of task cards","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111360450&partnerID=40&md5=0dfb7b7b307c3f0687f5911003a61f5b","volume":"2913"},
{"id":"dorodnykhEnduserDevelopmentKnowledge2021a","abstract":"The complexity of creating artificial intelligence applications remains high. One of the factors that cause such complexity is the high qualification requirements for developers in the field of programming. Development complexity can be reduced by using methods and tools based on a paradigm known as End-user development. One of the problems that requires the application of the methods of this paradigm is the development of intelligent systems for supporting the search and troubleshooting onboard aircraft. Some tasks connected with this problem are identified, including the task of dynamic formation of task cards for troubleshooting in terms of forming a list of operations. This paper presents a solution to this problem based on some principles of End-user development: model-driven development, visual programming, and wizard form-filling. In particular, an extension of the Prototyping expert systems based on transformations technology, which implements the End-user development, is proposed in the context of the problem to be solved for Sukhoi Superjet aircraft. The main contribution of the work is as follows: expanded the main technology method by supporting event trees formalism (as a popular expert method for formalizing scenarios for the development of problem situations and their localization); created a domain-specific tool (namely, Extended event tree editor) for building standard and extended event trees, including for diagnostic tasks; developed a module for supporting transformations of XML-like event tree representation format for the knowledge base prototyping system - Personal knowledge base designer. A description of the proposed extension and the means of its implementation, as well as an illustrative example, are provided. Copyright © 2021 for this paper by its authors.","author":[{"family":"Dorodnykh","given":"N.O."},{"family":"Kotlov","given":"Y.V."},{"family":"Nikolaychuk","given":"O.A."},{"family":"Popov","given":"V.M."},{"family":"Yurin","given":"A.Y."}],"citation-key":"dorodnykhEnduserDevelopmentKnowledge2021a","container-title":"CEUR Workshop Proceedings","editor":[{"family":"Bychkov I.","given":"Feoktistov A.","suffix":"Tchernykh A."}],"ISSN":"16130073","issued":{"date-parts":[[2021]]},"page":"60-73","publisher":"CEUR-WS","title":"End-user development of knowledge bases for semi-automated formation of task cards","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111360450&partnerID=40&md5=0dfb7b7b307c3f0687f5911003a61f5b","volume":"2913"},
{"id":"dsouzaWorkshopSoftwareArchitectures","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"D’Souza","given":"Meenakshi"},{"family":"Mohalik","given":"Swarup Kumar"},{"family":"Jayaraman","given":"Mahesh Babu"}],"citation-key":"dsouzaWorkshopSoftwareArchitectures","source":"Google Scholar","title":"Workshop on Software Architectures for Adaptive Autonomous Systems (SAAAS)","type":"article-journal","URL":"https://pdfs.semanticscholar.org/b1d6/f9387fdefc8d4eb0054162cb1c040de8d69f.pdf"},
{"id":"Duala-Ekoko:2012:AAQ:2337223.2337255","author":[{"family":"Duala-Ekoko","given":"Ekwa"},{"family":"Robillard","given":"Martin P."}],"citation-key":"Duala-Ekoko:2012:AAQ:2337223.2337255","collection-title":"ICSE '12","container-title":"Proceedings of the 34th international conference on software engineering","event-place":"Piscataway, NJ, USA","ISBN":"978-1-4673-1067-3","issued":{"date-parts":[[2012]]},"page":"266-276","publisher":"IEEE Press","publisher-place":"Piscataway, NJ, USA","title":"Asking and answering questions about unfamiliar APIs: An exploratory study","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=2337223.2337255"},
{"id":"Dubey2020","abstract":"We witnessed great advancement in artificial intelligence (AI) powered technologies over the past few decades. Wide use of AI technologies has led to the creation of an ecosystem where human and AI systems are partners, complementing each other with their strengths. To build a successful human-AI team, there are several considerations, including context awareness, effective communication, pro-activeness, etc. In this paper, we present a taxonomy of human-AI teaming concepts. We extend a multi-agent framework, Java Agent Development Framework (JADE), to support the proposed taxonomy. Our solution framework, Human-AI Collaboration (HACO), enables a model-driven development of human-AI teaming systems through graphical user interface. In this paper, we present the solution architecture for extending JADE with human-AI teaming taxonomy. A user study performed to assess the usefulness of HACO, shows that HACO is a promising framework. We evaluated the proposed framework by developing a set of use cases for a contact center and observed a signification reduction in the overall development effort. The framework video can be viewed at https://youtu.be/lNyrrk8dMqU. © 2020 Association for Computing Machinery.","author":[{"family":"Dubey","given":"A."},{"family":"Abhinav","given":"K."},{"family":"Jain","given":"S."},{"family":"Arora","given":"V."},{"family":"Puttaveerana","given":"A."}],"citation-key":"Dubey2020","collection-title":"ACM International Conference Proceeding Series","DOI":"10.1145/3385032.3385044","ISBN":"978-1-4503-7594-8","issued":{"date-parts":[[2020]]},"publisher":"Association for Computing Machinery","title":"HACO: A framework for developing human-AI teaming","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082668318&doi=10.1145%2f3385032.3385044&partnerID=40&md5=7cdb8a0cae1ea89101167421b92937b6"},
{"id":"duongAutomatedFruitRecognition2020","author":[{"family":"Duong","given":"L. T."},{"family":"Nguyen","given":"P. T."},{"family":"Di Sipio","given":"C."},{"family":"Di Ruscio","given":"D."}],"citation-key":"duongAutomatedFruitRecognition2020","container-title":"COMPUTERS AND ELECTRONICS IN AGRICULTURE","DOI":"10.1016/j.compag.2020.105326","issued":{"date-parts":[[2020]]},"title":"Automated fruit recognition using EfficientNet and MixNet","type":"article-journal","volume":"171"},
{"id":"duttSelfAwarenessCyberPhysicalSystems2016","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Dutt","given":"Nikil"},{"family":"TaheriNejad","given":"Nima"}],"citation-key":"duttSelfAwarenessCyberPhysicalSystems2016","DOI":"10.1109/VLSID.2016.129","ISBN":"978-1-4673-8700-2","issued":{"date-parts":[[2016,1]]},"page":"5-6","publisher":"IEEE","source":"CrossRef","title":"Self-Awareness in Cyber-Physical Systems","type":"paper-conference","URL":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7434906"},
{"id":"ebert50YearsSoftware2018","abstract":"A survey of software professionals worldwide suggests the past, present, and future challenges of software engineering. This article is part of a theme issue on software engineering’s 50th anniversary.","author":[{"family":"Ebert","given":"C."}],"citation-key":"ebert50YearsSoftware2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.3571228","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"94-101","source":"IEEE Xplore","title":"50 Years of Software Engineering: Progress and Perils","title-short":"50 Years of Software Engineering","type":"article-journal","volume":"35"},
{"id":"ebertGlobalSoftwareIT2011","accessed":{"date-parts":[[2017,6,23]]},"author":[{"family":"Ebert","given":"Christof"}],"citation-key":"ebertGlobalSoftwareIT2011","issued":{"date-parts":[[2011]]},"publisher":"John Wiley & Sons","source":"Google Scholar","title":"Global software and IT: a guide to distributed development, projects, and outsourcing","title-short":"Global software and IT","type":"book","URL":"http://books.google.com/books?hl=en&lr=&id=Bj7poEQLZOUC&oi=fnd&pg=PT11&dq=%22Time-to-pro%EF%AC%81t+means+that+you+must+cut+out+delays+from+the+introduction%22+%22complexity.+Open+source+software+only+delivers+core+features+and%22+%22For+that+very+reason,+security+breaches+are+typically+%EF%AC%81xed%22+&ots=l5lCIeb6BB&sig=mK3SgFCs5N3Rvnu70r_9cOw0l5I"},
{"id":"echelonBuildingIoTIndustrial","abstract":"In the first in a two-part series, Echelon’s Robert Dolin describes the requirements that the IP-enabled “Internet of Things” (IoT) must meet to be suitable for use in industrial control network environments.","accessed":{"date-parts":[[2016,11,1]]},"author":[{"family":"Echelon","given":"Robert Dolin"}],"citation-key":"echelonBuildingIoTIndustrial","container-title":"Embedded","title":"Building an IoT for industrial control: Part 1 – What is Industrial IoT?","title-short":"Building an IoT for industrial control","type":"webpage","URL":"http://www.embedded.com/design/real-world-applications/4426952/1/Building-an-IoT-for-industrial-control--Part-1--What-is-Industrial-IoT-"},
{"id":"ECL","author":[{"family":"Kolovos","given":"Dimitrios S."}],"citation-key":"ECL","container-title":"Model driven architecture - foundations and applications","editor":[{"family":"Paige","given":"Richard F."},{"family":"Hartman","given":"Alan"},{"family":"Rensink","given":"Arend"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-642-02674-4","issued":{"date-parts":[[2009]]},"page":"146-157","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","title":"Establishing correspondences between models with the epsilon comparison language","type":"paper-conference"},
{"id":"EclipseSmartHomeFlexible","accessed":{"date-parts":[[2016,12,8]]},"citation-key":"EclipseSmartHomeFlexible","title":"Eclipse SmartHome - A Flexible Framework for the Smart Home - Binding development","type":"webpage","URL":"http://www.eclipse.org/smarthome/documentation/development/bindings/how-to.html"},
{"id":"EclipseZoneGettingStarted","accessed":{"date-parts":[[2016,12,4]]},"citation-key":"EclipseZoneGettingStarted","title":"EclipseZone - Getting started with OSGi: Interacting ...","type":"webpage","URL":"http://www.eclipsezone.com/eclipse/forums/m92131032.html"},
{"id":"EditorialBoard2018","accessed":{"date-parts":[[2018,11,7]]},"citation-key":"EditorialBoard2018","container-title":"Internet of Things","DOI":"10.1016/S2542-6605(18)30096-9","ISSN":"25426605","issued":{"date-parts":[[2018,9]]},"page":"ii","source":"Crossref","title":"Editorial Board","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2542660518300969","volume":"1-2"},
{"id":"efremovIntegratedApproachCommon2015","abstract":"The recent advances in technology enabled transition to the Internet of Things (IoT), in which physical objects around us become an integral part of the global information system. A major technical challenge however is to make these numerous objects interact seamlessly with each other. The latest works related to concepts, such as Web of Things or Social Web of Things, partly address the issue. In our paper we further investigate the topic and point out several problems that need to be efficiently solved for the Internet of Things to work on large scale numbers. One of the main tasks is to make devices easily discoverable. Thus, an efficient way to handle and store their metadata is required. Another problem is connected with providing different models of inter-device communication, asynchronous being the most important, as many of today’s widely used web standards were not designed for it. Finally, we propose a general cloud-based IoT architecture aimed at solving the above-described problems.","accessed":{"date-parts":[[2019,9,10]]},"author":[{"family":"Efremov","given":"Sergey"},{"family":"Pilipenko","given":"Nikolay"},{"family":"Voskov","given":"Leonid"}],"citation-key":"efremovIntegratedApproachCommon2015","container-title":"Procedia Engineering","container-title-short":"Procedia Engineering","DOI":"10.1016/j.proeng.2015.01.486","ISSN":"18777058","issued":{"date-parts":[[2015]]},"page":"1215-1223","source":"DOI.org (Crossref)","title":"An Integrated Approach to Common Problems in the Internet of Things","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1877705815005135","volume":"100"},
{"id":"einarssonSmartHomeMLDomainSpecificModeling2017","accessed":{"date-parts":[[2017,11,22]]},"author":[{"family":"Einarsson","given":"Atli F."},{"family":"Patreksson","given":"Patrekur"},{"family":"Hamdaqa","given":"Mohammad"},{"family":"Hamou-Lhadj","given":"Abdelwahab"}],"citation-key":"einarssonSmartHomeMLDomainSpecificModeling2017","DOI":"10.1109/IEEE.ICIOT.2017.35","ISBN":"978-1-5386-2011-3","issued":{"date-parts":[[2017,6]]},"page":"82-88","publisher":"IEEE","source":"CrossRef","title":"SmartHomeML: Towards a Domain-Specific Modeling Language for Creating Smart Home Applications","title-short":"SmartHomeML","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/8039058/"},
{"id":"eisenbergSearchingModelsHybrid","abstract":"The Model-Driven Engineering (MDE) [3] paradigm advocates for the use of models as an abstraction layer to represent complex systems. Model transformations are a central technique within MDE [10]. They either modify existing models or create new ones from scratch. Generally, these models should represent an optimal state of the system that has to be found within a large space of possible solutions. Model-driven optimization [1, 2, 4–6, 9] is a research area within MDE that proposes to automatically find optimal solutions which are constructed by a set of transformation rules given certain objectives. In order to search into large solution spaces, model-driven optimization approaches combine the expressiveness of models and domain-specific modeling languages, with the computational effectiveness of Artificial Intelligence (AI) methods to find the best model for a particular scenario.","author":[{"family":"Eisenberg","given":"Martin"},{"family":"Pichler","given":"Hans-Peter"},{"family":"Garmendia","given":"Antonio"}],"citation-key":"eisenbergSearchingModelsHybrid","page":"2","source":"Zotero","title":"Searching for Models with Hybrid AI Techniques","type":"article-journal"},
{"id":"ekaputraOntologyChangeOntologyBased2015","accessed":{"date-parts":[[2015,6,24]]},"author":[{"family":"Ekaputra","given":"Fajar Juang"}],"citation-key":"ekaputraOntologyChangeOntologyBased2015","container-title":"The Semantic Web. Latest Advances and New Domains","editor":[{"family":"Gandon","given":"Fabien"},{"family":"Sabou","given":"Marta"},{"family":"Sack","given":"Harald"},{"family":"Amato","given":"Claudia","non-dropping-particle":"d’"},{"family":"Cudré-Mauroux","given":"Philippe"},{"family":"Zimmermann","given":"Antoine"}],"event-place":"Cham","ISBN":"978-3-319-18817-1 978-3-319-18818-8","issued":{"date-parts":[[2015]]},"page":"711-720","publisher":"Springer International Publishing","publisher-place":"Cham","source":"CrossRef","title":"Ontology Change in Ontology-Based Information Integration Systems","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-319-18818-8_44","volume":"9088"},
{"id":"ekstrandLensKitPythonNextGeneration2020","accessed":{"date-parts":[[2021,5,3]]},"author":[{"family":"Ekstrand","given":"Michael D."}],"citation-key":"ekstrandLensKitPythonNextGeneration2020","container-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","DOI":"10.1145/3340531.3412778","event":"CIKM '20: The 29th ACM International Conference on Information and Knowledge Management","event-place":"Virtual Event Ireland","ISBN":"978-1-4503-6859-9","issued":{"date-parts":[[2020,10,19]]},"note":"00003","page":"2999-3006","publisher":"ACM","publisher-place":"Virtual Event Ireland","source":"DOI.org (Crossref)","title":"LensKit for Python: Next-Generation Software for Recommender Systems Experiments","title-short":"LensKit for Python","type":"paper-conference","URL":"https://dl.acm.org/doi/10.1145/3340531.3412778"},
{"id":"ElHamlaoui2019167","abstract":"In the last decade, Service Level Agreements (SLAs) play a pivotal role in Cloud Computing especially for guaranteeing quality, availability and responsibility. SLA involves different actors including customers and service providers. The problem that arises is how to establish an SLA contract between those actors and especially how to help the customer to choose the provider that offers the adequate services. Another important point is the measures to guarantee that the provider respects its contract with the consumer. Our approach embraces model driven engineering principles to automate the generation of the SLA contract and its real-time monitoring. For this purpose, we propose three languages dedicated respectively to the customer, the supplier, and the contract specification. Since we cannot predict QoS values at advance, we propose to use machine learning to learn QoS behavior at run-time. © Springer Nature Switzerland AG 2019.","author":[{"family":"El Hamlaoui","given":"M."},{"family":"Fissaa","given":"T."},{"family":"Laghouaouta","given":"Y."},{"family":"Nassar","given":"M."}],"citation-key":"ElHamlaoui2019167","container-title":"Lecture Notes in Networks and Systems","DOI":"10.1007/978-3-319-97719-5_12","ISSN":"23673370","issued":{"date-parts":[[2019]]},"page":"167-184","publisher":"Springer","title":"Support cloud SLA establishment using MDE","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063299209&doi=10.1007%2f978-3-319-97719-5_12&partnerID=40&md5=78747c9999533ac370999a8301a4f99f","volume":"49"},
{"id":"elhamlaouiSupportCloudSLA2019a","abstract":"In the last decade, Service Level Agreements (SLAs) play a pivotal role in Cloud Computing especially for guaranteeing quality, availability and responsibility. SLA involves different actors including customers and service providers. The problem that arises is how to establish an SLA contract between those actors and especially how to help the customer to choose the provider that offers the adequate services. Another important point is the measures to guarantee that the provider respects its contract with the consumer. Our approach embraces model driven engineering principles to automate the generation of the SLA contract and its real-time monitoring. For this purpose, we propose three languages dedicated respectively to the customer, the supplier, and the contract specification. Since we cannot predict QoS values at advance, we propose to use machine learning to learn QoS behavior at run-time. © Springer Nature Switzerland AG 2019.","author":[{"family":"El Hamlaoui","given":"M."},{"family":"Fissaa","given":"T."},{"family":"Laghouaouta","given":"Y."},{"family":"Nassar","given":"M."}],"citation-key":"elhamlaouiSupportCloudSLA2019a","container-title":"Lecture Notes in Networks and Systems","DOI":"10.1007/978-3-319-97719-5_12","ISSN":"23673370","issued":{"date-parts":[[2019]]},"page":"167-184","publisher":"Springer","title":"Support cloud SLA establishment using MDE","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063299209&doi=10.1007%2f978-3-319-97719-5_12&partnerID=40&md5=78747c9999533ac370999a8301a4f99f","volume":"49"},
{"id":"Elnagar2020383","abstract":"Deep Learning (DL) modeling has been a recent topic of interest. With the accelerating need to embed Deep Learning Networks (DLNs) to the Internet of Things (IoT) applications, many DL optimization techniques were developed to enable applying DL to IoTs. However, despite the plethora of DL optimization techniques, there is always a trade-off between accuracy, latency, and cost. Moreover, there are no specific criteria for selecting the best optimization model for a specific scenario. Therefore, this research aims at providing a DL optimization model that eases the selection and re-using DLNs on IoTs. In addition, the research presents an initial design for a DL optimization model management framework. This framework would help organizations choose the optimal DL optimization model that maximizes performance without sacrificing quality. The research would add to the IS design science knowledge as well as the industry by providing insights to many IT managers to apply DLNs to IoTs such as machines and robots. © 2020, Springer Nature Switzerland AG.","author":[{"family":"Elnagar","given":"S."},{"family":"Osei-Bryson","given":"K.-M."}],"citation-key":"Elnagar2020383","container-title":"Lecture Notes in Business Information Processing","DOI":"10.1007/978-3-030-63396-7_26","editor":[{"family":"Themistocleous M., Papadaki M.","given":"Kamal M.M."}],"ISBN":"9783030633950","ISSN":"18651348","issued":{"date-parts":[[2020]]},"page":"383-398","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Towards applying deep learning to the internet of things: A model and a framework","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097576449&doi=10.1007%2f978-3-030-63396-7_26&partnerID=40&md5=c1e0847f4a2128dc22316a93d536f013","volume":"402"},
{"id":"EMFFacet","accessed":{"date-parts":[[2015,9,24]]},"citation-key":"EMFFacet","title":"EMF Facet","type":"webpage","URL":"http://www.eclipse.org/facet/"},
{"id":"EnablingAutonomousApplications","accessed":{"date-parts":[[2016,9,3]]},"citation-key":"EnablingAutonomousApplications","title":"Enabling Autonomous Applications for IoT - Alta Devices Alta Devices","type":"webpage","URL":"http://www.altadevices.com/energy-harvesting/enabling-autonomous-applications-for-iot/"},
{"id":"eramoAIDOaRtAIaugmentedAutomation2021","abstract":"With the emergence of Cyber-Physical Systems (CPS), the increasing complexity in development and operation demands for an efficient engineering process. In the recent years DevOps promotes closer continuous integration of system development and its operational deployment perspectives. In this context, the use of Artificial Intelligence (AI) is beneficial to improve the system design and integration activities, however, it is still limited despite its high potential. AIDOaRT is a 3 years long H2020-ECSEL European project involving 32 organizations, grouped in clusters from 7 different countries, focusing on AIaugmented automation supporting modelling, coding, testing, monitoring and continuous development of Cyber-Physical Systems (CPS). The project proposes to apply Model-Driven Engineering (MDE) principles and techniques to provide a framework offering proper AI-enhanced methods and related tooling for building trustable CPSs. The framework is intended to work within the DevOps practices combining software development and information technology (IT) operations. In this regard, the project points at enabling AI for IT operations (AIOps) to automate decision making process and complete system development tasks. This paper presents an overview of the project with the aim to discuss context, objectives and the proposed approach.","accessed":{"date-parts":[[2022,5,24]]},"author":[{"family":"Eramo","given":"Romina"},{"family":"Muttillo","given":"Vittoriano"},{"family":"Berardinelli","given":"Luca"},{"family":"Bruneliere","given":"Hugo"},{"family":"Gomez","given":"Abel"},{"family":"Bagnato","given":"Alessandra"},{"family":"Sadovykh","given":"Andrey"},{"family":"Cicchetti","given":"Antonio"}],"citation-key":"eramoAIDOaRtAIaugmentedAutomation2021","container-title":"2021 24th Euromicro Conference on Digital System Design (DSD)","DOI":"10.1109/DSD53832.2021.00053","event":"2021 24th Euromicro Conference on Digital System Design (DSD)","event-place":"Palermo, Italy","ISBN":"978-1-66542-703-6","issued":{"date-parts":[[2021,9]]},"page":"303-310","publisher":"IEEE","publisher-place":"Palermo, Italy","source":"DOI.org (Crossref)","title":"AIDOaRt: AI-augmented Automation for DevOps, a Model-based Framework for Continuous Development in Cyber-Physical Systems","title-short":"AIDOaRt","type":"paper-conference","URL":"https://ieeexplore.ieee.org/document/9556443/"},
{"id":"eramoModeldrivenDesignRuntimeInteraction2019","accessed":{"date-parts":[[2019,9,21]]},"author":[{"family":"Eramo","given":"Romina"},{"family":"Marchand de Kerchove","given":"Florent"},{"family":"Colange","given":"Maximilien"},{"family":"Tucci","given":"Michele"},{"family":"Ouy","given":"Julien"},{"family":"Bruneliere","given":"Hugo"},{"family":"Di Ruscio","given":"Davide"}],"citation-key":"eramoModeldrivenDesignRuntimeInteraction2019","container-title":"The Journal of Object Technology","container-title-short":"JOT","DOI":"10.5381/jot.2019.18.2.a1","ISSN":"1660-1769","issue":"2","issued":{"date-parts":[[2019]]},"page":"1:1","source":"DOI.org (Crossref)","title":"Model-driven Design-Runtime Interaction in Safety Critical System Development: an Experience Report.","title-short":"Model-driven Design-Runtime Interaction in Safety Critical System Development","type":"article-journal","URL":"http://www.jot.fm/contents/issue_2019_02/article1.html","volume":"18"},
{"id":"erdogmus50YearsSoftware2018","abstract":"This theme issue on software engineering’s 50th anniversary presents a range of contributions—from pioneers and well-established software engineers, to younger contributors whose imprint on the field is perhaps yet to come. These contributions come in a variety of formats that provide a balanced look at our field’s past, present, and likely future. The topics include both timeless ideas that appeared to fade for a while, only to pop up again in a new incarnation, and entirely new paradigms that have disrupted the field.","author":[{"family":"Erdogmus","given":"H."},{"family":"Medvidović","given":"N."},{"family":"Paulisch","given":"F."}],"citation-key":"erdogmus50YearsSoftware2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.3571240","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018]]},"note":"00000","page":"20-24","source":"IEEE Xplore","title":"50 Years of Software Engineering","type":"article-journal","volume":"35"},
{"id":"erginLanguageGraphBasedModel2014","accessed":{"date-parts":[[2015,9,15]]},"author":[{"family":"Ergin","given":"Hüseyin"},{"family":"Syriani","given":"Eugene"}],"citation-key":"erginLanguageGraphBasedModel2014","container-title":"Theory and Practice of Model Transformations","issued":{"date-parts":[[2014]]},"page":"91–105","publisher":"Springer","source":"Google Scholar","title":"Towards a Language for Graph-Based Model Transformation Design Patterns","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-319-08789-4_7"},
{"id":"erlenhovCurrentFutureBots2019","accessed":{"date-parts":[[2021,1,9]]},"author":[{"family":"Erlenhov","given":"Linda"},{"family":"Gomes de Oliveira Neto","given":"Francisco"},{"family":"Scandariato","given":"Riccardo"},{"family":"Leitner","given":"Philipp"}],"citation-key":"erlenhovCurrentFutureBots2019","container-title":"2019 IEEE/ACM 1st International Workshop on Bots in Software Engineering (BotSE)","DOI":"10.1109/BotSE.2019.00009","event":"2019 IEEE/ACM 1st International Workshop on Bots in Software Engineering (BotSE)","event-place":"Montreal, QC, Canada","ISBN":"978-1-72812-262-5","issued":{"date-parts":[[2019,5]]},"note":"00015","page":"7-11","publisher":"IEEE","publisher-place":"Montreal, QC, Canada","source":"DOI.org (Crossref)","title":"Current and Future Bots in Software Development","type":"paper-conference","URL":"https://ieeexplore.ieee.org/document/8823643/"},
{"id":"ernstAIDrivenDevelopmentHere2022","accessed":{"date-parts":[[2022,2,19]]},"author":[{"family":"Ernst","given":"Neil A."},{"family":"Bavota","given":"Gabriele"},{"family":"Menzies","given":"Tim"}],"citation-key":"ernstAIDrivenDevelopmentHere2022","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2021.3133805","ISSN":"0740-7459, 1937-4194","issue":"2","issued":{"date-parts":[[2022,3]]},"note":"00000","page":"106-110","source":"DOI.org (Crossref)","title":"AI-Driven Development Is Here: Should You Worry?","title-short":"AI-Driven Development Is Here","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/9713901/","volume":"39"},
{"id":"escobar-avilaUnsupervisedSoftwareCategorization2015","author":[{"family":"Escobar-Avila","given":"J."},{"family":"Linares-Vásquez","given":"M."},{"family":"Haiduc","given":"S."}],"citation-key":"escobar-avilaUnsupervisedSoftwareCategorization2015","container-title":"2015 IEEE 23rd international conference on program comprehension","DOI":"10.1109/ICPC.2015.33","ISSN":"1092-8138","issued":{"date-parts":[[2015,5]]},"page":"229-239","title":"Unsupervised software categorization using bytecode","type":"paper-conference"},
{"id":"escottContinuousModernisationPlaybook","author":[{"family":"Escott","given":"Eban"},{"family":"Tansey","given":"Indi"}],"citation-key":"escottContinuousModernisationPlaybook","note":"00000","page":"96","source":"Zotero","title":"The Continuous Modernisation Playbook","type":"article-journal"},
{"id":"espinazopaganQueryingLargeModels2014","accessed":{"date-parts":[[2015,3,20]]},"author":[{"family":"Espinazo Pagán","given":"Javier"},{"family":"García Molina","given":"Jesús"}],"citation-key":"espinazopaganQueryingLargeModels2014","container-title":"Information and Software Technology","DOI":"10.1016/j.infsof.2014.01.005","ISSN":"09505849","issue":"6","issued":{"date-parts":[[2014,6]]},"page":"586-622","source":"CrossRef","title":"Querying large models efficiently","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0950584914000160","volume":"56"},
{"id":"Essaidi2013240","abstract":"Transformation design is a key step in model-driven engineering, and it is a very challenging task, particularly in context of the model-driven data warehouse. Currently, this process is ensured by human experts. The authors propose a new methodology using machine learning techniques to automatically derive these transformation rules. The main goal is to automatically derive the transformation rules to be applied in the model-driven data warehouse process. The proposed solution allows for a simple design of the decision support systems and the reduction of time and costs of development. The authors use the inductive logic programming framework to learn these transformation rules from examples of previous projects. Then, they find that in model-driven data warehouse application, dependencies exist between transformations. Therefore, the authors investigate a new machine learning methodology, learning dependent-concepts, that is suitable to solve this kind of problem. The experimental evaluation shows that the dependent-concept learning approach gives significantly better results. © 2014 by IGI Global. All rights reserved.","author":[{"family":"Essaidi","given":"M."},{"family":"Osmani","given":"A."},{"family":"Rouveirol","given":"C."}],"citation-key":"Essaidi2013240","DOI":"10.4018/978-1-4666-4494-6.ch011","ISBN":"978-1-4666-4495-3 1-4666-4494-X 978-1-4666-4494-6","issued":{"date-parts":[[2013]]},"page":"240-267","publisher":"IGI Global","title":"Model-driven data warehouse automation: A dependent-concept learning approach","type":"book","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945126422&doi=10.4018%2f978-1-4666-4494-6.ch011&partnerID=40&md5=885cb7a49d21dfa0e6cb6705f6d46e93"},
{"id":"Essaidi2014151","abstract":"This chapter studies a new machine learning application with a possible challenging benchmark for relational learning systems. We are interested in the automation of a model-driven data warehouse using machine learning techniques. The main goal is to automatically derive the transformation rules to be applied in the model-driven process. This aims to reduce the contribution of transformation designers, thereby reducing the time and cost of development. We propose to express the model transformation problem as an Inductive Logic Programming (ILP) one: existing project traces (or project experiences) are used to define the background knowledge and examples. The Aleph ILP engine is used to learn best transformation rules. In our application, we need to deal with several dependent-concepts. Taking into account the work in Predicate Invention, Layered Learning, Cascade Learning and Context Learning, we propose a new methodology that automatically updates the background knowledge of concepts to be learned. Experimental results support the conclusion that our approach is suitable to solve this kind of problem. © 2015 Imperial College Press. All rights reserved.","author":[{"family":"Essaidi","given":"M."},{"family":"Osmani","given":"A."},{"family":"Rouveirol","given":"C."}],"citation-key":"Essaidi2014151","DOI":"10.1142/9781783265091_0017","ISBN":"978-1-78326-509-1 978-1-78326-508-4","issued":{"date-parts":[[2014]]},"page":"151-172","publisher":"Imperial College Press","title":"Learning dependent-concepts in ILP: Application to model-driven data warehouses","type":"book","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988643004&doi=10.1142%2f9781783265091_0017&partnerID=40&md5=9ac012b343f9b390a54ce50c387d10b1"},
{"id":"Essaidi20162730","abstract":"Transformation design is a key step in model-driven engineering, and it is a very challenging task, particularly in context of the model-driven data warehouse. Currently, this process is ensured by human experts. The authors propose a new methodology using machine learning techniques to automatically derive these transformation rules. The main goal is to automatically derive the transformation rules to be applied in the model-driven data warehouse process. The proposed solution allows for a simple design of the decision support systems and the reduction of time and costs of development. The authors use the inductive logic programming framework to learn these transformation rules from examples of previous projects. Then, they find that in model-driven data warehouse application, dependencies exist between transformations. Therefore, the authors investigate a new machine learning methodology, learning dependent-concepts, that is suitable to solve this kind of problem. The experimental evaluation shows that the dependent-concept learning approach gives significantly better results. © 2017 by IGI Global. All rights reserved.","author":[{"family":"Essaidi","given":"M."},{"family":"Osmani","given":"A."},{"family":"Rouveirol","given":"C."}],"citation-key":"Essaidi20162730","DOI":"10.4018/978-1-5225-1759-7.ch113","ISBN":"978-1-5225-1760-3 1-5225-1759-6 978-1-5225-1759-7","issued":{"date-parts":[[2016]]},"page":"2730-2758","publisher":"IGI Global","title":"Model-driven data warehouse automation: A dependent-concept learning approach","type":"book","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018554045&doi=10.4018%2f978-1-5225-1759-7.ch113&partnerID=40&md5=00b5d9f5737ff553f08be855b66f1963","volume":"4"},
{"id":"Etani2015","abstract":"Big data application has many data resources and data. In the first stage of software engineering, a service overview or a system overview cannot be seen. In this paper, we propose that two processes of “Big data analytics” and “Implementation of data modeling” should be collaborated with Model-driven architecture (MDA). Data modeling with those two process in MDA should be repeated fast in order to verify the data model and to find a new data resource for a service. Our first research goal of big data application is to predict side effect of drug which is one of screening methods in drug discovery. This prediction model is constructed with data mining methods at the intersection of statistics, machine learning and database system. Moreover, a new service for drug discovery by new uses for old drugs can be found in data modeling and developed. We demonstrate that the prediction model and the data model for drug discovery are implemented as a prototype system to verify those models and their practicality. © 2015, Etani.","author":[{"family":"Etani","given":"N."}],"citation-key":"Etani2015","container-title":"Journal of Big Data","DOI":"10.1186/s40537-015-0024-1","ISSN":"21961115","issue":"1","issued":{"date-parts":[[2015]]},"publisher":"SpringerOpen","title":"Database application model and its service for drug discovery in Model-driven architecture","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013981862&doi=10.1186%2fs40537-015-0024-1&partnerID=40&md5=d4f428f8589c368b214350208ae3dd82","volume":"2"},
{"id":"etaniDatabaseApplicationModel2015a","abstract":"Big data application has many data resources and data. In the first stage of software engineering, a service overview or a system overview cannot be seen. In this paper, we propose that two processes of “Big data analytics” and “Implementation of data modeling” should be collaborated with Model-driven architecture (MDA). Data modeling with those two process in MDA should be repeated fast in order to verify the data model and to find a new data resource for a service. Our first research goal of big data application is to predict side effect of drug which is one of screening methods in drug discovery. This prediction model is constructed with data mining methods at the intersection of statistics, machine learning and database system. Moreover, a new service for drug discovery by new uses for old drugs can be found in data modeling and developed. We demonstrate that the prediction model and the data model for drug discovery are implemented as a prototype system to verify those models and their practicality. © 2015, Etani.","author":[{"family":"Etani","given":"N."}],"citation-key":"etaniDatabaseApplicationModel2015a","container-title":"Journal of Big Data","DOI":"10.1186/s40537-015-0024-1","ISSN":"21961115","issue":"1","issued":{"date-parts":[[2015]]},"publisher":"SpringerOpen","title":"Database application model and its service for drug discovery in Model-driven architecture","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013981862&doi=10.1186%2fs40537-015-0024-1&partnerID=40&md5=d4f428f8589c368b214350208ae3dd82","volume":"2"},
{"id":"etienChainingModelTransformations2012","author":[{"family":"Etien","given":"Anne"},{"family":"Aranega","given":"Vincent"},{"family":"Blanc","given":"Xavier"},{"family":"Paige","given":"Richard F."}],"citation-key":"etienChainingModelTransformations2012","container-title":"Proceedings of the First Workshop on the Analysis of Model Transformations - AMT '12","DOI":"10.1145/2432497.2432500","issued":{"date-parts":[[2012]]},"page":"9–14","title":"Chaining model transformations","type":"article-journal"},
{"id":"etienCombiningIndependentModel2010","abstract":"Model transformation is one of the key principles of Model Driven Engineering. Many approaches have been proposed to design and realize them. However, for all the approaches, model transformations are considered as single entities that can only be chained if their input and output metamodels are compatible. This approach has the major drawback to focus on the satisfaction of the compliance property when building a transformation chain. In this paper we propose a mechanism for combining independent model transformations which jointly work towards a common objective but do not initially handle compatible metamodels. Our proposal is independent of any model transformation approach. It has been validated using Gaspard, an environment dedicated to code generation for embedded systems.","accessed":{"date-parts":[[2015,3,24]]},"author":[{"family":"Etien","given":"Anne"},{"family":"Muller","given":"Alexis"},{"family":"Legrand","given":"Thomas"},{"family":"Blanc","given":"Xavier"}],"citation-key":"etienCombiningIndependentModel2010","collection-title":"SAC '10","container-title":"Proceedings of the 2010 ACM Symposium on Applied Computing","DOI":"10.1145/1774088.1774557","event-place":"New York, NY, USA","ISBN":"978-1-60558-639-7","issued":{"date-parts":[[2010]]},"page":"2237–2243","publisher":"ACM","publisher-place":"New York, NY, USA","source":"ACM Digital Library","title":"Combining Independent Model Transformations","type":"paper-conference","URL":"http://doi.acm.org/10.1145/1774088.1774557"},
{"id":"etienLocalizedModelTransformations2013","author":[{"family":"Etien","given":"Anne"},{"family":"Muller","given":"Alexis"},{"family":"Legrand","given":"Thomas"},{"family":"Paige","given":"Richard F."}],"citation-key":"etienLocalizedModelTransformations2013","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-013-0379-8","issued":{"date-parts":[[2013]]},"title":"Localized model transformations for building large-scale transformations","type":"article-journal"},
{"id":"etzlstorferEvolutionModelingEcosystems2017","accessed":{"date-parts":[[2017,8,31]]},"author":[{"family":"Etzlstorfer","given":"Juergen"},{"family":"Kapsammer","given":"Elisabeth"},{"family":"Schwinger","given":"Wieland"}],"citation-key":"etzlstorferEvolutionModelingEcosystems2017","DOI":"10.5220/0006167900900099","ISBN":"978-989-758-210-3","issued":{"date-parts":[[2017]]},"page":"90-99","publisher":"SCITEPRESS - Science and Technology Publications","source":"CrossRef","title":"On the Evolution of Modeling Ecosystems: An Evaluation of Co-Evolution Approaches:","title-short":"On the Evolution of Modeling Ecosystems","type":"paper-conference","URL":"http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006167900900099"},
{"id":"Evans2009","abstract":"This paper describes the design, implementation, and application of a new algorithm to detect cloned code. It operates on the abstract syntax trees formed by many compilers as an intermediate representation. It extends prior work by identifying clones even when arbitrary subtrees have been changed. These subtrees may represent structural rather than simply lexical code differences. In several hundred thousand lines of Java and C# code, 20–50% of the clones that we find involve these structural changes, which are not accounted for by previous methods. Our method also identifies cloning in declarations, so it is somewhat more general than conventional procedural abstraction.","author":[{"family":"Evans","given":"William S."},{"family":"Fraser","given":"Christopher W."},{"family":"Ma","given":"Fei"}],"citation-key":"Evans2009","container-title":"Software Quality Journal","DOI":"10.1007/s11219-009-9074-y","ISSN":"1573-1367","issue":"4","issued":{"date-parts":[[2009,12,1]]},"page":"309-330","title":"Clone detection via structural abstraction","type":"article-journal","URL":"https://doi.org/10.1007/s11219-009-9074-y","volume":"17"},
{"id":"ExemplarsSoftwareEngineering","accessed":{"date-parts":[[2016,9,24]]},"citation-key":"ExemplarsSoftwareEngineering","title":"Exemplars | Software Engineering for Self-Adaptive Systems","type":"webpage","URL":"https://www.hpi.uni-potsdam.de/giese/public/selfadapt/exemplars/"},
{"id":"ExploreEclipseOSGi","accessed":{"date-parts":[[2016,12,4]]},"citation-key":"ExploreEclipseOSGi","title":"Explore Eclipse's OSGi console","type":"webpage","URL":"https://www.ibm.com/developerworks/library/os-ecl-osgiconsole/"},
{"id":"ExtremeDataManagement2019","accessed":{"date-parts":[[2022,3,9]]},"citation-key":"ExtremeDataManagement2019","container-title":"2019 Amity International Conference on Artificial Intelligence (AICAI)","DOI":"10.1109/AICAI.2019.8701403","event":"2019 Amity International Conference on Artificial Intelligence (AICAI)","event-place":"Dubai, United Arab Emirates","ISBN":"978-1-5386-9346-9","issued":{"date-parts":[[2019,2]]},"note":"00000","page":"i-i","publisher":"IEEE","publisher-place":"Dubai, United Arab Emirates","source":"DOI.org (Crossref)","title":"Extreme Data Management Analysis and Visualization for Exascale Supercomputers and Experimental Facilities","type":"paper-conference","URL":"https://ieeexplore.ieee.org/document/8701403/"},
{"id":"fabiofumarolaDataModelingNoSQL14:24:10UTC","abstract":"The Information Technology have led us into an era where the production,","accessed":{"date-parts":[[2018,4,30]]},"author":[{"family":"Fabio Fumarola","given":""}],"citation-key":"fabiofumarolaDataModelingNoSQL14:24:10UTC","genre":"Data & Analytics","issued":{"literal":"14:24:10 UTC"},"title":"5 Data Modeling for NoSQL 1/2","type":"speech","URL":"https://www.slideshare.net/fabiofumarola1/data-modeling-for-nosql-12"},
{"id":"FacebookOpenSourced","accessed":{"date-parts":[[2021,6,7]]},"citation-key":"FacebookOpenSourced","note":"00000","title":"Facebook Open Sourced this Architecture for Personalized Neural Recommendation Systems | by Jesus Rodriguez | DataSeries | May, 2021 | Medium","type":"webpage","URL":"https://medium.com/dataseries/facebook-open-sourced-this-architecture-for-personalized-neural-recommendation-systems-97db3fef35bb"},
{"id":"falessiApplyingEmpiricalSoftware2010","abstract":"In the last 15 years, software architecture has emerged as an important software engineering field for managing the development and maintenance of large, software-intensive systems. Software architecture community has developed numerous methods, techniques, and tools to support the architecture process (analysis, design, and review). Historically, most advances in software architecture have been driven by talented people and industrial experience, but there is now a growing need to systematically gather empirical evidence about the advantages or otherwise of tools and methods rather than just rely on promotional anecdotes or rhetoric. The aim of this paper is to promote and facilitate the application of the empirical paradigm to software architecture. To this end, we describe the challenges and lessons learned when assessing software architecture research that used controlled experiments, replications, expert opinion, systematic literature reviews, observational studies, and surveys. Our research will support the emergence of a body of knowledge consisting of the more widely-accepted and well-formed software architecture. theories.","accessed":{"date-parts":[[2020,4,8]]},"author":[{"family":"Falessi","given":"Davide"},{"family":"Babar","given":"Muhammad Ali"},{"family":"Cantone","given":"Giovanni"},{"family":"Kruchten","given":"Philippe"}],"citation-key":"falessiApplyingEmpiricalSoftware2010","container-title":"Empirical Software Engineering","container-title-short":"Empir Software Eng","DOI":"10.1007/s10664-009-9121-0","ISSN":"1382-3256, 1573-7616","issue":"3","issued":{"date-parts":[[2010,6]]},"page":"250-276","source":"DOI.org (Crossref)","title":"Applying empirical software engineering to software architecture: challenges and lessons learned","title-short":"Applying empirical software engineering to software architecture","type":"article-journal","URL":"http://link.springer.com/10.1007/s10664-009-9121-0","volume":"15"},
{"id":"falzoneModelBasedRapid2018","accessed":{"date-parts":[[2021,1,30]]},"author":[{"family":"Falzone","given":"Emanuele"},{"family":"Bernaschina","given":"Carlo"}],"citation-key":"falzoneModelBasedRapid2018","container-title":"Web Engineering","DOI":"10.1007/978-3-319-91662-0_43","editor":[{"family":"Mikkonen","given":"Tommi"},{"family":"Klamma","given":"Ralf"},{"family":"Hernández","given":"Juan"}],"event-place":"Cham","ISBN":"978-3-319-91661-3 978-3-319-91662-0","issued":{"date-parts":[[2018]]},"note":"00000","page":"496-500","publisher":"Springer International Publishing","publisher-place":"Cham","source":"DOI.org (Crossref)","title":"Model Based Rapid Prototyping and Evolution of Web Application","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-319-91662-0_43","volume":"10845"},
{"id":"families2persons","author":[{"family":"Allilaire","given":"Freddy"},{"family":"Jouault","given":"Fr�d�ric"}],"citation-key":"families2persons","issued":{"date-parts":[[2007]]},"title":"A simple illustration of model to model transformation","type":"article-journal","URL":"https://www.eclipse.org/atl/documentation/old/ATLUseCase_Families2Persons.pdf"},
{"id":"Fang2021","abstract":"Data from the cellular network have been proved as one of the most promising way to understand large-scale human mobility for various ubiquitous computing applications due to the high penetration of cellphones and low collection cost. Existing mobility models driven by cellular network data suffer from sparse spatialoral observations because user locations are recorded with cellphone activities, e.g., calls, text, or internet access. In this paper, we design a human mobility recovery system called CellSense to take the sparse cellular billing data (CBR) as input and outputs dense continuous records to recover the sensing gap when using cellular networks as sensing systems to sense the human mobility. There is limited work on this kind of recovery systems at large scale because even though it is straightforward to design a recovery system based on regression models, it is very challenging to evaluate these models at large scale due to the lack of the ground truth data. In this paper, we explore a new opportunity based on the upgrade of cellular infrastructures to obtain cellular network signaling data as the ground truth data, which log the interaction between cellphones and cellular towers at signal levels (e.g., attaching, detaching, paging) even without billable activities. Based on the signaling data, we design a system CellSense for human mobility recovery by integrating collective mobility patterns with individual mobility modeling, which achieves the 35.3% improvement over the state-of-the-art models. The key application of our recovery model is to take regular sparse CBR data that a researcher already has, and to recover the missing data due to sensing gaps of CBR data to produce a dense cellular data for them to train a machine learning model for their use cases, e.g., next location prediction. © 2021 ACM.","author":[{"family":"Fang","given":"Z."},{"family":"Yang","given":"Y."},{"family":"Yang","given":"G."},{"family":"Xian","given":"Y."},{"family":"Zhang","given":"F."},{"family":"Zhang","given":"D."}],"citation-key":"Fang2021","container-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","DOI":"10.1145/3478087","ISSN":"24749567","issue":"3","issued":{"date-parts":[[2021]]},"publisher":"Association for Computing Machinery","title":"CellSense: Human mobility recovery via cellular network data enhancement","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115176170&doi=10.1145%2f3478087&partnerID=40&md5=f5324933b0518e57278bb11d961c9b60","volume":"5"},
{"id":"fangCellSenseHumanMobility2021a","abstract":"Data from the cellular network have been proved as one of the most promising way to understand large-scale human mobility for various ubiquitous computing applications due to the high penetration of cellphones and low collection cost. Existing mobility models driven by cellular network data suffer from sparse spatialoral observations because user locations are recorded with cellphone activities, e.g., calls, text, or internet access. In this paper, we design a human mobility recovery system called CellSense to take the sparse cellular billing data (CBR) as input and outputs dense continuous records to recover the sensing gap when using cellular networks as sensing systems to sense the human mobility. There is limited work on this kind of recovery systems at large scale because even though it is straightforward to design a recovery system based on regression models, it is very challenging to evaluate these models at large scale due to the lack of the ground truth data. In this paper, we explore a new opportunity based on the upgrade of cellular infrastructures to obtain cellular network signaling data as the ground truth data, which log the interaction between cellphones and cellular towers at signal levels (e.g., attaching, detaching, paging) even without billable activities. Based on the signaling data, we design a system CellSense for human mobility recovery by integrating collective mobility patterns with individual mobility modeling, which achieves the 35.3% improvement over the state-of-the-art models. The key application of our recovery model is to take regular sparse CBR data that a researcher already has, and to recover the missing data due to sensing gaps of CBR data to produce a dense cellular data for them to train a machine learning model for their use cases, e.g., next location prediction. © 2021 ACM.","author":[{"family":"Fang","given":"Z."},{"family":"Yang","given":"Y."},{"family":"Yang","given":"G."},{"family":"Xian","given":"Y."},{"family":"Zhang","given":"F."},{"family":"Zhang","given":"D."}],"citation-key":"fangCellSenseHumanMobility2021a","container-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","DOI":"10.1145/3478087","ISSN":"24749567","issue":"3","issued":{"date-parts":[[2021]]},"publisher":"Association for Computing Machinery","title":"CellSense: Human Mobility Recovery via Cellular Network Data Enhancement","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115176170&doi=10.1145%2f3478087&partnerID=40&md5=f5324933b0518e57278bb11d961c9b60","volume":"5"},
{"id":"Fard2020755","abstract":"A growing number of companies rely on machine learning as a key element for gaining a competitive edge from their collected Big Data. An in-database machine learning system can provide many advantages in this scenario, e.g., eliminating the overhead of data transfer, avoiding the maintenance costs of a separate analytical system, and addressing data security and provenance concerns. In this paper, we present our distributed machine learning subsystem within the Vertica database. This subsystem, Vertica-ML, includes machine learning functionalities with SQL API which cover a complete data science workflow as well as model management. We treat machine learning models in Vertica as first-class database objects like tables and views; therefore, they enjoy a similar mechanism for archiving and managing. We explain the architecture of the subsystem, and present a set of experiments to evaluate the performance of the machine learning algorithms implemented on top of it. © 2020 Association for Computing Machinery.","author":[{"family":"Fard","given":"A."},{"family":"Le","given":"A."},{"family":"Larionov","given":"G."},{"family":"Dhillon","given":"W."},{"family":"Bear","given":"C."}],"citation-key":"Fard2020755","collection-title":"Proceedings of the ACM SIGMOD International Conference on Management of Data","DOI":"10.1145/3318464.3386137","ISBN":"978-1-4503-6735-6","ISSN":"07308078","issued":{"date-parts":[[2020]]},"page":"755-768","publisher":"Association for Computing Machinery","title":"Vertica-ML: Distributed machine learning in vertica database","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086269333&doi=10.1145%2f3318464.3386137&partnerID=40&md5=82319203d8198216f60ee68c13225dcb"},
{"id":"Fayyad201731","abstract":"This panel aims to address areas that are widely acknowledged to be of critical importance to the success of Data Science projects and to the healthy growth of KDD/Data Science as a field of scientific research. However, despite this acknowledgement of their criticality, these areas receive insufficient attention in the major conferences in the field. Furthermore, there is a lack of actual actions and tools to address these areas in actual practice. These areas are summarized as follows: 1. Ask any data scientist or machine learning practitioner what they spend the majority of their time working on, and you will most likely get an answer that indicates that 80% to 90% of their time is spent on \"Data Chasing\", \"Data Sourcing\", \"Data Wrangling\", \"Data Cleaning\" and generally what researchers would refer to-often dismissively-as \"Data Preparation\". The process of producing statistical or data mining models from data is typically \"messy\" and certainly lacks management tools to help manage, replicate, reconstruct, and capture all the knowledge that goes in 90% of activities of a Data Scientists. The intensive Data Engineering work that goes into exploring and determining the representation of problem and the significant amount of \"data cleaning\" that ensues creates a plethora of extracts, files, and many artifacts that are only meaningful to the data scientist. 2. The severe lack of Benchmarks in the field, especially ones at big data scale is an impediment to true, objective, measurable progress on performance. The results of each paper are highly dependent on the large degree of freedom an author has on configuring competitive models and on determining which data sets to use (often Data that is not available to others to replicate results on) 3. Monitoring the health of models in production, and deploying models into production environments efficiently and effectively is a black art and often an ignored area. Many models are effectively \"orphans\" with no means of getting appropriate health monitoring. The task of deploying a built model to production is frequently beyond the capabilities of a Data Scientists and the understanding of the IT team. For a typical company, a Machine Learning or Data Science expert is a major investment; yet these people are in such hot demand, that likelihood of churn is high. Typically, when a data scientist is replaced, the process pretty much starts over with a tabula rasa⋯ In fact, I would argue most data scientists coming back to tasks they built themselves 1-2 years before are unable to reconstruct what they did. For this panel, we have selected a unique set of experts who have different experiences and perspectives on these important problems and how they should be dealt with in real environments. It is our hope that the panel discussion will not only produce recommendations on what to do about these painful impediments to successful project deployments, but also serve as an eye opener for the research community to the importance of paying close attention to issues of Data and Model Management in KDD, as well the need to think carefully about the lifecycle of models and how they can be managed, maintained, and deployed systematically. Without addressing these critical deployment and practice issues, our field will be challenged to grow in a healthy and sustainable way. The expert panelists for this panel along with the panel moderator: Usama Fayyad are listed below along with their biographical sketches. © 2017 Copyright held by the owner/author(s).","author":[{"family":"Fayyad","given":"U.M."},{"family":"Candel","given":"A."},{"family":"De La Rubia","given":"E.A."},{"family":"Pafka","given":"S."},{"family":"Chong","given":"A."},{"family":"Lee","given":"J.-Y."}],"citation-key":"Fayyad201731","collection-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","DOI":"10.1145/3097983.3120998","ISBN":"978-1-4503-4887-4","issued":{"date-parts":[[2017]]},"page":"31-32","publisher":"Association for Computing Machinery","title":"Benchmarks and process management in data science: Will we ever get over the mess?","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029030495&doi=10.1145%2f3097983.3120998&partnerID=40&md5=66dd98e25d03d1eeb80f75f69434402e","volume":"Part F129685"},
{"id":"fearyMultipleViewsSafetyCritical2016","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Feary","given":"Michael"},{"family":"Martinie","given":"Célia"},{"family":"Palanque","given":"Philippe"},{"family":"Tscheligi","given":"Manfred"}],"citation-key":"fearyMultipleViewsSafetyCritical2016","DOI":"10.1145/2851581.2886430","ISBN":"978-1-4503-4082-3","issued":{"date-parts":[[2016]]},"page":"1069-1072","publisher":"ACM Press","source":"CrossRef","title":"Multiple Views on Safety-Critical Automation: Aircrafts, Autonomous Vehicles, Air Traffic Management and Satellite Ground Segments Perspectives","title-short":"Multiple Views on Safety-Critical Automation","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?doid=2851581.2886430"},
{"id":"felfernigOverviewRecommenderSystems2019","abstract":"The Internet Of Things (IoT) is an emerging paradigm that envisions a networked infrastructure enabling different types of devices to be interconnected. It creates different kinds of artifacts (e.g., services and applications) in various application domains such as health monitoring, sports monitoring, animal monitoring, enhanced retail services, and smart homes. Recommendation technologies can help to more easily identify relevant artifacts and thus will become one of the key technologies in future IoT solutions. In this article, we provide an overview of existing applications of recommendation technologies in the IoT context and present new recommendation techniques on the basis of real-world IoT scenarios.","accessed":{"date-parts":[[2020,12,14]]},"author":[{"family":"Felfernig","given":"Alexander"},{"family":"Polat-Erdeniz","given":"Seda"},{"family":"Uran","given":"Christoph"},{"family":"Reiterer","given":"Stefan"},{"family":"Atas","given":"Muesluem"},{"family":"Tran","given":"Thi Ngoc Trang"},{"family":"Azzoni","given":"Paolo"},{"family":"Kiraly","given":"Csaba"},{"family":"Dolui","given":"Koustabh"}],"citation-key":"felfernigOverviewRecommenderSystems2019","container-title":"Journal of Intelligent Information Systems","container-title-short":"J Intell Inf Syst","DOI":"10.1007/s10844-018-0530-7","ISSN":"0925-9902, 1573-7675","issue":"2","issued":{"date-parts":[[2019,4]]},"note":"00020","page":"285-309","source":"DOI.org (Crossref)","title":"An overview of recommender systems in the internet of things","type":"article-journal","URL":"http://link.springer.com/10.1007/s10844-018-0530-7","volume":"52"},
{"id":"Feng2019368","abstract":"For engineering applications, the dynamic system responses can be significantly affected by uncertainties in the system parameters including material and geometric properties as well as by uncertainties in the excitations. The reliability of dynamic systems is widely evaluated based on the first-passage theory. To improve the computational efficiency, surrogate models are widely used to approximate the relationship between the system inputs and outputs. In this paper, a new machine learning based metamodel, namely the extended support vector regression (X-SVR), is proposed for the reliability analysis of dynamic systems via utilizing the first-passage theory. Furthermore, the capability of X-SVR is enhanced by a new kernel function developed from the vectorized Gegenbauer polynomial, especially for solving complex engineering problems. Through the proposed approach, the relationship between the extremum of the dynamic responses and the input uncertain parameters is approximated by training the X-SVR model such that the probability of failure can be efficiently predicted without using other computational tools for numerical analysis, such as the finite element analysis (FEM). The feasibility and performance of the proposed surrogate model in dynamic reliability analysis is investigated by comparing it with the conventional ε-insensitive support vector regression (ε-SVR) with Gaussian kernel and Monte Carlo simulation (MSC). Four numerical examples are adopted to evidently demonstrate the practicability and efficiency of the proposed X-SVR method. © 2019 Elsevier Ltd","author":[{"family":"Feng","given":"J."},{"family":"Liu","given":"L."},{"family":"Wu","given":"D."},{"family":"Li","given":"G."},{"family":"Beer","given":"M."},{"family":"Gao","given":"W."}],"citation-key":"Feng2019368","container-title":"Mechanical Systems and Signal Processing","DOI":"10.1016/j.ymssp.2019.02.027","ISSN":"08883270","issued":{"date-parts":[[2019]]},"page":"368-391","publisher":"Academic Press","title":"Dynamic reliability analysis using the extended support vector regression (X-SVR)","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061910019&doi=10.1016%2fj.ymssp.2019.02.027&partnerID=40&md5=39bef0edfddd12243735cfe7fa313866","volume":"126"},
{"id":"Ferdjoukh20131044","abstract":"This work is a contribution of Artificial Intelligence to Software Engineering. We present a comprehensive approach to metamodel instantiation using CSP. The generation of models which conform to a given metamodel is a crucial issue in Software Engineering, especially when it comes to produce a variate and large dataset of relevant models to test model transformations or to properly design new metamodels. We define an original constraint modeling of the problem of generating a model conform to a metamodel, also taking into account its additional OCL constraints. The generation process we describe appears to be quicker, more efficient and flexible than any other state-of-the-art approach. © 2013 IEEE.","author":[{"family":"Ferdjoukh","given":"A."},{"family":"Baert","given":"A.-E."},{"family":"Chateau","given":"A."},{"family":"Coletta","given":"R."},{"family":"Nebut","given":"C."}],"citation-key":"Ferdjoukh20131044","collection-title":"Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI","DOI":"10.1109/ICTAI.2013.156","ISBN":"978-1-4799-2971-9","ISSN":"10823409","issued":{"date-parts":[[2013]]},"page":"1044-1051","title":"A CSP approach for metamodel instantiation","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897742023&doi=10.1109%2fICTAI.2013.156&partnerID=40&md5=2b7af224236f6e577c647472382cf91f"},
{"id":"Feriotto2013103","abstract":"In Italy, the Central Institute for Cataloguing and Documentation (ICCD) rules the cataloguing of the rich and articulated Italian Cultural Heritage through a complex system of shared methodology and formats. ICCD established different catalogue datasheets for each typology of Cultural Good, providing also detailed instructions to fill them. However, the computer assisted process necessary to the management of these complex datasheets (in some cases up to nearly 500 fields) is inadequate. TekneHub carried out a survey at Italian Museums and Soprintendenze ai beni storici, artistici ed etnoantropologici to analyse problems and needs through all the steps of the cataloging activity, thus drawing a precise picture of the issues to be addressed. Fhoster, an Italian hi-tech startup specialized in model-driven Application Platform as a Service (APaaS), offered its flagship platform, called Livebase, to support the research. Livebase is a cloud-computing service integrating an environment to quickly design custom applications and a hosting environment to immediately deploy such applications. Livebase can be accessed and used by authenticated users via a simple web browser. The unique model-driven approach of the Livebase platform allowed Fhoster and TekneHub to quickly test many alternative designs to match the ICCD datasheet chosen as a test (Naturalistic Palaeontological Goods - form BNP 3.01), and to implement specific functions to facilitate the cataloguing activity. As a test bed, the Geological Museum 'Capellini' offered all the paper entry catalogue of its collection of fossil fish from the Middle Eocene of Bolca (Vicenza, Italy). The application interface has been shaped taking into account consistency and intuitiveness. In conclusion, the application created on the Livebase platform strongly improves the efficiency of the cataloguing activity and, at the same time, allows a quick and effective query of the catalogue through basic and advanced filters still compliant with the complex Italian cataloguing standards. © 2013 IEEE.","author":[{"family":"Feriotto","given":"C."},{"family":"Biancardi","given":"M."},{"family":"Hohenstein","given":"U.T."},{"family":"Breda","given":"M."},{"family":"Leonforte","given":"A."}],"citation-key":"Feriotto2013103","collection-title":"Proceedings of the DigitalHeritage 2013 - Federating the 19th Int'l VSMM, 10th Eurographics GCH, and 2nd UNESCO Memory of the World Conferences, Plus Special Sessions fromCAA, Arqueologica 2.0 et al.","DOI":"10.1109/DigitalHeritage.2013.6744738","ISBN":"978-1-4799-3169-9","issued":{"date-parts":[[2013]]},"page":"103-106","title":"Cloud computing for cataloguing and valorization of the Cultural Heritage.: Experimentation of the LiveBase platform for the fast development of cataloguing","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896756574&doi=10.1109%2fDigitalHeritage.2013.6744738&partnerID=40&md5=6a30f93b66591517b4f3a1f99f22aa31","volume":"2"},
{"id":"feriottoCloudComputingCataloguing2013a","abstract":"In Italy, the Central Institute for Cataloguing and Documentation (ICCD) rules the cataloguing of the rich and articulated Italian Cultural Heritage through a complex system of shared methodology and formats. ICCD established different catalogue datasheets for each typology of Cultural Good, providing also detailed instructions to fill them. However, the computer assisted process necessary to the management of these complex datasheets (in some cases up to nearly 500 fields) is inadequate. TekneHub carried out a survey at Italian Museums and Soprintendenze ai beni storici, artistici ed etnoantropologici to analyse problems and needs through all the steps of the cataloging activity, thus drawing a precise picture of the issues to be addressed. Fhoster, an Italian hi-tech startup specialized in model-driven Application Platform as a Service (APaaS), offered its flagship platform, called Livebase, to support the research. Livebase is a cloud-computing service integrating an environment to quickly design custom applications and a hosting environment to immediately deploy such applications. Livebase can be accessed and used by authenticated users via a simple web browser. The unique model-driven approach of the Livebase platform allowed Fhoster and TekneHub to quickly test many alternative designs to match the ICCD datasheet chosen as a test (Naturalistic Palaeontological Goods - form BNP 3.01), and to implement specific functions to facilitate the cataloguing activity. As a test bed, the Geological Museum 'Capellini' offered all the paper entry catalogue of its collection of fossil fish from the Middle Eocene of Bolca (Vicenza, Italy). The application interface has been shaped taking into account consistency and intuitiveness. In conclusion, the application created on the Livebase platform strongly improves the efficiency of the cataloguing activity and, at the same time, allows a quick and effective query of the catalogue through basic and advanced filters still compliant with the complex Italian cataloguing standards. © 2013 IEEE.","author":[{"family":"Feriotto","given":"C."},{"family":"Biancardi","given":"M."},{"family":"Hohenstein","given":"U.T."},{"family":"Breda","given":"M."},{"family":"Leonforte","given":"A."}],"citation-key":"feriottoCloudComputingCataloguing2013a","container-title":"Proceedings of the DigitalHeritage 2013 - Federating the 19th Int'l VSMM, 10th Eurographics GCH, and 2nd UNESCO Memory of the World Conferences, Plus Special Sessions fromCAA, Arqueologica 2.0 et al.","DOI":"10.1109/DigitalHeritage.2013.6744738","ISBN":"978-1-4799-3169-9","issued":{"date-parts":[[2013]]},"page":"103-106","title":"Cloud computing for cataloguing and valorization of the Cultural Heritage.: Experimentation of the LiveBase platform for the fast development of cataloguing","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896756574&doi=10.1109%2fDigitalHeritage.2013.6744738&partnerID=40&md5=6a30f93b66591517b4f3a1f99f22aa31","volume":"2"},
{"id":"Fernández-Isabel20151745","abstract":"Road traffic is a pervasive aspect in modern societies that affects millions people. The study of its multiple aspects is a very demanding task. Due to its complexity, traffic simulations become a key tool. Their development demands multidisciplinary teams, where communication problems are frequent. Modeldriven engineering alleviates this situation providing graphical instruments for designing Modelling Languages (MLs) and semiautomatic transformations. This work presents a model-driven infrastructure composed by an integrative ML, a model editor, and a code generator. The ML is based on related literature and facilitates modelling different theories and simulations based on them. It considers the roles of individuals involved in road traffic, and partially adopts agent-based methodologies to model their decision-making. A case study shows how to produce a simulation specification adapting an existing traffic theory to the ML, and adjust this specification to a simulation platform for testing. It provides the basis for comparison with related work. © 2015, IEEE.","author":[{"family":"Fernández-Isabel","given":"A."},{"family":"Fuentes-Fernández","given":"R."}],"citation-key":"Fernández-Isabel20151745","collection-title":"Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015","DOI":"10.15439/2015F248","editor":[{"family":"Paprzycki M., Maciaszek L.","given":"Ganzha M.","suffix":"Maciaszek L."}],"ISBN":"978-83-60810-65-1","issued":{"date-parts":[[2015]]},"page":"1745-1756","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Developing an integrative modelling language for enhancing road traffic simulations","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958063967&doi=10.15439%2f2015F248&partnerID=40&md5=065fbbbf67e14800c8921cc7f1b56704"},
{"id":"Fernández-Isabel2016406","abstract":"Traffic is a key aspect of everyday life. Its study, as it happens with other complex phenomena, has found in simulation a basic tool. However, the use of simulations faces important limitations. Building them requires considering different aspects of traffic (e.g. urbanism, car features, and individual drivers) with their specific theories, that must be integrated to provide a coherent model. There is also a variety of simulation platforms with different requirements. Many of these problems demand multidisciplinary teams, where the different backgrounds can hinder the communication and validation of simulations. The Model-Driven Engineering (MDE) of simulations has been proposed in other fields to address these issues. Such approaches develop graphical Modelling Languages (MLs) that researchers use to model their problems, and then semi-automatically generate simulations from those models. Working in this way promotes communication, platform independence, incremental development, and reutilisation. This paper presents the first steps for a MDE framework for traffic simulations. It introduces a tailored extensible ML for domain experts. The ML is focused on human actions, so it adopts an Agent-Based Modelling perspective. Regarding traffic aspects, it includes concepts commonly found in related literature following the Driver-Vehicle-Environment model. The language is also suitable to accommodate additional theories using its extension mechanisms. The approach is supported by an infrastructure developed using Eclipse MDE projects: the ML is specified with Ecore, and a model editor and a code generator tools are provided. A case study illustrates how to develop a simulation based on a driver's behaviour theory for a specific target platform using these elements. © 2016 The Institute of Electronics, Information and Communication Engineers.","author":[{"family":"Fernández-Isabel","given":"A."},{"family":"Fuentes-Fernández","given":"R."}],"citation-key":"Fernández-Isabel2016406","container-title":"IEICE Transactions on Information and Systems","DOI":"10.1587/transinf.2015EDP7156","ISSN":"09168532","issue":"2","issued":{"date-parts":[[2016]]},"page":"406-414","publisher":"Maruzen Co., Ltd.","title":"An integrative modelling language for agent-based simulation of traffic","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957990045&doi=10.1587%2ftransinf.2015EDP7156&partnerID=40&md5=747e458e09fe438bca52ab29812d2812","volume":"E99D"},
{"id":"Fernández-Isabel2017219","abstract":"Road traffic and its influence over individuals is an important aspect of our life nowadays. Its study in order to understand its dynamics and the factors that affect it is a relevant field of research. Traffic simulations have become a fundamental tool for these studies. They provide a controlled environment to analyse traffic settings. However, they present some shortcomings. One of the main ones is the need of multidisciplinary groups of experts to work with complex models. Communication problems and misunderstandings frequently appear in them, which produce mistakes and bring increased costs. Some works have addressed these issues adopting abstract concepts that can act as bridges among different groups to model and implement simulations. Works that use intelligent agents to represent individuals, and their related simulation platforms, belong to this category. Nevertheless, these platforms are still programmer-oriented, so other experts find difficult to ground their abstract models in them. As a further step, Model-Driven Engineering (MDE) has been proposed to work with models and simulations. It offers the possibility of working with models at multiple levels of abstraction and focused on different aspects. These models can be oriented to specific experts’ backgrounds. The work presented follows this approach and introduces a generic Modelling Language (ML) through a model, that can be specialized to meet different needs in road traffic simulations. The case study illustrates how that model can be successively modified to model people’ behaviour in traffic both at the traffic expert and platform-oriented levels. This allows reducing the learning curve of experts with backgrounds non-related to software simulations. © 2017, ComSIS Consortium. All rights reserved.","author":[{"family":"Fernández-Isabel","given":"A."},{"family":"Fuentes-Fernández","given":"R."}],"citation-key":"Fernández-Isabel2017219","container-title":"Computer Science and Information Systems","DOI":"10.2298/CSIS161010001F","ISSN":"18200214","issue":"1","issued":{"date-parts":[[2017]]},"page":"219-237","publisher":"ComSIS Consortium","title":"Extending a generic traffic model to specific agent platform requirements","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011711118&doi=10.2298%2fCSIS161010001F&partnerID=40&md5=85829ad0eacb595e22fde3bbde12654d","volume":"14"},
{"id":"fernandez-isabelDevelopingIntegrativeModelling2015a","abstract":"Road traffic is a pervasive aspect in modern societies that affects millions people. The study of its multiple aspects is a very demanding task. Due to its complexity, traffic simulations become a key tool. Their development demands multidisciplinary teams, where communication problems are frequent. Modeldriven engineering alleviates this situation providing graphical instruments for designing Modelling Languages (MLs) and semiautomatic transformations. This work presents a model-driven infrastructure composed by an integrative ML, a model editor, and a code generator. The ML is based on related literature and facilitates modelling different theories and simulations based on them. It considers the roles of individuals involved in road traffic, and partially adopts agent-based methodologies to model their decision-making. A case study shows how to produce a simulation specification adapting an existing traffic theory to the ML, and adjust this specification to a simulation platform for testing. It provides the basis for comparison with related work. © 2015, IEEE.","author":[{"family":"Fernández-Isabel","given":"A."},{"family":"Fuentes-Fernández","given":"R."}],"citation-key":"fernandez-isabelDevelopingIntegrativeModelling2015a","container-title":"Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015","DOI":"10.15439/2015F248","editor":[{"family":"Paprzycki M.","given":"Maciaszek L.","suffix":"Maciaszek L., Ganzha M."}],"ISBN":"978-83-60810-65-1","issued":{"date-parts":[[2015]]},"page":"1745-1756","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Developing an integrative modelling language for enhancing road traffic simulations","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958063967&doi=10.15439%2f2015F248&partnerID=40&md5=065fbbbf67e14800c8921cc7f1b56704"},
{"id":"fernandez-isabelExtendingGenericTraffic2017a","abstract":"Road traffic and its influence over individuals is an important aspect of our life nowadays. Its study in order to understand its dynamics and the factors that affect it is a relevant field of research. Traffic simulations have become a fundamental tool for these studies. They provide a controlled environment to analyse traffic settings. However, they present some shortcomings. One of the main ones is the need of multidisciplinary groups of experts to work with complex models. Communication problems and misunderstandings frequently appear in them, which produce mistakes and bring increased costs. Some works have addressed these issues adopting abstract concepts that can act as bridges among different groups to model and implement simulations. Works that use intelligent agents to represent individuals, and their related simulation platforms, belong to this category. Nevertheless, these platforms are still programmer-oriented, so other experts find difficult to ground their abstract models in them. As a further step, Model-Driven Engineering (MDE) has been proposed to work with models and simulations. It offers the possibility of working with models at multiple levels of abstraction and focused on different aspects. These models can be oriented to specific experts’ backgrounds. The work presented follows this approach and introduces a generic Modelling Language (ML) through a model, that can be specialized to meet different needs in road traffic simulations. The case study illustrates how that model can be successively modified to model people’ behaviour in traffic both at the traffic expert and platform-oriented levels. This allows reducing the learning curve of experts with backgrounds non-related to software simulations. © 2017, ComSIS Consortium. All rights reserved.","author":[{"family":"Fernández-Isabel","given":"A."},{"family":"Fuentes-Fernández","given":"R."}],"citation-key":"fernandez-isabelExtendingGenericTraffic2017a","container-title":"Computer Science and Information Systems","DOI":"10.2298/CSIS161010001F","ISSN":"18200214","issue":"1","issued":{"date-parts":[[2017]]},"page":"219-237","publisher":"ComSIS Consortium","title":"Extending a generic traffic model to specific agent platform requirements","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011711118&doi=10.2298%2fCSIS161010001F&partnerID=40&md5=85829ad0eacb595e22fde3bbde12654d","volume":"14"},
{"id":"ferryCloudMFModelDrivenManagement2018","accessed":{"date-parts":[[2018,2,2]]},"author":[{"family":"Ferry","given":"Nicolas"},{"family":"Chauvel","given":"Franck"},{"family":"Song","given":"Hui"},{"family":"Rossini","given":"Alessandro"},{"family":"Lushpenko","given":"Maksym"},{"family":"Solberg","given":"Arnor"}],"citation-key":"ferryCloudMFModelDrivenManagement2018","container-title":"ACM Transactions on Internet Technology","DOI":"10.1145/3125621","ISSN":"15335399","issue":"2","issued":{"date-parts":[[2018,1,20]]},"page":"1-24","source":"CrossRef","title":"CloudMF: Model-Driven Management of Multi-Cloud Applications","title-short":"CloudMF","type":"article-journal","URL":"http://dl.acm.org/citation.cfm?doid=3182619.3125621","volume":"18"},
{"id":"Feth2017135","abstract":"The verification and validation (V&V) of autonomous systems is a complex and difficult task, especially when artificial intelligence is used to achieve autonomy. However, without proper V&V, sufficient evidence to argue safety is not attainable. We propose in this work the use of a Safety Supervisor (SSV) to circumvent this issue. However, the design of an adequate SSV is a challenge in itself. To assist in this task, we present a conceptual framework and a corresponding metamodel, which are motivated and justified by existing work in the field. The conceptual framework supports the alignment of future research in the field of runtime safety monitoring. Our vision is for the different parts of the framework to be filled with exchangeable solutions so that a concrete SSV can be derived systematically and efficiently, and that new solutions can be embedded in it and get evaluated against existing approaches. To exemplify our vision, we present an SSV that is based on the ISO 22839 standard for forward collision mitigation. © Springer International Publishing AG 2017.","author":[{"family":"Feth","given":"P."},{"family":"Schneider","given":"D."},{"family":"Adler","given":"R."}],"citation-key":"Feth2017135","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-319-66266-4_9","editor":[{"family":"Bitsch F., Tonetta S.","given":"Schoitsch E."}],"ISBN":"9783319662657","ISSN":"03029743","issued":{"date-parts":[[2017]]},"page":"135-148","publisher":"Springer Verlag","title":"A conceptual safety supervisor definition and evaluation framework for autonomous Systems","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029457317&doi=10.1007%2f978-3-319-66266-4_9&partnerID=40&md5=8d41a294e500353059dae975229f00b6","volume":"10488 LNCS"},
{"id":"fischerStackOverflowConsidered2017","abstract":"Online programming discussion platforms such as Stack Overflow serve as a rich source of information for software developers. Available information include vibrant discussions and oftentimes ready-to-use code snippets. Anecdotes report that software developers copy and paste code snippets from those information sources for convenience reasons. Such behavior results in a constant flow of community-provided code snippets into production software. To date, the impact of this behaviour on code security is unknown. We answer this highly important question by quantifying the proliferation of security-related code snippets from Stack Overflow in Android applications available on Google Play. Access to the rich source of information available on Stack Overflow including ready-to-use code snippets provides huge benefits for software developers. However, when it comes to code security there are some caveats to bear in mind: Due to the complex nature of code security, it is very difficult to provide ready-to-use and secure solutions for every problem. Hence, integrating a security-related code snippet from Stack Overflow into production software requires caution and expertise. Unsurprisingly, we observed insecure code snippets being copied into Android applications millions of users install from Google Play every day. To quantitatively evaluate the extent of this observation, we scanned Stack Overflow for code snippets and evaluated their security score using a stochastic gradient descent classifier. In order to identify code reuse in Android applications, we applied state-of-the-art static analysis. Our results are alarming: 15.4% of the 1.3 million Android applications we analyzed, contained security-related code snippets from Stack Overflow. Out of these 97.9% contain at least one insecure code snippet.","accessed":{"date-parts":[[2021,6,18]]},"author":[{"family":"Fischer","given":"Felix"},{"family":"Böttinger","given":"Konstantin"},{"family":"Xiao","given":"Huang"},{"family":"Stransky","given":"Christian"},{"family":"Acar","given":"Yasemin"},{"family":"Backes","given":"Michael"},{"family":"Fahl","given":"Sascha"}],"citation-key":"fischerStackOverflowConsidered2017","container-title":"arXiv:1710.03135 [cs]","issued":{"date-parts":[[2017,10,9]]},"note":"00161","source":"arXiv.org","title":"Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Security","title-short":"Stack Overflow Considered Harmful?","type":"article-journal","URL":"http://arxiv.org/abs/1710.03135"},
{"id":"fleckModelTransformationModularization2017","accessed":{"date-parts":[[2017,2,27]]},"author":[{"family":"Fleck","given":"Martin"},{"family":"Troya","given":"Javier"},{"family":"Kessentini","given":"Marouane"},{"family":"Wimmer","given":"Manuel"},{"family":"Alkhazi","given":"Bader"}],"citation-key":"fleckModelTransformationModularization2017","container-title":"IEEE Transactions on Software Engineering","DOI":"10.1109/TSE.2017.2654255","ISSN":"0098-5589, 1939-3520","issued":{"date-parts":[[2017]]},"page":"1-1","source":"CrossRef","title":"Model Transformation Modularization as a Many-Objective Optimization Problem","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7820199/"},
{"id":"fleder2009blockbuster","author":[{"family":"Fleder","given":"Daniel"},{"family":"Hosanagar","given":"Kartik"}],"citation-key":"fleder2009blockbuster","container-title":"Management science","issue":"5","issued":{"date-parts":[[2009]]},"page":"697-712","title":"Blockbuster culture's next rise or fall: The impact of recommender systems on sales diversity","type":"article-journal","volume":"55"},
{"id":"fleureyQualifyingInputTest2007","author":[{"family":"Fleurey","given":"Franck"},{"family":"Baudry","given":"Benoit"},{"family":"Muller","given":"Pierre-Alain"},{"family":"Traon","given":"Yves Le"}],"citation-key":"fleureyQualifyingInputTest2007","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-007-0074-8","issue":"2","issued":{"date-parts":[[2007]]},"page":"185–203","title":"Qualifying input test data for model transformations","type":"article-journal","volume":"8"},
{"id":"fogarasScalingLinkbasedSimilarity2005","author":[{"family":"Fogaras","given":"Dániel"},{"family":"Rácz","given":"Balázs"}],"citation-key":"fogarasScalingLinkbasedSimilarity2005","collection-title":"WWW '05","container-title":"Proceedings of the 14th international conference on world wide web","event-place":"New York, NY, USA","ISBN":"1-59593-046-9","issued":{"date-parts":[[2005]]},"page":"641-650","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Scaling link-based similarity search","type":"paper-conference","URL":"http://doi.acm.org/10.1145/1060745.1060839"},
{"id":"Foreward2011","citation-key":"Foreward2011","issued":{"date-parts":[[2011]]},"note":"00000","title":"Foreward","type":"book"},
{"id":"FOSDEM2016OSCAR","accessed":{"date-parts":[[2016,2,9]]},"citation-key":"FOSDEM2016OSCAR","title":"FOSDEM 2016 - OSCAR: Address the new challenges of open-source software quality","type":"webpage","URL":"https://fosdem.org/2016/schedule/event/oscar/"},
{"id":"fowkesParameterfreeProbabilisticAPI2016","author":[{"family":"Fowkes","given":"Jaroslav"},{"family":"Sutton","given":"Charles"}],"citation-key":"fowkesParameterfreeProbabilisticAPI2016","collection-title":"FSE 2016","container-title":"Proceedings of the 2016 24th ACM SIGSOFT international symposium on foundations of software engineering","event-place":"New York, NY, USA","ISBN":"978-1-4503-4218-6","issued":{"date-parts":[[2016]]},"page":"254-265","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Parameter-free probabilistic API mining across GitHub","type":"paper-conference","URL":"http://doi.acm.org/10.1145/2950290.2950319"},
{"id":"Fraj20211483","abstract":"In this paper, we present a reactive system for running flexible business process. We define two flexibility patterns based on BPMN (Business Process Model Notation) that deals with changes of resource requirements for business process. The business processes are built on an abstract level, using a BPMN model for the specification of the cloud service business process structure based on flexibility patterns, and the state-chart diagram for the specification of the cloud service business process behaviour. In fact, our approach is based on model driven engineering to facilitate the business process design for developers and free them from the lower cloud details during the running time of such applications. Moreover, the use of flexibility actions ensures the capacity of making a compromise between adapting rapidly and easily business process when running errors occur and keeping the effectiveness of these updated application models. This update is controlled by the real time system which is based on a machine learning algorithm to depict the appropriate cloud service to involve in the business process model using the right flexibility action. Finally, we present some results of our system. © 2021 IEEE.","author":[{"family":"Fraj","given":"I.B."},{"family":"Hlaoui","given":"Y.B."},{"family":"BenAyed","given":"L."}],"citation-key":"Fraj20211483","collection-title":"Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021","DOI":"10.1109/COMPSAC51774.2021.00220","editor":[{"family":"Chan W.K., Claycomb B.","given":"Takakura H.","suffix":"Yang J.-J., Teranishi Y., Towey D., Segura S., Shahriar H., Reisman S., Ahamed S.I."}],"ISBN":"978-1-66542-463-9","issued":{"date-parts":[[2021]]},"page":"1483-1489","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A reactive system for specifying and running flexible cloud service business processes based on machine learning","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115875633&doi=10.1109%2fCOMPSAC51774.2021.00220&partnerID=40&md5=69e8b854cd5f1d3d06837a533b14771b"},
{"id":"frakesTermConflationInformation1984","author":[{"family":"Frakes","given":"William B"}],"citation-key":"frakesTermConflationInformation1984","container-title":"Proceedings of the 7th annual international ACM SIGIR conference on Research and development in information retrieval","issued":{"date-parts":[[1984]]},"page":"383-389","title":"Term conflation for information retrieval","type":"paper-conference"},
{"id":"FrameworkVerificationModel","citation-key":"FrameworkVerificationModel","title":"A framework for verification of model transformations","type":"article-journal"},
{"id":"franceModelDrivenEngineering2012","call-number":"QA76.76.D47 M6258 2012","citation-key":"franceModelDrivenEngineering2012","collection-number":"7590","collection-title":"Lecture notes in computer science","editor":[{"family":"France","given":"Robert"}],"event-place":"Berlin ; New York","ISBN":"978-3-642-33665-2","issued":{"date-parts":[[2012]]},"note":"00012 \nOCLC: ocn873588823","number-of-pages":"828","publisher":"Springer","publisher-place":"Berlin ; New York","source":"Library of Congress ISBN","title":"Model driven engineering languages and systems: 15th International Conference, MODELS 2012, Innsbruck, Austria, September 30-October 5, 2012: proceedings","title-short":"Model driven engineering languages and systems","type":"book"},
{"id":"franceProvidingSupportModel2007","accessed":{"date-parts":[[2015,9,24]]},"author":[{"family":"France","given":"Robert"},{"family":"Fleurey","given":"Franck"},{"family":"Reddy","given":"Raghu"},{"family":"Baudry","given":"Benoit"},{"family":"Ghosh","given":"Sudipto"}],"citation-key":"franceProvidingSupportModel2007","container-title":"Enterprise Distributed Object Computing Conference, 2007. EDOC 2007. 11th IEEE International","issued":{"date-parts":[[2007]]},"page":"253–253","publisher":"IEEE","source":"Google Scholar","title":"Providing support for model composition in metamodels","type":"paper-conference","URL":"http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4383998"},
{"id":"franceRepositoryModelDriven2007","author":[{"family":"France","given":"Robert"},{"family":"Bieman","given":"Jim"},{"family":"Cheng","given":"Betty H. C."}],"citation-key":"franceRepositoryModelDriven2007","container-title":"Models in Software Engineering","DOI":"10.1007/978-3-540-69489-2_38","issued":{"date-parts":[[2007]]},"page":"311–317","title":"Repository for Model Driven Development (ReMoDD)","type":"article-journal","volume":"4364"},
{"id":"franchUsingQualityModels2003","accessed":{"date-parts":[[2017,2,25]]},"author":[{"family":"Franch","given":"Xavier"},{"family":"Carvallo","given":"Juan Pablo"}],"citation-key":"franchUsingQualityModels2003","container-title":"IEEE software","issue":"1","issued":{"date-parts":[[2003]]},"page":"34–41","source":"Google Scholar","title":"Using quality models in software package selection","type":"article-journal","URL":"http://ieeexplore.ieee.org/abstract/document/1159027/","volume":"20"},
{"id":"franzagoProtocolSystematicMapping2016","author":[{"family":"Franzago","given":"Mirco"},{"family":"Ruscio","given":"Davide Di"},{"family":"Malavolta","given":"Ivano"},{"family":"Muccini","given":"Henry"}],"citation-key":"franzagoProtocolSystematicMapping2016","container-title":"CoRR","issued":{"date-parts":[[2016]]},"note":"00000 \n_eprint: 1611.02619","title":"Protocol for a Systematic Mapping Study on Collaborative Model-Driven Software Engineering","type":"article-journal","URL":"http://arxiv.org/abs/1611.02619","volume":"abs/1611.02619"},
{"id":"fredericksPlanningOptimizationDynamically2019","abstract":"The large number of possible configurations of modern software-based systems, combined with the large number of possible environmental situations of such systems, prohibits enumerating all adaptation options at design time and necessitates planning at run time to dynamically identify an appropriate configuration for a situation. While numerous planning techniques exist, they typically assume a detailed state-based model of the system and that the situations that warrant adaptations are known. Both of these assumptions can be violated in complex, real-world systems. As a result, adaptation planning must rely on simple models that capture what can be changed (input parameters) and observed in the system and environment (output and context parameters). We therefore propose planning as optimization: the use of optimization strategies to discover optimal system configurations at runtime for each distinct situation that is also dynamically identified at runtime. We apply our approach to CrowdNav, an open-source traffic routing system with the characteristics of a real-world system. We identify situations via clustering and conduct an empirical study that compares Bayesian optimization and two types of evolutionary optimization (NSGA-II and novelty search) in CrowdNav.","accessed":{"date-parts":[[2020,10,5]]},"author":[{"family":"Fredericks","given":"Erik M."},{"family":"Gerostathopoulos","given":"Ilias"},{"family":"Krupitzer","given":"Christian"},{"family":"Vogel","given":"Thomas"}],"citation-key":"fredericksPlanningOptimizationDynamically2019","container-title":"2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","DOI":"10.1109/SASO.2019.00010","issued":{"date-parts":[[2019,6]]},"page":"1-10","source":"arXiv.org","title":"Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations","title-short":"Planning as Optimization","type":"article-journal","URL":"http://arxiv.org/abs/1905.01071"},
{"id":"fredKnowledgeDiscoveryKnowledge2015","accessed":{"date-parts":[[2015,11,10]]},"citation-key":"fredKnowledgeDiscoveryKnowledge2015","collection-title":"Communications in Computer and Information Science","editor":[{"family":"Fred","given":"Ana"},{"family":"Dietz","given":"Jan L. G."},{"family":"Aveiro","given":"David"},{"family":"Liu","given":"Kecheng"},{"family":"Filipe","given":"Joaquim"}],"event-place":"Cham","ISBN":"978-3-319-25839-3 978-3-319-25840-9","issued":{"date-parts":[[2015]]},"publisher":"Springer International Publishing","publisher-place":"Cham","source":"CrossRef","title":"Knowledge Discovery, Knowledge Engineering and Knowledge Management","type":"book","URL":"http://link.springer.com/10.1007/978-3-319-25840-9","volume":"553"},
{"id":"freitasQueryingLinkedData2011","author":[{"family":"Freitas","given":"André"},{"family":"Oliveira","given":"João Gabriel"},{"family":"O'Riain","given":"Seán"},{"family":"Curry","given":"Edward"},{"family":"Da Silva","given":"João Carlos Pereira"}],"citation-key":"freitasQueryingLinkedData2011","collection-title":"NLDB'11","container-title":"Proceedings of the 16th international conference on natural language processing and information systems","event-place":"Berlin, Heidelberg","ISBN":"978-3-642-22326-6","issued":{"date-parts":[[2011]]},"page":"40-51","publisher":"Springer-Verlag","publisher-place":"Berlin, Heidelberg","title":"Querying linked data using semantic relatedness: A vocabulary independent approach","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=2026011.2026017"},
{"id":"freitasTreoBesteffortNatural2011","abstract":"Linked Data promises an unprecedented availability of data on the Web. However, this vision comes together with the associated challenges of querying highly heterogeneous and distributed data. In order to query Linked Data on the Web today, end-users need to be aware of which datasets potentially contain the data and the data model behind these datasets. This query paradigm, deeply attached to the traditional perspective of structured queries over databases, does not suit the heterogeneity and scale of the Web, where it is impractical for data consumers to have an a priori understanding of the structure and location of available datasets. This work describes Treo, a best-effort natural language query mechanism for Linked Data, which focuses on the problem of bridging the semantic gap between end-user natural language queries and Linked Datasets.","author":[{"family":"Freitas","given":"André"},{"family":"Oliveira","given":"João"},{"family":"O'Riain","given":"Seán"},{"family":"Curry","given":"Edward"},{"family":"Pereira da Silva","given":"João"}],"citation-key":"freitasTreoBesteffortNatural2011","collection-title":"Lecture notes in computer science","container-title":"Proceedings of the 16th international conference on applications of natural language to information systems, NLDB 2011 (poster)","editor":[{"family":"Muñoz","given":"Rafael"},{"family":"Montoyo","given":"Andrés"},{"family":"Métais","given":"Elisabeth"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-642-22326-6","issued":{"date-parts":[[2011]]},"page":"286-289","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","title":"Treo: Best-effort natural language queries over linked data","type":"paper-conference","URL":"http://www.edwardcurry.org/publications/Freitas_Treo_NLDB_2011.pdf","volume":"6716"},
{"id":"fritsche2020avoiding","author":[{"family":"Fritsche","given":"Lars"},{"family":"Kosiol","given":"Jens"},{"family":"Schürr","given":"Andy"},{"family":"Taentzer","given":"Gabriele"}],"citation-key":"fritsche2020avoiding","container-title":"International Journal on Software Tools for Technology Transfer","issued":{"date-parts":[[2020]]},"note":"00002","page":"1–34","publisher":"Springer","title":"Avoiding unnecessary information loss: correct and efficient model synchronization based on triple graph grammars","type":"article-journal"},
{"id":"Froger201932","abstract":"As the first level of a BPM strategy, being able to design event-oriented models of processes is a must-have competence for every modern business. Unfortunately, industrial procedures have reached a certain complexity making the designing task complex enough to discourage businesses facing the blank page. Moreover, the 21st century witnesses the emergence of myriads of norms and external regulations that businesses want to abide by. Although domain experts have a limited process modelling and norm interpretation knowledge, they know how to describe their activities and their sequencing. With progresses made in the artificial intelligence, particularly in the natural language processing domain, it becomes possible to automatize the task of creating a process in compliance with norms. This paper presents a business-oriented prototype assisting users in getting certifiable specific business processes. We detail the metamodel used to separately model norms and business’ existing procedures and then, the algorithm envisaged to deduce a corresponding cartography of processes. © Springer Nature Switzerland AG 2019.","author":[{"family":"Froger","given":"M."},{"family":"Bénaben","given":"F."},{"family":"Truptil","given":"S."},{"family":"Boissel-Dallier","given":"N."}],"citation-key":"Froger201932","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-23554-3_3","editor":[{"family":"Ferreira J.E., Musaev A.","given":"Zhang L.-J."}],"ISBN":"9783030235536","ISSN":"03029743","issued":{"date-parts":[[2019]]},"page":"32-47","publisher":"Springer Verlag","title":"Generating personalized and certifiable workflow designs: A prototype","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068206758&doi=10.1007%2f978-3-030-23554-3_3&partnerID=40&md5=9e28a56aa9fac9af04a5ec49ac86442f","volume":"11515 LNCS"},
{"id":"frostChallengesOpportunitiesAutonomous2010","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Frost","given":"C."}],"citation-key":"frostChallengesOpportunitiesAutonomous2010","container-title":"Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2010 Symposium","issued":{"date-parts":[[2010]]},"source":"Google Scholar","title":"Challenges and opportunities for autonomous systems in space","type":"paper-conference","URL":"http://books.google.com/books?hl=en&lr=&id=2lH3kI2g2yMC&oi=fnd&pg=PA89&dq=%22encounters+an+unplanned-for+situation,+it+stops+and+waits+for+human+help+(e.g.+it%22+%22of+the+implementation+details,+however,+intelligent+autonomous+systems+are%22+%22The+Roomba+user+provides+high-level+goals+(vacuum+the+floor,+but+don%E2%80%99t+vacuum%22+&ots=ErAPABO1Yh&sig=sSQZsesfdEbvr-v9TCuY3WAQkAk"},
{"id":"Fu2021","abstract":"Segmenting optic disc (OD) in abnormal fundus images is a challenge task because of many distractions such as illumination variations, blurry boundary, occlusion of retinal vessels and big bright lesions. Data-driven deep learning is effective and robust to illumination variations, blurry boundary and occlusion in the normal fundus images but sensitive to big bright lesions in abnormal images. In this paper, an automatic OD segmentation method fusing U-net with model-driven probability bubble approach is proposed in abnormal fundus images. The probability bubble is conceived according to the position relationship between retinal vessels and OD, and the localization result is fused into the output layer of U-net through calculating the joint probability. The proposed method takes the advantage of the deep learning architecture and improves the architecture's performance by including the model-driven position constraint when lack of sufficient training data. Experiments show that the proposed method successfully removes the distraction of bright lesions in abnormal fundus images and obtains a satisfying OD segmentation on three public databases: Kaggle, MESSIDOR and NIVE, and it outperforms existing methods with a very high accuracy. © 2021","author":[{"family":"Fu","given":"Y."},{"family":"Chen","given":"J."},{"family":"Li","given":"J."},{"family":"Pan","given":"D."},{"family":"Yue","given":"X."},{"family":"Zhu","given":"Y."}],"citation-key":"Fu2021","container-title":"Pattern Recognition","DOI":"10.1016/j.patcog.2021.107971","ISSN":"00313203","issued":{"date-parts":[[2021]]},"publisher":"Elsevier Ltd","title":"Optic disc segmentation by U-net and probability bubble in abnormal fundus images","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104132508&doi=10.1016%2fj.patcog.2021.107971&partnerID=40&md5=987bd6cf601df8e6cc4783b4d4a2f71f","volume":"117"},
{"id":"Fu2021","abstract":"Deep learning-based methods have achieved notable progress in removing blocking artifacts caused by lossy JPEG compression on images. However, most deep learning-based methods handle this task by designing black-box network architectures to directly learn the relationships between the compressed images and their clean versions. These network architectures are always lack of sufficient interpretability, which limits their further improvements in deblocking performance. To address this issue, in this article, we propose a model-driven deep unfolding method for JPEG artifacts removal, with interpretable network structures. First, we build a maximum posterior (MAP) model for deblocking using convolutional dictionary learning and design an iterative optimization algorithm using proximal operators. Second, we unfold this iterative algorithm into a learnable deep network structure, where each module corresponds to a specific operation of the iterative algorithm. In this way, our network inherits the benefits of both the powerful model ability of data-driven deep learning method and the interpretability of traditional model-driven method. By training the proposed network in an end-to-end manner, all learnable modules can be automatically explored to well characterize the representations of both JPEG artifacts and image content. Experiments on synthetic and real-world datasets show that our method is able to generate competitive or even better deblocking results, compared with state-of-the-art methods both quantitatively and qualitatively. IEEE","author":[{"family":"Fu","given":"X."},{"family":"Wang","given":"M."},{"family":"Cao","given":"X."},{"family":"Ding","given":"X."},{"family":"Zha","given":"Z."}],"citation-key":"Fu2021","container-title":"IEEE Transactions on Neural Networks and Learning Systems","DOI":"10.1109/TNNLS.2021.3083504","ISSN":"2162237X","issued":{"date-parts":[[2021]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A model-driven deep unfolding method for JPEG artifacts removal","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107352265&doi=10.1109%2fTNNLS.2021.3083504&partnerID=40&md5=de1ba1a7e3e61307ef97b3ee3409910e"},
{"id":"Fukas202219","abstract":"The use of Artificial Intelligence (AI) must be systematically managed and coordinated to optimally support corporate goals and enable AI to create added value for organizations. This poses new challenges for traditional Information Technology (IT) management. Although initial approaches to managing AI as an extension of traditional IT management exist, the management of AI is still in its infancy. Therefore, the goal of our research is the development of an integrated management framework that combines insights from AI maturity model research with an overarching AI management perspective. In a multi-method and design science-oriented research process, an AI maturity model, an AI management metamodel, and a web-based AI maturity assessment and management tool combining both previous models are developed and evaluated. In addition, several smaller studies are conducted to demonstrate how AI-based information systems can be managed corresponding to the different dimensions of the integrated AI management framework. © 2022 Copyright for this paper by its authors.","author":[{"family":"Fukas","given":"P."}],"citation-key":"Fukas202219","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Looy A.V., Weber B.","given":"Rosemann M."}],"ISSN":"16130073","issued":{"date-parts":[[2022]]},"page":"19-27","publisher":"CEUR-WS","title":"The management of artificial intelligence: Developing a framework based on the artificial intelligence maturity principle","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130763237&partnerID=40&md5=0470ee8a2332eeaf51d2d7d78d596809","volume":"3139"},
{"id":"fukasManagementArtificialIntelligence2022a","abstract":"The use of Artificial Intelligence (AI) must be systematically managed and coordinated to optimally support corporate goals and enable AI to create added value for organizations. This poses new challenges for traditional Information Technology (IT) management. Although initial approaches to managing AI as an extension of traditional IT management exist, the management of AI is still in its infancy. Therefore, the goal of our research is the development of an integrated management framework that combines insights from AI maturity model research with an overarching AI management perspective. In a multi-method and design science-oriented research process, an AI maturity model, an AI management metamodel, and a web-based AI maturity assessment and management tool combining both previous models are developed and evaluated. In addition, several smaller studies are conducted to demonstrate how AI-based information systems can be managed corresponding to the different dimensions of the integrated AI management framework. © 2022 Copyright for this paper by its authors.","author":[{"family":"Fukas","given":"P."}],"citation-key":"fukasManagementArtificialIntelligence2022a","container-title":"CEUR Workshop Proceedings","editor":[{"family":"Looy A.V.","given":"Rosemann M.","suffix":"Weber B."}],"ISSN":"16130073","issued":{"date-parts":[[2022]]},"page":"19-27","publisher":"CEUR-WS","title":"The Management of Artificial Intelligence: Developing a Framework Based on the Artificial Intelligence Maturity Principle","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130763237&partnerID=40&md5=0470ee8a2332eeaf51d2d7d78d596809","volume":"3139"},
{"id":"fumarolaDataModelingRelationships","author":[{"family":"Fumarola","given":"Dr Fabio"}],"citation-key":"fumarolaDataModelingRelationships","page":"45","source":"Zotero","title":"Data Modeling for Relationships Handling and Data Distribution","type":"article-journal"},
{"id":"FundingTenders","accessed":{"date-parts":[[2019,10,30]]},"citation-key":"FundingTenders","title":"Funding & tenders","type":"webpage","URL":"https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/myarea/project/813884/program/31045243/organisation/999859511/roles/edit?name=Lowcomote"},
{"id":"fuOpticDiscSegmentation2021a","abstract":"Segmenting optic disc (OD) in abnormal fundus images is a challenge task because of many distractions such as illumination variations, blurry boundary, occlusion of retinal vessels and big bright lesions. Data-driven deep learning is effective and robust to illumination variations, blurry boundary and occlusion in the normal fundus images but sensitive to big bright lesions in abnormal images. In this paper, an automatic OD segmentation method fusing U-net with model-driven probability bubble approach is proposed in abnormal fundus images. The probability bubble is conceived according to the position relationship between retinal vessels and OD, and the localization result is fused into the output layer of U-net through calculating the joint probability. The proposed method takes the advantage of the deep learning architecture and improves the architecture's performance by including the model-driven position constraint when lack of sufficient training data. Experiments show that the proposed method successfully removes the distraction of bright lesions in abnormal fundus images and obtains a satisfying OD segmentation on three public databases: Kaggle, MESSIDOR and NIVE, and it outperforms existing methods with a very high accuracy. © 2021","author":[{"family":"Fu","given":"Y."},{"family":"Chen","given":"J."},{"family":"Li","given":"J."},{"family":"Pan","given":"D."},{"family":"Yue","given":"X."},{"family":"Zhu","given":"Y."}],"citation-key":"fuOpticDiscSegmentation2021a","container-title":"Pattern Recognition","DOI":"10.1016/j.patcog.2021.107971","ISSN":"00313203","issued":{"date-parts":[[2021]]},"publisher":"Elsevier Ltd","title":"Optic disc segmentation by U-net and probability bubble in abnormal fundus images","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104132508&doi=10.1016%2fj.patcog.2021.107971&partnerID=40&md5=987bd6cf601df8e6cc4783b4d4a2f71f","volume":"117"},
{"id":"Fursin2014309","abstract":"Empirical auto-tuning and machine learning techniques have been showing high potential to improve execution time, power consumption, code size, reliability and other important metrics of various applications for more than two decades. However, they are still far from widespread production use due to lack of native support for auto-tuning in an ever changing and complex software and hardware stack, large and multi-dimensional optimization spaces, excessively long exploration times, and lack of unified mechanisms for preserving and sharing of optimization knowledge and research material. We present a possible collaborative approach to solve above problems using Collective Mind knowledge management system. In contrast with previous cTuning framework, this modular infrastructure allows to preserve and share through the Internet the whole auto-tuning setups with all related artifacts and their software and hardware dependencies besides just performance data. It also allows to gradually structure, systematize and describe all available research material including tools, benchmarks, data sets, search strategies and machine learning models. Researchers can take advantage of shared components and data with extensible meta-description to quickly and collaboratively validate and improve existing auto-tuning and benchmarking techniques or prototype new ones. The community can now gradually learn and improve complex behavior of all existing computer systems while exposing behavior anomalies or model mispredictions to an interdisciplinary community in a reproducible way for further analysis. We present several practical, collaborative and model-driven auto-tuning scenarios. We also decided to release all material at c-mind.org/repo to set up an example for a collaborative and reproducible research as well as our new publication model in computer engineering where experimental results are continuously shared and validated by the community. © 2014 - IOS Press and the authors. All rights reserved.","author":[{"family":"Fursin","given":"G."},{"family":"Miceli","given":"R."},{"family":"Lokhmotov","given":"A."},{"family":"Gerndt","given":"M."},{"family":"Baboulin","given":"M."},{"family":"Malony","given":"A.D."},{"family":"Chamski","given":"Z."},{"family":"Novillo","given":"D."},{"family":"Del Vento","given":"D."}],"citation-key":"Fursin2014309","container-title":"Scientific Programming","DOI":"10.3233/SPR-140396","ISSN":"10589244","issue":"4","issued":{"date-parts":[[2014]]},"page":"309-329","publisher":"IOS Press","title":"Collective mind: Towards practical and collaborative auto-tuning","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923646864&doi=10.3233%2fSPR-140396&partnerID=40&md5=35e68b6898c79dacdd4631d62b041827","volume":"22"},
{"id":"fursinCollectiveMindPractical2014a","abstract":"Empirical auto-tuning and machine learning techniques have been showing high potential to improve execution time, power consumption, code size, reliability and other important metrics of various applications for more than two decades. However, they are still far from widespread production use due to lack of native support for auto-tuning in an ever changing and complex software and hardware stack, large and multi-dimensional optimization spaces, excessively long exploration times, and lack of unified mechanisms for preserving and sharing of optimization knowledge and research material. We present a possible collaborative approach to solve above problems using Collective Mind knowledge management system. In contrast with previous cTuning framework, this modular infrastructure allows to preserve and share through the Internet the whole auto-tuning setups with all related artifacts and their software and hardware dependencies besides just performance data. It also allows to gradually structure, systematize and describe all available research material including tools, benchmarks, data sets, search strategies and machine learning models. Researchers can take advantage of shared components and data with extensible meta-description to quickly and collaboratively validate and improve existing auto-tuning and benchmarking techniques or prototype new ones. The community can now gradually learn and improve complex behavior of all existing computer systems while exposing behavior anomalies or model mispredictions to an interdisciplinary community in a reproducible way for further analysis. We present several practical, collaborative and model-driven auto-tuning scenarios. We also decided to release all material at c-mind.org/repo to set up an example for a collaborative and reproducible research as well as our new publication model in computer engineering where experimental results are continuously shared and validated by the community. © 2014 - IOS Press and the authors. All rights reserved.","author":[{"family":"Fursin","given":"G."},{"family":"Miceli","given":"R."},{"family":"Lokhmotov","given":"A."},{"family":"Gerndt","given":"M."},{"family":"Baboulin","given":"M."},{"family":"Malony","given":"A.D."},{"family":"Chamski","given":"Z."},{"family":"Novillo","given":"D."},{"family":"Del Vento","given":"D."}],"citation-key":"fursinCollectiveMindPractical2014a","container-title":"Scientific Programming","DOI":"10.3233/SPR-140396","ISSN":"10589244","issue":"4","issued":{"date-parts":[[2014]]},"page":"309-329","publisher":"IOS Press","title":"Collective mind: Towards practical and collaborative auto-tuning","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923646864&doi=10.3233%2fSPR-140396&partnerID=40&md5=35e68b6898c79dacdd4631d62b041827","volume":"22"},
{"id":"gadepallyBigDAWGManagingHeterogenous","author":[{"family":"Gadepally","given":"Dr Vijay"}],"citation-key":"gadepallyBigDAWGManagingHeterogenous","page":"48","source":"Zotero","title":"BigDAWG: Managing Heterogenous Data and Streaming","type":"article-journal"},
{"id":"gainLowcodeAutoMLaugmentedData2021","abstract":"There is a lack of knowledge concerning the low-code autoML (automated machine learning) frameworks that can be used to enrich data for several purposes concerning either data engineering or software engineering. In this paper, 34 autoML frameworks have been reviewed based on the latest commits and augmentation properties of their GitHub content. The PyCaret framework was the result of the review due to requirements concerning adaptability by Google Colaboratory (Colab) and the BI (business intelligence) tool. Finally, the low-code autoMLaugmented data pipeline from raw data to dashboards and low-code apps has been drawn based on the experiments concerned classifications of the \"Census Income\" dataset. The constructed pipeline preferred the same data to be a ground for different reports, dashboards, and applications. However, the constructed low-code autoML-augmented data pipeline contains changeable building blocks such as libraries and visualisations.","accessed":{"date-parts":[[2022,3,14]]},"author":[{"family":"Gain","given":"Ulla"},{"family":"Hotti","given":"Virpi"}],"citation-key":"gainLowcodeAutoMLaugmentedData2021","container-title":"Journal of Physics: Conference Series","container-title-short":"J. Phys.: Conf. Ser.","DOI":"10.1088/1742-6596/1828/1/012015","ISSN":"1742-6588, 1742-6596","issue":"1","issued":{"date-parts":[[2021,2,1]]},"note":"00005","page":"012015","source":"DOI.org (Crossref)","title":"Low-code AutoML-augmented Data Pipeline – A Review and Experiments","type":"article-journal","URL":"https://iopscience.iop.org/article/10.1088/1742-6596/1828/1/012015","volume":"1828"},
{"id":"galassoCodeSophisticationCode2022","abstract":"A typical approach to programming is to first code the main execution scenario, and then focus on filling out alternative behaviors and corner cases. But, almost always, there exist unusual conditions that trigger atypical behaviors, which are hard to predict in program specifications, and are thus often not coded. In this paper, we consider the problem of detecting and recommending such missing behaviors, a task that we call code sophistication. Previous research on coding assistants usually focuses on recommending code fragments based on specifications of the intended behavior. In contrast, code sophistication happens in the absence of a specification, aiming to help developers complete the logic of their programs with missing and unspecified behaviors. We outline the research challenges to this problem and present early results showing how program logic can be completed by leveraging code structure and information about the usage of input parameters.","accessed":{"date-parts":[[2022,1,25]]},"author":[{"family":"Galasso","given":"Jessie"},{"family":"Famelis","given":"Michalis"},{"family":"Sahraoui","given":"Houari"}],"citation-key":"galassoCodeSophisticationCode2022","container-title":"arXiv:2201.07674 [cs]","issued":{"date-parts":[[2022,1,19]]},"note":"00000","source":"arXiv.org","title":"Code Sophistication: From Code Recommendation to Logic Recommendation","title-short":"Code Sophistication","type":"article-journal","URL":"http://arxiv.org/abs/2201.07674"},
{"id":"gallardoModelingCollaborationProtocols2013","author":[{"family":"Gallardo","given":"Jesús"},{"family":"Bravo","given":"Crescencio"},{"family":"Redondo","given":"Miguel A."},{"family":"Lara","given":"Juan","non-dropping-particle":"de"}],"citation-key":"gallardoModelingCollaborationProtocols2013","container-title":"Journal of Visual Languages & Computing","DOI":"10.1016/j.jvlc.2012.10.006","issue":"1","issued":{"date-parts":[[2013]]},"page":"10–23","title":"Modeling collaboration protocols for collaborative modeling tools: Experiences and applications","type":"article-journal","volume":"24"},
{"id":"ganserStagedModelEvolution2015","accessed":{"date-parts":[[2015,12,1]]},"author":[{"family":"Ganser","given":"Andreas"},{"family":"Lichter","given":"Horst"},{"family":"Roth","given":"Alexander"},{"family":"Rumpe","given":"Bernhard"}],"citation-key":"ganserStagedModelEvolution2015","container-title":"Software Quality Journal","DOI":"10.1007/s11219-015-9298-y","ISSN":"0963-9314, 1573-1367","issued":{"date-parts":[[2015,11,25]]},"source":"CrossRef","title":"Staged model evolution and proactive quality guidance for model libraries","type":"article-journal","URL":"http://link.springer.com/10.1007/s11219-015-9298-y"},
{"id":"Gao20182627","abstract":"In this letter, we propose a model-driven deep learning (DL) approach that combines DL with the expert knowledge to replace the existing orthogonal frequency-division multiplexing receiver in wireless communications. Different from the data-driven fully connected deep neural network (FC-DNN) method, we adopt the block-by-block signal processing method that divides the receiver into channel estimation subnet and signal detection subnet. Each subnet is constructed by a DNN and uses the existing simple and traditional solution as initialization. The proposed model-driven DL receiver offers more accurate channel estimation comparing with the linear minimum mean-squared error method and exhibits higher data recovery accuracy comparing with the existing methods and FC-DNN. Simulation results further demonstrate the robustness of the proposed approach in terms of signal-to-noise ratio and its superiority to the FC-DNN approach in the computational complexities or the memory usage. © 1997-2012 IEEE.","author":[{"family":"Gao","given":"X."},{"family":"Jin","given":"S."},{"family":"Wen","given":"C.-K."},{"family":"Li","given":"G.Y."}],"citation-key":"Gao20182627","container-title":"IEEE Communications Letters","DOI":"10.1109/LCOMM.2018.2877965","ISSN":"10897798","issue":"12","issued":{"date-parts":[[2018]]},"page":"2627-2630","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"ComNet: Combination of deep learning and expert knowledge in OFDM receivers","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055710120&doi=10.1109%2fLCOMM.2018.2877965&partnerID=40&md5=8cd867d37be7f8856ea36c0faa5d3cd8","volume":"22"},
{"id":"gaoCollaborativeFilteringRecommendation2019","abstract":"With the popularization of Internet of Things (IOT) technology, a large number of multi-source heterogeneous data are constantly generated and collected by cloud platforms, which indicates that the problem of large data in IOT has become increasingly prominent, especially for massive tags and information in IOT which is urgent to use appropriate data mining algorithms to mine the value of these data. A collaborative filtering recommendation algorithm based on multi-information source fusion (CFR-MIF) is proposed where a feature vector and time weight function are introduced to improve the accuracy of top-N recommendation. It can conveniently and effectively process the IoT data, and furthermore integrate, manage and store the massive data collected from different industries and data formats. Besides, It also provides data mining services in the whole IoT realizing prediction and decision-making, which reverses control these sensor networks, so as to control the movement and development process of objective in the Internet of Things. The experimental results based on DeviceLens 1M data set show that the proposed algorithm greatly improves the accuracy of recommendation results, recall rate and F1 value compared with other advanced algorithms.","accessed":{"date-parts":[[2022,2,3]]},"author":[{"family":"Gao","given":"Ying"},{"family":"Ran","given":"Lingxi"}],"citation-key":"gaoCollaborativeFilteringRecommendation2019","container-title":"IEEE Access","container-title-short":"IEEE Access","DOI":"10.1109/ACCESS.2019.2935224","ISSN":"2169-3536","issued":{"date-parts":[[2019]]},"note":"00007","page":"123583-123591","source":"DOI.org (Crossref)","title":"Collaborative Filtering Recommendation Algorithm for Heterogeneous Data Mining in the Internet of Things","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/8801822/","volume":"7"},
{"id":"gaoCollaborativeLearningBasedIndustrial2020","abstract":"The industrial Internet of things (IIoT), a new computing mode in Industry 4.0, is deployed to connect IoT devices and use communication technology to respond to control commands and handle industrial data. IIoT is typically employed to improve the efficiency of computing and sensing and can be used in many scenarios, such as intelligent manufacturing and video surveillance. To build an IIoT system, we need a collection of software to manage and monitor each system component when there are large-scale devices. Application programming interface (API) is an effective way to invoke public services provided by different platforms. Developers can invoke different APIs to operate IoT devices without knowing the implementation process. We can design a workflow to configure how and when to invoke target APIs. Thus, APIs are a powerful tool for rapidly developing industrial systems. However, the increasing number of APIs exacerbates the problem of finding suitable APIs. Current related recommendation methods have defects. For example, most existing methods focus on the relation between users and APIs but neglect the valuable relations among the users or APIs themselves. To address these problems, this article studies implicit knowledge in IIoT by using collaborative learning techniques. Considering the increased dimensions and dynamics of IoT devices, we explore the possible relationships between users and between APIs. We enhance the matrix factorization (MF) model with the mined implicit knowledge that are implicit relationships on both sides. We build an ensemble model by using all implicit knowledge. We conduct experiments on a collected real-world dataset and simulate industrial system scenarios. The experimental results verify the effectiveness and superiority of the proposed models.","accessed":{"date-parts":[[2020,10,6]]},"author":[{"family":"Gao","given":"Honghao"},{"family":"Qin","given":"Xi"},{"family":"Barroso","given":"Ramon J. Duran"},{"family":"Hussain","given":"Walayat"},{"family":"Xu","given":"Yueshen"},{"family":"Yin","given":"Yuyu"}],"citation-key":"gaoCollaborativeLearningBasedIndustrial2020","container-title":"IEEE Transactions on Emerging Topics in Computational Intelligence","container-title-short":"IEEE Trans. Emerg. Top. Comput. Intell.","DOI":"10.1109/TETCI.2020.3023155","ISSN":"2471-285X","issued":{"date-parts":[[2020]]},"note":"00000","page":"1-11","source":"DOI.org (Crossref)","title":"Collaborative Learning-Based Industrial IoT API Recommendation for Software-Defined Devices: The Implicit Knowledge Discovery Perspective","title-short":"Collaborative Learning-Based Industrial IoT API Recommendation for Software-Defined Devices","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/9208715/"},
{"id":"garcesEndtoendFinegrainedTraceability","author":[{"family":"Garces","given":"Victor Guana"}],"citation-key":"garcesEndtoendFinegrainedTraceability","note":"00000","page":"155","source":"Zotero","title":"End-to-end Fine-grained Traceability Analysis in Model Transformations and Transformation Chains","type":"article-journal"},
{"id":"garcia-dominguezStresstestingRemoteModel2017","abstract":"Recent research in scalable model-driven engineering now allows very large models to be stored and queried. Due to their size, rather than transferring such models over the network in their entirety, it is typically more efficient to access them remotely using networked services (e.g. model repositories, model indexes). Little attention has been paid so far to the nature of these services, and whether they remain responsive with an increasing number of concurrent clients. This paper extends a previous empirical study on the impact of certain key decisions on the scalability of concurrent model queries on two domains, using an Eclipse Connected Data Objects model repository, four configurations of the Hawk model index and a Neo4j-based configuration of the NeoEMF model store. The study evaluates the impact of the network protocol, the API design, the caching layer, the query language and the type of database and analyses the reasons for their varying levels of performance. The design of the API was shown to make a bigger difference compared to the network protocol (HTTP/TCP) used. Where available, the query-specific indexed and derived attributes in Hawk outperformed the comprehensive generic caching in CDO. Finally, the results illustrate the still ongoing evolution of graph databases: two tools using different versions of the same backend had very different performance, with one slower than CDO and the other faster than it.","accessed":{"date-parts":[[2017,8,28]]},"author":[{"family":"Garcia-Dominguez","given":"Antonio"},{"family":"Barmpis","given":"Konstantinos"},{"family":"Kolovos","given":"Dimitrios S."},{"family":"Wei","given":"Ran"},{"family":"Paige","given":"Richard F."}],"citation-key":"garcia-dominguezStresstestingRemoteModel2017","container-title":"Software & Systems Modeling","container-title-short":"Softw Syst Model","DOI":"10.1007/s10270-017-0606-9","ISSN":"1619-1366, 1619-1374","issued":{"date-parts":[[2017,6,30]]},"page":"1-29","source":"link.springer.com","title":"Stress-testing remote model querying APIs for relational and graph-based stores","type":"article-journal","URL":"https://link.springer.com/article/10.1007/s10270-017-0606-9"},
{"id":"gargMUDABlueAutomaticCategorization2004","author":[{"family":"Garg","given":"Pankaj K."},{"family":"Kawaguchi","given":"Shinji"},{"family":"Matsushita","given":"Makoto"},{"family":"Inoue","given":"Katsuro"}],"citation-key":"gargMUDABlueAutomaticCategorization2004","container-title":"2013 20th Asia-Pacific Software Engineering Conference (APSEC)","ISSN":"1530-1362","issued":{"date-parts":[[2004]]},"page":"184-193","title":"MUDABlue: An automatic categorization system for open source repositories","type":"article-journal"},
{"id":"garousiGuidelinesIncludingGrey2019","accessed":{"date-parts":[[2021,1,8]]},"author":[{"family":"Garousi","given":"Vahid"},{"family":"Felderer","given":"Michael"},{"family":"Mäntylä","given":"Mika V."}],"citation-key":"garousiGuidelinesIncludingGrey2019","container-title":"Information and Software Technology","container-title-short":"Information and Software Technology","DOI":"10.1016/j.infsof.2018.09.006","ISSN":"09505849","issued":{"date-parts":[[2019,2]]},"note":"00162","page":"101-121","source":"DOI.org (Crossref)","title":"Guidelines for including grey literature and conducting multivocal literature reviews in software engineering","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0950584918301939","volume":"106"},
{"id":"gasparicWhatRecommendationSystems2016","author":[{"family":"Gasparic","given":"Marko"},{"family":"Janes","given":"Andrea"}],"citation-key":"gasparicWhatRecommendationSystems2016","container-title":"J. Syst. Softw.","ISSN":"0164-1212","issue":"C","issued":{"date-parts":[[2016,3]]},"page":"101-113","title":"What recommendation systems for software engineering recommend","type":"article-journal","URL":"http://dx.doi.org/10.1016/j.jss.2015.11.036","volume":"113"},
{"id":"gasparicWhatRecommendationSystems2016a","abstract":"A recommendation system for software engineering (RSSE) is a software application that provides information items estimated to be valuable for a software engineering task in a given context. Present the results of a systematic literature review to reveal the typical functionality offered by existing RSSEs, research gaps, and possible research directions. We evaluated 46 papers studying the benefits, the data requirements, the information and recommendation types, and the effort requirements of RSSE systems. We include papers describing tools that support source code related development published between 2003 and 2013. The results show that RSSEs typically visualize source code artifacts. They aim to improve system quality, make the development process more efficient and less expensive, lower developer’s cognitive load, and help developers to make better decisions. They mainly support reuse actions and debugging, implementation, and maintenance phases. The majority of the systems are reactive. Unexploited opportunities lie in the development of recommender systems outside the source code domain. Furthermore, current RSSE systems use very limited context information and rely on simple models. Context-adapted and proactive behavior could improve the acceptance of RSSE systems in practice.","accessed":{"date-parts":[[2019,6,13]]},"author":[{"family":"Gasparic","given":"Marko"},{"family":"Janes","given":"Andrea"}],"citation-key":"gasparicWhatRecommendationSystems2016a","container-title":"Journal of Systems and Software","ISSN":"01641212","issued":{"date-parts":[[2016,3]]},"page":"101-113","title":"What recommendation systems for software engineering recommend: A systematic literature review","title-short":"What recommendation systems for software engineering recommend","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0164121215002605","volume":"113"},
{"id":"Ge:2010_catalog_coverage","author":[{"family":"Ge","given":"Mouzhi"},{"family":"Delgado-Battenfeld","given":"Carla"},{"family":"Jannach","given":"Dietmar"}],"citation-key":"Ge:2010_catalog_coverage","collection-title":"RecSys '10","container-title":"Proceedings of the fourth ACM conference on recommender systems","event-place":"New York, NY, USA","ISBN":"978-1-60558-906-0","issued":{"date-parts":[[2010]]},"page":"257-260","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Beyond accuracy: Evaluating recommender systems by coverage and serendipity","type":"paper-conference","URL":"http://doi.acm.org/10.1145/1864708.1864761"},
{"id":"geDataMiningAnalytics2017","abstract":"Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. As a computational engine to data mining and analytics, machine learning serves as basic tools for information extraction, data pattern recognition and predictions. From the perspective of machine learning, this paper provides a review on existing data mining and analytics applications in the process industry over the past several decades. The state-of-the-art of data mining and analytics are reviewed through eight unsupervised learning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms. Several perspectives are highlighted and discussed for future researches on data mining and analytics in the process industry.","accessed":{"date-parts":[[2022,2,3]]},"author":[{"family":"Ge","given":"Zhiqiang"},{"family":"Song","given":"Zhihuan"},{"family":"Ding","given":"Steven X."},{"family":"Huang","given":"Biao"}],"citation-key":"geDataMiningAnalytics2017","container-title":"IEEE Access","container-title-short":"IEEE Access","DOI":"10.1109/ACCESS.2017.2756872","ISSN":"2169-3536","issued":{"date-parts":[[2017]]},"note":"00608","page":"20590-20616","source":"DOI.org (Crossref)","title":"Data Mining and Analytics in the Process Industry: The Role of Machine Learning","title-short":"Data Mining and Analytics in the Process Industry","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/8051033/","volume":"5"},
{"id":"GeneralizedAutomaticClustering","accessed":{"date-parts":[[2015,5,7]]},"citation-key":"GeneralizedAutomaticClustering","title":"A generalized automatic clustering algorithm in a multiobjective framework","type":"webpage","URL":"http://www.sciencedirect.com/science/article/pii/S1568494612003493"},
{"id":"generoBuildingMeasurebasedPrediction2007","author":[{"family":"Genero","given":"Marcela"},{"family":"Manso","given":"Esperanza"},{"family":"Visaggio","given":"Aaron"},{"family":"Canfora","given":"Gerardo"},{"family":"Piattini","given":"Mario"}],"citation-key":"generoBuildingMeasurebasedPrediction2007","container-title":"Empirical Software Engineering","DOI":"10.1007/s10664-007-9038-4","issue":"5","issued":{"date-parts":[[2007]]},"page":"517–549","title":"Building measure-based prediction models for UML class diagram maintainability","type":"article-journal","volume":"12"},
{"id":"generoSurveyMetricsUML2005","author":[{"family":"Genero","given":"Marcela"},{"family":"Piattini","given":"Mario"},{"family":"Calero","given":"Coral"}],"citation-key":"generoSurveyMetricsUML2005","container-title":"The Journal of Object Technology","DOI":"10.5381/jot.2005.4.9.a1","issue":"9","issued":{"date-parts":[[2005]]},"page":"59","title":"A Survey of Metrics for UML Class Diagrams.","type":"article-journal","volume":"4"},
{"id":"gerostathopoulosTRAPPedTrafficSelfAdaptive2019","abstract":"Optimizing the traffic flow in a city is a challenging problem, especially in a future traffic system of self-driving cars and sharing vehicles. This is due to the interactions between the individual traffic agents (vehicles) that compete for the use of the common infrastructure (streets) given traffic dynamics such as stop-and-go effects, changing lanes, and other. The goal of this paper is to provide a solution to the above problem that works in a fully decentralized and participatory way, i.e. autonomous agents collaborate without a centralized data collector and arbitrator. Such a solution should be scalable, privacypreserving, and flexible with respect to the degree of autonomy of agents. A self-adaptive framework to support this research is introduced: TRAPP – Traffic Reconfigurations via Adaptive Participatory Planning. The framework relies on a microscopic traffic simulator, SUMO, for simulating urban mobility scenarios, and on a decentralized multi-agent planning system, EPOS, for decentralized combinatorial optimization, applied here in traffic flows. A data-driven interoperation of the two tools in the proposed framework allows high modularity and customization for experimenting with different scenarios, optimization objectives and agents’ behavior and as such providing new perspectives for resilient future traffic infrastructures.","accessed":{"date-parts":[[2020,10,5]]},"author":[{"family":"Gerostathopoulos","given":"Ilias"},{"family":"Pournaras","given":"Evangelos"}],"citation-key":"gerostathopoulosTRAPPedTrafficSelfAdaptive2019","container-title":"2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","DOI":"10.1109/SEAMS.2019.00014","event":"2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","event-place":"Montreal, QC, Canada","ISBN":"978-1-72813-368-3","issued":{"date-parts":[[2019,5]]},"page":"32-38","publisher":"IEEE","publisher-place":"Montreal, QC, Canada","source":"DOI.org (Crossref)","title":"TRAPPed in Traffic? A Self-Adaptive Framework for Decentralized Traffic Optimization","title-short":"TRAPPed in Traffic?","type":"paper-conference","URL":"https://ieeexplore.ieee.org/document/8787057/"},
{"id":"gessertNoSQLDatabaseSystems2017","abstract":"Today, data is generated and consumed at unprecedented scale. This has lead to novel approaches for scalable data management subsumed under the term “NoSQL” database systems to handle the ever-increasing data volume and request loads. However, the heterogeneity and diversity of the numerous existing systems impede the well-informed selection of a data store appropriate for a given application context. Therefore, this article gives a top-down overview of the field: instead of contrasting the implementation specifics of individual representatives, we propose a comparative classification model that relates functional and non-functional requirements to techniques and algorithms employed in NoSQL databases. This NoSQL Toolbox allows us to derive a simple decision tree to help practitioners and researchers filter potential system candidates based on central application requirements.","accessed":{"date-parts":[[2019,11,20]]},"author":[{"family":"Gessert","given":"Felix"},{"family":"Wingerath","given":"Wolfram"},{"family":"Friedrich","given":"Steffen"},{"family":"Ritter","given":"Norbert"}],"citation-key":"gessertNoSQLDatabaseSystems2017","container-title":"Computer Science - Research and Development","container-title-short":"Comput Sci Res Dev","DOI":"10.1007/s00450-016-0334-3","ISSN":"1865-2034, 1865-2042","issue":"3-4","issued":{"date-parts":[[2017,7]]},"note":"cites: autiliSoftwareExoskeletonProtect2019","page":"353-365","source":"DOI.org (Crossref)","title":"NoSQL database systems: a survey and decision guidance","title-short":"NoSQL database systems","type":"article-journal","URL":"http://link.springer.com/10.1007/s00450-016-0334-3","volume":"32"},
{"id":"Getir2014","abstract":"Development of agent systems is without question a complex task when autonomous, reactive and proactive characteristics of agents are considered. Furthermore, internal agent behavior model and interaction within the agent organizations become even more complex and hard to implement when new requirements and interactions for new agent environments such as the Semantic Web are taken into account. We believe that the use of both domain specific modeling and a Domain-specific Modeling Language (DSML) may provide the required abstraction and support a more fruitful methodology for the development of Multi-agent Systems (MASs) especially when they are working on the Semantic Web environment. Although syntax definition based on a metamodel is an essential part of a modeling language, an additional and required part would be the determination and implementation of DSML constraints that constitute the (formal) semantics which cannot be defined solely with a metamodel. Hence, in this paper, formal semantics of a MAS DSML called Semantic Web enabled Multi-agent Systems (SEA-ML) is introduced. SEA-ML is a modeling language for agent systems that specifically takes into account the interactions of semantic web agents with semantic web services. What is more, SEA-ML also supports the modeling of semantic agents from their internals to MAS perspective. Based on the defined abstract and concrete syntax definitions, we first give the formal representation of SEA-ML's semantics and then discuss its use on MAS validation. In order to define and implement semantics of SEA-ML, we employ Alloy language which is declarative and has a strong description capability originating from both relational and first-order logic in order to easily define complex structures and behaviors of these systems. Differentiating from similar contributions of other researchers on formal semantics definition for MAS development languages, SEA-ML's semantics, presented in this paper, defines both static and dynamic aspects of the interaction between software agents and semantic web services, in addition to the definition of the semantics already required for agent internals and MAS communication. Implementation with Alloy makes definition of SEA-ML's semantics to include relations and sets with a simple notation for MAS model definitions. We discuss how the automatic analysis and hence checking of SEA-ML models can be realized with the defined semantics. Design of an agent-based electronic barter system is exemplified in order to give some flavor of the use of SEA-ML's formal semantics. Lessons learned during the development of such a MAS DSML semantics are also reported in this paper. © 2014 World Scientific Publishing Company.","author":[{"family":"Getir","given":"S."},{"family":"Challenger","given":"M."},{"family":"Kardas","given":"G."}],"citation-key":"Getir2014","container-title":"International Journal of Cooperative Information Systems","DOI":"10.1142/S0218843014500051","ISSN":"02188430","issue":"3","issued":{"date-parts":[[2014]]},"publisher":"World Scientific Publishing Co. Pte Ltd","title":"The formal semantics of a domain-specific modeling language for semantic web enabled multi-agent systems","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906262282&doi=10.1142%2fS0218843014500051&partnerID=40&md5=31032a2502b33c62cf07c745c9b5713b","volume":"23"},
{"id":"GettingStarted","accessed":{"date-parts":[[2016,12,2]]},"citation-key":"GettingStarted","title":"0_Getting Started |","type":"webpage","URL":"http://self-star.imag.fr/?page_id=63"},
{"id":"Gharibi2019","abstract":"Developing a deep learning model is an iterative, experimental process that produces tens to hundreds of models before arriving at a satisfactory result. While there has been a surge in the number of software tools that aim to facilitate deep learning, the process of managing the models and their artifacts is still surprisingly challenging and time-consuming. Existing model-management solutions are either tailored for commercial platforms or require significant code changes. In this paper, we introduce a lightweight system, named ModelKB, that can automatically extract and manage the model's metadata and provenance information (e.g., the used datasets and hyperparameters). Our overarching goal is to automate the management of deep learning experiments with minimal user intervention. Moreover, ModelKB provides a stepping stone to facilitate model selection and reproducibility. © 2019 ACM.","author":[{"family":"Gharibi","given":"G."},{"family":"Walunj","given":"V."},{"family":"Alanazi","given":"R."},{"family":"Rella","given":"S."},{"family":"Lee","given":"Y."}],"citation-key":"Gharibi2019","collection-title":"Proceedings of the ACM SIGMOD International Conference on Management of Data","DOI":"10.1145/3329486.3329495","ISBN":"978-1-4503-6797-4","ISSN":"07308078","issued":{"date-parts":[[2019]]},"publisher":"Association for Computing Machinery","title":"Automated management of deep learning experiments","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074450392&doi=10.1145%2f3329486.3329495&partnerID=40&md5=b32e5a10e157a3af156ae099af14ecd5"},
{"id":"Gharibi201928","abstract":"Deep Learning has improved the state-of-the-art results in an ever-growing number of domains. This success heavily relies on the development and training of deep learning models, also known as deep neural networks (DNN). Often, developing a DNN is an ad-hoc, iterative process that results in producing tens to hundreds of models before arriving at a satisfactory result. While there has been a surge in the number of tools and frameworks that aim at facilitating deep learning, the issues of model management have been largely ignored. In particular, deep learning practitioners have to manually track their experiments using text files, spreadsheets or folder hierarchies, which is expensive, time-consuming, and error-prone. In this paper, we present our ongoing work and vision towards automating end-to-end model management in deep learning. Specifically, we introduce a tool prototype, named ModelKB, that can automatically (1) extract and store the model's metadata-including its architecture, weights, and configuration; (2) visualize, query, and compare experiments; and (3) reproduce experiments. Our overarching goal is to automate the model management process with minimal user intervention using the user's favorite framework. We report the current status of ModelKB, a pilot user study, and the challenges of automating model management in deep learning. © 2019 IEEE.","author":[{"family":"Gharibi","given":"G."},{"family":"Walunj","given":"V."},{"family":"Rella","given":"S."},{"family":"Lee","given":"Y."}],"citation-key":"Gharibi201928","collection-title":"Proceedings - 2019 IEEE/ACM 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2019","DOI":"10.1109/RAISE.2019.00013","ISBN":"978-1-72812-272-4","issued":{"date-parts":[[2019]]},"page":"28-34","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"ModelKB: Towards automated management of the modeling lifecycle in deep learning","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072912350&doi=10.1109%2fRAISE.2019.00013&partnerID=40&md5=411165c84d5da81e78b4a9cfe9a7302a"},
{"id":"Gharibi2021","abstract":"Deep learning has improved the state-of-the-art results in an ever-growing number of domains. This success heavily relies on the development and training of deep learning models–an experimental, iterative process that produces tens to hundreds of models before arriving at a satisfactory result. While there has been a surge in the number of tools and frameworks that aim at facilitating deep learning, the process of managing the models and their artifacts is still surprisingly challenging and time-consuming. Existing model-management solutions are either tailored for commercial platforms or require significant code changes. Moreover, most of the existing solutions address a single phase of the modeling lifecycle, such as experiment monitoring, while ignoring other essential tasks, such as model deployment. In this paper, we present a software system to facilitate and accelerate the deep learning lifecycle, named ModelKB. ModelKB can automatically manage the modeling lifecycle end-to-end, including (1) monitoring and tracking experiments; (2) visualizing, searching for, and comparing models and experiments; (3) deploying models locally and on the cloud; and (4) sharing and publishing trained models. Moreover, our system provides a stepping-stone for enhanced reproducibility. ModelKB currently supports TensorFlow 2.0, Keras, and PyTorch, and it can be extended to other deep learning frameworks easily. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.","author":[{"family":"Gharibi","given":"G."},{"family":"Walunj","given":"V."},{"family":"Nekadi","given":"R."},{"family":"Marri","given":"R."},{"family":"Lee","given":"Y."}],"citation-key":"Gharibi2021","container-title":"Empirical Software Engineering","DOI":"10.1007/s10664-020-09894-9","ISSN":"13823256","issue":"2","issued":{"date-parts":[[2021]]},"publisher":"Springer","title":"Automated end-to-end management of the modeling lifecycle in deep learning","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101321601&doi=10.1007%2fs10664-020-09894-9&partnerID=40&md5=e08470aa6b9653a16a05ff82f96fc8ff","volume":"26"},
{"id":"Ghasemi2021","abstract":"Simulation Optimization (SO) techniques refer to a set of methods that have been applied to stochastic optimization problems, structured so that the optimizer(s) are integrated with simulation experiments. Although SO techniques provide promising solutions for large and complex stochastic problems, the simulation model execution is potentially expensive in terms of computation time. Thus, the overall purpose of this research is to advance the evolutionary SO methods literature by researching the use of metamodeling within these techniques. Accordingly, we present a new Evolutionary Learning Based Simulation Optimization (ELBSO) method embedded within Ordinal Optimization. In ELBSO a Machine Learning (ML) based simulation metamodel is created using Genetic Programming (GP) to replace simulation experiments aimed at reducing computation. ELBSO is evaluated on a Stochastic Job Shop Scheduling Problem (SJSSP), which is a well known complex production planning problem in most industries such as semiconductor manufacturing. To build the metamodel from SJSSP instances that replace simulation replications, we employ a novel training vector to train GP. This then is integrated into an evolutionary two-phased Ordinal Optimization approach to optimize an SJSSP which forms the ELBSO method. Using a variety of experimental SJSSP instances, ELBSO is compared with evolutionary optimization methods from the literature and typical dispatching rules. Our findings include the superiority of ELBSO over all other algorithms in terms of the quality of solutions and computation time. Furthermore, the integrated procedures and results provided within this article establish a basis for future SO applications to large and complex stochastic problems. © 2021 Elsevier B.V.","author":[{"family":"Ghasemi","given":"A."},{"family":"Ashoori","given":"A."},{"family":"Heavey","given":"C."}],"citation-key":"Ghasemi2021","container-title":"Applied Soft Computing","DOI":"10.1016/j.asoc.2021.107309","ISSN":"15684946","issued":{"date-parts":[[2021]]},"publisher":"Elsevier Ltd","title":"Evolutionary learning based simulation optimization for stochastic job shop scheduling problems","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103102274&doi=10.1016%2fj.asoc.2021.107309&partnerID=40&md5=bfbbf8ca1247e84564786ff6d008f92a","volume":"106"},
{"id":"Ghose2001","abstract":"Taste tests are being increasingly used by marketers to influence consumers to change their preferences toward their brands. This research indicates how perceptual and preferential taste tests can be used in conjunction with visual maps to provide support to marketing managers for making better brand positioning and targeting decisions on the basis of taste for different segments of consumers. An empirical blind taste-test study is used to illustrate the concepts. The preferential taste judgment part of the empirical study is designed to capture violations of the `Independence of Irrelevant Alternatives' (IIA) effect that is commonly observed in consumers' actual purchases. The present paper also uses a hypothetical example to indicate the importance of considering the location of consumer `ideal points' before making formulation changes in a brand as part of a targeting strategy. Various managerial implications of using the suggested perceptual preferential taste-mapping analyses are also discussed. Appropriate measurements of consumer tastes provide insights for identifying and targeting viable market segments.","author":[{"family":"Ghose","given":"Sanjoy"},{"family":"Lowengart","given":"Oded"}],"citation-key":"Ghose2001","container-title":"Journal of Targeting, Measurement and Analysis for Marketing","DOI":"10.1057/palgrave.jt.5740031","ISSN":"1479-1862","issue":"1","issued":{"date-parts":[[2001,8,1]]},"page":"26-41","title":"Taste tests: Impacts of consumer perceptions and preferences on brand positioning strategies","type":"article-journal","URL":"https://doi.org/10.1057/palgrave.jt.5740031","volume":"10"},
{"id":"giacobbeImplementationInfluxDBMonitoring2020","accessed":{"date-parts":[[2021,1,5]]},"archive_location":"10.1007/978-3-030-21005-2_15","author":[{"family":"Giacobbe","given":"Maurizio"},{"family":"Chaouch","given":"Chakib"},{"family":"Scarpa","given":"Marco"},{"family":"Puliafito","given":"Antonio"}],"citation-key":"giacobbeImplementationInfluxDBMonitoring2020","collection-title":"Smart Innovation, Systems and Technologies","container-title":"Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1","DOI":"10.1007/978-3-030-21005-2_15","editor":[{"family":"Bouhlel","given":"Med Salim"},{"family":"Rovetta","given":"Stefano"}],"issued":{"date-parts":[[2020]]},"note":"00000","page":"155-162","source":"10.1007/978-3-030-21005-2_15","title":"An Implementation of InfluxDB for Monitoring and Analytics in Distributed IoT Environments","type":"article-journal","URL":"http://link.springer.com/10.1007/978-3-030-21005-2_15","volume":"146"},
{"id":"giacobbeImplementationInfluxDBMonitoring2020a","accessed":{"date-parts":[[2021,1,5]]},"author":[{"family":"Giacobbe","given":"Maurizio"},{"family":"Chaouch","given":"Chakib"},{"family":"Scarpa","given":"Marco"},{"family":"Puliafito","given":"Antonio"}],"citation-key":"giacobbeImplementationInfluxDBMonitoring2020a","container-title":"Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1","DOI":"10.1007/978-3-030-21005-2_15","editor":[{"family":"Bouhlel","given":"Med Salim"},{"family":"Rovetta","given":"Stefano"}],"event-place":"Cham","ISBN":"978-3-030-21004-5 978-3-030-21005-2","issued":{"date-parts":[[2020]]},"note":"00000","page":"155-162","publisher":"Springer International Publishing","publisher-place":"Cham","source":"DOI.org (Crossref)","title":"An Implementation of InfluxDB for Monitoring and Analytics in Distributed IoT Environments","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-030-21005-2_15","volume":"146"},
{"id":"giannottiEXplainableMachineLearning","author":[{"family":"Giannotti","given":"Fosca"}],"citation-key":"giannottiEXplainableMachineLearning","note":"00000","page":"44","source":"Zotero","title":"eXplainable machine learning for Trustworthy AI","type":"article-journal"},
{"id":"gibaldiMLAHandbookWriters2009","call-number":"LB2369 .G53 2009","citation-key":"gibaldiMLAHandbookWriters2009","edition":"7th ed","editor":[{"family":"Gibaldi","given":"Joseph"},{"family":"Modern Language Association of America","given":""}],"event-place":"New York","ISBN":"978-1-60329-024-1 978-1-60329-025-8","issued":{"date-parts":[[2009]]},"number-of-pages":"292","publisher":"Modern Language Association of America","publisher-place":"New York","source":"Library of Congress ISBN","title":"MLA handbook for writers of research papers","type":"book"},
{"id":"Giese2011","author":[{"literal":"Gabrysiak"},{"family":"Gregor, Holger Giese","given":"Alexander Lüders"},{"family":"Seibel","given":"Andreas"}],"citation-key":"Giese2011","container-title":"ICSE 2011 workshop on flexible modeling tools","issued":{"date-parts":[[2011]]},"title":"How can metamodels be used flexibly","type":"paper-conference","volume":"22"},
{"id":"Gilkeson201484","abstract":"Numerical noise is an inevitable by-product of Computational Fluid Dynamics (CFD) simulations which can lead to challenges in finding optimum designs. This article draws attention to the issue, illustrating the difficulties it can cause for road vehicle aerodynamics simulations. Firstly a benchmark problem is used to assess a range of turbulence models and grid types. Large noise amplitudes up to 22% are evident for solutions computed on unstructured tetrahedral grids whereas computations on hexahedral and polyhedral grid structures exhibit substantially less noise. The Spalart Allmaras turbulence model is shown to be far less susceptible to noise levels than two other commonly-used models for this application. Secondly, multi-objective aerodynamic shape optimization is applied to a fairing for a practical road vehicle which is parameterised in terms of three design variables. Moving Least Squares (MLS) metamodels are constructed from 50 high-fidelity CFD solutions for two objective functions. Subsequent optimization is successful for the first objective, however numerical noise levels in excess of 7% give rise to difficulties for the second one. A revision to the problem leads to success and the construction of a small Pareto front. Further analysis underlines the inherent capability of MLS metamodels in dealing with noisy CFD responses. © 2014 Elsevier Ltd.","author":[{"family":"Gilkeson","given":"C.A."},{"family":"Toropov","given":"V.V."},{"family":"Thompson","given":"H.M."},{"family":"Wilson","given":"M.C.T."},{"family":"Foxley","given":"N.A."},{"family":"Gaskell","given":"P.H."}],"citation-key":"Gilkeson201484","container-title":"Computers and Fluids","DOI":"10.1016/j.compfluid.2014.02.004","ISSN":"00457930","issued":{"date-parts":[[2014]]},"page":"84-97","title":"Dealing with numerical noise in CFD-based design optimization","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896741286&doi=10.1016%2fj.compfluid.2014.02.004&partnerID=40&md5=ea87b5316c0649d8571d87b3bb841369","volume":"94"},
{"id":"gilkesonDealingNumericalNoise2014a","abstract":"Numerical noise is an inevitable by-product of Computational Fluid Dynamics (CFD) simulations which can lead to challenges in finding optimum designs. This article draws attention to the issue, illustrating the difficulties it can cause for road vehicle aerodynamics simulations. Firstly a benchmark problem is used to assess a range of turbulence models and grid types. Large noise amplitudes up to 22% are evident for solutions computed on unstructured tetrahedral grids whereas computations on hexahedral and polyhedral grid structures exhibit substantially less noise. The Spalart Allmaras turbulence model is shown to be far less susceptible to noise levels than two other commonly-used models for this application. Secondly, multi-objective aerodynamic shape optimization is applied to a fairing for a practical road vehicle which is parameterised in terms of three design variables. Moving Least Squares (MLS) metamodels are constructed from 50 high-fidelity CFD solutions for two objective functions. Subsequent optimization is successful for the first objective, however numerical noise levels in excess of 7% give rise to difficulties for the second one. A revision to the problem leads to success and the construction of a small Pareto front. Further analysis underlines the inherent capability of MLS metamodels in dealing with noisy CFD responses. © 2014 Elsevier Ltd.","author":[{"family":"Gilkeson","given":"C.A."},{"family":"Toropov","given":"V.V."},{"family":"Thompson","given":"H.M."},{"family":"Wilson","given":"M.C.T."},{"family":"Foxley","given":"N.A."},{"family":"Gaskell","given":"P.H."}],"citation-key":"gilkesonDealingNumericalNoise2014a","container-title":"Computers and Fluids","DOI":"10.1016/j.compfluid.2014.02.004","ISSN":"00457930","issued":{"date-parts":[[2014]]},"page":"84-97","title":"Dealing with numerical noise in CFD-based design optimization","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896741286&doi=10.1016%2fj.compfluid.2014.02.004&partnerID=40&md5=ea87b5316c0649d8571d87b3bb841369","volume":"94"},
{"id":"gilWingsIntelligentWorkflowBased2011","accessed":{"date-parts":[[2022,3,8]]},"author":[{"family":"Gil","given":"Yolanda"},{"family":"Ratnakar","given":"Varun"},{"family":"Kim","given":"Jihie"},{"family":"Gonzalez-Calero","given":"Pedro"},{"family":"Groth","given":"Paul"},{"family":"Moody","given":"Joshua"},{"family":"Deelman","given":"Ewa"}],"citation-key":"gilWingsIntelligentWorkflowBased2011","container-title":"IEEE Intelligent Systems","container-title-short":"IEEE Intell. Syst.","DOI":"10.1109/MIS.2010.9","ISSN":"1541-1672","issue":"1","issued":{"date-parts":[[2011,1]]},"note":"00236","page":"62-72","source":"DOI.org (Crossref)","title":"Wings: Intelligent Workflow-Based Design of Computational Experiments","title-short":"Wings","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/5396300/","volume":"26"},
{"id":"Girum2019119","abstract":"Deep learning has shown unprecedented success in a variety of applications, such as computer vision and medical image analysis. However, there is still potential to improve segmentation in multimodal images by embedding prior knowledge via learning-based shape modeling and registration to learn the modality invariant anatomical structure of organs. For example, in radiotherapy automatic prostate segmentation is essential in prostate cancer diagnosis, therapy, and post-therapy assessment from T2-weighted MR or CT images. In this paper, we present a fully automatic deep generative model-driven multimodal prostate segmentation method using convolutional neural network (DGMNet). The novelty of our method comes with its embedded generative neural network for learning-based shape modeling and its ability to adapt for different imaging modalities via learning-based registration. The proposed method includes a multi-task learning framework that combines a convolutional feature extraction and an embedded regression and classification based shape modeling. This enables the network to predict the deformable shape of an organ. We show that generative neural network-based shape modeling trained on a reliable contrast imaging modality (such as MRI) can be directly applied to low contrast imaging modality (such as CT) to achieve accurate prostate segmentation. The method was evaluated on MRI and CT datasets acquired from different clinical centers with large variations in contrast and scanning protocols. Experimental results reveal that our method can be used to automatically and accurately segment the prostate gland in different imaging modalities. © Springer Nature Switzerland AG 2019.","author":[{"family":"Girum","given":"K.B."},{"family":"Créhange","given":"G."},{"family":"Hussain","given":"R."},{"family":"Walker","given":"P.M."},{"family":"Lalande","given":"A."}],"citation-key":"Girum2019119","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-32486-5_15","editor":[{"family":"Nguyen D., Jiang S.","given":"Xing L."}],"ISBN":"9783030324858","ISSN":"03029743","issued":{"date-parts":[[2019]]},"page":"119-127","publisher":"Springer","title":"Deep generative model-driven multimodal prostate segmentation in radiotherapy","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075677733&doi=10.1007%2f978-3-030-32486-5_15&partnerID=40&md5=054d2b01bc326413d95375dbc145ff59","volume":"11850 LNCS"},
{"id":"GitHubFacebookresearchDlrm","accessed":{"date-parts":[[2021,6,7]]},"citation-key":"GitHubFacebookresearchDlrm","note":"00000","title":"GitHub - facebookresearch/dlrm: An implementation of a deep learning recommendation model (DLRM)","type":"webpage","URL":"https://github.com/facebookresearch/dlrm"},
{"id":"glauberCollaborativeFilteringVs2019","abstract":"Recommendation Systems (SR) suggest items exploring user preferences, helping them with the information overload problem. Two approaches to SR have received more prominence, Collaborative Filtering, and Content-Based Filtering. Moreover, even though studies are indicating their advantages and disadvantages, few results empirically prove their characteristics, similarities, and differences. In this work, an experimental methodology is proposed to perform comparisons between recommendation algorithms for different approaches going beyond the \"precision of the predictions\". For the experiments, three algorithms of recommendation were tested: a baseline for Collaborative Filtration and two algorithms for Content-based Filtering that were developed for this evaluation. The experiments demonstrate the behavior of these systems in different data sets, its main characteristics and especially the complementary aspect of the two main approaches.","accessed":{"date-parts":[[2020,1,11]]},"author":[{"family":"Glauber","given":"Rafael"},{"family":"Loula","given":"Angelo"}],"citation-key":"glauberCollaborativeFilteringVs2019","container-title":"arXiv:1912.08932 [cs]","issued":{"date-parts":[[2019,12,18]]},"source":"arXiv.org","title":"Collaborative Filtering vs. Content-Based Filtering: differences and similarities","title-short":"Collaborative Filtering vs. Content-Based Filtering","type":"article-journal","URL":"http://arxiv.org/abs/1912.08932"},
{"id":"gleitzeFindingUniversalExecution2021","abstract":"When using multiple models to describe a (software) system, one can use a network of model transformations to keep the models consistent after changes. No strategy exists, however, to orchestrate the execution of transformations if the network has an arbitrary topology. In this paper, we analyse how often and in which order transformations need to be executed. We argue why linear execution bounds are too restrictive to be useful in practice and prove that there is no upper bound for the number of necessary executions. To avoid non-termination, we propose a conservative strategy that makes execution failures easier to understand. These insights help developers and users of transformation networks to understand under which circumstances their networks can terminate. Additionally, the proposed strategy helps them to find the cause when a network cannot restore consistency.","accessed":{"date-parts":[[2021,3,29]]},"author":[{"family":"Gleitze","given":"Joshua"},{"family":"Klare","given":"Heiko"},{"family":"Burger","given":"Erik"}],"citation-key":"gleitzeFindingUniversalExecution2021","container-title":"Fundamental Approaches to Software Engineering","DOI":"10.1007/978-3-030-71500-7_5","editor":[{"family":"Guerra","given":"Esther"},{"family":"Stoelinga","given":"Mariëlle"}],"event-place":"Cham","ISBN":"978-3-030-71499-4 978-3-030-71500-7","issued":{"date-parts":[[2021]]},"note":"00000","page":"87-107","publisher":"Springer International Publishing","publisher-place":"Cham","source":"DOI.org (Crossref)","title":"Finding a Universal Execution Strategy for Model Transformation Networks","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-030-71500-7_5","volume":"12649"},
{"id":"gloriaDesignImplementationIoT2017","accessed":{"date-parts":[[2019,9,9]]},"author":[{"family":"Glória","given":"André"},{"family":"Cercas","given":"Francisco"},{"family":"Souto","given":"Nuno"}],"citation-key":"gloriaDesignImplementationIoT2017","container-title":"Procedia Computer Science","container-title-short":"Procedia Computer Science","DOI":"10.1016/j.procs.2017.05.343","ISSN":"18770509","issued":{"date-parts":[[2017]]},"page":"568-575","source":"DOI.org (Crossref)","title":"Design and implementation of an IoT gateway to create smart environments","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1877050917310128","volume":"109"},
{"id":"GmailConcettoDi","accessed":{"date-parts":[[2015,4,24]]},"citation-key":"GmailConcettoDi","title":"Gmail - Concetto di \"scenario misto\"","type":"webpage","URL":"https://mail.google.com/mail/u/0/?ui=2&ik=c6f0013e0f&view=pt&search=inbox&type=14ce63765de5b01c&msg=14c64feb393c287b&siml=14c64feb393c287b"},
{"id":"gobertConceptualModelingManipulation","abstract":"An increasing number of organisations rely on NoSQL technologies to manage their mission-critical data. However, those technologies were not intended to replace relational database management systems, but rather to complement them. Hence the recent emergence of heterogeneous database architectures, commonly called hybrid polystores, that rely on a combination of several, possibly overlapping relational and NoSQL databases. Unfortunately, there is still a lack of models, methods and tools for data modeling and manipulation in such architectures. With the aim to fill this gap, we present HyDRa, a conceptual framework to design and manipulate hybrid polystores. HyDRa includes a textual modeling language to specify (1) the conceptual schema of the polystore, (2) the physical schemas of each of its databases, and (3) a set of mapping rules to express possibly complex correspondences between the conceptual schema elements and the physical databases. HyDRa provides the generation of a conceptual API, allowing developers to query hybrid polystores at a conceptual level, and to automatically enforce cross-database data integrity constraints. The use of HyDRa is supported by an Eclipse plugin, offering syntax highlighting, auto-completion and conceptual data access API generation.","author":[{"family":"Gobert","given":"Maxime"},{"family":"Meurice","given":"Loup"},{"family":"Cleve","given":"Anthony"}],"citation-key":"gobertConceptualModelingManipulation","note":"00000","page":"14","source":"Zotero","title":"Conceptual Modeling and Manipulation of Hybrid Polystores","type":"article-journal"},
{"id":"gomez-abajoSystematicEngineeringMutation2020","accessed":{"date-parts":[[2020,10,24]]},"author":[{"family":"Gómez-Abajo","given":"Pablo"},{"family":"Guerra","given":"Esther"},{"family":"Lara","given":"Juan","non-dropping-particle":"de"},{"family":"Merayo","given":"Mercedes G."}],"citation-key":"gomez-abajoSystematicEngineeringMutation2020","container-title":"The Journal of Object Technology","container-title-short":"JOT","DOI":"10.5381/jot.2020.19.3.a5","ISSN":"1660-1769","issue":"3","issued":{"date-parts":[[2020]]},"note":"00000","page":"3:1","source":"DOI.org (Crossref)","title":"Systematic Engineering of Mutation Operators.","type":"article-journal","URL":"http://www.jot.fm/contents/issue_2020_03/article5.html","volume":"19"},
{"id":"gomez-uribeNetflixRecommenderSystem2015","author":[{"family":"Gomez-Uribe","given":"Carlos A."},{"family":"Hunt","given":"Neil"}],"citation-key":"gomez-uribeNetflixRecommenderSystem2015","container-title":"ACM Transactions on Management Information Systems","container-title-short":"ACM Trans. Manage. Inf. Syst.","ISSN":"2158-656X","issue":"4","issued":{"date-parts":[[2015,12]]},"page":"13:1-13:19","title":"The netflix recommender system: Algorithms, business value, and innovation","type":"article-journal","URL":"http://doi.acm.org/10.1145/2843948","volume":"6"},
{"id":"Gómez2020141","abstract":"Pedagogical content knowledge (PCK) is a construct used to represent teacher’s understanding. PCK have used for different purposes, among which are the design of technological tools and curriculum materials. An Intelligent Tutoring Systems (ITS) is a type of Intelligent System, which incorporates AI techniques to know what to teach, who to teach and how to teach individually to each learner. The main module of an ITS is the tutor module. Thus, this study presented the design of the Tutor Module of an ITS using the theoretical assumptions of the METAGOGIC metamodel and the perceptions of the science teachers about their PCK. The research phases developed allowed: collection of the Science Teachers’ Perceptions, content Analysis of these Perceptions and finally the design of Model for the Tutor Module of an ITS based-on Science Teachers PCK using METAGOGIC Metamodel. © 2020, Springer Nature Switzerland AG.","author":[{"family":"Gómez","given":"A.A."},{"family":"Flórez","given":"E.P."},{"family":"Márquez","given":"L.A."}],"citation-key":"Gómez2020141","container-title":"Communications in Computer and Information Science","DOI":"10.1007/978-3-030-45344-2_12","editor":[{"family":"Villalba-Condori K.O., Aduriz-Bravo A.","given":"Lavonen J.","suffix":"Wong L.-H., Wang T.-H."}],"ISBN":"9783030453435","ISSN":"18650929","issued":{"date-parts":[[2020]]},"page":"141-157","publisher":"Springer","title":"Design of the tutor module for an intelligent tutoring system (ITS) based on science teachers’ pedagogical content knowledge (PCK)","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085027113&doi=10.1007%2f978-3-030-45344-2_12&partnerID=40&md5=f69991b602bd01ea41e295258a314d6c","volume":"1191 CCIS"},
{"id":"gomezDesignTutorModule2020a","abstract":"Pedagogical content knowledge (PCK) is a construct used to represent teacher’s understanding. PCK have used for different purposes, among which are the design of technological tools and curriculum materials. An Intelligent Tutoring Systems (ITS) is a type of Intelligent System, which incorporates AI techniques to know what to teach, who to teach and how to teach individually to each learner. The main module of an ITS is the tutor module. Thus, this study presented the design of the Tutor Module of an ITS using the theoretical assumptions of the METAGOGIC metamodel and the perceptions of the science teachers about their PCK. The research phases developed allowed: collection of the Science Teachers’ Perceptions, content Analysis of these Perceptions and finally the design of Model for the Tutor Module of an ITS based-on Science Teachers PCK using METAGOGIC Metamodel. © 2020, Springer Nature Switzerland AG.","author":[{"family":"Gómez","given":"A.A."},{"family":"Flórez","given":"E.P."},{"family":"Márquez","given":"L.A."}],"citation-key":"gomezDesignTutorModule2020a","container-title":"Communications in Computer and Information Science","DOI":"10.1007/978-3-030-45344-2_12","editor":[{"family":"Villalba-Condori K.O.","given":"Wang T.-H.","suffix":"Aduriz-Bravo A., Lavonen J., Wong L.-H."}],"ISBN":"9783030453435","ISSN":"18650929","issued":{"date-parts":[[2020]]},"page":"141-157","publisher":"Springer","title":"Design of the Tutor Module for an Intelligent Tutoring System (ITS) Based on Science Teachers’ Pedagogical Content Knowledge (PCK)","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085027113&doi=10.1007%2f978-3-030-45344-2_12&partnerID=40&md5=f69991b602bd01ea41e295258a314d6c","volume":"1191 CCIS"},
{"id":"gomezMapBasedTransparentPersistence2015","abstract":"The progressive industrial adoption of Model-Driven Engineering (MDE) is fostering the development of large tool ecosystems like the Eclipse Modeling project. These tools are built on top of a set of base technologies that have been primarily designed for small-scale scenarios, where models are manually developed. In particular, efficient runtime manipulation for large-scale models is an under-studied problem and this is hampering the application of MDE to several industrial scenarios. In this paper we introduce and evaluate a map-based persistence model for MDE tools. We use this model to build a transparent persistence layer for modeling tools, on top of a map-based database engine. The layer can be plugged into the Eclipse Modeling Framework, lowering execution times and memory consumption levels of other existing approaches. Empirical tests are performed based on a typical industrial scenario, model-driven reverse engineering, where very large software models originate from the analysis of massive code bases. The layer is freely distributed and can be immediately used for enhancing the scalability of any existing Eclipse Modeling tool.","accessed":{"date-parts":[[2015,4,7]]},"author":[{"family":"Gómez","given":"Abel"},{"family":"Tisi","given":"Massimo"},{"family":"Sunyé","given":"Gerson"},{"family":"Cabot","given":"Jordi"}],"citation-key":"gomezMapBasedTransparentPersistence2015","collection-number":"9033","collection-title":"Lecture Notes in Computer Science","container-title":"Fundamental Approaches to Software Engineering","editor":[{"family":"Egyed","given":"Alexander"},{"family":"Schaefer","given":"Ina"}],"ISBN":"978-3-662-46674-2 978-3-662-46675-9","issued":{"date-parts":[[2015,4,11]]},"page":"19-34","publisher":"Springer Berlin Heidelberg","source":"link.springer.com","title":"Map-Based Transparent Persistence for Very Large Models","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-662-46675-9_2"},
{"id":"gomezTemporalEMFTemporalMetamodeling2018","abstract":"Existing modeling tools provide direct access to the most current version of a model but very limited support to inspect the model state in the past. This typically requires looking for a model version (usually stored in some kind of external versioning system like Git) roughly corresponding to the desired period and using it to manually retrieve the required data. This approximate answer is not enough in scenarios that require a more precise and immediate response to temporal queries like complex collaborative co-engineering processes or runtime models.","accessed":{"date-parts":[[2021,4,19]]},"author":[{"family":"Gómez","given":"Abel"},{"family":"Cabot","given":"Jordi"},{"family":"Wimmer","given":"Manuel"}],"citation-key":"gomezTemporalEMFTemporalMetamodeling2018","container-title":"Conceptual Modeling","DOI":"10.1007/978-3-030-00847-5_26","editor":[{"family":"Trujillo","given":"Juan C."},{"family":"Davis","given":"Karen C."},{"family":"Du","given":"Xiaoyong"},{"family":"Li","given":"Zhanhuai"},{"family":"Ling","given":"Tok Wang"},{"family":"Li","given":"Guoliang"},{"family":"Lee","given":"Mong Li"}],"event-place":"Cham","ISBN":"978-3-030-00846-8 978-3-030-00847-5","issued":{"date-parts":[[2018]]},"note":"00000","page":"365-381","publisher":"Springer International Publishing","publisher-place":"Cham","source":"DOI.org (Crossref)","title":"TemporalEMF: A Temporal Metamodeling Framework","title-short":"TemporalEMF","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-030-00847-5_26","volume":"11157"},
{"id":"Gonçalves201577","abstract":"Multi-agent systems (MAS) involve a wide variety of agents that interact with each other to achieve their goals. Usually each agent has a particular internal architecture defining its main structure that gives support to the interaction among the entities of MAS. Many modelling languages have been proposed in recent years to represent the internal architectures of such agents, for instance the UML profiles. In particular, we highlight MAS-ML, an MAS modelling language that performs a conservative extension of UML while incorporating agent-related concepts to represent proactive agents. However, such languages fail to support the modelling of the heterogeneous architectures that can be used to develop the agents of an MAS. Even worse, little has been done to provide tools to help the systematic design of agents. This paper, therefore, aims to extend the MAS-ML metamodel and evolve its tool to support the modelling of not only proactive agents but also several other architectures described in the literature. © 2015 Elsevier Inc. All rights reserved.","author":[{"family":"Gonçalves","given":"E.J.T."},{"family":"Cortés","given":"M.I."},{"family":"Campos","given":"G.A.L."},{"family":"Lopes","given":"Y.S."},{"family":"Freire","given":"E.S.S."},{"family":"Da Silva","given":"V.T."},{"family":"De Oliveira","given":"K.S.F."},{"family":"De Oliveira","given":"M.A."}],"citation-key":"Gonçalves201577","container-title":"Journal of Systems and Software","DOI":"10.1016/j.jss.2015.06.008","ISSN":"01641212","issued":{"date-parts":[[2015]]},"page":"77-109","publisher":"Elsevier Inc.","title":"MAS-ML 2.0: Supporting the modelling of multi-agent systems with different agent architectures","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937781230&doi=10.1016%2fj.jss.2015.06.008&partnerID=40&md5=73e035bcb532d66a9922bd4d9842e13c","volume":"108"},
{"id":"gonzalezATLTestWhiteBoxTest2012","author":[{"family":"González","given":"Carlos A."},{"family":"Cabot","given":"Jordi"}],"citation-key":"gonzalezATLTestWhiteBoxTest2012","container-title":"Model Driven Engineering Languages and Systems","DOI":"10.1007/978-3-642-33666-9_29","issued":{"date-parts":[[2012]]},"page":"449–464","title":"ATLTest: A White-Box Test Generation Approach for ATL Transformations","type":"article-journal","volume":"7590"},
{"id":"Gorodetsky2015765","abstract":"A behavioral paradigm of artificial intelligence (AI) systems is considered. In this paradigm, it is assumed that the system’s “intelligence” emerges as a result of the individual behaviors and interaction of a set of distributed entities (robots, software agents, and the like) between themselves and with the external environment. An outline of the state of art in the field of behavioral models of artificial intelligence systems is given and, for such models, a unified semantically interpreted behavioral metamodel in the form of a domain-independent reference ontology and its extensions for two particular practically important classes of applications are proposed. The first class of applications deals with the team work of underwater robots autonomously inspecting underwater space in order to ensure its security. The second one corresponds to self-organizing systems composed of a large number of small satellites that autonomously communicate, observe, and inspect outer space. Directions of future research in the field of behavioral models of the distributed entities that cooperatively accomplish an autonomous mission are outlined. © 2015, Pleiades Publishing, Ltd.","author":[{"family":"Gorodetsky","given":"V.I."},{"family":"Samoylov","given":"V.V."},{"family":"Trotskii","given":"D.V."}],"citation-key":"Gorodetsky2015765","container-title":"Journal of Computer and Systems Sciences International","DOI":"10.1134/S1064230715030089","ISSN":"10642307","issue":"5","issued":{"date-parts":[[2015]]},"page":"765-782","publisher":"Maik Nauka-Interperiodica Publishing","title":"The reference ontology of collective behavior of autonomous agents and its extensions","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943606619&doi=10.1134%2fS1064230715030089&partnerID=40&md5=b86ba218c9e13d5a272761dd170a38cd","volume":"54"},
{"id":"gorodetskyReferenceOntologyCollective2015a","abstract":"A behavioral paradigm of artificial intelligence (AI) systems is considered. In this paradigm, it is assumed that the system’s “intelligence” emerges as a result of the individual behaviors and interaction of a set of distributed entities (robots, software agents, and the like) between themselves and with the external environment. An outline of the state of art in the field of behavioral models of artificial intelligence systems is given and, for such models, a unified semantically interpreted behavioral metamodel in the form of a domain-independent reference ontology and its extensions for two particular practically important classes of applications are proposed. The first class of applications deals with the team work of underwater robots autonomously inspecting underwater space in order to ensure its security. The second one corresponds to self-organizing systems composed of a large number of small satellites that autonomously communicate, observe, and inspect outer space. Directions of future research in the field of behavioral models of the distributed entities that cooperatively accomplish an autonomous mission are outlined. © 2015, Pleiades Publishing, Ltd.","author":[{"family":"Gorodetsky","given":"V.I."},{"family":"Samoylov","given":"V.V."},{"family":"Trotskii","given":"D.V."}],"citation-key":"gorodetskyReferenceOntologyCollective2015a","container-title":"Journal of Computer and Systems Sciences International","DOI":"10.1134/S1064230715030089","ISSN":"10642307","issue":"5","issued":{"date-parts":[[2015]]},"page":"765-782","publisher":"Maik Nauka-Interperiodica Publishing","title":"The reference ontology of collective behavior of autonomous agents and its extensions","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943606619&doi=10.1134%2fS1064230715030089&partnerID=40&md5=b86ba218c9e13d5a272761dd170a38cd","volume":"54"},
{"id":"gorrepotuSub1GHzMiniatureWireless2018","abstract":"Considering the Sub-1 GHz frequency as a solution to address the key requirements in wireless networks as it supports multiple nodes and covers longer distances in contrast to other existing and widely used wireless technologies like GSM, BLE, Bluetooth and WiFi. Consequently the Sub-1 GHz spectrum requires lower power from the transceiver than the 2.4 GHz band making it a great choice for battery operated IoT sensor devices. For deploying nodes to cover large area and long range, sensing devices must be small, energy efficient and cost effective. IoT Sensor devices using the Sub-1 GHz spectrum can handle interference better. The lower frequency ISM bands enable the Sub-1 GHz transmissions to weave better between buildings in an urban environment. This paper deals with the design and development of hardware as well as software of a Sub-1 GHz gateway and miniature sensor node for IoT applications. CC1310 SoC, a Sub-1 GHz family microcontroller is used in the design of Sub-1 GHz, 868 MHz board.","accessed":{"date-parts":[[2018,11,7]]},"author":[{"family":"Gorrepotu","given":"Ramesh"},{"family":"Korivi","given":"Narendra Swaroop"},{"family":"Chandu","given":"Kavitha"},{"family":"Deb","given":"Subimal"}],"citation-key":"gorrepotuSub1GHzMiniatureWireless2018","container-title":"Internet of Things","DOI":"10.1016/j.iot.2018.08.002","ISSN":"25426605","issued":{"date-parts":[[2018,9]]},"page":"27-39","source":"Crossref","title":"Sub-1GHz miniature wireless sensor node for IoT applications","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2542660518300222","volume":"1-2"},
{"id":"gouesBridgingGapResearch2018","abstract":"Software engineers must solve practical problems under deadline pressure. They rely on the best-codified knowledge available, turning to weaker results and their expert judgment when sound science is unavailable. Meanwhile, software engineering researchers seek fully validated results, resulting in a lag to practical guidance. To bridge this gap, research results should be systematically distilled into actionable guidance in a way that respects differences in strength and scope among the results. Starting with the practitioners’ need for actionable guidance, this article reviews the evolution of software engineering research expectations, identifies types of results and their strengths, and draws on evidence-based medicine for a concrete example of deriving pragmatic guidance from mixed-strength research results. It advances a levels-of-evidence framework to allow researchers to clearly identify the strengths of their claims and the supporting evidence for their results and to work with practitioners to synthesize actionable recommendations from diverse types of evidence. This article is part of a special issue on software engineering’s 50th anniversary.","author":[{"family":"Goues","given":"C. L."},{"family":"Jaspan","given":"C."},{"family":"Ozkaya","given":"I."},{"family":"Shaw","given":"M."},{"family":"Stolee","given":"K. T."}],"citation-key":"gouesBridgingGapResearch2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.3571235","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"50-57","source":"IEEE Xplore","title":"Bridging the Gap: From Research to Practical Advice","title-short":"Bridging the Gap","type":"article-journal","volume":"35"},
{"id":"Govindasamy2021229","abstract":"Since its first mentioning in the literature, the concept of Digital Twin has gained traction in both industry and academia. However, there are still many open challenges when applying Digital Twins to industry-scale use cases. Applying Model-Driven Engineering techniques to the creation and maintenance of Digital Twins (also referred to as Model-Driven Digital Twin Engineering) promises automation and consistency throughout the life cycle of a Digital Twin. The exemplar provided in this paper can be used to identify open challenges when it comes to Model-Driven Digital Twin Engineering, and to demonstrate how approaches can solve them. This exemplar applies Digital Twins to an indoor air quality management use case, where CO2, temperature, and humidity values of rooms within a building are measured. These values can be used to derive actions to improve work productivity and reduce the risk for virus infections. We describe three applications that make use of this Digital Twin (i.e., runtime visualization, physical simulation, and ML-based predictions), and provide an online repository with the artefacts of this exemplar. © 2021 IEEE.","author":[{"family":"Govindasamy","given":"H.S."},{"family":"Jayaraman","given":"R."},{"family":"Taspinar","given":"B."},{"family":"Lehner","given":"D."},{"family":"Wimmer","given":"M."}],"citation-key":"Govindasamy2021229","collection-title":"Companion Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021","DOI":"10.1109/MODELS-C53483.2021.00040","ISBN":"978-1-66542-484-4","issued":{"date-parts":[[2021]]},"page":"229-232","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Air quality management: An exemplar for model-driven digital twin engineering","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124047367&doi=10.1109%2fMODELS-C53483.2021.00040&partnerID=40&md5=0f7a3891d552bd007358edf2528e312b"},
{"id":"GrahamjensonListRecommender","accessed":{"date-parts":[[2017,3,10]]},"citation-key":"GrahamjensonListRecommender","title":"grahamjenson/list_of_recommender_systems: A List of Recommender Systems and Resources","type":"webpage","URL":"https://github.com/grahamjenson/list_of_recommender_systems"},
{"id":"grayExplicitImplicitModels2022","accessed":{"date-parts":[[2022,4,12]]},"author":[{"family":"Gray","given":"Jeff"},{"family":"Rumpe","given":"Bernhard"}],"citation-key":"grayExplicitImplicitModels2022","container-title":"Software and Systems Modeling","container-title-short":"Softw Syst Model","DOI":"10.1007/s10270-022-01001-4","ISSN":"1619-1366, 1619-1374","issued":{"date-parts":[[2022,4,7]]},"page":"s10270-022-01001-4","source":"DOI.org (Crossref)","title":"Explicit versus implicit models: What are good languages for modeling?","title-short":"Explicit versus implicit models","type":"article-journal","URL":"https://link.springer.com/10.1007/s10270-022-01001-4"},
{"id":"greifenbergEngineeringTaggingLanguages","accessed":{"date-parts":[[2015,9,24]]},"author":[{"family":"Greifenberg","given":"Timo"},{"family":"Look","given":"Markus"},{"family":"Roidl","given":"Sebastian"},{"family":"Rumpe","given":"Bernhard"}],"citation-key":"greifenbergEngineeringTaggingLanguages","source":"Google Scholar","title":"Engineering Tagging Languages for DSLs","type":"article-journal","URL":"http://www.se-rwth.de/publications/Engineering-Tagging-Languages-for-DSLs.pdf"},
{"id":"Grossberg:2013:ART:2405841.2405958","author":[{"family":"Grossberg","given":"Stephen"}],"citation-key":"Grossberg:2013:ART:2405841.2405958","container-title":"Neural Netw.","ISSN":"0893-6080","issued":{"date-parts":[[2013,1]]},"page":"1-47","title":"Adaptive resonance theory: How a brain learns to consciously attend, learn, and recognize a changing world","type":"article-journal","URL":"http://dx.doi.org/10.1016/j.neunet.2012.09.017","volume":"37"},
{"id":"Gu2016DeepAPI","author":[{"family":"Gu","given":"Xiaodong"},{"family":"Zhang","given":"Hongyu"},{"family":"Zhang","given":"Dongmei"},{"family":"Kim","given":"Sunghun"}],"citation-key":"Gu2016DeepAPI","container-title":"24th ACM SIGSOFT international symposium on foundations of software engineering","event-place":"New York","ISBN":"978-1-4503-4218-6","issued":{"date-parts":[[2016]]},"page":"631-642","publisher":"ACM","publisher-place":"New York","title":"Deep API learning","type":"paper-conference"},
{"id":"Gu2018DeepCode","author":[{"family":"Gu","given":"Xiaodong"},{"family":"Zhang","given":"Hongyu"},{"family":"Kim","given":"Sunghun"}],"citation-key":"Gu2018DeepCode","container-title":"40th international conference on software engineering","event-place":"New York","ISBN":"978-1-4503-5638-1","issued":{"date-parts":[[2018]]},"page":"933-944","publisher":"ACM","publisher-place":"New York","title":"Deep code search","type":"paper-conference"},
{"id":"guanaChainTrackerModelTransformationTrace2014","author":[{"family":"Guana","given":"Victor"},{"family":"Stroulia","given":"Eleni"}],"citation-key":"guanaChainTrackerModelTransformationTrace2014","container-title":"Theory and Practice of Model Transformations","DOI":"10.1007/978-3-319-08789-4_11","issued":{"date-parts":[[2014]]},"page":"146–153","title":"ChainTracker, a Model-Transformation Trace Analysis Tool for Code-Generation Environments","type":"article-journal","volume":"8568"},
{"id":"guerraAutomatedVerificationModel2012","author":[{"family":"Guerra","given":"Esther"},{"family":"Lara","given":"Juan"},{"family":"Wimmer","given":"Manuel"},{"family":"Kappel","given":"Gerti"},{"family":"Kusel","given":"Angelika"},{"family":"Retschitzegger","given":"Werner"},{"family":"Schönböck","given":"Johannes"},{"family":"Schwinger","given":"Wieland"}],"citation-key":"guerraAutomatedVerificationModel2012","container-title":"Automated Software Engineering","DOI":"10.1007/s10515-012-0102-y","issue":"1","issued":{"date-parts":[[2012]]},"page":"5–46","title":"Automated verification of model transformations based on visual contracts","type":"article-journal","volume":"20"},
{"id":"GuestEditorialSpecial","accessed":{"date-parts":[[2015,8,19]]},"citation-key":"GuestEditorialSpecial","title":"Guest Editorial: Special issue on constrained decision-making in robotics - Online First - Springer","type":"webpage","URL":"http://link.springer.com/article/10.1007/s10514-015-9489-1"},
{"id":"Guha:1998:CEC:276305.276312","author":[{"family":"Guha","given":"Sudipto"},{"family":"Rastogi","given":"Rajeev"},{"family":"Shim","given":"Kyuseok"}],"citation-key":"Guha:1998:CEC:276305.276312","container-title":"SIGMOD Rec.","ISSN":"0163-5808","issue":"2","issued":{"date-parts":[[1998,6]]},"page":"73-84","title":"CURE: An efficient clustering algorithm for large databases","type":"article-journal","URL":"http://doi.acm.org/10.1145/276305.276312","volume":"27"},
{"id":"GuideIntelligentCode","citation-key":"GuideIntelligentCode","title":"A Guide to Intelligent Code Completion Using Eclipse Code Recommenders","type":"post-weblog","URL":"https://medium.com/codetrails/insert-knowledge-here-a2f71c2862d2"},
{"id":"GuideLowcodePlatforms","accessed":{"date-parts":[[2020,4,8]]},"citation-key":"GuideLowcodePlatforms","title":"A Guide to Low-code Platforms - Federico Tomassetti - Software Architect","type":"webpage","URL":"https://tomassetti.me/a-guide-to-low-code-platforms/"},
{"id":"Gunawardana:2009:UAB:1639714.1639735","author":[{"family":"Gunawardana","given":"Asela"},{"family":"Meek","given":"Christopher"}],"citation-key":"Gunawardana:2009:UAB:1639714.1639735","collection-title":"RecSys '09","container-title":"Proceedings of the third ACM conference on recommender systems","event-place":"New York, NY, USA","ISBN":"978-1-60558-435-5","issued":{"date-parts":[[2009]]},"page":"117-124","publisher":"ACM","publisher-place":"New York, NY, USA","title":"A unified approach to building hybrid recommender systems","type":"paper-conference","URL":"http://doi.acm.org/10.1145/1639714.1639735"},
{"id":"Guo:2013:NBS:2540128.2540506","author":[{"family":"Guo","given":"Guibing"},{"family":"Zhang","given":"Jie"},{"family":"Yorke-Smith","given":"Neil"}],"citation-key":"Guo:2013:NBS:2540128.2540506","collection-title":"IJCAI '13","container-title":"Proceedings of the twenty-third international joint conference on artificial intelligence","event-place":"Beijing, China","ISBN":"978-1-57735-633-2","issued":{"date-parts":[[2013]]},"page":"2619-2625","publisher":"AAAI Press","publisher-place":"Beijing, China","title":"A novel bayesian similarity measure for recommender systems","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=2540128.2540506"},
{"id":"Guo2015","abstract":"Architectural design spaces of microprocessors are often exponentially large with respect to the pending processor parameters. To avoid simulating all configurations in the design space, machine learning and statistical techniques have been utilized to build regression models for characterizing the relationship between architectural configurations and responses (e.g., performance or power consumption). However, this article shows that the accuracy variability of many learning techniques over different design spaces and benchmarks can be significant enough to mislead the decision-making. This clearly indicates a high risk of applying techniques that work well on previous modeling tasks (each involving a design space, benchmark, and design objective) to a new task, due to which the powerful tools might be impractical. Inspired by ensemble learning in the machine learning domain, we propose a robust framework called ELSE to reduce the accuracy variability of design space modeling. Rather than employing a single learning technique as in previous investigations, ELSE employs distinct learning techniques to build multiple base regression models for each modeling task. This is not a trivial combination of different techniques (e.g., always trusting the regression model with the smallest error). Instead, ELSE carefully maintains the diversity of base regression models and constructs a metamodel from the base models that can provide accurate predictions even when the base models are far from accurate. Consequently, we are able to reduce the number of cases in which the final prediction errors are unacceptably large. Experimental results validate the robustness of ELSE: compared with the widely used artificial neural network over 52 distinct modeling tasks, ELSE reduces the accuracy variability by about 62%. Moreover, ELSE reduces the average prediction error by 27% and 85% for the investigated MIPS and POWER design spaces, respectively. © 2015 ACM.","author":[{"family":"Guo","given":"Q."},{"family":"Chen","given":"T."},{"family":"Zhou","given":"Z.-H."},{"family":"Temam","given":"O."},{"family":"Li","given":"L."},{"family":"Qian","given":"D."},{"family":"Chen","given":"Y."}],"citation-key":"Guo2015","container-title":"ACM Transactions on Design Automation of Electronic Systems","DOI":"10.1145/2668118","ISSN":"10844309","issue":"2","issued":{"date-parts":[[2015]]},"publisher":"Association for Computing Machinery","title":"Robust design space modeling","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924081921&doi=10.1145%2f2668118&partnerID=40&md5=489d93a6eba050c7a4de048d4b61d8fa","volume":"20"},
{"id":"Guo20221583","abstract":"Automated assessment of patients with Parkinson's disease (PD) is urgently required in clinical practice to improve the diagnostic efficiency and objectivity and to remotely monitor the motor disorder symptoms and general health of these patients, especially in view of the travel restrictions due to the recent coronavirus epidemic. Gait motor disorder is one of the critical manifestations of PD, and automated assessment of gait is vital to realize automated assessment of PD patients. To this end, we propose a novel two-stream spatial-temporal attention graph convolutional network (2s-ST-AGCN) for video assessment of PD gait motor disorder. Specifically, the skeleton sequence of human body is extracted from videos to construct spatial-temporal graphs of joints and bones, and a two-stream spatial-temporal graph convolutional network is then built to simultaneously model the static spatial information and dynamic temporal variations. The multi-scale spatial-temporal attention-aware mechanism is also designed to effectively extract the discriminative spatial-temporal features. The deep supervision strategy is then embedded to minimize classification errors, thereby guiding the weight update process of the hidden layer to promote significant discriminative features. Besides, two model-driven terms are integrated into this deep learning framework to strengthen multi-scale similarity in the deep supervision and realize sparsification of discriminative features. Extensive experiments on the clinical video dataset show that the proposed model exhibits good performance with an accuracy of 65.66% and an acceptable accuracy of 98.90%, which is much better than that of the existing sensor- and vision-based methods for Parkinsonian gait assessment. Thus, the proposed method is potentially useful for assessing PD gait motor disorder in clinical practice. © 1999-2012 IEEE.","author":[{"family":"Guo","given":"R."},{"family":"Shao","given":"X."},{"family":"Zhang","given":"C."},{"family":"Qian","given":"X."}],"citation-key":"Guo20221583","container-title":"IEEE Transactions on Multimedia","DOI":"10.1109/TMM.2021.3068609","ISSN":"15209210","issued":{"date-parts":[[2022]]},"page":"1583-1594","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Multi-scale sparse graph convolutional network for the assessment of parkinsonian gait","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103273529&doi=10.1109%2fTMM.2021.3068609&partnerID=40&md5=d04d0357529465653c5785217173de1e","volume":"24"},
{"id":"Guo2022547","abstract":"In order to achieve reliable communication with a high data rate of massive multiple-input multiple-output (MIMO) systems in frequency division duplex (FDD) mode, the estimated channel state information (CSI) at the receiver needs to be fed back to the transmitter. However, the feedback overhead becomes exorbitant with the increasing number of antennas. In this letter, a two stages low rank (TSLR) CSI feedback scheme for millimeter wave (mmWave) massive MIMO systems is proposed to reduce the feedback overhead based on model-driven deep learning. Besides, we design a deep iterative neural network, named FISTA-Net, by unfolding the fast iterative shrinkage thresholding algorithm (FISTA) to achieve more efficient CSI feedback. Moreover, a shrinkage thresholding network (ST-Net) is designed in FISTA-Net based on the attention mechanism, which can choose the threshold adaptively. Simulation results show that the proposed TSLR CSI feedback scheme and FISTA-Net outperform the existing algorithms in various scenarios. © 1997-2012 IEEE.","author":[{"family":"Guo","given":"J."},{"family":"Wang","given":"L."},{"family":"Li","given":"F."},{"family":"Xue","given":"J."}],"citation-key":"Guo2022547","container-title":"IEEE Communications Letters","DOI":"10.1109/LCOMM.2021.3138927","ISSN":"10897798","issue":"3","issued":{"date-parts":[[2022]]},"page":"547-551","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"CSI feedback with model-driven deep learning of massive MIMO systems","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122281968&doi=10.1109%2fLCOMM.2021.3138927&partnerID=40&md5=76fcb5adae51d727c60b90bb72a97777","volume":"26"},
{"id":"guthDetailedAnalysisIoT2018","accessed":{"date-parts":[[2019,9,17]]},"author":[{"family":"Guth","given":"Jasmin"},{"family":"Breitenbücher","given":"Uwe"},{"family":"Falkenthal","given":"Michael"},{"family":"Fremantle","given":"Paul"},{"family":"Kopp","given":"Oliver"},{"family":"Leymann","given":"Frank"},{"family":"Reinfurt","given":"Lukas"}],"citation-key":"guthDetailedAnalysisIoT2018","container-title":"Internet of Everything","DOI":"10.1007/978-981-10-5861-5_4","editor":[{"family":"Di Martino","given":"Beniamino"},{"family":"Li","given":"Kuan-Ching"},{"family":"Yang","given":"Laurence T."},{"family":"Esposito","given":"Antonio"}],"event-place":"Singapore","ISBN":"978-981-10-5860-8 978-981-10-5861-5","issued":{"date-parts":[[2018]]},"page":"81-101","publisher":"Springer Singapore","publisher-place":"Singapore","source":"DOI.org (Crossref)","title":"A Detailed Analysis of IoT Platform Architectures: Concepts, Similarities, and Differences","title-short":"A Detailed Analysis of IoT Platform Architectures","type":"chapter","URL":"http://link.springer.com/10.1007/978-981-10-5861-5_4"},
{"id":"haddadProceedings2005ACM2005","citation-key":"haddadProceedings2005ACM2005","DOI":"10.1145/1066677","editor":[{"family":"Haddad","given":"Hisham"},{"family":"Liebrock","given":"Lorie M."},{"family":"Omicini","given":"Andrea"},{"family":"Wainwright","given":"Roger L."}],"ISBN":"1-58113-964-0","issued":{"date-parts":[[2005]]},"publisher":"ACM","title":"Proceedings of the 2005 ACM Symposium on Applied Computing (SAC), Santa Fe, New Mexico, USA, March 13-17, 2005","type":"book","URL":"http://doi.acm.org/10.1145/1066677"},
{"id":"hadipourAutomaticWashingSystem2018","abstract":"The illumination of the streets and public area in metropolitan cities is a vital service, which is not only related to the type of the light but also the dirtiness of the surface of the light. In this paper, both subjects are considered to increase the productivity of the light. To achieve this goal, a novel Automatic washing system (AWS) of LED street/public light surface was designed, manufactured and installed practically. The proposed mechanism consists of two main parts comprising mechanical and electrical systems. AWS operates based on internet interconnection technique known as Internet of Things (IoT) with a high productivity. The system has the potential to be designed and employed by four types of control system; (i) using a timer switch, (ii) using a GSM 900, (iii) using a push button manually by an operator, and (iv) using a remote-control module such as GSM, SIM 808 or GPRS/GPS/SMS through the Ethernet network. A practical system has been manufactured and installed in Kermanshah city in Iran, due to its low cost, low maintenance, upgradability, and feasibility of installing different recognition sensors such as rain and dust sensors. © 2018 Elsevier B.V. All rights reserved.","accessed":{"date-parts":[[2018,11,7]]},"author":[{"family":"Hadipour","given":"Morteza"},{"family":"Derakhshandeh","given":"Javad Farrokhi"},{"family":"Shiran","given":"Mohsen Aghazadeh"},{"family":"Rezaei","given":"Reza"}],"citation-key":"hadipourAutomaticWashingSystem2018","container-title":"Internet of Things","DOI":"10.1016/j.iot.2018.08.006","ISSN":"25426605","issued":{"date-parts":[[2018,9]]},"page":"74-80","source":"Crossref","title":"Automatic washing system of LED street lighting via Internet of Things","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2542660518300465","volume":"1-2"},
{"id":"Hadjiski2020","abstract":"A tight integration between model-based approach and Case-Based Reasoning (CBR) as data-driven AI technology for advanced process control of periodical industrial plants described by parabolic Partial Differential Equation (PDE) is considered. Via First Principle Model Parametrization using batch parameters as main features instead thermo-dynamical parameters, a discrete virtual Version Space (VS) is proposed as a Case Base for modified CBR. An open loop control is accepted with small sub-optimality and it is derived for each point of VS. In this way, the big part of the model driven calculations are transferred in predominant off-line procedure.For the on-line control remains a modest volume of data-driven CBR calculations. This significantly reduces the requirements for computer power and resources for design, commissioning, and maintenance. The proposed control strategy could be seamlessly incorporated into the existing SCADA- or DSC-based industrial control systems. Some simulation results are presented. © 2020 IEEE.","author":[{"family":"Hadjiski","given":"M."},{"family":"Deliiski","given":"N."}],"citation-key":"Hadjiski2020","collection-title":"2020 International Conference Automatics and Informatics, ICAI 2020 - Proceedings","DOI":"10.1109/ICAI50593.2020.9311313","ISBN":"978-1-72819-308-3","issued":{"date-parts":[[2020]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Advanced process control of distributed parameter plants by integration first principle modeling and case-based reasoning : Part 1: Framework of DPP control with initial uncertainty","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100093100&doi=10.1109%2fICAI50593.2020.9311313&partnerID=40&md5=5cf6c35d07f8697058c7d275790f7479"},
{"id":"HaertelHHLV17","author":[{"family":"Härtel","given":"Johannes"},{"family":"Härtel","given":"Lukas"},{"family":"Heinz","given":"Marcel"},{"family":"Lämmel","given":"Ralf"},{"family":"Varanovich","given":"Andrei"}],"citation-key":"HaertelHHLV17","container-title":"The Art, Science, and Engineering of Programming Journal","issue":"1","issued":{"date-parts":[[2017]]},"title":"Interconnected linguistic architecture","type":"article-journal","volume":"1"},
{"id":"Halkidi01onclustering","author":[{"family":"Halkidi","given":"Maria"},{"family":"Batistakis","given":"Yannis"},{"family":"Vazirgiannis","given":"Michalis"}],"citation-key":"Halkidi01onclustering","container-title":"Journal of Intelligent Information Systems","issued":{"date-parts":[[2001]]},"page":"107-145","title":"On clustering validation techniques","type":"article-journal","volume":"17"},
{"id":"hallWEKADataMining2009","author":[{"family":"Hall","given":"Mark"},{"family":"Frank","given":"Eibe"},{"family":"Holmes","given":"Geoffrey"},{"family":"Pfahringer","given":"Bernhard"},{"family":"Reutemann","given":"Peter"},{"family":"Witten","given":"Ian H."}],"citation-key":"hallWEKADataMining2009","container-title":"SIGKDD Explor. Newsl.","ISSN":"1931-0145","issue":"1","issued":{"date-parts":[[2009,11]]},"page":"10-18","title":"The WEKA data mining software: An update","type":"article-journal","URL":"http://doi.acm.org/10.1145/1656274.1656278","volume":"11"},
{"id":"Hamdi2021699","abstract":"The present paper is focused on information extraction from key fields of invoices using two different methods based on sequence labeling. Invoices are semi-structured documents in which data can be located based on the context. Common information extraction systems are model-driven, using heuristics and lists of trigger words curated by domain experts. Their performances are generally high on documents they have been trained for but processing new templates often requires new manual annotations, which is tedious and time-consuming to produce. Recent works on deep learning applied to business documents claimed a gain in terms of time and performance. While these systems do not need manual curation, they nevertheless require a large amount of data to achieve good results. In this paper, we present a series of experiments using neural networks approaches to study the trade-off between data requirements and performance in the extraction of information from key fields of invoices (such as dates, document numbers, types, amounts..). The main contribution of this paper is a system that achieves competitive results using a small amount of data compared to the state-of-the-art systems that need to be trained on large datasets, that are costly and impractical to produce in real-world applications. © 2021, Springer Nature Switzerland AG.","author":[{"family":"Hamdi","given":"A."},{"family":"Carel","given":"E."},{"family":"Joseph","given":"A."},{"family":"Coustaty","given":"M."},{"family":"Doucet","given":"A."}],"citation-key":"Hamdi2021699","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-86331-9_45","editor":[{"family":"Llados J., Lopresti D.","given":"Uchida S."}],"ISBN":"9783030863302","ISSN":"03029743","issued":{"date-parts":[[2021]]},"page":"699-714","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Information extraction from invoices","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115289937&doi=10.1007%2f978-3-030-86331-9_45&partnerID=40&md5=a138a33bd6a6507686014bf4f528587c","volume":"12822 LNCS"},
{"id":"hamidModelDrivenMethodologyApproach2014","accessed":{"date-parts":[[2015,10,29]]},"author":[{"family":"Hamid","given":"Brahim"}],"citation-key":"hamidModelDrivenMethodologyApproach2014","container-title":"Model and Data Engineering","issued":{"date-parts":[[2014]]},"page":"29–44","publisher":"Springer","source":"Google Scholar","title":"A Model-Driven Methodology Approach for Developing a Repository of Models","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-319-11587-0_5"},
{"id":"hamiltonWhatErrorsTell2018","abstract":"Margaret Hamilton talks about her experiences over the last 60 years and how a “theory of errors” was derived from the errors made along the way. Its axioms of control led to the Universal Systems Language (USL) together with its automation and preventative paradigm, development-before-the-fact. The pressing issues haven’t gone away, largely because the traditional paradigm continues in force. With a preventative paradigm, most errors aren’t allowed into a system in the first place, just by the way the system is defined. Unlike a traditional approach, with a preventative approach the more reliable the system, the higher the productivity in its lifecycle. This article is part of a theme issue on software engineering’s 50th anniversary.","author":[{"family":"Hamilton","given":"M. H."}],"citation-key":"hamiltonWhatErrorsTell2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.290110447","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"32-37","source":"IEEE Xplore","title":"What the Errors Tell Us","type":"article-journal","volume":"35"},
{"id":"hammadDeepCloneModelingClones2020","abstract":"Programmers often reuse code from source code repositories to reduce the development effort. Code clones are candidates for reuse in exploratory or rapid development, as they represent often repeated functionality in software systems. To facilitate code clone reuse, we propose DeepClone, a novel approach utilizing a deep learning algorithm for modeling code clones to predict the next set of tokens (possibly a complete clone method body) based on the code written so far. The predicted tokens require minimal customization to fit the context. DeepClone applies natural language processing techniques to learn from a large code corpus, and generates code tokens using the model learned. We have quantitatively evaluated our solution to assess (1) our model's quality and its accuracy in token prediction, and (2) its performance and effectiveness in clone method prediction. We also discuss various application scenarios for our approach.","accessed":{"date-parts":[[2021,2,2]]},"author":[{"family":"Hammad","given":"Muhammad"},{"family":"Babur","given":"Önder"},{"family":"Basit","given":"Hamid Abdul"},{"family":"Brand","given":"Mark","dropping-particle":"van den"}],"citation-key":"hammadDeepCloneModelingClones2020","container-title":"arXiv:2007.11671 [cs]","issued":{"date-parts":[[2020,12,5]]},"note":"00003","source":"arXiv.org","title":"DeepClone: Modeling Clones to Generate Code Predictions","title-short":"DeepClone","type":"article-journal","URL":"http://arxiv.org/abs/2007.11671"},
{"id":"HammoudehGarcia2019329","abstract":"Ten years after its first release, the Robot Operating System (ROS) is arguably the most popular software framework used to program robots. It achieved such status despite its shortcomings compared to alternatives similarly centered on manual programming and, perhaps surprisingly, to model-driven engineering (MDE) approaches. Based on our experience as users and developers of both ROS and MDE tools, we identified possible ways to leverage the accessibility of ROS and its large software ecosystem, while providing quality assurance measures through selected MDE techniques. After describing our vision on how to combine MDE and manually written code, we present the first technical contribution in this pursuit: a family of three metamodels to respectively model ROS nodes, communication interfaces, and systems composed from subsystems. Such metamodels can be used, through the accompanying Eclipse-based tooling made publicly available, to model ROS systems of arbitrary complexity and generate with correctness guarantees the software artifacts for their composition and deployment. Furthermore, they account for specifications on these aspects by the Object Management Group (OMG), in order to be amenable to hybrid systems coupling ROS and other frameworks. We also report on our experience with a large and complex corpus of ROS software used in a commercially deployed robot (the Care-O-bot 4), to explain the rationale of the presented work, including the shortcomings of standard ROS tools and of previous efforts on ROS modeling. © 2019 IEEE.","author":[{"family":"Hammoudeh Garcia","given":"N."},{"family":"Ludtke","given":"M."},{"family":"Kortik","given":"S."},{"family":"Kahl","given":"B."},{"family":"Bordignon","given":"M."}],"citation-key":"HammoudehGarcia2019329","collection-title":"Proceedings - 3rd IEEE International Conference on Robotic Computing, IRC 2019","DOI":"10.1109/IRC.2019.00060","ISBN":"978-1-5386-9245-5","issued":{"date-parts":[[2019]]},"page":"329-336","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Bootstrapping MDE development from ROS manual code - part 1: Metamodeling","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064108077&doi=10.1109%2fIRC.2019.00060&partnerID=40&md5=482487740bc4cb6fb0c5408ad50649d3"},
{"id":"hammoudehgarciaBootstrappingMDEDevelopment2019a","abstract":"Ten years after its first release, the Robot Operating System (ROS) is arguably the most popular software framework used to program robots. It achieved such status despite its shortcomings compared to alternatives similarly centered on manual programming and, perhaps surprisingly, to model-driven engineering (MDE) approaches. Based on our experience as users and developers of both ROS and MDE tools, we identified possible ways to leverage the accessibility of ROS and its large software ecosystem, while providing quality assurance measures through selected MDE techniques. After describing our vision on how to combine MDE and manually written code, we present the first technical contribution in this pursuit: a family of three metamodels to respectively model ROS nodes, communication interfaces, and systems composed from subsystems. Such metamodels can be used, through the accompanying Eclipse-based tooling made publicly available, to model ROS systems of arbitrary complexity and generate with correctness guarantees the software artifacts for their composition and deployment. Furthermore, they account for specifications on these aspects by the Object Management Group (OMG), in order to be amenable to hybrid systems coupling ROS and other frameworks. We also report on our experience with a large and complex corpus of ROS software used in a commercially deployed robot (the Care-O-bot 4), to explain the rationale of the presented work, including the shortcomings of standard ROS tools and of previous efforts on ROS modeling. © 2019 IEEE.","author":[{"family":"Hammoudeh Garcia","given":"N."},{"family":"Ludtke","given":"M."},{"family":"Kortik","given":"S."},{"family":"Kahl","given":"B."},{"family":"Bordignon","given":"M."}],"citation-key":"hammoudehgarciaBootstrappingMDEDevelopment2019a","container-title":"Proceedings - 3rd IEEE International Conference on Robotic Computing, IRC 2019","DOI":"10.1109/IRC.2019.00060","ISBN":"978-1-5386-9245-5","issued":{"date-parts":[[2019]]},"page":"329-336","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Bootstrapping MDE Development from ROS Manual Code - Part 1: Metamodeling","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064108077&doi=10.1109%2fIRC.2019.00060&partnerID=40&md5=482487740bc4cb6fb0c5408ad50649d3"},
{"id":"Hamrani2021","abstract":"This paper presents a machine learning (ML) surrogate modeling for fast processing in meshless/meshfree methods. The main idea is to leverage the universal approximation (UA) propriety of supervised ML models (shallow/deep learning and other regression models) to surrogate the heavy shape function construction in meshless methods. The resulting ML metamodel preserves the same accuracy of the meshless interpolation while avoiding costly matrix inversion operations. The total computation time for solving 3D test simulation problems (using more than 20k nodes) is reduced by a factor of 1k in the case of the Gaussian process (GP) metamodel. © 2021 World Scientific Publishing Company.","author":[{"family":"Hamrani","given":"A."},{"family":"Akbarzadeh","given":"A."},{"family":"Madramootoo","given":"C.A."},{"family":"Bouarab","given":"F.Z."}],"citation-key":"Hamrani2021","container-title":"International Journal of Computational Methods","DOI":"10.1142/S021987622141022X","ISSN":"02198762","issued":{"date-parts":[[2021]]},"publisher":"World Scientific","title":"Machine learning surrogate modeling for meshless methods: Leveraging universal approximation","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127473127&doi=10.1142%2fS021987622141022X&partnerID=40&md5=5f6d7840bf1bd780fd1a8574ffdd09c4"},
{"id":"Han20201980","abstract":"This paper proposes a model-driven deep learning-based downlink channel reconstruction scheme for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The spatial non-stationarity, which is the key feature of the future extremely large aperture massive MIMO system, is considered. Instead of the channel matrix, the channel model parameters are learned by neural networks to save the overhead and improve the accuracy of channel reconstruction. By viewing the channel as an image, we introduce You Only Look Once (YOLO), a powerful neural network for object detection, to enable a rapid estimation process of the model parameters, including the detection of angles and delays of the paths and the identification of visibility regions of the scatterers. The deep learning-based scheme avoids the complicated iterative process introduced by the algorithm-based parameter extraction methods. A low-complexity algorithm-based refiner further refines the YOLO estimates toward high accuracy. Given the efficiency of model-driven deep learning and the combination of neural network and algorithm, the proposed scheme can rapidly and accurately reconstruct the non-stationary downlink channel. Moreover, the proposed scheme is also applicable to widely concerned stationary systems and achieves comparable reconstruction accuracy as an algorithm-based method with greatly reduced time consumption. © 1983-2012 IEEE.","author":[{"family":"Han","given":"Y."},{"family":"Li","given":"M."},{"family":"Jin","given":"S."},{"family":"Wen","given":"C.-K."},{"family":"Ma","given":"X."}],"citation-key":"Han20201980","container-title":"IEEE Journal on Selected Areas in Communications","DOI":"10.1109/JSAC.2020.3000836","ISSN":"07338716","issue":"9","issued":{"date-parts":[[2020]]},"page":"1980-1993","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Deep learning-based FDD non-stationary massive MIMO downlink channel reconstruction","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086716698&doi=10.1109%2fJSAC.2020.3000836&partnerID=40&md5=7e1f56f404fe727034b937464fc4bb8e","volume":"38"},
{"id":"HandsonManageYour","accessed":{"date-parts":[[2016,9,27]]},"citation-key":"HandsonManageYour","title":"Hands-on: Manage your devices with Lightweight M2M and connect them to your cloud | EclipseCon Europe 2016","type":"webpage","URL":"https://www.eclipsecon.org/europe2016/session/hands-manage-your-devices-lightweight-m2m-and-connect-them-your-cloud"},
{"id":"happelPotentialsChallengesRecommendation2008","abstract":"By surveying recommendation systems in software development, we found that existing approaches have been focusing on “you might like what similar developers like” scenarios. However structured artifacts and semantically well-defined development activities bear large potentials for further recommendation scenarios. We introduce a novel “landscape” of software development recommendation systems and line out several scenarios for knowledge sharing and collaboration. Basic challenges are improving context-awareness and particularly addressing information providers.","accessed":{"date-parts":[[2019,6,13]]},"author":[{"family":"Happel","given":"Hans-Jörg"},{"family":"Maalej","given":"Walid"}],"citation-key":"happelPotentialsChallengesRecommendation2008","container-title":"Proceedings of the 2008 international workshop on Recommendation systems for software engineering - RSSE '08","event-place":"Atlanta, Georgia","ISBN":"978-1-60558-228-3","issued":{"date-parts":[[2008]]},"page":"11","publisher":"ACM Press","publisher-place":"Atlanta, Georgia","title":"Potentials and challenges of recommendation systems for software development","type":"paper-conference","URL":"http://portal.acm.org/citation.cfm?doid=1454247.1454251"},
{"id":"Haresh20201407","abstract":"Inexpensive sensing and computation, as well as insurance innovations, have made smart dashboard cameras ubiquitous. Increasingly, simple model-driven computer vision algorithms focused on lane departures or safe following distances are finding their way into these devices. Unfortunately, the long-tailed distribution of road hazards means that these hand-crafted pipelines are inadequate for driver safety systems. We propose to apply data-driven anomaly detection ideas from deep learning to dashcam videos, which hold the promise of bridging this gap. Unfortunately, there exists almost no literature applying anomaly understanding to moving cameras, and correspondingly there is also a lack of relevant datasets. To counter this issue, we present a large and diverse dataset of truck dashcam videos, namely RetroTrucks, that includes normal and anomalous driving scenes. We apply: (i) one-class classification loss and (ii) reconstruction-based loss, for anomaly detection on RetroTrucks as well as on existing static-camera datasets. We introduce formulations for modeling object interactions in this context as priors. Our experiments indicate that our dataset is indeed more challenging than standard anomaly detection datasets, and previous anomaly detection methods do not perform well here out-of-the-box. In addition, we share insights into the behavior of these two important families of anomaly detection approaches on dashcam data. © 2020 IEEE.","author":[{"family":"Haresh","given":"S."},{"family":"Kumar","given":"S."},{"family":"Zia","given":"M.Z."},{"family":"Tran","given":"Q.-H."}],"citation-key":"Haresh20201407","collection-title":"IEEE Intelligent Vehicles Symposium, Proceedings","DOI":"10.1109/IV47402.2020.9304576","issued":{"date-parts":[[2020]]},"page":"1407-1414","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Towards anomaly detection in dashcam videos","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099882196&doi=10.1109%2fIV47402.2020.9304576&partnerID=40&md5=0158cd1c62770047963c577a52e0d19c"},
{"id":"hareshAnomalyDetectionDashcam2020a","abstract":"Inexpensive sensing and computation, as well as insurance innovations, have made smart dashboard cameras ubiquitous. Increasingly, simple model-driven computer vision algorithms focused on lane departures or safe following distances are finding their way into these devices. Unfortunately, the long-tailed distribution of road hazards means that these hand-crafted pipelines are inadequate for driver safety systems. We propose to apply data-driven anomaly detection ideas from deep learning to dashcam videos, which hold the promise of bridging this gap. Unfortunately, there exists almost no literature applying anomaly understanding to moving cameras, and correspondingly there is also a lack of relevant datasets. To counter this issue, we present a large and diverse dataset of truck dashcam videos, namely RetroTrucks, that includes normal and anomalous driving scenes. We apply: (i) one-class classification loss and (ii) reconstruction-based loss, for anomaly detection on RetroTrucks as well as on existing static-camera datasets. We introduce formulations for modeling object interactions in this context as priors. Our experiments indicate that our dataset is indeed more challenging than standard anomaly detection datasets, and previous anomaly detection methods do not perform well here out-of-the-box. In addition, we share insights into the behavior of these two important families of anomaly detection approaches on dashcam data. © 2020 IEEE.","author":[{"family":"Haresh","given":"S."},{"family":"Kumar","given":"S."},{"family":"Zia","given":"M.Z."},{"family":"Tran","given":"Q.-H."}],"citation-key":"hareshAnomalyDetectionDashcam2020a","container-title":"IEEE Intelligent Vehicles Symposium, Proceedings","DOI":"10.1109/IV47402.2020.9304576","issued":{"date-parts":[[2020]]},"page":"1407-1414","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Towards Anomaly Detection in Dashcam Videos","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099882196&doi=10.1109%2fIV47402.2020.9304576&partnerID=40&md5=0158cd1c62770047963c577a52e0d19c"},
{"id":"hartelClassificationAPIsHierarchical2018","abstract":"APIs can be classified according to the programming domains (e.g., GUIs, databases, collections, or security) that they address. Such classification is vital in searching repositories (e.g., the Maven Central Repository for Java) and for understanding the technology stack used in software projects. We apply hierarchical clustering to a curated suite of Java APIs to compare the computed API clusters with preexisting API classifications. Clustering entails various parameters (e.g., the choice of IDF versus LSI versus LDA). We describe the corresponding variability in terms of a feature model. We exercise all possible configurations to determine the maximum correlation with respect to two baselines: i) a smaller suite of APIs manually classified in previous research; ii) a larger suite of APIs from the Maven Central Repository, thereby taking advantage of crowd-sourced classification while relying on a threshold-based approach for identifying important APIs and versions thereof, subject to an API dependency analysis on GitHub. We discuss the configurations found in this way and we examine the influence of particular features on the correlation between computed clusters and baselines. To this end, we also leverage interactive exploration of the parameter space and the resulting dendrograms. In this manner, we can also identify issues with the use of classifiers (e.g., missing classifiers) in the baselines and limitations of the clustering approach.","author":[{"family":"Härtel","given":"Johannes"}],"citation-key":"hartelClassificationAPIsHierarchical2018","issued":{"date-parts":[[2018]]},"page":"11","source":"Zotero","title":"Classification of APIs by Hierarchical Clustering","type":"article-journal"},
{"id":"Hartmann2019300","abstract":"Although artificial intelligence and machine learning are currently extremely fashionable, applying machine learning on real-life problems remains very challenging. Data scientists need to evaluate various learning algorithms and tune their numerous parameters, based on their assumptions and experience, against concrete problems and training data sets. This is a long, tedious, and resource expensive task. Meta-learning is a recent technique to overcome, i.e. automate this problem. It aims at using machine learning itself to automatically learn the most appropriate algorithms and parameters for a machine learning problem. As it turns out, there are many parallels between meta-modelling - in the sense of model-driven engineering - and meta-learning. Both rely on abstractions, the meta data, to model a predefined class of problems and to define the variabilities of the models conforming to this definition. Both are used to define the output and input relationships and then fitting the right models to represent that behaviour. In this paper, we envision how a meta-model for meta-learning can look like. We discuss possible variabilities, for what types of learning it could be appropriate for, how concrete learning models can be generated from it, and how models can be finally selected. Last but not least, we discuss a possible integration into existing modelling tools. © 2019 IEEE.","author":[{"family":"Hartmann","given":"T."},{"family":"Moawad","given":"A."},{"family":"Schockaert","given":"C."},{"family":"Fouquet","given":"F."},{"family":"Le Traon","given":"Y."}],"citation-key":"Hartmann2019300","collection-title":"Proceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems, MODELS 2019","DOI":"10.1109/MODELS.2019.00014","editor":[{"family":"Kessentini M., Yue T.","given":"Yue T.","suffix":"Pretschner A., Voss S., Burgueno L., Burgueno L."}],"ISBN":"978-1-72812-535-0","issued":{"date-parts":[[2019]]},"page":"300-305","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Meta-modelling meta-learning","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076083013&doi=10.1109%2fMODELS.2019.00014&partnerID=40&md5=1bc899e03db04f26cf6d0a482f1e19a1"},
{"id":"hartmannNextEvolutionMDE2017","accessed":{"date-parts":[[2017,10,2]]},"author":[{"family":"Hartmann","given":"Thomas"},{"family":"Moawad","given":"Assaad"},{"family":"Fouquet","given":"Francois"},{"family":"Le Traon","given":"Yves"}],"citation-key":"hartmannNextEvolutionMDE2017","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-017-0600-2","ISSN":"1619-1366, 1619-1374","issued":{"date-parts":[[2017,5,29]]},"source":"CrossRef","title":"The next evolution of MDE: a seamless integration of machine learning into domain modeling","title-short":"The next evolution of MDE","type":"article-journal","URL":"http://link.springer.com/10.1007/s10270-017-0600-2"},
{"id":"hassamAssistanceSystemOCL2011","author":[{"family":"Hassam","given":"Kahina"},{"family":"Sadou","given":"Salah"},{"family":"Gloahec","given":"Vincent Le"},{"family":"Fleurquin","given":"Regis"}],"citation-key":"hassamAssistanceSystemOCL2011","container-title":"2011 15th European Conference on Software Maintenance and Reengineering","DOI":"10.1109/CSMR.2011.21","issued":{"date-parts":[[2011]]},"page":"151–160","title":"Assistance System for OCL Constraints Adaptation during Metamodel Evolution","type":"article-journal"},
{"id":"haugeAdoptionOpenSource2010","accessed":{"date-parts":[[2015,11,9]]},"author":[{"family":"Hauge","given":"Øyvind"},{"family":"Ayala","given":"Claudia"},{"family":"Conradi","given":"Reidar"}],"citation-key":"haugeAdoptionOpenSource2010","container-title":"Information and Software Technology","DOI":"10.1016/j.infsof.2010.05.008","ISSN":"09505849","issue":"11","issued":{"date-parts":[[2010,11]]},"page":"1133-1154","source":"CrossRef","title":"Adoption of open source software in software-intensive organizations – A systematic literature review","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0950584910000972","volume":"52"},
{"id":"haugeEmpiricalStudySelection2009","accessed":{"date-parts":[[2017,2,25]]},"author":[{"family":"Hauge","given":"Oyvind"},{"family":"Osterlie","given":"Thomas"},{"family":"Sorensen","given":"Carl-Fredrik"},{"family":"Gerea","given":"Marinela"}],"citation-key":"haugeEmpiricalStudySelection2009","container-title":"Emerging Trends in Free/Libre/Open Source Software Research and Development, 2009. FLOSS'09. ICSE Workshop on","issued":{"date-parts":[[2009]]},"page":"42–47","publisher":"IEEE","source":"Google Scholar","title":"An empirical study on selection of Open Source Software-Preliminary results","type":"paper-conference","URL":"http://ieeexplore.ieee.org/abstract/document/5071359/"},
{"id":"haugheyNOSQLDataLake2017","author":[{"family":"Haughey","given":"Tom"}],"citation-key":"haugheyNOSQLDataLake2017","issued":{"date-parts":[[2017]]},"page":"28","source":"Zotero","title":"NOSQL and Data Lake Architecture","type":"article-journal"},
{"id":"haveliwalaTopicsensitivePageRank2002","author":[{"family":"Haveliwala","given":"Taher H."}],"citation-key":"haveliwalaTopicsensitivePageRank2002","collection-title":"WWW '02","container-title":"Proceedings of the 11th international conference on world wide web","event-place":"New York, NY, USA","ISBN":"1-58113-449-5","issued":{"date-parts":[[2002]]},"page":"517-526","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Topic-sensitive PageRank","type":"paper-conference","URL":"http://doi.acm.org/10.1145/511446.511513"},
{"id":"HCI-009","author":[{"family":"Ekstrand","given":"Michael D."},{"family":"Riedl","given":"John T."},{"family":"Konstan","given":"Joseph A."}],"citation-key":"HCI-009","container-title":"Foundations and Trends® in Human–Computer Interaction","ISSN":"1551-3955","issue":"2","issued":{"date-parts":[[2011]]},"page":"81-173","title":"Collaborative filtering recommender systems","type":"article-journal","URL":"http://dx.doi.org/10.1561/1100000009","volume":"4"},
{"id":"He2019","abstract":"This paper presents TurboNet, a novel model-driven deep learning (DL) architecture for turbo decoding that combines DL with the traditional max-log-maximum a posteriori (MAP) algorithm. To design TurboNet, we unfold the original iterative structure for turbo decoding and replace each iteration by a deep neural network (DNN) decoding unit. In particular, the DNN decoding unit is obtained by parameterizing the max-log- MAP algorithm rather than replace the whole decoder with a black box fully connected DNN architecture. With the proposed architecture, the parameters can be efficiently learned from training data, and thus TurboNet learns to appropriately use systematic and parity information to offer higher error correction capabilities and decrease computational complexity compared with existing methods. Furthermore, simulation results prove TurboNet's superiority in signal-to-noise ratio generalizations. © 2019 IEEE.","author":[{"family":"He","given":"Y."},{"family":"Zhang","given":"J."},{"family":"Wen","given":"C.-K."},{"family":"Jin","given":"S."}],"citation-key":"He2019","collection-title":"Proceedings - 2019 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2019","DOI":"10.1109/VTS-APWCS.2019.8851650","ISBN":"978-1-72811-204-6","issued":{"date-parts":[[2019]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"TurboNet: A model-driven DNN decoder based on max-log-MAP algorithm for turbo code","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073533122&doi=10.1109%2fVTS-APWCS.2019.8851650&partnerID=40&md5=14580e4caf2f1bff2b470e0d84236208"},
{"id":"He2019584","abstract":"In this paper, we propose a model-driven deep learning network for multiple-input multiple-output (MIMO) detection. The structure of the network is specially designed by unfolding the iterative algorithm. Some trainable parameters are optimized through deep learning techniques to improve the detection performance. Since the number of trainable variables of the network is equal to that of the layers, the network can be easily trained within a very short time. Furthermore, the network can handle time-varying channel with only a single training. Numerical results show that the proposed approach can improve the performance of the iterative algorithm significantly under Rayleigh and correlated MIMO channels. © 2018 IEEE.","author":[{"family":"He","given":"H."},{"family":"Wen","given":"C.-K."},{"family":"Jin","given":"S."},{"family":"Li","given":"G.Y."}],"citation-key":"He2019584","collection-title":"2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings","DOI":"10.1109/GlobalSIP.2018.8646357","ISBN":"978-1-72811-295-4","issued":{"date-parts":[[2019]]},"page":"584-588","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A model-driven deep learning network for MIMO detection","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063081799&doi=10.1109%2fGlobalSIP.2018.8646357&partnerID=40&md5=5cd7f46876c872cad079438b8010bc73"},
{"id":"He201977","abstract":"Intelligent communication is gradually becoming a mainstream direction. As a major branch of machine learning, deep learning (DL) has been applied in physical layer communications and has demonstrated an impressive performance improvement in recent years. However, most existing works related to DL focus on data-driven approaches, which consider the communication system as a black box and train it by using a huge volume of data. Training a network requires sufficient computing resources and extensive time, both of which are rarely found in communication devices. By contrast, model-driven DL approaches combine communication domain knowledge with DL to reduce the demand for computing resources and training time. This article discusses the recent advancements in model-driven DL approaches in physical layer communications, including transmission schemes, receiver design, and channel information recovery. Several open issues for future research are also highlighted. © 2002-2012 IEEE.","author":[{"family":"He","given":"H."},{"family":"Jin","given":"S."},{"family":"Wen","given":"C.-K."},{"family":"Gao","given":"F."},{"family":"Li","given":"G.Y."},{"family":"Xu","given":"Z."}],"citation-key":"He201977","container-title":"IEEE Wireless Communications","DOI":"10.1109/MWC.2019.1800447","ISSN":"15361284","issue":"5","issued":{"date-parts":[[2019]]},"page":"77-83","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Model-driven deep learning for physical layer communications","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065964103&doi=10.1109%2fMWC.2019.1800447&partnerID=40&md5=c6f2f945a68998813d9d93a95df30187","volume":"26"},
{"id":"He20201702","abstract":"In this paper, we investigate the model-driven deep learning (DL) for MIMO detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters. Since the number of trainable parameters is much fewer than the data-driven DL based signal detector, the model-driven DL based MIMO detector can be rapidly trained with a much smaller data set. The proposed MIMO detector can be extended to soft-input soft-output detection easily. Furthermore, we investigate joint MIMO channel estimation and signal detection (JCESD), where the detector takes channel estimation error and channel statistics into consideration while channel estimation is refined by detected data and considers the detection error. Based on numerical results, the model-driven DL based MIMO detector significantly improves the performance of corresponding traditional iterative detector, outperforms other DL-based MIMO detectors and exhibits superior robustness to various mismatches. © 1991-2012 IEEE.","author":[{"family":"He","given":"H."},{"family":"Wen","given":"C.-K."},{"family":"Jin","given":"S."},{"family":"Li","given":"G.Y."}],"citation-key":"He20201702","container-title":"IEEE Transactions on Signal Processing","DOI":"10.1109/TSP.2020.2976585","ISSN":"1053587X","issued":{"date-parts":[[2020]]},"page":"1702-1715","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Model-driven deep learning for mimo detection","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081400998&doi=10.1109%2fTSP.2020.2976585&partnerID=40&md5=1c7e4dce2af82b5f40fa0650d1e27347","volume":"68"},
{"id":"He20202216","abstract":"Massive multiuser multiple-input multiple-output (MU-MIMO) has been the mainstream technology in fifth-generation wireless systems. To reduce high hardware costs and power consumption in massive MU-MIMO, low-resolution digital-to-analog converters (DAC) for each antenna and radio frequency (RF) chain in downlink transmission is used, which brings challenges for precoding design. To circumvent these obstacles, we develop a model-driven deep learning (DL) network for massive MU-MIMO with finite-alphabet precoding in this article. The architecture of the network is specially designed by unfolding an iterative algorithm. Compared with the traditional state-of-the-art techniques, the proposed DL-based precoder shows significant advantages in performance, complexity, and robustness to channel estimation error under Rayleigh fading channel. © 1997-2012 IEEE.","author":[{"family":"He","given":"H."},{"family":"Zhang","given":"M."},{"family":"Jin","given":"S."},{"family":"Wen","given":"C.-K."},{"family":"Li","given":"G.Y."}],"citation-key":"He20202216","container-title":"IEEE Communications Letters","DOI":"10.1109/LCOMM.2020.3002082","ISSN":"10897798","issue":"10","issued":{"date-parts":[[2020]]},"page":"2216-2220","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Model-driven deep learning for massive MU-MIMO with finite-alphabet precoding","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092701532&doi=10.1109%2fLCOMM.2020.3002082&partnerID=40&md5=2f9a0e8d81dc3158beb263a0e2f7c41d","volume":"24"},
{"id":"He2021","abstract":"Hyperspectral images (HSIs) are crucial for many research works. Spectral super-resolution (SSR) is a method used to obtain high-spatial-resolution (HR) HSIs from HR multispectral images. Traditional SSR methods include model-driven algorithms and deep learning. By unfolding a variational method, this article proposes an optimization-driven convolutional neural network (CNN) with a deep spatial-spectral prior, resulting in physically interpretable networks. Unlike the fully data-driven CNN, auxiliary spectral response function (SRF) is utilized to guide CNNs to group the bands with spectral relevance. In addition, the channel attention module (CAM) and the reformulated spectral angle mapper loss function are applied to achieve an effective reconstruction model. Finally, experiments on two types of data sets, including natural and remote sensing images, demonstrate the spectral enhancement effect of the proposed method, and also, the classification results on the remote sensing data set verified the validity of the information enhanced by the proposed method. IEEE","author":[{"family":"He","given":"J."},{"family":"Li","given":"J."},{"family":"Yuan","given":"Q."},{"family":"Shen","given":"H."},{"family":"Zhang","given":"L."}],"citation-key":"He2021","container-title":"IEEE Transactions on Neural Networks and Learning Systems","DOI":"10.1109/TNNLS.2021.3056181","ISSN":"2162237X","issued":{"date-parts":[[2021]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Spectral response function-guided deep optimization-driven network for spectral super-resolution","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101743298&doi=10.1109%2fTNNLS.2021.3056181&partnerID=40&md5=3bde0ec62ce3fc03d13824617a497f51"},
{"id":"hearstSupportVectorMachines1998","author":[{"family":"Hearst","given":"Marti A."}],"citation-key":"hearstSupportVectorMachines1998","container-title":"IEEE Intelligent Systems","ISSN":"1541-1672","issue":"4","issued":{"date-parts":[[1998,7]]},"page":"18-28","title":"Support vector machines","type":"article-journal","URL":"http://dx.doi.org/10.1109/5254.708428","volume":"13"},
{"id":"heAutoMLSurveyStateoftheart2021","accessed":{"date-parts":[[2021,5,3]]},"author":[{"family":"He","given":"Xin"},{"family":"Zhao","given":"Kaiyong"},{"family":"Chu","given":"Xiaowen"}],"citation-key":"heAutoMLSurveyStateoftheart2021","container-title":"Knowledge-Based Systems","container-title-short":"Knowledge-Based Systems","DOI":"10.1016/j.knosys.2020.106622","ISSN":"09507051","issued":{"date-parts":[[2021,1]]},"note":"00144","page":"106622","source":"DOI.org (Crossref)","title":"AutoML: A survey of the state-of-the-art","title-short":"AutoML","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0950705120307516","volume":"212"},
{"id":"hein2009model","author":[{"family":"Hein","given":"Christian"},{"family":"Ritter","given":"Tom"},{"family":"Wagner","given":"Michael"}],"citation-key":"hein2009model","container-title":"Workshop future trends of model-driven development","issued":{"date-parts":[[2009]]},"page":"50-52","title":"Model-driven tool integration with modelbus","type":"paper-conference"},
{"id":"heitmannUsingLinkedData2010","abstract":"While recommender systems can greatly enhance the user experience, the entry barriers in terms of data acquisition are very high, making it hard for new service providers to compete with existing recommendation services. This paper proposes to build open recommender systems which can utilise Linked Data to mitigate the new-user, new-item and sparsity problems of collaborative recommender systems. We describe how to aggregate data about object centred sociality from different sources and how to process it for collaborative recommendation. To demonstrate the validity of our approach, we augment the data from a closed collaborative music recommender system with Linked Data, and significantly improve its precision and recall.","author":[{"family":"Heitmann","given":"Benjamin"},{"family":"Hayes","given":"Conor"}],"citation-key":"heitmannUsingLinkedData2010","container-title":"Artificial Intelligence","issued":{"date-parts":[[2010]]},"page":"76-81","title":"Using linked data to build open, collaborative recommender systems","type":"article-journal","URL":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.174.2755 \thttp://www.aaai.org/ocs/index.php/SSS/SSS10/paper/viewPDFInterstitial/1067/1452"},
{"id":"henderson-sellersMultiLevelMetaModellingUnderpin2013","author":[{"family":"Henderson-Sellers","given":"Brian"},{"family":"Gonzalez-Perez","given":"Cesar"}],"citation-key":"henderson-sellersMultiLevelMetaModellingUnderpin2013","container-title":"Domain Engineering","DOI":"10.1007/978-3-642-36654-3_12","issued":{"date-parts":[[2013]]},"page":"291–316","title":"Multi-Level Meta-Modelling to Underpin the Abstract and Concrete Syntax for Domain-Specific Modelling Languages","type":"article-journal"},
{"id":"Henkel:2005:CCR:1062455.1062512","author":[{"family":"Henkel","given":"Johannes"},{"family":"Diwan","given":"Amer"}],"citation-key":"Henkel:2005:CCR:1062455.1062512","collection-title":"ICSE '05","container-title":"Proceedings of the 27th international conference on software engineering","event-place":"New York, NY, USA","ISBN":"1-58113-963-2","issued":{"date-parts":[[2005]]},"page":"274-283","publisher":"ACM","publisher-place":"New York, NY, USA","title":"CatchUp!: Capturing and replaying refactorings to support API evolution","type":"paper-conference","URL":"http://doi.acm.org/10.1145/1062455.1062512"},
{"id":"henningenRetrievingSoftwareObjects1991","author":[{"family":"Henningen","given":"Scott"}],"citation-key":"henningenRetrievingSoftwareObjects1991","container-title":"Proceedings of the 14th annual international ACM SIGIR conference on research and development in information retrieval. Chicago, illinois, USA, october 13-16, 1991 (special issue of the SIGIR forum).","DOI":"10.1145/122860.122886","issued":{"date-parts":[[1991]]},"page":"251-260","title":"Retrieving software objects in an example-based programming environment","type":"paper-conference","URL":"https://doi.org/10.1145/122860.122886"},
{"id":"HereWhatVoicecontrolled","accessed":{"date-parts":[[2015,4,16]]},"citation-key":"HereWhatVoicecontrolled","title":"Here’s what voice-controlled Android home automation looks like [video]","type":"webpage","URL":"http://www.androidauthority.com/voice-controlled-android-home-automation-video-205316/"},
{"id":"Hernández-Orozco2021","abstract":"We show how complexity theory can be introduced in machine learning to help bring together apparently disparate areas of current research. We show that this model-driven approach may require less training data and can potentially be more generalizable as it shows greater resilience to random attacks. In an algorithmic space the order of its element is given by its algorithmic probability, which arises naturally from computable processes. We investigate the shape of a discrete algorithmic space when performing regression or classification using a loss function parametrized by algorithmic complexity, demonstrating that the property of differentiation is not required to achieve results similar to those obtained using differentiable programming approaches such as deep learning. In doing so we use examples which enable the two approaches to be compared (small, given the computational power required for estimations of algorithmic complexity). We find and report that 1) machine learning can successfully be performed on a non-smooth surface using algorithmic complexity; 2) that solutions can be found using an algorithmic-probability classifier, establishing a bridge between a fundamentally discrete theory of computability and a fundamentally continuous mathematical theory of optimization methods; 3) a formulation of an algorithmically directed search technique in non-smooth manifolds can be defined and conducted; 4) exploitation techniques and numerical methods for algorithmic search to navigate these discrete non-differentiable spaces can be performed; in application of the (a) identification of generative rules from data observations; (b) solutions to image classification problems more resilient against pixel attacks compared to neural networks; (c) identification of equation parameters from a small data-set in the presence of noise in continuous ODE system problem, (d) classification of Boolean NK networks by (1) network topology, (2) underlying Boolean function, and (3) number of incoming edges. © Copyright © 2021 Hernández-Orozco, Zenil, Riedel, Uccello, Kiani and Tegnér.","author":[{"family":"Hernández-Orozco","given":"S."},{"family":"Zenil","given":"H."},{"family":"Riedel","given":"J."},{"family":"Uccello","given":"A."},{"family":"Kiani","given":"N.A."},{"family":"Tegnér","given":"J."}],"citation-key":"Hernández-Orozco2021","container-title":"Frontiers in Artificial Intelligence","DOI":"10.3389/frai.2020.567356","ISSN":"26248212","issued":{"date-parts":[[2021]]},"publisher":"Frontiers Media S.A.","title":"Algorithmic probability-guided machine learning on non-differentiable spaces","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115854060&doi=10.3389%2ffrai.2020.567356&partnerID=40&md5=ec5b2022bf6a9c7424edafb98dbbf5d8","volume":"3"},
{"id":"Herrmann201979","abstract":"Machine learning based motion modelling methods such as statistical modelling require a large amount of input data. In practice, the management of the data can become a problem in itself for artists who want to control the quality of the motion models. As a solution to this problem, we present a motion data and model management system and integrate it with a statistical motion modelling pipeline. The system is based on a data storage server with a REST interface that enables the efficient storage of different versions of motion data and models. The database system is combined with a motion preprocessing tool that provides functions for batch editing, retargeting and annotation of the data. For the application of the motion models in a game engine, the framework provides a stateful motion synthesis server that can load the models directly from the data storage server. Additionally, the framework makes use of a Kubernetes compute cluster to execute time consuming processes such as the preprocessing and modelling of the data. The system is evaluated in a use case for the simulation of manual assembly workers. © 2019 The Author(s) Eurographics Proceedings © 2019 The Eurographics Association.","author":[{"family":"Herrmann","given":"E."},{"family":"Du","given":"H."},{"family":"Antakli","given":"A."},{"family":"Rubinstein","given":"D."},{"family":"Schubotz","given":"R."},{"family":"Sprenger","given":"J."},{"family":"Hosseini","given":"S."},{"family":"Cheema","given":"N."},{"family":"Zinnikus","given":"I."},{"family":"Manns","given":"M."},{"family":"Fischer","given":"K."},{"family":"Slusallek","given":"P."}],"citation-key":"Herrmann201979","collection-title":"Italian Chapter Conference 2019 - Smart Tools and Apps in computer Graphics, STAG 2019","DOI":"10.2312/stag.20191366","editor":[{"family":"Agus M., Corsini M.","given":"Pintus R."}],"ISBN":"978-3-03868-100-7","issued":{"date-parts":[[2019]]},"page":"79-88","publisher":"Eurographics Association","title":"Motion data and model management for applied statistical motion synthesis","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086737324&doi=10.2312%2fstag.20191366&partnerID=40&md5=bae8d75e5fbf7e855aba09b78649813a"},
{"id":"heTurboNetModeldrivenDNN2019a","abstract":"This paper presents TurboNet, a novel model-driven deep learning (DL) architecture for turbo decoding that combines DL with the traditional max-log-maximum a posteriori (MAP) algorithm. To design TurboNet, we unfold the original iterative structure for turbo decoding and replace each iteration by a deep neural network (DNN) decoding unit. In particular, the DNN decoding unit is obtained by parameterizing the max-log- MAP algorithm rather than replace the whole decoder with a black box fully connected DNN architecture. With the proposed architecture, the parameters can be efficiently learned from training data, and thus TurboNet learns to appropriately use systematic and parity information to offer higher error correction capabilities and decrease computational complexity compared with existing methods. Furthermore, simulation results prove TurboNet's superiority in signal-to-noise ratio generalizations. © 2019 IEEE.","author":[{"family":"He","given":"Y."},{"family":"Zhang","given":"J."},{"family":"Wen","given":"C.-K."},{"family":"Jin","given":"S."}],"citation-key":"heTurboNetModeldrivenDNN2019a","container-title":"Proceedings - 2019 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2019","DOI":"10.1109/VTS-APWCS.2019.8851650","ISBN":"978-1-72811-204-6","issued":{"date-parts":[[2019]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"TurboNet: A model-driven DNN decoder based on max-log-MAP algorithm for turbo code","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073533122&doi=10.1109%2fVTS-APWCS.2019.8851650&partnerID=40&md5=14580e4caf2f1bff2b470e0d84236208"},
{"id":"hidasiSessionbasedRecommendationsRecurrent2015","author":[{"family":"Hidasi","given":"Balázs"},{"family":"Karatzoglou","given":"Alexandros"},{"family":"Baltrunas","given":"Linas"},{"family":"Tikk","given":"Domonkos"}],"citation-key":"hidasiSessionbasedRecommendationsRecurrent2015","container-title":"CoRR","issued":{"date-parts":[[2015]]},"title":"Session-based recommendations with recurrent neural networks","type":"article-journal","URL":"http://arxiv.org/abs/1511.06939","volume":"abs/1511.06939"},
{"id":"Hildebrandt2017128","abstract":"Lossless lightweight data compression is a very important optimization technique in various application domains like database systems, information retrieval or machine learning. Despite this importance, currently, there exists no comprehensive and non-Technical abstraction. To overcome this issue, we have developed a systematic approach using metamodeling that focuses on the non-Technical concepts of these algorithms. In this paper, we describe COLLATE, the metamodel we developed, and show that each algorithm can be described as a model conforming with COLLATE. Furthermore, we use COLLATE to specify a compression algorithm language COALA, so that lightweight data compression algorithms can be specified and modified in a descriptive and abstract way. Additionally, we present an approach to transform such descriptive algorithms into executable code. As we are going to show, our abstract and non-Technical approach offers several advantages. © 2011 Springer-Verlag Berlin Heidelberg.","author":[{"family":"Hildebrandt","given":"J."},{"family":"Habich","given":"D."},{"family":"Kuhn","given":"T."},{"family":"Damme","given":"P."},{"family":"Lehner","given":"W."}],"citation-key":"Hildebrandt2017128","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Cabanillas C., Farshidi S.","given":"Espana S."}],"ISSN":"16130073","issued":{"date-parts":[[2017]]},"page":"128-141","publisher":"CEUR-WS","title":"Metamodeling lightweight data compression algorithms and its application scenarios","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034960838&partnerID=40&md5=33294c448edfceb4dead3c958f0f06de","volume":"1979"},
{"id":"hintzeViolinPlotsBox1998","author":[{"family":"Hintze","given":"Jerry L."},{"literal":"Ray D. Nelson"}],"citation-key":"hintzeViolinPlotsBox1998","container-title":"The American Statistician","issue":"2","issued":{"date-parts":[[1998]]},"page":"181-184","title":"Violin plots: A box plot-density trace synergism","type":"article-journal","URL":"https://amstat.tandfonline.com/doi/abs/10.1080/00031305.1998.10480559","volume":"52"},
{"id":"Hirschberg:1977:ALC:322033.322044","author":[{"family":"Hirschberg","given":"Daniel S."}],"citation-key":"Hirschberg:1977:ALC:322033.322044","container-title":"Journal of the ACM","container-title-short":"J. ACM","ISSN":"0004-5411","issue":"4","issued":{"date-parts":[[1977,10]]},"page":"664-675","title":"Algorithms for the longest common subsequence problem","type":"article-journal","URL":"http://doi.acm.org/10.1145/322033.322044","volume":"24"},
{"id":"HitchhikerGuideIoT","accessed":{"date-parts":[[2016,9,27]]},"citation-key":"HitchhikerGuideIoT","title":"Hitchhiker's Guide to IoT Standards and Protocols - DZone IoT","type":"webpage","URL":"https://dzone.com/articles/hitchhikers-guide-to-iot-standards-and-protocols?edition=216186&utm_source=Spotlight&utm_medium=email&utm_campaign=iot%202016-09-27"},
{"id":"hnetynkaUsingComponentEnsembles2020","accessed":{"date-parts":[[2021,1,8]]},"author":[{"family":"Hnetynka","given":"Petr"},{"family":"Bures","given":"Tomas"},{"family":"Gerostathopoulos","given":"Ilias"},{"family":"Pacovsky","given":"Jan"}],"citation-key":"hnetynkaUsingComponentEnsembles2020","container-title":"Proceedings of the IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","DOI":"10.1145/3387939.3391599","event":"SEAMS '20: IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","event-place":"Seoul Republic of Korea","ISBN":"978-1-4503-7962-5","issued":{"date-parts":[[2020,6,29]]},"note":"00003","page":"156-162","publisher":"ACM","publisher-place":"Seoul Republic of Korea","source":"DOI.org (Crossref)","title":"Using component ensembles for modeling autonomic component collaboration in smart farming","type":"paper-conference","URL":"https://dl.acm.org/doi/10.1145/3387939.3391599"},
{"id":"hoareRoleFormalTechniques1996","accessed":{"date-parts":[[2016,11,20]]},"author":[{"family":"Hoare","given":"C. A. R."}],"citation-key":"hoareRoleFormalTechniques1996","container-title":"Proceedings of the 18th international conference on Software engineering","issued":{"date-parts":[[1996]]},"page":"233–234","publisher":"IEEE Computer Society","source":"Google Scholar","title":"The role of formal techniques: past, current and future or how did software get so reliable without proof?","title-short":"The role of formal techniques","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=227765"},
{"id":"hodaRiseEvolutionAgile2018","abstract":"Agile software development has dominated the second half of the past 50 years of software engineering. Retrospectives, one of the most common agile practices, enables reflection on past performance, discussion of current progress, and charting forth directions for future improvement. Because of agile’s burgeoning popularity as the software development model of choice and a significant research subdomain of software engineering, it demands a retrospective of its own. This article provides a historical overview of agile’s main focus areas and a holistic synthesis of its trends, their evolution over the past two decades, agile’s current status, and, forecast from these, agile’s likely future. This article is part of a theme issue on software engineering’s 50th anniversary.","author":[{"family":"Hoda","given":"R."},{"family":"Salleh","given":"N."},{"family":"Grundy","given":"J."}],"citation-key":"hodaRiseEvolutionAgile2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.290111318","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"58-63","source":"IEEE Xplore","title":"The Rise and Evolution of Agile Software Development","type":"article-journal","volume":"35"},
{"id":"hoislCatalogReusableDesign","abstract":"In the process of model-driven development (MDD) of software artifacts, domain-specific modeling languages (DSMLs) are an integral part. They act as the communication vehicle for aligning the requirements of the domain expert with the needs of the software engineer. With the rise of the UML as de facto standard for modeling software systems, MOF/UMLbased DSMLs are now widely used for MDD. This paper documents design decisions from ten DSML projects which are based on the MOF/UML and which we conducted over the last years. We present our experiences in the form of reusable decision templates for all decision points detected in each phase of the DSML development process. Furthermore, we report also on identified decision dependencies which may occur within a single decision or between two decisions.","author":[{"family":"Hoisl","given":"Bernhard"},{"family":"Sobernig","given":"Stefan"},{"family":"Schefer-Wenzl","given":"Sigrid"},{"family":"Strembeck","given":"Mark"},{"family":"Baumgrass","given":"Anne"}],"citation-key":"hoislCatalogReusableDesign","note":"00000","page":"24","source":"Zotero","title":"A Catalog of Reusable Design Decisions for Developing UML- and MOF-based Domain-Specific Modeling Languages","type":"article-journal"},
{"id":"hollerMachinetomachineInternetThings2014","abstract":"This book outlines the background and overall vision for the Internet of Things (IoT) and M2M communications and services, including major standards. Key technologies are described: Everything from physical instrumentation devices to the cloud infrastructures used to collect data, derive information and map it to current processes, as well as system architectures and regulatory requirements. Real world service use case studies provide the hands-on knowledge needed to successfully develop and implement M2M and IoT technologies sustainably and profitably","call-number":"TK5105.875.I57 F76 2014","citation-key":"hollerMachinetomachineInternetThings2014","editor":[{"family":"Höller","given":"Jan"}],"event-place":"Amsterdam","ISBN":"978-0-12-407684-6 978-0-08-099401-7","issued":{"date-parts":[[2014]]},"note":"OCLC: ocn877027010","number-of-pages":"331","publisher":"Elsevier Academic Press","publisher-place":"Amsterdam","source":"Library of Congress ISBN","title":"From machine-to-machine to the Internet of things: introduction to a new age of intelligence","title-short":"From machine-to-machine to the Internet of things","type":"book"},
{"id":"holmes_strathcona_nodate","abstract":"Using the application programming interfaces (API) of large software systems requires developers to understand details about the interfaces that are often not explicitly defined. However, documentation about the API is often incomplete or out of date. Existing systems that make use of the API provide a form of implicit information on how to use that code. Manually searching through existing projects to find relevant source code is tedious and time consuming. We have created the Strathcona Example Recommendation Tool to assist developers in finding relevant fragments of code, or examples, of an API’s use. These examples can be used by developers to provide insight on how they are supposed to interact with the API.","author":[{"family":"Holmes","given":"Reid"},{"family":"Walker","given":"Robert J"},{"family":"Murphy","given":"Gail C"}],"citation-key":"holmes_strathcona_nodate","issued":{"date-parts":[[2015]]},"page":"4","title":"Strathcona Example Recommendation Tool","type":"article-journal"},
{"id":"holmesStrathconaExampleRecommendation2005","author":[{"family":"Holmes","given":"Reid"},{"family":"Walker","given":"Robert J."},{"family":"Murphy","given":"Gail C."}],"citation-key":"holmesStrathconaExampleRecommendation2005","container-title":"Proceedings of the 10th european software engineering conference held jointly with 13th ACM SIGSOFT international symposium on foundations of software engineering, 2005, lisbon, portugal, september 5-9, 2005","DOI":"10.1145/1081706.1081744","issued":{"date-parts":[[2005]]},"page":"237-240","title":"Strathcona example recommendation tool","type":"paper-conference","URL":"https://doi.org/10.1145/1081706.1081744"},
{"id":"holzmannCodeVault2018","abstract":"So, what has changed since that first NATO software engineering conference in 1968? Depending on your point of view, nothing much has changed, or everything has changed. The part that didn’t change much is that we still struggle with writing code that’s robust enough to trust. The part that has changed dramatically is the performance of the hardware that runs our code. This article is part of a theme issue on software engineering’s 50th anniversary.","author":[{"family":"Holzmann","given":"G. J."}],"citation-key":"holzmannCodeVault2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.3571225","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"85-87","source":"IEEE Xplore","title":"Code Vault","type":"article-journal","volume":"35"},
{"id":"HomeServerNoob","accessed":{"date-parts":[[2015,4,22]]},"citation-key":"HomeServerNoob","title":"Home server noob. Can't get CouchPotato to communicate with Deluge. : HomeServer","type":"webpage","URL":"http://www.reddit.com/r/HomeServer/comments/2r15vh/home_server_noob_cant_get_couchpotato_to/"},
{"id":"HomeSystemsConsulting","accessed":{"date-parts":[[2015,4,8]]},"citation-key":"HomeSystemsConsulting","title":"Home Systems Consulting","type":"webpage","URL":"http://www.hsyco.com/"},
{"id":"HORIZON2020","accessed":{"date-parts":[[2015,4,8]]},"citation-key":"HORIZON2020","title":"HORIZON 2020","type":"webpage","URL":"http://een.unioncamerepuglia.it/Italiano/News/HORIZON-2020/"},
{"id":"HoseinDoost20191985","abstract":"In emergency response environments, variant entities with specific behaviors and interaction between them form a complex system that can be well modeled by multi-agent systems. To build such complex systems, instead of writing the code from scratch, one can follow the model-driven development approach, which aims to generate software from design models automatically. To achieve this goal, two important prerequisites are: a domain-specific modeling language for designing an emergency response environment model, and transformation programs for automatic code generation from a model. In addition, for modeling with the language, a modeling tool is required, and for executing the generated code there is a need to a platform. In this paper, a model-driven framework for developing multi-agent systems in emergency response environments is provided which includes several items. A domain-specific modeling language as well as a modeling tool is developed for this domain. The language and the tool are called ERE-ML and ERE-ML Tool, respectively. Using the ERE-ML Tool, a designer can model an emergency response situation and then validate the model against the predefined constraints. Furthermore, several model to code transformations are defined for automatic multi-agent system code generation from an emergency response environment model. For executing the generated code, an extension of JAMDER platform is also provided. To evaluate our framework, several case studies including the Victorian bushfire disaster are modeled to show the ability of the framework in modeling real-world situations and automatic transformation of the model into the code. © 2017, Springer-Verlag GmbH Germany.","author":[{"family":"HoseinDoost","given":"S."},{"family":"Adamzadeh","given":"T."},{"family":"Zamani","given":"B."},{"family":"Fatemi","given":"A."}],"citation-key":"HoseinDoost20191985","container-title":"Software and Systems Modeling","DOI":"10.1007/s10270-017-0627-4","ISSN":"16191366","issue":"3","issued":{"date-parts":[[2019]]},"page":"1985-2012","publisher":"Springer Verlag","title":"A model-driven framework for developing multi-agent systems in emergency response environments","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031489970&doi=10.1007%2fs10270-017-0627-4&partnerID=40&md5=443c1cafd658af497641eec10908552b","volume":"18"},
{"id":"hossainIEEEPressEditorial","author":[{"family":"Hossain","given":"Ekram"},{"family":"Fortino","given":"Giancarlo"},{"family":"Grier","given":"David Alan"},{"family":"Heirman","given":"Donald"},{"family":"Li","given":"Xiaoou"},{"family":"Molisch","given":"Andreas"},{"family":"Nahavandi","given":"Saeid"},{"family":"Perez","given":"Ray"},{"family":"Reed","given":"Jeffrey"},{"family":"Shafer","given":"Linda"},{"family":"Shahidehpour","given":"Mohammad"},{"family":"Spurgeon","given":"Sarah"},{"family":"Tekalp","given":"Ahmet Murat"}],"citation-key":"hossainIEEEPressEditorial","page":"693","source":"Zotero","title":"IEEE Press Editorial Board","type":"article-journal"},
{"id":"Hou:2013:CCA:2550526.2550556","author":[{"family":"Hou","given":"Daqing"},{"family":"Mo","given":"Lingfeng"}],"citation-key":"Hou:2013:CCA:2550526.2550556","collection-title":"ICSM '13","container-title":"Proceedings of the 2013 IEEE international conference on software maintenance","event-place":"Washington, DC, USA","ISBN":"978-0-7695-4981-1","issued":{"date-parts":[[2013]]},"page":"60-69","publisher":"IEEE Computer Society","publisher-place":"Washington, DC, USA","title":"Content categorization of API discussions","type":"paper-conference","URL":"http://dx.doi.org/10.1109/ICSM.2013.17"},
{"id":"HowCanUse","accessed":{"date-parts":[[2018,7,27]]},"citation-key":"HowCanUse","title":"How Can I Use This Method? - IEEE Conference Publication","type":"webpage","URL":"https://ieeexplore.ieee.org/document/7194634/"},
{"id":"HowExactlyMachine","accessed":{"date-parts":[[2017,3,10]]},"citation-key":"HowExactlyMachine","title":"How exactly is machine learning used in recommendation engines? - Quora","type":"webpage","URL":"https://www.quora.com/How-exactly-is-machine-learning-used-in-recommendation-engines"},
{"id":"HttpPdmaidsDibris","citation-key":"HttpPdmaidsDibris","note":"00000","title":"http://pdm-aids.dibris.unige.it/questionnaire.php","type":"document"},
{"id":"Huang:2012:LCD:2343876.2343884","author":[{"family":"Huang","given":"Lan"},{"family":"Milne","given":"David"},{"family":"Frank","given":"Eibe"},{"family":"Witten","given":"Ian H."}],"citation-key":"Huang:2012:LCD:2343876.2343884","container-title":"J. Am. Soc. Inf. Sci. Technol.","ISSN":"1532-2882","issue":"8","issued":{"date-parts":[[2012,8]]},"page":"1593-1608","title":"Learning a concept-based document similarity measure","type":"article-journal","URL":"http://dx.doi.org/10.1002/asi.22689","volume":"63"},
{"id":"Huang2020","abstract":"The increasing amount of solid waste is becoming a significant problem that needs to be addressed urgently. The reliable and accurate classification method is a crucial step in waste disposal because different types of wastes have different disposal ways. The existing waste classification models driven by deep learning are not easy to achieve accurate results and still need to be improved due to the various architecture networks adopted. Their performance on different datasets is varied, and there is also a lack of specific large-scale datasets for training. We propose a new combination classification model based on three pretrained CNN models (VGG19, DenseNet169, and NASNetLarge) for processing the ImageNet database and achieve high classification accuracy. In our proposed model, the transfer learning model based on each pretrained model is constructed as a candidate classifier, and the optimal output of three candidate classifiers is selected as the final classification result. The experiments based on two waste image datasets demonstrate that the proposed model achieves 96.5% and 94% classification accuracy and outperforms several counterpart methods. © 2020 John Wiley & Sons, Ltd.","author":[{"family":"Huang","given":"G.-L."},{"family":"He","given":"J."},{"family":"Xu","given":"Z."},{"family":"Huang","given":"G."}],"citation-key":"Huang2020","container-title":"Concurrency and Computation: Practice and Experience","DOI":"10.1002/cpe.5751","ISSN":"15320626","issue":"19","issued":{"date-parts":[[2020]]},"publisher":"John Wiley and Sons Ltd","title":"A combination model based on transfer learning for waste classification","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083773188&doi=10.1002%2fcpe.5751&partnerID=40&md5=cf57d9c8039ed89f9bf24c48c381c831","volume":"32"},
{"id":"Huang20221457","abstract":"Massive machine-type communications (mMTC) are expected to support a large amount of randomly deployed users for short package transmissions. Noncoherent random access provides an efficient and practical multi-access protocol for mMTC, and also poses new challenges for the receiver design. In this paper, we leverage two well-known methods, i.e., message passing and deep learning, to jointly detect the user activity and the desired data for the noncoherent mMTC. First, by exploiting the exact distribution information of the received signal, a generalized approximate message passing (GAMP)-based algorithm is proposed, which is shown to jointly detect the user activity and the desired data by two modules: inter-user interference elimination and data detection for each user. Inspired by the two-module GAMP-based algorithm, we then propose a model-driven deep learning method, which utilizes the deep neural networks (DNNs) to approximate both the two modules. The loss function for training the DNNs is derived by formulating the two-module detection as an unconstrained optimization problem. Simulation results reveal that the proposed GAMP-based algorithm outperforms the proposed deep learning method when the channel distribution is perfectly known, while it suffers from a significant performance degradation for the case with imperfect channel distribution information. © 1983-2012 IEEE.","author":[{"family":"Huang","given":"J."},{"family":"Zhang","given":"H."},{"family":"Huang","given":"C."},{"family":"Yang","given":"L."},{"family":"Zhang","given":"W."}],"citation-key":"Huang20221457","container-title":"IEEE Journal on Selected Areas in Communications","DOI":"10.1109/JSAC.2022.3143260","ISSN":"07338716","issue":"5","issued":{"date-parts":[[2022]]},"page":"1457-1472","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Noncoherent massive random access for inhomogeneous networks: From message passing to deep learning","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123312361&doi=10.1109%2fJSAC.2022.3143260&partnerID=40&md5=e6e26e6088b8cc55c06d8069a1703d53","volume":"40"},
{"id":"huangSimilarityMeasuresText2008","author":[{"family":"Huang","given":"A."}],"citation-key":"huangSimilarityMeasuresText2008","container-title":"Proceedings of the sixth new zealand computer science research student conference (NZCSRSC2008), christchurch, new zealand","issued":{"date-parts":[[2008]]},"page":"49-56","title":"Similarity measures for text document clustering","type":"paper-conference","URL":"http://scholar.google.com.au/scholar.bib?q=info:enBKVjSSXjQJ:scholar.google.com/&output=citation&hl=en&as_sdt=2000&ct=citation&cd=0"},
{"id":"huebscherSurveyAutonomicComputing2008","accessed":{"date-parts":[[2016,8,29]]},"author":[{"family":"Huebscher","given":"Markus C."},{"family":"McCann","given":"Julie A."}],"citation-key":"huebscherSurveyAutonomicComputing2008","container-title":"ACM Computing Surveys (CSUR)","issue":"3","issued":{"date-parts":[[2008]]},"page":"7","source":"Google Scholar","title":"A survey of autonomic computing—degrees, models, and applications","type":"article-journal","URL":"http://dl.acm.org/citation.cfm?id=1380585","volume":"40"},
{"id":"hulsbuschShowingFullSemantics2010","abstract":"Model transformation is a prime technique in modern, model-driven software design. One of the most challenging issues is to show that the semantics of the models is not affected by the transformation. So far, there is hardly any research into this issue, in particular in those cases where the source and target languages are different. In this paper, we are using two different state-of-the-art proof techniques (explicit bisimulation construction versus borrowed contexts) to show bisimilarity preservation of a given model transformation between two simple (self-defined) languages, both of which are equipped with a graph transformation-based operational semantics. The contrast between these proof techniques is interesting because they are based on different model transformation strategies: triple graph grammars versus in situ transformation. We proceed to compare the proofs and discuss scalability to a more realistic setting.","accessed":{"date-parts":[[2015,3,24]]},"author":[{"family":"Hülsbusch","given":"Mathias"},{"family":"König","given":"Barbara"},{"family":"Rensink","given":"Arend"},{"family":"Semenyak","given":"Maria"},{"family":"Soltenborn","given":"Christian"},{"family":"Wehrheim","given":"Heike"}],"citation-key":"hulsbuschShowingFullSemantics2010","collection-number":"6396","collection-title":"Lecture Notes in Computer Science","container-title":"Integrated Formal Methods","editor":[{"family":"Méry","given":"Dominique"},{"family":"Merz","given":"Stephan"}],"ISBN":"978-3-642-16264-0 978-3-642-16265-7","issued":{"date-parts":[[2010]]},"page":"183-198","publisher":"Springer Berlin Heidelberg","source":"link.springer.com","title":"Showing Full Semantics Preservation in Model Transformation - A Comparison of Techniques","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-642-16265-7_14"},
{"id":"hurleySimplifyMachineLearning2020","abstract":"How to use Pipelines to standardize data preprocessing, data transformation, and modeling steps of a machine learning workflow","accessed":{"date-parts":[[2021,3,18]]},"author":[{"family":"Hurley","given":"David"}],"citation-key":"hurleySimplifyMachineLearning2020","container-title":"Medium","issued":{"date-parts":[[2020,7,2]]},"note":"00000","title":"Simplify Machine Learning Workflows","type":"webpage","URL":"https://towardsdatascience.com/simplify-machine-learning-workflows-e9d4f404aaeb"},
{"id":"husarAutonomousSystemsModeling2013","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Husar","given":"Rosteslaw M."},{"family":"Stracener","given":"Jerrell"}],"citation-key":"husarAutonomousSystemsModeling2013","container-title":"Procedia Computer Science","DOI":"10.1016/j.procs.2013.09.268","ISSN":"18770509","issued":{"date-parts":[[2013]]},"page":"242-247","source":"CrossRef","title":"Autonomous Systems Modeling During Early Architecture Development","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S1877050913010685","volume":"20"},
{"id":"hutchinsonEmpiricalAssessmentMDE2011","accessed":{"date-parts":[[2015,10,29]]},"author":[{"family":"Hutchinson","given":"John"},{"family":"Whittle","given":"Jon"},{"family":"Rouncefield","given":"Mark"},{"family":"Kristoffersen","given":"Steinar"}],"citation-key":"hutchinsonEmpiricalAssessmentMDE2011","container-title":"Proceedings of the 33rd International Conference on Software Engineering","issued":{"date-parts":[[2011]]},"page":"471–480","publisher":"ACM","source":"Google Scholar","title":"Empirical assessment of MDE in industry","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=1985858"},
{"id":"hutchinsonModeldrivenEngineeringPractices2013","author":[{"family":"Hutchinson","given":"John"},{"family":"Whittle","given":"Jon"},{"family":"Rouncefield","given":"Mark"}],"citation-key":"hutchinsonModeldrivenEngineeringPractices2013","container-title":"Science of Computer Programming","DOI":"10.1016/j.scico.2013.03.017","issued":{"date-parts":[[2013]]},"title":"Model-driven engineering practices in industry: Social, organizational and managerial factors that lead to success or failure","type":"article-journal"},
{"id":"HybridApproachMetamodel","accessed":{"date-parts":[[2015,7,19]]},"citation-key":"HybridApproachMetamodel","title":"Hybrid Approach for Metamodel and Model Co-evolution - Springer","type":"webpage","URL":"http://link.springer.com/chapter/10.1007%2F978-3-319-19578-0_46"},
{"id":"Iberraken2019245","abstract":"In this paper, a design of a multi-controller architecture (MCA) is presented. It effectively links model-based approaches and Artificial Intelligence (AI) developments for intelligent vehicles navigation in a highway. In this MCA, the model-based approach appears in the path planning (based on analytical target set-points definition) and the control law (based on a Lyapunov stability analysis). The AI-based approach appears in the proposed Two-Sequential Level Bayesian Decision Network (TSLBDN) for handling lane change maneuvers in uncertain environment and changing dynamiclbehaviors of the surrounding vehicles. In addition, a combination of both trajectory prediction (based on dynamic target set-points and elliptic limit-cycles) and maneuver recognition based on Dynamic Bayesian Network (DBN) is proposed to infers surrounding vehicles actions. Several simulation results show the efficiency of the model-driven/data driven overall proposed control architecture. © 2019 IEEE.","author":[{"family":"Iberraken","given":"D."},{"family":"Adouanc","given":"L."},{"family":"Denis","given":"D."}],"citation-key":"Iberraken2019245","collection-title":"IEEE Intelligent Vehicles Symposium, Proceedings","DOI":"10.1109/IVS.2019.8813830","ISBN":"978-1-72810-560-4","issued":{"date-parts":[[2019]]},"page":"245-251","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Multi-controller architecture for reliable autonomous vehicle navigation: Combination of model-driven and data-driven formalization","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072291854&doi=10.1109%2fIVS.2019.8813830&partnerID=40&md5=ef22c18f0c2ab2342424f336f473a968","volume":"2019-June"},
{"id":"Ickin202072","abstract":"Quality of Experience (QoE) models need good generalization that necessitates sufficient amount of user-labeled datasets associated with measurements related to underlying QoE factors. However, obtaining QoE datasets is often costly, since they are preferably collected from many subjects with diverse background, and eventually dataset sizes and representations are limited. Models can be improved by sharing and merging those collected local datasets, however regulations such as GDPR make data sharing difficult, as those local user datasets might contain sensitive information about the subjects. A privacy-preserving machine learning approach such as Federated Learning (FL) is a potential candidate that enables sharing of QoE data models between collaborators without exposing ground truth, but only by means of sharing the securely aggregated form of extracted model parameters. While FL can enable a seamless QoE model management, if collaborators do not have the same level of data quality, more iterations of information sharing over a communication channel might be necessary for models to reach an acceptable accuracy. In this paper, we present an ensemble based Bayesian synthetic data generation method for FL, LOO (Leave-One-Out), which reduces the training time by 30% and the network footprint in the communication channel by 60%. © 2020 IEEE.","author":[{"family":"Ickin","given":"S."},{"family":"Vandikas","given":"K."},{"family":"Moradi","given":"F."},{"family":"Taghia","given":"J."},{"family":"Hu","given":"W."}],"citation-key":"Ickin202072","collection-title":"Proceedings of the 2020 IEEE Conference on Network Softwarization: Bridging the Gap Between AI and Network Softwarization, NetSoft 2020","DOI":"10.1109/NetSoft48620.2020.9165379","editor":[{"family":"De Turck F., Chemouil P.","given":"Wauters T.","suffix":"Zhani M.F., Cerroni W., Pasquini R., Zhu Z."}],"ISBN":"978-1-72815-684-2","issued":{"date-parts":[[2020]]},"page":"72-76","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Ensemble-based synthetic data synthesis for federated QoE modeling","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091982675&doi=10.1109%2fNetSoft48620.2020.9165379&partnerID=40&md5=52ccb7a76f86ec86bca5aec4acf32466"},
{"id":"IEEESoftwareBlog","accessed":{"date-parts":[[2020,10,5]]},"citation-key":"IEEESoftwareBlog","title":"IEEE Software Blog: Autonomous Computing Systems: The Convergence of Control Theory and Computing Systems","type":"webpage","URL":"http://blog.ieeesoftware.org/2019/07/autonomous-computing-systems.html"},
{"id":"iglesiaMAPEKFormalTemplates2015","accessed":{"date-parts":[[2016,9,19]]},"author":[{"family":"Iglesia","given":"Didac Gil De La"},{"family":"Weyns","given":"Danny"}],"citation-key":"iglesiaMAPEKFormalTemplates2015","container-title":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","issue":"3","issued":{"date-parts":[[2015]]},"page":"15","source":"Google Scholar","title":"MAPE-K formal templates to rigorously design behaviors for self-adaptive systems","type":"article-journal","URL":"http://dl.acm.org/citation.cfm?id=2724719","volume":"10"},
{"id":"ihirweLowcodeEngineeringInternet2020","author":[{"family":"Ihirwe","given":"Felicien"},{"family":"Ruscio","given":"Davide Di"},{"family":"Mazzini","given":"Silvia"},{"family":"Pierini","given":"Pierluigi"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"ihirweLowcodeEngineeringInternet2020","container-title":"CoRR","issued":{"date-parts":[[2020]]},"note":"_eprint: 2009.01876","title":"Low-code Engineering for Internet of things: A state of research","type":"article-journal","URL":"https://arxiv.org/abs/2009.01876","volume":"abs/2009.01876"},
{"id":"ihirweLowcodeEngineeringInternet2020a","author":[{"family":"Ihirwe","given":"Felicien"},{"family":"Di Ruscio","given":"D."},{"family":"Mazzini","given":"Silvia"},{"family":"Pierini","given":"Pierluigi"},{"family":"Pierantonio","given":"A."}],"citation-key":"ihirweLowcodeEngineeringInternet2020a","container-title":"Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings","DOI":"10.1145/3417990.3420208","ISBN":"978-1-4503-8135-2","issued":{"date-parts":[[2020]]},"page":"522–529","publisher":"Association for Computing Machinery, Inc","title":"Low-code engineering for internet of things: A state of research","type":"paper-conference"},
{"id":"ilahiChallengesCountermeasuresAdversarial2020","abstract":"Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in quickly adapting to the surrounding environments. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications (e.g., smart grids, traffic controls, and autonomous vehicles) unless its vulnerabilities are addressed and mitigated. Thus, this paper provides a comprehensive survey that discusses emerging attacks in DRL-based systems and the potential countermeasures to defend against these attacks. We first cover some fundamental backgrounds about DRL and present emerging adversarial attacks on machine learning techniques. We then investigate more details of the vulnerabilities that the adversary can exploit to attack DRL along with the state-of-the-art countermeasures to prevent such attacks. Finally, we highlight open issues and research challenges for developing solutions to deal with attacks for DRL-based intelligent systems.","accessed":{"date-parts":[[2021,4,2]]},"author":[{"family":"Ilahi","given":"Inaam"},{"family":"Usama","given":"Muhammad"},{"family":"Qadir","given":"Junaid"},{"family":"Janjua","given":"Muhammad Umar"},{"family":"Al-Fuqaha","given":"Ala"},{"family":"Hoang","given":"Dinh Thai"},{"family":"Niyato","given":"Dusit"}],"citation-key":"ilahiChallengesCountermeasuresAdversarial2020","container-title":"arXiv:2001.09684 [cs]","issued":{"date-parts":[[2020,1,27]]},"note":"00006","source":"arXiv.org","title":"Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning","type":"article-journal","URL":"http://arxiv.org/abs/2001.09684"},
{"id":"imamDataModelingGuidelines2018","abstract":"Good database design is key to high data availability and consistency in traditional databases, and numerous techniques exist to abet designers in modeling schemas appropriately. These schemas are strictly enforced by traditional database engines. However, with the emergence of schema-free databases (NoSQL) coupled with voluminous and highly diversified datasets (big data), such aid becomes even more important as schemas in NoSQL are enforced by application developers, which requires a high level of competence. Precisely, existing modeling techniques and guides used in traditional databases are insufficient for bigdata storage settings. As a synthesis, new modeling guidelines for NoSQL document-store databases are posed. These guidelines cut across both logical and physical stages of database designs. Each is developed based on solid empirical insights, yet they are prepared to be intuitive to developers and practitioners. To realize this goal, we employ an exploratory approach to the investigation of techniques, empirical methods and expert consultations. We analyze how industry experts prioritize requirements and analyze the relationships between datasets on the one hand and error prospects and awareness on the other hand. Few proprietary guidelines were extracted from a heuristic evaluation of 5 NoSQL databases. In this regard, the proposed guidelines have great potential to function as an imperative instrument of knowledge transfer from academia to NoSQL database modeling practices.","accessed":{"date-parts":[[2019,11,11]]},"author":[{"family":"Imam","given":"Abdullahi Abubakar"},{"family":"Basri","given":"Shuib"},{"family":"Ahmad","given":"Rohiza"},{"family":"Watada","given":"Junzo"},{"family":"T.","given":"Maria"},{"family":"Ahmad","given":"Malek"}],"citation-key":"imamDataModelingGuidelines2018","container-title":"International Journal of Advanced Computer Science and Applications","container-title-short":"ijacsa","DOI":"10.14569/IJACSA.2018.091066","ISSN":"21565570, 2158107X","issue":"10","issued":{"date-parts":[[2018]]},"source":"DOI.org (Crossref)","title":"Data Modeling Guidelines for NoSQL Document-Store Databases","type":"article-journal","URL":"http://thesai.org/Publications/ViewPaper?Volume=9&Issue=10&Code=ijacsa&SerialNo=66","volume":"9"},
{"id":"incLowCodePlatformRapidly2020","abstract":"This article talks about the importance of low-code platform in the analytics world and Low code vs traditional application development.","accessed":{"date-parts":[[2021,3,18]]},"author":[{"family":"Inc","given":"Gramener"}],"citation-key":"incLowCodePlatformRapidly2020","container-title":"Gramener Blog","issued":{"date-parts":[[2020,10,13]]},"note":"00000","title":"Low-Code Platform: Rapidly Build Enterprise-Grade Analytics Apps","title-short":"Low-Code Platform","type":"post-weblog","URL":"https://blog.gramener.com/low-code-platform-for-enterprise-analytics-applications/"},
{"id":"IndustrialCyberPhysicalSystems","accessed":{"date-parts":[[2016,1,26]]},"citation-key":"IndustrialCyberPhysicalSystems","title":"Industrial Cyber-Physical Systems Center (iCyPhy)","type":"webpage","URL":"http://www.icyphy.org/"},
{"id":"IndustryEclipseKura","accessed":{"date-parts":[[2016,9,27]]},"citation-key":"IndustryEclipseKura","title":"Industry 4.0 with Eclipse Kura | EclipseCon Europe 2016","type":"webpage","URL":"https://www.eclipsecon.org/europe2016/session/industry-40-eclipse-kura"},
{"id":"InternetThingsCS","accessed":{"date-parts":[[2016,9,11]]},"citation-key":"InternetThingsCS","title":"Internet of Things [CS Open CourseWare]","type":"webpage","URL":"http://ocw.cs.pub.ro/courses/iot"},
{"id":"InternetThingsRoad","accessed":{"date-parts":[[2016,9,3]]},"citation-key":"InternetThingsRoad","title":"The Internet of Things is on the Road to Autonomous Driving","type":"webpage","URL":"http://www.intel.com/content/www/us/en/internet-of-things/infographics/iot-autonomous-driving-infographic.html"},
{"id":"IntocpsAuDk","accessed":{"date-parts":[[2016,2,9]]},"citation-key":"IntocpsAuDk","title":"into-cps.au.dk","type":"webpage","URL":"http://into-cps.au.dk/"},
{"id":"IntroductionBuildingMachine","abstract":"Chapter 1. Introduction In this first chapter, we will introduce machine learning pipelines and outline all the steps that go into building them. We’ll explain what needs to happen to … - Selection from Building Machine Learning Pipelines [Book]","accessed":{"date-parts":[[2021,3,18]]},"citation-key":"IntroductionBuildingMachine","note":"00000","title":"1. Introduction - Building Machine Learning Pipelines [Book]","type":"webpage","URL":"https://www.oreilly.com/library/view/building-machine-learning/9781492053187/ch01.html"},
{"id":"IntroductionControlSystems","accessed":{"date-parts":[[2016,11,1]]},"citation-key":"IntroductionControlSystems","title":"An Introduction To Control Systems","type":"webpage","URL":"https://www.facstaff.bucknell.edu/mastascu/eControlHTML/Intro/Intro1.html"},
{"id":"IntroductionParallelComputing","accessed":{"date-parts":[[2017,2,23]]},"citation-key":"IntroductionParallelComputing","title":"Introduction to Parallel Computing","type":"webpage","URL":"https://computing.llnl.gov/tutorials/parallel_comp/#Whatis"},
{"id":"inverardiProducingSoftwareIntegration2013","abstract":"Software is increasingly produced according to a certain goal and by integrating existing software produced by third-parties, typically black-box, and often provided without a machine readable documentation. This implies that development processes of the next future have to explicitly deal with an inherent incompleteness of information about existing software, notably on its behaviour. Therefore, on one side a software producer will less and less know the precise behaviour of a third party software service, on the other side she will need to use it to build her own application. In this paper we present an innovative development process to automatically produce dependable software systems by integrating existing services under uncertainty and according to the specied goal. Moreover, we (i) discuss important challenges that must be faced while producing the kind of systems we are targeting, (ii) give an overview of the state of art related to the identied challenges, and finally (iii) provide research directions to address these challenges.","author":[{"family":"Inverardi","given":"P"},{"family":"Autili","given":"M"},{"family":"Di Ruscio","given":"D"},{"family":"Pelliccione","given":"P"},{"family":"Tivoli","given":"M"}],"citation-key":"inverardiProducingSoftwareIntegration2013","container-title":"Proceeding ESEC/FSE 2013 Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering","DOI":"10.1145/2491411.2505428","ISBN":"978-1-4503-2237-9","issued":{"date-parts":[[2013]]},"note":"00000","page":"2–12","publisher":"ACM, Association for Computing Machinery","title":"Producing software by integration: challenges and research directions (keynote)","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?doid=2491411.2505428"},
{"id":"IoTSecurityAction","accessed":{"date-parts":[[2016,9,27]]},"citation-key":"IoTSecurityAction","title":"IoT Security in action! | EclipseCon Europe 2016","type":"webpage","URL":"https://www.eclipsecon.org/europe2016/session/iot-security-action"},
{"id":"IoTVsM2M","accessed":{"date-parts":[[2016,8,21]]},"citation-key":"IoTVsM2M","title":"IoT vs. M2M, CPS, WoT....: Are these terms synonyms? | John Soldatos | Pulse | LinkedIn","type":"webpage","URL":"https://www.linkedin.com/pulse/iot-vs-m2m-cps-wot-terms-synonyms-john-soldatos"},
{"id":"Iovino2012OnTI","author":[{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Malavolta","given":"Ivano"}],"citation-key":"Iovino2012OnTI","container-title":"Journal of Object Technology","issued":{"date-parts":[[2012]]},"page":"3: 1-33","title":"On the impact significance of metamodel evolution in MDE","type":"article-journal","volume":"11"},
{"id":"iovinoMetamodelDeprecationManage2020","author":[{"family":"Iovino","given":"L."},{"family":"Di Salle","given":"A."},{"family":"Di Ruscio","given":"D."},{"family":"Pierantonio","given":"A."}],"citation-key":"iovinoMetamodelDeprecationManage2020","container-title":"Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings","DOI":"10.1145/3417990.3419625","ISBN":"978-1-4503-8135-2","issued":{"date-parts":[[2020]]},"note":"00000","page":"306–315","publisher":"Association for Computing Machinery, Inc","title":"Metamodel deprecation to manage technical debt in model co-evolution","type":"paper-conference"},
{"id":"ISINKAYE2015261","author":[{"family":"Isinkaye","given":"F.O."},{"family":"Folajimi","given":"Y.O."},{"family":"Ojokoh","given":"B.A."}],"citation-key":"ISINKAYE2015261","container-title":"Egyptian Informatics Journal","ISSN":"1110-8665","issue":"3","issued":{"date-parts":[[2015]]},"page":"261 - 273","title":"Recommendation systems: Principles, methods and evaluation","type":"article-journal","URL":"http://www.sciencedirect.com/science/article/pii/S1110866515000341","volume":"16"},
{"id":"islamLeveragingAutomatedSentiment2017","accessed":{"date-parts":[[2018,1,31]]},"author":[{"family":"Islam","given":"Md Rakibul"},{"family":"Zibran","given":"Minhaz F."}],"citation-key":"islamLeveragingAutomatedSentiment2017","DOI":"10.1109/MSR.2017.9","ISBN":"978-1-5386-1544-7","issued":{"date-parts":[[2017,5]]},"page":"203-214","publisher":"IEEE","source":"CrossRef","title":"Leveraging Automated Sentiment Analysis in Software Engineering","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/7962370/"},
{"id":"islamSemanticTextSimilarity2008","author":[{"family":"Islam","given":"Aminul"},{"family":"Inkpen","given":"Diana"}],"citation-key":"islamSemanticTextSimilarity2008","container-title":"ACM Trans. Knowl. Discov. Data","ISSN":"1556-4681","issue":"2","issued":{"date-parts":[[2008,7]]},"page":"10:1-10:25","title":"Semantic text similarity using corpus-based word similarity and string similarity","type":"article-journal","URL":"http://doi.acm.org/10.1145/1376815.1376819","volume":"2"},
{"id":"jaccardDistributionFloraAlpine1912","author":[{"family":"Jaccard","given":"Paul"}],"citation-key":"jaccardDistributionFloraAlpine1912","container-title":"New Phytologist","issue":"2","issued":{"date-parts":[[1912]]},"page":"37-50","title":"The distribution of the flora in the alpine zone","type":"article-journal","volume":"11"},
{"id":"jacksonAutomaticallyReasoningMetamodeling2015","accessed":{"date-parts":[[2015,9,15]]},"author":[{"family":"Jackson","given":"Ethan K."},{"family":"Levendovszky","given":"Tihamer"},{"family":"Balasubramanian","given":"Daniel"}],"citation-key":"jacksonAutomaticallyReasoningMetamodeling2015","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-013-0315-y","ISSN":"1619-1366, 1619-1374","issue":"1","issued":{"date-parts":[[2015,2]]},"page":"271-285","source":"CrossRef","title":"Automatically reasoning about metamodeling","type":"article-journal","URL":"http://link.springer.com/10.1007/s10270-013-0315-y","volume":"14"},
{"id":"jainDataClusteringReview1999","accessed":{"date-parts":[[2015,4,24]]},"author":[{"family":"Jain","given":"Anil K."},{"family":"Murty","given":"M. Narasimha"},{"family":"Flynn","given":"Patrick J."}],"citation-key":"jainDataClusteringReview1999","container-title":"ACM computing surveys (CSUR)","issue":"3","issued":{"date-parts":[[1999]]},"page":"264–323","source":"Google Scholar","title":"Data clustering: a review","title-short":"Data clustering","type":"article-journal","URL":"http://dl.acm.org/citation.cfm?id=331504","volume":"31"},
{"id":"Jamei2015133","abstract":"Deployment of smart meters has been greatly increased over the recent years. Most of the installed smart meters have been equipped with Advanced Metering Infrastructure (AMI) which enables a bidirectional wireless communication to gather the usage data from gas, electricity and water meters. The insecure wireless channel used by AMI meters jeopardizes the privacy of costumers and brings up cybersecurity issues since it allows hackers to monitor the energy usage data from different houses. To show the penetrability of the system, Received Signal Strength (RSS) - based localization of smart meters incorporating Maximum Likelihood (ML) estimator has been proposed in this paper. By decoding the received signal from a smart meter, one can localize the unoccupied houses or track the people's daily routines. The effectiveness of the proposed ML location estimator has been examined through MATLAB simulation, under the assumption of a log-normal path loss model and Frequency Shift Keying (FSK) modulation and demodulation. Particle Swarm Optimization (PSO) has been used to find the ML estimation. Finally, the effect of the variance, the number of the sensors and the path loss exponent has been studied on the average Miss Distance Error (MDE). © Springer International Publishing Switzerland 2015.","author":[{"family":"Jamei","given":"M."},{"family":"Sarwat","given":"A.I."},{"family":"Iyengar","given":"S.S."},{"family":"Kaleem","given":"F."}],"citation-key":"Jamei2015133","container-title":"Advances in Intelligent Systems and Computing","DOI":"10.1007/978-3-319-08422-0_20","ISBN":"9783319084213","ISSN":"21945357","issued":{"date-parts":[[2015]]},"page":"133-139","publisher":"Springer Verlag","title":"Security breach possibility with RSS-Based localization of smart meters incorporating maximum likelihood estimator","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906536356&doi=10.1007%2f978-3-319-08422-0_20&partnerID=40&md5=723f22aca34a84c45a975ec4fa87cbf3","volume":"1089"},
{"id":"jeanjeanIDECodeReifying2021","abstract":"To cope with the ever-growing number of programming languages, manufacturers of Integrated Development Environments (IDE) have recently defined protocols as a way to use and share multiple language services (e.g., auto-completion, type checker, language runtime) in language-agnostic environments (i.e., the user interface provided by the IDE): the most notable are the Language Server Protocol (LSP) for textual editors, and the Debug Adapter Protocol (DAP) for debugging facilities. These protocols rely on a proper specification of the services that are commonly found in the tool support of general-purpose languages, and define a fixed set of capabilities to offer in the IDE. However, new languages appear regularly offering unique constructs (e.g., Domain-Specific Languages), and supported by dedicated services to be offered as new capabilities in IDEs. This trend leads to the multiplication of new protocols, hard to combine and possibly incompatible (e.g., overlap, different technological stacks). Beyond the proposition of specific protocols, the goal of this paper is to stress out the importance of being able to specify language protocols and to offer IDEs to be configured with such protocol specifications. We present our vision by discussing the main concepts for the specification of language protocols, and an approach that can make use of these specifications in order to deploy an IDE as a set of coordinated, individually deployed, language capabilities (e.g., microservice choreography). IDEs went from directly supporting languages to protocols, and we envision in this paper the next step: IDE as Code, where language protocols are created or inferred on demand and serve as support of an adaptation loop taking in charge of the (re)configuration of the IDE.","author":[{"family":"Jeanjean","given":"Pierre"},{"family":"Combemale","given":"Benoit"},{"family":"Barais","given":"Olivier"}],"citation-key":"jeanjeanIDECodeReifying2021","issued":{"date-parts":[[2021]]},"note":"00000","page":"6","source":"Zotero","title":"IDE as Code: Reifying Language Protocols as First-Class Citizens","type":"article-journal"},
{"id":"jehSimRankMeasureStructuralcontext2002","author":[{"family":"Jeh","given":"Glen"},{"family":"Widom","given":"Jennifer"}],"citation-key":"jehSimRankMeasureStructuralcontext2002","collection-title":"KDD '02","container-title":"Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining","event-place":"New York, NY, USA","ISBN":"1-58113-567-X","issued":{"date-parts":[[2002]]},"page":"538-543","publisher":"ACM","publisher-place":"New York, NY, USA","title":"SimRank: A measure of structural-context similarity","type":"paper-conference","URL":"http://doi.acm.org/10.1145/775047.775126"},
{"id":"Jeon2022","abstract":"Wireless systems continue to go towards higher carrier frequencies, including terahertz bands, to take advantage of higher bandwidth channels. At the same time, antenna arrays remain important with continued increases in array elements. Yet, the power consumption of RF and digital circuits can increase proportionally to both the amount of signal bandwidth and the number of antennas. The use of one-bit analog-to-digital converters (ADCs) at the receiver is a cost-and power-efficient solution for wideband and/or massive antenna wireless systems. The nonlinearity of one-bit received signals brings challenges in physical-layer design at the receiver. At the same time, the binary nature of these signals opens new opportunities for artificial intelligence (AI) based physical-layer (PHY) design. This article covers recent progress in incorporating AI into the design of classical PHY techniques and emerging studies on establishing AIinspired frameworks that fundamentally replace classical model-driven techniques with data-driven AI techniques. It concludes with a discussion, including practical challenges and future research directions. IEEE","author":[{"family":"Jeon","given":"Y."},{"family":"Kim","given":"D."},{"family":"Hong","given":"S."},{"family":"Lee","given":"N."},{"family":"Heath","given":"R.W."}],"citation-key":"Jeon2022","container-title":"IEEE Communications Magazine","DOI":"10.1109/MCOM.007.2200002","ISSN":"01636804","issued":{"date-parts":[[2022]]},"page":"1-7","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Artificial intelligence for physical-layer design of MIMO communications with one-bit ADCs","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130844999&doi=10.1109%2fMCOM.007.2200002&partnerID=40&md5=75038c8e975dbfa6b9ed8e4038bf17a6"},
{"id":"Jha20212374","abstract":"We propose a Bayesian deep learning framework for model driven online sparse channel estimation task in Multi-user MIMO systems. Tools from Bayesian neural network and stochastic variational Bayesian Inference are utilized to capture aleatoric and epistemic uncertainty estimates. We treat the network prediction as an auxiliary variable to allow inference performance to be unaffected by the stage of training of the network. In addition to providing uncertainty estimates, being Bayesian, the framework enables us the possibility to marginalize over penalty parameters and is well suited for online scenario with changing environments. Our simulations show that the framework is robust to model mismatch, and efficiently captures uncertainty in the predictions. © 1983-2012 IEEE.","author":[{"family":"Jha","given":"N.K."},{"family":"Lau","given":"V.K.N."}],"citation-key":"Jha20212374","container-title":"IEEE Journal on Selected Areas in Communications","DOI":"10.1109/JSAC.2021.3087249","ISSN":"07338716","issue":"8","issued":{"date-parts":[[2021]]},"page":"2374-2387","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Online downlink multi-user channel estimation for mmWave systems using bayesian neural network","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110611425&doi=10.1109%2fJSAC.2021.3087249&partnerID=40&md5=2f5efa07b6ca5daa3326ed97e48d515f","volume":"39"},
{"id":"Jha20227051","abstract":"We propose a model-driven Bayesian deep learning framework for multiple access uplink systems in Multiuser MIMO systems. Utilizing tools from Streaming Variational Inference, we combine graphical models with neural networks to enable fast online machine learning. The proposed distributed inference framework is shown to be robust and suitable for the online scenario. Our simulations demonstrate the robustness of the proposed solution in online propagation environments and its ability to capture uncertainty. © 2014 IEEE.","author":[{"family":"Jha","given":"N.K."},{"family":"Lau","given":"V.K.N."}],"citation-key":"Jha20227051","container-title":"IEEE Internet of Things Journal","DOI":"10.1109/JIOT.2021.3113679","ISSN":"23274662","issue":"9","issued":{"date-parts":[[2022]]},"page":"7051-7064","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Transformer-based online bayesian neural networks for grant-free uplink access in CRAN with streaming variational inference","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115671012&doi=10.1109%2fJIOT.2021.3113679&partnerID=40&md5=9014237a16ec6d8f6b752bc8c1450792","volume":"9"},
{"id":"jhaAdversarialMachineLearning","author":[{"family":"Jha","given":"Somesh"}],"citation-key":"jhaAdversarialMachineLearning","page":"71","source":"Zotero","title":"Adversarial Machine Learning (AML)","type":"article-journal"},
{"id":"jhaTransformerBasedOnlineBayesian2022a","abstract":"We propose a model-driven Bayesian deep learning framework for multiple access uplink systems in Multiuser MIMO systems. Utilizing tools from Streaming Variational Inference, we combine graphical models with neural networks to enable fast online machine learning. The proposed distributed inference framework is shown to be robust and suitable for the online scenario. Our simulations demonstrate the robustness of the proposed solution in online propagation environments and its ability to capture uncertainty. © 2014 IEEE.","author":[{"family":"Jha","given":"N.K."},{"family":"Lau","given":"V.K.N."}],"citation-key":"jhaTransformerBasedOnlineBayesian2022a","container-title":"IEEE Internet of Things Journal","DOI":"10.1109/JIOT.2021.3113679","ISSN":"23274662","issue":"9","issued":{"date-parts":[[2022]]},"page":"7051-7064","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Transformer-Based Online Bayesian Neural Networks for Grant-Free Uplink Access in CRAN with Streaming Variational Inference","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115671012&doi=10.1109%2fJIOT.2021.3113679&partnerID=40&md5=9014237a16ec6d8f6b752bc8c1450792","volume":"9"},
{"id":"Jia2021","abstract":"Building energy models (BEM) are developed for understanding a building's energy performance. A meta-model of the whole building energy analysis is often used for the BEM calibration and energy prediction. The literature review shows that studies with a focus on the development of room-level metamodels are missing. This study aims to address this research gap through a case study of a residential building with 138 apartments in Doha, Qatar. Five parameters, including cooling setpoint, number of occupants, lighting power density, equipment power density, and interior solar reflectance, are selected as input parameters to create ninety-six different scenarios. Three machine-learning models are used as metamodels to generalize the relationship between cooling energy and the model parameters, including Multiple Linear Regression, Support Vector Regression, and Artificial Neural Networks. The three meta-models' prediction accuracies are evaluated by the Normalized Mean Bias Error (NMBE), Coefficient of Variation of the Root Mean Squared Error CV (RMSE), and R square (R2). The results show that the ANN model performs best. A new generic BEM is then established to validate the meta-model. The results indicate that the proposed metamodel is accurate and efficient in predicting the cooling energy in summer and transitional months for a building with a similar floor configuration. Copyright © 2021 by ASME.","author":[{"family":"Jia","given":"B."},{"family":"Hou","given":"D."},{"family":"Wang","given":"L.L."},{"family":"Hassan","given":"I.G."}],"citation-key":"Jia2021","collection-title":"Proceedings of the 2021 ASME Verification and Validation Symposium, VVS 2021","DOI":"10.1115/VVS2021-65272","ISBN":"978-0-7918-8478-2","issued":{"date-parts":[[2021]]},"publisher":"American Society of Mechanical Engineers (ASME)","title":"Estimation of room-level cooling energy in hot/arid climate by machine learning-based approaches","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109635848&doi=10.1115%2fVVS2021-65272&partnerID=40&md5=74508e9cd2c5dd5b6f1e0b2c72a0c0e7"},
{"id":"jiaEstimationRoomlevelCooling2021a","abstract":"Building energy models (BEM) are developed for understanding a building's energy performance. A meta-model of the whole building energy analysis is often used for the BEM calibration and energy prediction. The literature review shows that studies with a focus on the development of room-level metamodels are missing. This study aims to address this research gap through a case study of a residential building with 138 apartments in Doha, Qatar. Five parameters, including cooling setpoint, number of occupants, lighting power density, equipment power density, and interior solar reflectance, are selected as input parameters to create ninety-six different scenarios. Three machine-learning models are used as metamodels to generalize the relationship between cooling energy and the model parameters, including Multiple Linear Regression, Support Vector Regression, and Artificial Neural Networks. The three meta-models' prediction accuracies are evaluated by the Normalized Mean Bias Error (NMBE), Coefficient of Variation of the Root Mean Squared Error CV (RMSE), and R square (R2). The results show that the ANN model performs best. A new generic BEM is then established to validate the meta-model. The results indicate that the proposed metamodel is accurate and efficient in predicting the cooling energy in summer and transitional months for a building with a similar floor configuration. Copyright © 2021 by ASME.","author":[{"family":"Jia","given":"B."},{"family":"Hou","given":"D."},{"family":"Wang","given":"L.L."},{"family":"Hassan","given":"I.G."}],"citation-key":"jiaEstimationRoomlevelCooling2021a","container-title":"Proceedings of the 2021 ASME Verification and Validation Symposium, VVS 2021","DOI":"10.1115/VVS2021-65272","ISBN":"978-0-7918-8478-2","issued":{"date-parts":[[2021]]},"publisher":"American Society of Mechanical Engineers (ASME)","title":"Estimation of room-level cooling energy in hot/arid climate by machine learning-based approaches","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109635848&doi=10.1115%2fVVS2021-65272&partnerID=40&md5=74508e9cd2c5dd5b6f1e0b2c72a0c0e7"},
{"id":"Jiang2020283","abstract":"Model-based methods are dominant in current systems for their optimal designs under given models, but may suffer from inaccurate modeling assumptions. Recently, data-based deep learning methods have achieved remarkable performances by training a large amount of data but encounter some challenges such as, lack of available training data and explainability. In this paper, we propose a novel hybrid idea to integrate the strengths of both data and model-driven methods, named model based method enhanced by data, which is training affordable, theoretically interpretable and model flexible. To show the idea more concretely, we consider a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channel state information (CSI) acquisition approach. Specifically, we utilize a universal mixture of Gaussian (MoG) model to deal with the nongaussianity of the noise and interference in complex communication environments, which can adaptively adjust involved parameters to fit the true distribution by observed data. We propose a variational Bayesian framework to derive the specific form of minimum mean square error (MMSE) estimator. Simulations are performed to verify the efficiency of our proposed method and the accuracy of our analysis. © 2020 IEEE.","author":[{"family":"Jiang","given":"J.-C."},{"family":"Wang","given":"H.-M."}],"citation-key":"Jiang2020283","collection-title":"2020 IEEE/CIC International Conference on Communications in China, ICCC 2020","DOI":"10.1109/ICCC49849.2020.9238821","ISBN":"978-1-72817-327-6","issued":{"date-parts":[[2020]]},"page":"283-288","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Data-enhanced bayesian MIMO-OFDM channel estimation strategy with universal noise model","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097521271&doi=10.1109%2fICCC49849.2020.9238821&partnerID=40&md5=77303e74e28271f526e4155301c6e8e9"},
{"id":"Jiang20217655","abstract":"Orthogonal frequency division multiplexing (OFDM) has been widely applied in many wireless communi- cation systems. The artificial intelligence (AI)-aided OFDM receivers are currently brought to the forefront to replace and improve the traditional OFDM receivers. In this paper, we first compare two AI-aided OFDM receivers, namely, data-driven fully connected deep neural network and model-driven ComNet, through extensive simulation and real-time video transmission using a 5G rapid prototyping system for an over-the-air (OTA) test. We find a performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and the real environment. We develop a novel online training system, which is called SwitchNet receiver, to address this issue. This receiver has a flexible and extendable architecture and can adapt to real channels by training only several parameters online. From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to OTA environments and promising for future communication systems. At the end of this paper, we discuss potential challenges and future research inspired by our initial study in this paper. © 2002-2012 IEEE.","author":[{"family":"Jiang","given":"P."},{"family":"Wang","given":"T."},{"family":"Han","given":"B."},{"family":"Gao","given":"X."},{"family":"Zhang","given":"J."},{"family":"Wen","given":"C.-K."},{"family":"Jin","given":"S."},{"family":"Li","given":"G.Y."}],"citation-key":"Jiang20217655","container-title":"IEEE Transactions on Wireless Communications","DOI":"10.1109/TWC.2021.3087191","ISSN":"15361276","issue":"11","issued":{"date-parts":[[2021]]},"page":"7655-7668","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"AI-Aided online adaptive OFDM receiver: Design and experimental results","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112211168&doi=10.1109%2fTWC.2021.3087191&partnerID=40&md5=d9580b492b56db3bbb97dbd28ad17370","volume":"20"},
{"id":"jiangDataEnhancedBayesianMIMOOFDM2020a","abstract":"Model-based methods are dominant in current systems for their optimal designs under given models, but may suffer from inaccurate modeling assumptions. Recently, data-based deep learning methods have achieved remarkable performances by training a large amount of data but encounter some challenges such as, lack of available training data and explainability. In this paper, we propose a novel hybrid idea to integrate the strengths of both data and model-driven methods, named model based method enhanced by data, which is training affordable, theoretically interpretable and model flexible. To show the idea more concretely, we consider a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channel state information (CSI) acquisition approach. Specifically, we utilize a universal mixture of Gaussian (MoG) model to deal with the nongaussianity of the noise and interference in complex communication environments, which can adaptively adjust involved parameters to fit the true distribution by observed data. We propose a variational Bayesian framework to derive the specific form of minimum mean square error (MMSE) estimator. Simulations are performed to verify the efficiency of our proposed method and the accuracy of our analysis. © 2020 IEEE.","author":[{"family":"Jiang","given":"J.-C."},{"family":"Wang","given":"H.-M."}],"citation-key":"jiangDataEnhancedBayesianMIMOOFDM2020a","container-title":"2020 IEEE/CIC International Conference on Communications in China, ICCC 2020","DOI":"10.1109/ICCC49849.2020.9238821","ISBN":"978-1-72817-327-6","issued":{"date-parts":[[2020]]},"page":"283-288","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Data-Enhanced Bayesian MIMO-OFDM Channel Estimation Strategy with Universal Noise Model","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097521271&doi=10.1109%2fICCC49849.2020.9238821&partnerID=40&md5=77303e74e28271f526e4155301c6e8e9"},
{"id":"jiangSemanticSimilarityBased1997","abstract":"This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task.","author":[{"family":"Jiang","given":"J.J."},{"family":"Conrath","given":"D.W."}],"citation-key":"jiangSemanticSimilarityBased1997","container-title":"Proc. of the int'l. Conf. on research in computational linguistics","issued":{"date-parts":[[1997]]},"page":"19-33","title":"Semantic similarity based on corpus statistics and lexical taxonomy","type":"paper-conference","URL":"http://www.cse.iitb.ac.in/~cs626-449/Papers/WordSimilarity/4.pdf"},
{"id":"jiangWhyHowDevelopers2017","author":[{"family":"Jiang","given":"Jing"},{"family":"Lo","given":"David"},{"family":"He","given":"Jiahuan"},{"family":"Xia","given":"Xin"},{"family":"Kochhar","given":"Pavneet Singh"},{"family":"Zhang","given":"Li"}],"citation-key":"jiangWhyHowDevelopers2017","container-title":"Empirical Softw. Engg.","ISSN":"1382-3256","issue":"1","issued":{"date-parts":[[2017,2]]},"page":"547-578","title":"Why and how developers fork what from whom in GitHub","type":"article-journal","URL":"https://doi.org/10.1007/s10664-016-9436-6","volume":"22"},
{"id":"Jindal20213202","abstract":"The goal of this tutorial is to educate the audience about the state of the art in ML for cloud data systems, both in research and in practice. The tutorial is divided in two parts: the progress, and the path forward. Part I covers the recent successes in deploying machine learning solutions for cloud data systems. We will discuss the practical considerations taken into account and the progress made at various levels. The goal is to compare and contrast the promise of ML for systems with the ground actually covered in industry. Finally, Part II discusses practical issues of machine learning in the enterprise covering the generation of explanations, model debugging, model deployment, model management, constraints on eyes-on data usage and anonymization, and a discussion of the technical debt that can accrue through machine learning and models in the enterprise. © The authors.","author":[{"family":"Jindal","given":"A."},{"family":"Interlandi","given":"M."}],"citation-key":"Jindal20213202","container-title":"Proceedings of the VLDB Endowment","DOI":"10.14778/3476311.3476408","editor":[{"family":"Dong X.L.","given":"Naumann F."}],"ISSN":"21508097","issue":"12","issued":{"date-parts":[[2021]]},"page":"3202-3205","publisher":"VLDB Endowment","title":"Machine learning for cloud data systems: The progress so far and the path forward","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119991142&doi=10.14778%2f3476311.3476408&partnerID=40&md5=8aa9ddee2b592c6e3587f36a2d78b107","volume":"14"},
{"id":"jinghanSurveyNoSQLDatabase2011","abstract":"With the development of the Internet and cloud computing, there need databases to be able to store and process big data effectively, demand for high-performance when reading and writing, so the traditional relational database is facing many new challenges. Especially in large scale and high-concurrency applications, such as search engines and SNS, using the relational database to store and query dynamic user data has appeared to be inadequate. In this case, NoSQL database created. This paper describes the background, basic characteristics, data model of NoSQL. In addition, this paper classifies NoSQL databases according to the CAP theorem. Finally, the mainstream NoSQL databases are separately described in detail, and extract some properties to help enterprises to choose NoSQL.","author":[{"literal":"Jing Han"},{"literal":"Haihong E"},{"literal":"Guan Le"},{"literal":"Jian Du"}],"citation-key":"jinghanSurveyNoSQLDatabase2011","container-title":"2011 6th International Conference on Pervasive Computing and Applications","DOI":"10.1109/ICPCA.2011.6106531","event":"2011 6th International Conference on Pervasive Computing and Applications","issued":{"date-parts":[[2011,10]]},"note":"01100","page":"363-366","source":"IEEE Xplore","title":"Survey on NoSQL database","type":"paper-conference"},
{"id":"johannKiefMorrisInfrastructure2017","abstract":"Cloud specialist Kief Morris joins Software Engineering Radio host Sven Johann to discuss the benefits of infrastructure as code, including security, auditability, testing, documentation, and traceability.","author":[{"family":"Johann","given":"Sven"}],"citation-key":"johannKiefMorrisInfrastructure2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"117-120","source":"IEEE Computer Society","title":"Kief Morris on Infrastructure as Code","type":"article-magazine","volume":"34"},
{"id":"JointICTPIAEASchool","accessed":{"date-parts":[[2021,1,5]]},"citation-key":"JointICTPIAEASchool","note":"00000","title":"Joint ICTP-IAEA School on LoRa Enabled Radiation and Environmental Monitoring Sensors","type":"webpage","URL":"http://wireless.ictp.it/school_2018/"},
{"id":"josebaAutomaticImpactAnalysis","author":[{"family":"Joseba","given":"Agirre"},{"family":"Leire","given":"Etxeberria"},{"family":"Goiuria","given":"Sagardui"}],"citation-key":"josebaAutomaticImpactAnalysis","container-title":"AMT @MoDELS 2013","title":"Automatic impact analysis of software architecture migration on Model Driven Software Development","type":"article-journal"},
{"id":"journals/bmcbi/SchlickerDRL06","author":[{"family":"Schlicker","given":"Andreas"},{"family":"Domingues","given":"Francisco S."},{"family":"Rahnenführer","given":"Jörg"},{"family":"Lengauer","given":"Thomas"}],"citation-key":"journals/bmcbi/SchlickerDRL06","container-title":"BMC Bioinformatics","issued":{"literal":"2009-11-10, 2006"},"page":"302","title":"A new measure for functional similarity of gene products based on Gene Ontology.","type":"article-journal","URL":"http://dblp.uni-trier.de/db/journals/bmcbi/bmcbi7.html#SchlickerDRL06","volume":"7"},
{"id":"jungBuildingAutomationSmart2013","accessed":{"date-parts":[[2016,5,30]]},"author":[{"family":"Jung","given":"Markus"},{"family":"Weidinger","given":"J."},{"family":"Kastner","given":"W."},{"family":"Olivieri","given":"A."}],"citation-key":"jungBuildingAutomationSmart2013","DOI":"10.1109/WAINA.2013.200","ISBN":"978-1-4673-6239-9 978-0-7695-4952-1","issued":{"date-parts":[[2013,3]]},"page":"1361-1367","publisher":"IEEE","source":"CrossRef","title":"Building Automation and Smart Cities: An Integration Approach Based on a Service-Oriented Architecture","title-short":"Building Automation and Smart Cities","type":"paper-conference","URL":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6550585"},
{"id":"Jurgelaitis202163","abstract":"Model Driven Architecture (MDA) together with Unified Modelling Language (UML) presents a framework which transfers the emphasis of development from source code to the higher level of abstraction i.e., models. In this paper, we demonstrate the application of MDA principles for generating smart contract code executed on a blockchain. Even though blockchain smart contracts are not in all cases a classic type of object-oriented software, which UML is intended for, we demonstrate the possibility to adapt to this specific implementation platform. MDA Platform Specific Model (PSM) is used as an input for transformation algorithm which maps PSM metamodel elements to Go Chaincode elements and produces Go chaincode. In PSM, UML class and sequence diagrams are used for specifying structural and behavioural aspects of the smart contract. MOFM2T transformation language and Acceleo tool are employed for the implementation of this algorithm. The results of the algorithm execution were demonstrated using example chaincode for machine learning model validation. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","author":[{"family":"Jurgelaitis","given":"M."},{"family":"Drungilas","given":"V."},{"family":"Čeponienė","given":"L."},{"family":"Vaičiukynas","given":"E."},{"family":"Butkienė","given":"R."},{"family":"Čeponis","given":"J."}],"citation-key":"Jurgelaitis202163","container-title":"Advances in Intelligent Systems and Computing","DOI":"10.1007/978-3-030-72654-6_7","editor":[{"family":"Rocha A., Adeli H.","given":"Dzemyda G.","suffix":"Moreira F., Correia A.M.R."}],"ISBN":"9783030726539","ISSN":"21945357","issued":{"date-parts":[[2021]]},"page":"63-73","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Smart contract code generation from platform specific model for hyperledger go","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107328038&doi=10.1007%2f978-3-030-72654-6_7&partnerID=40&md5=48577072d0511adaafc017a3f4889ec1","volume":"1368 AISC"},
{"id":"jurgelaitisSmartContractCode2021a","abstract":"Model Driven Architecture (MDA) together with Unified Modelling Language (UML) presents a framework which transfers the emphasis of development from source code to the higher level of abstraction i.e., models. In this paper, we demonstrate the application of MDA principles for generating smart contract code executed on a blockchain. Even though blockchain smart contracts are not in all cases a classic type of object-oriented software, which UML is intended for, we demonstrate the possibility to adapt to this specific implementation platform. MDA Platform Specific Model (PSM) is used as an input for transformation algorithm which maps PSM metamodel elements to Go Chaincode elements and produces Go chaincode. In PSM, UML class and sequence diagrams are used for specifying structural and behavioural aspects of the smart contract. MOFM2T transformation language and Acceleo tool are employed for the implementation of this algorithm. The results of the algorithm execution were demonstrated using example chaincode for machine learning model validation. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","author":[{"family":"Jurgelaitis","given":"M."},{"family":"Drungilas","given":"V."},{"family":"Čeponienė","given":"L."},{"family":"Vaičiukynas","given":"E."},{"family":"Butkienė","given":"R."},{"family":"Čeponis","given":"J."}],"citation-key":"jurgelaitisSmartContractCode2021a","container-title":"Advances in Intelligent Systems and Computing","DOI":"10.1007/978-3-030-72654-6_7","editor":[{"family":"Rocha A.","given":"Correia A.M.R.","suffix":"Adeli H., Dzemyda G., Moreira F."}],"ISBN":"9783030726539","ISSN":"21945357","issued":{"date-parts":[[2021]]},"page":"63-73","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Smart Contract Code Generation from Platform Specific Model for Hyperledger Go","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107328038&doi=10.1007%2f978-3-030-72654-6_7&partnerID=40&md5=48577072d0511adaafc017a3f4889ec1","volume":"1368 AISC"},
{"id":"JustAddData","accessed":{"date-parts":[[2021,5,14]]},"citation-key":"JustAddData","note":"00012","title":"Just Add Data: Automated Predictive Modeling and BioSignature Discovery | bioRxiv","type":"webpage","URL":"https://www.biorxiv.org/content/10.1101/2020.05.04.075747v1.full"},
{"id":"Kaindl2021542","abstract":"Interaction design is considered important for achieving usable Web user interfaces. Communicative acts as abstractions from speech acts can model basic building blocks (‘atoms’) of communication, like a question or an answer. When, e.g., a question and an answer are glued together as a so-called adjacency pair, a simple ‘molecule’ of a dialogue is modeled. Deliberately complex discourse structures can be modeled using relations from Rhetorical Structure Theory (RST). The content of a communicative act can refer to ontologies of the domain of discourse. Taking all this together, we created a new discourse metamodel that specifies what discourse models may look like. Such discourse models can specify an interaction design. Since manual creation of user interfaces is hard and expensive, automated generation may become more and more important. This tutorial also demonstrates how such an interaction design can be used for automated Web user-interface generation. This is based on model-transformation rules according to the model-driven architecture. Based on AI optimization techniques, the graphical user interfaces (GUIs) are automatically tailored to a device such as a smartphone according to a given device specification. Since the usability of fully-automatically generated GUIs is still not satisfactory, unique customization techniques are employed as well. We also address low-vision accessibility of Web-pages, by combining automated design-time generation of Web-pages with responsive design for improving accessibility. © 2021, Springer Nature Switzerland AG.","author":[{"family":"Kaindl","given":"H."}],"citation-key":"Kaindl2021542","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-74296-6_47","editor":[{"family":"Brambilla M., Chbeir R.","given":"Frasincar F.","suffix":"Manolescu I."}],"ISBN":"9783030742959","ISSN":"03029743","issued":{"date-parts":[[2021]]},"page":"542-546","publisher":"Springer Science and Business Media Deutschland GmbH","title":"High-level interaction design with discourse models for automated web GUI generation","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111114605&doi=10.1007%2f978-3-030-74296-6_47&partnerID=40&md5=cb18717482c33843e468fabfb69e0be5","volume":"12706 LNCS"},
{"id":"Kanetaki2021V","abstract":"Faced with the disruption generated by the COVID-19 pandemic, the advent of enforced and exclusive online learning presented a challenging opportunity for researchers worldwide, to quickly adapt curricula to this new reality and gather electronic data by tracking students' satisfaction after attending online modules. Many researchers have looked into the subject of student satisfaction to discover if there is a link between personal satisfaction and academic achievement. Using a set of data, filtered out of a statistical analysis applied on an online survey, with 129 variables, this study investigates students' satisfaction prediction in a first-semester Mechanical Engineering CAD module combined with the evaluation and the effectiveness of specific curriculum reforms. A hybrid machine learning model that has been created, initially consists of a Generalized Linear Model (GLAR), based on critical variables that have been filtered out after a correlation analysis. Its fitting errors are utilized as an extra predictor, that is used as an input to an artificial neural network. The model has been trained using as a basis the 70% of the population (consisting of 165 observations) to predict the satisfaction of the remaining 30%. After several trials and gradual improvement, the metamodel's architecture is produced. The trained hybrid model's final form had a coefficient of determination equal to 1 (R = 1). This indicates that the data fitting method was successful in linking the independent variables with the dependent variable 100 percent of the time (satisfaction prediction). © 2021 The authors and IOS Press.","author":[{"family":"Kanetaki","given":"Z."},{"family":"Stergiou","given":"C."},{"family":"Bekas","given":"G."},{"family":"Troussas","given":"C."},{"family":"Sgouropoulou","given":"C."}],"citation-key":"Kanetaki2021V","container-title":"Frontiers in Artificial Intelligence and Applications","DOI":"10.3233/FAIA210085","editor":[{"family":"Frasson C., Kabassi K.","given":"Voulodimos A."}],"ISBN":"9781643682044","ISSN":"09226389","issued":{"date-parts":[[2021]]},"page":"V-VI","publisher":"IOS Press BV","title":"Creating a metamodel for predicting learners satisfaction by utilizing an educational information system during COVID-19 pandemic","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116772720&doi=10.3233%2fFAIA210085&partnerID=40&md5=d20da58ca3c447d1ac05e2740e089da6","volume":"338"},
{"id":"kanetakiCreatingMetamodelPredicting2021a","abstract":"Faced with the disruption generated by the COVID-19 pandemic, the advent of enforced and exclusive online learning presented a challenging opportunity for researchers worldwide, to quickly adapt curricula to this new reality and gather electronic data by tracking students' satisfaction after attending online modules. Many researchers have looked into the subject of student satisfaction to discover if there is a link between personal satisfaction and academic achievement. Using a set of data, filtered out of a statistical analysis applied on an online survey, with 129 variables, this study investigates students' satisfaction prediction in a first-semester Mechanical Engineering CAD module combined with the evaluation and the effectiveness of specific curriculum reforms. A hybrid machine learning model that has been created, initially consists of a Generalized Linear Model (GLAR), based on critical variables that have been filtered out after a correlation analysis. Its fitting errors are utilized as an extra predictor, that is used as an input to an artificial neural network. The model has been trained using as a basis the 70% of the population (consisting of 165 observations) to predict the satisfaction of the remaining 30%. After several trials and gradual improvement, the metamodel's architecture is produced. The trained hybrid model's final form had a coefficient of determination equal to 1 (R = 1). This indicates that the data fitting method was successful in linking the independent variables with the dependent variable 100 percent of the time (satisfaction prediction). © 2021 The authors and IOS Press.","author":[{"family":"Kanetaki","given":"Z."},{"family":"Stergiou","given":"C."},{"family":"Bekas","given":"G."},{"family":"Troussas","given":"C."},{"family":"Sgouropoulou","given":"C."}],"citation-key":"kanetakiCreatingMetamodelPredicting2021a","container-title":"Frontiers in Artificial Intelligence and Applications","DOI":"10.3233/FAIA210085","editor":[{"family":"Frasson C.","given":"Voulodimos A.","suffix":"Kabassi K."}],"ISBN":"9781643682044","ISSN":"09226389","issued":{"date-parts":[[2021]]},"page":"V-VI","publisher":"IOS Press BV","title":"Creating a metamodel for predicting learners satisfaction by utilizing an educational information system during COVID-19 pandemic","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116772720&doi=10.3233%2fFAIA210085&partnerID=40&md5=d20da58ca3c447d1ac05e2740e089da6","volume":"338"},
{"id":"karasneh2013online","author":[{"family":"Karasneh","given":"Bilal"},{"family":"Chaudron","given":"Michel RV"}],"citation-key":"karasneh2013online","container-title":"EESSMOD@ MoDELS","issued":{"date-parts":[[2013]]},"page":"61-66","title":"Online Img2UML repository: An online repository for UML models.","type":"paper-conference"},
{"id":"Karatzoglou:2017:DLR:3109859.3109933","author":[{"family":"Karatzoglou","given":"Alexandros"},{"family":"Hidasi","given":"Balázs"}],"citation-key":"Karatzoglou:2017:DLR:3109859.3109933","collection-title":"RecSys '17","container-title":"Proceedings of the eleventh ACM conference on recommender systems","event-place":"New York, NY, USA","ISBN":"978-1-4503-4652-8","issued":{"date-parts":[[2017]]},"page":"396-397","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Deep learning for recommender systems","type":"paper-conference","URL":"http://doi.acm.org/10.1145/3109859.3109933"},
{"id":"karsaiDistributedManagedResearch2014","accessed":{"date-parts":[[2016,3,10]]},"author":[{"family":"Karsai","given":"Gabor"},{"family":"Balasubramanian","given":"Daniel"},{"family":"Dubey","given":"Abhishek"},{"family":"Otte","given":"William R."}],"citation-key":"karsaiDistributedManagedResearch2014","DOI":"10.1109/ISORC.2014.36","ISBN":"978-1-4799-4430-9","issued":{"date-parts":[[2014,6]]},"page":"1-8","publisher":"IEEE","source":"CrossRef","title":"Distributed and Managed: Research Challenges and Opportunities of the Next Generation Cyber-Physical Systems","title-short":"Distributed and Managed","type":"paper-conference","URL":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6899124"},
{"id":"karsaiModelintegratedDevelopmentCyberphysical2008","accessed":{"date-parts":[[2016,3,10]]},"author":[{"family":"Karsai","given":"Gabor"},{"family":"Sztipanovits","given":"Janos"}],"citation-key":"karsaiModelintegratedDevelopmentCyberphysical2008","container-title":"Software Technologies for Embedded and Ubiquitous Systems","issued":{"date-parts":[[2008]]},"page":"46–54","publisher":"Springer","source":"Google Scholar","title":"Model-integrated development of cyber-physical systems","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-540-87785-1_5"},
{"id":"Karypis:1999:CHC:619043.621303","author":[{"family":"Karypis","given":"George"},{"family":"Han","given":"Eui-Hong (Sam)"},{"family":"Kumar","given":"Vipin"}],"citation-key":"Karypis:1999:CHC:619043.621303","container-title":"Computer","ISSN":"0018-9162","issue":"8","issued":{"date-parts":[[1999,8]]},"page":"68-75","title":"Chameleon: Hierarchical clustering using dynamic modeling","type":"article-journal","URL":"http://dx.doi.org/10.1109/2.781637","volume":"32"},
{"id":"Karypis:2001:EIT:502585.502627","author":[{"family":"Karypis","given":"George"}],"citation-key":"Karypis:2001:EIT:502585.502627","collection-title":"CIKM '01","container-title":"Procs. of the tenth international conf. on information and knowledge management","event-place":"New York, NY, USA","ISBN":"1-58113-436-3","issued":{"date-parts":[[2001]]},"page":"247-254","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Evaluation of item-based top-n recommendation algorithms","type":"paper-conference"},
{"id":"Kaselimi20203054","abstract":"Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), describes various processes aiming to identify the individual contribution of appliances, given the aggregate power signal. In this paper, a non-causal adaptive context-aware bidirectional deep learning model for energy disaggregation is introduced. The proposed model, CoBiLSTM, harnesses the representational power of deep recurrent Long Short-Term Memory (LSTM) neural networks, while fitting two basic properties of NILM problem which state of the art methods do not appropriately account for: non-causality and adaptivity to contextual factors (e.g., seasonality). A Bayesian-optimized framework is introduced to select the best configuration of the proposed regression model, driven by a self-training adaptive mechanism. Furthermore, the proposed model is structured in a modular way to address multi-dimensionality issues that arise when the number of appliances increases. Experimental results indicate the proposed method's superiority compared to the current state of the art. © 2010-2012 IEEE.","author":[{"family":"Kaselimi","given":"M."},{"family":"Doulamis","given":"N."},{"family":"Voulodimos","given":"A."},{"family":"Protopapadakis","given":"E."},{"family":"Doulamis","given":"A."}],"citation-key":"Kaselimi20203054","container-title":"IEEE Transactions on Smart Grid","DOI":"10.1109/TSG.2020.2974347","ISSN":"19493053","issue":"4","issued":{"date-parts":[[2020]]},"page":"3054-3067","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Context aware energy disaggregation using adaptive bidirectional LSTM models","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086308828&doi=10.1109%2fTSG.2020.2974347&partnerID=40&md5=4543ea4eae93c8d2d96c90aff62ddca7","volume":"11"},
{"id":"kaselimiContextAwareEnergy2020a","abstract":"Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), describes various processes aiming to identify the individual contribution of appliances, given the aggregate power signal. In this paper, a non-causal adaptive context-aware bidirectional deep learning model for energy disaggregation is introduced. The proposed model, CoBiLSTM, harnesses the representational power of deep recurrent Long Short-Term Memory (LSTM) neural networks, while fitting two basic properties of NILM problem which state of the art methods do not appropriately account for: non-causality and adaptivity to contextual factors (e.g., seasonality). A Bayesian-optimized framework is introduced to select the best configuration of the proposed regression model, driven by a self-training adaptive mechanism. Furthermore, the proposed model is structured in a modular way to address multi-dimensionality issues that arise when the number of appliances increases. Experimental results indicate the proposed method's superiority compared to the current state of the art. © 2010-2012 IEEE.","author":[{"family":"Kaselimi","given":"M."},{"family":"Doulamis","given":"N."},{"family":"Voulodimos","given":"A."},{"family":"Protopapadakis","given":"E."},{"family":"Doulamis","given":"A."}],"citation-key":"kaselimiContextAwareEnergy2020a","container-title":"IEEE Transactions on Smart Grid","DOI":"10.1109/TSG.2020.2974347","ISSN":"19493053","issue":"4","issued":{"date-parts":[[2020]]},"page":"3054-3067","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Context Aware Energy Disaggregation Using Adaptive Bidirectional LSTM Models","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086308828&doi=10.1109%2fTSG.2020.2974347&partnerID=40&md5=4543ea4eae93c8d2d96c90aff62ddca7","volume":"11"},
{"id":"Kasrin202176","abstract":"The recent evolution of the Internet of Things into a cyber-physical reality has spawned various challenges from a data management perspective. In addition, IoT platform designers are faced with another set of questions. How can platforms be extended to smoothly integrate new data management functionalities? Currently, data processing related tasks are typically realized by manually developed code and functions which creates difficulties in maintenance and growth. Hence we need to explore other approaches to integration for IoT platforms. In this paper we cover both these aspects: (1) we explore several emerging data management challenges, and (2) we propose an IoT platform integration model that can combine disparate functionalities under one roof. For the first, we focus on the following challenges: sensor data quality, privacy in data streams, machine learning model management, and resource-aware data management. For the second, we propose an information-integration model for IoT platforms. The model revolves around the concept of a Data-Sharing Market where data management functionalities can share and exchange information about their data with other functionalities. In addition, data-sharing markets themselves can be combined into networks of markets where information flows from one market to another, which creates a web of information exchange about data resources. To motivate this work we present a use-case application in smart cities. © 2021, The Author(s).","author":[{"family":"Kasrin","given":"N."},{"family":"Benabbas","given":"A."},{"family":"Elmamooz","given":"G."},{"family":"Nicklas","given":"D."},{"family":"Steuer","given":"S."},{"family":"Sünkel","given":"M."}],"citation-key":"Kasrin202176","container-title":"CCF Transactions on Pervasive Computing and Interaction","DOI":"10.1007/s42486-020-00054-y","ISSN":"2524521X","issue":"1","issued":{"date-parts":[[2021]]},"page":"76-93","publisher":"Springer","title":"Data-sharing markets for integrating IoT data processing functionalities","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108005821&doi=10.1007%2fs42486-020-00054-y&partnerID=40&md5=12301b8e8700eff6afa6c81e9f2aedf0","volume":"3"},
{"id":"katirtzisSummarizingSoftwareAPI","abstract":"As developers often use third-party libraries to facilitate software development, the lack of proper API documentation for these libraries undermines their reuse potential. And although several approaches extract usage examples for libraries, they are usually tied to specific language implementations, while their produced examples are often redundant and are not presented as concise and readable snippets. In this work, we propose a novel approach that extracts API call sequences from client source code and clusters them to produce a diverse set of source code snippets that effectively covers the target API. We further construct a summarization algorithm to present concise and readable snippets to the users. Upon evaluating our system on software libraries, we indicate that it achieves high coverage in API methods, while the produced snippets are of high quality and closely match handwritten examples.","author":[{"family":"Katirtzis","given":"Nikolaos"},{"family":"Diamantopoulos","given":"Themistoklis"},{"family":"Sutton","given":"Charles"}],"citation-key":"katirtzisSummarizingSoftwareAPI","page":"17","source":"Zotero","title":"Summarizing Software API Usage Examples using Clustering Techniques","type":"article-journal"},
{"id":"katsamakasWhyMostOpen2007","accessed":{"date-parts":[[2017,6,23]]},"author":[{"family":"Katsamakas","given":"Evangelos"},{"family":"Georgantzas","given":"Nicholas"}],"citation-key":"katsamakasWhyMostOpen2007","container-title":"Emerging Trends in FLOSS Research and Development, 2007. FLOSS'07. First International Workshop on","issued":{"date-parts":[[2007]]},"page":"3–3","publisher":"IEEE","source":"Google Scholar","title":"Why most open source development projects do not succeed?","type":"paper-conference","URL":"http://ieeexplore.ieee.org/abstract/document/4273074/"},
{"id":"kaufman:clustering1990","author":[{"family":"Kaufman","given":"L."},{"family":"Rousseeuw","given":"P.J."}],"citation-key":"kaufman:clustering1990","issued":{"date-parts":[[1990]]},"publisher":"Wiley","title":"Finding Groups in Data: an introduction to cluster analysis","type":"book"},
{"id":"kaufman2009finding","author":[{"family":"Kaufman","given":"Leonard"},{"family":"Rousseeuw","given":"Peter J"}],"citation-key":"kaufman2009finding","issued":{"date-parts":[[2009]]},"publisher":"John Wiley & Sons","title":"Finding groups in data: an introduction to cluster analysis","type":"book","volume":"344"},
{"id":"KaufmanL1987Cbmo","author":[{"family":"Kaufman","given":"L"},{"family":"Rousseeuw","given":"Peter"}],"citation-key":"KaufmanL1987Cbmo","container-title":"Statistical data analysis based on the L1 norm and related methods","ISBN":"0-444-70273-3","issued":{"date-parts":[[1987]]},"page":"405-416","publisher":"North-Holland; Amsterdam","title":"Clustering by means of medoids","type":"chapter","URL":"$$Uhttps://lirias.kuleuven.be/retrieve/377090$$DKaufmanRousseeuw_ClusteringByMedoids_L1Norm_1987.pdf \t [Available for KU Leuven users]"},
{"id":"Kaur2021671","abstract":"Skin cancer is a prevalent kind of cancer, and early diagnosis significantly improves the chance of survival. The purpose of this article is to develop a deep learning feature engineering model with an optimised xg-boost classifier for the purpose of classifying dermal cell pictures and detecting skin cancer. Utilization of Methodology Classification method based on features mapped on nonlinear space using Resnet 50 basis feature engineering, followed by learning via Xg-boost structure optimization. Structure optimization is accomplished by grey wolf optimization. Within the Results The deep learning with xgboost model developed here was evaluated on standard datasets and combined datasets, and the metric accuracy and precision were found to be 98.34 percent and 97.35 percent, respectively. Conclude that a practitioner may use model-driven architecture to rapidly develop deep learning models for skin cancer prediction. © 2021 IEEE.","author":[{"family":"Kaur","given":"R."},{"family":"Kaur","given":"N."}],"citation-key":"Kaur2021671","collection-title":"2021 International Conference on Computational Performance Evaluation, ComPE 2021","DOI":"10.1109/ComPE53109.2021.9751930","editor":[{"family":"Paul S.","given":"Verma J.K."}],"ISBN":"978-1-66543-656-4","issued":{"date-parts":[[2021]]},"page":"671-675","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Improved skin cancer detection classification residual network feature engineering","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128982462&doi=10.1109%2fComPE53109.2021.9751930&partnerID=40&md5=fb5d3c6e3769c095793f8c1716176c73"},
{"id":"Kaushal20201861","abstract":"In social networks, the problem of identity linkage is to find whether a pair of user identities on two social networks belong to the same individual or not. Prior works typically first collect ground truth datasets of user identities across social networks belonging to the same individuals and then build a machine learning model driven by features from user identities. User behaviors in different social networks drive the construction of these datasets, and as a consequence, behavioral biases get manifested in them. Our work performs a detailed investigation into these dataset biases, a work which has mostly remained under-explored in the identity linkage research. More specifically, we characterize, detect, and quantify behavioral biases in the dataset that manifest in the form of lexical differences in user-generated content, particularly in usernames and display names configured by users. We study these biases on more than 1 million user identity pairs obtained by leveraging two user behaviors, namely cross-posting and self-disclosure. We find that users who self-disclose their usernames and display names on different social networks show higher lexical similarity than users who cross-post. These behavioral biases lower down the performance (precision and recall) of learning models by 5-20%. Inspired by discrimination measurement metrics, we propose and implement a framework to quantify the extent of these biases and find that 15 - 20% of test data get affected. © 2020 ACM.","author":[{"family":"Kaushal","given":"R."},{"family":"Gupta","given":"S."},{"family":"Kumaraguru","given":"P."}],"citation-key":"Kaushal20201861","collection-title":"Proceedings of the ACM Symposium on Applied Computing","DOI":"10.1145/3341105.3374015","ISBN":"978-1-4503-6866-7","issued":{"date-parts":[[2020]]},"page":"1861-1868","publisher":"Association for Computing Machinery","title":"Investigation of biases in identity linkage DataSets","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083035521&doi=10.1145%2f3341105.3374015&partnerID=40&md5=dbcc367f9d6ab9d3ea8300f9a00e74f9"},
{"id":"kazmanManagingEnergyConsumption2018","abstract":"A look at the software for an automated weather station shows that energy can be treated like any other architectural quality attribute. It’s no different, from the perspective of architectural design, than modifiability, performance, or availability. It can be modeled and prototyped, and we can reason about the design tradeoffs required to achieve better energy use.","author":[{"family":"Kazman","given":"R."},{"family":"Haziyev","given":"S."},{"family":"Yakuba","given":"A."},{"family":"Tamburri","given":"D. A."}],"citation-key":"kazmanManagingEnergyConsumption2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.3571227","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"102-107","source":"IEEE Xplore","title":"Managing Energy Consumption as an Architectural Quality Attribute","type":"article-journal","volume":"35"},
{"id":"Kazmi2017449","abstract":"There exists a class of problems in e-commerce and retail businesses where the shopping behavior of customers is analyzed in order to predict their repeat behavior for products or retail stores. This analysis plays a crucial role in advertisement budgeting, product placement and relevant customer targeting. Researchers have addressed this problem by using standard predictive models, which use ad hoc features. We propose a metamodel that abstracts the different dimensions of data present in transactional datasets. These dimensions can be customer, product, offer, target, marketplace and transactions. Our framework also has abstract functions for comprehensive feature set generation, and includes different machine learning algorithms to learn prediction model. Our framework works end-To-end from feature engineering to reporting repeat probabilities of customers for products (or marketplace, brand, website or storechain). Moreover, the predicted repeat behavior of customers for different products along with their transactional history is used by our offer optimization model i-Prescribe to suggest products to be offered to customers with the goal of maximizing the return on investment of given marketing budget. We prove that our abstract features work on two different data-challenge datasets, by sharing experimental results. © 2016 IEEE.","author":[{"family":"Kazmi","given":"A.H."},{"family":"Shroff","given":"G."},{"family":"Agarwal","given":"P."}],"citation-key":"Kazmi2017449","collection-title":"Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016","DOI":"10.1109/WI.2016.0072","ISBN":"978-1-5090-4470-2","issued":{"date-parts":[[2017]]},"page":"449-452","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Generic framework to predict repeat behavior of customers using their transaction history","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013037512&doi=10.1109%2fWI.2016.0072&partnerID=40&md5=28cce5952734f16162b8dc2f62d52627"},
{"id":"KDMWelcome","accessed":{"date-parts":[[2018,4,30]]},"citation-key":"KDMWelcome","title":"KDM - Welcome","type":"webpage","URL":"http://kdm.dataview.org/"},
{"id":"KeepAllYour","accessed":{"date-parts":[[2021,1,11]]},"citation-key":"KeepAllYour","note":"00000","title":"Keep all your packages up to date with Dependabot - The GitHub Blog","type":"webpage","URL":"https://github.blog/2020-06-01-keep-all-your-packages-up-to-date-with-dependabot/"},
{"id":"kehrerUnderstandComplexChanges","author":[{"family":"Kehrer","given":"Timo"}],"citation-key":"kehrerUnderstandComplexChanges","page":"79","source":"Zotero","title":"Understand complex changes and improve the quality of your UML and domain-specific models","type":"article-journal"},
{"id":"kephartVisionAutonomicComputing2003","accessed":{"date-parts":[[2016,8,26]]},"author":[{"family":"Kephart","given":"Jeffrey O."},{"family":"Chess","given":"David M."}],"citation-key":"kephartVisionAutonomicComputing2003","container-title":"Computer","issue":"1","issued":{"date-parts":[[2003]]},"page":"41–50","source":"Google Scholar","title":"The vision of autonomic computing","type":"article-journal","URL":"http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1160055","volume":"36"},
{"id":"kerstenFivePredictionsComing2018","abstract":"To help celebrate software engineering’s 50th anniversary, department editor Mik Kersten considers how software engineering will evolve over the coming 50 years. His five predictions aren’t intended to be precise; they aim to provide discussion topics for the shape of software engineering trends to come. This article is part of a theme issue on software engineering’s 50th anniversary.","author":[{"family":"Kersten","given":"M."}],"citation-key":"kerstenFivePredictionsComing2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.3571232","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"7-9","source":"IEEE Xplore","title":"Five Predictions for the Coming Decades of Software","type":"article-journal","volume":"35"},
{"id":"KESSENTINI201949","author":[{"family":"Kessentini","given":"Wael"},{"family":"Sahraoui","given":"Houari"},{"family":"Wimmer","given":"Manuel"}],"citation-key":"KESSENTINI201949","container-title":"Information and Software Technology","DOI":"https://doi.org/10.1016/j.infsof.2018.09.003","ISSN":"0950-5849","issued":{"date-parts":[[2019]]},"page":"49 - 67","title":"Automated metamodel/model co-evolution: A search-based approach","type":"article-journal","URL":"http://www.sciencedirect.com/science/article/pii/S0950584918301915","volume":"106"},
{"id":"kessentiniAutomatedCoevolutionMetamodels2018","accessed":{"date-parts":[[2021,1,30]]},"author":[{"family":"Kessentini","given":"Wael"},{"family":"Sahraoui","given":"Houari"},{"family":"Wimmer","given":"Manuel"}],"citation-key":"kessentiniAutomatedCoevolutionMetamodels2018","container-title":"Search-Based Software Engineering","DOI":"10.1007/978-3-319-99241-9_12","editor":[{"family":"Colanzi","given":"Thelma Elita"},{"family":"McMinn","given":"Phil"}],"event-place":"Cham","ISBN":"978-3-319-99240-2 978-3-319-99241-9","issued":{"date-parts":[[2018]]},"note":"00000","page":"229-245","publisher":"Springer International Publishing","publisher-place":"Cham","source":"DOI.org (Crossref)","title":"Automated Co-evolution of Metamodels and Transformation Rules: A Search-Based Approach","title-short":"Automated Co-evolution of Metamodels and Transformation Rules","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-319-99241-9_12","volume":"11036"},
{"id":"kessentiniIntegratingDesignerIntheloop2018","accessed":{"date-parts":[[2021,1,30]]},"author":[{"family":"Kessentini","given":"Wael"},{"family":"Wimmer","given":"Manuel"},{"family":"Sahraoui","given":"Houari"}],"citation-key":"kessentiniIntegratingDesignerIntheloop2018","container-title":"Proceedings of the 21th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems","DOI":"10.1145/3239372.3239375","event":"MODELS '18: ACM/IEEE 21th International Conference on Model Driven Engineering Languages and Systems","event-place":"Copenhagen Denmark","ISBN":"978-1-4503-4949-9","issued":{"date-parts":[[2018,10,14]]},"note":"00009","page":"101-111","publisher":"ACM","publisher-place":"Copenhagen Denmark","source":"DOI.org (Crossref)","title":"Integrating the Designer in-the-loop for Metamodel/Model Co-Evolution via Interactive Computational Search","type":"paper-conference","URL":"https://dl.acm.org/doi/10.1145/3239372.3239375"},
{"id":"kessentiniSearchbasedMetamodelMatching2014","abstract":"The use of different domain-specific modeling languages and diverse versions of the same modeling language often entails the need to translate models between the different languages and language versions. The first step in establishing a transformation between two languages is to find their corresponding concepts, i.e., finding correspondences between their metamodel elements. Although, metamodels use heterogeneous terminologies and structures, they often still describe similar language concepts. In this paper, we propose to combine structural metrics (e.g., number of properties per concept) and syntactic metrics to generate correspondences between metamodels. Because metamodel matching requires to cope with a huge search space of possible element combinations, we adapted a local and a global metaheuristic search algorithm to find the best set of correspondences between metamodels. The efficiency and effectiveness of our proposal is evaluated on different matching scenarios based on existing benchmarks. In addition, we compared our technique to state-of-the-art ontology matching and model matching approaches.","accessed":{"date-parts":[[2018,7,11]]},"author":[{"family":"Kessentini","given":"Marouane"},{"family":"Ouni","given":"Ali"},{"family":"Langer","given":"Philip"},{"family":"Wimmer","given":"Manuel"},{"family":"Bechikh","given":"Slim"}],"citation-key":"kessentiniSearchbasedMetamodelMatching2014","container-title":"Journal of Systems and Software","DOI":"10.1016/j.jss.2014.06.040","ISSN":"01641212","issued":{"date-parts":[[2014,11]]},"page":"1-14","source":"Crossref","title":"Search-based metamodel matching with structural and syntactic measures","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0164121214001484","volume":"97"},
{"id":"khakpourFormalModelingEvolving2012","accessed":{"date-parts":[[2016,1,12]]},"author":[{"family":"Khakpour","given":"Narges"},{"family":"Jalili","given":"Saeed"},{"family":"Talcott","given":"Carolyn"},{"family":"Sirjani","given":"Marjan"},{"family":"Mousavi","given":"MohammadReza"}],"citation-key":"khakpourFormalModelingEvolving2012","container-title":"Science of Computer Programming","DOI":"10.1016/j.scico.2011.09.004","ISSN":"01676423","issue":"1","issued":{"date-parts":[[2012,11]]},"page":"3-26","source":"CrossRef","title":"Formal modeling of evolving self-adaptive systems","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0167642311001742","volume":"78"},
{"id":"khalilSupportingEvolutionUML2013","accessed":{"date-parts":[[2015,4,2]]},"author":[{"family":"Khalil","given":"Amal"},{"family":"Dingel","given":"Juergen"}],"citation-key":"khalilSupportingEvolutionUML2013","issued":{"date-parts":[[2013]]},"publisher":"Technical Report, School of Computing. Queens University, Canada","source":"Google Scholar","title":"Supporting the evolution of UML models in model driven software developmeny: A Survey","title-short":"Supporting the evolution of UML models in model driven software developmeny","type":"report","URL":"http://research.cs.queensu.ca/TechReports/Reports/2013-602.pdf"},
{"id":"Khan:2016:STS:3004996.3005218","author":[{"family":"Khan","given":"Saif Ur Rehman"},{"family":"Lee","given":"Sai Peck"},{"family":"Ahmad","given":"Raja Wasim"},{"family":"Akhunzada","given":"Adnan"},{"family":"Chang","given":"Victor"}],"citation-key":"Khan:2016:STS:3004996.3005218","container-title":"Int. J. Inf. Manag.","ISSN":"0268-4012","issue":"6","issued":{"date-parts":[[2016,12]]},"page":"963-975","title":"A survey on test suite reduction frameworks and tools","type":"article-journal","URL":"https://doi.org/10.1016/j.ijinfomgt.2016.05.025","volume":"36"},
{"id":"khanFederatedLearningInternet2020","abstract":"The Internet of Things (IoT) will be ripe for the deployment of novel machine learning algorithms for both network and application management. However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT. To overcome this challenge, federated learning can be a promising solution that enables on-device machine learning without the need to migrate the private end-user data to a central cloud. In federated learning, only learning model updates are transferred between end-devices and the aggregation server. Although federated learning can offer better privacy preservation than centralized machine learning, it has still privacy concerns. In this paper, first, we present the recent advances of federated learning towards enabling federated learning-powered IoT applications. A set of metrics such as sparsification, robustness, quantization, scalability, security, and privacy, is delineated in order to rigorously evaluate the recent advances. Second, we devise a taxonomy for federated learning over IoT networks. Third, we propose two IoT use cases of dispersed federated learning that can offer better privacy preservation than federated learning. Finally, we present several open research challenges with their possible solutions.","accessed":{"date-parts":[[2020,12,22]]},"author":[{"family":"Khan","given":"Latif U."},{"family":"Saad","given":"Walid"},{"family":"Han","given":"Zhu"},{"family":"Hossain","given":"Ekram"},{"family":"Hong","given":"Choong Seon"}],"citation-key":"khanFederatedLearningInternet2020","container-title":"arXiv:2009.13012 [cs]","issued":{"date-parts":[[2020,9,27]]},"note":"00003","source":"arXiv.org","title":"Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges","title-short":"Federated Learning for Internet of Things","type":"article-journal","URL":"http://arxiv.org/abs/2009.13012"},
{"id":"kharlamovSemanticApproachPolystores2016","accessed":{"date-parts":[[2018,4,17]]},"author":[{"family":"Kharlamov","given":"E."},{"family":"Mailis","given":"T."},{"family":"Bereta","given":"K."},{"family":"Bilidas","given":"D."},{"family":"Brandt","given":"S."},{"family":"Jimenez-Ruiz","given":"E."},{"family":"Lamparter","given":"S."},{"family":"Neuenstadt","given":"C."},{"family":"Ozcep","given":"O."},{"family":"Soylu","given":"A."},{"family":"Svingos","given":"C."},{"family":"Xiao","given":"G."},{"family":"Zheleznyakov","given":"D."},{"family":"Calvanese","given":"D."},{"family":"Horrocks","given":"I."},{"family":"Giese","given":"M."},{"family":"Ioannidis","given":"Y."},{"family":"Kotidis","given":"Y."},{"family":"Moller","given":"R."},{"family":"Waaler","given":"A."}],"citation-key":"kharlamovSemanticApproachPolystores2016","DOI":"10.1109/BigData.2016.7840898","ISBN":"978-1-4673-9005-7","issued":{"date-parts":[[2016,12]]},"page":"2565-2573","publisher":"IEEE","source":"CrossRef","title":"A semantic approach to polystores","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/7840898/"},
{"id":"khelladiDetectingComplexChanges2016","accessed":{"date-parts":[[2020,2,19]]},"author":[{"family":"Khelladi","given":"Djamel Eddine"},{"family":"Hebig","given":"Regina"},{"family":"Bendraou","given":"Reda"},{"family":"Robin","given":"Jacques"},{"family":"Gervais","given":"Marie-Pierre"}],"citation-key":"khelladiDetectingComplexChanges2016","container-title":"Information Systems","container-title-short":"Information Systems","DOI":"10.1016/j.is.2016.05.002","ISSN":"03064379","issued":{"date-parts":[[2016,12]]},"page":"220-241","source":"DOI.org (Crossref)","title":"Detecting complex changes and refactorings during (Meta)model evolution","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0306437916302356","volume":"62"},
{"id":"khomhSoftwareEngineeringMachineLearning2018","abstract":"The First Symposium on Software Engineering for Machine Learning Applications (SEMLA) aimed to create a space in which machine learning (ML) and software engineering (SE) experts could come together to discuss challenges, new insights, and practical ideas regarding the engineering of ML and AI-based systems. Key challenges discussed included the accuracy of systems built using ML and AI models, the testing of those systems, industrial applications of AI, and the rift between the ML and SE communities. This article is part of a theme issue on software engineering’s 50th anniversary.","author":[{"family":"Khomh","given":"F."},{"family":"Adams","given":"B."},{"family":"Cheng","given":"J."},{"family":"Fokaefs","given":"M."},{"family":"Antoniol","given":"G."}],"citation-key":"khomhSoftwareEngineeringMachineLearning2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.3571224","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"note":"00032","page":"81-84","source":"IEEE Xplore","title":"Software Engineering for Machine-Learning Applications: The Road Ahead","title-short":"Software Engineering for Machine-Learning Applications","type":"article-journal","volume":"35"},
{"id":"Khoshavi20201139","abstract":"In recent years, the big data booming has boosted the development of highly accurate prediction models driven from machine learning (ML) and deep learning (DL) algorithms. These models can be orchestrated on the customized hardware in the safety-critical missions to accelerate the inference process in ML/DL -powered IoT. However, the radiation-induced transient faults and black/white -box attacks can potentially impact the individual parameters in ML/DL models which may result in generating noisy data/labels or compromising the pre-trained model. In this paper, we propose Fiji-FIN 1, a suitable framework for evaluating the resiliency of IoT devices during the ML/DL model execution with respect to the major security challenges such as bit perturbation attacks and soft errors. Fiji-FIN is capable of injecting both single bit/event flip/upset and multi-bit flip/upset faults on the architectural ML/DL accelerator embedded in ML/DL -powered IoT. Fiji-FIN is significantly more accurate compared to the existing software-level fault injections paradigms on ML/DL -driven IoT devices. © 2020 IEEE.","author":[{"family":"Khoshavi","given":"N."},{"family":"Broyles","given":"C."},{"family":"Bi","given":"Y."},{"family":"Roohi","given":"A."}],"citation-key":"Khoshavi20201139","collection-title":"Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020","DOI":"10.1109/ICMLA51294.2020.00183","editor":[{"family":"Wani M.A., Luo F.","given":"Li X.","suffix":"Dou D., Bonchi F."}],"ISBN":"978-1-72818-470-8","issued":{"date-parts":[[2020]]},"page":"1139-1144","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Fiji-FIN: A fault injection framework on quantized neural network inference accelerator","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102511116&doi=10.1109%2fICMLA51294.2020.00183&partnerID=40&md5=8b1722ecde2094e79ad6749298216691"},
{"id":"Khrouf:2013:HER:2507157.2507171","author":[{"family":"Khrouf","given":"Houda"},{"family":"Troncy","given":"Raphaël"}],"citation-key":"Khrouf:2013:HER:2507157.2507171","collection-title":"RecSys '13","container-title":"Proceedings of the 7th ACM conference on recommender systems","event-place":"New York, NY, USA","ISBN":"978-1-4503-2409-0","issued":{"date-parts":[[2013]]},"page":"185-192","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Hybrid event recommendation using linked data and user diversity","type":"paper-conference","URL":"http://doi.acm.org/10.1145/2507157.2507171"},
{"id":"khusroRecommenderSystemsIssues2016","abstract":"A recommender system is an Information Retrieval technology that improves access and proactively recommends relevant items to users by considering the users' explicitly mentioned preferences and objective behaviors. A recommender system is one of the major techniques that handle information overload problem of Information Retrieval by suggesting users with appropriate and relevant items. Today, several recommender systems have been developed for different domains however, these are not precise enough to fulfil the information needs of users. Therefore, it is necessary to build high quality recommender systems. In designing such recommenders, designers face several issues and challenges that need proper attention. This paper investigates and reports the current trends, issues, challenges, and research opportunities in developing high-quality recommender systems. If properly followed, these issues and challenges will introduce new research avenues and the goal towards fine-tuned and high-quality recommender systems can be achieved.","author":[{"family":"Khusro","given":"Shah"},{"family":"Ali","given":"Zafar"},{"family":"Ullah","given":"Irfan"}],"citation-key":"khusroRecommenderSystemsIssues2016","container-title":"Information science and applications (ICISA) 2016","DOI":"10.1007/978-981-10-0557-2₁12","editor":[{"family":"Kim","given":"Kuinam J."},{"family":"Joukov","given":"Nikolai"}],"event-place":"Singapore","ISBN":"978-981-10-0557-2","issued":{"date-parts":[[2016]]},"page":"1179-1189","publisher":"Springer Singapore","publisher-place":"Singapore","title":"Recommender systems: Issues, challenges, and research opportunities","type":"chapter","URL":"https://doi.org/10.1007/978-981-10-0557-2₁12"},
{"id":"kienzleModeldrivenSustainabilityEvaluation2020","accessed":{"date-parts":[[2020,7,18]]},"author":[{"family":"Kienzle","given":"Jörg"},{"family":"Mussbacher","given":"Gunter"},{"family":"Combemale","given":"Benoit"},{"family":"Bastin","given":"Lucy"},{"family":"Bencomo","given":"Nelly"},{"family":"Bruel","given":"Jean-Michel"},{"family":"Becker","given":"Christoph"},{"family":"Betz","given":"Stefanie"},{"family":"Chitchyan","given":"Ruzanna"},{"family":"Cheng","given":"Betty H. C."},{"family":"Klingert","given":"Sonja"},{"family":"Paige","given":"Richard F."},{"family":"Penzenstadler","given":"Birgit"},{"family":"Seyff","given":"Norbert"},{"family":"Syriani","given":"Eugene"},{"family":"Venters","given":"Colin C."}],"citation-key":"kienzleModeldrivenSustainabilityEvaluation2020","container-title":"Communications of the ACM","container-title-short":"Commun. ACM","DOI":"10.1145/3371906","ISSN":"0001-0782, 1557-7317","issue":"3","issued":{"date-parts":[[2020,2,24]]},"note":"00000","page":"80-91","source":"DOI.org (Crossref)","title":"Toward model-driven sustainability evaluation","type":"article-journal","URL":"https://dl.acm.org/doi/10.1145/3371906","volume":"63"},
{"id":"kim_f_2018","abstract":"Code search is an unavoidable activity in software development. Various approaches and techniques have been explored in the literature to support code search tasks. Most of these approaches focus on serving user queries provided as natural language free-form input. However, there exists a wide range of use-case scenarios where a code-to-code approach would be most beneficial. For example, research directions in code transplantation, code diversity, patch recommendation can leverage a code-to-code search engine to find essential ingredients for their techniques. In this paper, we propose FaCoY, a novel approach for statically finding code fragments which may be semantically similar to user input code. FaCoY implements a query alternation strategy: instead of directly matching code query tokens with code in the search space, FaCoY first attempts to identify other tokens which may also be relevant in implementing the functional behavior of the input code. With various experiments, we show that (1) FaCoY is more effective than online code-to-code search engines; (2) FaCoY can detect more semantic code clones (i.e., Type-4) in BigCloneBench than the state-of-theart; (3) FaCoY, while static, can detect code fragments which are indeed similar with respect to runtime execution behavior; and (4) FaCoY can be useful in code/patch recommendation.","accessed":{"date-parts":[[2019,9,4]]},"author":[{"family":"Kim","given":"Kisub"},{"family":"Kim","given":"Dongsun"},{"family":"Bissyandé","given":"Tegawendé F."},{"family":"Choi","given":"Eunjong"},{"family":"Li","given":"Li"},{"family":"Klein","given":"Jacques"},{"family":"Traon","given":"Yves Le"}],"citation-key":"kim_f_2018","container-title":"Proceedings of the 40th International Conference on Software Engineering - ICSE '18","event-place":"Gothenburg, Sweden","ISBN":"978-1-4503-5638-1","issued":{"date-parts":[[2018]]},"page":"946-957","publisher":"ACM Press","publisher-place":"Gothenburg, Sweden","title":"FaCoY: a code-to-code search engine","title-short":"F <span style=\"font-variant","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?doid=3180155.3180187"},
{"id":"kim2014convolutional","author":[{"family":"Kim","given":"Yoon"}],"citation-key":"kim2014convolutional","container-title":"Proceedings of the 2014 conf. on empirical methods in NLP, EMNLP 2014, october 25-29, 2014, doha, qatar","issued":{"date-parts":[[2014]]},"page":"1746-1751","title":"Convolutional neural networks for sentence classification","type":"paper-conference"},
{"id":"Kim2017282","abstract":"Our legal question answering system combines legal information retrieval and textual entailment, and we describe a legal question answering system that exploits a deep convolutional neural network. We have evaluated our system using the training/test data from the competition on legal information extraction/entailment (COLIEE). The competition focuses on the legal information processing related to answering yes/no questions from Japanese legal bar exams, and it consists of three phases: ad-hoc legal information retrieval, textual entailment, and a learning model-driven combination of the two phases. Phase 1 requires the identification of Japan civil law articles relevant to a legal bar exam query. For that phase, we have implemented a combined TF-IDF and Ranking SVM information retrieval component. Phase 2 requires the system to answer “Yes” or “No” to previously unseen queries, by comparing extracted meanings of queries with relevant articles. Our training of an entailment model focuses on features based on word embeddings, syntactic similarities and identification of negation/antonym relations. We augment our textual entailment component with a convolutional neural network with dropout regularization and Rectified Linear Units. To our knowledge, our study is the first to adapt deep learning for textual entailment. Experimental evaluation demonstrates the effectiveness of the convolutional neural network and dropout regularization. The results show that our deep learning-based method outperforms our baseline SVM-based supervised model and K-means clustering. © Springer International Publishing AG 2017.","author":[{"family":"Kim","given":"M.-Y."},{"family":"Xu","given":"Y."},{"family":"Goebel","given":"R."}],"citation-key":"Kim2017282","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-319-50953-2_20","editor":[{"family":"Otake M., Satoh K.","given":"Kurahashi S.","suffix":"Ota Y., Bekki D."}],"ISBN":"9783319509525","ISSN":"03029743","issued":{"date-parts":[[2017]]},"page":"282-294","publisher":"Springer Verlag","title":"Applying a convolutional neural network to legal question answering","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018432283&doi=10.1007%2f978-3-319-50953-2_20&partnerID=40&md5=b2748ae62238740694ac8d8a278eb81f","volume":"10091 LNCS"},
{"id":"kimApplyingConvolutionalNeural2017a","abstract":"Our legal question answering system combines legal information retrieval and textual entailment, and we describe a legal question answering system that exploits a deep convolutional neural network. We have evaluated our system using the training/test data from the competition on legal information extraction/entailment (COLIEE). The competition focuses on the legal information processing related to answering yes/no questions from Japanese legal bar exams, and it consists of three phases: ad-hoc legal information retrieval, textual entailment, and a learning model-driven combination of the two phases. Phase 1 requires the identification of Japan civil law articles relevant to a legal bar exam query. For that phase, we have implemented a combined TF-IDF and Ranking SVM information retrieval component. Phase 2 requires the system to answer “Yes” or “No” to previously unseen queries, by comparing extracted meanings of queries with relevant articles. Our training of an entailment model focuses on features based on word embeddings, syntactic similarities and identification of negation/antonym relations. We augment our textual entailment component with a convolutional neural network with dropout regularization and Rectified Linear Units. To our knowledge, our study is the first to adapt deep learning for textual entailment. Experimental evaluation demonstrates the effectiveness of the convolutional neural network and dropout regularization. The results show that our deep learning-based method outperforms our baseline SVM-based supervised model and K-means clustering. © Springer International Publishing AG 2017.","author":[{"family":"Kim","given":"M.-Y."},{"family":"Xu","given":"Y."},{"family":"Goebel","given":"R."}],"citation-key":"kimApplyingConvolutionalNeural2017a","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-319-50953-2_20","editor":[{"family":"Otake M.","given":"Bekki D.","suffix":"Satoh K., Kurahashi S., Ota Y."}],"ISBN":"9783319509525","ISSN":"03029743","issued":{"date-parts":[[2017]]},"page":"282-294","publisher":"Springer Verlag","title":"Applying a convolutional neural network to legal question answering","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018432283&doi=10.1007%2f978-3-319-50953-2_20&partnerID=40&md5=b2748ae62238740694ac8d8a278eb81f","volume":"10091 LNCS"},
{"id":"KlingJWBC12","author":[{"family":"Kling","given":"Wolfgang"},{"family":"Jouault","given":"Frédéric"},{"family":"Wagelaar","given":"Dennis"},{"family":"Brambilla","given":"Marco"},{"family":"Cabot","given":"Jordi"}],"citation-key":"KlingJWBC12","collection-title":"LNCS","container-title":"Proc. SLE 2011","issued":{"date-parts":[[2012]]},"page":"180-200","publisher":"Springer","title":"MoScript: A DSL for querying and manipulating model repositories","type":"paper-conference","volume":"6940"},
{"id":"Kochovski2021215","abstract":"The new wave of Artificial Intelligence (AI) implementation has made it possible to deploy and (re)use AI models seamlessly. Modern software engineering techniques make it possible to containerize and orchestrate AI services globally, and across the whole computing continuum from the Cloud to the Edge. However, the data processed by AI services may be subject to various privacy and governance constraints, and thus subject to governmental regulations. In this work we present an advanced Smart Contract that is built to achieve regulatory compliance in cross-border AI model sharing between the European Union and the Republic of Korea. Key feature of the Smart Contract are specially developed oracle adapters that are used to achieve fine-grained control on AI model management. © 2021, Springer Nature Switzerland AG.","author":[{"family":"Kochovski","given":"P."},{"family":"Kum","given":"S."},{"family":"Moon","given":"J."},{"family":"Vujić","given":"A."},{"family":"Stankovski","given":"V."}],"citation-key":"Kochovski2021215","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-92916-9_20","editor":[{"family":"Tserpes K., Banares J.A.","given":"Djemame K.","suffix":"Tuffin B., Altmann J., Ben-Yehuda O.A., Stankovski V."}],"ISBN":"9783030929152","ISSN":"03029743","issued":{"date-parts":[[2021]]},"page":"215-222","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Smart contract for cross-border AI model management","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121856650&doi=10.1007%2f978-3-030-92916-9_20&partnerID=40&md5=d3769d8d66e53d11431cd0b46507fdc8","volume":"13072 LNCS"},
{"id":"koegel2010emfstore","author":[{"family":"Koegel","given":"Maximilian"},{"family":"Helming","given":"Jonas"}],"citation-key":"koegel2010emfstore","container-title":"2010 ACM/IEEE 32nd international conference on software engineering","issued":{"date-parts":[[2010]]},"page":"307-308","title":"EMFStore: a model repository for EMF models","type":"paper-conference","volume":"2"},
{"id":"Kohavi:1995:SCB:1643031.1643047","author":[{"family":"Kohavi","given":"Ron"}],"citation-key":"Kohavi:1995:SCB:1643031.1643047","collection-title":"IJCAI'95","container-title":"Proceedings of the 14th international joint conference on artificial intelligence - volume 2","event-place":"San Francisco, CA, USA","ISBN":"1-55860-363-8","issued":{"date-parts":[[1995]]},"page":"1137-1143","publisher":"Morgan Kaufmann Publishers Inc.","publisher-place":"San Francisco, CA, USA","title":"A study of cross-validation and bootstrap for accuracy estimation and model selection","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=1643031.1643047"},
{"id":"kolahdouz-rahimiEvaluationModelTransformation2014","accessed":{"date-parts":[[2015,6,25]]},"author":[{"family":"Kolahdouz-Rahimi","given":"S."},{"family":"Lano","given":"K."},{"family":"Pillay","given":"S."},{"family":"Troya","given":"J."},{"family":"Van Gorp","given":"P."}],"citation-key":"kolahdouz-rahimiEvaluationModelTransformation2014","container-title":"Science of Computer Programming","DOI":"10.1016/j.scico.2013.07.013","ISSN":"01676423","issued":{"date-parts":[[2014,6]]},"page":"5-40","source":"CrossRef","title":"Evaluation of model transformation approaches for model refactoring","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0167642313001871","volume":"85"},
{"id":"kolovosDifferentModelsModel2009","author":[{"family":"Kolovos","given":"DS"},{"family":"Di Ruscio","given":"D"},{"family":"Pierantonio","given":"A"},{"family":"Paige","given":"RF"}],"citation-key":"kolovosDifferentModelsModel2009","container-title":"Proceedings of the 2009 ICSE workshop on comparison and versioning of software models, CVSM 2009","DOI":"10.1109/CVSM.2009.5071714","ISSN":"null","issued":{"date-parts":[[2009]]},"title":"Different models for model matching: An analysis of approaches to support model differencing","type":"paper-conference"},
{"id":"kolovosDomainspecificLanguagesDesign2019","abstract":"The need for levels of availability and scalability beyond those supported by relational databases has led to the emergence of a new generation of purpose-specific databases grouped under the term NoSQL. In general, NoSQL databases are designed with horizontal scalability as a primary concern and deliver increased availability and fault tolerance at a cost of temporary inconsistency and reduced durability of data. To balance the requirements for data consistency and availability, organisations increasingly migrate towards hybrid data persistence architectures comprising both relational and NoSQL databases. The consensus is that this trend will only become stronger in the future; critical data will continue to be stored in ACID (largely relational) databases while non-critical data will be progressively migrated to high-availability NoSQL databases.","author":[{"family":"Kolovos","given":"Dimitris S"},{"family":"Medhat","given":"Fady"},{"family":"Paige","given":"Richard F"},{"family":"Di Ruscio","given":"Davide"},{"family":"Storm","given":"Tijs","non-dropping-particle":"ven der"},{"family":"Scholze","given":"Sebastian"},{"family":"Zolotas","given":"Athanasios"}],"citation-key":"kolovosDomainspecificLanguagesDesign2019","container-title":"11th Workshop on Modelling in Software Engineering (MiSE’2019) hosted by ICSE 2019","issued":{"date-parts":[[2019]]},"source":"Zotero","title":"Domain-specific Languages for the Design, Deployment and Manipulation of Heterogeneous Databases","type":"paper-conference","URL":"http://vps.diruscio.org/nc/s/tCdFXFci6FWeXjw"},
{"id":"kolovosDomainspecificLanguagesDesign2019a","author":[{"family":"Kolovos","given":"D."},{"family":"Medhat","given":"F."},{"family":"Paige","given":"R."},{"family":"Di Ruscio","given":"D."},{"family":"Van Der Storm","given":"T."},{"family":"Scholze","given":"S."},{"family":"Zolotas","given":"A."}],"citation-key":"kolovosDomainspecificLanguagesDesign2019a","container-title":"Proceedings - 2019 IEEE/ACM 11th International Workshop on Modelling in Software Engineering, MiSE 2019","DOI":"10.1109/MiSE.2019.00021","ISBN":"978-1-72812-231-1","issued":{"date-parts":[[2019]]},"note":"00000","page":"89–92","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Domain-specific languages for the design, deployment and manipulation of heterogeneous databases","type":"paper-conference","URL":"http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8870905"},
{"id":"kolovosEugeniaDisciplinedAutomated2015","accessed":{"date-parts":[[2015,4,23]]},"author":[{"family":"Kolovos","given":"Dimitrios S."},{"family":"García-Domínguez","given":"Antonio"},{"family":"Rose","given":"Louis M."},{"family":"Paige","given":"Richard F."}],"citation-key":"kolovosEugeniaDisciplinedAutomated2015","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-015-0455-3","ISSN":"1619-1366, 1619-1374","issued":{"date-parts":[[2015,2,26]]},"source":"CrossRef","title":"Eugenia: towards disciplined and automated development of GMF-based graphical model editors","title-short":"Eugenia","type":"article-journal","URL":"http://link.springer.com/10.1007/s10270-015-0455-3"},
{"id":"kolovosPreface2015","author":[{"family":"Kolovos","given":"Dimitris"},{"family":"Di Ruscio","given":"Davide"},{"family":"Matragkas","given":"Nicholas"},{"family":"Cuadrado","given":"Jesús Sánchez"},{"family":"Rath","given":"Istvan"},{"family":"Tisi","given":"Massimo"}],"citation-key":"kolovosPreface2015","issued":{"date-parts":[[2015]]},"note":"00000","publisher":"CEUR-WS","title":"Preface","type":"book","URL":"http://ceur-ws.org/","volume":"1406"},
{"id":"kolovosProceedings2ndWorkshop2014","citation-key":"kolovosProceedings2ndWorkshop2014","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Kolovos","given":"Dimitris S."},{"family":"Ruscio","given":"Davide Di"},{"family":"Matragkas","given":"Nicholas Drivalos"},{"family":"Lara","given":"Juan","dropping-particle":"de"},{"family":"Ráth","given":"István"},{"family":"Tisi","given":"Massimo"}],"issued":{"date-parts":[[2014]]},"publisher":"CEUR-WS.org","title":"Proceedings of the 2nd Workshop on Scalability in Model Driven Engineering co-located with the Software Technologies: Applications and Foundations Conference, BigMDE@STAF2014, York, UK, July 24, 2014","type":"book","URL":"http://ceur-ws.org/Vol-1206","volume":"1206"},
{"id":"kolovosProceedings3rdWorkshop2015","citation-key":"kolovosProceedings3rdWorkshop2015","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Kolovos","given":"Dimitris S."},{"family":"Ruscio","given":"Davide Di"},{"family":"Matragkas","given":"Nicholas Drivalos"},{"family":"Cuadrado","given":"Jesús Sánchez"},{"family":"Ráth","given":"István"},{"family":"Tisi","given":"Massimo"}],"issued":{"date-parts":[[2015]]},"publisher":"CEUR-WS.org","title":"Proceedings of the 3rd Workshop on Scalable Model Driven Engineering part of the Software Technologies: Applications and Foundations (STAF 2015) federation of conferences, L'Aquila, Italy, July 23, 2015","type":"book","URL":"http://ceur-ws.org/Vol-1406","volume":"1406"},
{"id":"kolovosProceedings4rdWorkshop2016","citation-key":"kolovosProceedings4rdWorkshop2016","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Kolovos","given":"Dimitris S."},{"family":"Ruscio","given":"Davide Di"},{"family":"Matragkas","given":"Nicholas Drivalos"},{"family":"Cuadrado","given":"Jesús Sánchez"},{"family":"Ráth","given":"István"},{"family":"Tisi","given":"Massimo"}],"issued":{"date-parts":[[2016]]},"publisher":"CEUR-WS.org","title":"Proceedings of the 4rd Workshop on Scalable Model Driven Engineering part of the Software Technologies: Applications and Foundations (STAF 2016) federation of conferences, Vienna, Austria, July 8, 2016","type":"book","URL":"http://ceur-ws.org/Vol-1652","volume":"1652"},
{"id":"koModelTransformationVerification2013","author":[{"family":"Ko","given":"Jong-Won"},{"family":"Chung","given":"Kyung-Yong"},{"family":"Han","given":"Jung-Soo"}],"citation-key":"koModelTransformationVerification2013","container-title":"Multimedia Tools and Applications","DOI":"10.1007/s11042-013-1581-y","issued":{"date-parts":[[2013]]},"title":"Model transformation verification using similarity and graph comparison algorithm","type":"article-journal"},
{"id":"Koseler201915","abstract":"Accompanying the Big Data (BD) paradigm is a resurgence in machine learning (ML). Using ML techniques to work with BD is a complex task, requiring specialized knowledge of the problem space, domain specific concepts, and appropriate ML approaches. However, specialists who possess that knowledge and programming ability are difficult to find and expensive to train. Model-Driven Engineering (MDE) allows developers to implement quality software through modeling using high-level domain specific concepts. In this research, we attempt to fill the gap between MDE and the industrial need for development of ML software by demonstrating the plausibility of applying MDE to BD. Specifically, we apply MDE to the setting of the thriving industry of professional baseball analytics. Our case study involves developing an MDE solution for the binary classification problem of predicting if a baseball pitch will be a fastball. We employ and refine an existing, but untested, ML Domain-Specific Modeling Language (DSML); devise model instances representing prediction features; create a code generation scheme; and evaluate our solution. We show our MDE solution is comparable to the one developed through traditional programming, distribute all our artifacts for public use and extension, and discuss the impact of our work and lessons we learned. Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved","author":[{"family":"Koseler","given":"K."},{"family":"McGraw","given":"K."},{"family":"Stephan","given":"M."}],"citation-key":"Koseler201915","collection-title":"MODELSWARD 2019 - Proceedings of the 7th International Conference on Model-Driven Engineering and Software Development","DOI":"10.5220/0007245800150026","editor":[{"family":"Hammoudi S., Pires L.F.","given":"Selic B."}],"ISBN":"978-989-758-358-2","issued":{"date-parts":[[2019]]},"page":"15-26","publisher":"SciTePress","title":"Realization of a machine learning domain specific modeling language: A baseball analytics case study","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064616104&doi=10.5220%2f0007245800150026&partnerID=40&md5=2af6cd3675123a74658da05ad7f17f81"},
{"id":"koselerRealizationMachineLearning2019a","abstract":"Accompanying the Big Data (BD) paradigm is a resurgence in machine learning (ML). Using ML techniques to work with BD is a complex task, requiring specialized knowledge of the problem space, domain specific concepts, and appropriate ML approaches. However, specialists who possess that knowledge and programming ability are difficult to find and expensive to train. Model-Driven Engineering (MDE) allows developers to implement quality software through modeling using high-level domain specific concepts. In this research, we attempt to fill the gap between MDE and the industrial need for development of ML software by demonstrating the plausibility of applying MDE to BD. Specifically, we apply MDE to the setting of the thriving industry of professional baseball analytics. Our case study involves developing an MDE solution for the binary classification problem of predicting if a baseball pitch will be a fastball. We employ and refine an existing, but untested, ML Domain-Specific Modeling Language (DSML); devise model instances representing prediction features; create a code generation scheme; and evaluate our solution. We show our MDE solution is comparable to the one developed through traditional programming, distribute all our artifacts for public use and extension, and discuss the impact of our work and lessons we learned. Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved","author":[{"family":"Koseler","given":"K."},{"family":"McGraw","given":"K."},{"family":"Stephan","given":"M."}],"citation-key":"koselerRealizationMachineLearning2019a","container-title":"MODELSWARD 2019 - Proceedings of the 7th International Conference on Model-Driven Engineering and Software Development","DOI":"10.5220/0007245800150026","editor":[{"family":"Hammoudi S.","given":"Selic B.","suffix":"Pires L.F."}],"ISBN":"978-989-758-358-2","issued":{"date-parts":[[2019]]},"page":"15-26","publisher":"SciTePress","title":"Realization of a machine learning domain specific modeling language: A baseball analytics case study","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064616104&doi=10.5220%2f0007245800150026&partnerID=40&md5=2af6cd3675123a74658da05ad7f17f81"},
{"id":"koshimaReconciliationFrameworkSupport2013","author":[{"family":"Koshima","given":"Amanuel Alemayehu"},{"family":"Englebert","given":"Vincent"},{"family":"Thiran","given":"Philippe"}],"citation-key":"koshimaReconciliationFrameworkSupport2013","container-title":"Domain Engineering","DOI":"10.1007/978-3-642-36654-3_10","issued":{"date-parts":[[2013]]},"page":"239–259","title":"A Reconciliation Framework to Support Cooperative Work with DSM","type":"article-journal"},
{"id":"kotsiantis2007supervised","author":[{"family":"Kotsiantis","given":"Sotiris B"},{"family":"Zaharakis","given":"I"},{"family":"Pintelas","given":"P"}],"citation-key":"kotsiantis2007supervised","container-title":"Emerging artificial intelligence applications in computer engineering","issued":{"date-parts":[[2007]]},"page":"3-24","title":"Supervised machine learning: A review of classification techniques","type":"article-journal","volume":"160"},
{"id":"Kourouklidis2021160","abstract":"Once a machine learning (ML) model is produced and used for commercial purposes, it is desirable to continuously monitor it for any potential performance degradation. Domain experts in the area of ML, commonly lack the required expertise in the area of software engineering, needed to implement a robust and scalable monitoring solution. This paper presents an approach based on model-driven engineering (MDE) principles, for detecting and responding to events that can affect a ML model's performance. The proposed solution allows ML experts to schedule the execution of drift detecting algorithms on a computing cluster and receive email notifications of the outcome without requiring extensive software engineering knowledge. © 2021 IEEE.","author":[{"family":"Kourouklidis","given":"P."},{"family":"Kolovos","given":"D."},{"family":"Noppen","given":"J."},{"family":"Matragkas","given":"N."}],"citation-key":"Kourouklidis2021160","collection-title":"Companion Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021","DOI":"10.1109/MODELS-C53483.2021.00028","ISBN":"978-1-66542-484-4","issued":{"date-parts":[[2021]]},"page":"160-164","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A model-driven engineering approach for monitoring machine learning models","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124011155&doi=10.1109%2fMODELS-C53483.2021.00028&partnerID=40&md5=4a0eb7e5a44eaf3d9f83e539a7e9763f"},
{"id":"kramerSelfManagedSystemsArchitectural2007","accessed":{"date-parts":[[2016,9,24]]},"author":[{"family":"Kramer","given":"Jeff"},{"family":"Magee","given":"Jeff"}],"citation-key":"kramerSelfManagedSystemsArchitectural2007","DOI":"10.1109/FOSE.2007.19","ISBN":"978-0-7695-2829-8","issued":{"date-parts":[[2007,5]]},"page":"259-268","publisher":"IEEE","source":"CrossRef","title":"Self-Managed Systems: an Architectural Challenge","title-short":"Self-Managed Systems","type":"paper-conference","URL":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4221625"},
{"id":"krauseMetamodelSpecificCoupledEvolution2013","author":[{"family":"Krause","given":"Christian"},{"family":"Dyck","given":"Johannes"},{"family":"Giese","given":"Holger"}],"citation-key":"krauseMetamodelSpecificCoupledEvolution2013","container-title":"Theory and Practice of Model Transformations","DOI":"10.1007/978-3-642-38883-5_10","issued":{"date-parts":[[2013]]},"page":"76–91","title":"Metamodel-Specific Coupled Evolution Based on Dynamically Typed Graph Transformations","type":"article-journal","volume":"7909"},
{"id":"Kravchenko201861","abstract":"Web blocks such as navigation menus, advertisements, headers, and footers are key components of Web pages that define not only the appearance, but also the way humans interact with different parts of the page. For machines, however, classifying and interacting with these blocks is a surprisingly hard task. Yet, Web block classification has varied applications in the fields of wrapper induction, assistance to visually impaired people, Web adaptation, Web page topic clustering, and Web search. Our system for Web block classification, BERyL, performs automated classification of Web blocks through a combination of machine learning and declarative, model-driven feature extraction based on Datalog rules. BERyL uses refined feature sets for the classification of individual blocks to achieve accurate classification for all the block types we have observed so far. The high accuracy is achieved through these carefully selected features, some even tuned to the specific block type. At the same time, BERyL avoids a high cost of feature engineering through a model-driven rather than programmatic approach to extracting features. Not only does this reduce the time for feature engineering, the model-driven, declarative approach also allows for semi-automatic optimisation of the feature extraction system. We perform evaluation to validate these claims on a selected range of Web blocks. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.","author":[{"family":"Kravchenko","given":"A."}],"citation-key":"Kravchenko201861","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-662-58039-4_4","ISSN":"03029743","issued":{"date-parts":[[2018]]},"page":"61-78","publisher":"Springer Verlag","title":"BERyL: A system for web block classification","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053482506&doi=10.1007%2f978-3-662-58039-4_4&partnerID=40&md5=8360a2a22a5b7df34743956d1fd91a55","volume":"10990 LNCS"},
{"id":"kravchenkoBERyLSystemWeb2018a","abstract":"Web blocks such as navigation menus, advertisements, headers, and footers are key components of Web pages that define not only the appearance, but also the way humans interact with different parts of the page. For machines, however, classifying and interacting with these blocks is a surprisingly hard task. Yet, Web block classification has varied applications in the fields of wrapper induction, assistance to visually impaired people, Web adaptation, Web page topic clustering, and Web search. Our system for Web block classification, BERyL, performs automated classification of Web blocks through a combination of machine learning and declarative, model-driven feature extraction based on Datalog rules. BERyL uses refined feature sets for the classification of individual blocks to achieve accurate classification for all the block types we have observed so far. The high accuracy is achieved through these carefully selected features, some even tuned to the specific block type. At the same time, BERyL avoids a high cost of feature engineering through a model-driven rather than programmatic approach to extracting features. Not only does this reduce the time for feature engineering, the model-driven, declarative approach also allows for semi-automatic optimisation of the feature extraction system. We perform evaluation to validate these claims on a selected range of Web blocks. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.","author":[{"family":"Kravchenko","given":"A."}],"citation-key":"kravchenkoBERyLSystemWeb2018a","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-662-58039-4_4","ISSN":"03029743","issued":{"date-parts":[[2018]]},"page":"61-78","publisher":"Springer Verlag","title":"BERyL: A System for Web Block Classification","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053482506&doi=10.1007%2f978-3-662-58039-4_4&partnerID=40&md5=8360a2a22a5b7df34743956d1fd91a55","volume":"10990 LNCS"},
{"id":"Krenker11","author":[{"family":"Krenker","given":"Andrej"},{"family":"Bester","given":"Janez"},{"family":"Kos","given":"Andrej"}],"citation-key":"Krenker11","container-title":"Artificial neural networks","editor":[{"family":"Suzuki","given":"Kenji"}],"event-place":"Rijeka","issued":{"date-parts":[[2011]]},"publisher":"IntechOpen","publisher-place":"Rijeka","title":"Introduction to the artificial neural networks","type":"chapter"},
{"id":"Krishnan2017","abstract":"When a Deep Neural Network makes a misprediction, it can be challenging for a developer to understand why. While there are many models for interpretability in terms of predictive features, it may be more natural to isolate a small set of training examples that have the greatest influence on the prediction. However, it is often the case that every training example contributes to a prediction in some way but with varying degrees of responsibility. We present Partition Aware Local Model (PALM), which is a tool that learns and summarizes this responsibility structure to aide machine learning debugging. PALM approximates a complex model (e.g., a deep neural network) using a two-part surrogate model: a meta-model that partitions the training data, and a set of sub-models that approximate the patterns within each partition. These sub-models can be arbitrarily complex to capture intricate local patterns. However, the metamodel is constrained to be a decision tree. This way the user can examine the structure of the meta-model, determine whether the rules match intuition, and link problematic test examples to responsible training data efficiently. Queries to PALM are nearly 30x faster than nearest neighbor queries for identifying relevant data, which is a key property for interactive applications. © 2017 ACM.","author":[{"family":"Krishnan","given":"S."},{"family":"Wu","given":"E."}],"citation-key":"Krishnan2017","collection-title":"Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics, HILDA 2017","DOI":"10.1145/3077257.3077271","ISBN":"978-1-4503-5029-7","issued":{"date-parts":[[2017]]},"publisher":"Association for Computing Machinery, Inc","title":"PALM: Machine learning explanations for iterative debugging","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021684051&doi=10.1145%2f3077257.3077271&partnerID=40&md5=42fbac18841848cdf43e61e7a79faee1"},
{"id":"krishnanPALMMachineLearning2017a","abstract":"When a Deep Neural Network makes a misprediction, it can be challenging for a developer to understand why. While there are many models for interpretability in terms of predictive features, it may be more natural to isolate a small set of training examples that have the greatest influence on the prediction. However, it is often the case that every training example contributes to a prediction in some way but with varying degrees of responsibility. We present Partition Aware Local Model (PALM), which is a tool that learns and summarizes this responsibility structure to aide machine learning debugging. PALM approximates a complex model (e.g., a deep neural network) using a two-part surrogate model: a meta-model that partitions the training data, and a set of sub-models that approximate the patterns within each partition. These sub-models can be arbitrarily complex to capture intricate local patterns. However, the metamodel is constrained to be a decision tree. This way the user can examine the structure of the meta-model, determine whether the rules match intuition, and link problematic test examples to responsible training data efficiently. Queries to PALM are nearly 30x faster than nearest neighbor queries for identifying relevant data, which is a key property for interactive applications. © 2017 ACM.","author":[{"family":"Krishnan","given":"S."},{"family":"Wu","given":"E."}],"citation-key":"krishnanPALMMachineLearning2017a","container-title":"Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics, HILDA 2017","DOI":"10.1145/3077257.3077271","ISBN":"978-1-4503-5029-7","issued":{"date-parts":[[2017]]},"publisher":"Association for Computing Machinery, Inc","title":"PALM: Machine learning explanations for iterative debugging","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021684051&doi=10.1145%2f3077257.3077271&partnerID=40&md5=42fbac18841848cdf43e61e7a79faee1"},
{"id":"krupitzerSurveyEngineeringApproaches2015","accessed":{"date-parts":[[2016,1,12]]},"author":[{"family":"Krupitzer","given":"Christian"},{"family":"Roth","given":"Felix Maximilian"},{"family":"VanSyckel","given":"Sebastian"},{"family":"Schiele","given":"Gregor"},{"family":"Becker","given":"Christian"}],"citation-key":"krupitzerSurveyEngineeringApproaches2015","container-title":"Pervasive and Mobile Computing","DOI":"10.1016/j.pmcj.2014.09.009","ISSN":"15741192","issued":{"date-parts":[[2015,2]]},"page":"184-206","source":"CrossRef","title":"A survey on engineering approaches for self-adaptive systems","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S157411921400162X","volume":"17"},
{"id":"kuangNewSearchEngine","abstract":"The original Yahoo! search engine consists of manually organized topic hierarchy of webpages for easy browsing. Modern search engines (such as Google and Bing), on the other hand, return a flat list of webpages based on keywords. It would be ideal if hierarchical browsing and keyword search can be seamlessly combined. The main difficulty in doing so is to automatically (i.e., not manually) classify and rank a massive number of webpages into various hierarchies (such as topics, media types, regions of the world). In this paper we report our attempt towards building this integrated search engine, called SEE (Search Engine with hiErarchy). We implement a hierarchical classification system based on Support Vector Machines, and embed it in SEE. We also design a novel user interface that allows users to dynamically adjust their desire for a higher accuracy vs. more results in any (sub)category of the hierarchy. Though our current search engine is still small (indexing about 1.2 million webpages), the results, including a small user study, have shown a great promise for integrating such techniques in the next-generation search engine.","author":[{"family":"Kuang","given":"Da"},{"family":"Li","given":"Xiao"},{"family":"Ling","given":"Charles X"}],"citation-key":"kuangNewSearchEngine","page":"6","source":"Zotero","title":"A New Search Engine Integrating Hierarchical Browsing and Keyword Search","type":"article-journal"},
{"id":"kuhn2005enriching","author":[{"family":"Kuhn","given":"Adrian"},{"family":"Ducasse","given":"Stéphane"},{"family":"Girba","given":"Tudor"}],"citation-key":"kuhn2005enriching","container-title":"12th working conference on reverse engineering (WCRE'05)","issued":{"date-parts":[[2005]]},"page":"10-pp","title":"Enriching reverse engineering with semantic clustering","type":"paper-conference"},
{"id":"kulaDevelopersUpdateTheir2018","accessed":{"date-parts":[[2021,1,9]]},"author":[{"family":"Kula","given":"Raula Gaikovina"},{"family":"German","given":"Daniel M."},{"family":"Ouni","given":"Ali"},{"family":"Ishio","given":"Takashi"},{"family":"Inoue","given":"Katsuro"}],"citation-key":"kulaDevelopersUpdateTheir2018","container-title":"Empirical Software Engineering","container-title-short":"Empir Software Eng","DOI":"10.1007/s10664-017-9521-5","ISSN":"1382-3256, 1573-7616","issue":"1","issued":{"date-parts":[[2018,2]]},"note":"00001","page":"384-417","source":"DOI.org (Crossref)","title":"Do developers update their library dependencies?: An empirical study on the impact of security advisories on library migration","title-short":"Do developers update their library dependencies?","type":"article-journal","URL":"http://link.springer.com/10.1007/s10664-017-9521-5","volume":"23"},
{"id":"kullbackInformationSufficiency1951","author":[{"family":"Kullback","given":"S."},{"family":"Leibler","given":"R. A."}],"citation-key":"kullbackInformationSufficiency1951","container-title":"Ann. Math. Statist.","issue":"1","issued":{"date-parts":[[1951]]},"page":"79-86","title":"On information and sufficiency","type":"article-journal","volume":"22"},
{"id":"kumarToolRecommenderSystem2021","abstract":"Abstract\n \n Background\n Galaxy is a web-based and open-source scientific data-processing platform. Researchers compose pipelines in Galaxy to analyse scientific data. These pipelines, also known as workflows, can be complex and difficult to create from thousands of tools, especially for researchers new to Galaxy. To help researchers with creating workflows, a system is developed to recommend tools that can facilitate further data analysis.\n \n \n Findings\n A model is developed to recommend tools using a deep learning approach by analysing workflows composed by researchers on the European Galaxy server. The higher-order dependencies in workflows, represented as directed acyclic graphs, are learned by training a gated recurrent units neural network, a variant of a recurrent neural network. In the neural network training, the weights of tools used are derived from their usage frequencies over time and the sequences of tools are uniformly sampled from training data. Hyperparameters of the neural network are optimized using Bayesian optimization. Mean accuracy of 98% in recommending tools is achieved for the top-1 metric.\n \n \n Conclusions\n The model is accessed by a Galaxy API to provide researchers with recommended tools in an interactive manner using multiple user interface integrations on the European Galaxy server. High-quality and highly used tools are shown at the top of the recommendations. The scripts and data to create the recommendation system are available under MIT license at https://github.com/anuprulez/galaxy_tool_recommendation.","accessed":{"date-parts":[[2022,3,8]]},"author":[{"family":"Kumar","given":"Anup"},{"family":"Rasche","given":"Helena"},{"family":"Grüning","given":"Björn"},{"family":"Backofen","given":"Rolf"}],"citation-key":"kumarToolRecommenderSystem2021","container-title":"GigaScience","DOI":"10.1093/gigascience/giaa152","ISSN":"2047-217X","issue":"1","issued":{"date-parts":[[2021,1,6]]},"note":"00003","page":"giaa152","source":"DOI.org (Crossref)","title":"Tool recommender system in Galaxy using deep learning","type":"article-journal","URL":"https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giaa152/6065533","volume":"10"},
{"id":"kuselRealityCheckModel","author":[{"family":"Kusel","given":"A"},{"family":"Schonbock","given":"J"},{"family":"Wimmer","given":"M"},{"family":"Retschitzegger","given":"W"},{"family":"Schwinger","given":"W"},{"family":"Kappel","given":"G"}],"citation-key":"kuselRealityCheckModel","title":"Reality Check for Model Transformation Reuse: The ATL Transformation Zoo Case Study","type":"article-journal"},
{"id":"kuselReuseModeltomodelTransformation2015","accessed":{"date-parts":[[2015,6,17]]},"author":[{"family":"Kusel","given":"A."},{"family":"Schönböck","given":"J."},{"family":"Wimmer","given":"M."},{"family":"Kappel","given":"G."},{"family":"Retschitzegger","given":"W."},{"family":"Schwinger","given":"W."}],"citation-key":"kuselReuseModeltomodelTransformation2015","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-013-0343-7","ISSN":"1619-1366, 1619-1374","issue":"2","issued":{"date-parts":[[2015,5]]},"page":"537-572","source":"CrossRef","title":"Reuse in model-to-model transformation languages: are we there yet?","title-short":"Reuse in model-to-model transformation languages","type":"article-journal","URL":"http://link.springer.com/10.1007/s10270-013-0343-7","volume":"14"},
{"id":"Kushwaha202016","abstract":"Artificial Intelligence has been increasingly gaining acceptance across advanced functions in numerous fields and industries. This includes marketing, customer support, and leads generation in healthcare, transportation, education, and off late in e-commerce. Machine learning as a subset of artificial intelligence techniques provides various algorithms that enable machines to learn from historical data and make realtime predictions on numbers and texts. Most of the businesses nowadays are trying to increase their reach and making sure that they are available to cater to the customers when they need help. This also enables the companies to market and respond to the queries of potential customers on a realtime basis. Chatter robots or chatbot is one such application of machine learning which allows the business to provide round the clock support to customers and potential leads for marketing questions. Most of the business fail to venture in the domain of hosting chatbot on the website as they do not have enough conversational data with them to train the machine learning algorithm and wait for years to collect enough sample. With the proposed language model-driven chatbots, businesses starting fresh in the domain of the hosting this application can use the user-generated content on social media to fuel the backend framework for the chatbots and start hosting the application. © 2020, IFIP International Federation for Information Processing.","author":[{"family":"Kushwaha","given":"A.K."},{"family":"Kar","given":"A.K."}],"citation-key":"Kushwaha202016","container-title":"IFIP Advances in Information and Communication Technology","DOI":"10.1007/978-3-030-64849-7_3","editor":[{"family":"Sharma S.K., Dwivedi Y.K.","given":"Metri B.","suffix":"Rana N.P."}],"ISBN":"9783030648480","ISSN":"18684238","issued":{"date-parts":[[2020]]},"page":"16-28","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Language model-driven chatbot for business to address marketing and selection of products","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098232302&doi=10.1007%2f978-3-030-64849-7_3&partnerID=40&md5=cc73673959a884bf2ac550d8b425547d","volume":"617"},
{"id":"Kusiak201590","abstract":"The main objective of the metamodelling is replacing the model of analysed process by its simple (with respect to the computation time) approximation. Metamodel gives a significant reduction of computation time of considered process simulation, as well as its further analysis (sensitivity analysis, optimization, etc.). The paper discusses the idea of metamodelling and compares the effectiveness of three techniques: Response Surface Methodology (RSM), Kriging method and Artificial Neural Network (ANN) applied to the benchmark functions. An example of the use of the considered metamodelling techniques in optimization of the problem of laminar cooling of rolled Dual Phase (DP) steel strips is presented. Metamodelling and optimization of a real industrial metal forming problems seems a novel approach in the field of research on Artificial Intelligence and Optimization practical applications. © 2015 Elsevier B.V. All rights reserved.","author":[{"family":"Kusiak","given":"J."},{"family":"Sztangret","given":"Ł."},{"family":"Pietrzyk","given":"M."}],"citation-key":"Kusiak201590","container-title":"Advances in Engineering Software","DOI":"10.1016/j.advengsoft.2015.02.002","ISSN":"09659978","issued":{"date-parts":[[2015]]},"page":"90-97","publisher":"Elsevier Ltd","title":"Effective strategies of metamodelling of industrial metallurgical processes","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940518621&doi=10.1016%2fj.advengsoft.2015.02.002&partnerID=40&md5=3590da91f08facee7200c8200bd810cd","volume":"89"},
{"id":"kusiakEffectiveStrategiesMetamodelling2015a","abstract":"The main objective of the metamodelling is replacing the model of analysed process by its simple (with respect to the computation time) approximation. Metamodel gives a significant reduction of computation time of considered process simulation, as well as its further analysis (sensitivity analysis, optimization, etc.). The paper discusses the idea of metamodelling and compares the effectiveness of three techniques: Response Surface Methodology (RSM), Kriging method and Artificial Neural Network (ANN) applied to the benchmark functions. An example of the use of the considered metamodelling techniques in optimization of the problem of laminar cooling of rolled Dual Phase (DP) steel strips is presented. Metamodelling and optimization of a real industrial metal forming problems seems a novel approach in the field of research on Artificial Intelligence and Optimization practical applications. © 2015 Elsevier B.V. All rights reserved.","author":[{"family":"Kusiak","given":"J."},{"family":"Sztangret","given":"Ł."},{"family":"Pietrzyk","given":"M."}],"citation-key":"kusiakEffectiveStrategiesMetamodelling2015a","container-title":"Advances in Engineering Software","DOI":"10.1016/j.advengsoft.2015.02.002","ISSN":"09659978","issued":{"date-parts":[[2015]]},"page":"90-97","publisher":"Elsevier Ltd","title":"Effective strategies of metamodelling of industrial metallurgical processes","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940518621&doi=10.1016%2fj.advengsoft.2015.02.002&partnerID=40&md5=3590da91f08facee7200c8200bd810cd","volume":"89"},
{"id":"kutsche2008bizycle","author":[{"family":"Kutsche","given":"Ralf"},{"family":"Milanovic","given":"Nikola"},{"family":"Bauhoff","given":"Gregor"},{"family":"Baum","given":"Timo"},{"family":"Cartsburg","given":"Mario"},{"family":"Kumpe","given":"Daniel"},{"family":"Widiker","given":"Jürgen"}],"citation-key":"kutsche2008bizycle","container-title":"Proceedings of the MDTPI at ECMDA","issued":{"date-parts":[[2008]]},"title":"Bizycle: Model-based interoperability platform for software and data integration","type":"article-journal","volume":"430"},
{"id":"kyriazisSmartAutonomousReliable2013","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Kyriazis","given":"Dimosthenis"},{"family":"Varvarigou","given":"Theodora"}],"citation-key":"kyriazisSmartAutonomousReliable2013","container-title":"Procedia Computer Science","DOI":"10.1016/j.procs.2013.09.059","ISSN":"18770509","issued":{"date-parts":[[2013]]},"page":"442-448","source":"CrossRef","title":"Smart, Autonomous and Reliable Internet of Things","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S1877050913008521","volume":"21"},
{"id":"L04","abstract":"We present the R2D2 redundancy detector. R2D2 identifies redundant code fragments in large software systems written in Lisp. For each pair of code fragments, R2D2 uses a combination of techniques ranging from syntax-based analysis to semantics-based analysis, that detects positive and negative evidences regarding the redundancy of the analyzed code fragments. These evidences are combined according to a well-defined model and sufficiently redundant fragments are reported to the user. R2D2 explores several techniques and heuristics to operate within reasonable time and space bounds and is designed to be extensible.","author":[{"family":"Leitão","given":"António Menezes"}],"citation-key":"L04","container-title":"Software Quality Journal","DOI":"10.1023/B:SQJO.0000039793.31052.72","ISSN":"1573-1367","issue":"4","issued":{"date-parts":[[2004,12,1]]},"page":"361-382","title":"Detection of redundant code using R2D2","type":"article-journal","URL":"https://doi.org/10.1023/B:SQJO.0000039793.31052.72","volume":"12"},
{"id":"lacavaEvaluatingRecommenderSystems2021","abstract":"Abstract\n \n Motivation\n Many researchers with domain expertise are unable to easily apply machine learning (ML) to their bioinformatics data due to a lack of ML and/or coding expertise. Methods that have been proposed thus far to automate ML mostly require programming experience as well as expert knowledge to tune and apply the algorithms correctly. Here, we study a method of automating biomedical data science using a web-based AI platform to recommend model choices and conduct experiments. We have two goals in mind: first, to make it easy to construct sophisticated models of biomedical processes; and second, to provide a fully automated AI agent that can choose and conduct promising experiments for the user, based on the user’s experiments as well as prior knowledge. To validate this framework, we conduct an experiment on 165 classification problems, comparing to state-of-the-art, automated approaches. Finally, we use this tool to develop predictive models of septic shock in critical care patients.\n \n \n Results\n We find that matrix factorization-based recommendation systems outperform metalearning methods for automating ML. This result mirrors the results of earlier recommender systems research in other domains. The proposed AI is competitive with state-of-the-art automated ML methods in terms of choosing optimal algorithm configurations for datasets. In our application to prediction of septic shock, the AI-driven analysis produces a competent ML model (AUROC 0.85±0.02) that performs on par with state-of-the-art deep learning results for this task, with much less computational effort.\n \n \n Availability and implementation\n PennAI is available free of charge and open-source. It is distributed under the GNU public license (GPL) version 3.\n \n \n Supplementary information\n Supplementary data are available at Bioinformatics online.","accessed":{"date-parts":[[2022,3,8]]},"author":[{"family":"La Cava","given":"William"},{"family":"Williams","given":"Heather"},{"family":"Fu","given":"Weixuan"},{"family":"Vitale","given":"Steve"},{"family":"Srivatsan","given":"Durga"},{"family":"Moore","given":"Jason H"}],"citation-key":"lacavaEvaluatingRecommenderSystems2021","container-title":"Bioinformatics","DOI":"10.1093/bioinformatics/btaa698","editor":[{"family":"Wren","given":"Jonathan"}],"ISSN":"1367-4803, 1460-2059","issue":"2","issued":{"date-parts":[[2021,4,19]]},"note":"00003","page":"250-256","source":"DOI.org (Crossref)","title":"Evaluating recommender systems for AI-driven biomedical informatics","type":"article-journal","URL":"https://academic.oup.com/bioinformatics/article/37/2/250/5885079","volume":"37"},
{"id":"Lai2020","abstract":"To support human decision making with machine learning models, we often need to elucidate patterns embedded in the models that are unsalient, unknown, or counterintuitive to humans. While existing approaches focus on explaining machine predictions with real-time assistance, we explore model-driven tutorials to help humans understand these patterns in a train- ing phase. We consider both tutorials with guidelines from scientific papers, analogous to current practices of science communication, and automatically selected examples from training data with explanations. We use deceptive review detection as a testbed and conduct large-scale, randomized human-subject experiments to examine the effectiveness of such tutorials. We find that tutorials indeed improve human performance, with and without real-time assistance. In particular, although deep learning provides superior predictive performance than simple models, tutorials and explanations from simple models are more useful to humans. Our work suggests future directions for human-centered tutorials and explanations towards a synergy between humans and AI. © 2020 ACM.","author":[{"family":"Lai","given":"V."},{"family":"Liu","given":"H."},{"family":"Tan","given":"C."}],"citation-key":"Lai2020","collection-title":"Conference on Human Factors in Computing Systems - Proceedings","DOI":"10.1145/3313831.3376873","ISBN":"978-1-4503-6708-0","issued":{"date-parts":[[2020]]},"publisher":"Association for Computing Machinery","title":"\"Why is 'Chicago' deceptive?\" towards building model-driven tutorials for humans","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091294383&doi=10.1145%2f3313831.3376873&partnerID=40&md5=bada1e25bc2db2592f9d239ab6280ada"},
{"id":"laiRobustOnlinePath2016","accessed":{"date-parts":[[2016,3,17]]},"author":[{"family":"Lai","given":"Shupeng"},{"family":"Wang","given":"Kangli"},{"family":"Qin","given":"Hailong"},{"family":"Cui","given":"Jin Q."},{"family":"Chen","given":"Ben M."}],"citation-key":"laiRobustOnlinePath2016","container-title":"Control Theory and Technology","DOI":"10.1007/s11768-016-6007-8","ISSN":"2095-6983, 2198-0942","issue":"1","issued":{"date-parts":[[2016,2]]},"page":"83-96","source":"CrossRef","title":"A robust online path planning approach in cluttered environments for micro rotorcraft drones","type":"article-journal","URL":"http://link.springer.com/10.1007/s11768-016-6007-8","volume":"14"},
{"id":"laiWhyChicagoDeceptive2020a","abstract":"To support human decision making with machine learning models, we often need to elucidate patterns embedded in the models that are unsalient, unknown, or counterintuitive to humans. While existing approaches focus on explaining machine predictions with real-time assistance, we explore model-driven tutorials to help humans understand these patterns in a train- ing phase. We consider both tutorials with guidelines from scientific papers, analogous to current practices of science communication, and automatically selected examples from training data with explanations. We use deceptive review detection as a testbed and conduct large-scale, randomized human-subject experiments to examine the effectiveness of such tutorials. We find that tutorials indeed improve human performance, with and without real-time assistance. In particular, although deep learning provides superior predictive performance than simple models, tutorials and explanations from simple models are more useful to humans. Our work suggests future directions for human-centered tutorials and explanations towards a synergy between humans and AI. © 2020 ACM.","author":[{"family":"Lai","given":"V."},{"family":"Liu","given":"H."},{"family":"Tan","given":"C."}],"citation-key":"laiWhyChicagoDeceptive2020a","container-title":"Conference on Human Factors in Computing Systems - Proceedings","DOI":"10.1145/3313831.3376873","ISBN":"978-1-4503-6708-0","issued":{"date-parts":[[2020]]},"publisher":"Association for Computing Machinery","title":"\"Why is 'Chicago' deceptive?\" Towards Building Model-Driven Tutorials for Humans","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091294383&doi=10.1145%2f3313831.3376873&partnerID=40&md5=bada1e25bc2db2592f9d239ab6280ada"},
{"id":"Lakshminarayan20192043","abstract":"Big data characterized by variety can be divided into 3 principal categories: numeric structured data, semi-structured data, and unstructured multimedia data involving audio, video, and text. Decision making requires multiple analytical engines suitable for each type of data, programming languages, algorithms, visualization tools, and user interfaces. More often than not, industrial analytics is conducted ad hoc by lashing together analytics components such as distributed data sources, analytics engines, and algorithms. This kind of piecemeal approach ignores scale, security, governance, reliability, model management and fault tolerance that are paramount for industrial strength analytics. A unified, versatile, and robust architecture that combines various components in a single integrated platform is the need of the hour. Teradata Vantage (TD Vantage) is such a platform for delivering production quality enterprise analytics at scale. In this paper, we outline the proposed TD Vantage (available in the market and under continuous development) that unifies data, engines, and algorithms operating in a seamless symphony. We will demonstrate its capabilities through three proofs of concept biz: image data using TensorFlow, text data using Spark, and transaction data using Aster (now renamed Machine Learning Engine or MLE), with Teradata orchestrating interactions among the various components. © 2019 IEEE.","author":[{"family":"Lakshminarayan","given":"C."},{"family":"Ramakrishnan","given":"T."},{"family":"Al-Omari","given":"A."},{"family":"Bouaziz","given":"K."},{"family":"Ahmad","given":"F."},{"family":"Raghavan","given":"S."},{"family":"Agarwal","given":"P."}],"citation-key":"Lakshminarayan20192043","collection-title":"Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019","DOI":"10.1109/BigData47090.2019.9006321","editor":[{"family":"Baru C., Huan J.","given":"Khan L.","suffix":"Hu X.T., Ak R., Tian Y., Barga R., Zaniolo C., Lee K., Ye Y.F."}],"ISBN":"978-1-72810-858-2","issued":{"date-parts":[[2019]]},"page":"2043-2046","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Enterprise-wide machine learning using teradata vantage: An integrated analytics platform","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081411837&doi=10.1109%2fBigData47090.2019.9006321&partnerID=40&md5=d3d6544c72ee144263f30a2ca4285b5f"},
{"id":"Lakshminarayan20196110","abstract":"Ease-of-use analytics at scale is the holy grail of industrial strength machine learning. In order to reap benefits from the mother-lode of business related data; tools, technologies, and analytical functions should operate in perpetual symphony to derive insightful business outcomes. While there have been advances in APIs, algorithms, and user interfaces, building an end to end workflow spanning data ingestion, data preparation, model training, model scoring, visualization and finally continuous improvement and model management received limited investment. In this paper we demonstrate an analytical workflow that integrates multiple analytical tools and techniques for image recognition wrapped in the model management framework. As analytics in industry is maturing, analytics implementations are no longer one-off, but are components of Analytics Operations known as AnalyticsOps. We introduce the notion of Model Quality Index (MQI) to track model performance. The MQI is similar to Process Capability Index (PCI) common in 6 σprograms in manufacturing. Our solution combines relational databases (Teradata DB), Machine Learning (Teradata/Aster), Deep Learning (TensorFlow), Hadoop Distributed File System (HDFS), and user interface tools over a communication fabric (Teradata QueryGrid). In particular, we demonstrate a hand written word recognition use-case for an enterprise customer cast in a model management workflow for repeatable deployments across a range of businesses. © 2019 IEEE.","author":[{"family":"Lakshminarayan","given":"C."},{"family":"Ramakrishnan","given":"T."},{"family":"Al-Omari","given":"A."},{"family":"Bouaziz","given":"K."},{"family":"Ahmad","given":"F."},{"family":"Raghavan","given":"S."},{"family":"Agarwal","given":"P."}],"citation-key":"Lakshminarayan20196110","collection-title":"Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019","DOI":"10.1109/BigData47090.2019.9005445","editor":[{"family":"Baru C., Huan J.","given":"Khan L.","suffix":"Hu X.T., Ak R., Tian Y., Barga R., Zaniolo C., Lee K., Ye Y.F."}],"ISBN":"978-1-72810-858-2","issued":{"date-parts":[[2019]]},"page":"6110-6112","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Model management and handwritten character recognition: An enterprise solution","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081399232&doi=10.1109%2fBigData47090.2019.9005445&partnerID=40&md5=95ceaaa645cf245fcde4f21c0fc96734"},
{"id":"lalandaAutonomicComputing2013","accessed":{"date-parts":[[2016,9,29]]},"author":[{"family":"Lalanda","given":"Philippe"},{"family":"McCann","given":"Julie A."},{"family":"Diaconescu","given":"Ada"}],"citation-key":"lalandaAutonomicComputing2013","collection-title":"Undergraduate Topics in Computer Science","event-place":"London","ISBN":"978-1-4471-5006-0 978-1-4471-5007-7","issued":{"date-parts":[[2013]]},"publisher":"Springer London","publisher-place":"London","source":"CrossRef","title":"Autonomic Computing","type":"book","URL":"http://link.springer.com/10.1007/978-1-4471-5007-7"},
{"id":"Landauer1998","author":[{"family":"Landauer","given":"T.K."},{"family":"Foltz","given":"P.W."},{"family":"Laham","given":"D."}],"citation-key":"Landauer1998","container-title":"Discourse processes","issued":{"date-parts":[[1998]]},"page":"259-284","title":"An introduction to latent semantic analysis","type":"article-journal","volume":"25"},
{"id":"landauer2006latent","author":[{"family":"Landauer","given":"Thomas K"}],"citation-key":"landauer2006latent","issued":{"date-parts":[[2006]]},"publisher":"Wiley Online Library","title":"Latent semantic analysis","type":"book"},
{"id":"langerEMFProfilesLightweight2012","accessed":{"date-parts":[[2015,9,23]]},"author":[{"family":"Langer","given":"Philip"},{"family":"Wieland","given":"Konrad"},{"family":"Wimmer","given":"Manuel"},{"family":"Cabot","given":"Jordi"}],"citation-key":"langerEMFProfilesLightweight2012","container-title":"The Journal of Object Technology","DOI":"10.5381/jot.2012.11.1.a8","ISSN":"1660-1769","issue":"1","issued":{"date-parts":[[2012]]},"page":"8:1","source":"CrossRef","title":"EMF Profiles: A Lightweight Extension Approach for EMF Models.","title-short":"EMF Profiles","type":"article-journal","URL":"http://www.jot.fm/contents/issue_2012_04/article8.html","volume":"11"},
{"id":"langerPosterioriOperationDetection2013","accessed":{"date-parts":[[2015,6,24]]},"author":[{"family":"Langer","given":"Philip"},{"family":"Wimmer","given":"Manuel"},{"family":"Brosch","given":"Petra"},{"family":"Herrmannsdörfer","given":"Markus"},{"family":"Seidl","given":"Martina"},{"family":"Wieland","given":"Konrad"},{"family":"Kappel","given":"Gerti"}],"citation-key":"langerPosterioriOperationDetection2013","container-title":"Journal of Systems and Software","DOI":"10.1016/j.jss.2012.09.037","ISSN":"01641212","issue":"2","issued":{"date-parts":[[2013,2]]},"page":"551-566","source":"CrossRef","title":"A posteriori operation detection in evolving software models","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0164121212002762","volume":"86"},
{"id":"Langford2021182","abstract":"Increasingly, safety-critical systems include artificial intelligence and machine learning components (i.e., Learning-Enabled Components (LECs)). However, when behavior is learned in a training environment that fails to fully capture real-world phenomena, the response of an LEC to untrained phenomena is uncertain, and therefore cannot be assured as safe. Automated methods are needed for self-assessment and adaptation to decide when learned behavior can be trusted. This work introduces a model-driven approach to manage self-adaptation of a Learning-Enabled System (LES) to account for run-time contexts for which the learned behavior of LECs cannot be trusted. The resulting framework enables an LES to monitor and evaluate goal models at run time to determine whether or not LECs can be expected to meet functional objectives. Using this framework enables stakeholders to have more confidence that LECs are used only in contexts comparable to those validated at design time. © 2021 IEEE.","author":[{"family":"Langford","given":"M.A."},{"family":"Chan","given":"K.H."},{"family":"Fleck","given":"J.E."},{"family":"Mckinley","given":"P.K."},{"family":"Cheng","given":"B.H.C."}],"citation-key":"Langford2021182","collection-title":"Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS 2021","DOI":"10.1109/MODELS50736.2021.00027","ISBN":"978-1-66543-495-9","issued":{"date-parts":[[2021]]},"page":"182-193","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"MoDALAS: Model-driven assurance for learning-enabled autonomous systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123430406&doi=10.1109%2fMODELS50736.2021.00027&partnerID=40&md5=a4d3ec3fe451d1e73c61d57e4b9a1e79"},
{"id":"Lano2020277","abstract":"In this paper we examine how model transformation specifications can be derived from requirements and examples, using a combination of natural language processing (NLP), machine learning (ML) and inductive logic programming (ILP) techniques, together with search-based software engineering (SBSE) for metamodel matching. The AI techniques are employed in order to improve the performance and accuracy of the base SBSE approach, and enable this to be used for a wider range of transformation cases. We propose a specific approach for the co-use of the techniques, and evaluate this on a range of transformation examples from different sources. © 2020 ACM.","author":[{"family":"Lano","given":"K."},{"family":"Fang","given":"S."},{"family":"Umar","given":"M.A."},{"family":"Yassipour-Tehrani","given":"S."}],"citation-key":"Lano2020277","collection-title":"Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings","DOI":"10.1145/3417990.3421386","ISBN":"978-1-4503-8135-2","issued":{"date-parts":[[2020]]},"page":"277-286","publisher":"Association for Computing Machinery, Inc","title":"Enhancing model transformation synthesis using natural language processing","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096766157&doi=10.1145%2f3417990.3421386&partnerID=40&md5=11d6ee9232eb90caf3712390b1ae03bc"},
{"id":"Lano2021173","abstract":"Model-driven engineering (MDE) of software systems from precise specifications has become established as an important approach for rigorous software development. However, the use of MDE requires specialised skills and tools, which has limited its adoption.In this paper we describe techniques for automating the derivation of software specifications from requirements statements, in order to reduce the effort required in creating MDE specifications, and hence to improve the usability and agility of MDE. Natural language processing (NLP) and Machine learning (ML) are used to recognise the required data and behaviour elements of systems from textual and graphical documents, and formal specification models of the systems are created. These specifications can then be used as the basis of manual software development, or as the starting point for automated software production using MDE. © 2021 IEEE.","author":[{"family":"Lano","given":"K."},{"family":"Yassipour-Tehrani","given":"S."},{"family":"Umar","given":"M.A."}],"citation-key":"Lano2021173","collection-title":"Companion Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021","DOI":"10.1109/MODELS-C53483.2021.00030","ISBN":"978-1-66542-484-4","issued":{"date-parts":[[2021]]},"page":"173-180","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Automated requirements formalisation for agile MDE","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124017516&doi=10.1109%2fMODELS-C53483.2021.00030&partnerID=40&md5=bcdcf680a1ed1d48fa0655c2496c7546"},
{"id":"Lano2022","abstract":"In this article, we address how the production of model transformations (MT) can be accelerated by automation of transformation synthesis from requirements, examples, and metamodels. We introduce a synthesis process based on metamodel matching, correspondence patterns between metamodels, and completeness and consistency analysis of matches. We describe how the limitations of metamodel matching can be addressed by combining matching with automated requirements analysis and model transformation by example (MTBE) techniques.We show that in practical examples a large percentage of required transformation functionality can usually be constructed automatically, thus potentially reducing development effort. We also evaluate the efficiency of synthesised transformations.Our novel contributions are:The concept of correspondence patterns between metamodels of a transformation.Requirements analysis of transformations using natural language processing (NLP) and machine learning (ML).Symbolic MTBE using \"predictive specification\"to infer transformations from examples.Transformation generation in multiple MT languages and in Java, from an abstract intermediate language. © 2021 Association for Computing Machinery.","author":[{"family":"Lano","given":"K."},{"family":"Kolahdouz-Rahimi","given":"S."},{"family":"Fang","given":"S."}],"citation-key":"Lano2022","container-title":"ACM Transactions on Software Engineering and Methodology","DOI":"10.1145/3471907","ISSN":"1049331X","issue":"2","issued":{"date-parts":[[2022]]},"publisher":"Association for Computing Machinery","title":"Model transformation development using automated requirements analysis, metamodel matching, and transformation by example","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130755580&doi=10.1145%2f3471907&partnerID=40&md5=f15e997dedf7462b07afecd215b95b6a","volume":"31"},
{"id":"lanoAutomatedRequirementsFormalisation2021a","abstract":"Model-driven engineering (MDE) of software systems from precise specifications has become established as an important approach for rigorous software development. However, the use of MDE requires specialised skills and tools, which has limited its adoption.In this paper we describe techniques for automating the derivation of software specifications from requirements statements, in order to reduce the effort required in creating MDE specifications, and hence to improve the usability and agility of MDE. Natural language processing (NLP) and Machine learning (ML) are used to recognise the required data and behaviour elements of systems from textual and graphical documents, and formal specification models of the systems are created. These specifications can then be used as the basis of manual software development, or as the starting point for automated software production using MDE. © 2021 IEEE.","author":[{"family":"Lano","given":"K."},{"family":"Yassipour-Tehrani","given":"S."},{"family":"Umar","given":"M.A."}],"citation-key":"lanoAutomatedRequirementsFormalisation2021a","container-title":"Companion Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021","DOI":"10.1109/MODELS-C53483.2021.00030","ISBN":"978-1-66542-484-4","issued":{"date-parts":[[2021]]},"page":"173-180","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Automated Requirements Formalisation for Agile MDE","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124017516&doi=10.1109%2fMODELS-C53483.2021.00030&partnerID=40&md5=bcdcf680a1ed1d48fa0655c2496c7546"},
{"id":"lanoConstraintbasedSpecificationModel2013","author":[{"family":"Lano","given":"K."},{"family":"Kolahdouz-Rahimi","given":"S."}],"citation-key":"lanoConstraintbasedSpecificationModel2013","container-title":"Journal of Systems and Software","DOI":"10.1016/j.jss.2012.09.006","issue":"2","issued":{"date-parts":[[2013]]},"page":"412–436","title":"Constraint-based specification of model transformations","type":"article-journal","volume":"86"},
{"id":"lanoEnhancingModelTransformation2020a","abstract":"In this paper we examine how model transformation specifications can be derived from requirements and examples, using a combination of natural language processing (NLP), machine learning (ML) and inductive logic programming (ILP) techniques, together with search-based software engineering (SBSE) for metamodel matching. The AI techniques are employed in order to improve the performance and accuracy of the base SBSE approach, and enable this to be used for a wider range of transformation cases. We propose a specific approach for the co-use of the techniques, and evaluate this on a range of transformation examples from different sources. © 2020 ACM.","author":[{"family":"Lano","given":"K."},{"family":"Fang","given":"S."},{"family":"Umar","given":"M.A."},{"family":"Yassipour-Tehrani","given":"S."}],"citation-key":"lanoEnhancingModelTransformation2020a","container-title":"Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings","DOI":"10.1145/3417990.3421386","ISBN":"978-1-4503-8135-2","issued":{"date-parts":[[2020]]},"page":"277-286","publisher":"Association for Computing Machinery, Inc","title":"Enhancing model transformation synthesis using natural language processing","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096766157&doi=10.1145%2f3417990.3421386&partnerID=40&md5=11d6ee9232eb90caf3712390b1ae03bc"},
{"id":"lanzaPolymetricViewsLightweight2003","author":[{"family":"Lanza","given":"M."},{"family":"Ducasse","given":"S."}],"citation-key":"lanzaPolymetricViewsLightweight2003","container-title":"IEEE Transactions on Software Engineering","DOI":"10.1109/TSE.2003.1232284","issue":"9","issued":{"date-parts":[[2003]]},"page":"782–795","title":"Polymetric views - A lightweight visual approach to reverse engineering","type":"article-journal","volume":"29"},
{"id":"laraAbstractingModellingLanguages2012","author":[{"family":"Lara","given":"Juan"},{"family":"Guerra","given":"Esther"},{"family":"Sánchez-Cuadrado","given":"Jesús"}],"citation-key":"laraAbstractingModellingLanguages2012","container-title":"Advanced Information Systems Engineering","DOI":"10.1007/978-3-642-31095-9_9","issued":{"date-parts":[[2012]]},"page":"127–143","title":"Abstracting Modelling Languages: A Reutilization Approach","type":"article-journal","volume":"7328"},
{"id":"laraAutomatedReuseModel2019","author":[{"family":"Lara","given":"Juan De"},{"family":"Guerra","given":"Esther"},{"family":"Ruscio","given":"Davide Di"},{"family":"Rocco","given":"Juri Di"},{"family":"Cuadrado","given":"Jesus Sanchez"},{"family":"Iovino","given":"Ludovico"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"laraAutomatedReuseModel2019","container-title":"ACM Transactions on Software Engineering and Methodology","issued":{"date-parts":[[2019]]},"page":"57","source":"Zotero","title":"Automated Reuse of Model Transformations through Typing Requirements Models","type":"article-magazine"},
{"id":"laraModeldrivenEngineeringDomainspecific2013","author":[{"family":"Lara","given":"Juan"},{"family":"Guerra","given":"Esther"},{"family":"Cuadrado","given":"Jesús Sánchez"}],"citation-key":"laraModeldrivenEngineeringDomainspecific2013","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-013-0367-z","issued":{"date-parts":[[2013]]},"title":"Model-driven engineering with domain-specific meta-modelling languages","type":"article-journal"},
{"id":"laraProceedingsWorkshopExtreme2013","citation-key":"laraProceedingsWorkshopExtreme2013","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Lara","given":"Juan","dropping-particle":"de"},{"family":"Ruscio","given":"Davide Di"},{"family":"Pierantonio","given":"Alfonso"}],"issued":{"date-parts":[[2013]]},"publisher":"CEUR-WS.org","title":"Proceedings of the Workshop on Extreme Modeling co-located with ACM/IEEE 16th International Conference on Model Driven Engineering Languages & Systems (MoDELS 2013), Miami, Florida, USA, September 29, 2013","type":"book","URL":"http://ceur-ws.org/Vol-1089","volume":"1089"},
{"id":"laraTypesTypeRequirements2011","author":[{"family":"Lara","given":"Juan"},{"family":"Guerra","given":"Esther"}],"citation-key":"laraTypesTypeRequirements2011","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-011-0221-0","issue":"3","issued":{"date-parts":[[2011]]},"page":"453–474","title":"From types to type requirements: genericity for model-driven engineering","type":"article-journal","volume":"12"},
{"id":"larruceaSoftwareEngineeringInternet2017a","accessed":{"date-parts":[[2019,8,22]]},"author":[{"family":"Larrucea","given":"Xabier"},{"family":"Combelles","given":"Annie"},{"family":"Favaro","given":"John"},{"family":"Taneja","given":"Kunal"}],"citation-key":"larruceaSoftwareEngineeringInternet2017a","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2017.28","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017,1]]},"page":"24-28","source":"DOI.org (Crossref)","title":"Software Engineering for the Internet of Things","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7819384/","volume":"34"},
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{"id":"Lawrence981","author":[{"family":"Lawrence","given":"Page"},{"family":"Sergey","given":"Brin"},{"family":"Motwani","given":"Rajeev"},{"family":"Winograd","given":"Terry"}],"citation-key":"Lawrence981","genre":"Technical report","issued":{"date-parts":[[1998]]},"publisher":"Stanford University","title":"The PageRank citation ranking: Bringing order to the web","type":"report"},
{"id":"LeClair2018AdaptingNT","author":[{"family":"LeClair","given":"Alexander"},{"family":"Eberhart","given":"Zachary"},{"family":"McMillan","given":"Collin"}],"citation-key":"LeClair2018AdaptingNT","container-title":"CoRR","issued":{"date-parts":[[2018]]},"title":"Adapting neural text classification for improved software categorization","type":"article-journal","volume":"abs/1806.01742"},
{"id":"LectureIoTData","accessed":{"date-parts":[[2021,1,5]]},"citation-key":"LectureIoTData","note":"00000","title":"Lecture 6: IoT Data Processing","type":"webpage","URL":"https://www2.slideshare.net/PayamBarnaghi/lecture-6-iot-data-processing?qid=8711baae-0a4e-45df-ba7a-9eb987306850&v=&b=&from_search=14"},
{"id":"LecturesSENG371","accessed":{"date-parts":[[2016,9,19]]},"citation-key":"LecturesSENG371","title":"Lectures SENG 371 Software Evolution","type":"webpage","URL":"http://www.engr.uvic.ca/~seng371/lectures.html"},
{"id":"leeCyberPhysicalSystems2008","accessed":{"date-parts":[[2016,2,3]]},"author":[{"family":"Lee","given":"Edward A."}],"citation-key":"leeCyberPhysicalSystems2008","DOI":"10.1109/ISORC.2008.25","ISBN":"978-0-7695-3132-8","issued":{"date-parts":[[2008,5]]},"page":"363-369","publisher":"IEEE","source":"CrossRef","title":"Cyber Physical Systems: Design Challenges","title-short":"Cyber Physical Systems","type":"paper-conference","URL":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4519604"},
{"id":"leeInternetThingsIoT2015","accessed":{"date-parts":[[2016,11,1]]},"author":[{"family":"Lee","given":"In"},{"family":"Lee","given":"Kyoochun"}],"citation-key":"leeInternetThingsIoT2015","container-title":"Business Horizons","DOI":"10.1016/j.bushor.2015.03.008","ISSN":"00076813","issue":"4","issued":{"date-parts":[[2015,7]]},"page":"431-440","source":"CrossRef","title":"The Internet of Things (IoT): Applications, investments, and challenges for enterprises","title-short":"The Internet of Things (IoT)","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0007681315000373","volume":"58"},
{"id":"leePresentFutureCyberPhysical2015","accessed":{"date-parts":[[2016,1,26]]},"author":[{"family":"Lee","given":"Edward"}],"citation-key":"leePresentFutureCyberPhysical2015","container-title":"Sensors","DOI":"10.3390/s150304837","ISSN":"1424-8220","issue":"3","issued":{"date-parts":[[2015,2,26]]},"page":"4837-4869","source":"CrossRef","title":"The Past, Present and Future of Cyber-Physical Systems: A Focus on Models","title-short":"The Past, Present and Future of Cyber-Physical Systems","type":"article-journal","URL":"http://www.mdpi.com/1424-8220/15/3/4837/","volume":"15"},
{"id":"leeSelfAdaptiveFrameworkBased2019","abstract":"The Internet of Things (IoT) connects a wide range of objects and the types of environments in which IoT can be deployed dynamically change. Therefore, these environments can be modified dynamically at runtime considering the emergence of other requirements. Self-adaptive software alters its behavior to satisfy the requirements in a dynamic environment. In this context, the concept of self-adaptive software is suitable for some dynamic IoT environments (e.g., smart greenhouses, smart homes, and reality applications). In this study, we propose a self-adaptive framework for decision-making in an IoT environment at runtime. The framework comprises a finite-state machine model design and a game theoretic decision-making method for extracting efficient strategies. The framework was implemented as a prototype and experiments were conducted to evaluate its runtime performance. The results demonstrate that the proposed framework can be applied to IoT environments at runtime. In addition, a smart greenhouse-based use case is included to illustrate the usability of the proposed framework.","accessed":{"date-parts":[[2021,12,7]]},"author":[{"family":"Lee","given":"Euijong"},{"family":"Seo","given":"Young-Duk"},{"family":"Kim","given":"Young-Gab"}],"citation-key":"leeSelfAdaptiveFrameworkBased2019","container-title":"Sensors","container-title-short":"Sensors","DOI":"10.3390/s19132996","ISSN":"1424-8220","issue":"13","issued":{"date-parts":[[2019,7,7]]},"note":"00013","page":"2996","source":"DOI.org (Crossref)","title":"Self-Adaptive Framework Based on MAPE Loop for Internet of Things","type":"article-journal","URL":"https://www.mdpi.com/1424-8220/19/13/2996","volume":"19"},
{"id":"Lei2021","abstract":"Deep learning requires a large amount of datasets to train deep neural network models for specific tasks, and thus training of a new model is a very costly task. Research on transfer networks used to reduce training costs will be the next turning point in deep learning research. The use of source task models to help reduce the training costs of the target task models, especially heterogeneous systems, is a problem we are studying. In order to quickly obtain an excellent target task model driven by the source task model, we propose a novel transfer learning approach. The model linearly transforms the feature mapping of the target domain and increases the weight value for feature matching to realize the knowledge transfer between heterogeneous networks and add a domain discriminator based on the principle of generative adversarial to speed up feature mapping and learning. Most importantly, this paper proposes a new objective function optimization scheme to complete the model training. It successfully combines the generative adversarial network with the weight feature matching method to ensure that the target model learns the most beneficial features from the source domain for its task. Compared with the previous transfer algorithm, our training results are excellent under the same benchmark for image recognition tasks. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","author":[{"family":"Lei","given":"F."},{"family":"Cheng","given":"J."},{"family":"Yang","given":"Y."},{"family":"Tang","given":"X."},{"family":"Sheng","given":"V.S."},{"family":"Huang","given":"C."}],"citation-key":"Lei2021","container-title":"Electronics (Switzerland)","DOI":"10.3390/electronics10131525","ISSN":"20799292","issue":"13","issued":{"date-parts":[[2021]]},"publisher":"MDPI AG","title":"Improving heterogeneous network knowledge transfer based on the principle of generative adversarial","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108312813&doi=10.3390%2felectronics10131525&partnerID=40&md5=5ba4600e63289505975fb08a4fdefff2","volume":"10"},
{"id":"Lejeune2021","abstract":"Many recent advances in machine learning have been motivated by classification problems. For example, classification methods are used to differentiate between “spam” and “non-spam” emails, identify hand written digits, and recognize the content of photos. For each application, a different model and model architecture will often perform best. Therefore, machine learning research has been enabled by readily available benchmark datasets. In particular, benchmark datasets have been used by researchers to demonstrate that novel methods can achieve high accuracy, and to demonstrate common vulnerabilities of classification methods to adversarial attacks. In the recent mechanics literature, there has been substantial interest in machine learning driven metamodels. Metamodels, or models of models, are appealing because once trained, they typically require orders of magnitude less compute time than full fidelity simulations. However, a better understanding of which machine learning methods and model architectures will perform best on mechanical data has been limited. Here we introduce an open source dataset “BIC” (Buckling Instability Classification) where a heterogeneous column is subject to a fixed level of applied displacement and is classified as either “Stable” or “Unstable.” In addition to introducing this benchmark dataset, we show baseline metamodel performance, and show two different types of adversarial attack. We anticipate that the open source BIC dataset will enable the future development of improved methods for classification problems in mechanics. © 2020 Elsevier Ltd","author":[{"family":"Lejeune","given":"E."}],"citation-key":"Lejeune2021","container-title":"CAD Computer Aided Design","DOI":"10.1016/j.cad.2020.102948","ISSN":"00104485","issued":{"date-parts":[[2021]]},"publisher":"Elsevier Ltd","title":"Geometric stability classification: Datasets, metamodels, and adversarial attacks","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094134503&doi=10.1016%2fj.cad.2020.102948&partnerID=40&md5=30943c6f7980dde551b8b8d804296246","volume":"131"},
{"id":"León20213","abstract":"Precision Medicine has emerged as a computational approach to provide a personalized diagnosis, based on the individual variability in genes, environment, and lifestyle. Success in such aim requires extensible, adaptive, and ontologically well-grounded Information Systems to store, manage, and analyze the large amounts of data generated by the scientific community. Using an existing adaptive information system (Delfos platform) supported by a conceptual schema and an AI algorithm, the contribution of this work is to describe how the system has been improved to address specific challenges regarding the clinical significance of DNA variants. To do so, the following topics are addressed: i) provide an ontologically-consistent representation of the problem domain; ii) improve the management of clinical significance conflicts; iii) ease the addition of new data sources; and iv) provide a scalable environment more aligned with the data analysis requirements in a clinical context. The aim of the work has been achieved by using a Model-Driven Engineering approach. © 2021, Springer Nature Switzerland AG.","author":[{"family":"León","given":"A."},{"family":"García S","given":"A."},{"family":"Costa","given":"M."},{"family":"Vañó Ribelles","given":"A."},{"family":"Pastor","given":"O."}],"citation-key":"León20213","container-title":"Lecture Notes in Business Information Processing","DOI":"10.1007/978-3-030-79108-7_1","editor":[{"family":"Nurcan S.","given":"Korthaus A."}],"ISBN":"9783030791070","ISSN":"18651348","issued":{"date-parts":[[2021]]},"page":"3-10","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Evolution of an adaptive information system for precision medicine","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111350382&doi=10.1007%2f978-3-030-79108-7_1&partnerID=40&md5=5d1fda23bcd3b1b0709bc704a19dc26e","volume":"424 LNBIP"},
{"id":"leonEvolutionAdaptiveInformation2021a","abstract":"Precision Medicine has emerged as a computational approach to provide a personalized diagnosis, based on the individual variability in genes, environment, and lifestyle. Success in such aim requires extensible, adaptive, and ontologically well-grounded Information Systems to store, manage, and analyze the large amounts of data generated by the scientific community. Using an existing adaptive information system (Delfos platform) supported by a conceptual schema and an AI algorithm, the contribution of this work is to describe how the system has been improved to address specific challenges regarding the clinical significance of DNA variants. To do so, the following topics are addressed: i) provide an ontologically-consistent representation of the problem domain; ii) improve the management of clinical significance conflicts; iii) ease the addition of new data sources; and iv) provide a scalable environment more aligned with the data analysis requirements in a clinical context. The aim of the work has been achieved by using a Model-Driven Engineering approach. © 2021, Springer Nature Switzerland AG.","author":[{"family":"León","given":"A."},{"family":"García S","given":"A."},{"family":"Costa","given":"M."},{"family":"Vañó Ribelles","given":"A."},{"family":"Pastor","given":"O."}],"citation-key":"leonEvolutionAdaptiveInformation2021a","container-title":"Lecture Notes in Business Information Processing","DOI":"10.1007/978-3-030-79108-7_1","editor":[{"family":"Nurcan S.","given":"Korthaus A."}],"ISBN":"9783030791070","ISSN":"18651348","issued":{"date-parts":[[2021]]},"page":"3-10","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Evolution of an Adaptive Information System for Precision Medicine","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111350382&doi=10.1007%2f978-3-030-79108-7_1&partnerID=40&md5=5d1fda23bcd3b1b0709bc704a19dc26e","volume":"424 LNBIP"},
{"id":"leopoldTextCategorizationSupport2002","author":[{"family":"Leopold","given":"Edda"},{"family":"Kindermann","given":"Jörg"}],"citation-key":"leopoldTextCategorizationSupport2002","container-title":"Machine Learning","issue":"1-3","issued":{"date-parts":[[2002]]},"page":"423-444","title":"Text categorization with support vector machines. How to represent texts in input space?","type":"article-journal","volume":"46"},
{"id":"lepallecSupportQualityMetrics2013","author":[{"family":"Le Pallec","given":"Xavier"},{"family":"Dupuy-Chessa","given":"Sophie"}],"citation-key":"lepallecSupportQualityMetrics2013","container-title":"Proceedings of the Second Workshop on Graphical Modeling Language Development - GMLD '13","DOI":"10.1145/2489820.2489825","issued":{"date-parts":[[2013]]},"page":"23–31","title":"Support for quality metrics in metamodelling","type":"article-journal"},
{"id":"Leppänen2020308","abstract":"The complex and opportunistic environment in which edge computing systems operate, poses a fundamental challenge for online edge system orchestration, resource provisioning and real-time responsiveness in response to user movement. Such a challenge needs to addressed throughout the edge system lifecycle, starting from the software development methodologies. In this paper, we propose a novel development process for modeling opportunistic edge computing services, which rely on (i) ETSI MEC reference architecture and Opportunistic Internet of Things Service modeling for the early stage of system analysis and design, i.e. domain model and service metamodel; and on (ii) feature engineering for evaluating those opportunistic aspects with data analysis. To address the identified opportunistic properties, at the service design phase we construct (both automatically and through domain expertise) Opportunistic Feature Vectors for Edge, containing the numerical representations of those properties. Such vectors enable further data analysis and machine learning techniques in the development of distributed, effective and efficient edge computing systems. Lastly, we exemplify the integrated process with a microservice-based user mobility management service, based on a real-world data set, for online analysis in MEC systems. © 2020 Elsevier B.V.","author":[{"family":"Leppänen","given":"T."},{"family":"Savaglio","given":"C."},{"family":"Fortino","given":"G."}],"citation-key":"Leppänen2020308","container-title":"Computer Communications","DOI":"10.1016/j.comcom.2020.04.011","ISSN":"01403664","issued":{"date-parts":[[2020]]},"page":"308-319","publisher":"Elsevier B.V.","title":"Service modeling for opportunistic edge computing systems with feature engineering","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083559297&doi=10.1016%2fj.comcom.2020.04.011&partnerID=40&md5=4b986bcd78dc35dd0c5d0ba8fb26d2c0","volume":"157"},
{"id":"leppanenServiceModelingOpportunistic2020a","abstract":"The complex and opportunistic environment in which edge computing systems operate, poses a fundamental challenge for online edge system orchestration, resource provisioning and real-time responsiveness in response to user movement. Such a challenge needs to addressed throughout the edge system lifecycle, starting from the software development methodologies. In this paper, we propose a novel development process for modeling opportunistic edge computing services, which rely on (i) ETSI MEC reference architecture and Opportunistic Internet of Things Service modeling for the early stage of system analysis and design, i.e. domain model and service metamodel; and on (ii) feature engineering for evaluating those opportunistic aspects with data analysis. To address the identified opportunistic properties, at the service design phase we construct (both automatically and through domain expertise) Opportunistic Feature Vectors for Edge, containing the numerical representations of those properties. Such vectors enable further data analysis and machine learning techniques in the development of distributed, effective and efficient edge computing systems. Lastly, we exemplify the integrated process with a microservice-based user mobility management service, based on a real-world data set, for online analysis in MEC systems. © 2020 Elsevier B.V.","author":[{"family":"Leppänen","given":"T."},{"family":"Savaglio","given":"C."},{"family":"Fortino","given":"G."}],"citation-key":"leppanenServiceModelingOpportunistic2020a","container-title":"Computer Communications","DOI":"10.1016/j.comcom.2020.04.011","ISSN":"01403664","issued":{"date-parts":[[2020]]},"page":"308-319","publisher":"Elsevier B.V.","title":"Service modeling for opportunistic edge computing systems with feature engineering","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083559297&doi=10.1016%2fj.comcom.2020.04.011&partnerID=40&md5=4b986bcd78dc35dd0c5d0ba8fb26d2c0","volume":"157"},
{"id":"levenshtein1966bcc","author":[{"family":"Levenshtein","given":"VI"}],"citation-key":"levenshtein1966bcc","container-title":"Soviet Physics Doklady","issued":{"date-parts":[[1966]]},"page":"707","title":"Binary codes capable of correcting deletions, insertions and reversals","type":"article-journal","volume":"10"},
{"id":"Li201928737","abstract":"The Kriging method based on machine learning is an attractive tool. In this work, a sequential Kriging method assisted by trust region strategy (SKM-TRS) is proposed to solve unconstrained black-box problems. In this SKM-TRS, the complex and expensive objective function is approximated by Kriging model. And then, a sub-optimization problem, which is constructed by Kriging and a distance factor, is minimized by the improved trust region strategy to determine next update point during each iteration cycle. The proposed method is verified by ten well-known benchmark optimization problems and a proxy cache size optimization of the streaming media video data due to fragment popularity distribution. The final test results demonstrate the efficiency and robustness of the SKM-TRS in contrast with Efficient Global Optimization (EGO), Trust Region Implementation in Kriging-based optimization with Expected improvement (TRIKE) and an Adaptive Metamodel based Global Optimization algorithm (AMGO). © 2018, Springer Science+Business Media, LLC, part of Springer Nature.","author":[{"family":"Li","given":"Y."},{"family":"Zhang","given":"Q."},{"family":"Wu","given":"Y."},{"family":"Wang","given":"S."}],"citation-key":"Li201928737","container-title":"Multimedia Tools and Applications","DOI":"10.1007/s11042-018-6563-7","ISSN":"13807501","issue":"20","issued":{"date-parts":[[2019]]},"page":"28737-28756","publisher":"Springer New York LLC","title":"A sequential Kriging method assisted by trust region strategy for proxy cache size optimization of the streaming media video data due to fragment popularity distribution","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053380980&doi=10.1007%2fs11042-018-6563-7&partnerID=40&md5=3354d7a1851971976d2cf3cf801add06","volume":"78"},
{"id":"Li202050","abstract":"Autonomous vehicles rely on their perception systems to acquire information about their immediate surroundings. It is necessary to detect the presence of other vehicles, pedestrians, and other relevant entities. Safety concerns and the need for accurate estimations have led to the introduction of lidar systems to complement camera-or radar-based perception systems. This article presents a review of state-of-the-art automotive lidar technologies and the perception algorithms used with those technologies. Lidar systems are introduced first by analyzing such a system?s main components, from laser transmitter to beamscanning mechanism. The advantages/disadvantages and the current status of various solutions are introduced and compared. Then, the specific perception pipeline for lidar data processing is detailed from an autonomous vehicle perspective. The model-driven approaches and emerging deep learning (DL) solutions are reviewed. Finally, we provide an overview of the limitations, challenges, and trends for automotive lidars and perception systems. © 1991-2012 IEEE.","author":[{"family":"Li","given":"Y."},{"family":"Ibanez-Guzman","given":"J."}],"citation-key":"Li202050","container-title":"IEEE Signal Processing Magazine","DOI":"10.1109/MSP.2020.2973615","ISSN":"10535888","issue":"4","issued":{"date-parts":[[2020]]},"page":"50-61","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Lidar for autonomous driving: The principles, challenges, and trends for automotive lidar and perception systems","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087808843&doi=10.1109%2fMSP.2020.2973615&partnerID=40&md5=6b92342bb3dce2b82c042ba07f6054cb","volume":"37"},
{"id":"Li2021172","abstract":"Recently researches about receiver structures for orthogonal time-frequency space (OTFS) have been received widespread attention. Previous OTFS receiver algorithms are based on model-driven, which would lead to complex structures. Motivated by recent advances in data-driven receivers, this paper proposes a data-driven OTFS receiver with a deep neural network (DNN). We demonstrate that the proposed data-driven receiver for OTFS can be generalized to different high mobility scenarios. Specifically, this scheme combines the power of deep learning (DL), which is widely used in various fields. With DL, the proposed algorithm can achieve excellent robustness and strong generalization ability for channel parameters, which are ubiquitous challenges in the design of receiver algorithms. Through a good deal of numerical experiments, simulation results show that the proposed data-driven receiver based on DNN for OTFS can achieve superior performance than comparison methods. © 2021 IEEE.","author":[{"family":"Li","given":"Q."},{"family":"Gong","given":"Y."},{"family":"Meng","given":"F."},{"family":"Xu","given":"Z."}],"citation-key":"Li2021172","collection-title":"Proceedings of 2021 7th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2021","DOI":"10.1109/IC-NIDC54101.2021.9660432","ISBN":"978-1-66540-582-9","issued":{"date-parts":[[2021]]},"page":"172-176","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Data-driven receiver for OTFS system with deep learning","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124802885&doi=10.1109%2fIC-NIDC54101.2021.9660432&partnerID=40&md5=d8505399763acf83fa8ad8f014a69cd1"},
{"id":"Li202190","abstract":"This article studies the problem of RFID-based gesture recognition, which is practically important in various human-computer interaction scenarios, for example, smart homes, intelligent logistics, and smart cities. However, the existing solutions normally suffer from two major limitations: the model-driven methods are sensitive to specific environmental factors, and usually do not adapt well to a complex scenario that is full of multipath; the data-driven methods normally need the collection of massive RFID training data, and deploying the model in the remote cloud leads to long response delay. To overcome the above limitations, this article proposes a cross-domain augmentation-based AI learning (CAL) framework in the context of cloud-edge computing. In the CAL framework, we can simulate massive RFID phase profiles by converting the computer vision data that contains the gesture movement information, instead of costing lots of manpower to actually collect RFID training data. The simulated RFID phase profiles are used to train an AI model in the high-performance cloud. Note that since many sources of this kind of computer vision data are available online, we actually do not even need any manpower to collect training data. To achieve time-efficient recognition, knowledge distillation is applied to get a light and accurate model, which is deployed at the edge side. Thus, recognition response delay can be significantly reduced because the edge server where the AI model is actually deployed is much closer to users than the cloud server. We use commercial off-the-shelf RFID, Kinect, a high-performance server, and a laptop to implement the CAL framework. Extensive experiments are conducted to evaluate the performance of CAL. The results reveal that gesture recognition accuracy of CAL can reach nearly 90 percent without collection of any RFID training data. © 1986-2012 IEEE.","author":[{"family":"Li","given":"M."},{"family":"Fu","given":"L."},{"family":"Wang","given":"X."}],"citation-key":"Li202190","container-title":"IEEE Network","DOI":"10.1109/MNET.011.2100035","ISSN":"08908044","issue":"5","issued":{"date-parts":[[2021]]},"page":"90-97","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A cross-domain augmentation-based AI learning framework for in-network gesture recognition","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119451833&doi=10.1109%2fMNET.011.2100035&partnerID=40&md5=eedf8479d90880fea766925d5b098ed1","volume":"35"},
{"id":"liangModeldrivenClusterResource2022","abstract":"Since emerging edge applications such as Internet of Things (IoT) analytics and augmented reality have tight latency constraints, hardware AI accelerators have been recently proposed to speed up deep neural network (DNN) inference run by these applications. Resource-constrained edge servers and accelerators tend to be multiplexed across multiple IoT applications, introducing the potential for performance interference between latency-sensitive workloads. In this paper, we design analytic models to capture the performance of DNN inference workloads on shared edge accelerators, such as GPU and edgeTPU, under different multiplexing and concurrency behaviors. After validating our models using extensive experiments, we use them to design various cluster resource management algorithms to intelligently manage multiple applications on edge accelerators while respecting their latency constraints. We implement a prototype of our system in Kubernetes and show that our system can host 2.3X more DNN applications in heterogeneous multi-tenant edge clusters with no latency violations when compared to traditional knapsack hosting algorithms.","accessed":{"date-parts":[[2022,1,25]]},"author":[{"family":"Liang","given":"Qianlin"},{"family":"Hanafy","given":"Walid A."},{"family":"Ali-Eldin","given":"Ahmed"},{"family":"Shenoy","given":"Prashant"}],"citation-key":"liangModeldrivenClusterResource2022","container-title":"arXiv:2201.07312 [cs, eess]","issued":{"date-parts":[[2022,1,18]]},"note":"00000","source":"arXiv.org","title":"Model-driven Cluster Resource Management for AI Workloads in Edge Clouds","type":"article-journal","URL":"http://arxiv.org/abs/2201.07312"},
{"id":"Liao20201724","abstract":"In this letter, an efficient massive multiple-input multiple-output (MIMO) detector is proposed by employing a deep neural network (DNN). Specifically, we first unfold an existing iterative detection algorithm into the DNN structure, such that the detection task can be implemented by deep learning (DL) approach. We then introduce two auxiliary parameters at each layer to better cancel multiuser interference (MUI). The first parameter is to generate the residual error vector while the second one is to adjust the relationship among previous layers. We further design the training procedure to optimize the auxiliary parameters with pre-processed inputs. The so derived MIMO detector falls into the category of model-driven DL. The simulation results show that the proposed MIMO detector can achieve preferable detection performance compared to the existing detectors for massive MIMO systems. © 1997-2012 IEEE.","author":[{"family":"Liao","given":"J."},{"family":"Zhao","given":"J."},{"family":"Gao","given":"F."},{"family":"Li","given":"G.Y."}],"citation-key":"Liao20201724","container-title":"IEEE Communications Letters","DOI":"10.1109/LCOMM.2020.2989672","ISSN":"10897798","issue":"8","issued":{"date-parts":[[2020]]},"page":"1724-1728","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A model-driven deep learning method for massive MIMO detection","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089945597&doi=10.1109%2fLCOMM.2020.2989672&partnerID=40&md5=1b437b75b1d9b8bcad426a2a9d6faaba","volume":"24"},
{"id":"Liao202249","abstract":"OpenMP 5.0 introduced the directive to support compile-time selection from a set of directive variants based on OpenMP context. OpenMP 5.1 extended context information to include user-defined conditions that enable user-guided runtime adaptation. However, defining conditions that capture the complex interactions between applications and hardware platforms to select an optimized variant is challenging for programmers. This paper explores a novel approach to automate runtime adaptation through machine learning. We design a new directive to describe semantics for model-driven adaptation and also develop a prototype implementation. Using the Smith-Waterman algorithm as a use-case, our experiments demonstrate that the proposed adaptive OpenMP extension automatically chooses the code variants that deliver the best performance in heterogeneous platforms that consist of CPU and GPU processing capabilities. Using decision tree models for tuning has an accuracy of up to 93.1% in selecting the optimal variant, with negligible runtime overhead. © 2022, Anjia Wang and Yonghong Yan, and Lawrence Livermore National Security, LLC, under exclusive license to Springer Nature Switzerland AG, part of Springer Nature.","author":[{"family":"Liao","given":"C."},{"family":"Wang","given":"A."},{"family":"Georgakoudis","given":"G."},{"family":"Supinski","given":"B.R.","non-dropping-particle":"de"},{"family":"Yan","given":"Y."},{"family":"Beckingsale","given":"D."},{"family":"Gamblin","given":"T."}],"citation-key":"Liao202249","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-97759-7_3","editor":[{"family":"Bhalachandra S., Daley C.","given":"Melesse Vergara V."}],"ISBN":"9783030977580","ISSN":"03029743","issued":{"date-parts":[[2022]]},"page":"49-69","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Extending OpenMP for machine learning-driven adaptation","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130911973&doi=10.1007%2f978-3-030-97759-7_3&partnerID=40&md5=adb216bafbf4aa56475a343063ca6587","volume":"13194 LNCS"},
{"id":"liaoDataAdapterQuerying2016","abstract":"As the growing of applications with big data in cloud computing become popular, many existing systems expect to expand their service to support the explosive increase of data. We propose a data adapter system to support hybrid database architecture including a relational database (RDB) and NoSQL database. It can support query from application and deal with database transformation at the same time. We provide three modes of query approach in data adapter system: blocking transformation mode (BT mode), blocking dump mode (BD mode), and direct access mode (DA mode). We provide a data synchronization mechanism and describe the design and implementation in detail. This paper focuses on velocity with proposed three modes and partly variety with data stored in RDB, NoSQL database and temporary files. With the proposed data adapter system, we can provide a seamless mechanism to use RDB and NoSQL database at the same time.","accessed":{"date-parts":[[2018,5,8]]},"author":[{"family":"Liao","given":"Ying-Ti"},{"family":"Zhou","given":"Jiazheng"},{"family":"Lu","given":"Chia-Hung"},{"family":"Chen","given":"Shih-Chang"},{"family":"Hsu","given":"Ching-Hsien"},{"family":"Chen","given":"Wenguang"},{"family":"Jiang","given":"Mon-Fong"},{"family":"Chung","given":"Yeh-Ching"}],"citation-key":"liaoDataAdapterQuerying2016","container-title":"Future Generation Computer Systems","DOI":"10.1016/j.future.2016.02.002","ISSN":"0167739X","issued":{"date-parts":[[2016,12]]},"page":"111-121","source":"Crossref","title":"Data adapter for querying and transformation between SQL and NoSQL database","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0167739X16300085","volume":"65"},
{"id":"liaoExtendingOpenMPMachine2022a","abstract":"OpenMP 5.0 introduced the directive to support compile-time selection from a set of directive variants based on OpenMP context. OpenMP 5.1 extended context information to include user-defined conditions that enable user-guided runtime adaptation. However, defining conditions that capture the complex interactions between applications and hardware platforms to select an optimized variant is challenging for programmers. This paper explores a novel approach to automate runtime adaptation through machine learning. We design a new directive to describe semantics for model-driven adaptation and also develop a prototype implementation. Using the Smith-Waterman algorithm as a use-case, our experiments demonstrate that the proposed adaptive OpenMP extension automatically chooses the code variants that deliver the best performance in heterogeneous platforms that consist of CPU and GPU processing capabilities. Using decision tree models for tuning has an accuracy of up to 93.1% in selecting the optimal variant, with negligible runtime overhead. © 2022, Anjia Wang and Yonghong Yan, and Lawrence Livermore National Security, LLC, under exclusive license to Springer Nature Switzerland AG, part of Springer Nature.","author":[{"family":"Liao","given":"C."},{"family":"Wang","given":"A."},{"family":"Georgakoudis","given":"G."},{"family":"Supinski","given":"B.R.","non-dropping-particle":"de"},{"family":"Yan","given":"Y."},{"family":"Beckingsale","given":"D."},{"family":"Gamblin","given":"T."}],"citation-key":"liaoExtendingOpenMPMachine2022a","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-97759-7_3","editor":[{"family":"Bhalachandra S.","given":"Melesse Vergara V.","suffix":"Daley C."}],"ISBN":"9783030977580","ISSN":"03029743","issued":{"date-parts":[[2022]]},"page":"49-69","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Extending OpenMP for Machine Learning-Driven Adaptation","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130911973&doi=10.1007%2f978-3-030-97759-7_3&partnerID=40&md5=adb216bafbf4aa56475a343063ca6587","volume":"13194 LNCS"},
{"id":"Lin:1998:IDS:645527.657297","author":[{"family":"Lin","given":"Dekang"}],"citation-key":"Lin:1998:IDS:645527.657297","collection-title":"ICML '98","container-title":"Proceedings of the fifteenth international conference on machine learning","event-place":"San Francisco, CA, USA","ISBN":"1-55860-556-8","issued":{"date-parts":[[1998]]},"page":"296-304","publisher":"Morgan Kaufmann Publishers Inc.","publisher-place":"San Francisco, CA, USA","title":"An information-theoretic definition of similarity","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=645527.657297"},
{"id":"Linares-Vasquez:2014:ACT:2597008.2597155","author":[{"family":"Linares-Vásquez","given":"Mario"},{"family":"Bavota","given":"Gabriele"},{"family":"Di Penta","given":"Massimiliano"},{"family":"Oliveto","given":"Rocco"},{"family":"Poshyvanyk","given":"Denys"}],"citation-key":"Linares-Vasquez:2014:ACT:2597008.2597155","collection-title":"ICPC 2014","container-title":"Proceedings of the 22Nd international conference on program comprehension","event-place":"New York, NY, USA","ISBN":"978-1-4503-2879-1","issued":{"date-parts":[[2014]]},"page":"83-94","publisher":"ACM","publisher-place":"New York, NY, USA","title":"How do API changes trigger stack overflow discussions? A study on the android SDK","type":"paper-conference","URL":"http://doi.acm.org/10.1145/2597008.2597155"},
{"id":"Linares-Vasquez:2014:UML:2617668.2617703","author":[{"family":"Linares-Vásquez","given":"Mario"},{"family":"Mcmillan","given":"Collin"},{"family":"Poshyvanyk","given":"Denys"},{"family":"Grechanik","given":"Mark"}],"citation-key":"Linares-Vasquez:2014:UML:2617668.2617703","container-title":"Empirical Softw. Engg.","ISSN":"1382-3256","issue":"3","issued":{"date-parts":[[2014,6]]},"page":"582-618","title":"On using machine learning to automatically classify software applications into domain categories","type":"article-journal","URL":"http://dx.doi.org/10.1007/s10664-012-9230-z","volume":"19"},
{"id":"linares-vasquezAPIChangeFault2013","abstract":"During the recent years, the market of mobile software applications (apps) has maintained an impressive upward trajectory. Many small and large software development companies invest considerable resources to target available opportunities. As of today, the markets for such devices feature over 850K+ apps for Android and 900K+ for iOS. Availability, cost, functionality, and usability are just some factors that determine the success or lack of success for a given app. Among the other factors, reliability is an important criteria: users easily get frustrated by repeated failures, crashes, and other bugs; hence, abandoning some apps in favor of others. This paper reports a study analyzing how the fault- and change-proneness of APIs used by 7,097 (free) Android apps relates to applications’ lack of success, estimated from user ratings. Results of this study provide important insights into a crucial issue: making heavy use of fault- and change-prone APIs can negatively impact the success of these apps.","accessed":{"date-parts":[[2019,10,4]]},"author":[{"family":"Linares-Vásquez","given":"Mario"},{"family":"Bavota","given":"Gabriele"},{"family":"Bernal-Cárdenas","given":"Carlos"},{"family":"Di Penta","given":"Massimiliano"},{"family":"Oliveto","given":"Rocco"},{"family":"Poshyvanyk","given":"Denys"}],"citation-key":"linares-vasquezAPIChangeFault2013","container-title":"Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering - ESEC/FSE 2013","DOI":"10.1145/2491411.2491428","event":"the 2013 9th Joint Meeting","event-place":"Saint Petersburg, Russia","ISBN":"978-1-4503-2237-9","issued":{"date-parts":[[2013]]},"page":"477","publisher":"ACM Press","publisher-place":"Saint Petersburg, Russia","source":"DOI.org (Crossref)","title":"API change and fault proneness: a threat to the success of Android apps","title-short":"API change and fault proneness","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?doid=2491411.2491428"},
{"id":"linares-vasquezAutomaticallyDetectingSimilar2016","accessed":{"date-parts":[[2017,9,25]]},"author":[{"family":"Linares-Vásquez","given":"Mario"},{"family":"Holtzhauer","given":"Andrew"},{"family":"Poshyvanyk","given":"Denys"}],"citation-key":"linares-vasquezAutomaticallyDetectingSimilar2016","container-title":"Program Comprehension (ICPC), 2016 IEEE 24th International Conference on","issued":{"date-parts":[[2016]]},"page":"1–10","publisher":"IEEE","source":"Google Scholar","title":"On automatically detecting similar android apps","type":"paper-conference","URL":"http://ieeexplore.ieee.org/abstract/document/7503721/"},
{"id":"Linden:2003:ARI:642462.642471","author":[{"family":"Linden","given":"Greg"},{"family":"Smith","given":"Brent"},{"family":"York","given":"Jeremy"}],"citation-key":"Linden:2003:ARI:642462.642471","container-title":"IEEE Internet Computing","ISSN":"1089-7801","issue":"1","issued":{"date-parts":[[2003,1]]},"page":"76-80","title":"Amazon.Com recommendations: Item-to-item collaborative filtering","type":"article-journal","URL":"http://dx.doi.org/10.1109/MIC.2003.1167344","volume":"7"},
{"id":"linModelingUsersMobile","abstract":"In this paper, we investigate the feasibility of identifying a small set of privacy profiles as a way of helping users manage their mobile app privacy preferences. Our analysis does not limit itself to looking at permissions people feel comfortable granting to an app. Instead it relies on static code analysis to determine the purpose for which an app requests each of its permissions, distinguishing for instance between apps relying on particular permissions to deliver their core functionality and apps requesting these permissions to share information with advertising networks or social networks. Using privacy preferences that reflect people’s comfort with the purpose for which different apps request their permissions, we use clustering techniques to identify privacy profiles. A major contribution of this work is to show that, while people’s mobile app privacy preferences are diverse, it is possible to identify a small number of privacy profiles that collectively do a good job at capturing these diverse preferences.","author":[{"family":"Lin","given":"Jialiu"},{"family":"Liu","given":"Bin"},{"family":"Sadeh","given":"Norman"},{"family":"Hong","given":"Jason I"}],"citation-key":"linModelingUsersMobile","note":"00247","page":"14","source":"Zotero","title":"Modeling Users’ Mobile App Privacy Preferences: Restoring Usability in a Sea of Permission Settings","type":"article-journal"},
{"id":"linSentimentAnalysisSoftware2018","abstract":"Sentiment analysis has been applied to various software engineering (SE) tasks, such as evaluating app reviews or analyzing developers’ emotions in commit messages. Studies indicate that sentiment analysis tools provide unreliable results when used out-of-the-box, since they are not designed to process SE datasets. The silver bullet for a successful application of sentiment analysis tools to SE datasets might be their customization to the specific usage context. We describe our experience in building a software library recommender exploiting developers’ opinions mined from Stack Overflow. To reach our goal, we retrained—on a set of 40k manually labeled sentences/words extracted from Stack Overflow—a state-of-the-art sentiment analysis tool exploiting deep learning. Despite such an effort- and time-consuming training process, the results were negative. We changed our focus and performed a thorough investigation of the accuracy of commonly used tools to identify the sentiment of SE related texts. Meanwhile, we also studied the impact of different datasets on tool performance. Our results should warn the research community about the strong limitations of current sentiment analysis tools.","author":[{"family":"Lin","given":"Bin"},{"family":"Zampetti","given":"Fiorella"},{"family":"Bavota","given":"Gabriele"},{"family":"Penta","given":"Massimiliano Di"},{"family":"Lanza","given":"Michele"},{"family":"Oliveto","given":"Rocco"}],"citation-key":"linSentimentAnalysisSoftware2018","issued":{"date-parts":[[2018]]},"page":"11","source":"Zotero","title":"Sentiment Analysis for Software Engineering: How Far Can We Go?","type":"article-journal"},
{"id":"linsteadSourcererMiningSearching2009","accessed":{"date-parts":[[2019,9,4]]},"author":[{"family":"Linstead","given":"Erik"},{"family":"Bajracharya","given":"Sushil"},{"family":"Ngo","given":"Trung"},{"family":"Rigor","given":"Paul"},{"family":"Lopes","given":"Cristina"},{"family":"Baldi","given":"Pierre"}],"citation-key":"linsteadSourcererMiningSearching2009","container-title":"Data Mining and Knowledge Discovery","ISSN":"1384-5810, 1573-756X","issue":"2","issued":{"date-parts":[[2009,4]]},"page":"300-336","title":"Sourcerer: mining and searching internet-scale software repositories","title-short":"Sourcerer","type":"article-journal","URL":"http://link.springer.com/10.1007/s10618-008-0118-x","volume":"18"},
{"id":"liPreprocessingMethodsPipelines","abstract":"0","accessed":{"date-parts":[[2021,3,18]]},"author":[{"family":"Li","given":"Canchen"}],"citation-key":"liPreprocessingMethodsPipelines","container-title":"0","note":"0","source":"0","title":"Preprocessing Methods and Pipelines of Data Mining","title-short":"0","type":"article-journal","URL":"0"},
{"id":"liSystematicMappingStudy2015","accessed":{"date-parts":[[2021,1,7]]},"author":[{"family":"Li","given":"Zengyang"},{"family":"Avgeriou","given":"Paris"},{"family":"Liang","given":"Peng"}],"citation-key":"liSystematicMappingStudy2015","container-title":"Journal of Systems and Software","container-title-short":"Journal of Systems and Software","DOI":"10.1016/j.jss.2014.12.027","ISSN":"01641212","issued":{"date-parts":[[2015,3]]},"note":"00391","page":"193-220","source":"DOI.org (Crossref)","title":"A systematic mapping study on technical debt and its management","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0164121214002854","volume":"101"},
{"id":"Liu:2006:GDS:1150402.1150522","author":[{"family":"Liu","given":"Chao"},{"family":"Chen","given":"Chen"},{"family":"Han","given":"Jiawei"},{"family":"Yu","given":"Philip S."}],"citation-key":"Liu:2006:GDS:1150402.1150522","collection-title":"KDD '06","container-title":"Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining","event-place":"New York, NY, USA","ISBN":"1-59593-339-5","issued":{"date-parts":[[2006]]},"page":"872-881","publisher":"ACM","publisher-place":"New York, NY, USA","title":"GPLAG: Detection of software plagiarism by program dependence graph analysis","type":"paper-conference","URL":"http://doi.acm.org/10.1145/1150402.1150522"},
{"id":"Liu2015","abstract":"This work introduces a multimaterial density-based topology optimization method suitable for nonlinear structural problems. The proposed method consists of three stages: continuous density distribution, clustering, and metamodel-based optimization. The initial continuous density distribution is generated following a synthesis strategy without penalization, e.g., the hybrid cellular automaton (HCA) method. In the clustering stage, unsupervised machine learning (e.g., K-means clustering) is used to optimally classify the continuous density distribution into a finite number of clusters based on their similarity. Finally, a metamodel (e.g., Kriging interpolation) is generated and iteratively updated following a global optimization algorithm (e.g., genetic algorithms) to ultimately converge to an optimal material distribution. The proposed methodology is demonstrated with the design of multimaterial stiff (minimum compliance) structures, compliant mechanisms, and a thin-walled S-rail structure for crashworthiness. Copyright © 2015 by ASME.","author":[{"family":"Liu","given":"K."},{"family":"Tovar","given":"A."},{"family":"Nutwell","given":"E."},{"family":"Detwiler","given":"D."}],"citation-key":"Liu2015","collection-title":"Proceedings of the ASME Design Engineering Technical Conference","DOI":"10.1115/DETC201546534","ISBN":"978-0-7918-5708-3","issued":{"date-parts":[[2015]]},"publisher":"American Society of Mechanical Engineers (ASME)","title":"Towards nonlinear multimaterial topology optimization using unsupervised machine learning and metamodel-based optimization","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979018872&doi=10.1115%2fDETC201546534&partnerID=40&md5=d1654a98553e41e561c15bbd6c2b5650","volume":"2B-2015"},
{"id":"Liu2016","abstract":"This study presents an efficient multimaterial design optimization algorithm that is suitable for nonlinear structures. The proposed algorithm consists of three steps: conceptual design generation, design characterization by machine learning, and metamodel-based multi-objective optimization. The conceptual design can be generated from extracting finite element analysis information or by using structure optimization. The conceptual design is then characterized by using machine learning techniques to dramatically reduce the dimension of the design space. Finally, metamodels are derived using Efficient Global Optimization (EGO) followed by multi-objective design optimization to find the optimal material distribution. The proposed methodology is demonstrated using examples from multiple physics and compared with traditional multimaterial topology optimization method. © Copyright 2016 by ASME.","author":[{"family":"Liu","given":"K."},{"family":"Detwiler","given":"D."},{"family":"Tovar","given":"A."}],"citation-key":"Liu2016","collection-title":"Proceedings of the ASME Design Engineering Technical Conference","DOI":"10.1115/DETC2016-60471","ISBN":"978-0-7918-5011-4","issued":{"date-parts":[[2016]]},"publisher":"American Society of Mechanical Engineers (ASME)","title":"Machine learning and metamodel-based design optimization of nonlinear multimaterial structures","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007574710&doi=10.1115%2fDETC2016-60471&partnerID=40&md5=6924196ecad9a6cc0a220f8af07be03f","volume":"2B-2016"},
{"id":"Liu2017","abstract":"This study presents an efficient multimaterial design optimization algorithm that is suitable for nonlinear structures. The proposed algorithm consists of three steps: conceptual design generation, clustering, and metamodel-based global optimization. The conceptual design is generated using a structural optimization algorithm for linear models or a heuristic design algorithm for nonlinear models. Then, the conceptual design is clustered into a predefined number of clusters (materials) using a machine learning algorithm. Finally, the global optimization problem aims to find the optimal material parameters of the clustered design using metamodels. The metamodels are built using sampling and cross-validation and sequentially updated using an expected improvement function until convergence. The proposed methodology is demonstrated using examples from multiple physics and compared with traditional multimaterial topology optimization (MTOP) method. The proposed approach is applied to a nonlinear, multi-objective design problems for crashworthiness. Copyright © 2017 by ASME.","author":[{"family":"Liu","given":"K."},{"family":"Detwiler","given":"D."},{"family":"Tovar","given":"A."}],"citation-key":"Liu2017","container-title":"Journal of Mechanical Design, Transactions of the ASME","DOI":"10.1115/1.4037620","ISSN":"10500472","issue":"10","issued":{"date-parts":[[2017]]},"publisher":"American Society of Mechanical Engineers (ASME)","title":"Optimal design of nonlinear multimaterial structures for crashworthiness using cluster analysis","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028599752&doi=10.1115%2f1.4037620&partnerID=40&md5=742e005d287c3e2f2d1e7dd952e93f08","volume":"139"},
{"id":"Liu2019","abstract":"The existing spectrum sensing methods mostly make decisions using model-driven test statistics, such as energy and eigenvalues. A weakness of these model-driven methods is the difficulty in accurately modeling for practical environment. In contrast to the model-driven approach, in this paper, we use a deep neural network to automatically learn features from data itself, and develop a data-driven detection approach. Inspired by the powerful capability of convolutional neural network (CNN) in extracting features of matrix-shaped data, we use the sample covariance matrix as the input of CNN, proposing a novel covariance matrix-aware CNN-based detection scheme, which consists of offline training and online detection. Different from the existing deep learning-based detection methods which replace the whole detection system by an end-to-end neural network, in this work, we use CNN for offline test statistic design and develop a practical threshold-based online detection mechanism. Specially, according to the maximum a posteriori probability (MAP) criterion, we derive the cost function for offline training in the spectrum sensing model, which guarantees the optimality of the designed test statistic. Simulation results have shown that whether the PU signals are independent or correlated, the detection performance of the proposed method is close to the optimal bound of estimator-correlator detector. Particularly, when the PU signals are correlated with a correlation coefficient 0.7, the probability of detection of the proposed method outperforms the conventional maximum eigenvalue detection method by nearly 7.5 times at SNR = -14dB. © 2019 IEEE.","author":[{"family":"Liu","given":"C."},{"family":"Liu","given":"X."},{"family":"Liang","given":"Y.-C."}],"citation-key":"Liu2019","collection-title":"IEEE International Conference on Communications","DOI":"10.1109/ICC.2019.8761360","ISBN":"978-1-5386-8088-9","ISSN":"15503607","issued":{"date-parts":[[2019]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Deep CNN for spectrum sensing in cognitive radio","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067094519&doi=10.1109%2fICC.2019.8761360&partnerID=40&md5=420046ae3bedcb763a8de664dddccca1","volume":"2019-May"},
{"id":"Liu202188","abstract":"Reconfigurable intelligent surface (RIS) is regarded as a key technology for the next generation of wireless communications. Recently, the combination of RIS and spatial modulation (SM) or space shift keying (SSK) has attracted a lot of interest in the wireless communication area by achieving a trade-off between spectral and energy efficiency. In this paper, by generalizing RIS-aided SM/SSK system to a special case of conventional SM system, we investigated deep learning based detection in RIS-aided SM/SSK systems. Based on the idea of deep unfolding, we studied the model-driven deep learning detection for RIS-aided SM systems and compare the performance against the data-driven deep learning detectors. © 2021 IEEE.","author":[{"family":"Liu","given":"J."},{"family":"Renzo","given":"M.D."}],"citation-key":"Liu202188","collection-title":"Proceedings - 2021 IEEE 4th 5G World Forum, 5GWF 2021","DOI":"10.1109/5GWF52925.2021.00023","ISBN":"978-1-66544-308-1","issued":{"date-parts":[[2021]]},"page":"88-92","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Data-driven and model-driven deep learning detection for RIS-aided spatial modulation","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123314470&doi=10.1109%2f5GWF52925.2021.00023&partnerID=40&md5=29d6886417c9d778cc9d002aca875a8f"},
{"id":"Liu20223","abstract":"The application of machine learning and deep learning is widely used in the business of the power grid. However, the business of the power grid is complicated, and the online service of deep learning faces greater performance challenges. In order to solve this problem, this paper proposes an online service EOSP based on go-tensorflow. EOSP service is divided into 3 modules, namely model configuration module, execution engine module and model management module. The model configuration module mainly includes functions such as online model configuration and model configuration information synchronization. The execution engine can execute graphical model calls, and has optimized performance based on the characteristics of golang language coroutines. The model management module is responsible for model registration, update, uninstallation and version management. Experiments show that the EOSP service is highly stable, which greatly reduces the time consumption of online services. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.","author":[{"family":"Liu","given":"P."},{"family":"Lu","given":"Y."},{"family":"Wang","given":"G."},{"family":"Zhou","given":"W."}],"citation-key":"Liu20223","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-97774-0_1","editor":[{"family":"Qiu M., Gai K.","given":"Qiu H."}],"ISBN":"9783030977733","ISSN":"03029743","issued":{"date-parts":[[2022]]},"page":"3-13","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Efficient online service based on go-tensorflow in the middle-station scenario of grid service","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127049907&doi=10.1007%2f978-3-030-97774-0_1&partnerID=40&md5=6fc6b2ed337d7703b40ab94dbb70d2ed","volume":"13202 LNCS"},
{"id":"liuDatadrivenModeldrivenDeep2021a","abstract":"Reconfigurable intelligent surface (RIS) is regarded as a key technology for the next generation of wireless communications. Recently, the combination of RIS and spatial modulation (SM) or space shift keying (SSK) has attracted a lot of interest in the wireless communication area by achieving a trade-off between spectral and energy efficiency. In this paper, by generalizing RIS-aided SM/SSK system to a special case of conventional SM system, we investigated deep learning based detection in RIS-aided SM/SSK systems. Based on the idea of deep unfolding, we studied the model-driven deep learning detection for RIS-aided SM systems and compare the performance against the data-driven deep learning detectors. © 2021 IEEE.","author":[{"family":"Liu","given":"J."},{"family":"Renzo","given":"M.D."}],"citation-key":"liuDatadrivenModeldrivenDeep2021a","container-title":"Proceedings - 2021 IEEE 4th 5G World Forum, 5GWF 2021","DOI":"10.1109/5GWF52925.2021.00023","ISBN":"978-1-66544-308-1","issued":{"date-parts":[[2021]]},"page":"88-92","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Data-driven and Model-driven Deep Learning Detection for RIS-aided Spatial Modulation","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123314470&doi=10.1109%2f5GWF52925.2021.00023&partnerID=40&md5=29d6886417c9d778cc9d002aca875a8f"},
{"id":"liuFollowMyRecommendations","abstract":"Modern smartphone platforms have millions of apps, many of which request permissions to access private data and resources, like user accounts or location. While these smartphone platforms provide varying degrees of control over these permissions, the sheer number of decisions that users are expected to manage has been shown to be unrealistically high. Prior research has shown that users are often unaware of, if not uncomfortable with, many of their permission settings. Prior work also suggests that it is theoretically possible to predict many of the privacy settings a user would want by asking the user a small number of questions. However, this approach has neither been operationalized nor evaluated with actual users before. We report on a field study (n=72) in which we implemented and evaluated a Personalized Privacy Assistant (PPA) with participants using their own Android devices. The results of our study are encouraging. We find that 78.7% of the recommendations made by the PPA were adopted by users. Following initial recommendations on permission settings, participants were motivated to further review and modify their settings with daily “privacy nudges.” Despite showing substantial engagement with these nudges, participants only changed 5.1% of the settings previously adopted based on the PPA’s recommendations. The PPA and its recommendations were perceived as useful and usable. We discuss the implications of our results for mobile permission management and the design of personalized privacy assistant solutions.","author":[{"family":"Liu","given":"Bin"},{"family":"Andersen","given":"Mads Schaarup"},{"family":"Schaub","given":"Florian"},{"family":"Almuhimedi","given":"Hazim"},{"family":"Zhang","given":"Shikun"},{"family":"Sadeh","given":"Norman"},{"family":"Acquisti","given":"Alessandro"},{"family":"Agarwal","given":"Yuvraj"}],"citation-key":"liuFollowMyRecommendations","page":"16","source":"Zotero","title":"Follow My Recommendations: A Personalized Privacy Assistant for Mobile App Permissions","type":"article-journal"},
{"id":"liuJointProceedingsMODELS2014","citation-key":"liuJointProceedingsMODELS2014","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Liu","given":"Yan"},{"family":"Zschaler","given":"Steffen"},{"family":"Baudry","given":"Benoit"},{"family":"Ghosh","given":"Sudipto"},{"family":"Ruscio","given":"Davide Di"},{"family":"Jackson","given":"Ethan K."},{"family":"Wimmer","given":"Manuel"}],"issued":{"date-parts":[[2014]]},"note":"00000","publisher":"CEUR-WS.org","title":"Joint Proceedings of MODELS'13 Invited Talks, Demonstration Session, Poster Session, and ACM Student Research Competition co-located with the 16th International Conference on Model Driven Engineering Languages and Systems (MODELS 2013), Miami, USA, September 29 - October 4, 2013","type":"book","URL":"http://ceur-ws.org/Vol-1115","volume":"1115"},
{"id":"liuOptimalDesignNonlinear2017a","abstract":"This study presents an efficient multimaterial design optimization algorithm that is suitable for nonlinear structures. The proposed algorithm consists of three steps: conceptual design generation, clustering, and metamodel-based global optimization. The conceptual design is generated using a structural optimization algorithm for linear models or a heuristic design algorithm for nonlinear models. Then, the conceptual design is clustered into a predefined number of clusters (materials) using a machine learning algorithm. Finally, the global optimization problem aims to find the optimal material parameters of the clustered design using metamodels. The metamodels are built using sampling and cross-validation and sequentially updated using an expected improvement function until convergence. The proposed methodology is demonstrated using examples from multiple physics and compared with traditional multimaterial topology optimization (MTOP) method. The proposed approach is applied to a nonlinear, multi-objective design problems for crashworthiness. Copyright © 2017 by ASME.","author":[{"family":"Liu","given":"K."},{"family":"Detwiler","given":"D."},{"family":"Tovar","given":"A."}],"citation-key":"liuOptimalDesignNonlinear2017a","container-title":"Journal of Mechanical Design, Transactions of the ASME","DOI":"10.1115/1.4037620","ISSN":"10500472","issue":"10","issued":{"date-parts":[[2017]]},"publisher":"American Society of Mechanical Engineers (ASME)","title":"Optimal design of nonlinear multimaterial structures for crashworthiness using cluster analysis","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028599752&doi=10.1115%2f1.4037620&partnerID=40&md5=742e005d287c3e2f2d1e7dd952e93f08","volume":"139"},
{"id":"Lo2022","abstract":"Federated learning has received fast-growing interests from academia and industry to tackle the challenges of data hungriness and privacy in machine learning. A federated learning system can be viewed as a large-scale distributed system with different components and stakeholders as numerous client devices participate in federated learning. Designing a federated learning system requires software system design thinking apart from the machine learning knowledge. Although much effort has been put into federated learning from the machine learning technique aspects, the software architecture design concerns in building federated learning systems have been largely ignored. Therefore, in this paper, we present a collection of architectural patterns to deal with the design challenges of federated learning systems. Architectural patterns present reusable solutions to a commonly occurring problem within a given context during software architecture design. The presented patterns are based on the results of a systematic literature review and include three client management patterns, four model management patterns, three model training patterns, four model aggregation patterns, and one configuration pattern. The patterns are associated to the particular state transitions in a federated learning model lifecycle, serving as a guidance for effective use of the patterns in the design of federated learning systems. © 2022 Elsevier Inc.","author":[{"family":"Lo","given":"S.K."},{"family":"Lu","given":"Q."},{"family":"Zhu","given":"L."},{"family":"Paik","given":"H.-Y."},{"family":"Xu","given":"X."},{"family":"Wang","given":"C."}],"citation-key":"Lo2022","container-title":"Journal of Systems and Software","DOI":"10.1016/j.jss.2022.111357","ISSN":"01641212","issued":{"date-parts":[[2022]]},"publisher":"Elsevier Inc.","title":"Architectural patterns for the design of federated learning systems","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130338616&doi=10.1016%2fj.jss.2022.111357&partnerID=40&md5=0b01334bb06be844f4684740cd833b8e","volume":"191"},
{"id":"Logan20213705","abstract":"Reusing data is difficult even within well-defined science communities and only gets worse when combining data from multiple communities and disciplines. Through the lens of current work on constructing an environmental epidemiological data set from multiple disciplinary sources, we demonstrate the need for a new tool ecosystem to support heterogeneous Big Data science. Extending existing community standards for schemas and/or data formats through human auditing and wrangling of the data is not feasible at scale. This work therefore suggests new approaches for the multi-disciplinary communities to build a shared tool ecosystem for big data. We discuss both the larger context of data wrangling of epidemiological data sets for novel artificial intelligence algorithms and the specific lessons from working with these multi-disciplinary data sets. Adopting a more model-driven, automatable approach promises not only better efficiency but also removes key sources of human-generated errors and promotes reuse and reproducibility of science data. © 2021 IEEE.","author":[{"family":"Logan","given":"J."},{"family":"Agasthya","given":"G."},{"family":"Hanson","given":"H."},{"family":"Wolf","given":"M."},{"family":"Lee","given":"H."},{"family":"Dewji","given":"S."},{"family":"Yoon","given":"H.-J."},{"family":"Kapadia","given":"A."}],"citation-key":"Logan20213705","collection-title":"Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021","DOI":"10.1109/BigData52589.2021.9671683","editor":[{"family":"Chen Y., Ludwig H.","given":"Tu Y.","suffix":"Fayyad U., Zhu X., Hu X.T., Byna S., Liu X., Zhang J., Pan S., Papalexakis V., Wang J., Cuzzocrea A., Ordonez C."}],"ISBN":"978-1-66543-902-2","issued":{"date-parts":[[2021]]},"page":"3705-3708","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Creating a tools ecosystem for cross-discipline environmental data reuse","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125292245&doi=10.1109%2fBigData52589.2021.9671683&partnerID=40&md5=4dfc7b84d856284e56cf1494fbe55c9c"},
{"id":"loganCreatingToolsEcosystem2021a","abstract":"Reusing data is difficult even within well-defined science communities and only gets worse when combining data from multiple communities and disciplines. Through the lens of current work on constructing an environmental epidemiological data set from multiple disciplinary sources, we demonstrate the need for a new tool ecosystem to support heterogeneous Big Data science. Extending existing community standards for schemas and/or data formats through human auditing and wrangling of the data is not feasible at scale. This work therefore suggests new approaches for the multi-disciplinary communities to build a shared tool ecosystem for big data. We discuss both the larger context of data wrangling of epidemiological data sets for novel artificial intelligence algorithms and the specific lessons from working with these multi-disciplinary data sets. Adopting a more model-driven, automatable approach promises not only better efficiency but also removes key sources of human-generated errors and promotes reuse and reproducibility of science data. © 2021 IEEE.","author":[{"family":"Logan","given":"J."},{"family":"Agasthya","given":"G."},{"family":"Hanson","given":"H."},{"family":"Wolf","given":"M."},{"family":"Lee","given":"H."},{"family":"Dewji","given":"S."},{"family":"Yoon","given":"H.-J."},{"family":"Kapadia","given":"A."}],"citation-key":"loganCreatingToolsEcosystem2021a","container-title":"Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021","DOI":"10.1109/BigData52589.2021.9671683","editor":[{"family":"Chen Y.","given":"Ordonez C.","suffix":"Ludwig H., Tu Y., Fayyad U., Zhu X., Hu X.T., Byna S., Liu X., Zhang J., Pan S., Papalexakis V., Wang J., Cuzzocrea A."}],"ISBN":"978-1-66543-902-2","issued":{"date-parts":[[2021]]},"page":"3705-3708","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Creating a Tools Ecosystem for Cross-Discipline Environmental Data Reuse","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125292245&doi=10.1109%2fBigData52589.2021.9671683&partnerID=40&md5=4dfc7b84d856284e56cf1494fbe55c9c"},
{"id":"Lomas2021618","abstract":"The use of the Internet today has grown by leaps and bounds both in mobile phones, appliances, televisions, computers, so the link between objects and people is more daily. Services such as cloud computing and the IoT Internet of things have had a significant advance along with machine learning for managing predictions. For this reason, this article presents a cross-platform architecture for the analysis of vehicular traffic in a smart city with machine learning tools based on model engineering to generate prediction tools. For the architecture design, MDA Model-Driven Architecture techniques were used, and services were implemented in AWS Amazon Web Service. To validate the proposal, the usability of the interface was analyzed, and load tests were applied to the services. © 2021, Springer Nature Switzerland AG.","author":[{"family":"Lomas","given":"E."},{"family":"Cevallos","given":"P."},{"family":"Alulema","given":"D."},{"family":"Alulema","given":"V."},{"family":"Saenz","given":"M."}],"citation-key":"Lomas2021618","container-title":"Communications in Computer and Information Science","DOI":"10.1007/978-3-030-71503-8_48","editor":[{"family":"Botto-Tobar M., Montes Leon S.","given":"Camacho O.","suffix":"Chavez D., Torres-Carrion P., Zambrano Vizuete M."}],"ISBN":"9783030715021","ISSN":"18650929","issued":{"date-parts":[[2021]]},"page":"618-628","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Development of a cross-platform architecture for the analysis of vehicular traffic in a smart city with machine learning tools","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107326741&doi=10.1007%2f978-3-030-71503-8_48&partnerID=40&md5=ff216326abbc51ffac68399c8501d373","volume":"1388 CCIS"},
{"id":"lomasDevelopmentCrossPlatformArchitecture2021a","abstract":"The use of the Internet today has grown by leaps and bounds both in mobile phones, appliances, televisions, computers, so the link between objects and people is more daily. Services such as cloud computing and the IoT Internet of things have had a significant advance along with machine learning for managing predictions. For this reason, this article presents a cross-platform architecture for the analysis of vehicular traffic in a smart city with machine learning tools based on model engineering to generate prediction tools. For the architecture design, MDA Model-Driven Architecture techniques were used, and services were implemented in AWS Amazon Web Service. To validate the proposal, the usability of the interface was analyzed, and load tests were applied to the services. © 2021, Springer Nature Switzerland AG.","author":[{"family":"Lomas","given":"E."},{"family":"Cevallos","given":"P."},{"family":"Alulema","given":"D."},{"family":"Alulema","given":"V."},{"family":"Saenz","given":"M."}],"citation-key":"lomasDevelopmentCrossPlatformArchitecture2021a","container-title":"Communications in Computer and Information Science","DOI":"10.1007/978-3-030-71503-8_48","editor":[{"family":"Botto-Tobar M.","given":"Zambrano Vizuete M.","suffix":"Montes Leon S., Camacho O., Chavez D., Torres-Carrion P."}],"ISBN":"9783030715021","ISSN":"18650929","issued":{"date-parts":[[2021]]},"page":"618-628","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Development of a Cross-Platform Architecture for the Analysis of Vehicular Traffic in a Smart City with Machine Learning Tools","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107326741&doi=10.1007%2f978-3-030-71503-8_48&partnerID=40&md5=ff216326abbc51ffac68399c8501d373","volume":"1388 CCIS"},
{"id":"LondonbasedGyanaRaises","accessed":{"date-parts":[[2020,3,2]]},"citation-key":"LondonbasedGyanaRaises","title":"London-based Gyana raises $3.9M for a no-code approach to data science – TechCrunch","type":"webpage","URL":"https://techcrunch.com/2020/02/27/london-based-gyana-raises-3-9m-for-a-no-code-approach-to-data-science/amp/?guce_referrer=aHR0cHM6Ly90LmNvL0p4U1pmVFJ4dms_YW1wPTE&guce_referrer_sig=AQAAAK7PsQ7LRtmCbJPzeDGcZKBNQWYD7Kx1bOzyc7RPk9m25HkGQKbBfxKc&guccounter=2"},
{"id":"lopez-fernandezAssessingQualityMetamodels2014","accessed":{"date-parts":[[2015,9,15]]},"author":[{"family":"López-Fernández","given":"Jesús J."},{"family":"Guerra","given":"Esther"},{"family":"Lara","given":"Juan","non-dropping-particle":"de"}],"citation-key":"lopez-fernandezAssessingQualityMetamodels2014","container-title":"11th Workshop on Model Driven Engineering, Verification and Validation MoDeVVa 2014","issued":{"date-parts":[[2014]]},"page":"3","source":"Google Scholar","title":"Assessing the Quality of Meta-models","type":"paper-conference","URL":"http://ceur-ws.org/Vol-1235/MoDeVVa2014-complete.pdf#page=9"},
{"id":"lopez-fernandezExampledrivenMetamodelDevelopment2015","accessed":{"date-parts":[[2015,9,15]]},"author":[{"family":"López-Fernández","given":"Jesús J."},{"family":"Cuadrado","given":"Jesús Sánchez"},{"family":"Guerra","given":"Esther"},{"family":"Lara","given":"Juan","non-dropping-particle":"de"}],"citation-key":"lopez-fernandezExampledrivenMetamodelDevelopment2015","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-013-0392-y","ISSN":"1619-1366, 1619-1374","issue":"4","issued":{"date-parts":[[2015,10]]},"page":"1323-1347","source":"CrossRef","title":"Example-driven meta-model development","type":"article-journal","URL":"http://link.springer.com/10.1007/s10270-013-0392-y","volume":"14"},
{"id":"López2022967","abstract":"The application of machine learning (ML) algorithms to address problems related to model-driven engineering (MDE) is currently hindered by the lack of curated datasets of software models. There are several reasons for this, including the lack of large collections of good quality models, the difficulty to label models due to the required domain expertise, and the relative immaturity of the application of ML to MDE. In this work, we present ModelSet, a labelled dataset of software models intended to enable the application of ML to address software modelling problems. To create it we have devised a method designed to facilitate the exploration and labelling of model datasets by interactively grouping similar models using off-the-shelf technologies like a search engine. We have built an Eclipse plug-in to support the labelling process, which we have used to label 5,466 Ecore meta-models and 5,120 UML models with its category as the main label plus additional secondary labels of interest. We have evaluated the ability of our labelling method to create meaningful groups of models in order to speed up the process, improving the effectiveness of classical clustering methods. We showcase the usefulness of the dataset by applying it in a real scenario: enhancing the MAR search engine. We use ModelSet to train models able to infer useful metadata to navigate search results. The dataset and the tooling are available at https://figshare.com/s/5a6c02fa8ed20782935c and a live version at http://modelset.github.io. © 2021, The Author(s).","author":[{"family":"López","given":"J.A.H."},{"family":"Cánovas Izquierdo","given":"J.L."},{"family":"Cuadrado","given":"J.S."}],"citation-key":"López2022967","container-title":"Software and Systems Modeling","DOI":"10.1007/s10270-021-00929-3","ISSN":"16191366","issue":"3","issued":{"date-parts":[[2022]]},"page":"967-986","publisher":"Springer Science and Business Media Deutschland GmbH","title":"ModelSet: a dataset for machine learning in model-driven engineering","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117576971&doi=10.1007%2fs10270-021-00929-3&partnerID=40&md5=4dfe6f4506c21606c462b383c383b789","volume":"21"},
{"id":"lopezMARStructurebasedSearch2020","abstract":"The availability of shared software models provides opportunities for reusing, adapting and learning from them. Public models are typically stored in a variety of locations, including model repositories, regular source code repositories, web pages, etc. To profit from them developers need effective search mechanisms to locate the models relevant for their tasks. However, to date, there has been little success in creating a generic and efficient search engine specially tailored to the modelling domain. In this paper we present MAR, a search engine for models. MAR is generic in the sense that it can index any type of model if its meta-model is known. MAR uses a query-by-example approach, that is, it uses example models as queries. The search takes the model structure into account using the notion of bag of paths, which encodes the structure of a model using paths between model elements and is a representation amenable for indexing. MAR is built over HBase using a specific design to deal with large repositories. Our benchmarks show that the engine is efficient and has fast response times in most cases. We have also evaluated the precision of the search engine by creating model mutants which simulate user queries. A REST API is available to perform queries and an Eclipse plug-in allows end users to connect to the search engine from model editors. We have currently indexed more than 50.000 models of different kinds, including Ecore meta-models, BPMN diagrams and UML models. MAR is available at http://mar-search.org.","accessed":{"date-parts":[[2020,10,23]]},"author":[{"family":"López","given":"José Antonio Hernández"},{"family":"Cuadrado","given":"Jesús Sánchez"}],"citation-key":"lopezMARStructurebasedSearch2020","container-title":"arXiv:2008.11858 [cs]","issued":{"date-parts":[[2020,8,26]]},"note":"00000","source":"arXiv.org","title":"MAR: A structure-based search engine for models","title-short":"MAR","type":"article-journal","URL":"http://arxiv.org/abs/2008.11858"},
{"id":"lopezMARStructurebasedSearch2020a","accessed":{"date-parts":[[2021,4,29]]},"author":[{"family":"López","given":"José Antonio Hernández"},{"family":"Cuadrado","given":"Jesús Sánchez"}],"citation-key":"lopezMARStructurebasedSearch2020a","container-title":"Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems","DOI":"10.1145/3365438.3410947","event":"MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems","event-place":"Virtual Event Canada","ISBN":"978-1-4503-7019-6","issued":{"date-parts":[[2020,10,18]]},"note":"00001","page":"57-67","publisher":"ACM","publisher-place":"Virtual Event Canada","source":"DOI.org (Crossref)","title":"MAR: a structure-based search engine for models","title-short":"MAR","type":"paper-conference","URL":"https://dl.acm.org/doi/10.1145/3365438.3410947"},
{"id":"lopezMARStructurebasedSearch2020b","abstract":"The availability of shared software models provides opportunities for reusing, adapting and learning from them. Public models are typically stored in a variety of locations, including model repositories, regular source code repositories, web pages, etc. To profit from them developers need effective search mechanisms to locate the models relevant for their tasks. However, to date, there has been little success in creating a generic and efficient search engine specially tailored to the modelling domain.","accessed":{"date-parts":[[2021,4,29]]},"author":[{"family":"López","given":"José Antonio Hernández"},{"family":"Cuadrado","given":"Jesús Sánchez"}],"citation-key":"lopezMARStructurebasedSearch2020b","container-title":"Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems","DOI":"10.1145/3365438.3410947","event":"MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems","event-place":"Virtual Event Canada","ISBN":"978-1-4503-7019-6","issued":{"date-parts":[[2020,10,18]]},"note":"00001","page":"57-67","publisher":"ACM","publisher-place":"Virtual Event Canada","source":"DOI.org (Crossref)","title":"MAR: a structure-based search engine for models","title-short":"MAR","type":"paper-conference","URL":"https://dl.acm.org/doi/10.1145/3365438.3410947"},
{"id":"LopsCB","author":[{"family":"Lops","given":"Pasquale"},{"family":"Gemmis","given":"Marco","non-dropping-particle":"de"},{"family":"Semeraro","given":"Giovanni"}],"citation-key":"LopsCB","container-title":"Recommender systems handbook","editor":[{"family":"Ricci","given":"Francesco"},{"family":"Rokach","given":"Lior"},{"family":"Shapira","given":"Bracha"},{"family":"Kantor","given":"Paul B."}],"ISBN":"978-0-387-85819-7","issued":{"date-parts":[[2011]]},"page":"73-105","publisher":"Springer","title":"Content-based recommender systems: State of the art and trends.","type":"chapter","URL":"http://dblp.uni-trier.de/db/reference/rsh/rsh2011.html#LopsGS11"},
{"id":"lorenzoniMachineLearningModel2021","abstract":"Data scientists often develop machine learning models to solve a variety of problems in the industry and academy but not without facing several challenges in terms of Model Development. The problems regarding Machine Learning Development involves the fact that such professionals do not realize that they usually perform ad-hoc practices that could be improved by the adoption of activities presented in the Software Engineering Development Lifecycle. Of course, since machine learning systems are different from traditional Software systems, some differences in their respective development processes are to be expected. In this context, this paper is an effort to investigate the challenges and practices that emerge during the development of ML models from the software engineering perspective by focusing on understanding how software developers could benefit from applying or adapting the traditional software engineering process to the Machine Learning workflow.","accessed":{"date-parts":[[2021,3,25]]},"author":[{"family":"Lorenzoni","given":"Giuliano"},{"family":"Alencar","given":"Paulo"},{"family":"Nascimento","given":"Nathalia"},{"family":"Cowan","given":"Donald"}],"citation-key":"lorenzoniMachineLearningModel2021","container-title":"arXiv:2102.07574 [cs]","issued":{"date-parts":[[2021,2,15]]},"note":"00000","source":"arXiv.org","title":"Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review","title-short":"Machine Learning Model Development from a Software Engineering Perspective","type":"article-journal","URL":"http://arxiv.org/abs/2102.07574"},
{"id":"lourencoChoosingRightNoSQL2015","accessed":{"date-parts":[[2021,2,6]]},"author":[{"family":"Lourenço","given":"João Ricardo"},{"family":"Cabral","given":"Bruno"},{"family":"Carreiro","given":"Paulo"},{"family":"Vieira","given":"Marco"},{"family":"Bernardino","given":"Jorge"}],"citation-key":"lourencoChoosingRightNoSQL2015","container-title":"Journal of Big Data","container-title-short":"Journal of Big Data","DOI":"10.1186/s40537-015-0025-0","ISSN":"2196-1115","issue":"1","issued":{"date-parts":[[2015,12]]},"note":"00120","page":"18","source":"DOI.org (Crossref)","title":"Choosing the right NoSQL database for the job: a quality attribute evaluation","title-short":"Choosing the right NoSQL database for the job","type":"article-journal","URL":"https://journalofbigdata.springeropen.com/articles/10.1186/s40537-015-0025-0","volume":"2"},
{"id":"LowcodeAbstractionLevels","accessed":{"date-parts":[[2020,4,8]]},"citation-key":"LowcodeAbstractionLevels","title":"Low-code and abstraction levels - Stefan Dreverman - Medium","type":"webpage","URL":"https://medium.com/@stefan.dreverman/low-code-and-abstraction-levels-e9412e9e5329"},
{"id":"LowCodeDevelopment","accessed":{"date-parts":[[2020,4,8]]},"citation-key":"LowCodeDevelopment","title":"Low Code Development Platforms: A Complete Guide | QuickBase","type":"webpage","URL":"https://www.quickbase.com/resources/articles/low-code-development-platforms"},
{"id":"LowCodeDevelopmentPlatform","abstract":"Low-Code Development Platform Economic (Free) Survey Unlimited free version Free Trial Period OutSystems 60 days 30 days 15 days Visual LANSA Appian Kissflow (14 days) Mendix FileMaker (45 days) Microsoft PowerApps Zoho Creator (15 days) Kony Heroku (verifies accoun...","accessed":{"date-parts":[[2020,2,11]]},"citation-key":"LowCodeDevelopmentPlatform","container-title":"Google Docs","title":"Low-Code Development Platform Economic (Free) Survey","type":"webpage","URL":"https://docs.google.com/document/d/1F1pLpNudMnth3bxYd1RyfjkxxUYeTbi-qa3BTJmat_8/edit?ts=5e3d9277&usp=embed_facebook"},
{"id":"LowcodeNocodeDevelopment","accessed":{"date-parts":[[2020,3,29]]},"citation-key":"LowcodeNocodeDevelopment","title":"Low-code and no-code development platforms","type":"webpage","URL":"https://www.computerweekly.com/feature/Low-code-and-no-code-development-platforms"},
{"id":"LowcodePlatformsFuture2020","abstract":"The future of low-code platforms is improving which eliminates the progression of the hard side of coding. The trend of low coding is now evolving towards data sciences and analytics.","accessed":{"date-parts":[[2021,3,18]]},"citation-key":"LowcodePlatformsFuture2020","container-title":"Big Data Analytics News","issued":{"date-parts":[[2020,7,15]]},"note":"00000","section":"Analytics","title":"Low-code platforms: The Future of Data Analytics","title-short":"Low-code platforms","type":"post-weblog","URL":"https://bigdataanalyticsnews.com/low-code-platforms-future-of-data-analytics/"},
{"id":"LowCodePlatformsSurvey","abstract":"An online LaTeX editor that's easy to use. No installation, real-time collaboration, version control, hundreds of LaTeX templates, and more.","accessed":{"date-parts":[[2020,2,11]]},"citation-key":"LowCodePlatformsSurvey","title":"Low-Code Platforms Survey_MoDELS conference_v2","type":"webpage","URL":"https://www.overleaf.com/4361461464kxwpjrzcvszz"},
{"id":"LowcodeWillDigital","citation-key":"LowcodeWillDigital","note":"00000","page":"21","source":"Zotero","title":"Low-code will save the Digital Transformation","type":"article-journal"},
{"id":"loza14recsys","abstract":"In this paper, we discuss the development of a hybrid multi-strategy book recommendation system using Linked Open Data. Our approach builds on training individual base recommenders and using global popularity scores as generic recommenders. The results of the individual recommenders are combined using stacking regression and rank aggregation. We show that this approach delivers very good results in different recommendation settings and also allows for incorporating diversity of recommendations.","author":[{"family":"Ristoski","given":"Petar"},{"family":"Loza Mencía","given":"Eneldo"},{"family":"Paulheim","given":"Heiko"}],"citation-key":"loza14recsys","collection-title":"Communications in computer and information science","container-title":"Semantic web evaluation challenge, proceedings (ESWC 2014)","ISBN":"978-3-319-12023-2","issued":{"date-parts":[[2014,5]]},"page":"150-156","publisher":"Springer","title":"A hybrid multi-strategy recommender system using linked open data","type":"chapter","URL":"http://2014.eswc-conferences.org/sites/default/files/eswc2014-challenges_rs_submission_12.pdf","volume":"475"},
{"id":"Lu2007","abstract":"Published scientific articles are linked together into a graph, the citation graph, through their citations. This paper explores the notion of similarity based on connectivity alone, and proposes several algorithms to quantify it. Our metrics take advantage of the local neighborhoods of the nodes in the citation graph. Two variants of link-based similarity estimation between two nodes are described, one based on the separate local neighborhoods of the nodes, and another based on the joint local neighborhood expanded from both nodes at the same time. The algorithms are implemented and evaluated on a subgraph of the citation graph of computer science in a retrieval context. The results are compared with text-based similarity, and demonstrate the complementarity of link-based and text-based retrieval.","author":[{"family":"Lu","given":"Wangzhong"},{"family":"Janssen","given":"J."},{"family":"Milios","given":"E."},{"family":"Japkowicz","given":"N."},{"family":"Zhang","given":"Yongzheng"}],"citation-key":"Lu2007","container-title":"Knowledge and Information Systems","DOI":"10.1007/s10115-006-0023-9","ISSN":"0219-3116","issue":"1","issued":{"date-parts":[[2007,1,1]]},"page":"105-129","title":"Node similarity in the citation graph","type":"article-journal","URL":"https://doi.org/10.1007/s10115-006-0023-9","volume":"11"},
{"id":"Lu20192108","abstract":"Accurate channel estimation and signal detection are very difficult for orthogonal frequency division multiplexing (OFDM) receiver under the limit of one-bit complex quantization which can greatly reduce power loss and systematic complexity. In this paper, we propose an improved one-bit OFDM receiver based on model-driven deep learning (DL). Different from the conventional one-bit receiver based on autoencoder architecture of DL, our proposed one-bit receiver consists of channel estimation module and signal detection module, and each of which is constructed by a deep neural network after using traditional communication method as initialization. Simulation results show that our scheme is superior to AE-OFDM based on autoencoder in view of bit error rate (BER) performance. © 2019 IEEE.","author":[{"family":"Lu","given":"Z."},{"family":"Wei","given":"L."},{"family":"Xu","given":"Y."}],"citation-key":"Lu20192108","collection-title":"2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019","DOI":"10.1109/ICCC47050.2019.9064391","ISBN":"978-1-72814-743-7","issued":{"date-parts":[[2019]]},"page":"2108-2112","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"An improved one-bit OFDM receiver based on model-driven deep learning","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084031900&doi=10.1109%2fICCC47050.2019.9064391&partnerID=40&md5=b9e28cd50bdf94a67c6138370bd027c0"},
{"id":"luan_aroma:_2018","abstract":"We next introduce several notations and definitions which are used to compute the features of a code snippet. Definition 1 (Keyword tokens). This is the set of all tokens in a language whose values are fixed as part of the language.","accessed":{"date-parts":[[2019,6,13]]},"author":[{"family":"Luan","given":"Sifei"},{"family":"Yang","given":"Di"},{"family":"Barnaby","given":"Celeste"},{"family":"Sen","given":"Koushik"},{"family":"Chandra","given":"Satish"}],"citation-key":"luan_aroma:_2018","container-title":"arXiv:1812.01158 [cs]","issued":{"date-parts":[[2018,12]]},"title":"Aroma: Code Recommendation via Structural Code Search","title-short":"Aroma","type":"article-journal","URL":"http://arxiv.org/abs/1812.01158"},
{"id":"lucasCollabRDLLanguageCoordinate2017","accessed":{"date-parts":[[2017,2,27]]},"author":[{"family":"Lucas","given":"Edson M."},{"family":"Oliveira","given":"Toacy C."},{"family":"Farias","given":"Kleinner"},{"family":"Alencar","given":"Paulo S.C."}],"citation-key":"lucasCollabRDLLanguageCoordinate2017","container-title":"Journal of Systems and Software","DOI":"10.1016/j.jss.2017.01.031","ISSN":"01641212","issued":{"date-parts":[[2017,2]]},"source":"CrossRef","title":"CollabRDL: A language to coordinate collaborative reuse","title-short":"CollabRDL","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0164121217300225"},
{"id":"lucene","citation-key":"lucene","title":"Apache lucene core","type":"article-journal","URL":"https://lucene.apache.org/core/"},
{"id":"lucioFTGPMIntegrated2013","abstract":"In this paper, we describe our ongoing work on model transformation chains. Model transformation chains refer to the sequences of model transformations in Model Driven Engineering (MDE). The transformations represent and formalise typical model/software engineering activities, and their chaining is the natural composition of such activities. Model transformation chains found in industrial practice vary widely, depending on the specific domain they are used in. By explicitly modelling development activities, these activities can be analysed and the MDE process may be improved. As a step towards such analyses, we propose an integrated framework to describe all the artifacts involved in model transformation chains, as well as the means to execute “enact” those chains. We describe the Formalism Transformation Graph + Process Model (FTG+PM) which is at the heart of our framework in detail.","accessed":{"date-parts":[[2015,3,24]]},"author":[{"family":"Lúcio","given":"Levi"},{"family":"Mustafiz","given":"Sadaf"},{"family":"Denil","given":"Joachim"},{"family":"Vangheluwe","given":"Hans"},{"family":"Jukss","given":"Maris"}],"citation-key":"lucioFTGPMIntegrated2013","collection-number":"7916","collection-title":"Lecture Notes in Computer Science","container-title":"SDL 2013: Model-Driven Dependability Engineering","editor":[{"family":"Khendek","given":"Ferhat"},{"family":"Toeroe","given":"Maria"},{"family":"Gherbi","given":"Abdelouahed"},{"family":"Reed","given":"Rick"}],"ISBN":"978-3-642-38910-8 978-3-642-38911-5","issued":{"date-parts":[[2013]]},"page":"182-202","publisher":"Springer Berlin Heidelberg","source":"link.springer.com","title":"FTG+PM: An Integrated Framework for Investigating Model Transformation Chains","title-short":"FTG+PM","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-642-38911-5_11"},
{"id":"lucioModelTransformationIntents2014","accessed":{"date-parts":[[2015,3,20]]},"author":[{"family":"Lúcio","given":"Levi"},{"family":"Amrani","given":"Moussa"},{"family":"Dingel","given":"Jürgen"},{"family":"Lambers","given":"Leen"},{"family":"Salay","given":"Rick"},{"family":"Selim","given":"Gehan MK"},{"family":"Syriani","given":"Eugene"},{"family":"Wimmer","given":"Manuel"}],"citation-key":"lucioModelTransformationIntents2014","container-title":"Software & Systems Modeling","issued":{"date-parts":[[2014]]},"page":"1–38","source":"Google Scholar","title":"Model transformation intents and their properties","type":"article-journal","URL":"http://link.springer.com/article/10.1007/s10270-014-0429-x"},
{"id":"lucioTechniqueAutomaticValidation2010","author":[{"family":"Lúcio","given":"Levi"},{"family":"Barroca","given":"Bruno"},{"family":"Amaral","given":"Vasco"}],"citation-key":"lucioTechniqueAutomaticValidation2010","container-title":"Model Driven Engineering Languages and Systems","DOI":"10.1007/978-3-642-16145-2_10","issued":{"date-parts":[[2010]]},"page":"136–150","title":"A Technique for Automatic Validation of Model Transformations","type":"article-journal","volume":"6394"},
{"id":"luckeyHighqualitySpecificationSelfadaptive2013","accessed":{"date-parts":[[2016,9,21]]},"author":[{"family":"Luckey","given":"Markus"},{"family":"Engels","given":"Gregor"}],"citation-key":"luckeyHighqualitySpecificationSelfadaptive2013","container-title":"Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","issued":{"date-parts":[[2013]]},"page":"143–152","publisher":"IEEE Press","source":"Google Scholar","title":"High-quality specification of self-adaptive software systems","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=2487359"},
{"id":"lucredioMOOGLEMetamodelbasedModel2010","author":[{"family":"Lucrédio","given":"Daniel"},{"family":"M. Fortes","given":"Renata P."},{"family":"Whittle","given":"Jon"}],"citation-key":"lucredioMOOGLEMetamodelbasedModel2010","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-010-0167-7","issue":"2","issued":{"date-parts":[[2010]]},"page":"183–208","title":"MOOGLE: a metamodel-based model search engine","type":"article-journal","volume":"11"},
{"id":"ludovicoModelRepairQualityBased2020","abstract":"Domain modeling is a core activity in Model-Driven Engineering, and these models must be correct. A large number of artifacts may be constructed on top of these domain models, such as instance models, transformations, and editors. Similar to any other software artifact, domain models are subject to the introduction of errors during the modeling process. There are a number of existing tools that reduce the burden of manually dealing with correctness issues in models. Although various approaches have been proposed to support the quality assessment of modeling artifacts in the past decade, the quality of the automatically repaired models has not been the focus of repairing processes. In this paper, we propose the integration of an automatic evaluation of domain models based on a quality model with a framework for personalized and automatic model repair. The framework uses reinforcement learning to find the best sequence of actions for repairing a broken model.","accessed":{"date-parts":[[2022,5,24]]},"author":[{"family":"Ludovico","given":"Iovino"},{"family":"Barriga","given":"Angela"},{"family":"Rutle","given":"Adrian"},{"family":"Heldal","given":"Rogardt"}],"citation-key":"ludovicoModelRepairQualityBased2020","container-title":"The Journal of Object Technology","container-title-short":"JOT","DOI":"10.5381/jot.2020.19.2.a17","ISSN":"1660-1769","issue":"2","issued":{"date-parts":[[2020]]},"page":"17:1","source":"DOI.org (Crossref)","title":"Model Repair with Quality-Based Reinforcement Learning.","type":"article-journal","URL":"http://www.jot.fm/contents/issue_2020_02/article17.html","volume":"19"},
{"id":"LUNG2004227","abstract":"The artifacts constituting a software system are sometimes unnecessarily coupled with one another or may drift over time. As a result, support of software partitioning, recovery, and restructuring is often necessary. This paper presents studies on applying the numerical taxonomy clustering technique to software applications. The objective is to facilitate those activities just mentioned and to improve design, evaluation and evolution. Numerical taxonomy is mathematically simple and yet it is a useful mechanism for component clustering and software partitioning. The technique can be applied at various levels of abstraction or to different software life-cycle phases. We have applied the technique to: (1) software partitioning at the software architecture design phase; (2) grouping of components based on the source code to recover the software architecture in the reverse engineering process; (3) restructuring of a software to support evolution in the maintenance stage; and (4) improving cohesion and reducing coupling for source code. In this paper, we provide an introduction to the numerical taxonomy, discuss our experiences in applying the approach to various areas, and relate the technique to the context of similar work.","author":[{"family":"Lung","given":"Chung-Horng"},{"family":"Zaman","given":"Marzia"},{"family":"Nandi","given":"Amit"}],"citation-key":"LUNG2004227","container-title":"Journal of Systems and Software","ISSN":"0164-1212","issue":"2","issued":{"date-parts":[[2004]]},"page":"227 - 244","title":"Applications of clustering techniques to software partitioning, recovery and restructuring","type":"article-journal","URL":"http://www.sciencedirect.com/science/article/pii/S0164121203002346","volume":"73"},
{"id":"Luo2019504","abstract":"Accurate and automatic analysis of breast MRI plays a vital role in early diagnosis and successful treatment planning for breast cancer. Due to the heterogeneity nature, precise diagnosis of tumors remains a challenging task. In this paper, we propose to identify breast tumor in MRI by Cosine Margin Sigmoid Loss (CMSL) with deep learning (DL) and localize possible cancer lesion by COrrelation Attention Map (COAM) based on the learned features. The CMSL embeds tumor features onto a hyper-sphere and imposes a decision margin through cosine constraints. In this way, the DL model could learn more separable inter-class features and more compact intra-class features in the angular space. Furthermore, we utilize the correlations among feature vectors to generate attention maps that could accurately localize cancer candidates with only image-level labels. We build the largest breast cancer dataset involving 10,290 DCE-MRI scan volumes for developing and evaluating the proposed methods. The model driven by CMSL achieved a classification accuracy of 0.855 and AUC of 0.902 on the testing set, with sensitivity and specificity of 0.857 and 0.852, respectively, outperforming competitive methods overall. In addition, the proposed COAM accomplished more accurate localization of the cancer center compared with other state-of-the-art weakly supervised localization method. © Springer Nature Switzerland AG 2019.","author":[{"family":"Luo","given":"L."},{"family":"Chen","given":"H."},{"family":"Wang","given":"X."},{"family":"Dou","given":"Q."},{"family":"Lin","given":"H."},{"family":"Zhou","given":"J."},{"family":"Li","given":"G."},{"family":"Heng","given":"P.-A."}],"citation-key":"Luo2019504","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-32251-9_55","editor":[{"family":"Shen D., Yap P.-T.","given":"Liu T.","suffix":"Peters T.M., Khan A., Staib L.H., Essert C., Zhou S."}],"ISBN":"9783030322502","ISSN":"03029743","issued":{"date-parts":[[2019]]},"page":"504-512","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Deep angular embedding and feature correlation attention for breast MRI cancer analysis","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075644267&doi=10.1007%2f978-3-030-32251-9_55&partnerID=40&md5=e5dd41a371a19b3d5cfaf040a8035d26","volume":"11767 LNCS"},
{"id":"luongFACOSFindingAPI2021","abstract":"Collecting API examples, usages, and mentions relevant to a specific API method over discussions on venues such as Stack Overflow is not a trivial problem. It requires efforts to correctly recognize whether the discussion refers to the API method that developers/tools are searching for. The content of the thread, which consists of both text paragraphs describing the involvement of the API method in the discussion and the code snippets containing the API invocation, may refer to the given API method. Leveraging this observation, we develop FACOS, a context-specific algorithm to capture the semantic and syntactic information of the paragraphs and code snippets in a discussion. FACOS combines a syntactic word-based score with a score from a predictive model fine-tuned from CodeBERT. FACOS beats the state-of-the-art approach by 13.9% in terms of F1-score.","accessed":{"date-parts":[[2021,11,21]]},"author":[{"family":"Luong","given":"Kien"},{"family":"Hadi","given":"Mohammad"},{"family":"Thung","given":"Ferdian"},{"family":"Fard","given":"Fatemeh"},{"family":"Lo","given":"David"}],"citation-key":"luongFACOSFindingAPI2021","container-title":"arXiv:2111.07238 [cs]","issued":{"date-parts":[[2021,11,13]]},"note":"00000","source":"arXiv.org","title":"FACOS: Finding API Relevant Contents on Stack Overflow with Semantic and Syntactic Analysis","title-short":"FACOS","type":"article-journal","URL":"http://arxiv.org/abs/2111.07238"},
{"id":"Lutz2021583","abstract":"Computer simulations are used in precision medicine to assist in adapting treatment plans for varying patient characteristics, especially for diseases like HIV where these characteristics have a major impact on disease trajectory. However, simulations are computationally intensive, which can be prohibitive at scale. Meta-models for HIV progression have been developed previously to approximate these simulation results more efficiently, but we are interested in determining how much data is required to build an accurate metamodel. Using many different amounts of data from two HIV simulation models, we build machine learning classification meta-models to predict if an HIV patient is at risk for AIDS based on treatment parameters. Our findings indicate that the amount required to achieve high meta-model accuracy varies for different computer simulations. We are able to achieve near-perfect accuracy with one of our models using limited data, while the other model requires more extensive data to achieve stable accuracy. © 2021 Society for Modeling & Simulation International (SCS).","author":[{"family":"Lutz","given":"C.B."},{"family":"Giabbanelli","given":"P.J."},{"family":"Fisher","given":"A."},{"family":"Mago","given":"V.K."}],"citation-key":"Lutz2021583","collection-title":"Simulation Series","editor":[{"family":"Martin C.R., Blas M.J.","given":"Psijas A.I."}],"ISSN":"07359276","issue":"2","issued":{"date-parts":[[2021]]},"number":"2","page":"583-594","publisher":"The Society for Modeling and Simulation International","title":"How many costly simulations do we need to create accurate metamodels? A case study on predicting hiv viral load in response to clinically relevant intervention scenarios","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118574816&partnerID=40&md5=9eea6773e6551052e5a35c095cf09935","volume":"53"},
{"id":"lv_codehow:_2015","abstract":"Over the years of software development, a vast amount of source code has been accumulated. Many code search tools were proposed to help programmers reuse previouslywritten code by performing free-text queries over a large-scale codebase. Our experience shows that the accuracy of these code search tools are often unsatisfactory. One major reason is that existing tools lack of query understanding ability. In this paper, we propose CodeHow, a code search technique that can recognize potential APIs a user query refers to. Having understood the potentially relevant APIs, CodeHow expands the query with the APIs and performs code retrieval by applying the Extended Boolean model, which considers the impact of both text similarity and potential APIs on code search. We deploy the backend of CodeHow as a Microsoft Azure service and implement the frontend as a Visual Studio extension. We evaluate CodeHow on a large-scale codebase consisting of 26K C# projects downloaded from GitHub. The experimental results show that when the top 1 results are inspected, CodeHow achieves a precision score of 0.794 (i.e., 79.4% of the first returned results are relevant code snippets). The results also show that CodeHow outperforms conventional code search tools. Furthermore, we perform a controlled experiment and a survey of Microsoft developers. The results confirm the usefulness and effectiveness of CodeHow in programming practices.","accessed":{"date-parts":[[2019,9,11]]},"author":[{"family":"Lv","given":"Fei"},{"family":"Zhang","given":"Hongyu"},{"family":"Lou","given":"Jian-guang"},{"family":"Wang","given":"Shaowei"},{"family":"Zhang","given":"Dongmei"},{"family":"Zhao","given":"Jianjun"}],"citation-key":"lv_codehow:_2015","container-title":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","event-place":"Lincoln, NE, USA","ISBN":"978-1-5090-0025-8","issued":{"date-parts":[[2015,11]]},"page":"260-270","publisher":"IEEE","publisher-place":"Lincoln, NE, USA","title":"CodeHow: Effective Code Search Based on API Understanding and Extended Boolean Model (E)","title-short":"CodeHow","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/7372014/"},
{"id":"Ma20212388","abstract":"This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for wideband millimeter-wave (mmWave) massive hybrid multiple-input multiple-output (MIMO) systems, where the angle-delay domain channels' sparsity is exploited for reducing the overhead. First, we consider the uplink channel estimation for time-division duplexing systems. To reduce the uplink pilot overhead for estimating high-dimensional channels from a limited number of radio frequency (RF) chains at the base station (BS), we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Particularly, by exploiting the channels' structured sparsity from an a priori model and learning the integrated trainable parameters from the data samples, the proposed multiple-measurement-vectors learned approximate message passing (MMV-LAMP) network with the devised redundant dictionary can jointly recover multiple subcarriers' channels with significantly enhanced performance. Moreover, we consider the downlink channel estimation and feedback for frequency-division duplexing systems. Similarly, the pilots at the BS and channel estimator at the users can be jointly trained as an encoder and a decoder, respectively. Besides, to further reduce the channel feedback overhead, only the received pilots on part of the subcarriers are fed back to the BS, which can exploit the MMV-LAMP network to reconstruct the spatial-frequency channel matrix. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms state-of-the-art approaches. © 1983-2012 IEEE.","author":[{"family":"Ma","given":"X."},{"family":"Gao","given":"Z."},{"family":"Gao","given":"F."},{"family":"DI Renzo","given":"M."}],"citation-key":"Ma20212388","container-title":"IEEE Journal on Selected Areas in Communications","DOI":"10.1109/JSAC.2021.3087269","ISSN":"07338716","issue":"8","issued":{"date-parts":[[2021]]},"page":"2388-2406","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Model-driven deep learning based channel estimation and feedback for millimeter-wave massive hybrid MIMO systems","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110699290&doi=10.1109%2fJSAC.2021.3087269&partnerID=40&md5=f1490aa707e2a235a005ae41ec3d266a","volume":"39"},
{"id":"Maarek:1991:IRA:126244.126254","author":[{"family":"Maarek","given":"Yoëlle S."},{"family":"Berry","given":"Daniel M."},{"family":"Kaiser","given":"Gail E."}],"citation-key":"Maarek:1991:IRA:126244.126254","container-title":"IEEE Transactions on Software Engineering","container-title-short":"IEEE Trans. Softw. Eng.","ISSN":"0098-5589","issue":"8","issued":{"date-parts":[[1991,8]]},"page":"800-813","title":"An information retrieval approach for automatically constructing software libraries","type":"article-journal","URL":"http://dx.doi.org/10.1109/32.83915","volume":"17"},
{"id":"maccioniQUEPAQUeryingExploring2016","abstract":"Polystore systems (or simply polystores) have been recently proposed to support a common scenario in which enterprise data are stored in a variety of database technologies relying on different data models and languages. Polystores provide a loosely coupled integration of data sources and support the direct access, with the local language, to each specific storage engine to exploit its distinctive features. Given the absence of a global schema, new challenges for accessing data arise in these environments. In fact, it is usually hard to know in advance if a query to a specific data store can be satisfied with data stored elsewhere in the polystore.","accessed":{"date-parts":[[2018,4,16]]},"author":[{"family":"Maccioni","given":"Antonio"},{"family":"Basili","given":"Edoardo"},{"family":"Torlone","given":"Riccardo"}],"citation-key":"maccioniQUEPAQUeryingExploring2016","DOI":"10.1145/2882903.2899393","ISBN":"978-1-4503-3531-7","issued":{"date-parts":[[2016]]},"page":"2133-2136","publisher":"ACM Press","source":"CrossRef","title":"QUEPA: QUerying and Exploring a Polystore by Augmentation","title-short":"QUEPA","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?doid=2882903.2899393"},
{"id":"MachineLearningAutomation","accessed":{"date-parts":[[2021,4,21]]},"citation-key":"MachineLearningAutomation","note":"00000","title":"Machine Learning Automation - Run:AI","type":"webpage","URL":"https://www.run.ai/guides/machine-learning-operations/machine-learning-automation/"},
{"id":"MachineLearningPipelines","abstract":"ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free.","accessed":{"date-parts":[[2021,3,18]]},"citation-key":"MachineLearningPipelines","container-title":"ResearchGate","note":"00001","title":"Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles","title-short":"(17) (PDF) Machine Learning Pipelines","type":"webpage","URL":"https://www.researchgate.net/publication/342377391_Machine_Learning_Pipelines_Provenance_Reproducibility_and_FAIR_Data_Principles"},
{"id":"macias-escrivaSelfadaptiveSystemsSurvey2013","accessed":{"date-parts":[[2016,1,12]]},"author":[{"family":"Macías-Escrivá","given":"Frank D."},{"family":"Haber","given":"Rodolfo"},{"family":"Toro","given":"Raul","non-dropping-particle":"del"},{"family":"Hernandez","given":"Vicente"}],"citation-key":"macias-escrivaSelfadaptiveSystemsSurvey2013","container-title":"Expert Systems with Applications","DOI":"10.1016/j.eswa.2013.07.033","ISSN":"09574174","issue":"18","issued":{"date-parts":[[2013,12]]},"page":"7267-7279","source":"CrossRef","title":"Self-adaptive systems: A survey of current approaches, research challenges and applications","title-short":"Self-adaptive systems","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0957417413005125","volume":"40"},
{"id":"mahdavinejadMachineLearningInternet2018","abstract":"Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration.","accessed":{"date-parts":[[2019,9,7]]},"author":[{"family":"Mahdavinejad","given":"Mohammad Saeid"},{"family":"Rezvan","given":"Mohammadreza"},{"family":"Barekatain","given":"Mohammadamin"},{"family":"Adibi","given":"Peyman"},{"family":"Barnaghi","given":"Payam"},{"family":"Sheth","given":"Amit P."}],"citation-key":"mahdavinejadMachineLearningInternet2018","container-title":"Digital Communications and Networks","container-title-short":"Digital Communications and Networks","DOI":"10.1016/j.dcan.2017.10.002","ISSN":"23528648","issue":"3","issued":{"date-parts":[[2018,8]]},"page":"161-175","source":"DOI.org (Crossref)","title":"Machine learning for internet of things data analysis: a survey","title-short":"Machine learning for internet of things data analysis","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S235286481730247X","volume":"4"},
{"id":"maiaDragonflyToolSimulating2019","abstract":"Drone simulators can provide an abstraction of different applications of drones and facilitate reasoning about distinct situations, in order to evaluate the effectiveness of these applications. In this paper we describe Dragonfly, a simulator of the behaviours of individual and collection of drones in various environments, involving random contextual variables and different environmental settings. Dragonfly supports the use of several drones in applications and evaluates the satisfaction of requirements under normal and exceptional situations. It simulates adaptive behaviours of drones due to exceptional situations. The adaption of drones is based on the use of wrappers implemented using aspect-oriented programming.","accessed":{"date-parts":[[2020,10,5]]},"author":[{"family":"Maia","given":"Paulo Henrique"},{"family":"Vieira","given":"Lucas"},{"family":"Chagas","given":"Matheus"},{"family":"Yu","given":"Yijun"},{"family":"Zisman","given":"Andrea"},{"family":"Nuseibeh","given":"Bashar"}],"citation-key":"maiaDragonflyToolSimulating2019","container-title":"2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","DOI":"10.1109/SEAMS.2019.00022","event":"2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","event-place":"Montreal, QC, Canada","ISBN":"978-1-72813-368-3","issued":{"date-parts":[[2019,5]]},"page":"107-113","publisher":"IEEE","publisher-place":"Montreal, QC, Canada","source":"DOI.org (Crossref)","title":"Dragonfly: a Tool for Simulating Self-Adaptive Drone Behaviours","title-short":"Dragonfly","type":"paper-conference","URL":"https://ieeexplore.ieee.org/document/8787155/"},
{"id":"maiwaldWhatAreReal2019","accessed":{"date-parts":[[2021,3,19]]},"author":[{"family":"Maiwald","given":"Benjamin"},{"family":"Riedle","given":"Benjamin"},{"family":"Scherzinger","given":"Stefanie"}],"citation-key":"maiwaldWhatAreReal2019","container-title":"Advances in Conceptual Modeling","DOI":"10.1007/978-3-030-34146-6_9","editor":[{"family":"Guizzardi","given":"Giancarlo"},{"family":"Gailly","given":"Frederik"},{"family":"Suzana Pitangueira Maciel","given":"Rita"}],"event-place":"Cham","ISBN":"978-3-030-34145-9 978-3-030-34146-6","issued":{"date-parts":[[2019]]},"note":"00000","page":"95-105","publisher":"Springer International Publishing","publisher-place":"Cham","source":"DOI.org (Crossref)","title":"What Are Real JSON Schemas Like?: An Empirical Analysis of Structural Properties","title-short":"What Are Real JSON Schemas Like?","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-030-34146-6_9","volume":"11787"},
{"id":"maki_context","abstract":"Recommendation System in Software Engineering (RSSE) represents a new promising research area, whose goal is to help software developers in their tasks by providing them with contextdependent insights extracted from their current project or taken from best practices. A key challenge here is to retrieve the context from the programming task in order to provide useful recommendations. In this paper, we conduct a survey of RSSEs with a particular focus on different approaches used to extract the context. We propose a feature model to represent some important characteristics of such extraction and identify some open issues.","author":[{"family":"Maki","given":"Sana"},{"family":"Kpodjedo","given":"Sègla"},{"family":"Boussaidi","given":"Ghizlane El"}],"citation-key":"maki_context","issued":{"date-parts":[[2015]]},"page":"10","title":"Context Extraction in Recommendation Systems in Software Engineering: A Preliminary Survey","type":"article-journal"},
{"id":"Malathy20215771","abstract":"Voltage stability primarily depends on the voltage magnitude, phase angle, real and reactive power constraint of the electric power system. Even during emergencies like contingency (outages), the stability of the electric power structure will be enhanced by improving the Loadability Limit (LL) of the transmission sector. Flexibility in the real and reactive power flow in the transmission system is achieved by the Flexible AC Transmission System (FACTS) devices. These devices can be placed anywhere in the transmission sector. To get effective control over the power flow through the transmission lines and to achieve the maximum loadability with the minimal installation cost, optimal choice and placement of FACTS devices are essential. In this manuscript, efforts had been taken to analyze the LL with outages for hybrid electric power structures. The proposed method is simulated and tested with the hybrid version of standard IEEE 30 bus system. Three types of FACTS devices like Thyristor Controlled Series Capacitor (TCSC), Static VAr Compensator (SVC) and Unified Power Flow Controller (UPFC) are efficiently selected and placed in the transmission lines. For the optimal positioning and placement of these devices, the Contingency Severity Index (CSI) and Fast Voltage Stability Index (FVSI) have been used. Differential Evolution (DE) and Modified Differential Evolution (MDE) algorithms are applied to optimize the obtained results. DE is an evolutionary search based soft computing algorithm popularly handed to resolve multifarious problems. MDE is the enhanced version of DE that embraces a prior knowledge about the solution space at every stage of the search. The main focus of this work is (i) to identify the weak branches and buses in the system using CSI and FVSI. (ii) to optimize the number, location and settings of FACTS devices using various soft computing techniques like DE and MDE. (iii) to evaluate ML of a transmission system with FACTS devices under normal and contingency conditions using DE and MDE. (iv) to calculate the cost required for the installation of FACTS devices. (v) to enhance ML of pool and hybrid model of deregulated electric power market with contingency using the optimal number, rating and positioning of FACTS devices. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.","author":[{"family":"Malathy","given":"P."},{"family":"Shunmugalatha","given":"A."}],"citation-key":"Malathy20215771","container-title":"Journal of Ambient Intelligence and Humanized Computing","DOI":"10.1007/s12652-020-02111-x","ISSN":"18685137","issue":"6","issued":{"date-parts":[[2021]]},"page":"5771-5782","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Estimation of loadability limit with N-1 and N-2 outages using evolutionary computation techniques","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085361176&doi=10.1007%2fs12652-020-02111-x&partnerID=40&md5=4719a75f8eea2d90248d8af4d6cd95f7","volume":"12"},
{"id":"maLibRadarFastAccurate2016","accessed":{"date-parts":[[2018,5,16]]},"author":[{"family":"Ma","given":"Ziang"},{"family":"Wang","given":"Haoyu"},{"family":"Guo","given":"Yao"},{"family":"Chen","given":"Xiangqun"}],"citation-key":"maLibRadarFastAccurate2016","DOI":"10.1145/2889160.2889178","ISBN":"978-1-4503-4205-6","issued":{"date-parts":[[2016]]},"page":"653-656","publisher":"ACM Press","source":"Crossref","title":"LibRadar: fast and accurate detection of third-party libraries in Android apps","title-short":"LibRadar","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?doid=2889160.2889178"},
{"id":"mangharamThreeChallengesCyberphysical2016","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Mangharam","given":"Rahul"},{"family":"Abbas","given":"Houssam"},{"family":"Behl","given":"Madhur"},{"family":"Jang","given":"Kuk"},{"family":"Pajic","given":"Miroslav"},{"family":"Jiang","given":"Zhihao"}],"citation-key":"mangharamThreeChallengesCyberphysical2016","container-title":"2016 8th International Conference on Communication Systems and Networks (COMSNETS)","issued":{"date-parts":[[2016]]},"page":"1–8","publisher":"IEEE","source":"Google Scholar","title":"Three challenges in cyber-physical systems","type":"paper-conference","URL":"http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7440015"},
{"id":"Manning:2008:IIR:1394399","author":[{"family":"Manning","given":"Christopher D."},{"family":"Raghavan","given":"Prabhakar"},{"family":"Schütze","given":"Hinrich"}],"citation-key":"Manning:2008:IIR:1394399","event-place":"New York, NY, USA","ISBN":"0-521-86571-9 978-0-521-86571-5","issued":{"date-parts":[[2008]]},"publisher":"Cambridge University Press","publisher-place":"New York, NY, USA","title":"Introduction to information retrieval","type":"book"},
{"id":"mansoNoredundantMetricsUML2003","author":[{"family":"Manso","given":"Ma Esperanza"},{"family":"Genero","given":"Marcela"},{"family":"Piattini","given":"Mario"}],"citation-key":"mansoNoredundantMetricsUML2003","container-title":"Advanced Information Systems Engineering","DOI":"10.1007/3-540-45017-3_11","issued":{"date-parts":[[2003]]},"page":"127–142","title":"No-redundant Metrics for UML Class Diagram Structural Complexity","type":"article-journal","volume":"2681"},
{"id":"mansoorMOMMMultiobjectiveModel2015","accessed":{"date-parts":[[2015,6,17]]},"author":[{"family":"Mansoor","given":"Usman"},{"family":"Kessentini","given":"Marouane"},{"family":"Langer","given":"Philip"},{"family":"Wimmer","given":"Manuel"},{"family":"Bechikh","given":"Slim"},{"family":"Deb","given":"Kalyanmoy"}],"citation-key":"mansoorMOMMMultiobjectiveModel2015","container-title":"Journal of Systems and Software","DOI":"10.1016/j.jss.2014.11.043","ISSN":"01641212","issued":{"date-parts":[[2015,5]]},"page":"423-439","source":"CrossRef","title":"MOMM: Multi-objective model merging","title-short":"MOMM","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S016412121400274X","volume":"103"},
{"id":"mansouryFeedbackLoopBias2020","abstract":"Recommendation algorithms are known to suffer from popularity bias; a few popular items are recommended frequently while the majority of other items are ignored. These recommendations are then consumed by the users, their reaction will be logged and added to the system: what is generally known as a feedback loop. In this paper, we propose a method for simulating the users interaction with the recommenders in an offline setting and study the impact of feedback loop on the popularity bias amplification of several recommendation algorithms. We then show how this bias amplification leads to several other problems such as declining the aggregate diversity, shifting the representation of users' taste over time and also homogenization of the users experience. In particular, we show that the impact of feedback loop is generally stronger for the users who belong to the minority group.","accessed":{"date-parts":[[2022,3,24]]},"author":[{"family":"Mansoury","given":"Masoud"},{"family":"Abdollahpouri","given":"Himan"},{"family":"Pechenizkiy","given":"Mykola"},{"family":"Mobasher","given":"Bamshad"},{"family":"Burke","given":"Robin"}],"citation-key":"mansouryFeedbackLoopBias2020","container-title":"arXiv:2007.13019 [cs]","issued":{"date-parts":[[2020,7,25]]},"source":"arXiv.org","title":"Feedback Loop and Bias Amplification in Recommender Systems","type":"article-journal","URL":"http://arxiv.org/abs/2007.13019"},
{"id":"mantzCoevolvingMetamodelsTheir2015","accessed":{"date-parts":[[2015,10,29]]},"author":[{"family":"Mantz","given":"Florian"},{"family":"Taentzer","given":"Gabriele"},{"family":"Lamo","given":"Yngve"},{"family":"Wolter","given":"Uwe"}],"citation-key":"mantzCoevolvingMetamodelsTheir2015","container-title":"Science of Computer Programming","DOI":"10.1016/j.scico.2015.01.002","ISSN":"01676423","issued":{"date-parts":[[2015,6]]},"page":"2-43","source":"CrossRef","title":"Co-evolving meta-models and their instance models: A formal approach based on graph transformation","title-short":"Co-evolving meta-models and their instance models","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0167642315000106","volume":"104"},
{"id":"mantzCustomizingModelMigrations2013","author":[{"family":"Mantz","given":"Florian"},{"family":"Taentzer","given":"Gabriele"},{"family":"Lamo","given":"Yngve"}],"citation-key":"mantzCustomizingModelMigrations2013","container-title":"Proceedings of the 2013 International Workshop on Principles of Software Evolution - IWPSE 2013","DOI":"10.1145/2501543.2501545","issued":{"date-parts":[[2013]]},"page":"1","title":"Customizing model migrations by rule schemes","type":"article-journal"},
{"id":"ManuallyConfigureTelegraf","accessed":{"date-parts":[[2021,1,11]]},"citation-key":"ManuallyConfigureTelegraf","note":"00000","title":"Manually configure Telegraf for InfluxDB v2.0 | InfluxDB OSS 2.0 Documentation","type":"webpage","URL":"https://docs.influxdata.com/influxdb/v2.0/write-data/no-code/use-telegraf/manual-config/"},
{"id":"MAO201757","author":[{"family":"Mao","given":"Ke"},{"family":"Capra","given":"Licia"},{"family":"Harman","given":"Mark"},{"family":"Jia","given":"Yue"}],"citation-key":"MAO201757","container-title":"Journal of Systems and Software","ISSN":"0164-1212","issued":{"date-parts":[[2017]]},"page":"57 - 84","title":"A survey of the use of crowdsourcing in software engineering","type":"article-journal","URL":"http://www.sciencedirect.com/science/article/pii/S0164121216301832","volume":"126"},
{"id":"Mao20222870","abstract":"For conventional signaling, the length of the orthogonal pilot is required at least equal to the total number of user antennas. However, it is not recommended in the Internet of Things (IoT) due to the expensive cost paid in massive connectivities. Thanks to the sporadic nature of the massive connected users where a considerable fraction of users are inactive within a coherence time, the nonorthogonal pilot can be utilized with the joint channel estimation and active-user detection being modeled as a compressive sensing problem. According to the different antenna configuration methods employed by the base station, the constructed problems in this work are formulated into the single measurement vector and the multiple measurement vectors recovery problems. Also, we develop a model-driven deep learning algorithm to solve the problems based on the traditional alternative direction method of multipliers (ADMM) algorithm, where the iteration operation is unfolded into the network layer. The network parameters are learned with the help of the stochastic gradient descent algorithm. Simulation results show that the proposed approach can achieve better performance than an ADMM algorithm under the same computational complexity. © 2014 IEEE.","author":[{"family":"Mao","given":"Z."},{"family":"Liu","given":"X."},{"family":"Peng","given":"M."},{"family":"Chen","given":"Z."},{"family":"Wei","given":"G."}],"citation-key":"Mao20222870","container-title":"IEEE Internet of Things Journal","DOI":"10.1109/JIOT.2021.3097133","ISSN":"23274662","issue":"4","issued":{"date-parts":[[2022]]},"page":"2870-2881","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Joint channel estimation and active-user detection for massive access in internet of things-A deep learning approach","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110845607&doi=10.1109%2fJIOT.2021.3097133&partnerID=40&md5=aacac6e3fdce82bb9bd0fdec04fcee10","volume":"9"},
{"id":"maozFrameworkRelatingSyntactic","accessed":{"date-parts":[[2015,9,10]]},"author":[{"family":"Maoz","given":"Shahar"},{"family":"Ringert","given":"Jan Oliver"}],"citation-key":"maozFrameworkRelatingSyntactic","source":"Google Scholar","title":"A Framework for Relating Syntactic and Semantic Model Differences","type":"article-journal","URL":"http://www.cs.tau.ac.il/~ringert/publications/MR15synsemdiff.pdf"},
{"id":"maqbool2007hierarchical","author":[{"family":"Maqbool","given":"Onaiza"},{"family":"Babri","given":"Haroon"}],"citation-key":"maqbool2007hierarchical","container-title":"IEEE Transactions on Software Engineering","issue":"11","issued":{"date-parts":[[2007]]},"page":"759-780","title":"Hierarchical clustering for software architecture recovery","type":"article-journal","volume":"33"},
{"id":"Margaria2021393","abstract":"With the heterogeneity of the industry 4.0 world, and more generally of the Cyberphysical Systems realm, the quest towards a platform approach to solve the interoperability problem is front and centre to any system and system-of-systems project. Traditional approaches cover individual aspects, like data exchange formats and published interfaces. They may adhere to some standard, however they hardly cover the production of the integration layer, which is implemented as bespoke glue code that is hard to produce and even harder to maintain. Therefore, the traditional integration approach often leads to poor code quality, further increasing the time and cost and reducing the agility, and a high reliance on the individual development skills. We are instead tackling the interoperability challenge by building a model driven/low-code Digital Thread platform that 1) systematizes the integration methodology, 2) provides methods and techniques for the individual integrations based on a layered Domain Specific Languages (DSL) approach, 3) through the DSLs it covers the integration space domain by domain, technology by technology, and is thus highly generalizable and reusable, 4) showcases a first collection of examples from the domains of robotics, IoT, data analytics, AI/ML and web applications, 5) brings cohesiveness to the aforementioned heterogeneous platform, and 6) is easier to understand and maintain, even by not specialized programmers. We showcase the power, versatility and the potential of the Digital Thread platform on four interoperability case studies: the generic extension to REST services, to robotics through the UR family of robots, to the integration of various external databases (for data integration) and to the provision of data analytics capabilities in R. © 2021, The Author(s).","author":[{"family":"Margaria","given":"T."},{"family":"Chaudhary","given":"H.A.A."},{"family":"Guevara","given":"I."},{"family":"Ryan","given":"S."},{"family":"Schieweck","given":"A."}],"citation-key":"Margaria2021393","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-89159-6_25","editor":[{"family":"Margaria T., Margaria T.","given":"Steffen B."}],"ISBN":"9783030891589","ISSN":"03029743","issued":{"date-parts":[[2021]]},"page":"393-413","publisher":"Springer Science and Business Media Deutschland GmbH","title":"The interoperability challenge: Building a model-driven digital thread platform for CPS","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118123247&doi=10.1007%2f978-3-030-89159-6_25&partnerID=40&md5=e744fbe407311e72660f74fc785511fc","volume":"13036 LNCS"},
{"id":"margariaInteroperabilityChallengeBuilding2021a","abstract":"With the heterogeneity of the industry 4.0 world, and more generally of the Cyberphysical Systems realm, the quest towards a platform approach to solve the interoperability problem is front and centre to any system and system-of-systems project. Traditional approaches cover individual aspects, like data exchange formats and published interfaces. They may adhere to some standard, however they hardly cover the production of the integration layer, which is implemented as bespoke glue code that is hard to produce and even harder to maintain. Therefore, the traditional integration approach often leads to poor code quality, further increasing the time and cost and reducing the agility, and a high reliance on the individual development skills. We are instead tackling the interoperability challenge by building a model driven/low-code Digital Thread platform that 1) systematizes the integration methodology, 2) provides methods and techniques for the individual integrations based on a layered Domain Specific Languages (DSL) approach, 3) through the DSLs it covers the integration space domain by domain, technology by technology, and is thus highly generalizable and reusable, 4) showcases a first collection of examples from the domains of robotics, IoT, data analytics, AI/ML and web applications, 5) brings cohesiveness to the aforementioned heterogeneous platform, and 6) is easier to understand and maintain, even by not specialized programmers. We showcase the power, versatility and the potential of the Digital Thread platform on four interoperability case studies: the generic extension to REST services, to robotics through the UR family of robots, to the integration of various external databases (for data integration) and to the provision of data analytics capabilities in R. © 2021, The Author(s).","author":[{"family":"Margaria","given":"T."},{"family":"Chaudhary","given":"H.A.A."},{"family":"Guevara","given":"I."},{"family":"Ryan","given":"S."},{"family":"Schieweck","given":"A."}],"citation-key":"margariaInteroperabilityChallengeBuilding2021a","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-89159-6_25","editor":[{"family":"Margaria T.","given":"Steffen B.","suffix":"Margaria T."}],"ISBN":"9783030891589","ISSN":"03029743","issued":{"date-parts":[[2021]]},"page":"393-413","publisher":"Springer Science and Business Media Deutschland GmbH","title":"The Interoperability Challenge: Building a Model-Driven Digital Thread Platform for CPS","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118123247&doi=10.1007%2f978-3-030-89159-6_25&partnerID=40&md5=e744fbe407311e72660f74fc785511fc","volume":"13036 LNCS"},
{"id":"marozzoWorkflowManagementSystem2018","abstract":"The extraction of useful information from data is often a complex process that can be conveniently modeled as a data analysis workflow. When very large data sets must be analyzed and/or complex data mining algorithms must be executed, data analysis workflows may take very long times to complete their execution. Therefore, efficient systems are required for the scalable execution of data analysis workflows, by exploiting the computing services of the Cloud platforms where data is increasingly being stored. The objective of the paper is to demonstrate how Cloud software technologies can be integrated to implement an effective environment for designing and executing scalable data analysis workflows. We describe the design and implementation of the Data Mining Cloud Framework (DMCF), a data analysis system that integrates a visual workflow language and a parallel runtime with the Software-as-aService (SaaS) model. DMCF was designed taking into account the needs of real data mining applications, with the goal of simplifying the development of data mining applications compared to generic workflow management systems that are not specifically designed for this domain. The result is a high-level environment that, through an integrated visual workflow language, minimizes the programming effort, making easier to domain experts the use of common patterns specifically designed for the development and the parallel execution of data mining applications. The DMCF’s visual workflow language, system architecture and runtime mechanisms are presented. We also discuss several data mining workflows developed with DMCF and the scalability obtained executing such workflows on a public Cloud.","accessed":{"date-parts":[[2022,2,3]]},"author":[{"family":"Marozzo","given":"Fabrizio"},{"family":"Talia","given":"Domenico"},{"family":"Trunfio","given":"Paolo"}],"citation-key":"marozzoWorkflowManagementSystem2018","container-title":"IEEE Transactions on Services Computing","container-title-short":"IEEE Trans. Serv. Comput.","DOI":"10.1109/TSC.2016.2589243","ISSN":"1939-1374","issue":"3","issued":{"date-parts":[[2018,5,1]]},"note":"00048","page":"480-492","source":"DOI.org (Crossref)","title":"A Workflow Management System for Scalable Data Mining on Clouds","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/7506329/","volume":"11"},
{"id":"martinezAutomatingExtractionModelBased2015","accessed":{"date-parts":[[2016,1,25]]},"author":[{"family":"Martinez","given":"Jabier"},{"family":"Ziadi","given":"Tewfik"},{"family":"Bissyande","given":"Tegawende F."},{"family":"Klein","given":"Jacques"},{"family":"Traon","given":"Yves","dropping-particle":"le"}],"citation-key":"martinezAutomatingExtractionModelBased2015","DOI":"10.1109/ASE.2015.44","ISBN":"978-1-5090-0025-8","issued":{"date-parts":[[2015,11]]},"page":"396-406","publisher":"IEEE","source":"CrossRef","title":"Automating the Extraction of Model-Based Software Product Lines from Model Variants (T)","type":"paper-conference","URL":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7372028"},
{"id":"marussySpecificationLanguageConsistent2020","accessed":{"date-parts":[[2020,10,24]]},"author":[{"family":"Marussy","given":"Kristóf"},{"family":"Semeráth","given":"Oszkár"},{"family":"A. Babikian","given":"Aren"},{"family":"Varró","given":"Dániel"}],"citation-key":"marussySpecificationLanguageConsistent2020","container-title":"The Journal of Object Technology","container-title-short":"JOT","DOI":"10.5381/jot.2020.19.3.a12","ISSN":"1660-1769","issue":"3","issued":{"date-parts":[[2020]]},"note":"00000","page":"3:1","source":"DOI.org (Crossref)","title":"A Specification Language for Consistent Model Generation based on Partial Models.","type":"article-journal","URL":"http://www.jot.fm/contents/issue_2020_03/article12.html","volume":"19"},
{"id":"Masing2022814","abstract":"Realizing desired properties 'by construction' is a highly appealing goal in the design of safety-critical embedded systems. As verification and validation tasks in this domain are often both challenging and time-consuming, the by-construction paradigm is a promising solution to increase design productivity and reduce design errors. In the XANDAR project, partners from industry and academia develop a toolchain that will advance current development processes by employing a modelbased X-by-Construction (XbC) approach. XANDAR defines a development process, metamodel extensions, a library of safety and security patterns, and investigates many further techniques for design automation, verification, and validation. The developed toolchain will use a hypervisor-based platform, targeting future centralized, AI-capable high-performance embedded processing systems. It is co-developed and validated in both an avionics use case for situation perception and pilot assistance as well as an automotive use case for autonomous driving. © 2022 EDAA.","author":[{"family":"Masing","given":"L."},{"family":"Dorr","given":"T."},{"family":"Schade","given":"F."},{"family":"Becker","given":"J."},{"family":"Keramidas","given":"G."},{"family":"Antonopoulos","given":"C.P."},{"family":"Mavropoulos","given":"M."},{"family":"Tiganourias","given":"E."},{"family":"Kelefouras","given":"V."},{"family":"Antonopoulos","given":"K."},{"family":"Voros","given":"N."},{"family":"Durak","given":"U."},{"family":"Ahlbrecht","given":"A."},{"family":"Zaeske","given":"W."},{"family":"Panagiotou","given":"C."},{"family":"Karadimas","given":"D."},{"family":"Adler","given":"N."},{"family":"Sailer","given":"A."},{"family":"Weber","given":"R."},{"family":"Wilhelm","given":"T."},{"family":"Nemeth","given":"G."},{"family":"Siddiqui","given":"F."},{"family":"Khan","given":"R."},{"family":"Garousi","given":"V."},{"family":"Sezer","given":"S."},{"family":"Morales","given":"V."}],"citation-key":"Masing2022814","collection-title":"Proceedings of the 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022","DOI":"10.23919/DATE54114.2022.9774534","editor":[{"family":"Bolchini C., Verbauwhede I.","given":"Vatajelu I."}],"ISBN":"978-3-9819263-6-1","issued":{"date-parts":[[2022]]},"page":"814-818","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"XANDAR: Exploiting the X-by-construction paradigm in model-based development of safety-critical systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130798783&doi=10.23919%2fDATE54114.2022.9774534&partnerID=40&md5=5d1f18a3ffb87b73a4f78ee543d72f70"},
{"id":"masingXANDARExploitingXbyConstruction2022a","abstract":"Realizing desired properties 'by construction' is a highly appealing goal in the design of safety-critical embedded systems. As verification and validation tasks in this domain are often both challenging and time-consuming, the by-construction paradigm is a promising solution to increase design productivity and reduce design errors. In the XANDAR project, partners from industry and academia develop a toolchain that will advance current development processes by employing a modelbased X-by-Construction (XbC) approach. XANDAR defines a development process, metamodel extensions, a library of safety and security patterns, and investigates many further techniques for design automation, verification, and validation. The developed toolchain will use a hypervisor-based platform, targeting future centralized, AI-capable high-performance embedded processing systems. It is co-developed and validated in both an avionics use case for situation perception and pilot assistance as well as an automotive use case for autonomous driving. © 2022 EDAA.","author":[{"family":"Masing","given":"L."},{"family":"Dorr","given":"T."},{"family":"Schade","given":"F."},{"family":"Becker","given":"J."},{"family":"Keramidas","given":"G."},{"family":"Antonopoulos","given":"C.P."},{"family":"Mavropoulos","given":"M."},{"family":"Tiganourias","given":"E."},{"family":"Kelefouras","given":"V."},{"family":"Antonopoulos","given":"K."},{"family":"Voros","given":"N."},{"family":"Durak","given":"U."},{"family":"Ahlbrecht","given":"A."},{"family":"Zaeske","given":"W."},{"family":"Panagiotou","given":"C."},{"family":"Karadimas","given":"D."},{"family":"Adler","given":"N."},{"family":"Sailer","given":"A."},{"family":"Weber","given":"R."},{"family":"Wilhelm","given":"T."},{"family":"Nemeth","given":"G."},{"family":"Siddiqui","given":"F."},{"family":"Khan","given":"R."},{"family":"Garousi","given":"V."},{"family":"Sezer","given":"S."},{"family":"Morales","given":"V."}],"citation-key":"masingXANDARExploitingXbyConstruction2022a","container-title":"Proceedings of the 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022","DOI":"10.23919/DATE54114.2022.9774534","editor":[{"family":"Bolchini C.","given":"Vatajelu I.","suffix":"Verbauwhede I."}],"ISBN":"978-3-9819263-6-1","issued":{"date-parts":[[2022]]},"page":"814-818","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"XANDAR: Exploiting the X-by-Construction Paradigm in Model-based Development of Safety-critical Systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130798783&doi=10.23919%2fDATE54114.2022.9774534&partnerID=40&md5=5d1f18a3ffb87b73a4f78ee543d72f70"},
{"id":"Masthead2017","accessed":{"date-parts":[[2019,8,22]]},"citation-key":"Masthead2017","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2017.5","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017,1]]},"page":"8-8","source":"DOI.org (Crossref)","title":"Masthead","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7819374/","volume":"34"},
{"id":"Masthead2018","abstract":"Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.","citation-key":"Masthead2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.3571248","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"c2-c2","source":"IEEE Xplore","title":"Masthead","type":"article-journal","volume":"35"},
{"id":"Masthead2020","accessed":{"date-parts":[[2020,9,1]]},"citation-key":"Masthead2020","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2020.2972660","ISSN":"0740-7459, 1937-4194","issue":"4","issued":{"date-parts":[[2020,7]]},"note":"00000","page":"C2-C2","source":"DOI.org (Crossref)","title":"Masthead","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/9121616/","volume":"37"},
{"id":"Masthead2020a","accessed":{"date-parts":[[2020,9,2]]},"citation-key":"Masthead2020a","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2020.2972672","ISSN":"0740-7459, 1937-4194","issue":"5","issued":{"date-parts":[[2020,9]]},"note":"00000","page":"C2-C2","source":"DOI.org (Crossref)","title":"Masthead","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/9173632/","volume":"37"},
{"id":"mathewSoftwareEngineeringTop2018","abstract":"For this theme issue on the 50th anniversary of software engineering (SE), Redirections offers an overview of the twists, turns, and numerous redirections seen over the years in the SE research literature. Nearly a dozen topics have dominated the past few decades of SE research—and these have been redirected many times. Some are gaining popularity, whereas others are becoming increasingly rare. This article is part of a theme issue on software engineering’s 50th anniversary.","author":[{"family":"Mathew","given":"G."},{"family":"Menzies","given":"T."}],"citation-key":"mathewSoftwareEngineeringTop2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.3571230","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"88-93","source":"IEEE Xplore","title":"Software Engineering’s Top Topics, Trends, and Researchers","type":"article-journal","volume":"35"},
{"id":"mattihalliPlantLeafDiseases2018","abstract":"Leaf diseases in plants cause major production and economic losses as well as reduction in both quality and quantity of agricultural crop. It’s better to detect the leaf diseases in early on leaf health and disease detection can facilitate the control of diseases through proper management strategies.","accessed":{"date-parts":[[2018,11,7]]},"author":[{"family":"Mattihalli","given":"Channamallikarjuna"},{"family":"Gedefaye","given":"Edemialem"},{"family":"Endalamaw","given":"Fasil"},{"family":"Necho","given":"Adugna"}],"citation-key":"mattihalliPlantLeafDiseases2018","container-title":"Internet of Things","DOI":"10.1016/j.iot.2018.08.007","ISSN":"25426605","issued":{"date-parts":[[2018,9]]},"page":"67-73","source":"Crossref","title":"Plant leaf diseases detection and auto-medicine","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2542660518300453","volume":"1-2"},
{"id":"mattsonDemonstratingBigDAWGPolystore","abstract":"In most Big Data applications, the data is heterogeneous. As we have been arguing in a series of papers, storage engines should be well suited to the data they hold. Therefore, a system supporting Big Data applications should be able to expose multiple storage engines through a single interface. We call such systems, polystore systems. Our reference implementation of the polystore concept is called BigDAWG (short for the Big Data Analytics Working Group). In this demonstration, we will show the BigDAWG system and a number of polystore applications built to help ocean metagenomics researchers handle their heterogenous Big Data.","author":[{"family":"Mattson","given":"Tim"},{"family":"Gadepally","given":"Vijay"},{"family":"She","given":"Zuohao"},{"family":"Dziedzic","given":"Adam"},{"family":"Parkhurst","given":"Jeff"}],"citation-key":"mattsonDemonstratingBigDAWGPolystore","page":"9","source":"Zotero","title":"Demonstrating the BigDAWG Polystore System for Ocean Metagenomic Analysis","type":"article-journal"},
{"id":"mayminUsingScoutingReports2021a","abstract":"Draft decisions by National Basketball Association (NBA) teams are notoriously poor. Analytics can help but are often dismissed for being too overfit, complex, risky, and incomplete. To address these concerns, we train separate leave-one-out random forests machine learning models for each collegiate NBA prospect from 2006 through 2019 with a conservative utility function on a novel comprehensive dataset including the raw text of scouting reports, combine measurements, on-court stats, mock draft placements, and more. Despite being unable to draft high school or international players, the resulting model outperforms the actual decisions of all but one NBA team, with an average gain of $100 million. Target shuffling shows that the model does not overfit and feature shuffling shows that handedness and ESPN mock draft rating, but not other mock drafts, are most important. NBA teams may be missing value by not following a disciplined, model-driven, prescriptive analytics approach to decision making. © 2021 Operational Research Society 2021.","author":[{"family":"Maymin","given":"P."}],"citation-key":"mayminUsingScoutingReports2021a","container-title":"Journal of Business Analytics","DOI":"10.1080/2573234X.2021.1873077","ISSN":"2573234X","issue":"1","issued":{"date-parts":[[2021]]},"page":"40-54","publisher":"Taylor and Francis Ltd.","title":"Using Scouting Reports Text To Predict NCAA → NBA Performance","type":"article-journal","volume":"4"},
{"id":"mayminUsingScoutingReports2021b","abstract":"Draft decisions by National Basketball Association (NBA) teams are notoriously poor. Analytics can help but are often dismissed for being too overfit, complex, risky, and incomplete. To address these concerns, we train separate leave-one-out random forests machine learning models for each collegiate NBA prospect from 2006 through 2019 with a conservative utility function on a novel comprehensive dataset including the raw text of scouting reports, combine measurements, on-court stats, mock draft placements, and more. Despite being unable to draft high school or international players, the resulting model outperforms the actual decisions of all but one NBA team, with an average gain of $100 million. Target shuffling shows that the model does not overfit and feature shuffling shows that handedness and ESPN mock draft rating, but not other mock drafts, are most important. NBA teams may be missing value by not following a disciplined, model-driven, prescriptive analytics approach to decision making. © 2021 Operational Research Society 2021.","author":[{"family":"Maymin","given":"P."}],"citation-key":"mayminUsingScoutingReports2021b","container-title":"Journal of Business Analytics","DOI":"10.1080/2573234X.2021.1873077","ISSN":"2573234X","issue":"1","issued":{"date-parts":[[2021]]},"page":"40-54","publisher":"Taylor and Francis Ltd.","title":"Using Scouting Reports Text To Predict NCAA → NBA Performance","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100648235&doi=10.1080%2f2573234X.2021.1873077&partnerID=40&md5=bee39fa5c88e4454a15f7e1cc2612fd0","volume":"4"},
{"id":"mazaheriRecommenderSystemScientific2021","abstract":"Scientific datasets and analysis pipelines are increasingly being shared publicly in the interest of open science. However, mechanisms are lacking to reliably identify which pipelines and datasets can appropriately be used together. Given the increasing number of high-quality public datasets and pipelines, this lack of clear compatibility threatens the findability and reusability of these resources. We investigate the feasibility of a collaborative filtering system to recommend pipelines and datasets based on provenance records from previous executions. We evaluate our system using datasets and pipelines extracted from the Canadian Open Neuroscience Platform, a national initiative for open neuroscience. The recommendations provided by our system (AUC$=0.83$) are significantly better than chance and outperform recommendations made by domain experts using their previous knowledge as well as pipeline and dataset descriptions (AUC$=0.63$). In particular, domain experts often neglect low-level technical aspects of a pipeline-dataset interaction, such as the level of pre-processing, which are captured by a provenance-based system. We conclude that provenance-based pipeline and dataset recommenders are feasible and beneficial to the sharing and usage of open-science resources. Future work will focus on the collection of more comprehensive provenance traces, and on deploying the system in production.","accessed":{"date-parts":[[2022,3,8]]},"author":[{"family":"Mazaheri","given":"Mandana"},{"family":"Kiar","given":"Gregory"},{"family":"Glatard","given":"Tristan"}],"citation-key":"mazaheriRecommenderSystemScientific2021","container-title":"arXiv:2108.09275 [cs]","issued":{"date-parts":[[2021,8,20]]},"note":"00000","source":"arXiv.org","title":"A Recommender System for Scientific Datasets and Analysis Pipelines","type":"article-journal","URL":"http://arxiv.org/abs/2108.09275"},
{"id":"mazaheriRecommenderSystemScientific2021a","abstract":"Scientific datasets and analysis pipelines are increasingly being shared publicly in the interest of open science. However, mechanisms are lacking to reliably identify which pipelines and datasets can appropriately be used together. Given the increasing number of high-quality public datasets and pipelines, this lack of clear compatibility threatens the findability and reusability of these resources. We investigate the feasibility of a collaborative filtering system to recommend pipelines and datasets based on provenance records from previous executions. We evaluate our system using datasets and pipelines extracted from the Canadian Open Neuroscience Platform, a national initiative for open neuroscience. The recommendations provided by our system (AUC= 0.83) are significantly better than chance and outperform recommendations made by domain experts using their previous knowledge as well as pipeline and dataset descriptions (AUC= 0.63). In particular, domain experts often neglect low-level technical aspects of a pipeline-dataset interaction, such as the level of pre-processing, which are captured by a provenance-based system. We conclude that provenance-based pipeline and dataset recommenders are feasible and beneficial to the sharing and usage of open-science resources. Future work will focus on the collection of more comprehensive provenance traces, and on deploying the system in production.","accessed":{"date-parts":[[2022,4,11]]},"author":[{"family":"Mazaheri","given":"Mandana"},{"family":"Kiar","given":"Gregory"},{"family":"Glatard","given":"Tristan"}],"citation-key":"mazaheriRecommenderSystemScientific2021a","container-title":"2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS)","DOI":"10.1109/WORKS54523.2021.00006","event":"2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS)","event-place":"St. Louis, MO, USA","ISBN":"978-1-66541-136-3","issued":{"date-parts":[[2021,11]]},"page":"1-8","publisher":"IEEE","publisher-place":"St. Louis, MO, USA","source":"DOI.org (Crossref)","title":"A Recommender System for Scientific Datasets and Analysis Pipelines","type":"paper-conference","URL":"https://ieeexplore.ieee.org/document/9652602/"},
{"id":"mazanekToolDemonstrationTransformation2011","accessed":{"date-parts":[[2016,2,9]]},"author":[{"family":"Mazanek","given":"Steffen"},{"family":"Rutetzki","given":"Christian"},{"family":"Minas","given":"Mark"}],"citation-key":"mazanekToolDemonstrationTransformation2011","container-title":"Applications of Graph Transformations with Industrial Relevance","issued":{"date-parts":[[2011]]},"page":"97–104","publisher":"Springer","source":"Google Scholar","title":"Tool demonstration of the transformation judge","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-642-34176-2_10"},
{"id":"mccallumBuildingMDaocmhianineLSepaercniincgSTeaecrhchniEqunegsines","abstract":"Domain-speci c search engines are growing in popularity because they o er increased accuracy and extra functionality not possible with the general, Web-wide search engines. For example, www.campsearch.com allows complex queries by age-group, size, location and cost over summer camps. Unfortunately these domain-speci c search engines are di cult and timeconsuming to maintain. This paper proposes the use of machine learning techniques to greatly automate the creation and maintenance of domain-speci c search engines. We describe new research in reinforcement learning, information extraction and text classi cation that enables e cient spidering, identifying informative text segments, and populating topic hierarchies. Using these techniques, we have built a demonstration system: a search engine for computer science research papers. It already contains over 50,000 papers and is publicly available at www.cora.justresearch.com.","author":[{"family":"McCallum","given":"Andrew"},{"family":"Nigam","given":"Kamal"},{"family":"Rennie","given":"Jason"},{"family":"Seymore","given":"Kristie"}],"citation-key":"mccallumBuildingMDaocmhianineLSepaercniincgSTeaecrhchniEqunegsines","page":"12","source":"Zotero","title":"BuildingMDaocmhianine-LSepaercniincgSTeaecrhchniEqunegsines with","type":"article-journal"},
{"id":"mcewenDesigningInternetThings2014","author":[{"family":"McEwen","given":"Adrian"},{"family":"Cassimally","given":"Hakim"}],"citation-key":"mcewenDesigningInternetThings2014","edition":"Reprinted with corrections","event-place":"Chichester","ISBN":"978-1-118-43062-0 978-1-118-43063-7 978-1-118-43065-1","issued":{"date-parts":[[2014]]},"note":"OCLC: 862794270","number-of-pages":"324","publisher":"Wiley","publisher-place":"Chichester","source":"Gemeinsamer Bibliotheksverbund ISBN","title":"Designing the Internet of things","type":"book"},
{"id":"McKinney20211279","abstract":"Canonical anomaly detection has been achieved through various means ranging from statistical tests and clustering methods to categorical decision-making and rule-based systems. Each method has its own pros and cons; however, many depend on assumptions. These assumptions can be model driven, such as assuming white Gaussian inputs, or method driven such as linear regression. In any case, assumptions are being made either about the structure of the data or its relationship with other random variables.This work presents a deep learning methodology for anomaly detection, a sampling technique for large data sets, and feature importance analysis. The anomaly detection technique uses an ensemble of learners to predict relationships between benign features and characterizes deviations from these patterns as \"surprisal\"scores. This method identifies malicious network traffic without previous attack behavior knowledge and is applied to data from the Canadian Institute for Cybersecurity. © 2021 IEEE.","author":[{"family":"McKinney","given":"E."},{"family":"Mortensen","given":"D."}],"citation-key":"McKinney20211279","collection-title":"Conference Record - Asilomar Conference on Signals, Systems and Computers","DOI":"10.1109/IEEECONF53345.2021.9723308","editor":[{"family":"M.B.","given":"Matthews"}],"ISBN":"978-1-66545-828-3","ISSN":"10586393","issued":{"date-parts":[[2021]]},"page":"1279-1283","publisher":"IEEE Computer Society","title":"Deep anomaly detection for network traffic","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127061316&doi=10.1109%2fIEEECONF53345.2021.9723308&partnerID=40&md5=dc0f7bfac75fbd60ba71fe4fa087bf31","volume":"2021-October"},
{"id":"mckinneyDeepAnomalyDetection2021a","abstract":"Canonical anomaly detection has been achieved through various means ranging from statistical tests and clustering methods to categorical decision-making and rule-based systems. Each method has its own pros and cons; however, many depend on assumptions. These assumptions can be model driven, such as assuming white Gaussian inputs, or method driven such as linear regression. In any case, assumptions are being made either about the structure of the data or its relationship with other random variables.This work presents a deep learning methodology for anomaly detection, a sampling technique for large data sets, and feature importance analysis. The anomaly detection technique uses an ensemble of learners to predict relationships between benign features and characterizes deviations from these patterns as \"surprisal\"scores. This method identifies malicious network traffic without previous attack behavior knowledge and is applied to data from the Canadian Institute for Cybersecurity. © 2021 IEEE.","author":[{"family":"McKinney","given":"E."},{"family":"Mortensen","given":"D."}],"citation-key":"mckinneyDeepAnomalyDetection2021a","container-title":"Conference Record - Asilomar Conference on Signals, Systems and Computers","DOI":"10.1109/IEEECONF53345.2021.9723308","editor":[{"family":"M.B","given":"Matthews"}],"ISBN":"978-1-66545-828-3","ISSN":"10586393","issued":{"date-parts":[[2021]]},"page":"1279-1283","publisher":"IEEE Computer Society","title":"Deep Anomaly Detection for Network Traffic","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127061316&doi=10.1109%2fIEEECONF53345.2021.9723308&partnerID=40&md5=dc0f7bfac75fbd60ba71fe4fa087bf31","volume":"2021-October"},
{"id":"McMillan:2011:CSA:2117694.2119646","author":[{"family":"McMillan","given":"Collin"},{"family":"Linares-Vasquez","given":"Mario"},{"family":"Poshyvanyk","given":"Denys"},{"family":"Grechanik","given":"Mark"}],"citation-key":"McMillan:2011:CSA:2117694.2119646","collection-title":"ICSM '11","container-title":"Proceedings of the 2011 27th IEEE international conference on software maintenance","event-place":"Washington, DC, USA","ISBN":"978-1-4577-0663-9","ISSN":"1063-6773","issued":{"date-parts":[[2011]]},"page":"343-352","publisher":"IEEE Computer Society","publisher-place":"Washington, DC, USA","title":"Categorizing software applications for maintenance","type":"paper-conference","URL":"https://doi.org/10.1109/ICSM.2011.6080801"},
{"id":"mcmillanDetectingSimilarSoftware2012","accessed":{"date-parts":[[2017,3,14]]},"author":[{"family":"McMillan","given":"Collin"},{"family":"Grechanik","given":"Mark"},{"family":"Poshyvanyk","given":"Denys"}],"citation-key":"mcmillanDetectingSimilarSoftware2012","container-title":"Software Engineering (ICSE), 2012 34th International Conference on","event-place":"Zurich, Switzerland","issued":{"date-parts":[[2012]]},"page":"364–374","publisher":"IEEE","publisher-place":"Zurich, Switzerland","source":"Google Scholar","title":"Detecting similar software applications","type":"paper-conference","URL":"http://ieeexplore.ieee.org/abstract/document/6227178/"},
{"id":"mcmillanRecommendingSourceCode2010","author":[{"family":"McMillan","given":"Collin"},{"family":"Poshyvanyk","given":"Denys"},{"family":"Grechanik","given":"Mark"}],"citation-key":"mcmillanRecommendingSourceCode2010","collection-title":"RSSE '10","container-title":"Proceedings of the 2Nd international workshop on recommendation systems for software engineering","event-place":"New York, NY, USA","ISBN":"978-1-60558-974-9","issued":{"date-parts":[[2010]]},"page":"21-25","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Recommending source code examples via API call usages and documentation","type":"paper-conference","URL":"http://doi.acm.org/10.1145/1808920.1808925"},
{"id":"MDD4DRESProgram","accessed":{"date-parts":[[2016,3,10]]},"citation-key":"MDD4DRESProgram","title":"MDD4DRES Program","type":"webpage","URL":"http://www.mdd4dres.org/program/#JM"},
{"id":"MDE","author":[{"family":"Schmidt","given":"D. C."}],"citation-key":"MDE","container-title":"Computer","DOI":"10.1109/MC.2006.58","ISSN":"1558-0814","issue":"2","issued":{"date-parts":[[2006,2]]},"page":"25-31","title":"Guest editor's introduction: Model-driven engineering","type":"article-journal","volume":"39"},
{"id":"MDEAdoptionThreelegged2017","citation-key":"MDEAdoptionThreelegged2017","issued":{"date-parts":[[2017]]},"note":"00000","title":"MDE Adoption—A Three-legged Chair","type":"book"},
{"id":"meadHalfCenturySoftware2018","abstract":"From the aspirational title of the 1968 NATO conference, software engineering has evolved to a well-defined engineering discipline with strong educational underpinnings. The supporting educational foundation has grown from a few courses in programming languages and data structures, evolving through structured programming, correctness formalisms, and state machine abstractions to full curricula and degree programs. With this context in mind, the authors discuss the evolution of software engineering education and pedagogy, software engineering principles, and future needs, drawing specifically on their experience at Carnegie Mellon University. Reflecting on the software development profession today, they believe that formal software engineering education is needed at least as much as it was in earlier decades. However, it must address the increasing diversity of the developer community, and it must be an education based on the enduring principles that will last a lifetime. This article is part of a theme issue on software engineering’s 50th anniversary.","author":[{"family":"Mead","given":"N. R."},{"family":"Garlan","given":"D."},{"family":"Shaw","given":"M."}],"citation-key":"meadHalfCenturySoftware2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.290110743","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"25-31","source":"IEEE Xplore","title":"Half a Century of Software Engineering Education: The CMU Exemplar","title-short":"Half a Century of Software Engineering Education","type":"article-journal","volume":"35"},
{"id":"meijerCorelationalModelData2011","accessed":{"date-parts":[[2018,5,9]]},"author":[{"family":"Meijer","given":"Erik"},{"family":"Bierman","given":"Gavin"}],"citation-key":"meijerCorelationalModelData2011","container-title":"Communications of the ACM","DOI":"10.1145/1924421.1924436","ISSN":"00010782","issue":"4","issued":{"date-parts":[[2011,4,1]]},"page":"49","source":"Crossref","title":"A co-relational model of data for large shared data banks","type":"article-journal","URL":"http://portal.acm.org/citation.cfm?doid=1924421.1924436","volume":"54"},
{"id":"meloContextAugmentedSoftwareDevelopment2019","accessed":{"date-parts":[[2020,3,2]]},"author":[{"family":"Melo","given":"Glaucia"},{"family":"Alencar","given":"Paulo"},{"family":"Cowan","given":"Don"}],"citation-key":"meloContextAugmentedSoftwareDevelopment2019","container-title":"2019 IEEE International Conference on Big Data (Big Data)","DOI":"10.1109/BigData47090.2019.9006245","event":"2019 IEEE International Conference on Big Data (Big Data)","event-place":"Los Angeles, CA, USA","ISBN":"978-1-72810-858-2","issued":{"date-parts":[[2019,12]]},"page":"3449-3457","publisher":"IEEE","publisher-place":"Los Angeles, CA, USA","source":"DOI.org (Crossref)","title":"Context-Augmented Software Development in Traditional and Big Data Projects: Literature Review and Preliminary Framework","title-short":"Context-Augmented Software Development in Traditional and Big Data Projects","type":"paper-conference","URL":"https://ieeexplore.ieee.org/document/9006245/"},
{"id":"melvilleRecommenderSystems2010","author":[{"family":"Melville","given":"Prem"},{"family":"Sindhwani","given":"Vikas"}],"citation-key":"melvilleRecommenderSystems2010","container-title":"Encyclopedia of machine learning","editor":[{"family":"Sammut","given":"Claude"},{"family":"Webb","given":"Geoffrey I."}],"ISBN":"978-0-387-30768-8","issued":{"date-parts":[[2010]]},"page":"829-838","publisher":"Springer","title":"Recommender systems.","type":"chapter","URL":"http://dblp.uni-trier.de/db/reference/ml/ml2010.html#MelvilleS10"},
{"id":"mendoncaDevelopingSelfAdaptiveMicroservice2021","accessed":{"date-parts":[[2021,3,26]]},"author":[{"family":"Mendonca","given":"Nabor C."},{"family":"Jamshidi","given":"Pooyan"},{"family":"Garlan","given":"David"},{"family":"Pahl","given":"Claus"}],"citation-key":"mendoncaDevelopingSelfAdaptiveMicroservice2021","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2019.2955937","ISSN":"0740-7459, 1937-4194","issue":"2","issued":{"date-parts":[[2021,3]]},"note":"00019","page":"70-79","source":"DOI.org (Crossref)","title":"Developing Self-Adaptive Microservice Systems: Challenges and Directions","title-short":"Developing Self-Adaptive Microservice Systems","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/8913688/","volume":"38"},
{"id":"Meng2019","abstract":"The model-driven power allocation (PA) algorithms in the wireless cellular networks with interfering multiple-access channel (IMAC) have been investigated for decades. Nowadays, the data-driven model-free machine learning-based approaches are rapidly developing in this field, and among them the deep reinforcement learning (DRL) is proved to be of great potential. Different from supervised learning, the DRL takes advantages of exploration and exploitation to maximize the objective function under certain constraints. In our paper, we propose a two-step training framework. First, with the off-line learning in simulated environment, a deep Q network (DQN) is trained with deep Q learning (DQL) algorithm, which is well-designed to be in consistent with this PA issue. Second, the DQN will be further fine-tuned with real data in on-line training procedure. The simulation results show that the proposed DQN achieves the highest averaged sum-rate, comparing to the ones with present standard DQL training. With different user densities, our DQN outperforms benchmark algorithms and thus a good generalization ability is verified. © 2019 IEEE.","author":[{"family":"Meng","given":"F."},{"family":"Chen","given":"P."},{"family":"Wu","given":"L."}],"citation-key":"Meng2019","collection-title":"IEEE International Conference on Communications","DOI":"10.1109/ICC.2019.8761431","ISBN":"978-1-5386-8088-9","ISSN":"15503607","issued":{"date-parts":[[2019]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Power allocation in multi-user cellular networks with deep Q learning approach","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070230399&doi=10.1109%2fICC.2019.8761431&partnerID=40&md5=b80fabbbfa34b1f03e252312c18a122e","volume":"2019-May"},
{"id":"menziesFiveLawsSE2020","accessed":{"date-parts":[[2020,7,9]]},"author":[{"family":"Menzies","given":"Tim"}],"citation-key":"menziesFiveLawsSE2020","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2019.2954841","ISSN":"0740-7459, 1937-4194","issue":"1","issued":{"date-parts":[[2020,1]]},"page":"81-85","source":"DOI.org (Crossref)","title":"The Five Laws of SE for AI","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/8938116/","volume":"37"},
{"id":"menziesShockinglySimpleKEYS2021","accessed":{"date-parts":[[2021,2,24]]},"author":[{"family":"Menzies","given":"Tim"}],"citation-key":"menziesShockinglySimpleKEYS2021","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2020.3043014","ISSN":"0740-7459, 1937-4194","issue":"2","issued":{"date-parts":[[2021,3]]},"note":"00000","page":"114-118","source":"DOI.org (Crossref)","title":"Shockingly Simple:\"KEYS\" for Better AI for SE","title-short":"Shockingly Simple","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/9354395/","volume":"38"},
{"id":"menziesSoftwareAnalyticsWhat2018","abstract":"Knowing what factors control software projects is very useful because humans might not understand those factors. Developers sometimes develop their own ideas about good and bad software, on the basis of just a few past projects. Using software analytics, we can correct those misconceptions. Software analytics lets software engineers learn about AI techniques. Once they learn those techniques, they can build and ship innovative AI tools. That is, software analytics is the training ground for the next generation of AI-literate software engineers. This article is part of a special issue on software engineering’s 50th anniversary.","author":[{"family":"Menzies","given":"T."},{"family":"Zimmermann","given":"T."}],"citation-key":"menziesSoftwareAnalyticsWhat2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.290111035","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"64-70","source":"IEEE Xplore","title":"Software Analytics: What’s Next?","title-short":"Software Analytics","type":"article-journal","volume":"35"},
{"id":"merilinnaStateArtPractice2006","accessed":{"date-parts":[[2017,2,25]]},"author":[{"family":"Merilinna","given":"Janne"},{"family":"Matinlassi","given":"Mari"}],"citation-key":"merilinnaStateArtPractice2006","container-title":"Software Engineering and Advanced Applications, 2006. SEAA'06. 32nd EUROMICRO Conference on","issued":{"date-parts":[[2006]]},"page":"170–177","publisher":"IEEE","source":"Google Scholar","title":"State of the art and practice of opensource component integration","type":"paper-conference","URL":"http://ieeexplore.ieee.org/abstract/document/1690138/"},
{"id":"MessageRoSE20182018","citation-key":"MessageRoSE20182018","issued":{"date-parts":[[2018]]},"note":"00000","publisher":"IEEE Computer Society","title":"Message from the RoSE 2018 Co-Organizers","type":"book","volume":"137815"},
{"id":"meyerSoftwareEngineering2015","accessed":{"date-parts":[[2017,3,7]]},"citation-key":"meyerSoftwareEngineering2015","collection-title":"Lecture Notes in Computer Science","DOI":"10.1007/978-3-319-28406-4","editor":[{"family":"Meyer","given":"Bertrand"},{"family":"Nordio","given":"Martin"}],"event-place":"Cham","ISBN":"978-3-319-28405-7 978-3-319-28406-4","issued":{"date-parts":[[2015]]},"publisher":"Springer International Publishing","publisher-place":"Cham","source":"CrossRef","title":"Software Engineering","type":"book","URL":"http://link.springer.com/10.1007/978-3-319-28406-4","volume":"8987"},
{"id":"Michalke20211565","abstract":"Lane detection is an essential part of the perception sub-architecture of any automated driving (AD) or advanced driver assistance system (ADAS). When focusing on low-cost, large scale products for automated driving, model-driven approaches for the detection of lane markings have proven good performance. More recently, data-driven approaches have been proposed that target the drivable area / freespace mainly in inner-city applications. Focus of these approaches is less on lane-based driving due to the fact that the lane concept does not fully apply in unstructured, residential inner-city environments. So-far the concept of drivable area is seldom used for highway and inter-urban applications due to the specific requirements of these scenarios that require clear lane associations of all traffic participants. We believe that lane-based, mapless driving in inter-urban and highway scenarios is still not fully handled with sufficient robustness and availability. Especially for challenging weather situations such as heavy rain, fog, low-standing sun, darkness or reflections in puddles, the mapless detection of lane markings decreases significantly or completely fails. We see potential in applying specifically designed data-driven freespace approaches in more lane-based driving applications for highways and inter-urban use. Therefore, we propose to classify specifically a drivable corridor of the ego lane on pixel level with a deep learning approach. Our approach is kept computationally efficient with only 0.66 million parameters allowing its application in large scale products. Thus, we were able to easily integrate into an online AD system of a test vehicle. We demonstrate the performance of our approach under challenging conditions qualitatively and quantitatively in comparison to a state-of-the-art model-driven approach. We see the current approach as part of a fallback path whenever model-driven approaches cannot cope with a challenging scenario. We give insights how such a fallback path can be integrated into an AD system, thereby extending the overall system availability. © 2021 IEEE.","author":[{"family":"Michalke","given":"T."},{"family":"Wust","given":"C."},{"family":"Feng","given":"D."},{"family":"Dolgov","given":"M."},{"family":"Glaser","given":"C."},{"family":"Timm","given":"F."}],"citation-key":"Michalke20211565","collection-title":"IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC","DOI":"10.1109/ITSC48978.2021.9564647","ISBN":"978-1-72819-142-3","issued":{"date-parts":[[2021]]},"page":"1565-1571","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Where can i drive? A system approach: Deep ego corridor estimation for robust automated driving","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118444594&doi=10.1109%2fITSC48978.2021.9564647&partnerID=40&md5=32810fb6e0b111330dd9e5553b0e3359","volume":"2021-September"},
{"id":"michalkeWhereCanDrive2021a","abstract":"Lane detection is an essential part of the perception sub-architecture of any automated driving (AD) or advanced driver assistance system (ADAS). When focusing on low-cost, large scale products for automated driving, model-driven approaches for the detection of lane markings have proven good performance. More recently, data-driven approaches have been proposed that target the drivable area / freespace mainly in inner-city applications. Focus of these approaches is less on lane-based driving due to the fact that the lane concept does not fully apply in unstructured, residential inner-city environments. So-far the concept of drivable area is seldom used for highway and inter-urban applications due to the specific requirements of these scenarios that require clear lane associations of all traffic participants. We believe that lane-based, mapless driving in inter-urban and highway scenarios is still not fully handled with sufficient robustness and availability. Especially for challenging weather situations such as heavy rain, fog, low-standing sun, darkness or reflections in puddles, the mapless detection of lane markings decreases significantly or completely fails. We see potential in applying specifically designed data-driven freespace approaches in more lane-based driving applications for highways and inter-urban use. Therefore, we propose to classify specifically a drivable corridor of the ego lane on pixel level with a deep learning approach. Our approach is kept computationally efficient with only 0.66 million parameters allowing its application in large scale products. Thus, we were able to easily integrate into an online AD system of a test vehicle. We demonstrate the performance of our approach under challenging conditions qualitatively and quantitatively in comparison to a state-of-the-art model-driven approach. We see the current approach as part of a fallback path whenever model-driven approaches cannot cope with a challenging scenario. We give insights how such a fallback path can be integrated into an AD system, thereby extending the overall system availability. © 2021 IEEE.","author":[{"family":"Michalke","given":"T."},{"family":"Wust","given":"C."},{"family":"Feng","given":"D."},{"family":"Dolgov","given":"M."},{"family":"Glaser","given":"C."},{"family":"Timm","given":"F."}],"citation-key":"michalkeWhereCanDrive2021a","container-title":"IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC","DOI":"10.1109/ITSC48978.2021.9564647","ISBN":"978-1-72819-142-3","issued":{"date-parts":[[2021]]},"page":"1565-1571","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Where can i drive? A System Approach: Deep Ego Corridor Estimation for Robust Automated Driving","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118444594&doi=10.1109%2fITSC48978.2021.9564647&partnerID=40&md5=32810fb6e0b111330dd9e5553b0e3359","volume":"2021-September"},
{"id":"miEmpiricalCharacterizationIFTTT2017","abstract":"IFTTT is a popular trigger-action programming platform whose applets can automate more than 400 services of IoT devices and web applications. We conduct an empirical study of IFTTT using a combined approach of analyzing data collected for 6 months and performing controlled experiments using a custom testbed. We profile the interactions among different entities, measure how applets are used by end users, and test the performance of applet execution. Overall we observe the fast growth of the IFTTT ecosystem and its increasing usage for automating IoT-related tasks, which correspond to 52% of all services and 16% of the applet usage. We also observe several performance inefficiencies and identify their causes.","accessed":{"date-parts":[[2019,6,26]]},"author":[{"family":"Mi","given":"Xianghang"},{"family":"Qian","given":"Feng"},{"family":"Zhang","given":"Ying"},{"family":"Wang","given":"XiaoFeng"}],"citation-key":"miEmpiricalCharacterizationIFTTT2017","container-title":"Proceedings of the 2017 Internet Measurement Conference on - IMC '17","DOI":"10.1145/3131365.3131369","event":"the 2017 Internet Measurement Conference","event-place":"London, United Kingdom","ISBN":"978-1-4503-5118-8","issued":{"date-parts":[[2017]]},"page":"398-404","publisher":"ACM Press","publisher-place":"London, United Kingdom","source":"DOI.org (Crossref)","title":"An empirical characterization of IFTTT: ecosystem, usage, and performance","title-short":"An empirical characterization of IFTTT","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?doid=3131365.3131369"},
{"id":"Mihalcea:2006:CKM:1597538.1597662","author":[{"family":"Mihalcea","given":"Rada"},{"family":"Corley","given":"Courtney"},{"family":"Strapparava","given":"Carlo"}],"citation-key":"Mihalcea:2006:CKM:1597538.1597662","collection-title":"AAAI'06","container-title":"Proceedings of the 21st national conference on artificial intelligence - volume 1","event-place":"Boston, Massachusetts","ISBN":"978-1-57735-281-5","issued":{"date-parts":[[2006]]},"page":"775-780","publisher":"AAAI Press","publisher-place":"Boston, Massachusetts","title":"Corpus-based and knowledge-based measures of text semantic similarity","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=1597538.1597662"},
{"id":"millerMDAGuideVersion2003","author":[{"family":"Miller","given":"J."},{"family":"Mukerji","given":"J."}],"citation-key":"millerMDAGuideVersion2003","issued":{"date-parts":[[2003]]},"publisher":"Object Management Group (OMG)","title":"MDA Guide Version 1.0.1","type":"report"},
{"id":"millerWordNetLexicalDatabase1995","author":[{"family":"Miller","given":"George A."}],"citation-key":"millerWordNetLexicalDatabase1995","container-title":"Communications of the ACM","container-title-short":"Commun. ACM","ISSN":"0001-0782","issue":"11","issued":{"date-parts":[[1995,11]]},"page":"39-41","title":"WordNet: A lexical database for english","type":"article-journal","URL":"http://doi.acm.org/10.1145/219717.219748","volume":"38"},
{"id":"minoliBlockchainMechanismsIoT2018","abstract":"The deployment of Internet of Things (IoT) results in an enlarged attack surface that requires end-to-end security mitigation. IoT applications range from mission-critical predicaments (e.g., Smart Grid, Intelligent Transportation Systems, video surveillance, e-health) to business-oriented applications (e.g., banking, logistics, insurance, and contract law). There is a need for comprehensive support of security in the IoT, especially for mission-critical applications, but also for the down-stream business applications. A number of security techniques and approaches have been proposed and/or utilized. Blockchain mechanisms (BCMs) play a role in securing many IoT-oriented applications by becoming part of a security mosaic, in the context of a defenses-in-depth/Castle Approach. A blockchain is a database that stores all processed transactions – or data – in chronological order, in a set of computer memories that are tamperproof to adversaries. These transactions are then shared by all participating users. Information is stored and/or published as a public ledger that is infeasible to modify; every user or node in the system retains the same ledger as all other users or nodes in the network. This paper highlights some IoT environments where BCMs play an important role, while at the same time pointing out that BCMs are only part of the IoT Security (IoTSec) solution.","accessed":{"date-parts":[[2018,11,7]]},"author":[{"family":"Minoli","given":"Daniel"},{"family":"Occhiogrosso","given":"Benedict"}],"citation-key":"minoliBlockchainMechanismsIoT2018","container-title":"Internet of Things","DOI":"10.1016/j.iot.2018.05.002","ISSN":"25426605","issued":{"date-parts":[[2018,9]]},"page":"1-13","source":"Crossref","title":"Blockchain mechanisms for IoT security","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2542660518300167","volume":"1-2"},
{"id":"miorandiInternetThingsVision2012","abstract":"The term ‘‘Internet-of-Things’’ is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. Internet-of-Things envisions a future in which digital and physical entities can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this article, we present a survey of technologies, applications and research challenges for Internetof-Things.","accessed":{"date-parts":[[2019,9,3]]},"author":[{"family":"Miorandi","given":"Daniele"},{"family":"Sicari","given":"Sabrina"},{"family":"De Pellegrini","given":"Francesco"},{"family":"Chlamtac","given":"Imrich"}],"citation-key":"miorandiInternetThingsVision2012","container-title":"Ad Hoc Networks","container-title-short":"Ad Hoc Networks","DOI":"10.1016/j.adhoc.2012.02.016","ISSN":"15708705","issue":"7","issued":{"date-parts":[[2012,9]]},"page":"1497-1516","source":"DOI.org (Crossref)","title":"Internet of things: Vision, applications and research challenges","title-short":"Internet of things","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1570870512000674","volume":"10"},
{"id":"miorNoSESchemaDesign2016","accessed":{"date-parts":[[2021,3,24]]},"author":[{"family":"Mior","given":"Michael J."},{"family":"Salem","given":"Kenneth"},{"family":"Aboulnaga","given":"Ashraf"},{"family":"Liu","given":"Rui"}],"citation-key":"miorNoSESchemaDesign2016","container-title":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","DOI":"10.1109/ICDE.2016.7498239","event":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","event-place":"Helsinki, Finland","ISBN":"978-1-5090-2020-1","issued":{"date-parts":[[2016,5]]},"note":"00068","page":"181-192","publisher":"IEEE","publisher-place":"Helsinki, Finland","source":"DOI.org (Crossref)","title":"NoSE: Schema design for NoSQL applications","title-short":"NoSE","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/7498239/"},
{"id":"Miranda:2008:ICF:1486927.1487083","author":[{"family":"Miranda","given":"Catarina"},{"family":"Jorge","given":"Alípio M."}],"citation-key":"Miranda:2008:ICF:1486927.1487083","collection-title":"WI-IAT '08","container-title":"Proceedings of the 2008 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology - volume 01","event-place":"Washington, DC, USA","ISBN":"978-0-7695-3496-1","issued":{"date-parts":[[2008]]},"page":"389-392","publisher":"IEEE Computer Society","publisher-place":"Washington, DC, USA","title":"Incremental collaborative filtering for binary ratings","type":"paper-conference","URL":"http://dx.doi.org/10.1109/WIIAT.2008.263"},
{"id":"MiSE2017ProgramPreparatory","abstract":"Program\t1 Registrants (33)\t3 Preparatory emails\t5 To presenters\t6 To attendees\t6 Program Sunday 21 May 2017 09:00 - 09:05 Welcome from the organizers 09:05 - 10:30 Keynote 1: Empirical Studies into UML in Practice: Pitfalls and Prospects, Michel Chaudron [abstract] [Session chair: Davide] ...","accessed":{"date-parts":[[2017,5,8]]},"citation-key":"MiSE2017ProgramPreparatory","container-title":"Google Docs","title":"MiSE2017 - Program and preparatory emails","type":"webpage","URL":"https://docs.google.com/document/d/1FVBtlKZzdkNVYqea-9nB2YCpicTohzntjEcx2ZKcqjA/edit?usp=sharing&usp=embed_facebook"},
{"id":"Mishty20211730","abstract":"We demonstrate the design of efficient and high-performance artificial intelligence (AI)/deep learning accelerators with customized spin transfer torque (STT)-MRAM (STT-MRAM) and a reconfigurable core. Based on model-driven detailed design space exploration, we present the design methodology of an innovative scratchpad-assisted on-chip STT-MRAM-based buffer system for high-performance accelerators. Using analytically derived expression of memory occupancy time of AI model weights and activation maps, the volatility of STT-MRAM is adjusted with process and temperature variation aware scaling of thermal stability factor to optimize the retention time, energy, read/write latency, and area of STT-MRAM. From the analysis of AI workloads and accelerator implementation in 14-nm technology, we verify the efficacy of our AI accelerator with STT-MRAM (STT-AI). Compared to an SRAM-based implementation, the STT-AI accelerator achieves 75% area and 3% power savings at isoaccuracy. Furthermore, with a relaxed bit error rate and negligible AI accuracy tradeoff, the designed STT-AI Ultra accelerator achieves 75.4% and 3.5% savings in area and power, respectively, over regular SRAM-based accelerators. © 1993-2012 IEEE.","author":[{"family":"Mishty","given":"K."},{"family":"Sadi","given":"M."}],"citation-key":"Mishty20211730","container-title":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","DOI":"10.1109/TVLSI.2021.3105958","ISSN":"10638210","issue":"10","issued":{"date-parts":[[2021]]},"page":"1730-1742","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Designing efficient and high-performance AI accelerators with customized STT-MRAM","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116994360&doi=10.1109%2fTVLSI.2021.3105958&partnerID=40&md5=d43ec143655f36b193a67a5dff9ae715","volume":"29"},
{"id":"mishtyDesigningEfficientHighPerformance2021a","abstract":"We demonstrate the design of efficient and high-performance artificial intelligence (AI)/deep learning accelerators with customized spin transfer torque (STT)-MRAM (STT-MRAM) and a reconfigurable core. Based on model-driven detailed design space exploration, we present the design methodology of an innovative scratchpad-assisted on-chip STT-MRAM-based buffer system for high-performance accelerators. Using analytically derived expression of memory occupancy time of AI model weights and activation maps, the volatility of STT-MRAM is adjusted with process and temperature variation aware scaling of thermal stability factor to optimize the retention time, energy, read/write latency, and area of STT-MRAM. From the analysis of AI workloads and accelerator implementation in 14-nm technology, we verify the efficacy of our AI accelerator with STT-MRAM (STT-AI). Compared to an SRAM-based implementation, the STT-AI accelerator achieves 75% area and 3% power savings at isoaccuracy. Furthermore, with a relaxed bit error rate and negligible AI accuracy tradeoff, the designed STT-AI Ultra accelerator achieves 75.4% and 3.5% savings in area and power, respectively, over regular SRAM-based accelerators. © 1993-2012 IEEE.","author":[{"family":"Mishty","given":"K."},{"family":"Sadi","given":"M."}],"citation-key":"mishtyDesigningEfficientHighPerformance2021a","container-title":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","DOI":"10.1109/TVLSI.2021.3105958","ISSN":"10638210","issue":"10","issued":{"date-parts":[[2021]]},"page":"1730-1742","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Designing Efficient and High-Performance AI Accelerators with Customized STT-MRAM","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116994360&doi=10.1109%2fTVLSI.2021.3105958&partnerID=40&md5=d43ec143655f36b193a67a5dff9ae715","volume":"29"},
{"id":"misraSoftwareClusteringUnifying2012","author":[{"family":"Misra","given":"J."},{"family":"Annervaz","given":"K. M."},{"family":"Kaulgud","given":"V."},{"family":"Sengupta","given":"S."},{"family":"Titus","given":"G."}],"citation-key":"misraSoftwareClusteringUnifying2012","container-title":"2012 19th working conference on reverse engineering","DOI":"10.1109/WCRE.2012.21","ISSN":"2375-5369","issued":{"date-parts":[[2012,10]]},"page":"113-122","title":"Software clustering: Unifying syntactic and semantic features","type":"paper-conference"},
{"id":"MisurazioneDiGas","accessed":{"date-parts":[[2015,4,4]]},"citation-key":"MisurazioneDiGas","title":"Misurazione di gas o di ulteriore protocollo di studio Vaillant e di e-Bus di controllo / Riscaldamento / Casa intelligente 1-wire con le proprie mani / ab-log.ru","type":"webpage","URL":"http://www.ab-log.ru/smart-house/heating-automation/gaz_meter"},
{"id":"mitchellFAIRDataPipeline2021","abstract":"Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used. Public trust in policy decisions resulting from such analyses is easily damaged and is often low, with cynicism arising where claims of \"following the science\" are made without accompanying evidence. Tracing the provenance of such decisions back through open software to primary data would clarify this evidence, enhancing the transparency of the decision-making process. Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline developed during the COVID-19 pandemic that allows easy annotation of data as they are consumed by analyses, while tracing the provenance of scientific outputs back through the analytical source code to data sources. Such a tool provides a mechanism for the public, and fellow scientists, to better assess the trust that should be placed in scientific evidence, while allowing scientists to support policy-makers in openly justifying their decisions. We believe that tools such as this should be promoted for use across all areas of policy-facing research.","accessed":{"date-parts":[[2022,2,24]]},"author":[{"family":"Mitchell","given":"Sonia Natalie"},{"family":"Lahiff","given":"Andrew"},{"family":"Cummings","given":"Nathan"},{"family":"Hollocombe","given":"Jonathan"},{"family":"Boskamp","given":"Bram"},{"family":"Reddyhoff","given":"Dennis"},{"family":"Field","given":"Ryan"},{"family":"Zarebski","given":"Kristian"},{"family":"Wilson","given":"Antony"},{"family":"Burke","given":"Martin"},{"family":"Archibald","given":"Blair"},{"family":"Bessell","given":"Paul"},{"family":"Blackwell","given":"Richard"},{"family":"Boden","given":"Lisa A."},{"family":"Brett","given":"Alys"},{"family":"Brett","given":"Sam"},{"family":"Dundas","given":"Ruth"},{"family":"Enright","given":"Jessica"},{"family":"Gonzalez-Beltran","given":"Alejandra N."},{"family":"Harris","given":"Claire"},{"family":"Hinder","given":"Ian"},{"family":"Hughes","given":"Christopher David"},{"family":"Knight","given":"Martin"},{"family":"Mano","given":"Vino"},{"family":"McMonagle","given":"Ciaran"},{"family":"Mellor","given":"Dominic"},{"family":"Mohr","given":"Sibylle"},{"family":"Marion","given":"Glenn"},{"family":"Matthews","given":"Louise"},{"family":"McKendrick","given":"Iain J."},{"family":"Pooley","given":"Christopher Mark"},{"family":"Porphyre","given":"Thibaud"},{"family":"Reeves","given":"Aaron"},{"family":"Townsend","given":"Edward"},{"family":"Turner","given":"Robert"},{"family":"Walton","given":"Jeremy"},{"family":"Reeve","given":"Richard"}],"citation-key":"mitchellFAIRDataPipeline2021","container-title":"arXiv:2110.07117 [cs, q-bio]","issued":{"date-parts":[[2021,10,13]]},"note":"00000","source":"arXiv.org","title":"FAIR Data Pipeline: provenance-driven data management for traceable scientific workflows","title-short":"FAIR Data Pipeline","type":"article-journal","URL":"http://arxiv.org/abs/2110.07117"},
{"id":"mittelmannPersonalKnowledgeManagement2016","accessed":{"date-parts":[[2020,11,21]]},"author":[{"family":"Mittelmann","given":"Angelika"}],"citation-key":"mittelmannPersonalKnowledgeManagement2016","container-title":"Procedia Computer Science","container-title-short":"Procedia Computer Science","DOI":"10.1016/j.procs.2016.09.105","ISSN":"18770509","issued":{"date-parts":[[2016]]},"page":"117-124","source":"DOI.org (Crossref)","title":"Personal Knowledge Management as Basis for Successful Organizational Knowledge Management in the Digital Age","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1877050916322505","volume":"99"},
{"id":"MMR02","author":[{"family":"Melnik","given":"S."},{"family":"Garcia-Molina","given":"H."},{"family":"Rahm","given":"E."}],"citation-key":"MMR02","container-title":"Proceedings. 18th international conference on data engineering, 2002","DOI":"10.1109/ICDE.2002.994702","ISSN":"1063-6382","issued":{"date-parts":[[2002]]},"page":"117-128","title":"Similarity flooding: a versatile graph matching algorithm and its application to schema matching","type":"paper-conference"},
{"id":"mmsim2","author":[{"family":"Falleri","given":"Jean-Rémy"},{"family":"Huchard","given":"Marianne"},{"family":"Lafourcade","given":"Mathieu"},{"family":"Nebut","given":"Clémentine"}],"citation-key":"mmsim2","collection-title":"Lecture notes in computer science","container-title":"Model driven engineering languages and systems","DOI":"10.1007/978-3-540-87875-9_24","editor":[{"family":"Czarnecki","given":"Krzysztof"},{"family":"Ober","given":"Ileana"},{"family":"Bruel","given":"Jean-Michel"},{"family":"Uhl","given":"Axel"},{"family":"Völter","given":"Markus"}],"ISBN":"978-3-540-87874-2","issued":{"date-parts":[[2008]]},"page":"326-340","publisher":"Springer Berlin Heidelberg","title":"Metamodel matching for automatic model transformation generation","type":"chapter","URL":"http://dx.doi.org/10.1007/978-3-540-87875-9_24","volume":"5301"},
{"id":"mobasherAttacksRemediesCollaborative2007","accessed":{"date-parts":[[2021,4,2]]},"author":[{"family":"Mobasher","given":"Bamshad"},{"family":"Burke","given":"Robin"},{"family":"Bhaumik","given":"Runa"},{"family":"Sandvig","given":"J.J."}],"citation-key":"mobasherAttacksRemediesCollaborative2007","container-title":"IEEE Intelligent Systems","container-title-short":"IEEE Intell. Syst.","DOI":"10.1109/MIS.2007.45","ISSN":"1541-1672","issue":"3","issued":{"date-parts":[[2007,5]]},"note":"00116","page":"56-63","source":"DOI.org (Crossref)","title":"Attacks and Remedies in Collaborative Recommendation","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/4216981/","volume":"22"},
{"id":"MobileAutonomousSystems","accessed":{"date-parts":[[2016,1,23]]},"citation-key":"MobileAutonomousSystems","title":"Mobile Autonomous Systems Laboratory | Electrical Engineering and Computer Science | MIT OpenCourseWare","type":"webpage","URL":"http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-186-mobile-autonomous-systems-laboratory-january-iap-2005/index.htm"},
{"id":"mocriiIoTbasedSmartHomes2018","abstract":"This article presents a review of major technologies of IoT-based smart homes. It starts with definition of the smart home that sets the perspective adopted in the review. In addition to describing the complementary user and system functions of the smart home, it introduces its general, IoT-based architecture and sets smart homes within the larger context of the smart grid. The following sections concentrate on software solutions and components of smart home management systems, related communication technologies, and issues of privacy and security associated with the connected nature of modern smart homes. A separate section presents current challenges of smart home technologies and their dispersion, and points to some interesting solutions and future trends.","accessed":{"date-parts":[[2018,11,7]]},"author":[{"family":"Mocrii","given":"Dragos"},{"family":"Chen","given":"Yuxiang"},{"family":"Musilek","given":"Petr"}],"citation-key":"mocriiIoTbasedSmartHomes2018","container-title":"Internet of Things","DOI":"10.1016/j.iot.2018.08.009","ISSN":"25426605","issued":{"date-parts":[[2018,9]]},"page":"81-98","source":"Crossref","title":"IoT-based smart homes: A review of system architecture, software, communications, privacy and security","title-short":"IoT-based smart homes","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2542660518300477","volume":"1-2"},
{"id":"ModeldrivenEngineeringScientific","accessed":{"date-parts":[[2017,2,23]]},"citation-key":"ModeldrivenEngineeringScientific","title":"Model-driven Engineering of Scientific Applications - Mohamed Almorsy Abdelrazek","type":"webpage","URL":"https://sites.google.com/site/mohamedalmorsy/home/research/model-driven-engineering-of-scientific-applications"},
{"id":"MODELS20","abstract":"An online LaTeX editor that's easy to use. No installation, real-time collaboration, version control, hundreds of LaTeX templates, and more.","accessed":{"date-parts":[[2020,2,13]]},"citation-key":"MODELS20","title":"MODELS20","type":"webpage","URL":"https://www.overleaf.com/8979411763njfrwbxdyfcg"},
{"id":"ModelTypingSpringer","accessed":{"date-parts":[[2015,4,1]]},"citation-key":"ModelTypingSpringer","title":"On model typing - Springer","type":"webpage","URL":"http://link.springer.com/article/10.1007%2Fs10270-006-0036-6"},
{"id":"MoDeS3","accessed":{"date-parts":[[2016,8,21]]},"citation-key":"MoDeS3","title":"MoDeS3","type":"webpage","URL":"http://modes3.tumblr.com/"},
{"id":"mohagheghiMetamodelSpecifyingQuality2008","accessed":{"date-parts":[[2015,12,2]]},"author":[{"family":"Mohagheghi","given":"Parastoo"},{"family":"Dehlen","given":"Vegard"}],"citation-key":"mohagheghiMetamodelSpecifyingQuality2008","container-title":"Proc. The Nordic Workshop on Model Driven Engineering","issued":{"date-parts":[[2008]]},"page":"51–65","source":"Google Scholar","title":"A metamodel for specifying quality models in model-driven engineering","type":"paper-conference","URL":"http://www.sintef-group.com/globalassets/upload/ikt/9012/qualitymetamodel_final.pdf"},
{"id":"Moin2022987","abstract":"Models are used in both Software Engineering (SE) and Artificial Intelligence (AI). SE models may specify the architecture at different levels of abstraction and for addressing different concerns at various stages of the software development life-cycle, from early conceptualization and design, to verification, implementation, testing and evolution. However, AI models may provide smart capabilities, such as prediction and decision-making support. For instance, in Machine Learning (ML), which is currently the most popular sub-discipline of AI, mathematical models may learn useful patterns in the observed data and can become capable of making predictions. The goal of this work is to create synergy by bringing models in the said communities together and proposing a holistic approach to model-driven software development for intelligent systems that require ML. We illustrate how software models can become capable of creating and dealing with ML models in a seamless manner. The main focus is on the domain of the Internet of Things (IoT), where both ML and model-driven SE play a key role. In the context of the need to take a Cyber-Physical System-of-Systems perspective of the targeted architecture, an integrated design environment for both SE and ML sub-systems would best support the optimization and overall efficiency of the implementation of the resulting system. In particular, we implement the proposed approach, called ML-Quadrat, based on ThingML, and validate it using a case study from the IoT domain, as well as through an empirical user evaluation. It transpires that the proposed approach is not only feasible, but may also contribute to the performance leap of software development for smart Cyber-Physical Systems (CPS) which are connected to the IoT, as well as an enhanced user experience of the practitioners who use the proposed modeling solution. © 2022, The Author(s).","author":[{"family":"Moin","given":"A."},{"family":"Challenger","given":"M."},{"family":"Badii","given":"A."},{"family":"Günnemann","given":"S."}],"citation-key":"Moin2022987","container-title":"Software and Systems Modeling","DOI":"10.1007/s10270-021-00967-x","ISSN":"16191366","issue":"3","issued":{"date-parts":[[2022]]},"page":"987-1014","publisher":"Springer Science and Business Media Deutschland GmbH","title":"A model-driven approach to machine learning and software modeling for the IoT: Generating full source code for smart Internet of Things (IoT) services and cyber-physical systems (CPS)","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123102237&doi=10.1007%2fs10270-021-00967-x&partnerID=40&md5=3222ad0204118cf61ab0ed25216acbfb","volume":"21"},
{"id":"moinINVALIDSCITEVALUEINVALID_SCITE_VALUE","abstract":"INVALID_SCITE_VALUE","author":[{"family":"Moin","given":"Armin"},{"family":"Challenger","given":"Moharram"},{"family":"Badii","given":"Atta"},{"family":"Günnemann","given":"Stephan"}],"citation-key":"moinINVALIDSCITEVALUEINVALID_SCITE_VALUE","container-title":"INVALID_SCITE_VALUE","container-title-short":"INVALID_SCITE_VALUE","DOI":"INVALID_SCITE_VALUE","ISSN":"INVALID_SCITE_VALUE","issued":{"literal":"INVALID_SCITE_VALUE"},"source":"INVALID_SCITE_VALUE","title":"INVALID_SCITE_VALUE","title-short":"INVALID_SCITE_VALUE","type":"article-journal","URL":"INVALID_SCITE_VALUE"},
{"id":"moinMDE4QAIModelDrivenEngineering2021","abstract":"Over the past decade, Artificial Intelligence (AI) has provided enormous new possibilities and opportunities, but also new demands and requirements for software systems. In particular, Machine Learning (ML) has proven useful in almost every vertical application domain. Although other sub-disciplines of AI, such as intelligent agents and Multi-Agent Systems (MAS) did not become promoted to the same extent, they still possess the potential to be integrated into the mainstream technology stacks and ecosystems, for example, due to the ongoing prevalence of the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS). However, in the decade ahead, an unprecedented paradigm shift from classical computing towards Quantum Computing (QC) is expected, with perhaps a quantum-classical hybrid model. We expect the Model-Driven Engineering (MDE) paradigm to be an enabler and a facilitator, when it comes to the quantum and the quantum-classical hybrid applications as it has already proven beneficial in the highly complex domains of IoT, smart CPS and AI with inherently heterogeneous hardware and software platforms, and APIs. This includes not only automated code generation, but also automated model checking and verification, as well as model analysis in the early design phases, and model-to-model transformations both at the design-time and at the runtime. In this paper, the vision is focused on MDE for Quantum AI, and a holistic approach integrating all of the above.","accessed":{"date-parts":[[2022,5,24]]},"author":[{"family":"Moin","given":"Armin"},{"family":"Challenger","given":"Moharram"},{"family":"Badii","given":"Atta"},{"family":"Günnemann","given":"Stephan"}],"citation-key":"moinMDE4QAIModelDrivenEngineering2021","DOI":"10.48550/ARXIV.2107.06708","issued":{"date-parts":[[2021]]},"publisher":"arXiv","source":"DOI.org (Datacite)","title":"MDE4QAI: Towards Model-Driven Engineering for Quantum Artificial Intelligence","title-short":"MDE4QAI","type":"article-journal","URL":"https://arxiv.org/abs/2107.06708","version":"1"},
{"id":"mongodbSchemaDesignExample10:25:57UTC","abstract":"One of the challenges that comes with moving to MongoDB is figuring how to","accessed":{"date-parts":[[2018,4,30]]},"author":[{"family":"MongoDB","given":""}],"citation-key":"mongodbSchemaDesignExample10:25:57UTC","issued":{"literal":"10:25:57 UTC"},"title":"Schema Design By Example","type":"speech","URL":"https://www.slideshare.net/mongodb/schema-design-by-example"},
{"id":"mongodbTransitioningSQLMongoDB10:56:52UTC","abstract":"Learn how to transition from SQL to MongoDB with this presentation.","accessed":{"date-parts":[[2018,4,30]]},"author":[{"family":"MongoDB","given":""}],"citation-key":"mongodbTransitioningSQLMongoDB10:56:52UTC","genre":"Technology","issued":{"literal":"10:56:52 UTC"},"title":"Transitioning from SQL to MongoDB","type":"speech","URL":"https://www.slideshare.net/mongodb/transition-sql2mongo-1?next_slideshow=1"},
{"id":"MonitoringYourHome","accessed":{"date-parts":[[2021,1,7]]},"citation-key":"MonitoringYourHome","note":"00000","title":"Monitoring your home network with InfluxDB on Raspberry Pi with Docker | by Pete Shima | Medium","type":"webpage","URL":"https://medium.com/@petey5000/monitoring-your-home-network-with-influxdb-on-raspberry-pi-with-docker-78a23559ffea"},
{"id":"monperrusMeasuringModels","author":[{"family":"Monperrus","given":"Martin"},{"family":"Jezequel","given":"Jean-Marc"},{"family":"Champeau","given":"Joel"},{"family":"Hoeltzener","given":"Brigitte"}],"citation-key":"monperrusMeasuringModels","title":"Measuring models","type":"article-journal"},
{"id":"Montini2013721","abstract":"Nowadays there are various forms for performing software design, planning, and manufacturing. To each of these it is required a proper process definition to achieve metrological forecasting goal. In this investigation, the research area is Artificial Intelligence algorithms applied to projects for Production Lines design, characterized as Manufacturing Cells. In this type of approach the design project is aimed at improving the understanding and assertiveness in the planning of the operation, through an Intelligent Agent use. The Intelligent Agent was proposed as a model-driven and was aimed at identifying the code capacity installed in a specific programming language. © 2013 IEEE.","author":[{"family":"Montini","given":"D.A."},{"family":"Tasinaffo","given":"P.M."},{"family":"Dias","given":"L.A.V."},{"family":"Neto","given":"A.A."},{"family":"Da Cunha","given":"A.M."},{"family":"Montini","given":"A.A."}],"citation-key":"Montini2013721","collection-title":"Proceedings of the 2013 10th International Conference on Information Technology: New Generations, ITNG 2013","DOI":"10.1109/ITNG.2013.139","ISBN":"978-0-7695-4967-5","issued":{"date-parts":[[2013]]},"page":"721-726","title":"A meta-algorithm for planning optimization in a software production line","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886682095&doi=10.1109%2fITNG.2013.139&partnerID=40&md5=22eac0802809d2eefe3ed8df081533e2"},
{"id":"MoralEducationSelfManagement","accessed":{"date-parts":[[2016,9,21]]},"citation-key":"MoralEducationSelfManagement","title":"E M C I: Moral Education: Self-Management - Lecture 5","type":"webpage","URL":"http://spu.edu/depts/iccs/emci/courses/lectures/self_management_lec5.htm"},
{"id":"moreno-llorenaFunctionalCharacterizationCollaborative2011","accessed":{"date-parts":[[2015,4,1]]},"author":[{"family":"Moreno-Llorena","given":"Jaime"},{"family":"Claros","given":"Iván"},{"family":"Martín","given":"Rafael"},{"family":"Cobos","given":"Ruth"},{"family":"Lara","given":"Juan","non-dropping-particle":"de"},{"family":"Guerra","given":"Esther"}],"citation-key":"moreno-llorenaFunctionalCharacterizationCollaborative2011","container-title":"Cooperative Design, Visualization, and Engineering","issued":{"date-parts":[[2011]]},"page":"182–185","publisher":"Springer","source":"Google Scholar","title":"Towards a functional characterization of collaborative systems","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-642-23734-8_30"},
{"id":"Moreno:2015:IUT:2818754.2818860","author":[{"family":"Moreno","given":"Laura"},{"family":"Bavota","given":"Gabriele"},{"family":"Di Penta","given":"Massimiliano"},{"family":"Oliveto","given":"Rocco"},{"family":"Marcus","given":"Andrian"}],"citation-key":"Moreno:2015:IUT:2818754.2818860","container-title":"37th international conference on software engineering","event-place":"Piscataway","ISBN":"978-1-4799-1934-5","issued":{"date-parts":[[2015]]},"page":"880-890","publisher":"IEEE","publisher-place":"Piscataway","title":"How can I use this method?","type":"paper-conference"},
{"id":"Morin20162160","abstract":"We use machine learning to generate metamodels for sawing simulation. Simulation is widely used in the wood industry for decision making. These simulators are particular since their response for a given input is a structured object, i.e., a basket of lumbers. We demonstrate how we use simple machine learning algorithms (e.g., a tree) to obtain a good approximation of the simulator's response. The generated metamodels are guaranteed to output physically realistic baskets (i.e., there exists at least one log that can produce the basket). We also propose to use kernel ridge regression. While having the power to exploit the structure of a basket, it can predict previously unseen baskets. We finally evaluate the impact of possibly predicting unrealistic baskets using ridge regression jointly with a nearest neighbor approach in the output space. All metamodels are evaluated using standard machine learning metrics and novel metrics especially designed for the problem. © 2015 IEEE.","author":[{"family":"Morin","given":"M."},{"family":"Paradis","given":"F."},{"family":"Rolland","given":"A."},{"family":"Wery","given":"J."},{"family":"Gaudreault","given":"J."},{"family":"Laviolette","given":"F."}],"citation-key":"Morin20162160","collection-title":"Proceedings - Winter Simulation Conference","DOI":"10.1109/WSC.2015.7408329","ISBN":"978-1-4673-9743-8","ISSN":"08917736","issued":{"date-parts":[[2016]]},"page":"2160-2171","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Machine learning-based metamodels for sawing simulation","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962909563&doi=10.1109%2fWSC.2015.7408329&partnerID=40&md5=fae22320ca439ee5b84825aade0844ac","volume":"2016-February"},
{"id":"morinModelBasedSoftwareEngineering2017","accessed":{"date-parts":[[2019,8,22]]},"author":[{"family":"Morin","given":"Brice"},{"family":"Harrand","given":"Nicolas"},{"family":"Fleurey","given":"Franck"}],"citation-key":"morinModelBasedSoftwareEngineering2017","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2017.11","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017,1]]},"page":"30-36","source":"DOI.org (Crossref)","title":"Model-Based Software Engineering to Tame the IoT Jungle","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7819419/","volume":"34"},
{"id":"Morris2018","abstract":"Computational sprinting speeds up query execution by increasing power usage for short bursts. Sprinting policy decides when and how long to sprint. Poor policies inflate response time significantly. We propose a model-driven approach that chooses between sprinting policies based on their expected response time. However, sprinting alters query executions at runtime, creating a complex dependency between queuing and processing time. Our performance modeling approach employs offline profiling, machine learning, and first-principles simulation. Collectively, these modeling techniques capture the effects of sprinting on response time. We validated our modeling approach with 3 sprinting mechanisms across 9 workloads. Our performance modeling approach predicted response time with median error below 4% in most tests and median error of 11% in the worst case. We demonstrated model-driven sprinting for cloud providers seeking to colocate multiple workloads on AWS Burstable Instances while meeting service level objectives. Model-driven sprinting uncovered policies that achieved response time goals, allowing more workloads to colocate on a node. Compared to AWS Burstable policies, our approach increased revenue per node by 1.6X. © 2018 Association for Computing Machinery.","author":[{"family":"Morris","given":"N."},{"family":"Stewart","given":"C."},{"family":"Chen","given":"L."},{"family":"Birke","given":"R."},{"family":"Kelley","given":"J."}],"citation-key":"Morris2018","collection-title":"Proceedings of the 13th EuroSys Conference, EuroSys 2018","DOI":"10.1145/3190508.3190543","ISBN":"978-1-4503-5584-1","issued":{"date-parts":[[2018]]},"publisher":"Association for Computing Machinery, Inc","title":"Model-driven computational sprinting","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052014119&doi=10.1145%2f3190508.3190543&partnerID=40&md5=5caf49863f9cade1d124c7ce429d5ae7","volume":"2018-January"},
{"id":"morrisonSoftwareArchitecture2nd2005","citation-key":"morrisonSoftwareArchitecture2nd2005","collection-title":"Lecture Notes in Computer Science","DOI":"10.1007/b136986","editor":[{"family":"Morrison","given":"Ronald"},{"family":"Oquendo","given":"Flávio"}],"ISBN":"3-540-26275-X","issued":{"date-parts":[[2005]]},"publisher":"Springer","title":"Software Architecture, 2nd European Workshop, EWSA 2005, Pisa, Italy, June 13-14, 2005, Proceedings","type":"book","URL":"https://doi.org/10.1007/b136986","volume":"3527"},
{"id":"mostermanCyberphysicalSystemsChallenges2016","accessed":{"date-parts":[[2016,2,2]]},"author":[{"family":"Mosterman","given":"Pieter J."},{"family":"Zander","given":"Justyna"}],"citation-key":"mostermanCyberphysicalSystemsChallenges2016","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-015-0469-x","ISSN":"1619-1366, 1619-1374","issue":"1","issued":{"date-parts":[[2016,2]]},"page":"5-16","source":"CrossRef","title":"Cyber-physical systems challenges: a needs analysis for collaborating embedded software systems","title-short":"Cyber-physical systems challenges","type":"article-journal","URL":"http://link.springer.com/10.1007/s10270-015-0469-x","volume":"15"},
{"id":"mostermanIndustryCyberPhysicalSystem2016","accessed":{"date-parts":[[2016,2,2]]},"author":[{"family":"Mosterman","given":"Pieter J."},{"family":"Zander","given":"Justyna"}],"citation-key":"mostermanIndustryCyberPhysicalSystem2016","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-015-0493-x","ISSN":"1619-1366, 1619-1374","issue":"1","issued":{"date-parts":[[2016,2]]},"page":"17-29","source":"CrossRef","title":"Industry 4.0 as a Cyber-Physical System study","type":"article-journal","URL":"http://link.springer.com/10.1007/s10270-015-0493-x","volume":"15"},
{"id":"MSCAITN2016","accessed":{"date-parts":[[2015,11,19]]},"citation-key":"MSCAITN2016","title":"MSCA-ITN-2016","type":"webpage","URL":"https://ec.europa.eu/research/participants/portal/desktop/en/opportunities/h2020/topics/2056-msca-itn-2016.html"},
{"id":"mucciniSelfadaptationCyberphysicalSystems2016","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Muccini","given":"Henry"},{"family":"Sharaf","given":"Mohammad"},{"family":"Weyns","given":"Danny"}],"citation-key":"mucciniSelfadaptationCyberphysicalSystems2016","DOI":"10.1145/2897053.2897069","ISBN":"978-1-4503-4187-5","issued":{"date-parts":[[2016]]},"page":"75-81","publisher":"ACM Press","source":"CrossRef","title":"Self-adaptation for cyber-physical systems: a systematic literature review","title-short":"Self-adaptation for cyber-physical systems","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?doid=2897053.2897069"},
{"id":"mullerAutonomicComputingNow2009","accessed":{"date-parts":[[2016,9,29]]},"author":[{"family":"Müller","given":"Hausi A."},{"family":"Kienle","given":"Holger M."},{"family":"Stege","given":"Ulrike"}],"citation-key":"mullerAutonomicComputingNow2009","collection-editor":[{"family":"Hutchison","given":"David"},{"family":"Kanade","given":"Takeo"},{"family":"Kittler","given":"Josef"},{"family":"Kleinberg","given":"Jon M."},{"family":"Mattern","given":"Friedemann"},{"family":"Mitchell","given":"John C."},{"family":"Naor","given":"Moni"},{"family":"Nierstrasz","given":"Oscar"},{"family":"Pandu Rangan","given":"C."},{"family":"Steffen","given":"Bernhard"},{"family":"Sudan","given":"Madhu"},{"family":"Terzopoulos","given":"Demetri"},{"family":"Tygar","given":"Doug"},{"family":"Vardi","given":"Moshe Y."},{"family":"Weikum","given":"Gerhard"}],"container-title":"Software Engineering","editor":[{"family":"De Lucia","given":"Andrea"},{"family":"Ferrucci","given":"Filomena"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-540-95887-1 978-3-540-95888-8","issued":{"date-parts":[[2009]]},"page":"32-54","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","source":"CrossRef","title":"Autonomic Computing Now You See It, Now You Don’t","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-540-95888-8_2","volume":"5413"},
{"id":"Mumuni2022191","abstract":"Monocular depth estimation (MDE) provides information (from a single image) about overall scene layout, and is useful in robotics for autonomous navigation and vision-aided guidance. Advancements in deep learning, particularly self-supervised convolutional neural networks (CNNs), have led to the development of MDE models capable of providing highly accurate per-pixel depth maps. However, these models are typically tuned for specific datasets, leading to sharp performance degradation in real-world scenarios, particularly in robot vision tasks—where the natural environments are too varied and complex to be sufficiently described by standard datasets. Motivated by the approach of biological vision, whose immense success relies on optimal combination of multiple depth cues and knowledge about the underlying environments, we exploit structure from motion (SfM) through optical flow as an additional depth cue and prior knowledge about depth distribution in the environment to improve monocular depth prediction. Meanwhile, there is a general incompatibility between the outputs of these models—whereas SfM measures absolute distances, MDE is scale ambiguous, returning only depth ratios. Consequently, we show how it is possible to promote MDE cue from ordinal scale to the same metric scale as SfM, thus, enabling their optimal integration in a Bayesian optimal manner. Additionally, we generalize the relationship between camera tilt angles and resulting MDE distortions, and show how this can be used to further improve depth perception robustness and accuracy (up to 6.2%) for a mobile robot whose heading is subject to arbitrary angular inclinations. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.","author":[{"family":"Mumuni","given":"F."},{"family":"Mumuni","given":"A."}],"citation-key":"Mumuni2022191","container-title":"International Journal of Intelligent Robotics and Applications","DOI":"10.1007/s41315-022-00226-2","ISSN":"23665971","issue":"2","issued":{"date-parts":[[2022]]},"page":"191-206","publisher":"Springer","title":"Bayesian cue integration of structure from motion and CNN-based monocular depth estimation for autonomous robot navigation","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125531676&doi=10.1007%2fs41315-022-00226-2&partnerID=40&md5=6ffc132f69e4783ea9fb133e1245f442","volume":"6"},
{"id":"munappyDataPipelineManagement2020","abstract":"Data pipelines involve a complex chain of interconnected activities that starts with a data source and ends in a data sink. Data pipelines are important for data-driven organizations since a data pipeline can process data in multiple formats from distributed data sources with minimal human intervention, accelerate data life cycle activities, and enhance productivity in data-driven enterprises. However, there are challenges and opportunities in implementing data pipelines but practical industry experiences are seldom reported. The findings of this study are derived by conducting a qualitative multiple-case study and interviews with the representatives of three companies. The challenges include data quality issues, infrastructure maintenance problems, and organizational barriers. On the other hand, data pipelines are implemented to enable traceability, fault-tolerance, and reduce human errors through maximizing automation thereby producing high-quality data. Based on multiplecase study research with five use cases from three case companies, this paper identifies the key challenges and benefits associated with the implementation and use of data pipelines.","accessed":{"date-parts":[[2022,2,24]]},"author":[{"family":"Munappy","given":"Aiswarya Raj"},{"family":"Bosch","given":"Jan"},{"family":"Olsson","given":"Helena Homström"}],"citation-key":"munappyDataPipelineManagement2020","container-title":"Product-Focused Software Process Improvement","DOI":"10.1007/978-3-030-64148-1_11","editor":[{"family":"Morisio","given":"Maurizio"},{"family":"Torchiano","given":"Marco"},{"family":"Jedlitschka","given":"Andreas"}],"event-place":"Cham","ISBN":"978-3-030-64147-4 978-3-030-64148-1","issued":{"date-parts":[[2020]]},"note":"00000","page":"168-184","publisher":"Springer International Publishing","publisher-place":"Cham","source":"DOI.org (Crossref)","title":"Data Pipeline Management in Practice: Challenges and Opportunities","title-short":"Data Pipeline Management in Practice","type":"chapter","URL":"https://link.springer.com/10.1007/978-3-030-64148-1_11","volume":"12562"},
{"id":"murLineeGuidaIniziative","author":[{"family":"Mur","given":"Pnrr"}],"citation-key":"murLineeGuidaIniziative","note":"00000","page":"47","source":"Zotero","title":"Linee Guida per le iniziative di sistema Missione 4: Istruzione e ricerca Componente 2: Dalla ricerca all’impresa","type":"article-journal"},
{"id":"Murtagh2012","author":[{"family":"Murtagh","given":"Fionn"},{"family":"Contreras","given":"Pedro"}],"citation-key":"Murtagh2012","container-title":"Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery","issued":{"date-parts":[[2012,1]]},"page":"86-97","title":"Algorithms for hierarchical clustering: An overview","type":"article-journal","volume":"2"},
{"id":"mussbacherAssessmentGridIntelligent2020","accessed":{"date-parts":[[2021,10,30]]},"author":[{"family":"Mussbacher","given":"Gunter"},{"family":"Combemale","given":"Benoit"},{"family":"Abrahão","given":"Silvia"},{"family":"Bencomo","given":"Nelly"},{"family":"Burgueño","given":"Loli"},{"family":"Engels","given":"Gregor"},{"family":"Kienzle","given":"Jörg"},{"family":"Kühn","given":"Thomas"},{"family":"Mosser","given":"Sébastien"},{"family":"Sahraoui","given":"Houari"},{"family":"Weyssow","given":"Martin"}],"citation-key":"mussbacherAssessmentGridIntelligent2020","container-title":"Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings","DOI":"10.1145/3417990.3421396","event":"MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems","event-place":"Virtual Event Canada","ISBN":"978-1-4503-8135-2","issued":{"date-parts":[[2020,10,16]]},"note":"00003","page":"1-10","publisher":"ACM","publisher-place":"Virtual Event Canada","source":"DOI.org (Crossref)","title":"Towards an assessment grid for intelligent modeling assistance","type":"paper-conference","URL":"https://dl.acm.org/doi/10.1145/3417990.3421396"},
{"id":"nagornyBigDataAnalysis","abstract":"The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.","author":[{"family":"Nagorny","given":"Kevin"},{"family":"Lima-Monteiro","given":"Pedro"},{"family":"Barata","given":"Jose"},{"family":"Colombo","given":"Armando Walter"}],"citation-key":"nagornyBigDataAnalysis","note":"00000","page":"29","source":"Zotero","title":"Big Data Analysis in Smart Manufacturing: A Review","type":"article-journal"},
{"id":"Nagy2021","abstract":"Dependable cyber-physical systems (CPS) are increasingly used in various application fields, such as urban mobility, smart city, industrial IoT and telecommunication. Beside functional requirements, dependable CPS systems have to meet several extra-functional requirements such as reliability, availability, fault-Tolerance and performance the complexity of modern CPS systems significantly increased since the extensive use of distributed services, redundant architectures and advanced safety mechanisms. In addition, several new technologies have emerged in the edge, such as embedded GPU-s, AI acceleration and virtualisation tools, which enhance the extra-functional properties, such as latency and performance of dependable CPS systems. Because of the increased complexity and the cutting edge technologies, evaluating the extra-functional requirements becomes difficult for modern CPS systems. Consequently, several new analysis techniques have also been developed. We developed an open-source demonstrator for dependable edge-based CPS systems in the field of smart city and urban mobility. With the demonstrator, the researchers can compare and evaluate different technologies, safety mechanisms and analysis techniques the demonstrator consists of several emerging technologies such as hardware accelerators, load-balance mechanisms, containerisation and container deployment tools the architecture of the demonstrator was developed following the edge computing paradigm and model-driven engineering approach the demonstrator contains distributed redundant and fault-Tolerant services. We also developed a hardware-in-The-loop (HIL) test environment to simulate various environmental scenarios and evaluate extra-functional properties. © 2021 IEEE.","author":[{"family":"Nagy","given":"S.J."},{"family":"Szabo","given":"R."},{"family":"Vajda","given":"M.L."},{"family":"Voros","given":"A."}],"citation-key":"Nagy2021","collection-title":"2021 10th Latin-American Symposium on Dependable Computing, LADC 2021 - Proceedings","DOI":"10.1109/LADC53747.2021.9672569","ISBN":"978-1-66547-831-1","issued":{"date-parts":[[2021]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Demonstrator for dependable edge-based cyber-physical systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125298833&doi=10.1109%2fLADC53747.2021.9672569&partnerID=40&md5=b270f2c49b3b33aecb7514a05f8224f8"},
{"id":"nagyDemonstratorDependableEdgebased2021a","abstract":"Dependable cyber-physical systems (CPS) are increasingly used in various application fields, such as urban mobility, smart city, industrial IoT and telecommunication. Beside functional requirements, dependable CPS systems have to meet several extra-functional requirements such as reliability, availability, fault-Tolerance and performance the complexity of modern CPS systems significantly increased since the extensive use of distributed services, redundant architectures and advanced safety mechanisms. In addition, several new technologies have emerged in the edge, such as embedded GPU-s, AI acceleration and virtualisation tools, which enhance the extra-functional properties, such as latency and performance of dependable CPS systems. Because of the increased complexity and the cutting edge technologies, evaluating the extra-functional requirements becomes difficult for modern CPS systems. Consequently, several new analysis techniques have also been developed. We developed an open-source demonstrator for dependable edge-based CPS systems in the field of smart city and urban mobility. With the demonstrator, the researchers can compare and evaluate different technologies, safety mechanisms and analysis techniques the demonstrator consists of several emerging technologies such as hardware accelerators, load-balance mechanisms, containerisation and container deployment tools the architecture of the demonstrator was developed following the edge computing paradigm and model-driven engineering approach the demonstrator contains distributed redundant and fault-Tolerant services. We also developed a hardware-in-The-loop (HIL) test environment to simulate various environmental scenarios and evaluate extra-functional properties. © 2021 IEEE.","author":[{"family":"Nagy","given":"S.J."},{"family":"Szabo","given":"R."},{"family":"Vajda","given":"M.L."},{"family":"Voros","given":"A."}],"citation-key":"nagyDemonstratorDependableEdgebased2021a","container-title":"2021 10th Latin-American Symposium on Dependable Computing, LADC 2021 - Proceedings","DOI":"10.1109/LADC53747.2021.9672569","ISBN":"978-1-66547-831-1","issued":{"date-parts":[[2021]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Demonstrator for dependable edge-based cyber-physical systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125298833&doi=10.1109%2fLADC53747.2021.9672569&partnerID=40&md5=b270f2c49b3b33aecb7514a05f8224f8"},
{"id":"nairFindingFasterConfigurations","abstract":"Finding good configurations of a software system is often challenging since the number of configuration options can be large. Software engineers often make poor choices about configuration or, even worse, they usually use a sub-optimal configuration in production, which leads to inadequate performance. To assist engineers in finding the better configuration, this article introduces FLASH, a sequential model-based method that sequentially explores the configuration space by reflecting on the configurations evaluated so far to determine the next best configuration to explore. FLASH scales up to software systems that defeat the prior state-of-the-art model-based methods in this area. FLASH runs much faster than existing methods and can solve both single-objective and multi-objective optimization problems. The central insight of this article is to use the prior knowledge of the configuration space (gained from prior runs) to choose the next promising configuration. This strategy reduces the effort (i.e., number of measurements) required to find the better configuration. We evaluate FLASH using 30 scenarios based on 7 software systems to demonstrate that FLASH saves effort in 100% and 80% of cases in single-objective and multi-objective problems respectively by up to several orders of magnitude compared to state-of-the-art techniques.","author":[{"family":"Nair","given":"Vivek"},{"family":"Yu","given":"Zhe"},{"family":"Menzies","given":"Tim"},{"family":"Siegmund","given":"Norbert"},{"family":"Apel","given":"Sven"}],"citation-key":"nairFindingFasterConfigurations","page":"17","source":"Zotero","title":"Finding Faster Configurations using FLASH","type":"article-journal"},
{"id":"nanayakkaraSurveyFindingTrends2021","abstract":"Social media have become very popular in the last few decades. Users rely on social network sites like Twitter, Facebook, YouTube, and LinkedIn for both information and entertainment needs. Social media analytics with data mining technology could be an analysis axis centered on extracting trends, patterns, and rules from the social media pool, to serve the people and organizations to have optimum choices concerning many disciplines. The traditional media analytical techniques appear obsolete and inadequate to gratify this immense array of unstructured social media knowledge characterized by three key problems namely; size, noise, and dynamism, predominantly shifting from the batch scale to the streaming one. The objective of this study is to investigate the data mining techniques that were used by social media networks during the years 2010 and 2020. The effort is a systematic review of content analysis in studies within the field of social media analytics that was published in principal databases. 125 articles were reviewed in this paper. Content analysis was implemented based on their approach, tools utilized, language, the dataset used, country, year, and nature of the experiment. The review discovered that 22 data mining techniques were employed with social media data while frequently used in Artificial Neural Network (ANN), Bayesian networks (BN) and Support Vector Machine (SVM), K-means Clustering, and Neuro-Fuzzy Logic Approach. The study has focused to assist the involved analyzers and educators to capture the research trends and problems associated with the Social media analytics process with future research initiatives.","accessed":{"date-parts":[[2022,2,3]]},"author":[{"family":"Nanayakkara","given":"A. C."},{"family":"Kumara","given":"B. T. G. S."},{"family":"Rathnayaka","given":"R. M. K. T."}],"citation-key":"nanayakkaraSurveyFindingTrends2021","container-title":"Sri Lanka Journal of Social Sciences and Humanities","container-title-short":"SL J. Soc. Sci. Hum.","DOI":"10.4038/sljssh.v1i2.36","ISSN":"2773-692X, 2773-6911","issue":"2","issued":{"date-parts":[[2021,8,1]]},"note":"00000","page":"37","source":"DOI.org (Crossref)","title":"A Survey of Finding Trends in Data Mining Techniques for Social Media Analysis","type":"article-journal","URL":"https://sljssh.sljol.info/article/10.4038/sljssh.v1i2.36/","volume":"1"},
{"id":"Narayanankutty202192","abstract":"IoT networks today face a myriad of security vulnerabilities in their infrastructure due to its wide attack surface. Large-scale networks are increasingly adopting a Software-Defined Networking approach, it allows for simplified network control and management through network virtualization. Since traditional security mechanisms are incapable of handling virtualized environments, SDSec or Software-Defined Security is introduced as a solution to support virtualized infrastructure, specifically aimed at providing security solutions to SDN frameworks. To further aid large scale design and development of SDN frameworks, Model-Driven Engineering (MDE) has been proposed to be used at the design phase, since abstraction, automation and analysis are inherently key aspects of MDE. This provides an efficient approach to reducing large problems through models that abstract away the complex technicality of the total system. Making adaptations to these models to address security issues faced in IoT networks, largely reduces cost and improves efficiency. These models can be simulated, analysed and supports architecture model adaptation; model changes are then reflected back to the real system. We propose a model-driven security approach for SDSec networks that can self-Adapt using machine learning to mitigate security threats. The overall design time changes can be monitored at run time through machine learning techniques (e.g. deep, reinforcement learning) for real time analysis. This approach can be tested in IoT simulation environments, for instance using the CAPS IoT modeling and simulation framework. Using self-Adaptation of models and advanced machine learning for data analysis would ensure that the SDSec architecture adapts and improves over time. This largely reduces the overall attack surface to achieve improved end-To-end security in IoT environments. © 2021 IEEE.","author":[{"family":"Narayanankutty","given":"H."}],"citation-key":"Narayanankutty202192","collection-title":"Proceedings - 2021 IEEE 18th International Conference on Software Architecture Companion, ICSA-C 2021","DOI":"10.1109/ICSA-C52384.2021.00023","ISBN":"978-1-66543-910-7","issued":{"date-parts":[[2021]]},"page":"92-93","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Self-adapting model-based SDSec for IoT networks using machine learning","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106640097&doi=10.1109%2fICSA-C52384.2021.00023&partnerID=40&md5=3a162f13a9bfc2e1ccd4c070ee839967"},
{"id":"nassifAutomaticallyCategorizingSoftware2018","abstract":"Informal language and the absence of a standard taxonomy for software technologies make it difficult to reliably analyze technology trends on discussion forums and other on-line venues. We propose an automated approach called Witt for the categorization of software technology (an expanded version of the hypernym discovery problem). Witt takes as input a phrase describing a software technology or concept and returns a general category that describes it (e.g., integrated development environment), along with attributes that further qualify it (commercial, php, etc.). By extension, the approach enables the dynamic creation of lists of all technologies of a given type (e.g., web application frameworks). Our approach relies on Stack Overflow and Wikipedia, and involves numerous original domain adaptations and a new solution to the problem of normalizing automatically-detected hypernyms. We compared Witt with six independent taxonomy tools and found that, when applied to software terms, Witt demonstrated better coverage than all evaluated alternate solutions, without a corresponding degradation in false positive rate.","accessed":{"date-parts":[[2018,8,8]]},"author":[{"family":"Nassif","given":"Mathieu"},{"family":"Treude","given":"Christoph"},{"family":"Robillard","given":"Martin"}],"citation-key":"nassifAutomaticallyCategorizingSoftware2018","container-title":"IEEE Transactions on Software Engineering","DOI":"10.1109/TSE.2018.2836450","ISSN":"0098-5589, 1939-3520","issued":{"date-parts":[[2018]]},"page":"1-1","source":"Crossref","title":"Automatically Categorizing Software Technologies","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/8359344/"},
{"id":"nasticPatRICIANovelProgramming2013","accessed":{"date-parts":[[2016,5,30]]},"author":[{"family":"Nastic","given":"Stefan"},{"family":"Sehic","given":"Sanjin"},{"family":"Vogler","given":"Michael"},{"family":"Truong","given":"Hong-Linh"},{"family":"Dustdar","given":"Schahram"}],"citation-key":"nasticPatRICIANovelProgramming2013","DOI":"10.1109/SOCA.2013.48","ISBN":"978-1-4799-2702-9 978-1-4799-2701-2","issued":{"date-parts":[[2013,12]]},"page":"53-60","publisher":"IEEE","source":"CrossRef","title":"PatRICIA -- A Novel Programming Model for IoT Applications on Cloud Platforms","type":"paper-conference","URL":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6717285"},
{"id":"nasticProvisioningSoftwareDefinedIoT2014","accessed":{"date-parts":[[2016,5,30]]},"author":[{"family":"Nastic","given":"Stefan"},{"family":"Sehic","given":"Sanjin"},{"family":"Le","given":"Duc-Hung"},{"family":"Truong","given":"Hong-Linh"},{"family":"Dustdar","given":"Schahram"}],"citation-key":"nasticProvisioningSoftwareDefinedIoT2014","DOI":"10.1109/FiCloud.2014.52","ISBN":"978-1-4799-4357-9","issued":{"date-parts":[[2014,8]]},"page":"288-295","publisher":"IEEE","source":"CrossRef","title":"Provisioning Software-Defined IoT Cloud Systems","type":"paper-conference","URL":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6984208"},
{"id":"naumovDeepLearningRecommendation2019","abstract":"With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks. These networks differ significantly from other deep learning networks due to their need to handle categorical features and are not well studied or understood. In this paper, we develop a state-of-the-art deep learning recommendation model (DLRM) and provide its implementation in both PyTorch and Caffe2 frameworks. In addition, we design a specialized parallelization scheme utilizing model parallelism on the embedding tables to mitigate memory constraints while exploiting data parallelism to scale-out compute from the fully-connected layers. We compare DLRM against existing recommendation models and characterize its performance on the Big Basin AI platform, demonstrating its usefulness as a benchmark for future algorithmic experimentation and system co-design.","accessed":{"date-parts":[[2021,6,7]]},"author":[{"family":"Naumov","given":"Maxim"},{"family":"Mudigere","given":"Dheevatsa"},{"family":"Shi","given":"Hao-Jun Michael"},{"family":"Huang","given":"Jianyu"},{"family":"Sundaraman","given":"Narayanan"},{"family":"Park","given":"Jongsoo"},{"family":"Wang","given":"Xiaodong"},{"family":"Gupta","given":"Udit"},{"family":"Wu","given":"Carole-Jean"},{"family":"Azzolini","given":"Alisson G."},{"family":"Dzhulgakov","given":"Dmytro"},{"family":"Mallevich","given":"Andrey"},{"family":"Cherniavskii","given":"Ilia"},{"family":"Lu","given":"Yinghai"},{"family":"Krishnamoorthi","given":"Raghuraman"},{"family":"Yu","given":"Ansha"},{"family":"Kondratenko","given":"Volodymyr"},{"family":"Pereira","given":"Stephanie"},{"family":"Chen","given":"Xianjie"},{"family":"Chen","given":"Wenlin"},{"family":"Rao","given":"Vijay"},{"family":"Jia","given":"Bill"},{"family":"Xiong","given":"Liang"},{"family":"Smelyanskiy","given":"Misha"}],"citation-key":"naumovDeepLearningRecommendation2019","container-title":"arXiv:1906.00091 [cs]","issued":{"date-parts":[[2019,5,31]]},"note":"00000","source":"arXiv.org","title":"Deep Learning Recommendation Model for Personalization and Recommendation Systems","type":"article-journal","URL":"http://arxiv.org/abs/1906.00091"},
{"id":"navarreteIntroducingSubjectiveKnowledge","abstract":"Knowledge-based applications that deal with uncertainty usually represent it by means of a confidence score that expresses the probability that a given fact is true. However, different users may have distinct opinions about the same fact, something that is not considered in existing proposals. This is critical in a number of areas where individual opinions need to be taken into account when making informed decisions, particularly when these are to be made by consensus. This paper introduces Subjective Knowledge Graphs (SKG), an extension to Probabilistic Knowledge Graphs that considers the individual opinions of separate users about the same facts, and allows reasoning about them. We show how SKGs can be implemented using standard graph databases and how the results of the queries can be enriched with the associated degrees of uncertainty.","author":[{"family":"Navarrete","given":"Francisco J"},{"family":"Vallecillo","given":"Antonio"}],"citation-key":"navarreteIntroducingSubjectiveKnowledge","note":"00000","page":"10","source":"Zotero","title":"Introducing Subjective Knowledge Graphs","type":"article-journal"},
{"id":"nazabalDataEngineeringData2020","abstract":"Consider the situation where a data analyst wishes to carry out an analysis on a given dataset. It is widely recognized that most of the analyst’s time will be taken up with data engineering tasks such as acquiring, understanding, cleaning and preparing the data. In this paper we provide a description and classification of such tasks into high-levels groups, namely data organization, data quality and feature engineering. We also make available four datasets and example analyses that exhibit a wide variety of these problems, to help encourage the development of tools and techniques to help reduce this burden and push forward research towards the automation or semi-automation of the data engineering process.","accessed":{"date-parts":[[2020,7,21]]},"author":[{"family":"Nazabal","given":"Alfredo"},{"family":"Williams","given":"Christopher K. I."},{"family":"Colavizza","given":"Giovanni"},{"family":"Smith","given":"Camila Rangel"},{"family":"Williams","given":"Angus"}],"citation-key":"nazabalDataEngineeringData2020","container-title":"arXiv:2004.12929 [cs]","issued":{"date-parts":[[2020,4,27]]},"note":"00000","source":"arXiv.org","title":"Data Engineering for Data Analytics: A Classification of the Issues, and Case Studies","title-short":"Data Engineering for Data Analytics","type":"article-journal","URL":"http://arxiv.org/abs/2004.12929"},
{"id":"nejatiNextGenerationSoftwareVerification2021","abstract":"In recent years, automated software verification has progressed significantly. We can now effectively explore complex software structures through automated testing or to prove properties of complex programs, such as compilers using formal methods. But, for the most part, software testing and formal software verification techniques have advanced independently with relatively few insights on how their research thrusts compare or can be combined.","accessed":{"date-parts":[[2021,5,10]]},"author":[{"family":"Nejati","given":"Shiva"}],"citation-key":"nejatiNextGenerationSoftwareVerification2021","container-title":"IEEE Software","DOI":"10.1109/MS.2021.3049322","ISSN":"0740-7459","issue":"03","issued":{"date-parts":[[2021,5,1]]},"note":"00000","page":"126-130","publisher":"IEEE Computer Society","source":"www.computer.org","title":"Next-Generation Software Verification: An AI Perspective","title-short":"Next-Generation Software Verification","type":"article-journal","URL":"https://www.computer.org/csdl/magazine/so/2021/03/09407305/1sVEM1i36Jq","volume":"38"},
{"id":"Neto2017293","abstract":"The preprocessing stage in knowledge discovery projects is costly, normally taking between 50% and 80% of the total project time. It is in this stage that data in a relational database are transformed for applying a data mining technique. This stage is a complex task that demands from database designers a strong interaction with experts having a broad knowledge about the application domain. Frameworks aiming to systemize this stage have significant limitations when applied to Credit Behavioral Scoring solutions. This paper proposes a framework based on the Model Driven Development approach to systemize the mentioned stage. This work has three main contributions: 1) improving the discriminant power of data mining techniques by means of the construction of new input variables which embed temporal knowledge for the technique; 2) reducing the time of data transformation using automatic code generation, and 3) allowing artificial intelligence and statistics modelers to perform the data transformation without the help of database experts. In order to validate the proposed framework, two comparative studies were conducted. Experiments showed that the proposed framework delivers a performance equivalent or superior to those of existing frameworks and reduces the time of data transformation with a confidence level of 95%. © 2016","author":[{"family":"Neto","given":"R."},{"family":"Jorge Adeodato","given":"P."},{"family":"Carolina Salgado","given":"A."}],"citation-key":"Neto2017293","container-title":"Expert Systems with Applications","DOI":"10.1016/j.eswa.2016.10.059","ISSN":"09574174","issued":{"date-parts":[[2017]]},"page":"293-305","publisher":"Elsevier Ltd","title":"A framework for data transformation in Credit Behavioral Scoring applications based on Model Driven Development","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007545437&doi=10.1016%2fj.eswa.2016.10.059&partnerID=40&md5=eede38e92df0ad7a39f2faeee4f6579d","volume":"72"},
{"id":"Neumann201911","abstract":"The interaction between instructors and students is one of the key concepts to improve the student's learning process. To personalize learning on a massive scale, social bots can be used as supporting technology. However, their development for virtual learning environments currently requires deep technical knowledge. This leaves learner communities relying on highly-skilled developers to generate and tailor these social bots. Participatory design, end-user development and model-driven principles bear the potential to close this technical gap. In this paper, we propose a model-driven approach for creating social bots. Using our framework, learners can create, train and utilize these for self-hosted virtual learning environments relying on OpenAPI specifications offered, e.g. by Blackboard. We support both retrieval-based bots that react to certain events in predefined ways, as well as generative bots by utilizing open source deep learning technologies. Our first evaluation shows the usefulness of model-driven generation and utilization of social bots. We see the potential of this approach to move the development closer to the actual learner. © 2019 IEEE.","author":[{"family":"Neumann","given":"A.T."},{"family":"De Lange","given":"P."},{"family":"Klamma","given":"R."}],"citation-key":"Neumann201911","collection-title":"Proceedings - 2019 IEEE 5th International Conference on Collaboration and Internet Computing, CIC 2019","DOI":"10.1109/CIC48465.2019.00011","ISBN":"978-1-72816-739-8","issued":{"date-parts":[[2019]]},"page":"11-19","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Collaborative creation and training of social bots in learning communities","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080858915&doi=10.1109%2fCIC48465.2019.00011&partnerID=40&md5=b0745d83ea9e30a639cd47e0308b26cb"},
{"id":"neumannCollaborativeCreationTraining2019a","abstract":"The interaction between instructors and students is one of the key concepts to improve the student's learning process. To personalize learning on a massive scale, social bots can be used as supporting technology. However, their development for virtual learning environments currently requires deep technical knowledge. This leaves learner communities relying on highly-skilled developers to generate and tailor these social bots. Participatory design, end-user development and model-driven principles bear the potential to close this technical gap. In this paper, we propose a model-driven approach for creating social bots. Using our framework, learners can create, train and utilize these for self-hosted virtual learning environments relying on OpenAPI specifications offered, e.g. by Blackboard. We support both retrieval-based bots that react to certain events in predefined ways, as well as generative bots by utilizing open source deep learning technologies. Our first evaluation shows the usefulness of model-driven generation and utilization of social bots. We see the potential of this approach to move the development closer to the actual learner. © 2019 IEEE.","author":[{"family":"Neumann","given":"A.T."},{"family":"De Lange","given":"P."},{"family":"Klamma","given":"R."}],"citation-key":"neumannCollaborativeCreationTraining2019a","container-title":"Proceedings - 2019 IEEE 5th International Conference on Collaboration and Internet Computing, CIC 2019","DOI":"10.1109/CIC48465.2019.00011","ISBN":"978-1-72816-739-8","issued":{"date-parts":[[2019]]},"page":"11-19","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Collaborative creation and training of social bots in learning communities","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080858915&doi=10.1109%2fCIC48465.2019.00011&partnerID=40&md5=b0745d83ea9e30a639cd47e0308b26cb"},
{"id":"NewSimilarityMeasure","accessed":{"date-parts":[[2015,11,2]]},"citation-key":"NewSimilarityMeasure","title":"A New Similarity Measure for an Ontology Matching System - Springer","type":"webpage","URL":"http://link.springer.com/chapter/10.1007/978-3-319-25840-9_17?wt_mc=alerts.TOCseries"},
{"id":"NewSoftRobot","accessed":{"date-parts":[[2016,8,29]]},"citation-key":"NewSoftRobot","title":"New Soft Robot is Completely Autonomous and Has No Electronics!","type":"webpage","URL":"http://sciencenewsjournal.com/new-soft-robot-completely-autonomous-no-electronics/"},
{"id":"Ng:2002:CMC:627342.628263","author":[{"family":"Ng","given":"Raymond T."},{"family":"Han","given":"Jiawei"}],"citation-key":"Ng:2002:CMC:627342.628263","container-title":"IEEE Trans. on Knowl. and Data Eng.","ISSN":"1041-4347","issue":"5","issued":{"date-parts":[[2002,9]]},"page":"1003-1016","title":"CLARANS: A method for clustering objects for spatial data mining","type":"article-journal","URL":"http://dx.doi.org/10.1109/TKDE.2002.1033770","volume":"14"},
{"id":"Nguyen:2015:CRV:2942298.2942305","author":[{"family":"Nguyen","given":"Phuong T."},{"family":"Tomeo","given":"Paolo"},{"family":"Di Noia","given":"Tommaso"},{"family":"Di Sciascio","given":"Eugenio"}],"citation-key":"Nguyen:2015:CRV:2942298.2942305","container-title":"Proceedings of the 14th international conference on the semantic web - ISWC 2015 - volume 9366","event-place":"New York, NY, USA","ISBN":"978-3-319-25006-9","issued":{"date-parts":[[2015]]},"page":"605-621","publisher":"Springer-Verlag New York, Inc.","publisher-place":"New York, NY, USA","title":"Content-based recommendations via DBpedia and freebase: A case study in the music domain","type":"paper-conference","URL":"http://dx.doi.org/10.1007/978-3-319-25007-6_35"},
{"id":"Nguyen:2015:ESP:2740908.2742141","author":[{"family":"Nguyen","given":"Phuong T."},{"family":"Tomeo","given":"Paolo"},{"family":"Di Noia","given":"Tommaso"},{"family":"Di Sciascio","given":"Eugenio"}],"citation-key":"Nguyen:2015:ESP:2740908.2742141","collection-title":"WWW '15 companion","container-title":"Proceedings of the 24th international conference on world wide web","event-place":"New York, NY, USA","ISBN":"978-1-4503-3473-0","issued":{"date-parts":[[2015]]},"page":"1477-1482","publisher":"ACM","publisher-place":"New York, NY, USA","title":"An evaluation of SimRank and personalized PageRank to build a recommender system for the web of data","type":"paper-conference","URL":"http://doi.acm.org/10.1145/2740908.2742141"},
{"id":"Nguyen:2016:ACR:2950290.2950333","author":[{"family":"Nguyen","given":"Anh Tuan"},{"family":"Hilton","given":"Michael"},{"family":"Codoban","given":"Mihai"},{"family":"Nguyen","given":"Hoan Anh"},{"family":"Mast","given":"Lily"},{"family":"Rademacher","given":"Eli"},{"family":"Nguyen","given":"Tien N."},{"family":"Dig","given":"Danny"}],"citation-key":"Nguyen:2016:ACR:2950290.2950333","collection-title":"FSE 2016","container-title":"Proceedings of the 2016 24th ACM SIGSOFT international symposium on foundations of software engineering","event-place":"New York, NY, USA","ISBN":"978-1-4503-4218-6","issued":{"date-parts":[[2016]]},"page":"511-522","publisher":"ACM","publisher-place":"New York, NY, USA","title":"API code recommendation using statistical learning from fine-grained changes","type":"paper-conference","URL":"http://doi.acm.org/10.1145/2950290.2950333"},
{"id":"Nguyen:2017:ACD:3098344.3098399","author":[{"family":"Nguyen","given":"Anh Tuan"},{"family":"Nguyen","given":"Tien N."}],"citation-key":"Nguyen:2017:ACD:3098344.3098399","collection-title":"ICSE-C '17","container-title":"Proceedings of the 39th international conference on software engineering companion","event-place":"Piscataway, NJ, USA","ISBN":"978-1-5386-1589-8","issued":{"date-parts":[[2017]]},"page":"164-166","publisher":"IEEE Press","publisher-place":"Piscataway, NJ, USA","title":"Automatic categorization with deep neural network for open-source java projects","type":"paper-conference","URL":"https://doi.org/10.1109/ICSE-C.2017.118"},
{"id":"Nguyen:2019:JSS:CrossRec","author":[{"family":"Nguyen","given":"Phuong T."},{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"},{"family":"Di Penta","given":"Massimiliano"}],"citation-key":"Nguyen:2019:JSS:CrossRec","container-title":"Journal of Systems and Software","issued":{"date-parts":[[2019]]},"title":"CrossRec: Recommending highly relevant third-party libraries - manuscript under review","type":"article-journal"},
{"id":"Nguyen:2019:JSS:CrossSim","author":[{"family":"Nguyen","given":"Phuong T."},{"family":"Di Rocco","given":"Juri"},{"family":"Rubei","given":"Riccardo"},{"family":"Di Ruscio","given":"Davide"}],"citation-key":"Nguyen:2019:JSS:CrossSim","container-title":"Software Quality Journal","issued":{"date-parts":[[2019]]},"title":"An automated approach to assess the similarity of GitHub repositories - manuscript under revision","type":"article-journal"},
{"id":"Nguyen2019272","abstract":"Manual classification methods of metamodel repositories require highly trained personnel and the results are usually influenced by the subjectivity of human perception. Therefore, automated metamodel classification is very desirable and stringent. In this work, Machine Learning techniques have been employed for metamodel automated classification. In particular, a tool implementing a feed-forward neural network is introduced to classify metamodels. An experimental evaluation over a dataset of 555 metamodels demonstrates that the technique permits to learn from manually classified data and effectively categorize incoming unlabeled data with a considerably high prediction rate: the best performance comprehends 95.40% as success rate, 0.945 as precision, 0.938 as recall, and 0.942 as F1 score. © 2019 IEEE.","author":[{"family":"Nguyen","given":"P.T."},{"family":"Di Rocco","given":"J."},{"family":"Di Ruscio","given":"D."},{"family":"Pierantonio","given":"A."},{"family":"Iovino","given":"L."}],"citation-key":"Nguyen2019272","collection-title":"Proceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems, MODELS 2019","DOI":"10.1109/MODELS.2019.00011","editor":[{"family":"Kessentini M., Yue T.","given":"Yue T.","suffix":"Pretschner A., Voss S., Burgueno L., Burgueno L."}],"ISBN":"978-1-72812-535-0","issued":{"date-parts":[[2019]]},"page":"272-282","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Automated classification of metamodel repositories: A machine learning approach","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076112592&doi=10.1109%2fMODELS.2019.00011&partnerID=40&md5=79e6cc806bf218c9bb48cc0816bfaf81"},
{"id":"Nguyen20211797","abstract":"Modeling is a ubiquitous activity in the process of software development. In recent years, such an activity has reached a high degree of intricacy, guided by the heterogeneity of the components, data sources, and tasks. The democratized use of models has led to the necessity for suitable machinery for mining modeling repositories. Among others, the classification of metamodels into independent categories facilitates personalized searches by boosting the visibility of metamodels. Nevertheless, the manual classification of metamodels is not only a tedious but also an error-prone task. According to our observation, misclassification is the norm which leads to a reduction in reachability as well as reusability of metamodels. Handling such complexity requires suitable tooling to leverage raw data into practical knowledge that can help modelers with their daily tasks. In our previous work, we proposed AURORA as a machine learning classifier for metamodel repositories. In this paper, we present a thorough evaluation of the system by taking into consideration different settings as well as evaluation metrics. More importantly, we improve the original AURORA tool by changing its internal design. Experimental results demonstrate that the proposed amendment is beneficial to the classification of metamodels. We also compared our approach with two baseline algorithms, namely gradient boosted decision tree and support vector machines. Eventually, we see that AURORA outperforms the baselines with respect to various quality metrics. © 2021, The Author(s).","author":[{"family":"Nguyen","given":"P.T."},{"family":"Di Rocco","given":"J."},{"family":"Iovino","given":"L."},{"family":"Di Ruscio","given":"D."},{"family":"Pierantonio","given":"A."}],"citation-key":"Nguyen20211797","container-title":"Software and Systems Modeling","DOI":"10.1007/s10270-021-00913-x","ISSN":"16191366","issue":"6","issued":{"date-parts":[[2021]]},"page":"1797-1821","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Evaluation of a machine learning classifier for metamodels","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114606004&doi=10.1007%2fs10270-021-00913-x&partnerID=40&md5=a23dbe04192a75afa1ec7fd1028640f7","volume":"20"},
{"id":"Nguyen20217333","abstract":"The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we present several linear receivers based on the Bussgang decomposition that show significant performance gains over conventional linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based detector, namely OBMNet, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method. © 2002-2012 IEEE.","author":[{"family":"Nguyen","given":"L.V."},{"family":"Swindlehurst","given":"A.L."},{"family":"Nguyen","given":"D.H.N."}],"citation-key":"Nguyen20217333","container-title":"IEEE Transactions on Wireless Communications","DOI":"10.1109/TWC.2021.3082844","ISSN":"15361276","issue":"11","issued":{"date-parts":[[2021]]},"page":"7333-7345","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Linear and deep neural network-based receivers for massive MIMO systems with one-bit ADCs","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107229127&doi=10.1109%2fTWC.2021.3082844&partnerID=40&md5=e07c0c408c207b0d2cf681722401c5ca","volume":"20"},
{"id":"nguyenAutomatedApproachAssess2019","author":[{"family":"Nguyen","given":"Phuong T"},{"family":"Di Rocco","given":"Juri"},{"family":"Rubei","given":"Riccardo"},{"family":"Di Ruscio","given":"Davide"}],"citation-key":"nguyenAutomatedApproachAssess2019","container-title":"Software Quality Journal","issued":{"date-parts":[[2019]]},"title":"An Automated Approach to Assess the Similarity of GitHub Repositories","type":"article-newspaper"},
{"id":"nguyenAutomatedApproachAssess2020","author":[{"family":"Nguyen","given":"Phuong T."},{"family":"Rocco","given":"Juri Di"},{"family":"Rubei","given":"Riccardo"},{"family":"Ruscio","given":"Davide Di"}],"citation-key":"nguyenAutomatedApproachAssess2020","container-title":"Software Quality Journal","DOI":"10.1007/s11219-019-09483-0","issued":{"date-parts":[[2020,2]]},"note":"00000","title":"An automated approach to assess the similarity of GitHub repositories","type":"article-journal","URL":"https://doi.org/10.1007%2Fs11219-019-09483-0"},
{"id":"nguyenAutomatedClassificationMetamodel2019","author":[{"family":"Nguyen","given":"Phuong T."},{"family":"DI ROCCO","given":"Juri"},{"family":"DI RUSCIO","given":"Davide"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Iovino","given":"Ludovico"}],"citation-key":"nguyenAutomatedClassificationMetamodel2019","container-title":"IEEE / ACM 22nd international conference on model driven engineering languages and systems (MODELS)","issued":{"date-parts":[[2019]]},"publisher":"Springer","title":"Automated classification of metamodel repositories: A machine learning approach","type":"paper-conference"},
{"id":"nguyenAutomatedClassificationMetamodel2019b","abstract":"Manual classification methods of metamodel repositories require highly trained personnel and the results are usually influenced by the subjectivity of human perception. Therefore, automated metamodel classification is very desirable and stringent. In this work, Machine Learning techniques have been employed for metamodel automated classification. In particular, a tool implementing a feed-forward neural network is introduced to classify metamodels. An experimental evaluation over a dataset of 555 metamodels demonstrates that the technique permits to learn from manually classified data and effectively categorize incoming unlabeled data with a considerably high prediction rate: the best performance comprehends 95.40% as success rate, 0.945 as precision, 0.938 as recall, and 0.942 as F1 score. © 2019 IEEE.","author":[{"family":"Nguyen","given":"P.T."},{"family":"Di Rocco","given":"J."},{"family":"Di Ruscio","given":"D."},{"family":"Pierantonio","given":"A."},{"family":"Iovino","given":"L."}],"citation-key":"nguyenAutomatedClassificationMetamodel2019b","container-title":"Proceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems, MODELS 2019","DOI":"10.1109/MODELS.2019.00011","editor":[{"family":"Kessentini M.","given":"Burgueno L.","suffix":"Yue T., Yue T., Pretschner A., Voss S., Burgueno L."}],"ISBN":"978-1-72812-535-0","issued":{"date-parts":[[2019]]},"page":"272-282","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Automated Classification of Metamodel Repositories: A Machine Learning Approach","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076112592&doi=10.1109%2fMODELS.2019.00011&partnerID=40&md5=79e6cc806bf218c9bb48cc0816bfaf81"},
{"id":"nguyenBuildingInformationSystems2019","author":[{"family":"Nguyen","given":"Phuong T"},{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"}],"citation-key":"nguyenBuildingInformationSystems2019","container-title":"2nd Workshop on Flexible Advanced Information Systems (FAiSE) at CAiSE 2019","event":"2nd Workshop on Flexible Advanced Information Systems (FAiSE) at CAiSE 2019","event-place":"Rome (Italy)","issued":{"date-parts":[[2019]]},"publisher-place":"Rome (Italy)","title":"Building information systems by means of collaborative-filtering recommendation techniques","type":"paper-conference","URL":"http://vps.diruscio.org/nc/s/C6eS5s74DyZtSnH"},
{"id":"nguyenConvolutionalNeuralNetworks2021","author":[{"family":"Nguyen","given":"Thanh Phuong"},{"family":"Di Ruscio","given":"D."},{"family":"Pierantonio","given":"A."},{"family":"Di Rocco","given":"J."},{"family":"Iovino","given":"L."}],"citation-key":"nguyenConvolutionalNeuralNetworks2021","container-title":"THE JOURNAL OF SYSTEMS AND SOFTWARE","DOI":"10.1016/j.jss.2020.110860","issued":{"date-parts":[[2021]]},"note":"00000","title":"Convolutional neural networks for enhanced classification mechanisms of metamodels","type":"article-journal","URL":"https://pdf.sciencedirectassets.com/271629/1-s2.0-S0164121220X00112/1-s2.0-S0164121220302508/main.pdf?X-Amz-Security-Token=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&amp;X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Date=20210117T172906Z&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Expires=300&amp;X-Amz-Credential=ASIAQ3PHCVTYS3E4DEPF/20210117/us-east-1/s3/aws4_request&amp;X-Amz-Signature=ad929863728e1cd26de76e77a7dd09473b5df70f6cf7e13db65cf04ee4b0a095&amp;hash=bac4b9d04daa65d44fd729cf851d32064223c3617c578247a68a69ec413d6ef3&amp;host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&amp;pii=S0164121220302508&amp;tid=spdf-63631104-233d-4ddf-a057-47ad007ca4f5&amp;sid=edfc577261ea1349798837408324b7b16c01gxrqb&amp;type=client","volume":"172"},
{"id":"nguyenCrossRecSupportingSoftware2019","abstract":"When creating a new software system, or when evolving an existing one, developers do not reinvent the wheel but, rather, seek available libraries that suit their purpose. In such a context, open source software repositories contain rich resources that can provide developers with helpful advice to support their tasks. However, the heterogeneity of resources and the dependencies among them are the main obstacles to the e ective mining and exploitation of the available data. In this sense, advanced techniques and tools are needed to mine the metadata to bring in meaningful recommendations. In this paper, we present CrossRec, a recommender system to assist open source software developers in selecting suitable third-party libraries. CrossRec exploits a collaborative ltering technique to recommend libraries to developers by relying on the set of dependencies, which are currently included in the project being developed. We perform an empirical evaluation to compare the proposed approach with three state-of-theart baselines, i.e., LibRec, LibFinder, and LibCUP on three considerably large datasets. The experimental results show that CrossRec overcomes the limitation of the baselines by recommending also libraries with a speci c version. More importantly, it outperforms LibRec and LibCUP with respect to various quality metrics.","author":[{"family":"Nguyen","given":"Phuong T"},{"family":"Rocco","given":"Juri Di"},{"family":"Ruscio","given":"Davide Di"},{"family":"Penta","given":"Massimiliano Di"}],"citation-key":"nguyenCrossRecSupportingSoftware2019","container-title":"Journal of Systems and Software - Elsevier","issued":{"date-parts":[[2019]]},"note":"00000","page":"54","source":"Zotero","title":"CrossRec: Supporting Software Developers by Recommending Third-party Libraries","type":"article-journal"},
{"id":"nguyenEnablingHeterogeneousRecommendations2019","abstract":"Open source software (OSS) forges contain rich data sources that are useful for supporting development activities. Research has been done to promote techniques and tools for providing open source developers with innovative features aiming at obtaining improvements in terms of development effort, cost savings, and developer productivity, just to mention a few. In the context of the EU H2020 CROSSMINER project we are conceiving a set of recommendations to assist software programmers in different phases of the development process. To this end, we defined a graph-based representation able to encode in a homogeneous manner different aspects of OSS ecosystems as well as to incorporate various well-founded recommendation techniques. Following the proposed paradigm, we have implemented recommender systems for providing various artifacts, such as third-party libraries and API usage. The preliminary results we achieved so far are promising: the proposed systems are able to suggest highly relevant items with respect to the current development context. In this paper, we describe what has been achieved so far as well as our planned medium and longer-term objectives. Furthermore, as a proof of concept, we present a use case where we built a context-aware recommender system to recommend API function calls and usage patterns.","author":[{"family":"Nguyen","given":"Phuong T"},{"family":"Rocco","given":"Juri Di"},{"family":"Ruscio","given":"Davide Di"}],"citation-key":"nguyenEnablingHeterogeneousRecommendations2019","container-title":"23rd Evaluation and Assessment in Software Engineering (EASE 2019)","event-place":"Copenhagen, Denmark","issued":{"date-parts":[[2019]]},"page":"6","publisher-place":"Copenhagen, Denmark","source":"Zotero","title":"Enabling heterogeneous recommendations in OSS development: what's done and what's next in CROSSMINER","type":"paper-conference","URL":"http://vps.diruscio.org/nc/s/gd36SsJm5MBQg68"},
{"id":"nguyenEvaluationMachineLearning","abstract":"Modeling is a ubiquitous activity in the process of software development. In recent years, such an activity has reached a high degree of intricacy, guided by the heterogeneity of the components, data sources, and tasks. The democratized use of models has led to the necessity for suitable machinery for mining modeling repositories. Among others, the classification of metamodels into independent categories facilitates personalized searches by boosting the visibility of metamodels. Nevertheless, the manual classification of metamodels is not only a tedious but also an error-prone task. According to our observation, misclassification is the norm which leads to a reduction in reachability as well as re-usability of metamodels. Handling such complexity requires suitable tooling to leverage raw data into practical knowledge that can help modelers with their daily tasks. In our previous work, we proposed AURORA as a Machine Learning classifier for metamodels repositories. In this paper, we present a thorough evaluation of the system by taking into consideration different settings as well as evaluation metrics.","author":[{"family":"Nguyen","given":"Phuong T"},{"family":"Rocco","given":"Juri Di"},{"family":"Iovino","given":"Ludovico"},{"family":"Ruscio","given":"Davide Di"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"nguyenEvaluationMachineLearning","note":"00000","page":"25","source":"Zotero","title":"Evaluation of Machine Learning Classifiers for Metamodels","type":"article-journal"},
{"id":"nguyenEvaluationMachineLearning2021a","abstract":"Modeling is a ubiquitous activity in the process of software development. In recent years, such an activity has reached a high degree of intricacy, guided by the heterogeneity of the components, data sources, and tasks. The democratized use of models has led to the necessity for suitable machinery for mining modeling repositories. Among others, the classification of metamodels into independent categories facilitates personalized searches by boosting the visibility of metamodels. Nevertheless, the manual classification of metamodels is not only a tedious but also an error-prone task. According to our observation, misclassification is the norm which leads to a reduction in reachability as well as reusability of metamodels. Handling such complexity requires suitable tooling to leverage raw data into practical knowledge that can help modelers with their daily tasks. In our previous work, we proposed AURORA as a machine learning classifier for metamodel repositories. In this paper, we present a thorough evaluation of the system by taking into consideration different settings as well as evaluation metrics. More importantly, we improve the original AURORA tool by changing its internal design. Experimental results demonstrate that the proposed amendment is beneficial to the classification of metamodels. We also compared our approach with two baseline algorithms, namely gradient boosted decision tree and support vector machines. Eventually, we see that AURORA outperforms the baselines with respect to various quality metrics. © 2021, The Author(s).","author":[{"family":"Nguyen","given":"P.T."},{"family":"Di Rocco","given":"J."},{"family":"Iovino","given":"L."},{"family":"Di Ruscio","given":"D."},{"family":"Pierantonio","given":"A."}],"citation-key":"nguyenEvaluationMachineLearning2021a","container-title":"Software and Systems Modeling","DOI":"10.1007/s10270-021-00913-x","ISSN":"16191366","issue":"6","issued":{"date-parts":[[2021]]},"page":"1797-1821","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Evaluation of a machine learning classifier for metamodels","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114606004&doi=10.1007%2fs10270-021-00913-x&partnerID=40&md5=a23dbe04192a75afa1ec7fd1028640f7","volume":"20"},
{"id":"nguyenKnowledgeawareRecommenderSystem2018","author":[{"family":"Nguyen","given":"Phuong T."},{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"}],"citation-key":"nguyenKnowledgeawareRecommenderSystem2018","collection-title":"KaRS 2018","container-title":"Proceedings of the 1st workshop on knowledge-aware and conversational recommender system","event-place":"New York, NY, USA","issued":{"date-parts":[[2018]]},"publisher":"ACM","publisher-place":"New York, NY, USA","title":"Knowledge-aware recommender system for software development","type":"paper-conference"},
{"id":"nguyenMiningSoftwareRepositories2018","author":[{"family":"Nguyen","given":"Phuong"},{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"}],"citation-key":"nguyenMiningSoftwareRepositories2018","collection-title":"CEUR WORKSHOP PROCEEDINGS","container-title":"CEUR workshop proceedings","issued":{"date-parts":[[2018]]},"publisher":"CEUR-WS","title":"Mining software repositories to support OSS developers: A recommender systems approach","type":"paper-conference","URL":"http://ceur-ws.org/","volume":"2140"},
{"id":"nguyenRecommendingAPIFunction2021","author":[{"family":"Nguyen","given":"Phuong T."},{"family":"Rocco","given":"Juri Di"},{"family":"Sipio","given":"Claudio Di"},{"family":"Ruscio","given":"Davide Di"},{"family":"Penta","given":"Massimiliano Di"}],"citation-key":"nguyenRecommendingAPIFunction2021","container-title":"CoRR","issued":{"date-parts":[[2021]]},"note":"00000 \n_eprint: 2102.07508","title":"Recommending API Function Calls and Code Snippets to Support Software Development","type":"article-journal","URL":"https://arxiv.org/abs/2102.07508","volume":"abs/2102.07508"},
{"id":"Nicolae2021","abstract":"With achievement of low ohmic semiconductors, the impact of the package on power switches performance has increased. Moreover, determining the best trade-off between technological performance and production costs results in a large number of simulation. Also, an optimal trade-off may be not achieved because this optimization follows an trial and error approach. Therefore it is important to reduce the simulation time in order to find global optimum designs with consideration to the requirements and with regard to the performance-cost trade-off. By achieving this, we pave the way to automatic design optimization as machine learning and artificial intelligence algorithms find their use for a large variety of problems. In this paper we propose a solution for the estimation of circuit performance in the matter of On resistance (Ron), in significantly less amount of time. We achieve this by building a metamodel that can predict the Ron based on geometry properties of the device about 6 million times faster than simulation. © 2021 IEEE.","author":[{"family":"Nicolae","given":"G."},{"family":"Buzo","given":"A."},{"family":"Feuerbaum","given":"C."},{"family":"Diaconu","given":"C.V."},{"family":"Cucu","given":"H."},{"family":"Pelz","given":"G."},{"family":"Burileanu","given":"C."}],"citation-key":"Nicolae2021","collection-title":"IEEE Electrical Design of Advanced Packaging and Systems Symposium","DOI":"10.1109/EDAPS53774.2021.9656996","ISBN":"978-1-66546-613-4","ISSN":"21511225","issued":{"date-parts":[[2021]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Metamodel-based prediction of On Resistance for microelectronic power switches","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124170861&doi=10.1109%2fEDAPS53774.2021.9656996&partnerID=40&md5=d2cb7066c3aa110fd88e2018c556d960","volume":"2021-December"},
{"id":"nielsenNeuralNetworksDeep2018","author":[{"family":"Nielsen","given":"Michael A."}],"citation-key":"nielsenNeuralNetworksDeep2018","issued":{"date-parts":[[2018]]},"title":"Neural networks and deep learning","type":"article-journal","URL":"http://neuralnetworksanddeeplearning.com/"},
{"id":"Niemann:2013:NCF:2487575.2487656","author":[{"family":"Niemann","given":"Katja"},{"family":"Wolpers","given":"Martin"}],"citation-key":"Niemann:2013:NCF:2487575.2487656","collection-title":"KDD '13","container-title":"Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining","event-place":"New York, NY, USA","ISBN":"978-1-4503-2174-7","issued":{"date-parts":[[2013]]},"page":"955-963","publisher":"ACM","publisher-place":"New York, NY, USA","title":"A new collaborative filtering approach for increasing the aggregate diversity of recommender systems","type":"paper-conference","URL":"http://doi.acm.org/10.1145/2487575.2487656"},
{"id":"nikanjamFaultsDeepReinforcement2021","abstract":"A growing demand is witnessed in both industry and academia for employing Deep Learning (DL) in various domains to solve real-world problems. Deep Reinforcement Learning (DRL) is the application of DL in the domain of Reinforcement Learning (RL). Like any software systems, DRL applications can fail because of faults in their programs. In this paper, we present the first attempt to categorize faults occurring in DRL programs. We manually analyzed 761 artifacts of DRL programs (from Stack Overflow posts and GitHub issues) developed using well-known DRL frameworks (OpenAI Gym, Dopamine, Keras-rl, Tensorforce) and identified faults reported by developers/users. We labeled and taxonomized the identified faults through several rounds of discussions. The resulting taxonomy is validated using an online survey with 19 developers/researchers. To allow for the automatic detection of faults in DRL programs, we have defined a meta-model of DRL programs and developed DRLinter, a model-based fault detection approach that leverages static analysis and graph transformations. The execution flow of DRLinter consists in parsing a DRL program to generate a model conforming to our meta-model and applying detection rules on the model to identify faults occurrences. The effectiveness of DRLinter is evaluated using 15 synthetic DRLprograms in which we injected faults observed in the analyzed artifacts of the taxonomy. The results show that DRLinter can successfully detect faults in all synthetic faulty programs.","accessed":{"date-parts":[[2021,1,9]]},"author":[{"family":"Nikanjam","given":"Amin"},{"family":"Morovati","given":"Mohammad Mehdi"},{"family":"Khomh","given":"Foutse"},{"family":"Braiek","given":"Houssem Ben"}],"citation-key":"nikanjamFaultsDeepReinforcement2021","container-title":"arXiv:2101.00135 [cs]","issued":{"date-parts":[[2021,1,5]]},"note":"00000","source":"arXiv.org","title":"Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach","title-short":"Faults in Deep Reinforcement Learning Programs","type":"article-journal","URL":"http://arxiv.org/abs/2101.00135"},
{"id":"Niknam202046","abstract":"There is a growing interest in the wireless communications community to complement the traditional model-driven design approaches with data-driven machine learning (ML)-based solutions. While conventional ML approaches rely on the assumption of having the data and processing heads in a central entity, this is not always feasible in wireless communications applications because of the inaccessibility of private data and large communication overhead required to transmit raw data to central ML processors. As a result, decentralized ML approaches that keep the data where it is generated are much more appealing. Due to its privacy-preserving nature, federated learning is particularly relevant for many wireless applications, especially in the context of fifth generation (5G) networks. In this article, we provide an accessible introduction to the general idea of federated learning, discuss several possible applications in 5G networks, and describe key technical challenges and open problems for future research on federated learning in the context of wireless communications. © 1979-2012 IEEE.","author":[{"family":"Niknam","given":"S."},{"family":"Dhillon","given":"H.S."},{"family":"Reed","given":"J.H."}],"citation-key":"Niknam202046","container-title":"IEEE Communications Magazine","DOI":"10.1109/MCOM.001.1900461","ISSN":"01636804","issue":"6","issued":{"date-parts":[[2020]]},"page":"46-51","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Federated learning for wireless communications: Motivation, opportunities, and challenges","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088533872&doi=10.1109%2fMCOM.001.1900461&partnerID=40&md5=4fa992c67efaa8379d42d7b1ab0c602d","volume":"58"},
{"id":"Nisa201932","abstract":"Many machine learning methods involve iterative optimization and are amenable to a variety of alternate formulations. Many currently popular formulations for some machine learning methods based on core operations that essentially correspond to sparse matrix-vector products. A reformulation using sparse matrix-matrix products primitives can potentially enable significant performance enhancement. Sampled Dense-Dense Matrix Multiplication (SDDMM) is a primitive that has been shown to be usable as a core component in reformulations of many machine learning factor analysis algorithms such as Alternating Least Squares (ALS), Latent Dirichlet Allocation (LDA), Sparse Factor Analysis (SFA), and Gamma Poisson (GaP). It requires the computation of the product of two input dense matrices but only at locations of the result matrix corresponding to nonzero entries in a sparse third input matrix. In this paper, we address the development of cuSDDMM, a multi-node GPU-accelerated implementation for SDDMM. We analyze the data reuse characteristics of SDDMM and develop a model-driven strategy for choice of tiling permutation and tile-size choice. cuSDDMM improves significantly (up to 4.6x) over the best currently available GPU implementation of SDDMM (in the BIDMach Machine Learning library). © 2018 IEEE.","author":[{"family":"Nisa","given":"I."},{"family":"Sukumaran-Rajam","given":"A."},{"family":"Kurt","given":"S.E."},{"family":"Hong","given":"C."},{"family":"Sadayappan","given":"P."}],"citation-key":"Nisa201932","collection-title":"Proceedings - 25th IEEE International Conference on High Performance Computing, HiPC 2018","DOI":"10.1109/HiPC.2018.00013","ISBN":"978-1-5386-8386-6","issued":{"date-parts":[[2019]]},"page":"32-41","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Sampled dense matrix multiplication for high-performance machine learning","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063136630&doi=10.1109%2fHiPC.2018.00013&partnerID=40&md5=ceaa14d6266d900ec18e9206ef2479c2"},
{"id":"nisaSampledDenseMatrix2019a","abstract":"Many machine learning methods involve iterative optimization and are amenable to a variety of alternate formulations. Many currently popular formulations for some machine learning methods based on core operations that essentially correspond to sparse matrix-vector products. A reformulation using sparse matrix-matrix products primitives can potentially enable significant performance enhancement. Sampled Dense-Dense Matrix Multiplication (SDDMM) is a primitive that has been shown to be usable as a core component in reformulations of many machine learning factor analysis algorithms such as Alternating Least Squares (ALS), Latent Dirichlet Allocation (LDA), Sparse Factor Analysis (SFA), and Gamma Poisson (GaP). It requires the computation of the product of two input dense matrices but only at locations of the result matrix corresponding to nonzero entries in a sparse third input matrix. In this paper, we address the development of cuSDDMM, a multi-node GPU-accelerated implementation for SDDMM. We analyze the data reuse characteristics of SDDMM and develop a model-driven strategy for choice of tiling permutation and tile-size choice. cuSDDMM improves significantly (up to 4.6x) over the best currently available GPU implementation of SDDMM (in the BIDMach Machine Learning library). © 2018 IEEE.","author":[{"family":"Nisa","given":"I."},{"family":"Sukumaran-Rajam","given":"A."},{"family":"Kurt","given":"S.E."},{"family":"Hong","given":"C."},{"family":"Sadayappan","given":"P."}],"citation-key":"nisaSampledDenseMatrix2019a","container-title":"Proceedings - 25th IEEE International Conference on High Performance Computing, HiPC 2018","DOI":"10.1109/HiPC.2018.00013","ISBN":"978-1-5386-8386-6","issued":{"date-parts":[[2019]]},"page":"32-41","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Sampled Dense Matrix Multiplication for High-Performance Machine Learning","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063136630&doi=10.1109%2fHiPC.2018.00013&partnerID=40&md5=ceaa14d6266d900ec18e9206ef2479c2"},
{"id":"niuAPIUsagePattern2017","accessed":{"date-parts":[[2018,2,2]]},"author":[{"family":"Niu","given":"Haoran"},{"family":"Keivanloo","given":"Iman"},{"family":"Zou","given":"Ying"}],"citation-key":"niuAPIUsagePattern2017","container-title":"Journal of Systems and Software","DOI":"10.1016/j.jss.2016.07.026","ISSN":"01641212","issued":{"date-parts":[[2017,7]]},"page":"127-139","source":"CrossRef","title":"API usage pattern recommendation for software development","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0164121216301200","volume":"129"},
{"id":"noiaRecommenderSystemsLinked2015","author":[{"family":"Noia","given":"Tommaso Di"},{"family":"Ostuni","given":"Vito Claudio"}],"citation-key":"noiaRecommenderSystemsLinked2015","container-title":"Reasoning web. Web logic rules - 11th international summer school 2015, berlin, germany, july 31 - august 4, 2015, tutorial lectures","DOI":"10.1007/978-3-319-21768-0_4","issued":{"date-parts":[[2015]]},"page":"88-113","title":"Recommender systems and linked open data","type":"paper-conference","URL":"https://doi.org/10.1007/978-3-319-21768-0_4"},
{"id":"nonamiAutonomousControlSystems2013","citation-key":"nonamiAutonomousControlSystems2013","collection-number":"65","collection-title":"International series on intelligent systems, control and automation: science and engineering","editor":[{"family":"Nonami","given":"Kenzo"},{"literal":"International Conference on Intelligent Unmanned Systems"},{"literal":"International Society of Intelligent Unmanned Systems"}],"event-place":"Tokyo","ISBN":"978-4-431-54276-6 978-4-431-54275-9","issued":{"date-parts":[[2013]]},"note":"OCLC: 931078578","number-of-pages":"315","publisher":"Springer","publisher-place":"Tokyo","source":"Gemeinsamer Bibliotheksverbund ISBN","title":"Autonomous control systems and vehicles: intelligent unmanned systems ; [International Conference on Intelligent Unmanned Systems (ICIUS) 2011 ... Chiba University, Japan ; collection of excellent papers that where updated after presentation]","title-short":"Autonomous control systems and vehicles","type":"book"},
{"id":"Nordmann20155032","abstract":"This paper introduces a model-driven approach for engineering complex movement control architectures based on motion primitives, which in recent years have been a central development towards adaptive and flexible control of complex and compliant robots. We consider rich motor skills realized through the composition of motion primitives as our domain. In this domain we analyze the control architectures of representative example systems to identify common abstractions. It turns out that the introduced notion of motion primitives implemented as dynamical systems with machine learning capabilities, provide the computational building block for a large class of such control architectures. Building on the identified concepts, we introduce domain-specific languages that allow the compact specification of movement control architectures based on motion primitives and their coordination respectively. Using a proper tool chain, we show how to employ this model-driven approach in a case study for the real world example of automatic laundry grasping with the KUKA LWR-IV, where executable source-code is automatically generated from the domain-specific language specification. © 2015 IEEE.","author":[{"family":"Nordmann","given":"A."},{"family":"Wrede","given":"S."},{"family":"Steil","given":"J."}],"citation-key":"Nordmann20155032","collection-title":"Proceedings - IEEE International Conference on Robotics and Automation","DOI":"10.1109/ICRA.2015.7139899","ISBN":"978-1-4799-6923-4","ISSN":"10504729","issue":"June","issued":{"date-parts":[[2015]]},"number":"June","page":"5032-5039","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Modeling of movement control architectures based on motion primitives using domain-specific languages","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938253208&doi=10.1109%2fICRA.2015.7139899&partnerID=40&md5=35c5178b8d694b39984bf2728e34c27d","volume":"2015-June"},
{"id":"nordmannSurveyDomainspecificLanguages2014","accessed":{"date-parts":[[2015,4,21]]},"author":[{"family":"Nordmann","given":"Arne"},{"family":"Hochgeschwender","given":"Nico"},{"family":"Wrede","given":"Sebastian"}],"citation-key":"nordmannSurveyDomainspecificLanguages2014","container-title":"Simulation, Modeling, and Programming for Autonomous Robots","issued":{"date-parts":[[2014]]},"page":"195–206","publisher":"Springer","source":"Google Scholar","title":"A survey on domain-specific languages in robotics","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-319-11900-7_17"},
{"id":"northropUltralargescaleSystemsSoftware2006","author":[{"family":"Northrop","given":"Linda"},{"family":"Feiler","given":"Peter H"},{"family":"Pollak","given":"Bill"},{"family":"Pipitone","given":"Daniel"}],"citation-key":"northropUltralargescaleSystemsSoftware2006","event-place":"Pittsburgh, Pa.","ISBN":"978-0-9786956-0-6","issued":{"date-parts":[[2006]]},"note":"OCLC: 71323012","publisher":"Software Engineering Institute, Carnegie Mellon University","publisher-place":"Pittsburgh, Pa.","source":"Open WorldCat","title":"Ultra-large-scale systems: the software challenge of the future","title-short":"Ultra-large-scale systems","type":"book"},
{"id":"NoSQLDataModeling","accessed":{"date-parts":[[2015,3,26]]},"citation-key":"NoSQLDataModeling","title":"NoSQL Data Modeling | eBay Tech Blog","type":"post-weblog","URL":"http://www.ebaytechblog.com/2014/10/10/nosql-data-modeling/#.VRPVNfmJtrc"},
{"id":"NoSQLDataModelinga","accessed":{"date-parts":[[2018,5,6]]},"citation-key":"NoSQLDataModelinga","title":"NoSQL Data Modeling Techniques – Highly Scalable Blog","type":"webpage","URL":"https://highlyscalable.wordpress.com/2012/03/01/nosql-data-modeling-techniques/"},
{"id":"NotionAllinoneWorkspace","abstract":"A new tool that blends your everyday work apps into one. It's the all-in-one workspace for you and your team","accessed":{"date-parts":[[2020,2,11]]},"citation-key":"NotionAllinoneWorkspace","container-title":"Notion","title":"Notion – The all-in-one workspace for your notes, tasks, wikis, and databases.","type":"webpage","URL":"https://www.notion.so"},
{"id":"NotionNotes","citation-key":"NotionNotes","title":"Notion notes","type":"document","URL":"https://www.notion.so/Publications-SoSyM-and-Visions-81b70721668c4e5d83b78bac2dbde571"},
{"id":"Novák2014121","abstract":"Stricter requirements on the quality of industrial plant operation together with environmental limits and decreasing energy consumption bring more complex automation systems. The intelligent control techniques, which are based on approaches from diverse disciplines including statistics, artificial intelligence or signal processing, have been widely used during the last years and their benefits have been proved. They cannot be developed and tested without simulation models and access to online and historical data. This article proposes a platform for the integration of simulations and industrial SCADA systems supporting complex data access and simulation code re-use. The idea of the presented framework is to connect simulations, data sources, optimizers, other calculations and SCADA systems into one integrated environment seamlessly. A technical level of the framework provides integration of stakeholders and a semantic level captures engineering knowledge in inter-mapped ontologies and configures the technical level, which is often called model-driven configuration. The semantic level utilizes a formal model implemented as set of ontologies. The major contribution of the article are the layered model of the integration architecture and formulation of the integration requirements in the industrial automation domain. The proposed solution has been implemented and tested on a software prototype level. It is demonstrated on two use-cases covering both design and integration of simulation models from the industrial perspective. The proposed architecture is intended to be as general as possible, however it has been tested on signal-oriented simulators only. It is the main limitation of this contribution and it should be addressed in upcoming work. © 2014 Elsevier B.V. All rights reserved.","author":[{"family":"Novák","given":"P."},{"family":"Šindelář","given":"R."},{"family":"Mordinyi","given":"R."}],"citation-key":"Novák2014121","container-title":"Simulation Modelling Practice and Theory","DOI":"10.1016/j.simpat.2014.05.010","ISSN":"1569190X","issued":{"date-parts":[[2014]]},"page":"121-140","publisher":"Elsevier","title":"Integration framework for simulations and SCADA systems","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903841730&doi=10.1016%2fj.simpat.2014.05.010&partnerID=40&md5=91a8fd5c05a54385d774ff0f7048f822","volume":"47"},
{"id":"novielliLoveJoyAnger2020","accessed":{"date-parts":[[2020,7,9]]},"author":[{"family":"Novielli","given":"Nicole"},{"family":"Calefato","given":"Fabio"},{"family":"Lanubile","given":"Filippo"}],"citation-key":"novielliLoveJoyAnger2020","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2020.2968557","ISSN":"0740-7459, 1937-4194","issue":"3","issued":{"date-parts":[[2020,5]]},"note":"00000","page":"86-91","source":"DOI.org (Crossref)","title":"Love, Joy, Anger, Sadness, Fear, and Surprise: SE Needs Special Kinds of AI: A Case Study on Text Mining and SE","title-short":"Love, Joy, Anger, Sadness, Fear, and Surprise","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/9068371/","volume":"37"},
{"id":"ntiMinireviewMachineLearning2022","abstract":"The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the high number of free BDA tools, platforms, and data mining tools makes it challenging to select the appropriate one for the right task. This paper presents a comprehensive mini-literature review of ML in BDA, using a keyword search; a total of 1512 published articles was identified. The articles were screened to 140 based on the study proposed novel taxonomy. The study outcome shows that deep neural networks (15%), support vector machines (15%), artificial neural networks (14%), decision trees (12%), and ensemble learning techniques (11%) are widely applied in BDA. The related applications fields, challenges, and most importantly the openings for future research, are detailed.","accessed":{"date-parts":[[2022,2,3]]},"author":[{"family":"Nti","given":"Isaac Kofi"},{"family":"Quarcoo","given":"Juanita Ahia"},{"family":"Aning","given":"Justice"},{"family":"Fosu","given":"Godfred Kusi"}],"citation-key":"ntiMinireviewMachineLearning2022","container-title":"Big Data Mining and Analytics","container-title-short":"Big Data Min. Anal.","DOI":"10.26599/BDMA.2021.9020028","ISSN":"2096-0654","issue":"2","issued":{"date-parts":[[2022,6]]},"note":"00000","page":"81-97","source":"DOI.org (Crossref)","title":"A mini-review of machine learning in big data analytics: Applications, challenges, and prospects","title-short":"A mini-review of machine learning in big data analytics","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/9691296/","volume":"5"},
{"id":"Numediart","accessed":{"date-parts":[[2016,1,23]]},"citation-key":"Numediart","title":"numediart","type":"webpage","URL":"http://www.numediart.org/2015/06/23/hci-seminar-research-advances-in-interactive-systems-modeling-%C2%BB/"},
{"id":"oakesBuildingDomainSpecificMachine2022","abstract":"Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents six key challenges that a domain expert faces in transforming their problem into a computational workflow, and then into an executable implementation. These challenges arise out of our conceptual framework which presents the \"route\" of options that a domain expert may choose to take while developing their solution. To ground our conceptual framework in the state-of-the-practice, this article discusses a selection of available textual and graphical workflow systems and their support for these six challenges. Case studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain-specificity and machine learning usage of their problem, workflow, and implementation. The state-of-the-practice informs our discussion of the six key challenges, where we identify which challenges are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains.","accessed":{"date-parts":[[2022,3,22]]},"author":[{"family":"Oakes","given":"Bentley James"},{"family":"Famelis","given":"Michalis"},{"family":"Sahraoui","given":"Houari"}],"citation-key":"oakesBuildingDomainSpecificMachine2022","container-title":"arXiv:2203.08638 [cs]","issued":{"date-parts":[[2022,3,16]]},"source":"arXiv.org","title":"Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice","title-short":"Building Domain-Specific Machine Learning Workflows","type":"article-journal","URL":"http://arxiv.org/abs/2203.08638"},
{"id":"obrenovicQuotesIEEESoftware2018","abstract":"This alternative view of IEEE Software history presents quotes organized in conversations. Each conversation pairs a quote from the magazine’s early days (1984–1990) with a more contemporary quote, with at least 20 years between the two. The aim is to illustrate that some key ideas and topics are classic and have value even decades later. Additional pairs of quotes are available in the Web Extra at https://extras.computer.org/extra/mso2018050010s1.pdf. This article is part of a theme issue on software engineering’s 50th anniversary.","author":[{"family":"Obrenović","given":"Ž"}],"citation-key":"obrenovicQuotesIEEESoftware2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.3571243","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"10-13","source":"IEEE Xplore","title":"Quotes from IEEE Software History","type":"article-journal","volume":"35"},
{"id":"ODMD14a","author":[{"family":"Ostuni","given":"Vito Claudio"},{"family":"Di Noia","given":"Tommaso"},{"family":"Mirizzi","given":"Roberto"},{"family":"Di Sciascio","given":"Eugenio"}],"citation-key":"ODMD14a","collection-title":"Lecture notes in business information processing","container-title":"The 15th international conference on electronic commerce and web technologies","issued":{"date-parts":[[2014]]},"publisher":"Springer","title":"A linked data recommender system using a neighborhood-based graph kernel","type":"paper-conference","URL":"http://sisinflab.poliba.it/sisinflab/publications/ 2014/ODMD14a"},
{"id":"odonovanIndustrialBigData2015","abstract":"The term smart manufacturing refers to a future-state of manufacturing, where the real-time transmission and analysis of data from across the factory creates manufacturing intelligence, which can be used to have a positive impact across all aspects of operations. In recent years, many initiatives and groups have been formed to advance smart manufacturing, with the most prominent being the Smart Manufacturing Leadership Coalition (SMLC), Industry 4.0, and the Industrial Internet Consortium. These initiatives comprise industry, academic and government partners, and contribute to the development of strategic policies, guidelines, and roadmaps relating to smart manufacturing adoption. In turn, many of these recommendations may be implemented using data-centric technologies, such as Big Data, Machine Learning, Simulation, Internet of Things and Cyber Physical Systems, to realise smart operations in the factory. Given the importance of machine uptime and availability in smart manufacturing, this research centres on the application of data-driven analytics to industrial equipment maintenance. The main contributions of this research are a set of data and system requirements for implementing equipment maintenance applications in industrial environments, and an information system model that provides a scalable and fault tolerant big data pipeline for integrating, processing and analysing industrial equipment data. These contributions are considered in the context of highly regulated large-scale manufacturing environments, where legacy (e.g. automation controllers) and emerging instrumentation (e.g. internet-aware smart sensors) must be supported to facilitate initial smart manufacturing efforts.","accessed":{"date-parts":[[2022,3,14]]},"author":[{"family":"O’Donovan","given":"P."},{"family":"Leahy","given":"K."},{"family":"Bruton","given":"K."},{"family":"O’Sullivan","given":"D. T. J."}],"citation-key":"odonovanIndustrialBigData2015","container-title":"Journal of Big Data","container-title-short":"Journal of Big Data","DOI":"10.1186/s40537-015-0034-z","ISSN":"2196-1115","issue":"1","issued":{"date-parts":[[2015,12]]},"note":"00000","page":"25","source":"DOI.org (Crossref)","title":"An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities","type":"article-journal","URL":"http://www.journalofbigdata.com/content/2/1/25","volume":"2"},
{"id":"Ogden202151","abstract":"Deep learning (DL) models are rapidly expanding in popularity in large part due to rapid innovations in model accuracy, as well as companies' enthusiasm in integrating deep learning into the existing application logic. This trend will inevitably lead to a deployment scenario, akin to the content delivery network for web objects, where many deep learning models-each with different popularity-run on a shared edge with limited resources. In this paper, we set out to answer the key question of how to manage many deep learning models at the edge effectively. Via an empirical study based on profiling more than twenty deep learning models and extrapolating from an open-source Microsoft Azure workload trace, we pinpoint a promising avenue of leveraging cheaper CPUs, rather than commonly promoted accelerators, for edge-based deep inference serving. Based on our empirical insights, we formulate the DL model management problem as a classical caching problem, which we refer to as model-level caching. As an initial step towards realizing model-level caching, we propose a simple cache eviction policy, called CremeBrulee, by adapting BeladyMIN to explicitly consider DL model-specific factors when calculating each in-cache object's utility. Using a small-scale testbed, we demonstrate that CremeBrulee can achieve a 50% reduction in memory while keeping load latency below 92% of execution latency and less than 36% of the penalty of using a random approach to model eviction. Further, when scaling to more models and requests in a simulation, we demonstrate that CremeBrulee can keep the model load delay lower than other eviction policies that only consider workload characteristics by up to 16.6%. Relevant research artifacts are available at https://github.com/cake-lab/CremeBrulee © 2021 IEEE.","author":[{"family":"Ogden","given":"S.S."},{"family":"Gilman","given":"G.R."},{"family":"Walls","given":"R.J."},{"family":"Guo","given":"T."}],"citation-key":"Ogden202151","collection-title":"Proceedings - 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2021","DOI":"10.1109/ACSOS52086.2021.00027","editor":[{"family":"El-Araby E., Kalogeraki V.","given":"Pianini D.","suffix":"Lassabe F., Porter B., Ghahremani S., Nunes I., Bakhouya M., Tomforde S."}],"ISBN":"978-1-66541-261-2","issued":{"date-parts":[[2021]]},"page":"51-60","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Many models at the edge: Scaling deep inference via model-level caching","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124800268&doi=10.1109%2fACSOS52086.2021.00027&partnerID=40&md5=01ee5efefc436dd0d8e1353259f66b66"},
{"id":"Okewu2020273","abstract":"In this work, we adopt an engineering problem-solving approach to the open-air defecation health problem. We model social and behaviour change communication intervention among other components of a water-sanitation-hygiene (WASH) system in response to the menace of open defecation in rural and urban communities globally. We also used experimental outcomes to show empirically that patterns in data captured in the WASH process could be learnt for effective decision making using deep learning neural networks as an intelligent software engineering technique. Eradicating open defecation is one of the indicators used for measuring progress made towards the attainment of Sustainable Development Goal 6 (SDG 6). We use the Adum-Aiona community in Nigeria as case study in designing community-based total sanitation programs using software model-driven engineering approaches with the aim of promoting their implementation. This is because even when toilets and other sanitary infrastructure are available, behavior and social change efforts are needed to promote their large-scale use. Also, we demonstrate that besides being used to model software systems, computational models (software architecture) are useful in documenting and promoting understanding of concepts in virtually all fields of human endeavour. Our motivation is that enhancing understanding of open defecation through software modelling would help SDG 6 implementors and actors attain set sanitation goals in both rural and urban communities towards the SDGs target year 2030. © 2020, Springer Nature Switzerland AG.","author":[{"family":"Okewu","given":"E."},{"family":"Misra","given":"S."},{"family":"Lius","given":"F.-S."}],"citation-key":"Okewu2020273","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-58817-5_21","editor":[{"family":"Gervasi O., Murgante B.","given":"Misra S.","suffix":"Garau C., Blecic I., Taniar D., Apduhan B.O., Rocha A.M.A.C., Tarantino E., Torre C.M., Karaca Y."}],"ISBN":"9783030588168","ISSN":"03029743","issued":{"date-parts":[[2020]]},"page":"273-288","publisher":"Springer Science and Business Media Deutschland GmbH","title":"A software engineering approach to implementation of SDG 6 in adum-aiona community of nigeria","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092662126&doi=10.1007%2f978-3-030-58817-5_21&partnerID=40&md5=380fe898dfd400c711e9ede1055bd803","volume":"12254 LNCS"},
{"id":"omaEnergyefficientModelFog2018","abstract":"A huge number of devices like sensors in addition to computers are interconnected in the IoT (Internet of Things). In the cloud computing model, sensor data is transmitted to servers in networks and processed on the servers in a cloud. Here, networks are congested and servers are overloaded due to heavy traffic from sensors. In order to reduce the delay time and network traffic and increase the performance of the system, data and processes are distributed to not only servers in a cloud but also fog nodes in fog computing models. While the traffic of servers in a cloud can be reduced, the total electric energy consumed by fog nodes increases to process sensor data. In this paper, we newly propose a treebased fog computing (TBFC) model to distribute processes and data to servers and fog nodes so that the total electric energy consumption of nodes can be reduced in the IoT. In the evaluation, we show the total electric energy consumption of nodes in the TBFC model is smaller than the cloud computing model.","accessed":{"date-parts":[[2018,11,7]]},"author":[{"family":"Oma","given":"Ryuji"},{"family":"Nakamura","given":"Shigenari"},{"family":"Duolikun","given":"Dilawaer"},{"family":"Enokido","given":"Tomoya"},{"family":"Takizawa","given":"Makoto"}],"citation-key":"omaEnergyefficientModelFog2018","container-title":"Internet of Things","DOI":"10.1016/j.iot.2018.08.003","ISSN":"25426605","issued":{"date-parts":[[2018,9]]},"page":"14-26","source":"Crossref","title":"An energy-efficient model for fog computing in the Internet of Things (IoT)","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2542660518300386","volume":"1-2"},
{"id":"OpenVsClosedloop","accessed":{"date-parts":[[2016,11,1]]},"citation-key":"OpenVsClosedloop","title":"Open- vs. closed-loop control | Control Engineering","type":"webpage","URL":"http://www.controleng.com/single-article/open-vs-closed-loop-control/f8d8023a15738d0fcfe78d6a2d71dd60.html"},
{"id":"OrchestratingATLModel","citation-key":"OrchestratingATLModel","title":"Orchestrating ATL Model Transformations","type":"article-journal"},
{"id":"OSGiModularityTutorial","accessed":{"date-parts":[[2016,12,2]]},"citation-key":"OSGiModularityTutorial","title":"OSGi Modularity - Tutorial","type":"webpage","URL":"http://www.vogella.com/tutorials/OSGi/article.html#introduction-into-software-modularity-with-osgi"},
{"id":"ostuniTopnRecommendationsImplicit2013","author":[{"family":"Ostuni","given":"Vito Claudio"},{"family":"Di Noia","given":"Tommaso"},{"family":"Di Sciascio","given":"Eugenio"},{"family":"Mirizzi","given":"Roberto"}],"citation-key":"ostuniTopnRecommendationsImplicit2013","collection-title":"RecSys '13","container-title":"Proceedings of the 7th ACM conference on recommender systems","event-place":"New York, NY, USA","ISBN":"978-1-4503-2409-0","issued":{"date-parts":[[2013]]},"page":"85-92","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Top-n recommendations from implicit feedback leveraging linked open data","type":"paper-conference","URL":"http://doi.acm.org/10.1145/2507157.2507172"},
{"id":"oubelliScalableModelBased2018","accessed":{"date-parts":[[2018,8,21]]},"author":[{"family":"Oubelli","given":"Lynda Ait"},{"family":"Aït Ameur","given":"Yamine"},{"family":"Bedouet","given":"Judicael"},{"family":"Kervarc","given":"Romain"},{"family":"Chausserie-Laprée","given":"Benoit"},{"family":"Larzul","given":"Béatrice"}],"citation-key":"oubelliScalableModelBased2018","container-title":"Computer Languages, Systems & Structures","DOI":"10.1016/j.cl.2018.08.001","ISSN":"14778424","issued":{"date-parts":[[2018,8]]},"source":"Crossref","title":"A scalable model based approach for data model evolution: Application to space missions data models","title-short":"A scalable model based approach for data model evolution","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1477842418300447"},
{"id":"ouelletControlSwarmsAutonomous2011","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Ouellet","given":"Dany"},{"family":"Givigi","given":"Sidney N."},{"family":"Beaulieu","given":"Alain JG"}],"citation-key":"ouelletControlSwarmsAutonomous2011","container-title":"Systems Conference (SysCon), 2011 IEEE International","issued":{"date-parts":[[2011]]},"page":"512–519","publisher":"IEEE","source":"Google Scholar","title":"Control of swarms of autonomous robots using Model Driven Development-A state-based approach","type":"paper-conference","URL":"http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5929129"},
{"id":"ouniSearchbasedSoftwareLibrary2017","accessed":{"date-parts":[[2018,1,29]]},"author":[{"family":"Ouni","given":"Ali"},{"family":"Kula","given":"Raula Gaikovina"},{"family":"Kessentini","given":"Marouane"},{"family":"Ishio","given":"Takashi"},{"family":"German","given":"Daniel M."},{"family":"Inoue","given":"Katsuro"}],"citation-key":"ouniSearchbasedSoftwareLibrary2017","container-title":"Information and Software Technology","DOI":"10.1016/j.infsof.2016.11.007","ISSN":"09505849","issued":{"date-parts":[[2017,3]]},"page":"55-75","source":"CrossRef","title":"Search-based software library recommendation using multi-objective optimization","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0950584916303652","volume":"83"},
{"id":"OverviewAutonomousSystems","accessed":{"date-parts":[[2016,8,26]]},"citation-key":"OverviewAutonomousSystems","title":"Overview of the Autonomous Systems Area | Wallenberg ASP","type":"webpage","URL":"http://wasp-sweden.org/research/overview-of-autonomous-systems-area/"},
{"id":"PaaSword","accessed":{"date-parts":[[2015,4,8]]},"citation-key":"PaaSword","title":"PaaSword","type":"webpage","URL":"https://sites.google.com/site/paaswordeu/"},
{"id":"Padget201435","abstract":"We describe an approach to the representation of requirements using answer set programming and how this leads to a vision for the role of artificial intelligence techniques in software engineering with a particular focus on adaptive business systems. We outline how the approach has developed over several years through a combination of commercial software development and artificial intelligence research, resulting in: (i) a metamodel that incorporates the notion of runtime requirements, (ii) a formal language for their representation and its supporting computational model (InstAL), and (iii) a software architecture that enables monitoring of distributed systems. The metamodel is the result of several years experience in the development of business systems for e-tailing, while InstAL and the runtime monitor is on-going research to support the specification, verification and application of normative frameworks in distributed intelligent systems. Our approach derives from the view that in order to build agile systems, the components need to be structured more like software that controls robots, in that it is designed to be relatively resilient in the face of a non-deterministic, dynamic, complex environment about which there is incomplete information. Thus, degrees of autonomy become a strength and an opportunity, but must somehow be constrained by informing these autonomous components what should be done in a certain situation or what system state ought to be achieved through norms as expressions of requirements. Because such a system made up of autonomous components is potentially behaviourally complex and not just complicated, it becomes essential to monitor both whether norms/requirements are being fulfilled and if not why not. Finally, because control over the system can be expressed through requirements in the form of data that can be changed, a route is opened to adjustment and dynamic re-direction of running systems. © 2014 IEEE.","author":[{"family":"Padget","given":"J."},{"family":"Elakehal","given":"E.E."},{"family":"Satoh","given":"K."},{"family":"Ishikawa","given":"F."}],"citation-key":"Padget201435","collection-title":"2014 IEEE 1st International Workshop on Artificial Intelligence for Requirements Engineering, AIRE 2014 - Proceedings","DOI":"10.1109/AIRE.2014.6894854","ISBN":"978-1-4799-6355-3","issued":{"date-parts":[[2014]]},"page":"35-42","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"On requirements representation and reasoning using answer set programming","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908881544&doi=10.1109%2fAIRE.2014.6894854&partnerID=40&md5=b95be4002802805343aba765f0f4a697"},
{"id":"padgetRequirementsRepresentationReasoning2014a","abstract":"We describe an approach to the representation of requirements using answer set programming and how this leads to a vision for the role of artificial intelligence techniques in software engineering with a particular focus on adaptive business systems. We outline how the approach has developed over several years through a combination of commercial software development and artificial intelligence research, resulting in: (i) a metamodel that incorporates the notion of runtime requirements, (ii) a formal language for their representation and its supporting computational model (InstAL), and (iii) a software architecture that enables monitoring of distributed systems. The metamodel is the result of several years experience in the development of business systems for e-tailing, while InstAL and the runtime monitor is on-going research to support the specification, verification and application of normative frameworks in distributed intelligent systems. Our approach derives from the view that in order to build agile systems, the components need to be structured more like software that controls robots, in that it is designed to be relatively resilient in the face of a non-deterministic, dynamic, complex environment about which there is incomplete information. Thus, degrees of autonomy become a strength and an opportunity, but must somehow be constrained by informing these autonomous components what should be done in a certain situation or what system state ought to be achieved through norms as expressions of requirements. Because such a system made up of autonomous components is potentially behaviourally complex and not just complicated, it becomes essential to monitor both whether norms/requirements are being fulfilled and if not why not. Finally, because control over the system can be expressed through requirements in the form of data that can be changed, a route is opened to adjustment and dynamic re-direction of running systems. © 2014 IEEE.","author":[{"family":"Padget","given":"J."},{"family":"Elakehal","given":"E.E."},{"family":"Satoh","given":"K."},{"family":"Ishikawa","given":"F."}],"citation-key":"padgetRequirementsRepresentationReasoning2014a","container-title":"2014 IEEE 1st International Workshop on Artificial Intelligence for Requirements Engineering, AIRE 2014 - Proceedings","DOI":"10.1109/AIRE.2014.6894854","ISBN":"978-1-4799-6355-3","issued":{"date-parts":[[2014]]},"page":"35-42","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"On requirements representation and reasoning using answer set programming","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908881544&doi=10.1109%2fAIRE.2014.6894854&partnerID=40&md5=b95be4002802805343aba765f0f4a697"},
{"id":"Padhi2021","abstract":"Digital era deficiencies traditionally exist in healthcare applications because of the unbalanced distribution of medical resources, especially in rural areas globally. Cognitive data intelligence, which constitute the integration of cognitive computing, massive data analytics, and tiny artificial intelligence, especially tiny machine learning, can be used to palpate a patient’s health status, physiologically and psychologically transforming the current healthcare system. To remotely detect patients’ emotional state of diagnosing diseases, the integration of 6G enabled Tactile Internet, cognitive data intelligence, and Internet of Healthcare Everything is proposed to form the 6GCIoHE system that aims at achieving global ubiquitous accessibility, extremely low latency, high reliability, and elevated performance in cognitive healthcare in real time to ensure patients receive prompt treatment, especially for the haptic actions. Judiciously, a model-driven methodology is proffered to facilitate the 6GCIoHE system design and development that adopts different refinement levels to incorporate the cognitive healthcare requirements through the interactions of semantic management, process management, cognitive intelligence capabilities, and knowledge sources. Based on the 6GCIoHE system architecture, applications, and challenges, the aim of this study was accomplished by developing a novel theoretical framework to captivate further research within the cognitive healthcare field. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","author":[{"family":"Padhi","given":"P.K."},{"family":"Charrua-Santos","given":"F."}],"citation-key":"Padhi2021","container-title":"Applied System Innovation","DOI":"10.3390/asi4030066","ISSN":"25715577","issue":"3","issued":{"date-parts":[[2021]]},"publisher":"MDPI","title":"6g enabled tactile internet and cognitive internet of healthcare everything: Towards a theoretical framework","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115164376&doi=10.3390%2fasi4030066&partnerID=40&md5=44f4b7c619111c02ed49faf8436d555c","volume":"4"},
{"id":"pageLearningAutonomousSystems2017","accessed":{"date-parts":[[2021,1,13]]},"author":[{"family":"Page","given":"Brian R."},{"family":"Ziaeefard","given":"Saeedeh"},{"family":"Moridian","given":"Barzin"},{"family":"Mahmoudian","given":"Nina"}],"citation-key":"pageLearningAutonomousSystems2017","container-title":"2017 IEEE Frontiers in Education Conference (FIE)","DOI":"10.1109/FIE.2017.8190555","event":"2017 IEEE Frontiers in Education Conference (FIE)","event-place":"Indianapolis, IN","ISBN":"978-1-5090-5920-1","issued":{"date-parts":[[2017,10]]},"note":"00002","page":"1-7","publisher":"IEEE","publisher-place":"Indianapolis, IN","source":"DOI.org (Crossref)","title":"Learning autonomous systems — An interdisciplinary project-based experience","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/8190555/"},
{"id":"paigeEvolvingModelsModelDriven2015","accessed":{"date-parts":[[2015,10,19]]},"author":[{"family":"Paige","given":"Richard F."},{"family":"Matragkas","given":"Nicholas"},{"family":"Rose","given":"Louis M."}],"citation-key":"paigeEvolvingModelsModelDriven2015","container-title":"Journal of Systems and Software","issued":{"date-parts":[[2015]]},"source":"Google Scholar","title":"Evolving Models in Model-Driven Engineering: State-of-the-Art and Future Challenges","title-short":"Evolving Models in Model-Driven Engineering","type":"article-journal","URL":"http://www.sciencedirect.com/science/article/pii/S0164121215001909"},
{"id":"paigeProceedings2015ACM2015","citation-key":"paigeProceedings2015ACM2015","editor":[{"family":"Paige","given":"Richard F."},{"family":"Ruscio","given":"Davide Di"},{"family":"Völter","given":"Markus"}],"ISBN":"978-1-4503-3686-4","issued":{"date-parts":[[2015]]},"publisher":"ACM","title":"Proceedings of the 2015 ACM SIGPLAN International Conference on Software Language Engineering, SLE 2015, Pittsburgh, PA, USA, October 25-27, 2015","type":"book","URL":"http://dl.acm.org/citation.cfm?id=2814251"},
{"id":"paigeRigorousIdentificationEncoding2010","author":[{"family":"Paige","given":"Richard F."},{"family":"Drivalos","given":"Nikolaos"},{"family":"Kolovos","given":"Dimitrios S."},{"family":"Fernandes","given":"Kiran J."},{"family":"Power","given":"Christopher"},{"family":"Olsen","given":"Goran K."},{"family":"Zschaler","given":"Steffen"}],"citation-key":"paigeRigorousIdentificationEncoding2010","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-010-0158-8","issue":"4","issued":{"date-parts":[[2010]]},"page":"469–487","title":"Rigorous identification and encoding of trace-links in model-driven engineering","type":"article-journal","volume":"10"},
{"id":"pakdeetrakulwongRecommendationSystemsSoftware2014","accessed":{"date-parts":[[2017,6,19]]},"author":[{"family":"Pakdeetrakulwong","given":"Udsanee"},{"family":"Wongthongtham","given":"Pornpit"},{"family":"Siricharoen","given":"Waralak V."}],"citation-key":"pakdeetrakulwongRecommendationSystemsSoftware2014","ISBN":"978-1-908320-39-1","issued":{"date-parts":[[2014,12]]},"page":"137-142","publisher":"IEEE","title":"Recommendation systems for software engineering: A survey from software development life cycle phase perspective","title-short":"Recommendation systems for software engineering","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/7038793/"},
{"id":"palyartMDE4HPCApproachUsing2011","accessed":{"date-parts":[[2017,2,23]]},"author":[{"family":"Palyart","given":"Marc"},{"family":"Lugato","given":"David"},{"family":"Ober","given":"Ileana"},{"family":"Bruel","given":"Jean-Michel"}],"citation-key":"palyartMDE4HPCApproachUsing2011","container-title":"International SDL Forum","issued":{"date-parts":[[2011]]},"page":"247–261","publisher":"Springer","source":"Google Scholar","title":"MDE4HPC: an approach for using model-driven engineering in high-performance computing","title-short":"MDE4HPC","type":"paper-conference","URL":"http://link.springer.com/10.1007%2F978-3-642-25264-8_19"},
{"id":"PAM","author":[{"family":"Fowkes","given":"Jaroslav"},{"family":"Sutton","given":"Charles"}],"citation-key":"PAM","title":"PAM: Probabilistic API miner","type":"article-journal","URL":"https://github.com/mast-group/api-mining"},
{"id":"panachEvaluatingModelDrivenDevelopment2021","accessed":{"date-parts":[[2021,5,28]]},"author":[{"family":"Panach","given":"Jose Ignacio"},{"family":"Dieste","given":"Oscar"},{"family":"Marin","given":"Beatriz"},{"family":"Espana","given":"Sergio"},{"family":"Vegas","given":"Sira"},{"family":"Pastor","given":"Oscar"},{"family":"Juristo","given":"Natalia"}],"citation-key":"panachEvaluatingModelDrivenDevelopment2021","container-title":"IEEE Transactions on Software Engineering","container-title-short":"IIEEE Trans. Software Eng.","DOI":"10.1109/TSE.2018.2884706","ISSN":"0098-5589, 1939-3520, 2326-3881","issue":"1","issued":{"date-parts":[[2021,1,1]]},"note":"00000","page":"130-145","source":"DOI.org (Crossref)","title":"Evaluating Model-Driven Development Claims with Respect to Quality: A Family of Experiments","title-short":"Evaluating Model-Driven Development Claims with Respect to Quality","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/8565892/","volume":"47"},
{"id":"panDevelopingHybridIntrusion2015","abstract":"Synchrophasor systems provide an immense volume of data for wide area monitoring and control of power systems to meet the increasing demand of reliable energy. The construction of traditional intrusion detection systems (IDSs) that use manually created rules based upon expert knowledge is knowledge-intensive and is not suitable in the context of this big data problem. This paper presents a systematic and automated approach to build a hybrid IDS that learns temporal state-based specifications for power system scenarios including disturbances, normal control operations, and cyber-attacks. A data mining technique called common path mining is used to automatically and accurately learn patterns for scenarios from a fusion of synchrophasor measurement data, and power system audit logs. As a proof of concept, an IDS prototype was implemented and validated. The IDS prototype accurately classifies disturbances, normal control operations, and cyber-attacks for the distance protection scheme for a two-line three-bus power transmission system.","accessed":{"date-parts":[[2022,2,3]]},"author":[{"family":"Pan","given":"Shengyi"},{"family":"Morris","given":"Thomas"},{"family":"Adhikari","given":"Uttam"}],"citation-key":"panDevelopingHybridIntrusion2015","container-title":"IEEE Transactions on Smart Grid","container-title-short":"IEEE Trans. Smart Grid","DOI":"10.1109/TSG.2015.2409775","ISSN":"1949-3053, 1949-3061","issue":"6","issued":{"date-parts":[[2015,11]]},"note":"00314","page":"3104-3113","source":"DOI.org (Crossref)","title":"Developing a Hybrid Intrusion Detection System Using Data Mining for Power Systems","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7063234/","volume":"6"},
{"id":"Panichella:2013:EUT:2486788.2486857","author":[{"family":"Panichella","given":"Annibale"},{"family":"Dit","given":"Bogdan"},{"family":"Oliveto","given":"Rocco"},{"family":"Di Penta","given":"Massimiliano"},{"family":"Poshyvanyk","given":"Denys"},{"family":"De Lucia","given":"Andrea"}],"citation-key":"Panichella:2013:EUT:2486788.2486857","collection-title":"ICSE '13","container-title":"Proceedings of the 2013 international conference on software engineering","event-place":"Piscataway, NJ, USA","ISBN":"978-1-4673-3076-3","issued":{"date-parts":[[2013]]},"page":"522-531","publisher":"IEEE Press","publisher-place":"Piscataway, NJ, USA","title":"How to effectively use topic models for software engineering tasks? An approach based on genetic algorithms","type":"paper-conference","URL":"http://dl.acm.org.univaq.clas.cineca.it/citation.cfm?id=2486788.2486857"},
{"id":"papagelisQualitativeAnalysisUserbased2005","author":[{"family":"Papagelis","given":"Manos"},{"family":"Plexousakis","given":"Dimitris"}],"citation-key":"papagelisQualitativeAnalysisUserbased2005","container-title":"Engineering Applications of Artificial Intelligence","container-title-short":"Eng. Appl. Artif. Intell.","ISSN":"0952-1976","issue":"7","issued":{"date-parts":[[2005,10]]},"page":"781-789","title":"Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents","type":"article-journal","URL":"http://dx.doi.org/10.1016/j.engappai.2005.06.010","volume":"18"},
{"id":"PapyrusIoTModeling","accessed":{"date-parts":[[2016,8,21]]},"citation-key":"PapyrusIoTModeling","title":"Papyrus for IoT – A Modeling Solution for IoT","type":"webpage","URL":"https://www.eclipse.org/community/eclipse_newsletter/2016/april/article3.php"},
{"id":"ParallelProgrammingModel","accessed":{"date-parts":[[2017,2,23]]},"citation-key":"ParallelProgrammingModel","title":"1.3 A Parallel Programming Model","type":"webpage","URL":"http://www.mcs.anl.gov/~itf/dbpp/text/node9.html"},
{"id":"Park202083","abstract":"In recent years, several kinds of machine learning tools have developed, each involving complex functions and tasks, which means usage knowledge varies between tools. Integrating the environment for effective AI machine learning can be regarded as a complicated task and may even consist of several separate tasks, such as building a test environment, data acquisition, data cleansing, machine learning training, and model management. In terms of the cognitive engineering approach, most tasks not only require knowledge-based cognitive control over skill-based or rule-based behaviours higher cognitive loads and workloads as well. Since complex knowledge and higher cognitive loads are required, the use of AI machine learning is limited and leads to ineffective work procedures. Thus, this research analysed the AI development process via various methods of cognitive task analysis in order to identify which tasks induce cognitive workload. Then, a new integrated AI development system was created, which was expected to reduce the number of ineffective tasks and workload. Experiments were conducted twice to validate the system’s effectiveness, and the results indicate that there were significant differences between the several different AI development tasks. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.","author":[{"family":"Park","given":"D."},{"family":"Park","given":"H."},{"family":"Song","given":"S."}],"citation-key":"Park202083","container-title":"Advances in Intelligent Systems and Computing","DOI":"10.1007/978-3-030-51828-8_12","editor":[{"family":"Ahram T.","given":"Falcao C."}],"ISBN":"9783030518271","ISSN":"21945357","issued":{"date-parts":[[2020]]},"page":"83-96","publisher":"Springer","title":"Designing the ai developing system through ecological interface design","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088753678&doi=10.1007%2f978-3-030-51828-8_12&partnerID=40&md5=87029524958e9149179a9a1cd564f3bf","volume":"1217 AISC"},
{"id":"parkIoTRoutingArchitecture2014","accessed":{"date-parts":[[2016,11,2]]},"author":[{"family":"Park","given":"Soochang"},{"family":"Crespi","given":"Noel"},{"family":"Park","given":"Hosung"},{"family":"Kim","given":"Sang-Ha"}],"citation-key":"parkIoTRoutingArchitecture2014","container-title":"Internet of Things (WF-IoT), 2014 IEEE World Forum on","issued":{"date-parts":[[2014]]},"page":"442–445","publisher":"IEEE","source":"Google Scholar","title":"IoT routing architecture with autonomous systems of things","type":"paper-conference","URL":"http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6803207"},
{"id":"parkSelfmanagementSystemBased2006","accessed":{"date-parts":[[2016,9,21]]},"author":[{"family":"Park","given":"Jeongmin"},{"family":"Yoo","given":"Giljong"},{"family":"Jeong","given":"Chulho"},{"family":"Lee","given":"Eunseok"}],"citation-key":"parkSelfmanagementSystemBased2006","container-title":"Asia-Pacific Network Operations and Management Symposium","issued":{"date-parts":[[2006]]},"page":"372–382","publisher":"Springer","source":"Google Scholar","title":"Self-management system based on self-healing mechanism","type":"paper-conference","URL":"http://link.springer.com/chapter/10.1007/11876601_38"},
{"id":"parnasCriteriaBeUsed1972","abstract":"This paper discusses modularization as a mechanism for improving the flexibility and comprehensibility of a system while allowing the shortening of its development time. The effectiveness of a \"modularization\" is dependent upon the criteria used in dividing the system into modules. A system design problem is presented and both a conventional and unconventional decomposition are described. It is shown that the unconventional decompositions have distinct advantages for the goals outlined. The criteria used in arriving at the decompositions are discussed. The unconventional decomposition, if implemented with the conventional assumption that a module consists of one or more subroutines, will be less efficient in most cases. An alternative approach to implementation which does not have this effect is sketched.","author":[{"family":"Parnas","given":"D L"}],"citation-key":"parnasCriteriaBeUsed1972","issue":"12","issued":{"date-parts":[[1972]]},"page":"6","source":"Zotero","title":"On the Criteria To Be Used in Decomposing Systems into Modules","type":"article-journal","volume":"15"},
{"id":"parnin2012crowd","author":[{"family":"Parnin","given":"Chris"},{"family":"Treude","given":"Christoph"},{"family":"Grammel","given":"Lars"},{"family":"Storey","given":"Margaret-Anne"}],"citation-key":"parnin2012crowd","container-title":"Georgia Institute of Technology, Tech. Rep","issued":{"date-parts":[[2012]]},"title":"Crowd documentation: Exploring the coverage and the dynamics of API discussions on Stack Overflow","type":"article-journal"},
{"id":"parra-ullauriEventdrivenTemporalModels2022","abstract":"Abstract\n \n Modern software systems are increasingly expected to show higher degrees of autonomy and self-management to cope with uncertain and diverse situations. As a consequence, autonomous systems can exhibit unexpected and surprising behaviours. This is exacerbated due to the ubiquity and complexity of Artificial Intelligence (AI)-based systems. This is the case of Reinforcement Learning (RL), where autonomous agents learn through trial-and-error how to find good solutions to a problem. Thus, the underlying decision-making criteria may become opaque to users that interact with the system and who may require explanations about the system’s reasoning. Available work for eXplainable Reinforcement Learning (XRL) offers different trade-offs: e.g. for runtime explanations, the approaches are model-specific or can only analyse results after-the-fact. Different from these approaches, this paper aims to provide an\n online model-agnostic\n approach for XRL towards trustworthy and understandable AI. We present ETeMoX, an architecture based on temporal models to keep track of the decision-making processes of RL systems. In cases where the resources are limited (e.g. storage capacity or time to response), the architecture also integrates complex event processing, an event-driven approach, for detecting matches to event patterns that need to be stored, instead of keeping the entire history. The approach is applied to a mobile communications case study that uses RL for its decision-making. In order to test the generalisability of our approach, three variants of the underlying RL algorithms are used: Q-Learning, SARSA and DQN. The encouraging results show that using the proposed configurable architecture, RL developers are able to obtain explanations about the evolution of a metric, relationships between metrics, and were able to track situations of interest happening over time windows.","accessed":{"date-parts":[[2022,5,24]]},"author":[{"family":"Parra-Ullauri","given":"Juan Marcelo"},{"family":"García-Domínguez","given":"Antonio"},{"family":"Bencomo","given":"Nelly"},{"family":"Zheng","given":"Changgang"},{"family":"Zhen","given":"Chen"},{"family":"Boubeta-Puig","given":"Juan"},{"family":"Ortiz","given":"Guadalupe"},{"family":"Yang","given":"Shufan"}],"citation-key":"parra-ullauriEventdrivenTemporalModels2022","container-title":"Software and Systems Modeling","container-title-short":"Softw Syst Model","DOI":"10.1007/s10270-021-00952-4","ISSN":"1619-1366, 1619-1374","issue":"3","issued":{"date-parts":[[2022,6]]},"page":"1091-1113","source":"DOI.org (Crossref)","title":"Event-driven temporal models for explanations - ETeMoX: explaining reinforcement learning","title-short":"Event-driven temporal models for explanations - ETeMoX","type":"article-journal","URL":"https://link.springer.com/10.1007/s10270-021-00952-4","volume":"21"},
{"id":"Pasi2019574","abstract":"The Social Web promotes social interactions among people through Web 2.0 technologies. In this context, User-Generated Content (UGC) spreads across social media platforms in the absence of traditional intermediaries that can verify both the believability of the content and the reliability of the sources that generated it. For this reason, the problem of how to assess the credibility of UGC is receiving nowadays increasing attention. In the literature, several approaches have tackled this issue mainly as a classification problem, by categorizing information into genuine and fake. The majority of the proposed solutions follows a data-driven approach, by employing supervised or semi-supervised machine learning techniques that act on multiple features related to credibility. Despite its effectiveness, however, machine learning may present some possible drawbacks, including data-dependency and the possible inscrutability of the contribution that single or interacting features have in the final classification process. In this paper, a Multi-Criteria Decision Making approach is proposed, aimed to assess the credibility of UGC. A given information item (alternative) is evaluated with respect to the considered credibility features (criteria) based on prior domain knowledge, where an overall credibility estimate is obtained by means of a suitable model-driven approach based on aggregation operators. The credibility estimate allows to classify credible UGC with respect to non-credible one, and can also be used to provide a ranking of the alternatives with respect to credibility. To consider interactions among features, the Choquet integral is employed. © 2019 Elsevier Inc.","author":[{"family":"Pasi","given":"G."},{"family":"Viviani","given":"M."},{"family":"Carton","given":"A."}],"citation-key":"Pasi2019574","container-title":"Information Sciences","DOI":"10.1016/j.ins.2019.07.037","ISSN":"00200255","issued":{"date-parts":[[2019]]},"page":"574-588","publisher":"Elsevier Inc.","title":"A Multi-Criteria Decision Making approach based on the Choquet integral for assessing the credibility of User-Generated Content","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068747985&doi=10.1016%2fj.ins.2019.07.037&partnerID=40&md5=28df48e15386b68ec54a1ce4fd9a2007","volume":"503"},
{"id":"Passant:2010:DMR:1940334.1940349","author":[{"family":"Passant","given":"Alexandre"}],"citation-key":"Passant:2010:DMR:1940334.1940349","collection-title":"ISWC'10","container-title":"Proceedings of the 9th international semantic web conference on the semantic web - volume part II","event-place":"Berlin, Heidelberg","ISBN":"3-642-17748-4 978-3-642-17748-4","issued":{"date-parts":[[2010]]},"page":"209-224","publisher":"Springer-Verlag","publisher-place":"Berlin, Heidelberg","title":"Dbrec: Music recommendations using DBpedia","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=1940334.1940349"},
{"id":"passantMeasuringSemanticDistance2010","author":[{"family":"Passant","given":"Alexandre"}],"citation-key":"passantMeasuringSemanticDistance2010","container-title":"AAAI spring symposium: Linked data meets artificial intelligence","issued":{"date-parts":[[2010]]},"publisher":"AAAI","title":"Measuring semantic distance on linking data and using it for resources recommendations.","type":"paper-conference","URL":"http://dblp.uni-trier.de/db/conf/aaaiss/aaaiss2010-7.html#Passant10"},
{"id":"pastorAdvancedInformationSystems2005","citation-key":"pastorAdvancedInformationSystems2005","collection-title":"Lecture Notes in Computer Science","DOI":"10.1007/b136788","editor":[{"family":"Pastor","given":"Oscar"},{"family":"Cunha","given":"João Falcão","dropping-particle":"e"}],"ISBN":"3-540-26095-1","issued":{"date-parts":[[2005]]},"publisher":"Springer","title":"Advanced Information Systems Engineering, 17th International Conference, CAiSE 2005, Porto, Portugal, June 13-17, 2005, Proceedings","type":"book","URL":"https://doi.org/10.1007/b136788","volume":"3520"},
{"id":"patelEnablingHighlevelApplication2015","accessed":{"date-parts":[[2016,5,30]]},"author":[{"family":"Patel","given":"Pankesh"},{"family":"Cassou","given":"Damien"}],"citation-key":"patelEnablingHighlevelApplication2015","container-title":"Journal of Systems and Software","issued":{"date-parts":[[2015]]},"page":"62–84","source":"Google Scholar","title":"Enabling high-level application development for the Internet of Things","type":"article-journal","URL":"http://www.sciencedirect.com/science/article/pii/S0164121215000187","volume":"103"},
{"id":"pattersonCarbonEmissionsLarge2021","abstract":"The computation demand for machine learning (ML) has grown rapidly recently, which comes with a number of costs. Estimating the energy cost helps measure its environmental impact and finding greener strategies, yet it is challenging without detailed information. We calculate the energy use and carbon footprint of several recent large models-T5, Meena, GShard, Switch Transformer, and GPT-3-and refine earlier estimates for the neural architecture search that found Evolved Transformer. We highlight the following opportunities to improve energy efficiency and CO2 equivalent emissions (CO2e): Large but sparsely activated DNNs can consume <1/10th the energy of large, dense DNNs without sacrificing accuracy despite using as many or even more parameters. Geographic location matters for ML workload scheduling since the fraction of carbon-free energy and resulting CO2e vary ~5X-10X, even within the same country and the same organization. We are now optimizing where and when large models are trained. Specific datacenter infrastructure matters, as Cloud datacenters can be ~1.4-2X more energy efficient than typical datacenters, and the ML-oriented accelerators inside them can be ~2-5X more effective than off-the-shelf systems. Remarkably, the choice of DNN, datacenter, and processor can reduce the carbon footprint up to ~100-1000X. These large factors also make retroactive estimates of energy cost difficult. To avoid miscalculations, we believe ML papers requiring large computational resources should make energy consumption and CO2e explicit when practical. We are working to be more transparent about energy use and CO2e in our future research. To help reduce the carbon footprint of ML, we believe energy usage and CO2e should be a key metric in evaluating models, and we are collaborating with MLPerf developers to include energy usage during training and inference in this industry standard benchmark.","accessed":{"date-parts":[[2022,4,4]]},"author":[{"family":"Patterson","given":"David"},{"family":"Gonzalez","given":"Joseph"},{"family":"Le","given":"Quoc"},{"family":"Liang","given":"Chen"},{"family":"Munguia","given":"Lluis-Miquel"},{"family":"Rothchild","given":"Daniel"},{"family":"So","given":"David"},{"family":"Texier","given":"Maud"},{"family":"Dean","given":"Jeff"}],"citation-key":"pattersonCarbonEmissionsLarge2021","container-title":"arXiv:2104.10350 [cs]","issued":{"date-parts":[[2021,4,23]]},"source":"arXiv.org","title":"Carbon Emissions and Large Neural Network Training","type":"article-journal","URL":"http://arxiv.org/abs/2104.10350"},
{"id":"pautassoMicroservicesPracticePart2017","abstract":"Service-oriented architecture (SOA) and microservices insiders Mike Amundsen, James Lewis, and Nicolai Josuttis share their experiences and predictions with department editors Cesare Pautasso and Olaf Zimmermann.","author":[{"family":"Pautasso","given":"Cesare"},{"family":"Zimmermann","given":"Olaf"},{"family":"Amundsen","given":"Mike"},{"family":"Lewis","given":"James"},{"family":"Josuttis","given":"Nicolai"},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""}],"citation-key":"pautassoMicroservicesPracticePart2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"91-98","source":"IEEE Computer Society","title":"Microservices in Practice, Part 1: Reality Check and Service Design","title-short":"Microservices in Practice, Part 1","type":"article-magazine","volume":"34"},
{"id":"Pazzani2007","author":[{"family":"Pazzani","given":"Michael J."},{"family":"Billsus","given":"Daniel"}],"citation-key":"Pazzani2007","container-title":"The adaptive web: Methods and strategies of web personalization","DOI":"10.1007/978-3-540-72079-9₁0","editor":[{"family":"Brusilovsky","given":"Peter"},{"family":"Kobsa","given":"Alfred"},{"family":"Nejdl","given":"Wolfgang"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-540-72079-9","issued":{"date-parts":[[2007]]},"page":"325-341","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","title":"Content-based recommendation systems","type":"chapter"},
{"id":"pelliccioneArtificialIntelligenceSoftware","abstract":"ML and AI are increasingly dominating the high-tech industry. Organizations and technology companies are leveraging their big data to create new products or improve their processes to reach the next level in their market. However, ML and AI are not a silver bullet and Software 2.0 is not the end of software developers or software engineering. In this lecture I will introduce the course and I will argument on how software engineering can help ML and AI to become the key technology for (autonomous) systems of the near future. Software engineering best practices and achievements reached in the last decades might help, e.g., (i) democratising the use of ML/AI, (ii) composing, reusing, chaining ML/AI models to solve more complex problems, and (iii) supporting for reasoning about correctness, repeatability, explainability, traceability, fairness, ethics, while building an ML/AI pipeline.","author":[{"family":"Pelliccione","given":"Patrizio"},{"family":"Ruscio","given":"Davide Di"},{"family":"Begel","given":"Andrew"},{"family":"Crnkovic","given":"Ivica"}],"citation-key":"pelliccioneArtificialIntelligenceSoftware","note":"00000","page":"5","source":"Zotero","title":"Artificial Intelligence and Software Engineering","type":"article-journal"},
{"id":"penaModeldrivenArchitectureApproach2006","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Pena","given":"Joaquin"},{"family":"Hinchey","given":"Michael G."},{"family":"Sterritt","given":"Roy"},{"family":"Ruiz-Cortes","given":"Antonio"},{"family":"Resinas","given":"Manuel"}],"citation-key":"penaModeldrivenArchitectureApproach2006","container-title":"2006 2nd IEEE International Symposium on Dependable, Autonomic and Secure Computing","issued":{"date-parts":[[2006]]},"page":"19–30","publisher":"IEEE","source":"Google Scholar","title":"A model-driven architecture approach for modeling, specifying and deploying policies in autonomous and autonomic systems","type":"paper-conference","URL":"http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4030862"},
{"id":"pereiraPlatformEnableSelfadaptive2020","abstract":"Self-Adaptive Systems (SASs) relect on both their state and on the environment and change their behavior to satisfy the expected objectives. Cloud systems are self-adaptive by nature, especially considering the resources used in a pay-as-you-go manner. Satisfying trustworthiness (worthiness of a service based on evidences of its trust) properties also demands self-adaptation capabilities. Unfortunately, developers lack an easy-to-use platform to support the assessment of such properties and to execute the required adaptions. This paper presents TMA, a platform that implements a MAPE-K control loop for cloud systems, supported by a distributed monitoring system based on probes. Quality Models are used to express trustworthiness properties, resulting in scores, which are used to plan adaptations through evaluation rules. These plans are executed by actuators. A demo shows the scaling up/down of the number of containers in a cloud application of a set of web services from TPC Benchmarks, as a result of changes observed in the environment.","accessed":{"date-parts":[[2021,1,8]]},"author":[{"family":"Pereira","given":"José D'Abruzzo"},{"family":"Silva","given":"Rui"},{"family":"Antunes","given":"Nuno"},{"family":"Silva","given":"Jorge L. M."},{"family":"França","given":"Breno","non-dropping-particle":"de"},{"family":"Moraes","given":"Regina"},{"family":"Vieira","given":"Marco"}],"citation-key":"pereiraPlatformEnableSelfadaptive2020","container-title":"Proceedings of the IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","DOI":"10.1145/3387939.3391608","event":"SEAMS '20: IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","event-place":"Seoul Republic of Korea","ISBN":"978-1-4503-7962-5","issued":{"date-parts":[[2020,6,29]]},"note":"00003","page":"71-77","publisher":"ACM","publisher-place":"Seoul Republic of Korea","source":"DOI.org (Crossref)","title":"A platform to enable self-adaptive cloud applications using trustworthiness properties","type":"paper-conference","URL":"https://dl.acm.org/doi/10.1145/3387939.3391608"},
{"id":"perez-sanchezOnlineLearningAlgorithm2013","accessed":{"date-parts":[[2016,1,12]]},"author":[{"family":"Pérez-Sánchez","given":"Beatriz"},{"family":"Fontenla-Romero","given":"Oscar"},{"family":"Guijarro-Berdiñas","given":"Bertha"},{"family":"Martínez-Rego","given":"David"}],"citation-key":"perez-sanchezOnlineLearningAlgorithm2013","container-title":"Expert Systems with Applications","DOI":"10.1016/j.eswa.2013.06.066","ISSN":"09574174","issue":"18","issued":{"date-parts":[[2013,12]]},"page":"7294-7304","source":"CrossRef","title":"An online learning algorithm for adaptable topologies of neural networks","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0957417413004624","volume":"40"},
{"id":"Pérez-Soler2020207","abstract":"Chatbots are software services accessed via conversation in natural language. They are increasingly used to help in all kinds of procedures like booking flights, querying visa information or assigning tasks to developers. They can be embedded in webs and social networks, and be used from mobile devices without installing dedicated apps. While many frameworks and platforms have emerged for their development, identifying the most appropriate one for building a particular chatbot requires a high investment of time. Moreover, some of them are closed – resulting in customer lock-in – or require deep technical knowledge. To tackle these issues, we propose a model-driven engineering approach to chatbot development. It comprises a neutral meta-model and a domain-specific language (DSL) for chatbot description; code generators and parsers for several chatbot platforms; and a platform recommender. Our approach supports forward and reverse engineering, and model-based analysis. We demonstrate its feasibility presenting a prototype tool and an evaluation based on migrating third party Dialogflow bots to Rasa. © 2020, Springer Nature Switzerland AG.","author":[{"family":"Pérez-Soler","given":"S."},{"family":"Guerra","given":"E."},{"family":"Lara","given":"J.","non-dropping-particle":"de"}],"citation-key":"Pérez-Soler2020207","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-62522-1_15","editor":[{"family":"Dobbie G., Frank U.","given":"Kappel G.","suffix":"Liddle S.W., Mayr H.C."}],"ISBN":"9783030625214","ISSN":"03029743","issued":{"date-parts":[[2020]]},"page":"207-222","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Model-driven chatbot development","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097391175&doi=10.1007%2f978-3-030-62522-1_15&partnerID=40&md5=448d366d1928970f463d3bdc3b395360","volume":"12400 LNCS"},
{"id":"Perez201973","abstract":"Architectural Technical Debt (ATD) is a metaphor used to describe consciously decisions taken by software architects to accomplish short-term goals but possibly negatively affecting the long-term health of the system. However, difficulties arise when repayment strategies are defined because software architects need to be aware of the consequences of these strategies over others decisions in the software architecture. This article proposes REBEL, a semi-automated model-driven approach that exploits natural language processing, machine learning and model checking techniques on heterogeneous project artifacts to build a model that allows to locate and visualize the impact produced by the consciously injected ATD and its repayment strategy on the other architectural decisions. The technique is illustrated with a data analytics project in Colombia where software architects are unaware of the consequences of the repayment strategies. This proposal seeks to support teams of architects to make explicit the current and future impact of the ATD injected as a result of decisions taken, focusing on the architectural level rather than code level. © 2019 IEEE.","author":[{"family":"Perez","given":"B."},{"family":"Correal","given":"D."},{"family":"Astudillo","given":"H."}],"citation-key":"Perez201973","collection-title":"Proceedings - 2019 IEEE/ACM International Conference on Technical Debt, TechDebt 2019","DOI":"10.1109/TechDebt.2019.00025","ISBN":"978-1-72813-371-3","issued":{"date-parts":[[2019]]},"page":"73-77","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A proposed model-driven approach to manage architectural technical debt life cycle","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071146151&doi=10.1109%2fTechDebt.2019.00025&partnerID=40&md5=a6bf5dc2c6baf5b49904d2b7fb3432d3"},
{"id":"Perrotin2020372","abstract":"The increasing number of cyberattacks requires to incorporate security concerns all along the system development life-cycle. In this context, detecting and evaluating vulnerabilities early in system modelling helps fix security issues and improves resilience of systems. Nowadays, due to the increasing complexity of modern systems, the level of responsibility dedicated to human operator has growning up. This is particularly visible in Socio-Technical Systems (STS) where humans are considered as subsystems. Thus, to improve the resilience of the overall system, it is necessary to manage the vulnerability of humans. We developed a language called HoS-ML and a specific tool allowing a system architect to evaluate human vulnerability in STS during early stage of the system design. In this paper we present an industrial STS case study using our approach. We briefly present the language and his metamodel before to model a real industrial case study to illustrate our approach.. © 2020 ACM.","author":[{"family":"Perrotin","given":"P."},{"family":"Sadou","given":"S."},{"family":"Hairion","given":"D."},{"family":"Beugnard","given":"A."}],"citation-key":"Perrotin2020372","collection-title":"Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings","DOI":"10.1145/3417990.3420045","ISBN":"978-1-4503-8135-2","issued":{"date-parts":[[2020]]},"page":"372-379","publisher":"Association for Computing Machinery, Inc","title":"Detecting human vulnerably in socio-technical systems: A naval case study","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096796272&doi=10.1145%2f3417990.3420045&partnerID=40&md5=bddb028fbf779cd7af9b86077ebc5c38"},
{"id":"perrotinDetectingHumanVulnerably2020a","abstract":"The increasing number of cyberattacks requires to incorporate security concerns all along the system development life-cycle. In this context, detecting and evaluating vulnerabilities early in system modelling helps fix security issues and improves resilience of systems. Nowadays, due to the increasing complexity of modern systems, the level of responsibility dedicated to human operator has growning up. This is particularly visible in Socio-Technical Systems (STS) where humans are considered as subsystems. Thus, to improve the resilience of the overall system, it is necessary to manage the vulnerability of humans. We developed a language called HoS-ML and a specific tool allowing a system architect to evaluate human vulnerability in STS during early stage of the system design. In this paper we present an industrial STS case study using our approach. We briefly present the language and his metamodel before to model a real industrial case study to illustrate our approach.. © 2020 ACM.","author":[{"family":"Perrotin","given":"P."},{"family":"Sadou","given":"S."},{"family":"Hairion","given":"D."},{"family":"Beugnard","given":"A."}],"citation-key":"perrotinDetectingHumanVulnerably2020a","container-title":"Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings","DOI":"10.1145/3417990.3420045","ISBN":"978-1-4503-8135-2","issued":{"date-parts":[[2020]]},"page":"372-379","publisher":"Association for Computing Machinery, Inc","title":"Detecting human vulnerably in socio-technical systems: A naval case study","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096796272&doi=10.1145%2f3417990.3420045&partnerID=40&md5=bddb028fbf779cd7af9b86077ebc5c38"},
{"id":"pervaizExaminingChallengesDevelopment2019","abstract":"The developing world has increasingly relied on data driven policies. Numerous development agencies have pushed for on-ground data collection to support the development work they pursue. Many governments have launched their own efforts for frequent information gathering. Overall, the amount of data collected is tremendous, yet there are significant issues in doing useful analysis. Most of these barriers manifest in data cleaning and merging, and require a data engineer to support some parts of the analysis. In this paper, we investigate the challenges of cleaning development data through an interview based study. We conducted face to face interviews of 13 stakeholders, eight from international development organizations and five government workers from Pakistan, including both managers and data analysts. From analysis of the interviews we identified common challenges faced in processing development data including correcting open text fields, merging hierarchical data, and extracting data from textual formats such as PDF. We construct a basic taxonomy of data cleaning challenges, and identify areas where support tools can improve the process. Ultimately, the objective is to empower regular data users to easily do the necessary data cleaning and scrubbing for analysis.","accessed":{"date-parts":[[2022,3,14]]},"author":[{"family":"Pervaiz","given":"Fahad"},{"family":"Vashistha","given":"Aditya"},{"family":"Anderson","given":"Richard"}],"citation-key":"pervaizExaminingChallengesDevelopment2019","container-title":"Proceedings of the Conference on Computing & Sustainable Societies - COMPASS 19","DOI":"10.1145/3314344.3332496","event":"the 2nd ACM SIGCAS Conference","event-place":"Accra, Ghana","ISBN":"978-1-4503-6714-1","issued":{"date-parts":[[2019]]},"note":"00010","page":"13-21","publisher":"ACM Press","publisher-place":"Accra, Ghana","source":"DOI.org (Crossref)","title":"Examining the challenges in development data pipeline","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?doid=3314344.3332496"},
{"id":"pescadorPatternBasedDevelopmentDomainSpecific2015","accessed":{"date-parts":[[2015,9,24]]},"author":[{"family":"Pescador","given":"Ana"},{"family":"Garmendia","given":"Antonio"},{"family":"Guerra","given":"Esther"},{"family":"Cuadrado","given":"Jesús Sánchez"},{"family":"Lara","given":"Juan","non-dropping-particle":"de"}],"citation-key":"pescadorPatternBasedDevelopmentDomainSpecific2015","issued":{"date-parts":[[2015]]},"publisher":"MODELS","source":"Google Scholar","title":"Pattern-Based Development of Domain-Specific Modelling Languages","type":"paper-conference","URL":"http://www.miso.es/pubs/DSLtao.pdf"},
{"id":"petcuProcessingExtremeData2016","accessed":{"date-parts":[[2022,3,9]]},"author":[{"family":"Petcu","given":"Dana"},{"family":"Iuhasz","given":"Gabriel"},{"family":"Pop","given":"Daniel"},{"family":"Talia","given":"Domenico"},{"family":"Carretero","given":"Jesus"},{"family":"Prodan","given":"Radu"},{"family":"Fahringer","given":"Thomas"},{"family":"Grasso","given":"Ivan"},{"family":"Doallo","given":"Ramon"},{"family":"Martin","given":"Maria J."},{"family":"Fraguela","given":"Basilio B."},{"family":"Trobec","given":"Roman"},{"family":"Depolli","given":"Matjaz"},{"family":"Rodriguez","given":"Francisco Almeida"},{"family":"De Sande","given":"Francisco"},{"family":"Da Costa","given":"Georges"},{"family":"Pierson","given":"Jean-Marc"},{"family":"Anastasiadis","given":"Stergios"},{"family":"Bartzokas","given":"Aristides"},{"family":"Lolis","given":"Christos"},{"family":"Goncalves","given":"Pedro"},{"family":"Brito","given":"Fabrice"},{"family":"Brown","given":"Nick"}],"citation-key":"petcuProcessingExtremeData2016","container-title":"Scalable Computing: Practice and Experience","container-title-short":"SCPE","DOI":"10.12694/scpe.v16i4.1134","ISSN":"1895-1767","issue":"4","issued":{"date-parts":[[2016,1,30]]},"note":"00007","page":"467-490","source":"DOI.org (Crossref)","title":"On Processing Extreme Data","type":"article-journal","URL":"http://www.scpe.org/index.php/scpe/article/view/1134","volume":"16"},
{"id":"Petroll2021","abstract":"The design freedom and functional integration of additive manufacturing is increasingly being implemented in existing products. One of the biggest challenges are competing optimization goals and functions. This leads to multidisciplinary optimization problems which needs to be solved in parallel. To solve this problem, the authors require a synthetic data set to train a deep learning metamodel. The research presented shows how to create a data set with the right quality and quantity. It is discussed what are the requirements for solving an MDO problem with a metamodel taking into account functional and production-specific boundary conditions. A data set of generic designs is then generated and validated. The generation of the generic design proposals is accompanied by a specific product development example of a drone combustion engine. © 2021 die Autoren.","author":[{"family":"Petroll","given":"C."},{"family":"Denk","given":"M."},{"family":"Holtmannspötter","given":"J."},{"family":"Paetzold","given":"K."},{"family":"Höfer","given":"P."}],"citation-key":"Petroll2021","collection-title":"Proceedings of the 32nd Symposium Design for X, DFX 2021","DOI":"10.35199/dfx2021.11","editor":[{"family":"Krause D., Paetzold K.","given":"Wartzack S."}],"issued":{"date-parts":[[2021]]},"publisher":"The Design Society","title":"Synthetic data generation for deep learning models","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121677494&doi=10.35199%2fdfx2021.11&partnerID=40&md5=71a7d89d361e520db55f925578071d41"},
{"id":"petrollSyntheticDataGeneration2021a","abstract":"The design freedom and functional integration of additive manufacturing is increasingly being implemented in existing products. One of the biggest challenges are competing optimization goals and functions. This leads to multidisciplinary optimization problems which needs to be solved in parallel. To solve this problem, the authors require a synthetic data set to train a deep learning metamodel. The research presented shows how to create a data set with the right quality and quantity. It is discussed what are the requirements for solving an MDO problem with a metamodel taking into account functional and production-specific boundary conditions. A data set of generic designs is then generated and validated. The generation of the generic design proposals is accompanied by a specific product development example of a drone combustion engine. © 2021 die Autoren.","author":[{"family":"Petroll","given":"C."},{"family":"Denk","given":"M."},{"family":"Holtmannspötter","given":"J."},{"family":"Paetzold","given":"K."},{"family":"Höfer","given":"P."}],"citation-key":"petrollSyntheticDataGeneration2021a","container-title":"Proceedings of the 32nd Symposium Design for X, DFX 2021","DOI":"10.35199/dfx2021.11","editor":[{"family":"Krause D.","given":"Wartzack S.","suffix":"Paetzold K."}],"issued":{"date-parts":[[2021]]},"publisher":"The Design Society","title":"Synthetic data generation for deep learning models","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121677494&doi=10.35199%2fdfx2021.11&partnerID=40&md5=71a7d89d361e520db55f925578071d41"},
{"id":"pettigrewTastingProjectiveTechnique2008","abstract":"Purpose – The purpose of this paper is to investigate the benefits of tasting as a projective technique (PT) in explicating consumers' thoughts and feelings towards food and beverage products.Design/methodology/approach – In total, ten focus groups were conducted with 35 consumers, 14 wine producers, and 13 mediators. The mediator category included those involved in marketing, wholesaling, retailing, and judging wine. Participants in each focus group were given the same four wines to taste. Initially they were invited to discuss their views on wine quality. The participants were then presented with the wines and asked to discuss their responses to them, particularly their perceptions of the quality of the wines.Findings – The primary findings related to: the changes in apparent certainty levels amongst professionals and high‐involvement informants; exposure of real and contradictory preferences; role of cognitive, affective, and sensory responses to wine; and interpretation of the language of tasting.Research limitations/implications – Tasting as a PT has the potential to generate additional and insightful data that can increase our appreciation of the complexities involved in consumption experiences. In particular, it can reveal the uncertainty that can affect consumers' product evaluations and explicate the multiple evaluation pathways that can be used by consumers of food and beverage products.Originality/value – The paper is of value in showing that the ability of PTs to yield both stated and actual preferences provides insight into the salient external factors that impact on consumption decisions and gives an indication of where marketers could most effectively focus their product development and promotional attention.","author":[{"family":"Pettigrew","given":"Simone"},{"family":"Charters","given":"Stephen"}],"citation-key":"pettigrewTastingProjectiveTechnique2008","container-title":"Qualitative Market Research: An International Journal","issue":"3","issued":{"date-parts":[[2008]]},"page":"331-343","title":"Tasting as a projective technique","type":"article-journal","URL":"https://doi.org/10.1108/13522750810879048","volume":"11"},
{"id":"pezoaFoundationsJSONSchema2016","accessed":{"date-parts":[[2021,1,29]]},"author":[{"family":"Pezoa","given":"Felipe"},{"family":"Reutter","given":"Juan L."},{"family":"Suarez","given":"Fernando"},{"family":"Ugarte","given":"Martín"},{"family":"Vrgoč","given":"Domagoj"}],"citation-key":"pezoaFoundationsJSONSchema2016","container-title":"Proceedings of the 25th International Conference on World Wide Web","DOI":"10.1145/2872427.2883029","event":"WWW '16: 25th International World Wide Web Conference","event-place":"Montréal Québec Canada","ISBN":"978-1-4503-4143-1","issued":{"date-parts":[[2016,4,11]]},"note":"00140","page":"263-273","publisher":"International World Wide Web Conferences Steering Committee","publisher-place":"Montréal Québec Canada","source":"DOI.org (Crossref)","title":"Foundations of JSON Schema","type":"paper-conference","URL":"https://dl.acm.org/doi/10.1145/2872427.2883029"},
{"id":"phuong_t_nguyen_2018_1476035","author":[{"family":"Rocco","given":"Di"},{"literal":"Juri"},{"family":"Nguyen","given":"Phuong T."},{"family":"Rubei","given":"Riccardo"},{"family":"Di Ruscio","given":"Davide"}],"citation-key":"phuong_t_nguyen_2018_1476035","DOI":"10.5281/zenodo.1476035","issued":{"date-parts":[[2018,10]]},"title":"An automated approach to assess the similarity of GitHub repositories - online appendix","type":"article-journal","URL":"https://doi.org/10.5281/zenodo.1479309"},
{"id":"pierantonioalfonsoKeynoteJCRSTAFa","author":[{"literal":"Pierantonio, Alfonso"}],"citation-key":"pierantonioalfonsoKeynoteJCRSTAFa","note":"00000","title":"Keynote JCR STAF","type":"article-journal"},
{"id":"pierantonioOpenAccessAll2020","accessed":{"date-parts":[[2020,10,26]]},"author":[{"family":"Pierantonio","given":"Alfonso"},{"family":"Brand","given":"Mark","non-dropping-particle":"van den"},{"family":"Combemale","given":"Benoit"}],"citation-key":"pierantonioOpenAccessAll2020","container-title":"The Journal of Object Technology","container-title-short":"JOT","DOI":"10.5381/jot.2020.19.1.e1","ISSN":"1660-1769","issue":"1","issued":{"date-parts":[[2020]]},"note":"00000","page":"1","source":"DOI.org (Crossref)","title":"Open Access: all you wanted to know and never dared to ask.","title-short":"Open Access","type":"article-journal","URL":"http://www.jot.fm/contents/issue_2020_01/editorial1.html","volume":"19"},
{"id":"Pinna Puissant2015461","abstract":"One of the main challenges in model-driven software engineering is to automate the resolution of design model inconsistencies. We propose to use the artificial intelligence technique of automated planning for the purpose of resolving such inconsistencies through the generation of one or more resolution plans. We implemented Badger, a regression planner in Prolog that generates such plans. We assess its scalability on the resolution of different types of structural inconsistencies in UML models using both generated models and reverse-engineered models of varying sizes, the largest ones containing more than 10,000 model elements. We illustrate the metamodel-independence of our approach by applying it to the resolution of code smells in a Java program. We discuss how the user can adapt the order in which resolution plans are presented by modifying the cost function of the planner algorithm. © 2013, Springer-Verlag Berlin Heidelberg.","author":[{"family":"Pinna Puissant","given":"J."},{"family":"Van Der Straeten","given":"R."},{"family":"Mens","given":"T."}],"citation-key":"Pinna Puissant2015461","container-title":"Software and Systems Modeling","DOI":"10.1007/s10270-013-0317-9","ISSN":"16191366","issue":"1","issued":{"date-parts":[[2015]]},"page":"461-481","publisher":"Springer Verlag","title":"Resolving model inconsistencies using automated regression planning","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922337776&doi=10.1007%2fs10270-013-0317-9&partnerID=40&md5=5f228b2e1f19cf790f9efb976dbf8b2d","volume":"14"},
{"id":"PinnaPuissant2012146","abstract":"One of the main challenges in model-driven software engineering is to deal with design model inconsistencies. Automated techniques to detect and resolve these inconsistencies are essential. We propose to use the artificial intelligence technique of automated planning for the purpose of resolving software model inconsistencies. We implemented a regression planner in Prolog and validated it on the resolution of different types of structural inconsistencies for generated models of varying sizes. We discuss the scalability results of the approach obtained through several stress-tests and discuss the limitations of our approach. © 2012 Springer-Verlag.","author":[{"family":"Pinna Puissant","given":"J."},{"family":"Van Der Straeten","given":"R."},{"family":"Mens","given":"T."}],"citation-key":"PinnaPuissant2012146","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-642-31491-9_13","ISBN":"9783642314902","ISSN":"03029743","issued":{"date-parts":[[2012]]},"page":"146-161","title":"Badger: A regression planner to resolve design model inconsistencies","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864055583&doi=10.1007%2f978-3-642-31491-9_13&partnerID=40&md5=1035e22c5b864e5c94e009d50f0cacae","volume":"7349 LNCS"},
{"id":"PMID:25142186","author":[{"family":"Zeng","given":"Wei"},{"family":"Zeng","given":"An"},{"family":"Liu","given":"Hao"},{"family":"Shang","given":"Ming-Sheng"},{"family":"Zhou","given":"Tao"}],"citation-key":"PMID:25142186","container-title":"Scientific reports","DOI":"10.1038/srep06140","ISSN":"2045-2322","issued":{"date-parts":[[2014]]},"page":"6140","title":"Uncovering the information core in recommender systems","type":"article-journal","URL":"http://europepmc.org/articles/PMC4139954","volume":"4"},
{"id":"polyakovMachineLearningCybersecurity","author":[{"family":"Polyakov","given":"Alexander"}],"citation-key":"polyakovMachineLearningCybersecurity","page":"23","source":"Zotero","title":"Machine Learning for Cybersecurity 10","type":"article-journal"},
{"id":"PolystoreDatabasesBe","accessed":{"date-parts":[[2018,4,16]]},"citation-key":"PolystoreDatabasesBe","title":"Polystore Databases to be Examined at IEEE, CIDR Conferences | Intel Science & Technology Center for Big Data","type":"webpage","URL":"http://istc-bigdata.org/index.php/polystore-databases-at-ieee-cidr-conferences/"},
{"id":"pontaMetadataCodecentricUsagebased2018","abstract":"The use of open-source software (OSS) is ever-increasing, and so is the number of open-source vulnerabilities being discovered and publicly disclosed. The gains obtained from the reuse of community-developed libraries may be offset by the cost of detecting, assessing, and mitigating their vulnerabilities in a timely fashion. In this paper we present a novel method to detect, assess and mitigate OSS vulnerabilities that improves on state-of-the-art approaches, which commonly depend on metadata to identify vulnerable OSS dependencies. Our solution instead is code-centric and combines static and dynamic analysis to determine the reachability of the vulnerable portion of libraries used (directly or transitively) by an application. Taking this usage into account, our approach then supports developers in choosing among the existing non-vulnerable library versions. VULAS, the tool implementing our code-centric and usage-based approach, is officially recommended by SAP to scan its Java software, and has been successfully used to perform more than 250000 scans of about 500 applications since December 2016. We report on our experience and on the lessons we learned when maturing the tool from a research prototype to an industrial-grade solution.","accessed":{"date-parts":[[2018,10,8]]},"author":[{"family":"Ponta","given":"Serena E."},{"family":"Plate","given":"Henrik"},{"family":"Sabetta","given":"Antonino"}],"citation-key":"pontaMetadataCodecentricUsagebased2018","container-title":"arXiv:1806.05893 [cs]","issued":{"date-parts":[[2018,6,15]]},"source":"arXiv.org","title":"Beyond Metadata: Code-centric and Usage-based Analysis of Known Vulnerabilities in Open-source Software","title-short":"Beyond Metadata","type":"article-journal","URL":"http://arxiv.org/abs/1806.05893"},
{"id":"ponzanelliMiningStackOverflowTurn2014","accessed":{"date-parts":[[2017,3,28]]},"author":[{"family":"Ponzanelli","given":"Luca"},{"family":"Bavota","given":"Gabriele"},{"family":"Di Penta","given":"Massimiliano"},{"family":"Oliveto","given":"Rocco"},{"family":"Lanza","given":"Michele"}],"citation-key":"ponzanelliMiningStackOverflowTurn2014","DOI":"10.1145/2597073.2597077","event-place":"Hyderabad, India","ISBN":"978-1-4503-2863-0","issued":{"date-parts":[[2014]]},"page":"102-111","publisher":"ACM Press","publisher-place":"Hyderabad, India","source":"CrossRef","title":"Mining StackOverflow to turn the IDE into a self-confident programming prompter","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?doid=2597073.2597077"},
{"id":"ponzanelliPrompterTurningIDE2016","abstract":"Developers often require knowledge beyond the one they possess, which boils down to asking co-workers for help or consulting additional sources of information, such as Application Programming Interfaces (API) documentation, forums, and Q&A websites. However, it requires time and energy to formulate one’s problem, peruse and process the results. We propose a novel approach that, given a context in the Integrated Development Environment (IDE), automatically retrieves pertinent discussions from Stack Overflow, evaluates their relevance using a multi-faceted ranking model, and, if a given confidence threshold is surpassed, notifies the developer. We have implemented our approach in PROMPTER, an Eclipse plug-in. PROMPTER was evaluated in two empirical studies. The first study was aimed at evaluatingPROMPTER’s ranking model and involved 33 participants.","accessed":{"date-parts":[[2019,9,4]]},"author":[{"family":"Ponzanelli","given":"Luca"},{"family":"Bavota","given":"Gabriele"},{"family":"Di Penta","given":"Massimiliano"},{"family":"Oliveto","given":"Rocco"},{"family":"Lanza","given":"Michele"}],"citation-key":"ponzanelliPrompterTurningIDE2016","container-title":"Empirical Software Engineering","ISSN":"1382-3256, 1573-7616","issue":"5","issued":{"date-parts":[[2016,10]]},"page":"2190-2231","title":"Prompter: Turning the IDE into a self-confident programming assistant","title-short":"Prompter","type":"article-journal","URL":"http://link.springer.com/10.1007/s10664-015-9397-1","volume":"21"},
{"id":"ponzanelliSeahawkStackOverflow2013","author":[{"family":"Ponzanelli","given":"L."},{"family":"Bacchelli","given":"A."},{"family":"Lanza","given":"M."}],"citation-key":"ponzanelliSeahawkStackOverflow2013","container-title":"2013 35th international conference on software engineering (ICSE)","DOI":"10.1109/ICSE.2013.6606701","ISSN":"0270-5257","issued":{"date-parts":[[2013,5]]},"page":"1295-1298","title":"Seahawk: Stack overflow in the IDE","type":"paper-conference"},
{"id":"poojaraServerlessDataPipeline2022","abstract":"With the increasing number of Internet of Things (IoT) devices, massive amounts of raw data is being generated. The latency, cost, and other challenges in cloud-based IoT data processing have driven the adoption of Edge and Fog computing models, where some data processing tasks are moved closer to data sources. Properly dealing with the flow of such data requires building data pipelines, to control the complete life cycle of data streams from data acquisition at the data source, edge and fog processing, to Cloud side storage and analytics. Data analytics tasks need to be executed dynamically at different distances from the data sources and often on very heterogeneous hardware devices. This can be streamlined by the use of a Serverless (or FaaS) cloud computing model, where tasks are defined as virtual functions, which can be migrated from edge to cloud (and vice versa) and executed in an event-driven manner on data streams. In this work, we investigate the benefits of building Serverless data pipelines (SDP) for IoT data analytics and evaluate three different approaches for designing SDPs: (1) Off-the-shelf data flow tool (DFT) based, (2) Object storage service (OSS) based and (3) MQTT based. Further, we applied these strategies on three fog applications (Aeneas, PocketSphinx, and custom Video processing application) and evaluated the performance by comparing their processing time (computation time, network communication and disk access time), and resource utilization. Results show that DFT is unsuitable for compute-intensive applications such as video or image processing, whereas OSS is best suitable for this task. However, DFT is nicely fit for bandwidthintensive applications due to the minimum use of network resources. On the other hand, MQTT-based SDP is observed with increase in CPU and Memory usage as the number of users rose, and experienced a drop in data units in the pipeline for PocketSphinx and custom video processing applications, however it performed well for Aeneas which had low size data units.","accessed":{"date-parts":[[2022,3,14]]},"author":[{"family":"Poojara","given":"Shivananda R."},{"family":"Dehury","given":"Chinmaya Kumar"},{"family":"Jakovits","given":"Pelle"},{"family":"Srirama","given":"Satish Narayana"}],"citation-key":"poojaraServerlessDataPipeline2022","container-title":"Future Generation Computer Systems","container-title-short":"Future Generation Computer Systems","DOI":"10.1016/j.future.2021.12.012","ISSN":"0167739X","issued":{"date-parts":[[2022,5]]},"note":"00000","page":"91-105","source":"DOI.org (Crossref)","title":"Serverless data pipeline approaches for IoT data in fog and cloud computing","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0167739X21004933","volume":"130"},
{"id":"portugalUseMachineLearning2015","accessed":{"date-parts":[[2017,3,10]]},"author":[{"family":"Portugal","given":"Ivens"},{"family":"Alencar","given":"Paulo"},{"family":"Cowan","given":"Donald"}],"citation-key":"portugalUseMachineLearning2015","container-title":"arXiv preprint arXiv:1511.05263","issued":{"date-parts":[[2015]]},"source":"Google Scholar","title":"The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review","title-short":"The Use of Machine Learning Algorithms in Recommender Systems","type":"article-journal","URL":"https://arxiv.org/abs/1511.05263"},
{"id":"pottsSoftwareengineeringResearchRevisited1993","accessed":{"date-parts":[[2017,7,3]]},"author":[{"family":"Potts","given":"Colin"}],"citation-key":"pottsSoftwareengineeringResearchRevisited1993","container-title":"IEEE software","issue":"5","issued":{"date-parts":[[1993]]},"page":"19–28","source":"Google Scholar","title":"Software-engineering research revisited","type":"article-journal","URL":"http://ieeexplore.ieee.org/abstract/document/232392/","volume":"10"},
{"id":"potvinWhyGoogleStores2016","accessed":{"date-parts":[[2017,5,25]]},"author":[{"family":"Potvin","given":"Rachel"},{"family":"Levenberg","given":"Josh"}],"citation-key":"potvinWhyGoogleStores2016","container-title":"Communications of the ACM","issue":"7","issued":{"date-parts":[[2016]]},"page":"78–87","source":"Google Scholar","title":"Why Google stores billions of lines of code in a single repository","type":"article-journal","URL":"http://dl.acm.org/citation.cfm?id=2854146","volume":"59"},
{"id":"Pourpanah:2016:HMF:2884077.2884195","author":[{"family":"Pourpanah","given":"Farhad"},{"family":"Lim","given":"Chee Peng"},{"family":"Saleh","given":"Junita Mohamad"}],"citation-key":"Pourpanah:2016:HMF:2884077.2884195","container-title":"Expert Systems with Applications","container-title-short":"Expert Syst. Appl.","ISSN":"0957-4174","issue":"C","issued":{"date-parts":[[2016,5]]},"page":"74-85","title":"A hybrid model of fuzzy ARTMAP and genetic algorithm for data classification and rule extraction","type":"article-journal","URL":"https://doi.org/10.1016/j.eswa.2015.11.009","volume":"49"},
{"id":"prasadConvolutionalNeuralNetworks","author":[{"family":"Prasad","given":"Ashu"}],"citation-key":"prasadConvolutionalNeuralNetworks","page":"28","source":"Zotero","title":"Convolutional Neural Networks with Tensor ow","type":"article-journal"},
{"id":"prev-93837","citation-key":"prev-93837","issued":{"date-parts":[[2017]]},"title":"MDE Adoption—A three-legged chair","type":"article-journal"},
{"id":"Project2ACLAutonomous","accessed":{"date-parts":[[2016,8,26]]},"citation-key":"Project2ACLAutonomous","title":"project2 - ACL - Autonomous Systems and Robotics - Research Groups - Research - ACSE - The University of Sheffield","type":"webpage","URL":"https://www.sheffield.ac.uk/acse/research/groups/asrg/acl/project2"},
{"id":"prokschHowBuildRecommendation2015","abstract":"Software developers must interact with large amounts of different types of information and perform many different activities to build a software system. To ease the finding of information and hone workflows, there has been growing interest in building recommenders that are intended to help software developers work more effectively. Building an effective recommender requires a deep understanding of the problem that is the target of a recommender, analysis of different aspects of the approach taken to perform the recommendations and design and evaluation of the mechanisms used to present recommendations to a developer. In this chapter, we outline the different steps that must be taken to develop an effective recommender system to aid software development.","accessed":{"date-parts":[[2020,10,11]]},"author":[{"family":"Proksch","given":"Sebastian"},{"family":"Bauer","given":"Veronika"},{"family":"Murphy","given":"Gail C."}],"citation-key":"prokschHowBuildRecommendation2015","container-title":"Software Engineering","DOI":"10.1007/978-3-319-28406-4_1","editor":[{"family":"Meyer","given":"Bertrand"},{"family":"Nordio","given":"Martin"}],"event-place":"Cham","ISBN":"978-3-319-28405-7 978-3-319-28406-4","issued":{"date-parts":[[2015]]},"note":"00000","page":"1-42","publisher":"Springer International Publishing","publisher-place":"Cham","source":"DOI.org (Crossref)","title":"How to Build a Recommendation System for Software Engineering","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-319-28406-4_1","volume":"8987"},
{"id":"provoostDingNetSelfAdaptiveInternetofThings2019","accessed":{"date-parts":[[2021,1,10]]},"author":[{"family":"Provoost","given":"Michiel"},{"family":"Weyns","given":"Danny"}],"citation-key":"provoostDingNetSelfAdaptiveInternetofThings2019","container-title":"2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","DOI":"10.1109/SEAMS.2019.00033","event":"2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","event-place":"Montreal, QC, Canada","ISBN":"978-1-72813-368-3","issued":{"date-parts":[[2019,5]]},"note":"00005","page":"195-201","publisher":"IEEE","publisher-place":"Montreal, QC, Canada","source":"DOI.org (Crossref)","title":"DingNet: A Self-Adaptive Internet-of-Things Exemplar","title-short":"DingNet","type":"paper-conference","URL":"https://ieeexplore.ieee.org/document/8787065/"},
{"id":"PtolemyProjectHome","accessed":{"date-parts":[[2016,1,26]]},"citation-key":"PtolemyProjectHome","title":"Ptolemy Project Home Page","type":"webpage","URL":"http://ptolemy.eecs.berkeley.edu/"},
{"id":"PublicationsGEMOCInitiative","accessed":{"date-parts":[[2015,9,28]]},"citation-key":"PublicationsGEMOCInitiative","title":"Publications » The GEMOC Initiative","type":"webpage","URL":"http://gemoc.org/publications/"},
{"id":"pulgattiDataMigrationDifferent","author":[{"family":"Pulgatti","given":"Leandro Duarte"}],"citation-key":"pulgattiDataMigrationDifferent","page":"80","source":"Zotero","title":"Data Migration Between Different Data Models of NoSql Databases","type":"article-journal"},
{"id":"Pylianidis202145","abstract":"In this work we compare the performance of a location-specific and a location-agnostic machine learning metamodel for crop nitrogen response rate prediction. We conduct a case study for grass-only pasture in several locations in New Zealand. We generate a large dataset of APSIM simulation outputs and train machine learning models based on that data. Initially, we examine how the models perform at the location where the location-specific model was trained. We then perform the Mann–Whitney U test to see if the difference in the predictions of the two models (i.e. location-specific and location-agnostic) is significant. We expand this procedure to other locations to investigate the generalization capability of the models. We find that there is no statistically significant difference in the predictions of the two models. This is both interesting and useful because the location-agnostic model generalizes better than the location-specific model which means that it can be applied to virgin sites with similar confidence to experienced sites. © 2021, Springer Nature Switzerland AG.","author":[{"family":"Pylianidis","given":"C."},{"family":"Snow","given":"V."},{"family":"Holzworth","given":"D."},{"family":"Bryant","given":"J."},{"family":"Athanasiadis","given":"I.N."}],"citation-key":"Pylianidis202145","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-68780-9_5","editor":[{"family":"Del Bimbo A., Cucchiara R.","given":"Sclaroff S.","suffix":"Farinella G.M., Mei T., Bertini M., Escalante H.J., Vezzani R."}],"ISBN":"9783030687793","ISSN":"03029743","issued":{"date-parts":[[2021]]},"page":"45-54","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Location-specific vs location-agnostic machine learning metamodels for predicting pasture nitrogen response rate","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103278180&doi=10.1007%2f978-3-030-68780-9_5&partnerID=40&md5=3fdf3f03bef8b52d2ecf43dcc0c3d073","volume":"12666 LNCS"},
{"id":"Qiang20202508","abstract":"This letter provides a deep learning framework for massive grant-free random access in 6G cellular internet of things (IoT) networks. A model-driven deep learning algorithm for joint activity detection and channel estimation is proposed based on the principle of approximate massage passing (AMP). This algorithm only needs to learn four key parameters, but not the whole algorithm architecture. More importantly, it does not require the prior information about active probabilities and channel variance, and can significantly improve the performance with a finite number of training data. Simulation results validate the effectiveness of the proposed deep learning algorithm. © 1997-2012 IEEE.","author":[{"family":"Qiang","given":"Y."},{"family":"Shao","given":"X."},{"family":"Chen","given":"X."}],"citation-key":"Qiang20202508","container-title":"IEEE Communications Letters","DOI":"10.1109/LCOMM.2020.3011571","ISSN":"10897798","issue":"11","issued":{"date-parts":[[2020]]},"page":"2508-2512","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A model-driven deep learning algorithm for joint activity detection and channel estimation","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096175552&doi=10.1109%2fLCOMM.2020.3011571&partnerID=40&md5=dfa4a77c09af283f84939735d76bc90f","volume":"24"},
{"id":"Qu20193751","abstract":"With the development and application of advanced technologies such as Cyber Physical System, Internet of Things, Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., more manufacturing enterprises are transforming to intelligent enterprises. Smart manufacturing systems (SMSs) have become the focus of attention of some countries and manufacturing enterprises. At present, there are some applications of SMSs in different industrial fields. However, there is still a lack of a unified definition of SMSs and a unified analysis of requirements. In order to have a comprehensive understanding of SMSs, this paper summarized the evolution, definition, objectives, functional requirements, business requirements, technical requirements, and components of SMSs. At the same time, it points out the current development status and level. Based on above, an autonomous SMSs model driven by dynamic demand and key performance indicators is proposed. Through the review of this paper, the reference can be provided for the transformation of more manufacturing enterprises from the traditional to the intellectualized ones. © 2019, Springer-Verlag London Ltd., part of Springer Nature.","author":[{"family":"Qu","given":"Y.J."},{"family":"Ming","given":"X.G."},{"family":"Liu","given":"Z.W."},{"family":"Zhang","given":"X.Y."},{"family":"Hou","given":"Z.T."}],"citation-key":"Qu20193751","container-title":"International Journal of Advanced Manufacturing Technology","DOI":"10.1007/s00170-019-03754-7","ISSN":"02683768","issue":"9-12","issued":{"date-parts":[[2019]]},"page":"3751-3768","publisher":"Springer London","title":"Smart manufacturing systems: state of the art and future trends","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065648019&doi=10.1007%2fs00170-019-03754-7&partnerID=40&md5=0742779c394cfb277d7123254e4e2c83","volume":"103"},
{"id":"Quinlan:1986:IDT:637962.637969","author":[{"family":"Quinlan","given":"J. R."}],"citation-key":"Quinlan:1986:IDT:637962.637969","container-title":"Machine Learning","container-title-short":"Mach. Learn.","ISSN":"0885-6125","issue":"1","issued":{"date-parts":[[1986,3]]},"page":"81-106","title":"Induction of decision trees","type":"article-journal","URL":"http://dx.doi.org/10.1023/A:1022643204877","volume":"1"},
{"id":"quSmartManufacturingSystems2019a","abstract":"With the development and application of advanced technologies such as Cyber Physical System, Internet of Things, Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., more manufacturing enterprises are transforming to intelligent enterprises. Smart manufacturing systems (SMSs) have become the focus of attention of some countries and manufacturing enterprises. At present, there are some applications of SMSs in different industrial fields. However, there is still a lack of a unified definition of SMSs and a unified analysis of requirements. In order to have a comprehensive understanding of SMSs, this paper summarized the evolution, definition, objectives, functional requirements, business requirements, technical requirements, and components of SMSs. At the same time, it points out the current development status and level. Based on above, an autonomous SMSs model driven by dynamic demand and key performance indicators is proposed. Through the review of this paper, the reference can be provided for the transformation of more manufacturing enterprises from the traditional to the intellectualized ones. © 2019, Springer-Verlag London Ltd., part of Springer Nature.","author":[{"family":"Qu","given":"Y.J."},{"family":"Ming","given":"X.G."},{"family":"Liu","given":"Z.W."},{"family":"Zhang","given":"X.Y."},{"family":"Hou","given":"Z.T."}],"citation-key":"quSmartManufacturingSystems2019a","container-title":"International Journal of Advanced Manufacturing Technology","DOI":"10.1007/s00170-019-03754-7","ISSN":"02683768","issue":"9-12","issued":{"date-parts":[[2019]]},"page":"3751-3768","publisher":"Springer London","title":"Smart manufacturing systems: state of the art and future trends","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065648019&doi=10.1007%2fs00170-019-03754-7&partnerID=40&md5=0742779c394cfb277d7123254e4e2c83","volume":"103"},
{"id":"Rabbi201749","abstract":"Epistemic logic plays an important role in artificial intelligence for reasoning about multi-agent systems. Current approaches for modelling multi-agent systems with epistemic logic use Kripke semantics where the knowledge base of an agent is represented as atomic propositions, but intelligent agents need to be equipped with formulas to derive implicit information. In this paper, we propose a metamodelling approach where agents’ state of affairs are separated in different scopes, and the knowledge base of an agent is represented by a propositional logic language restricted to Horn clauses. We propose to use a model driven approach for the diagrammatic representation of multi-agent systems knowledge (and nested knowledge). We use a message passing for updating the state of affairs of agents and use belief revision to update the knowledge base of agents. © Springer International Publishing AG 2017.","author":[{"family":"Rabbi","given":"F."},{"family":"Lamo","given":"Y."},{"family":"Kristensen","given":"L.M."}],"citation-key":"Rabbi201749","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-319-59294-7_5","editor":[{"family":"Carrascosa C., Julian Inglada V.","given":"Criado Pacheco N.","suffix":"Osman N."}],"ISBN":"9783319592930","ISSN":"03029743","issued":{"date-parts":[[2017]]},"page":"49-57","publisher":"Springer Verlag","title":"An MDE approach for modelling and reasoning about multi-agent systems","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022229600&doi=10.1007%2f978-3-319-59294-7_5&partnerID=40&md5=629d4375b45c520e3806ce89438bcde8","volume":"10207 LNAI"},
{"id":"rabbiMDEApproachModelling2017a","abstract":"Epistemic logic plays an important role in artificial intelligence for reasoning about multi-agent systems. Current approaches for modelling multi-agent systems with epistemic logic use Kripke semantics where the knowledge base of an agent is represented as atomic propositions, but intelligent agents need to be equipped with formulas to derive implicit information. In this paper, we propose a metamodelling approach where agents’ state of affairs are separated in different scopes, and the knowledge base of an agent is represented by a propositional logic language restricted to Horn clauses. We propose to use a model driven approach for the diagrammatic representation of multi-agent systems knowledge (and nested knowledge). We use a message passing for updating the state of affairs of agents and use belief revision to update the knowledge base of agents. © Springer International Publishing AG 2017.","author":[{"family":"Rabbi","given":"F."},{"family":"Lamo","given":"Y."},{"family":"Kristensen","given":"L.M."}],"citation-key":"rabbiMDEApproachModelling2017a","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-319-59294-7_5","editor":[{"family":"Carrascosa C.","given":"Osman N.","suffix":"Julian Inglada V., Criado Pacheco N."}],"ISBN":"9783319592930","ISSN":"03029743","issued":{"date-parts":[[2017]]},"page":"49-57","publisher":"Springer Verlag","title":"An MDE approach for modelling and reasoning about multi-agent systems","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022229600&doi=10.1007%2f978-3-319-59294-7_5&partnerID=40&md5=629d4375b45c520e3806ce89438bcde8","volume":"10207 LNAI"},
{"id":"Rafique2018D126","abstract":"Networks are complex interacting systems involving cloud operations, core and metro transport, and mobile connectivity all the way to video streaming and similar user applications.With localized and highly engineered operational tools, it is typical of these networks to take days to weeks for any changes, upgrades, or service deployments to take effect. Machine learning, a sub-domain of artificial intelligence, is highly suitable for complex system representation. In this tutorial paper, we review several machine learning concepts tailored to the optical networking industry and discuss algorithm choices, data and model management strategies, and integration into existing network control and management tools. We then describe four networking case studies in detail, covering predictive maintenance, virtual network topology management, capacity optimization, and optical spectral analysis. © 2009-2012 OSA.","author":[{"family":"Rafique","given":"D."},{"family":"Velasco","given":"L."}],"citation-key":"Rafique2018D126","container-title":"Journal of Optical Communications and Networking","DOI":"10.1364/JOCN.10.00D126","ISSN":"19430620","issue":"10","issued":{"date-parts":[[2018]]},"page":"D126-D143","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Machine learning for network automation: Overview, architecture, and applications [Invited Tutorial]","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055964092&doi=10.1364%2fJOCN.10.00D126&partnerID=40&md5=c814a681aa393fa4cde0e0f638cd779e","volume":"10"},
{"id":"Ragkhitwetsagul2018","abstract":"Copying and pasting of source code is a common activity in software engineering. Often, the code is not copied as it is and it may be modified for various purposes; e.g. refactoring, bug fixing, or even software plagiarism. These code modifications could affect the performance of code similarity analysers including code clone and plagiarism detectors to some certain degree. We are interested in two types of code modification in this study: pervasive modifications, i.e. transformations that may have a global effect, and local modifications, i.e. code changes that are contained in a single method or code block. We evaluate 30 code similarity detection techniques and tools using five experimental scenarios for Java source code. These are (1) pervasively modified code, created with tools for source code and bytecode obfuscation, and boiler-plate code, (2) source code normalisation through compilation and decompilation using different decompilers, (3) reuse of optimal configurations over different data sets, (4) tool evaluation using ranked-based measures, and (5) local + global code modifications. Our experimental results show that in the presence of pervasive modifications, some of the general textual similarity measures can offer similar performance to specialised code similarity tools, whilst in the presence of boiler-plate code, highly specialised source code similarity detection techniques and tools outperform textual similarity measures. Our study strongly validates the use of compilation/decompilation as a normalisation technique. Its use reduced false classifications to zero for three of the tools. Moreover, we demonstrate that optimal configurations are very sensitive to a specific data set. After directly applying optimal configurations derived from one data set to another, the tools perform poorly on the new data set. The code similarity analysers are thoroughly evaluated not only based on several well-known pair-based and query-based error measures but also on each specific type of pervasive code modification. This broad, thorough study is the largest in existence and potentially an invaluable guide for future users of similarity detection in source code.","author":[{"family":"Ragkhitwetsagul","given":"Chaiyong"},{"family":"Krinke","given":"Jens"},{"family":"Clark","given":"David"}],"citation-key":"Ragkhitwetsagul2018","container-title":"Empirical Software Engineering","DOI":"10.1007/s10664-017-9564-7","ISSN":"1573-7616","issue":"4","issued":{"date-parts":[[2018,8,1]]},"page":"2464-2519","title":"A comparison of code similarity analysers","type":"article-journal","URL":"https://doi.org/10.1007/s10664-017-9564-7","volume":"23"},
{"id":"ragoneSchemasummarizationLinkeddatabasedFeature2017","author":[{"family":"Ragone","given":"Azzurra"},{"family":"Tomeo","given":"Paolo"},{"family":"Magarelli","given":"Corrado"},{"family":"Di Noia","given":"Tommaso"},{"family":"Palmonari","given":"Matteo"},{"family":"Maurino","given":"Andrea"},{"family":"Di Sciascio","given":"Eugenio"}],"citation-key":"ragoneSchemasummarizationLinkeddatabasedFeature2017","collection-title":"SAC '17","container-title":"Proceedings of the symposium on applied computing","event-place":"New York, NY, USA","ISBN":"978-1-4503-4486-9","issued":{"date-parts":[[2017]]},"page":"330-335","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Schema-summarization in linked-data-based feature selection for recommender systems","type":"paper-conference","URL":"http://doi.acm.org/10.1145/3019612.3019837"},
{"id":"Rajaei2021149","abstract":"Specific deep-learning tools for graph-structured data, i.e. graph-learning, are successfully used in several domains. Their use in Model-Driven Engineering (MDE) requires MDE practitioners to have a good understanding of technical aspects of the graph-learning process. For instance, automatic translators need to be developed, in order to encode models in the most effective input format for deep-learning neural networks. With this work, we aim at assisting MDE practitioners in applying deep learning on their models. For this purpose, we introduce a Domain-Specific Language (DSL) for configuring the encoding of models into suitable input for graph-learning tools. This DSL is interpreted to automatically translate MDE datasets, enabling their use in machine-learning pipelines. To evaluate this research, we consider the AIDS dataset as instances of a corresponding metamodel. We use our DSL to automatically encode models of this dataset into the format expected by a graph-learning tool. The experimental evaluation demonstrates that we are able to obtain the same encoding used in related work. © 2021 Copyright (c) 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)","author":[{"family":"Rajaei","given":"Z."},{"family":"Kolahdouz-Rahimi","given":"S."},{"family":"Tisi","given":"M."},{"family":"Jouault","given":"F."}],"citation-key":"Rajaei2021149","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Iovino L.","given":"Kristensen L.M."}],"ISSN":"16130073","issued":{"date-parts":[[2021]]},"page":"149-161","publisher":"CEUR-WS","title":"A DSL for encoding models for graph-learning processes","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118923715&partnerID=40&md5=0392ba00df3c85c19cc98510b244e3b9","volume":"2999"},
{"id":"rajInternetThingsEnabling2017","author":[{"family":"Raj","given":"Pethuru"},{"family":"Raman","given":"Anupama C."}],"call-number":"TK5105.8857 .R35 2017","citation-key":"rajInternetThingsEnabling2017","event-place":"Boca Raton","ISBN":"978-1-4987-6128-4","issued":{"date-parts":[[2017]]},"number-of-pages":"364","publisher":"CRC Press/Taylor & Francis Group","publisher-place":"Boca Raton","source":"Library of Congress ISBN","title":"The Internet of things: enabling technologies, platforms, and use cases","title-short":"The Internet of things","type":"book"},
{"id":"Ramaswamy201450","abstract":"A Human-Machine system is a complex system consisting of many components and services that dynamically compose to achieve a specific goal. The functional and non-functional attributes of the components are considered to make 'who does, what, and when' decisions depending on the operational context. However, non-functional properties are not given sufficient importance compared to that of the functional requirements during the developmental stages. This paper highlights the importance of non-functional properties in human-machine systems and proposes a metamodel for modeling those properties. A case study on assistive lane keeping in automobiles is presented to demonstrate how the non-functional properties can be modeled. This is a part of the intermediate results of a research in progress for modeling decision architectures for autonomous systems. Copyright © 2014, Association for the Advancement of Artificial Intelligence. All rights reserved.","author":[{"family":"Ramaswamy","given":"A."},{"family":"Monsuez","given":"B."},{"family":"Tapus","given":"A."}],"citation-key":"Ramaswamy201450","collection-title":"AAAI Spring Symposium - Technical Report","ISBN":"978-1-57735-655-4","issued":{"date-parts":[[2014]]},"page":"50-55","publisher":"AI Access Foundation","title":"Modeling non-functional properties for human-machine systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904888679&partnerID=40&md5=8255705b33be09a555b7c43d9218c3a7","volume":"SS-14-02"},
{"id":"ramaswamyModeldrivenSoftwareDevelopment2014","accessed":{"date-parts":[[2015,3,20]]},"author":[{"family":"Ramaswamy","given":"Arunkumar"},{"family":"Monsuez","given":"Bruno"},{"family":"Tapus","given":"Adriana"}],"citation-key":"ramaswamyModeldrivenSoftwareDevelopment2014","DOI":"10.1145/2593770.2593781","ISBN":"978-1-4503-2849-4","issued":{"date-parts":[[2014]]},"page":"43-48","publisher":"ACM Press","source":"CrossRef","title":"Model-driven software development approaches in robotics research","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?doid=2593770.2593781"},
{"id":"ramosUsingTFIDFDetermine1999","author":[{"family":"Ramos","given":"Juan"}],"citation-key":"ramosUsingTFIDFDetermine1999","issued":{"date-parts":[[1999]]},"title":"Using TF-IDF to determine word relevance in document queries","type":"article-journal"},
{"id":"randObjectiveCriteriaEvaluation1971","author":[{"family":"Rand","given":"W.M."}],"citation-key":"randObjectiveCriteriaEvaluation1971","container-title":"Journal of the American Statistical association","ISSN":"0162-1459","issue":"336","issued":{"date-parts":[[1971]]},"page":"846-850","title":"Objective criteria for the evaluation of clustering methods","type":"article-journal","volume":"66"},
{"id":"Rasiman202235","abstract":"[Context and Motivation] Requirements Traceability (RT) aims to follow and describe the lifecycle of a requirement. RT is employed either because it is mandated, or because the product team perceives benefits. [Problem] RT practices such as the establishment and maintenance of trace links are generally carried out manually, thereby being prone to mistakes, vulnerable to changes, time-consuming, and difficult to maintain. Automated tracing tools have been proposed; yet, their adoption is low, often because of the limited evidence of their effectiveness. We focus on vertical traceability that links artifacts having different levels of abstraction. [Results] We design an automated tool for recovering traces between JIRA issues (user stories and bugs) and revisions in a model-driven development (MDD) context. Based on existing literature that uses process and text-based data, we created 123 features to train a machine learning classifier. This classifier was validated via three MDD industry datasets. For a trace recommendation scenario, we obtained an average F 2 -score of 69% with the best tested configuration. For an automated trace maintenance scenario, we obtained an F 0.5 -score of 76%. [Contribution] Our findings provide insights on the effectiveness of state-of-the-art trace link recovery techniques in an MDD context by using real-world data from a large company in the field of low-code development. © 2022, Springer Nature Switzerland AG.","author":[{"family":"Rasiman","given":"R."},{"family":"Dalpiaz","given":"F."},{"family":"España","given":"S."}],"citation-key":"Rasiman202235","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-98464-9_4","editor":[{"family":"Gervasi V.","given":"Vogelsang A."}],"ISBN":"9783030984632","ISSN":"03029743","issued":{"date-parts":[[2022]]},"page":"35-51","publisher":"Springer Science and Business Media Deutschland GmbH","title":"How effective is automated trace link recovery in model-driven development?","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127083036&doi=10.1007%2f978-3-030-98464-9_4&partnerID=40&md5=dc16353591d80cf0a9382bfcd345419e","volume":"13216 LNCS"},
{"id":"Rausch2021127","abstract":"It is often prohibitively time-consuming to do sensitivity analysis, uncertainty quantification, and optimization with complex state-based quantitative models because each model execution or solution takes so long to complete, and many such executions are necessary to complete the analysis. One way to approach this problem is to use metamodels that emulate the behavior of the base model but run much faster. These metamodels may be automatically constructed using machine learning techniques, and then the relevant analysis may be conducted on the fast-running metamodel in place of the slow-running model. In this work, we evaluate the effectiveness of several different types of metamodels in emulating seven publicly available PRISM and Möbius models. In our evaluation, we found that the metamodels are reasonably accurate and are several thousand times faster than the corresponding models they emulate. Furthermore, we find that stacking-based metamodels are significantly more accurate than state-of-the-practice metamodels. We show that metamodeling is a powerful and practical tool for modelers interested in understanding the behavior of their models, because it makes feasible analysis techniques that would otherwise take too long to run on the original models. © 2021, Springer Nature Switzerland AG.","author":[{"family":"Rausch","given":"M."},{"family":"Sanders","given":"W.H."}],"citation-key":"Rausch2021127","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-85172-9_7","editor":[{"family":"Abate A.","given":"Marin A."}],"ISBN":"9783030851712","ISSN":"03029743","issued":{"date-parts":[[2021]]},"page":"127-145","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Evaluating the effectiveness of metamodeling in emulating quantitative models","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115141001&doi=10.1007%2f978-3-030-85172-9_7&partnerID=40&md5=4350fd7e971b8653a48429f1b5ec4e9a","volume":"12846 LNCS"},
{"id":"rauschEvaluatingEffectivenessMetamodeling2021a","abstract":"It is often prohibitively time-consuming to do sensitivity analysis, uncertainty quantification, and optimization with complex state-based quantitative models because each model execution or solution takes so long to complete, and many such executions are necessary to complete the analysis. One way to approach this problem is to use metamodels that emulate the behavior of the base model but run much faster. These metamodels may be automatically constructed using machine learning techniques, and then the relevant analysis may be conducted on the fast-running metamodel in place of the slow-running model. In this work, we evaluate the effectiveness of several different types of metamodels in emulating seven publicly available PRISM and Möbius models. In our evaluation, we found that the metamodels are reasonably accurate and are several thousand times faster than the corresponding models they emulate. Furthermore, we find that stacking-based metamodels are significantly more accurate than state-of-the-practice metamodels. We show that metamodeling is a powerful and practical tool for modelers interested in understanding the behavior of their models, because it makes feasible analysis techniques that would otherwise take too long to run on the original models. © 2021, Springer Nature Switzerland AG.","author":[{"family":"Rausch","given":"M."},{"family":"Sanders","given":"W.H."}],"citation-key":"rauschEvaluatingEffectivenessMetamodeling2021a","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-85172-9_7","editor":[{"family":"Abate A.","given":"Marin A."}],"ISBN":"9783030851712","ISSN":"03029743","issued":{"date-parts":[[2021]]},"page":"127-145","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Evaluating the Effectiveness of Metamodeling in Emulating Quantitative Models","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115141001&doi=10.1007%2f978-3-030-85172-9_7&partnerID=40&md5=4350fd7e971b8653a48429f1b5ec4e9a","volume":"12846 LNCS"},
{"id":"raySurveyInternetThings2018","abstract":"Internet of Things is a platform where every day devices become smarter, every day processing becomes intelligent, and every day communication becomes informative. While the Internet of Things is still seeking its own shape, its effects have already stared in making incredible strides as a universal solution media for the connected scenario. Architecture specific study does always pave the conformation of related field. The lack of overall architectural knowledge is presently resisting the researchers to get through the scope of Internet of Things centric approaches. This literature surveys Internet of Things oriented architectures that are capable enough to improve the understanding of related tool, technology, and methodology to facilitate developer’s requirements. Directly or indirectly, the presented architectures propose to solve real-life problems by building and deployment of powerful Internet of Things notions. Further, research challenges have been investigated to incorporate the lacuna inside the current trends of architectures to motivate the academics and industries get involved into seeking the possible way outs to apt the exact power of Internet of Things. A main contribution of this survey paper is that it summarizes the current state-of-the-art of Internet of Things architectures in various domains systematically.","accessed":{"date-parts":[[2019,9,10]]},"author":[{"family":"Ray","given":"P.P."}],"citation-key":"raySurveyInternetThings2018","container-title":"Journal of King Saud University - Computer and Information Sciences","container-title-short":"Journal of King Saud University - Computer and Information Sciences","DOI":"10.1016/j.jksuci.2016.10.003","ISSN":"13191578","issue":"3","issued":{"date-parts":[[2018,7]]},"page":"291-319","source":"DOI.org (Crossref)","title":"A survey on Internet of Things architectures","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1319157816300799","volume":"30"},
{"id":"raySurveyIoTCloud2016","abstract":"Internet of Things (IoT) envisages overall merging of several “things” while utilizing internet as the backbone of the communication system to establish a smart interaction between people and surrounding objects. Cloud, being the crucial component of IoT, provides valuable application specific services in many application domains. A number of IoT cloud providers are currently emerging into the market to leverage suitable and specific IoT based services. In spite of huge possible involvement of these IoT clouds, no standard cum comparative analytical study has been found across the literature databases. This article surveys popular IoT cloud platforms in light of solving several service domains such as application development, device management, system management, heterogeneity management, data management, tools for analysis, deployment, monitoring, visualization, and research. A comparison is presented for overall dissemination of IoT clouds according to their applicability. Further, few challenges are also described that the researchers should take on in near future. Ultimately, the goal of this article is to provide detailed knowledge about the existing IoT cloud service providers and their pros and cons in concrete form.","accessed":{"date-parts":[[2019,9,7]]},"author":[{"family":"Ray","given":"Partha Pratim"}],"citation-key":"raySurveyIoTCloud2016","container-title":"Future Computing and Informatics Journal","container-title-short":"Future Computing and Informatics Journal","DOI":"10.1016/j.fcij.2017.02.001","ISSN":"23147288","issue":"1-2","issued":{"date-parts":[[2016,12]]},"page":"35-46","source":"DOI.org (Crossref)","title":"A survey of IoT cloud platforms","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2314728816300149","volume":"1"},
{"id":"RealWorldIoT","accessed":{"date-parts":[[2016,9,27]]},"citation-key":"RealWorldIoT","title":"Real World IoT: Architectures and Projects with Eclipse IoT | EclipseCon Europe 2016","type":"webpage","URL":"https://www.eclipsecon.org/europe2016/session/real-world-iot-architectures-and-projects-eclipse-iot"},
{"id":"REBY199735","abstract":"The classification and recognition of individual characteristics and behaviours constitute a preliminary step and is an important objective in the behavioural sciences. Current statistical methods do not always give satisfactory results. To improve performance in this area, we present a methodology based on one of the principles of artificial neural networks: the backpropagation gradient. After summarizing the theoretical construction of the model, we describe how to parameterize a neural network using the example of the individual recognition of vocalizations of four fallow deer (Dama dama). With 100% recognition and %90% prediction success, the results are very promising.","author":[{"family":"Reby","given":"David"},{"family":"Lek","given":"Sovan"},{"family":"Dimopoulos","given":"Ioannis"},{"family":"Joachim","given":"Jean"},{"family":"Lauga","given":"Jacques"},{"family":"Aulagnier","given":"Stéphane"}],"citation-key":"REBY199735","container-title":"Behavioural Processes","ISSN":"0376-6357","issue":"1","issued":{"date-parts":[[1997]]},"page":"35 - 43","title":"Artificial neural networks as a classification method in the behavioural sciences","type":"article-journal","volume":"40"},
{"id":"reedTFICFNewTerm2006","author":[{"family":"Reed","given":"Joel W."},{"family":"Jiao","given":"Yu"},{"family":"Potok","given":"Thomas E."},{"family":"Klump","given":"Brian A."},{"family":"Elmore","given":"Mark T."},{"family":"Hurson","given":"Ali R."}],"citation-key":"reedTFICFNewTerm2006","collection-title":"ICMLA '06","container-title":"Proceedings of the 5th international conference on machine learning and applications","event-place":"Washington, DC, USA","ISBN":"0-7695-2735-3","issued":{"date-parts":[[2006]]},"page":"258-263","publisher":"IEEE Computer Society","publisher-place":"Washington, DC, USA","title":"TF-ICF: A new term weighting scheme for clustering dynamic data streams","type":"paper-conference","URL":"http://dx.doi.org/10.1109/ICMLA.2006.50"},
{"id":"ReliableDataProcessing2021","citation-key":"ReliableDataProcessing2021","issued":{"date-parts":[[2021]]},"note":"00000","page":"15","source":"Zotero","title":"Reliable Data Processing with Minimal Toil","type":"article-journal"},
{"id":"Rendon:2011:CIE:1959666.1959695","author":[{"family":"Rendón","given":"Eréndira"},{"family":"Abundez","given":"Itzel M."},{"family":"Gutierrez","given":"Citlalih"},{"family":"Zagal","given":"Sergio Díaz"},{"family":"Arizmendi","given":"Alejandra"},{"family":"Quiroz","given":"Elvia M."},{"family":"Arzate","given":"H. Elsa"}],"citation-key":"Rendon:2011:CIE:1959666.1959695","collection-title":"AMERICAN-MATH'11/CEA'11","container-title":"Proceedings of the 2011 american conference on applied mathematics and the 5th WSEAS international conference on computer engineering and applications","event-place":"Stevens Point, Wisconsin, USA","ISBN":"978-960-474-270-7","issued":{"date-parts":[[2011]]},"page":"158-163","publisher":"World Scientific and Engineering Academy and Society (WSEAS)","publisher-place":"Stevens Point, Wisconsin, USA","title":"A comparison of internal and external cluster validation indexes","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=1959666.1959695"},
{"id":"Rendon2011","abstract":".","author":[{"family":"Rendón","given":"Eréndira"},{"family":"Abundez","given":"Itzel"},{"family":"Arizmendi","given":"Alejandra"},{"family":"Quiroz","given":"Elvia M."}],"citation-key":"Rendon2011","container-title":"Int. Journal of Compt. and Comm.","issue":"1","issued":{"date-parts":[[2011,3]]},"page":"27-34","title":"Internal versus External cluster validation indexes","type":"article-journal","volume":"5"},
{"id":"RePEc:eee:intfor:v:14:y:1998:i:1:p:35-62","author":[{"family":"Zhang","given":"Guoqiang"},{"family":"Eddy Patuwo","given":"B."},{"family":"Y. Hu","given":"Michael"}],"citation-key":"RePEc:eee:intfor:v:14:y:1998:i:1:p:35-62","container-title":"International Journal of Forecasting","issue":"1","issued":{"date-parts":[[1998]]},"page":"35-62","title":"Forecasting with artificial neural networks:: The state of the art","type":"article-journal","volume":"14"},
{"id":"RepubblicaItNews","abstract":"Repubblica è il quotidiano online aggiornato 24 ore su 24 su politica, cronaca, economia, sport, esteri, spettacoli, musica, cultura, scienza, tecnologia.","accessed":{"date-parts":[[2020,1,15]]},"citation-key":"RepubblicaItNews","container-title":"Repubblica.it","title":"La Repubblica.it - News in tempo reale - Le notizie e i video di politica, cronaca, economia, sport","type":"webpage","URL":"http://www.repubblica.it/"},
{"id":"ResearchInsightsServerless","accessed":{"date-parts":[[2021,1,17]]},"citation-key":"ResearchInsightsServerless","note":"00000","title":"(Research) Insights for Serverless Application Engineering","type":"webpage","URL":"https://www.computer.org/csdl/magazine/so/2021/01/09305894/1pNkwYVzrUc"},
{"id":"resnikUsingInformationContent1995","author":[{"family":"Resnik","given":"Philip"}],"citation-key":"resnikUsingInformationContent1995","collection-title":"IJCAI'95","container-title":"Proceedings of the 14th international joint conference on artificial intelligence - volume 1","event-place":"San Francisco, CA, USA","ISBN":"1-55860-363-8 978-1-55860-363-9","issued":{"date-parts":[[1995]]},"page":"448-453","publisher":"Morgan Kaufmann Publishers Inc.","publisher-place":"San Francisco, CA, USA","title":"Using information content to evaluate semantic similarity in a taxonomy","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=1625855.1625914"},
{"id":"Results1stCall","abstract":"10 projects were selected for co-financing under the first H2020 call for proposals on Smart System Integration.","accessed":{"date-parts":[[2015,4,8]]},"citation-key":"Results1stCall","container-title":"Digital Agenda for Europe","title":"Results of the 1st call on Smart System Integration under H2020","type":"webpage","URL":"ec.europa.eu//digital-agenda/en/news/results-1st-call-smart-system-integration-under-h2020"},
{"id":"Reynolds2019624","abstract":"The ability for systems to make decisions by themselves is increasing with advances in different areas of AI such as machine learning and optimisation techniques for autonomous systems among other. Humans are handing over more decisions to systems that provide no explanations for their judgements unless they are enabled explicitly in their design. Trust based on a program being well written and tested correctly is not appropriate for AI-based autonomous systems. Unlike traditional software, this new software increasingly exhibit emergent behaviours making it unpredictable due to unexpected situations. Self-explanation is sometimes implemented, tracking decisions to give explanations to users. A more consistent, proven approach to self-explanation would be needed for making trustable systems. The paper proposes a research agenda to define an architecture to enable self-explanation for autonomous decision-making systems. The approach will be model-driven to facilitate reuse, the rapid development of tools and suitable abstractions for demonstrating concepts. The architecture will be informed by existing research in provenance ontology and model version research. The evaluation of the architecture is expected to be done using two case studies. The first will implement self-explanation as a primary concern in the building of a system. The second case will attempt to fit self-explanation to an existing system. © 2019 IEEE.","author":[{"family":"Reynolds","given":"O."}],"citation-key":"Reynolds2019624","collection-title":"Proceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2019","DOI":"10.1109/MODELS-C.2019.00095","editor":[{"family":"Burgueno L., Burgueno L.","given":"Pretschner A.","suffix":"Voss S., Chaudron M., Kienzle J., Volter M., Gerard S., Zahedi M., Bousse E., Rensink A., Polack F., Engels G., Kappel G."}],"ISBN":"978-1-72815-125-0","issued":{"date-parts":[[2019]]},"page":"624-628","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Towards model-driven self-explanation for autonomous decision-making systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075932569&doi=10.1109%2fMODELS-C.2019.00095&partnerID=40&md5=07147073941bdf8f30acd17f5c521d48"},
{"id":"riahisfarRoadmapSecurityChallenges2018","abstract":"Unquestionably, communicating entities (object, or things) in the Internet of Things (IoT) context are playing an active role in human activities, systems and processes. The high connectivity of intelligent objects and their severe constraints lead to many security challenges, which are not included in the classical formulation of security problems and solutions. The Security Shield for IoT has been identified by DARPA (Defense Advanced Research Projects Agency) as one of the four projects with a potential impact broader than the Internet itself. To help interested researchers contribute to this research area, an overview of the IoT security roadmap overview is presented in this paper based on a novel cognitive and systemic approach. The role of each component of the approach is explained, we also study its interactions with the other main components, and their impact on the overall. A case study is presented to highlight the components and interactions of the systemic and cognitive approach. Then, security questions about privacy, trust, identification, and access control are discussed. According to the novel taxonomy of the IoT framework, different research challenges are highlighted, important solutions and research activities are revealed, and interesting research directions are proposed. In addition, current standardization activities are surveyed and discussed to the ensure the security of IoT components and applications.","accessed":{"date-parts":[[2019,9,10]]},"author":[{"family":"Riahi Sfar","given":"Arbia"},{"family":"Natalizio","given":"Enrico"},{"family":"Challal","given":"Yacine"},{"family":"Chtourou","given":"Zied"}],"citation-key":"riahisfarRoadmapSecurityChallenges2018","container-title":"Digital Communications and Networks","container-title-short":"Digital Communications and Networks","DOI":"10.1016/j.dcan.2017.04.003","ISSN":"23528648","issue":"2","issued":{"date-parts":[[2018,4]]},"page":"118-137","source":"DOI.org (Crossref)","title":"A roadmap for security challenges in the Internet of Things","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2352864817300214","volume":"4"},
{"id":"Ricci2011","author":[{"family":"Ricci","given":"Francesco"},{"family":"Rokach","given":"Lior"},{"family":"Shapira","given":"Bracha"}],"citation-key":"Ricci2011","container-title":"Recommender systems handbook","DOI":"10.1007/978-0-387-85820-3₁","editor":[{"family":"Ricci","given":"Francesco"},{"family":"Rokach","given":"Lior"},{"family":"Shapira","given":"Bracha"},{"family":"Kantor","given":"Paul B."}],"event-place":"Boston, MA","ISBN":"978-0-387-85820-3","issued":{"date-parts":[[2011]]},"page":"1-35","publisher":"Springer US","publisher-place":"Boston, MA","title":"Introduction to recommender systems handbook","type":"chapter"},
{"id":"richardsonVendorLandscapeFractured2016","author":[{"family":"Richardson","given":"Clay"},{"family":"Rymer","given":"John R"}],"citation-key":"richardsonVendorLandscapeFractured2016","issued":{"date-parts":[[2016]]},"page":"23","source":"Zotero","title":"Vendor Landscape: The Fractured, Fertile Terrain Of Low-Code Application Platforms","type":"article-journal"},
{"id":"Ries202141","abstract":"Since the emergence of deep learning (DL) a decade ago, only few software engineering development methods have been defined for systems based on this machine learning approach. Moreover, rare are the DL approaches addressing specifically requirements engineering. In this paper, we define a model-driven engineering (MDE) method based on traditional requirements engineering to improve datasets requirements engineering. Our MDE method is composed of a process supported by tools to aid customers and analysts in eliciting, specifying and validating dataset structural requirements for DL-based systems. Our model driven engineering approach uses the UML semi-formal modeling language for the analysis of datasets structural requirements, and the Alloy formal language for the requirements model execution based on our informal translational semantics. The model executions results are then presented to the customer for improving the dataset validation activity. Our approach aims at validating DL-based dataset structural requirements by modeling and instantiating their datatypes. We illustrate our approach with a case study on the requirements engineering of the structure of a dataset for classification of five-segments digits images. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.","author":[{"family":"Ries","given":"B."},{"family":"Guelfi","given":"N."},{"family":"Jahić","given":"B."}],"citation-key":"Ries202141","collection-title":"MODELSWARD 2021 - Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development","editor":[{"family":"Hammoudi S., Pires L.F.","given":"Seidewitz E.","suffix":"Soley R."}],"ISBN":"978-989-758-487-9","issued":{"date-parts":[[2021]]},"page":"41-52","publisher":"SciTePress","title":"An MDE method for improving deep learning dataset requirements engineering using alloy and UML","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103060952&partnerID=40&md5=4945b5c1de311257ad57a8d74cfc36ba"},
{"id":"riesMDEMethodImproving2021a","abstract":"Since the emergence of deep learning (DL) a decade ago, only few software engineering development methods have been defined for systems based on this machine learning approach. Moreover, rare are the DL approaches addressing specifically requirements engineering. In this paper, we define a model-driven engineering (MDE) method based on traditional requirements engineering to improve datasets requirements engineering. Our MDE method is composed of a process supported by tools to aid customers and analysts in eliciting, specifying and validating dataset structural requirements for DL-based systems. Our model driven engineering approach uses the UML semi-formal modeling language for the analysis of datasets structural requirements, and the Alloy formal language for the requirements model execution based on our informal translational semantics. The model executions results are then presented to the customer for improving the dataset validation activity. Our approach aims at validating DL-based dataset structural requirements by modeling and instantiating their datatypes. We illustrate our approach with a case study on the requirements engineering of the structure of a dataset for classification of five-segments digits images. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.","author":[{"family":"Ries","given":"B."},{"family":"Guelfi","given":"N."},{"family":"Jahić","given":"B."}],"citation-key":"riesMDEMethodImproving2021a","container-title":"MODELSWARD 2021 - Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development","editor":[{"family":"Hammoudi S.","given":"Soley R.","suffix":"Pires L.F., Seidewitz E."}],"ISBN":"978-989-758-487-9","issued":{"date-parts":[[2021]]},"page":"41-52","publisher":"SciTePress","title":"An MDE method for improving deep learning dataset requirements engineering using alloy and UML","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103060952&partnerID=40&md5=4945b5c1de311257ad57a8d74cfc36ba"},
{"id":"Rigou2020","abstract":"Drafting a formal or semi-formal model describing the functional requirements of a system from a textual specification is a prerequisite in the context of a model-driven engineering approach, such as the model-driven architecture initiative proposed by OMG. This model, called a platform-independent model (PIM), is used to derive automatically or semi-automatically the source code of a system. Different knowledge-based approaches have been proposed to extract a PIM from a textual specification automatically. These approaches use a predefined set of rules to perform this discovery. These approaches impose several restrictions on the way a specification is written. The emergence of machine learning techniques and more specifically of deep learning and their obvious success among others in several tasks in automatic language processing, such as speech recognition and translation, suggests the possibility of using these techniques to reach our objective. In this paper, we review state of the art in the domain and we sketch a rough deep learning approach to achieve our objective of extracting a PIM from the textual specification of a system. © 2020 IEEE.","author":[{"family":"Rigou","given":"Y."},{"family":"Lamontagne","given":"D."},{"family":"Khriss","given":"I."}],"citation-key":"Rigou2020","collection-title":"2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2020","DOI":"10.1109/IRASET48871.2020.9092144","editor":[{"family":"Benhala B., Mansouri K.","given":"Raihani A.","suffix":"Qbadou M."}],"ISBN":"978-1-72814-979-0","issued":{"date-parts":[[2020]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A sketch of a deep learning approach for discovering UML class diagrams from system's textual specification","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085470615&doi=10.1109%2fIRASET48871.2020.9092144&partnerID=40&md5=d8f77226baad815a391feca40f911045"},
{"id":"Rivera2020631","abstract":"Digital Twins (DT) are software systems representing different aspects of a physical or conceptual counterpart - -the real twin, which is instrumented with several sensors or computing devices that generate, consume and transfer data to its DT with different purposes. In other words, DT systems are, to a large extent, IoT-intensive systems. Indeed, by exploiting and managing IoT data, artificial intelligence, and big data and simulation capabilities, DTs have emerged as a promising approach to manage the virtual manifestation of real-world entities throughout their entire lifecycle. Their proliferation will contribute to realizing the long-craved convergence of virtual and physical spaces to augment things and human capabilities. In this context, despite the proposal of noteworthy contributions, we argue that DTs have not been sufficiently investigated from a software engineering perspective. To address this, in this paper we propose GEMINIS, an architectural reference model that adopts self-adaptation, control, and model-driven engineering techniques to specify the structural and behavioural aspects of DTs and enable the evolution of their internal models. Moreover, we introduce an approach for engineering IoT-intensive Digital Twin Software Systems (DTSS) using GEMINIS' capabilities to deal with uncertain conditions that are inherent to the nature of mirrored physical environments and that might compromise the fidelity of a DT. With GEMINIS and the proposed approach, we aim to advance the engineering of DTSS as well as IoT and cyber-physical systems by providing practitioners with guidelines to model and specify inherent structural and behavioural characteristics of DTs, addressing common design concerns. © 2020 ACM.","author":[{"family":"Rivera","given":"L.F."},{"family":"Müller","given":"H.A."},{"family":"Villegas","given":"N.M."},{"family":"Tamura","given":"G."},{"family":"Jiménez","given":"M."}],"citation-key":"Rivera2020631","collection-title":"Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020","DOI":"10.1145/3387940.3392195","ISBN":"978-1-4503-7963-2","issued":{"date-parts":[[2020]]},"page":"631-638","publisher":"Association for Computing Machinery, Inc","title":"On the engineering of IoT-Intensive digital twin software systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093092203&doi=10.1145%2f3387940.3392195&partnerID=40&md5=ef97a6c62c8feab8c5a482a95493fd62"},
{"id":"riveraEngineeringIoTIntensiveDigital2020a","abstract":"Digital Twins (DT) are software systems representing different aspects of a physical or conceptual counterpart - -the real twin, which is instrumented with several sensors or computing devices that generate, consume and transfer data to its DT with different purposes. In other words, DT systems are, to a large extent, IoT-intensive systems. Indeed, by exploiting and managing IoT data, artificial intelligence, and big data and simulation capabilities, DTs have emerged as a promising approach to manage the virtual manifestation of real-world entities throughout their entire lifecycle. Their proliferation will contribute to realizing the long-craved convergence of virtual and physical spaces to augment things and human capabilities. In this context, despite the proposal of noteworthy contributions, we argue that DTs have not been sufficiently investigated from a software engineering perspective. To address this, in this paper we propose GEMINIS, an architectural reference model that adopts self-adaptation, control, and model-driven engineering techniques to specify the structural and behavioural aspects of DTs and enable the evolution of their internal models. Moreover, we introduce an approach for engineering IoT-intensive Digital Twin Software Systems (DTSS) using GEMINIS' capabilities to deal with uncertain conditions that are inherent to the nature of mirrored physical environments and that might compromise the fidelity of a DT. With GEMINIS and the proposed approach, we aim to advance the engineering of DTSS as well as IoT and cyber-physical systems by providing practitioners with guidelines to model and specify inherent structural and behavioural characteristics of DTs, addressing common design concerns. © 2020 ACM.","author":[{"family":"Rivera","given":"L.F."},{"family":"Müller","given":"H.A."},{"family":"Villegas","given":"N.M."},{"family":"Tamura","given":"G."},{"family":"Jiménez","given":"M."}],"citation-key":"riveraEngineeringIoTIntensiveDigital2020a","container-title":"Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020","DOI":"10.1145/3387940.3392195","ISBN":"978-1-4503-7963-2","issued":{"date-parts":[[2020]]},"page":"631-638","publisher":"Association for Computing Machinery, Inc","title":"On the Engineering of IoT-Intensive Digital Twin Software Systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093092203&doi=10.1145%2f3387940.3392195&partnerID=40&md5=ef97a6c62c8feab8c5a482a95493fd62"},
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{"id":"robillardIntroductionRecommendationSystems2014","accessed":{"date-parts":[[2017,3,8]]},"author":[{"family":"Robillard","given":"Martin P."},{"family":"Walker","given":"Robert J."}],"citation-key":"robillardIntroductionRecommendationSystems2014","container-title":"Recommendation Systems in Software Engineering","issued":{"date-parts":[[2014]]},"page":"1–11","publisher":"Springer","source":"Google Scholar","title":"An introduction to recommendation systems in software engineering","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-642-45135-5_1"},
{"id":"robillardRecommendationSystemsSoftware2010","accessed":{"date-parts":[[2017,6,8]]},"author":[{"family":"Robillard","given":"Martin"},{"family":"Walker","given":"Robert"},{"family":"Zimmermann","given":"Thomas"}],"citation-key":"robillardRecommendationSystemsSoftware2010","container-title":"IEEE software","issue":"4","issued":{"date-parts":[[2010]]},"page":"80–86","source":"Google Scholar","title":"Recommendation systems for software engineering","type":"article-journal","URL":"http://ieeexplore.ieee.org/abstract/document/5235134/","volume":"27"},
{"id":"robillardRecommendationSystemsSoftware2014","accessed":{"date-parts":[[2017,3,10]]},"citation-key":"robillardRecommendationSystemsSoftware2014","DOI":"10.1007/978-3-642-45135-5","editor":[{"family":"Robillard","given":"Martin P."},{"family":"Maalej","given":"Walid"},{"family":"Walker","given":"Robert J."},{"family":"Zimmermann","given":"Thomas"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-642-45134-8 978-3-642-45135-5","issued":{"date-parts":[[2014]]},"publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","source":"CrossRef","title":"Recommendation Systems in Software Engineering","type":"book","URL":"http://link.springer.com/10.1007/978-3-642-45135-5"},
{"id":"roblesExtensiveDatasetUML2017","accessed":{"date-parts":[[2021,5,10]]},"author":[{"family":"Robles","given":"Gregorio"},{"family":"Ho-Quang","given":"Truong"},{"family":"Hebig","given":"Regina"},{"family":"Chaudron","given":"Michel R.V."},{"family":"Fernandez","given":"Miguel Angel"}],"citation-key":"roblesExtensiveDatasetUML2017","container-title":"2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR)","DOI":"10.1109/MSR.2017.48","event":"2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR)","event-place":"Buenos Aires, Argentina","ISBN":"978-1-5386-1544-7","issued":{"date-parts":[[2017,5]]},"note":"00026","page":"519-522","publisher":"IEEE","publisher-place":"Buenos Aires, Argentina","source":"DOI.org (Crossref)","title":"An Extensive Dataset of UML Models in GitHub","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/7962411/"},
{"id":"RoboticsAutonomousSystems","accessed":{"date-parts":[[2016,8,26]]},"citation-key":"RoboticsAutonomousSystems","title":"Robotics and autonomous systems: apply for innovation funding - News stories - GOV.UK","type":"webpage","URL":"https://www.gov.uk/government/news/robotics-and-autonomous-systems-apply-for-innovation-funding"},
{"id":"RoboticsProgrammingLaboratory","accessed":{"date-parts":[[2016,1,12]]},"citation-key":"RoboticsProgrammingLaboratory","title":"Robotics Programming Laboratory","type":"webpage","URL":"http://se.inf.ethz.ch/courses/2013b_fall/rpl/#lectures"},
{"id":"roccoResilienceSiriusEditors2018","author":[{"family":"Rocco","given":"Juri Di"},{"family":"Ruscio","given":"Davide Di"},{"family":"Narayanankutty","given":"Hrishikesh"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"roccoResilienceSiriusEditors2018","collection-title":"CEUR Workshop Proceedings","container-title":"Proceedings of MODELS 2018 Workshops: ModComp, MRT, OCL, FlexMDE, EXE, COMMitMDE, MDETools, GEMOC, MORSE, MDE4IoT, MDEbug, MoDeVVa, ME, MULTI, HuFaMo, AMMoRe, PAINS co-located with ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018), Copenhagen, Denmark, October, 14, 2018","editor":[{"family":"Hebig","given":"Regina"},{"family":"Berger","given":"Thorsten"}],"issued":{"date-parts":[[2018]]},"note":"00000","page":"620–630","publisher":"CEUR-WS.org","title":"Resilience in Sirius Editors: Understanding the Impact of Metamodel Changes","type":"paper-conference","URL":"http://ceur-ws.org/Vol-2245/me_paper_6.pdf","volume":"2245"},
{"id":"roccoTopFilterApproachRecommend2020","author":[{"family":"Rocco","given":"Juri Di"},{"family":"Ruscio","given":"Davide Di"},{"family":"Sipio","given":"Claudio Di"},{"family":"Nguyen","given":"Phuong T."},{"family":"Rubei","given":"Riccardo"}],"citation-key":"roccoTopFilterApproachRecommend2020","container-title":"ESEM '20: ACM / IEEE International Symposium on Empirical Software Engineering and Measurement, Bari, Italy, October 5-7, 2020","DOI":"10.1145/3382494.3410690","editor":[{"family":"Baldassarre","given":"Maria Teresa"},{"family":"Lanubile","given":"Filippo"},{"family":"Kalinowski","given":"Marcos"},{"family":"Sarro","given":"Federica"}],"issued":{"date-parts":[[2020]]},"note":"00000","page":"21:1–21:11","publisher":"ACM","title":"TopFilter: An Approach to Recommend Relevant GitHub Topics","type":"paper-conference","URL":"https://doi.org/10.1145/3382494.3410690"},
{"id":"roccoUsingATLTransformation2016","author":[{"family":"Rocco","given":"Juri Di"},{"family":"Ruscio","given":"Davide Di"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Cuadrado","given":"Jesús Sánchez"},{"family":"Lara","given":"Juan","dropping-particle":"de"},{"family":"Guerra","given":"Esther"}],"citation-key":"roccoUsingATLTransformation2016","collection-title":"Lecture Notes in Computer Science","container-title":"Theory and Practice of Model Transformations - 9th International Conference, ICMT@STAF 2016, Vienna, Austria, July 4-5, 2016, Proceedings","DOI":"10.1007/978-3-319-42064-6_5","editor":[{"family":"Gorp","given":"Pieter Van"},{"family":"Engels","given":"Gregor"}],"issued":{"date-parts":[[2016]]},"note":"00000","page":"70–78","publisher":"Springer","title":"Using ATL Transformation Services in the MDEForge Collaborative Modeling Platform","type":"paper-conference","URL":"https://doi.org/10.1007/978-3-319-42064-6_5","volume":"9765"},
{"id":"roccoUsingATLTransformation2016a","author":[{"family":"Rocco","given":"Juri Di"},{"family":"Ruscio","given":"Davide Di"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Cuadrado","given":"Jesús Sánchez"},{"family":"Lara","given":"Juan","dropping-particle":"de"},{"family":"Guerra","given":"Esther"}],"citation-key":"roccoUsingATLTransformation2016a","collection-title":"Lecture Notes in Computer Science","container-title":"Theory and Practice of Model Transformations - 9th International Conference, ICMT@STAF 2016, Vienna, Austria, July 4-5, 2016, Proceedings","DOI":"10.1007/978-3-319-42064-6_5","editor":[{"family":"Gorp","given":"Pieter Van"},{"family":"Engels","given":"Gregor"}],"issued":{"date-parts":[[2016]]},"note":"00000","page":"70–78","publisher":"Springer","title":"Using ATL Transformation Services in the MDEForge Collaborative Modeling Platform","type":"paper-conference","URL":"https://doi.org/10.1007/978-3-319-42064-6_5","volume":"9765"},
{"id":"ROCKRobustClustering1999","citation-key":"ROCKRobustClustering1999","collection-title":"ICDE '99","container-title":"Proceedings of the 15th international conference on data engineering","event-place":"Washington, DC, USA","ISBN":"0-7695-0071-4","issued":{"date-parts":[[1999]]},"page":"512-","publisher":"IEEE Computer Society","publisher-place":"Washington, DC, USA","title":"ROCK: A robust clustering algorithm for categorical attributes","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=846218.847264"},
{"id":"rodriguez-graciaCollaborativeTestbedWeb2014","accessed":{"date-parts":[[2015,4,27]]},"author":[{"family":"Rodríguez-Gracia","given":"D."},{"family":"Criado","given":"J."},{"family":"Iribarne","given":"L."},{"family":"Padilla","given":"N."}],"citation-key":"rodriguez-graciaCollaborativeTestbedWeb2014","container-title":"Computers in Human Behavior","DOI":"10.1016/j.chb.2014.11.096","ISSN":"07475632","issued":{"date-parts":[[2014,12]]},"source":"CrossRef","title":"A collaborative testbed web tool for learning model transformation in software engineering education","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0747563214007158"},
{"id":"rodriguezMetamodelDependenciesExecutable2011","accessed":{"date-parts":[[2021,2,19]]},"author":[{"family":"Rodríguez","given":"Carlos"},{"family":"Sánchez","given":"Mario"},{"family":"Villalobos","given":"Jorge"}],"citation-key":"rodriguezMetamodelDependenciesExecutable2011","container-title":"Objects, Models, Components, Patterns","DOI":"10.1007/978-3-642-21952-8_8","editor":[{"family":"Bishop","given":"Judith"},{"family":"Vallecillo","given":"Antonio"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-642-21951-1 978-3-642-21952-8","issued":{"date-parts":[[2011]]},"note":"00000","page":"83-98","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","source":"DOI.org (Crossref)","title":"Metamodel Dependencies for Executable Models","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-642-21952-8_8","volume":"6705"},
{"id":"rohrModeldrivenDevelopmentSelfmanaging2006","accessed":{"date-parts":[[2016,9,21]]},"author":[{"family":"Rohr","given":"Matthias"},{"family":"Boskovic","given":"Marko"},{"family":"Giesecke","given":"Simon"},{"family":"Hasselbring","given":"Wilhelm"}],"citation-key":"rohrModeldrivenDevelopmentSelfmanaging2006","issued":{"date-parts":[[2006]]},"source":"Google Scholar","title":"Model-driven development of self-managing software systems","type":"article-journal","URL":"http://eprints.uni-kiel.de/14544/1/MODELS2006.pdf"},
{"id":"Rojas:1996:NNS:235222","author":[{"family":"Rojas","given":"Raúl"}],"citation-key":"Rojas:1996:NNS:235222","event-place":"Berlin, Heidelberg","ISBN":"3-540-60505-3","issued":{"date-parts":[[1996]]},"publisher":"Springer-Verlag","publisher-place":"Berlin, Heidelberg","title":"Neural networks: A systematic introduction","type":"book"},
{"id":"Rokach2005","citation-key":"Rokach2005","container-title":"Data mining and knowledge discovery handbook","DOI":"10.1007/0-387-25465-X₁5","editor":[{"family":"Maimon","given":"Oded"},{"family":"Rokach","given":"Lior"}],"event-place":"Boston, MA","ISBN":"978-0-387-25465-4","issued":{"date-parts":[[2005]]},"page":"321-352","publisher":"Springer US","publisher-place":"Boston, MA","title":"Clustering methods","type":"chapter","URL":"http://dx.doi.org/10.1007/0-387-25465-X₁5"},
{"id":"Roldán2020","abstract":"The Internet of Things (IoT) is growing globally at a fast pace: people now find themselves surrounded by a variety of IoT devices such as smartphones and wearables in their everyday lives. Additionally, smart environments, such as smart healthcare systems, smart industries and smart cities, benefit from sensors and actuators interconnected through the IoT. However, the increase in IoT devices has brought with it the challenge of promptly detecting and combating the cybersecurity attacks and threats that target them, including malware, privacy breaches and denial of service attacks, among others. To tackle this challenge, this paper proposes an intelligent architecture that integrates Complex Event Processing (CEP) technology and the Machine Learning (ML) paradigm in order to detect different types of IoT security attacks in real time. In particular, such an architecture is capable of easily managing event patterns whose conditions depend on values obtained by ML algorithms. Additionally, a model-driven graphical tool for security attack pattern definition and automatic code generation is provided, hiding all the complexity derived from implementation details from domain experts. The proposed architecture has been applied in the case of a healthcare IoT network to validate its ability to detect attacks made by malicious devices. The results obtained demonstrate that this architecture satisfactorily fulfils its objectives. © 2020 Elsevier Ltd","author":[{"family":"Roldán","given":"J."},{"family":"Boubeta-Puig","given":"J."},{"family":"Luis Martínez","given":"J."},{"family":"Ortiz","given":"G."}],"citation-key":"Roldán2020","container-title":"Expert Systems with Applications","DOI":"10.1016/j.eswa.2020.113251","ISSN":"09574174","issued":{"date-parts":[[2020]]},"publisher":"Elsevier Ltd","title":"Integrating complex event processing and machine learning: An intelligent architecture for detecting IoT security attacks","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079340111&doi=10.1016%2fj.eswa.2020.113251&partnerID=40&md5=43a93e58809b1a17db000345efc6d7ae","volume":"149"},
{"id":"rosaSelfmanagementDistributedSystems2013","accessed":{"date-parts":[[2016,9,21]]},"author":[{"family":"Rosa","given":"Liliana"},{"family":"Rodrigues","given":"Luís"},{"family":"Lopes","given":"Antónia"}],"citation-key":"rosaSelfmanagementDistributedSystems2013","container-title":"Software Engineering for Self-Adaptive Systems II","issued":{"date-parts":[[2013]]},"page":"162–190","publisher":"Springer","source":"Google Scholar","title":"Self-management of Distributed Systems Using High-Level Goal Policies","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-642-35813-5_7"},
{"id":"roseComparisonModelMigration2010","accessed":{"date-parts":[[2015,3,20]]},"author":[{"family":"Rose","given":"Louis M."},{"family":"Herrmannsdoerfer","given":"Markus"},{"family":"Williams","given":"James R."},{"family":"Kolovos","given":"Dimitrios S."},{"family":"Garcés","given":"Kelly"},{"family":"Paige","given":"Richard F."},{"family":"Polack","given":"Fiona AC"}],"citation-key":"roseComparisonModelMigration2010","container-title":"Model Driven Engineering Languages and Systems","issued":{"date-parts":[[2010]]},"page":"61–75","publisher":"Springer","source":"Google Scholar","title":"A comparison of model migration tools","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-642-16145-2_5"},
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{"id":"roughan10Lessons102011","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Roughan","given":"Matthew"},{"family":"Willinger","given":"Walter"},{"family":"Maennel","given":"Olaf"},{"family":"Perouli","given":"Debbie"},{"family":"Bush","given":"Randy"}],"citation-key":"roughan10Lessons102011","container-title":"IEEE Journal on Selected Areas in Communications","DOI":"10.1109/JSAC.2011.111006","ISSN":"0733-8716","issue":"9","issued":{"date-parts":[[2011,10]]},"page":"1810-1821","source":"CrossRef","title":"10 Lessons from 10 Years of Measuring and Modeling the Internet's Autonomous Systems","type":"article-journal","URL":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6027863","volume":"29"},
{"id":"rousseeuwSilhouettesGraphicalAid1987","abstract":"A new graphical display is proposed for partitioning techniques. Each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation. This silhouette shows which objects lie well within their cluster, and which ones are merely somewhere in between clusters. The entire clustering is displayed by combining the silhouettes into a single plot, allowing an appreciation of the relative quality of the clusters and an overview of the data configuration. The average silhouette width provides an evaluation of clustering validity, and might be used to select an ‘appropriate’ number of clusters.","author":[{"family":"Rousseeuw","given":"Peter J."}],"citation-key":"rousseeuwSilhouettesGraphicalAid1987","container-title":"Journal of Computational and Applied Mathematics","ISSN":"0377-0427","issued":{"date-parts":[[1987]]},"page":"53 - 65","title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","type":"article-journal","URL":"http://www.sciencedirect.com/science/article/pii/0377042787901257","volume":"20"},
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{"id":"roy-hubaraMethodDatabaseModel2019","accessed":{"date-parts":[[2021,2,6]]},"author":[{"family":"Roy-Hubara","given":"Noa"},{"family":"Shoval","given":"Peretz"},{"family":"Sturm","given":"Arnon"}],"citation-key":"roy-hubaraMethodDatabaseModel2019","container-title":"Enterprise, Business-Process and Information Systems Modeling","DOI":"10.1007/978-3-030-20618-5_18","editor":[{"family":"Reinhartz-Berger","given":"Iris"},{"family":"Zdravkovic","given":"Jelena"},{"family":"Gulden","given":"Jens"},{"family":"Schmidt","given":"Rainer"}],"event-place":"Cham","ISBN":"978-3-030-20617-8 978-3-030-20618-5","issued":{"date-parts":[[2019]]},"note":"00000","page":"261-275","publisher":"Springer International Publishing","publisher-place":"Cham","source":"DOI.org (Crossref)","title":"A Method for Database Model Selection","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-030-20618-5_18","volume":"352"},
{"id":"roy-hubaraModelingGraphDatabase2017","accessed":{"date-parts":[[2021,3,24]]},"author":[{"family":"Roy-Hubara","given":"Noa"},{"family":"Rokach","given":"Lior"},{"family":"Shapira","given":"Bracha"},{"family":"Shoval","given":"Peretz"}],"citation-key":"roy-hubaraModelingGraphDatabase2017","container-title":"IT Professional","container-title-short":"IT Prof.","DOI":"10.1109/MITP.2017.4241458","ISSN":"1520-9202","issue":"6","issued":{"date-parts":[[2017,11]]},"note":"00021","page":"34-43","source":"DOI.org (Crossref)","title":"Modeling Graph Database Schema","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/8123463/","volume":"19"},
{"id":"roy-hubaraQuestDatabaseSelection","abstract":"New types of database have emerged over the last decade, aimed at answering new requirements in the Big Data era. The new databases, in additional to the Relational model, may fit to specific types of applications. Therefore, new challenges have also emerged, including the issue of which database model to select for a given application, and how to design the database based on the selected model. To the best of our knowledge, these two challenges have not been addressed by any systematic method. In this research we plan to devise a structured method for database model selection and design based on variety of factors, including data-related requirements, functional requirements, and non-functional requirements. Based on these requirements the method will recommend which database models are the most appropriate for that application and will suggest a design for the recommended models.","author":[{"family":"Roy-Hubara","given":"Noa"}],"citation-key":"roy-hubaraQuestDatabaseSelection","page":"9","source":"Zotero","title":"The Quest for a Database Selection and Design Method","type":"article-journal"},
{"id":"Ru2020804","abstract":"Reliable channel estimation is a crucial task for orthogonal frequency division multiplexing (OFDM) systems to achieve high data rate. In this paper, a deep learning-based channel estimation method that combined with image super-resolution (SR) and convolutional neural network (CNN) is proposed. Using the idea of model-driven approach, the network is initialized by the least square estimation and then trained offline to extract valid features of two-dimensional channel response matrices for high accuracy channel estimates. The results show that the proposed method significantly outperforms the linear minimum mean squared error (LMMSE) estimator in mean square error (MSE) and has potential in spectrum saving. © 2020 IEEE.","author":[{"family":"Ru","given":"X."},{"family":"Wei","given":"L."},{"family":"Xu","given":"Y."}],"citation-key":"Ru2020804","collection-title":"2020 IEEE 5th International Conference on Signal and Image Processing, ICSIP 2020","DOI":"10.1109/ICSIP49896.2020.9339375","ISBN":"978-1-72816-896-8","issued":{"date-parts":[[2020]]},"page":"804-808","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Model-driven channel estimation for OFDM systems based on image super-resolution network","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101132485&doi=10.1109%2fICSIP49896.2020.9339375&partnerID=40&md5=1ff4c101cb8af63d6c6e70e2c6f316d0"},
{"id":"Rubei:ASE:2019","author":[{"family":"Rubei","given":"Riccardo"},{"family":"Di Sipio","given":"Claudio"},{"family":"Nguyen","given":"Phuong T."},{"family":"Di Rocco","given":"Juri"},{"literal":"Di Ruscio"}],"citation-key":"Rubei:ASE:2019","container-title":"34th IEEE/ACM international conference on automated software engineering, ASE 2019, san diego, california, USA, 2019","title":"Recommeding highly relevant StackOverflow posts with boosted multi-facet queries - manuscript under review","type":"paper-conference"},
{"id":"rubeiPostFinderMiningStack2020","abstract":"Context During the development of complex software systems, programmers look for external resources to understand better how to use speci c APIs and to get advice related to their current tasks. Stack Over ow provides developers with a broader insight into API usage as well as useful code examples. Given the circumstances, tools and techniques for mining Stack Over ow are highly desirable.","accessed":{"date-parts":[[2020,7,25]]},"author":[{"family":"Rubei","given":"Riccardo"},{"family":"Di Sipio","given":"Claudio"},{"family":"Nguyen","given":"Phuong T."},{"family":"Di Rocco","given":"Juri"},{"family":"Di Ruscio","given":"Davide"}],"citation-key":"rubeiPostFinderMiningStack2020","container-title":"Information and Software Technology","container-title-short":"Information and Software Technology","DOI":"10.1016/j.infsof.2020.106367","ISSN":"09505849","issued":{"date-parts":[[2020,11]]},"note":"00000","page":"106367","source":"DOI.org (Crossref)","title":"PostFinder: Mining Stack Overflow posts to support software developers","title-short":"PostFinder","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0950584920301361","volume":"127"},
{"id":"rubinDeclarativeApproachModel2008","accessed":{"date-parts":[[2015,9,24]]},"author":[{"family":"Rubin","given":"Julia"},{"family":"Chechik","given":"Marsha"},{"family":"Easterbrook","given":"Steve M."}],"citation-key":"rubinDeclarativeApproachModel2008","container-title":"Proceedings of the 2008 international workshop on Models in software engineering","issued":{"date-parts":[[2008]]},"page":"7–14","publisher":"ACM","source":"Google Scholar","title":"Declarative approach for model composition","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=1370734"},
{"id":"ruscio9thWorkshopModelling2017","author":[{"family":"Ruscio","given":"Davide Di"},{"family":"Chechik","given":"Marsha"},{"family":"Rumpe","given":"Bernhard"}],"citation-key":"ruscio9thWorkshopModelling2017","container-title":"9th IEEE/ACM International Workshop on Modelling in Software Engineering, MiSE@ICSE 2017, Buenos Aires, Argentina, May 21-22, 2017","DOI":"10.1109/MiSE.2017.15","issued":{"date-parts":[[2017]]},"note":"00000","page":"1","publisher":"IEEE","title":"9th Workshop on Modelling in Software Engineering (MiSE 2017)","type":"paper-conference","URL":"https://doi.org/10.1109/MiSE.2017.15"},
{"id":"ruscioAutomatedCoevolutionGMF2010","author":[{"family":"Ruscio","given":"Davide Di"},{"family":"Lämmel","given":"Ralf"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"ruscioAutomatedCoevolutionGMF2010","container-title":"CoRR","issued":{"date-parts":[[2010]]},"note":"00000 \n_eprint: 1006.5761","title":"Automated co-evolution of GMF editor models","type":"article-journal","URL":"http://arxiv.org/abs/1006.5761","volume":"abs/1006.5761"},
{"id":"ruscioDatamodellingApproachWeb2004","author":[{"family":"Ruscio","given":"Davide Di"},{"family":"Muccini","given":"Henry"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"ruscioDatamodellingApproachWeb2004","container-title":"Int. J. Web Eng. Technol.","DOI":"10.1504/IJWET.2004.005236","issue":"3","issued":{"date-parts":[[2004]]},"page":"320–337","title":"A data-modelling approach to web application synthesis","type":"article-journal","URL":"https://doi.org/10.1504/IJWET.2004.005236","volume":"1"},
{"id":"ruscioExtremeModellingXM2014","author":[{"family":"Ruscio","given":"Davide Di"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Lara","given":"Juan","dropping-particle":"de"}],"citation-key":"ruscioExtremeModellingXM2014","container-title":"J. Object Technol.","DOI":"10.5381/jot.2014.13.3.e1","issue":"3","issued":{"date-parts":[[2014]]},"note":"00000","title":"Extreme Modelling (XM) 2012 Special Section","type":"article-journal","URL":"https://doi.org/10.5381/jot.2014.13.3.e1","volume":"13"},
{"id":"ruscioInternationalWorkshopModel2012","author":[{"family":"Ruscio","given":"Davide Di"},{"family":"Kolovos","given":"Dimitris S."}],"citation-key":"ruscioInternationalWorkshopModel2012","container-title":"J. Object Technol.","DOI":"10.5381/jot.2012.11.3.e1","issue":"3","issued":{"date-parts":[[2012]]},"note":"00000","title":"International Workshop on Model Comparison","type":"article-journal","URL":"https://doi.org/10.5381/jot.2012.11.3.e1","volume":"11"},
{"id":"ruscioMaintainerScriptModernization2009","author":[{"family":"Ruscio","given":"Davide Di"},{"family":"Pelliccione","given":"Patrizio"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Zacchiroli","given":"Stefano"}],"citation-key":"ruscioMaintainerScriptModernization2009","container-title":"CoRR","issued":{"date-parts":[[2009]]},"note":"00000 \n_eprint: 0909.5087","title":"Towards maintainer script modernization in FOSS distributions","type":"article-journal","URL":"http://arxiv.org/abs/0909.5087","volume":"abs/0909.5087"},
{"id":"ruscioMaintainerScriptModernization2009a","author":[{"family":"Ruscio","given":"Davide Di"},{"family":"Pelliccione","given":"Patrizio"},{"family":"Pierantonio","given":"Alfonso"},{"family":"Zacchiroli","given":"Stefano"}],"citation-key":"ruscioMaintainerScriptModernization2009a","container-title":"CoRR","issued":{"date-parts":[[2009]]},"note":"_eprint: 0909.5087","title":"Towards maintainer script modernization in FOSS distributions","type":"article-journal","URL":"http://arxiv.org/abs/0909.5087","volume":"abs/0909.5087"},
{"id":"ruscioPostproceedingsSeventhSeminar2015","citation-key":"ruscioPostproceedingsSeventhSeminar2015","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Ruscio","given":"Davide Di"},{"family":"Zaytsev","given":"Vadim"}],"issued":{"date-parts":[[2015]]},"publisher":"CEUR-WS.org","title":"Post-proceedings of the Seventh Seminar on Advanced Techniques and Tools for Software Evolution, SATToSE 2014, L'Aquila, Italy, 9-11 July 2014","type":"book","URL":"http://ceur-ws.org/Vol-1354","volume":"1354"},
{"id":"ruscioProceedings2ndWorkshop2016","citation-key":"ruscioProceedings2ndWorkshop2016","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Ruscio","given":"Davide Di"},{"family":"Lara","given":"Juan","dropping-particle":"de"},{"family":"Pierantonio","given":"Alfonso"}],"issued":{"date-parts":[[2016]]},"publisher":"CEUR-WS.org","title":"Proceedings of the 2nd Workshop on Flexible Model Driven Engineering co-located with ACM/IEEE 19th International Conference on Model Driven Engineering Languages & Systems (MoDELS 2016), Saint-Malo, France, October 2, 2016","type":"book","URL":"http://ceur-ws.org/Vol-1694","volume":"1694"},
{"id":"ruscioProceedings3rdWorkshop2014","citation-key":"ruscioProceedings3rdWorkshop2014","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Ruscio","given":"Davide Di"},{"family":"Lara","given":"Juan","dropping-particle":"de"},{"family":"Pierantonio","given":"Alfonso"}],"issued":{"date-parts":[[2014]]},"publisher":"CEUR-WS.org","title":"Proceedings of the 3rd Workshop on Extreme Modeling co-located with ACM/IEEE 17th International Conference on Model Driven Engineering Languages & Systems, XM@MoDELS 2014, Valencia, Spain, September 29, 2014","type":"book","URL":"http://ceur-ws.org/Vol-1239","volume":"1239"},
{"id":"ruscioProceedingsWorkshopACadeMics2013","citation-key":"ruscioProceedingsWorkshopACadeMics2013","DOI":"10.1145/2491279","editor":[{"family":"Ruscio","given":"Davide Di"},{"family":"Kolovos","given":"Dimitris S."},{"family":"Rose","given":"Louis M."},{"family":"Al-Hilank","given":"Samir"}],"ISBN":"978-1-4503-2036-8","issued":{"date-parts":[[2013]]},"publisher":"ACM","title":"Proceedings of the workshop on ACadeMics Tooling with Eclipse, ACME@ECOOP 2013, Montpellier, France, July 2, 2013","type":"book","URL":"https://doi.org/10.1145/2491279"},
{"id":"ruscioProceedingsWorkshopFlexible2015","citation-key":"ruscioProceedingsWorkshopFlexible2015","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Ruscio","given":"Davide Di"},{"family":"Lara","given":"Juan","dropping-particle":"de"},{"family":"Pierantonio","given":"Alfonso"}],"issued":{"date-parts":[[2015]]},"note":"00000","publisher":"CEUR-WS.org","title":"Proceedings of the Workshop on Flexible Model Driven Engineering co-located with ACM/IEEE 18th International Conference on Model Driven Engineering Languages & Systems (MoDELS 2015), Ottawa, Canada, September 29, 2015","type":"book","URL":"http://ceur-ws.org/Vol-1470","volume":"1470"},
{"id":"ruscioProceedingsWorkshopScalability2013","citation-key":"ruscioProceedingsWorkshopScalability2013","editor":[{"family":"Ruscio","given":"Davide Di"},{"family":"Kolovos","given":"Dimitris S."},{"family":"Matragkas","given":"Nicholas"}],"ISBN":"978-1-4503-2165-5","issued":{"date-parts":[[2013]]},"note":"00000","publisher":"ACM","title":"Proceedings of the Workshop on Scalability in Model Driven Engineering, Budapest, Hungary, June 17, 2013","type":"book","URL":"http://dl.acm.org/citation.cfm?id=2487766"},
{"id":"ruscioTheoryPracticeModel2014","citation-key":"ruscioTheoryPracticeModel2014","collection-title":"Lecture Notes in Computer Science","DOI":"10.1007/978-3-319-08789-4","editor":[{"family":"Ruscio","given":"Davide Di"},{"family":"Varró","given":"Dániel"}],"ISBN":"978-3-319-08788-7","issued":{"date-parts":[[2014]]},"publisher":"Springer","title":"Theory and Practice of Model Transformations - 7th International Conference, ICMT@STAF 2014, York, UK, July 21-22, 2014. Proceedings","type":"book","URL":"https://doi.org/10.1007/978-3-319-08789-4","volume":"8568"},
{"id":"rymerForresterWaveLowCode2019","author":[{"family":"Rymer","given":"John R"},{"family":"Koplowitz","given":"Rob"}],"citation-key":"rymerForresterWaveLowCode2019","issued":{"date-parts":[[2019]]},"page":"17","source":"Zotero","title":"The Forrester Wave™: Low-Code Development Platforms For AD&D Professionals, Q1 2019","type":"article-journal"},
{"id":"sahaySupportingUnderstandingComparison","abstract":"Low-code development platforms (LCDPs) are easy to use visual environments that are being increasingly introduced and promoted by major IT players to permit citizen developers to build their software systems even if they lack a programming background. Understanding and evaluating the LCDP to be employed for the particular problem at hand are difficult tasks mainly because decision-makers have to choose among hundreds of heterogeneous platforms, which are difficult to evaluate without dedicated support. Thus, a detailed classification is needed to elaborate on the existing low-code platforms and to help users find out the most appropriate platforms based on their requirements.","author":[{"family":"Sahay","given":"Apurvanand"},{"family":"Indamutsa","given":"Arsene"},{"family":"Ruscio","given":"Davide Di"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"sahaySupportingUnderstandingComparison","page":"8","source":"Zotero","title":"Supporting the understanding and comparison of low-code development platforms","type":"article-journal"},
{"id":"sahaySupportingUnderstandingComparison2020","abstract":"Low-code development platforms (LCDPs) are easy to use visual environments that are being increasingly introduced and promoted by major IT players to permit citizen developers to build their software systems even if they lack a programming background. Understanding and evaluating the LCDP to be employed for the particular problem at hand are difficult tasks mainly because decision-makers have to choose among hundreds of heterogeneous platforms, which are difficult to evaluate without dedicated support. Thus, a detailed classification is needed to elaborate on the existing low-code platforms and to help users find out the most appropriate platforms based on their requirements.","author":[{"family":"Sahay","given":"Apurvanand"},{"family":"Indamutsa","given":"Arsene"},{"family":"Ruscio","given":"Davide Di"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"sahaySupportingUnderstandingComparison2020","container-title":"Euromicro Conference on Software Engineering and Advanced Applications (SEAA2020)","issued":{"date-parts":[[2020]]},"note":"00000","page":"8","source":"Zotero","title":"Supporting the understanding and comparison of low-code development platforms","type":"paper-conference"},
{"id":"sahaySupportingUnderstandingComparison2020a","accessed":{"date-parts":[[2021,4,9]]},"author":[{"family":"Sahay","given":"Apurvanand"},{"family":"Indamutsa","given":"Arsene"},{"family":"Di Ruscio","given":"Davide"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"sahaySupportingUnderstandingComparison2020a","container-title":"2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","DOI":"10.1109/SEAA51224.2020.00036","event":"2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","event-place":"Portoroz, Slovenia","ISBN":"978-1-72819-532-2","issued":{"date-parts":[[2020,8]]},"note":"00005","page":"171-178","publisher":"IEEE","publisher-place":"Portoroz, Slovenia","source":"DOI.org (Crossref)","title":"Supporting the understanding and comparison of low-code development platforms","type":"paper-conference","URL":"https://ieeexplore.ieee.org/document/9226356/"},
{"id":"sahaySupportingUnderstandingComparison2020b","accessed":{"date-parts":[[2021,5,3]]},"author":[{"family":"Sahay","given":"Apurvanand"},{"family":"Indamutsa","given":"Arsene"},{"family":"Di Ruscio","given":"Davide"},{"family":"Pierantonio","given":"Alfonso"}],"citation-key":"sahaySupportingUnderstandingComparison2020b","container-title":"2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","DOI":"10.1109/SEAA51224.2020.00036","event":"2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","event-place":"Portoroz, Slovenia","ISBN":"978-1-72819-532-2","issued":{"date-parts":[[2020,8]]},"note":"00005","page":"171-178","publisher":"IEEE","publisher-place":"Portoroz, Slovenia","source":"DOI.org (Crossref)","title":"Supporting the understanding and comparison of low-code development platforms","type":"paper-conference","URL":"https://ieeexplore.ieee.org/document/9226356/"},
{"id":"sahayUnderstandingRoleModel2020","author":[{"family":"Sahay","given":"A."},{"family":"Di Ruscio","given":"D."},{"family":"Pierantonio","given":"A."}],"citation-key":"sahayUnderstandingRoleModel2020","container-title":"Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings","DOI":"10.1145/3417990.3420197","ISBN":"978-1-4503-8135-2","issued":{"date-parts":[[2020]]},"note":"00000","page":"431–435","publisher":"Association for Computing Machinery, Inc","title":"Understanding the role of model transformation compositions in low-code development platforms","type":"paper-conference"},
{"id":"saidComparativeRecommenderSystem2014","accessed":{"date-parts":[[2021,5,3]]},"author":[{"family":"Said","given":"Alan"},{"family":"Bellogín","given":"Alejandro"}],"citation-key":"saidComparativeRecommenderSystem2014","container-title":"Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14","DOI":"10.1145/2645710.2645746","event":"the 8th ACM Conference","event-place":"Foster City, Silicon Valley, California, USA","ISBN":"978-1-4503-2668-1","issued":{"date-parts":[[2014]]},"note":"00175","page":"129-136","publisher":"ACM Press","publisher-place":"Foster City, Silicon Valley, California, USA","source":"DOI.org (Crossref)","title":"Comparative recommender system evaluation: benchmarking recommendation frameworks","title-short":"Comparative recommender system evaluation","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?doid=2645710.2645746"},
{"id":"Saied2015Could","author":[{"family":"Saied","given":"Mohamed Aymen"},{"family":"Abdeen","given":"Hani"},{"family":"Benomar","given":"Omar"},{"family":"Sahraoui","given":"Houari"}],"citation-key":"Saied2015Could","container-title":"23rd international conference on program comprehension","event-place":"Piscataway","issued":{"date-parts":[[2015]]},"page":"71-81","publisher":"IEEE","publisher-place":"Piscataway","title":"Could we infer unordered API usage patterns only using the library source code?","type":"paper-conference"},
{"id":"SAIED2018164","abstract":"Modern software systems are increasingly dependent on third-party libraries. It is widely recognized that using mature and well-tested third-party libraries can improve developers’ productivity, reduce time-to-market, and produce more reliable software. Today’s open-source repositories provide a wide range of libraries that can be freely downloaded and used. However, as software libraries are documented separately but intended to be used together, developers are unlikely to fully take advantage of these reuse opportunities. In this paper, we present a novel approach to automatically identify third-party library usage patterns, i.e., collections of libraries that are commonly used together by developers. Our approach employs a hierarchical clustering technique to group together software libraries based on external client usage. To evaluate our approach, we mined a large set of over 6000 popular libraries from Maven Central Repository and investigated their usage by over 38,000 client systems from the Github repository. Our experiments show that our technique is able to detect the majority (77%) of highly %consistent and cohesive library usage patterns across a considerable %number %of client systems.","author":[{"family":"Saied","given":"Mohamed Aymen"},{"family":"Ouni","given":"Ali"},{"family":"Sahraoui","given":"Houari"},{"family":"Kula","given":"Raula Gaikovina"},{"family":"Inoue","given":"Katsuro"},{"family":"Lo","given":"David"}],"citation-key":"SAIED2018164","container-title":"Journal of Systems and Software","ISSN":"0164-1212","issued":{"date-parts":[[2018]]},"page":"164 - 179","title":"Improving reusability of software libraries through usage pattern mining","type":"article-journal","URL":"http://www.sciencedirect.com/science/article/pii/S0164121218301699","volume":"145"},
{"id":"saiedMiningMultilevelAPI2015","author":[{"family":"Saied","given":"M. A."},{"family":"Benomar","given":"O."},{"family":"Abdeen","given":"H."},{"family":"Sahraoui","given":"H."}],"citation-key":"saiedMiningMultilevelAPI2015","container-title":"22nd international conference on software analysis, evolution, and reengineering","event-place":"Piscataway","ISSN":"1534-5351","issued":{"date-parts":[[2015]]},"page":"23-32","publisher":"IEEE","publisher-place":"Piscataway","title":"Mining multi-level API usage patterns","type":"paper-conference"},
{"id":"Saini2019714","abstract":"In Model-Driven Engineering (MDE), models are used to build and analyze complex systems. In the last decades, different modelling formalisms have been proposed for supporting software development. However, their adoption and practice strongly rely on mastering essential modelling skills to develop a complete and coherent model-based system. Moreover, it is often difficult for novice modellers to get direct and timely feedback and recommendations on their modelling strategies and decisions, particularly in large classroom settings which hinders their learning. Certainly, there is an opportunity to apply Artificial Intelligence (AI) techniques to an MDE learning environment to empower the provisioning of automated and intelligent modelling advocacy. In this paper, we propose a framework called ModBud (a modelling buddy) to educate novice modellers about the art of abstraction. ModBud uses natural language processing (NLP) and machine learning (ML) to create modelling bots with the aim of improving the modelling skills of novice modellers and assisting other practitioners, too. These bots could be used to support teaching with automatic creation or grading of models and enhance learning beyond the traditional classroom-based MDE education with timely feedback and personalized tutoring. Research challenges for the proposed framework are discussed and a research roadmap is presented. © 2019 IEEE.","author":[{"family":"Saini","given":"R."},{"family":"Mussbacher","given":"G."},{"family":"Guo","given":"J.L.C."},{"family":"Kienzle","given":"J."}],"citation-key":"Saini2019714","collection-title":"Proceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2019","DOI":"10.1109/MODELS-C.2019.00108","editor":[{"family":"Burgueno L., Burgueno L.","given":"Pretschner A.","suffix":"Voss S., Chaudron M., Kienzle J., Volter M., Gerard S., Zahedi M., Bousse E., Rensink A., Polack F., Engels G., Kappel G."}],"ISBN":"978-1-72815-125-0","issued":{"date-parts":[[2019]]},"page":"714-719","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Teaching modelling literacy: An artificial intelligence approach","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075956606&doi=10.1109%2fMODELS-C.2019.00108&partnerID=40&md5=fb6e84b5d44979f4864c04e1e9197de7"},
{"id":"Saini2020334","abstract":"Model-Driven Software Engineering encompasses various modelling formalisms for supporting software development. One such formalism is domain modelling which bridges the gap between requirements expressed in natural language and analyzable and more concise domain models expressed in class diagrams. Due to the lack of modelling skills among novice modellers and time constraints in industrial projects, it is often not possible to build an accurate domain model manually. To address this challenge, we aim to develop an approach to extract domain models from problem descriptions written in natural language by combining rules based on natural language processing with machine learning. As a first step, we report on an automated and tool-supported approach with an accuracy of extracted domain models higher than existing approaches. In addition, the approach generates trace links for each model element of a domain model. The trace links enable novice modellers to execute queries on the extracted domain models to gain insights into the modelling decisions taken for improving their modelling skills. Furthermore, to evaluate our approach, we propose a novel comparison metric and discuss our experimental design. Finally, we present a research agenda detailing research directions and discuss corresponding challenges. © 2020 IEEE.","author":[{"family":"Saini","given":"R."},{"family":"Mussbacher","given":"G."},{"family":"Guo","given":"J.L.C."},{"family":"Kienzle","given":"J."}],"citation-key":"Saini2020334","collection-title":"Proceedings of the IEEE International Conference on Requirements Engineering","DOI":"10.1109/RE48521.2020.00044","editor":[{"family":"Breaux T., Zisman A.","given":"Fricker S.","suffix":"Glinz M."}],"ISBN":"978-1-72817-438-9","ISSN":"1090705X","issued":{"date-parts":[[2020]]},"page":"334-339","publisher":"IEEE Computer Society","title":"Towards queryable and traceable domain models","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093961426&doi=10.1109%2fRE48521.2020.00044&partnerID=40&md5=2b277ed837d1cb77673ace1ecc76293e","volume":"2020-August"},
{"id":"sainiAutomatedInteractiveTraceable2022","accessed":{"date-parts":[[2022,5,24]]},"author":[{"family":"Saini","given":"Rijul"},{"family":"Mussbacher","given":"Gunter"},{"family":"Guo","given":"Jin L. C."},{"family":"Kienzle","given":"Jörg"}],"citation-key":"sainiAutomatedInteractiveTraceable2022","container-title":"Software and Systems Modeling","container-title-short":"Softw Syst Model","DOI":"10.1007/s10270-021-00942-6","ISSN":"1619-1366, 1619-1374","issue":"3","issued":{"date-parts":[[2022,6]]},"page":"1015-1045","source":"DOI.org (Crossref)","title":"Automated, interactive, and traceable domain modelling empowered by artificial intelligence","type":"article-journal","URL":"https://link.springer.com/10.1007/s10270-021-00942-6","volume":"21"},
{"id":"sainiQueryableTraceableDomain2020a","abstract":"Model-Driven Software Engineering encompasses various modelling formalisms for supporting software development. One such formalism is domain modelling which bridges the gap between requirements expressed in natural language and analyzable and more concise domain models expressed in class diagrams. Due to the lack of modelling skills among novice modellers and time constraints in industrial projects, it is often not possible to build an accurate domain model manually. To address this challenge, we aim to develop an approach to extract domain models from problem descriptions written in natural language by combining rules based on natural language processing with machine learning. As a first step, we report on an automated and tool-supported approach with an accuracy of extracted domain models higher than existing approaches. In addition, the approach generates trace links for each model element of a domain model. The trace links enable novice modellers to execute queries on the extracted domain models to gain insights into the modelling decisions taken for improving their modelling skills. Furthermore, to evaluate our approach, we propose a novel comparison metric and discuss our experimental design. Finally, we present a research agenda detailing research directions and discuss corresponding challenges. © 2020 IEEE.","author":[{"family":"Saini","given":"R."},{"family":"Mussbacher","given":"G."},{"family":"Guo","given":"J.L.C."},{"family":"Kienzle","given":"J."}],"citation-key":"sainiQueryableTraceableDomain2020a","container-title":"Proceedings of the IEEE International Conference on Requirements Engineering","DOI":"10.1109/RE48521.2020.00044","editor":[{"family":"Breaux T.","given":"Glinz M.","suffix":"Zisman A., Fricker S."}],"ISBN":"978-1-72817-438-9","ISSN":"1090705X","issued":{"date-parts":[[2020]]},"page":"334-339","publisher":"IEEE Computer Society","title":"Towards Queryable and Traceable Domain Models","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093961426&doi=10.1109%2fRE48521.2020.00044&partnerID=40&md5=2b277ed837d1cb77673ace1ecc76293e","volume":"2020-August"},
{"id":"sainiTeachingModellingLiteracy2019","abstract":"In Model-Driven Engineering (MDE), models are used to build and analyze complex systems. In the last decades, different modelling formalisms have been proposed for supporting software development. However, their adoption and practice strongly rely on mastering essential modelling skills to develop a complete and coherent model-based system. Moreover, it is often difficult for novice modellers to get direct and timely feedback and recommendations on their modelling strategies and decisions, particularly in large classroom settings which hinders their learning. Certainly, there is an opportunity to apply Artificial Intelligence (AI) techniques to an MDE learning environment to empower the provisioning of automated and intelligent modelling advocacy. In this paper, we propose a framework called ModBud (a modelling buddy) to educate novice modellers about the art of abstraction. ModBud uses natural language processing (NLP) and machine learning (ML) to create modelling bots with the aim of improving the modelling skills of novice modellers and assisting other practitioners, too. These bots could be used to support teaching with automatic creation or grading of models and enhance learning beyond the traditional classroom-based MDE education with timely feedback and personalized tutoring. Research challenges for the proposed framework are discussed and a research roadmap is presented.","accessed":{"date-parts":[[2022,5,24]]},"author":[{"family":"Saini","given":"Rijul"},{"family":"Mussbacher","given":"Gunter"},{"family":"Guo","given":"Jin L.C."},{"family":"Kienzle","given":"Joerg"}],"citation-key":"sainiTeachingModellingLiteracy2019","container-title":"2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)","DOI":"10.1109/MODELS-C.2019.00108","event":"2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)","event-place":"Munich, Germany","ISBN":"978-1-72815-125-0","issued":{"date-parts":[[2019,9]]},"page":"714-719","publisher":"IEEE","publisher-place":"Munich, Germany","source":"DOI.org (Crossref)","title":"Teaching Modelling Literacy: An Artificial Intelligence Approach","title-short":"Teaching Modelling Literacy","type":"paper-conference","URL":"https://ieeexplore.ieee.org/document/8904688/"},
{"id":"sainiTeachingModellingLiteracy2019b","abstract":"In Model-Driven Engineering (MDE), models are used to build and analyze complex systems. In the last decades, different modelling formalisms have been proposed for supporting software development. However, their adoption and practice strongly rely on mastering essential modelling skills to develop a complete and coherent model-based system. Moreover, it is often difficult for novice modellers to get direct and timely feedback and recommendations on their modelling strategies and decisions, particularly in large classroom settings which hinders their learning. Certainly, there is an opportunity to apply Artificial Intelligence (AI) techniques to an MDE learning environment to empower the provisioning of automated and intelligent modelling advocacy. In this paper, we propose a framework called ModBud (a modelling buddy) to educate novice modellers about the art of abstraction. ModBud uses natural language processing (NLP) and machine learning (ML) to create modelling bots with the aim of improving the modelling skills of novice modellers and assisting other practitioners, too. These bots could be used to support teaching with automatic creation or grading of models and enhance learning beyond the traditional classroom-based MDE education with timely feedback and personalized tutoring. Research challenges for the proposed framework are discussed and a research roadmap is presented. © 2019 IEEE.","author":[{"family":"Saini","given":"R."},{"family":"Mussbacher","given":"G."},{"family":"Guo","given":"J.L.C."},{"family":"Kienzle","given":"J."}],"citation-key":"sainiTeachingModellingLiteracy2019b","container-title":"Proceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2019","DOI":"10.1109/MODELS-C.2019.00108","editor":[{"family":"Burgueno L.","given":"Kappel G.","suffix":"Burgueno L., Pretschner A., Voss S., Chaudron M., Kienzle J., Volter M., Gerard S., Zahedi M., Bousse E., Rensink A., Polack F., Engels G."}],"ISBN":"978-1-72815-125-0","issued":{"date-parts":[[2019]]},"page":"714-719","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Teaching modelling literacy: An artificial intelligence approach","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075956606&doi=10.1109%2fMODELS-C.2019.00108&partnerID=40&md5=fb6e84b5d44979f4864c04e1e9197de7"},
{"id":"Sakai2017","abstract":"We developed a method to automatically generate humanlike trunk motions based on speech (i.e., the neck and waist motions involved in speech) for a conversational android from its speech in real time. To generate humanlike movements, the android's mechanical limitation (i.e., limited number of joints) needs to be compensated for. By enforcing the synchronization of speech and motion in the android, the method enables us to compensate for its mechanical limitations. Moreover, motion can be modulated to express emotions by tuning the parameters in the dynamical model. This method is based on a spring-damper dynamical model driven by voice features to simulate the human trunk movements involved in speech. In contrast to the existing methods based on machine learning, our system can easily modulate the motions generated due to speech patterns because the model's parameters correspond to muscle stiffness. The experimental results show that the android motions generated by our model can be perceived as more natural and thus motivate users to talk longer with it compared to a system that simply copies human motions. In addition, our model generates emotional speech motions by tuning its parameters. © 2017 Sakai, Minato, Ishi and Ishiguro.","author":[{"family":"Sakai","given":"K."},{"family":"Minato","given":"T."},{"family":"Ishi","given":"C.T."},{"family":"Ishiguro","given":"H."}],"citation-key":"Sakai2017","container-title":"Frontiers Robotics AI","DOI":"10.3389/frobt.2017.00049","ISSN":"22969144","issue":"OCT","issued":{"date-parts":[[2017]]},"publisher":"Frontiers Media S.A.","title":"Novel speech motion generation by modeling dynamics of human speech production","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056753103&doi=10.3389%2ffrobt.2017.00049&partnerID=40&md5=e1d12ae941825d4abfa7515ae68e86cc","volume":"4"},
{"id":"sakaiNovelSpeechMotion2017a","abstract":"We developed a method to automatically generate humanlike trunk motions based on speech (i.e., the neck and waist motions involved in speech) for a conversational android from its speech in real time. To generate humanlike movements, the android's mechanical limitation (i.e., limited number of joints) needs to be compensated for. By enforcing the synchronization of speech and motion in the android, the method enables us to compensate for its mechanical limitations. Moreover, motion can be modulated to express emotions by tuning the parameters in the dynamical model. This method is based on a spring-damper dynamical model driven by voice features to simulate the human trunk movements involved in speech. In contrast to the existing methods based on machine learning, our system can easily modulate the motions generated due to speech patterns because the model's parameters correspond to muscle stiffness. The experimental results show that the android motions generated by our model can be perceived as more natural and thus motivate users to talk longer with it compared to a system that simply copies human motions. In addition, our model generates emotional speech motions by tuning its parameters. © 2017 Sakai, Minato, Ishi and Ishiguro.","author":[{"family":"Sakai","given":"K."},{"family":"Minato","given":"T."},{"family":"Ishi","given":"C.T."},{"family":"Ishiguro","given":"H."}],"citation-key":"sakaiNovelSpeechMotion2017a","container-title":"Frontiers Robotics AI","DOI":"10.3389/frobt.2017.00049","ISSN":"22969144","issue":"OCT","issued":{"date-parts":[[2017]]},"publisher":"Frontiers Media S.A.","title":"Novel speech motion generation by modeling dynamics of human speech production","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056753103&doi=10.3389%2ffrobt.2017.00049&partnerID=40&md5=e1d12ae941825d4abfa7515ae68e86cc","volume":"4"},
{"id":"salaDebtHunterMachineLearningbased2021","abstract":"Due to limited time, budget or resources, a team is prone to introduce code that does not follow the best software development practices. This code that introduces instability in the software projects is known as Technical Debt (TD). Often, TD intentionally manifests in source code, which is known as Self-Admitted Technical Debt (SATD). This paper presents DebtHunter, a natural language processing (NLP)- and machine learning (ML)- based approach for identifying and classifying SATD in source code comments. The proposed classification approach combines two classification phases for differentiating between the multiple debt types. Evaluations over 10 open source systems, containing more than 259k comments, showed that the approach was able to improve the performance of others in the literature. The presented approach is supported by a tool that can help developers to effectively manage SATD. The tool complements the analysis over Java source code by allowing developers to also examine the associated issue tracker. DebtHunter can be used in a continuous evolution environment to monitor the development process and make developers aware of how and where SATD is introduced, thus helping them to manage and resolve it.","author":[{"family":"Sala","given":"Irene"},{"family":"Tommasel","given":"Antonela"},{"family":"Fontana","given":"Francesca Arcelli"}],"citation-key":"salaDebtHunterMachineLearningbased2021","issued":{"date-parts":[[2021]]},"note":"00000","page":"6","source":"Zotero","title":"DebtHunter: A Machine Learning-based Approach for Detecting Self-Admitted Technical Debt","type":"article-journal"},
{"id":"salaDebtHunterMachineLearningbased2021a","accessed":{"date-parts":[[2021,10,17]]},"author":[{"family":"Sala","given":"Irene"},{"family":"Tommasel","given":"Antonela"},{"family":"Arcelli Fontana","given":"Francesca"}],"citation-key":"salaDebtHunterMachineLearningbased2021a","container-title":"Evaluation and Assessment in Software Engineering","DOI":"10.1145/3463274.3464455","event":"EASE 2021: Evaluation and Assessment in Software Engineering","event-place":"Trondheim Norway","ISBN":"978-1-4503-9053-8","issued":{"date-parts":[[2021,6,21]]},"note":"00000","page":"278-283","publisher":"ACM","publisher-place":"Trondheim Norway","source":"DOI.org (Crossref)","title":"DebtHunter: A Machine Learning-based Approach for Detecting Self-Admitted Technical Debt","title-short":"DebtHunter","type":"paper-conference","URL":"https://dl.acm.org/doi/10.1145/3463274.3464455"},
{"id":"salehieSelfadaptiveSoftwareLandscape2009","accessed":{"date-parts":[[2016,1,12]]},"author":[{"family":"Salehie","given":"Mazeiar"},{"family":"Tahvildari","given":"Ladan"}],"citation-key":"salehieSelfadaptiveSoftwareLandscape2009","container-title":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","issue":"2","issued":{"date-parts":[[2009]]},"page":"14","source":"Google Scholar","title":"Self-adaptive software: Landscape and research challenges","title-short":"Self-adaptive software","type":"article-journal","URL":"http://dl.acm.org/citation.cfm?id=1516538","volume":"4"},
{"id":"Salemi2016","abstract":"Simulation metamodeling is building a statistical model based on simulation output as an approximation to the system performance measure being estimated by the simulation model. In high-dimensional metamodeling problems, larger numbers of design points are needed to build an accurate and precise metamodel. Metamodeling techniques that are functions of all of these design points experience difficulties because of numerical instabilities and high computation times. We introduce a procedure to implement a local smoothing method called Moving Least Squares (MLS) regression in high-dimensional stochastic simulation metamodeling problems. Although MLS regression is known to work well when there are a very large number of design points, current procedures are focused on two- and three-dimensional cases. Furthermore, our procedure accounts for the fact that we can make replications and control the placement of design points in stochastic simulation. We provide a bound on the expected approximation error, show that the MLS predictor is consistent under certain conditions, and test the procedure with two examples that demonstrate better results than other existing simulation metamodeling techniques. © 2016 ACM.","author":[{"family":"Salemi","given":"P."},{"family":"Nelson","given":"B.L."},{"family":"Staum","given":"J."}],"citation-key":"Salemi2016","container-title":"ACM Transactions on Modeling and Computer Simulation","DOI":"10.1145/2724708","ISSN":"10493301","issue":"3","issued":{"date-parts":[[2016]]},"publisher":"Association for Computing Machinery","title":"Moving least squares regression for high-dimensional stochastic simulation metamodeling","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954484012&doi=10.1145%2f2724708&partnerID=40&md5=0d6cea95be2964d5fd20e4c5e68b7ddf","volume":"26"},
{"id":"samadControlSystemsInternet2016","accessed":{"date-parts":[[2016,11,1]]},"author":[{"family":"Samad","given":"Tariq"}],"citation-key":"samadControlSystemsInternet2016","container-title":"IEEE Control Systems","DOI":"10.1109/MCS.2015.2495022","ISSN":"1066-033X","issue":"1","issued":{"date-parts":[[2016,2]]},"page":"13-16","source":"CrossRef","title":"Control Systems and the Internet of Things [Technical Activities]","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7393961/","volume":"36"},
{"id":"Samea2018388","abstract":"Signatures are one of the most important behavioral biometric feature which are used to recognize an individual identity. These handwritten signatures are captured as actual input signals that are written on some electronic gadgets by the user. The divergent writing patterns of individuals primarily due to variation in style, shape and steadiness create real time challenges in differentiating real signatures from the fake ones. In order to overcome the said challenge of signature recognition, this article introduces model driven approach for dynamic signature verification. Particularly, a UMLPDSV (Unified Modeling Language Profile for Dynamic Signature Verification) has been proposed to specify the signature verification requirements at high abstraction level. This provides the basis to automatically generate target models of different machine learning tools (e.g. RapidMiner process, Matlab code etc.) to perform dynamic signature verification. The applicability of UMLPDSV has been validated through internet banking case study. © 2018, Springer Nature Switzerland AG.","author":[{"family":"Samea","given":"F."},{"family":"Anwar","given":"M.W."},{"family":"Azam","given":"F."},{"family":"Khan","given":"M."},{"family":"Shinwari","given":"M.F."}],"citation-key":"Samea2018388","container-title":"Communications in Computer and Information Science","DOI":"10.1007/978-3-319-99972-2_32","editor":[{"family":"Damasevicius R.","given":"Vasiljeviene G."}],"ISBN":"9783319999715","ISSN":"18650929","issued":{"date-parts":[[2018]]},"page":"388-398","publisher":"Springer Verlag","title":"An introduction to UMLPDSV for real-time dynamic signature verification","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053631632&doi=10.1007%2f978-3-319-99972-2_32&partnerID=40&md5=5e777bb2a0ef86eaf2483f2285e51979","volume":"920"},
{"id":"Samuel_2019","abstract":"Understandability and reproducibility of scientific results are vital in every field of science. Several reproducibility measures are being taken to make the data used in the publications findable and accessible. However, there are many challenges faced by scientists from the beginning of an experiment to the end in particular for data management. The explosive growth of heterogeneous research data and understanding how this data has been derived is one of the research problems faced in this context. Interlinking the data, the steps and the results from the computational and non-computational processes of a scientific experiment is important for the reproducibility. We introduce the notion of end-to-end provenance management’’ of scientific experiments to help scientists understand and reproduce the experimental results. The main contributions of this thesis are: (1) We propose a provenance modelREPRODUCE-ME’’ to describe the scientific experiments using semantic web technologies by extending existing standards. (2) We study computational reproducibility and important aspects required to achieve it. (3) Taking into account the REPRODUCE-ME provenance model and the study on computational reproducibility, we introduce our tool, ProvBook, which is designed and developed to demonstrate computational reproducibility. It provides features to capture and store provenance of Jupyter notebooks and helps scientists to compare and track their results of different executions. (4) We provide a framework, CAESAR (CollAborative Environment for Scientific Analysis with Reproducibility) for the end-to-end provenance management. This collaborative framework allows scientists to capture, manage, query and visualize the complete path of a scientific experiment consisting of computational and non-computational steps in an interoperable way. We apply our contributions to a set of scientific experiments in microscopy research projects.","author":[{"family":"Samuel","given":"Sheeba"}],"citation-key":"Samuel_2019","DOI":"10.22032/dbt.40396","event-place":"Jena","issued":{"date-parts":[[2019]]},"note":"00002","publisher-place":"Jena","title":"A provenance-based semantic approach to support understandability, reproducibility, and reuse of scientific experiments","type":"thesis","URL":"https://www.db-thueringen.de/receive/dbt_mods_00040396"},
{"id":"sanchez-cuadradoBottomUpMetaModellingInteractive2012","author":[{"family":"Sánchez-Cuadrado","given":"Jesús"},{"family":"Lara","given":"Juan"},{"family":"Guerra","given":"Esther"}],"citation-key":"sanchez-cuadradoBottomUpMetaModellingInteractive2012","container-title":"Model Driven Engineering Languages and Systems","DOI":"10.1007/978-3-642-33666-9_2","issued":{"date-parts":[[2012]]},"page":"3–19","title":"Bottom-Up Meta-Modelling: An Interactive Approach","type":"article-journal","volume":"7590"},
{"id":"sanchezBuildingModularYAWL2012","abstract":"Nowadays, novel strategies to develop and adapt workflow engines in efficient ways are required in order to have BPM and workflow solutions with the capacity to support frequent changes in the corporate environment. One key strategy is to build new engines by reusing as much as possible from existing components. This requires two things. Firstly, the mechanisms and technologies to build a library of reusable, extensible and adaptable workflow components. And secondly, a platform to integrate those components and form full applications. In this paper we show that Cumbia, being a platform for the development of workflow engines based on the modularisation of workflows according to concerns, suits this task. This is illustrated with YOC, a Cumbia-based implementation of YAWL.","accessed":{"date-parts":[[2021,2,21]]},"author":[{"family":"Sanchez","given":"Mario"},{"family":"Puentes","given":"Diana"},{"family":"Villalobos","given":"Jorge"}],"citation-key":"sanchezBuildingModularYAWL2012","container-title":"International Journal of Business Process Integration and Management","container-title-short":"IJBPIM","DOI":"10.1504/IJBPIM.2012.047912","ISSN":"1741-8763, 1741-8771","issue":"1","issued":{"date-parts":[[2012]]},"note":"00000","page":"41","source":"DOI.org (Crossref)","title":"Building a modular YAWL engine with Cumbia","type":"article-journal","URL":"http://www.inderscience.com/link.php?id=47912","volume":"6"},
{"id":"sanchezcuadradoApproachesModelTransformation2008","author":[{"family":"Sánchez Cuadrado","given":"Jesús"},{"family":"García Molina","given":"Jesús"}],"citation-key":"sanchezcuadradoApproachesModelTransformation2008","container-title":"Theory and Practice of Model Transformations","DOI":"10.1007/978-3-540-69927-9_12","issued":{"date-parts":[[2008]]},"page":"168–182","title":"Approaches for Model Transformation Reuse: Factorization and Composition","type":"article-journal","volume":"5063"},
{"id":"sanchezcuadradoComponentModelModel2014","abstract":"Model-driven engineering promotes an active use of models to conduct the software development process. In this way, models are used to specify, simulate, verify, test and generate code for the final systems. Model transformations are key enablers for this approach, being used to manipulate instance models of a certain modelling language. However, while other development paradigms make available techniques to increase productivity through reutilization, there are few proposals for the reuse of model transformations across different modelling languages. As a result, transformations have to be developed from scratch even if other similar ones exist. In this paper, we propose a technique for the flexible reutilization of model transformations. Our proposal is based on generic programming for the definition and instantiation of transformation templates, and on component-based development for the encapsulation and composition of transformations. We have designed a component model for model transformations, supported by an implementation currently targeting the Atlas Transformation Language (ATL). To evaluate its reusability potential, we report on a generic transformation component to analyse workflow models through their transformation into Petri nets, which we have reused for eight workflow languages, including UML Activity Diagrams, YAWL and two versions of BPMN.","author":[{"family":"Sanchez Cuadrado","given":"J."},{"family":"Guerra","given":"E."},{"family":"De Lara","given":"J."}],"citation-key":"sanchezcuadradoComponentModelModel2014","container-title":"IEEE Transactions on Software Engineering","DOI":"10.1109/TSE.2014.2339852","ISSN":"0098-5589","issue":"11","issued":{"date-parts":[[2014,11]]},"page":"1042-1060","source":"IEEE Xplore","title":"A Component Model for Model Transformations","type":"article-journal","volume":"40"},
{"id":"sanchezSemanticbasedPrivacySettings2020","abstract":"By 2020, an individual is expected to own an average of 6.58 devices that share and integrate a wealth of personal user data. The management of privacy preferences across these devices is a complex task for which users are ill-equipped, which increases privacy risks. In this paper we propose an approach that exploits Semantic Web (SW) technology to manage the user’s IoT privacy preferences and negotiate the permissions for data sharing with third parties. SW technology comprises a web of data that can be processed by machines through a formal, universally shared representation. In our approach, SW enables a lightweight and interoperable communication between a Personal Data Manager (PDM) and the Third Parties (TPs) that request access to the user’s personal data. The PDM can handle multiple heterogeneous personal IoT devices and manages the negotiation process between the user and the TPs in a way that can relieve users from the burden of specifying their privacy requirement for each TP. The core of the approach is the definition of the Privacy Preference for IoT (PPIoT) Ontology which is based on the Privacy Preference Ontology, the W3C Semantic Sensor Network Ontology, the Fair Information Practices (FIP) principles, and state-of-the-art recommendation techniques for privacy protection in the IoT. This ontology aims to capture the complexity of privacy management in the IoT paradigm in light of the recent General Data Protection Regulation (GDPR) of the European Union. Along with presenting the ontology, in this paper we will provide an example on how to use the PPIoT ontology for the management of privacy preferences in the fitness IoT domain and we will show how the PDM handles the process of negotiation between the user and the TPs. The approach is based on an interactive PPIoT-based Privacy Preference Model (PPM) that meets the requirements of the GDPR to have transparent and simple TP privacy policies. Finally, we will report the results of an evaluation on a mockup fitness app that implements this PPM. The main contributions of this paper are: (i) to propose an ontology for privacy preference in the IoT context, which covers a knowledge gap in existing literature and can be used for IoT privacy management, (ii) to propose an interactive PPIoT-based Privacy Preference Model, which is in accordance with the GDPR objectives.","accessed":{"date-parts":[[2021,3,31]]},"author":[{"family":"Sanchez","given":"Odnan Ref"},{"family":"Torre","given":"Ilaria"},{"family":"Knijnenburg","given":"Bart P."}],"citation-key":"sanchezSemanticbasedPrivacySettings2020","container-title":"Future Generation Computer Systems","container-title-short":"Future Generation Computer Systems","DOI":"10.1016/j.future.2019.10.024","ISSN":"0167739X","issued":{"date-parts":[[2020,10]]},"note":"00003","page":"879-898","source":"DOI.org (Crossref)","title":"Semantic-based privacy settings negotiation and management","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0167739X18317035","volume":"111"},
{"id":"sandhuBigDataCloud2022","abstract":"With the recent advancements in computer technologies, the amount of data available is increasing day by day. However, excessive amounts of data create great challenges for users. Meanwhile, cloud computing services provide a powerful environment to store large volumes of data. They eliminate various requirements, such as dedicated space and maintenance of expensive computer hardware and software. Handling big data is a time-consuming task that requires large computational clusters to ensure successful data storage and processing. In this work, the definition, classification, and characteristics of big data are discussed, along with various cloud services, such as Microsoft Azure, Google Cloud, Amazon Web Services, International Business Machine cloud, Hortonworks, and MapR. A comparative analysis of various cloud-based big data frameworks is also performed. Various research challenges are defined in terms of distributed database storage, data security, heterogeneity, and data visualization.","author":[{"family":"Sandhu","given":"Amanpreet Kaur"}],"citation-key":"sandhuBigDataCloud2022","container-title":"Big Data Mining and Analytics","issued":{"date-parts":[[2022]]},"note":"00000","page":"9","source":"Zotero","title":"Big Data with Cloud Computing: Discussions and Challenges","type":"article-journal"},
{"id":"sandhuIntegrationArtificialIntelligence2021","accessed":{"date-parts":[[2021,3,6]]},"author":[{"family":"Sandhu","given":"Amandeep Kaur"},{"family":"Batth","given":"Ranbir Singh"}],"citation-key":"sandhuIntegrationArtificialIntelligence2021","container-title":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","DOI":"10.1109/ICCAKM50778.2021.9357738","event":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","event-place":"Dubai, United Arab Emirates","ISBN":"978-1-72819-491-2","issued":{"date-parts":[[2021,1,19]]},"note":"00000","page":"357-362","publisher":"IEEE","publisher-place":"Dubai, United Arab Emirates","source":"DOI.org (Crossref)","title":"Integration of Artificial Intelligence into software reuse: An overview of Software Intelligence","title-short":"Integration of Artificial Intelligence into software reuse","type":"paper-conference","URL":"https://ieeexplore.ieee.org/document/9357738/"},
{"id":"Sankaran201747","abstract":"Traditional software engineering programming paradigms are mostly object or procedure oriented, driven by deterministic algorithms. With the advent of deep learning and cognitive sciences there is an emerging trend for data-driven programming, creating a shift in the programming paradigm among the software engineering communities. Visualizing and interpreting the execution of a current large scale data-driven software development is challenging. Further, for deep learning development there are many libraries in multiple programming languages such as TensorFlow (Python), CAFFE (C++), Theano (Python), Torch (Lua), and Deeplearning4j (Java), driving a huge need for interoperability across libraries. We propose a model driven development based solution framework, that facilitates intuitive designing of deep learning models in a platform agnostic fashion. This framework could potentially generate library specific code, perform program translation across languages, and debug the training process of a deep learning model from a fault localization and repair perspective. Further we identify open research problems in this emerging domain, and discuss some new software tooling requirements to serve this new age data-driven programming paradigm. © 2017 IEEE.","author":[{"family":"Sankaran","given":"A."},{"family":"Aralikatte","given":"R."},{"family":"Mani","given":"S."},{"family":"Khare","given":"S."},{"family":"Panwar","given":"N."},{"family":"Gantayat","given":"N."}],"citation-key":"Sankaran201747","collection-title":"Proceedings - 2017 IEEE/ACM 39th International Conference on Software Engineering: New Ideas and Emerging Results Track, ICSE-NIER 2017","DOI":"10.1109/ICSE-NIER.2017.13","ISBN":"978-1-5386-2675-7","issued":{"date-parts":[[2017]]},"page":"47-50","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"DARVIZ: Deep abstract representation, visualization, and verification of deep learning models","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026730806&doi=10.1109%2fICSE-NIER.2017.13&partnerID=40&md5=8b7d2a471beccbb90ba2ad78df2425a8"},
{"id":"sankaranDARVIZDeepAbstract2017","abstract":"Traditional software engineering programming paradigms are mostly object or procedure oriented, driven by deterministic algorithms. With the advent of deep learning and cognitive sciences there is an emerging trend for data-driven programming, creating a shift in the programming paradigm among the software engineering communities. Visualizing and interpreting the execution of a current large scale data-driven software development is challenging. Further, for deep learning development there are many libraries in multiple programming languages such as TensorFlow (Python), CAFFE (C++), Theano (Python), Torch (Lua), and Deeplearning4j (Java), driving a huge need for interoperability across libraries. We propose a model driven development based solution framework, that facilitates intuitive designing of deep learning models in a platform agnostic fashion. This framework could potentially generate library specific code, perform program translation across languages, and debug the training process of a deep learning model from a fault localization and repair perspective. Further we identify open research problems in this emerging domain, and discuss some new software tooling requirements to serve this new age data-driven programming paradigm. © 2017 IEEE.","author":[{"family":"Sankaran","given":"A."},{"family":"Aralikatte","given":"R."},{"family":"Mani","given":"S."},{"family":"Khare","given":"S."},{"family":"Panwar","given":"N."},{"family":"Gantayat","given":"N."}],"citation-key":"sankaranDARVIZDeepAbstract2017","container-title":"Proceedings - 2017 IEEE/ACM 39th International Conference on Software Engineering: New Ideas and Emerging Results Track, ICSE-NIER 2017","DOI":"10.1109/ICSE-NIER.2017.13","ISBN":"978-1-5386-2675-7","issued":{"date-parts":[[2017]]},"page":"47-50","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"DARVIZ: Deep abstract representation, visualization, and verification of deep learning models","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026730806&doi=10.1109%2fICSE-NIER.2017.13&partnerID=40&md5=8b7d2a471beccbb90ba2ad78df2425a8"},
{"id":"santhanamBotsSoftwareEngineering2022","abstract":"Bots have emerged from research prototypes to deployable systems due to the recent developments in machine learning, natural language processing and understanding techniques. In software engineering, bots range from simple automated scripts to decision-making autonomous systems. The spectrum of applications of bots in software engineering is so wide and diverse, that a comprehensive overview and categorization of such bots is needed. Existing works considered selective bots to be analyzed and failed to provide the overall picture. Hence it is significant to categorize bots in software engineering through analyzing why, what and how the bots are applied in software engineering. We approach the problem with a systematic mapping study based on the research articles published in this topic. This study focuses on classification of bots used in software engineering, the various dimensions of the characteristics, the more frequently researched area, potential research spaces to be explored and the perception of bots in the developer community. This study aims to provide an introduction and a broad overview of bots used in software engineering. Discussions of the feedback and results from several studies provide interesting insights and prospective future directions.","accessed":{"date-parts":[[2022,4,26]]},"author":[{"family":"Santhanam","given":"Sivasurya"},{"family":"Hecking","given":"Tobias"},{"family":"Schreiber","given":"Andreas"},{"family":"Wagner","given":"Stefan"}],"citation-key":"santhanamBotsSoftwareEngineering2022","container-title":"PeerJ Computer Science","DOI":"10.7717/peerj-cs.866","ISSN":"2376-5992","issued":{"date-parts":[[2022,2,9]]},"page":"e866","source":"DOI.org (Crossref)","title":"Bots in software engineering: a systematic mapping study","title-short":"Bots in software engineering","type":"article-journal","URL":"https://peerj.com/articles/cs-866","volume":"8"},
{"id":"Saracevic:1995:EEI:215206.215351","author":[{"family":"Saracevic","given":"Tefko"}],"citation-key":"Saracevic:1995:EEI:215206.215351","collection-title":"SIGIR '95","container-title":"Proceedings of the 18th annual international ACM SIGIR conference on research and development in information retrieval","event-place":"New York, NY, USA","ISBN":"0-89791-714-6","issued":{"date-parts":[[1995]]},"page":"138-146","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Evaluation of evaluation in information retrieval","type":"paper-conference","URL":"http://doi.acm.org/10.1145/215206.215351"},
{"id":"Sarazin2021200","abstract":"The increasing complexity of modern systems, cost reduction policies and ever increasing safety requirements are bringing new challenges to the maintenance domain. In many fields, periodic maintenance actions become either insufficient or too expensive. In this context, Condition-Based Maintenance (CBM) strategies, and Prognostics and Health Management (PHM) in particular, are offering an interesting alternative by allowing systems to be maintained only when needed. These strategies rely on a constant monitoring and analysis of the systems operating conditions in order to detect and identify a failure when it occurs and even sometimes beforehand. Nowadays, two main approaches are explored to detect failures in PHM solutions: one based on machine learning, the other based on expertise and capitalised system knowledge. This work proposes to combine a Complex Event Processing (CEP), to manage incoming data’s volumetry and velocity, with an Expert System (ES) in charge of exploiting the capitalized knowledge. This paper focuses on the configuration of a CEP from rules contained in a CBM ES using a Model Driven Architecture (MDA). This configuration is a challenge, especially regarding the management of rules with temporal parameters and the need for intermediate results to deal with the rule’s complexity. © 2021, Springer Nature Switzerland AG.","author":[{"family":"Sarazin","given":"A."},{"family":"Truptil","given":"S."},{"family":"Montarnal","given":"A."},{"family":"Bascans","given":"J."},{"family":"Lorca","given":"X."}],"citation-key":"Sarazin2021200","container-title":"Communications in Computer and Information Science","DOI":"10.1007/978-3-030-67445-8_9","editor":[{"family":"Hammoudi S., Pires L.F.","given":"Selic B."}],"ISBN":"9783030674441","ISSN":"18650929","issued":{"date-parts":[[2021]]},"page":"200-224","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Model transformation from CBM to EPL rules to detect failure symptoms","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101354230&doi=10.1007%2f978-3-030-67445-8_9&partnerID=40&md5=92d271261b05e5eb522baad1dab20552","volume":"1361"},
{"id":"Sarwar:2001:ICF:371920.372071","author":[{"family":"Sarwar","given":"Badrul"},{"family":"Karypis","given":"George"},{"family":"Konstan","given":"Joseph"},{"family":"Riedl","given":"John"}],"citation-key":"Sarwar:2001:ICF:371920.372071","container-title":"10th international conference on world wide web","event-place":"New York","ISBN":"1-58113-348-0","issued":{"date-parts":[[2001]]},"page":"285-295","publisher":"ACM","publisher-place":"New York","title":"Item-based collaborative filtering recommendation algorithms","type":"paper-conference"},
{"id":"sasPerilsPitfallsClassifying","abstract":"Empirical results in software engineering have long started to show that findings and evidence are unlikely to be applicable to all software systems, or any domain: results need to be evaluated in specified contexts, and limited to the type of systems that they were extracted from.","author":[{"family":"Sas","given":"Cezar"},{"family":"Capiluppi","given":"Andrea"}],"citation-key":"sasPerilsPitfallsClassifying","note":"00000","page":"11","source":"Zotero","title":"The Perils and Pitfalls of Classifying Software Systems","type":"article-journal"},
{"id":"SATToSE2017Postproceedings2017","citation-key":"SATToSE2017Postproceedings2017","issued":{"date-parts":[[2017]]},"note":"00000","publisher":"CEUR-WS","title":"SATToSE 2017: The post-proceedings editorial","type":"book","volume":"2070"},
{"id":"Sauer20182999","abstract":"Within the Transregional Collaborative Research Centre 73, a self-learning engineering workbench is being developed. It assists product developers in designing sheet-bulk metal formed (SBMF) parts by computing the effects of given product and process characteristics on the product properties. This contribution presents a novel approach to using deep learning methods for the properties prediction. By making use of a parameter study of 20 SBMF part designs, a metamodel is trained and used to predict the total equivalent plastic strain on local level as an indicator for part manufacturability. © 2018 Faculty of Mechanical Engineering and Naval Architecture. All Rights Reserved.","author":[{"family":"Sauer","given":"C."},{"family":"Schleich","given":"B."},{"family":"Wartzack","given":"S."}],"citation-key":"Sauer20182999","collection-title":"Proceedings of International Design Conference, DESIGN","DOI":"10.21278/idc.2018.0147","editor":[{"family":"Bojcetic N., Storga M.","given":"Marjanovic D.","suffix":"Skec S., Pavkovic N."}],"ISBN":"978-953-7738-59-4","ISSN":"18479073","issued":{"date-parts":[[2018]]},"page":"2999-3010","publisher":"Faculty of Mechanical Engineering and Naval Architecture","title":"Deep learning in sheet-bulk metal forming part design","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054911080&doi=10.21278%2fidc.2018.0147&partnerID=40&md5=92b321ea5d7c506caf75f34923877133","volume":"6"},
{"id":"sauerDeepLearningSheetbulk2018a","abstract":"Within the Transregional Collaborative Research Centre 73, a self-learning engineering workbench is being developed. It assists product developers in designing sheet-bulk metal formed (SBMF) parts by computing the effects of given product and process characteristics on the product properties. This contribution presents a novel approach to using deep learning methods for the properties prediction. By making use of a parameter study of 20 SBMF part designs, a metamodel is trained and used to predict the total equivalent plastic strain on local level as an indicator for part manufacturability. © 2018 Faculty of Mechanical Engineering and Naval Architecture. All Rights Reserved.","author":[{"family":"Sauer","given":"C."},{"family":"Schleich","given":"B."},{"family":"Wartzack","given":"S."}],"citation-key":"sauerDeepLearningSheetbulk2018a","container-title":"Proceedings of International Design Conference, DESIGN","DOI":"10.21278/idc.2018.0147","editor":[{"family":"Bojcetic N.","given":"Pavkovic N.","suffix":"Storga M., Marjanovic D., Skec S."}],"ISBN":"978-953-7738-59-4","ISSN":"18479073","issued":{"date-parts":[[2018]]},"page":"2999-3010","publisher":"Faculty of Mechanical Engineering and Naval Architecture","title":"Deep learning in sheet-bulk metal forming part design","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054911080&doi=10.21278%2fidc.2018.0147&partnerID=40&md5=92b321ea5d7c506caf75f34923877133","volume":"6"},
{"id":"savolainenSELECTIONLOWCODEPLATFORMS","author":[{"family":"Savolainen","given":"Paula"}],"citation-key":"savolainenSELECTIONLOWCODEPLATFORMS","page":"86","source":"Zotero","title":"SELECTION OF LOW-CODE PLATFORMS BASED ON ORGANIZATION AND APPLICATION TYPE","type":"article-journal"},
{"id":"scavuzzoInteroperableDataMigration2014","accessed":{"date-parts":[[2018,5,9]]},"author":[{"family":"Scavuzzo","given":"Marco"},{"family":"Nitto","given":"Elisabetta Di"},{"family":"Ceri","given":"Stefano"}],"citation-key":"scavuzzoInteroperableDataMigration2014","DOI":"10.1109/EDOCW.2014.32","ISBN":"978-1-4799-5467-4","issued":{"date-parts":[[2014,9]]},"page":"154-162","publisher":"IEEE","source":"Crossref","title":"Interoperable Data Migration between NoSQL Columnar Databases","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/6975356/"},
{"id":"schaarschmidtAutomatedPolyglotPersistence","abstract":"In this paper, we present an innovative solution for providing automated polyglot persistence based on service level agreements defined over functional and non-functional requirements of database systems. Complex applications require polyglot persistence to deal with a wide range of database related needs. Until now, the overhead and the required know-how to manage multiple database systems prevents many applications from employing efficient polyglot persistence solutions. Instead, developers are often forced to implement one-size-fits-all solutions that do not scale well and cannot easily be upgraded. Therefore, we introduce the concept for a Polyglot Persistence Mediator (PPM), which allows for runtime decisions on routing data to different backends according to schema-based annotations. This enables applications to either employ polyglot persistence right from the beginning or employ new systems at any point with minimal overhead. We have implemented and evaluated the concept of automated polyglot persistence for a REST-based Database-as-a-Service setting. Evaluations were performed on various EC2 setups, showing a scalable writeperformance increase of 50-100% for a typical polyglot persistence scenario as well as drastically reduced latencies for reads and queries.","author":[{"family":"Schaarschmidt","given":"Michael"},{"family":"Gessert","given":"Felix"},{"family":"Ritter","given":"Norbert"}],"citation-key":"schaarschmidtAutomatedPolyglotPersistence","page":"10","source":"Zotero","title":"Towards Automated Polyglot Persistence","type":"article-journal"},
{"id":"schaefferSurveyGraphClustering2007","author":[{"family":"Schaeffer","given":"Satu Elisa"}],"citation-key":"schaefferSurveyGraphClustering2007","container-title":"Computer Science Review","container-title-short":"Comput. Sci. Rev.","ISSN":"1574-0137","issue":"1","issued":{"date-parts":[[2007,8]]},"page":"27-64","title":"Survey: Graph clustering","type":"article-journal","URL":"http://dx.doi.org/10.1016/j.cosrev.2007.05.001","volume":"1"},
{"id":"schaferAdaptiveWeb2007","author":[{"family":"Schafer","given":"J. Ben"},{"family":"Frankowski","given":"Dan"},{"family":"Herlocker","given":"Jon"},{"family":"Sen","given":"Shilad"}],"citation-key":"schaferAdaptiveWeb2007","editor":[{"family":"Brusilovsky","given":"Peter"},{"family":"Kobsa","given":"Alfred"},{"family":"Nejdl","given":"Wolfgang"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-540-72078-2","issued":{"date-parts":[[2007]]},"page":"291-324","publisher":"Springer-Verlag","publisher-place":"Berlin, Heidelberg","title":"The adaptive web","type":"chapter"},
{"id":"schaferDyadRankingUsing2018","accessed":{"date-parts":[[2022,3,8]]},"author":[{"family":"Schäfer","given":"Dirk"},{"family":"Hüllermeier","given":"Eyke"}],"citation-key":"schaferDyadRankingUsing2018","container-title":"Machine Learning","container-title-short":"Mach Learn","DOI":"10.1007/s10994-017-5694-9","ISSN":"0885-6125, 1573-0565","issue":"5","issued":{"date-parts":[[2018,5]]},"note":"00015","page":"903-941","source":"DOI.org (Crossref)","title":"Dyad ranking using Plackett–Luce models based on joint feature representations","type":"article-journal","URL":"http://link.springer.com/10.1007/s10994-017-5694-9","volume":"107"},
{"id":"Schatten2017246","abstract":"Massively multi-player on-line role-playing games (MMO-RPGs) present a large-scale, digital environment that fosters organizational behaviour of players in which multi-agent systems (MASs) can be used for various purposes including but not limited to automated testing, bot detection or analysis of social player behaviour and human – artificial agent interaction. A work-in-progress model-driven MAS development environment for such games is presented. An open-source MMORPG called The Mana World (TMW) is used as an example scenario on which the various components of the system are tested. © Springer International Publishing AG 2017.","author":[{"family":"Schatten","given":"M."},{"family":"Okreša Ðurić","given":"B."},{"family":"Tomičić","given":"I."},{"family":"Ivković","given":"N."}],"citation-key":"Schatten2017246","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-319-59930-4_20","editor":[{"family":"Demazeau Y., Davidsson P.","given":"Vale Z.","suffix":"Bajo J."}],"ISBN":"9783319599298","ISSN":"03029743","issued":{"date-parts":[[2017]]},"page":"246-258","publisher":"Springer Verlag","title":"Agents as bots – An initial attempt towards model-driven MMORPG gameplay","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021712181&doi=10.1007%2f978-3-319-59930-4_20&partnerID=40&md5=8495bf650b2a062fb70bddfe88893903","volume":"10349 LNCS"},
{"id":"Schatten2017359","abstract":"A work-in-progress agent-based framework for automated testing of an open-source massively multi-player on-line role playing game (MMORPG) called The Mana World is presented. The implemented system, in its current state, allows for model-driven development of tests using a graphical user interface (GUI), implementation of automated artificial players (bots) and their use in testing the quests (player tasks) of the game. The system is implemented using Python, SPADE, SWI Prolog and AToM3. © Springer International Publishing AG 2017.","author":[{"family":"Schatten","given":"M."},{"family":"Okreaša Ðurić","given":"B."},{"family":"Tomičič","given":"I."},{"family":"Ivkovič","given":"N."}],"citation-key":"Schatten2017359","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-319-59930-4_38","editor":[{"family":"Demazeau Y., Davidsson P.","given":"Vale Z.","suffix":"Bajo J."}],"ISBN":"9783319599298","ISSN":"03029743","issued":{"date-parts":[[2017]]},"page":"359-363","publisher":"Springer Verlag","title":"Automated MMORPG testing – An agent-based approach","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021755847&doi=10.1007%2f978-3-319-59930-4_38&partnerID=40&md5=c6c0f4d52654563a36a8d3f20e624ccd","volume":"10349 LNCS"},
{"id":"schattenAutomatedMMORPGTesting2017a","abstract":"A work-in-progress agent-based framework for automated testing of an open-source massively multi-player on-line role playing game (MMORPG) called The Mana World is presented. The implemented system, in its current state, allows for model-driven development of tests using a graphical user interface (GUI), implementation of automated artificial players (bots) and their use in testing the quests (player tasks) of the game. The system is implemented using Python, SPADE, SWI Prolog and AToM3. © Springer International Publishing AG 2017.","author":[{"family":"Schatten","given":"M."},{"family":"Okreaša Ðurić","given":"B."},{"family":"Tomičič","given":"I."},{"family":"Ivkovič","given":"N."}],"citation-key":"schattenAutomatedMMORPGTesting2017a","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-319-59930-4_38","editor":[{"family":"Demazeau Y.","given":"Bajo J.","suffix":"Davidsson P., Vale Z."}],"ISBN":"9783319599298","ISSN":"03029743","issued":{"date-parts":[[2017]]},"page":"359-363","publisher":"Springer Verlag","title":"Automated MMORPG testing – An agent-based approach","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021755847&doi=10.1007%2f978-3-319-59930-4_38&partnerID=40&md5=c6c0f4d52654563a36a8d3f20e624ccd","volume":"10349 LNCS"},
{"id":"schatzDesignSpaceExplorationConstraintBased2010","author":[{"family":"Schätz","given":"Bernhard"},{"family":"Hölzl","given":"Florian"},{"family":"Lundkvist","given":"Torbjörn"}],"citation-key":"schatzDesignSpaceExplorationConstraintBased2010","container-title":"2010 17th IEEE International Conference and Workshops on Engineering of Computer Based Systems","DOI":"10.1109/ECBS.2010.25","issued":{"date-parts":[[2010]]},"page":"173–182","title":"Design-Space Exploration through Constraint-Based Model-Transformation","type":"article-journal"},
{"id":"schelterDeequDataQuality","abstract":"Modern machine learning (ML) systems are comprised of complex ML pipelines which typically have many implicit assumptions about the data they consume (e.g., about the scales of variables, the presence of missing values or the dictionary of categorical values). Violations of these assumptions can result in crashes or wrong predictions. We therefore present Deequ, a library that allows users to explicitly specify their assumptions about the data in a declarative way. Deequ enables the efficient automatic validation of these assumptions on large datasets. It is an open source library based on Apache Spark and meets the requirements of production use cases at Amazon.","author":[{"family":"Schelter","given":"Sebastian"},{"family":"Grafberger","given":"Stefan"},{"family":"Schmidt","given":"Philipp"},{"family":"Rukat","given":"Tammo"},{"family":"Kiessling","given":"Mario"},{"family":"Taptunov","given":"Andrey"},{"family":"Biessmann","given":"Felix"},{"family":"Lange","given":"Dustin"}],"citation-key":"schelterDeequDataQuality","note":"00001","page":"3","source":"Zotero","title":"Deequ - Data Quality Validation for Machine Learning Pipelines","type":"article-journal"},
{"id":"schlegelDesignAbstractionProcesses2010","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Schlegel","given":"Christian"},{"family":"Steck","given":"Andreas"},{"family":"Brugali","given":"Davide"},{"family":"Knoll","given":"Alois"}],"citation-key":"schlegelDesignAbstractionProcesses2010","container-title":"International Conference on Simulation, Modeling, and Programming for Autonomous Robots","issued":{"date-parts":[[2010]]},"page":"324–335","publisher":"Springer","source":"Google Scholar","title":"Design abstraction and processes in robotics: from code-driven to model-driven engineering","title-short":"Design abstraction and processes in robotics","type":"paper-conference","URL":"http://link.springer.com/content/pdf/10.1007/978-3-642-17319-6_31.pdf"},
{"id":"schonbockModelDrivenCoevolutionAgile2015","accessed":{"date-parts":[[2015,10,29]]},"author":[{"family":"Schonbock","given":"J."},{"family":"Etzlstorfer","given":"J."},{"family":"Kapsammer","given":"E."},{"family":"Kusel","given":"A."},{"family":"Retschitzegger","given":"W."},{"family":"Schwinger","given":"W."}],"citation-key":"schonbockModelDrivenCoevolutionAgile2015","DOI":"10.1109/HICSS.2015.603","ISBN":"978-1-4799-7367-5","issued":{"date-parts":[[2015,1]]},"page":"5094-5103","publisher":"IEEE","source":"CrossRef","title":"Model-Driven Co-evolution for Agile Development","type":"paper-conference","URL":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7070425"},
{"id":"sculleyHiddenTechnicalDebt","abstract":"Machine learning offers a fantastically powerful toolkit for building useful complex prediction systems quickly. This paper argues it is dangerous to think of these quick wins as coming for free. Using the software engineering framework of technical debt, we find it is common to incur massive ongoing maintenance costs in real-world ML systems. We explore several ML-specific risk factors to account for in system design. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, configuration issues, changes in the external world, and a variety of system-level anti-patterns.","author":[{"family":"Sculley","given":"D"},{"family":"Holt","given":"Gary"},{"family":"Golovin","given":"Daniel"},{"family":"Davydov","given":"Eugene"},{"family":"Phillips","given":"Todd"},{"family":"Ebner","given":"Dietmar"},{"family":"Chaudhary","given":"Vinay"},{"family":"Young","given":"Michael"},{"family":"Crespo","given":"Jean-François"},{"family":"Dennison","given":"Dan"}],"citation-key":"sculleyHiddenTechnicalDebt","note":"00000","page":"9","source":"Zotero","title":"Hidden Technical Debt in Machine Learning Systems","type":"article-journal"},
{"id":"SEfSAS3challenges","citation-key":"SEfSAS3challenges","title":"SEfSAS3-challenges","type":"article-journal"},
{"id":"Segundo2017301","abstract":"This paper presents a decision support system (DSS) called DSScreening to rapidly detect inborn errors of metabolism (IEMs) in newborn screening (NS). The system has been created using the Aide-DS framework, which uses techniques imported from model-driven software engineering (MDSE) and soft computing, and it is available through eGuider, a web portal for the enactment of computerised clinical practice guidelines and protocols. MDSE provides the context and techniques to build new software artefacts based on models which conform to a specific metamodel. It also offers separation of concern, to disassociate medical from technological knowledge, thus allowing changes in one domain without affecting the other. The changes might include, for instance, the addition of new disorders to the DSS or new measures to the computation related to a disorder. Artificial intelligence and soft computing provide fuzzy logic to manage uncertainty and ambiguous situations. Fuzzy logic is embedded in an inference system to build a fuzzy inference system (FIS); specifically, a single-input rule modules connected zero-order Takagi-Sugeno FIS. The automatic creation of FISs is performed by the Aide-DS framework, which is capable of embedding the generated FISs in computerized clinical guidelines. It can also create a desktop application to execute the FIS. Technologically, it supports the addition of new target languages for the desktop applications and the inclusion of new ways of acquiring data. DSScreening has been tested by comparing its predictions with the results of 152 real analyses from two groups: (1) NS samples and (2) clinical samples belonging to individuals of all ages with symptoms that do not necessarily correspond to an IEM. The system has reduced the time needed by 98.7% when compared to the interpretation time spent by laboratory professionals. Besides, it has correctly classified 100% of the NS samples and obtained an accuracy of 70% for samples belonging to individuals with clinical symptoms. © 2017 Elsevier Ltd","author":[{"family":"Segundo","given":"U."},{"family":"Aldámiz-Echevarría","given":"L."},{"family":"López-Cuadrado","given":"J."},{"family":"Buenestado","given":"D."},{"family":"Andrade","given":"F."},{"family":"Pérez","given":"T.A."},{"family":"Barrena","given":"R."},{"family":"Pérez-Yarza","given":"E.G."},{"family":"Pikatza","given":"J.M."}],"citation-key":"Segundo2017301","container-title":"Expert Systems with Applications","DOI":"10.1016/j.eswa.2017.02.022","ISSN":"09574174","issued":{"date-parts":[[2017]]},"page":"301-318","publisher":"Elsevier Ltd","title":"Improvement of newborn screening using a fuzzy inference system","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013790845&doi=10.1016%2fj.eswa.2017.02.022&partnerID=40&md5=af42efad86da7073ac56fe757e31f87c","volume":"78"},
{"id":"sehrawatDataMiningIoT2018","abstract":"Internet of Things (IoT) has provided enormous opportunities to make prevailing smart environment by influencing the increasing ubiquity of Radio Frequency Identification Devices (RFID), wireless network, and sensor devices. Recently, a large number of industrial IoT applications have embarked their presence. Rapid technological growth introduces tremendous information on the network. Big Data is an idea to assemble huge amount of data from IoT enabled devices like sensors, actuators in IoT smart environment to help monitor specific conditions, procedures, and system performance. In this new generation, it becomes more challenging to extract most relevant information quickly and efficiently. To solve this problem, a data mining technique widely known as automatic text summarization may also prove to be fruitful. Text summarization creates summarized information from a large text corpus. Various latest techniques used for text summarization viz. Classification, Particle Swarm Optimization, Genetic Algorithms, clustering, neural network and various hybridized approaches are presented in this paper. The latest and relevant algorithms may be customized in the context of IoT applications. This paper is aimed at reviewing these techniques and also discusses the challenges as well as other related research issues.","accessed":{"date-parts":[[2022,2,3]]},"author":[{"family":"Sehrawat","given":"Deepti"},{"literal":"Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, Haryana, India"},{"family":"Gill","given":"Nasib Singh"},{"literal":"Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, Haryana, India"}],"citation-key":"sehrawatDataMiningIoT2018","container-title":"International Journal of Computer Sciences and Engineering","container-title-short":"ijcse","DOI":"10.26438/ijcse/v6i4.289295","ISSN":"23472693","issue":"4","issued":{"date-parts":[[2018,4,30]]},"note":"00000","page":"289-295","source":"DOI.org (Crossref)","title":"Data Mining in IoT and its Challenges","type":"article-journal","URL":"http://www.ijcseonline.org/full_paper_view.php?paper_id=1886","volume":"6"},
{"id":"seibelDedicatedLanguageContext2012","abstract":"Model-Driven Engineering (MDE) automates development activities by employing model transformations. Thereby, a plethora of model transformation approaches with individual capabilities have been developed. In certain cases, complex and automated MDE activities require the interaction of various, potentially heterogeneous, model transformations. This can be achieved by a loosely coupled and highly cohesive composition of model transformations implemented in different model transformation languages. However, existing approaches either do not support context composition, using other model transformations as additional context, or they violate the important black-box principle because they require adapting model transformations for context composition. In this paper, we present a dedicated model transformation composition framework (MoTCoF) that does not require the adaptation of model transformations and, thus, treats model transformations as true black-boxes. We illustrate our approach with an application example taken from an industrial case study.","accessed":{"date-parts":[[2015,3,24]]},"author":[{"family":"Seibel","given":"Andreas"},{"family":"Hebig","given":"Regina"},{"family":"Neumann","given":"Stefan"},{"family":"Giese","given":"Holger"}],"citation-key":"seibelDedicatedLanguageContext2012","collection-number":"6940","collection-title":"Lecture Notes in Computer Science","container-title":"Software Language Engineering","editor":[{"family":"Sloane","given":"Anthony"},{"family":"Aßmann","given":"Uwe"}],"ISBN":"978-3-642-28829-6 978-3-642-28830-2","issued":{"date-parts":[[2012]]},"page":"19-39","publisher":"Springer Berlin Heidelberg","source":"link.springer.com","title":"A Dedicated Language for Context Composition and Execution of True Black-Box Model Transformations","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-642-28830-2_2"},
{"id":"SelfmanagingInformationSystems","accessed":{"date-parts":[[2016,9,24]]},"citation-key":"SelfmanagingInformationSystems","title":"Self-managing information systems","type":"webpage","URL":"http://www.inf.u-szeged.hu/~jelasity/selfstar05.html"},
{"id":"selimAutomatedVerificationModel2013","author":[{"family":"Selim","given":"Gehan M. K."},{"family":"Büttner","given":"Fabian"},{"family":"Cordy","given":"James R."},{"family":"Dingel","given":"Juergen"},{"family":"Wang","given":"Shige"}],"citation-key":"selimAutomatedVerificationModel2013","container-title":"Model-Driven Engineering Languages and Systems","DOI":"10.1007/978-3-642-41533-3_42","issued":{"date-parts":[[2013]]},"page":"690–706","title":"Automated Verification of Model Transformations in the Automotive Industry","type":"article-journal","volume":"8107"},
{"id":"selimModelTransformationsMigrating2012","author":[{"family":"Selim","given":"Gehan M. K."},{"family":"Wang","given":"Shige"},{"family":"Cordy","given":"James R."},{"family":"Dingel","given":"Juergen"}],"citation-key":"selimModelTransformationsMigrating2012","container-title":"Modelling Foundations and Applications","DOI":"10.1007/978-3-642-31491-9_9","issued":{"date-parts":[[2012]]},"page":"90–101","title":"Model Transformations for Migrating Legacy Models: An Industrial Case Study","type":"article-journal","volume":"7349"},
{"id":"Sen2013236","abstract":"It is essential to estimate the Channel and detect symbol in multiple-input and multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. Symbol detection by applying the maximum likelihood (ML) detector gives excellent performance but in systems with higher number of antennas and greater constellation size, the computational complexity of this algorithm becomes quite high. In this paper we apply a recently developed modified Differential Evolution (DE) algorithm with novel mutation, crossover as well as parameter adaptation strategies (MDE-pBX) for reducing the search space of the ML detector and the computational complexity of symbol detection in MIMO-OFDM systems. The performance of MDE-pBX have been compared with two classical symbol detectors namely ML and ZF and two famous evolutionary algorithm namely SaDE and CLPSO. © 2013 Springer International Publishing.","author":[{"family":"Sen","given":"A."},{"family":"Roy","given":"S."},{"family":"Das","given":"S."}],"citation-key":"Sen2013236","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-319-03753-0_22","ISBN":"9783319037523","ISSN":"03029743","issue":"PART 1","issued":{"date-parts":[[2013]]},"page":"236-247","title":"A modified differential evolution for symbol detection in MIMO-OFDM system","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893291922&doi=10.1007%2f978-3-319-03753-0_22&partnerID=40&md5=2630bbf0470ea2dd4c0462aa26a347d1","volume":"8297 LNCS"},
{"id":"serbanAdoptionEffectsSoftware2020","abstract":"Background. The increasing reliance on applications with machine learning (ML) components calls for mature engineering techniques that ensure these are built in a robust and future-proof manner. Aim. We aim to empirically determine the state of the art in how teams develop, deploy and maintain software with ML components.\nMethod. We mined both academic and grey literature and identified 29 engineering best practices for ML applications. We conducted a survey among 313 practitioners to determine the degree of adoption for these practices and to validate their perceived effects. Using the survey responses, we quantified practice adoption, differentiated along demographic characteristics, such as geography or team size. We also tested correlations and investigated linear and non-linear relationships between practices and their perceived effect using various statistical models.\nResults. Our findings indicate, for example, that larger teams tend to adopt more practices, and that traditional software engineering practices tend to have lower adoption than ML specific practices. Also, the statistical models can accurately predict perceived effects such as agility, software quality and traceability, from the degree of adoption for specific sets of practices. Combining practice adoption rates with practice importance, as revealed by statistical models, we identify practices that are important but have low adoption, as well as practices that are widely adopted but are less important for the effects we studied.\nConclusion. Overall, our survey and the analysis of responses received provide a quantitative basis for assessment and step-wise improvement of practice adoption by ML teams.","accessed":{"date-parts":[[2020,11,21]]},"author":[{"family":"Serban","given":"Alex"},{"family":"Blom","given":"Koen","non-dropping-particle":"van der"},{"family":"Hoos","given":"Holger"},{"family":"Visser","given":"Joost"}],"citation-key":"serbanAdoptionEffectsSoftware2020","container-title":"Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)","DOI":"10.1145/3382494.3410681","event":"ESEM '20: ACM / IEEE International Symposium on Empirical Software Engineering and Measurement","event-place":"Bari Italy","ISBN":"978-1-4503-7580-1","issued":{"date-parts":[[2020,10,5]]},"page":"1-12","publisher":"ACM","publisher-place":"Bari Italy","source":"DOI.org (Crossref)","title":"Adoption and Effects of Software Engineering Best Practices in Machine Learning","type":"paper-conference","URL":"https://dl.acm.org/doi/10.1145/3382494.3410681"},
{"id":"serbanSurveyIntelligentAssistants2013","abstract":"Research and industry increasingly make use of large amounts of data to guide decision-making. To do this, however, data needs to be analyzed in typically nontrivial refinement processes, which require technical expertise about methods and algorithms, experience with how a precise analysis should proceed, and knowledge about an exploding number of analytic approaches. To alleviate these problems, a plethora of different systems have been proposed that “intelligently” help users to analyze their data.\n This article provides a first survey to almost 30 years of research on intelligent discovery assistants (IDAs). It explicates the types of help IDAs can provide to users and the kinds of (background) knowledge they leverage to provide this help. Furthermore, it provides an overview of the systems developed over the past years, identifies their most important features, and sketches an ideal future IDA as well as the challenges on the road ahead.","accessed":{"date-parts":[[2022,3,21]]},"author":[{"family":"Serban","given":"Floarea"},{"family":"Vanschoren","given":"Joaquin"},{"family":"Kietz","given":"Jörg-Uwe"},{"family":"Bernstein","given":"Abraham"}],"citation-key":"serbanSurveyIntelligentAssistants2013","container-title":"ACM Computing Surveys","container-title-short":"ACM Comput. Surv.","DOI":"10.1145/2480741.2480748","ISSN":"0360-0300, 1557-7341","issue":"3","issued":{"date-parts":[[2013,6]]},"page":"1-35","source":"DOI.org (Crossref)","title":"A survey of intelligent assistants for data analysis","type":"article-journal","URL":"https://dl.acm.org/doi/10.1145/2480741.2480748","volume":"45"},
{"id":"ServerlessApplicationsWhy","accessed":{"date-parts":[[2021,1,17]]},"citation-key":"ServerlessApplicationsWhy","note":"00000","title":"Serverless Applications: Why, When, and How?","type":"webpage","URL":"https://www.computer.org/csdl/magazine/so/2021/01/09190031/1mYZaiUIVhu"},
{"id":"sevillaruizInferringVersionedSchemas2015","abstract":"While the concept of database schema plays a central role in relational database systems, most NoSQL systems are schemaless: these databases are created without having to formally define its schema. Instead, it is implicit in the stored data. This lack of schema definition offers a greater flexibility; more specifically, the schemaless databases ease both the recording of non-uniform data and data evolution. However, this comes at the cost of losing some of the benefits provided by schemas. In this article, a MDE-based reverse engineering approach for inferring the schema of aggregate-oriented NoSQL databases is presented. We show how the obtained schemas can be used to build database utilities that tackle some of the problems encountered using implicit schemas: a schema diagram viewer and a data validator generator are presented.","accessed":{"date-parts":[[2018,5,7]]},"author":[{"family":"Sevilla Ruiz","given":"Diego"},{"family":"Morales","given":"Severino Feliciano"},{"family":"García Molina","given":"Jesús"}],"citation-key":"sevillaruizInferringVersionedSchemas2015","container-title":"Conceptual Modeling","DOI":"10.1007/978-3-319-25264-3_35","editor":[{"family":"Johannesson","given":"Paul"},{"family":"Lee","given":"Mong Li"},{"family":"Liddle","given":"Stephen W."},{"family":"Opdahl","given":"Andreas L."},{"family":"Pastor López","given":"Óscar"}],"event-place":"Cham","ISBN":"978-3-319-25263-6 978-3-319-25264-3","issued":{"date-parts":[[2015]]},"page":"467-480","publisher":"Springer International Publishing","publisher-place":"Cham","source":"Crossref","title":"Inferring Versioned Schemas from NoSQL Databases and Its Applications","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-319-25264-3_35","volume":"9381"},
{"id":"shafiqMachineLearningSoftware2020","abstract":"Objective: This article addresses the aforementioned problem and aims to present a state-of-the-art on the growing number of uses of machine learning in software engineering.\nMethod: We conduct a systematic mapping study on applications of machine learning to software engineering following the standard guidelines and principles of empirical software engineering.\nResults: This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages. Overall, 227 articles were rigorously selected and analyzed as a result of this study.\nConclusion: From the selected articles, we explore a variety of aspects that should be helpful to academics and practitioners alike in understanding the potential of adopting machine learning techniques during software engineering projects.","accessed":{"date-parts":[[2020,10,26]]},"author":[{"family":"Shafiq","given":"Saad"},{"family":"Mashkoor","given":"Atif"},{"family":"Mayr-Dorn","given":"Christoph"},{"family":"Egyed","given":"Alexander"}],"citation-key":"shafiqMachineLearningSoftware2020","container-title":"arXiv:2005.13299 [cs]","issued":{"date-parts":[[2020,5,27]]},"note":"00000","source":"arXiv.org","title":"Machine Learning for Software Engineering: A Systematic Mapping","title-short":"Machine Learning for Software Engineering","type":"article-journal","URL":"http://arxiv.org/abs/2005.13299"},
{"id":"shahrivarBusinessModelCommercial2018","abstract":"Context\nCommercial open source software (COSS) and community open source software (OSS) are two types of open source software. The former is the newer concept with the grounds for research such as business model. However, in the literature of open source software, the revenue model has been studied as a business model, which is one component of the business model. Therefore, there is a need for a more complete review of the COSS business model with all components.\nObjective\nThe purpose of this research is to describe and present the COSS business model with all its components.\nMethod\nA systematic literature review of the COSS business model was conducted and 1157 studies were retrieved through search in six academic databases. The result of the process of selecting the primary studies was 21 studies. By backward snowballing, we discovered 10 other studies, and thus a total of 31 studies were found. Then, the grounded theory coding procedures were used to determine the characteristics and components of the COSS business model.\nResults\nThe COSS business model was presented with value proposition, value creation & delivery, and value capture. This business model includes eight components: COSS products and complementarities, COSS clients and users, COSS competitive strategies, organizational aspects of COSS, position of COSS producers in the value network, resources and capabilities of COSS business, COSS revenue sources, and COSS cost-benefit.\nConclusion\nThis study provides a complete illustration of the COSS business model. Identifies COSS generic competitive strategies. By cost-benefit component, we have considered both tangible and intangible components. This business model is especially effective in developing countries. In future research, it is necessary to review the management of the COSS community, the organization, the new revenue models for disruptive ability of open source software, and the localization of open source software.","accessed":{"date-parts":[[2018,9,14]]},"author":[{"family":"Shahrivar","given":"Shahrokh"},{"family":"Elahi","given":"Shaban"},{"family":"Hassanzadeh","given":"Alireza"},{"family":"Montazer","given":"Gholamali"}],"citation-key":"shahrivarBusinessModelCommercial2018","container-title":"Information and Software Technology","container-title-short":"Information and Software Technology","DOI":"10.1016/j.infsof.2018.06.018","ISSN":"0950-5849","issued":{"date-parts":[[2018,11,1]]},"page":"202-214","source":"ScienceDirect","title":"A business model for commercial open source software: A systematic literature review","title-short":"A business model for commercial open source software","type":"article-journal","URL":"http://www.sciencedirect.com/science/article/pii/S0950584918301277","volume":"103"},
{"id":"shaoQuantifyMusicArtist2008","author":[{"family":"Shao","given":"Bo"},{"family":"Li","given":"Tao"},{"family":"Ogihara","given":"Mitsunori"}],"citation-key":"shaoQuantifyMusicArtist2008","collection-title":"WIDM '08","container-title":"Proceedings of the 10th ACM workshop on web information and data management","event-place":"New York, NY, USA","ISBN":"978-1-60558-260-3","issued":{"date-parts":[[2008]]},"page":"119-124","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Quantify music artist similarity based on style and mood","type":"paper-conference","URL":"http://doi.acm.org/10.1145/1458502.1458522"},
{"id":"Sharma:2017:CGR:3084226.3084287","author":[{"family":"Sharma","given":"Abhishek"},{"family":"Thung","given":"Ferdian"},{"family":"Kochhar","given":"Pavneet Singh"},{"family":"Sulistya","given":"Agus"},{"family":"Lo","given":"David"}],"citation-key":"Sharma:2017:CGR:3084226.3084287","collection-title":"EASE'17","container-title":"Proceedings of the 21st international conference on evaluation and assessment in software engineering","event-place":"New York, NY, USA","ISBN":"978-1-4503-4804-1","issued":{"date-parts":[[2017]]},"page":"314-319","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Cataloging GitHub repositories","type":"paper-conference","URL":"http://doi.acm.org.univaq.clas.cineca.it/10.1145/3084226.3084287"},
{"id":"Shekhar201337","abstract":"This paper describes preliminary work in developing a model-driven approach to conducting price/performance tradeoffs for Cloud-based MapReduce application deployment. The need for this work stems from the significant variability in both the MapReduce application characteristics and price/performance characteristics of the underlying cloud platform. Our approach involves a model-based machine learning capability that trains itself from executing a variety of MapReduce applications on different cloud service providers, and in turn providing useful price/performance tradeoff information to MapReduce application users. Additionally, the model-based platform serves to automate the deployment of a MapReduce application to the cloud by incorporating the tradeoff choices. Copyright © 2013 for the individual papers by the papers' authors.","author":[{"family":"Shekhar","given":"S."},{"family":"Caglar","given":"F."},{"family":"An","given":"K."},{"family":"Kuroda","given":"T."},{"family":"Gokhale","given":"A."},{"family":"Gokhale","given":"S."}],"citation-key":"Shekhar201337","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Ober I., Dabholkar A.","given":"Hill J.","suffix":"Bruel J.-M., Felderer M., Lugato D., Gokhale A."}],"ISSN":"16130073","issued":{"date-parts":[[2013]]},"page":"37-41","publisher":"CEUR-WS","title":"A model-driven approach for price/performance tradeoffs in cloud-based MapReduce application deployment","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922953691&partnerID=40&md5=7115c7072386dd9b6820534ff82d5025","volume":"1118"},
{"id":"Shen20211226","abstract":"Deep learning is an effective approach for signal detection which is a challenging issue in wireless communication systems. Some DNN deep learning methods have shortcomings, like complex structures, many training parameters, and converging difficulty, which may cause information loss and affect accuracy and reliability if the received signals pass through the continuous layers of the neural networks. To overcome those, in our paper, the signal detection part is replaced with a conditional generative adversarial (cGAN) network at the OFDM receiver, and we preprocess the received signals using LS and ZF algorithms based on model-driven as the initial input of the neural network. And the cGAN model introduces pilots as the condition to counter-train the two deep learning networks for generating signals closer to being under real channels. In addition, with an adaptive loss function (GAN Loss), the cGAN model has a certain corrective effect on optimizing the neural network for recovering data. The simulation results illustrate that the cGAN model performs better than the existing DNN models under various signal-to-noise ratios (SNR), especially facing low SNRs and short pilot sequences, that is, it has better robustness for restoring effectively the transmitted signals under real channels. © 2021 IEEE.","author":[{"family":"Shen","given":"X."},{"family":"Wei","given":"L."},{"family":"Xu","given":"Y."}],"citation-key":"Shen20211226","collection-title":"International Conference on Communication Technology Proceedings, ICCT","DOI":"10.1109/ICCT52962.2021.9658003","ISBN":"978-1-66543-206-1","issued":{"date-parts":[[2021]]},"page":"1226-1230","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Signal detection method at the OFDM receiver based on conditional GAN","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124380897&doi=10.1109%2fICCT52962.2021.9658003&partnerID=40&md5=57ca12be342a1e0b5dea0b5b8d86eeaa","volume":"2021-October"},
{"id":"shenSignalDetectionMethod2021a","abstract":"Deep learning is an effective approach for signal detection which is a challenging issue in wireless communication systems. Some DNN deep learning methods have shortcomings, like complex structures, many training parameters, and converging difficulty, which may cause information loss and affect accuracy and reliability if the received signals pass through the continuous layers of the neural networks. To overcome those, in our paper, the signal detection part is replaced with a conditional generative adversarial (cGAN) network at the OFDM receiver, and we preprocess the received signals using LS and ZF algorithms based on model-driven as the initial input of the neural network. And the cGAN model introduces pilots as the condition to counter-train the two deep learning networks for generating signals closer to being under real channels. In addition, with an adaptive loss function (GAN Loss), the cGAN model has a certain corrective effect on optimizing the neural network for recovering data. The simulation results illustrate that the cGAN model performs better than the existing DNN models under various signal-to-noise ratios (SNR), especially facing low SNRs and short pilot sequences, that is, it has better robustness for restoring effectively the transmitted signals under real channels. © 2021 IEEE.","author":[{"family":"Shen","given":"X."},{"family":"Wei","given":"L."},{"family":"Xu","given":"Y."}],"citation-key":"shenSignalDetectionMethod2021a","container-title":"International Conference on Communication Technology Proceedings, ICCT","DOI":"10.1109/ICCT52962.2021.9658003","ISBN":"978-1-66543-206-1","issued":{"date-parts":[[2021]]},"page":"1226-1230","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Signal Detection Method at the OFDM Receiver Based on Conditional GAN","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124380897&doi=10.1109%2fICCT52962.2021.9658003&partnerID=40&md5=57ca12be342a1e0b5dea0b5b8d86eeaa","volume":"2021-October"},
{"id":"shevtsovControlTheoreticalSoftwareAdaptation2018","abstract":"Modern software applications are subject to uncertain operating conditions, such as dynamics in the availability of services and variations of system goals. Consequently, runtime changes cannot be ignored, but often cannot be predicted at design time. Control theory has been identified as a principled way of addressing runtime changes and it has been applied successfully to modify the structure and behavior of software applications. Most of the times, however, the adaptation targeted the resources that the software has available for execution (CPU, storage, etc.) more than the software application itself. This paper investigates the research efforts that have been conducted to make software adaptable by modifying the software rather than the resource allocated to its execution. This paper aims to identify: the focus of research on control-theoretical software adaptation; how software is modeled and what control mechanisms are used to adapt software; what software qualities and controller guarantees are considered. To that end, we performed a systematic literature review in which we extracted data from 42 primary studies selected from 1,512 papers that resulted from an automatic search. The results of our investigation show that even though the behavior of software is considered non-linear, research efforts use linear models to represent it, with some success. Also, the control strategies that are most often considered are classic control, mostly in the form of Proportional and Integral controllers, and Model Predictive Control. The paper also discusses sensing and actuating strategies that are prominent for software adaptation and the (often neglected) proof of formal properties. Finally, we distill open challenges for control-theoretical software adaptation.","accessed":{"date-parts":[[2021,10,5]]},"author":[{"family":"Shevtsov","given":"Stepan"},{"family":"Berekmeri","given":"Mihaly"},{"family":"Weyns","given":"Danny"},{"family":"Maggio","given":"Martina"}],"citation-key":"shevtsovControlTheoreticalSoftwareAdaptation2018","container-title":"IEEE Transactions on Software Engineering","container-title-short":"IIEEE Trans. Software Eng.","DOI":"10.1109/TSE.2017.2704579","ISSN":"0098-5589, 1939-3520, 2326-3881","issue":"8","issued":{"date-parts":[[2018,8,1]]},"note":"00070","page":"784-810","source":"DOI.org (Crossref)","title":"Control-Theoretical Software Adaptation: A Systematic Literature Review","title-short":"Control-Theoretical Software Adaptation","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/7929422/","volume":"44"},
{"id":"Shi:2014:CFB:2620784.2556270","author":[{"family":"Shi","given":"Yue"},{"family":"Larson","given":"Martha"},{"family":"Hanjalic","given":"Alan"}],"citation-key":"Shi:2014:CFB:2620784.2556270","container-title":"ACM Computing Surveys","container-title-short":"ACM Comput. Surv.","ISSN":"0360-0300","issue":"1","issued":{"date-parts":[[2014,5]]},"page":"3:1-3:45","title":"Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges","type":"article-journal","URL":"http://doi.acm.org/10.1145/2556270","volume":"47"},
{"id":"Shi2020130","abstract":"Direction-of-arrival (DOA) estimation is an important task in many unmanned aerial vehicle (UAV) applications. However, the complicated electromagnetic wave propagation in urban environments substantially deteriorates the performance of many conventional model-driven DOA estimation approaches. To alleviate this, a deep learning based DOA estimation approach is proposed in this paper. Specifically, a complex-valued convolutional neural network (CCNN) is designed to fit the electromagnetic UAV signal with complex envelope better. In the CCNN design, we construct some mapping functions using quantum probabilities, and further analyze some factors which may impact the convergence of complex-valued neural networks. Numerical simulations show that the proposed CCNN converges faster than the real convolutional neural network, and the DOA estimation result is more accurate and robust. © 2020, Posts and Telecom Press Co Ltd. All rights reserved.","author":[{"family":"Shi","given":"B."},{"family":"Ma","given":"X."},{"family":"Zhang","given":"W."},{"family":"Shao","given":"H.Z."},{"family":"Shi","given":"Q.J."},{"family":"Lin","given":"J.R."}],"citation-key":"Shi2020130","container-title":"Journal of Communications and Information Networks","ISSN":"20961081","issue":"2","issued":{"date-parts":[[2020]]},"page":"130-137","publisher":"Posts and Telecom Press Co Ltd","title":"Complex-valued convolutional neural networks design and its application on UAV DOA estimation in urban environments","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113143658&partnerID=40&md5=deec2b2f397becbf8e99183a65d1ec44","volume":"5"},
{"id":"Shi2021","abstract":"Traffic state estimation (TSE) bifurcates into two main categories, model-driven and data-driven (e.g., machine learning, ML) approaches, while each suffers from either deficient physics or small data. To mitigate these limitations, recent studies introduced hybrid methods, such as physics-informed deep learning (PIDL), which contains both model-driven and data-driven components. This paper contributes an improved paradigm, called physics-informed deep learning with a fundamental diagram learner (PIDL + FDL), which integrates ML terms into the model-driven component to learn a functional form of a fundamental diagram (FD), i.e., a mapping from traffic density to flow or velocity. The proposed PIDL + FDL has the advantages of performing the TSE learning, model parameter identification, and FD estimation simultaneously. This paper focuses on highway TSE with observed data from loop detectors, using traffic density or velocity as traffic variables. We demonstrate the use of PIDL + FDL to solve popular first-order and second-order traffic flow models and reconstruct the FD relation as well as model parameters that are outside the FD term. We then evaluate the PIDL + FDL-based TSE using the Next Generation SIMulation (NGSIM) dataset. The experimental results show the superiority of the PIDL + FDL in terms of improved estimation accuracy and data efficiency over advanced baseline TSE methods, and additionally, the capacity to properly learn the unknown underlying FD relation. IEEE","author":[{"family":"Shi","given":"R."},{"family":"Mo","given":"Z."},{"family":"Huang","given":"K."},{"family":"Di","given":"X."},{"family":"Du","given":"Q."}],"citation-key":"Shi2021","container-title":"IEEE Transactions on Intelligent Transportation Systems","DOI":"10.1109/TITS.2021.3106259","ISSN":"15249050","issued":{"date-parts":[[2021]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A physics-informed deep learning paradigm for traffic state and fundamental diagram estimation","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114735339&doi=10.1109%2fTITS.2021.3106259&partnerID=40&md5=2413829d71218feee064a9f14da9efa4"},
{"id":"Shi2021540","abstract":"Traffic state estimation (TSE) reconstructs the traffic variables (e.g., density or average velocity) on road segments using partially observed data, which is important for traffic managements. Traditional TSE approaches mainly bifurcate into two categories: model-driven and data-driven, and each of them has shortcomings. To mitigate these limitations, hybrid TSE methods, which combine both model-driven and data-driven, are becoming a promising solution. This paper introduces a hybrid framework, physics-informed deep learning (PIDL), to combine second-order traffic flow models and neural networks to solve the TSE problem. PIDL can encode traffic flow models into deep neural networks to regularize the learning process to achieve improved data efficiency and estimation accuracy. We focus on highway TSE with observed data from loop detectors and probe vehicles, using both density and average velocity as the traffic variables. With numerical examples, we show the use of PIDL to solve a popular second-order traffic flow model, i.e., a Greenshields-based Aw-Rascle-Zhang (ARZ) model, and discover the model parameters. We then evaluate the PIDL-based TSE method using the Next Generation SIMulation (NGSIM) dataset. Experimental results demonstrate the proposed PIDL-based approach to outperform advanced baseline methods in terms of data efficiency and estimation accuracy. © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved","author":[{"family":"Shi","given":"R."},{"family":"Mo","given":"Z."},{"family":"Di","given":"X."}],"citation-key":"Shi2021540","collection-title":"35th AAAI Conference on Artificial Intelligence, AAAI 2021","ISBN":"978-1-71383-597-4","issued":{"date-parts":[[2021]]},"page":"540-547","publisher":"Association for the Advancement of Artificial Intelligence","title":"Physics-informed deep learning for traffic state estimation: A hybrid paradigm informed by second-order traffic models","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129927870&partnerID=40&md5=732554314ae873028be2d7b6ddb023c0","volume":"1"},
{"id":"Shi2021917","abstract":"The nuclear power industry is currently a strategic sector in the national economy, along with nuclear energy being considered to be an essential source of national power supply and security. Under such circumstances, nuclear power plants (NPPs) have been constructed globally in recent decades, providing stable and large amounts of electricity to many countries and regions for quite a long time. However, due to the specialty of NPPs, safety management is always the top priority in their daily operations, like fault diagnosis and monitoring. At the same time, with the great development of artificial intelligence, machine learning and deep learning methods have been infiltrating lots of disciplines and resulting in more intelligent transformations in real industries. By the strength of better performance, machine learning and deep learning models have been introduced into research and practice of safety management in NPPs, not only producing more academic papers but also ensuring stable and safe operations of NPPs. Focusing on the safety management of NPPs, this article starts with the common trend of data analytics, also the evolving process of algorithms applied in academia and real practice, which is from model-driven methods to data-driven approaches. Then detailed applications of conventional machine learning, advanced deep learning and other related intelligent approaches used in the safety management of NPPs are comprehensively categorized and reviewed. Further, we make necessary summaries and discussions, proposing new ideas and perspectives to better promote the theoretical and practical development of safety management in NPPs. © 2021 IEEE.","author":[{"family":"Shi","given":"Y."},{"family":"Xue","given":"X."},{"family":"Qu","given":"Y."},{"family":"Xue","given":"J."},{"family":"Zhang","given":"L."}],"citation-key":"Shi2021917","collection-title":"IEEE International Conference on Data Mining Workshops, ICDMW","DOI":"10.1109/ICDMW53433.2021.00120","editor":[{"family":"Xue B., Pechenizkiy M.","given":"Koh Y.S."}],"ISBN":"978-1-66542-427-1","ISSN":"23759232","issued":{"date-parts":[[2021]]},"page":"917-924","publisher":"IEEE Computer Society","title":"Machine learning and deep learning methods used in safety management of nuclear power plants: A survey","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124753069&doi=10.1109%2fICDMW53433.2021.00120&partnerID=40&md5=c1eb1246467fa089c5a18b3763952a51","volume":"2021-December"},
{"id":"shiComplexvaluedConvolutionalNeural2020a","abstract":"Direction-of-arrival (DOA) estimation is an important task in many unmanned aerial vehicle (UAV) applications. However, the complicated electromagnetic wave propagation in urban environments substantially deteriorates the performance of many conventional model-driven DOA estimation approaches. To alleviate this, a deep learning based DOA estimation approach is proposed in this paper. Specifically, a complex-valued convolutional neural network (CCNN) is designed to fit the electromagnetic UAV signal with complex envelope better. In the CCNN design, we construct some mapping functions using quantum probabilities, and further analyze some factors which may impact the convergence of complex-valued neural networks. Numerical simulations show that the proposed CCNN converges faster than the real convolutional neural network, and the DOA estimation result is more accurate and robust. © 2020, Posts and Telecom Press Co Ltd. All rights reserved.","author":[{"family":"Shi","given":"B."},{"family":"Ma","given":"X."},{"family":"Zhang","given":"W."},{"family":"Shao","given":"H.Z."},{"family":"Shi","given":"Q.J."},{"family":"Lin","given":"J.R."}],"citation-key":"shiComplexvaluedConvolutionalNeural2020a","container-title":"Journal of Communications and Information Networks","ISSN":"20961081","issue":"2","issued":{"date-parts":[[2020]]},"page":"130-137","publisher":"Posts and Telecom Press Co Ltd","title":"Complex-valued convolutional neural networks design and its application on UAV DOA estimation in urban environments","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113143658&partnerID=40&md5=deec2b2f397becbf8e99183a65d1ec44","volume":"5"},
{"id":"shinDynamicAdaptationSoftwaredefined2020","accessed":{"date-parts":[[2021,1,8]]},"author":[{"family":"Shin","given":"Seung Yeob"},{"family":"Nejati","given":"Shiva"},{"family":"Sabetzadeh","given":"Mehrdad"},{"family":"Briand","given":"Lionel C."},{"family":"Arora","given":"Chetan"},{"family":"Zimmer","given":"Frank"}],"citation-key":"shinDynamicAdaptationSoftwaredefined2020","container-title":"Proceedings of the IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","DOI":"10.1145/3387939.3391603","event":"SEAMS '20: IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","event-place":"Seoul Republic of Korea","ISBN":"978-1-4503-7962-5","issued":{"date-parts":[[2020,6,29]]},"note":"00001","page":"137-148","publisher":"ACM","publisher-place":"Seoul Republic of Korea","source":"DOI.org (Crossref)","title":"Dynamic adaptation of software-defined networks for IoT systems: a search-based approach","title-short":"Dynamic adaptation of software-defined networks for IoT systems","type":"paper-conference","URL":"https://dl.acm.org/doi/10.1145/3387939.3391603"},
{"id":"shinNoSQLDatabaseDesign2017","abstract":"In the Big Data era, relational databases and NoSQL databases coexist in Polyglot Persistence environment. Although data management is more essential in an environment where a variety of databases are, NoSQL databases only concentrate on solving non-functional requirements to run well on large clusters. This situation makes consistent data management standards difficult. To solve this problem, this study proposes NoSQL database design method using conceptual data model based on Peter Chen’s framework. The proposed design method is applied to the e-commerce business area in order to examine the applicability of it.","author":[{"family":"Shin","given":"Kwangchul"},{"family":"Hwang","given":"Chulhyun"},{"family":"Jung","given":"Hoekyung"}],"citation-key":"shinNoSQLDatabaseDesign2017","issue":"5","issued":{"date-parts":[[2017]]},"page":"5","source":"Zotero","title":"NoSQL Database Design Using UML Conceptual Data Model Based on Peter Chen’s Framework","type":"article-journal","volume":"12"},
{"id":"shiPhysicsInformedDeepLearning2021a","abstract":"Traffic state estimation (TSE) reconstructs the traffic variables (e.g., density or average velocity) on road segments using partially observed data, which is important for traffic managements. Traditional TSE approaches mainly bifurcate into two categories: model-driven and data-driven, and each of them has shortcomings. To mitigate these limitations, hybrid TSE methods, which combine both model-driven and data-driven, are becoming a promising solution. This paper introduces a hybrid framework, physics-informed deep learning (PIDL), to combine second-order traffic flow models and neural networks to solve the TSE problem. PIDL can encode traffic flow models into deep neural networks to regularize the learning process to achieve improved data efficiency and estimation accuracy. We focus on highway TSE with observed data from loop detectors and probe vehicles, using both density and average velocity as the traffic variables. With numerical examples, we show the use of PIDL to solve a popular second-order traffic flow model, i.e., a Greenshields-based Aw-Rascle-Zhang (ARZ) model, and discover the model parameters. We then evaluate the PIDL-based TSE method using the Next Generation SIMulation (NGSIM) dataset. Experimental results demonstrate the proposed PIDL-based approach to outperform advanced baseline methods in terms of data efficiency and estimation accuracy. © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved","author":[{"family":"Shi","given":"R."},{"family":"Mo","given":"Z."},{"family":"Di","given":"X."}],"citation-key":"shiPhysicsInformedDeepLearning2021a","container-title":"35th AAAI Conference on Artificial Intelligence, AAAI 2021","ISBN":"978-1-71383-597-4","issued":{"date-parts":[[2021]]},"page":"540-547","publisher":"Association for the Advancement of Artificial Intelligence","title":"Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129927870&partnerID=40&md5=732554314ae873028be2d7b6ddb023c0","volume":"1"},
{"id":"siegwartIntroductionAutonomousMobile2004","author":[{"family":"Siegwart","given":"Roland"},{"family":"Nourbakhsh","given":"Illah Reza"}],"call-number":"TJ211.415 .S54 2004","citation-key":"siegwartIntroductionAutonomousMobile2004","collection-title":"Intelligent robots and autonomous agents","event-place":"Cambridge, Mass","ISBN":"978-0-262-19502-7","issued":{"date-parts":[[2004]]},"number-of-pages":"321","publisher":"MIT Press","publisher-place":"Cambridge, Mass","source":"Library of Congress ISBN","title":"Introduction to autonomous mobile robots","type":"book"},
{"id":"sierraSurveySelfadmittedTechnical2019","accessed":{"date-parts":[[2022,4,6]]},"author":[{"family":"Sierra","given":"Giancarlo"},{"family":"Shihab","given":"Emad"},{"family":"Kamei","given":"Yasutaka"}],"citation-key":"sierraSurveySelfadmittedTechnical2019","container-title":"Journal of Systems and Software","container-title-short":"Journal of Systems and Software","DOI":"10.1016/j.jss.2019.02.056","ISSN":"01641212","issued":{"date-parts":[[2019,6]]},"page":"70-82","source":"DOI.org (Crossref)","title":"A survey of self-admitted technical debt","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0164121219300457","volume":"152"},
{"id":"Sikdar2014225","abstract":"In this paper we propose a modified differential evolution (MDE) based feature selection and ensemble learning algorithms for biochemical entity recognizer. Identification and classification of chemical entities are relatively more complex and challenging compared to the other related tasks. As chemical entities we focus on IUPAC and IUPAC related entities. The algorithm performs feature selection within the framework of a robust machine learning algorithm, namely Conditional Random Field. Features are identified and implemented mostly without using any domain specific knowledge and/or resources. In this paper we modify traditional differential evolution to perform two tasks, viz. determining relevant set of features as well as determining proper voting weights for constructing an ensemble. The feature selection technique produces a set of potential solutions on the final population. We develop many models of CRF using these feature combinations. In order to further improve the performance the outputs of these classifiers are combined together using a classifier ensemble technique based on modified DE. Our experiments with the benchmark datasets yield the recall, precision and F-measure values of 82.34%, 88.26% and 85.20%, respectively. © 2014 Springer-Verlag Berlin Heidelberg.","author":[{"family":"Sikdar","given":"U.K."},{"family":"Ekbal","given":"A."},{"family":"Saha","given":"S."}],"citation-key":"Sikdar2014225","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-642-54906-9_18","ISBN":"9783642549052","ISSN":"03029743","issue":"PART 1","issued":{"date-parts":[[2014]]},"page":"225-236","publisher":"Springer Verlag","title":"Modified differential evolution for biochemical name recognizer","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958527890&doi=10.1007%2f978-3-642-54906-9_18&partnerID=40&md5=0886c3b875b64a91c62dfca702fde8ec","volume":"8403 LNCS"},
{"id":"simGettingWholeStory2011","abstract":"When analyzing data elicited using the “war stories” technique, previously introduced by Lutters and Seaman (Inf Softw Technol 49(6):576–587, 2007), we encountered unexpected challenges in applying standard qualitative analysis techniques. After reviewing the literature on stories and storytelling, we realized that a richer analysis would be possible if we accorded more respect to the data’s structure and nature as stories, rather than treating our participants’ utterances simply as textual data. We report on five lessons learned regarding how we can better analyze war stories as stories: 1) war stories tend to be about exceptional situations; 2) war stories tend to be diverse and resistant to being combined into a single grand narrative; 3) the humanities can be a valuable resource for analyzing war stories; 4) war stories are not just text, they are also performances; and 5) war stories are not just data, they are also instructive and evocative.","accessed":{"date-parts":[[2020,3,31]]},"author":[{"family":"Sim","given":"Susan Elliott"},{"family":"Alspaugh","given":"Thomas A."}],"citation-key":"simGettingWholeStory2011","container-title":"Empirical Software Engineering","container-title-short":"Empir Software Eng","DOI":"10.1007/s10664-011-9157-9","ISSN":"1382-3256, 1573-7616","issue":"4","issued":{"date-parts":[[2011,8]]},"page":"460-486","source":"DOI.org (Crossref)","title":"Getting the whole story: an experience report on analyzing data elicited using the war stories procedure","title-short":"Getting the whole story","type":"article-journal","URL":"http://link.springer.com/10.1007/s10664-011-9157-9","volume":"16"},
{"id":"SimilarityMatrix","abstract":"Petrinet\n\nSubject/dataset,petrinet2.ecore,PetriNet.ecore,petrinet_extendable.ecore,PetriNets.ecore,petri_nets.ecore,petrinet_tgg_rule.ecore,PetrinetDsl.ecore,PetriNet_extended.ecore,PetriNetModel.ecore,petri.ecore\npetrinet2.ecore,100,33,33,66,33,0,25,33,33,50\nPetriNet.ecore,20,100,64,20,37,0,20,4...","accessed":{"date-parts":[[2020,2,11]]},"citation-key":"SimilarityMatrix","container-title":"Google Docs","title":"Similarity matrix","type":"webpage","URL":"https://docs.google.com/spreadsheets/d/1jJ7FGuN1I7cWJZw4J6dO-KYaU118AFtpbvNzC01re0c/edit?usp=sharing&usp=embed_facebook"},
{"id":"SimplifyingModelTransformation","accessed":{"date-parts":[[2015,3,24]]},"citation-key":"SimplifyingModelTransformation","title":"Simplifying Model Transformation Chains by Rule Composition - Springer","type":"webpage","URL":"http://link.springer.com/chapter/10.1007%2F978-3-642-21210-9_28"},
{"id":"Sinha20218579","abstract":"The severe spread of the COVID-19 pandemic has created a situation of public health emergency and global awareness. In our research, we analyzed the demographical factors affecting the global pandemic spread along with the features that lead to death due to the infection. Modeling results stipulate that the mortality rate increase as the age increase and it is found that most of the death cases belong to the age group 60–80. Cluster-based analysis of age groups is also conducted to analyze the maximum targeted age-groups. An association between positive COVID-19 cases and deceased cases are also presented, with the impact on male and female death cases due to corona. Additionally, we have also presented an artificial intelligence-based statistical approach to predict the survival chances of corona infected people in South Korea with the analysis of the impact on the exploratory factors, including age-groups, gender, temporal evolution, etc. To analyze the coronavirus cases, we applied machine learning with hyperparameters tuning and deep learning models with an autoencoder-based approach for estimating the influence of the disparate features on the spread of the disease and predict the survival possibilities of the quarantined patients in isolation. The model calibrated in the study is based on positive corona infection cases and presents the analysis over different aspects that proven to be impactful to analyze the temporal trends in the current situation along with the exploration of deceased cases due to coronavirus. Analysis delineates key points in the outbreak spreading, indicating that the models driven by machine intelligence and deep learning can be effective in providing a quantitative view of the epidemical outbreak. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.","author":[{"family":"Sinha","given":"A."},{"family":"Rathi","given":"M."}],"citation-key":"Sinha20218579","container-title":"Applied Intelligence","DOI":"10.1007/s10489-021-02352-z","ISSN":"0924669X","issue":"12","issued":{"date-parts":[[2021]]},"page":"8579-8597","publisher":"Springer","title":"COVID-19 prediction using AI analytics for South Korea","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104070618&doi=10.1007%2fs10489-021-02352-z&partnerID=40&md5=74718984dc00d646bd5493cb57639a01","volume":"51"},
{"id":"sinnapoluIntegratingWearablesCloudbased2018","abstract":"Researchers and physicians have come a long way in inventing various types of wearable devices for health monitoring which makes it easier for medical professionals to monitor patients. Considering a situation, when a patient is driving, his/her health cannot be monitored or assisted immediately in case of emergency due to enormous drawbacks in the communication or the reporting system which is of today’s prime issue. The cloud-based communication helps solving the issue to some extent but inventing an application to integrate any wearable device to the Internet of things (IoT) and the cloud, considering portability and robustness will solve the prime issue. In this paper, we demonstrate a prototype working model along with the healthdetect iOS app for monitoring health data (heart rate) using wearables, if a serious heart rate data is detected by this app, from proximity sensor on the wearables, the microcontroller in the vehicle enables the healthlocateapp to locate and route to the nearest hospitals for the driver to drive. If the condition is critical and he/she is not responding for in-vehicle button press or driver related activity, then the microcontroller sends CAN message to activate the auto pilot to pull over for assistance.","accessed":{"date-parts":[[2018,11,7]]},"author":[{"family":"Sinnapolu","given":"GiriBabu"},{"family":"Alawneh","given":"Shadi"}],"citation-key":"sinnapoluIntegratingWearablesCloudbased2018","container-title":"Internet of Things","DOI":"10.1016/j.iot.2018.08.004","ISSN":"25426605","issued":{"date-parts":[[2018,9]]},"page":"40-54","source":"Crossref","title":"Integrating wearables with cloud-based communication for health monitoring and emergency assistance","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2542660518300404","volume":"1-2"},
{"id":"Sirres2018","abstract":"Source code terms such as method names and variable types are often different from conceptual words mentioned in a search query. This vocabulary mismatch problem can make code search inefficient. In this paper, we present COde voCABUlary (CoCaBu), an approach to resolving the vocabulary mismatch problem when dealing with free-form code search queries. Our approach leverages common developer questions and the associated expert answers to augment user queries with the relevant, but missing, structural code entities in order to improve the performance of matching relevant code examples within large code repositories. To instantiate this approach, we build GitSearch, a code search engine, on top of GitHub and Stack Overflow Q&A data. We evaluate GitSearch in several dimensions to demonstrate that (1) its code search results are correct with respect to user-accepted answers; (2) the results are qualitatively better than those of existing Internet-scale code search engines; (3) our engine is competitive against web search engines, such as Google, in helping users solve programming tasks; and (4) GitSearch provides code examples that are acceptable or interesting to the community as answers for Stack Overflow questions.","author":[{"family":"Sirres","given":"Raphael"},{"family":"Bissyandé","given":"Tegawendé F."},{"family":"Kim","given":"Dongsun"},{"family":"Lo","given":"David"},{"family":"Klein","given":"Jacques"},{"family":"Kim","given":"Kisub"},{"family":"Traon","given":"Yves Le"}],"citation-key":"Sirres2018","container-title":"Empirical Software Engineering","DOI":"10.1007/s10664-017-9544-y","ISSN":"1573-7616","issue":"5","issued":{"date-parts":[[2018,10,1]]},"page":"2622-2654","title":"Augmenting and structuring user queries to support efficient free-form code search","type":"article-journal","URL":"https://doi.org/10.1007/s10664-017-9544-y","volume":"23"},
{"id":"sirresAugmentingStructuringUser2018","author":[{"family":"Sirres","given":"Raphael"},{"family":"Bissyandé","given":"Tegawendé F."},{"family":"Kim","given":"Dongsun"},{"family":"Lo","given":"David"},{"family":"Klein","given":"Jacques"},{"family":"Kim","given":"Kisub"},{"family":"Traon","given":"Yves Le"}],"citation-key":"sirresAugmentingStructuringUser2018","collection-title":"ICSE '18","container-title":"Proceedings of the 40th international conference on software engineering","event-place":"New York, NY, USA","ISBN":"978-1-4503-5638-1","issued":{"date-parts":[[2018]]},"page":"945-945","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Augmenting and structuring user queries to support efficient free-form code search","type":"paper-conference","URL":"http://doi.acm.org/10.1145/3180155.3182513"},
{"id":"sivieriBuildingInternetThings2016","accessed":{"date-parts":[[2016,3,23]]},"author":[{"family":"Sivieri","given":"Alessandro"},{"family":"Mottola","given":"Luca"},{"family":"Cugola","given":"Gianpaolo"}],"citation-key":"sivieriBuildingInternetThings2016","container-title":"Computer Communications","DOI":"10.1016/j.comcom.2016.02.004","ISSN":"01403664","issued":{"date-parts":[[2016,2]]},"source":"CrossRef","title":"Building Internet of Things software with ELIoT","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0140366416300238"},
{"id":"SmartAnythingEverywhere","accessed":{"date-parts":[[2015,4,8]]},"citation-key":"SmartAnythingEverywhere","title":"Smart Anything Everywhere | EU H2020","type":"webpage","URL":"https://smartanythingeverywhere.eu/"},
{"id":"SMR:SMR567","author":[{"family":"Dit","given":"Bogdan"},{"family":"Revelle","given":"Meghan"},{"family":"Gethers","given":"Malcom"},{"family":"Poshyvanyk","given":"Denys"}],"citation-key":"SMR:SMR567","container-title":"Journal of Software: Evolution and Process","ISSN":"2047-7481","issue":"1","issued":{"date-parts":[[2013]]},"page":"53-95","title":"Feature location in source code: a taxonomy and survey","type":"article-journal","URL":"http://dx.doi.org/10.1002/smr.567","volume":"25"},
{"id":"SoftwareEngineeringSelfAdaptive","accessed":{"date-parts":[[2016,2,17]]},"citation-key":"SoftwareEngineeringSelfAdaptive","title":"Software Engineering for Self-Adaptive Systems: A Second Research Roadmap - Springer","type":"webpage","URL":"http://link.springer.com/chapter/10.1007/978-3-642-35813-5_1"},
{"id":"soldatosBuildingBlocksIoT2016","accessed":{"date-parts":[[2021,1,5]]},"author":[{"family":"Soldatos","given":"John"}],"citation-key":"soldatosBuildingBlocksIoT2016","DOI":"10.13052/rp-9788793519046","issued":{"date-parts":[[2016]]},"note":"00012","page":"1-294","source":"DOI.org (Crossref)","title":"Building Blocks for IoT Analytics","type":"chapter","URL":"http://riverpublishers.com/dissertations_xml/9788793519046/9788793519046.xml"},
{"id":"Song20201385","abstract":"Artificial intelligence is being utilized in multipath industrial networks to enhance service supporting ability. However, existing obstacles in controlling receive buffer restrict throughput even when higher bandwidth is available. Therefore, in this article, we propose a smart collaborative automation (SCA) scheme to improve resource usage and overcome buffer limitations. First, a mathematical model is established to describe primary system operations with considerations of chunk loss. The inf-supremum methodology and probability theory are adopted to track congestion window variations. Second, differences in disordered chunk expectations are analyzed to locate the critical condition of round numbers. Specific algorithm details are provided via simplifying comparison to achieve comprehensive policy selections. Third, evaluation topologies and environments are created with reasonable parameter settings. Validation results demonstrate that model-driven SCA can reduce unexpected occupations at the receiver-side. Comparing to intuition-driven schemes, overall performances, in terms of the sender's transmission capacity and receiver's buffer utilization, are improved under different experimental configurations. © 2005-2012 IEEE.","author":[{"family":"Song","given":"F."},{"family":"Ai","given":"Z."},{"family":"Zhou","given":"Y."},{"family":"You","given":"I."},{"family":"Choo","given":"K.-K.R."},{"family":"Zhang","given":"H."}],"citation-key":"Song20201385","container-title":"IEEE Transactions on Industrial Informatics","DOI":"10.1109/TII.2019.2950109","ISSN":"15513203","issue":"2","issued":{"date-parts":[[2020]]},"page":"1385-1394","publisher":"IEEE Computer Society","title":"Smart collaborative automation for receive buffer control in multipath industrial networks","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078530650&doi=10.1109%2fTII.2019.2950109&partnerID=40&md5=d9b4ff8dc67cbbc7a3fe4030fa93aa40","volume":"16"},
{"id":"songSmartCollaborativeAutomation2020a","abstract":"Artificial intelligence is being utilized in multipath industrial networks to enhance service supporting ability. However, existing obstacles in controlling receive buffer restrict throughput even when higher bandwidth is available. Therefore, in this article, we propose a smart collaborative automation (SCA) scheme to improve resource usage and overcome buffer limitations. First, a mathematical model is established to describe primary system operations with considerations of chunk loss. The inf-supremum methodology and probability theory are adopted to track congestion window variations. Second, differences in disordered chunk expectations are analyzed to locate the critical condition of round numbers. Specific algorithm details are provided via simplifying comparison to achieve comprehensive policy selections. Third, evaluation topologies and environments are created with reasonable parameter settings. Validation results demonstrate that model-driven SCA can reduce unexpected occupations at the receiver-side. Comparing to intuition-driven schemes, overall performances, in terms of the sender's transmission capacity and receiver's buffer utilization, are improved under different experimental configurations. © 2005-2012 IEEE.","author":[{"family":"Song","given":"F."},{"family":"Ai","given":"Z."},{"family":"Zhou","given":"Y."},{"family":"You","given":"I."},{"family":"Choo","given":"K.-K.R."},{"family":"Zhang","given":"H."}],"citation-key":"songSmartCollaborativeAutomation2020a","container-title":"IEEE Transactions on Industrial Informatics","DOI":"10.1109/TII.2019.2950109","ISSN":"15513203","issue":"2","issued":{"date-parts":[[2020]]},"page":"1385-1394","publisher":"IEEE Computer Society","title":"Smart collaborative automation for receive buffer control in multipath industrial networks","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078530650&doi=10.1109%2fTII.2019.2950109&partnerID=40&md5=d9b4ff8dc67cbbc7a3fe4030fa93aa40","volume":"16"},
{"id":"SoussiNiaimi202219","abstract":"During the past years, there were so many researches focusing on traffic prediction and ways to resolve future traffic congestion; at the very beginning, the goal was to build a mechanism capable of predicting the traffic for short-term; meanwhile, others did focus on the traffic prediction using different perspectives and methods, in order to obtain better and more precise results. The main aim was to come up with enhancements to the accuracy and precision of the outcomes and get a longer-term vision, also build a prediction’s system for the traffic jams and solve them by taking preventive measures (Bolshinsky and Freidman in Traffic flow forecast survey 2012, [1]) basing on artificial intelligence decisions with the given predictions. There are many algorithms; some of them are using statistical physics methods; others use genetic algorithms… the common goal was to achieve a kind of framework that will allow us to move forward and backward in time to have a practical and effective traffic prediction. In addition to moving forward and backward in time, the application of the new framework allows us to locate future traffic jams (congestions). This paper reviews the evolution of the existing traffic prediction’s approaches and the edge given by AI to make the best decisions; we will focus on the model-driven and data-driven approaches. We start by analyzing all advantages and disadvantages of each approach to reach our goal in order to pursue the best approaches for the best output possible. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","author":[{"family":"Soussi Niaimi","given":"B.-E."},{"family":"Bouhorma","given":"M."},{"family":"Zili","given":"H."}],"citation-key":"SoussiNiaimi202219","container-title":"Smart Innovation, Systems and Technologies","DOI":"10.1007/978-981-16-3637-0_2","editor":[{"family":"Ben Ahmed M., Teodorescu H.L.","given":"Mazri T.","suffix":"Subashini P., Boudhir A."}],"ISBN":"9789811636363","ISSN":"21903018","issued":{"date-parts":[[2022]]},"page":"19-31","publisher":"Springer Science and Business Media Deutschland GmbH","title":"The evolution of the traffic congestion prediction and AI application","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116901659&doi=10.1007%2f978-981-16-3637-0_2&partnerID=40&md5=cc4a741557305c33c5d511235f44c820","volume":"237"},
{"id":"soussiniaimiEvolutionTrafficCongestion2022","abstract":"During the past years, there were so many researches focusing on traffic prediction and ways to resolve future traffic congestion; at the very beginning, the goal was to build a mechanism capable of predicting the traffic for short-term; meanwhile, others did focus on the traffic prediction using different perspectives and methods, in order to obtain better and more precise results. The main aim was to come up with enhancements to the accuracy and precision of the outcomes and get a longer-term vision, also build a prediction’s system for the traffic jams and solve them by taking preventive measures (Bolshinsky and Freidman in Traffic flow forecast survey 2012, [1]) basing on artificial intelligence decisions with the given predictions. There are many algorithms; some of them are using statistical physics methods; others use genetic algorithms… the common goal was to achieve a kind of framework that will allow us to move forward and backward in time to have a practical and effective traffic prediction. In addition to moving forward and backward in time, the application of the new framework allows us to locate future traffic jams (congestions). This paper reviews the evolution of the existing traffic prediction’s approaches and the edge given by AI to make the best decisions; we will focus on the model-driven and data-driven approaches. We start by analyzing all advantages and disadvantages of each approach to reach our goal in order to pursue the best approaches for the best output possible. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","author":[{"family":"Soussi Niaimi","given":"B.-E."},{"family":"Bouhorma","given":"M."},{"family":"Zili","given":"H."}],"citation-key":"soussiniaimiEvolutionTrafficCongestion2022","container-title":"Smart Innovation, Systems and Technologies","DOI":"10.1007/978-981-16-3637-0_2","editor":[{"family":"Ben Ahmed M.","given":"Boudhir A.","suffix":"Teodorescu H.L., Mazri T., Subashini P."}],"ISBN":"9789811636363","ISSN":"21903018","issued":{"date-parts":[[2022]]},"page":"19-31","publisher":"Springer Science and Business Media Deutschland GmbH","title":"The Evolution of the Traffic Congestion Prediction and AI Application","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116901659&doi=10.1007%2f978-981-16-3637-0_2&partnerID=40&md5=cc4a741557305c33c5d511235f44c820","volume":"237"},
{"id":"soussiniaimiEvolutionTrafficCongestion2022a","abstract":"During the past years, there were so many researches focusing on traffic prediction and ways to resolve future traffic congestion; at the very beginning, the goal was to build a mechanism capable of predicting the traffic for short-term; meanwhile, others did focus on the traffic prediction using different perspectives and methods, in order to obtain better and more precise results. The main aim was to come up with enhancements to the accuracy and precision of the outcomes and get a longer-term vision, also build a prediction’s system for the traffic jams and solve them by taking preventive measures (Bolshinsky and Freidman in Traffic flow forecast survey 2012, [1]) basing on artificial intelligence decisions with the given predictions. There are many algorithms; some of them are using statistical physics methods; others use genetic algorithms… the common goal was to achieve a kind of framework that will allow us to move forward and backward in time to have a practical and effective traffic prediction. In addition to moving forward and backward in time, the application of the new framework allows us to locate future traffic jams (congestions). This paper reviews the evolution of the existing traffic prediction’s approaches and the edge given by AI to make the best decisions; we will focus on the model-driven and data-driven approaches. We start by analyzing all advantages and disadvantages of each approach to reach our goal in order to pursue the best approaches for the best output possible. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","author":[{"family":"Soussi Niaimi","given":"B.-E."},{"family":"Bouhorma","given":"M."},{"family":"Zili","given":"H."}],"citation-key":"soussiniaimiEvolutionTrafficCongestion2022a","container-title":"Smart Innovation, Systems and Technologies","DOI":"10.1007/978-981-16-3637-0_2","editor":[{"family":"Ben Ahmed M.","given":"Boudhir A.","suffix":"Teodorescu H.L., Mazri T., Subashini P."}],"ISBN":"9789811636363","ISSN":"21903018","issued":{"date-parts":[[2022]]},"page":"19-31","publisher":"Springer Science and Business Media Deutschland GmbH","title":"The Evolution of the Traffic Congestion Prediction and AI Application","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116901659&doi=10.1007%2f978-981-16-3637-0_2&partnerID=40&md5=cc4a741557305c33c5d511235f44c820","volume":"237"},
{"id":"spearmanProofMeasurementAssociation1904","author":[{"family":"Spearman","given":"Charles"}],"citation-key":"spearmanProofMeasurementAssociation1904","container-title":"The American journal of psychology","issue":"1","issued":{"date-parts":[[1904]]},"page":"72-101","title":"The proof and measurement of association between two things","type":"article-journal","volume":"15"},
{"id":"spinellisSoftwareEngineeringInternetThings2017a","accessed":{"date-parts":[[2017,2,27]]},"author":[{"family":"Spinellis","given":"Diomidis"}],"citation-key":"spinellisSoftwareEngineeringInternetThings2017a","container-title":"IEEE Software","issue":"1","issued":{"date-parts":[[2017]]},"page":"4–6","source":"Google Scholar","title":"Software-Engineering the Internet of Things","type":"article-journal","URL":"http://ieeexplore.ieee.org/abstract/document/7819398/","volume":"34"},
{"id":"spinellisSuccessHeavenlyMarriage2018","abstract":"For a field that sprang out of a so-called software crisis, software engineering has done rather well over the past half-century. By riding on the coattails of Moore’s law, it has progressed phenomenally. The field’s achievements are visible through the large, complex, yet effective software systems that power our everyday lives. By looking at the drivers of the field’s progress and taking stock of its achievements, we can appreciate the challenges in front of us and confidently plan for the future. This article is part of a theme issue on software engineering’s 50th anniversary.","author":[{"family":"Spinellis","given":"D."}],"citation-key":"spinellisSuccessHeavenlyMarriage2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.3571251","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"3-6","source":"IEEE Xplore","title":"The Success of a Heavenly Marriage","type":"article-journal","volume":"35"},
{"id":"Spruegel2018","abstract":"In modern product development, the use of sophisticated simulation tools for assessing the effects of design changes on the intended product behavior is essential. However, setting up valid simulations requires expert knowledge, acquired skills, and sufficient expertise. Design engineers, who perform finite element analysis (FEA) infrequently, must be assisted and their FEA results need to be checked for plausibility. An automatic plausibility check for finite element (FE) simulations in linear structural mechanics can identify non-plausible simulations and warn the user to interpret the results cautiously or ask for expert help. In this context, currently available tools can only compare very similar simulations. However, as the amount of available simulation data in the industry increases more and more, a data-driven simulation check is an obvious next step. Nevertheless, the question arises how simulation data of very different parts and simulations can be transferred to a single software tool, how this tool can learn the relevant rules behind plausible simulations, and how it can be applied to new simulations. In this context, it is especially important to train a metamodel that is able to generalize the rules so that it can later on be applied to unknown simulations. This paper presents an approach to transfer different FE meshes, corresponding FE results and boundary conditions to an individual matrix of fixed size for very different structural mechanic FE simulation. The novel approach uses spherical detector surfaces to project three-dimensional information on its surface. It allows generating the so-called “DNA of an FE simulation”; classification algorithms i.e. Support Vector Machines or Deep Learning Neural Networks such as Convolutional Neural Networks (CNN) can then classify this information. The whole methodology reduces the dimension of a 3D finite element simulation to a 2D matrix of numeric values. The matrix contains all the relevant information for the classification in “plausible” or “non-plausible”. An implausible simulation contains errors, which would be quickly identified by an experienced simulation engineer, whereas a plausible simulation does not contain such errors. As less experienced simulation users in design departments are not trained to find such errors in their simulation setup, they cannot detect them and take adequate countermeasures. In the paper, every single step of the novel methodology for plausibility checking of structural mechanics simulations will be illustrated and explained in detail for simplified parts and corresponding simulations. © Proceedings of NordDesign: Design in the Era of Digitalization, NordDesign 2018. All rights reserved.","author":[{"family":"Spruegel","given":"T.C."},{"family":"Rothfelder","given":"R."},{"family":"Bickel","given":"S."},{"family":"Grauf","given":"A."},{"family":"Sauer","given":"C."},{"family":"Schleich","given":"B."},{"family":"Wartzack","given":"S."}],"citation-key":"Spruegel2018","collection-title":"Proceedings of NordDesign: Design in the Era of Digitalization, NordDesign 2018","ISBN":"978-91-7685-185-2","issued":{"date-parts":[[2018]]},"publisher":"The Design Society","title":"Methodology for plausibility checking of structural mechanics simulations using Deep Learning on existing simulation data","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057150108&partnerID=40&md5=3fa21ccaf4f28ff72326348e85cbde09"},
{"id":"Sridhar2020351","abstract":"As the influence of machine learning grows over decisions in businesses and human life, so grows the need for Model Governance. In this paper, we motivate the need for, define the problem of, and propose a solution for Model Governance in production ML. We show that through our approach one can meaningfully track and understand the who, where, what, when, and how an ML prediction came to be. To the best of our knowledge, this is the first work providing a comprehensive framework for production Model Governance, building upon previous work in developer-focused Model Management. © Proceedings of the 2018 USENIX Annual Technical Conference, USENIX ATC 2018. All rights reserved.","author":[{"family":"Sridhar","given":"V."},{"family":"Subramanian","given":"S."},{"family":"Arteaga","given":"D."},{"family":"Sundararaman","given":"S."},{"family":"Roselli","given":"D."},{"family":"Talagala","given":"N."}],"citation-key":"Sridhar2020351","collection-title":"Proceedings of the 2018 USENIX Annual Technical Conference, USENIX ATC 2018","ISBN":"978-1-939133-02-1","issued":{"date-parts":[[2020]]},"page":"351-357","publisher":"USENIX Association","title":"Model governance: Reducing the anarchy of production ML","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075761598&partnerID=40&md5=393aa3aa505e90fb80de134aad651b4f"},
{"id":"srinivasanWebAppSecurity2017","abstract":"Web app developers often face challenges in using the many available security-testing frameworks, owing to those frameworks' inherent complexity and the lack of proper documentation. No up-to-date criteria exist that can help practitioners and organizations select an appropriate framework. Consequently, numerous vulnerabilities go undetected in the final product, creating a potential for major attacks. To help practitioners select the right framework, researchers classified 26 frameworks, using 27 criteria.","author":[{"family":"Srinivasan","given":"Satish M."},{"family":"Sangwan","given":"Raghvinder S."},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""}],"citation-key":"srinivasanWebAppSecurity2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"99-102","source":"IEEE Computer Society","title":"Web App Security: A Comparison and Categorization of Testing Frameworks","title-short":"Web App Security","type":"article-magazine","volume":"34"},
{"id":"sriramInternetThingsPerspectives2015","accessed":{"date-parts":[[2020,12,19]]},"author":[{"family":"Sriram","given":"Ram D."},{"family":"Sheth","given":"Amit"}],"citation-key":"sriramInternetThingsPerspectives2015","container-title":"IT Professional","container-title-short":"IT Prof.","DOI":"10.1109/MITP.2015.43","ISSN":"1520-9202","issue":"3","issued":{"date-parts":[[2015,5]]},"note":"00024","page":"60-63","source":"DOI.org (Crossref)","title":"Internet of Things Perspectives","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7116432/","volume":"17"},
{"id":"SS04","author":[{"family":"Spinellis","given":"D."},{"family":"Szyperski","given":"C."}],"citation-key":"SS04","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2004,1]]},"page":"28-33","title":"How is open source affecting software development?","type":"article-journal","volume":"21"},
{"id":"stahlModeldrivenSoftwareDevelopment2006","author":[{"family":"Stahl","given":"Thomas"},{"family":"Völter","given":"Markus"}],"call-number":"QA76.76.D47 S69713 2006","citation-key":"stahlModeldrivenSoftwareDevelopment2006","event-place":"Chichester, England ; Hoboken, NJ","ISBN":"978-0-470-02570-3","issued":{"date-parts":[[2006]]},"note":"01472 \nOCLC: ocm64453466","number-of-pages":"428","publisher":"John Wiley","publisher-place":"Chichester, England ; Hoboken, NJ","source":"Library of Congress ISBN","title":"Model-driven software development: technology, engineering, management","title-short":"Model-driven software development","type":"book"},
{"id":"stankovicResearchDirectionsInternet2014","accessed":{"date-parts":[[2016,3,11]]},"author":[{"family":"Stankovic","given":"John A."}],"citation-key":"stankovicResearchDirectionsInternet2014","container-title":"IEEE Internet of Things Journal","DOI":"10.1109/JIOT.2014.2312291","ISSN":"2327-4662","issue":"1","issued":{"date-parts":[[2014,2]]},"page":"3-9","source":"CrossRef","title":"Research Directions for the Internet of Things","type":"article-journal","URL":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6774858","volume":"1"},
{"id":"stansburyGraduateProgramUnmanned2015","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Stansbury","given":"Richard S."},{"family":"Moncayo","given":"Hever"},{"family":"Currier","given":"Patrick"}],"citation-key":"stansburyGraduateProgramUnmanned2015","issued":{"date-parts":[[2015]]},"source":"Google Scholar","title":"A Graduate Program in Unmanned and Autonomous Systems Engineering","type":"article-journal","URL":"http://se.asee.org/proceedings/ASEE2015/papers2015/79.pdf"},
{"id":"steinbach00comparison","author":[{"family":"Steinbach","given":"M."},{"family":"Karypis","given":"G."},{"family":"Kumar","given":"V."}],"citation-key":"steinbach00comparison","container-title":"KDD workshop on text mining","issued":{"date-parts":[[2000]]},"title":"A comparison of document clustering techniques","type":"paper-conference","URL":"http://citeseer.ist.psu.edu/steinbach00comparison.html"},
{"id":"stephanCognizantVirtualSoftware2019","abstract":"We present our new ideas on taking the first steps towards cultivating synergy between model-driven engineering (MDE), machine learning, and software clones. Specifically, we describe our vision in realizing a cognizant virtual software modeling assistant that uses the latter two to improve software design and MDE. Software engineering has benefited greatly from knowledge-based cognizant source code completion and assistance, but MDE has few and limited analogous capabilities. We outline our research directions by describing our vision for a prototype assistant that provides suggestions to modelers performing model creation or extension in the form of 1) complete models for insertion or guidance, and 2) granular single-step operations. These suggestions are derived by detecting clones of the in-progress model and existing domain, organizational, and exemplar models. We overview our envisioned workflow between modeler and assistant, and, using Simulink as an example, illustrate different manifestations including multiple overlays with percentages and employing variant elements.","accessed":{"date-parts":[[2021,4,30]]},"author":[{"family":"Stephan","given":"Matthew"}],"citation-key":"stephanCognizantVirtualSoftware2019","container-title":"2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","DOI":"10.1109/ICSE-NIER.2019.00014","event":"2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","event-place":"Montreal, QC, Canada","ISBN":"978-1-72811-758-4","issued":{"date-parts":[[2019,5]]},"note":"00002","page":"21-24","publisher":"IEEE","publisher-place":"Montreal, QC, Canada","source":"DOI.org (Crossref)","title":"Towards a Cognizant Virtual Software Modeling Assistant using Model Clones","type":"paper-conference","URL":"https://ieeexplore.ieee.org/document/8805738/"},
{"id":"stereotypes2004","author":[{"literal":"Jiang"},{"family":"Yanbing, Weizhong Shao","given":"Lu Zhang","suffix":"Zhiyi Ma, Xiangwen Meng"},{"family":"Ma.","given":"Haohai"}],"citation-key":"stereotypes2004","container-title":"International conference on the unified modeling language","issued":{"date-parts":[[2004]]},"page":"54-68","title":"On the classification of uml’s meta model extension mechanism","type":"paper-conference"},
{"id":"Stevens201754","abstract":"The study of models, and related concepts such as metamodels, is largely situated within the software engineering community under the banner of model-driven development. Yet these concepts have some obvious parallels with concepts developed within the artificial intelligence community under the banners of ontologies and the semantic web. Although a considerable amount of work has been done that aims to relate the development of ontologies to the model-driven development of software, the place of bidirectional transformations within these connected worlds is (almost) unstudied. Yet, experts in the study of ontologies have experienced the need to check and restore consistency, and have developed techniques, terminology and tools that relate to these tasks. In this paper we provide a high-level introduction to the work that has been done, aiming to promote further study and perhaps collaboration between these communities. Copyright © 2017 by the paper's authors.","author":[{"family":"Stevens","given":"P."},{"family":"Gibbons","given":"J."}],"citation-key":"Stevens201754","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Johnson M.","given":"Eramo R."}],"ISSN":"16130073","issued":{"date-parts":[[2017]]},"page":"54-58","publisher":"CEUR-WS","title":"On ontologology","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019246553&partnerID=40&md5=bf8dd48d63d77a58878a1fc67b9f12fe","volume":"1827"},
{"id":"stevensOntologology2017a","abstract":"The study of models, and related concepts such as metamodels, is largely situated within the software engineering community under the banner of model-driven development. Yet these concepts have some obvious parallels with concepts developed within the artificial intelligence community under the banners of ontologies and the semantic web. Although a considerable amount of work has been done that aims to relate the development of ontologies to the model-driven development of software, the place of bidirectional transformations within these connected worlds is (almost) unstudied. Yet, experts in the study of ontologies have experienced the need to check and restore consistency, and have developed techniques, terminology and tools that relate to these tasks. In this paper we provide a high-level introduction to the work that has been done, aiming to promote further study and perhaps collaboration between these communities. Copyright © 2017 by the paper's authors.","author":[{"family":"Stevens","given":"P."},{"family":"Gibbons","given":"J."}],"citation-key":"stevensOntologology2017a","container-title":"CEUR Workshop Proceedings","editor":[{"family":"Johnson M.","given":"Eramo R."}],"ISSN":"16130073","issued":{"date-parts":[[2017]]},"page":"54-58","publisher":"CEUR-WS","title":"On ontologology","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019246553&partnerID=40&md5=bf8dd48d63d77a58878a1fc67b9f12fe","volume":"1827"},
{"id":"stilgoeMachineLearningSocial2018","abstract":"Self-driving cars, a quintessentially ‘smart’ technology, are not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. Understanding and governing the politics of this technology means asking ‘Who is learning, what are they learning and how are they learning?’ Focusing on the successes and failures of social learning around the much-publicized crash of a Tesla Model S in 2016, I argue that trajectories and rhetorics of machine learning in transport pose a substantial governance challenge. ‘Self-driving’ or ‘autonomous’ cars are misnamed. As with other technologies, they are shaped by assumptions about social needs, solvable problems, and economic opportunities. Governing these technologies in the public interest means improving social learning by constructively engaging with the contingencies of machine learning.","author":[{"family":"Stilgoe","given":"Jack"}],"citation-key":"stilgoeMachineLearningSocial2018","container-title":"Social Studies of Science","issue":"1","issued":{"date-parts":[[2018]]},"page":"25-56","title":"Machine learning, social learning and the governance of self-driving cars","type":"article-journal","volume":"48"},
{"id":"stolABCSoftwareEngineering2018","accessed":{"date-parts":[[2020,2,20]]},"author":[{"family":"Stol","given":"Klaas-Jan"},{"family":"Fitzgerald","given":"Brian"}],"citation-key":"stolABCSoftwareEngineering2018","container-title":"ACM Transactions on Software Engineering and Methodology","container-title-short":"ACM Trans. Softw. Eng. Methodol.","DOI":"10.1145/3241743","ISSN":"1049331X","issue":"3","issued":{"date-parts":[[2018,9,17]]},"note":"00000","page":"1-51","source":"DOI.org (Crossref)","title":"The ABC of Software Engineering Research","type":"article-journal","URL":"http://dl.acm.org/citation.cfm?doid=3276753.3241743","volume":"27"},
{"id":"strittmatter2016challenges","author":[{"family":"Strittmatter","given":"Misha"},{"family":"Hinkel","given":"Georg"},{"family":"Langhammer","given":"Michael"},{"family":"Jung","given":"Reiner"},{"family":"Heinrich","given":"Robert"}],"citation-key":"strittmatter2016challenges","container-title":"CEUR workshop proceedings","issued":{"date-parts":[[2016]]},"page":"30-39","publisher":"CEUR","title":"Challenges in the evolution of metamodels: Smells and anti-patterns of a historically-grown metamodel","type":"paper-conference","volume":"1706"},
{"id":"strittmatterChallengesEvolutionMetamodels","abstract":"In model-driven engineering, modeling languages are developed to serve as basis for system design, simulation and code generation. Like any software artifact, modeling languages evolve over time. If, however, the metamodel that defines the language is badly designed, the effort needed for its maintenance is unnecessarily increased. In this paper, we present bad smells and anti-patterns that we discovered in a thorough metamodel review of the Palladio Component Model (PCM). The PCM is a good representative for big and old metamodels that have grown over time. Thus, these results are meaningful, as they reflect the types of smells that accumulate in such metamodels over time. Related work deals mainly with automatically detectable bad smells, anti-patterns and defects. However, there are smells and anti-patterns, which cannot be detected automatically. They should not be neglected. Thus, in this paper, we focus on both: automatically and non-automatically detectable smells.","author":[{"family":"Strittmatter","given":"Misha"},{"family":"Hinkel","given":"Georg"},{"family":"Langhammer","given":"Michael"}],"citation-key":"strittmatterChallengesEvolutionMetamodels","page":"10","source":"Zotero","title":"Challenges in the Evolution of Metamodels: Smells and Anti-Patterns of a Historically-Grown Metamodel","type":"article-journal"},
{"id":"strittmatterChallengesEvolvingMetamodels","abstract":"Like every other software artifact, metamodels are subject to change even in later phases of the software life cycle. In this problem description paper, we first classify metamodel changes. We then elaborate on the challenges of metamodel evolution. The main challenges are the tight coupling of code to metamodels and the pervasiveness of metamodel dependencies. As this is a problem description paper, we will only present a brief overview of possible solutions.","author":[{"family":"Strittmatter","given":"Misha"},{"family":"Heinrich","given":"Robert"}],"citation-key":"strittmatterChallengesEvolvingMetamodels","page":"4","source":"Zotero","title":"Challenges in evolving Metamodels","type":"article-journal"},
{"id":"Subahi2020","abstract":"�Program synthesis is defined as a software development step aims at achieving an automatic process of code generation that is satisfactory given high-level specifications. There are various program synthesis applications built on Machine Learning (ML) and Natural Language Processing (NLP) based approaches. Recently, there have been remarkable advancements in the Artificial Intelligent (AI) domain. The rise in advanced ML techniques has been remarkable. Deep Learning (DL), for instance, is considered an example of a currently attractive research field that has led to advances in the areas of ML and NLP. With this advancement, there is a need to gain greater benefits from these approaches to cognify synthesis processes for next-generation model-driven engineering (MDE) framework. In this work, a systematic domain analysis is conducted to explore the extent to the automatic generation of code can be enabled via the next generation of cognified MDE frameworks that support recent DL and NLP techniques. After identifying critical features that might be considered when distinguishing synthesis systems, it will be possible to introduce a conceptual design for the future involving program synthesis/MDE frameworks. By searching different research database sources, 182 articles related to program synthesis approaches and their applications were identified. After defining research questions, structuring the domain analysis, and applying inclusion and exclusion criteria on the classification scheme, 170 out of 182 articles were considered in a three-phase systematic analysis, guided by some research questions. The analysis is introduced as a key contribution. The results are documented using feature diagrams as a comprehensive feature model of program synthesis showing alternative techniques and architectures. The achieved outcomes serve as motivation for introducing a conceptual architectural design of the next generation of cognified MDE frameworks. © 2020 by the author. Licensee MDPI, Basel, Switzerland.","author":[{"family":"Subahi","given":"A.F."}],"citation-key":"Subahi2020","container-title":"Computers","DOI":"10.3390/computers9020027","ISSN":"2073431X","issue":"2","issued":{"date-parts":[[2020]]},"publisher":"MDPI AG","title":"Cognification of program synthesis—a systematic feature-oriented analysis and future direction","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083357975&doi=10.3390%2fcomputers9020027&partnerID=40&md5=9accdd8c3869cdc70c256788a9acb83e","volume":"9"},
{"id":"subahiCognificationProgramSynthesis2020a","abstract":"�Program synthesis is defined as a software development step aims at achieving an automatic process of code generation that is satisfactory given high-level specifications. There are various program synthesis applications built on Machine Learning (ML) and Natural Language Processing (NLP) based approaches. Recently, there have been remarkable advancements in the Artificial Intelligent (AI) domain. The rise in advanced ML techniques has been remarkable. Deep Learning (DL), for instance, is considered an example of a currently attractive research field that has led to advances in the areas of ML and NLP. With this advancement, there is a need to gain greater benefits from these approaches to cognify synthesis processes for next-generation model-driven engineering (MDE) framework. In this work, a systematic domain analysis is conducted to explore the extent to the automatic generation of code can be enabled via the next generation of cognified MDE frameworks that support recent DL and NLP techniques. After identifying critical features that might be considered when distinguishing synthesis systems, it will be possible to introduce a conceptual design for the future involving program synthesis/MDE frameworks. By searching different research database sources, 182 articles related to program synthesis approaches and their applications were identified. After defining research questions, structuring the domain analysis, and applying inclusion and exclusion criteria on the classification scheme, 170 out of 182 articles were considered in a three-phase systematic analysis, guided by some research questions. The analysis is introduced as a key contribution. The results are documented using feature diagrams as a comprehensive feature model of program synthesis showing alternative techniques and architectures. The achieved outcomes serve as motivation for introducing a conceptual architectural design of the next generation of cognified MDE frameworks. © 2020 by the author. Licensee MDPI, Basel, Switzerland.","author":[{"family":"Subahi","given":"A.F."}],"citation-key":"subahiCognificationProgramSynthesis2020a","container-title":"Computers","DOI":"10.3390/computers9020027","ISSN":"2073431X","issue":"2","issued":{"date-parts":[[2020]]},"publisher":"MDPI AG","title":"Cognification of program synthesis—a systematic feature-oriented analysis and future direction","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083357975&doi=10.3390%2fcomputers9020027&partnerID=40&md5=9accdd8c3869cdc70c256788a9acb83e","volume":"9"},
{"id":"Suchänek202074","abstract":"Knowledge representation in OWL ontologies gained a lot of popularity with the development of Big Data, Artificial Intelligence, Semantic Web, and Linked Open Data. OWL ontologies are very versatile, and there are many tools for analysis, design, documentation, and mapping. They can capture concepts and categories, their properties and relations. Normalized Systems (NS) provide a way of code generation from a model of so-called NS Elements resulting in an information system with proven evolvability. The model used in NS contains domain-specific knowledge that can be represented in an OWL ontology. This work clarifies the potential advantages of having OWL representation of the NS model, discusses the design of a bi-directional transformation between NS models and domain ontologies in OWL, and describes its implementation. It shows how the resulting ontology enables further work on the analytical level and leverages the system design. Moreover, due to the fact that NS metamodel is metacircular, the transformation can generate ontology of NS metamodel itself. It is expected that the results of this work will help with the design of larger real-world applications as well as the metamodel and that the transformation tool will be further extended with additional features which we proposed. © Copyright 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.","author":[{"family":"Suchänek","given":"M."},{"family":"Mannaert","given":"H."},{"family":"Uhnäk","given":"P."},{"family":"Pergl","given":"R."}],"citation-key":"Suchänek202074","collection-title":"ENASE 2020 - Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering","editor":[{"family":"Ali R., Kaindl H.","given":"Maciaszek L.","suffix":"Maciaszek L."}],"ISBN":"978-989-758-421-3","issued":{"date-parts":[[2020]]},"page":"74-85","publisher":"SciTePress","title":"Bi-directional transformation between normalized systems elements and domain ontologies in OWL","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088385688&partnerID=40&md5=df36b44338dd85b1c2007e585a204b4d"},
{"id":"suchanekBidirectionalTransformationNormalized2020a","abstract":"Knowledge representation in OWL ontologies gained a lot of popularity with the development of Big Data, Artificial Intelligence, Semantic Web, and Linked Open Data. OWL ontologies are very versatile, and there are many tools for analysis, design, documentation, and mapping. They can capture concepts and categories, their properties and relations. Normalized Systems (NS) provide a way of code generation from a model of so-called NS Elements resulting in an information system with proven evolvability. The model used in NS contains domain-specific knowledge that can be represented in an OWL ontology. This work clarifies the potential advantages of having OWL representation of the NS model, discusses the design of a bi-directional transformation between NS models and domain ontologies in OWL, and describes its implementation. It shows how the resulting ontology enables further work on the analytical level and leverages the system design. Moreover, due to the fact that NS metamodel is metacircular, the transformation can generate ontology of NS metamodel itself. It is expected that the results of this work will help with the design of larger real-world applications as well as the metamodel and that the transformation tool will be further extended with additional features which we proposed. © Copyright 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.","author":[{"family":"Suchänek","given":"M."},{"family":"Mannaert","given":"H."},{"family":"Uhnäk","given":"P."},{"family":"Pergl","given":"R."}],"citation-key":"suchanekBidirectionalTransformationNormalized2020a","container-title":"ENASE 2020 - Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering","editor":[{"family":"Ali R.","given":"Maciaszek L.","suffix":"Kaindl H., Maciaszek L."}],"ISBN":"978-989-758-421-3","issued":{"date-parts":[[2020]]},"page":"74-85","publisher":"SciTePress","title":"Bi-directional transformation between normalized systems elements and domain ontologies in OWL","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088385688&partnerID=40&md5=df36b44338dd85b1c2007e585a204b4d"},
{"id":"Sun:2014:ESN:2627508.2627514","author":[{"family":"Sun","given":"Xiaobing"},{"family":"Liu","given":"Xiangyue"},{"family":"Hu","given":"Jiajun"},{"family":"Zhu","given":"Junwu"}],"citation-key":"Sun:2014:ESN:2627508.2627514","collection-title":"EAST 2014","container-title":"Proceedings of the 2014 3rd int. Workshop on evidential assessment of soft. Tech.","event-place":"New York, NY, USA","ISBN":"978-1-4503-2965-1","issued":{"date-parts":[[2014]]},"page":"32-39","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Empirical studies on the NLP techniques for source code data preprocessing","type":"paper-conference"},
{"id":"Sun2021","abstract":"Most existing deep learning based single-image super-resolution (SISR) methods mainly improve the reconstruction performances from the perspective of data-driven, i.e., widening or deepening the networks according to the huge scale of the training data. However, it will bring a huge amount of weights and biases, and cost the expensive computations. Recently, some people have proposed a new frame for designing the deep networks according to the algorithms deduced from the ℓ2-optimization problem. But they did not consider the case with outliers. Since ℓ1-norm can describe the sparsity of the outliers better than ℓ2-norm, we propose an effective deep network designed according to the new algorithm deduced from the ℓ1-optimization problem. In our proposed method, an effective iterative algorithm for the ℓ1 reconstructed optimization problem is deduced based on the split Bregman algorithm, majorization–minimization algorithm, and soft thresholding operator. Then according to the deduced iterative algorithm, an effective deep network, named ℓ1 Model-Driven Recursive Multi-Scale Denoising Network (ℓ1-MRMDN), is designed. Due to the iteration form of the deduced algorithm, the proposed ℓ1-MRMDN contains an inner recursion and an outer recursion. Therefore, our proposed method can not only relieve its sensitiveness to the outliers because of the ℓ1 data fidelity term, but also avoid designing the deep network blindly via the guidance of prior knowledge. Extensive experimental results illustrate that our proposed method is superior to some related popular SISR methods. © 2021","author":[{"family":"Sun","given":"Z."},{"family":"Zhao","given":"J."},{"family":"Zhou","given":"Z."},{"family":"Gao","given":"Q."}],"citation-key":"Sun2021","container-title":"Knowledge-Based Systems","DOI":"10.1016/j.knosys.2021.107115","ISSN":"09507051","issued":{"date-parts":[[2021]]},"publisher":"Elsevier B.V.","title":"L1 model-driven recursive multi-scale denoising network for image super-resolution","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105491569&doi=10.1016%2fj.knosys.2021.107115&partnerID=40&md5=c315b429b9a2dc6c0ec4b84210149687","volume":"225"},
{"id":"Sun2022","abstract":"There is a growing interest in the wireless technology to complement the traditional model-driven design approaches with data-driven machine learning- (ML-) based solutions. Telling a white lie is a distinct type of prosocial behavior, because in terms of the nature of lies, it is a lie but its motivation is to benefit someone else. It is unclear how children behave when they are caught in a conflict between prosocial motivation and the psychological cost of losing in a competition. Big data analysis can improve work efficiency, make analysis work more organized, and make analysis results more accurate. So the purpose of this study was to investigate the motivation of children to tell white lies by using big data analysis to examine the effects of different competitive situations on white lie behavior among 6- to 11-year olds. A final-round-of-game paradigm was used to elicit prosocial white lies in children under varying competitive conditions. These were explored in two studies. In the study, two groups of children (N=177, Mage=104.41 months, SD=1.74, 50.8% boys) participated in either baseline conditions or a competition against others. More children tended to tell the truth in the others-competition context group, and boys tended to be more truthful. These findings show that a decision of whether to tell a white lie is influenced by the psychological cost to children. © 2022 Yunrui Sun et al.","author":[{"family":"Sun","given":"Y."},{"family":"Lyu","given":"Y."},{"family":"Ma","given":"J."}],"citation-key":"Sun2022","container-title":"Wireless Communications and Mobile Computing","DOI":"10.1155/2022/1127915","ISSN":"15308669","issued":{"date-parts":[[2022]]},"publisher":"Hindawi Limited","title":"Competitive contexts reduce children's motivation to tell white lies based on big data analysis","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129969344&doi=10.1155%2f2022%2f1127915&partnerID=40&md5=6a7c5d2dd248b60c2ebae52475196b21","volume":"2022"},
{"id":"sunAIEnhancedOffloadingEdge2019","abstract":"The Industrial Internet of Things (IIoT) enables intelligent industrial operations by incorporating artificial intelligence (AI) and big data technologies. An AI-enabled framework typically requires prompt and private cloud-based service to process and aggregate manufacturing data. Thus, integrating intelligence into edge computing is without doubt a promising development trend. Nevertheless, edge intelligence brings heterogeneity to the edge servers, in terms of not only computing capability, but also service accuracy. Most works on offloading in edge computing focus on finding the power-delay trade-off, ignoring service accuracy provided by edge servers as well as the accuracy required by IIoT devices. In this vein, in this article we introduce an intelligent computing architecture with cooperative edge and cloud computing for IIoT. Based on the computing architecture, an AI enhanced offloading framework is proposed for service accuracy maximization, which considers service accuracy as a new metric besides delay, and intelligently disseminates the traffic to edge servers or through an appropriate path to remote cloud. A case study is performed on transfer learning to show the performance gain of the proposed framework.","accessed":{"date-parts":[[2020,12,17]]},"author":[{"family":"Sun","given":"Wen"},{"family":"Liu","given":"Jiajia"},{"family":"Yue","given":"Yanlin"}],"citation-key":"sunAIEnhancedOffloadingEdge2019","container-title":"IEEE Network","container-title-short":"IEEE Network","DOI":"10.1109/MNET.001.1800510","ISSN":"0890-8044, 1558-156X","issue":"5","issued":{"date-parts":[[2019,9]]},"note":"00000","page":"68-74","source":"DOI.org (Crossref)","title":"AI-Enhanced Offloading in Edge Computing: When Machine Learning Meets Industrial IoT","title-short":"AI-Enhanced Offloading in Edge Computing","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/8863729/","volume":"33"},
{"id":"sunCompetitiveContextsReduce2022a","abstract":"There is a growing interest in the wireless technology to complement the traditional model-driven design approaches with data-driven machine learning- (ML-) based solutions. Telling a white lie is a distinct type of prosocial behavior, because in terms of the nature of lies, it is a lie but its motivation is to benefit someone else. It is unclear how children behave when they are caught in a conflict between prosocial motivation and the psychological cost of losing in a competition. Big data analysis can improve work efficiency, make analysis work more organized, and make analysis results more accurate. So the purpose of this study was to investigate the motivation of children to tell white lies by using big data analysis to examine the effects of different competitive situations on white lie behavior among 6- to 11-year olds. A final-round-of-game paradigm was used to elicit prosocial white lies in children under varying competitive conditions. These were explored in two studies. In the study, two groups of children (N=177, Mage=104.41 months, SD=1.74, 50.8% boys) participated in either baseline conditions or a competition against others. More children tended to tell the truth in the others-competition context group, and boys tended to be more truthful. These findings show that a decision of whether to tell a white lie is influenced by the psychological cost to children. © 2022 Yunrui Sun et al.","author":[{"family":"Sun","given":"Y."},{"family":"Lyu","given":"Y."},{"family":"Ma","given":"J."}],"citation-key":"sunCompetitiveContextsReduce2022a","container-title":"Wireless Communications and Mobile Computing","DOI":"10.1155/2022/1127915","ISSN":"15308669","issued":{"date-parts":[[2022]]},"publisher":"Hindawi Limited","title":"Competitive Contexts Reduce Children's Motivation to Tell White Lies Based on Big Data Analysis","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129969344&doi=10.1155%2f2022%2f1127915&partnerID=40&md5=6a7c5d2dd248b60c2ebae52475196b21","volume":"2022"},
{"id":"sunConvergenceRecommenderSystems2020","accessed":{"date-parts":[[2020,12,18]]},"author":[{"family":"Sun","given":"Chuan"},{"family":"Li","given":"Hui"},{"family":"Li","given":"Xiuhua"},{"family":"Wen","given":"Junhao"},{"family":"Xiong","given":"Qingyu"},{"family":"Zhou","given":"Wei"}],"citation-key":"sunConvergenceRecommenderSystems2020","container-title":"IEEE Access","container-title-short":"IEEE Access","DOI":"10.1109/ACCESS.2020.2978896","ISSN":"2169-3536","issued":{"date-parts":[[2020]]},"note":"00000","page":"47118-47132","source":"DOI.org (Crossref)","title":"Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey","title-short":"Convergence of Recommender Systems and Edge Computing","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/9026896/","volume":"8"},
{"id":"Sünderhauf2018405","abstract":"The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning. We explain the need for better evaluation metrics, highlight the importance and unique challenges for deep robotic learning in simulation, and explore the spectrum between purely data-driven and model-driven approaches. We hope this paper provides a motivating overview of important research directions to overcome the current limitations, and helps to fulfill the promising potentials of deep learning in robotics. © 2018, © The Author(s) 2018.","author":[{"family":"Sünderhauf","given":"N."},{"family":"Brock","given":"O."},{"family":"Scheirer","given":"W."},{"family":"Hadsell","given":"R."},{"family":"Fox","given":"D."},{"family":"Leitner","given":"J."},{"family":"Upcroft","given":"B."},{"family":"Abbeel","given":"P."},{"family":"Burgard","given":"W."},{"family":"Milford","given":"M."},{"family":"Corke","given":"P."}],"citation-key":"Sünderhauf2018405","container-title":"International Journal of Robotics Research","DOI":"10.1177/0278364918770733","ISSN":"02783649","issue":"4-5","issued":{"date-parts":[[2018]]},"page":"405-420","publisher":"SAGE Publications Inc.","title":"The limits and potentials of deep learning for robotics","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046709786&doi=10.1177%2f0278364918770733&partnerID=40&md5=0bc3b31b095684a8cbbab2e29e82f2a3","volume":"37"},
{"id":"sunderhaufLimitsPotentialsDeep2018a","abstract":"The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning. We explain the need for better evaluation metrics, highlight the importance and unique challenges for deep robotic learning in simulation, and explore the spectrum between purely data-driven and model-driven approaches. We hope this paper provides a motivating overview of important research directions to overcome the current limitations, and helps to fulfill the promising potentials of deep learning in robotics. © 2018, © The Author(s) 2018.","author":[{"family":"Sünderhauf","given":"N."},{"family":"Brock","given":"O."},{"family":"Scheirer","given":"W."},{"family":"Hadsell","given":"R."},{"family":"Fox","given":"D."},{"family":"Leitner","given":"J."},{"family":"Upcroft","given":"B."},{"family":"Abbeel","given":"P."},{"family":"Burgard","given":"W."},{"family":"Milford","given":"M."},{"family":"Corke","given":"P."}],"citation-key":"sunderhaufLimitsPotentialsDeep2018a","container-title":"International Journal of Robotics Research","DOI":"10.1177/0278364918770733","ISSN":"02783649","issue":"4-5","issued":{"date-parts":[[2018]]},"page":"405-420","publisher":"SAGE Publications Inc.","title":"The limits and potentials of deep learning for robotics","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046709786&doi=10.1177%2f0278364918770733&partnerID=40&md5=0bc3b31b095684a8cbbab2e29e82f2a3","volume":"37"},
{"id":"sunhareInternetThingsData2020","abstract":"Advancement in the fields of electronic communication, data processing, and internet technologies enable easy access to and interaction with a variety of physical devices throughout the globe. Our whole world is enveloped by a blanket of innumerable smart devices equipped with the sensors and actuators. Extensive research on the Internet of things (IoT) with cloud technologies, make it possible to accumulate tremendous data created from this heterogeneous environment and transform it into precious knowledge by utilizing data mining technologies. Furthermore, this generated knowledge will play a key role in intelligent decision making, system performance boosting, and optimum management of resources and services. With this background, this paper presents a systematic and detailed review of various data mining techniques employed in the large and small scale IoT applications to formulate an intelligent environment. It also presents an overview of cloud-assisted IoT Big data mining system to better understand the importance of data mining for an IoT environment.","accessed":{"date-parts":[[2022,2,3]]},"author":[{"family":"Sunhare","given":"Priyank"},{"family":"Chowdhary","given":"Rameez R."},{"family":"Chattopadhyay","given":"Manju K."}],"citation-key":"sunhareInternetThingsData2020","container-title":"Journal of King Saud University - Computer and Information Sciences","container-title-short":"Journal of King Saud University - Computer and Information Sciences","DOI":"10.1016/j.jksuci.2020.07.002","ISSN":"13191578","issued":{"date-parts":[[2020,7]]},"note":"00022","page":"S131915782030416X","source":"DOI.org (Crossref)","title":"Internet of things and data mining: An application oriented survey","title-short":"Internet of things and data mining","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S131915782030416X"},
{"id":"sunkleAIdrivenStreamlinedModeling2022","accessed":{"date-parts":[[2022,5,24]]},"author":[{"family":"Sunkle","given":"Sagar"},{"family":"Saxena","given":"Krati"},{"family":"Patil","given":"Ashwini"},{"family":"Kulkarni","given":"Vinay"}],"citation-key":"sunkleAIdrivenStreamlinedModeling2022","container-title":"Software and Systems Modeling","container-title-short":"Softw Syst Model","DOI":"10.1007/s10270-022-00982-6","ISSN":"1619-1366, 1619-1374","issue":"3","issued":{"date-parts":[[2022,6]]},"page":"1-23","publisher":"Springer Science and Business Media Deutschland GmbH","source":"DOI.org (Crossref)","title":"AI-driven streamlined modeling: experiences and lessons learned from multiple domains","title-short":"AI-driven streamlined modeling","type":"article-journal","URL":"https://link.springer.com/10.1007/s10270-022-00982-6","volume":"21"},
{"id":"suriModelbasedDevelopmentModular2017","accessed":{"date-parts":[[2018,1,3]]},"author":[{"family":"Suri","given":"Kunal"},{"family":"Cuccuru","given":"Arnaud"},{"family":"Cadavid","given":"Juan"},{"family":"Gerard","given":"Sebastien"},{"family":"Gaaloul","given":"Walid"},{"family":"Tata","given":"Samir"}],"citation-key":"suriModelbasedDevelopmentModular2017","DOI":"10.5220/0006210504870495","ISBN":"978-989-758-210-3","issued":{"date-parts":[[2017]]},"page":"487-495","publisher":"SCITEPRESS - Science and Technology Publications","source":"CrossRef","title":"Model-based Development of Modular Complex Systems for Accomplishing System Integration for Industry 4.0:","title-short":"Model-based Development of Modular Complex Systems for Accomplishing System Integration for Industry 4.0","type":"paper-conference","URL":"http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006210504870495"},
{"id":"SurveyClusteringData","accessed":{"date-parts":[[2015,4,16]]},"citation-key":"SurveyClusteringData","title":"A Survey of Clustering Data Mining Techniques - Springer","type":"webpage","URL":"http://link.springer.com/chapter/10.1007%2F3-540-28349-8_2"},
{"id":"SurveyNoSQLDatabase","abstract":"With the development of the Internet and cloud computing, there need databases to be able to store and process big data effectively, demand for high-performance when reading and writing, so the traditional relational database is facing many new challenges. Especially in large scale and high-concurrency applications, such as search engines and SNS, using the relational database to store and query dynamic user data has appeared to be inadequate. In this case, NoSQL database created. This paper describes the background, basic characteristics, data model of NoSQL. In addition, this paper classifies NoSQL databases according to the CAP theorem. Finally, the mainstream NoSQL databases are separately described in detail, and extract some properties to help enterprises to choose NoSQL.","accessed":{"date-parts":[[2021,3,22]]},"citation-key":"SurveyNoSQLDatabase","note":"01100","title":"Survey on NoSQL database","type":"webpage","URL":"http://ieeexplore.ieee.org/abstract/document/6106531/?casa_token=skk-O-EQilsAAAAA:E0LtNJ8JtgHBiTRq54qaAudBrRo6Iz4BFciGElfCEkBSW7ZVSzK8lyjhT-MGt35cwpStASMZ"},
{"id":"sutskeverSequenceSequenceLearning2014","abstract":"Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT'14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous best result on this task. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.","accessed":{"date-parts":[[2021,3,31]]},"author":[{"family":"Sutskever","given":"Ilya"},{"family":"Vinyals","given":"Oriol"},{"family":"Le","given":"Quoc V."}],"citation-key":"sutskeverSequenceSequenceLearning2014","container-title":"arXiv:1409.3215 [cs]","issued":{"date-parts":[[2014,12,14]]},"note":"14365","source":"arXiv.org","title":"Sequence to Sequence Learning with Neural Networks","type":"article-journal","URL":"http://arxiv.org/abs/1409.3215"},
{"id":"Svozil1997","author":[{"family":"Svozil","given":"Daniel"},{"family":"Kvasnicka","given":"Vladimir"},{"family":"Pospíchal","given":"Jiří"}],"citation-key":"Svozil1997","container-title":"Chemometrics and Intelligent Laboratory Systems","issued":{"date-parts":[[1997,11]]},"page":"43-62","title":"Introduction to multi-layer feed-forward neural networks","type":"article-journal","volume":"39"},
{"id":"svyatkovskiyPythiaAIassistedCode2019","abstract":"In this paper, we propose a novel end-to-end approach for AI-assisted code completion called Pythia. It generates ranked lists of method and API recommendations which can be used by software developers at edit time. The system is currently deployed as part of Intellicode extension in Visual Studio Code IDE. Pythia exploits state-of-the-art large-scale deep learning models trained on code contexts extracted from abstract syntax trees. It is designed to work at a high throughput predicting the best matching code completions on the order of 100 ms. We describe the architecture of the system, perform comparisons to frequency-based approach and invocation-based Markov Chain language model, and discuss challenges serving Pythia models on lightweight client devices. The offline evaluation results obtained on 2700 Python open source software GitHub repositories show a top-5 accuracy of 92%, surpassing the baseline models by 20% averaged over classes, for both intra and cross-project settings.","accessed":{"date-parts":[[2021,7,1]]},"author":[{"family":"Svyatkovskiy","given":"Alexey"},{"family":"Zhao","given":"Ying"},{"family":"Fu","given":"Shengyu"},{"family":"Sundaresan","given":"Neel"}],"citation-key":"svyatkovskiyPythiaAIassistedCode2019","container-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","DOI":"10.1145/3292500.3330699","issued":{"date-parts":[[2019,7,25]]},"note":"00037","page":"2727-2735","source":"arXiv.org","title":"Pythia: AI-assisted Code Completion System","title-short":"Pythia","type":"article-journal","URL":"http://arxiv.org/abs/1912.00742"},
{"id":"SwarmRobotsCan","accessed":{"date-parts":[[2016,8,30]]},"citation-key":"SwarmRobotsCan","title":"Swarm robots can learn by simply observing -- ScienceDaily","type":"webpage","URL":"https://www.sciencedaily.com/releases/2016/08/160830083653.htm"},
{"id":"syrianiAToMPMWebbasedModeling2013","accessed":{"date-parts":[[2015,6,24]]},"author":[{"family":"Syriani","given":"Eugene"},{"family":"Vangheluwe","given":"Hans"},{"family":"Mannadiar","given":"Raphael"},{"family":"Hansen","given":"Conner"},{"family":"Van Mierlo","given":"Simon"},{"family":"Ergin","given":"Hüseyin"}],"citation-key":"syrianiAToMPMWebbasedModeling2013","container-title":"Demos/Posters/StudentResearch@ MoDELS","issued":{"date-parts":[[2013]]},"page":"21–25","publisher":"Citeseer","source":"Google Scholar","title":"AToMPM: A Web-based Modeling Environment.","title-short":"AToMPM","type":"paper-conference","URL":"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.407.6965&rep=rep1&type=pdf"},
{"id":"syrianiChallengesAddressingQuality2012","author":[{"family":"Syriani","given":"Eugene"},{"family":"Gray","given":"Jeff"}],"citation-key":"syrianiChallengesAddressingQuality2012","container-title":"2012 IEEE Fifth International Conference on Software Testing, Verification and Validation","DOI":"10.1109/ICST.2012.198","issued":{"date-parts":[[2012]]},"page":"929–937","title":"Challenges for Addressing Quality Factors in Model Transformation","type":"article-journal"},
{"id":"syrianiChallengesAddressingQuality2012a","accessed":{"date-parts":[[2015,10,29]]},"author":[{"family":"Syriani","given":"Eugene"},{"family":"Gray","given":"Jeff"}],"citation-key":"syrianiChallengesAddressingQuality2012a","container-title":"Software Testing, Verification and Validation (ICST), 2012 IEEE Fifth International Conference on","issued":{"date-parts":[[2012]]},"page":"929–937","publisher":"IEEE","source":"Google Scholar","title":"Challenges for addressing quality factors in model transformation","type":"paper-conference","URL":"http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6200115"},
{"id":"syrianiModelingModelTransformation2013","author":[{"family":"Syriani","given":"Eugene"},{"family":"Gray","given":"Jeff"},{"family":"Vangheluwe","given":"Hans"}],"citation-key":"syrianiModelingModelTransformation2013","container-title":"Domain Engineering","DOI":"10.1007/978-3-642-36654-3_9","issued":{"date-parts":[[2013]]},"page":"211–237","title":"Modeling a Model Transformation Language","type":"article-journal"},
{"id":"syrianiProceedings23rdACM2020","accessed":{"date-parts":[[2021,1,7]]},"author":[{"family":"Syriani","given":"Eugene"},{"literal":"Association for Computing Machinery"},{"literal":"Special Interest Group on Software Engineering"}],"citation-key":"syrianiProceedings23rdACM2020","ISBN":"978-1-4503-7019-6","issued":{"date-parts":[[2020]]},"note":"00000 \nOCLC: 1227082484","source":"Open WorldCat","title":"Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems.","type":"book","URL":"https://dl.acm.org/action/showBook?doi=10.1145/3365438"},
{"id":"syrianiTCoreFrameworkCustombuilt2013","author":[{"family":"Syriani","given":"Eugene"},{"family":"Vangheluwe","given":"Hans"},{"family":"LaShomb","given":"Brian"}],"citation-key":"syrianiTCoreFrameworkCustombuilt2013","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-013-0370-4","issued":{"date-parts":[[2013]]},"title":"T-Core: a framework for custom-built model transformation engines","type":"article-journal"},
{"id":"szvetitsSystematicLiteratureReview2013","accessed":{"date-parts":[[2015,7,4]]},"author":[{"family":"Szvetits","given":"Michael"},{"family":"Zdun","given":"Uwe"}],"citation-key":"szvetitsSystematicLiteratureReview2013","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-013-0394-9","ISSN":"1619-1366, 1619-1374","issued":{"date-parts":[[2013,12,17]]},"source":"CrossRef","title":"Systematic literature review of the objectives, techniques, kinds, and architectures of models at runtime","type":"article-journal","URL":"http://link.springer.com/10.1007/s10270-013-0394-9"},
{"id":"TableContents2017","accessed":{"date-parts":[[2019,8,22]]},"citation-key":"TableContents2017","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2017.23","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017,1]]},"page":"2-3","source":"DOI.org (Crossref)","title":"Table of contents","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7819381/","volume":"34"},
{"id":"Tadejko2020169","abstract":"Cognitive Services are cloud computing services available to help developers build intelligent applications based on Machine Learning (ML) methods with pre-trained models as a service. Machine Learning platforms are one of the fastest growing services of the cloud because ML and Artificial Intelligence (AI) platforms are available through diverse delivery models such as cognitive computing, automated machine learning, model management. Cognitive Computing is delivered as a set of APIs. Due to the nature of the technologies involved in ML ecosystems and Knowledge Hierarchy—Data, Information, Knowledge, Wisdom (DIKW) Pyramid, there is a natural overlap of a technologies and Knowledge Management (KM) processes. The modern architecture of software solutions can be developed with the use of a wide technology stack, including cloud computing technologies and Cognitive Services (CS). We can use a wide range of ML tools at all levels of the DIKW pyramid. In this paper, we propose a new CS based approach to build an architecture of Knowledge Management system. We have analyzed the possibilities of using CS at all levels of the DIKW pyramid. We discussed some of the relevant aspects of Cloud CS and ML in Knowledge Management context and possibilities implementation of Cognitive Services on knowledge processing. © Springer Nature Switzerland AG 2020.","author":[{"family":"Tadejko","given":"P."}],"citation-key":"Tadejko2020169","container-title":"Lecture Notes on Data Engineering and Communications Technologies","DOI":"10.1007/978-3-030-34706-2_9","ISSN":"23674512","issued":{"date-parts":[[2020]]},"page":"169-190","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Cloud cognitive services based on machine learning methods in architecture of modern knowledge management solutions","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078996708&doi=10.1007%2f978-3-030-34706-2_9&partnerID=40&md5=da203377d411a4362b9f051977d457a7","volume":"40"},
{"id":"taghaviNewInsightsDeveloping2018","accessed":{"date-parts":[[2020,4,21]]},"author":[{"family":"Taghavi","given":"Mona"},{"family":"Bentahar","given":"Jamal"},{"family":"Bakhtiyari","given":"Kaveh"},{"family":"Hanachi","given":"Chihab"}],"citation-key":"taghaviNewInsightsDeveloping2018","container-title":"The Computer Journal","DOI":"10.1093/comjnl/bxx056","ISSN":"0010-4620, 1460-2067","issue":"3","issued":{"date-parts":[[2018,3,1]]},"page":"319-348","source":"DOI.org (Crossref)","title":"New Insights Towards Developing Recommender Systems","type":"article-journal","URL":"https://academic.oup.com/comjnl/article/61/3/319/3893562","volume":"61"},
{"id":"tahaModelingBasicAspects2013","accessed":{"date-parts":[[2016,2,5]]},"author":[{"family":"Taha","given":"Walid"},{"family":"Philippsen","given":"Roland"}],"citation-key":"tahaModelingBasicAspects2013","container-title":"arXiv preprint arXiv:1303.2792","issued":{"date-parts":[[2013]]},"source":"Google Scholar","title":"Modeling basic aspects of cyber-physical systems","type":"article-journal","URL":"http://arxiv.org/abs/1303.2792"},
{"id":"tairasCorpusbasedAnalysisDomainspecific2015","accessed":{"date-parts":[[2015,6,9]]},"author":[{"family":"Tairas","given":"Robert"},{"family":"Cabot","given":"Jordi"}],"citation-key":"tairasCorpusbasedAnalysisDomainspecific2015","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-013-0352-6","ISSN":"1619-1366, 1619-1374","issue":"2","issued":{"date-parts":[[2015,5]]},"page":"889-904","source":"CrossRef","title":"Corpus-based analysis of domain-specific languages","type":"article-journal","URL":"http://link.springer.com/10.1007/s10270-013-0352-6","volume":"14"},
{"id":"taivalsaariRoadmapProgrammableWorld2017","abstract":"The Internet of Things (IoT) represents the next significant step in the evolution of the Internet and software development. Although most IoT research focuses on data acquisition, analytics, and visualization, a subtler but equally important transition is underway. Hardware advances are making it possible to embed fully fledged virtual machines and dynamic language runtimes virtually everywhere, leading to a Programmable World in which all our everyday things are connected and programmable dynamically. The emergence of millions of remotely programmable devices in our surroundings will pose significant software development challenges. A roadmap from today's cloud-centric, data-centric IoT systems to the Programmable World highlights the technical challenges that deserve to be part of developer education and deserve deeper investigation beyond those IoT topics that receive the most attention today.","author":[{"family":"Taivalsaari","given":"Antero"},{"family":"Mikkonen","given":"Tommi"},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""}],"citation-key":"taivalsaariRoadmapProgrammableWorld2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"72-80","source":"IEEE Computer Society","title":"A Roadmap to the Programmable World: Software Challenges in the IoT Era","title-short":"A Roadmap to the Programmable World","type":"article-magazine","volume":"34"},
{"id":"Tan2020566","abstract":"Big data and artificial intelligence methods are combined with information technology methods for engineering construction to develop an information model for slope design during construction of hydropower stations using the BIM technique and an information model. An information model management platform for slope construction was developed for hydropower projects based on intelligent construction theory for sense, analysis, and control with integrated scheduling, quality control and safety management. Results for the construction of the Baihetan Hydropower Project as an example show that the platform provides comprehensive digital management for design results, construction processes and slope construction for large hydropower projects. The system more effectively controls the construction progress, reduces safety risks and provides a comprehensive data archive for the entire slope construction process to improve the construction efficiency and economics. © 2020, Tsinghua University Press. All right reserved.","author":[{"family":"Tan","given":"Y."},{"family":"Chen","given":"W."},{"family":"Guo","given":"Z."},{"family":"Lin","given":"E."},{"family":"Lin","given":"P."},{"family":"Zhou","given":"M."},{"family":"Li","given":"J."}],"citation-key":"Tan2020566","container-title":"Qinghua Daxue Xuebao/Journal of Tsinghua University","DOI":"10.16511/j.cnki.qhdxxb.2020.26.004","ISSN":"10000054","issue":"7","issued":{"date-parts":[[2020]]},"page":"566-574","publisher":"Press of Tsinghua University","title":"Information model for slope construction in hydropower projects [水电工程边坡施工全过程信息模型研究与应用]","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086581787&doi=10.16511%2fj.cnki.qhdxxb.2020.26.004&partnerID=40&md5=ffbbd5e55216362acf5a8f3bb358e332","volume":"60"},
{"id":"Tang2019385","abstract":"Code generation is a model-driven engineering approach that enables developers to generate source code automatically and achieves extremely high development productivity. Specifically, generating code from a descriptive text reduces the time and expense of software development significantly. However, the performance of existing methods is not satisfying, since they are either of low accuracy (lack of specifics of the generated code) or too complicated (lack of efficiency in training). In this work, we proposed three novel methods by combining neural architectures and syntax rules, aiming at explicitly capturing the syntactical characteristics of target code. First, we proposed three models based on the Combination of Deep learning and Syntax rules (CDS models). Then, we evaluated CDS models with BLEU metric by comparing our models with existing methods. The results show that our models outperform existing methods for the challenging code generation task. Finally, we conducted a comparative study between the three CDS models. With further analysis we provided advice on the choice of neural architectures by considering both task accuracy and efficiency. Experimental results show that (1) there is a trade-off between speed and accuracy of the model, and (2) one of our CDS models (i.e., the CDS-POOLING model) outperforms other existing methods for the challenging code generation task. © 2019 Knowledge Systems Institute Graduate School. All rights reserved.","author":[{"family":"Tang","given":"X."},{"family":"Wang","given":"Z."},{"family":"Qi","given":"J."},{"family":"Li","given":"Z."}],"citation-key":"Tang2019385","collection-title":"Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE","DOI":"10.18293/SEKE2019-170","ISBN":"1-891706-48-9","ISSN":"23259000","issued":{"date-parts":[[2019]]},"page":"385-390","publisher":"Knowledge Systems Institute Graduate School","title":"Improving code generation from descriptive text by combining deep learning and syntax rules","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071390647&doi=10.18293%2fSEKE2019-170&partnerID=40&md5=f109811ea84fa9ae6dfc4d14bea7fbae","volume":"2019-July"},
{"id":"Tang2022251","abstract":"In this paper, for the long code decoding problem, we analyze the performance of belief propagation (BP) decoder in neural network. The decoding of long codes has always been a concern of LDPC decoding. In recent years, the application of neural networks in the communication field has gradually become widespread. As a result, we are considering and combining the two. The decoding method proposed in this paper uses model-driven deep learning. The network we propose is a neural standardized BP LDPC decoding network. Model-driven deep learning absorbs the advantages of both model-driven and data-driven, which combines them adaptively. The network structure proposed in this paper takes advantage of model-driven to expand the iterative process of decoding between check nodes and variable nodes into the neural network. We can increase the number of iterations by increasing the CN layer and VN layer of the hidden layer. Furthermore, by changing the SNR to detect its relationship with system robustness, and, finally, determine the appropriate SNR range. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","author":[{"family":"Tang","given":"Y."},{"family":"Zhou","given":"L."},{"family":"Zhang","given":"S."},{"family":"Chen","given":"Y."}],"citation-key":"Tang2022251","container-title":"Smart Innovation, Systems and Technologies","DOI":"10.1007/978-981-16-5164-9_30","editor":[{"family":"Jain L.C., Kountchev R.","given":"Hu B.","suffix":"Kountcheva R."}],"ISBN":"9789811651632","ISSN":"21903018","issued":{"date-parts":[[2022]]},"page":"251-257","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Modern-driven deep learning for belief propagation LDPC decoding","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123310266&doi=10.1007%2f978-981-16-5164-9_30&partnerID=40&md5=8b6d316c097c5db5f4cb7a4c7a0c21ee","volume":"257"},
{"id":"tangBridgingGapRequirement2015","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Tang","given":"Shan"},{"family":"Li","given":"Liping"},{"family":"Cao","given":"Xiaoxia"},{"family":"Tan","given":"Wenan"}],"citation-key":"tangBridgingGapRequirement2015","container-title":"Software Engineering and Service Science (ICSESS), 2015 6th IEEE International Conference on","issued":{"date-parts":[[2015]]},"page":"1102–1105","publisher":"IEEE","source":"Google Scholar","title":"Bridging the gap between requirement analysis and architecture design of self-adaptive systems","type":"paper-conference","URL":"http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7339244"},
{"id":"Teh:EtAl:06","abstract":"We consider problems involving groups of data, where each observation within a group is a draw from a mixture model, and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this setting it is natural to consider sets of Dirichlet processes, one for each group, where the well-known clustering property of the Dirichlet process provides a nonparametric prior for the number of mixture componentswithin each group. Given our desire to tie the mixture models in the various groups, we consider a hierarchical model, specifically one in which the base measure for the child Dirichlet processes is itself distributed according to a Dirichlet process. Such a base measure being discrete, the child Dirichlet processes necessarily share atoms. Thus, as desired, the mixture models in the different groups necessarily share mixture components. We discuss representations of hierarchical Dirichlet processes in terms of a stick-breaking process, and a generalization of the Chinese restaurant process that we refer to as the \"Chinese restaurant franchise\". We present Markov chain Monte Carlo algorithms for posterior inference in hierarchical Dirichlet process mixtures, and describe applications to problems in information retrieval and text modelling.","author":[{"family":"Teh","given":"Yee Whye"},{"family":"Jordan","given":"Michael I."},{"family":"Beal","given":"Matthew J."},{"family":"Blei","given":"David M."}],"citation-key":"Teh:EtAl:06","container-title":"Journal of the American Statistical Association","issue":"476","issued":{"date-parts":[[2006]]},"page":"1566-1581","title":"Hierarchical dirichlet processes","type":"article-journal","URL":"http://www.gatsby.ucl.ac.uk/~ywteh/research/npbayes/jasa2006.pdf","volume":"101"},
{"id":"TemporalEMFTemporalMeta","accessed":{"date-parts":[[2018,8,10]]},"citation-key":"TemporalEMFTemporalMeta","title":"TemporalEMF: A Temporal (meta) modeling Framework","type":"webpage","URL":"https://modeling-languages.com/temporal-modeling-framework-emf/"},
{"id":"thummalapentaParsewebProgrammerAssistant2007","author":[{"family":"Thummalapenta","given":"Suresh"},{"family":"Xie","given":"Tao"}],"citation-key":"thummalapentaParsewebProgrammerAssistant2007","collection-title":"ASE '07","container-title":"Proceedings of the twenty-second IEEE/ACM international conference on automated software engineering","event-place":"New York, NY, USA","ISBN":"978-1-59593-882-4","issued":{"date-parts":[[2007]]},"page":"204-213","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Parseweb: A programmer assistant for reusing open source code on the web","type":"paper-conference","URL":"http://doi.acm.org/10.1145/1321631.1321663"},
{"id":"Thung2013Automated","citation-key":"Thung2013Automated","title":"Thung et al. - 2013 - Automated library recommendation","type":"article-journal"},
{"id":"thungAPIRecommendationSystem2016","accessed":{"date-parts":[[2017,6,19]]},"author":[{"family":"Thung","given":"Ferdian"}],"citation-key":"thungAPIRecommendationSystem2016","container-title":"Automated Software Engineering (ASE), 2016 31st IEEE/ACM International Conference on","issued":{"date-parts":[[2016]]},"page":"896–899","publisher":"IEEE","source":"Google Scholar","title":"API recommendation system for software development","type":"paper-conference","URL":"http://ieeexplore.ieee.org/abstract/document/7582836/"},
{"id":"thungAutomaticRecommendationAPI2013","author":[{"family":"Thung","given":"Ferdian"},{"family":"Wang","given":"Shaowei"},{"family":"Lo","given":"David"},{"family":"Lawall","given":"Julia"}],"citation-key":"thungAutomaticRecommendationAPI2013","container-title":"Proceedings of the 28th IEEE/ACM International Conference on Automated Software Engineering","event-place":"Silicon Valley, CA, USA","issued":{"date-parts":[[2013]]},"page":"290–300","publisher":"IEEE Press","publisher-place":"Silicon Valley, CA, USA","source":"Google Scholar","title":"Automatic recommendation of API methods from feature requests","type":"paper-conference"},
{"id":"thungDetectingSimilarApplications2012","accessed":{"date-parts":[[2017,3,14]]},"author":[{"family":"Thung","given":"Ferdian"},{"family":"Lo","given":"David"},{"family":"Jiang","given":"Lingxiao"}],"citation-key":"thungDetectingSimilarApplications2012","container-title":"Software Maintenance (ICSM), 2012 28th IEEE International Conference on","issued":{"date-parts":[[2012]]},"page":"600–603","publisher":"IEEE","source":"Google Scholar","title":"Detecting similar applications with collaborative tagging","type":"paper-conference","URL":"http://ieeexplore.ieee.org/abstract/document/6405331/"},
{"id":"thurimellaGuidelinesManagingRequirements2017","abstract":"Requirements are identified and elaborated on the basis of stakeholders' decisions. The reasoning behind those decisions can be expressed as rationales. Systematic rationale management offers both short-term benefits, such as clearer requirements leading to fewer defects, and long-term benefits, such as simplified requirements evolution. However, little guidance exists for managing requirements rationales. This article presents guidelines to pragmatically capture, trace, maintain, and reuse such rationales. A list of questions augments the guidelines, improving their usability.","author":[{"family":"Thurimella","given":"Anil Kumar"},{"family":"Schubanz","given":"Mathias"},{"family":"Pleuss","given":"Andreas"},{"family":"Botterweck","given":"Goetz"},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""}],"citation-key":"thurimellaGuidelinesManagingRequirements2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"82-90","source":"IEEE Computer Society","title":"Guidelines for Managing Requirements Rationales","type":"article-magazine","volume":"34"},
{"id":"ThWorkshopFlexible2018","citation-key":"ThWorkshopFlexible2018","issued":{"date-parts":[[2018]]},"note":"00000","publisher":"CEUR-WS","title":"4 th workshop on flexible model-driven engineering (FlexMDE 2018)","type":"book","volume":"2245"},
{"id":"Tiarks2011","abstract":"Code reuse through copying and pasting leads to so-called software clones. These clones can be roughly categorized into identical fragments (type-1 clones), fragments with parameter substitution (type-2 clones), and similar fragments that differ through modified, deleted, or added statements (type-3 clones). Although there has been extensive research on detecting clones, detection of type-3 clones is still an open research issue due to the inherent vagueness in their definition. In this paper, we analyze type-3 clones detected by state-of-the-art tools and investigate type-3 clones in terms of their syntactic differences. Then, we derive their underlying semantic abstractions from their syntactic differences. Finally, we investigate whether there are code characteristics that indicate that a tool-suggested clone candidate is a real type-3 clone from a human's perspective. Our findings can help developers of clone detectors and clone refactoring tools to improve their tools.","author":[{"family":"Tiarks","given":"Rebecca"},{"family":"Koschke","given":"Rainer"},{"family":"Falke","given":"Raimar"}],"citation-key":"Tiarks2011","container-title":"Software Quality Journal","DOI":"10.1007/s11219-010-9115-6","ISSN":"1573-1367","issue":"2","issued":{"date-parts":[[2011,6,1]]},"page":"295-331","title":"An extended assessment of type-3 clones as detected by state-of-the-art tools","type":"article-journal","URL":"https://doi.org/10.1007/s11219-010-9115-6","volume":"19"},
{"id":"tichyEmpiricalSoftwareResearch2011","accessed":{"date-parts":[[2017,2,25]]},"author":[{"family":"Tichy","given":"Walter"}],"citation-key":"tichyEmpiricalSoftwareResearch2011","container-title":"Ubiquity","issue":"June","issued":{"date-parts":[[2011]]},"page":"2","source":"Google Scholar","title":"Empirical software research: an interview with Dag Sjøberg, University of Oslo, Norway","title-short":"Empirical software research","type":"article-journal","URL":"http://dl.acm.org/citation.cfm?id=1998374","volume":"2011"},
{"id":"tisiLowcomoteTrainingNext2019","author":[{"family":"Tisi","given":"M."},{"family":"Mottu","given":"J. -M."},{"family":"Kolovos","given":"D. S."},{"family":"Lara","given":"J.","non-dropping-particle":"de"},{"family":"Guerra","given":"E."},{"family":"Di Ruscio","given":"D."},{"family":"Pierantonio","given":"A."},{"family":"Wimmer","given":"M."}],"citation-key":"tisiLowcomoteTrainingNext2019","container-title":"CEUR Workshop Proceedings","ISSN":"16130073","issued":{"date-parts":[[2019]]},"note":"00000","publisher":"CEUR-WS","title":"Lowcomote: Training the next generation of experts in scalable low-code engineering platforms","type":"paper-conference","URL":"http://ceur-ws.org/","volume":"2405"},
{"id":"Tong2021298","abstract":"The traditional information management model has poor data transmission efficiency in the process of pushing information services. To solve this problem, this paper designs a hotel marketing information management model based on deep learning. Using Oracle relational database and MVC architecture to build a marketing information database, then use deep learning to extract information features, and classify marketing information of different service categories, connect hotel management and client, and integrate model management functions to provide information services for hotel managers and customers. The experimental results show that the data throughput and transmission rate of the above model are higher than those of the traditional model, and the information transmission efficiency is improved. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.","author":[{"family":"Tong","given":"L."},{"family":"Wang","given":"F."}],"citation-key":"Tong2021298","container-title":"Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST","DOI":"10.1007/978-3-030-82562-1_28","editor":[{"family":"Fu W., Xu Y.","given":"Wang S.","suffix":"Zhang Y."}],"ISBN":"9783030825614","ISSN":"18678211","issued":{"date-parts":[[2021]]},"page":"298-310","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Design of hotel marketing information management model based on deep learning","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113384978&doi=10.1007%2f978-3-030-82562-1_28&partnerID=40&md5=33a2eb6d8416680e36f72caf00310217","volume":"387"},
{"id":"tornedeExtremeAlgorithmSelection2020","abstract":"Algorithm selection (AS) deals with selecting an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem, e.g., choosing solvers for SAT problems. Benchmark suites for AS usually comprise candidate sets consisting of at most tens of algorithms, whereas in combined algorithm selection and hyperparameter optimization problems the number of candidates becomes intractable, impeding to learn effective meta-models and thus requiring costly online performance evaluations. Therefore, here we propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms, facilitating meta learning. We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation in which both problem instances and algorithms are described. We find the latter to improve significantly over the current state of the art in various metrics.","accessed":{"date-parts":[[2022,3,8]]},"author":[{"family":"Tornede","given":"Alexander"},{"family":"Wever","given":"Marcel"},{"family":"Hüllermeier","given":"Eyke"}],"citation-key":"tornedeExtremeAlgorithmSelection2020","container-title":"arXiv:2001.10741 [cs, stat]","DOI":"10.1007/978-3-030-61527-7_21","issued":{"date-parts":[[2020]]},"note":"00011","page":"309-324","source":"arXiv.org","title":"Extreme Algorithm Selection With Dyadic Feature Representation","type":"article-journal","URL":"http://arxiv.org/abs/2001.10741","volume":"12323"},
{"id":"totterdaleCASESTUDYUTILIZATION2018","abstract":"Research data must be collected and maintained in compliance with a myriad of laws and regulations that protect the privacy of participant’s information, and should be captured in a manner that is cost effective and timely. This paper discusses research data collection, explores challenges and approaches for collecting data, and describes how low-code development technology can be utilized to facilitate the secure and efficient collection of research data in the healthcare industry. This paper is based on research being conducted in the healthcare industry but has broad applicability across other industries and research areas that collect personally identifiable information, or other sensitive data protected by U.S. or international laws and regulations.","author":[{"family":"Totterdale","given":"Robert L"}],"citation-key":"totterdaleCASESTUDYUTILIZATION2018","issue":"2","issued":{"date-parts":[[2018]]},"page":"8","source":"Zotero","title":"CASE STUDY: THE UTILIZATION OF LOW-CODE DEVELOPMENT TECHNOLOGY TO SUPPORT RESEARCH DATA COLLECTION","type":"article-journal","volume":"19"},
{"id":"Toutiaee20201097","abstract":"We propose a new framework for 2-D interpreting (features and samples) black-box machine learning models via a metamodeling technique, by which we study the output and input relationships of the underlying machine learning model. The metamodel can be estimated from data generated via a trained complex model by running the computer experiment on samples of data in the region of interest. We utilize a Gaussian process as a surrogate to capture the response surface of a complex model, in which we incorporate two parts in the process: interpolated values that are modeled by a stationary Gaussian process Z governed by a prior covariance function, and a mean function μ that captures the known trends in the underlying model. The optimization procedure for the variable importance parameter θ is to maximize the likelihood function. This θ corresponds to the correlation of individual variables with the target response. There is no need for any pre-assumed models since it depends on empirical observations. Experiments demonstrate the potential of the interpretable model through quantitative assessment of the predicted samples. © 2020 IEEE.","author":[{"family":"Toutiaee","given":"M."},{"family":"Miller","given":"J.A."}],"citation-key":"Toutiaee20201097","collection-title":"Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020","DOI":"10.1109/BigData50022.2020.9378132","editor":[{"family":"Wu X., Jermaine C.","given":"Xiong L.","suffix":"Hu X.T., Kotevska O., Lu S., Xu W., Aluru S., Zhai C., Al-Masri E., Chen Z., Saltz J."}],"ISBN":"978-1-72816-251-5","issued":{"date-parts":[[2020]]},"page":"1097-1102","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Gaussian function on response surface estimation","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103813544&doi=10.1109%2fBigData50022.2020.9378132&partnerID=40&md5=b4fb40d2d962d534d6e828b21eee4aff"},
{"id":"trakadasArtificialIntelligenceBasedCollaboration2020","abstract":"The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.","accessed":{"date-parts":[[2020,12,17]]},"author":[{"family":"Trakadas","given":"Panagiotis"},{"family":"Simoens","given":"Pieter"},{"family":"Gkonis","given":"Panagiotis"},{"family":"Sarakis","given":"Lambros"},{"family":"Angelopoulos","given":"Angelos"},{"family":"Ramallo-González","given":"Alfonso P."},{"family":"Skarmeta","given":"Antonio"},{"family":"Trochoutsos","given":"Christos"},{"family":"Calvο","given":"Daniel"},{"family":"Pariente","given":"Tomas"},{"family":"Chintamani","given":"Keshav"},{"family":"Fernandez","given":"Izaskun"},{"family":"Irigaray","given":"Aitor Arnaiz"},{"family":"Parreira","given":"Josiane Xavier"},{"family":"Petrali","given":"Pierluigi"},{"family":"Leligou","given":"Nelly"},{"family":"Karkazis","given":"Panagiotis"}],"citation-key":"trakadasArtificialIntelligenceBasedCollaboration2020","container-title":"Sensors","DOI":"10.3390/s20195480","issue":"19","issued":{"date-parts":[[2020,1]]},"note":"00000","number":"19","page":"5480","publisher":"Multidisciplinary Digital Publishing Institute","source":"www.mdpi.com","title":"An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications","title-short":"An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing","type":"article-journal","URL":"https://www.mdpi.com/1424-8220/20/19/5480","volume":"20"},
{"id":"tranDependableControlSystems2015","accessed":{"date-parts":[[2016,11,1]]},"author":[{"family":"Tran","given":"Tri"},{"family":"Ha","given":"Q.P."}],"citation-key":"tranDependableControlSystems2015","container-title":"ISA Transactions","DOI":"10.1016/j.isatra.2015.08.008","ISSN":"00190578","issued":{"date-parts":[[2015,11]]},"page":"303-313","source":"CrossRef","title":"Dependable control systems with Internet of Things","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0019057815001901","volume":"59"},
{"id":"tranMultiBackEndsModel2013","accessed":{"date-parts":[[2015,6,24]]},"author":[{"family":"Tran","given":"Ngoc Viet"},{"family":"Ganser","given":"Andreas"},{"family":"Lichter","given":"Horst"}],"citation-key":"tranMultiBackEndsModel2013","container-title":"Computational Science and Its Applications–ICCSA 2013","issued":{"date-parts":[[2013]]},"page":"160–174","publisher":"Springer","source":"Google Scholar","title":"Multi Back-Ends for a Model Library Abstraction Layer","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-642-39646-5_12"},
{"id":"Tsai2014265","abstract":"This work tries to bring a marriage between two areas of computer science, social network analysis and machine learning, by exploiting ranking-based learning models for preference prediction on social networks. In the field of social network analysis, the diffusion of information on social networks has been studied for decades. This paper proposes the study of diffusion of preference on social networks. In general, there are two types of approaches proposed to predict the diffusion of information on a network, model-driven and data-driven approaches. The former assumes an underlying mechanism for diffusion while the latter tries to learn a more flexible model with the given data. This paper first proposes a simple modification on the existing model-driven binary diffusion approaches for preference list diffusion, and then addresses some concerns by proposing a rank-learning based data-driven approach. To evaluate the approaches, we propose two scenarios which data can be obtained from publicly available sources, namely predicting the preference propagation about the citation behavior and the microblogging behavior. The experiments show that the proposed ranking-based data-driven method outperforms all the other competitors significantly in both evaluation scenarios. © 2014 IEEE.","author":[{"family":"Tsai","given":"C.-H."},{"family":"Lou","given":"J.-K."},{"family":"Lu","given":"W.-C."},{"family":"Lin","given":"S.-D."}],"citation-key":"Tsai2014265","collection-title":"ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","DOI":"10.1109/ASONAM.2014.6921595","editor":[{"family":"Wu X., Wu X.","given":"Ester M.","suffix":"Xu G."}],"ISBN":"978-1-4799-5877-1","issued":{"date-parts":[[2014]]},"page":"265-272","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Exploiting rank-learning models to predict the diffusion of preferences on social networks","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84911155042&doi=10.1109%2fASONAM.2014.6921595&partnerID=40&md5=48c452f46a528637b9c901d28d9f4573"},
{"id":"tsaiExploitingRanklearningModels2014a","abstract":"This work tries to bring a marriage between two areas of computer science, social network analysis and machine learning, by exploiting ranking-based learning models for preference prediction on social networks. In the field of social network analysis, the diffusion of information on social networks has been studied for decades. This paper proposes the study of diffusion of preference on social networks. In general, there are two types of approaches proposed to predict the diffusion of information on a network, model-driven and data-driven approaches. The former assumes an underlying mechanism for diffusion while the latter tries to learn a more flexible model with the given data. This paper first proposes a simple modification on the existing model-driven binary diffusion approaches for preference list diffusion, and then addresses some concerns by proposing a rank-learning based data-driven approach. To evaluate the approaches, we propose two scenarios which data can be obtained from publicly available sources, namely predicting the preference propagation about the citation behavior and the microblogging behavior. The experiments show that the proposed ranking-based data-driven method outperforms all the other competitors significantly in both evaluation scenarios. © 2014 IEEE.","author":[{"family":"Tsai","given":"C.-H."},{"family":"Lou","given":"J.-K."},{"family":"Lu","given":"W.-C."},{"family":"Lin","given":"S.-D."}],"citation-key":"tsaiExploitingRanklearningModels2014a","container-title":"ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","DOI":"10.1109/ASONAM.2014.6921595","editor":[{"family":"Wu X.","given":"Xu G.","suffix":"Wu X., Ester M."}],"ISBN":"978-1-4799-5877-1","issued":{"date-parts":[[2014]]},"page":"265-272","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Exploiting rank-learning models to predict the diffusion of preferences on social networks","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84911155042&doi=10.1109%2fASONAM.2014.6921595&partnerID=40&md5=48c452f46a528637b9c901d28d9f4573"},
{"id":"tsamardinosBootstrappingOutofsamplePredictions2018","accessed":{"date-parts":[[2021,5,14]]},"author":[{"family":"Tsamardinos","given":"Ioannis"},{"family":"Greasidou","given":"Elissavet"},{"family":"Borboudakis","given":"Giorgos"}],"citation-key":"tsamardinosBootstrappingOutofsamplePredictions2018","container-title":"Machine Learning","container-title-short":"Mach Learn","DOI":"10.1007/s10994-018-5714-4","ISSN":"0885-6125, 1573-0565","issue":"12","issued":{"date-parts":[[2018,12]]},"note":"00055","page":"1895-1922","source":"DOI.org (Crossref)","title":"Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation","type":"article-journal","URL":"http://link.springer.com/10.1007/s10994-018-5714-4","volume":"107"},
{"id":"tsamardinosPerformanceEstimationPropertiesCrossValidationBased2014","accessed":{"date-parts":[[2021,5,14]]},"author":[{"family":"Tsamardinos","given":"Ioannis"},{"family":"Rakhshani","given":"Amin"},{"family":"Lagani","given":"Vincenzo"}],"citation-key":"tsamardinosPerformanceEstimationPropertiesCrossValidationBased2014","collection-editor":[{"family":"Hutchison","given":"David"},{"family":"Kanade","given":"Takeo"},{"family":"Kittler","given":"Josef"},{"family":"Kleinberg","given":"Jon M."},{"family":"Kobsa","given":"Alfred"},{"family":"Mattern","given":"Friedemann"},{"family":"Mitchell","given":"John C."},{"family":"Naor","given":"Moni"},{"family":"Nierstrasz","given":"Oscar"},{"family":"Pandu Rangan","given":"C."},{"family":"Steffen","given":"Bernhard"},{"family":"Terzopoulos","given":"Demetri"},{"family":"Tygar","given":"Doug"},{"family":"Weikum","given":"Gerhard"}],"container-title":"Artificial Intelligence: Methods and Applications","DOI":"10.1007/978-3-319-07064-3_1","editor":[{"family":"Likas","given":"Aristidis"},{"family":"Blekas","given":"Konstantinos"},{"family":"Kalles","given":"Dimitris"}],"event-place":"Cham","ISBN":"978-3-319-07063-6 978-3-319-07064-3","issued":{"date-parts":[[2014]]},"note":"00000","page":"1-14","publisher":"Springer International Publishing","publisher-place":"Cham","source":"DOI.org (Crossref)","title":"Performance-Estimation Properties of Cross-Validation-Based Protocols with Simultaneous Hyper-Parameter Optimization","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-319-07064-3_1","volume":"8445"},
{"id":"Tsantalis:2018:AER:3180155.3180206","author":[{"family":"Tsantalis","given":"Nikolaos"},{"family":"Mansouri","given":"Matin"},{"family":"Eshkevari","given":"Laleh M."},{"family":"Mazinanian","given":"Davood"},{"family":"Dig","given":"Danny"}],"citation-key":"Tsantalis:2018:AER:3180155.3180206","collection-title":"ICSE '18","container-title":"Proceedings of the 40th international conference on software engineering","event-place":"New York, NY, USA","ISBN":"978-1-4503-5638-1","issued":{"date-parts":[[2018]]},"page":"483-494","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Accurate and efficient refactoring detection in commit history","type":"paper-conference","URL":"http://doi.acm.org/10.1145/3180155.3180206"},
{"id":"TSEFOCUSJournalPaper","abstract":"Supporting software development with API function calls and usage patterns Link: https://github.com/MDEGroup/FOCUS/tree/master/TSE-FOCUS Journal: Transactions on Software Engineering (submission instruction) Introduction API function calls recommendation Issues (Redundancy, execution time) Liter...","accessed":{"date-parts":[[2020,2,11]]},"citation-key":"TSEFOCUSJournalPaper","container-title":"Google Docs","title":"TSE-FOCUS Journal Paper","type":"webpage","URL":"https://docs.google.com/document/d/1_40QPw-9Ddk7yZ2fQy1HRaPtK5I1dI_TxOSuOWcHKkU/edit?usp=embed_facebook"},
{"id":"Tun202113","abstract":"Designing a software model using Unified Modeling Language (UML) does not fully integrate some essential model elements, including goals and non-functional requirements. However, it can extend model elements and relationships through the UML profile to develop model components. In this study, we propose a goal-centralized metamodel that explicitly captures functional and non-functional requirements-based goal-oriented analysis for machine learning systems in an integrated manner. This study aims to present the integration approaches to specific ML model elements that focus on modeling and analyzing goals and scenarios. © 2021 IEEE.","author":[{"family":"Tun","given":"H.T."},{"family":"Husen","given":"J.H."},{"family":"Yoshioka","given":"N."},{"family":"Washizaki","given":"H."},{"family":"Fukazawa","given":"Y."}],"citation-key":"Tun202113","collection-title":"Proceedings - Asia-Pacific Software Engineering Conference, APSEC","DOI":"10.1109/APSECW53869.2021.00013","ISBN":"978-1-66543-813-1","ISSN":"15301362","issued":{"date-parts":[[2021]]},"page":"13-16","publisher":"IEEE Computer Society","title":"Goal-centralized metamodel based requirements integration for machine learning systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127432084&doi=10.1109%2fAPSECW53869.2021.00013&partnerID=40&md5=0a7b1b54c2ffafd9d411e94612a0cbf2"},
{"id":"Turney:2010:FMV:1861751.1861756","author":[{"family":"Turney","given":"Peter D."},{"family":"Pantel","given":"Patrick"}],"citation-key":"Turney:2010:FMV:1861751.1861756","container-title":"J. Artif. Int. Res.","ISSN":"1076-9757","issue":"1","issued":{"date-parts":[[2010,1]]},"page":"141-188","title":"From frequency to meaning: Vector space models of semantics","type":"article-journal","URL":"http://dl.acm.org/citation.cfm?id=1861751.1861756","volume":"37"},
{"id":"tversky1977features","abstract":"Questions the metric and dimensional assumptions that underlie the geometric representation of similarity on both theoretical and empirical grounds. A new set-theoretical approach to similarity is developed in which objects are represented as collections of features and similarity is described as a feature-matching process. Specifically, a set of qualitative assumptions is shown to imply the contrast model, which expresses the similarity between objects as a linear combination of the measures of their common and distinctive features. Several predictions of the contrast model are tested in studies of similarity with both semantic and perceptual stimuli. The model is used to uncover, analyze, and explain a variety of empirical phenomena such as the role of common and distinctive features, the relations between judgments of similarity and difference, the presence of asymmetric similarities, and the effects of context on judgments of similarity. The contrast model generalizes standard representations of similarity data in terms of clusters and trees. It is also used to analyze the relations of prototypicality and family resemblance. (39 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)","author":[{"family":"Tversky","given":"Amos"}],"citation-key":"tversky1977features","container-title":"Psychological Review","ISSN":"19391471","issue":"4","issued":{"date-parts":[[1977]]},"page":"327-352","title":"Features of similarity","type":"article-journal","volume":"84"},
{"id":"ugurelWhatCodeAutomatic2002","author":[{"family":"Ugurel","given":"Secil"},{"family":"Krovetz","given":"Robert"},{"family":"Giles","given":"C. Lee"}],"citation-key":"ugurelWhatCodeAutomatic2002","collection-title":"KDD '02","container-title":"Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining","event-place":"New York, NY, USA","ISBN":"1-58113-567-X","issued":{"date-parts":[[2002]]},"page":"632-638","publisher":"ACM","publisher-place":"New York, NY, USA","title":"What's the code?: Automatic classification of source code archives","type":"paper-conference","URL":"http://doi.acm.org/10.1145/775047.775141"},
{"id":"undefinedDarkitectureRealitySkirted2017","abstract":"Just as physicists infer dark matter's presence on the basis of its gravitational effects on visible matter, we can conceptualize a \"darkitecture\" that outlines visible software architectures.","author":[{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""}],"citation-key":"undefinedDarkitectureRealitySkirted2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"103-105","source":"IEEE Computer Society","title":"Darkitecture: The Reality Skirted by Architecture","title-short":"Darkitecture","type":"article-magazine","volume":"34"},
{"id":"undefinedKeyAbstractionsIoTOriented2017","abstract":"Despite the progress in Internet of Things (IoT) research, a general software engineering approach for systematic development of IoT systems and applications is still missing. A synthesis of the state of the art in the area can help frame the key abstractions related to such development. Such a framework could be the basis for guidelines for IoT-oriented software engineering.","author":[{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""}],"citation-key":"undefinedKeyAbstractionsIoTOriented2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"38-45","source":"IEEE Computer Society","title":"Key Abstractions for IoT-Oriented Software Engineering","type":"article-magazine","volume":"34"},
{"id":"undefinedPracticesTechnologiesComputer2017","abstract":"Computer games are rich, complex, and often large-scale software applications. They're a significant, interesting, and often compelling domain for innovative research in software engineering techniques and technologies. Computer games are progressively changing the everyday world in many positive ways. Game developers, whether focusing on entertainment market opportunities or game-based applications in nonentertainment domains such as education, healthcare, defense, or scientific research (that is, serious games), thus share a common interest in how best to engineer game software. This article examines techniques and technologies that inform contemporary computer game software engineering.","author":[{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""}],"citation-key":"undefinedPracticesTechnologiesComputer2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"110-116","source":"IEEE Computer Society","title":"Practices and Technologies in Computer Game Software Engineering","type":"article-magazine","volume":"34"},
{"id":"undefinedSoftwareEngineeringInternetThings2017","author":[{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""}],"citation-key":"undefinedSoftwareEngineeringInternetThings2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"4-6","source":"IEEE Computer Society","title":"Software-Engineering the Internet of Things","type":"article-magazine","volume":"34"},
{"id":"undefinedValueDoubt2017","abstract":"Doubt is key to becoming a good programmer. If you don't doubt the correctness of your work, you have no incentive to look for the hidden spoilers that are always there.","author":[{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""}],"citation-key":"undefinedValueDoubt2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"106-109","source":"IEEE Computer Society","title":"The Value of Doubt","type":"article-magazine","volume":"34"},
{"id":"ungerAutonomousSystemsDevelopments2012","accessed":{"date-parts":[[2016,8,21]]},"citation-key":"ungerAutonomousSystemsDevelopments2012","collection-title":"Studies in Computational Intelligence","editor":[{"family":"Unger","given":"Herwig"},{"family":"Kyamaky","given":"Kyandoghere"},{"family":"Kacprzyk","given":"Janusz"}],"event-place":"Berlin, Heidelberg","ISBN":"978-3-642-24805-4 978-3-642-24806-1","issued":{"date-parts":[[2012]]},"publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","source":"CrossRef","title":"Autonomous Systems: Developments and Trends","title-short":"Autonomous Systems","type":"book","URL":"http://link.springer.com/10.1007/978-3-642-24806-1","volume":"391"},
{"id":"UniversitySouthAustralia","accessed":{"date-parts":[[2016,8,21]]},"citation-key":"UniversitySouthAustralia","title":"University of South Australia > Course","type":"webpage","URL":"http://programs.unisa.edu.au/public/pcms/course.aspx?pageid=101801&y=2016"},
{"id":"Unsal2022227","abstract":"Data-centric approaches have been used to develop predictive methods for elucidating uncharacterized properties of proteins; however, studies indicate that these methods should be further improved to effectively solve critical problems in biomedicine and biotechnology, which can be achieved by better representing the data at hand. Novel data representation approaches mostly take inspiration from language models that have yielded ground-breaking improvements in natural language processing. Lately, these approaches have been applied to the field of protein science and have displayed highly promising results in terms of extracting complex sequence–structure–function relationships. In this study we conducted a detailed investigation over protein representation learning by first categorizing/explaining each approach, subsequently benchmarking their performances on predicting: (1) semantic similarities between proteins, (2) ontology-based protein functions, (3) drug target protein families and (4) protein–protein binding affinity changes following mutations. We evaluate and discuss the advantages and disadvantages of each method over the benchmark results, source datasets and algorithms used, in comparison with classical model-driven approaches. Finally, we discuss current challenges and suggest future directions. We believe that the conclusions of this study will help researchers to apply machine/deep learning-based representation techniques to protein data for various predictive tasks, and inspire the development of novel methods. © 2022, The Author(s), under exclusive licence to Springer Nature Limited.","author":[{"family":"Unsal","given":"S."},{"family":"Atas","given":"H."},{"family":"Albayrak","given":"M."},{"family":"Turhan","given":"K."},{"family":"Acar","given":"A.C."},{"family":"Doğan","given":"T."}],"citation-key":"Unsal2022227","container-title":"Nature Machine Intelligence","DOI":"10.1038/s42256-022-00457-9","ISSN":"25225839","issue":"3","issued":{"date-parts":[[2022]]},"page":"227-245","publisher":"Nature Research","title":"Learning functional properties of proteins with language models","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126907351&doi=10.1038%2fs42256-022-00457-9&partnerID=40&md5=35346d9b6b10605bc8d19730e5b08971","volume":"4"},
{"id":"UsingRecommenderSystems","citation-key":"UsingRecommenderSystems","note":"00000","title":"Using Recommender Systems to Improve Proactive Modeling","type":"article-newspaper"},
{"id":"UsingTorPrivoxy","accessed":{"date-parts":[[2015,3,30]]},"citation-key":"UsingTorPrivoxy","title":"Using Tor, Privoxy and Polipo ~ A little bit of everything","type":"post-weblog","URL":"http://teebeenator.blogspot.it/2014/03/using-tor-privoxy-and-polipo.html"},
{"id":"Utomo2019283","abstract":"The implementation of Education 3.0 in educational institutions, mainly in higher education institutions (HEIs) has been growing from day to day. The implementation of Education 3.0 has brought the institution towards better education. But on the other hand, the implementation also gives new problems to the institution. The problems are increased administrative processes, insufficient mobility access, and unavailability of access for parents and industry. The problems can be solved by using ICT tools that have been used in many educational institutions called academic information system (AIS). AIS can be used as a way out to overcome the problems mentioned above. To be used in support the Education 3.0, AIS must be transformed in accordance with the needs of Education 3.0. In the process of transforming an information system, a model is needed as a guide. This paper will discuss three models namely model-driven architecture (MDA), service-oriented architecture (SOA) and substitution-augmentation-modification-redefinition (SAMR) to find out which model best suits the characteristics of Education 3.0. At the end of the discussion, it can be concluded that SAMR is best suited to Education 3.0 characteristics. © BEIESP.","author":[{"family":"Utomo","given":"H.P."},{"family":"Bon","given":"A.T."},{"family":"Hendayun","given":"M."}],"citation-key":"Utomo2019283","container-title":"International Journal of Innovative Technology and Exploring Engineering","DOI":"10.35940/ijitee.K1044.09811S219","ISSN":"22783075","issue":"11 Special issue 2","issued":{"date-parts":[[2019]]},"page":"283-287","publisher":"Blue Eyes Intelligence Engineering and Sciences Publication","title":"SAMR as a framework for modeling of academic information system in higher education institution toward education 3.0","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074328372&doi=10.35940%2fijitee.K1044.09811S219&partnerID=40&md5=094ead30e97f2a36f304ab89cdf290c3","volume":"8"},
{"id":"utomoSAMRFrameworkModeling2019a","abstract":"The implementation of Education 3.0 in educational institutions, mainly in higher education institutions (HEIs) has been growing from day to day. The implementation of Education 3.0 has brought the institution towards better education. But on the other hand, the implementation also gives new problems to the institution. The problems are increased administrative processes, insufficient mobility access, and unavailability of access for parents and industry. The problems can be solved by using ICT tools that have been used in many educational institutions called academic information system (AIS). AIS can be used as a way out to overcome the problems mentioned above. To be used in support the Education 3.0, AIS must be transformed in accordance with the needs of Education 3.0. In the process of transforming an information system, a model is needed as a guide. This paper will discuss three models namely model-driven architecture (MDA), service-oriented architecture (SOA) and substitution-augmentation-modification-redefinition (SAMR) to find out which model best suits the characteristics of Education 3.0. At the end of the discussion, it can be concluded that SAMR is best suited to Education 3.0 characteristics. © BEIESP.","author":[{"family":"Utomo","given":"H.P."},{"family":"Bon","given":"A.T."},{"family":"Hendayun","given":"M."}],"citation-key":"utomoSAMRFrameworkModeling2019a","container-title":"International Journal of Innovative Technology and Exploring Engineering","DOI":"10.35940/ijitee.K1044.09811S219","ISSN":"22783075","issue":"11 Special issue 2","issued":{"date-parts":[[2019]]},"page":"283-287","publisher":"Blue Eyes Intelligence Engineering and Sciences Publication","title":"SAMR as a framework for modeling of academic information system in higher education institution toward education 3.0","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074328372&doi=10.35940%2fijitee.K1044.09811S219&partnerID=40&md5=094ead30e97f2a36f304ab89cdf290c3","volume":"8"},
{"id":"Vakil-Baghmisheh2003","abstract":"We present an algorithmic variant of the simplified fuzzy ARTMAP (SFAM) network, whose structure resembles those of feed-forward networks. Its difference with Kasuba's model is discussed, and their performances are compared on two benchmarks. We show that our algorithm is much faster than Kasuba's algorithm, and by increasing the number of training samples, the difference in speed grows enormously.","author":[{"family":"Vakil-Baghmisheh","given":"Mohammad-Taghi"},{"family":"Pavešić","given":"Nikola"}],"citation-key":"Vakil-Baghmisheh2003","container-title":"Neural Processing Letters","DOI":"10.1023/A:1026004816362","ISSN":"1573-773X","issue":"3","issued":{"date-parts":[[2003,6,1]]},"page":"273-316","title":"A fast simplified fuzzy ARTMAP network","type":"article-journal","URL":"https://doi.org/10.1023/A:1026004816362","volume":"17"},
{"id":"vallecilloTypingModelTransformations2012","author":[{"family":"Vallecillo","given":"Antonio"},{"family":"Gogolla","given":"Martin"}],"citation-key":"vallecilloTypingModelTransformations2012","container-title":"Theory and Practice of Model Transformations","DOI":"10.1007/978-3-642-30476-7_4","issued":{"date-parts":[[2012]]},"page":"56–71","title":"Typing Model Transformations Using Tracts","type":"article-journal","volume":"7307"},
{"id":"vanamstelUsingMetricsAssessing","author":[{"family":"van Amstel","given":""},{"family":"van den Brand","given":""}],"citation-key":"vanamstelUsingMetricsAssessing","title":"Using Metrics for Assessing the Quality of ATL Model Transformations","type":"article-journal"},
{"id":"vandenHeuvel2020169","abstract":"This paper explores a novel vision for the disciplined, repeatable, and transparent model-driven development and Machine-Learning operations (ML-Ops) of intelligent enterprise applications. The proposed framework treats model abstractions of AI/ML models (named AI/ML Blueprints) as first-class citizens and promotes end-to-end transparency and portability from raw data detection- to model verification, and, policy-driven model management. This framework is grounded on the intelligent Application Architecture (iA2) and entails a first attempt to incorporate requirements stemming from (more) intelligent enterprise applications into a logically-structured architecture. The logical separation is grounded on the need to enact MLOps and logically separate basic data manipulation requirements (data-processing layer), from more advanced functionality needed to instrument applications with intelligence (data intelligence layer), and continuous deployment, testing and monitoring of intelligent application (knowledge-driven application layer). Finally, the paper sets out exploring a foundational metamodel underpinning blueprint-model-driven MLOps for iA2 applications, and presents its main findings and open research agenda. © Springer Nature Switzerland AG 2020.","author":[{"family":"Heuvel","given":"W.-J.","non-dropping-particle":"van den"},{"family":"Tamburri","given":"D.A."}],"citation-key":"vandenHeuvel2020169","container-title":"Lecture Notes in Business Information Processing","DOI":"10.1007/978-3-030-52306-0_11","editor":[{"family":"Shishkov B., Shishkov B.","given":"Shishkov B."}],"ISBN":"9783030523053","ISSN":"18651348","issued":{"date-parts":[[2020]]},"page":"169-181","publisher":"Springer","title":"Model-driven ml-ops for intelligent enterprise applications: vision, approaches and challenges","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088507116&doi=10.1007%2f978-3-030-52306-0_11&partnerID=40&md5=362d44357f3ef99062b8b2cf0865d113","volume":"391 LNBIP"},
{"id":"vanderAalst201433","abstract":"Recently, process mining emerged as a new scientific discipline on the interface between process models and event data. On the one hand, conventional Business Process Management (BPM) andWorkflow Management (WfM) approaches and tools are mostly model-driven with little consideration for event data. On the other hand, Data Mining (DM), Business Intelligence (BI), and Machine Learning (ML) focus on data without considering end-to-end process models. Process mining aims to bridge the gap between BPM and WfM on the one hand and DM, BI, and ML on the other hand. Here, the challenge is to turn torrents of event data (\"Big Data\") into valuable insights related to process performance and compliance. Fortunately, process mining results can be used to identify and understand bottlenecks, inefficiencies, deviations, and risks. This tutorial paper introduces basic process mining techniques that can be used for process discovery and conformance checking. Moreover, some very general decomposition results are discussed. These allow for the decomposition and distribution of process discovery and conformance checking problems, thus enabling process mining in the large. © Springer International Publishing Switzerland 2014.","author":[{"family":"Aalst","given":"W.M.P.","non-dropping-particle":"van der"}],"citation-key":"vanderAalst201433","container-title":"Lecture Notes in Business Information Processing","DOI":"10.1007/978-3-319-05461-2_2","ISBN":"9783319054605","ISSN":"18651348","issued":{"date-parts":[[2014]]},"page":"33-76","publisher":"Springer Verlag","title":"Process mining in the large: A tutorial","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904536698&doi=10.1007%2f978-3-319-05461-2_2&partnerID=40&md5=75456be882c6e1db94442473df066d2d","volume":"172 LNBIP"},
{"id":"vanderaalstProcessMiningLarge2014a","abstract":"Recently, process mining emerged as a new scientific discipline on the interface between process models and event data. On the one hand, conventional Business Process Management (BPM) andWorkflow Management (WfM) approaches and tools are mostly model-driven with little consideration for event data. On the other hand, Data Mining (DM), Business Intelligence (BI), and Machine Learning (ML) focus on data without considering end-to-end process models. Process mining aims to bridge the gap between BPM and WfM on the one hand and DM, BI, and ML on the other hand. Here, the challenge is to turn torrents of event data (\"Big Data\") into valuable insights related to process performance and compliance. Fortunately, process mining results can be used to identify and understand bottlenecks, inefficiencies, deviations, and risks. This tutorial paper introduces basic process mining techniques that can be used for process discovery and conformance checking. Moreover, some very general decomposition results are discussed. These allow for the decomposition and distribution of process discovery and conformance checking problems, thus enabling process mining in the large. © Springer International Publishing Switzerland 2014.","author":[{"family":"Aalst","given":"W.M.P.","non-dropping-particle":"van der"}],"citation-key":"vanderaalstProcessMiningLarge2014a","container-title":"Lecture Notes in Business Information Processing","DOI":"10.1007/978-3-319-05461-2_2","ISBN":"9783319054605","ISSN":"18651348","issued":{"date-parts":[[2014]]},"page":"33-76","publisher":"Springer Verlag","title":"Process mining in the large: A tutorial","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904536698&doi=10.1007%2f978-3-319-05461-2_2&partnerID=40&md5=75456be882c6e1db94442473df066d2d","volume":"172 LNBIP"},
{"id":"vanderdoncktApplyingDeepLearning2020","abstract":"When a self-adaptive system needs to adapt, it has to analyze the possible options for adaptation, i.e., the adaptation space. For systems with large adaptation spaces, this analysis process can be resource- and time-consuming. One approach to tackle this problem is using machine learning techniques to reduce the adaptation space to only the relevant adaptation options. However, existing approaches only handle threshold goals, while practical systems often need to address also optimization goals. To tackle this limitation, we propose a two-stage learning approach called Deep Learning for Adaptation Space Reduction (DLASeR). DLASeR applies a deep learner first to reduce the adaptation space for the threshold goals and then ranks these options for the optimization goal. A benefit of deep learning is that it does not require feature engineering. Results on two instances of the DeltaIoT artifact (with different sizes of adaptation space) show that DLASeR outperforms a state-of-the-art approach for settings with only threshold goals. The results for settings with both threshold goals and an optimization goal show that DLASeR is effective with a negligible effect on the realization of the adaptation goals. Finally, we observe no noteworthy effect on the effectiveness of DLASeR for larger sizes of adaptation spaces.","accessed":{"date-parts":[[2020,10,5]]},"author":[{"family":"Van Der Donckt","given":"Jeroen"},{"family":"Weyns","given":"Danny"},{"family":"Quin","given":"Federico"},{"family":"Van Der Donckt","given":"Jonas"},{"family":"Michiels","given":"Sam"}],"citation-key":"vanderdoncktApplyingDeepLearning2020","container-title":"Proceedings of the IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","DOI":"10.1145/3387939.3391605","event":"SEAMS '20: IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","event-place":"Seoul Republic of Korea","ISBN":"978-1-4503-7962-5","issued":{"date-parts":[[2020,6,29]]},"page":"20-30","publisher":"ACM","publisher-place":"Seoul Republic of Korea","source":"DOI.org (Crossref)","title":"Applying deep learning to reduce large adaptation spaces of self-adaptive systems with multiple types of goals","type":"paper-conference","URL":"https://dl.acm.org/doi/10.1145/3387939.3391605"},
{"id":"VanDerWaa2018","abstract":"Explainable AI becomes increasingly important as the use of intelligent systems becomes more widespread in high-risk domains. In these domains it is important that the user knows to which degree the system's decisions can be trusted. To facilitate this, we present the Intuitive Confidence Measure (ICM): A lazy learning meta-model that can predict how likely a given decision is correct. ICM is intended to be easy to understand which we validated in an experiment. We compared ICM with two different methods of computing confidence measures: The numerical output of the model and an actively learned metamodel. The validation was performed using a smart assistant for maritime professionals. Results show that ICM is easier to understand but that each user is unique in its desires for explanations. This user studies with domain experts shows what users need in their explanations and that personalization is crucial. © 2018 Copyright for the individual papers remains with the authors.","author":[{"family":"Van Der Waa","given":"J."},{"family":"Van DIggelen","given":"J."},{"family":"Neerincx","given":"M."}],"citation-key":"VanDerWaa2018","collection-title":"CEUR Workshop Proceedings","editor":[{"family":"Said A.","given":"Komatsu T."}],"ISSN":"16130073","issued":{"date-parts":[[2018]]},"publisher":"CEUR-WS","title":"The design and validation of an intuitive confidence measure","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044532830&partnerID=40&md5=2ed349cd86e9fe55b202cfbb8c2a5f7d","volume":"2068"},
{"id":"vanhooffFrameworkTransformationChain2006","author":[{"family":"Vanhooff","given":"Bert"},{"family":"Ayed","given":"Dhouha"},{"family":"Berbers","given":"Yolande"}],"citation-key":"vanhooffFrameworkTransformationChain2006","issued":{"date-parts":[[2006]]},"page":"3–8","title":"A Framework for Transformation Chain Development Processes","type":"article-journal"},
{"id":"vanhooffTransformationChainModeling2006","author":[{"family":"Vanhooff","given":"Bert"},{"family":"Baelen","given":"Stefan"},{"family":"Hovsepyan","given":"Aram"},{"family":"Joosen","given":"Wouter"},{"family":"Berbers","given":"Yolande"}],"citation-key":"vanhooffTransformationChainModeling2006","container-title":"Embedded Computer Systems: Architectures, Modeling, and Simulation","DOI":"10.1007/11796435_6","issued":{"date-parts":[[2006]]},"page":"39–48","title":"Towards a Transformation Chain Modeling Language","type":"article-journal","volume":"4017"},
{"id":"Vargas_sales_diversity_14","author":[{"family":"Vargas","given":"Saúl"},{"family":"Castells","given":"Pablo"}],"citation-key":"Vargas_sales_diversity_14","container-title":"Eighth ACM conference on recommender systems, RecSys '14, foster city, silicon valley, CA, USA - october 06 - 10, 2014","DOI":"10.1145/2645710.2645744","issued":{"date-parts":[[2014]]},"page":"145-152","title":"Improving sales diversity by recommending users to items","type":"paper-conference","URL":"http://doi.acm.org/10.1145/2645710.2645744"},
{"id":"vargasRankRelevanceNovelty2011","author":[{"family":"Vargas","given":"Saúl"},{"family":"Castells","given":"Pablo"}],"citation-key":"vargasRankRelevanceNovelty2011","collection-title":"RecSys '11","container-title":"Proceedings of the fifth ACM conference on recommender systems","event-place":"New York, NY, USA","ISBN":"978-1-4503-0683-6","issued":{"date-parts":[[2011]]},"page":"109-116","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Rank and relevance in novelty and diversity metrics for recommender systems","type":"paper-conference","URL":"http://doi.acm.org/10.1145/2043932.2043955"},
{"id":"vasilescuHowHealthyAre2014","accessed":{"date-parts":[[2015,4,24]]},"author":[{"family":"Vasilescu","given":"Bogdan"},{"family":"Serebrenik","given":"Alexander"},{"family":"Mens","given":"Tom"},{"family":"Brand","given":"Mark G.J.","non-dropping-particle":"van den"},{"family":"Pek","given":"Ekaterina"}],"citation-key":"vasilescuHowHealthyAre2014","container-title":"Science of Computer Programming","DOI":"10.1016/j.scico.2014.01.016","ISSN":"01676423","issued":{"date-parts":[[2014,9]]},"page":"251-272","source":"CrossRef","title":"How healthy are software engineering conferences?","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0167642314000318","volume":"89"},
{"id":"vassevAutonomyRequirementsEngineering2013","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Vassev","given":"Emil"},{"family":"Hinchey","given":"Mike"}],"citation-key":"vassevAutonomyRequirementsEngineering2013","container-title":"Proceedings of the International C* Conference on Computer Science and Software Engineering","issued":{"date-parts":[[2013]]},"page":"31–41","publisher":"ACM","source":"Google Scholar","title":"Autonomy requirements engineering: a case study on the BepiColombo mission","title-short":"Autonomy requirements engineering","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=2494472"},
{"id":"vathy-fogarassyUniformDataAccess2017","abstract":"Integration of data stored in heterogeneous database systems is a very challenging task and it may hide several difficulties. As NoSQL databases are growing in popularity, integration of different NoSQL systems and interoperability of NoSQL systems with SQL databases become an increasingly important issue. In this paper, we propose a novel data integration methodology to query data individually from different relational and NoSQL database systems. The suggested solution does not support joins and aggregates across data sources; it only collects data from different separated database management systems according to the filtering options and migrates them. The proposed method is based on a metamodel approach and it covers the structural, semantic and syntactic heterogeneities of source systems. To introduce the applicability of the proposed methodology, we developed a web-based application, which convincingly confirms the usefulness of the novel method.","accessed":{"date-parts":[[2018,5,7]]},"author":[{"family":"Vathy-Fogarassy","given":"Ágnes"},{"family":"Hugyák","given":"Tamás"}],"citation-key":"vathy-fogarassyUniformDataAccess2017","container-title":"Information Systems","DOI":"10.1016/j.is.2017.04.002","ISSN":"03064379","issued":{"date-parts":[[2017,9]]},"page":"93-105","source":"Crossref","title":"Uniform data access platform for SQL and NoSQL database systems","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0306437916303398","volume":"69"},
{"id":"vazEmpiricalStudyTask2019","accessed":{"date-parts":[[2021,6,18]]},"author":[{"family":"Vaz","given":"Luis"},{"family":"Steinmacher","given":"Igor"},{"family":"Marczak","given":"Sabrina"}],"citation-key":"vazEmpiricalStudyTask2019","container-title":"2019 ACM/IEEE 14th International Conference on Global Software Engineering (ICGSE)","DOI":"10.1109/ICGSE.2019.00041","event":"2019 ACM/IEEE 14th International Conference on Global Software Engineering (ICGSE)","event-place":"Montreal, QC, Canada","ISBN":"978-1-5386-9196-0","issued":{"date-parts":[[2019,5]]},"note":"00007","page":"48-57","publisher":"IEEE","publisher-place":"Montreal, QC, Canada","source":"DOI.org (Crossref)","title":"An Empirical Study on Task Documentation in Software Crowdsourcing on TopCoder","type":"paper-conference","URL":"https://ieeexplore.ieee.org/document/8807631/"},
{"id":"venChallengesStrategiesUse2008","accessed":{"date-parts":[[2015,11,11]]},"author":[{"family":"Ven","given":"Kris"},{"family":"Mannaert","given":"Herwig"}],"citation-key":"venChallengesStrategiesUse2008","container-title":"Information and Software Technology","DOI":"10.1016/j.infsof.2007.09.001","ISSN":"09505849","issue":"9-10","issued":{"date-parts":[[2008,8]]},"page":"991-1002","source":"CrossRef","title":"Challenges and strategies in the use of Open Source Software by Independent Software Vendors","type":"article-journal","URL":"http://linkinghub.elsevier.com/retrieve/pii/S0950584907001036","volume":"50"},
{"id":"venkateshScalableApplicationDesignIoT2017","abstract":"The Internet of Things envisions a web-connected infrastructure of sensing and actuation devices. However, the current state of the art presents another reality: monolithic end-to-end applications tightly coupled to a limited set of sensors and actuators. Growing such applications with new devices or behaviors, or extending the existing infrastructure with new applications, involves redesign and deployment. A proposed approach breaks these applications up into an equivalent set of functional units called context engines, whose I/O transformations are driven by general-purpose machine learning. This approach decreases computational redundancy and complexity with a minimal impact on accuracy. Researchers evaluated this approach's scalability--how the context engines' overhead grows as the input data and number of computational nodes increase. In a large-scale case study of residential smart-grid control, this approach provided better accuracy and scaling than the state-of-the-art single-stage approach.","author":[{"family":"Venkatesh","given":"Jagannathan"},{"family":"Aksanli","given":"Baris"},{"family":"Chan","given":"Christine S."},{"family":"Akyurek","given":"Alper S."},{"family":"Rosing","given":"Tajana S."},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""},{"family":"undefined","given":""}],"citation-key":"venkateshScalableApplicationDesignIoT2017","container-title":"IEEE Software","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017]]},"page":"62-70","source":"IEEE Computer Society","title":"Scalable-Application Design for the IoT","type":"article-magazine","volume":"34"},
{"id":"vermesanInternetThingsApplications2014","author":[{"family":"Vermesan","given":"Ovidiu"}],"citation-key":"vermesanInternetThingsApplications2014","event-place":"Place of publication not identified","ISBN":"978-87-93102-94-1","issued":{"date-parts":[[2014]]},"note":"OCLC: 884398675","publisher":"River Publishers","publisher-place":"Place of publication not identified","source":"Open WorldCat","title":"Internet of things applications - from research and innovation to market deployment.","type":"book"},
{"id":"vermesanInternetThingsConverging2013","author":[{"family":"Vermesan","given":"Ovidiu"}],"citation-key":"vermesanInternetThingsConverging2013","issued":{"date-parts":[[2013]]},"note":"OCLC: 943786361","publisher":"River Publishers","source":"Open WorldCat","title":"Internet of Things: Converging Technologies for Smart Environments","title-short":"Internet of Things","type":"book"},
{"id":"vermesanInternetThingsStrategic2011","accessed":{"date-parts":[[2016,6,3]]},"author":[{"family":"Vermesan","given":"Ovidiu"},{"family":"Friess","given":"Peter"},{"family":"Guillemin","given":"Patrick"},{"family":"Gusmeroli","given":"Sergio"},{"family":"Sundmaeker","given":"Harald"},{"family":"Bassi","given":"Alessandro"},{"family":"Jubert","given":"Ignacio Soler"},{"family":"Mazura","given":"Margaretha"},{"family":"Harrison","given":"Mark"},{"family":"Eisenhauer","given":"M."},{"literal":"others"}],"citation-key":"vermesanInternetThingsStrategic2011","container-title":"Internet of Things-Global Technological and Societal Trends","issued":{"date-parts":[[2011]]},"page":"9–52","source":"Google Scholar","title":"Internet of things strategic research roadmap","type":"article-journal","URL":"http://books.google.com/books?hl=en&lr=&id=Eug-RvslW30C&oi=fnd&pg=PA9&dq=%22by+individuals+and+organisations+around+the%22+%22Services+(IoS),+into+a+common+global+IT+platform+of+seamless+networks%22+%22networks+and+Internet.+Research+on+SOA,+Web/enterprise%22+%22interconnects+growing+population+of+users+while+promoting+their%22+&ots=3Tx7vGjxCw&sig=jz8DKE3sstPdA9juWvxatLigzxs"},
{"id":"vermolenReconstructingComplexMetamodel2012","author":[{"family":"Vermolen","given":"Sander D."},{"family":"Wachsmuth","given":"Guido"},{"family":"Visser","given":"Eelco"}],"citation-key":"vermolenReconstructingComplexMetamodel2012","collection-title":"Lecture Notes in Computer Science","container-title":"Software Language Engineering","issued":{"date-parts":[[2012]]},"page":"201–221","title":"Reconstructing Complex Metamodel Evolution","type":"chapter","volume":"6940"},
{"id":"vieiraMetricsMeasureChange2014","author":[{"family":"Vieira","given":"Andreza"},{"family":"Ramalho","given":"Franklin"}],"citation-key":"vieiraMetricsMeasureChange2014","collection-title":"Lecture Notes in Computer Science","container-title":"Product-Focused Software Process Improvement","issued":{"date-parts":[[2014]]},"page":"254–268","title":"Metrics to Measure the Change Impact in ATL Model Transformations","type":"chapter","volume":"8892"},
{"id":"vignagaTypingArtifactsMegamodeling2011","author":[{"family":"Vignaga","given":"Andrés"},{"family":"Jouault","given":"Frédéric"},{"family":"Bastarrica","given":"María Cecilia"},{"family":"Brunelière","given":"Hugo"}],"citation-key":"vignagaTypingArtifactsMegamodeling2011","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-011-0191-2","issue":"1","issued":{"date-parts":[[2011]]},"page":"105–119","title":"Typing artifacts in megamodeling","type":"article-journal","volume":"12"},
{"id":"vignagaTypingModelManagement2009","abstract":"Model management is essential for coping with the complexity introduced by the increasing number and varied nature of artifacts involved in MDE-based projects. Global Model Management (GMM) addresses this issue enabling the representation of artifacts, particularly transformation composition and execution, by a model called a megamodel. Typing information about artifacts can be used for preventing type errors during execution. In this work, we present a type system for GMM that improves its current typing approach and enables formal reasoning about the type of artifacts within a megamodel. This type system is able to capture non-trivial situations such as the use of higher order transformations.","accessed":{"date-parts":[[2015,4,1]]},"author":[{"family":"Vignaga","given":"Andrés"},{"family":"Jouault","given":"Frédéric"},{"family":"Bastarrica","given":"María Cecilia"},{"family":"Brunelière","given":"Hugo"}],"citation-key":"vignagaTypingModelManagement2009","collection-number":"5563","collection-title":"Lecture Notes in Computer Science","container-title":"Theory and Practice of Model Transformations","editor":[{"family":"Paige","given":"Richard F."}],"ISBN":"978-3-642-02407-8 978-3-642-02408-5","issued":{"date-parts":[[2009]]},"page":"197-212","publisher":"Springer Berlin Heidelberg","source":"link.springer.com","title":"Typing in Model Management","type":"chapter","URL":"http://link.springer.com/chapter/10.1007/978-3-642-02408-5_14"},
{"id":"Vijayakumar2021761","abstract":"Autonomous vehicles, without the help of a human, support challenging tasks for sensing the environment and vehicle navigation. The driving behavior is controlled automatically from the observed surroundings using many supervised learning methods that provide action output based on matching the visual inputs and training labels. Most essentially, deep learning algorithms offer improved processing of observed input data but with the increased training, the complexity in processing the real-time data eventually becomes complex. In this paper, an autonomous driving model driven by a behavioral model is designed incorporating (a) recognition, (b) planning and (c) prediction modules. Each module is designed to regulate the processing of input trajectory video data. Additionally, deep learning classifiers are included to improve the automated ability of planning and prediction modules. Initially, the recognition module is planned to limit the redundant data from the raw input data. Secondly, the planning module is designed with convolutional neural network (CNN) to classify the predictable and unpredictable objects from the surrounding trajectories occurring in the line of sight. Finally, the prediction module is designed with recurrent neural network (RNN) to predict the future driving patterns based on the present condition and past driving outputs. The simulation results show that the proposed hybrid deep learning behavioral model offers improved autonomous driving than other existing autonomous driving models. The results of different environments prove that the proposed hybrid model offers increased scalability in terms of improved recall rate of 95.15%, 96.13% and 97.72% in terrain, dense and light traffic zones, respectively, than existing methods. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","author":[{"family":"Vijayakumar","given":"V.A."},{"family":"Shanthini","given":"J."},{"family":"Karthick","given":"S."}],"citation-key":"Vijayakumar2021761","container-title":"Lecture Notes on Data Engineering and Communications Technologies","DOI":"10.1007/978-981-15-9509-7_62","ISSN":"23674512","issued":{"date-parts":[[2021]]},"page":"761-772","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Convolutional recurrent neural network framework for autonomous driving behavioral model","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102016402&doi=10.1007%2f978-981-15-9509-7_62&partnerID=40&md5=52107f047ace610f25b08278f1d58aed","volume":"57"},
{"id":"vijayakumarConvolutionalRecurrentNeural2021a","abstract":"Autonomous vehicles, without the help of a human, support challenging tasks for sensing the environment and vehicle navigation. The driving behavior is controlled automatically from the observed surroundings using many supervised learning methods that provide action output based on matching the visual inputs and training labels. Most essentially, deep learning algorithms offer improved processing of observed input data but with the increased training, the complexity in processing the real-time data eventually becomes complex. In this paper, an autonomous driving model driven by a behavioral model is designed incorporating (a) recognition, (b) planning and (c) prediction modules. Each module is designed to regulate the processing of input trajectory video data. Additionally, deep learning classifiers are included to improve the automated ability of planning and prediction modules. Initially, the recognition module is planned to limit the redundant data from the raw input data. Secondly, the planning module is designed with convolutional neural network (CNN) to classify the predictable and unpredictable objects from the surrounding trajectories occurring in the line of sight. Finally, the prediction module is designed with recurrent neural network (RNN) to predict the future driving patterns based on the present condition and past driving outputs. The simulation results show that the proposed hybrid deep learning behavioral model offers improved autonomous driving than other existing autonomous driving models. The results of different environments prove that the proposed hybrid model offers increased scalability in terms of improved recall rate of 95.15%, 96.13% and 97.72% in terrain, dense and light traffic zones, respectively, than existing methods. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","author":[{"family":"Vijayakumar","given":"V.A."},{"family":"Shanthini","given":"J."},{"family":"Karthick","given":"S."}],"citation-key":"vijayakumarConvolutionalRecurrentNeural2021a","container-title":"Lecture Notes on Data Engineering and Communications Technologies","DOI":"10.1007/978-981-15-9509-7_62","ISSN":"23674512","issued":{"date-parts":[[2021]]},"page":"761-772","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Convolutional Recurrent Neural Network Framework for Autonomous Driving Behavioral Model","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102016402&doi=10.1007%2f978-981-15-9509-7_62&partnerID=40&md5=52107f047ace610f25b08278f1d58aed","volume":"57"},
{"id":"viroliSASO2014Selected2016","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Viroli","given":"Mirko"},{"family":"Diaconescu","given":"Ada"},{"family":"Kandasamy","given":"Nagarajan"}],"citation-key":"viroliSASO2014Selected2016","container-title":"ACM Transactions on Autonomous and Adaptive Systems","DOI":"10.1145/2939206","ISSN":"15564665","issue":"2","issued":{"date-parts":[[2016,7,25]]},"page":"1-2","source":"CrossRef","title":"SASO 2014: Selected, Revised, and Extended Best Papers","title-short":"SASO 2014","type":"article-journal","URL":"http://dl.acm.org/citation.cfm?doid=2952298.2939206","volume":"11"},
{"id":"Viviani2017197","abstract":"In Social Media, large amounts of User Generated Content (UGC) generally diffuse without any form of trusted external control. In this context, the risk of running into misinformation is not negligible. For this reason, assessing the credibility of both information and its sources in Social Media platforms constitutes nowadays a fundamental issue for users. In the last years, several approaches have been proposed to address this issue. Most of them employ machine learning techniques to classify information and misinformation. Other approaches exploit multiple kinds of relationships connecting entities in Social Media applications, focusing on credibility and trust propagation. Unlike previous approaches, in this paper we propose a model-driven approach based on Multi-Criteria Decision Making (MCDM) and quantifier guided aggregation. An overall credibility estimate for each piece of information is obtained based on multiple criteria connected to both UGC and users generating it. The proposed model is evaluated in the context of opinion spam detection in review sites, on a real-world dataset crawled from Yelp, and it is compared with well-known supervised machine learning techniques. © Springer International Publishing AG 2017.","author":[{"family":"Viviani","given":"M."},{"family":"Pasi","given":"G."}],"citation-key":"Viviani2017197","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-319-52962-2_17","ISSN":"03029743","issued":{"date-parts":[[2017]]},"page":"197-207","publisher":"Springer Verlag","title":"A multi-criteria decision making approach for the assessment of information credibility in social media","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85012076998&doi=10.1007%2f978-3-319-52962-2_17&partnerID=40&md5=5b5d0a738b858286f10443f44f73dea4","volume":"10147 LNAI"},
{"id":"Vještica2020441","abstract":"One of the goals of Industry 4.0 is to enable mass customization of products and to satisfy specific needs of customers. This goal is often hard to achieve in traditional manufacturing systems. To enable fast production changes, an automatic and flexible production is needed. In this context we propose a Model-Driven Software Development (MDSD) approach and a Domain-Specific Modeling Language (DSML) to model production processes. The language supports two levels of abstraction. A Master-Level (ML) model is used by a process designer to model process steps. A Detail-Level (DL) model is used by Orchestrator, a cluster of industrial computers that manages production, to fill existing ML models with a specification of production logistic and smart resources. A code generator is used to generate machine-readable or human-readable instructions from DL models. Generated code is used for automatic execution of production processes within a simulation or a shop floor. In this paper we provide an application of a DSML, which is capable of modeling production processes that are ready for automatic code generation. © 2020, IFIP International Federation for Information Processing.","author":[{"family":"Vještica","given":"M."},{"family":"Dimitrieski","given":"V."},{"family":"Pisarić","given":"M."},{"family":"Kordić","given":"S."},{"family":"Ristić","given":"S."},{"family":"Luković","given":"I."}],"citation-key":"Vještica2020441","container-title":"IFIP Advances in Information and Communication Technology","DOI":"10.1007/978-3-030-57993-7_50","editor":[{"family":"Lalic B., Marjanovic U.","given":"Majstorovic V.","suffix":"von Cieminski G., Romero D."}],"ISBN":"9783030579920","ISSN":"18684238","issued":{"date-parts":[[2020]]},"page":"441-448","publisher":"Springer","title":"An application of a DSML in industry 4.0 production processes","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090174522&doi=10.1007%2f978-3-030-57993-7_50&partnerID=40&md5=ae94d9fc22ab504eee9d97675f0070b7","volume":"591 IFIP"},
{"id":"vladHypersonicModelAnalysis","author":[{"family":"Vlad","given":"Acretoaie"},{"family":"Harald","given":"Storrle"}],"citation-key":"vladHypersonicModelAnalysis","title":"Hypersonic: Model Analysis and Checking in the Cloud","type":"article-journal"},
{"id":"vogelsangRequirementsEngineeringMachine2019","accessed":{"date-parts":[[2020,12,17]]},"author":[{"family":"Vogelsang","given":"Andreas"},{"family":"Borg","given":"Markus"}],"citation-key":"vogelsangRequirementsEngineeringMachine2019","container-title":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","DOI":"10.1109/REW.2019.00050","event":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","event-place":"Jeju Island, Korea (South)","ISBN":"978-1-72815-165-6","issued":{"date-parts":[[2019,9]]},"note":"00000","page":"245-251","publisher":"IEEE","publisher-place":"Jeju Island, Korea (South)","source":"DOI.org (Crossref)","title":"Requirements Engineering for Machine Learning: Perspectives from Data Scientists","title-short":"Requirements Engineering for Machine Learning","type":"paper-conference","URL":"https://ieeexplore.ieee.org/document/8933800/"},
{"id":"vogelsangRequirementsEngineeringMachine2019a","abstract":"Machine learning (ML) is used increasingly in real-world applications. In this paper, we describe our ongoing endeavor to define characteristics and challenges unique to Requirements Engineering (RE) for ML-based systems. As a first step, we interviewed four data scientists to understand how ML experts approach elicitation, specification, and assurance of requirements and expectations. The results show that changes in the development paradigm, i.e., from coding to training, also demands changes in RE. We conclude that development of ML systems demands requirements engineers to: (1) understand ML performance measures to state good functional requirements, (2) be aware of new quality requirements such as explainability, freedom from discrimination, or specific legal requirements, and (3) integrate ML specifics in the RE process. Our study provides a first contribution towards an RE methodology for ML systems.","accessed":{"date-parts":[[2021,6,19]]},"author":[{"family":"Vogelsang","given":"Andreas"},{"family":"Borg","given":"Markus"}],"citation-key":"vogelsangRequirementsEngineeringMachine2019a","container-title":"arXiv:1908.04674 [cs]","issued":{"date-parts":[[2019,8,13]]},"note":"00035","source":"arXiv.org","title":"Requirements Engineering for Machine Learning: Perspectives from Data Scientists","title-short":"Requirements Engineering for Machine Learning","type":"article-journal","URL":"http://arxiv.org/abs/1908.04674"},
{"id":"voigtStructuralGraphbasedMetamodel2011","author":[{"family":"Voigt","given":"Konrad"}],"citation-key":"voigtStructuralGraphbasedMetamodel2011","issued":{"date-parts":[[2011]]},"title":"Structural Graph-based Metamodel Matching","type":"thesis"},
{"id":"volter2013model","author":[{"family":"Völter","given":"Markus"},{"family":"Stahl","given":"Thomas"},{"family":"Bettin","given":"Jorn"},{"family":"Haase","given":"Arno"},{"family":"Helsen","given":"Simon"}],"citation-key":"volter2013model","issued":{"date-parts":[[2013]]},"publisher":"John Wiley & Sons","title":"Model-driven software development: technology, engineering, management","type":"book"},
{"id":"VWMonDataLogger","accessed":{"date-parts":[[2015,3,27]]},"citation-key":"VWMonDataLogger","title":"VWMon: Data logger and remote control for the Vaillant heat pump | construction blog by Katja & Alexey","title-short":"VWMon","type":"post-weblog","URL":"http://baublog.ozerov.de/waermepumpe/vwmon-datenlogger-fuer-die-vaillant-waermepumpe/"},
{"id":"walensteinSimilarityPrograms2006","author":[{"family":"Walenstein","given":"Andrew"},{"family":"El-Ramly","given":"Mohammad"},{"family":"Cordy","given":"James R."},{"family":"Evans","given":"William S."},{"family":"Mahdavi","given":"Kiarash"},{"family":"Pizka","given":"Markus"},{"family":"Ramalingam","given":"Ganesan"},{"family":"Gudenberg","given":"Jürgen Wolff","non-dropping-particle":"von"}],"citation-key":"walensteinSimilarityPrograms2006","container-title":"Duplication, Redundancy, and Similarity in Software, 23.07. - 26.07.2006","issued":{"date-parts":[[2006]]},"title":"Similarity in programs","type":"paper-conference","URL":"http://drops.dagstuhl.de/opus/volltexte/2007/968"},
{"id":"Wan2022423","abstract":"The great success of deep learning (DL) has inspired researchers to develop more accurate and efficient symbol detectors for multi-input multi-output (MIMO) systems. Existing DL-based MIMO detectors, however, suffer several drawbacks. To address these issues, in this paper, we develop a model-driven DL detector based on variational Bayesian inference. Specifically, the proposed unrolled DL architecture is inspired by an inverse-free variational Bayesian learning framework which circumvents matrix inversion via maximizing a relaxed evidence lower bound. Two networks are respectively developed for independent and identically distributed (i.i.d.) Gaussian channels and arbitrarily correlated channels. The proposed networks, referred to as VBINet, have only a few learnable parameters and thus can be efficiently trained with a moderate amount of training samples. The proposed VBINet-based detectors can work in both offline and online training modes. An important advantage of our proposed networks over state-of-the-art MIMO detection networks such as OAMPNet and MMNet is that the VBINet can automatically learn the noise variance from data, thus yielding a significant performance improvement over the OAMPNet and MMNet in the presence of noise variance uncertainty. Simulation results show that the proposed VBINet-based detectors achieve competitive performance for both i.i.d. Gaussian and realistic 3GPP MIMO channels. © 1991-2012 IEEE.","author":[{"family":"Wan","given":"Q."},{"family":"Fang","given":"J."},{"family":"Huang","given":"Y."},{"family":"Duan","given":"H."},{"family":"Li","given":"H."}],"citation-key":"Wan2022423","container-title":"IEEE Transactions on Signal Processing","DOI":"10.1109/TSP.2022.3140926","ISSN":"1053587X","issued":{"date-parts":[[2022]]},"page":"423-437","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A variational bayesian inference-inspired unrolled deep network for MIMO detection","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122880349&doi=10.1109%2fTSP.2022.3140926&partnerID=40&md5=0af7b4866a8677c5417c52ba38720407","volume":"70"},
{"id":"Wang:2004:BEM:977401.978142","author":[{"family":"Wang","given":"Jianyong"},{"family":"Han","given":"Jiawei"}],"citation-key":"Wang:2004:BEM:977401.978142","collection-title":"ICDE '04","container-title":"Proceedings of the 20th international conference on data engineering","event-place":"Washington, DC, USA","ISBN":"0-7695-2065-0","issued":{"date-parts":[[2004]]},"page":"79-","publisher":"IEEE Computer Society","publisher-place":"Washington, DC, USA","title":"BIDE: Efficient mining of frequent closed sequences","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=977401.978142"},
{"id":"Wang20171920","abstract":"Process mining is an emerging discipline providing comprehensive sets of tools to provide fact-based insights and to support process improvements. This new discipline builds on process model-driven approaches and data-centric analysis techniques such as machine learning and data mining. Conformance checking approaches, i.e., techniques to compare and relate event logs and process models, are one of the three core process mining techniques. It is shown that conformance can be quantified and that deviations can be diagnosed. BPMN 2.0 model has so powerful expression ability that it can express complex patterns like multi-instance, sub-process, OR gateway and boundary event. However, there is no existing conformance checking algorithm supporting such complex patterns. To solve this problem, this paper proposes an algorithm (Acorn) for conformance checking for BPMN 2.0 model, which supports aforesaid complex patterns. The algorithm uses A* algorithm to find the minimum cost alignment, which is used to calculate fitness between BPMN 2.0 model and the log. In addition, virtual cost and expected cost are introduced for optimization. Experimental evaluations show that Acorn can find the best alignment by exploiting the meanings of BPMN 2.0 elements correctly and efficiently, and the introduction of virtual cost and expectation cost indeed reduces the search space. © 2017, Science Press. All right reserved.","author":[{"family":"Wang","given":"Y."},{"family":"Wen","given":"L."},{"family":"Yan","given":"Z."}],"citation-key":"Wang20171920","container-title":"Jisuanji Yanjiu yu Fazhan/Computer Research and Development","DOI":"10.7544/issn1000-1239.2017.20160756","ISSN":"10001239","issue":"9","issued":{"date-parts":[[2017]]},"page":"1920-1930","publisher":"Science Press","title":"Alignment based conformance checking algorithm for BPMN 2.0 model","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032805906&doi=10.7544%2fissn1000-1239.2017.20160756&partnerID=40&md5=981f7c5d81adc91e82a9621883debc5a","volume":"54"},
{"id":"Wang2020","abstract":"With the applications of deep learning networks booming in physical layer communication, deep-learning-based channel decoding methods have become a research hotspot. However, the high complexity hinders the application of deep neural network (DNN) on long code. In this paper, we propose a model-driven deep learning method for normalized min-sum (NMS) low-density parity-check (LDPC) decoding. First, we propose a neural normalized min-sum (NNMS) LDPC decoding network. By unfolding the iterative decoding progress between checking nodes (CNs) and variable nodes (VNs) into a feedforward propagation network, we can make use of the advantages of both model-driven deep learning methods and conventional normalized min-sum (CNMS) LDPC decoding method. Second, considering that the NNMS decoder needs large number of multipliers, we propose a shared neural normalized min-sum (SNNMS) decoding network to reduce the number of correction factors. Experimental results show that the BER performance of the proposed NNMS decoder is 1.5dB better than the conventional LDPC decoder, using fewer iterations. Furthermore, the proposed SNNMS decoder outperforms the proposed NNMS decoder and reduces the computation complexity. © 2020 IEEE.","author":[{"family":"Wang","given":"Q."},{"family":"Wang","given":"S."},{"family":"Fang","given":"H."},{"family":"Chen","given":"L."},{"family":"Chen","given":"L."},{"family":"Guo","given":"Y."}],"citation-key":"Wang2020","collection-title":"2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings","DOI":"10.1109/ICCWorkshops49005.2020.9145237","ISBN":"978-1-72817-440-2","issued":{"date-parts":[[2020]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A model-driven deep learning method for normalized min-sum LDPC decoding","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090273487&doi=10.1109%2fICCWorkshops49005.2020.9145237&partnerID=40&md5=d7fe7aed05f36baafa513b59ce239bdd"},
{"id":"Wang2020107","abstract":"Multiple-Input Multiple-Output (MIMO) is a key technology due to its high spectral efficiency and data rate in communication systems. Due to the high complexity of linear Minimum Mean Square Error (MMSE) detection, Gauss-Seidel iterative method is applied to MIMO detection as an approximate method of MMSE and achieves the effect of MMSE detection. In this paper, we propose a learnable Gauss-Seidel detector based on model-driven Deep Learning (DL) for MIMO systems. The proposed detector is designed by unfolding the Gauss-Seidel detection method. In the proposed detector, we add some parameters, and that can be learned to improve the detection performance. Simulation results show that the proposed detector has better detection performance than traditional Gauss-Seidel detector. © 2020 IEEE.","author":[{"family":"Wang","given":"Q."},{"family":"Hai","given":"H."},{"family":"Peng","given":"K."},{"family":"Xu","given":"B."},{"family":"Jiang","given":"X.-Q."}],"citation-key":"Wang2020107","collection-title":"2020 IEEE/CIC International Conference on Communications in China, ICCC 2020","DOI":"10.1109/ICCC49849.2020.9238938","ISBN":"978-1-72817-327-6","issued":{"date-parts":[[2020]]},"page":"107-111","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A learnable gauss-seidel detector for MIMO detection","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097560935&doi=10.1109%2fICCC49849.2020.9238938&partnerID=40&md5=18e20b07af475b1d1414002738aa21fd"},
{"id":"Wang20203100","abstract":"Deep learning (DL) methods have achieved state-of-the-art performance in the task of single image rain removal. Most of current DL architectures, however, are still lack of sufficient interpretability and not fully integrated with physical structures inside general rain streaks. To this issue, in this paper, we propose a model-driven deep neural network for the task, with fully interpretable network structures. Specifically, based on the convolutional dictionary learning mechanism for representing rain, we propose a novel single image deraining model and utilize the proximal gradient descent technique to design an iterative algorithm only containing simple operators for solving the model. Such a simple implementation scheme facilitates us to unfold it into a new deep network architecture, called rain convolutional dictionary network (RCDNet), with almost every network module one-to-one corresponding to each operation involved in the algorithm. By end-to-end training the proposed RCDNet, all the rain kernels and proximal operators can be automatically extracted, faithfully characterizing the features of both rain and clean background layers, and thus naturally lead to its better deraining performance, especially in real scenarios. Comprehensive experiments substantiate the superiority of the proposed network, especially its well generality to diverse testing scenarios and good interpretability for all its modules, as compared with state-of-the-arts both visually and quantitatively. © 2020 IEEE","author":[{"family":"Wang","given":"H."},{"family":"Xie","given":"Q."},{"family":"Zhao","given":"Q."},{"family":"Meng","given":"D."}],"citation-key":"Wang20203100","collection-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","DOI":"10.1109/CVPR42600.2020.00317","ISSN":"10636919","issued":{"date-parts":[[2020]]},"page":"3100-3109","publisher":"IEEE Computer Society","title":"A model-driven deep neural network for single image rain removal","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094849783&doi=10.1109%2fCVPR42600.2020.00317&partnerID=40&md5=be07775c6aa62c750d5e0545790caf4a"},
{"id":"Wang2021107","abstract":"For the task of metal artifact reduction (MAR), although deep learning (DL)-based methods have achieved promising performances, most of them suffer from two problems: 1) the CT imaging geometry constraint is not fully embedded into the network during training, leaving room for further performance improvement; 2) the model interpretability is lack of sufficient consideration. Against these issues, we propose a novel interpretable dual domain network, termed as InDuDoNet, which combines the advantages of model-driven and data-driven methodologies. Specifically, we build a joint spatial and Radon domain reconstruction model and utilize the proximal gradient technique to design an iterative algorithm for solving it. The optimization algorithm only consists of simple computational operators, which facilitate us to correspondingly unfold iterative steps into network modules and thus improve the interpretablility of the framework. Extensive experiments on synthesized and clinical data show the superiority of our InDuDoNet. Code is available in https://github.com/hongwang01/InDuDoNet. © 2021, Springer Nature Switzerland AG.","author":[{"family":"Wang","given":"H."},{"family":"Li","given":"Y."},{"family":"Zhang","given":"H."},{"family":"Chen","given":"J."},{"family":"Ma","given":"K."},{"family":"Meng","given":"D."},{"family":"Zheng","given":"Y."}],"citation-key":"Wang2021107","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-030-87231-1_11","editor":[{"family":"Bruijne M., de Bruijne M.","given":"Cattin P.C.","non-dropping-particle":"de","suffix":"Cotin S., Padoy N., Speidel S., Zheng Y., Essert C."}],"ISBN":"9783030872304","ISSN":"03029743","issued":{"date-parts":[[2021]]},"page":"107-118","publisher":"Springer Science and Business Media Deutschland GmbH","title":"InDuDoNet: An interpretable dual domain network for CT metal artifact reduction","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116489169&doi=10.1007%2f978-3-030-87231-1_11&partnerID=40&md5=b8d43a5c90f85f7bc0921d7c0213f515","volume":"12906 LNCS"},
{"id":"Wang20212270","abstract":"Deep learning (DL) has dramatically improved the peak-to-average power ratio (PAPR) performance. However, the high computational complexity and excessive training data constitute a significant hurdle. In this letter, a model-driven deep learning algorithm is proposed for PAPR reduction in orthogonal frequency division multiplexing (OFDM) system. Precisely, an iterative peak-canceling signal generation scheme is unfolded as a layer structure of the DL network. The scheme falls into the category of tone reservation technique. A set of trainable parameters, which optimizes the clipping threshold and weights time-domain kernel function, has been designed and introduced into the iterative scheme. Compared with the existing approaches, the simulation results demonstrate that the proposed algorithm achieves comparable PAPR performance with low complexity and training costs. © 2021 IEEE.","author":[{"family":"Wang","given":"X."},{"family":"Jin","given":"N."},{"family":"Wei","given":"J."}],"citation-key":"Wang20212270","container-title":"IEEE Communications Letters","DOI":"10.1109/LCOMM.2021.3076605","ISSN":"10897798","issue":"7","issued":{"date-parts":[[2021]]},"page":"2270-2274","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A model-driven DL algorithm for PAPR reduction in OFDM system","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105096082&doi=10.1109%2fLCOMM.2021.3076605&partnerID=40&md5=76d11892ab72727bed787aa9012450e3","volume":"25"},
{"id":"Wang20212385","abstract":"In this letter, we propose a pilot-assisted receiver scheme based on learnable successive interference cancellation (PA-LSIC) for uplink single-input multiple-output (SIMO) non-orthogonal multiple access (NOMA) systems. The PA-LSIC combines the successive interference cancellation (SIC) structure with the model-driven deep learning network. Considering the noise impact of channel estimation and the incomplete detection and cancellation in SIC process, we introduce some new parameters, such as noise cancellation factor and interference cancellation factor, which are optimized by using the back-propagation algorithm and random gradient descent algorithm. Numerical results show that the PA-LSIC has superior bit error rate (BER) performance and lower complexity during training and implementation. © 2021 IEEE.","author":[{"family":"Wang","given":"X."},{"family":"Zhu","given":"P."},{"family":"Li","given":"D."},{"family":"Xu","given":"Y."},{"family":"You","given":"X."}],"citation-key":"Wang20212385","container-title":"IEEE Communications Letters","DOI":"10.1109/LCOMM.2021.3070705","ISSN":"10897798","issue":"7","issued":{"date-parts":[[2021]]},"page":"2385-2389","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Pilot-assisted SIMO-NOMA signal detection with learnable successive interference cancellation","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103764410&doi=10.1109%2fLCOMM.2021.3070705&partnerID=40&md5=b3322a74a6d236e8d2cadd317ad9f3c9","volume":"25"},
{"id":"Wang2021639","abstract":"Machine learning models are widely deployed in production cloud to provide online inference services. Efficiently deploying inference services requires careful tuning of hardware and runtime configurations (e.g., GPU type, GPU memory, batch size), which can significantly improve the model serving performance and reduce cost. However, existing autoconfiguration approaches for general workloads, such as Bayesian optimization and white-box prediction, are inefficient in navigating the high-dimensional configuration space of model serving, incurring high sampling cost. In this paper, we present Morphling, a fast, near-optimal auto-configuration framework for cloud-native model serving. Morphling employs model-agnostic meta-learning to navigate the large configuration space. It trains a metamodel offline to capture the general performance trend under varying configurations. Morphling quickly adapts the metamodel to a new inference service by sampling a small number of configurations and uses it to find the optimal one. We have implemented Morphling as an auto-configuration service in Kubernetes, and evaluate its performance with popular CV and NLP models, as well as the production inference services in Alibaba. Compared with existing approaches, Morphling reduces the median search cost by 3x-22x, quickly converging to the optimal configuration by sampling only 30 candidates in a large search space consisting of 720 options. © 2021 Association for Computing Machinery.","author":[{"family":"Wang","given":"L."},{"family":"Yang","given":"L."},{"family":"Yu","given":"Y."},{"family":"Wang","given":"W."},{"family":"Li","given":"B."},{"family":"Sun","given":"X."},{"family":"He","given":"J."},{"family":"Zhang","given":"L."}],"citation-key":"Wang2021639","collection-title":"SoCC 2021 - Proceedings of the 2021 ACM Symposium on Cloud Computing","DOI":"10.1145/3472883.3486987","ISBN":"978-1-4503-8638-8","issued":{"date-parts":[[2021]]},"page":"639-653","publisher":"Association for Computing Machinery, Inc","title":"Morphling: Fast, near-optimal auto-configuration for cloud-native model serving","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119292829&doi=10.1145%2f3472883.3486987&partnerID=40&md5=78b239b313810e4a4057f27f1b39344a"},
{"id":"Wang2022","abstract":"The Macroscopic fundamental Diagram of departure (MFD-D) for airport traffic is a more comprehensive metric that concisely describes aggregate network behavior while capturing the intrinsic demand-supply dynamics compared with taxiing time. This paper adopts a \"bottom-up\"framework by deconstructing MFD-D into three key parameters: free-flow taxiing time, Runway Queue time due to capacity limitation and De-Conflict delay during taxiing, and proposes a regression analysis. A hybrid data and model-driven approach combined regression analysis, transient queuing model and machine learning technology is adopted to predict above parameters accordingly in order to enhance the interpretability and accuracy. The models are validated using operational data of Pudong International Airport in 2019, showing high performance of departure traffic congestion estimation in terms of both key parameters and the MFD-D configuration. The results show that the prediction accuracy of the hybrid model is significantly better than other benchmark models. © 2022 IEEE.","author":[{"family":"Wang","given":"S."},{"family":"Yang","given":"L."},{"family":"Wang","given":"Y."},{"family":"Cong","given":"W."}],"citation-key":"Wang2022","collection-title":"Integrated Communications, Navigation and Surveillance Conference, ICNS","DOI":"10.1109/ICNS54818.2022.9771493","ISBN":"978-1-66548-419-0","ISSN":"21554943","issued":{"date-parts":[[2022]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A data and model-driven approach to predict congestion of departure traffic at airport","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130747246&doi=10.1109%2fICNS54818.2022.9771493&partnerID=40&md5=87d84e229d377e7878b2b2fb111cf5d5","volume":"2022-April"},
{"id":"wangCoCoSumContextualCode2021","abstract":"Source code summaries are short natural language descriptions of code snippets that help developers better understand and maintain source code. There has been a surge of work on automatic code summarization to reduce the burden of writing summaries manually. However, most contemporary approaches mainly leverage the information within the boundary of the method being summarized (i.e., local context), and ignore the broader context that could assist with code summarization. This paper explores two global contexts, namely intra-class and inter-class contexts, and proposes the model CoCoSUM: Contextual Code Summarization with Multi-Relational Graph Neural Networks. CoCoSUM first incorporates class names as the intra-class context to generate the class semantic embeddings. Then, relevant Unified Modeling Language (UML) class diagrams are extracted as inter-class context and are encoded into the class relational embeddings using a novel Multi-Relational Graph Neural Network (MRGNN). Class semantic embeddings and class relational embeddings, together with the outputs from code token encoder and AST encoder, are passed to a decoder armed with a two-level attention mechanism to generate high-quality, context-aware code summaries. We conduct extensive experiments to evaluate our approach and compare it with other automatic code summarization models. The experimental results show that CoCoSUM is effective and outperforms state-of-the-art methods. Our source code and experimental data are available in the supplementary materials and will be made publicly available.","accessed":{"date-parts":[[2022,1,28]]},"author":[{"family":"Wang","given":"Yanlin"},{"family":"Shi","given":"Ensheng"},{"family":"Du","given":"Lun"},{"family":"Yang","given":"Xiaodi"},{"family":"Hu","given":"Yuxuan"},{"family":"Han","given":"Shi"},{"family":"Zhang","given":"Hongyu"},{"family":"Zhang","given":"Dongmei"}],"citation-key":"wangCoCoSumContextualCode2021","container-title":"arXiv:2107.01933 [cs]","issued":{"date-parts":[[2021,7,5]]},"note":"00000","source":"arXiv.org","title":"CoCoSum: Contextual Code Summarization with Multi-Relational Graph Neural Network","title-short":"CoCoSum","type":"article-journal","URL":"http://arxiv.org/abs/2107.01933"},
{"id":"wangMiningSuccinctHighcoverage2013","author":[{"family":"Wang","given":"J."},{"family":"Dang","given":"Y."},{"family":"Zhang","given":"H."},{"family":"Chen","given":"K."},{"family":"Xie","given":"T."},{"family":"Zhang","given":"D."}],"citation-key":"wangMiningSuccinctHighcoverage2013","container-title":"10th working conference on mining software repositories","event-place":"Piscataway","ISSN":"2160-1852","issued":{"date-parts":[[2013]]},"page":"319-328","publisher":"IEEE","publisher-place":"Piscataway","title":"Mining succinct and high-coverage API usage patterns from source code","type":"paper-conference"},
{"id":"wangPersonalizingLabelPrediction2022","abstract":"Objective: These factors inspire us to propose a method to identify these synonymous labels automatically and recommend personalized labels for different open-source projects.\nMethod: In this paper, we propose a Personalizing Label Prediction framework for Issues named PLPI. PLPI identifies labels with similar meanings by representing labels as semantic vectors and applying clustering methods. PLPI can predict personalized labels from the existing labels in the open-source project.\nResult: We conduct a comprehensive study to compare seven commonly adopted labeling models with our approach. The experimental results demonstrate the advantages of our approach. Finally, we show some representative examples and discuss the visualization results of synonyms clustering by dimension reduction.\nConclusion: The experimental results show that our method PLPI can improve label prediction performance and provide personalized label recommendation results for different open-source projects.","accessed":{"date-parts":[[2022,1,25]]},"author":[{"family":"Wang","given":"Jun"},{"family":"Zhang","given":"Xiaofang"},{"family":"Chen","given":"Lin"},{"family":"Xie","given":"Xiaoyuan"}],"citation-key":"wangPersonalizingLabelPrediction2022","container-title":"Information and Software Technology","container-title-short":"Information and Software Technology","DOI":"10.1016/j.infsof.2022.106845","ISSN":"09505849","issued":{"date-parts":[[2022,1]]},"note":"00000","page":"106845","source":"DOI.org (Crossref)","title":"Personalizing label prediction for GitHub issues","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0950584922000192"},
{"id":"wangVerifyingMetamodelCoverage2006","accessed":{"date-parts":[[2015,8,17]]},"author":[{"family":"Wang","given":"J."},{"family":"Kim","given":"S.-K."},{"family":"Carrington","given":"D."}],"citation-key":"wangVerifyingMetamodelCoverage2006","DOI":"10.1109/ASWEC.2006.55","ISBN":"978-0-7695-2551-8","issued":{"date-parts":[[2006]]},"page":"10 pp.-282","publisher":"IEEE","source":"CrossRef","title":"Verifying metamodel coverage of model transformations","type":"paper-conference","URL":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1615060"},
{"id":"wangWuKongScalableAccurate2015","author":[{"family":"Wang","given":"Haoyu"},{"family":"Guo","given":"Yao"},{"family":"Ma","given":"Ziang"},{"family":"Chen","given":"Xiangqun"}],"citation-key":"wangWuKongScalableAccurate2015","collection-title":"ISSTA 2015","container-title":"Proceedings of the 2015 international symposium on software testing and analysis","event-place":"New York, NY, USA","ISBN":"978-1-4503-3620-8","issued":{"date-parts":[[2015]]},"page":"71-82","publisher":"ACM","publisher-place":"New York, NY, USA","title":"WuKong: A scalable and accurate two-phase approach to android app clone detection","type":"paper-conference","URL":"http://doi.acm.org/10.1145/2771783.2771795"},
{"id":"Warwas2012109","abstract":"Modeling real world agent-based systems is a complex endeavour. An ideal domain specific agent modeling language would be tailored to a certain application domain (e.g. virtual worlds) as well as to the target execution environment (e.g. a legacy virtual reality platform). This includes the use of specialized domain concepts, information models, software languages (e.g. query languages for reasoning about an agent's knowledge), as well as custom views and diagrams for designing the system. At the same time it is desirable to reuse application domain independent model artifacts such as interaction protocols (e.g. auction protocols) or goal/plan decompositions of a certain problem domain that already proved their use in similar scenarios. Current agent modeling languages cover the core concepts of multiagent systems but are neither thought to be customized for a certain application domain nor to be extended by external researchers with new or alternative AI and agent concepts. In this paper we propose a model-driven framework for engineering multiagent systems, called BOCHICA, which is based on a platform independent core modeling language and can be tailored through several extension interfaces to the user's needs. The framework leverages the reuse of existing design patterns and reduces development time and costs for creating application domain specific modeling solutions. We evaluated our approach on a distributed semantic web based execution platform for virtual worlds.","author":[{"family":"Warwas","given":"S."},{"family":"Fischer","given":"K."},{"family":"Klusch","given":"M."},{"family":"Slusallek","given":"P."}],"citation-key":"Warwas2012109","collection-title":"ICAART 2012 - Proceedings of the 4th International Conference on Agents and Artificial Intelligence","ISBN":"978-989-8425-95-9","issued":{"date-parts":[[2012]]},"page":"109-118","title":"BOCHICA: A model-driven framework for engineering multiagent systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862134348&partnerID=40&md5=d550fba890ffdfe635e9cb831073cdfa","volume":"1"},
{"id":"waszkowskiLowcodePlatformAutomating2019","accessed":{"date-parts":[[2021,5,3]]},"author":[{"family":"Waszkowski","given":"Robert"}],"citation-key":"waszkowskiLowcodePlatformAutomating2019","container-title":"IFAC-PapersOnLine","container-title-short":"IFAC-PapersOnLine","DOI":"10.1016/j.ifacol.2019.10.060","ISSN":"24058963","issue":"10","issued":{"date-parts":[[2019]]},"note":"00014","page":"376-381","source":"DOI.org (Crossref)","title":"Low-code platform for automating business processes in manufacturing","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2405896319309152","volume":"52"},
{"id":"watzoldtModelingCollaborationsSelfadaptive2015","author":[{"family":"Wätzoldt","given":"Sebastian"},{"family":"Giese","given":"Holger"}],"citation-key":"watzoldtModelingCollaborationsSelfadaptive2015","collection-number":"96","collection-title":"Technische Berichte des Hasso-Plattner-Instituts für Softwaresystemtechnik an der Universität Potsdam","event-place":"Potsdam","ISBN":"978-3-86956-324-4","issued":{"date-parts":[[2015]]},"note":"OCLC: 910322896","number-of-pages":"72","publisher":"Univ.-Verl","publisher-place":"Potsdam","source":"Gemeinsamer Bibliotheksverbund ISBN","title":"Modeling collaborations in self-adaptive systems of systems: terms, characteristics, requirements, and scenarios","title-short":"Modeling collaborations in self-adaptive systems of systems","type":"book"},
{"id":"Weber2020403","abstract":"Industry 4.0 use cases such as predictive maintenance and product quality control make it necessary to create, use and maintain a multitude of different machine learning models. In this setting, model management systems help to organize models. However, concepts for model management systems currently focus on data scientists, but do not support non-expert users such as domain experts and business analysts. Thus, it is difficult for them to reuse existing models for their use cases. In this paper, we address these challenges and present an architecture, a metadata schema and a corresponding model management platform. © Springer Nature Switzerland AG 2020.","author":[{"family":"Weber","given":"C."},{"family":"Hirmer","given":"P."},{"family":"Reimann","given":"P."}],"citation-key":"Weber2020403","container-title":"Lecture Notes in Business Information Processing","DOI":"10.1007/978-3-030-53337-3_30","editor":[{"family":"Abramowicz W.","given":"Klein G."}],"ISBN":"9783030533366","ISSN":"18651348","issued":{"date-parts":[[2020]]},"page":"403-417","publisher":"Springer","title":"A model management platform for industry 4.0 – enabling management of machine learning models in manufacturing environments","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089216306&doi=10.1007%2f978-3-030-53337-3_30&partnerID=40&md5=5fe3f57bc41847e76bd4a6f07f279f8b","volume":"389 LNBIP"},
{"id":"Weber202091","abstract":"In manufacturing environments, machine learning models are being built for several use cases, such as predictive maintenance and product quality control. In this context, the various manufacturing processes, machines, and product variants make it necessary to create and use lots of different machine learning models. This calls for a software system that is able to manage all these diverse machine learning models and associated metadata. However, current model management systems do not associate models with business and domain context to provide non-expert users with tailored functions for model search and discovery. Moreover, none of the existing systems provides a comprehensive overview of all models within an organization. In our demonstration, we present the MMP, our model management platform that addresses these issues. The MMP provides a model metadata extractor, a model registry, and a context manager to store model metadata in a central metadata store. On top of this, the MMP provides frontend components that offer the above-mentioned functionalities. In our demonstration, we show two scenarios for model management in Industry 4.0 environments that illustrate the novel functionalities of the MMP. We demonstrate to the audience how the platform and its metadata, linking models to their business and domain context, help non-expert users to search and discover models. Furthermore, we show how to use MMP's powerful visualizations for model reporting, such as a dashboard and a model landscape view. © 2020 IEEE.","author":[{"family":"Weber","given":"C."},{"family":"Reimann","given":"P."}],"citation-key":"Weber202091","collection-title":"Proceedings - IEEE International Enterprise Distributed Object Computing Workshop, EDOCW","DOI":"10.1109/EDOCW49879.2020.00025","ISBN":"978-1-72816-471-7","ISSN":"15417719","issued":{"date-parts":[[2020]]},"page":"91-94","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"MMP - A platform to manage machine learning models in industry 4.0 environments","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096104642&doi=10.1109%2fEDOCW49879.2020.00025&partnerID=40&md5=52cb944596c0c1f609bb7655254c3a43","volume":"2020-October"},
{"id":"Wei201979","abstract":"This paper combines data-driven and model-driven methods for real-time misinformation detection. Our algorithm, named Quick- Stop, is an optimal stopping algorithm based on a probabilistic information spreading model obtained from labeled data. The algorithm consists of an offline machine learning algorithm for learning the probabilistic information spreading model and an online optimal stopping algorithm to detect misinformation. The online detection algorithm has both low computational and memory complexities. Our numerical evaluations with a real-world dataset show that QuickStop outperforms existing misinformation detection algorithms in terms of both accuracy and detection time (number of observations needed for detection). Our evaluations with synthetic data further show that QuickStop is robust to (offline) learning errors. © 2019 Copyright held by the owner/author(s).","author":[{"family":"Wei","given":"H."},{"family":"Kang","given":"X."},{"family":"Wang","given":"W."},{"family":"Ying","given":"L."}],"citation-key":"Wei201979","collection-title":"SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","DOI":"10.1145/3309697.3331513","ISBN":"978-1-4503-6678-6","issued":{"date-parts":[[2019]]},"page":"79-80","publisher":"Association for Computing Machinery, Inc","title":"QuickStop: A Markov optimal stopping approach for quickest misinformation detection","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069187623&doi=10.1145%2f3309697.3331513&partnerID=40&md5=07f039a9735e15fca8a7ca6833852521"},
{"id":"Wei201979","abstract":"This paper combines data-driven and model-driven methods for real-Time misinformation detection. Our algorithm, named Quick-Stop, is an optimal stopping algorithm based on a probabilistic information spreading model obtained from labeled data. The algorithm consists of an offline machine learning algorithm for learning the probabilistic information spreading model and an online optimal stopping algorithm to detect misinformation. The online detection algorithm has both low computational and memory complexities. Our numerical evaluations with a real-world dataset show that QuickStop outperforms existing misinformation detection algorithms in terms of both accuracy and detection time (number of observations needed for detection). Our evaluations with synthetic data further show that QuickStop is robust to (offline) learning errors. © 2019 Copyright is held by the owner/author(s).","author":[{"family":"Wei","given":"H."},{"family":"Kang","given":"X."},{"family":"Wang","given":"W."},{"family":"Ying","given":"L."}],"citation-key":"Wei201979","container-title":"Performance Evaluation Review","DOI":"10.1145/3309697.3331513","ISSN":"01635999","issue":"1","issued":{"date-parts":[[2019]]},"page":"79-80","publisher":"Association for Computing Machinery","title":"QuickStop: A markov optimal stopping approach for quickest misinformation detection","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086498251&doi=10.1145%2f3309697.3331513&partnerID=40&md5=b7f07b5ab08c3d41f1b7484cb3ea7f07","volume":"47"},
{"id":"Wei201979","abstract":"This paper combines data-driven and model-driven methods for real-time misinformation detection. Our algorithm, named Quick- Stop, is an optimal stopping algorithm based on a probabilistic information spreading model obtained from labeled data. The algorithm consists of an offline machine learning algorithm for learning the probabilistic information spreading model and an online optimal stopping algorithm to detect misinformation. The online detection algorithm has both low computational and memory complexities. Our numerical evaluations with a real-world dataset show that QuickStop outperforms existing misinformation detection algorithms in terms of both accuracy and detection time (number of observations needed for detection). Our evaluations with synthetic data further show that QuickStop is robust to (offline) learning errors. © 2019 Copyright held by the owner/author(s).","author":[{"family":"Wei","given":"H."},{"family":"Kang","given":"X."},{"family":"Wang","given":"W."},{"family":"Ying","given":"L."}],"citation-key":"Wei201979","collection-title":"SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","DOI":"10.1145/3309697.3331513","ISBN":"978-1-4503-6678-6","issued":{"date-parts":[[2019]]},"page":"79-80","publisher":"Association for Computing Machinery, Inc","title":"QuickStop: A Markov optimal stopping approach for quickest misinformation detection","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069187623&doi=10.1145%2f3309697.3331513&partnerID=40&md5=07f039a9735e15fca8a7ca6833852521"},
{"id":"Wei2020","abstract":"In this work, we consider the use of model-driven deep learning (DL) techniques for signal detection in massive multiple-input multiple-output (MIMO) system. Massive MIMO promises improved spectral efficiency, coverage and reliability, compared to conventional MIMO systems. Unfortunately, these benefits usually come at the cost of significantly increased computational complexity. To address this difficulty, a learned conjugate gradient descent network, referred to as LcgNet, is presented by unfolding the iterative conjugate gradient descent (CG) detector. In the proposed network, instead of calculating the exact values of the scalar step-sizes for every problem instance, we explicitly learn their universal values. We show that the performance of the proposed network can be greatly improved by augmenting the dimensions of these step-sizes. Furthermore, due to the limited learnable parameters to be optimized, the proposed networks are easy and fast to train. Numerical results demonstrate that this approach can achieve superior performance over some state-of-the-art MIMO detectors such as the CG detector, the linear minimum mean squared error (LMMSE) detector etc., with much lower computational complexity. © 2020 IEEE.","author":[{"family":"Wei","given":"Y."},{"family":"Zhao","given":"M.-M."},{"family":"Hong","given":"M."},{"family":"Zhao","given":"M.-J."},{"family":"Lei","given":"M."}],"citation-key":"Wei2020","collection-title":"IEEE International Conference on Communications","DOI":"10.1109/ICC40277.2020.9149227","ISBN":"978-1-72815-089-5","ISSN":"15503607","issued":{"date-parts":[[2020]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Learned conjugate gradient descent network for massive MIMO detection","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089424041&doi=10.1109%2fICC40277.2020.9149227&partnerID=40&md5=08b6cb8ad9457a47d075009b469dd4b5","volume":"2020-June"},
{"id":"Wei20206336","abstract":"In this work, we consider the use of model-driven deep learning techniques for massive multiple-input multiple-output (MIMO) detection. Compared with conventional MIMO systems, massive MIMO promises improved spectral efficiency, coverage and range. Unfortunately, these benefits are at the expense of significantly increased computational complexity. To reduce the complexity of signal detection and guarantee the performance, we present a learned conjugate gradient descent network (LcgNet), which is constructed by unfolding the iterative conjugate gradient descent (CG) detector. In the proposed network, instead of calculating the exact values of the scalar step-sizes, we explicitly learn their universal values. Also, we can enhance the proposed network by augmenting the dimensions of these step-sizes. Furthermore, in order to reduce the memory costs, a novel quantized LcgNet is proposed, where a low-resolution nonuniform quantizer is used to quantize the learned parameters. The quantizer is based on a specially designed soft staircase function with learnable parameters to adjust its shape. Meanwhile, due to fact that the number of learnable parameters is limited, the proposed networks are relatively easy to train. Numerical results demonstrate that the proposed network can achieve promising performance with much lower complexity. © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.","author":[{"family":"Wei","given":"Y."},{"family":"Zhao","given":"M.-M."},{"family":"Hong","given":"M."},{"family":"Zhao","given":"M.-J."},{"family":"Lei","given":"M."}],"citation-key":"Wei20206336","container-title":"IEEE Transactions on Signal Processing","DOI":"10.1109/TSP.2020.3035832","ISSN":"1053587X","issued":{"date-parts":[[2020]]},"page":"6336-6349","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Learned conjugate gradient descent network for massive MIMO detection","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101264155&doi=10.1109%2fTSP.2020.3035832&partnerID=40&md5=aeb27683ad7c05fb456fd55d6a4e4daf","volume":"68"},
{"id":"weiQuickStopMarkovOptimal2019b","abstract":"This paper combines data-driven and model-driven methods for real-Time misinformation detection. Our algorithm, named Quick-Stop, is an optimal stopping algorithm based on a probabilistic information spreading model obtained from labeled data. The algorithm consists of an offline machine learning algorithm for learning the probabilistic information spreading model and an online optimal stopping algorithm to detect misinformation. The online detection algorithm has both low computational and memory complexities. Our numerical evaluations with a real-world dataset show that QuickStop outperforms existing misinformation detection algorithms in terms of both accuracy and detection time (number of observations needed for detection). Our evaluations with synthetic data further show that QuickStop is robust to (offline) learning errors. © 2019 Copyright is held by the owner/author(s).","author":[{"family":"Wei","given":"H."},{"family":"Kang","given":"X."},{"family":"Wang","given":"W."},{"family":"Ying","given":"L."}],"citation-key":"weiQuickStopMarkovOptimal2019b","container-title":"Performance Evaluation Review","DOI":"10.1145/3309697.3331513","ISSN":"01635999","issue":"1","issued":{"date-parts":[[2019]]},"page":"79-80","publisher":"Association for Computing Machinery","title":"QuickStop: A markov optimal stopping approach for quickest misinformation detection","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086498251&doi=10.1145%2f3309697.3331513&partnerID=40&md5=b7f07b5ab08c3d41f1b7484cb3ea7f07","volume":"47"},
{"id":"weiQuickStopMarkovOptimal2019c","abstract":"This paper combines data-driven and model-driven methods for real-time misinformation detection. Our algorithm, named Quick- Stop, is an optimal stopping algorithm based on a probabilistic information spreading model obtained from labeled data. The algorithm consists of an offline machine learning algorithm for learning the probabilistic information spreading model and an online optimal stopping algorithm to detect misinformation. The online detection algorithm has both low computational and memory complexities. Our numerical evaluations with a real-world dataset show that QuickStop outperforms existing misinformation detection algorithms in terms of both accuracy and detection time (number of observations needed for detection). Our evaluations with synthetic data further show that QuickStop is robust to (offline) learning errors. © 2019 Copyright held by the owner/author(s).","author":[{"family":"Wei","given":"H."},{"family":"Kang","given":"X."},{"family":"Wang","given":"W."},{"family":"Ying","given":"L."}],"citation-key":"weiQuickStopMarkovOptimal2019c","container-title":"SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","DOI":"10.1145/3309697.3331513","ISBN":"978-1-4503-6678-6","issued":{"date-parts":[[2019]]},"page":"79-80","publisher":"Association for Computing Machinery, Inc","title":"QuickStop: A Markov optimal stopping approach for quickest misinformation detection","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069187623&doi=10.1145%2f3309697.3331513&partnerID=40&md5=07f039a9735e15fca8a7ca6833852521"},
{"id":"weissModelDrivenDevelopmentSelfDescribing2011","accessed":{"date-parts":[[2016,8,23]]},"author":[{"family":"Weiss","given":"Gereon"},{"family":"Becker","given":"Klaus"},{"family":"Kamphausen","given":"Benjamin"},{"family":"Radermacher","given":"Ansgar"},{"family":"Gerard","given":"Sebastien"}],"citation-key":"weissModelDrivenDevelopmentSelfDescribing2011","DOI":"10.1109/SEAA.2011.78","ISBN":"978-1-4577-1027-8","issued":{"date-parts":[[2011,8]]},"page":"477-484","publisher":"IEEE","source":"CrossRef","title":"Model-Driven Development of Self-Describing Components for Self-Adaptive Distributed Embedded Systems","type":"paper-conference","URL":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6068387"},
{"id":"Wen20181505","abstract":"Automatic trading systems are typical complex systems, and a successful automatic trading system should be excellent at prediction and decision. Stock prices are affected by the information from various sources, while traditional automatic trading systems only consider the historical trading data. For this issue, we design an automatic trading framework by considering the signals from stock prices and new information based on artificial neural networks. Specifically, we first analyze various kinds of financial events and their corresponding effects on stock prices, and then extract the financial events that have prominent effects on stock prices. Next, we design an automatic trading model driven by stock prices and financial event data. Experiments on real world datasets show that the proposed ANN-News model outperforms the conventional machine learning models by about 4% in prediction precision and 7% in return, respectively. Copyright © 2018 Acta Automatica Sinica. All rights reserved.","author":[{"family":"Wen","given":"D.-Y."},{"family":"Ma","given":"C.-Q."},{"family":"Wang","given":"K."}],"citation-key":"Wen20181505","container-title":"Zidonghua Xuebao/Acta Automatica Sinica","DOI":"10.16383/j.aas.2018.c170563","ISSN":"02544156","issue":"8","issued":{"date-parts":[[2018]]},"page":"1505-1517","publisher":"Science Press","title":"A multi-source data driven decision model for automatic trading systems [一种多源数据驱动的自动交易系统决策模型]","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056328781&doi=10.16383%2fj.aas.2018.c170563&partnerID=40&md5=d0d16623a439397cb20a4d34b6d0d427","volume":"44"},
{"id":"weynsApplyingArchitectureBasedAdaptation2018","abstract":"Architecture-based adaptation equips a software-intensive system with a feedback loop that enables the system to adapt itself at runtime to changes to maintain its required quality goals. To guarantee the required goals, existing adaptation approaches apply exhaustive verification techniques at runtime. However these approaches are restricted to small-scale settings, which often limits their applicability in practice. To tackle this problem, we introduce an innovative architecture-based adaptation approach to solve a concrete practical problem of VersaSense: automating the management of Internet-of-Things (IoT). The approach, called MARTAS, equips a software system with a feedback loop that employs Models At Run Time and Statistical techniques to reason about the system and adapt it to ensure the required goals. We apply MARTAS to a building security case system, which is a representative IoT system deployed by VersaSense. The application comprises a set of IoT devices that communicate sensor data over a time synchronized smart mess network to a central monitoring facility. We demonstrate how MARTAS outperforms a conservative approach that is typically applied in practice and a state-of-the-art adaptation approach for different quality goals, and we report lessons learned from this industrial case.","author":[{"family":"Weyns","given":"Danny"},{"family":"Iftikhar","given":"M. Usman"},{"family":"Hughes","given":"Danny"},{"family":"Matthys","given":"Nelson"}],"citation-key":"weynsApplyingArchitectureBasedAdaptation2018","collection-title":"Lecture Notes in Computer Science","container-title":"Software Architecture","DOI":"10.1007/978-3-030-00761-4_4","editor":[{"family":"Cuesta","given":"Carlos E."},{"family":"Garlan","given":"David"},{"family":"Pérez","given":"Jennifer"}],"event-place":"Cham","ISBN":"978-3-030-00761-4","issued":{"date-parts":[[2018]]},"note":"00022","page":"49-67","publisher":"Springer International Publishing","publisher-place":"Cham","source":"Springer Link","title":"Applying Architecture-Based Adaptation to Automate the Management of Internet-of-Things","type":"paper-conference"},
{"id":"weyssowRecommendingMetamodelConcepts2021","abstract":"The design of conceptually sound metamodels that embody proper semantics in relation to the application domain is particularly tedious in Model-Driven Engineering. As metamodels define complex relationships between domain concepts, it is crucial for a modeler to define these concepts thoroughly while being consistent with respect to the application domain. We propose an approach to assist a modeler in the design of a metamodel by recommending relevant domain concepts in several modeling scenarios. Our approach does not require to extract knowledge from the domain or to hand-design completion rules. Instead, we design a fully data-driven approach using a deep learning model that is able to abstract domain concepts by learning from both structural and lexical metamodel properties in a corpus of thousands of independent metamodels. We evaluate our approach on a test set containing 166 metamodels, unseen during the model training, with more than 5000 test samples. Our preliminary results show that the trained model is able to provide accurate top-$5$ lists of relevant recommendations for concept renaming scenarios. Although promising, the results are less compelling for the scenario of the iterative construction of the metamodel, in part because of the conservative strategy we use to evaluate the recommendations.","accessed":{"date-parts":[[2021,4,30]]},"author":[{"family":"Weyssow","given":"Martin"},{"family":"Sahraoui","given":"Houari"},{"family":"Syriani","given":"Eugene"}],"citation-key":"weyssowRecommendingMetamodelConcepts2021","container-title":"arXiv:2104.01642 [cs]","issued":{"date-parts":[[2021,4,4]]},"note":"00000","publisher":"Springer Science and Business Media Deutschland GmbH","source":"arXiv.org","title":"Recommending Metamodel Concepts during Modeling Activities with Pre-Trained Language Models","type":"article-journal","URL":"http://arxiv.org/abs/2104.01642"},
{"id":"whalenRequirementsArchitecturesSecure2016","accessed":{"date-parts":[[2016,9,28]]},"author":[{"family":"Whalen","given":"Michael W."},{"family":"Cofer","given":"Darren"},{"family":"Gacek","given":"Andrew"}],"citation-key":"whalenRequirementsArchitecturesSecure2016","container-title":"IEEE Software","issue":"4","issued":{"date-parts":[[2016]]},"page":"22–25","source":"Google Scholar","title":"Requirements and Architectures for Secure Vehicles","type":"article-journal","URL":"http://ieeexplore.ieee.org/abstract/document/7498541/","volume":"33"},
{"id":"WhatDifferenceAutonomous","accessed":{"date-parts":[[2016,8,26]]},"citation-key":"WhatDifferenceAutonomous","title":"What's the difference between autonomous systems, ISPs and RIRs? - Network Engineering Stack Exchange","type":"webpage","URL":"http://networkengineering.stackexchange.com/questions/25951/whats-the-difference-between-autonomous-systems-isps-and-rirs"},
{"id":"WhatDifferenceEvolution","accessed":{"date-parts":[[2020,2,10]]},"citation-key":"WhatDifferenceEvolution","title":"What is the difference between evolution and change? | WikiDiff","type":"webpage","URL":"https://wikidiff.com/evolution/change"},
{"id":"WhatLowCode2020","accessed":{"date-parts":[[2020,4,8]]},"citation-key":"WhatLowCode2020","title":"What Is Low-Code? [2020 Update]","type":"webpage","URL":"https://www.outsystems.com/blog/what-is-low-code.html"},
{"id":"WhenHowUse","citation-key":"WhenHowUse","title":"When and How to Use Multi-Level Modelling.pdf","type":"document"},
{"id":"whitmoreInternetThingsSurvey2015","accessed":{"date-parts":[[2016,5,31]]},"author":[{"family":"Whitmore","given":"Andrew"},{"family":"Agarwal","given":"Anurag"},{"family":"Da Xu","given":"Li"}],"citation-key":"whitmoreInternetThingsSurvey2015","container-title":"Information Systems Frontiers","DOI":"10.1007/s10796-014-9489-2","ISSN":"1387-3326, 1572-9419","issue":"2","issued":{"date-parts":[[2015,4]]},"page":"261-274","source":"CrossRef","title":"The Internet of Things—A survey of topics and trends","type":"article-journal","URL":"http://link.springer.com/10.1007/s10796-014-9489-2","volume":"17"},
{"id":"whittleIndustrialAdoptionModeldriven2013","accessed":{"date-parts":[[2017,2,22]]},"author":[{"family":"Whittle","given":"Jon"},{"family":"Hutchinson","given":"John"},{"family":"Rouncefield","given":"Mark"},{"family":"Burden","given":"H\\a","dropping-particle":"akan"},{"family":"Heldal","given":"Rogardt"}],"citation-key":"whittleIndustrialAdoptionModeldriven2013","container-title":"International Conference on Model Driven Engineering Languages and Systems","issued":{"date-parts":[[2013]]},"page":"1–17","publisher":"Springer","source":"Google Scholar","title":"Industrial adoption of model-driven engineering: Are the tools really the problem?","title-short":"Industrial adoption of model-driven engineering","type":"paper-conference","URL":"http://link.springer.com/chapter/10.1007/978-3-642-41533-3_1"},
{"id":"Wilcoxon1992","abstract":"The comparison of two treatments generally falls into one of the following two categories: (a) we may have a number of replications for each of the two treatments, which are unpaired, or (b) we may have a number of paired comparisons leading to a series of differences, some of which may be positive and some negative. The appropriate methods for testing the significance of the differences of the means in these two cases are described in most of the textbooks on statistical methods.","author":[{"family":"Wilcoxon","given":"Frank"}],"citation-key":"Wilcoxon1992","container-title":"Breakthroughs in statistics: Methodology and distribution","DOI":"10.1007/978-1-4612-4380-9₁6","editor":[{"family":"Kotz","given":"Samuel"},{"family":"Johnson","given":"Norman L."}],"event-place":"New York, NY","ISBN":"978-1-4612-4380-9","issued":{"date-parts":[[1992]]},"page":"196-202","publisher":"Springer New York","publisher-place":"New York, NY","title":"Individual comparisons by ranking methods","type":"chapter","URL":"https://doi.org/10.1007/978-1-4612-4380-9₁6"},
{"id":"WileyAutonomousSystem","accessed":{"date-parts":[[2016,8,22]]},"citation-key":"WileyAutonomousSystem","title":"Wiley: The Autonomous System: A Foundational Synthesis of the Sciences of the Mind - Szabolcs Michael de Gyurky, Mark A. Tarbell","type":"webpage","URL":"http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1118294246,subjectCd-EE79.html"},
{"id":"Williams2020","abstract":"The widespread growth of additive manufacturing, a field with a complex informatic \"digital thread\", has helped fuel the creation of design repositories, where multiple users can upload distribute, and download a variety of candidate designs for a variety of situations. Additionally, advancements in additive manufacturing process development, design frameworks, and simulation are increasing what is possible to fabricate with AM, further growing the richness of such repositories. Machine learning offers new opportunities to combine these design repository components' rich geometric data with their associated process and performance data to train predictive models capable of automatically assessing build metrics related to AM part manufacturability. Although design repositories that can be used to train these machine learning constructs are expanding, our understanding of what makes a particular design repository useful as a machine learning training dataset is minimal. In this study we use a metamodel to predict the extent to which individual design repositories can train accurate convolutional neural networks. To facilitate the creation and refinement of this metamodel, we constructed a large artificial design repository, and subsequently split it into sub-repositories. We then analyzed metadata regarding the size, complexity, and diversity of the subrepositories for use as independent variables predicting accuracy and the required training computational effort for training convolutional neural networks. The networks each predict one of three additive manufacturing build metrics: (1) part mass, (2) support material mass, and (3) build time. Our results suggest that metamodels predicting the convolutional neural network coefficient of determination, as opposed to computational effort, were most accurate. Moreover, the size of a design repository, the average complexity of its constituent designs, and the average and spread of design spatial diversity were the best predictors of convolutional neural network accuracy. © 2020 American Society of Mechanical Engineers (ASME). All rights reserved.","author":[{"family":"Williams","given":"G."},{"family":"Meisel","given":"N.A."},{"family":"Simpson","given":"T.W."},{"family":"McComb","given":"C."}],"citation-key":"Williams2020","collection-title":"Proceedings of the ASME Design Engineering Technical Conference","DOI":"10.1115/DETC2020-22518","ISBN":"978-0-7918-8400-3","issued":{"date-parts":[[2020]]},"publisher":"American Society of Mechanical Engineers (ASME)","title":"Deriving metamodels to relate machine learning quality to design repository characteristics in the context of additive manufacturing","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096309337&doi=10.1115%2fDETC2020-22518&partnerID=40&md5=b1448409b71ef411174bd270b41846aa","volume":"11A-2020"},
{"id":"williamsDerivingMetamodelsRelate2020a","abstract":"The widespread growth of additive manufacturing, a field with a complex informatic \"digital thread\", has helped fuel the creation of design repositories, where multiple users can upload distribute, and download a variety of candidate designs for a variety of situations. Additionally, advancements in additive manufacturing process development, design frameworks, and simulation are increasing what is possible to fabricate with AM, further growing the richness of such repositories. Machine learning offers new opportunities to combine these design repository components' rich geometric data with their associated process and performance data to train predictive models capable of automatically assessing build metrics related to AM part manufacturability. Although design repositories that can be used to train these machine learning constructs are expanding, our understanding of what makes a particular design repository useful as a machine learning training dataset is minimal. In this study we use a metamodel to predict the extent to which individual design repositories can train accurate convolutional neural networks. To facilitate the creation and refinement of this metamodel, we constructed a large artificial design repository, and subsequently split it into sub-repositories. We then analyzed metadata regarding the size, complexity, and diversity of the subrepositories for use as independent variables predicting accuracy and the required training computational effort for training convolutional neural networks. The networks each predict one of three additive manufacturing build metrics: (1) part mass, (2) support material mass, and (3) build time. Our results suggest that metamodels predicting the convolutional neural network coefficient of determination, as opposed to computational effort, were most accurate. Moreover, the size of a design repository, the average complexity of its constituent designs, and the average and spread of design spatial diversity were the best predictors of convolutional neural network accuracy. © 2020 American Society of Mechanical Engineers (ASME). All rights reserved.","author":[{"family":"Williams","given":"G."},{"family":"Meisel","given":"N.A."},{"family":"Simpson","given":"T.W."},{"family":"McComb","given":"C."}],"citation-key":"williamsDerivingMetamodelsRelate2020a","container-title":"Proceedings of the ASME Design Engineering Technical Conference","DOI":"10.1115/DETC2020-22518","ISBN":"978-0-7918-8400-3","issued":{"date-parts":[[2020]]},"publisher":"American Society of Mechanical Engineers (ASME)","title":"Deriving metamodels to relate machine learning quality to design repository characteristics in the context of additive manufacturing","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096309337&doi=10.1115%2fDETC2020-22518&partnerID=40&md5=b1448409b71ef411174bd270b41846aa","volume":"11A-2020"},
{"id":"williamsEngineeringSecurityVulnerability2018","abstract":"Around the turn of the 21st century, practices began to emerge to guide teams toward engineering software to stop attackers and users from utilizing unintended functionality by violating the system designer’s assumptions to cause a security breach. Yet, breaches are reported daily in the news in all domains—from the casual to the critical. The goal of this article is to help software engineers, software engineering educators, and security researchers understand opportunities for education and research through an analysis of current software security practices. The analysis is conducted on data on the use of a subset of 113 software security practices by 109 firms over 42 months, as reported in the Building Security In Maturity Model (BSIMM) Version 8 report. This article is part of a theme issue on software engineering’s 50th anniversary.","author":[{"family":"Williams","given":"L."},{"family":"McGraw","given":"G."},{"family":"Migues","given":"S."}],"citation-key":"williamsEngineeringSecurityVulnerability2018","container-title":"IEEE Software","DOI":"10.1109/MS.2018.290110854","ISSN":"0740-7459","issue":"5","issued":{"date-parts":[[2018,9]]},"page":"76-80","source":"IEEE Xplore","title":"Engineering Security Vulnerability Prevention, Detection, and Response","type":"article-journal","volume":"35"},
{"id":"williamsModelBasedAutonomousSystems1996","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Williams","given":"Brian C."}],"citation-key":"williamsModelBasedAutonomousSystems1996","container-title":"AIPS","issued":{"date-parts":[[1996]]},"page":"275–282","source":"Google Scholar","title":"Model-Based Autonomous Systems in the New Millenium.","type":"paper-conference","URL":"http://www.aaai.org/Papers/AIPS/1996/AIPS96-035.pdf"},
{"id":"Wills06googlespagerank","author":[{"family":"Wills","given":"Rebecca S."}],"citation-key":"Wills06googlespagerank","container-title":"The Mathematical Intelligencer","container-title-short":"Math. Intelligencer","issued":{"date-parts":[[2006]]},"page":"6-10","title":"Google's PageRank: The math behind the search engine","type":"article-journal"},
{"id":"wimmerCatalogueRefactoringsModeltoModel2012","author":[{"family":"Wimmer","given":"Manuel"},{"family":"Martínez","given":"Salvador"},{"family":"Jouault","given":"Frédéric"},{"family":"Cabot","given":"Jordi"}],"citation-key":"wimmerCatalogueRefactoringsModeltoModel2012","container-title":"The Journal of Object Technology","DOI":"10.5381/jot.2012.11.2.a2","issue":"2","issued":{"date-parts":[[2012]]},"page":"2:1","title":"A Catalogue of Refactorings for Model-to-Model Transformations.","type":"article-journal","volume":"11"},
{"id":"wimmerHowWebCan2008","author":[{"family":"Wimmer","given":"Manuel"},{"family":"Schauerhuber","given":"Andrea"},{"family":"Michael","given":"Strommer"},{"family":"Jürgen","given":"Flandorfer"},{"family":"Gerti","given":"Kappel"}],"citation-key":"wimmerHowWebCan2008","container-title":"Workshop Domänspezifische Modellierungssprachen","issued":{"date-parts":[[2008]]},"title":"How Web 2.0 can leverage Model Engineering in Practice","type":"article-journal"},
{"id":"wimmerPlugPlayModel2010","accessed":{"date-parts":[[2015,6,24]]},"author":[{"family":"Wimmer","given":"Manuel"},{"family":"Retschitzegger","given":"W."},{"family":"Kappel","given":"G."},{"family":"Schoenboeck","given":"J."},{"family":"Kusel","given":"A."},{"family":"Schwinger","given":"Wieland"}],"citation-key":"wimmerPlugPlayModel2010","container-title":"Proceedings of the 10th Workshop on Domain-Specific Modeling","issued":{"date-parts":[[2010]]},"page":"7","publisher":"ACM","source":"Google Scholar","title":"Plug & play model transformations: a DSL for resolving structural metamodel heterogeneities","title-short":"Plug & play model transformations","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=2060348"},
{"id":"wimmerReusingModelTransformations2011","author":[{"family":"Wimmer","given":"Manuel"}],"citation-key":"wimmerReusingModelTransformations2011","issued":{"date-parts":[[2011]]},"title":"Reusing Model Transformations across Heterogeneous Metamodels","type":"article-journal"},
{"id":"wintersSoftwareEngineeringGoogle","author":[{"family":"Winters","given":"Titus"},{"family":"Manschreck","given":"Tom"},{"family":"Wright","given":"Hyrum"}],"citation-key":"wintersSoftwareEngineeringGoogle","note":"00010","page":"602","source":"Zotero","title":"Software Engineering at Google","type":"article-journal"},
{"id":"wongPerformanceEvaluationClassification2015","author":[{"family":"Wong","given":"Tzu-Tsung"}],"citation-key":"wongPerformanceEvaluationClassification2015","container-title":"Pattern Recognition","ISSN":"0031-3203","issue":"9","issued":{"date-parts":[[2015]]},"page":"2839-2846","title":"Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation","type":"article-journal","volume":"48"},
{"id":"wortmannModelingLanguagesIndustry2019","abstract":"Industry 4.0 integrates cyber-physical systems with the Internet of Things to optimize the complete value-added chain. Successfully applying Industry 4.0 requires the cooperation of various stakeholders from different domains. Domain-specific modeling languages promise to facilitate their involvement through leveraging (domain-specific) models to primary development artifacts. We aim to assess the use of modeling in Industry 4.0 through the lens of modeling languages in a broad sense. Based on an extensive literature review, we updated our systematic mapping study on modeling languages and modeling techniques used in Industry 4.0 (Wortmann et al., Conference on model-driven engineering languages and systems (MODELS’17), IEEE, pp 281–291, 2017) to include publications until February 2018. Overall, the updated study considers 3344 candidate publications that were systematically investigated until 408 relevant publications were identified. Based on these, we developed an updated map of the research landscape on modeling languages and techniques for Industry 4.0. Research on modeling languages in Industry 4.0 focuses on contributing methods to solve the challenges of digital representation and integration. To this end, languages from systems engineering and knowledge representation are applied most often but rarely combined. There also is a gap between the communities researching and applying modeling languages for Industry 4.0 that originates from different perspectives on modeling and related standards. From the vantage point of modeling, Industry 4.0 is the combination of systems engineering, with cyber-physical systems, and knowledge engineering. Research currently is splintered along topics and communities and accelerating progress demands for multi-disciplinary, integrated research efforts.","accessed":{"date-parts":[[2019,9,25]]},"author":[{"family":"Wortmann","given":"Andreas"},{"family":"Barais","given":"Olivier"},{"family":"Combemale","given":"Benoit"},{"family":"Wimmer","given":"Manuel"}],"citation-key":"wortmannModelingLanguagesIndustry2019","container-title":"Software and Systems Modeling","container-title-short":"Softw Syst Model","DOI":"10.1007/s10270-019-00757-6","ISSN":"1619-1366, 1619-1374","issued":{"date-parts":[[2019,9,20]]},"source":"DOI.org (Crossref)","title":"Modeling languages in Industry 4.0: an extended systematic mapping study","title-short":"Modeling languages in Industry 4.0","type":"article-journal","URL":"http://link.springer.com/10.1007/s10270-019-00757-6"},
{"id":"Wu20202230","abstract":"Accurate health evaluation is crucial to reliable operation of complex degradation systems. Although traditional machine learning methods such as artificial neural network (ANN) and support vector machine (SVM) have been used widely, state assessment schemes based on a single classification model still suffer from low multiclass classification efficiency, high variance, and deviation. To solve these problems, this article proposes a novel health evaluation method based on stacking ensemble learning and generalized multiclass support vector machine (GMSVM) algorithm. The proposed health evaluation framework includes three parts: 1) abnormal value elimination and missing value processing are applied for multiple sensor data; 2) statistical features are extracted from the observed data and the Pearson correlation coefficient is applied for feature selection; and 3) ensemble generalized multiclass support vector machines (EGMSVMs) are utilized to evaluate the health situation of a degradation system. Unlike the binary classifiers and deep-learning-based classifiers, EGMSVMs utilize the stacking-based method to combine several GMSVMs as submodels and random forest as a metamodel, and the metamodel ensembles the results of submodels to reach a satisfied performance. Compared to traditional SVM- and ANN-based algorithms, EGMSVMs, in processing multiclass problems, achieve high efficiency and, meanwhile, low variance and deviation. The proposed method is verified using a hydraulic test rig. The experimental results show the feasibility and applicability of the proposed health evaluation framework. © 2020 IEEE.","author":[{"family":"Wu","given":"J."},{"family":"Guo","given":"P."},{"family":"Cheng","given":"Y."},{"family":"Zhu","given":"H."},{"family":"Wang","given":"X.-B."},{"family":"Shao","given":"X."}],"citation-key":"Wu20202230","container-title":"IEEE/ASME Transactions on Mechatronics","DOI":"10.1109/TMECH.2020.3009449","ISSN":"10834435","issue":"5","issued":{"date-parts":[[2020]]},"page":"2230-2240","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Ensemble generalized multiclass support-vector-machine-based health evaluation of complex degradation systems","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089299263&doi=10.1109%2fTMECH.2020.3009449&partnerID=40&md5=c1b64e79743948d839dd3897f741bf99","volume":"25"},
{"id":"Wu20211915","abstract":"While massive multiple-input multiple-output (MIMO) has achieved tremendous success in both theory and practice, it faces a crisis of sharp performance degradation in moderate or high-mobility scenarios (e.g., 30 km/h), due to the breach of uplink-downlink channel duality. Such a 'curse of mobility' has spurred the research on channel prediction in high-mobility scenarios. Instead of predicting channel response matrix in the space-frequency domain, we investigate it in the angle-delay domain by utilizing the high angle-delay resolution of wideband massive MIMO systems. Specifically, we study the general angle-delay domain channel characterization and obtain that: 1) the correlations between the angle-delay domain channel response matrix (ADCRM) elements are decoupled significantly; 2) when the number of antennas and bandwidth are limited, the decoupling is insufficient and residual correlations between the neighboring ADCRM elements exist. Then focusing on the ADCRM, we propose two channel prediction methods: a spatio-temporal autoregressive (ST-AR) model-driven unsupervised-learning method and a deep learning (DL) based data-driven supervised-learning method. While the model-driven method provides a principled way for channel prediction, the data-driven method is generalizable to various channel scenarios. In particular, ST-AR exploits the residual spatio-temporal correlations of the channel element with its most neighboring elements, and DL realizes element-wise angle-delay domain channel prediction utilizing a complex-valued neural network (CVNN). Simulation results under the 3GPP non-line-of-sight (NLOS) scenarios indicate that, compared to the state-of-the-art Prony-based angular-delay domain (PAD) prediction method, both the proposed ST-AR and the CVNN-based channel prediction methods can enhance the channel prediction accuracy. © 1983-2012 IEEE.","author":[{"family":"Wu","given":"C."},{"family":"Yi","given":"X."},{"family":"Zhu","given":"Y."},{"family":"Wang","given":"W."},{"family":"You","given":"L."},{"family":"Gao","given":"X."}],"citation-key":"Wu20211915","container-title":"IEEE Journal on Selected Areas in Communications","DOI":"10.1109/JSAC.2021.3078503","ISSN":"07338716","issue":"7","issued":{"date-parts":[[2021]]},"page":"1915-1930","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Channel prediction in high-mobility massive MIMO: From spatio-temporal autoregression to deep learning","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105893774&doi=10.1109%2fJSAC.2021.3078503&partnerID=40&md5=d2cce3a653d84b2b9eb13e9746d29d67","volume":"39"},
{"id":"wuEnsembleGeneralizedMulticlass2020a","abstract":"Accurate health evaluation is crucial to reliable operation of complex degradation systems. Although traditional machine learning methods such as artificial neural network (ANN) and support vector machine (SVM) have been used widely, state assessment schemes based on a single classification model still suffer from low multiclass classification efficiency, high variance, and deviation. To solve these problems, this article proposes a novel health evaluation method based on stacking ensemble learning and generalized multiclass support vector machine (GMSVM) algorithm. The proposed health evaluation framework includes three parts: 1) abnormal value elimination and missing value processing are applied for multiple sensor data; 2) statistical features are extracted from the observed data and the Pearson correlation coefficient is applied for feature selection; and 3) ensemble generalized multiclass support vector machines (EGMSVMs) are utilized to evaluate the health situation of a degradation system. Unlike the binary classifiers and deep-learning-based classifiers, EGMSVMs utilize the stacking-based method to combine several GMSVMs as submodels and random forest as a metamodel, and the metamodel ensembles the results of submodels to reach a satisfied performance. Compared to traditional SVM- and ANN-based algorithms, EGMSVMs, in processing multiclass problems, achieve high efficiency and, meanwhile, low variance and deviation. The proposed method is verified using a hydraulic test rig. The experimental results show the feasibility and applicability of the proposed health evaluation framework. © 2020 IEEE.","author":[{"family":"Wu","given":"J."},{"family":"Guo","given":"P."},{"family":"Cheng","given":"Y."},{"family":"Zhu","given":"H."},{"family":"Wang","given":"X.-B."},{"family":"Shao","given":"X."}],"citation-key":"wuEnsembleGeneralizedMulticlass2020a","container-title":"IEEE/ASME Transactions on Mechatronics","DOI":"10.1109/TMECH.2020.3009449","ISSN":"10834435","issue":"5","issued":{"date-parts":[[2020]]},"page":"2230-2240","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Ensemble Generalized Multiclass Support-Vector-Machine-Based Health Evaluation of Complex Degradation Systems","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089299263&doi=10.1109%2fTMECH.2020.3009449&partnerID=40&md5=c1b64e79743948d839dd3897f741bf99","volume":"25"},
{"id":"wuGraphNeuralNetworks2021","abstract":"Owing to the superiority of GNN in learning on graph data and its efficacy in capturing collaborative signals and sequential patterns, utilizing GNN techniques in recommender systems has gain increasing interests in academia and industry. In this survey, we provide a comprehensive review of the most recent works on GNN-based recommender systems. We proposed a classification scheme for organizing existing works. For each category, we briefly clarify the main issues, and detail the corresponding strategies adopted by the representative models. We also discuss the advantages and limitations of the existing strategies. Furthermore, we suggest several promising directions for future researches. We hope this survey can provide readers with a general understanding of the recent progress in this field, and shed some light on future developments.","accessed":{"date-parts":[[2021,10,19]]},"author":[{"family":"Wu","given":"Shiwen"},{"family":"Sun","given":"Fei"},{"family":"Zhang","given":"Wentao"},{"family":"Cui","given":"Bin"}],"citation-key":"wuGraphNeuralNetworks2021","container-title":"arXiv:2011.02260 [cs]","issued":{"date-parts":[[2021,4,19]]},"note":"00029","source":"arXiv.org","title":"Graph Neural Networks in Recommender Systems: A Survey","title-short":"Graph Neural Networks in Recommender Systems","type":"article-journal","URL":"http://arxiv.org/abs/2011.02260"},
{"id":"xhafaInternetThingsEngineering2018","accessed":{"date-parts":[[2018,11,7]]},"author":[{"family":"Xhafa","given":"Fatos"}],"citation-key":"xhafaInternetThingsEngineering2018","container-title":"Internet of Things","DOI":"10.1016/S2542-6605(18)30099-4","ISSN":"25426605","issued":{"date-parts":[[2018,9]]},"page":"iii","source":"Crossref","title":"Internet of Things: Engineering Cyber Physical Human Systems","title-short":"Internet of Things","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2542660518300994","volume":"1-2"},
{"id":"xia:tag:2013","author":[{"family":"Xia","given":"Xin"},{"family":"Lo","given":"David"},{"family":"Wang","given":"Xinyu"},{"family":"Zhou","given":"Bo"}],"citation-key":"xia:tag:2013","collection-title":"MSR '13","container-title":"Proceedings of the 10th working conference on mining software repositories","event-place":"Piscataway, NJ, USA","ISBN":"978-1-4673-2936-1","issued":{"date-parts":[[2013]]},"page":"287-296","publisher":"IEEE Press","publisher-place":"Piscataway, NJ, USA","title":"Tag recommendation in software information sites","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=2487085.2487140"},
{"id":"xieSystematicMappingStudy2021","abstract":"The development of artificial intelligence (AI) has made various industries eager to explore the benefits of AI. There is an increasing amount of research surrounding AI, most of which is centred on the development of new AI algorithms and techniques. However, the advent of AI is bringing an increasing set of practical problems related to AI model lifecycle management that need to be investigated. We address this gap by conducting a systematic mapping study on the lifecycle of AI model. Through quantitative research, we provide an overview of the field, identify research opportunities, and provide suggestions for future research. Our study yields 405 publications published from 2005 to 2020, mapped in 5 different main research topics, and 31 sub-topics. We observe that only a minority of publications focus on data management and model production problems, and that more studies should address the AI lifecycle from a holistic perspective.","accessed":{"date-parts":[[2021,3,23]]},"author":[{"family":"Xie","given":"Yuanhao"},{"family":"Cruz","given":"Luís"},{"family":"Heck","given":"Petra"},{"family":"Rellermeyer","given":"Jan S."}],"citation-key":"xieSystematicMappingStudy2021","container-title":"arXiv:2103.10248 [cs]","issued":{"date-parts":[[2021,3,11]]},"note":"00000","source":"arXiv.org","title":"Systematic Mapping Study on the Machine Learning Lifecycle","type":"article-journal","URL":"http://arxiv.org/abs/2103.10248"},
{"id":"Xiong2022","abstract":"While Machine Learning (ML) technologies are widely adopted in many mission critical fields to support intelligent decision-making, concerns remain about system resilience against ML-specific security attacks and privacy breaches as well as the trust that users have in these systems. In this article, we present our recent systematic and comprehensive survey on the state-of-the-art ML robustness and trustworthiness from a security engineering perspective, focusing on the problems in system threat analysis, design and evaluation faced in developing practical machine learning applications, in terms of robustness and user trust. Accordingly, we organize the presentation of this survey intended to facilitate the convey of the body of knowledge from this angle. We then describe a metamodel we created that represents the body of knowledge in a standard and visualized way. We further illustrate how to leverage the metamodel to guide a systematic threat analysis and security design process which extends and scales up the classic process. Finally, we propose the future research directions motivated by our findings. Our work differs itself from the existing surveys by (i) exploring the fundamental principles and best practices to support robust and trustworthy ML system development, and (ii) studying the interplay of robustness and user trust in the context of ML systems. We expect this survey provides a big picture for machine learning security practitioners. © 2022","author":[{"family":"Xiong","given":"P."},{"family":"Buffett","given":"S."},{"family":"Iqbal","given":"S."},{"family":"Lamontagne","given":"P."},{"family":"Mamun","given":"M."},{"family":"Molyneaux","given":"H."}],"citation-key":"Xiong2022","container-title":"Journal of Information Security and Applications","DOI":"10.1016/j.jisa.2022.103121","ISSN":"22142134","issued":{"date-parts":[[2022]]},"publisher":"Elsevier Ltd","title":"Towards a robust and trustworthy machine learning system development: An engineering perspective","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124314035&doi=10.1016%2fj.jisa.2022.103121&partnerID=40&md5=fa8bf24ca832c86ff0ff8108655259bf","volume":"65"},
{"id":"xiuExploratoryStudyMachine2021","accessed":{"date-parts":[[2021,1,17]]},"author":[{"family":"Xiu","given":"Minke"},{"family":"Jiang","given":"Zhen Ming Jack"},{"family":"Adams","given":"Bram"}],"citation-key":"xiuExploratoryStudyMachine2021","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2020.2975159","ISSN":"0740-7459, 1937-4194","issue":"1","issued":{"date-parts":[[2021,1]]},"note":"00000","page":"114-122","source":"DOI.org (Crossref)","title":"An Exploratory Study of Machine Learning Model Stores","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/9003231/","volume":"38"},
{"id":"Xu2015","abstract":"Data analysis is used as a common method in modern science research, which is across communication science, computer science and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along with the information increase and subject intersection. On the other hand, each clustering algorithm has its own strengths and weaknesses, due to the complexity of information. In this review paper, we begin at the definition of clustering, take the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyze the clustering algorithms from two perspectives, the traditional ones and the modern ones. All the discussed clustering algorithms will be compared in detail and comprehensively shown in Appendix Table 22.","author":[{"family":"Xu","given":"Dongkuan"},{"family":"Tian","given":"Yingjie"}],"citation-key":"Xu2015","container-title":"Annals of Data Science","DOI":"10.1007/s40745-015-0040-1","ISSN":"2198-5812","issue":"2","issued":{"date-parts":[[2015,6,1]]},"page":"165-193","title":"A comprehensive survey of clustering algorithms","type":"article-journal","URL":"https://doi.org/10.1007/s40745-015-0040-1","volume":"2"},
{"id":"xuREPERSPRecommendingPersonalized2017","accessed":{"date-parts":[[2017,11,21]]},"author":[{"family":"Xu","given":"Wenyuan"},{"family":"Sun","given":"Xiaobing"},{"family":"Hu","given":"Jiajun"},{"family":"Li","given":"Bin"}],"citation-key":"xuREPERSPRecommendingPersonalized2017","DOI":"10.1109/ICSME.2017.20","ISBN":"978-1-5386-0992-7","issued":{"date-parts":[[2017,9]]},"page":"648-652","publisher":"IEEE","source":"CrossRef","title":"REPERSP: Recommending Personalized Software Projects on GitHub","title-short":"REPERSP","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/8094473/"},
{"id":"Yang2021","abstract":"We present a deep learning based joint source channel coding (JSCC) scheme for wireless image transmission over multipath fading channels with non-linear signal clipping. The proposed encoder and decoder use convolutional neural networks (CNN) and directly map the source images to complex-valued baseband samples for orthogonal frequency division multiplexing (OFDM) transmission. The proposed model-driven machine learning approach eliminates the need for separate source and channel coding while integrating an OFDM datapath to cope with multipath fading channels. The end-to-end JSCC communication system combines trainable CNN layers with non-trainable but differentiable layers representing the multipath channel model and OFDM signal processing blocks. Our results show that injecting domain expert knowledge by incorporating OFDM baseband processing blocks into the machine learning framework significantly enhances the overall performance compared to an unstructured CNN. Our method outperforms conventional schemes that employ state-of-the-art but separate source and channel coding such as BPG and LDPC with OFDM. Moreover, our method is shown to be robust against non-linear signal clipping in OFDM for various channel conditions that do not match the model parameter used during the training. © 2021 IEEE.","author":[{"family":"Yang","given":"M."},{"family":"Bian","given":"C."},{"family":"Kim","given":"H.-S."}],"citation-key":"Yang2021","collection-title":"IEEE International Conference on Communications","DOI":"10.1109/ICC42927.2021.9500996","ISBN":"978-1-72817-122-7","ISSN":"15503607","issued":{"date-parts":[[2021]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Deep joint source channel coding for wireless image transmission with OFDM","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115691155&doi=10.1109%2fICC42927.2021.9500996&partnerID=40&md5=5dc5ac6dafd28970055954a21f311089"},
{"id":"yangActionableAnalyticsSoftware2018","author":[{"family":"Yang","given":"Ye"},{"family":"Falessi","given":"Davide"},{"family":"Menzies","given":"Tim"},{"family":"Hihn","given":"Jairus"}],"citation-key":"yangActionableAnalyticsSoftware2018","container-title":"IEEE Software","issue":"1","issued":{"date-parts":[[2018]]},"page":"51–53","source":"Google Scholar","title":"Actionable Analytics for Software Engineering","type":"article-journal","volume":"35"},
{"id":"yangIoTStreamProcessing2017","accessed":{"date-parts":[[2021,1,5]]},"author":[{"family":"Yang","given":"Shusen"}],"citation-key":"yangIoTStreamProcessing2017","container-title":"IEEE Communications Magazine","container-title-short":"IEEE Commun. Mag.","DOI":"10.1109/MCOM.2017.1600840","ISSN":"0163-6804, 1558-1896","issue":"8","issued":{"date-parts":[[2017,8]]},"note":"00090","page":"21-27","source":"DOI.org (Crossref)","title":"IoT Stream Processing and Analytics in the Fog","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/8004149/","volume":"55"},
{"id":"yangNaturalAttackPretrained2022","abstract":"Pre-trained models of code have achieved success in many important software engineering tasks. However, these powerful models are vulnerable to adversarial attacks that slightly perturb model inputs to make a victim model produce wrong outputs. Current works mainly attack models of code with examples that preserve operational program semantics but ignore a fundamental requirement for adversarial example generation: perturbations should be natural to human judges, which we refer to as naturalness requirement.","accessed":{"date-parts":[[2022,1,29]]},"author":[{"family":"Yang","given":"Zhou"},{"family":"Shi","given":"Jieke"},{"family":"He","given":"Junda"},{"family":"Lo","given":"David"}],"citation-key":"yangNaturalAttackPretrained2022","container-title":"arXiv:2201.08698 [cs]","DOI":"10.1145/3510003.3510146","issued":{"date-parts":[[2022,1,21]]},"note":"00000","source":"arXiv.org","title":"Natural Attack for Pre-trained Models of Code","type":"article-journal","URL":"http://arxiv.org/abs/2201.08698"},
{"id":"yangStackOverflowGithub2017","author":[{"family":"Yang","given":"Di"},{"family":"Martins","given":"Pedro"},{"family":"Saini","given":"Vaibhav"},{"family":"Lopes","given":"Cristina"}],"citation-key":"yangStackOverflowGithub2017","container-title":"Mining Software Repositories (MSR), 2017 IEEE/ACM 14th International Conference on","issued":{"date-parts":[[2017]]},"page":"280–290","publisher":"IEEE","source":"Google Scholar","title":"Stack overflow in github: any snippets there?","title-short":"Stack overflow in github","type":"paper-conference"},
{"id":"yaoIntelligentManufacturingSmart2017","abstract":"Smart manufacturing (SM) is emerging as a new version of intelligent manufacturing (IM), reflecting the magnitude and impact of smart technologies such the Internet of Things, Cloud Computing, Cyber-Physical Systems and Big Data on Industry 4.0. This paper addresses how IM evolves to SM along with artificial intelligence (AI) evolution. To this end, this study first summarizes how the symbolic AI (called AI 1.0) characterized by structured contents and centralized control structures evolves into the next-generation AI (called AI 2.0) characterized by unstructured contents, decentralized control structures and machine learning (especially deep learning), and explain show IM enabled by AI 1.0 evolves into SM by AI 2.0 accordingly. Then, the comparison of IM and SM is discussed in detail. Finally, the further development of smart manufacturing for Industry 4.0 is given.","accessed":{"date-parts":[[2020,12,17]]},"author":[{"family":"Yao","given":"Xifan"},{"family":"Zhou","given":"Jiajun"},{"family":"Zhang","given":"Jiangming"},{"family":"Boer","given":"Claudio R."}],"citation-key":"yaoIntelligentManufacturingSmart2017","container-title":"2017 5th International Conference on Enterprise Systems (ES)","DOI":"10.1109/ES.2017.58","event":"2017 5th International Conference on Enterprise Systems (ES)","event-place":"Beijing","ISBN":"978-1-5386-0936-1","issued":{"date-parts":[[2017,9]]},"note":"00053","page":"311-318","publisher":"IEEE","publisher-place":"Beijing","source":"DOI.org (Crossref)","title":"From Intelligent Manufacturing to Smart Manufacturing for Industry 4.0 Driven by Next Generation Artificial Intelligence and Further On","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/8119409/"},
{"id":"Yavanoglu20172186","abstract":"It is an undeniable fact that currently information is a pretty significant presence for all companies or organizations. Therefore protecting its security is crucial and the security models driven by real datasets has become quite important. The operations based on military, government, commercial and civilians are linked to the security and availability of computer systems and network. From this point of security, the network security is a significant issue because the capacity of attacks is unceasingly rising over the years and they turn into be more sophisticated and distributed. The objective of this review is to explain and compare the most commonly used datasets. This paper focuses on the datasets used in artificial intelligent and machine learning techniques, which are the primary tools for analyzing network traffic and detecting abnormalities. © 2017 IEEE.","author":[{"family":"Yavanoglu","given":"O."},{"family":"Aydos","given":"M."}],"citation-key":"Yavanoglu20172186","collection-title":"Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017","DOI":"10.1109/BigData.2017.8258167","editor":[{"family":"Nie J.-Y., Obradovic Z.","given":"Suzumura T.","suffix":"Ghosh R., Nambiar R., Wang C., Zang H., Baeza-Yates R., Baeza-Yates R., Hu X., Kepner J., Cuzzocrea A., Tang J., Toyoda M."}],"ISBN":"978-1-5386-2714-3","issued":{"date-parts":[[2017]]},"page":"2186-2193","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"A review on cyber security datasets for machine learning algorithms","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047880263&doi=10.1109%2fBigData.2017.8258167&partnerID=40&md5=ac945470bd7ac85304c63a6a8773e3a4","volume":"2018-January"},
{"id":"Ye202166","abstract":"Non-orthogonal multiple access (NOMA) is a promising evolution path to meet the requirements of massive machine type communications (mMTC) in 5G and beyond. However, the deployment of NOMA is hindered by the non-unified signal processing architectures of various NOMA schemes and the inflexibility resulting from the offline design paradigm. The block-wise optimized transceivers make its performance far from the limit. The recent breakthrough of deep learning and its positive applications to wireless communications have paved the way to tackle these challenges. This article studies the effectiveness and efficiency of deep learning in enhancing NOMA performance. Specifically, we first present the deep neural network (DNN), which is constructed via a uniform signal processing architecture, and use it as the unified multiuser receiver in both data and model-driven approaches. This enables the end-to-end optimization of NOMA transceivers due to the universal function approximation property of DNN. On the other hand, with DNN we can automatically extract the user access behaviors out of the time-series signals and optimize the transceivers to match these cross-lay-er behaviors. We further analyze the integration of non-orthogonal communication and neural computation to accomplish high-efficiency data transmission at low cost. Finally, we identify some essential future directions of deep-learning-en-hanced NOMA from the perspectives of online reconfigurability and adaptability toward the ever changing environment in future mMTC. © 2002-2012 IEEE.","author":[{"family":"Ye","given":"N."},{"family":"An","given":"J."},{"family":"Yu","given":"J."}],"citation-key":"Ye202166","container-title":"IEEE Wireless Communications","DOI":"10.1109/MWC.001.2000472","ISSN":"15361284","issue":"4","issued":{"date-parts":[[2021]]},"page":"66-73","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Deep-learning-enhanced NOMA transceiver design for massive MTC: Challenges, state of the art, and future directions","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114708633&doi=10.1109%2fMWC.001.2000472&partnerID=40&md5=befff4054987c64890f1db69ca80c9e9","volume":"28"},
{"id":"yeSupportingReuseDelivering2002","author":[{"family":"Ye","given":"Yunwen"},{"family":"Fischer","given":"Gerhard"}],"citation-key":"yeSupportingReuseDelivering2002","collection-title":"ICSE '02","container-title":"Proceedings of the 24th international conference on software engineering","event-place":"New York, NY, USA","ISBN":"1-58113-472-X","issued":{"date-parts":[[2002]]},"page":"513-523","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Supporting reuse by delivering task-relevant and personalized information","type":"paper-conference","URL":"http://doi.acm.org/10.1145/581339.581402"},
{"id":"Yin2022","abstract":"In order to fully exploit the advantages of massive multiple-input multiple-output (mMIMO), it is critical for the transmitter to accurately acquire the channel state information (CSI). Deep learning (DL)-based methods have been proposed for CSI compression and feedback to the transmitter. Although most existing DL-based methods consider the CSI matrix as an image, structural features of the CSI image are rarely exploited in neural network design. As such, we propose a model of self-information that dynamically measures the amount of information contained in each patch of a CSI image from the perspective of structural features. Then, by applying the self-information model, we propose a model-and-data-driven network for CSI compression and feedback, namely IdasNet. The IdasNet includes the design of a module of self-information deletion and selection (IDAS), an encoder of informative feature compression (IFC), and a decoder of informative feature recovery (IFR). In particular, the model-driven module of IDAS pre-compresses the CSI image by removing informative redundancy in terms of the self-information. The encoder of IFC then conducts feature compression to the pre-compressed CSI image and generates a feature codeword which contains two components, i.e., codeword values and position indices of the codeword values. Subsequently, the IFR decoder decouples the codeword values as well as position indices to recover the CSI image. Experimental results verify that the proposed IdasNet noticeably outperforms existing DL-based networks under various compression ratios while it has the number of network parameters reduced by orders-of-magnitude compared with various existing methods. IEEE","author":[{"family":"Yin","given":"Z."},{"family":"Xu","given":"W."},{"family":"Xie","given":"R."},{"family":"Zhang","given":"S."},{"family":"Ng","given":"D.W.K."},{"family":"You","given":"X."}],"citation-key":"Yin2022","container-title":"IEEE Transactions on Wireless Communications","DOI":"10.1109/TWC.2022.3170576","ISSN":"15361276","issued":{"date-parts":[[2022]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Deep CSI compression for massive MIMO: A self-information model-driven neural network","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129422322&doi=10.1109%2fTWC.2022.3170576&partnerID=40&md5=2e6552eb776c90d5d60231b91500fc26"},
{"id":"yinDynamicDataMining2020","abstract":"The research of data mining has aroused widespread concern in academia and industry. However, an important mark of the Internet of Things era is that sensor data replaces artificially compiled data. How to extract valuable knowledge and patterns from a large amount of data generated by sensors is a meaningful research topic. This paper proposes a dynamic data mining framework for processing sensor data. A sensor data mining model which can be used in the process of dynamic change is constructed. In this model, different sensor network environments are considered as different physical systems. The physical system and its parameters are trained by collecting and mining historical changes in sensor data; the associations between different sensor network environments are discovered by mining the associations between the parameters of different physical systems. In our limited experimental environment, the physical quantities considered included transmission distance, transmission delay, sensor data, data changes, and so on. Experiments were carried out on the designated experimental platform, and the results showed that the model could mine the dynamic data and find stable patterns. Through the analysis of the experimental results, it was found that the model had reference value for the dynamic mining of sensor data, and was expected to construct new methods for industrial big data analysis.","accessed":{"date-parts":[[2022,2,3]]},"author":[{"family":"Yin","given":"Yunfei"},{"family":"Long","given":"Lianjie"},{"family":"Deng","given":"Xiyu"}],"citation-key":"yinDynamicDataMining2020","container-title":"IEEE Access","container-title-short":"IEEE Access","DOI":"10.1109/ACCESS.2020.2976699","ISSN":"2169-3536","issued":{"date-parts":[[2020]]},"note":"00007","page":"41637-41648","source":"DOI.org (Crossref)","title":"Dynamic Data Mining of Sensor Data","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/9016236/","volume":"8"},
{"id":"yingSelectionPresentationPractices","abstract":"Code examples are an important source for answering questions about software libraries and applications. Many usage contexts for code examples require them to be distilled to their essence: e.g., when serving as cues to longer documents, or for reminding developers of a previously known idiom. We conducted a study to discover how code can be summarized and why. As part of the study, we collected 156 pairs of code examples and their summaries from 16 participants, along with over 26 hours of think-aloud verbalizations detailing the decisions of the participants during their summarization activities. Based on a qualitative analysis of this data we elicited a list of practices followed by the participants to summarize code examples and propose empirically-supported hypotheses justifying the use of specific practices. One main finding was that none of the participants exclusively extracted code verbatim for the summaries, motivating abstractive summarization. The results provide a grounded basis for the development of code example summarization and presentation technology.","author":[{"family":"Ying","given":"Annie T T"},{"family":"Robillard","given":"Martin P"}],"citation-key":"yingSelectionPresentationPractices","page":"12","source":"Zotero","title":"Selection and Presentation Practices for Code Example Summarization","type":"article-journal"},
{"id":"Yoon202010468","abstract":"Estimating a dense and accurate depth map is the key requirement for autonomous driving and robotics. Recent advances in deep learning have allowed depth estimation in full resolution from a single image. Despite this impressive result, many deep-learning-based monocular depth estimation (MDE) algorithms have failed to keep their accuracy yielding a meter-level estimation error. In many robotics applications, accurate but sparse measurements are readily available from Light Detection and Ranging (LiDAR). Although they are highly accurate, the sparsity limits full resolution depth map reconstruction. Targeting the problem of dense and accurate depth map recovery, this paper introduces the fusion of these two modalities as a depth completion (DC) problem by dividing the role of depth inference and depth regression. Utilizing the state-of-the-art MDE and our Gaussian process (GP) based depth-regression method, we propose a general solution that can flexibly work with various MDE modules by enhancing its depth with sparse range measurements. To overcome the major limitation of GP, we adopt Kernel Interpolation for Scalable Structured (KISS)-GP and mitigate the computational complexity from O(N3) to O(N). Our experiments demonstrate that the accuracy and robustness of our method outperform state-of-the-art unsupervised methods for sparse and biased measurements. © 2020 IEEE.","author":[{"family":"Yoon","given":"S."},{"family":"Kim","given":"A."}],"citation-key":"Yoon202010468","collection-title":"IEEE International Conference on Intelligent Robots and Systems","DOI":"10.1109/IROS45743.2020.9341769","ISBN":"978-1-72816-212-6","ISSN":"21530858","issued":{"date-parts":[[2020]]},"page":"10468-10475","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Balanced depth completion between dense depth inference and sparse range measurements via KISS-GP","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098842349&doi=10.1109%2fIROS45743.2020.9341769&partnerID=40&md5=6553ab2604681d5d71c8d8f668ea82b8"},
{"id":"Yu201919","abstract":"In this paper, we investigate the fast downlink beamforming for full-dimension multiple-input multiple-output systems under correlated Rician channels. Under the assumption that the base station (BS) has only statistical channel state information (CSI), we decouple each user's beamforming vector and derive their optimal beamforming vector through the maximization of the average signal-to-leakage-plus-noise ratio (SLNR) lower bound. Then, to reduce the computation time, a model-driven deep learning (DL)-based beamforming algorithm is proposed, as well as a data-driven algoriothm for comparison. In the model-driven DL-based beamforming algorithm, the process of obtaining the beamforming vector is separated into two parallel neural networks which are constructed and trained independently. The proposed algorithms can achieve similar ergodic rate as the optimal beamforming algorithm with much less computation time, and the model-driven algorithm requires less computing resource than the data-driven algorithm. © 2019 IEEE.","author":[{"family":"Yu","given":"X."},{"family":"Yang","given":"X."},{"family":"Li","given":"X."},{"family":"Jin","given":"S."}],"citation-key":"Yu201919","collection-title":"2019 IEEE/CIC International Conference on Communications in China, ICCC 2019","DOI":"10.1109/ICCChina.2019.8855848","ISBN":"978-1-72810-732-5","issued":{"date-parts":[[2019]]},"page":"19-24","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Deep learning based beamforming for FD-MIMO downlink transmission: (Invited paper)","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074113741&doi=10.1109%2fICCChina.2019.8855848&partnerID=40&md5=4cf428a673727bd78f17f9ab6abd42aa"},
{"id":"yuAPIBookEffectiveApproach2016","accessed":{"date-parts":[[2018,2,2]]},"author":[{"family":"Yu","given":"Haibo"},{"family":"Song","given":"Wenhao"},{"family":"Mine","given":"Tsunenori"}],"citation-key":"yuAPIBookEffectiveApproach2016","DOI":"10.1145/2993717.2993727","ISBN":"978-1-4503-4829-4","issued":{"date-parts":[[2016]]},"page":"45-53","publisher":"ACM Press","source":"CrossRef","title":"APIBook: an effective approach for finding APIs","title-short":"APIBook","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?doid=2993717.2993727"},
{"id":"yuDeepLearningBased2019a","abstract":"In this paper, we investigate the fast downlink beamforming for full-dimension multiple-input multiple-output systems under correlated Rician channels. Under the assumption that the base station (BS) has only statistical channel state information (CSI), we decouple each user's beamforming vector and derive their optimal beamforming vector through the maximization of the average signal-to-leakage-plus-noise ratio (SLNR) lower bound. Then, to reduce the computation time, a model-driven deep learning (DL)-based beamforming algorithm is proposed, as well as a data-driven algoriothm for comparison. In the model-driven DL-based beamforming algorithm, the process of obtaining the beamforming vector is separated into two parallel neural networks which are constructed and trained independently. The proposed algorithms can achieve similar ergodic rate as the optimal beamforming algorithm with much less computation time, and the model-driven algorithm requires less computing resource than the data-driven algorithm. © 2019 IEEE.","author":[{"family":"Yu","given":"X."},{"family":"Yang","given":"X."},{"family":"Li","given":"X."},{"family":"Jin","given":"S."}],"citation-key":"yuDeepLearningBased2019a","container-title":"2019 IEEE/CIC International Conference on Communications in China, ICCC 2019","DOI":"10.1109/ICCChina.2019.8855848","ISBN":"978-1-72810-732-5","issued":{"date-parts":[[2019]]},"page":"19-24","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Deep learning based beamforming for FD-MIMO downlink transmission: (Invited paper)","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074113741&doi=10.1109%2fICCChina.2019.8855848&partnerID=40&md5=4cf428a673727bd78f17f9ab6abd42aa"},
{"id":"Yue2019","abstract":"Blind image denoising is an important yet very challenging problem in computer vision due to the complicated acquisition process of real images. In this work we propose a new variational inference method, which integrates both noise estimation and image denoising into a unique Bayesian framework, for blind image denoising. Specifically, an approximate posterior, parameterized by deep neural networks, is presented by taking the intrinsic clean image and noise variances as latent variables conditioned on the input noisy image. This posterior provides explicit parametric forms for all its involved hyper-parameters, and thus can be easily implemented for blind image denoising with automatic noise estimation for the test noisy image. On one hand, as other data-driven deep learning methods, our method, namely variational denoising network (VDN), can perform denoising efficiently due to its explicit form of posterior expression. On the other hand, VDN inherits the advantages of traditional model-driven approaches, especially the good generalization capability of generative models. VDN has good interpretability and can be flexibly utilized to estimate and remove complicated non-i.i.d. noise collected in real scenarios. Comprehensive experiments are performed to substantiate the superiority of our method in blind image denoising. © 2019 Neural information processing systems foundation. All rights reserved.","author":[{"family":"Yue","given":"Z."},{"family":"Yong","given":"H."},{"family":"Zhao","given":"Q."},{"family":"Zhang","given":"L."},{"family":"Meng","given":"D."}],"citation-key":"Yue2019","collection-title":"Advances in Neural Information Processing Systems","ISSN":"10495258","issued":{"date-parts":[[2019]]},"publisher":"Neural information processing systems foundation","title":"Variational denoising network: Toward blind noise modeling and removal","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079825785&partnerID=40&md5=ca438397b8f3afbe44778afcd4bcd6a3","volume":"32"},
{"id":"yuEfficientSimRankComputation2013","author":[{"family":"Yu","given":"Weiren"},{"family":"Lin","given":"Xuemin"},{"family":"Zhang","given":"Wenjie"}],"citation-key":"yuEfficientSimRankComputation2013","container-title":"ICDE","editor":[{"family":"Jensen","given":"Christian S."},{"family":"Jermaine","given":"Christopher M."},{"family":"Zhou","given":"Xiaofang"}],"ISBN":"978-1-4673-4909-3","issued":{"date-parts":[[2013]]},"page":"601-612","publisher":"IEEE Computer Society","title":"Towards efficient SimRank computation on large networks.","type":"paper-conference","URL":"http://dblp.uni-trier.de/db/conf/icde/icde2013.html#YuLZ13"},
{"id":"yueVariationalDenoisingNetwork2019a","abstract":"Blind image denoising is an important yet very challenging problem in computer vision due to the complicated acquisition process of real images. In this work we propose a new variational inference method, which integrates both noise estimation and image denoising into a unique Bayesian framework, for blind image denoising. Specifically, an approximate posterior, parameterized by deep neural networks, is presented by taking the intrinsic clean image and noise variances as latent variables conditioned on the input noisy image. This posterior provides explicit parametric forms for all its involved hyper-parameters, and thus can be easily implemented for blind image denoising with automatic noise estimation for the test noisy image. On one hand, as other data-driven deep learning methods, our method, namely variational denoising network (VDN), can perform denoising efficiently due to its explicit form of posterior expression. On the other hand, VDN inherits the advantages of traditional model-driven approaches, especially the good generalization capability of generative models. VDN has good interpretability and can be flexibly utilized to estimate and remove complicated non-i.i.d. noise collected in real scenarios. Comprehensive experiments are performed to substantiate the superiority of our method in blind image denoising. © 2019 Neural information processing systems foundation. All rights reserved.","author":[{"family":"Yue","given":"Z."},{"family":"Yong","given":"H."},{"family":"Zhao","given":"Q."},{"family":"Zhang","given":"L."},{"family":"Meng","given":"D."}],"citation-key":"yueVariationalDenoisingNetwork2019a","container-title":"Advances in Neural Information Processing Systems","ISSN":"10495258","issued":{"date-parts":[[2019]]},"publisher":"Neural information processing systems foundation","title":"Variational denoising network: Toward blind noise modeling and removal","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079825785&partnerID=40&md5=ca438397b8f3afbe44778afcd4bcd6a3","volume":"32"},
{"id":"yuRapidApplicationDevelopment2011","abstract":"The development of large or medium-sized domain application systems usually involves intensive workforce due to its complexity. Reuse of existing components, especially those architectural ones, could dramatically reduce the production cost and improve the quality. However, the problems related with making and adapting reusable components among different systems often inhibit the introduction of reuse. Fortunately, domainoriented application systems, especially those data-centric ones, usually share similar behaviors no matter what they server for. This paper extracts the common behaviors existing in different domains and introduces the templates based application framework, called RADF. RADF provides application skeletons and confines domain specific coding in predefined templates of classes and configuration files. The proprietary behavior of domain specific applications could be realized via simply filling codes in these templates. RADF not only consolidates the programming paradigm and provides the supporting classes for default behaviors expected in different domains, but also allows manually extending and reassembling these supporting classes. Four cases of RADF-based development have proved that RADF helps rapid application development with significantly reduced number of manually-coded source lines.","accessed":{"date-parts":[[2019,6,27]]},"author":[{"family":"Yu","given":"Dongjin"}],"citation-key":"yuRapidApplicationDevelopment2011","container-title":"Journal of Software","container-title-short":"JSW","DOI":"10.4304/jsw.6.9.1795-1804","ISSN":"1796-217X","issue":"9","issued":{"date-parts":[[2011,8,1]]},"page":"1795-1804","source":"DOI.org (Crossref)","title":"Towards the Rapid Application Development Based on Predefined Frameworks","type":"article-journal","URL":"http://ojs.academypublisher.com/index.php/jsw/article/view/4988","volume":"6"},
{"id":"Yurin2021167","abstract":"Creating embedded decision-making modules for web applications that implement artificial intelligence methods in the form of knowledge bases is quite an interesting task. Specialized methodologies and software are being developed to solve them. At the same time, the use of generative and visual programming principles, as well as model transformations, can provide better results. In our previous works, we proposed to apply these principles combined with the model-driven approach for the automated creation of expert systems and knowledge bases. In this paper, we extend the previously developed method with new platforms, in particular: PHP (Hypertext Preprocessor) and Drools, as well as we add the possibility to use the decision tables formalism and Microsoft Excel tools for their construction. The modified (extended) method allows one to effectively create knowledge bases with a large number of logical rules and generate the source code for web embedded decision-making modules. This extension is implemented as a plugin for an expert system prototyping system, namely, Personal Knowledge Base Designer. This paper describes the extended method and examples of its application for the development of web application modules: for making decisions when detecting banned messages and identifying customers who violate rules of using the SMS notification service (“Detector”), and interpreting signs of emotions within the HR-Robot application (“EmSi-Interpreter”). The proposed method was also evaluated in solving educational (test) tasks. © 2021, Springer Nature Switzerland AG.","author":[{"family":"Yurin","given":"A.Y."},{"family":"Dorodnykh","given":"N.O."}],"citation-key":"Yurin2021167","container-title":"Communications in Computer and Information Science","DOI":"10.1007/978-3-030-68527-0_11","editor":[{"family":"Simian D.","given":"Stoica L.F."}],"ISBN":"9783030685263","ISSN":"18650929","issued":{"date-parts":[[2021]]},"page":"167-184","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Creating web decision-making modules on the basis of decision tables transformations","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102635372&doi=10.1007%2f978-3-030-68527-0_11&partnerID=40&md5=63a9867efcb6705f223416d78a41f59b","volume":"1341"},
{"id":"yurinCreatingWebDecisionMaking2021a","abstract":"Creating embedded decision-making modules for web applications that implement artificial intelligence methods in the form of knowledge bases is quite an interesting task. Specialized methodologies and software are being developed to solve them. At the same time, the use of generative and visual programming principles, as well as model transformations, can provide better results. In our previous works, we proposed to apply these principles combined with the model-driven approach for the automated creation of expert systems and knowledge bases. In this paper, we extend the previously developed method with new platforms, in particular: PHP (Hypertext Preprocessor) and Drools, as well as we add the possibility to use the decision tables formalism and Microsoft Excel tools for their construction. The modified (extended) method allows one to effectively create knowledge bases with a large number of logical rules and generate the source code for web embedded decision-making modules. This extension is implemented as a plugin for an expert system prototyping system, namely, Personal Knowledge Base Designer. This paper describes the extended method and examples of its application for the development of web application modules: for making decisions when detecting banned messages and identifying customers who violate rules of using the SMS notification service (“Detector”), and interpreting signs of emotions within the HR-Robot application (“EmSi-Interpreter”). The proposed method was also evaluated in solving educational (test) tasks. © 2021, Springer Nature Switzerland AG.","author":[{"family":"Yurin","given":"A.Y."},{"family":"Dorodnykh","given":"N.O."}],"citation-key":"yurinCreatingWebDecisionMaking2021a","container-title":"Communications in Computer and Information Science","DOI":"10.1007/978-3-030-68527-0_11","editor":[{"family":"Simian D.","given":"Stoica L.F."}],"ISBN":"9783030685263","ISSN":"18650929","issued":{"date-parts":[[2021]]},"page":"167-184","publisher":"Springer Science and Business Media Deutschland GmbH","title":"Creating Web Decision-Making Modules on the Basis of Decision Tables Transformations","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102635372&doi=10.1007%2f978-3-030-68527-0_11&partnerID=40&md5=63a9867efcb6705f223416d78a41f59b","volume":"1341"},
{"id":"Zahoor2020198","abstract":"Urbanization, exceptional increase in population and advancement in technology caused the automotive industry to grow rapidly &automobiles become essential part of daily life. Consequently, finding a parking space particularly in populous zones, is a challenging task. Researchers have proposed different solutions to assist the developments in smart parking systems. In this paper, we have investigated the key tools, techniques &challenges proposed in the recent research studies. Primarily, a Systematic Literature Review is carried out, total 35 studies are explored during time interval of (2015-2019). Subsequently, five major areas are recognized where smart parking is often functional i.e. Internet of Things (IoT) (13 studies), Cloud Computing (2 studies), Model-Driven Engineering (4 studies), Fog Computing (6 studies) and Artificial Intelligence (11 studies). Furthermore, (15) primary tools and (25) algorithms are presented. This article also portray the challenges cited by different studies. The findings of this study will definitely assist the practitioners while deciding the appropriate selections. © 2020 ACM.","author":[{"family":"Zahoor","given":"T."},{"family":"Azam","given":"F."},{"family":"Anwar","given":"M.W."},{"family":"Tariq","given":"A."},{"family":"Javaid","given":"H.A."}],"citation-key":"Zahoor2020198","collection-title":"ACM International Conference Proceeding Series","DOI":"10.1145/3436829.3436851","ISBN":"978-1-4503-7721-8","issued":{"date-parts":[[2020]]},"page":"198-203","publisher":"Association for Computing Machinery","title":"An investigation of smart parking tools, technologies, & challenges","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099199062&doi=10.1145%2f3436829.3436851&partnerID=40&md5=664f254e6f21cd56051cb6ea78b59b61"},
{"id":"zahoorInvestigationSmartParking2020a","abstract":"Urbanization, exceptional increase in population and advancement in technology caused the automotive industry to grow rapidly &automobiles become essential part of daily life. Consequently, finding a parking space particularly in populous zones, is a challenging task. Researchers have proposed different solutions to assist the developments in smart parking systems. In this paper, we have investigated the key tools, techniques &challenges proposed in the recent research studies. Primarily, a Systematic Literature Review is carried out, total 35 studies are explored during time interval of (2015-2019). Subsequently, five major areas are recognized where smart parking is often functional i.e. Internet of Things (IoT) (13 studies), Cloud Computing (2 studies), Model-Driven Engineering (4 studies), Fog Computing (6 studies) and Artificial Intelligence (11 studies). Furthermore, (15) primary tools and (25) algorithms are presented. This article also portray the challenges cited by different studies. The findings of this study will definitely assist the practitioners while deciding the appropriate selections. © 2020 ACM.","author":[{"family":"Zahoor","given":"T."},{"family":"Azam","given":"F."},{"family":"Anwar","given":"M.W."},{"family":"Tariq","given":"A."},{"family":"Javaid","given":"H.A."}],"citation-key":"zahoorInvestigationSmartParking2020a","container-title":"ACM International Conference Proceeding Series","DOI":"10.1145/3436829.3436851","ISBN":"978-1-4503-7721-8","issued":{"date-parts":[[2020]]},"page":"198-203","publisher":"Association for Computing Machinery","title":"An Investigation of Smart Parking Tools, Technologies, & Challenges","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099199062&doi=10.1145%2f3436829.3436851&partnerID=40&md5=664f254e6f21cd56051cb6ea78b59b61"},
{"id":"zambonelliKeyAbstractionsIoTOriented2017","accessed":{"date-parts":[[2019,8,22]]},"author":[{"family":"Zambonelli","given":"Franco"}],"citation-key":"zambonelliKeyAbstractionsIoTOriented2017","container-title":"IEEE Software","container-title-short":"IEEE Softw.","DOI":"10.1109/MS.2017.3","ISSN":"0740-7459","issue":"1","issued":{"date-parts":[[2017,1]]},"page":"38-45","source":"DOI.org (Crossref)","title":"Key Abstractions for IoT-Oriented Software Engineering","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/7819396/","volume":"34"},
{"id":"zambonelliSelfAdaptationSelfExpressionSelfAwareness2011","accessed":{"date-parts":[[2016,8,21]]},"author":[{"family":"Zambonelli","given":"Franco"},{"family":"Bicocchi","given":"Nicola"},{"family":"Cabri","given":"Giacomo"},{"family":"Leonardi","given":"Letizia"},{"family":"Puviani","given":"Mariachiara"}],"citation-key":"zambonelliSelfAdaptationSelfExpressionSelfAwareness2011","DOI":"10.1109/SASOW.2011.24","ISBN":"978-1-4577-2029-1 978-0-7695-4545-5","issued":{"date-parts":[[2011,10]]},"page":"108-113","publisher":"IEEE","source":"CrossRef","title":"On Self-Adaptation, Self-Expression, and Self-Awareness in Autonomic Service Component Ensembles","type":"paper-conference","URL":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6114583"},
{"id":"zampettiHowOpenSource2017","accessed":{"date-parts":[[2018,1,31]]},"author":[{"family":"Zampetti","given":"Fiorella"},{"family":"Scalabrino","given":"Simone"},{"family":"Oliveto","given":"Rocco"},{"family":"Canfora","given":"Gerardo"},{"family":"Di Penta","given":"Massimiliano"}],"citation-key":"zampettiHowOpenSource2017","DOI":"10.1109/MSR.2017.2","ISBN":"978-1-5386-1544-7","issued":{"date-parts":[[2017,5]]},"page":"334-344","publisher":"IEEE","source":"CrossRef","title":"How Open Source Projects Use Static Code Analysis Tools in Continuous Integration Pipelines","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/7962383/"},
{"id":"zayanEffectsUsingExamples2014","accessed":{"date-parts":[[2015,5,17]]},"author":[{"family":"Zayan","given":"Dina"},{"family":"Antkiewicz","given":"Micha\\l"},{"family":"Czarnecki","given":"Krzysztof"}],"citation-key":"zayanEffectsUsingExamples2014","container-title":"Proceedings of the 36th International Conference on Software Engineering","issued":{"date-parts":[[2014]]},"page":"955–966","publisher":"ACM","source":"Google Scholar","title":"Effects of using examples on structural model comprehension: a controlled experiment","title-short":"Effects of using examples on structural model comprehension","type":"paper-conference","URL":"http://dl.acm.org/citation.cfm?id=2568270"},
{"id":"zelkowitzExperimentalModelsValidating1998","accessed":{"date-parts":[[2020,7,21]]},"author":[{"family":"Zelkowitz","given":"M.V."},{"family":"Wallace","given":"D.R."}],"citation-key":"zelkowitzExperimentalModelsValidating1998","container-title":"Computer","container-title-short":"Computer","DOI":"10.1109/2.675630","ISSN":"00189162","issue":"5","issued":{"date-parts":[[1998,5]]},"note":"00000","page":"23-31","source":"DOI.org (Crossref)","title":"Experimental models for validating technology","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/675630/","volume":"31"},
{"id":"Zhang:1997:BND:593415.593443","author":[{"family":"Zhang","given":"Tian"},{"family":"Ramakrishnan","given":"Raghu"},{"family":"Livny","given":"Miron"}],"citation-key":"Zhang:1997:BND:593415.593443","container-title":"Data Mining and Knowledge Discovery","container-title-short":"Data Min. Knowl. Discov.","ISSN":"1384-5810","issue":"2","issued":{"date-parts":[[1997,1]]},"page":"141-182","title":"BIRCH: A new data clustering algorithm and its applications","type":"article-journal","URL":"https://doi.org/10.1023/A:1009783824328","volume":"1"},
{"id":"Zhang2017","abstract":"This paper develops a novel hybrid approach that integrates metamodeling, machine learning algorithms, and a variance decomposition technique to support global uncertainty and sensitivity (US) analysis under uncertainty. It consists of three main steps: (1) metamodel construction; (2) metamodel validation; and (3) global US analysis. A multi-input and multioutput metamodel, with least-squares support vector machine (LSSVM) and particle swarm optimization (PSO) algorithms incorporated, is built in order to simulate system behaviors of tunnel-induced building damage. Three indicators - mean absolute percentage error (MAPE), variance of absolute percentage error (VAPE), and mean square percentage error (MSPE) - are proposed to test the prediction performance of the metamodel. The extended Fourier amplitude sensitivity test (EFAST) is used to perform global US analysis on the basis of the well-trained metamodel. The novelty of the developed approach lies in its capability of learning from given data to identify relationships between model inputs and outputs to provide an access for conducting global US analysis. The collected data from the construction of the Wuhan Metro system (WMS) in China are used in a case study to demonstrate the effectiveness and applicability of the developed approach. Results indicate that the developed approach is capable of (1) predicting and assessing the magnitude of tunnel-induced building damage in terms of the cumulative distribution function (CDF) of model outputs, and (2) identifying the most significant and insignificant factors for possible dimension reduction to improve the understanding of the model behavior. This research contributes to (1) the body of knowledge by proposing a more appropriate research methodology that can cope with aleatory and epistemic uncertainty and support global US analysis based on given data, and (2) the state of practice by providing a data-driven metamodel technique to simulate system behaviors of tunnel-induced building damage with high reliability and reduce dependency on domain experts. © 2017 American Society of Civil Engineers.","author":[{"family":"Zhang","given":"L."},{"family":"Wu","given":"X."},{"family":"Zhu","given":"H."},{"family":"Abourizk","given":"S.M."}],"citation-key":"Zhang2017","container-title":"Journal of Computing in Civil Engineering","DOI":"10.1061/(ASCE)CP.1943-5487.0000714","ISSN":"08873801","issue":"6","issued":{"date-parts":[[2017]]},"publisher":"American Society of Civil Engineers (ASCE)","title":"Performing global uncertainty and sensitivity analysis from given data in tunnel construction","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029410032&doi=10.1061%2f%28ASCE%29CP.1943-5487.0000714&partnerID=40&md5=41a35455f42274d15fdc8783f24aafcc","volume":"31"},
{"id":"Zhang2019","abstract":"Wireless localization for mobile device has attracted more and more interests by increasing the demand for location based services. Fingerprint-based localization is promising, especially in non-Line-of-Sight (NLoS) or rich scattering environments, such as urban areas and indoor scenarios. In this paper, we propose a novel fingerprint-based localization technique based on deep learning framework under commercial long term evolution (LTE) systems. Specifically, we develop a software defined user equipment to collect the real time channel state information (CSI) knowledge from LTE base stations and extract the intrinsic features among CSI observations. On top of that, we propose a time domain fusion approach to assemble multiple positioning estimations. Experimental results demonstrated that the proposed localization technique can significantly improve the localization accuracy and robustness, e.g. achieves Mean Distance Error (MDE) of 0.47 meters for indoor and of 19.9 meters for outdoor scenarios, respectively. © 2019 IEEE.","author":[{"family":"Zhang","given":"H."},{"family":"Zhang","given":"Z."},{"family":"Zhang","given":"S."},{"family":"Xu","given":"S."},{"family":"Cao","given":"S."}],"citation-key":"Zhang2019","collection-title":"IEEE Vehicular Technology Conference","DOI":"10.1109/VTCFall.2019.8891257","ISBN":"978-1-72811-220-6","ISSN":"15502252","issued":{"date-parts":[[2019]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Fingerprint-based localization using commercial LTE signals: A field-trial study","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075257554&doi=10.1109%2fVTCFall.2019.8891257&partnerID=40&md5=4739040a82e1a127f515d71bc09933d2","volume":"2019-September"},
{"id":"Zhang2019376","abstract":"Most of the data is extracted and processed by Spark in Tencent Machine Learning Platform. However, seldom of them use Spark MLlib, an official machine learning (ML) library on top of Spark due to its inefficiency. In contrast, systems like parameter servers, XGBoost and TensorFlow are more used, which incur expensive cost of transferring data in and out of Spark ecosystem. In this paper, we identify the causes of inefficiency in Spark MLlib and solve the problem by building parameter servers on top of Spark. We propose PS2, a parameter server architecture that integrates Spark without hacking the core of Spark. With PS2, we leverage the power of Spark for data processing and ML training, and parameter servers for maintaining ML models. By carefully analyzing Tencent ML workloads, we figure out a widely existing computation pattern for ML models-element-wise operations among multiple high dimensional vectors. Based on this observation, we propose a new data abstraction, called Dimension Co-located Vector (DCV) for efficient model management in PS2. A DCV is a distributed vector that considers locality in parameter servers and enables efficient computation with multiple co-located distributed vectors. For ease-of-use, we also design a wide variety of advanced operators for operating DCVs. Finally, we carefully implement the PS2 system and evaluate it against existing systems on both public and Tencent workloads. Empirical results demonstrate that PS2 can outperform Spark MLlib by up to 55.6× and specialized ML systems like Petuum by up to 3.7×. © 2019 Association for Computing Machinery.","author":[{"family":"Zhang","given":"Z."},{"family":"Cui","given":"B."},{"family":"Shao","given":"Y."},{"family":"Yu","given":"L."},{"family":"Jiang","given":"J."},{"family":"Miao","given":"X."}],"citation-key":"Zhang2019376","collection-title":"Proceedings of the ACM SIGMOD International Conference on Management of Data","DOI":"10.1145/3299869.3314038","ISBN":"978-1-4503-5643-5","ISSN":"07308078","issued":{"date-parts":[[2019]]},"page":"376-388","publisher":"Association for Computing Machinery","title":"PS2: Parameter server on spark","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069480208&doi=10.1145%2f3299869.3314038&partnerID=40&md5=6ece78925eb323621569aacb5e249481"},
{"id":"Zhang2019611","abstract":"2D face analysis techniques, such as face landmarking, face recognition and face verification, are reasonably dependent on illumination conditions which are usually uncontrolled and unpredictable in the real world. The current massive data-driven approach, e.g., deep learning-based face recognition, requires a huge amount of labeled training face data that hardly cover the infinite lighting variations that can be encountered in real-life applications. An illumination robust preprocessing method thus remains a very interesting but also a significant challenge in reliable face analysis. In this paper we propose a novel model driven approach to improve lighting normalization of face images. Specifically, we propose to build the underlying reflectance model which characterizes interactions between skin surface, lighting source and camera sensor, and elaborate the formation of face color appearance. The proposed illumination processing pipeline enables generation of the Chromaticity Intrinsic Image (CII) in a log chromaticity space which is robust to illumination variations. Moreover, as an advantage over most prevailing methods, a photo-realistic color face image is subsequently reconstructed, which eliminates a wide variety of shadows whilst retaining the color information and identity details. Experimental results under different scenarios and using various face databases show the effectiveness of the proposed approach in dealing with lighting variations, including both soft and hard shadows, in face recognition. © 1979-2012 IEEE.","author":[{"family":"Zhang","given":"W."},{"family":"Zhao","given":"X."},{"family":"Morvan","given":"J.-M."},{"family":"Chen","given":"L."}],"citation-key":"Zhang2019611","container-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence","DOI":"10.1109/TPAMI.2018.2803179","ISSN":"01628828","issue":"3","issued":{"date-parts":[[2019]]},"page":"611-624","publisher":"IEEE Computer Society","title":"Improving shadow suppression for illumination robust face recognition","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041533373&doi=10.1109%2fTPAMI.2018.2803179&partnerID=40&md5=47ba92aa77f378d642d4fcf8ee93df76","volume":"41"},
{"id":"Zhang2020","abstract":"Accurate estimation of airspace capacity is essential to a safe, efficient and predictable air transportation system. Conventional approaches focus on controller workload using airspace complexity measurements that only consider operational conditions of controllers. However, such model-driven methods don't completely demonstrate airspace capacity in the real world because of lack of consideration for other critical factors such as weather. To address this challenge, we propose a new airspace capacity estimation model based on decision tree ensembles. Our model combines multi-source data to quantify the maximum transportation capacity of en route sector under different circumstances.This paper makes the following contributions: (a) we present an interpretable data-driven model that estimates the capacities of the National Airspace System (NAS), and highlight factor importance for airspace capacities; (b) the airspace capacity estimated by our proposed model is dynamically adjusted based on the real-time environment that has the potential to be a guide for temporary flight path changes or air traffic selections for an emergency landing; and (c) we promote the role of machine learning-based methods in future ATM and airspace optimization. © 2020 IEEE.","author":[{"family":"Zhang","given":"K."},{"family":"Liu","given":"Y."},{"family":"Wang","given":"J."},{"family":"Song","given":"H."},{"family":"Liu","given":"D."}],"citation-key":"Zhang2020","collection-title":"Integrated Communications, Navigation and Surveillance Conference, ICNS","DOI":"10.1109/ICNS50378.2020.9222986","ISBN":"978-1-72817-270-5","ISSN":"21554943","issued":{"date-parts":[[2020]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Tree-based airspace capacity estimation","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094875496&doi=10.1109%2fICNS50378.2020.9222986&partnerID=40&md5=7df938c2e15d7336634257f7cc5f8871","volume":"2020-September"},
{"id":"Zhang2020","abstract":"This paper considers a multiple-input multiple-output (MIMO) receiver with insufficient pilots in fast fading channel environment. Previous studies demonstrated that the pilot sequences should be relatively sufficient to obtain acceptable channel state information. To address this requirement, we investigate the model-driven deep learning based Turbo-MIMO receiver that includes joint channel estimation, signal detection and channel decoding (JCDD) modules. First, we use a short pilot sequence to produce a preliminary estimate of the channel matrix by linear minimum mean-squared error algorithm. Sub-sequently, we re-estimate the channel matrix with the assistance of more reliably estimated symbols and re-detect the data symbols utilizing the soft statistics from the channel decoder. Signal detection is realized in the receiver by representing the expectation propagation (EP) algorithm as multi-layer deep feed-forward networks to optimize the necessary damping factors, which can effectively compensate for the channel estimation error. Numerical results show that the proposed model-driven Turbo-MIMO receiver significantly outperforms the existing algorithms and is effective for the channel estimation with insufficient pilot sequences. © 2020 IEEE.","author":[{"family":"Zhang","given":"J."},{"family":"He","given":"H."},{"family":"Yang","given":"X."},{"family":"Wen","given":"C.-K."},{"family":"Jin","given":"S."},{"family":"Ma","given":"X."}],"citation-key":"Zhang2020","collection-title":"IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC","DOI":"10.1109/SPAWC48557.2020.9154227","ISBN":"978-1-72815-478-7","issued":{"date-parts":[[2020]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Model-driven deep learning based turbo-MIMO receiver","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090380606&doi=10.1109%2fSPAWC48557.2020.9154227&partnerID=40&md5=5a3d1b45d368de0316d66ef795c9d59d","volume":"2020-May"},
{"id":"Zhang20201513","abstract":"Distributed machine learning (ML) has triggered tremendous research interest in recent years. Stochastic gradient descent (SGD) is one of the most popular algorithms for training ML models, and has been implemented in almost all distributed ML systems, such as Spark MLlib, Petuum, MXNet, and TensorFlow. However, current implementations often incur huge communication and memory overheads when it comes to large models. One important reason for this inefficiency is the row-oriented scheme (RowSGD) that existing systems use to partition the training data, which forces them to adopt a centralized model management strategy that leads to vast amount of data exchange over the network.We propose a novel, column-oriented scheme (ColumnSGD) that partitions training data by columns rather than by rows. As a result, ML model can be partitioned by columns as well, leading to a distributed configuration where individual data and model partitions can be collocated on the same machine. Following this locality property, we develop a simple yet powerful computation framework that significantly reduces communication overheads and memory footprints compared to RowSGD, for large-scale ML models such as generalized linear models (GLMs) and factorization machines (FMs). We implement ColumnSGD on top of Apache Spark, and study its performance both analytically and experimentally. Experimental results on both public and real-world datasets show that ColumnSGD is up to 930 x faster than MLlib, 63 x faster than Petuum, and 14 x faster than MXNet. © 2020 IEEE.","author":[{"family":"Zhang","given":"Z."},{"family":"Wu","given":"W."},{"family":"Jiang","given":"J."},{"family":"Yu","given":"L."},{"family":"Cui","given":"B."},{"family":"Zhang","given":"C."}],"citation-key":"Zhang20201513","collection-title":"Proceedings - International Conference on Data Engineering","DOI":"10.1109/ICDE48307.2020.00134","ISBN":"978-1-72812-903-7","ISSN":"10844627","issued":{"date-parts":[[2020]]},"page":"1513-1524","publisher":"IEEE Computer Society","title":"ColumnSGD: A column-oriented framework for distributed stochastic gradient descent","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085857288&doi=10.1109%2fICDE48307.2020.00134&partnerID=40&md5=15fe6d6be6d72f9a1254414c09b8bb1c","volume":"2020-April"},
{"id":"Zhang2020903","abstract":"Traditional compressed sensing MRI methods that focus on constructing better prior regularizations or numerical iterative optimizations usually suffer from heavy computational burden. Recently developed deep learning based approaches rely too much on the selection of training data and deep architecture, thus have poor abilities of generalization. To address these issues, we propose an efficient and robust algorithm to achieve the balance between reconstruction accuracy and efficiency. We construct a model-driven priori expression process and a data-driven prediction process for details restoration and artifacts correction, in a complementary perspective, realizing an integration of domain knowledge and deep representation. Further, the iteratively alternating mechanism ensures that the output propagation can be corrected in time and guided towards the desired solution in expected direction. Detailed experiments on T1 and T2 weighted data demonstrate that compared with the state-of-the-art, our method achieves higher reconstruction accuracy for all three kinds of sampling patterns and five sampling ratios, as well as higher computation efficiency on both GPU and CPU. Further experiments show that our method provides stronger robustness to data variations and noise pollution. © 2020, Beijing China Science Journal Publishing Co. Ltd. All right reserved.","author":[{"family":"Zhang","given":"Y."},{"family":"Ma","given":"L."},{"family":"Liu","given":"R."},{"family":"Cheng","given":"S."},{"family":"Fan","given":"X."},{"family":"Luo","given":"Z."}],"citation-key":"Zhang2020903","container-title":"Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics","DOI":"10.3724/SP.J.1089.2020.17999","ISSN":"10039775","issue":"6","issued":{"date-parts":[[2020]]},"page":"903-910","publisher":"Institute of Computing Technology","title":"An efficient data-model dual-drive algorithm for compressed sensing MRI [数据与模型双驱动的高效压缩感知磁共振成像重构算法]","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087768742&doi=10.3724%2fSP.J.1089.2020.17999&partnerID=40&md5=4c08cae0d9d2454a87f7124d5ed876a6","volume":"32"},
{"id":"Zhang2021141","abstract":"As current 5G communication systems cannot fulfill the stringent requirements brought by emerging applications, 6G will innovatively employ deep learning (DL) techniques to fundamentally rethink the communication systems design problem from the bottom to top layers. Although recent evidence has shown the power of DL techniques in the communication domain, the exploration and utilization of DL techniques in communication systems is still in its infancy and should come in a progressive manner. To effectively and efficiently implement DL techniques in future 6G communications in the physical layer, we give some potential deployment strategies and key enabling technologies that relate to 6G in terms of joint design of block-structured and end-to-end DL, integration of model-driven and data-driven DL, combination of online and offline training, ubiquitous learning and explainable DL techniques. © 2021 IEEE.","author":[{"family":"Zhang","given":"S."},{"family":"Liu","given":"J."},{"family":"Rodrigues","given":"T.K."},{"family":"Kato","given":"N."}],"citation-key":"Zhang2021141","container-title":"IEEE Wireless Communications","DOI":"10.1109/MWC.001.2000516","ISSN":"15361284","issue":"5","issued":{"date-parts":[[2021]]},"page":"141-147","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Deep learning techniques for advancing 6G communications in the physical layer","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119990737&doi=10.1109%2fMWC.001.2000516&partnerID=40&md5=8ba793c0b9d66c517e97e4e3f5738227","volume":"28"},
{"id":"Zhang2021158","abstract":"Reconfigurable intelligent surface (RIS) is an emerging meta-surface that can provide additional communications links through reflecting the signals, and has been recognized as a strong candidate of 6G mobile communications systems. Meanwhile, it has been recently admitted that implementing artificial intelligence (AI) into RIS communications will extensively benefit the reconfiguration capacity and enhance the robustness to complicated transmission environments. Besides the conventional model-driven approaches, AI can also deal with the existing signal processing problems in a data-driven manner via digging the inherent characteristic from the real data. Hence, AI is particularly suitable for the signal processing problems over RIS networks under unideal scenarios like modeling mismatching, insufficient resource, hardware impairment, as well as dynamical transmissions. As one of the earliest survey papers, we will introduce the merging of AI and RIS, called AIRIS, over various signal processing topics, including environmental sensing, channel acquisition, beam-forming design, and resource scheduling, etc. We will also discuss the challenges of AIRIS and present some interesting future directions. © 2013 China Institute of Communications.","author":[{"family":"Zhang","given":"S."},{"family":"Li","given":"M."},{"family":"Jian","given":"M."},{"family":"Zhao","given":"Y."},{"family":"Gao","given":"F."}],"citation-key":"Zhang2021158","container-title":"China Communications","DOI":"10.23919/JCC.2021.07.013","ISSN":"16735447","issue":"7","issued":{"date-parts":[[2021]]},"page":"158-171","publisher":"Editorial Board of Journal on Communications","title":"AIRIS: Artificial intelligence enhanced signal processing in reconfigurable intelligent surface communications","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111663366&doi=10.23919%2fJCC.2021.07.013&partnerID=40&md5=47cbe476b6315e4d12f08537c5bc93ab","volume":"18"},
{"id":"Zhang2022","abstract":"High-dimensional model representation (HDMR), decomposing the high-dimensional problem into summands of different order component terms, has been widely researched to work out the dilemma of \"curse-of-dimensionality\" when using surrogate techniques to approximate high-dimensional problems in engineering design. However, the available one-metamodel-based HDMRs usually encounter the predicament of prediction uncertainty, while current multi-metamodels-based HDMRs cannot provide simple explicit expressions for black-box problems, and have high computational complexity in terms of constructing the model by the explored points and predicting the responses of unobserved locations. Therefore, aimed at such problems, a new stand-alone HDMR metamodeling technique, termed as Dendrite-HDMR, is proposed in this study based on the hierarchical Cut-HDMR and the white-box machine learning algorithm, Dendrite Net. The proposed Dendrite-HDMR not only provides succinct and explicit expressions in the form of Taylor expansion but also has relatively higher accuracy and stronger stability for most mathematical functions than other classical HDMRs with the assistance of the proposed adaptive sampling strategy, named KKMC, in which k-means clustering algorithm, k-Nearest Neighbor classification algorithm and the maximum curvature information of the provided expression are utilized to sample new points to refine the model. Finally, the Dendrite-HDMR technique is applied to solve the design optimization problem of the solid launch vehicle propulsion system with the purpose of improving the impulse-weight ratio, which represents the design level of the propulsion system. © 2022 American Institute of Physics Inc.. All rights reserved.","author":[{"family":"Zhang","given":"Q."},{"family":"Wu","given":"Y."},{"family":"Lu","given":"L."},{"family":"Qiao","given":"P."}],"citation-key":"Zhang2022","container-title":"Journal of Mechanical Design, Transactions of the ASME","DOI":"10.1115/1.4053526","ISSN":"10500472","issue":"8","issued":{"date-parts":[[2022]]},"publisher":"American Society of Mechanical Engineers (ASME)","title":"An adaptive dendrite-HDMR metamodeling technique for high-dimensional problems","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127829296&doi=10.1115%2f1.4053526&partnerID=40&md5=10133a97c61678fed1987917b3e2ac78","volume":"144"},
{"id":"Zhang20221037","abstract":"Precoding design exploiting deep learning methods has been widely studied for multiuser multiple-input multiple-output (MU-MIMO) systems. However, conventional neural precoding design applies black-box-based neural networks which are less interpretable. In this letter, we propose a deep learning-based precoding method based on an interpretable design of a neural precoding network, namely iPNet. In particular, the iPNet mimics the classic minimum mean-squared error (MMSE) precoding and approximates the matrix inversion in the design of the neural network architecture. Specifically, the proposed iPNet consists of a model-driven component network, responsible for augmenting the input channel state information (CSI), and a data-driven sub-network, responsible for precoding calculation from this augmented CSI. The latter data-driven module is explicitly interpreted as an unsupervised learner of the MMSE precoder. Simulation results show that by exploiting the augmented CSI, the proposed iPNet achieves noticeable performance gain over existing black-box designs and also exhibits enhanced generalizability against CSI mismatches. © 1997-2012 IEEE.","author":[{"family":"Zhang","given":"S."},{"family":"Xu","given":"J."},{"family":"Xu","given":"W."},{"family":"Wang","given":"N."},{"family":"Ng","given":"D.W.K."},{"family":"You","given":"X."}],"citation-key":"Zhang20221037","container-title":"IEEE Communications Letters","DOI":"10.1109/LCOMM.2022.3156946","ISSN":"10897798","issue":"5","issued":{"date-parts":[[2022]]},"page":"1037-1041","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Data augmentation empowered neural precoding for multiuser MIMO with MMSE model","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125715655&doi=10.1109%2fLCOMM.2022.3156946&partnerID=40&md5=48dead280716d79e435590a0d7be70c4","volume":"26"},
{"id":"Zhang20222368","abstract":"Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, when only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate. © 2002-2012 IEEE.","author":[{"family":"Zhang","given":"J."},{"family":"You","given":"M."},{"family":"Zheng","given":"G."},{"family":"Krikidis","given":"I."},{"family":"Zhao","given":"L."}],"citation-key":"Zhang20222368","container-title":"IEEE Transactions on Wireless Communications","DOI":"10.1109/TWC.2021.3111843","ISSN":"15361276","issue":"4","issued":{"date-parts":[[2022]]},"page":"2368-2382","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Model-driven learning for generic MIMO downlink beamforming with uplink channel information","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115128946&doi=10.1109%2fTWC.2021.3111843&partnerID=40&md5=5574fe17096e697e4221dc93d1dc616f","volume":"21"},
{"id":"Zhang20225095","abstract":"Traditional iterative-based reconstruction algorithms for compressed color imaging often suffer from long reconstruction time and low reconstruction accuracy at extreme low-rate subsampling. This paper proposes a model-driven deep learning framework for compressed color imaging. In the training step, extract the image blocks at the same position of the R, G, and B channel images as the ground truth, and then, singular value decomposition is performed on the measurement matrix to obtain the optimized measurement matrix and low-dimensional measurements; afterward, the ground-truth and optimized measurements are utilized to construct a large amount of training data pairs to train an ‘end-to-end’ deep unfolding model for compressed color imaging. In the test step, the single pretrained model is used to reconstruct high-quality images from optimized low-dimensional compressed measurements for each channel and synthesize a color image. Numerical experiments demonstrate that our proposed unified framework can achieve high accuracy and real-time reconstruction for the color image at extremely low subsampling rate. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.","author":[{"family":"Zhang","given":"C."},{"family":"Wu","given":"F."},{"family":"Zhu","given":"Y."},{"family":"Zhou","given":"J."},{"family":"Wei","given":"S."}],"citation-key":"Zhang20225095","container-title":"Soft Computing","DOI":"10.1007/s00500-022-06982-4","ISSN":"14327643","issue":"11","issued":{"date-parts":[[2022]]},"page":"5095-5103","publisher":"Springer Science and Business Media Deutschland GmbH","title":"A unified framework of deep unfolding for compressed color imaging","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128295499&doi=10.1007%2fs00500-022-06982-4&partnerID=40&md5=8c0297808b0c8bb8ebab6772dda17936","volume":"26"},
{"id":"zhangOptimalityNaiveBayes2004","abstract":"Naive Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surprising, because the conditional independence assumption on which it is based, is rarely true in realworld applications. An open question is: what is the true reason for the surprisingly good performance of naive Bayes in classification? In this paper, we propose a novel explanation on the superb classification performance of naive Bayes. We show that, essentially, the dependence distribution; i.e., how the local dependence of a node distributes in each class, evenly or unevenly, and how the local dependencies of all nodes work together, consistently (supporting a certain classification) or inconsistently (canceling each other out), plays a crucial role. Therefore, no matter how strong the dependences among attributes are, naive Bayes can still be optimal if the dependences distribute evenly in classes, or if the dependences cancel each other out. We propose and prove a sufficient and necessary conditions for the optimality of naive Bayes. Further, we investigate the optimality of naive Bayes under the Gaussian distribution. We present and prove a sufficient condition for the optimality of naive Bayes, in which the dependence between attributes do exist. This provides evidence that dependence among attributes may cancel out each other. In addition, we explore when naive Bayes works well.","author":[{"family":"Zhang","given":"Harry"}],"citation-key":"zhangOptimalityNaiveBayes2004","container-title":"Proceedings of the seventeenth international florida artificial intelligence research society conference (FLAIRS 2004)","editor":[{"family":"Barr","given":"Valerie"},{"family":"Markov","given":"Zdravko"}],"issued":{"date-parts":[[2004]]},"publisher":"AAAI Press","title":"The optimality of naive bayes","type":"paper-conference"},
{"id":"zhangPerformingGlobalUncertainty2017a","abstract":"This paper develops a novel hybrid approach that integrates metamodeling, machine learning algorithms, and a variance decomposition technique to support global uncertainty and sensitivity (US) analysis under uncertainty. It consists of three main steps: (1) metamodel construction; (2) metamodel validation; and (3) global US analysis. A multi-input and multioutput metamodel, with least-squares support vector machine (LSSVM) and particle swarm optimization (PSO) algorithms incorporated, is built in order to simulate system behaviors of tunnel-induced building damage. Three indicators - mean absolute percentage error (MAPE), variance of absolute percentage error (VAPE), and mean square percentage error (MSPE) - are proposed to test the prediction performance of the metamodel. The extended Fourier amplitude sensitivity test (EFAST) is used to perform global US analysis on the basis of the well-trained metamodel. The novelty of the developed approach lies in its capability of learning from given data to identify relationships between model inputs and outputs to provide an access for conducting global US analysis. The collected data from the construction of the Wuhan Metro system (WMS) in China are used in a case study to demonstrate the effectiveness and applicability of the developed approach. Results indicate that the developed approach is capable of (1) predicting and assessing the magnitude of tunnel-induced building damage in terms of the cumulative distribution function (CDF) of model outputs, and (2) identifying the most significant and insignificant factors for possible dimension reduction to improve the understanding of the model behavior. This research contributes to (1) the body of knowledge by proposing a more appropriate research methodology that can cope with aleatory and epistemic uncertainty and support global US analysis based on given data, and (2) the state of practice by providing a data-driven metamodel technique to simulate system behaviors of tunnel-induced building damage with high reliability and reduce dependency on domain experts. © 2017 American Society of Civil Engineers.","author":[{"family":"Zhang","given":"L."},{"family":"Wu","given":"X."},{"family":"Zhu","given":"H."},{"family":"Abourizk","given":"S.M."}],"citation-key":"zhangPerformingGlobalUncertainty2017a","container-title":"Journal of Computing in Civil Engineering","DOI":"10.1061/(ASCE)CP.1943-5487.0000714","ISSN":"08873801","issue":"6","issued":{"date-parts":[[2017]]},"publisher":"American Society of Civil Engineers (ASCE)","title":"Performing Global Uncertainty and Sensitivity Analysis from Given Data in Tunnel Construction","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029410032&doi=10.1061%2f%28ASCE%29CP.1943-5487.0000714&partnerID=40&md5=41a35455f42274d15fdc8783f24aafcc","volume":"31"},
{"id":"zhangTPPFAMUseThreshold2014","abstract":"The issue of category proliferation caused by the overlapping classes in fuzzy ARTMAP (FAM) is addressed in this paper. A new FAM-based neural architecture called TTPFAM is proposed, which can reduce category proliferation by performing a threshold filtering mechanism before a new category created during training, and improve the classification accuracy by combining prediction distributed by the dynamic Q-max rule and posterior probability estimated during testing. The TPPFAM can produce a small size of neural network architecture without degradation of the classification accuracy. The algorithm is evaluated in terms of the classification accuracy and the number of categories by experiments on both artificial and real data, and the results show that the performance of TPPFAM is better than that of the other models.","author":[{"family":"Zhang","given":"Yongquan"},{"family":"Ji","given":"Hongbing"},{"family":"Zhang","given":"Wenbo"}],"citation-key":"zhangTPPFAMUseThreshold2014","container-title":"Neurocomputing","ISSN":"0925-2312","issued":{"date-parts":[[2014]]},"page":"63 - 71","title":"TPPFAM: Use of threshold and posterior probability for category reduction in fuzzy ARTMAP","type":"article-journal","URL":"http://www.sciencedirect.com/science/article/pii/S0925231213008151","volume":"124"},
{"id":"zhangTreeBasedAirspaceCapacity2020a","abstract":"Accurate estimation of airspace capacity is essential to a safe, efficient and predictable air transportation system. Conventional approaches focus on controller workload using airspace complexity measurements that only consider operational conditions of controllers. However, such model-driven methods don't completely demonstrate airspace capacity in the real world because of lack of consideration for other critical factors such as weather. To address this challenge, we propose a new airspace capacity estimation model based on decision tree ensembles. Our model combines multi-source data to quantify the maximum transportation capacity of en route sector under different circumstances.This paper makes the following contributions: (a) we present an interpretable data-driven model that estimates the capacities of the National Airspace System (NAS), and highlight factor importance for airspace capacities; (b) the airspace capacity estimated by our proposed model is dynamically adjusted based on the real-time environment that has the potential to be a guide for temporary flight path changes or air traffic selections for an emergency landing; and (c) we promote the role of machine learning-based methods in future ATM and airspace optimization. © 2020 IEEE.","author":[{"family":"Zhang","given":"K."},{"family":"Liu","given":"Y."},{"family":"Wang","given":"J."},{"family":"Song","given":"H."},{"family":"Liu","given":"D."}],"citation-key":"zhangTreeBasedAirspaceCapacity2020a","container-title":"Integrated Communications, Navigation and Surveillance Conference, ICNS","DOI":"10.1109/ICNS50378.2020.9222986","ISBN":"978-1-72817-270-5","ISSN":"21554943","issued":{"date-parts":[[2020]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Tree-Based Airspace Capacity Estimation","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094875496&doi=10.1109%2fICNS50378.2020.9222986&partnerID=40&md5=7df938c2e15d7336634257f7cc5f8871","volume":"2020-September"},
{"id":"zhao2005","abstract":"Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In particular, clustering algorithms that build meaningful hierarchies out of large document collections are ideal tools for their interactive visualization and exploration as they provide data-views that are consistent, predictable, and at different levels of granularity. This paper focuses on document clustering algorithms that build such hierarchical solutions and (i) presents a comprehensive study of partitional and agglomerative algorithms that use different criterion functions and merging schemes, and (ii) presents a new class of clustering algorithms called constrained agglomerative algorithms, which combine features from both partitional and agglomerative approaches that allows them to reduce the early-stage errors made by agglomerative methods and hence improve the quality of clustering solutions. The experimental evaluation shows that, contrary to the common belief, partitional algorithms always lead to better solutions than agglomerative algorithms; making them ideal for clustering large document collections due to not only their relatively low computational requirements, but also higher clustering quality. Furthermore, the constrained agglomerative methods consistently lead to better solutions than agglomerative methods alone and for many cases they outperform partitional methods, as well.","author":[{"family":"Zhao","given":"Ying"},{"family":"Karypis","given":"George"},{"family":"Fayyad","given":"Usama"}],"citation-key":"zhao2005","container-title":"Data Mining and Knowledge Discovery","container-title-short":"Data Min. Knowl. Discov.","ISSN":"1384-5810","issue":"2","issued":{"date-parts":[[2005,3]]},"page":"141-168","title":"Hierarchical clustering algorithms for document datasets","type":"article-journal","URL":"http://dl.acm.org/citation.cfm?id=1061897.1061908","volume":"10"},
{"id":"zhaoUserbasedCollaborativefilteringRecommendation2010","author":[{"family":"Zhao","given":"Zhi-Dan"},{"family":"Shang","given":"Ming-sheng"}],"citation-key":"zhaoUserbasedCollaborativefilteringRecommendation2010","collection-title":"WKDD '10","container-title":"Proceedings of the 2010 third international conference on knowledge discovery and data mining","event-place":"Washington, DC, USA","ISBN":"978-0-7695-3923-2","issued":{"date-parts":[[2010]]},"page":"478-481","publisher":"IEEE Computer Society","publisher-place":"Washington, DC, USA","title":"User-based collaborative-filtering recommendation algorithms on hadoop","type":"paper-conference","URL":"https://doi.org/10.1109/WKDD.2010.54"},
{"id":"Zheng201210","abstract":"Almost all previous approaches on coronary artery centerline extraction are data-driven, which try to trace a centerline from an automatically detected or manually specified coronary ostium. No or little high level prior information is used; therefore, the centerline tracing procedure may terminate early at a severe occlusion or an anatomically inconsistent centerline course may be generated. In this work, we propose a model-driven approach to extracting the three major coronary arteries. The relative position of the major coronary arteries with respect to the heart chambers is stable, therefore the automatically segmented chambers can be used to predict the initial position of these coronary centerlines. The initial centerline is further refined using a machine learning based vesselness measurement. The proposed approach can handle variations in the length and topology of an artery, and it is more robust under severe occlusions than a data-driven approach. The extracted centerlines are already labeled, therefore no additional vessel labeling procedure is needed. Quantitative comparison on 54 cardiac CT datasets demonstrates the robustness of the proposed method over a state-of-the-art data-driven approach. © 2012 Springer-Verlag.","author":[{"family":"Zheng","given":"Y."},{"family":"Shen","given":"J."},{"family":"Tek","given":"H."},{"family":"Funka-Lea","given":"G."}],"citation-key":"Zheng201210","container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007/978-3-642-35428-1_2","ISBN":"9783642354274","ISSN":"03029743","issued":{"date-parts":[[2012]]},"page":"10-18","title":"Model-driven centerline extraction for severely occluded major coronary arteries","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870006976&doi=10.1007%2f978-3-642-35428-1_2&partnerID=40&md5=dfa4fff30f524a64f25c750a78ec5951","volume":"7588 LNCS"},
{"id":"Zheng20212485","abstract":"Objective: Steganography is a technique that hides secret information in multimedia without arousing suspicion from the steganalyzer. In a typical case, the steganography system consists of three parts: Alice, Bob, and Eve. The sender Alice hides the secret message in the carrier image(cover), turns it into another image(stego) that does not change its appearance perception, and then sends the stego to the receiver Bob through a public channel. Then, Bob can recover the secret messages from the stego. Eve acts as the steganalyzer to monitor their communications and determine whether secret information is hidden in the stego. In general, image information hiding includes two branches, namely, image steganography and image watermarking. Steganography hides secret information in a carrier and achieves the purpose of secret communication via the transmission of stego images, and the main evaluation metric is security against steganalysis. The principle of watermarking technology is similar to that of steganography, the difference is that its purpose is to use the watermark information embedded in the carrier to protect intellectual property rights, and the metric of watermarking emphasizes robustness against various watermarking attack. Traditional steganography and watermarking algorithms depend on the artificial designed complex feature, which requires the designer's domain knowledge and accumulated experience. Recent work has shown that deep neural networks are highly sensitive to minute perturbations of input images, giving rise to adversarial examples. This property is usually considered a weakness of learned models but can be exploited to enhance the capability of information hiding. Researchers have tried to use generative adversarial networks (GANs) to design steganographic algorithms and robust watermarking algorithms automatically. However, due to the unreasonable design of the neural network structure and other reasons as well as the lacking consideration of several practical problems, state-of-the-art GAN-based information hiding methods have several weaknesses.1) In real-world applications, the pixel value of the decoded image should be a float, but the networks proposed by existing methods set it to an integer. 2) Image steganography based on GANs has insufficient anti-steganalysis ability. 3) Watermarking technology based on deep learning has limited consideration of the types of attacking noises.4) The design of differential noise layer is unreasonable. Thus, the existing GAN-based steganography or watermarking algorithms have deficiencies in information extraction accuracy, embedding capacity, steganography security or watermark robustness, and watermark image quality. Method The paper proposes a new end-to-end steganographic model driven by GANs called image information hiding-GAN (IIH-GAN)) and robust blind watermarking model named image robust blind watermark-GAN (IRBW-GAN) for image steganography and robust blind watermarking, respectively. SE-ResNet, a more efficient encoder and decoder structure, is included in the network model, which can optimize the interdependence between network channels and enhance the global feature automatic selection, leading to a more accurate, high-quality information embedding and extraction. The proposed IIH-GAN uses a discriminator to cotrain with the encoder-decoder; thus, it maintains the distribution of the carrier image during adversarial training unchanged and enhances the security in resisting steganalysis. To solve the problem of decoding real images in real-world scenarios, IIH-GAN adds a round layer between the encoder and the decoder. IIH-GAN adds the adversarial examples to the GAN-based steganographic model to remedy the shortcomings of GAN-based steganography in resisting the powerful state-of-the-art deep learning-based steganalysis algorithms. In the watermark model, IRBW-GAN adds a differentiable noise layer between the encoder and the decoder that resists noise attacks, considering various noise attack types and high-intensity noise attacks. For non differentiable JPEG compression noise, a new type of differentiable network layer is proposed for simulation. The datasets used include celebA, BOSSBase, and common object in context(COCO). The existing advanced GAN-based steganography methods, such as Volkhonskiy, Hayes, Hu, and HiDDeN, are used for comparison experiments under the same evaluation metrics of image quality, capacity, decoding accuracy, and steganography security. The watermarking methods for comparison experiments include HiDDeN, ReDMark, and Liu. The watermark image quality and watermark extraction accuracy under various noises are compared. Noise types include Identity, Dropout, Cropout, Gaussian blur, JPEG Compression, Crop, Resize, Mean filtering, and Salt and pepper. Result: Experimental results show that the designed models have remarkable improvements in performance compared with state-of-the-art methods. When the detailed parameters of the trained steganalysis model are known, the adversarial examples are added to the proposed IIH-GAN. This method can reduce the detection accuracy of existing powerful deep learning-based steganalysis, YeNet, from 97.43% to 48.69%, which means the proposed IIH-GAN greatly improves the steganography security. The proposed watermarking model IRBW-GAN can achieve a relative embedding capacity as high as 1 bpp (bits-per-pixel) on a 256×256 pixels image. Compared with other models, the peak signal-to-noise ratio and structural similarity of IRBW-GAN are greatly improved, which means that the image generated by IRBW-GAN has a higher image quality. Compared with the state-of-the-art deep learning-based watermarking methods, when resisting various types and high-intensity noise attacks, IRBW-GAN model considerably improves the watermarked image quality and watermark extraction accuracy while increasing the watermark embedding capacity. The extraction accuracy is increased by approximately 20% compared with other methods under the attack of JEPG compression. The proposed simulated JPEG compression network layer is closer to the real JPEG compression, which can achieve a better robustness against image compression. Conclusion: The proposed IIH-GAN and IRBW-GAN achieve superior performance over state-of-the-art models in the fields of image steganography and watermarking, respectively. © 2021, Editorial Office of Journal of Image and Graphics. All right reserved.","author":[{"family":"Zheng","given":"G."},{"family":"Hu","given":"D."},{"family":"Ge","given":"H."},{"family":"Zheng","given":"S."}],"citation-key":"Zheng20212485","container-title":"Journal of Image and Graphics","DOI":"10.11834/jig.200404","ISSN":"10068961","issue":"10","issued":{"date-parts":[[2021]]},"page":"2485-2502","publisher":"Editorial and Publishing Board of JIG","title":"End-to-end image steganography and watermarking driven by generative adversarial networks [生成对抗网络驱动的图像隐写与水印模型]","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117700828&doi=10.11834%2fjig.200404&partnerID=40&md5=4d91f77a97f81fe87d9ae0f228120a6b","volume":"26"},
{"id":"zhengCodEXSourceCode2018","author":[{"family":"Zheng","given":"Mengya"},{"family":"Pan","given":"Xingyu"},{"family":"Lillis","given":"David"}],"citation-key":"zhengCodEXSourceCode2018","container-title":"Proceedings for the 26th AIAI irish conference on artificial intelligence and cognitive science trinity college dublin, dublin, ireland, december 6-7th, 2018.","issued":{"date-parts":[[2018]]},"page":"362-373","title":"CodEX: Source code plagiarism detection based on abstract syntax tree","type":"paper-conference","URL":"http://ceur-ws.org/Vol-2259/aics_33.pdf"},
{"id":"zhengEndtoendImageSteganography2021a","abstract":"Objective: Steganography is a technique that hides secret information in multimedia without arousing suspicion from the steganalyzer. In a typical case, the steganography system consists of three parts: Alice, Bob, and Eve. The sender Alice hides the secret message in the carrier image(cover), turns it into another image(stego) that does not change its appearance perception, and then sends the stego to the receiver Bob through a public channel. Then, Bob can recover the secret messages from the stego. Eve acts as the steganalyzer to monitor their communications and determine whether secret information is hidden in the stego. In general, image information hiding includes two branches, namely, image steganography and image watermarking. Steganography hides secret information in a carrier and achieves the purpose of secret communication via the transmission of stego images, and the main evaluation metric is security against steganalysis. The principle of watermarking technology is similar to that of steganography, the difference is that its purpose is to use the watermark information embedded in the carrier to protect intellectual property rights, and the metric of watermarking emphasizes robustness against various watermarking attack. Traditional steganography and watermarking algorithms depend on the artificial designed complex feature, which requires the designer's domain knowledge and accumulated experience. Recent work has shown that deep neural networks are highly sensitive to minute perturbations of input images, giving rise to adversarial examples. This property is usually considered a weakness of learned models but can be exploited to enhance the capability of information hiding. Researchers have tried to use generative adversarial networks (GANs) to design steganographic algorithms and robust watermarking algorithms automatically. However, due to the unreasonable design of the neural network structure and other reasons as well as the lacking consideration of several practical problems, state-of-the-art GAN-based information hiding methods have several weaknesses.1) In real-world applications, the pixel value of the decoded image should be a float, but the networks proposed by existing methods set it to an integer. 2) Image steganography based on GANs has insufficient anti-steganalysis ability. 3) Watermarking technology based on deep learning has limited consideration of the types of attacking noises.4) The design of differential noise layer is unreasonable. Thus, the existing GAN-based steganography or watermarking algorithms have deficiencies in information extraction accuracy, embedding capacity, steganography security or watermark robustness, and watermark image quality. Method The paper proposes a new end-to-end steganographic model driven by GANs called image information hiding-GAN (IIH-GAN)) and robust blind watermarking model named image robust blind watermark-GAN (IRBW-GAN) for image steganography and robust blind watermarking, respectively. SE-ResNet, a more efficient encoder and decoder structure, is included in the network model, which can optimize the interdependence between network channels and enhance the global feature automatic selection, leading to a more accurate, high-quality information embedding and extraction. The proposed IIH-GAN uses a discriminator to cotrain with the encoder-decoder; thus, it maintains the distribution of the carrier image during adversarial training unchanged and enhances the security in resisting steganalysis. To solve the problem of decoding real images in real-world scenarios, IIH-GAN adds a round layer between the encoder and the decoder. IIH-GAN adds the adversarial examples to the GAN-based steganographic model to remedy the shortcomings of GAN-based steganography in resisting the powerful state-of-the-art deep learning-based steganalysis algorithms. In the watermark model, IRBW-GAN adds a differentiable noise layer between the encoder and the decoder that resists noise attacks, considering various noise attack types and high-intensity noise attacks. For non differentiable JPEG compression noise, a new type of differentiable network layer is proposed for simulation. The datasets used include celebA, BOSSBase, and common object in context(COCO). The existing advanced GAN-based steganography methods, such as Volkhonskiy, Hayes, Hu, and HiDDeN, are used for comparison experiments under the same evaluation metrics of image quality, capacity, decoding accuracy, and steganography security. The watermarking methods for comparison experiments include HiDDeN, ReDMark, and Liu. The watermark image quality and watermark extraction accuracy under various noises are compared. Noise types include Identity, Dropout, Cropout, Gaussian blur, JPEG Compression, Crop, Resize, Mean filtering, and Salt and pepper. Result: Experimental results show that the designed models have remarkable improvements in performance compared with state-of-the-art methods. When the detailed parameters of the trained steganalysis model are known, the adversarial examples are added to the proposed IIH-GAN. This method can reduce the detection accuracy of existing powerful deep learning-based steganalysis, YeNet, from 97.43% to 48.69%, which means the proposed IIH-GAN greatly improves the steganography security. The proposed watermarking model IRBW-GAN can achieve a relative embedding capacity as high as 1 bpp (bits-per-pixel) on a 256×256 pixels image. Compared with other models, the peak signal-to-noise ratio and structural similarity of IRBW-GAN are greatly improved, which means that the image generated by IRBW-GAN has a higher image quality. Compared with the state-of-the-art deep learning-based watermarking methods, when resisting various types and high-intensity noise attacks, IRBW-GAN model considerably improves the watermarked image quality and watermark extraction accuracy while increasing the watermark embedding capacity. The extraction accuracy is increased by approximately 20% compared with other methods under the attack of JEPG compression. The proposed simulated JPEG compression network layer is closer to the real JPEG compression, which can achieve a better robustness against image compression. Conclusion: The proposed IIH-GAN and IRBW-GAN achieve superior performance over state-of-the-art models in the fields of image steganography and watermarking, respectively. © 2021, Editorial Office of Journal of Image and Graphics. All right reserved.","author":[{"family":"Zheng","given":"G."},{"family":"Hu","given":"D."},{"family":"Ge","given":"H."},{"family":"Zheng","given":"S."}],"citation-key":"zhengEndtoendImageSteganography2021a","container-title":"Journal of Image and Graphics","DOI":"10.11834/jig.200404","ISSN":"10068961","issue":"10","issued":{"date-parts":[[2021]]},"page":"2485-2502","publisher":"Editorial and Publishing Board of JIG","title":"End-to-end image steganography and watermarking driven by generative adversarial networks [生成对抗网络驱动的图像隐写与水印模型]","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117700828&doi=10.11834%2fjig.200404&partnerID=40&md5=4d91f77a97f81fe87d9ae0f228120a6b","volume":"26"},
{"id":"Zhou2021","abstract":"The absence of cyclic prefix (CP) can increase the spectral efficiency of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. However, CP removal will make signal detection challenging. In this paper, we develop a model-driven deep learning (DL)-based detector to resolve this problem. The prototype of the detector is the orthogonal approximate message passing (OAMP) algorithm, which has a strong ability to mitigate interference but involves matrix inversion with high complexity. We first use the conjugate gradient method to reduce the computational complexity of OAMP. Then, by unfolding the revised algorithm into a network and learning the optimal values of its parameters, the detection performance can be significantly improved. Complexity analysis indicates that the proposed scheme can reduce the running time to only a quarter of that required in OAMP while achieving remarkable performance in bit-error rate. © 2021 IEEE.","author":[{"family":"Zhou","given":"X."},{"family":"Zhang","given":"J."},{"family":"Wen","given":"C.-K."},{"family":"Zhang","given":"J."},{"family":"Jin","given":"S."}],"citation-key":"Zhou2021","collection-title":"2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings","DOI":"10.1109/ICCWorkshops50388.2021.9473616","ISBN":"978-1-72819-441-7","issued":{"date-parts":[[2021]]},"publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Model-driven deep learning-based signal detector for CP-Free MIMO-OFDM systems","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112816485&doi=10.1109%2fICCWorkshops50388.2021.9473616&partnerID=40&md5=7b74f0ef63f97e144ac8269632447714"},
{"id":"Zhou20211","abstract":"With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations. In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type. However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process. Spatiotemporal big data analysis is a powerful tool for the traffic prediction. The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network. Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models. The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted. The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction. The potential issues of graph neural network driven prediction mechanisms were also excavated. © 2021, Beijing Xintong Media Co., Ltd.. All rights reserved.","author":[{"family":"Zhou","given":"Y."},{"family":"Hu","given":"S."},{"family":"Li","given":"W."},{"family":"Cheng","given":"N."},{"family":"Lu","given":"N."},{"family":"Shen","given":"X."}],"citation-key":"Zhou20211","container-title":"Chinese Journal on Internet of Things","DOI":"10.11959/j.issn.2096-3750.2021.00235","ISSN":"20963750","issue":"3","issued":{"date-parts":[[2021]]},"page":"1-16","publisher":"Beijing Xintong Media Co., Ltd.","title":"Graph neural network driven traffic prediction technology: review and challenge","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127699130&doi=10.11959%2fj.issn.2096-3750.2021.00235&partnerID=40&md5=64eee253bb1cb42109c66bc071cb044d","volume":"5"},
{"id":"zhouMoreAccurateContent2014","author":[{"family":"Zhou","given":"Bo"},{"family":"Xia","given":"Xin"},{"family":"Lo","given":"David"},{"family":"Tian","given":"Cong"},{"family":"Wang","given":"Xinyu"}],"citation-key":"zhouMoreAccurateContent2014","collection-title":"ICPC 2014","container-title":"Proceedings of the 22Nd international conference on program comprehension","event-place":"New York, NY, USA","ISBN":"978-1-4503-2879-1","issued":{"date-parts":[[2014]]},"page":"95-105","publisher":"ACM","publisher-place":"New York, NY, USA","title":"Towards more accurate content categorization of API discussions","type":"paper-conference","URL":"http://doi.acm.org/10.1145/2597008.2597142"},
{"id":"Zhu20212667","abstract":"Microsoft's internal big-data infrastructure is one of the largest in the world - -with over 300k machines running billions of tasks from over 0.6M daily jobs. Operating this infrastructure is a costly and complex endeavor, and efficiency is paramount. In fact, for over 15 years, a dedicated engineering team has tuned almost every aspect of this infrastructure, achieving state-of-the-art efficiency (¿60% average CPU utilization across all clusters). Despite rich telemetry and strong expertise, faced with evolving hardware/software/workloads this manual tuning approach had reached its limit - -we had plateaued. In this paper, we present KEA, a multi-year effort to automate our tuning processes to be fully data/model-driven. KEA leverages a mix of domain knowledge and principled data science to capture the essence of our cluster dynamic behavior in a set of machine learning (ML) models based on collected system data. These models power automated optimization procedures for parameter tuning, and inform our leadership in critical decisions around engineering and capacity management (such as hardware and data center design, software investments, etc.). We combine \"observational” tuning (i.e., using models to predict system behavior without direct experimentation) with judicious use of \"flighting” (i.e., conservative testing in production). This allows us to support a broad range of applications that we discuss in this paper. KEA continuously tunes our cluster configurations and is on track to save Microsoft tens of millions of dollars per year. At the best of our knowledge, this paper is the first to discuss research challenges and practical learnings that emerge when tuning an exabyte-scale data infrastructure. © 2021 ACM.","author":[{"family":"Zhu","given":"Y."},{"family":"Krishnan","given":"S."},{"family":"Karanasos","given":"K."},{"family":"Tarte","given":"I."},{"family":"Power","given":"C."},{"family":"Modi","given":"A."},{"family":"Kumar","given":"M."},{"family":"Zhang","given":"D."},{"family":"Muthyala","given":"K."},{"family":"Jurgens","given":"N."},{"family":"Sakalanaga","given":"S."},{"family":"Darbha","given":"S."},{"family":"Iyer","given":"M."},{"family":"Agarwal","given":"A."},{"family":"Curino","given":"C."}],"citation-key":"Zhu20212667","collection-title":"Proceedings of the ACM SIGMOD International Conference on Management of Data","DOI":"10.1145/3448016.3457569","ISSN":"07308078","issued":{"date-parts":[[2021]]},"page":"2667-2680","publisher":"Association for Computing Machinery","title":"KEA: Tuning an exabyte-scale data infrastructure","type":"paper-conference","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108968081&doi=10.1145%2f3448016.3457569&partnerID=40&md5=864feff0a1e14f2bf9cf491edca6626b"},
{"id":"Zhu20215434","abstract":"This paper considers the massive connectivity problem in an asynchronous grant-free random access system, where a huge number of devices sporadically transmit data to a base station (BS) with imperfect synchronization. The goal is to design algorithms for joint user activity detection, delay detection, and channel estimation. By exploiting the sparsity on both user activity and delays, we formulate a hierarchical sparse signal recovery problem in both the single-antenna and the multiple-antenna scenarios. While traditional compressed sensing algorithms can be applied to these problems, they suffer high computational complexity and often require the perfect statistical information of channel and devices. This paper solves these problems by designing the Learned Approximate Message Passing (LAMP) network, which belongs to model-driven deep learning approaches and ensures efficient performance without tremendous training data. Particularly, in the multiple-antenna scenario, we design three different LAMP structures, namely, distributed, centralized and hybrid ones, to balance the performance and complexity. Simulation results demonstrate that the proposed LAMP networks can significantly outperform the conventional AMP method thanks to their ability of parameter learning. It is also shown that LAMP has robust performance to the maximal delay spread of the asynchronous users. © 2002-2012 IEEE.","author":[{"family":"Zhu","given":"W."},{"family":"Tao","given":"M."},{"family":"Yuan","given":"X."},{"family":"Guan","given":"Y."}],"citation-key":"Zhu20215434","container-title":"IEEE Transactions on Wireless Communications","DOI":"10.1109/TWC.2021.3067903","ISSN":"15361276","issue":"8","issued":{"date-parts":[[2021]]},"page":"5434-5448","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"Deep-learned approximate message passing for asynchronous massive connectivity","type":"article-journal","URL":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103794150&doi=10.1109%2fTWC.2021.3067903&partnerID=40&md5=df1c1dc4bb892bd729ac54f719b96b55","volume":"20"},
{"id":"zhuMiningAPIUsage2014","abstract":"Lack of effective usage examples in API documents has been proven to be a great obstacle to API learning. To deal with this issue, several approaches have been proposed to automatically extract usage examples from client code or related web pages, which are unfortunately not available for newly released API libraries. In this paper, we propose a novel approach to mining API usage examples from test code. Although test code can be a good source of usage examples, the issue of multiple test scenarios might lead to repetitive and interdependent API usages in a test method, which make it complicated and difficult to extract API usage examples. To address this issue, we study the JUnit test code and summarize a set of test code patterns. We employ a code pattern based heuristic slicing approach to separate test scenarios into code examples. Then we cluster the similar usage examples for recommendation. An evaluation on four open source software libraries demonstrates that the accuracy of our approach is much higher than the state-of-art approach eXoaDoc on test code. Furthermore, we have developed an Eclipse plugin tool UsETeC.","accessed":{"date-parts":[[2018,4,12]]},"author":[{"family":"Zhu","given":"Zixiao"},{"family":"Zou","given":"Yanzhen"},{"family":"Xie","given":"Bing"},{"family":"Jin","given":"Yong"},{"family":"Lin","given":"Zeqi"},{"family":"Zhang","given":"Lu"}],"citation-key":"zhuMiningAPIUsage2014","DOI":"10.1109/ICSME.2014.52","ISBN":"978-1-4799-6146-7","issued":{"date-parts":[[2014,9]]},"page":"301-310","publisher":"IEEE","source":"CrossRef","title":"Mining API Usage Examples from Test Code","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/6976096/"},
{"id":"zolotasRESTsecLowcodePlatform2018","abstract":"In the modern business world it is increasingly often that Enterprises opt to bring their business model online, in their effort to reach out to more end users and increase their customer base. While transitioning to the new model, enterprises consider securing their data of pivotal importance. In fact, many efforts have been introduced to automate this ‘webification’ process; however, they all fall short in some aspect: a) they either generate only the security infrastructure, assigning implementation to the developers, b) they embed mainstream, less powerful authorisation schemes, or c) they disregard the merits of the dominating REST architecture and adopt less suitable approaches. In this paper we present RESTsec, a Low-Code platform that supports rapid security requirements modelling for Enterprise Services, abiding by the state of the art ABAC authorisation scheme. RESTsec enables the developer to seamlessly embed the desired access control policy and generate the service, the security infrastructure and the code. Evaluation shows that our approach is valid and can help developers deliver secure by design enterprise services in a rapid and automated manner.","accessed":{"date-parts":[[2019,6,13]]},"author":[{"family":"Zolotas","given":"Christoforos"},{"family":"Chatzidimitriou","given":"Kyriakos C."},{"family":"Symeonidis","given":"Andreas L."}],"citation-key":"zolotasRESTsecLowcodePlatform2018","container-title":"Enterprise Information Systems","container-title-short":"Enterprise Information Systems","DOI":"10.1080/17517575.2018.1462403","ISSN":"1751-7575, 1751-7583","issue":"8-9","issued":{"date-parts":[[2018,10,21]]},"page":"1007-1033","source":"DOI.org (Crossref)","title":"RESTsec: a low-code platform for generating secure by design enterprise services","title-short":"RESTsec","type":"article-journal","URL":"https://www.tandfonline.com/doi/full/10.1080/17517575.2018.1462403","volume":"12"},
{"id":"zolotasTypeInferenceFlexible2018","accessed":{"date-parts":[[2018,1,29]]},"author":[{"family":"Zolotas","given":"Athanasios"},{"family":"Matragkas","given":"Nicholas"},{"family":"Devlin","given":"Sam"},{"family":"Kolovos","given":"Dimitrios S."},{"family":"Paige","given":"Richard F."}],"citation-key":"zolotasTypeInferenceFlexible2018","container-title":"Software & Systems Modeling","DOI":"10.1007/s10270-018-0658-5","ISSN":"1619-1366, 1619-1374","issued":{"date-parts":[[2018,1,23]]},"source":"CrossRef","title":"Type inference in flexible model-driven engineering using classification algorithms","type":"article-journal","URL":"http://link.springer.com/10.1007/s10270-018-0658-5"},
{"id":"ZoomingPanningHTML5","abstract":"Often Zooming and Panning are required Chart Interactions when plotting a chart with large data. By making zoomEnabled to true, you can zoom into area of interest.","accessed":{"date-parts":[[2015,4,2]]},"citation-key":"ZoomingPanningHTML5","container-title":"CanvasJS","title":"Zooming & Panning in HTML5 & JavaScript Chart","type":"webpage","URL":"http://canvasjs.com/docs/charts/basics-of-creating-html5-chart/zooming-panning/"}
]
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