@misc{01YourFirst, title = {01\_{{Your}} First Component with the {{IDE}} |}, url = {http://self-star.imag.fr/?page_id=196}, urldate = {2016-12-02} } @misc{02UsingComponent, title = {02\_{{Using}} Component Properties to Configure Instances |}, url = {http://self-star.imag.fr/?page_id=198}, urldate = {2016-12-02} } @misc{03ProvidingUsing, title = {03\_{{Providing}} and Using Services |}, url = {http://self-star.imag.fr/?page_id=204}, urldate = {2016-12-02} } @misc{04BuildingApplication, title = {04\_{{Building}} an Application from Multiple Bundles |}, url = {http://self-star.imag.fr/?page_id=206}, urldate = {2016-12-02} } @misc{05ICasaICasa, title = {05\_{{iCasa}} {{iCasa Architecture}}}, url = {http://adeleresearchgroup.github.io/iCasa/snapshot/architecture.html}, urldate = {2016-12-02} } @misc{06TutorialFollow, title = {06\_{{Tutorial Follow}} Me}, url = {http://self-star.imag.fr/?page_id=61}, urldate = {2016-12-02} } @inproceedings{10.1007/11871637_49, title = {Naive Bayes for Text Classification with Unbalanced Classes}, booktitle = {Knowledge Discovery in Databases: {{PKDD}} 2006}, author = {Frank, Eibe and Bouckaert, Remco R.}, editor = {F{\"u}rnkranz, Johannes and Scheffer, Tobias and Spiliopoulou, Myra}, year = {2006}, pages = {503--510}, publisher = {{Springer Berlin Heidelberg}}, address = {{Berlin, Heidelberg}}, 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.}, isbn = {978-3-540-46048-0} } @inproceedings{10.1007/978-3-030-20948-3_19, title = {Building Information Systems Using Collaborative-Filtering Recommendation Techniques}, booktitle = {Advanced Information Systems Engineering Workshops}, author = {Nguyen, Phuong T. and Di Rocco, Juri and Di Ruscio, Davide}, editor = {Proper, Henderik A. and Stirna, Janis}, year = {2019}, pages = {214--226}, publisher = {{Springer International Publishing}}, address = {{Cham}}, 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.}, isbn = {978-3-030-20948-3}, keywords = {Book recommendation,Collaborative-filtering,IoT} } @inproceedings{10.1007/978-3-319-19069-3_17, title = {Detecting Complex Changes during Metamodel Evolution}, booktitle = {Advanced Information Systems Engineering}, author = {Khelladi, Djamel Eddine and Hebig, Regina and Bendraou, Reda and Robin, Jacques and Gervais, Marie-Pierre}, editor = {Zdravkovic, Jelena and Kirikova, Marite and Johannesson, Paul}, year = {2015}, pages = {263--278}, publisher = {{Springer International Publishing}}, address = {{Cham}}, isbn = {978-3-319-19069-3} } @inproceedings{10.1007/978-3-319-60438-1_47, ids = {nguyen_modification_2017}, title = {Modification to K-Medoids and {{CLARA}} for Effective Document Clustering}, booktitle = {Foundations of Intelligent Systems}, author = {Nguyen, Phuong T. and Eckert, Kai and Ragone, Azzurra and Di Noia, Tommaso}, editor = {Kryszkiewicz, Marzena and Appice, Annalisa and {\'S}l{\k{e}}zak, Dominik and Rybinski, Henryk and Skowron, Andrzej and Ra{\'s}, Zbigniew W.}, year = {2017}, pages = {481--491}, publisher = {{Springer International Publishing}}, address = {{Cham}}, 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.}, isbn = {978-3-319-60438-1} } @inproceedings{10.1007/978-3-319-74730-9_33, title = {Developer-Centric Knowledge Mining from Large Open-Source Software Repositories ({{CROSSMINER}})}, booktitle = {Software Technologies: {{Applications}} and Foundations}, author = {{Bagnato et. al.}, Alessandra}, year = {2018}, pages = {375--384}, publisher = {{Springer International Publishing}}, 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.}, isbn = {978-3-319-74730-9}, noaddress = {Cham} } @inproceedings{10.1007/978-3-540-30104-2_12, title = {Qualitative Analysis of User-Based and Item-Based Prediction Algorithms for Recommendation Agents}, booktitle = {Engineering {{Applications}} of {{Artificial Intelligence}}}, author = {Papagelis, Manos and Plexousakis, Dimitris}, editor = {Klusch, Matthias and Ossowski, Sascha and Kashyap, Vipul and Unland, Rainer}, year = {2005}, month = oct, volume = {18}, pages = {152--166}, publisher = {{Pergamon Press, Inc.}}, address = {{Tarrytown, NY, USA}}, url = {http://dx.doi.org/10.1016/j.engappai.2005.06.010}, 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.}, acmid = {1707132}, isbn = {978-3-540-30104-2}, issue_date = {October, 2005}, nodoi = {10.1016/j.engappai.2005.06.010}, numpages = {9}, keywords = {Collaborative filtering,Recommendation algorithms,Similarity measures} } @inproceedings{10.1007/978-3-540-30549-1_43, title = {Multinomial Naive Bayes for Text Categorization Revisited}, booktitle = {{{AI}} 2004: {{Advances}} in Artificial Intelligence}, author = {Kibriya, Ashraf M. and Frank, Eibe and Pfahringer, Bernhard and Holmes, Geoffrey}, editor = {Webb, Geoffrey I. and Yu, Xinghuo}, year = {2005}, pages = {488--499}, publisher = {{Springer Berlin Heidelberg}}, address = {{Berlin, Heidelberg}}, 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.}, isbn = {978-3-540-30549-1} } @inproceedings{10.1007/978-3-642-03013-0_15, title = {{{MAPO}}: {{Mining}} and Recommending {{API}} Usage Patterns}, booktitle = {23rd European Conference on Object-Oriented Programming}, author = {Zhong, Hao and Xie, Tao and Zhang, Lu and Pei, Jian and Mei, Hong}, year = {2009}, pages = {318--343}, publisher = {{Springer}}, address = {{Berlin, Heidelberg}}, isbn = {978-3-642-03012-3} } @inproceedings{10.1007/978-3-642-37456-2_14, title = {Density-Based Clustering Based on Hierarchical Density Estimates}, booktitle = {Advances in Knowledge Discovery and Data Mining}, author = {Campello, Ricardo J. G. B. and Moulavi, Davoud and Sander, Joerg}, editor = {Pei, Jian and Tseng, Vincent S. and Cao, Longbing and Motoda, Hiroshi and Xu, Guandong}, year = {2013}, pages = {160--172}, publisher = {{Springer Berlin Heidelberg}}, address = {{Berlin, Heidelberg}}, 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.}, isbn = {978-3-642-37456-2} } @article{10.1109/SANER.2017.7884605, title = {Detecting Similar Repositories on {{GitHub}}}, author = {Zhang, Yun and Lo, David and Kochhar, Pavneet Singh and Xia, Xin and Li, Quanlai and Sun, Jianling}, year = {2017}, journal = {2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)}, volume = {00}, pages = {13--23}, publisher = {{IEEE Computer Society}}, address = {{Los Alamitos, CA, USA}}, nodoi = {doi.ieeecomputersociety.org/10.1109/SANER.2017.7884605} } @inproceedings{11697_100182, ids = {diruscioSupportingVariabilityExploration2016,ruscioSupportingVariabilityExploration2016,ruscioSupportingVariabilityExploration2016a}, title = {Supporting Variability Exploration and Resolution during Model Migration}, booktitle = {Modelling Foundations and Applications, 12th European Conference, {{ECMFA}} 2016, Held as Part of {{STAF}} 2016, Vienna, Austria, July 6-7, 2016, Proceedings}, author = {Di Ruscio, Davide and Etzlstorfer, Juergen and Iovino, Ludovico and Pierantonio, Alfonso and Schwinger, Wieland}, year = {2016}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {9764}, pages = {231--246}, publisher = {{Springer Verlag}}, doi = {10.1007/978-3-319-42061-5_15}, isbn = {978-3-319-42060-8}, keywords = {Computer Science (all),Theoretical Computer Science} } @inproceedings{11697_100188, ids = {diroccoSupportingUsersManage2015,roccoSupportingUsersManage2015,roccoSupportingUsersManage2015a}, title = {Supporting Users to Manage Breaking and Unresolvable Changes in Coupled Evolution}, booktitle = {{{DSM}} 2015 - Proceedings of the Workshop on Domain-Specific Modeling}, author = {Di Rocco, Juri and Di Ruscio, Davide and Pierantonio, Alfonso and Iovino, Ludovico}, year = {2015}, pages = {47--54}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/2846696.2846703}, isbn = {978-1-4503-3903-2}, keywords = {Breaking unresolvable changes,Code generators,Coupled evolution,MDE,Modeling and Simulation} } @article{11697_100190, title = {Collaborative Repositories in Model-Driven Engineering}, author = {Di Rocco, Juri and Di Ruscio, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2015}, journal = {IEEE SOFTWARE}, volume = {32}, pages = {28--34}, doi = {10.1109/MS.2015.61}, keywords = {MDE,MDEForge,model repositories,model-driven engineering,Software,software development,software engineering} } @article{11697_10461, title = {Managing the Evolution of {{FOSS}} Systems}, author = {Ruscio, Davide Di and {Pelliccione} and {P} and Alfonso, Pierantonio}, year = {2012}, journal = {ERCIM NEWS}, volume = {88}, pages = {319--342}, url = {http://ercim-news.ercim.eu/en88/special/managing-the-evolution-of-foss-systems} } @article{11697_106776, title = {Adopting {{MDE}} for Specifying and Executing Civilian Missions of Mobile Multi-Robot Systems}, author = {{Ciccozzi} and {Federico} and Davide, Di Ruscio and Malavolta, Ivano and Pelliccione, Patrizio}, year = {2016}, journal = {IEEE ACCESS}, volume = {4}, pages = {6451--6466}, doi = {10.1109/ACCESS.2016.2613642}, 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.} } @article{11697_106777, title = {A Model-Driven Approach to Detect Faults in {{FOSS}} Systems}, author = {Di Ruscio, Davide and Pelliccione, Patrizio}, year = {2015}, journal = {JOURNAL OF SOFTWARE}, volume = {27}, pages = {294--318}, doi = {10.1002/smr.1716}, keywords = {evolution of FOSS systems,fault prevention,linux distributions,model-driven engineering,Software} } @article{11697_106779, ids = {ciccozziModeldrivenEngineeringMissionCritical2017}, title = {Model-Driven Engineering for Mission-Critical {{IoT}} Systems}, author = {Ciccozzi, Federico and Crnkovic, Ivica and DI RUSCIO, Davide and Malavolta, Ivano and Pelliccione, Patrizio and Spalazzese, Romina}, year = {2017}, journal = {IEEE SOFTWARE}, volume = {34}, pages = {46--53}, doi = {10.1109/MS.2017.1}, keywords = {internet of things,IoT,Mission critical systems,mission-critical systems,model-driven engineering,software development,software engineering} } @inproceedings{11697_107894, ids = {inproceedings}, title = {Automated Chaining of Model Transformations with Incompatible Metamodels}, booktitle = {17th International Conference, {{MODELS}} 2014}, author = {Basciani, Francesco and Di Ruscio, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2014}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {8767}, pages = {602--618}, publisher = {{Springer Verlag}}, url = {http://springerlink.com/content/0302-9743/copyright/2005/}, keywords = {Computer Science (all),Theoretical Computer Science} } @inproceedings{11697_107911, title = {Mining Metrics for Understanding Metamodel Characteristics}, booktitle = {6th International Workshop on Modeling in Software Engineering, {{MiSE}} 2014 - Proceedings}, author = {Di Rocco, Juri and Di Ruscio, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2014}, pages = {55--60}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/2593770.2593774}, isbn = {978-1-4503-2849-4}, keywords = {Computer Science Applications1707 Computer Vision and Pattern Recognition,Electrical and Electronic Engineering,Metamodel metrics,Metamodeling,Model driven engineering,Software} } @inproceedings{11697_107914, title = {Mining Correlations of {{ATL}} Model Transformation and Metamodel Metrics}, booktitle = {Proceedings - 7th International Workshop on Modeling in Software Engineering, {{MiSE}} 2015}, author = {Di Rocco, Juri and Di Ruscio, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2015}, pages = {54--59}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MiSE.2015.17}, isbn = {978-1-4799-1934-5}, keywords = {Modeling and Simulation,Software} } @inproceedings{11697_107917, ids = {diroccoTraceabilityVisualizationMetamodel2013}, title = {Traceability Visualization in Metamodel Change Impact Detection}, booktitle = {Proceedings of the 2nd Workshop on Graphical Modeling Language Development, {{GMLD}} 2013 - in Conjunction with European Conference on Modelling Foundations and Applications, {{ECMFA}} 2013}, author = {Di Rocco, Juri and Di Ruscio, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2013}, pages = {51--62}, doi = {10.1145/2489820.2489824}, isbn = {978-1-4503-2044-3}, keywords = {Computer Science Applications1707 Computer Vision and Pattern Recognition,Modeling and Simulation,Software} } @article{11697_110553, title = {A Tool-Supported Methodology for Validation and Refinement of Early-Stage Domain Models}, author = {Autili, Marco and Bertolino, Antonia and DE ANGELIS, Guglielmo and DI RUSCIO, Davide and Di Sandro, Alessio}, year = {2016}, journal = {IEEE TRANSACTIONS ON SOFTWARE ENGINEERING}, volume = {42}, pages = {2--25}, doi = {10.1109/TSE.2015.2449319}, keywords = {Domain Modeling,Early Stage Model,Model Driven Engineering,Model Refinement,Model Validation,Natural Language Questionnaires,Semantic Model Quality,Software} } @inproceedings{11697_110621, title = {{{CHOReOSynt}}: {{Enforcing}} Choreography Realizability in the Future Internet}, booktitle = {Proceedings of the {{ACM SIGSOFT}} Symposium on the Foundations of Software Engineering}, author = {Autili, Marco and Di Ruscio, Davide and Di Salle, Amleto and Perucci, Alexander}, year = {2014}, pages = {723--726}, publisher = {{Association for Computing Machinery}}, doi = {10.1145/2635868.2661667}, isbn = {978-1-4503-3056-5}, keywords = {Choreography synthesis,Distributed coordination,Software} } @article{11697_110934, ids = {diruscioSpecialIssueFlexible2017,ruscioSpecialIssueFlexible2017}, title = {Special Issue on Flexible Model Driven Engineering}, author = {Di Ruscio, Davide and {de Lara}, Juan and Pierantonio, Alfonso}, year = {2017}, journal = {COMPUTER LANGUAGES, SYSTEMS \& STRUCTURES}, doi = {10.1016/j.cl.2016.12.003}, keywords = {Computer Networks and Communications,Software} } @inproceedings{11697_111412, ids = {diruscioSupportingCustomQuality2016,ruscioSupportingCustomQuality2016,ruscioSupportingCustomQuality2016a}, title = {Supporting Custom Quality Models to Analyse and Compare Open-Source Software}, booktitle = {Proceedings - 2016 10th International Conference on the Quality of Information and Communications Technology, {{QUATIC}} 2016}, author = {Di Ruscio, Davide and Kolovos, Dimitrios S. and Korkontzelos, Yannis and Matragkas, Nicholas and Vinju, Jurgen}, year = {2016}, pages = {94--99}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/QUATIC.2016.026}, isbn = {978-1-5090-3581-6}, keywords = {Computer Networks and Communications,Information Systems,Management of Technology and Innovation,Reliability and Quality,Risk,Safety,Software} } @inproceedings{11697_111413, title = {A Customizable Approach for the Automated Quality Assessment of Modelling Artifacts}, booktitle = {Proceedings - 2016 10th International Conference on the Quality of Information and Communications Technology, {{QUATIC}} 2016}, author = {Basciani, Francesco and Di Rocco, Juri and Di Ruscio, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2016}, pages = {88--93}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/QUATIC.2016.025}, isbn = {978-1-5090-3581-6}, keywords = {Artefact Quality,Computer Networks and Communications,Information Systems,Management of Technology and Innovation,MDE,Model Quality,Reliability and Quality,Risk,Safety,Software} } @inproceedings{11697_111414, ids = {atleeMessageWorkshopChairs2016}, title = {Message from the Workshop Chairs}, booktitle = {Proceedings - 8th International Workshop on Modeling in Software Engineering, {{MiSE}} 2016}, author = {Atlee, Joanne and Baillargeon, Robert and Di Ruscio, Davide and Rumpe, Bernhard}, year = {2016}, publisher = {{Association for Computing Machinery, Inc}}, isbn = {978-1-4503-4164-6}, keywords = {Modeling and Simulation,Software} } @inproceedings{11697_111418, title = {{{CEUR}} Workshop Proceedings: {{Preface}}}, booktitle = {{{CEUR}} Workshop Proceedings}, author = {Di Ruscio, Davide and De Lara, Juan and Pierantonio, Alfonso}, year = {2016}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1694}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{11697_111420, ids = {osmanSATToSE2016Postproceedings2016}, title = {{{SATToSE}} 2016: {{The}} Post-Proceedings Editorial}, booktitle = {{{CEUR}} Workshop Proceedings}, author = {Osman, Haidar and Di Ruscio, Davide and Zaytsev, Vadim and Lungu, Mircea and Bagge, Anya Helene}, year = {2016}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1791}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{11697_111424, title = {A Tool for Clustering Metamodel Repositories}, booktitle = {Proceedings of the {{MoDELS}} 2015 Demo and Poster Session Co-Located with {{ACM}}/{{IEEE}} 18th International Conference on Model Driven Engineering Languages and Systems ({{MoDELS}} 2015), Ottawa, Canada, September 27, 2015}, author = {Basciani, Francesco and DI RUSCIO, Davide and DI ROCCO, Juri and Pierantonio, Alfonso and Iovino, Ludovico}, year = {2015}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1554}, pages = {1--4}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @article{11697_111425, title = {Guest Editors' Introduction to the Fifth Issue of {{Experimental Software}} and {{Toolkits}} ({{EST}}): {{A}} Special Issue on {{Academics Modelling}} with {{Eclipse}} ({{ACME2012}})}, author = {Van Den Brand, Mark and Di Ruscio, Davide and Kolovos, Dimitrios S. and Rose, Louis M.}, year = {2015}, journal = {SCIENCE OF COMPUTER PROGRAMMING}, volume = {98}, pages = {1--2}, doi = {10.1016/j.scico.2014.11.001}, keywords = {Software} } @inproceedings{11697_111428, title = {Model Repositories: {{Will}} They Become Reality? {{A}} Position Statement}, booktitle = {Proceedings of the 3rd International Workshop on Model-Driven Engineering on and for the Cloud 18th International Conference on Model Driven Engineering Languages and Systems ({{MoDELS}} 2015), Ottawa, Canada, September 29, 2015}, author = {Basciani, Francesco and DI ROCCO, Juri and DI RUSCIO, Davide and Pierantonio, Alfonso and Iovino, Ludovico}, year = {2015}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1563}, pages = {37--42}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{11697_111431, title = {{{SATToSE}} 2015: {{The}} Post-Proceedings Editorial}, booktitle = {{{CEUR}} Workshop Proceedings}, author = {Osman, Haidar and Di Ruscio, Davide and Zaytsev, Vadim and Lungu, Mircea and Bagge, Anya Helene}, year = {2015}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1820}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{11697_111432, title = {{{OSSMETER}}: {{A}} Software Measurement Platform for Automatically Analysing Open Source Software Projects}, booktitle = {2015 10th Joint Meeting of the European Software Engineering Conference and the {{ACM SIGSOFT}} Symposium on the Foundations of Software Engineering, {{ESEC}}/{{FSE}} 2015 - Proceedings}, author = {Di Ruscio, Davide and Kolovos, Dimitrios S. and Korkontzelos, Ioannis and Matragkas, Nicholas and Vinju, Jurgen J.}, year = {2015}, pages = {970--973}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/2786805.2803186}, isbn = {978-1-4503-3675-8}, keywords = {Open source software,Software,Source code analysis,Text mining techniques} } @inproceedings{11697_111433, title = {{{OSSMETER}}: {{Automated}} Measurement and Analysis of Open Source Software}, booktitle = {{{CEUR}} Workshop Proceedings}, author = {Almeida, Bruno and Ananiadou, Sophia and Bagnato, Alessandra and Berreteaga Barbero, Alberto and Di Rocco, Juri and Di Ruscio, Davide and Kolovos, Dimitrios S. and Korkontzelos, Ioannis and Hansen, Scott and Mal{\'o}, Pedro and Matragkas, Nicholas and Paige, Richard F. and Vinju, Jurgen}, year = {2015}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1400}, pages = {36--43}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{11697_111437, ids = {diruscioRolePartsSystem2014,ruscioRolePartsSystem2014,ruscioRolePartsSystem2014a}, title = {The Role of Parts in the System Behaviour}, booktitle = {Software Engineering for Resilient Systems - 6th International Workshop, {{SERENE}} 2014, Budapest, Hungary, October 15-16, 2014. {{Proceedings}}}, author = {DI RUSCIO, Davide and Malavolta, Ivano and Pelliccione, Patrizio}, year = {2014}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {8785}, pages = {24--39}, publisher = {{Springer Verlag}}, url = {http://springerlink.com/content/0302-9743/copyright/2005/}, isbn = {978-3-319-12240-3}, keywords = {Computer Science (all),Theoretical Computer Science} } @inproceedings{11697_111440, title = {Dealing with the Coupled Evolution of Metamodels and Model-to-Text Transformations}, booktitle = {Proceedings of the Workshop on Models and Evolution Co-Located with {{ACM}}/{{IEEE}} 17th International Conference on Model Driven Engineering Languages and Systems ({{MoDELS}} 2014), Valencia, Spain, Sept 28, 2014}, author = {DI ROCCO, Juri and DI RUSCIO, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2014}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1331}, pages = {22--31}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{11697_111441, title = {Describing the Correlations between Metamodels and Transformations Aspects}, booktitle = {Post-Proceedings of the Seventh Seminar on Advanced Techniques and Tools for Software Evolution, {{SATToSE}} 2014, {{L}}'{{Aquila}}, Italy, 9-11 July 2014}, author = {DI ROCCO, Juri and DI RUSCIO, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2014}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1354}, pages = {90--101}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all),Metamodel metrics,Metamodeling,Model driven engineering,Transformation metrics} } @inproceedings{11697_111450, ids = {cicchettiPropagationChangesModel2006,cicchettiPropagationChangesModel2006a,cicchettiPropagationChangesModel2006b}, title = {Towards Propagation of Changes by Model Approximations}, booktitle = {Proceedings - 2006 10th {{IEEE}} International Enterprise Distributed Object Computing Conference Workshops, {{EDOCW2006}}}, author = {Cicchetti, Antonio and Di Ruscio, Davide and Eramo, Romina}, year = {2006}, pages = {24--24}, doi = {10.1109/EDOCW.2006.68}, isbn = {0-7695-2558-X}, keywords = {Computer Science Applications1707 Computer Vision and Pattern Recognition,Software} } @inproceedings{11697_111452, ids = {cicchettiSoftwareCustomizationModel2007,cicchettiSoftwareCustomizationModel2007a,cicchettiSoftwareCustomizationModel2007b}, title = {Software Customization in Model Driven Development of Web Applications}, booktitle = {Model Transformation Track of the 22th {{ACM}} Symposium on Applied Computing ({{SAC}} 2007)}, author = {Cicchetti, Antonio and Di Ruscio, Davide and Di Salle, Amleto}, year = {2007}, pages = {1025--1030}, doi = {10.1145/1244002.1244224}, keywords = {Model driven development,Model transformation,Model-view-controller,Software,Software customization,Web application} } @article{11697_111453, title = {Decoupling Web Application Concerns through Weaving Operations}, author = {Cicchetti, Antonio and Di Ruscio, Davide}, year = {2008}, journal = {SCIENCE OF COMPUTER PROGRAMMING}, volume = {70}, pages = {62--86}, doi = {10.1016/j.scico.2007.10.002}, keywords = {Abstract state machines,Metamodel specification,Model transformations,Model weaving,Software,Web applications} } @inproceedings{11697_111454, title = {Models of {{OSS}} Project Meta-Information: {{A}} Dataset of Three Forges}, booktitle = {11th Working Conference on Mining Software Repositories, {{MSR}} 2014 - Proceedings}, author = {Williams, James R. and Di Ruscio, Davide and Matragkas, Nicholas and Di Rocco, Juri and Kolovos, Dimitrios S.}, year = {2014}, pages = {408--411}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/2597073.2597132}, isbn = {978-1-4503-2863-0}, keywords = {Computer Science Applications1707 Computer Vision and Pattern Recognition,Data mining,Software} } @inproceedings{11697_111455, title = {A Family of Domain-Specific Languages for Specifying Civilian Missions of Multi-Robot Systems}, booktitle = {Proceedings of the 1st International Workshop on Model-Driven Robot Software Engineering Co-Located with International Conference on Software Technologies: {{Applications}} and Foundations ({{STAF}} 2014)}, author = {DI RUSCIO, Davide and Malavolta, Ivano and Pelliccione, Patrizio}, year = {2014}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1319}, pages = {16--29}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{11697_111464, ids = {delaraReusingModelTransformations2017,laraReusingModelTransformations2017}, title = {Reusing Model Transformations through Typing Requirements Models}, booktitle = {Fundamental Approaches to Software Engineering - 20th International Conference, {{FASE}} 2017, Held as Part of the European Joint Conferences on Theory and Practice of Software, {{ETAPS}} 2017, Uppsala, Sweden, April 22-29, 2017, Proceedings.}, author = {{de Lara}, Juan and DI ROCCO, Juri and DI RUSCIO, Davide and Guerra, Esther and Iovino, Ludovico and Pierantonio, Alfonso and Cuadrado, Jes{\'u}s S{\'a}nchez}, year = {2017}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {10202}, pages = {264--282}, publisher = {{Springer Verlag}}, doi = {10.1007/978-3-662-54494-5_15}, isbn = {978-3-662-54494-5}, keywords = {Computer Science (all),Theoretical Computer Science} } @inproceedings{11697_111465, title = {Qualifying Chains of Transformation with Coverage Based Evaluation Criteria}, booktitle = {Post-Proceedings of the Seventh Seminar on Advanced Techniques and Tools for Software Evolution, {{SATToSE}} 2014, {{L}}'{{Aquila}}, Italy, 9-11 July 2014}, author = {Basciani, Francesco and DI ROCCO, Juri and DI RUSCIO, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2014}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1354}, pages = {79--89}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @article{11697_119338, title = {Collaborative Model-Driven Software Engineering: A Classification Framework and a Research Map}, author = {Franzago, MIRCO GIOVANNI UMBERTO and DI RUSCIO, Davide and Malavolta, Ivano and Muccini, Henry}, year = {2017}, journal = {IEEE TRANSACTIONS ON SOFTWARE ENGINEERING}, pages = {1--1}, doi = {10.1109/TSE.2017.2755039}, keywords = {Collaborative Software Engineering,model-driven engineering,Model-Driven Engineering,Systematic Mapping study} } @inproceedings{11697_119347, title = {Envisioning the Future of Collaborative Model-Driven Software Engineering}, booktitle = {{{IEEE}}/{{ACM}} 39th International Conference on Software Engineering Companion, {{ICSE-C}} 2017}, author = {DI RUSCIO, Davide and Franzago, MIRCO GIOVANNI UMBERTO and Malavolta, Ivano and Muccini, Henry}, year = {2017}, pages = {219--221}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ICSE-C.2017.143}, isbn = {978-1-5386-1589-8}, keywords = {Collaborative MDSE,Collaborative Software Engineering,model-driven engineering,Model-Driven Engineering,Reliability and Quality,Risk,Safety,Software} } @incollection{11697_121392, ids = {chechikMessageWorkshopChairs2017}, title = {Message from the Workshop Chairs {{MiSE}} 2017}, booktitle = {Proceedings - 2017 {{IEEE}}/{{ACM}} 9th International Workshop on Modelling in Software Engineering, {{MiSE}} 2017}, author = {Chechik, Marsha and Di Ruscio, Davide and Rumpe, Bernhard}, year = {2017}, pages = {vii-vii}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MiSE.2017.11}, isbn = {978-1-5386-0426-7}, keywords = {Modeling and Simulation,Software} } @incollection{11697_121393, ids = {diruscio9thWorkshopModelling2017,ruscio9thWorkshopModelling2017}, title = {9th Workshop on Modelling in Software Engineering ({{MiSE}} 2017)}, booktitle = {9th {{IEEE}}/{{ACM International Workshop}} on {{Modelling}} in {{Software Engineering}}, {{MiSE}}@{{ICSE}} 2017, {{Buenos Aires}}, {{Argentina}}, {{May}} 21-22, 2017}, author = {Di Ruscio, Davide and Chechik, Marsha and Rumpe, Bernhard}, year = {2017}, pages = {1--1}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MiSE.2017.15}, isbn = {978-1-5386-0426-7}, keywords = {Modeling and Simulation,Software} } @inproceedings{11697_121394, title = {A Feature-Based Approach for Variability Exploration and Resolution in Model Transformation Migration}, booktitle = {Modelling Foundations and Applications - 13th European Conference, {{ECMFA}} 2017, Held as Part of {{STAF}} 2017, Marburg, Germany, July 19-20, 2017, Proceedings}, author = {Di Ruscio, Davide and Etzlstorfer, Juergen and Iovino, Ludovico and Pierantonio, Alfonso and Schwinger, Wieland}, year = {2017}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {10376}, pages = {71--89}, publisher = {{Springer Verlag}}, doi = {10.1007/978-3-319-61482-3_5}, isbn = {978-3-319-61481-6}, keywords = {Computer Science (all),Theoretical Computer Science} } @incollection{11697_121395, title = {Proceedings of the 3rd {{Workshop}} on {{Scalable Model Driven Engineering}} Part of the {{Software Technologies}}: {{Applications}} and {{Foundations}} ({{STAF}} 2015) Federation of Conferences}, booktitle = {{{CEUR}} Workshop Proceedings}, author = {Kolovos, Dimitris and Di Ruscio, Davide and Matragkas, Nicholas and Cuadrado, Jesus Sanchez and Rath, Istvan and Tisi, Massimo}, year = {2015}, volume = {1406}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{11697_121396, title = {Engineering the Software of Robotic Systems}, booktitle = {Proceedings - 2017 {{IEEE}}/{{ACM}} 39th International Conference on Software Engineering Companion, {{ICSE-C}} 2017}, author = {Ciccozzi, Federico and Di Ruscio, Davide and Malavolta, Ivano and Pelliccione, Patrizio and Tumova, Jana}, year = {2017}, pages = {507--508}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ICSE-C.2017.167}, isbn = {978-1-5386-1589-8}, keywords = {model-driven engineering,Model-Driven Engineering,Reliability and Quality,Risk,Robotics,Safety,Software,software engineering,Software Engineering} } @inproceedings{11697_121397, title = {Proceedings of the 2nd Workshop on Scalability in Model Driven Engineering Co-Located with the Software Technologies: {{Applications}} and Foundations Conference, {{BigMDE}}@{{STAF2014}}}, booktitle = {{{CEUR}} Workshop Proceedings}, author = {Di Ruscio, Davide and De Lara, Juan and Kolovos, Dimitris and Matragkas, Nicholas and Rath, Istvan and Tisi, Massimo}, year = {2014}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1206}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{11697_121399, title = {{{SATToSE}} 2014: {{The}} Post-Proceedings Editorial}, booktitle = {{{CEUR}} Workshop Proceedings}, author = {Di Ruscio, Davide and Zaytsev, Vadim}, year = {2014}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1354}, pages = {1--5}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{11697_121401, ids = {diruscioScalabilityModelDriven2013,ruscioScalabilityModelDriven2013,ruscioScalabilityModelDriven2013a}, title = {Scalability in Model Driven Engineering - {{BigMDE}}'13 Workshop Summary}, booktitle = {{{ACM}} International Conference Proceeding Series}, author = {Di Ruscio, Davide and Kolovos, Dimitrios and Matragkas, Nicholas}, year = {2013}, pages = {1--2}, doi = {10.1145/2487766.2487767}, isbn = {978-1-4503-2165-5}, keywords = {1707,BigMDE,Computer Networks and Communications,Human-Computer Interaction,MDE,Scalability,Software} } @inproceedings{11697_121402, title = {{{ACadeMics}} Tooling with {{Eclipse}}: {{ACME}}'13 Workshop Summmary}, booktitle = {{{ACadeMics}} Tooling with Eclipse, {{ACME}} 2013 - a Joint {{ECMFA}}/{{ECSA}}/{{ECOOP}} Workshop}, author = {Di Ruscio, Davide and Kolovos, Dimitrios and Rose, Louis and {Al-Hilank}, Samir}, year = {2013}, pages = {1--2}, doi = {10.1145/2491279.2491280}, isbn = {978-1-4503-2036-8}, keywords = {Software} } @inproceedings{11697_121403, title = {{{ACM}} Student Research Competition at {{MoDELS}} 2013}, booktitle = {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)}, author = {Di Ruscio, Davide and Jackson, Ethan}, year = {2013}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1115}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{11697_121404, ids = {diruscioSummaryExtremeModeling2012}, title = {Summary of the Extreme Modeling Workshop ({{XM}}'12)}, booktitle = {2012 Extreme Modeling Workshop, {{XM}} 2012 - Post-Proceedings, Satellite Event of the {{IEEE}}/{{ACM}} 15th International Conference on Model Driven Engineering Languages and Systems, {{MODELS}} 2012}, author = {Di Ruscio, Davide and Pierantonio, Alfonso and De Lara, Juan}, year = {2012}, pages = {1--2}, doi = {10.1145/2467307.2467308}, isbn = {978-1-4503-1804-4}, keywords = {Information Systems,Modeling and Simulation,Software} } @incollection{11697_121405, title = {Proceedings of the 2nd International Workshop on Model Comparison in Practice}, booktitle = {{{ACM}} International Conference Proceeding Series}, author = {Di Ruscio, Davide and Kolovos, Dimitris}, year = {2011}, isbn = {978-1-4503-0668-3}, keywords = {1707,Computer Networks and Communications,Human-Computer Interaction,Software} } @incollection{11697_121406, title = {Proceedings of the 1st International Workshop on Model Comparison in Practice}, booktitle = {{{ACM}} International Conference Proceeding Series}, author = {Di Ruscio, Davide and Kolovos, Dimitris}, year = {2010}, isbn = {978-1-60558-960-2}, keywords = {1707,Computer Networks and Communications,Human-Computer Interaction,Software} } @article{11697_121408, ids = {chechikReport9thWorkshop2017,chechikReport9thWorkshop2017a,chechikReport9thWorkshop2017b}, title = {Report from the 9th Workshop on Modelling in Software {{Engineering}}({{MiSE}} 2017)}, author = {Chechik, Marsha and DI RUSCIO, Davide}, year = {2017}, journal = {SOFTWARE ENGINEERING NOTES}, volume = {42}, pages = {21--24}, doi = {10.1145/3149485.3149520} } @inproceedings{11697_121409, ids = {afzalMegaMRt2ECSEL2017,afzalMegaMRt2ECSEL2017a,afzalMegaMRt2ECSEL2017b}, title = {The {{MegaM}}@{{Rt2 ECSEL}} Project: {{MegaModelling}} at Runtime - Scalable Model-Based Framework for Continuous Development and Runtime Validation of Complex Systems}, booktitle = {Euromicro Conference on Digital System Design, {{DSD}} 2017, Vienna, Austria, August 30 - Sept. 1, 2017}, author = {Afzal, Wasif and Bruneliere, Hugo and DI RUSCIO, Davide and Sadovykh, Andrey and Mazzini, Silvia and Cariou, Eric and Truscan, Dragos and Cabot, Jordi and Field, Daniel and Pomante, Luigi and Smrz, Pavel}, year = {2017}, publisher = {{IEEE Computer Society}}, url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8048781}, isbn = {978-1-5386-2146-2} } @inproceedings{11697_121412, title = {Consistency Recovery in Interactive Modeling}, booktitle = {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.}, author = {DI ROCCO, Juri and DI RUSCIO, Davide and Heinz, Marcel and Iovino, Ludovico and Laemmel, Ralf and Pierantonio, Alfonso}, year = {2017}, volume = {2019}, pages = {116--122}, publisher = {{CEUR-WS.org}}, url = {http://ceur-ws.org/Vol-2019/exe_6.pdf} } @inproceedings{11697_121413, title = {Developer-Centric Knowledge Mining from Large Open-Source Software Repositories ({{CROSSMINER}})}, booktitle = {Software Technologies: {{Applications}} and Foundations - {{STAF}} 2017 Collocated Workshops, Marburg, Germany, July 17-21, 2017, Revised Selected Papers}, author = {Bagnato, Alessandra and Barmpis, Konstantinos and Bessis, Nik and {Adrian Cabrera-Diego}, Luis and DI ROCCO, Juri and DI RUSCIO, Davide and Gergely, Tamas and Hansen, Scott and Kolovos, Dimitris S. and Krief, Philippe and Korkontzelos, Ioannis and Lauriere, Stephane and {Manrique Lopez de la Fuente}, Jose and Malo, Pedro and Paige, Richard F. and Spinellis, Diomidis and Thomas, Cedric and Vinju, Jurgen J.}, year = {2017}, volume = {10748}, publisher = {{Springer}}, doi = {10.1007/978-3-319-74730-9}, isbn = {978-3-319-74729-3} } @article{11697_126147, title = {Automated {{Selection}} of {{Optimal Model Transformation Chains}} via {{Shortest-Path Algorithms}}}, author = {Basciani, Francesco and Demidio, Mattia and Di Ruscio, Davide and Frigioni, Daniele and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2018}, journal = {IEEE TRANSACTIONS ON SOFTWARE ENGINEERING}, volume = {46}, pages = {251--279}, url = {http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=32}, keywords = {Adaptation models,Analytical models,Bridges,Ecosystems,Graph Algorithms,Model driven engineering,Model Transformation Composition,model-driven engineering,Model-driven engineering,Shortest Paths,Software,Unified modeling language} } @book{11697_128217, title = {Protocol for a Systematic Mapping Study on Collaborative Model-Driven Software Engineering}, author = {Franzago, MIRCO GIOVANNI UMBERTO and DI RUSCIO, Davide and Malavolta, Ivano and Muccini, Henry}, year = {2016}, volume = {abs/1611.02619}, publisher = {{arXiv}}, url = {http://arxiv.org/abs/1611.02619v1}, 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.}, keywords = {Computer Science - Software Engineering} } @article{11697_128309, ids = {afzalMegaMRt2ECSEL2018}, title = {The {{MegaM}}@{{Rt2 ECSEL}} Project: {{MegaModelling}} at {{Runtime}} \textendash{} {{Scalable}} Model-Based Framework for Continuous Development and Runtime Validation of Complex Systems}, author = {Afzal, Wasif and Bruneliere, Hugo and Di Ruscio, Davide and Sadovykh, Andrey and Mazzini, Silvia and Cariou, Eric and Truscan, Dragos and Cabot, Jordi and G{\'o}mez, Abel and Gorro{\~n}ogoitia, Jes{\'u}s and Pomante, Luigi and Smrz, Pavel}, year = {2018}, journal = {MICROPROCESSORS AND MICROSYSTEMS}, volume = {61}, pages = {86--95}, doi = {10.1016/j.micpro.2018.05.010}, keywords = {Artificial Intelligence,Computer Networks and Communications,Design time,Hardware and Architecture,Megamodelling,model-driven engineering,Model-driven engineering,Runtime,Software} } @inproceedings{11697_128310, ids = {diroccoResilienceSiriusEditors2018}, title = {Resilience in Sirius Editors: {{Understanding}} the Impact of Meta-Model Changes}, booktitle = {{{CEUR}} Workshop Proceedings}, author = {Di Rocco, Juri and Di Ruscio, Davide and Narayanankutty, Hrishikesh and Pierantonio, Alfonso}, year = {2018}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {2192}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Co-evolution,Computer Science (all),model-driven engineering,Model-Driven Engineering,Sirius Editors} } @inproceedings{11697_128311, ids = {diroccoSystematicRecoveryMDE2018,roccoSystematicRecoveryMDE2018}, title = {Systematic Recovery of {{MDE}} Technology Usage}, booktitle = {Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author = {Di Rocco, Juri and Di Ruscio, Davide and H{\"a}rtel, Johannes and Iovino, Ludovico and L{\"a}mmel, Ralf and Pierantonio, Alfonso}, year = {2018}, series = {{{LECTURE NOTES IN ARTIFICIAL INTELLIGENCE}}}, volume = {10888}, pages = {110--126}, publisher = {{Springer Verlag}}, doi = {10.1007/978-3-319-93317-7_5}, isbn = {978-3-319-93316-0}, keywords = {Computer Science (all),Theoretical Computer Science} } @inproceedings{11697_128312, ids = {8498236}, title = {{{CrossSim}}: {{Exploiting}} Mutual Relationships to Detect Similar {{OSS}} Projects}, booktitle = {44th Euromicro Conference on Software Engineering and Advanced Applications}, author = {Nguyen, Phuong T. and Di Rocco, Juri and Rubei, Riccardo and Di Ruscio, Davide}, year = {2018}, pages = {388--395}, doi = {10.1109/SEAA.2018.00069}, isbn = {978-1-5386-7383-6}, keywords = {Computational modeling,Ecosystems,Libraries,Mining software repositories,Open source software,Semantics,SimRank,software similarities,Software systems} } @inproceedings{11697_128313, title = {A Tool for Automatically Selecting Optimal Model Transformation Chains}, booktitle = {Proceedings of the 21st {{ACM}}/{{IEEE}} International Conference on Model Driven Engineering Languages and Systems: {{Companion}} Proceedings}, author = {Basciani, Francesco and Di Ruscio, Davide and D'Emidio, Mattia and Frigioni, Daniele and Pierantonio, Alfonso and Iovino, Ludovico}, year = {2018}, pages = {2--6}, doi = {10.1145/3270112.3270123}, isbn = {978-1-4503-5965-8} } @article{11697_132211, ids = {8632659}, title = {Quality-Driven {{Detection}} and {{Resolution}} of {{Metamodel Smells}}}, author = {Bettini, Lorenzo and Di Ruscio, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2019}, journal = {IEEE Access}, volume = {7}, pages = {16364--16376}, issn = {2169-3536}, url = {http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639}, 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.}, langid = {english}, keywords = {\#duplicate-citation-key,Analytical models,Companies,Computer Science (all),Containers,Customer relationship management,domain-specific languages,Domain-specific languages,Edelta language,Engineering (all),formal specification,maintainability,Materials Science (all),metamodel design,metamodel smells resolution,model-driven engineering,Object oriented modeling,Quality assurance,quality-driven detection,reusability,Software,software development practice,software metrics,software quality,software quality engineering,systems analysis,understandability} } @article{11697_132481, ids = {bozhinoskiSafetyMobileRobotic2019a}, title = {Requirements, {{Human Values}}, and the {{Development Technology Landscape}}}, author = {Carver, Jeffrey C. and Minku, Leandro L. and Penzenstadler, Birgit and {undefined} and {undefined} and {undefined} and {undefined}}, year = {2017}, month = jan, journal = {IEEE Software}, volume = {151}, number = {1}, pages = {150--179}, issn = {0740-7459}, url = {http://ieeexplore.ieee.org/document/7819412/}, langid = {english}, keywords = {\#duplicate-citation-key} } @article{11697_132481, ids = {bozhinoskiSafetyMobileRobotic2019a}, title = {Safety for {{Mobile Robotic System}}: A {{Systematic Mapping Study}} from a {{Software Engineering Perspective}}}, author = {Bozhinoski, Darko and Ruscio, Davide Di and Malavolta, Ivano and Pelliccione, Patrizio and Crnkovic, Ivica}, year = {2019}, journal = {Elsevier Journal of Systems and Software (JSS)}, volume = {151}, number = {to appear}, pages = {150--179}, url = {http://people.disim.univaq.it/diruscio/pubs/JSS_ROB_2019.pdf}, 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.}, langid = {english}, keywords = {\#duplicate-citation-key,Hardware and Architecture,Information Systems,Safety for mobile robots,Software,Systematic mapping study} } @inproceedings{11697_134269, title = {Collaborative Model-Driven Software Engineering}, booktitle = {40th International Conference on Software Engineering}, author = {Di Ruscio, Davide and Franzago, Mirco and Muccini, Henry and Malavolta, Ivano}, year = {2018}, pages = {535--535}, doi = {10.1145/3180155.3182543}, isbn = {978-1-4503-5638-1} } @article{11697_134405, ids = {bascianiToolsupportedApproachAssessing2019}, title = {A Tool-Supported Approach for Assessing the Quality of Modeling Artifacts}, author = {Basciani, Francesco and Di Rocco, Juri and Di Ruscio, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2019}, month = apr, journal = {JOURNAL OF COMPUTER LANGUAGES}, volume = {51}, pages = {173--192}, issn = {2590-1184}, doi = {10.1016/j.cola.2019.02.003}, 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.}, langid = {english}, keywords = {Assessment of metamodel quality,Assessment of model transformation quality,Automated quality assessment,Computer Networks and Communications,Human-Computer Interaction,Model driven engineering,Software} } @article{11697_135647, title = {Automated Reuse of Model Transformations through Typing Requirements Models}, author = {DE LARA, Juan and Guerra, Esther and DI RUSCIO, Davide and DI ROCCO, Juri and SANCHEZ CUADRADO, Jesus and Iovino, Ludovico and Pierantonio, Alfonso}, year = {9999}, journal = {ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY} } @article{11697_136218, ids = {DDHILP19,di2019understanding,diroccoUnderstandingMDEProjects2019,diroccoUnderstandingMDEProjects2020,roccoUnderstandingMDEProjects2020}, title = {Understanding {{MDE}} Projects: Megamodels to the Rescue for Architecture Recovery}, author = {DI ROCCO, Juri and DI RUSCIO, Davide and H{\"a}rtel, Johannes and Iovino, Ludovico and L{\"a}mmel, Ralf and Pierantonio, Alfonso}, year = {2019}, journal = {SOFTWARE AND SYSTEMS MODELING}, pages = {1--23}, publisher = {{Springer}}, doi = {10.1007/s10270-019-00748-7}, keywords = {Architecture recovery,Code generator,MDE,Megamodeling,Megamodeling Reverse engineering Architecture recovery MDE Code generator Model transformation,Model transformation,Reverse engineering} } @article{11697_13879, ids = {diruscioSimulatingUpgradesComplex2014,ruscioSimulatingUpgradesComplex2014,ruscioSimulatingUpgradesComplex2014a}, title = {Simulating Upgrades of Complex Systems: {{The}} Case of {{Free}} and {{Open Source Software}}}, author = {Di Ruscio, D and Pelliccione, P}, year = {2014}, journal = {INFORMATION AND SOFTWARE TECHNOLOGY}, volume = {56}, pages = {438--462} } @article{11697_19479, title = {Managing the Evolution of Data-Intensive {{Web}} Applications by Model-Driven Techniques}, author = {Cicchetti, A and DI RUSCIO, Davide and Iovino, L and Pierantonio, Alfonso}, year = {2012}, journal = {SOFTWARE AND SYSTEMS MODELING}, volume = {12}, pages = {1--31}, doi = {10.1007/s10270-011-0193-0}, 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.} } @incollection{11697_26169, title = {From Requirements to {{Java}} Code: {{An}} Architecture-Centric Approach for Producing Quality Systems}, booktitle = {Model-Driven Software Development: {{Integrating}} Quality Assurance}, author = {{ANTONIO} and {BUCCHIARONE} and RUSCIO, DAVIDE DI and HENRY, MUCCINI and PELLICCIONE, PATRIZIO}, year = {2008}, volume = {abs/0910.0493}, doi = {abs/0910.0493}, isbn = {978-1-60566-006-6} } @incollection{11697_26888, ids = {antonioModelDrivenApproach2010,cicchettiModelDrivenApproach2009,cicchettiModelDrivenApproach2009a}, title = {A Model Driven Approach to Upgrade Package Based Software Systems}, booktitle = {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}}}, author = {{ANTONIO} and {CICCHETTI} and RUSCIO, DAVIDE DI and PELLICCIONE, P and ALFONSO, PIERANTONIO and STEFANO, ZACCHIROLI}, year = {2010}, series = {Communications in {{Computer}} and {{Information Science}}}, volume = {69}, pages = {262--276}, publisher = {{SPRINGER}}, address = {{HEIDELBERG}}, doi = {10.1007/978-3-642-14819-4}, isbn = {978-3-642-14818-7} } @article{11697_287, ids = {balzeraniSupportingWebApplications2006b}, title = {Supporting {{Web}} Applications Development with a Product Line Architecture}, author = {Balzerani, L and Di Ruscio, D and Pierantonio, A and De Angelis, G}, year = {2006}, journal = {JOURNAL OF WEB ENGINEERING}, volume = {5}, pages = {25--42} } @inproceedings{11697_29795, title = {{{ByADL}}: {{An MDE}} Framework for Building Extensible Architecture Description Languages}, booktitle = {4th European Conference on Software Architecture, {{ECSA}} 2010}, author = {Ruscio, Davide Di and {Ivano} and {Malavolta} and Muccini, H and Patrizio, Pelliccione and Alfonso, Pierantonio}, year = {2010}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {Lecture Notes in Computer Science 6285}, pages = {527--531}, publisher = {{Springer}}, address = {{BERLIN HEIDELBERG}}, isbn = {978-3-642-15113-2} } @inproceedings{11697_30086, ids = {cicchettiWeavingConcernsModel2006,cicchettiWeavingConcernsModel2006a,cicchettiWeavingConcernsModel2006b}, title = {Weaving Concerns in Model Based Development of Data-Intensive {{Web}} Applications}, booktitle = {Proceedings of the {{ACM}} Symposium on Applied Computing}, author = {Cicchetti, A and Di Ruscio, D and Pierantonio, A}, year = {2006} } @inproceedings{11697_30601, title = {Managing Model Conflicts in Distributed Development}, booktitle = {Proc. of the {{ACM}}/{{IEEE}} 11th International Conference on Model Driven Engineering Languages and Systems ({{MODELS}} 2008)}, author = {{ANTONIO} and {CICCHETTI} and RUSCIO, DAVIDE DI and PIERANTONIO, A}, year = {2008}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {5301}, pages = {311--325}, doi = {10.1007/978-3-540-87875-9_23} } @inproceedings{11697_30610, title = {A Product Line Architecture for Web Applications}, booktitle = {Proc. {{ACM}} Symposium on Applied Computing ({{SAC}} 2005), Special Track on Web Technologies and Applications, {{ACM}} Press}, author = {Balzerani, L and Di Ruscio, D and Pierantonio, A and De Angelis, G}, year = {2005}, doi = {10.1145/1066677.1067059} } @inproceedings{11697_30657, title = {Composition of Model Differences}, booktitle = {1st European Workshop on Composition of Model Transformations - {{CMT}} 2006}, author = {CICCHETTI, A. and DI RUSCIO, D. and PIERANTONIO, A}, year = {2006} } @inproceedings{11697_31176, ids = {cicchettiModelDrivenApproach2009a,cicchettiModelDrivenApproach2009c,cicchettiModelDrivenApproach2009d}, title = {Towards a Model Driven Approach to Upgrade Complex Software Systems}, booktitle = {{{ENASE}} 2009 - 4th International Conference on Evaluation of Novel Approaches to Software Engineering, Proceedings}, author = {Cicchetti, A and Di Ruscio, D and Pelliccione, P and Pierantonio, A and Zacchiroli, S}, year = {2009}, pages = {121--133}, publisher = {{Elsevier B.V.}}, isbn = {978-989-8111-98-2} } @inproceedings{11697_32865, title = {Model-Driven Techniques to Enhance Architectural Languages Interoperability}, booktitle = {15th International Conference on Fundamental Approaches to Software Engineering ({{FASE}})}, author = {Ruscio, Davide Di and {Ivano} and {Malavolta} and Muccini, H and Patrizio, Pelliccione and Alfonso, Pierantonio}, year = {2012}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {7212}, pages = {26--42}, publisher = {{Springer-Verlag}}, address = {{BERLIN HEIDELBERG}}, doi = {10.1007/978-3-642-28872-2_2}, 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.}, isbn = {978-3-642-28871-5} } @incollection{11697_34038, title = {A Test-Driven Approach for Metamodel Development}, booktitle = {Emerging Technologies for the Evolution and Maintenance of Software Models}, author = {Cicchetti, A and Di Ruscio, D and Kolovos, D and Pierantonio, A}, year = {2012}, pages = {319--342}, publisher = {{IGI Global}}, address = {{NEY YORK}}, doi = {10.4018/978-1-61350-438-3.ch012} } @inproceedings{11697_35267, title = {Engineering {{MDA}} into Compositional Reasoning for Analyzing Middleware-Based Applications}, booktitle = {Software Architecture, 2nd European Workshop, {{EWSA}} 2005}, author = {Caporuscio, M and DI RUSCIO, D and Inverardi, P and Pelliccione, P and Pierantonio, A}, year = {2005}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {3527}, pages = {130--145}, publisher = {{Springer - LNCS series}}, isbn = {3-540-26275} } @inproceedings{11697_37088, title = {Model Patches in Model-Driven Engineering}, booktitle = {Models in Software Engineering, Workshops and Symposia at {{MODELS}} 2009}, author = {Cicchetti, A and DI RUSCIO, Davide and Pierantonio, Alfonso}, year = {2010}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {6002}, pages = {190--204}, 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.}, isbn = {978-3-642-12260-6} } @inproceedings{11697_37099, title = {Developing next Generation {{ADLs}} through {{MDE}} Techniques}, booktitle = {Proceedings - International Conference on Software Engineering}, author = {Di Ruscio, D and Malavolta, I and Muccini, H and Pelliccione, P and Pierantonio, A}, year = {2010}, series = {{{PROCEEDINGS}} - {{INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING}}}, pages = {85--94}, publisher = {{Association for Computing Machinery, Inc. (ACM)}}, address = {{NEW YORK, NY, USA}}, doi = {10.1145/1806799.1806816}, 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 \textendash{} 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.}, isbn = {978-1-60558-719-6} } @inproceedings{11697_37101, title = {Managing Dependent Changes in Coupled Evolution}, booktitle = {Theory and Practice of Model Transformations, Second International Conference, {{ICMT}} 2009}, author = {Cicchetti, A and Di Ruscio, D and Pierantonio, A}, year = {2009}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {5563}, pages = {35--51}, doi = {10.1007/978-3-642-02408-5_4}, isbn = {978-3-642-02407-8} } @inproceedings{11697_37103, title = {{{BeContent}}: {{A}} Model-Driven Platform for Designing and Maintaining Web Applications}, booktitle = {Web Engineering, 9th International Conference, {{ICWE}} 2009}, author = {Cicchetti, A and Di Ruscio, D and Eramo, R and Maccarrone, F and Pierantonio, A}, year = {2009}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {5648}, pages = {518--522}, doi = {10.1007/978-3-642-02818-2_52} } @inproceedings{11697_37139, title = {{{JTL}}: {{A}} Bidirectional and Change Propagating Transformation Language}, booktitle = {Third International Conference, {{SLE}} 2010, Eindhoven, the Netherlands}, author = {Cicchetti, A and Di Ruscio, D and Eramo, R and Pierantonio, A}, year = {2010}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {6563}, pages = {183--202}, isbn = {978-3-642-19439-9} } @inproceedings{11697_37543, title = {Model Transformations in the Development of Data-Intensive Web Applications}, booktitle = {Proc. 17th Conference on Advanced Information Systems Engineering ({{CAiSE}}'05), Springer {{LNCS}}}, author = {Di Ruscio, D and Pierantonio, A}, year = {2005}, volume = {3520}, pages = {475--490} } @inproceedings{11697_37559, ids = {diruscioWeavingSoftwareArchitecture2006,ruscioTowardsWeavingSoftwareArchitecture2006,ruscioTowardsWeavingSoftwareArchitecture2006a}, title = {Towards Weaving Software Architecture Models}, booktitle = {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}, author = {Di Ruscio, D and Muccini, H and Pierantonio, A and Pelliccione, P}, year = {2006}, publisher = {{IEEE Computer Society}}, address = {{NEW YORK}}, doi = {10.1109/MBD-MOMPES.2006.24}, isbn = {0-7695-2538-5} } @inproceedings{11697_38176, title = {Towards Maintainer Script Modernization in {{FOSS}} Distributions}, booktitle = {{{IWOCE}} 2009: {{INTERNATIONAL WORKSHOP ON OPEN COMPONENT ECOSYSTEM}}}, author = {Di Ruscio, D and Pelliccione, P and Pierantonio, A and Zacchiroli, S}, year = {2009}, volume = {abs/0909.5087}, pages = {11--20}, publisher = {{Association for Computing Machinery, Inc. (ACM)}}, address = {{NEW YORK, NY, USA}}, doi = {10.1145/1595800.1595803}, isbn = {978-1-60558-677-9} } @inproceedings{11697_39261, title = {Managing the Evolution of Free and Open Source Software Complex Systems}, booktitle = {V Conferenza Italiana Sul Software Libero - Milano 23-24 Giugno 2011}, author = {Ruscio, Davide Di and {Pelliccione} and {P}}, year = {2011} } @inproceedings{11697_40934, ids = {autiliSynthesizingAutomatabasedRepresentation2014,autiliSynthesizingAutomatabasedRepresentation2014a,autiliSynthesizingAutomatabasedRepresentation2014b}, title = {Synthesizing an Automata-Based Representation of {{BPMN2}} Choreography Diagrams}, booktitle = {1st International Workshop on Model-Driven Engineering for Component-Based Software Systems}, author = {Autili, Marco and DI RUSCIO, Davide and DI SALLE, Amleto and Inverardi, Paola}, year = {2014}, volume = {1281}, pages = {67--77}, publisher = {{CEUR-WS.org}}, address = {{AACHEN}}, url = {http://ceur-ws.org/Vol-1281/7.pdf}, 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.}, keywords = {choreography,service engineering} } @article{11697_4393, title = {Guest Editorial to the Special Issue on {{Success Stories}} in {{Model Driven Engineering}}}, author = {DI RUSCIO, Davide and Paige, R and Pierantonio, Alfonso}, year = {2014}, journal = {SCIENCE OF COMPUTER PROGRAMMING}, doi = {10.1016/j.scico.2013.12.006} } @inproceedings{11697_89156, ids = {diruscioSupportingEvolutionFree2013,ruscioSupportingEvolutionFree2013,ruscioSupportingEvolutionFree2013a}, title = {Supporting the Evolution of Free and Open Source Software Distributions}, booktitle = {Software Engineering for Resilient Systems - 5th International Workshop Proceedings}, author = {Di Ruscio, D and Pelliccione, P}, year = {2013}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, pages = {56--63}, publisher = {{Springer-Verlag}}, address = {{BERLIN HEIDELBERG}}, doi = {10.1007/978-3-642-40894-6_5}, 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.}, isbn = {978-3-642-40893-9} } @inproceedings{11697_89159, title = {Managing the Coupled Evolution of Metamodels and Textual Concrete Syntax Specifications}, booktitle = {39th Euromicro Conference Series on Software Engineering and Advanced Applications, {{SEAA}} 2013}, author = {Di Ruscio, D and Iovino, L and Pierantonio, A}, year = {2013}, pages = {114--121}, publisher = {{IEEE}}, doi = {10.1109/SEAA.2013.22}, 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}, isbn = {978-0-7695-5091-6} } @inproceedings{11697_89171, title = {Applying Model Differences to Automate Performance-Driven Refactoring of Software Models}, booktitle = {Computer Performance Engineering - 10th European Workshop, {{EPEW}} 2013, Venice, Italy, September 16-17, 2013. {{Proceedings}}. {{Lecture}} Notes in Computer Science}, author = {Arcelli, D and Cortellessa, Vittorio and DI RUSCIO, Davide}, year = {2013}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {8168}, pages = {312--324}, publisher = {{Springer}}, doi = {10.1007/978-3-642-40725-3_24}, 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.}, isbn = {978-3-642-40724-6}, keywords = {Computer Science (all),Theoretical Computer Science} } @inproceedings{11697_89209, title = {Engineering a Platform for Mission Planning of Autonomous and Resilient Quadrotors}, booktitle = {Software Engineering for Resilient Systems, 5th International Workshop, {{SERENE}} 2013, Kiev, Ukraine, October 3-4, 2013. {{Proceedings}}. {{Lecture}} Notes in Computer Science}, author = {DI RUSCIO, Davide and Malavolta, Ivano and Pelliccione, Patrizio}, year = {2013}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {8166}, pages = {33--47}, publisher = {{Springer-Verlag}}, address = {{BERLIN HEIDELBERG}}, doi = {10.1007/978-3-642-40894-6_3}, isbn = {978-3-642-40893-9}, keywords = {open source software} } @inproceedings{11697_89217, title = {A Methodological Approach for the Coupled Evolution of Metamodels and {{ATL}} Transformations}, booktitle = {6th International Conference on Theory and Practice of Model Transformations, {{ICMT}} 2013;}, author = {Di Ruscio, D and Iovino, L and Pierantonio, A}, year = {2013}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {7909}, pages = {60--75}, publisher = {{Springer-Verlag}}, address = {{BERLIN HEIDELBERG}}, doi = {10.1007/978-3-642-38883-5_9}, 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.}, isbn = {978-3-642-38882-8} } @inproceedings{11697_89278, title = {Automated {{Co-evolution}} of {{GMF}} Editor Models}, booktitle = {Software Language Engineering - Third International Conference, {{SLE}} 2010, Eindhoven, the Netherlands, October 12-13, 2010, Revised Selected Papers}, author = {DI RUSCIO, Davide and Lammel, R and Pierantonio, Alfonso}, year = {2011}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {abs/1006.5761}, pages = {143--162}, publisher = {{Springer-Verlag}}, address = {{BERLIN HEIDELBERG}}, doi = {10.1007/978-3-642-19440-5_9}, 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.}, isbn = {978-3-642-19439-9} } @inproceedings{11697_89297, ids = {10.1145/2000410.2000416,diruscioWhatNeededManaging2011}, title = {What Is Needed for Managing Co-Evolution in {{MDE}}?}, booktitle = {Proceedings of the 2nd International Workshop on Model Comparison in Practice}, author = {Di Ruscio, D and Iovino, L and Pierantonio, A}, year = {2011}, pages = {30--38}, address = {{Zurich, Switzerland}}, doi = {10.1145/2000410.2000416}, isbn = {978-1-4503-0668-3}, numpages = {9}, keywords = {metamodel co-evolution,model differences,model driven engineering} } @inproceedings{11697_89302, title = {Providing Lightweight and Adaptable Service Technology for Information and Communication ({{PLASTIC}}) in the Mobile {{eHealth}} Case Study}, booktitle = {{{ICSE}} Workshop on Principles of Engineering Service Oriented Systems}, author = {Autili, Marco and Berardinelli, Luca and Di Ruscio, Davide and Trubiani, Catia}, year = {2012}, pages = {69--70}, publisher = {{IEEE Computer Society}}, address = {{NEW YORK}}, doi = {10.1109/PESOS.2012.6225946}, 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.}, isbn = {978-1-4673-1754-2}, keywords = {Software} } @inproceedings{11697_89303, ids = {wagelaarTranslationalSemanticsCoevolution2012,wagelaarTranslationalSemanticsCoevolution2012a,wagelaarTranslationalSemanticsCoevolution2012b}, title = {Translational Semantics of a Co-Evolution Specific Language with the {{EMF}} Transformation Virtual Machine}, booktitle = {Theory and Practice of Model Transformations}, author = {Wagelaar, D and Iovino, L and Di Ruscio, D and Pierantonio, A}, year = {2012}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {7307}, pages = {192--207}, doi = {10.1007/978-3-642-30476-7_13}, 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.}, isbn = {978-3-642-30475-0} } @inproceedings{11697_89304, title = {Model Transformations}, booktitle = {Formal Methods for Model-Driven Engineering}, author = {Di Ruscio, D and Eramo, R and Pierantonio, A}, year = {2012}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {7320}, pages = {91--136}, publisher = {{Springer-Verlag}}, address = {{BERLIN HEIDELBERG}}, doi = {10.1007/978-3-642-30982-3_4}, isbn = {978-3-642-30981-6} } @inproceedings{11697_89344, title = {{{EVOSS}}: {{A}} Tool for Managing the Evolution of Free and Open Source Software Systems}, booktitle = {Proceedings of the 34th International Conference on Software Engineering ({{ICSE}} 2012)}, author = {Di Ruscio, D and Pelliccione, P and Pierantonio, A}, year = {2012}, pages = {1415--1418}, publisher = {{IEEE Computer Society}}, address = {{NEW YORK}}, doi = {10.1109/ICSE.2012.6227234}, 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.}, isbn = {978-1-4673-1066-6} } @inproceedings{11697_89346, title = {Evolutionary Togetherness: How to Manage Coupled Evolution in Metamodeling Ecosystems}, booktitle = {Procs. {{International}} Conference on Graph Transformations ({{ICGT2012}})}, author = {Di Ruscio, D and Iovino, L and Pierantonio, A}, year = {2012}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {7562}, pages = {20--37}, publisher = {{Springer-Verlag}}, address = {{BERLIN HEIDELBERG}}, doi = {10.1007/978-3-642-33654-6_2}, 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.}, isbn = {978-3-642-33653-9} } @article{11697_89593, ids = {cosmoSupportingSoftwareEvolution2011,cosmoSupportingSoftwareEvolution2011a,dicosmoSupportingSoftwareEvolution2011}, title = {Supporting Software Evolution in Component-Based {{FOSS}} Systems}, author = {Di Cosmo, R and Di Ruscio, D and Pelliccione, P and Pierantonio, A and Zacchiroli, S}, year = {2011}, journal = {SCIENCE OF COMPUTER PROGRAMMING}, volume = {76}, pages = {1144--1160}, doi = {10.1016/j.scico.2010.11.001} } @article{11697_89601, title = {Coupled Evolution in Model-Driven Engineering}, author = {Di Ruscio, D and Iovino, L and Pierantonio, A}, year = {2012}, journal = {IEEE SOFTWARE}, volume = {29}, pages = {78--84}, doi = {10.1109/MS.2012.153}, 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.} } @article{11697_9327, title = {A Metamodel Independent Approach to Difference Representation}, author = {Cicchetti, A and Di Ruscio, D and Pierantonio, A}, year = {2007}, journal = {JOURNAL OF OBJECT TECHNOLOGY}, volume = {6}, pages = {165--185} } @article{159059, title = {Fuzzy {{ARTMAP}}: {{A}} Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps}, author = {Carpenter, G. A. and Grossberg, S. and Markuzon, N. and Reynolds, J. H. and Rosen, D. B.}, year = {1992}, month = sep, journal = {IEEE Transactions on Neural Networks}, volume = {3}, number = {5}, pages = {698--713}, issn = {1045-9227}, doi = {10.1109/72.159059}, keywords = {adaptive resonance theory,analog multidimensional maps,Computational modeling,fuzzy ARTMAP,fuzzy logic,Fuzzy logic,Fuzzy neural networks,fuzzy set theory,Fuzzy sets,Fuzzy systems,incremental supervised learning,learning systems,Multidimensional systems,neural nets,neural network architecture,Neural networks,pattern recognition,Resonance,Salzberg's NGE systems,Simpson's FMMC system,Subspace constraints,Supervised learning} } @book{3rdworkshopFlexibleModel2017, title = {3rdworkshop on Flexible Model Driven Engineering ({{FlexMDE}} 2017)}, year = {2017}, journal = {CEUR Workshop Proceedings}, volume = {2019}, publisher = {{CEUR-WS}} } @misc{3rdworkshopFlexibleModel2017a, title = {3rdworkshop on Flexible Model Driven Engineering ({{FlexMDE}} 2017)}, year = {2017}, journal = {CEUR Workshop Proceedings}, volume = {2019}, pages = {385--386}, publisher = {{CEUR-WS}} } @inproceedings{5279910, title = {{{RASCAL}}: {{A}} Domain Specific Language for Source Code Analysis and Manipulation}, booktitle = {2009 Ninth {{IEEE}} International Working Conference on Source Code Analysis and Manipulation}, author = {Klint, P. and v. d. Storm, T. and Vinju, J.}, year = {2009}, month = sep, pages = {168--177}, doi = {10.1109/SCAM.2009.28}, keywords = {ad hoc integration,automated software engineering tool,complex software refactoring,conceptual-syntactic-semantic-technical level,domain specific language,Domain specific languages,Impedance,impedance mismatch,Informatics,Java,Libraries,Logic programming,meta-programming,object-oriented languages,Pattern matching,program diagnostics,RASCAL,Scalability,software engineering,software maintenance,source code analysis,source code manipulation,Storms,transformation} } @article{5287006, title = {What Makes {{APIs}} Hard to Learn? {{Answers}} from Developers}, author = {Robillard, M. P.}, year = {2009}, month = nov, journal = {IEEE Software}, volume = {26}, number = {6}, pages = {27--34}, issn = {0740-7459}, doi = {10.1109/MS.2009.193}, keywords = {API design,API learnability,API usability,application program interface,application program interfaces,Application software,code examples,code reuse,empirical study,Microsoft developers,Programming profession,software development management,software development technologies,software documentation,software reusability,Usability} } @inproceedings{6224306, title = {App Store Mining and Analysis: {{MSR}} for App Stores}, booktitle = {2012 9th {{IEEE}} Working Conference on Mining Software Repositories ({{MSR}})}, author = {Harman, M. and Jia, Y. and Zhang, Y.}, year = {2012}, month = jun, pages = {108--111}, issn = {2160-1860}, doi = {10.1109/MSR.2012.6224306}, keywords = {app download,app store analysis,app store mining,app store MSR,Blackberry app store,Business,business aspect,Clustering algorithms,Correlation,customer aspect,customer rating,customer review,data mining,Data mining,Feature extraction,feature information extraction,Measurement,pricing,Software,software development management,software packages,software repository mining,technical aspect} } @inproceedings{6233407, title = {Example {{Overflow}}: {{Using}} Social Media for Code Recommendation}, booktitle = {2012 Third International Workshop on Recommendation Systems for Software Engineering ({{RSSE}})}, author = {Zagalsky, A. and Barzilay, O. and Yehudai, A.}, year = {2012}, month = jun, pages = {38--42}, issn = {2327-0934} } @inproceedings{6233418, title = {Context-Based Recommendation to Support Problem Solving in Software Development}, booktitle = {2012 Third International Workshop on Recommendation Systems for Software Engineering ({{RSSE}})}, author = {Cordeiro, J. and Antunes, B. and Gomes, P.}, year = {2012}, month = jun, pages = {85--89}, issn = {2327-0934}, doi = {10.1109/RSSE.2012.6233418}, keywords = {Console View,Context,Context Modelling,context-based recommendation,Data mining,Eclipse,exception occurrence,exception stack traces,IDE,information searching,keyword-based approach,online front-ends,problem solving,Problem Solving,Problem-solving,Programming,question answering (information retrieval),question-answering Web resources retrieval,recommendation systems,recommender systems,Search engines,Servers,Software,Software Development,software development management,software development process,Web browser,Web pages,Web sites} } @inproceedings{6671293, title = {Automated Library Recommendation}, booktitle = {2013 20th Working Conf. on Reverse Engineering ({{WCRE}})}, author = {Thung, F. and Lo, D. and Lawall, J.}, year = {2013}, month = oct, pages = {182--191}, issn = {1095-1350}, keywords = {association rule mining,Association rules,automated library recommendation,Collaboration,collaborative filtering,data mining,Feature extraction,Generators,Itemsets,Libraries,recall rate,software projects,software reliability,third-party libraries} } @inproceedings{6671295, title = {Find Your Library Experts}, booktitle = {2013 20th Working Conference on Reverse Engineering ({{WCRE}})}, author = {Teyton, C{\'e}dric and Falleri, Jean-R{\'e}my and Morandat, Flor{\'e}al and Blanc, Xavier}, year = {2013}, month = oct, pages = {202--211}, issn = {1095-1350}, doi = {10.1109/WCRE.2013.6671295}, keywords = {Apache HBase project,Communities,data mining,GitHub developers,Indexes,Java,Java libraries,Libraries,Libtic search engine,Prototypes,public domain software,search engines,Software,software development,software maintenance,software repositories mining,third-party libraries} } @inproceedings{6671315, title = {Documenting {{APIs}} with Examples: {{Lessons}} Learned with the {{APIMiner}} Platform}, booktitle = {2013 20th Working Conference on Reverse Engineering ({{WCRE}})}, author = {Montandon, J. E. and Borges, H. and Felix, D. and Valente, M. T.}, year = {2013}, month = oct, pages = {401--408}, issn = {1095-1350}, doi = {10.1109/WCRE.2013.6671315}, keywords = {Android API,Androids,API documentation,API learning process,APIMiner platform,application program interfaces,application programming interfaces,Computer architecture,data mining,Documentation,field study,Google,Humanoid robots,Instruments,Java,JavaDoc,Measurement,private source code repository,software development,software engineering,source code examples,standard Java-based API documentation format,static slicing algorithm,system documentation} } @misc{67OpenSource, title = {67 Open Source Tools and Resources for the {{Internet}} of {{Things}} ({{IoT}})}, url = {http://techbeacon.com/67-open-source-tools-resources-iot}, urldate = {2016-09-27} } @inproceedings{7070485, title = {M3: {{A}} General Model for Code Analytics in Rascal}, booktitle = {2015 {{IEEE}} 1st International Workshop on Software Analytics ({{SWAN}})}, author = {Basten, B. and Hills, M. and Klint, P. and Landman, D. and Shahi, A. and Steindorfer, M. J. and Vinju, J. J.}, year = {2015}, month = mar, pages = {25--28}, doi = {10.1109/SWAN.2015.7070485}, keywords = {Abstracts,Analytical models,code analytics,Computational modeling,general model,Java,M3 framework,Measurement,Object oriented modeling,Rascal meta programming language,software libraries,source code,source code (software),specification languages,standard library} } @inproceedings{7372018, title = {Automated Tagging of Software Projects Using Bytecode and Dependencies (n)}, booktitle = {2015 30th {{IEEE}}/{{ACM}} International Conference on Automated Software Engineering ({{ASE}})}, author = {{Vargas-Baldrich}, S. and {Linares-V{\'a}squez}, M. and Poshyvanyk, D.}, year = {2015}, month = nov, pages = {289--294}, doi = {10.1109/ASE.2015.38}, keywords = {automatic software tagging approach,bytecode,categorization approach,closed source repository group software systems,Data mining,dependency relations,Feature extraction,information retrieval,Internet,Maven-based software projects,online repositories,open source community,open source repository group software systems,project management,public domain software,recommendation systems,Sally,Software algorithms,software assets,software management,Software systems,software tagging,Support vector machines,Tagging,term assignment} } @article{7569018, title = {Approaches to Co-Evolution of Metamodels and Models: {{A}} Survey}, author = {Hebig, R. and Khelladi, D. E. and Bendraou, R.}, year = {2017}, month = may, journal = {IEEE Transactions on Software Engineering}, volume = {43}, number = {5}, pages = {396--414}, issn = {2326-3881}, doi = {10.1109/TSE.2016.2610424}, keywords = {Atmospheric modeling,Biological system modeling,coevolution approaches,Companies,decision support,design notations and documentation,Libraries,metamodel-model coevolution,metamodels,models,Productivity,software engineering,solution technique taxonomy,Survey,Taxonomy,Unified modeling language} } @inproceedings{7816479, title = {Understanding the Factors That Impact the Popularity of {{GitHub}} Repositories}, booktitle = {2016 {{IEEE}} International Conference on Software Maintenance and Evolution ({{ICSME}})}, author = {Borges, H. and Hora, A. and Valente, M. T.}, year = {2016}, month = oct, pages = {334--344}, doi = {10.1109/ICSME.2016.31}, keywords = {Documentation,GitHub,GitHub projects,GitHub Repositories,HTML,Java,Libraries,open source developers,open source software,Open Source software,Organizations,programming language,project popularity,public domain software,Social coding,Software,software acceptance,software market,Software Popularity,software reviews,software system popularity,source code (software),stargazers button,time series} } @inproceedings{7961718, title = {An All-in-One Convolutional Neural Network for Face Analysis}, booktitle = {2017 12th {{IEEE}} Int. {{Conf}}. on Automatic Face Gesture Recognition ({{FG}} 2017)}, author = {Ranjan, R. and Sankaranarayanan, S. and Castillo, C. D. and Chellappa, R.}, year = {2017}, month = may, pages = {17--24}, doi = {10.1109/FG.2017.137}, keywords = {age estimation,all-in-one convolutional neural network,CNN shared parameters,Face,face alignment,face analysis,Face detection,face recognition,Face recognition,gender recognition,learning (artificial intelligence),multitask learning framework,neural nets,pose estimation,Pose estimation,Robustness,simultaneous face detection,smile detection,Training} } @inproceedings{8009930, title = {Towards Better Understanding of Software Quality Evolution through Commit-Impact Analysis}, booktitle = {2017 {{IEEE}} International Conference on Software Quality, Reliability and Security ({{QRS}})}, author = {Behnamghader, P. and Alfayez, R. and Srisopha, K. and Boehm, B.}, year = {2017}, month = jul, pages = {251--262}, doi = {10.1109/QRS.2017.36}, keywords = {Apache Java software systems,commit-impact analysis,Computer bugs,Measurement,mining software repositories,program compilers,Security,software metrics,software quality,Software quality,software quality evolution,software quality indicator,software quality metrics,Software systems,source code,source code (software),Tools} } @inproceedings{8025917, title = {Source Code Classification Using Neural Networks}, booktitle = {2017 14th International Joint Conference on Computer Science and Software Engineering ({{JCSSE}})}, author = {Gilda, S.}, year = {2017}, month = jul, pages = {1--6}, doi = {10.1109/JCSSE.2017.8025917}, keywords = {artificial neural network,Artificial neural network,convolutional neural network,feature extraction,Feature extraction,file extension,HTML,intelligent feature extraction,learning (artificial intelligence),Multi-layer neural network,multilayer neural network,neural nets,neural networks,pattern classification,programming languages,software development industry,software engineering,source code (software),source code classification,supervised learning,Supervised learning,Syntactics,Training,word embedding layers} } @inproceedings{8327318, title = {A Picture Is Worth a Thousand Words: {{Code}} Clone Detection Based on Image Similarity}, booktitle = {2018 {{IEEE}} 12th International Workshop on Software Clones ({{IWSC}})}, author = {Ragkhitwetsagul, C. and Krinke, J. and Marnette, B.}, year = {2018}, month = mar, pages = {44--50}, issn = {2572-6587}, doi = {10.1109/IWSC.2018.8327318}, keywords = {clone pairs,Cloning,code clone detection technique,code clone detectors,code image blurring,Detectors,Earth,Image color analysis,image conversion,image processing,image similarity,Jaccard similarity,Java,Java clone detection,normalisation technique,pervasive code modifications,public domain software,raw source code text,similarity computation,similarity measures,software maintenance,software systems,source code (software),syntax highlighting,type-3 clones,Visualization} } @inproceedings{8595234, title = {Exploring the Use of Automated {{API}} Migrating Techniques in Practice: {{An}} Experience Report on Android}, booktitle = {2018 {{IEEE}}/{{ACM}} 15th International Conference on Mining Software Repositories ({{MSR}})}, author = {Lamothe, M. and Shang, W.}, year = {2018}, month = may, pages = {503--514}, issn = {2574-3864}, keywords = {Android API,Android APIs,API consumers,API documentation,API migration,application program interfaces,automated API migrating techniques,Data mining,Documentation,freely available application program interfaces,historical code changes,historical code-change information,History,Keyword search,migration guidance,migration suggestions,Mining Software Repositories,mobile computing,open source software libraries,public domain software,robust applications,Software,software documentation,Software evolution,software libraries,software maintenance,Task analysis,Tools} } @article{ab.rahimSurveyApproachesVerifying2013, title = {A Survey of Approaches for Verifying Model Transformations}, author = {Ab.~Rahim, Lukman and Whittle, Jon}, year = {2013}, month = jun, journal = {Software \& Systems Modeling}, pages = {1--26}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-013-0358-0}, 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.}, langid = {english}, keywords = {software engineering} } @inproceedings{Abbar09context-awarerecommender, title = {Context-Aware Recommender Systems: {{A}} Service Oriented Approach}, booktitle = {{{VLDB PersDB}} Workshop}, author = {Abbar, Sofiane and Bouzeghoub, Mokrane and Lopez, St{\'e}phane}, year = {2009} } @article{Abdalhadi20221356, title = {An Optimal Proportional Integral Derivative Tuning for a Magnetic Levitation System Using Metamodeling Approach}, author = {Abdalhadi, A. and Wahid, H. and Burhanuddin, D.H.}, year = {2022}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, volume = {25}, number = {3}, pages = {1356--1366}, publisher = {{Institute of Advanced Engineering and Science}}, issn = {25024752}, doi = {10.11591/ijeecs.v25.i3.pp1356-1366}, abbrev_source_title = {Indones. J. Electrical Eng. Comput. Sci.}, affiliation = {Control and Mechatronics Engineering Division, School of Electrical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia; Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia}, correspondence_address1 = {Wahid, H.; School of Electrical Engineering, Johor, Malaysia; email: herman@utm.my}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{abdalkareemCodeReuseStackOverflow2017, title = {On Code Reuse from {{StackOverflow}}: {{An}} Exploratory Study on {{Android}} Apps}, author = {Abdalkareem, Rabe and Shihab, Emad and Rilling, Juergen}, year = {2017}, journal = {Information and Software Technology}, volume = {88}, pages = {148--158}, issn = {0950-5849}, url = {http://www.sciencedirect.com/science/article/pii/S0950584917303610}, 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.}, nodoi = {https://doi.org/10.1016/j.infsof.2017.04.005}, keywords = {Code reuse,Mobile app,StackOverflow} } @inproceedings{abdelhediLogicalUnifiedModeling2017, title = {Logical {{Unified Modeling}} for {{NoSQL Databases}}:}, shorttitle = {Logical {{Unified Modeling}} for {{NoSQL Databases}}}, author = {Abdelhedi, Fatma and Brahim, Amal Ait and Atigui, Faten and Zurfluh, Gilles}, year = {2017}, pages = {249--256}, publisher = {{SCITEPRESS - Science and Technology Publications}}, doi = {10.5220/0006311702490256}, abstract = {Big Data, NoSQL, UML Conceptual Model, MDA, QVT.}, isbn = {978-989-758-247-9 978-989-758-248-6 978-989-758-249-3}, langid = {english} } @inproceedings{abeywickramaSimSOTAEngineeringSimulating2013, title = {{{SimSOTA}}: Engineering and Simulating Feedback Loops for Self-Adaptive Systems}, shorttitle = {{{SimSOTA}}}, booktitle = {Proceedings of the {{International C}}* {{Conference}} on {{Computer Science}} and {{Software Engineering}}}, author = {Abeywickrama, Dhaminda B. and Hoch, Nicklas and Zambonelli, Franco}, year = {2013}, pages = {67--76}, publisher = {{ACM}}, url = {http://dl.acm.org/citation.cfm?id=2494446}, urldate = {2016-08-21} } @inproceedings{Abid2019355, title = {Towards Machine Learning for Learnability of {{MDD}} Tools}, author = {Abid, S.B. and Mahajan, V. and Lucio, L.}, year = {2019}, series = {Proceedings of the {{International Conference}} on {{Software Engineering}} and {{Knowledge Engineering}}, {{SEKE}}}, volume = {2019-July}, pages = {355--360}, publisher = {{Knowledge Systems Institute Graduate School}}, issn = {23259000}, doi = {10.18293/SEKE2019-050}, abbrev_source_title = {Proc. Int. Conf. Softw. Eng. Knowl. Eng., SEKE}, affiliation = {Model-based Systems Engineering (MbSE) fortiss GmbH, Munich, Germany; Technical University of Munich, Munich, Germany}, document_type = {Conference Paper}, isbn = {1-891706-48-9}, langid = {english}, source = {Scopus}, keywords = {notion} } @book{abrahaoModelDrivenEngineeringLanguages2014, title = {Model-{{Driven Engineering Languages}} and {{Systems}}: 17th {{International Conference}}, {{MODELS}} 2014, {{Valencia}}, {{Spain}}, {{September}} 283- {{October}} 4, 2014. {{Proceedings}}}, shorttitle = {Model-{{Driven Engineering Languages}} and {{Systems}}}, editor = {Abrahao, Silvia and Dingel, Juergen and Insfran, Emilio and Ramos, Isidro and Schulte, Wolfram}, year = {2014}, series = {Programming and {{Software Engineering}}}, edition = {1st ed. 2014}, number = {8767}, publisher = {{Springer International Publishing : Imprint: Springer}}, address = {{Cham}}, doi = {10.1007/978-3-319-11653-2}, 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}, isbn = {978-3-319-11653-2}, lccn = {005.13}, keywords = {Computer logic,Computer science,Computer simulation,Computer system failures,Logics and Meanings of Programs,Management information systems,Management of Computing and Information Systems,Programming languages (Electronic computers),Programming Languages; Compilers; Interpreters,Simulation and Modeling,Software engineering,Software Engineering,System Performance and Evaluation} } @article{abu-elkheirDataManagementInternet2013, title = {Data {{Management}} for the {{Internet}} of {{Things}}: {{Design Primitives}} and {{Solution}}}, shorttitle = {Data {{Management}} for the {{Internet}} of {{Things}}}, author = {{Abu-Elkheir}, Mervat and Hayajneh, Mohammad and Ali, Najah}, year = {2013}, month = nov, journal = {Sensors}, volume = {13}, number = {11}, pages = {15582--15612}, issn = {1424-8220}, doi = {10.3390/s131115582}, langid = {english}, keywords = {Data analysis,internet of things} } @book{ACMInternationalConference2010, title = {{{ACM International Conference Proceeding Series}}: {{Foreword}}}, year = {2010}, journal = {ACM International Conference Proceeding Series} } @misc{ACMInternationalConference2010a, title = {{{ACM International Conference Proceeding Series}}: {{Foreword}}}, year = {2010}, journal = {ACM International Conference Proceeding Series} } @book{acmsigchisymposiumonengineeringinteractivecomputingsystemsEICS13Proceedings2013, title = {{{EICS}} '13: Proceedings of the {{ACM SIGCHI Symposium}} on {{Engineering Interactive Computing Systems}} : {{June}} 24-27, 2013, {{London}}, {{United Kingdom}}}, shorttitle = {{{EICS}} '13}, author = {{ACM SIGCHI Symposium on Engineering Interactive Computing Systems} and Forbrig, Peter and Dewan, Prasun and {SIGCHI (Group : U.S.)} and City University (London, England) and {Springer (Firm)} and {IFIP Working Group 2.7/13.4} and {Association for Computing Machinery} and {ACM Digital Library}}, year = {2013}, url = {http://dl.acm.org/citation.cfm?id=2494603}, urldate = {2016-09-24}, langid = {english} } @book{ACMStudentResearch2017, title = {{{ACM}} Student Research Competition at {{MoDELS}} 2017}, year = {2017}, journal = {CEUR Workshop Proceedings}, volume = {2019}, publisher = {{CEUR-WS}} } @misc{ACMStudentResearch2017a, title = {{{ACM}} Student Research Competition at {{MoDELS}} 2017}, year = {2017}, journal = {CEUR Workshop Proceedings}, volume = {2019}, pages = {547--548}, publisher = {{CEUR-WS}} } @article{acretoaieHypersonicModelAnalysis2014, title = {Hypersonic: {{Model Analysis}} and {{Checking}} in the {{Cloud}}}, author = {Acretoaie, Vlad and St{\"o}rrle, Harald}, year = {2014} } @article{Adamopoulos_TIST, title = {On Unexpectedness in Recommender Systems: {{Or}} How to Better Expect the Unexpected}, author = {Adamopoulos, Panagiotis and Tuzhilin, Alexander}, year = {2014}, month = dec, journal = {ACM Transactions on Intelligent Systems and Technology}, volume = {5}, number = {4}, pages = {54:1-54:32}, publisher = {{ACM}}, address = {{New York, NY, USA}}, issn = {2157-6904}, url = {http://doi.acm.org/10.1145/2559952}, acmid = {2559952}, articleno = {54}, issue_date = {January 2015}, nodoi = {10.1145/2559952}, numpages = {32}, keywords = {Diversity,evaluation,novelty,recommendation systems,recommender systems,serendipity,unexpectedness,utility theory} } @inproceedings{Adomavicius:2008:CRS:1454008.1454068, title = {Context-Aware Recommender Systems}, booktitle = {Proceedings of the 2008 {{ACM}} Conference on Recommender Systems}, author = {Adomavicius, Gediminas and Tuzhilin, Alexander}, year = {2008}, series = {{{RecSys}} '08}, pages = {335--336}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/1454008.1454068}, acmid = {1454068}, isbn = {978-1-60558-093-7}, nodoi = {10.1145/1454008.1454068}, numpages = {2}, keywords = {collaborative filtering,contextual information,personalization,recommender systems,tutorial} } @article{Adomavicius:2012:aggrDiv, title = {Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques}, author = {Adomavicius, Gediminas and Kwon, YoungOk}, year = {2012}, month = may, journal = {IEEE Trans. on Knowl. and Data Eng.}, volume = {24}, number = {5}, pages = {896--911}, publisher = {{IEEE Educational Activities Department}}, address = {{Piscataway, NJ, USA}}, issn = {1041-4347}, url = {http://dx.doi.org/10.1109/TKDE.2011.15}, acmid = {2197127}, issue_date = {May 2012}, nodoi = {10.1109/TKDE.2011.15}, numpages = {16}, keywords = {collaborative filtering.,performance evaluation metrics,ranking functions,recommendation diversity} } @article{AdversarialMachineLearning, title = {Adversarial {{Machine Learning}} \textemdash{{An Introduction}}}, journal = {Machine Learning}, pages = {26}, langid = {english}, keywords = {adversarial machine learning} } @inproceedings{afzalMegaMRt2ECSEL2017, title = {The {{MegaM}}@{{Rt2 ECSEL Project}}: {{MegaModelling}} at {{Runtime}} - {{Scalable Model-Based Framework}} for {{Continuous Development}} and {{Runtime Validation}} of {{Complex Systems}}}, booktitle = {Euromicro {{Conference}} on {{Digital System Design}}, {{DSD}} 2017, {{Vienna}}, {{Austria}}, {{August}} 30 - {{Sept}}. 1, 2017}, author = {Afzal, Wasif and Bruneliere, Hugo and DI RUSCIO, Davide and Sadovykh, Andrey and Mazzini, Silvia and Cariou, Eric and Truscan, Dragos and Cabot, Jordi and Field, Daniel and Pomante, Luigi and Smrz, Pavel}, year = {2017}, publisher = {{IEEE Computer Society}}, url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8048781}, isbn = {978-1-5386-2146-2} } @article{afzalMegaMRt2ECSEL2018, title = {The {{MegaM}}@{{Rt2 ECSEL}} Project: {{MegaModelling}} at {{Runtime}} \textendash{} {{Scalable}} Model-Based Framework for Continuous Development and Runtime Validation of Complex Systems}, author = {Afzal, Wasif and Bruneliere, Hugo and Di Ruscio, Davide and Sadovykh, Andrey and Mazzini, Silvia and Cariou, Eric and Truscan, Dragos and Cabot, Jordi and G{\'o}mez, Abel and Gorro{\~n}ogoitia, Jes{\'u}s and Pomante, Luigi and Smrz, Pavel}, year = {2018}, journal = {MICROPROCESSORS AND MICROSYSTEMS}, volume = {61}, pages = {86--95}, doi = {10.1016/j.micpro.2018.05.010}, keywords = {Artificial Intelligence,Computer Networks and Communications,Design time,Hardware and Architecture,Megamodelling,Model-driven engineering,Runtime,Software} } @incollection{aggarwalNeighborhoodbasedCollaborativeFiltering2016, title = {Neighborhood-Based Collaborative Filtering}, booktitle = {Recommender Systems: {{The}} Textbook}, author = {Aggarwal, Charu}, year = {2016}, pages = {29--70}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-319-29659-3₂}, 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:}, isbn = {978-3-319-29659-3} } @inproceedings{AGIRRE10.534, title = {Exploring Knowledge Bases for Similarity}, booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation ({{LREC}}'10)}, author = {Agirre, Eneko and Cuadros, Montse and Rigau, German and Soroa, Aitor}, editor = {Chair), Nicoletta Calzolari (Conference and Choukri, Khalid and Maegaard, Bente and Mariani, Joseph and Odijk, Jan and Piperidis, Stelios and Rosner, Mike and Tapias, Daniel}, year = {19-21, 2010-05}, publisher = {{European Language Resources Association (ELRA)}}, address = {{Valletta, Malta}}, isbn = {2-9517408-6-7}, langid = {english} } @inproceedings{agirrePersonalizingPageRankWord2009, title = {Personalizing {{PageRank}} for Word Sense Disambiguation}, booktitle = {Proceedings of the 12th {{Conference}} of the {{European Chapter}} of the {{Association}} for {{Computational Linguistics}}}, author = {Agirre, Eneko and Soroa, Aitor}, year = {2009}, series = {{{EACL}} '09}, pages = {33--41}, publisher = {{Association for Computational Linguistics}}, address = {{Stroudsburg, PA, USA}}, url = {http://dl.acm.org/citation.cfm?id=1609067.1609070}, acmid = {1609070}, numpages = {9} } @article{agt-rickauerSupportingDomainModeling, title = {{Supporting Domain Modeling with Automated Knowledge Acquisition and Modeling Recommendations}}, author = {{Agt-Rickauer}, Henning}, pages = {196}, langid = {ngerman} } @article{Aha:1991:ILA:104713.104717, title = {Instance-Based Learning Algorithms}, author = {Aha, David W. and Kibler, Dennis and Albert, Marc K.}, year = {1991}, month = jan, journal = {Machine Learning}, volume = {6}, number = {1}, pages = {37--66}, publisher = {{Kluwer Academic Publishers}}, address = {{Hingham, MA, USA}}, issn = {0885-6125}, url = {https://doi.org/10.1023/A:1022689900470}, acmid = {104717}, issue_date = {Jan. 1991}, nodoi = {10.1023/A:1022689900470}, numpages = {30}, keywords = {incremental learning,instance-based concept descriptions,learning theory,noise,similarity,Supervised concept learning} } @inproceedings{Al-Azzoni202087, title = {Model Driven Approach for Neural Networks}, author = {{Al-Azzoni}, I.}, editor = {Alsmirat M., Jararweh Y., Aloqaily M., Lloret Mauri J.}, year = {2020}, series = {2020 {{International Conference}} on {{Intelligent Data Science Technologies}} and {{Applications}}, {{IDSTA}} 2020}, pages = {87--94}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/IDSTA50958.2020.9264067}, abbrev_source_title = {Int. Conf. Intell. Data Sci. Technol. Appl., IDSTA}, affiliation = {College of Engineering, Al Ain University, Al Ain, United Arab Emirates}, art_number = {9264067}, correspondence_address1 = {Al-Azzoni, I.; College of Engineering, United Arab Emirates; email: issam.alazzoni@aau.ac.ae}, document_type = {Conference Paper}, isbn = {978-1-72818-376-3}, langid = {english}, source = {Scopus} } @article{al-garadiSurveyMachineDeep2018, title = {A {{Survey}} of {{Machine}} and {{Deep Learning Methods}} for {{Internet}} of {{Things}} ({{IoT}}) {{Security}}}, author = {{Al-Garadi}, Mohammed Ali and Mohamed, Amr and {Al-Ali}, Abdulla and Du, Xiaojiang and Guizani, Mohsen}, year = {2018}, month = jul, journal = {arXiv:1807.11023 [cs]}, eprint = {1807.11023}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/1807.11023}, urldate = {2021-01-10}, 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.}, archiveprefix = {arXiv} } @article{Al-Janan2017686, title = {Optimizing the Double Inverted Pendulum's Performance via the Uniform Neuro Multiobjective Genetic Algorithm}, author = {{Al-Janan}, D.H. and Chang, H.-C. and Chen, Y.-P. and Liu, T.-K.}, year = {2017}, journal = {International Journal of Automation and Computing}, volume = {14}, number = {6}, pages = {686--695}, publisher = {{Chinese Academy of Sciences}}, issn = {14768186}, doi = {10.1007/s11633-017-1069-8}, abbrev_source_title = {Int. J. Autom. Comput.}, affiliation = {Institute of Engineering Science and Technology, National Kaohsiung First University of Science and Technology, Taiwan; Mechanical Engineering Departement, Engineeering Faculty, Semarang State University, Semarang, Center of Java, Indonesia}, correspondence_address1 = {Liu, T.-K.; Institute of Engineering Science and Technology, Taiwan; email: tkliu@nkfust.edu.tw}, document_type = {Article}, langid = {english}, source = {Scopus} } @inproceedings{Al-Subaihin:2016:CMA:2961111.2962600, title = {Clustering Mobile Apps Based on Mined Textual Features}, booktitle = {Proceedings of the 10th {{ACM}}/{{IEEE}} International Symposium on Empirical Software Engineering and Measurement}, author = {{Al-Subaihin}, A. A. and Sarro, F. and Black, S. and Capra, L. and Harman, M. and Jia, Y. and Zhang, Y.}, year = {2016}, series = {{{ESEM}} '16}, pages = {38:1-38:10}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/2961111.2962600}, acmid = {2962600}, articleno = {38}, isbn = {978-1-4503-4427-2}, nodoi = {10.1145/2961111.2962600}, numpages = {10} } @inproceedings{Alaa2019, title = {Demystifying Black-Box Models with Symbolic Metamodels}, author = {Alaa, A.M. and {van der Schaar}, M.}, year = {2019}, series = {Advances in {{Neural Information Processing Systems}}}, volume = {32}, publisher = {{Neural information processing systems foundation}}, issn = {10495258}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078483653&partnerID=40&md5=e624a02d5e67fdb84876eed77ec60513}, abbrev_source_title = {Adv. neural inf. proces. syst.}, affiliation = {ECE Department UCLA; UCLA, University of Cambridge, Alan Turing Institute, United Kingdom}, document_type = {Conference Paper}, langid = {english}, source = {Scopus} } @article{alaminEmpiricalStudyDeveloper2021, title = {An {{Empirical Study}} of {{Developer Discussions}} on {{Low-Code Software Development Challenges}}}, author = {Alamin, Md Abdullah Al and Malakar, Sanjay and Uddin, Gias and Afroz, Sadia and Haider, Tameem Bin and Iqbal, Anindya}, year = {2021}, month = may, journal = {2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR)}, eprint = {2103.11429}, eprinttype = {arxiv}, pages = {46--57}, doi = {10.1109/MSR52588.2021.00018}, 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.}, archiveprefix = {arXiv}, keywords = {Computer Science - Software Engineering} } @article{aldallalEmpiricalEvaluationImpact2018, title = {Empirical {{Evaluation}} of the {{Impact}} of {{Object-Oriented Code Refactoring}} on {{Quality Attributes}}: {{A Systematic Literature Review}}}, shorttitle = {Empirical {{Evaluation}} of the {{Impact}} of {{Object-Oriented Code Refactoring}} on {{Quality Attributes}}}, author = {Al Dallal, Jehad and Abdin, Anas}, year = {2018}, month = jan, journal = {IEEE Transactions on Software Engineering}, volume = {44}, number = {1}, pages = {44--69}, issn = {0098-5589, 1939-3520}, doi = {10.1109/TSE.2017.2658573} } @misc{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.}, url = {https://github.com/AlessioTonioni/Autonomous-Flight-ROS}, urldate = {2016-09-11} } @inproceedings{alexanderCertificationAutonomousSystems2007, title = {Certification of Autonomous Systems}, booktitle = {Proceedings of the 2nd {{Systems Engineering}} for {{Autonomous Systems}} ({{SEAS}}) {{Defence Technology Centre}} ({{DTC}}) {{Annual Technical Conference}}}, author = {Alexander, Robert and {Hall-May}, Martin and Kelly, Tim}, year = {2007}, publisher = {{Citeseer}}, url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.126.7288&rep=rep1&type=pdf}, urldate = {2016-08-21} } @article{alfonsoSelfadaptiveArchitecturesIoT2021, title = {Self-Adaptive {{Architectures}} in {{IoT Systems}}: {{A Systematic Literature Review}}}, shorttitle = {Self-Adaptive {{Architectures}} in {{IoT Systems}}}, author = {Alfonso, Iv{\'a}n and Garc{\'e}s, Kelly and Castro, Harold and Cabot, Jordi}, year = {2021}, month = sep, journal = {arXiv:2109.03312 [cs]}, eprint = {2109.03312}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2109.03312}, urldate = {2021-10-04}, 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.}, archiveprefix = {arXiv}, keywords = {Computer Science - Networking and Internet Architecture} } @article{Ali20192979, title = {Artificial Neural Network Based Screening of Cervical Cancer Using a Hierarchical Modular Neural Network Architecture ({{HMNNA}}) and Novel Benchmark Uterine Cervix Cancer Database}, author = {Ali, M. and Sarwar, A. and Sharma, V. and Suri, J.}, year = {2019}, journal = {Neural Computing and Applications}, volume = {31}, number = {7}, pages = {2979--2993}, publisher = {{Springer London}}, issn = {09410643}, doi = {10.1007/s00521-017-3246-7}, 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\textendash Maarquardt neural network algorithm. As compared to the standard back propagation algorithm, Levenberg\textendash 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. \textcopyright{} 2017, The Natural Computing Applications Forum.}, document_type = {Article}, source = {Scopus} } @incollection{allenEngineeringAcademicSoftware2017, title = {Engineering {{Academic Software}} ({{Dagstuhl Perspectives Workshop}} 16252)}, booktitle = {Dagstuhl {{Manifestos}}}, author = {Allen, Alice and Aragon, Cecilia and Becker, Christoph and Carver, Jeffrey and Chis, Andrei and Combemale, Benoit and Croucher, Mike and Crowston, Kevin and Garijo, Daniel and Gehani, Ashish and others}, year = {2017}, volume = {6}, publisher = {{Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik}}, url = {http://drops.dagstuhl.de/opus/volltexte/2017/7146/}, urldate = {2017-05-30} } @article{allhoffInternetThingsFoundational2018, title = {The {{Internet}} of {{Things}}: {{Foundational}} Ethical Issues}, shorttitle = {The {{Internet}} of {{Things}}}, author = {Allhoff, Fritz and Henschke, Adam}, year = {2018}, month = sep, journal = {Internet of Things}, volume = {1--2}, pages = {55--66}, issn = {25426605}, doi = {10.1016/j.iot.2018.08.005}, 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.}, langid = {english} } @inproceedings{almeidaOSSMETERAutomatedMeasurement2015, title = {{{OSSMETER}}: {{Automated}} Measurement and Analysis of Open Source Software}, booktitle = {{{CEUR Workshop Proceedings}}}, author = {Almeida, Bruno and Ananiadou, Sophia and Bagnato, Alessandra and Berreteaga Barbero, Alberto and DI ROCCO, Juri and DI RUSCIO, Davide and Kolovos, Dimitrios S. and Korkontzelos, Ioannis and Hansen, Scott and Mal{\'o}, Pedro and Matragkas, Nicholas and Paige, Richard F. and Vinju, Jurgen}, year = {2015}, volume = {1400}, pages = {36--43}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @article{almonteAutomatingConstructionRecommender2020, title = {Towards Automating the Construction of Recommender Systems for Low-Code Development Platforms}, author = {Almonte, Lissette and Cantador, Iv{\'a}n and Guerra, Esther}, year = {2020}, pages = {10}, 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.}, langid = {english} } @article{almonteRecommenderSystemsModeldriven2021, title = {Recommender Systems in Model-Driven Engineering: {{A}} Systematic Mapping Review}, shorttitle = {Recommender Systems in Model-Driven Engineering}, author = {Almonte, Lissette and Guerra, Esther and Cantador, Iv{\'a}n and {de Lara}, Juan}, year = {2021}, month = jul, journal = {Software and Systems Modeling}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-021-00905-x}, 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.}, langid = {english} } @inproceedings{almorsySuiteDomainspecificVisual2013, title = {A Suite of Domain-Specific Visual Languages for Scientific Software Application Modelling}, booktitle = {Visual {{Languages}} and {{Human-Centric Computing}} ({{VL}}/{{HCC}}), 2013 {{IEEE Symposium}} On}, author = {Almorsy, Mohamed and Grundy, John and Sadus, Richard and {van Straten}, Willem and Barnes, David G. and Kaluza, Owen}, year = {2013}, pages = {91--94}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/abstract/document/6645249/}, urldate = {2017-02-23} } @inproceedings{alomranChoosingNLPLibrary2017, title = {Choosing an {{NLP Library}} for {{Analyzing Software Documentation}}: {{A Systematic Literature Review}} and a {{Series}} of {{Experiments}}}, shorttitle = {Choosing an {{NLP Library}} for {{Analyzing Software Documentation}}}, author = {Al Omran, Fouad Nasser A and Treude, Christoph}, year = {2017}, month = may, pages = {187--197}, publisher = {{IEEE}}, doi = {10.1109/MSR.2017.42}, isbn = {978-1-5386-1544-7} } @article{Alreshedy2018SCCAC, title = {{{SCC}}: {{Automatic}} Classification of Code Snippets}, author = {Alreshedy, Kamel and Dharmaretnam, Dhanush and German, Daniel M. and Srinivasan, Venkatesh and Gulliver, T. Aaron}, year = {2018}, journal = {CoRR}, volume = {abs/1809.07945} } @inproceedings{alrubayeUseInformationRetrieval2019, title = {On the {{Use}} of {{Information Retrieval}} to {{Automate}} the {{Detection}} of {{Third-Party Java Library Migration}} at the {{Method Level}}}, booktitle = {2019 {{IEEE}}/{{ACM}} 27th {{International Conference}} on {{Program Comprehension}} ({{ICPC}})}, author = {Alrubaye, Hussein and Mkaouer, Mohamed Wiem and Ouni, Ali}, year = {2019}, month = may, pages = {347--357}, issn = {2643-7147}, doi = {10.1109/ICPC.2019.00053}, 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.} } @article{alsrehinIntelligentTransportationControl2019, title = {Intelligent {{Transportation}} and {{Control Systems Using Data Mining}} and {{Machine Learning Techniques}}: {{A Comprehensive Study}}}, shorttitle = {Intelligent {{Transportation}} and {{Control Systems Using Data Mining}} and {{Machine Learning Techniques}}}, author = {Alsrehin, Nawaf O. and Klaib, Ahmad F. and Magableh, Aws}, year = {2019}, journal = {IEEE Access}, volume = {7}, pages = {49830--49857}, issn = {2169-3536}, doi = {10.1109/ACCESS.2019.2909114}, 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.}, langid = {english} } @article{altulyanRecommenderSystemsInternet2020, title = {Recommender {{Systems}} for the {{Internet}} of {{Things}}: {{A Survey}}}, shorttitle = {Recommender {{Systems}} for the {{Internet}} of {{Things}}}, author = {Altulyan, May and Yao, Lina and Wang, Xianzhi and Huang, Chaoran and Kanhere, Salil S. and Sheng, Quan Z.}, year = {2020}, month = jul, journal = {arXiv:2007.06758 [cs, stat]}, eprint = {2007.06758}, eprinttype = {arxiv}, primaryclass = {cs, stat}, url = {http://arxiv.org/abs/2007.06758}, urldate = {2020-12-14}, 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.}, archiveprefix = {arXiv}, langid = {english}, keywords = {internet of things,machine learning,recommendation systems} } @article{alurSystemsComputingChallenges2016, title = {Systems {{Computing Challenges}} in the {{Internet}} of {{Things}}}, author = {Alur, Rajeev and Berger, Emery and Drobnis, Ann W. and Fix, Limor and Fu, Kevin and Hager, Gregory D. and Lopresti, Daniel and Nahrstedt, Klara and Mynatt, Elizabeth and Patel, Shwetak and others}, year = {2016}, journal = {arXiv preprint arXiv:1604.02980}, eprint = {1604.02980}, eprinttype = {arxiv}, url = {http://arxiv.org/abs/1604.02980}, urldate = {2016-08-21}, archiveprefix = {arXiv} } @article{alvarezMTCFlowTool2013, title = {{{MTC Flow}}: A Tool to Design, Develop and Deploy Model Transformation Chains}, author = {Alvarez, Camilo and Casallas, Rubby}, year = {2013}, journal = {Proceedings of the workshop on ACadeMics Tooling with Eclipse - ACME '13}, pages = {1--9}, doi = {10.1145/2491279.2491286} } @article{alvinoLessonsLearnedLarge, title = {Lessons {{Learned}} from {{Large Scale Real World Recommender Systems}}}, author = {Alvino, Chris}, pages = {22}, langid = {english} } @misc{AmbitiousPlan, title = {An Ambitious Plan :-)}, shorttitle = {An Ambitious Plan}, journal = {Google Docs}, url = {https://docs.google.com/document/d/1uWyVw2JEI6A6KcB1kMYx_9sXqTSgy8r5CfWc1xQSHRM/edit?ts=5be56d68&usp=embed_facebook}, urldate = {2020-02-11}, 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...}, langid = {british} } @inproceedings{amershiSoftwareEngineeringMachine2019, ids = {amershiSoftwareEngineeringMachine2019a}, title = {Software {{Engineering}} for {{Machine Learning}}: {{A Case Study}}}, shorttitle = {Software {{Engineering}} for {{Machine Learning}}}, booktitle = {2019 {{IEEE}}/{{ACM}} 41st {{International Conference}} on {{Software Engineering}}: {{Software Engineering}} in {{Practice}} ({{ICSE-SEIP}})}, author = {Amershi, Saleema and Begel, Andrew and Bird, Christian and DeLine, Robert and Gall, Harald and Kamar, Ece and Nagappan, Nachiappan and Nushi, Besmira and Zimmermann, Thomas}, year = {2019}, month = may, pages = {291--300}, publisher = {{IEEE}}, address = {{Montreal, QC, Canada}}, doi = {10.1109/ICSE-SEIP.2019.00042}, 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 \textemdash{} 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.}, isbn = {978-1-72811-760-7}, langid = {english} } @article{Ameur2022, title = {Merits of {{Bayesian}} Networks in Overcoming Small Data Challenges: A Meta-Model for Handling Missing Data}, author = {Ameur, H. and Njah, H. and Jamoussi, S.}, year = {2022}, journal = {International Journal of Machine Learning and Cybernetics}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {18688071}, doi = {10.1007/s13042-022-01577-9}, abbrev_source_title = {Intl. J. Mach. Learn. Cybern.}, affiliation = {Multimedia, InfoRmation Systems and Advanced Computing Laboratory, Sfax, Tunisia; Higher Institute of Computer Sciences and Multimedia, University of Sfax, Sfax, Tunisia; Higher Institute of Computer Sciences and Multimedia, University of Gabes, Gabes, Tunisia}, correspondence_address1 = {Ameur, H.; Multimedia, Tunisia; email: ameurhanen@gmail.com}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{amine_benelallam_2018_1489120, title = {Maven Central Dependency Graph}, author = {Benelallam, Amine and Harrand, Nicolas and Valero, C{\'e}sar Soto and Baudry, Benoit and Barais, Olivier}, year = {2018}, month = nov, doi = {10.5281/zenodo.1489120} } @article{Ammar2018247, title = {Automatic Planning: {{From}} Event-{{B}} to {{PDDL}}}, author = {Ammar, S. and Bhiri, M.T.}, editor = {Golfarelli M., Bellatreche L., Nakamatsu K., Ordonez C., Mery D., Benslimane D., Abdelwahed E.H., Jean S.}, year = {2018}, journal = {Communications in Computer and Information Science}, volume = {929}, pages = {247--254}, publisher = {{Springer Verlag}}, issn = {18650929}, doi = {10.1007/978-3-030-02852-7_21}, 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. \textcopyright{} Springer Nature Switzerland AG 2018.}, document_type = {Conference Paper}, isbn = {9783030028510}, source = {Scopus} } @inproceedings{Ammar2021261, title = {A Formal Approach Combining Event-b and Pddl for Planning Problems}, author = {Ammar, S. and Bhiri, M.T.}, editor = {{Fill H.-G., van Sinderen M.}, Maciaszek L., Maciaszek L.}, year = {2021}, series = {Proceedings of the 16th {{International Conference}} on {{Software Technologies}}, {{ICSOFT}} 2021}, pages = {261--268}, publisher = {{SciTePress}}, doi = {10.5220/0010577102610268}, 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 \textcopyright{} 2021 by SCITEPRESS \textendash{} Science and Technology Publications, Lda. All rights reserved}, document_type = {Conference Paper}, isbn = {978-989-758-523-4}, source = {Scopus} } @article{Amouzgar201828, title = {Radial Basis Functions with a Priori Bias as Surrogate Models: {{A}} Comparative Study}, author = {Amouzgar, K. and Bandaru, S. and Ng, A.H.C.}, year = {2018}, journal = {Engineering Applications of Artificial Intelligence}, volume = {71}, pages = {28--44}, publisher = {{Elsevier Ltd}}, issn = {09521976}, doi = {10.1016/j.engappai.2018.02.006}, abbrev_source_title = {Eng Appl Artif Intell}, affiliation = {School of Engineering Science, University of Sk\"ovde, Sk\"ovde, 541 28, Sweden}, coden = {EAAIE}, correspondence_address1 = {Amouzgar, K.; School of Engineering Science, Sweden; email: kaveh.amouzgar@his.se}, document_type = {Article}, langid = {english}, source = {Scopus} } @inproceedings{amraniTridimensionalApproachStudying2012, title = {A {{Tridimensional Approach}} for {{Studying}} the {{Formal Verification}} of {{Model Transformations}}}, booktitle = {2012 {{IEEE Fifth International Conference}} on {{Software Testing}}, {{Verification}} and {{Validation}} ({{ICST}})}, author = {Amrani, M. and Lucio, L. and Selim, G. and Combemale, B. and Dingel, J. and Vangheluwe, H. and Le Traon, Y. and Cordy, J.R.}, year = {2012}, month = apr, pages = {921--928}, doi = {10.1109/ICST.2012.197}, 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.} } @phdthesis{AnalisiSperimentazioneDi, title = {Analisi e Sperimentazione Di {{Algoritmi}} Di {{Outlier Detection}} in {{Sistemi GDO}}} } @article{AnalysisLicenseInconsistency, title = {Analysis of License Inconsistency in Large Collections of Open Source Projects}, url = {http://rdcu.be/tez8} } @misc{AnalysisMetamodelingPractices, title = {An Analysis of Metamodeling Practices for {{MOF}} and {{OCL}}}, url = {http://www.sciencedirect.com/science/article/pii/S1477842415000068}, urldate = {2015-06-12} } @misc{AnalyzeUnderstandText, title = {Analyze and {{Understand Text}}: {{Guide}} to {{Natural Language Processing}} - {{Strumenta}}}, url = {https://tomassetti.me/guide-natural-language-processing/?utm_source=newsletter&utm_medium=email&utm_campaign=onboardingsequence}, urldate = {2021-02-01} } @article{Anavangot20216314, title = {Signal Source Distribution Approximation to Speedup Scalar Quantizer Design}, author = {Anavangot, V. and Kumar, A.}, year = {2021}, journal = {IEEE Transactions on Signal Processing}, volume = {69}, pages = {6314--6328}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {1053587X}, doi = {10.1109/TSP.2021.3125602}, 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. \textcopyright{} 1991-2012 IEEE.}, coden = {ITPRE}, document_type = {Article}, source = {Scopus} } @book{Anderson:2006:LTW:1197299, title = {The Long Tail: {{Why}} the Future of Business Is Selling Less of More}, author = {Anderson, Chris}, year = {2006}, publisher = {{Hyperion}}, isbn = {1-4013-0237-8} } @inproceedings{andSwingSWTBack2010, title = {Swing to {{SWT}} and Back: {{Patterns}} for {{API}} Migration by Wrapping}, booktitle = {2010 {{IEEE}} International Conference on Software Maintenance}, author = {{and}}, year = {2010}, month = sep, pages = {1--10}, issn = {1063-6773}, doi = {10.1109/ICSM.2010.5610429}, keywords = {Adaptive arrays,API migration re-engineering,application code,application program interfaces,Containers,graphical user interfaces,Graphical user interfaces,Java,object-oriented API,object-oriented methods,Open source software,open-source GUI wrapper,wrapper-based migration,wrapper-based re-implementation,wrapping,Wrapping,XML} } @article{anelliElliotComprehensiveRigorous2021, title = {Elliot: A {{Comprehensive}} and {{Rigorous Framework}} for {{Reproducible Recommender Systems Evaluation}}}, shorttitle = {Elliot}, author = {Anelli, Vito Walter and Bellog{\'i}n, Alejandro and Ferrara, Antonio and Malitesta, Daniele and Merra, Felice Antonio and Pomo, Claudio and Donini, Francesco Maria and Di Noia, Tommaso}, year = {2021}, month = mar, journal = {arXiv:2103.02590 [cs]}, eprint = {2103.02590}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2103.02590}, urldate = {2021-03-09}, 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).}, archiveprefix = {arXiv}, keywords = {Computer Science - Information Retrieval} } @article{anelliSemanticInterpretationTopN2020, title = {Semantic {{Interpretation}} of {{Top-N Recommendations}}}, author = {Anelli, Vito Walter and Di Noia, Tommaso and Di Sciascio, Eugenio and Ragone, Azzurra and Trotta, Joseph}, year = {2020}, journal = {IEEE Transactions on Knowledge and Data Engineering}, pages = {1--1}, issn = {1041-4347, 1558-2191, 2326-3865}, doi = {10.1109/TKDE.2020.3010215}, 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.}, langid = {english} } @misc{angeliniCrackingNutsSledgehammer2022, title = {Cracking Nuts with a Sledgehammer: When Modern Graph Neural Networks Do Worse than Classical Greedy Algorithms}, shorttitle = {Cracking Nuts with a Sledgehammer}, author = {Angelini, Maria Chiara and {Ricci-Tersenghi}, Federico}, year = {2022}, month = jun, number = {arXiv:2206.13211}, eprint = {2206.13211}, eprinttype = {arxiv}, primaryclass = {cond-mat}, publisher = {{arXiv}}, url = {http://arxiv.org/abs/2206.13211}, urldate = {2022-08-03}, abstract = {The recent work ``Combinatorial Optimization with Physics-Inspired Graph Neural Networks'' [Nat Mach Intell 4 (2022) 367] introduces a physics-inspired unsupervised Graph Neural Network (GNN) to solve combinatorial optimization problems on sparse graphs. To test the performances of these GNNs, the authors of the work show numerical results for two fundamental problems: maximum cut and maximum independent set (MIS). They conclude that "the graph neural network optimizer performs on par or outperforms existing solvers, with the ability to scale beyond the state of the art to problems with millions of variables." In this comment, we show that a simple greedy algorithm, running in almost linear time, can find solutions for the MIS problem of much better quality than the GNN. The greedy algorithm is faster by a factor of \$10\^4\$ with respect to the GNN for problems with a million variables. We do not see any good reason for solving the MIS with these GNN, as well as for using a sledgehammer to crack nuts. In general, many claims of superiority of neural networks in solving combinatorial problems are at risk of being not solid enough, since we lack standard benchmarks based on really hard problems. We propose one of such hard benchmarks, and we hope to see future neural network optimizers tested on these problems before any claim of superiority is made.}, archiveprefix = {arXiv}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Condensed Matter - Disordered Systems and Neural Networks,Mathematics - Optimization and Control} } @article{aNoSQLImplementationConceptual2018, title = {{{NoSQL Implementation}} of a {{Conceptual Data Model}} : {{UML Class Diagram}} to a {{Document Oriented Model}}}, shorttitle = {{{NoSQL Implementation}} of a {{Conceptual Data Model}}}, author = {A, Benmakhlouf}, year = {2018}, month = apr, journal = {International Journal of Database Management Systems}, volume = {10}, number = {2}, pages = {01--10}, issn = {09755985, 09755705}, doi = {10.5121/ijdms.2018.10201} } @article{Ansari2022190, title = {{{ESAR}}, an Expert Shoplifting Activity Recognition System}, author = {Ansari, M.A. and Singh, D.K.}, year = {2022}, journal = {Cybernetics and Information Technologies}, volume = {22}, number = {1}, pages = {190--200}, publisher = {{Sciendo}}, issn = {13119702}, doi = {10.2478/cait-2022-0012}, abbrev_source_title = {Cybern. Inf. Technol.}, affiliation = {CSED, MNNIT, Prayagraj, Allahabad, India}, document_type = {Article}, langid = {english}, source = {Scopus} } @incollection{antonioModelDrivenApproach2010, title = {A {{Model Driven Approach}} to {{Upgrade Package Based Software Systems}}}, booktitle = {Evaluation of {{Novel Approaches}} to {{Software Engineering}}}, author = {Antonio, Cicchetti and DI RUSCIO, Davide and Pelliccione, Patrizio and Pierantonio, Alfonso and Stefano, Zacchiroli}, year = {2010}, volume = {69}, pages = {262--276}, publisher = {{SPRINGER}}, address = {{HEIDELBERG}}, doi = {10.1007/978-3-642-14819-4}, isbn = {978-3-642-14818-7} } @article{ApplicationofAIandMLinIoTPdf, title = {{{ApplicationofAIandMLinIoT}}.Pdf}, keywords = {internet of things} } @article{aranegaUsingFeatureModel2012, title = {Using {{Feature Model}} to {{Build Model Transformation Chains}}}, author = {Aranega, Vincent and Etien, Anne and Mosser, Sebastien}, year = {2012}, journal = {Model Driven Engineering Languages and Systems}, volume = {7590}, pages = {562--578}, doi = {10.1007/978-3-642-33666-9_36} } @inproceedings{arcainiModelingAnalyzingMAPEK2015, title = {Modeling and {{Analyzing MAPE-K Feedback Loops}} for {{Self-Adaptation}}}, booktitle = {2015 {{IEEE}}/{{ACM}} 10th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}}}, author = {Arcaini, Paolo and Riccobene, Elvinia and Scandurra, Patrizia}, year = {2015}, month = may, pages = {13--23}, publisher = {{IEEE}}, address = {{Florence, Italy}}, doi = {10.1109/SEAMS.2015.10}, 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.}, isbn = {978-0-7695-5567-6}, langid = {english} } @inproceedings{arcelliApplyingModelDifferences2013, title = {Applying Model Differences to Automate Performance-Driven Refactoring of Software Models}, booktitle = {Computer {{Performance Engineering}} - 10th {{European Workshop}}, {{EPEW}} 2013, {{Venice}}, {{Italy}}, {{September}} 16-17, 2013. {{Proceedings}}. {{Lecture Notes}} in {{Computer Science}}}, author = {Arcelli, D and Cortellessa, Vittorio and DI RUSCIO, Davide}, year = {2013}, volume = {8168}, pages = {312--324}, publisher = {{Springer}}, doi = {10.1007/978-3-642-40725-3_24}, 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.}, isbn = {978-3-642-40724-6}, keywords = {Computer Science (all),Theoretical Computer Science} } @misc{ArchivaDocumentationInstalling, title = {Archiva {{Documentation}} - {{Installing Apache Archiva}}}, url = {http://archiva.apache.org/docs/2.2.0/adminguide/installing.html}, urldate = {2015-04-16} } @article{Areferencearchitecturefortheinternetofthings, title = {A-Reference-Architecture-for-the-Internet-of-Things}, keywords = {iot,reference architecture,se4as} } @article{arendtEMFMetricsSpecification, title = {{{EMF Metrics}}: {{Specification}} and {{Calculation}} of {{Model Metrics}} within the {{Eclipse Modeling Framework}}}, author = {Arendt, Thorsten and Stepien, Pawel and Taentzer, Gabriele} } @article{arendtIntegrationSmellsRefactorings2012, title = {Integration of Smells and Refactorings within the {{Eclipse}} Modeling Framework}, author = {Arendt, Thorsten and Taentzer, Gabriele}, year = {2012}, journal = {Proceedings of the Fifth Workshop on Refactoring Tools - WRT '12}, pages = {8--15}, doi = {10.1145/2328876.2328878} } @article{arendtToolEnvironmentQuality2012, title = {A Tool Environment for Quality Assurance Based on the {{Eclipse Modeling Framework}}}, author = {Arendt, Thorsten and Taentzer, Gabriele}, year = {2012}, journal = {Automated Software Engineering}, volume = {20}, number = {2}, pages = {141--184}, doi = {10.1007/s10515-012-0114-7} } @book{arhippainenUseIntegrationThridparty2003, title = {Use and Integration of Thrid-Party Components in Software Development}, author = {Arhippainen, Leena}, year = {2003}, series = {{{VTT}} Publications}, number = {489}, publisher = {{VTT}}, address = {{Espoo}}, isbn = {978-951-38-6032-5 978-951-38-6033-2}, langid = {english}, keywords = {software engineering} } @inproceedings{ariasOrccadRobotController2010, title = {Orccad, Robot Controller Model and Its Support Using Eclipse Modeling Tools}, booktitle = {5th {{National Conference}} on {{Control Architecture}} of {{Robots}}}, author = {Arias, Soraya and Boudin, Florine and {Pissard-Gibollet}, Roger and Simon, Daniel}, year = {2010}, url = {https://hal.archives-ouvertes.fr/inria-00482559/}, urldate = {2016-08-21} } @article{arkinModelDrivenTransformationsMapping2013, title = {Model-{{Driven Transformations}} for {{Mapping Parallel Algorithms}} on {{Parallel Computing Platforms}}.}, author = {Arkin, Ethem and Tekinerdogan, Bedir}, year = {2013}, journal = {MDHPCL@ MoDELS}, volume = {2013}, pages = {63--72}, url = {http://ceur-ws.org/Vol-1118/08-paper.pdf}, urldate = {2017-02-23} } @article{arlot2010, ids = {arlot2010survey}, title = {A Survey of Cross-Validation Procedures for Model Selection}, author = {Arlot, Sylvain and Celisse, Alain}, year = {2010}, journal = {Statist. Surv.}, volume = {4}, pages = {40--79}, publisher = {{The American Statistical Association, the Bernoulli Society, the Institute of Mathematical Statistics, and the Statistical Society of Canada}}, added-at = {2017-04-15T09:32:51.000+0200}, biburl = {https://www.bibsonomy.org/bibtex/295dfb73fc5adfb8b1c5dba6435132f15/becker}, fjournal = {Statistics Surveys}, interhash = {ce81fa08863c054ec939cc798387b0b8}, intrahash = {95dfb73fc5adfb8b1c5dba6435132f15}, nodoi = {10.1214/09-SS054}, keywords = {cross validation inthesis diss citedby:scholar:count:1216 citedby:scholar:timestamp:2017-4-15}, timestamp = {2017-04-15T09:32:51.000+0200} } @article{Aspenberg2013245, title = {Robust Optimization of Front Members in a Full Frontal Car Impact}, author = {Aspenberg, D. and Jergeus, J. and Nilsson, L.}, year = {2013}, journal = {Engineering Optimization}, volume = {45}, number = {3}, pages = {245--264}, issn = {0305215X}, doi = {10.1080/0305215X.2012.669380}, abbrev_source_title = {Eng Optim}, affiliation = {Division of Solid Mechanics, Link\"oping University, SE-581 83, Link\"oping, Sweden; Volvo Cars Safety Centre, SE-405 31, G\"oteborg, Sweden}, coden = {EGOPA}, correspondence_address1 = {Aspenberg, D.; Division of Solid Mechanics, , SE-581 83, Link\"oping, Sweden; email: david.aspenberg@liu.se}, document_type = {Article}, langid = {english}, source = {Scopus} } @incollection{assmannReferenceArchitectureRoadmap2014, title = {A Reference Architecture and Roadmap for {{Models}}@ Run. Time Systems}, booktitle = {Models@ Run. Time}, author = {{A{\textbackslash}s smann}, Uwe and G{\"o}tz, Sebastian and J{\'e}z{\'e}quel, Jean-Marc and Morin, Brice and Trapp, Mario}, year = {2014}, pages = {1--18}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/978-3-319-08915-7_1}, urldate = {2016-08-21} } @article{atkinsonUnifyingApproachConnections, title = {A {{Unifying Approach}} to {{Connections}} for {{Multi-Level Modeling}}}, author = {Atkinson, Colin and Gerbig, Ralph and K{\"u}hne, Thomas}, url = {http://homepages.ecs.vuw.ac.nz/~tk/publications/papers/deep-connections.pdf}, urldate = {2015-09-24} } @inproceedings{Atouani202155, title = {Artifact and Reference Models for Generative Machine Learning Frameworks and Build Systems}, author = {Atouani, A. and Kirchhof, J.C. and Kusmenko, E. and Rumpe, B.}, editor = {Tilevich E., De Roover C.}, year = {2021}, series = {{{GPCE}} 2021 - {{Proceedings}} of the 20th {{ACM SIGPLAN International Conference}} on {{Generative Programming}}: {{Concepts}} and {{Experiences}}, Co-Located with {{SPLASH}} 2021}, pages = {55--68}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3486609.3487199}, abbrev_source_title = {GPCE - Proc. ACM SIGPLAN Int. Conf. Gener. Program.: Concepts Exp., co-located SPLASH}, affiliation = {Rwth Aachen University, Germany}, document_type = {Conference Paper}, isbn = {978-1-4503-9112-2}, langid = {english}, source = {Scopus} } @article{atouaniArtifactReferenceModels2021, title = {Artifact and {{Reference Models}} for {{Generative Machine Learning Frameworks}} and {{Build Systems}}}, author = {Atouani, Abdallah and Kirchhof, J{\"o}rg Christian and Kusmenko, Evgeny and Rumpe, Bernhard}, year = {2021}, pages = {14}, 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.}, langid = {english} } @article{atzeniDataModelDescriptions2011, title = {Data Model Descriptions and Translation Signatures in a Multi-Model Framework}, author = {Atzeni, Paolo and Gianforme, Giorgio and Cappellari, Paolo}, year = {2011}, month = dec, journal = {Annals of Mathematics and Artificial Intelligence}, volume = {63}, number = {3-4}, pages = {287--315}, issn = {1012-2443, 1573-7470}, doi = {10.1007/s10472-012-9277-y}, langid = {english} } @article{atzeniModelindependentSchemaTranslation2008, title = {Model-Independent Schema Translation}, author = {Atzeni, Paolo and Cappellari, Paolo and Torlone, Riccardo and Bernstein, Philip A. and Gianforme, Giorgio}, year = {2008}, month = nov, journal = {The VLDB Journal}, volume = {17}, number = {6}, pages = {1347--1370}, issn = {1066-8888, 0949-877X}, doi = {10.1007/s00778-008-0105-2}, langid = {english} } @incollection{atzeniModelsNoSQLDatabases2015, title = {Models for {{NoSQL Databases}}: {{A Contradiction}}?}, shorttitle = {Models for {{NoSQL Databases}}}, booktitle = {Advances in {{Conceptual Modeling}}}, author = {Atzeni, Paolo}, editor = {Jeusfeld, Manfred A. and Karlapalem, Kamalakar}, year = {2015}, volume = {9382}, pages = {133--133}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-319-25747-1_13}, isbn = {978-3-319-25746-4 978-3-319-25747-1}, langid = {english} } @article{atzeniRuntimeApproachModelgeneric2012, title = {A Runtime Approach to Model-Generic Translation of Schema and Data}, author = {Atzeni, Paolo and Bellomarini, Luigi and Bugiotti, Francesca and Celli, Fabrizio and Gianforme, Giorgio}, year = {2012}, month = may, journal = {Information Systems}, volume = {37}, number = {3}, pages = {269--287}, issn = {03064379}, doi = {10.1016/j.is.2011.11.003}, langid = {english} } @article{atzeniUniformAccessNoSQL2014, title = {Uniform Access to {{NoSQL}} Systems}, author = {Atzeni, Paolo and Bugiotti, Francesca and Rossi, Luca}, year = {2014}, month = jul, journal = {Information Systems}, volume = {43}, pages = {117--133}, issn = {03064379}, doi = {10.1016/j.is.2013.05.002}, langid = {english} } @incollection{atzeniUniversalMetamodelIts2009, title = {A {{Universal Metamodel}} and {{Its Dictionary}}}, booktitle = {Transactions on {{Large-Scale Data-}} and {{Knowledge-Centered Systems I}}}, author = {Atzeni, Paolo and Gianforme, Giorgio and Cappellari, Paolo}, editor = {Hameurlain, Abdelkader and K{\"u}ng, Josef and Wagner, Roland}, year = {2009}, volume = {5740}, pages = {38--62}, publisher = {{Springer Berlin Heidelberg}}, address = {{Berlin, Heidelberg}}, doi = {10.1007/978-3-642-03722-1_2}, 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.}, isbn = {978-3-642-03721-4 978-3-642-03722-1}, langid = {english} } @article{atzoriInternetThingsSurvey2010a, ids = {atzoriInternetThingsSurvey2010}, title = {The {{Internet}} of {{Things}}: {{A}} Survey}, shorttitle = {The {{Internet}} of {{Things}}}, author = {Atzori, Luigi and Iera, Antonio and Morabito, Giacomo}, year = {2010}, month = oct, journal = {Computer Networks}, volume = {54}, number = {15}, pages = {2787--2805}, issn = {13891286}, doi = {10.1016/j.comnet.2010.05.010}, 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.}, langid = {english}, keywords = {relevant} } @article{augusteijnNeuralNetworkClassification2002, title = {Neural Network Classification and Novelty Detection}, author = {Augusteijn, M. F. and {B. A. Folkert}}, year = {2002}, journal = {International Journal of Remote Sensing}, volume = {23}, number = {14}, pages = {2891--2902}, publisher = {{Taylor \& Francis}}, nodoi = {10.1080/01431160110055804} } @article{authorTopFilterApproachRecommend2020, title = {{{TopFilter}}: {{An Approach}} to {{Recommend Relevant GitHub Topics}}}, author = {Author, Anonymous}, year = {2020}, pages = {11}, langid = {english} } @inproceedings{autiliDevelopmentProcessContextAware2008, title = {A {{Development Process}} for {{Context-Aware Adaptive Services}}}, booktitle = {23rd {{IEEE}}/{{ACM International Conference}} on {{Automated Software Engineering}} - {{Workshop Proceedings}} ({{ASE Workshops}} 2008)}, author = {Autili, Marco and DI BENEDETTO, P and DI RUSCIO, Davide and Inverardi, Paola and Tivoli, Massimo}, year = {2008}, pages = {9--16}, publisher = {{IEEE Computer Society}}, address = {{NEW YORK}}, doi = {10.1109/ASEW.2008.4686288}, isbn = {978-1-4244-2776-5} } @inproceedings{autiliDevelopmentProcessRequirements2011, title = {A {{Development Process}} for {{Requirements Based Service Choreography}}}, booktitle = {Workshop on {{Requirements Engineering}} for {{Systems}}, {{Services}} and {{Systems-of-Systems}} ({{RESS}})}, author = {Autili, M and Di Ruscio, D and Inverardi, P and Lockerbie, J and Tivoli, M}, year = {2011}, pages = {59--62}, publisher = {{IEEE Computer Society}}, address = {{NEW YORK}}, doi = {10.1109/RESS.2011.6043925}, 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.}, isbn = {978-1-4577-0939-5} } @inproceedings{autiliDevelopmentProcessSelfadapting2007, title = {A {{Development Process}} for {{Self-adapting Service Oriented Applications}}}, booktitle = {Proceedings of the {{International Conference}} on {{Service-oriented Computing}} ({{ICSOC}})}, author = {Autili, Marco and Cortellessa, Vittorio and DI MARCO, Antinisca and DI RUSCIO, Davide and Inverardi, Paola and Tivoli, Massimo}, year = {2007}, volume = {4749}, pages = {442--448}, publisher = {{Springer-Verlag}}, address = {{BERLIN HEIDELBERG}}, doi = {10.1007/978-3-540-74974-5_41}, isbn = {978-3-540-74973-8} } @inproceedings{autiliDevelopmentProcessSelfadapting2007a, title = {A {{Development Process}} for {{Self-adapting Service Oriented Applications}}}, booktitle = {Proceedings of the {{International Conference}} on {{Service-oriented Computing}} ({{ICSOC}})}, author = {Autili, Marco and Cortellessa, Vittorio and DI MARCO, Antinisca and DI RUSCIO, Davide and Inverardi, Paola and Tivoli, Massimo}, year = {2007}, volume = {4749}, pages = {442--448}, publisher = {{Springer-Verlag}}, address = {{BERLIN HEIDELBERG}}, doi = {10.1007/978-3-540-74974-5_41}, isbn = {978-3-540-74973-8} } @inproceedings{autiliEAGLEEngineeringSoftwAre2011, title = {{{EAGLE}}: {{Engineering softwAre}} in the Ubiquitous {{Globe}} by {{Leveraging uncErtainty}}}, booktitle = {Proceedings of the 19th {{ACM SIGSOFT}} Symposium and the 13th {{European}} Conference on {{Foundations}} of Software Engineering}, author = {Autili, M and Cortellessa, V and Di Ruscio, D and Inverardi, P and Pelliccione, P and Tivoli, M}, year = {2011}, pages = {488--491}, publisher = {{Association for Computing Machinery, Inc. (ACM)}}, address = {{NEW YORK, NY, USA}}, doi = {10.1145/2025113.2025199}, 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."}, isbn = {978-1-4503-0443-6} } @inproceedings{autiliIntegrationArchitectureSynthesis2012, title = {Integration {{Architecture Synthesis}} for {{Taming Uncertainty}} in the {{Digital Space}}}, booktitle = {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}}}, author = {Autili, Marco and Cortellessa, Vittorio and DI RUSCIO, Davide and Inverardi, Paola and Pelliccione, Patrizio and Tivoli, Massimo}, year = {2012}, volume = {7539}, pages = {118--131}, publisher = {{Springer-Verlag}}, address = {{BERLIN HEIDELBERG}}, doi = {10.1007/978-3-642-34059-8_6}, abstract = {"\textbackslash "\textbackslash "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. \^A\textcopyright{} 2012 Springer-Verlag.\textbackslash "\textbackslash ""}, isbn = {978-3-642-34058-1} } @inproceedings{autiliIntegrationArchitectureSynthesis2012a, title = {Integration {{Architecture Synthesis}} for {{Taming Uncertainty}} in the {{Digital Space}}}, booktitle = {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}}}, author = {Autili, Marco and Cortellessa, Vittorio and DI RUSCIO, Davide and Inverardi, Paola and Pelliccione, Patrizio and Tivoli, Massimo}, year = {2012}, volume = {7539}, pages = {118--131}, publisher = {{Springer-Verlag}}, address = {{BERLIN HEIDELBERG}}, doi = {10.1007/978-3-642-34059-8_6}, abstract = {"\textbackslash "\textbackslash "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. \^A\textcopyright{} 2012 Springer-Verlag.\textbackslash "\textbackslash ""}, isbn = {978-3-642-34058-1} } @inproceedings{autiliModelbasedSynthesisProcess2013, title = {A Model-Based Synthesis Process for Choreography Realizability Enforcement}, booktitle = {Fundamental {{Approaches}} to {{Software Engineering}} ({{FASE}} 2013)}, author = {Autili, Marco and DI RUSCIO, Davide and DI SALLE, Amleto and Inverardi, Paola and Tivoli, Massimo}, year = {2013}, volume = {7793}, pages = {37--52}, publisher = {{Springer Berlin Heidelberg}}, doi = {10.1007/978-3-642-37057-1_4}, abstract = {""The near future in service-oriented system development envisions a ubiquitous world of available services that collaborate to fit users\^a\texteuro\texttrademark{} 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\^a\texteuro\texttrademark 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\^a\texteuro\texttrademark{} 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.""}, isbn = {978-3-642-37056-4}, keywords = {Choreography Realizability Enforcement,Service Choreographies,Service Oriented Architectures} } @inproceedings{autiliModelLANDWhereModels2011, title = {{{ModelLAND}}: {{Where Do Models Come}} From?}, booktitle = {Models@run.Time - {{Foundations}}, {{Applications}}, and {{Roadmaps}} [{{Dagstuhl Seminar}} 11481, {{November}} 27 - {{December}} 2, 2011]}, author = {Autili, Marco and Ruscio, Davide Di and Inverardi, Paola and Pelliccione, Patrizio and Tivoli, Massimo}, editor = {Bencomo, Nelly and France, Robert B. and Cheng, Betty H. C. and A{\ss}mann, Uwe}, year = {2011}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {8378}, pages = {162--187}, publisher = {{Springer}}, doi = {10.1007/978-3-319-08915-7_6} } @incollection{autiliModelLANDWhereModels2014, title = {{{ModelLAND}}: {{Where}} Do Models Come From?}, booktitle = {Models@run.Time - {{Foundations}}, {{Applications}}, and {{Roadmaps}} [{{Dagstuhl Seminar}} 11481, {{November}} 27 - {{December}} 2, 2011]}, author = {Autili, Marco and DI RUSCIO, Davide and Inverardi, Paola and Pelliccione, Patrizio and Tivoli, Massimo}, year = {2014}, volume = {LNCS 8378}, pages = {162--187}, publisher = {{Springer Verlag}}, doi = {10.1007/978-3-319-08915-7_6}, isbn = {978-3-319-08914-0}, keywords = {Computer Science (all),Theoretical Computer Science} } @incollection{autiliModelLANDWhereModels2014a, title = {{{ModelLAND}}: {{Where}} Do Models Come From?}, booktitle = {Models@run.Time - {{Foundations}}, {{Applications}}, and {{Roadmaps}} [{{Dagstuhl Seminar}} 11481, {{November}} 27 - {{December}} 2, 2011]}, author = {Autili, Marco and DI RUSCIO, Davide and Inverardi, Paola and Pelliccione, Patrizio and Tivoli, Massimo}, year = {2014}, volume = {LNCS 8378}, pages = {162--187}, publisher = {{Springer Verlag}}, doi = {10.1007/978-3-319-08915-7_6}, isbn = {978-3-319-08914-0}, keywords = {Computer Science (all),Theoretical Computer Science} } @inproceedings{autiliProvidingLightweightAdaptable2012, title = {Providing Lightweight and Adaptable Service Technology for Information and Communication ({{PLASTIC}}) in the Mobile {{eHealth}} Case Study}, booktitle = {{{ICSE Workshop}} on {{Principles}} of {{Engineering Service Oriented Systems}}}, author = {Autili, Marco and Berardinelli, Luca and DI RUSCIO, Davide and Trubiani, Catia}, year = {2012}, pages = {69--70}, publisher = {{IEEE Computer Society}}, address = {{NEW YORK}}, doi = {10.1109/PESOS.2012.6225946}, 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.}, isbn = {978-1-4673-1754-2}, keywords = {Software} } @misc{AutonomousSemiAutonomousSoftware, title = {Autonomous and {{Semi-Autonomous Software Systems}}}, url = {http://aosgrp.com/featured-research/autonomy_and_agents/autonomous_systems/autonomous_and_semi-autonom.html}, urldate = {2016-08-24} } @misc{AutonomousSystems, title = {Autonomous {{Systems}}}, url = {https://www.cranfield.ac.uk/Academic%20disciplines/Autonomous-Systems}, urldate = {2016-08-21} } @misc{AutonomousSystemsFormerly, title = {Autonomous {{Systems}} (Formerly {{Unmanned Systems}})}, url = {http://www.northropgrumman.com/Capabilities/AutonomousSystems/Pages/default.aspx}, urldate = {2016-08-26} } @misc{Autonomy, title = {Autonomy}, url = {http://aosgrp.com/featured-research/autonomy_and_agents/autonomous_systems/autonomy.html}, urldate = {2016-08-24} } @article{AutoTaskLearningGenerate2021, title = {{{AutoTask}}: {{Learning}} to {{Generate Machine Learning Pipelines}}}, year = {2021}, pages = {11}, langid = {english} } @article{avgeriouOverviewComparisonTechnical2021, title = {An {{Overview}} and {{Comparison}} of {{Technical Debt Measurement Tools}}}, author = {Avgeriou, Paris C. and Taibi, Davide and Ampatzoglou, Apostolos and Fontana, Francesca Arcelli and Besker, Terese and Chatzigeorgiou, Alexander and Lenarduzzi, Valentina and Martini, Antonio and Moschou, Athanasia and Pigazzini, Ilaria and Saarimaki, Nyyti and Sas, Darius Daniel and de Toledo, Saulo Soares and Tsintzira, Angeliki Agathi}, year = {2021}, month = may, journal = {IEEE Software}, volume = {38}, number = {03}, pages = {61--71}, publisher = {{IEEE Computer Society}}, issn = {0740-7459}, doi = {10.1109/MS.2020.3024958}, 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.}, langid = {english} } @article{avitabileDefeatingMassSurveillance, title = {Towards {{Defeating Mass Surveillance}} and {{SARS-CoV-2}}: {{The Pronto-C2 Fully Decentralized Automatic Contact Tracing System}}}, author = {Avitabile, Gennaro and Botta, Vincenzo and Iovino, Vincenzo and Visconti, Ivan}, pages = {25}, 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.}, langid = {english} } @inproceedings{azzaraPyoTMacroprogrammingFramework2014, title = {{{PyoT}}, a Macroprogramming Framework for the {{Internet}} of {{Things}}}, booktitle = {Industrial {{Embedded Systems}} ({{SIES}}), 2014 9th {{IEEE International Symposium}} On}, author = {Azzara, Andrea and Alessandrelli, Daniele and Bocchino, Stefano and Petracca, Matteo and Pagano, Paolo}, year = {2014}, pages = {96--103}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6871193}, urldate = {2016-05-30} } @article{baba-cheikhPreliminaryStudyOpensource2020, title = {A Preliminary Study of Open-Source {{IoT}} Development Frameworks}, author = {{Baba-Cheikh}, Zeineb and {El-Boussaidi}, Ghizlane and {Gascon-Samson}, Julien and Mili, Hafedh and Gu{\'e}h{\'e}neuc, Yann-Gael}, year = {2020}, pages = {8}, 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.}, langid = {english} } @inproceedings{Babur2016888, title = {Statistical Analysis of Large Sets of Models}, author = {Babur, {\"O}.}, editor = {Khurshid S., Lo D., Apel S.}, year = {2016}, series = {{{ASE}} 2016 - {{Proceedings}} of the 31st {{IEEE}}/{{ACM International Conference}} on {{Automated Software Engineering}}}, pages = {888--891}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/2970276.2975938}, abbrev_source_title = {ASE - Proc. IEEE/ACM Int. Conf. Autom. Softw. Eng.}, affiliation = {Eindhoven University of Technology, Eindhoven, 5600 MB, Netherlands}, correspondence_address1 = {Babur, \"O.; Eindhoven University of TechnologyNetherlands; email: O.Babur@tue.nl}, document_type = {Conference Paper}, isbn = {978-1-4503-3845-5}, langid = {english}, source = {Scopus} } @article{Babur2018129, title = {Models, More Models, and Then a Lot More}, author = {Babur, {\"O}. and Cleophas, L. and {van den Brand}, M. and Tekinerdogan, B. and Aksit, M.}, editor = {Zschaler S., Seidl M.}, year = {2018}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {10748 LNCS}, pages = {129--135}, publisher = {{Springer Verlag}}, issn = {03029743}, doi = {10.1007/978-3-319-74730-9_10}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Eindhoven University of Technology, Eindhoven, Netherlands; Wageningen University \& Research, Wageningen, Netherlands; University of Twente, Enschede, Netherlands}, correspondence_address1 = {Babur, \"O.; Eindhoven University of TechnologyNetherlands; email: O.Babur@tue.nl}, document_type = {Conference Paper}, isbn = {9783319747293}, langid = {english}, source = {Scopus} } @incollection{Babur2018778, title = {{{AMMoRe}} 2018: {{First}} International Workshop on Analytics and Mining of Model Repositories}, booktitle = {{{CEUR Workshop Proceedings}}}, author = {Babur, O. and Chaudron, M. R. V. and Cleophas, L. and Di Ruscio, D. and Kolovos, D.}, year = {2018}, series = {{{CEUR Workshop Proceedings}}}, volume = {2245}, pages = {778--779}, publisher = {{CEUR-WS}}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063109105&partnerID=40&md5=9b6af321cedc951abe47939224d38e2b}, 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. \textcopyright{} 2018 CEUR-WS. All rights reserved.}, document_type = {Conference Paper}, source = {Scopus} } @inproceedings{Babur2018778, title = {{{AMMoRe}} 2018: {{First}} International Workshop on Analytics and Mining of Model Repositories}, author = {Babur, {\"O}. and Chaudron, M.R.V. and Cleophas, L. and Ruscio, D.D. and Kolovos, D.}, editor = {Hebig R., Berger T.}, year = {2018}, series = {{{CEUR Workshop Proceedings}}}, volume = {2245}, pages = {778--779}, publisher = {{CEUR-WS}}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063109105&partnerID=40&md5=9b6af321cedc951abe47939224d38e2b}, abbrev_source_title = {CEUR Workshop Proc.}, affiliation = {Eindhoven University of Technology, Eindhoven, Netherlands; Chalmers | University of Gothenburg, Gothenburg, Sweden; Eindhoven University of Technology Eindhoven, Netherlands Stellenbosch University, Stellenbosch, South Africa; University of L'Aquila, L'Aquila, Italy; University of York York, United Kingdom}, document_type = {Conference Paper}, langid = {english}, source = {Scopus} } @incollection{baburAMMoRe2018First2018, title = {{{AMMoRe}} 2018: {{First}} International Workshop on Analytics and Mining of Model Repositories}, booktitle = {{{CEUR Workshop Proceedings}}}, author = {Babur, O. and Chaudron, M. R. V. and Cleophas, L. and Di Ruscio, D. and Kolovos, D.}, year = {2018}, volume = {2245}, pages = {778--779}, publisher = {{CEUR-WS}} } @incollection{baburAMMoRe2018First2018a, title = {{{AMMoRe}} 2018: {{First}} International Workshop on Analytics and Mining of Model Repositories}, booktitle = {{{CEUR Workshop Proceedings}}}, author = {Babur, O. and Chaudron, M. R. V. and Cleophas, L. and Di Ruscio, D. and Kolovos, D.}, year = {2018}, volume = {2245}, pages = {778--779}, publisher = {{CEUR-WS}} } @incollection{baburAMMoRe2018First2018b, title = {{{AMMoRe}} 2018: {{First}} International Workshop on Analytics and Mining of Model Repositories}, booktitle = {{{CEUR Workshop Proceedings}}}, author = {Babur, O. and Chaudron, M. R. V. and Cleophas, L. and Di Ruscio, D. and Kolovos, D.}, year = {2018}, volume = {2245}, pages = {778--779}, publisher = {{CEUR-WS}} } @misc{baburLabeledEcoreMetamodel2019, title = {A Labeled {{Ecore}} Metamodel Dataset for Domain Clustering}, author = {Babur, {\"O}nder}, year = {2019}, month = mar, publisher = {{Zenodo}}, doi = {10.5281/ZENODO.2585456}, abstract = {Manually labeled 555 metamodels mined from GitHub in April 2017. Domains: (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 Procedure 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. Format for the file names: ABSINDEX\_CLUSTER\_ITEMINDEX\_name\_hash.ecore}, copyright = {Creative Commons Attribution 4.0 International, Open Access}, keywords = {clustering,metamodel,model analytics,model-driven engineering} } @article{Bachinger2020263, title = {Concept for a Technical Infrastructure for Management of Predictive Models in Industrial Applications}, author = {Bachinger, F. and Kronberger, G.}, editor = {{Moreno-Diaz R., Quesada-Arencibia A.}, Pichler F.}, year = {2020}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {12013 LNCS}, pages = {263--270}, publisher = {{Springer}}, issn = {03029743}, doi = {10.1007/978-3-030-45093-9_32}, 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. \textcopyright{} 2020, Springer Nature Switzerland AG.}, document_type = {Conference Paper}, isbn = {9783030450922}, source = {Scopus} } @article{Bae2015, title = {A Personal Credit Rating Prediction Model Using Data Mining in Smart Ubiquitous Environments}, author = {Bae, J.K. and Kim, J.}, year = {2015}, journal = {International Journal of Distributed Sensor Networks}, volume = {2015}, publisher = {{Hindawi Publishing Corporation}}, issn = {15501329}, doi = {10.1155/2015/179060}, abbrev_source_title = {Int. J. Distrib. Sens. Netw.}, affiliation = {Department of Management Information Systems, Keimyung University, 1095 Dalgubeoldaero, Daegu, 704-701, South Korea; School of Business, Sogang University, 1 Sinsu-dong, Seoul, 121-742, South Korea}, art_number = {179060}, correspondence_address1 = {Bae, J.K.; Department of Management Information Systems, Keimyung University, 1095 Dalgubeoldaero, South Korea}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{Bae2022, title = {Limit Surface/States Searching Algorithm with a Deep Neural Network and {{Monte Carlo}} Dropout for Nuclear Power Plant Safety Assessment}, author = {Bae, J. and Park, J.W. and Lee, S.J.}, year = {2022}, journal = {Applied Soft Computing}, volume = {124}, publisher = {{Elsevier Ltd}}, issn = {15684946}, doi = {10.1016/j.asoc.2022.109007}, abbrev_source_title = {Appl. Soft Comput.}, affiliation = {Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulsan, 44919, South Korea}, art_number = {109007}, correspondence_address1 = {Lee, S.J.; Department of Nuclear Engineering, 50 UNIST-gil, South Korea; email: sjlee420@unist.ac.kr}, document_type = {Article}, langid = {english}, source = {Scopus} } @inproceedings{bagnatoDeveloperCentricKnowledgeMining2017, title = {Developer-{{Centric Knowledge Mining}} from {{Large Open-Source Software Repositories}} ({{CROSSMINER}})}, booktitle = {Software {{Technologies}}: {{Applications}} and {{Foundations}} - {{STAF}} 2017 {{Collocated Workshops}}, {{Marburg}}, {{Germany}}, {{July}} 17-21, 2017, {{Revised Selected Papers}}}, author = {Bagnato, Alessandra and Barmpis, Konstantinos and Bessis, Nik and {Cabrera-Diego}, Luis Adri{\'a}n and Rocco, Juri Di and Ruscio, Davide Di and Gergely, Tam{\'a}s and Hansen, Scott and Kolovos, Dimitris S. and Krief, Philippe and Korkontzelos, Ioannis and Lauri{\`e}re, St{\'e}phane and de la Fuente, Jose Manrique Lopez and Mal{\'o}, Pedro and Paige, Richard F. and Spinellis, Diomidis and Thomas, Cedric and Vinju, Jurgen J.}, editor = {Seidl, Martina and Zschaler, Steffen}, year = {2017}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {10748}, pages = {375--384}, publisher = {{Springer}}, doi = {10.1007/978-3-319-74730-9_33} } @article{Bai2022112, title = {Prior Information Aided Deep Learning Method for Grant-Free {{NOMA}} in {{mMTC}}}, author = {Bai, Y. and Chen, W. and Ai, B. and Zhong, Z. and Wassell, I.J.}, year = {2022}, journal = {IEEE Journal on Selected Areas in Communications}, volume = {40}, number = {1}, pages = {112--126}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {07338716}, doi = {10.1109/JSAC.2021.3126071}, 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. \textcopyright{} 1983-2012 IEEE.}, coden = {ISACE}, document_type = {Article}, source = {Scopus} } @unpublished{bakerSingularValueDecomposition2005, title = {Singular Value Decomposition Tutorial}, author = {Baker, Kirk and Baker, Kirk}, year = {2005}, added-at = {2009-04-17T10:27:21.000+0200}, biburl = {https://www.bibsonomy.org/bibtex/2abdcfb3746e47898ac93929ab88298af/voigtmannc}, interhash = {52abe308f6c46087df51a5876c1ab6e9}, intrahash = {abdcfb3746e47898ac93929ab88298af}, keywords = {algorithm datamining decomposition singular svd tutorial}, timestamp = {2009-04-17T10:27:21.000+0200} } @article{balabanPatternbasedApproachImproving2015, title = {A Pattern-Based Approach for Improving Model Quality}, author = {Balaban, Mira and Maraee, Azzam and Sturm, Arnon and Jelnov, Pavel}, year = {2015}, month = oct, journal = {Software \& Systems Modeling}, volume = {14}, number = {4}, pages = {1527--1555}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-013-0390-0}, langid = {english} } @article{Balasubramanian20153, title = {{{DREMS ML}}: {{A}} Wide Spectrum Architecture Design Language for Distributed Computing Platforms}, author = {Balasubramanian, D. and Dubey, A. and Otte, W. and Levendovszky, T. and Gokhale, A. and Kumar, P. and Emfinger, W. and Karsai, G.}, year = {2015}, journal = {Science of Computer Programming}, volume = {106}, pages = {3--29}, publisher = {{Elsevier B.V.}}, issn = {01676423}, doi = {10.1016/j.scico.2015.04.002}, 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. \textcopyright{} 2015 Elsevier B.V. All rights reserved.}, coden = {SCPGD}, document_type = {Article}, source = {Scopus} } @article{balogDeepCoderLearningWrite2016, title = {{{DeepCoder}}: {{Learning}} to {{Write Programs}}}, shorttitle = {{{DeepCoder}}}, author = {Balog, Matej and Gaunt, Alexander L. and Brockschmidt, Marc and Nowozin, Sebastian and Tarlow, Daniel}, year = {2016}, journal = {arXiv preprint arXiv:1611.01989}, eprint = {1611.01989}, eprinttype = {arxiv}, url = {https://arxiv.org/abs/1611.01989}, urldate = {2017-02-25}, archiveprefix = {arXiv} } @inproceedings{baltesSOTorrentReconstructingAnalyzing2018, title = {{{SOTorrent}}: {{Reconstructing}} and Analyzing the Evolution of Stack Overflow Posts}, booktitle = {Proceedings of the 15th International Conference on Mining Software Repositories}, author = {Baltes, Sebastian and Dumani, Lorik and Treude, Christoph and Diehl, Stephan}, year = {2018}, series = {{{MSR}} '18}, pages = {319--330}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/3196398.3196430}, acmid = {3196430}, isbn = {978-1-4503-5716-6}, nodoi = {10.1145/3196398.3196430}, numpages = {12}, keywords = {code snippets,open dataset,software evolution,stack overflow} } @inproceedings{balzeraniProductLineArchitecture2005, title = {A {{Product Line Architecture}} for {{Web Applications}}}, booktitle = {Proc. {{ACM Symposium}} on {{Applied Computing}} ({{SAC}} 2005), {{Special Track}} on {{Web Technologies}} and {{Applications}}, {{ACM Press}}}, author = {Balzerani, L and DI RUSCIO, Davide and Pierantonio, Alfonso and De Angelis, G.}, year = {2005}, doi = {10.1145/1066677.1067059} } @article{balzeraniSupportingWebApplications2006, title = {Supporting {{Web Applications}} Development with a {{PLA}}}, author = {Balzerani, Luca and Angelis, Guglielmo De and Ruscio, Davide Di and Pierantonio, Alfonso}, year = {2006}, journal = {J. Web Eng.}, volume = {5}, number = {1}, pages = {25--42}, url = {http://www.rintonpress.com/xjwe5/jwe-5-1/025-042.pdf} } @book{banchsInformationRetrievalTechnology2013, title = {Information {{Retrieval Technology}}}, editor = {Banchs, Rafael E. and Silvestri, Fabrizio and Liu, Tie-Yan and Zhang, Min and Gao, Sheng and Lang, Jun and Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Doug and Vardi, Moshe Y. and Weikum, Gerhard}, year = {2013}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {8281}, publisher = {{Springer Berlin Heidelberg}}, address = {{Berlin, Heidelberg}}, doi = {10.1007/978-3-642-45068-6}, isbn = {978-3-642-45067-9 978-3-642-45068-6} } @article{bansiyaHierarchicalModelObjectoriented2002, title = {A Hierarchical Model for Object-Oriented Design Quality Assessment}, author = {Bansiya, J. and Davis, C.G.}, year = {2002}, journal = {IEEE Transactions on Software Engineering}, volume = {28}, number = {1}, pages = {4--17}, doi = {10.1109/32.979986}, keywords = {OO metrics,quality assessment} } @article{Bao2021706, 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]}}, author = {Bao, Y. and Yang, Z. and Yang, Y. and Xie, J. and Zhou, Y. and Yue, T. and Huang, Z. and Guo, P.}, year = {2021}, journal = {Jisuanji Yanjiu yu Fazhan/Computer Research and Development}, volume = {58}, number = {4}, pages = {706--730}, publisher = {{Science Press}}, issn = {10001239}, doi = {10.7544/issn1000-1239.2021.20200757}, abbrev_source_title = {Jisuanji Yanjiu yu Fazhan}, affiliation = {School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China; Key Laboratory of Safety-critical Software, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China; Aviation Computing Technology Research Institute, Xi'an, 710065, China}, coden = {JYYFE}, correspondence_address1 = {Yang, Z.; School of Computer Science and Technology, China; email: yangzhibin168@163.com}, document_type = {Article}, langid = {chinese}, source = {Scopus}, keywords = {GOAL_Model-synthesis,notion} } @article{baresiBuildingSoftwareInternet2015, title = {Building {{Software}} for the {{Internet}} of {{Things}}}, author = {Baresi, Luciano and Mottola, Luca and Dustdar, Schahram}, year = {2015}, month = mar, journal = {IEEE Internet Computing}, volume = {19}, number = {2}, pages = {6--8}, issn = {1089-7801}, doi = {10.1109/MIC.2015.31}, langid = {english} } @article{Barmpis20141, title = {Evaluation of Contemporary Graph Databases for Ecient Persistence of Large-Scale Models}, author = {Barmpis, K. and Kolovos, D.S.}, year = {2014}, journal = {Journal of Object Technology}, volume = {13}, number = {3}, pages = {1--26}, publisher = {{Association Internationale pour les Technologies Objets}}, issn = {16601769}, doi = {10.5381/jot.2014.13.3.a3}, abbrev_source_title = {J. Object Technol.}, affiliation = {Department of Computer Science, University of York, Heslington, York, YO10 5DD, United Kingdom}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{Barriga20221135, title = {{{AI-powered}} Model Repair: An Experience Report\textemdash Lessons Learned, Challenges, and Opportunities}, author = {Barriga, A. and Rutle, A. and Heldal, R.}, year = {2022}, journal = {Software and Systems Modeling}, volume = {21}, number = {3}, pages = {1135--1157}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {16191366}, doi = {10.1007/s10270-022-00983-5}, abbrev_source_title = {Softw. Syst. Model.}, affiliation = {Western Norway University of Applied Sciences, Bergen, Norway}, correspondence_address1 = {Barriga, A.; Western Norway University of Applied SciencesNorway; email: abar@hvl.no}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Repair,notion} } @inproceedings{barrigaDesigningSimulatingIoT2022, title = {Designing and Simulating {{IoT}} Environments by Using a Model-Driven Approach {\textsuperscript{*}}}, booktitle = {2022 17th {{Iberian Conference}} on {{Information Systems}} and {{Technologies}} ({{CISTI}})}, author = {Barriga, Jose A. and Clemente, Pedro J.}, year = {2022}, month = jun, pages = {1--6}, publisher = {{IEEE}}, address = {{Madrid, Spain}}, doi = {10.23919/CISTI54924.2022.9820477}, isbn = {978-989-33-3436-2} } @inproceedings{barrigaExtensibleToolchainAnalyzing2020, title = {An Extensible Tool-Chain for Analyzing Datasets of Metamodels}, booktitle = {Proceedings - 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2020 - {{Companion Proceedings}}}, author = {Barriga, Angela and Di Ruscio, D. and Iovino, L. and Nguyen, Thanh Phuong and Pierantonio, A.}, year = {2020}, pages = {316--323}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3417990.3419626}, isbn = {978-1-4503-8135-2}, keywords = {Analysis,Dataset,Metamodels,Repositories} } @inproceedings{Barzdins202076, title = {Metamodel Specialization Based {{DSL}} for {{DL}} Lifecycle Data Management}, author = {Barzdins, P. and Celms, E. and Barzdins, J. and Kalnins, A. and Sprogis, A. and Grasmanis, M. and Rikacovs, S.}, year = {2020}, series = {Proceedings - 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2020 - {{Companion Proceedings}}}, pages = {76--77}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3417990.3420050}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS-C - Companion Proc.}, affiliation = {Institute of Mathematics and Computer Science, University of Latvia, Riga, Latvia; Innovation Labs LETA, Riga, Latvia}, document_type = {Conference Paper}, isbn = {978-1-4503-8135-2}, langid = {english}, source = {Scopus} } @article{Barzdins202217, title = {Metamodel Specialisation Based Tool Extension}, author = {Barzdins, P. and Kalnins, A. and Celms, E. and Barzdins, J. and Sprogis, A. and Grasmanis, M. and Rikacovs, S. and Barzdins, G.}, year = {2022}, journal = {Baltic Journal of Modern Computing}, volume = {10}, number = {1}, pages = {17--35}, publisher = {{University of Latvia}}, issn = {22558942}, doi = {10.22364/BJMC.2022.10.1.02}, abbrev_source_title = {Baltic J. Mod. Comp.}, affiliation = {Institute of Mathematics and Computer Science, University of Latvia, Rai\c{n}a bulvaris 29, Riga, LV-1459, Latvia; Innovation Labs LETA, Latvia, Riga, Satekles iela 2b, Riga, LV 1050, Latvia}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {notion} } @inproceedings{basciani2015model, title = {Model Repositories: {{Will}} They Become Reality?}, booktitle = {{{CloudMDE}}@ {{MoDELS}}}, author = {Basciani, Francesco and Di Rocco, Juri and Di Ruscio, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2015}, pages = {37--42} } @inproceedings{bascianiAutomatedChainingModel2014, title = {Automated Chaining of Model Transformations with Incompatible Metamodels}, booktitle = {17th {{International Conference}}, {{MODELS}} 2014}, author = {Basciani, Francesco and DI RUSCIO, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2014}, volume = {8767}, pages = {602--618}, publisher = {{Springer Verlag}}, url = {http://springerlink.com/content/0302-9743/copyright/2005/}, keywords = {Computer Science (all),Theoretical Computer Science} } @inproceedings{bascianiAutomatedClusteringMetamodel2016, title = {Automated Clustering of Metamodel Repositories}, booktitle = {Advanced {{Information Systems Engineering}}, 28th {{International Conference}}, {{CAiSE}} 2016, {{Ljubljana}}, {{Slovenia}}, {{June}} 13-17, 2016. {{Proceedings}}}, author = {Basciani, Francesco and DI ROCCO, Juri and DI RUSCIO, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2016}, volume = {9694}, pages = {342--358}, publisher = {{Springer Verlag}}, doi = {10.1007/978-3-319-39696-5_21}, isbn = {978-3-319-39695-8}, keywords = {Computer Science (all),MDEForge,Metamodel clustering,Model Driven Engineering,Model repositories,Theoretical Computer Science} } @inproceedings{bascianiAutomatedClusteringMetamodel2016a, title = {Automated Clustering of Metamodel Repositories}, booktitle = {Advanced {{Information Systems Engineering}}, 28th {{International Conference}}, {{CAiSE}} 2016, {{Ljubljana}}, {{Slovenia}}, {{June}} 13-17, 2016. {{Proceedings}}}, author = {Basciani, Francesco and DI ROCCO, Juri and DI RUSCIO, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2016}, volume = {9694}, pages = {342--358}, publisher = {{Springer Verlag}}, doi = {10.1007/978-3-319-39696-5_21}, isbn = {978-3-319-39695-8}, keywords = {Computer Science (all),MDEForge,Metamodel clustering,Model Driven Engineering,Model repositories,Theoretical Computer Science} } @inproceedings{bascianiAutomatedClusteringMetamodel2016b, title = {Automated Clustering of Metamodel Repositories}, booktitle = {Advanced Information Systems Engineering, 28th International Conference, {{CAiSE}} 2016, Ljubljana, Slovenia, June 13-17, 2016. {{Proceedings}}}, author = {Basciani, Francesco and Di Rocco, Juri and Di Ruscio, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2016}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {abs/1006.5761}, pages = {342--358}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-319-39696-5_21}, 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.}, isbn = {978-3-319-39695-8 978-3-319-39696-5}, langid = {english}, keywords = {Computer Science (all),MDEForge,Metamodel clustering,Model Driven Engineering,Model repositories,Theoretical Computer Science} } @article{bascianiExploringModelRepositories, title = {Exploring Model Repositories by Means of Megamodel-Aware Search Operators}, author = {Basciani, Francesco and Ruscio, Davide Di and Rocco, Juri Di and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2018}, journal = {CEUR Workshop Proceedings}, volume = {2245}, pages = {793--798}, 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.}, langid = {english} } @inproceedings{bascianiExploringModelRepositories2018, title = {Exploring Model Repositories by Means of Megamodel-Aware Search Operators}, booktitle = {{{CEUR Workshop Proceedings}}}, author = {Basciani, F. and Di Ruscio, D. and Di Rocco, J. and Iovino, L. and Pierantonio, A.}, year = {2018}, volume = {2245}, pages = {793--798}, publisher = {{CEUR-WS}} } @inproceedings{bascianiExploringModelRepositories2018a, title = {Exploring Model Repositories by Means of Megamodel-Aware Search Operators}, booktitle = {{{CEUR Workshop Proceedings}}}, author = {Basciani, F. and Di Ruscio, D. and Di Rocco, J. and Iovino, L. and Pierantonio, A.}, year = {2018}, volume = {2245}, pages = {793--798}, publisher = {{CEUR-WS}} } @inproceedings{bascianiMDEForgeExtensibleWebbased, title = {{{MDEForge}}: An Extensible {{Web-based}} Modeling Platform}, author = {Basciani, Francesco and Rocco, Juri Di and Ruscio, Davide Di and Salle, Amleto Di and Pierantonio, Alfonso}, pages = {10}, 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.}, langid = {english} } @inproceedings{bascianiMDEForgeExtensibleWebbased2014, title = {{{MDEForge}}: {{An}} Extensible {{Web-based}} Modeling Platform}, booktitle = {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}, author = {Basciani, Francesco and DI ROCCO, Juri and DI RUSCIO, Davide and DI SALLE, Amleto and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2014}, volume = {1242}, pages = {66--75}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{bascianiToolAutomaticallySelecting2018, title = {A Tool for Automatically Selecting Optimal Model Transformation Chains}, booktitle = {Proceedings of the 21st {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}: {{Companion Proceedings}}}, author = {Basciani, Francesco and Di Ruscio, Davide and D'Emidio, Mattia and Frigioni, Daniele and Pierantonio, Alfonso and Iovino, Ludovico}, year = {2018}, pages = {2--6}, doi = {10.1145/3270112.3270123}, isbn = {978-1-4503-5965-8} } @inproceedings{bascianiToolClusteringMetamodel2015, title = {A Tool for Clustering Metamodel Repositories}, booktitle = {Proceedings of the {{MoDELS}} 2015 {{Demo}} and {{Poster Session}} Co-Located with {{ACM}}/{{IEEE}} 18th {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}} ({{MoDELS}} 2015), {{Ottawa}}, {{Canada}}, {{September}} 27, 2015}, author = {Basciani, Francesco and DI RUSCIO, Davide and DI ROCCO, Juri and Pierantonio, Alfonso and Iovino, Ludovico}, year = {2015}, volume = {1554}, pages = {1--4}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{bascianiTyphonMLModelingEnvironment2020, title = {{{TyphonML}}: {{A}} Modeling Environment to Develop Hybrid Polystores}, booktitle = {Proceedings - 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2020 - {{Companion Proceedings}}}, author = {Basciani, F. and Di Rocco, J. and Di Ruscio, D. and Pierantonio, A. and Iovino, L.}, year = {2020}, pages = {6--10}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3417990.3421999}, isbn = {978-1-4503-8135-2}, keywords = {Data modelling,Database technologies,Hybrid polystore,Tools} } @article{basiliSoftwareEngineeringResearch2018, title = {Software {{Engineering Research}} and {{Industry}}: {{A Symbiotic Relationship}} to {{Foster Impact}}}, shorttitle = {Software {{Engineering Research}} and {{Industry}}}, author = {Basili, V. and Briand, L. and Bianculli, D. and Nejati, S. and Pastore, F. and Sabetzadeh, M.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {44--49}, issn = {0740-7459}, doi = {10.1109/MS.2018.290110216}, 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.}, keywords = {software engineering} } @inproceedings{Basmer201986, title = {Encoding Adaptability of Software Engineering Tools as Algorithm Configuration Problem: {{A}} Case Study}, author = {Basmer, M. and Kehrer, T.}, year = {2019}, series = {Proceedings - 2019 34th {{IEEE}}/{{ACM International Conference}} on {{Automated Software Engineering Workshops}}, {{ASEW}} 2019}, pages = {86--89}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ASEW.2019.00035}, abbrev_source_title = {Proc. - IEEE/ACM Int. Conf. Autom. Softw. Eng. Workshops, ASEW}, affiliation = {Department of Computer Science, Humboldt-Universit\"at zu Berlin, Germany}, art_number = {8967417}, document_type = {Conference Paper}, isbn = {978-1-72814-136-7}, langid = {english}, source = {Scopus} } @inproceedings{Bataleblu20153418, title = {Robust Trajectory Optimization of Space Launch Vehicle Using Computational Intelligence}, author = {Bataleblu, A.A. and Roshanian, J.}, year = {2015}, series = {2015 {{IEEE Congress}} on {{Evolutionary Computation}}, {{CEC}} 2015 - {{Proceedings}}}, pages = {3418--3425}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/CEC.2015.7257318}, abbrev_source_title = {IEEE Congr. Evol. Comput., CEC - Proc.}, affiliation = {Department of Aerospace Engineering, K. N. Toosi University of Technology, Tehran, Iran}, art_number = {7257318}, document_type = {Conference Paper}, isbn = {978-1-4799-7492-4}, langid = {english}, source = {Scopus} } @article{batotPromotingSocialDiversity2022, title = {Promoting Social Diversity for the Automated Learning of Complex {{MDE}} Artifacts}, author = {Batot, Edouard R. and Sahraoui, Houari}, year = {2022}, month = jun, journal = {Software and Systems Modeling}, volume = {21}, number = {3}, pages = {1159--1178}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-021-00969-9}, langid = {english} } @inproceedings{Bauer:2012:SAA:2473496.2473600, title = {A Structured Approach to Assess Third-Party Library Usage}, booktitle = {Proceedings of the 2012 {{IEEE}} International Conference on Software Maintenance}, author = {Bauer, Veronika and Heinemann, Lars and Deissenboeck, Florian}, year = {2012}, series = {{{ICSM}} '12}, pages = {483--492}, publisher = {{IEEE Computer Society}}, address = {{Washington, DC, USA}}, url = {http://dx.doi.org/10.1109/ICSM.2012.6405311}, acmid = {2473600}, isbn = {978-1-4673-2313-0}, nodoi = {10.1109/ICSM.2012.6405311}, numpages = {10}, keywords = {API,Libraries,library,Maintenance engineering,Measurement,Modeling,software maintenance,Software maintenance,software reuse,Software systems} } @incollection{bauerIoTReferenceModel2013, title = {{{IoT Reference Model}}}, booktitle = {Enabling {{Things}} to {{Talk}}}, author = {Bauer, Martin and Bui, Nicola and De Loof, Jourik and Magerkurth, Carsten and Nettstr{\"a}ter, Andreas and Stefa, Julinda and Walewski, Joachim W.}, editor = {Bassi, Alessandro and Bauer, Martin and Fiedler, Martin and Kramp, Thorsten and {van Kranenburg}, Rob and Lange, Sebastian and Meissner, Stefan}, year = {2013}, pages = {113--162}, publisher = {{Springer Berlin Heidelberg}}, address = {{Berlin, Heidelberg}}, url = {http://link.springer.com/10.1007/978-3-642-40403-0_7}, urldate = {2016-05-30}, isbn = {978-3-642-40402-3 978-3-642-40403-0}, langid = {english} } @article{bauerTestSuiteQuality2011, title = {Test {{Suite Quality}} for {{Model Transformation Chains}}}, author = {Bauer, Eduard and K{\"u}ster, Jochen M. and Engels, Gregor}, year = {2011}, journal = {Objects, Models, Components, Patterns}, volume = {6705}, pages = {3--19}, doi = {10.1007/978-3-642-21952-8_3} } @misc{BeautifulMonitoringGrafana, title = {Beautiful {{Monitoring With Grafana}} and {{InfluxDB}}}, url = {https://www2.slideshare.net/leesjensen/beautiful-monitoring-with-grafana-and-influxdb?qid=2eb80839-115d-421d-afaa-e6dcbd79c280&v=&b=&from_search=4}, urldate = {2021-01-05}, keywords = {grafana,influxdb} } @inproceedings{beckerSymbolicInvariantVerification2006, title = {Symbolic Invariant Verification for Systems with Dynamic Structural Adaptation}, booktitle = {Proceedings of the 28th International Conference on {{Software}} Engineering}, author = {Becker, Basil and Beyer, Dirk and Giese, Holger and Klein, Florian and Schilling, Daniela}, year = {2006}, pages = {72--81}, publisher = {{ACM}}, url = {http://dl.acm.org/citation.cfm?id=1134297}, urldate = {2015-04-07} } @article{beechamPreparingTomorrowSoftware2017, title = {Preparing {{Tomorrow}}'s {{Software Engineers}} for {{Work}} in a {{Global Environment}}}, author = {Beecham, Sarah and Clear, Tony and Barr, John and Daniels, Mats and Oudshoorn, Michael and Noll, John and {undefined} and {undefined} and {undefined} and {undefined}}, year = {2017}, month = jan, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {9--12}, issn = {0740-7459}, url = {http://ieeexplore.ieee.org/document/7819397/}, 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.}, langid = {english}, keywords = {software engineering} } @inproceedings{begelAnalyzeThis1452014, title = {Analyze This! 145 Questions for Data Scientists in Software Engineering}, booktitle = {Proceedings of the 36th {{International Conference}} on {{Software Engineering}}}, author = {Begel, Andrew and Zimmermann, Thomas}, year = {2014}, pages = {12--23}, publisher = {{ACM}}, url = {http://dl.acm.org/citation.cfm?id=2568233}, urldate = {2016-01-22} } @inproceedings{begoliHeterogeneousPolystorelikeData2016, title = {Towards a Heterogeneous, Polystore-like Data Architecture for the {{US Department}} of {{Veteran Affairs}} ({{VA}}) Enterprise Analytics}, author = {Begoli, Edmon and Kistler, Derek and Bates, Jack}, year = {2016}, month = dec, pages = {2550--2554}, publisher = {{IEEE}}, doi = {10.1109/BigData.2016.7840896}, isbn = {978-1-4673-9005-7}, langid = {english} } @inproceedings{Behjat2020, title = {Metamodel Based Forward and Inverse Design for Passive Vibration Suppression}, author = {Behjat, A. and Oddiraju, M. and Attarzadeh, M.A. and Nouh, M. and Chowdhury, S.}, year = {2020}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {11B-2020}, publisher = {{American Society of Mechanical Engineers (ASME)}}, doi = {10.1115/DETC2020-22747}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY 14260, United States}, art_number = {V11BT11A024}, correspondence_address1 = {Chowdhury, S.; Department of Mechanical and Aerospace Engineering, United States; email: soumacho@buffalo.edu}, document_type = {Conference Paper}, isbn = {978-0-7918-8401-0}, langid = {english}, source = {Scopus} } @article{BellogiN:2013:CSH:2397740.2398191, title = {A Comparative Study of Heterogeneous Item Recommendations in Social Systems}, author = {Bellog{\'i}n, Alejandro and Cantador, Iv{\'a}N and Castells, Pablo}, year = {2013}, month = feb, journal = {Inf. Sci.}, volume = {221}, pages = {142--169}, publisher = {{Elsevier Science Inc.}}, issn = {0020-0255}, url = {http://dx.doi.org/10.1016/j.ins.2012.09.039}, acmid = {2398191}, issue_date = {February, 2013}, noaddress = {New York, NY, USA}, nodoi = {10.1016/j.ins.2012.09.039}, numpages = {28}, keywords = {Collaborative tagging,Evaluation,Implicit feedback,Recommender system,Social network,Social Web} } @inproceedings{Bellogin2011, title = {Precision-Oriented Evaluation of Recommender Systems: {{An}} Algorithmic Comparison}, booktitle = {{{ACM RecSys}} '11}, author = {Bellogin, A. and Castells, P. and Cantador, I.}, year = {2011}, pages = {333--336} } @inproceedings{Benaben20191549, title = {Integrating Model-Driven Engineering as the next Challenge for Artificial Intelligence - Application to Risk and Crisis Management}, author = {Benaben, F. and Lauras, M. and Fertier, A. and Salatge, N.}, year = {2019}, series = {Proceedings - {{Winter Simulation Conference}}}, volume = {2019-December}, pages = {1549--1563}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {08917736}, doi = {10.1109/WSC40007.2019.9004828}, abbrev_source_title = {Proc. Winter Simul. Conf.}, affiliation = {IMT Mines Albi, Centre G\'enie Industriel, Albi, 81000, France}, art_number = {9004828}, coden = {WSCPD}, document_type = {Conference Paper}, isbn = {978-1-72813-283-9}, langid = {english}, source = {Scopus} } @inproceedings{Bencomo20121, title = {Summary of the 7th {{International Workshop}} on {{Models}}@run.Time}, author = {Bencomo, N. and Blair, G. and G{\"o}tz, S. and Morin, B. and Rumpe, B.}, year = {2012}, series = {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}, pages = {1--2}, doi = {10.1145/2422518.2422519}, abbrev_source_title = {Proc. Workshop Models@run.time, MRT - Being Part ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS}, affiliation = {INRIA Paris-Rocquencourt, France; Lancester University, United Kingdom; Technische Universit\"at, Dresden, Germany; Stiftelsen SINTEF, Norway; RWTH Aachen, Germany}, correspondence_address1 = {Bencomo, N.; INRIA Paris-RocquencourtFrance; email: nelly@acm.org}, document_type = {Conference Paper}, isbn = {978-1-4503-1799-3}, langid = {english}, source = {Scopus} } @book{bencomoModelsRunTime2014, title = {Models@run.Time}, editor = {Bencomo, Nelly and France, Robert and Cheng, Betty H. C. and A{\ss}mann, Uwe and Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Kobsa, Alfred and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Terzopoulos, Demetri and Tygar, Doug and Weikum, Gerhard}, year = {2014}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {8378}, publisher = {{Springer International Publishing}}, address = {{Cham}}, url = {http://link.springer.com/10.1007/978-3-319-08915-7}, urldate = {2016-08-21}, isbn = {978-3-319-08914-0 978-3-319-08915-7} } @article{bendraouComparisonSixUMLBased2010, title = {A {{Comparison}} of {{Six UML-Based Languages}} for {{Software Process Modeling}}}, author = {Bendraou, Reda and Jezequel, Jean-Marc and Gervais, Marie-Pierre and Blanc, Xavier}, year = {2010}, month = sep, journal = {IEEE Transactions on Software Engineering}, volume = {36}, number = {5}, pages = {662--675}, issn = {0098-5589}, doi = {10.1109/TSE.2009.85}, 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.}, langid = {english}, keywords = {comparison analysis,uml} } @article{benelallamMavenDependencyGraph2019, title = {The Maven Dependency Graph: A Temporal Graph-Based Representation of Maven Central}, author = {Benelallam, Amine and Harrand, Nicolas and {Soto-Valero}, C{\'e}sar and Baudry, Benoit and Barais, Olivier}, year = {2019}, journal = {CoRR}, volume = {abs/1901.05392}, eprint = {1901.05392}, eprinttype = {arxiv}, url = {http://arxiv.org/abs/1901.05392}, archiveprefix = {arXiv}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1901-05392}, timestamp = {Fri, 01 Feb 2019 13:39:59 +0100} } @incollection{bengioPracticalRecommendationsGradientbased2012, title = {Practical Recommendations for Gradient-Based Training of Deep Architectures}, booktitle = {Neural Networks: {{Tricks}} of the Trade: {{Second}} Edition}, author = {Bengio, Yoshua}, editor = {Montavon, Gr{\'e}goire and Orr, Genevi{\`e}ve B. and M{\"u}ller, Klaus-Robert}, year = {2012}, pages = {437--478}, publisher = {{Springer Berlin Heidelberg}}, address = {{Berlin, Heidelberg}}, doi = {10.1007/978-3-642-35289-8₂6}, 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.}, isbn = {978-3-642-35289-8} } @article{benoitGlobalizingModelingLanguages, title = {Globalizing {{Modeling Languages}}}, author = {Benoit, Comemale and Julien, DeAntoni and Benoit, Baudry and Robert B., France and {Jean-Marc}, Jezequel and Jeff, Gray}, doi = {10.1109/MC.2014.147} } @article{BenSalem2018719, title = {Automatic Selection for General Surrogate Models}, author = {Ben Salem, M. and Tomaso, L.}, year = {2018}, journal = {Structural and Multidisciplinary Optimization}, volume = {58}, number = {2}, pages = {719--734}, publisher = {{Springer Verlag}}, issn = {1615147X}, doi = {10.1007/s00158-018-1925-3}, abbrev_source_title = {Struct. Mutltidiscip. Opt.}, affiliation = {Ecole des mines de St-Etienne, Saint \'Etienne, France; ANSYS, Inc., Villeurbanne, France}, coden = {SMOTB}, correspondence_address1 = {Ben Salem, M.; ANSYS, France; email: Malek.ben-salem@emse.fr}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Search,notion} } @article{Benzaid2020124, title = {{{ZSM}} Security: {{Threat}} Surface and Best Practices}, author = {Benzaid, C. and Taleb, T.}, year = {2020}, journal = {IEEE Network}, volume = {34}, number = {3}, pages = {124--133}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {08908044}, doi = {10.1109/MNET.001.1900273}, 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. \textcopyright{} 1986-2012 IEEE.}, art_number = {8994962}, coden = {IENEE}, document_type = {Article}, source = {Scopus}, keywords = {Best practices,Mitigation measures,Model-driven,Network and service managements,Open Interface,OUT-OF-DOMAIN,Potential attack,Reusability,Security threats,System security} } @inproceedings{bergmayrOutplaceTransformationEvolution2014, title = {From Out-Place Transformation Evolution to in-Place Model Patching}, author = {Bergmayr, Alexander and Troya, Javier and Wimmer, Manuel}, year = {2014}, pages = {647--652}, publisher = {{ACM Press}}, doi = {10.1145/2642937.2642946}, isbn = {978-1-4503-3013-8}, langid = {english} } @article{Berkhin2006, ids = {B06,berkhin2006survey}, title = {A Survey of Clustering Data Mining Techniques.}, author = {Berkhin, Pavel and others}, year = {2006}, journal = {Grouping multidimensional data: Recent advances in clustering}, volume = {25}, pages = {25--71}, doi = {10.1007/3-540-28349-8_2}, keywords = {\#duplicate-citation-key} } @article{Berkhin2006, ids = {B06,berkhin2006survey}, title = {A Software Exoskeleton to Protect and Support Citizen's Ethics and Privacy in the Digital World}, author = {Autili, Marco and Di Ruscio, Davide and Inverardi, Paola and Pelliccione, Patrizio and Tivoli, Massimo}, year = {2019}, journal = {Grouping multidimensional data: Recent advances in clustering}, volume = {7}, pages = {25--71}, doi = {10.1007/3-540-28349-8_2}, keywords = {\#duplicate-citation-key} } @article{bermejo-alonsoOntologicalFrameworkAutonomous2010, title = {An Ontological Framework for Autonomous Systems Modelling}, author = {{Bermejo-Alonso}, Julita and Sanz, Ricardo and Rodr{\'i}guez, Manuel and Hern{\'a}ndez, Carlos}, year = {2010}, journal = {International Journal on Advances in Intelligent Systems}, volume = {3}, number = {3}, url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.641.7853&rep=rep1&type=pdf#page=57}, urldate = {2016-08-21} } @article{bermejoalonsoEngineeringOntologyAutonomous2011, title = {Engineering an {{Ontology}} for {{Autonomous Systems-The OASys Ontology}}}, author = {Bermejo Alonso, Julita and Sanz Bravo, Ricardo and Rodr{\'i}guez, Manuel and Hern{\'a}ndez Corbato, Carlos}, year = {2011}, url = {http://oa.upm.es/11957/}, urldate = {2016-08-21} } @book{Berry:1997:DMT:560675, title = {Data Mining Techniques: {{For}} Marketing, Sales, and Customer Support}, author = {Berry, Michael J. and Linoff, Gordon}, year = {1997}, publisher = {{John Wiley \& Sons, Inc.}}, address = {{New York, NY, USA}}, isbn = {0-471-17980-9} } @article{bertoaQualityAttributesSoftware2010, ids = {bertoaQualityAttributesSoftware}, title = {Quality Attributes for Software Metamodels}, author = {Bertoa, Manuel and Vallecillo, Antonio}, year = {2010}, journal = {M\'alaga, Spain}, url = {http://www.lcc.uma.es/~av/Publicaciones/10/qaoose10.pdf}, urldate = {2015-09-15} } @misc{BestDataPipeline, title = {The {{Best Data Pipeline Tools List}} for 2021 | {{Hevo Blog}}}, url = {https://hevodata.com/blog/data-pipeline-tools-list/}, urldate = {2021-03-18} } @article{bettiniDetectingMetamodelEvolutions2020, title = {Detecting {{Metamodel Evolutions}} in {{Repositories}} of {{Model-Driven Projects}}.}, author = {Bettini, Lorenzo and Di Ruscio, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2020}, journal = {The Journal of Object Technology}, volume = {19}, number = {2}, pages = {14:1}, issn = {1660-1769}, doi = {10.5381/jot.2020.19.2.a14}, 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.}, langid = {english}, keywords = {Evolution,Megamodels,Model-driven engineering,Quality,Reverse engineering} } @inproceedings{bettiniEdeltaApproachDefining2017, ids = {bettini2017edelta}, title = {Edelta: {{An}} Approach for Defining and Applying Reusable Metamodel Refactorings}, booktitle = {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.}, author = {Bettini, Lorenzo and DI RUSCIO, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2017}, volume = {2019}, pages = {71--80}, publisher = {{CEUR-WS.org}}, url = {http://ceur-ws.org/Vol-2019/me_4.pdf} } @inproceedings{bettiniEdeltaApproachDefining2017a, title = {Edelta: {{An Approach}} for {{Defining}} and {{Applying Reusable Metamodel Refactorings}}}, booktitle = {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.}, author = {Bettini, Lorenzo and DI RUSCIO, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2017}, volume = {2019}, pages = {71--80}, publisher = {{CEUR-WS.org}}, url = {http://ceur-ws.org/Vol-2019/me_4.pdf} } @inproceedings{bettiniEdeltaSupportingLive2020, title = {Edelta 2.0: {{Supporting}} Live Metamodel Evolutions}, booktitle = {Proceedings - 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2020 - {{Companion Proceedings}}}, author = {Bettini, Lorenzo and Di Ruscio, D. and Iovino, L. and Pierantonio, A.}, year = {2020}, pages = {324--333}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3417990.3419501}, isbn = {978-1-4503-8135-2}, keywords = {Edelta,Evolution,Metamodels,Refactoring} } @article{bettiniSupportingSafeMetamodel2022, title = {Supporting Safe Metamodel Evolution with Edelta}, author = {Bettini, L and Di Ruscio, D and Iovino, L and Pierantonio, A}, year = {2022}, journal = {INTERNATIONAL JOURNAL ON SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER}, doi = {10.1007/s10009-022-00646-2}, keywords = {Metamodel evolution,Model-driven engineering,Parallel evolution,Safe evolution} } @inproceedings{Beyer:2018:ACP:3196321.3196333, title = {Automatically Classifying Posts into Question Categories on Stack Overflow}, booktitle = {Proceedings of the 26th Conference on Program Comprehension}, author = {Beyer, Stefanie and Macho, Christian and Pinzger, Martin and Di Penta, Massimiliano}, year = {2018}, series = {{{ICPC}} '18}, pages = {211--221}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/3196321.3196333}, acmid = {3196333}, isbn = {978-1-4503-5714-2}, nodoi = {10.1145/3196321.3196333}, numpages = {11} } @article{beyhlSmartModelSearch, title = {Smart {{Model Search}} in {{Model Repositories}} by {{Modular Search Index Generation}} and {{Querying}} ({{Submitted}} to {{SLE2014}} - Confidential)}, author = {Beyhl, Thomas and Giese, Holger} } @article{beyhlSmartModelSearcha, title = {Smart {{Model Search}} in {{Model Repositories}} by {{Modular Search Index Generation}} and {{Querying}}}, author = {Beyhl, Thomas and Giese, Holger}, pages = {20}, 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.}, langid = {english} } @inproceedings{BezivinJRV05, title = {Modeling in the {{Large}} and {{Modeling}} in the {{Small}}}, booktitle = {European {{MDA}} Workshops {{MDAFA}} 2003 and {{MDAFA}} 2004, Revised Selected Papers}, author = {B{\'e}zivin, Jean and Jouault, Fr{\'e}d{\'e}ric and Rosenthal, Peter and Valduriez, Patrick}, year = {2005}, series = {{{LNCS}}}, volume = {3599}, pages = {33--46}, publisher = {{Springer}} } @inproceedings{BezivinJV04, ids = {bezivin2004need}, title = {On the Need for {{Megamodels}}}, booktitle = {Proc. of the {{OOPSLA}}/{{GPCE}}: {{Best}} Practices for Model-Driven Software Development Workshop}, author = {B{\'e}zivin, J. and Jouault, F. and Valduriez, P.}, year = {2004} } @article{bezivinUnificationPowerModels2005, title = {On the Unification Power of Models}, author = {B{\'e}zivin, Jean}, year = {2005}, month = may, journal = {Software \& Systems Modeling}, volume = {4}, number = {2}, pages = {171--188}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-005-0079-0}, langid = {english} } @inproceedings{bhandariSerendipitousRecommendationMobile2013, title = {Serendipitous Recommendation for Mobile Apps Using Item-Item Similarity Graph.}, booktitle = {{{AIRS}}}, author = {Bhandari, Upasna and Sugiyama, Kazunari and Datta, Anindya and Jindal, Rajni}, editor = {Banchs, Rafael E. and Silvestri, Fabrizio and Liu, Tie-Yan and Zhang, Min and Gao, Sheng and Lang, Jun}, year = {2013}, series = {Lecture Notes in Computer Science}, volume = {8281}, pages = {440--451}, publisher = {{Springer}}, url = {http://dblp.uni-trier.de/db/conf/airs/airs2013.html#BhandariSDJ13}, added-at = {2013-12-14T00:00:00.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/202e78647742172b1076224a8af342fea/dblp}, ee = {http://dx.doi.org/10.1007/978-3-642-45068-6{$_3$}8}, interhash = {9b6654f3f311708df5e2faf14c15d05a}, intrahash = {02e78647742172b1076224a8af342fea}, isbn = {978-3-642-45067-9}, keywords = {dblp}, timestamp = {2015-04-28T17:38:09.000+0200} } @inproceedings{Bhattacharjee20191607, title = {{{STRATUM}}: {{A BigData-as-a-Service}} for Lifecycle Management of {{IoT}} Analytics Applications}, author = {Bhattacharjee, A. and Barve, Y. and Khare, S. and Bao, S. and Kang, Z. and Gokhale, A. and Damiano, T.}, editor = {Baru C., Huan J., Hu X.T., Ak R., Tian Y., Barga R., Zaniolo C., Lee K., Ye Y.F., Khan L.}, year = {2019}, series = {Proceedings - 2019 {{IEEE International Conference}} on {{Big Data}}, {{Big Data}} 2019}, pages = {1607--1612}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/BigData47090.2019.9006518}, abbrev_source_title = {Proc. - IEEE Int. Conf. Big Data, Big Data}, affiliation = {Vanderbilt University, EECS Dept, Nashville, TN, United States; Lockheed Martin Advanced Technology Labs, Cherry Hill, NJ, United States}, art_number = {9006518}, document_type = {Conference Paper}, isbn = {978-1-72810-858-2}, langid = {english}, source = {Scopus} } @misc{BigDAWGPolystoreSystem, title = {The {{BigDAWG}} Polystore System and Architecture \textemdash{} {{Northwestern Scholars}}}, url = {https://www.scholars.northwestern.edu/en/publications/the-bigdawg-polystore-system-and-architecture}, urldate = {2018-04-16} } @inproceedings{BinTang2007, title = {Document Representation and Dimension Reduction for Text Clustering}, booktitle = {2013 {{IEEE}} 29th Int. {{Conf}}. on Data Engineering Workshops ({{ICDEW}})}, author = {Tang, B. and Spiteri, R. and Milios, E. and Zhang, R. and Wang, S. and Tougas, J. and Shafiei, M.}, year = {2007}, month = apr, pages = {770--779}, publisher = {{IEEE Computer Society}}, address = {{Los Alamitos, CA, USA}}, nodoi = {10.1109/ICDEW.2007.4401066} } @book{Bishop:1995:NNP:525960, title = {Neural Networks for Pattern Recognition}, author = {Bishop, Christopher M.}, year = {1995}, publisher = {{Oxford University Press, Inc.}}, address = {{New York, NY, USA}}, isbn = {0-19-853864-2} } @article{bislimovskaTextualContentBasedSearch2014, title = {Textual and {{Content-Based Search}} in {{Repositories}} of {{Web Application Models}}}, author = {Bislimovska, Bojana and Bozzon, Alessandro and Brambilla, Marco and Fraternali, Piero}, year = {2014}, journal = {ACM Transactions on the Web}, volume = {8}, number = {2}, pages = {1--47}, doi = {10.1145/2579991} } @article{bizer_linked_2009, title = {Linked Data - the Story so Far}, author = {Bizer, C. and Heath, T. and {Berners-Lee}, T.}, year = {2009}, journal = {Int. J. Semantic Web Inf. Syst.}, volume = {5}, number = {3}, pages = {1--22}, added-at = {2011-11-02T15:40:20.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/25e13b99f0fe4d28c1261158410041c70/mgraube}, interhash = {599c4dfb0c1625c0c4368a1ab8346646}, intrahash = {5e13b99f0fe4d28c1261158410041c70}, timestamp = {2011-11-02T15:40:20.000+0100} } @article{bjarnasonAligningRequirementsTesting2017, title = {Aligning {{Requirements}} and {{Testing}}: {{Working Together}} toward the {{Same Goal}}}, shorttitle = {Aligning {{Requirements}} and {{Testing}}}, author = {Bjarnason, Elizabeth and Borg, Markus and {undefined} and {undefined} and {undefined} and {undefined}}, year = {2017}, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {20--23}, issn = {0740-7459}, 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.}, keywords = {software engineering} } @article{bjarnasonChallengesPracticesAligning2014, title = {Challenges and Practices in Aligning Requirements with Verification and Validation: A Case Study of Six Companies}, shorttitle = {Challenges and Practices in Aligning Requirements with Verification and Validation}, author = {Bjarnason, Elizabeth and Runeson, Per and Borg, Markus and Unterkalmsteiner, Michael and Engstr{\"o}m, Emelie and Regnell, Bj{\"o}rn and Sabaliauskaite, Giedre and Loconsole, Annabella and Gorschek, Tony and Feldt, Robert}, year = {2014}, month = dec, journal = {Empirical Software Engineering}, volume = {19}, number = {6}, pages = {1809--1855}, issn = {1382-3256, 1573-7616}, doi = {10.1007/s10664-013-9263-y}, langid = {english} } @article{Blondel:2004:MSG:1035533.1035557, title = {A Measure of Similarity between Graph Vertices: {{Applications}} to Synonym Extraction and Web Searching}, author = {Blondel, Vincent D. and Gajardo, Anah{\'i} and Heymans, Maureen and Senellart, Pierre and Dooren, Paul Van}, year = {2004}, month = apr, journal = {SIAM Review}, volume = {46}, number = {4}, pages = {647--666}, publisher = {{Society for Industrial and Applied Mathematics}}, address = {{Philadelphia, PA, USA}}, issn = {0036-1445}, url = {http://dx.doi.org/10.1137/S0036144502415960}, acmid = {1035557}, issue_date = {2004}, nodoi = {10.1137/S0036144502415960}, numpages = {20}, keywords = {algorithms,eigenvalues of graphs,graph algorithms,graph theory} } @article{blouinKomprenModelingGenerating2012, title = {Kompren: Modeling and Generating Model Slicers}, author = {Blouin, Arnaud and Combemale, Beno{\^i}t and Baudry, Benoit and Beaudoux, Olivier}, year = {2012}, journal = {Software \& Systems Modeling}, doi = {10.1007/s10270-012-0300-x} } @article{blouinSlicingbasedTechniquesVisualizing, title = {Slicing-Based {{Techniques}} for {{Visualizing Large Metamodels}}}, author = {Blouin, Arnaud and Moha, Naouel and Baudry, Benoit and Saharaoui, Houaru}, 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.} } @book{Blum:1992:NNC:129269, title = {Neural Networks in {{C}}++: {{An}} Object-Oriented Framework for Building Connectionist Systems}, author = {Blum, Adam}, year = {1992}, publisher = {{John Wiley \& Sons, Inc.}}, address = {{New York, NY, USA}}, isbn = {0-471-53847-7} } @inproceedings{boardmanSystemSystemstheMeaning2006, title = {System of {{Systems-the}} Meaning of Of}, booktitle = {2006 {{IEEE}}/{{SMC International Conference}} on {{System}} of {{Systems Engineering}}}, author = {Boardman, John and Sauser, Brian}, year = {2006}, pages = {6--pp}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1652284}, urldate = {2016-08-21} } @inproceedings{Bogoclu20163344, title = {A Benchmark of Contemporary Metamodeling Algorithms}, author = {Bogoclu, C. and Roos, D.}, editor = {Stefanou G., Papadrakakis M., Plevris V., Papadopoulos V.}, year = {2016}, series = {{{ECCOMAS Congress}} 2016 - {{Proceedings}} of the 7th {{European Congress}} on {{Computational Methods}} in {{Applied Sciences}} and {{Engineering}}}, volume = {2}, pages = {3344--3360}, publisher = {{National Technical University of Athens}}, doi = {10.7712/100016.2039.7645}, abbrev_source_title = {ECCOMAS Congress - Proc. Euro. Congr. Comput. Methods Appl. Sci. Eng.}, affiliation = {Institute of Modeling and High-Performance Computing, Niederrhein University of Applied Sciences, Germany}, document_type = {Conference Paper}, isbn = {978-618-82844-0-1}, langid = {english}, source = {Scopus} } @article{bononiIoTSensorData, title = {{{IoT Sensor Data Management}}}, author = {Bononi, L and Felice, M Di}, pages = {28}, langid = {english}, keywords = {data processing,DONE,GOOD_SOURCE,internet of things,slides} } @article{bononiIoTSensorDataa, title = {{{IoT Sensor Data Processing}}}, author = {Bononi, L and Felice, M Di}, pages = {43}, langid = {english}, keywords = {data processing,DONE,internet of things,slides} } @article{boochHistorySoftwareEngineering2018, title = {The {{History}} of {{Software Engineering}}}, author = {Booch, G.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {108--114}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571234}, 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.}, keywords = {software engineering} } @incollection{books/sp/mining2012/AggarwalZ12a, title = {A Survey of Text Clustering Algorithms.}, booktitle = {Mining Text Data}, author = {Aggarwal, Charu C. and Zhai, ChengXiang}, editor = {Aggarwal, Charu C. and Zhai, ChengXiang}, year = {2012}, pages = {77--128}, publisher = {{Springer}}, url = {http://dblp.uni-trier.de/db/books/collections/Mining2012.html#AggarwalZ12a}, added-at = {2012-02-18T00:00:00.000+0100}, biburl = {http://www.bibsonomy.org/bibtex/28cfea77bb8aecd46ab2ba9db26c4338b/dblp}, ee = {http://dx.doi.org/10.1007/978-1-4614-3223-4{$_{4}$}}, interhash = {a8614bc450f82d917149afd58fabf02a}, intrahash = {8cfea77bb8aecd46ab2ba9db26c4338b}, isbn = {978-1-4419-8462-3}, keywords = {dblp}, timestamp = {2012-02-21T11:35:00.000+0100} } @inproceedings{Borg:2014:RSD:2652524.2652556, title = {A Replicated Study on Duplicate Detection: {{Using}} Apache Lucene to Search among Android Defects}, booktitle = {Proceedings of the 8th {{ACM}}/{{IEEE}} International Symposium on Empirical Software Engineering and Measurement}, author = {Borg, Markus and Runeson, Per and Johansson, Jens and M{\"a}ntyl{\"a}, Mika V.}, year = {2014}, series = {{{ESEM}} '14}, pages = {8:1-8:4}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/2652524.2652556}, acmid = {2652556}, articleno = {8}, isbn = {978-1-4503-2774-9}, nodoi = {10.1145/2652524.2652556}, numpages = {4}, keywords = {information retrieval,issue management,replication,software evolution} } @article{borgSupportingChangeImpact2016, title = {Supporting {{Change Impact Analysis Using}} a {{Recommendation System}}: {{An Industrial Case Study}} in a {{Safety-Critical Context}}}, shorttitle = {Supporting {{Change Impact Analysis Using}} a {{Recommendation System}}}, author = {Borg, Markus and Wnuk, Krzysztof and Regnell, Bjorn and Runeson, Per}, year = {2016}, journal = {IEEE Transactions on Software Engineering}, pages = {1--1}, issn = {0098-5589, 1939-3520}, doi = {10.1109/TSE.2016.2620458} } @article{Böttcher2021, title = {{{ELSA}}: {{An}} Efficient, Adaptive Ensemble Learning-Based Sampling Approach}, author = {B{\"o}ttcher, M. and Fuchs, A. and Leichsenring, F. and Graf, W. and Kaliske, M.}, year = {2021}, journal = {Advances in Engineering Software}, volume = {154}, publisher = {{Elsevier Ltd}}, issn = {09659978}, doi = {10.1016/j.advengsoft.2021.102974}, abbrev_source_title = {Adv Eng Software}, affiliation = {Institute for Structural Analysis, Technische Universit\"at Dresden, Dresden, 01062, Germany}, art_number = {102974}, coden = {AESOD}, correspondence_address1 = {Kaliske, M.; Institute for Structural Analysis, Germany; email: michael.kaliske@tu-dresden.de}, document_type = {Article}, langid = {english}, source = {Scopus} } @inproceedings{Bottou91stochasticgradient, title = {Stochastic Gradient Learning in Neural Networks}, booktitle = {In Proceedings of Neuro-N\^imes. {{EC2}}}, author = {Bottou, L{\'e}on}, year = {1991} } @inproceedings{Boubekeur202084, title = {Automatic Assessment of Students' Software Models Using a Simple Heuristic and Machine Learning}, author = {Boubekeur, Y. and Mussbacher, G. and McIntosh, S.}, year = {2020}, series = {Proceedings - 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2020 - {{Companion Proceedings}}}, pages = {84--93}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3417990.3418741}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS-C - Companion Proc.}, affiliation = {McGill University, Montre\'al, Canada; University of Waterloo, Waterloo, Canada}, document_type = {Conference Paper}, isbn = {978-1-4503-8135-2}, langid = {english}, source = {Scopus} } @inproceedings{Boubekeur202094, title = {Towards a Better Understanding of Interactions with a Domain Modeling Assistant}, author = {Boubekeur, Y. and Mussbacher, G.}, year = {2020}, series = {Proceedings - 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2020 - {{Companion Proceedings}}}, pages = {94--103}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3417990.3418742}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS-C - Companion Proc.}, affiliation = {McGill University, Montre\'al, Canada}, document_type = {Conference Paper}, isbn = {978-1-4503-8135-2}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Assistance,notion} } @inproceedings{boudeffaIntegratingDeployingHeterogeneous2019, title = {Integrating and Deploying Heterogeneous Components by Means of a Microservices Architecture in the {{CROSSMINER}} Project}, booktitle = {{{CEUR Workshop Proceedings}}}, author = {Boudeffa, A. and Abherve, A. and Bagnato, A. and Di Ruscio, D. and Mateus, M. and Almeida, B.}, year = {2019}, volume = {2405}, pages = {67--72}, publisher = {{CEUR-WS}}, keywords = {Deployment,Heterogeneous Legacy Components,Integration,Microservice,Open Source Software} } @book{bourqueGuideSoftwareEngineering2014, title = {Guide to the Software Engineering Body of Knowledge}, author = {Bourque, Pierre and Fairley, R. E and {IEEE Computer Society}}, year = {2014}, isbn = {978-0-7695-5166-1}, langid = {english} } @inproceedings{bousseGenerativeApproachDefine2015, title = {A {{Generative Approach}} to {{Define Rich Domain-Specific Trace Metamodels}}}, booktitle = {11th {{European Conference}} on {{Modelling Foundations}} and {{Applications}} ({{ECMFA}})}, author = {Bousse, Erwan and Mayerhofer, Tanja and Combemale, Benoit and Baudry, Benoit}, year = {2015}, url = {https://hal.inria.fr/hal-01154225/document}, urldate = {2015-06-24} } @inproceedings{bozhinoskiFLYAQEnablingNonexpert2015, title = {{{FLYAQ}}: {{Enabling}} Non-Expert Users to Specify and Generate Missions of Autonomous Multicopters}, booktitle = {Proceedings - 2015 30th {{IEEE}}/{{ACM International Conference}} on {{Automated Software Engineering}}, {{ASE}} 2015}, author = {Bozhinoski, Darko and DI RUSCIO, Davide and Malavolta, Ivano and Pelliccione, Patrizio and Tivoli, Massimo}, year = {2015}, pages = {801--806}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ASE.2015.104}, isbn = {978-1-5090-0024-1}, keywords = {Domain-specific Languages,Model-Driven Engineering,Multicopter,Software} } @inproceedings{bozhinoskiFLYAQEnablingNonexpert2015a, title = {{{FLYAQ}}: {{Enabling}} Non-Expert Users to Specify and Generate Missions of Autonomous Multicopters}, booktitle = {Proceedings - 2015 30th {{IEEE}}/{{ACM International Conference}} on {{Automated Software Engineering}}, {{ASE}} 2015}, author = {Bozhinoski, Darko and DI RUSCIO, Davide and Malavolta, Ivano and Pelliccione, Patrizio and Tivoli, Massimo}, year = {2015}, pages = {801--806}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ASE.2015.104}, isbn = {978-1-5090-0024-1}, keywords = {Domain-specific Languages,Model-Driven Engineering,Multicopter,Software} } @inproceedings{breuTenPrinciplesLiving2010, title = {Ten {{Principles}} for {{Living Models}} - {{A Manifesto}} of {{Change-Driven Software Engineering}}}, booktitle = {2010 {{International Conference}} on {{Complex}}, {{Intelligent}} and {{Software Intensive Systems}}}, author = {Breu, Ruth}, year = {2010}, month = feb, pages = {1--8}, publisher = {{IEEE}}, address = {{Krakow, TBD, Poland}}, doi = {10.1109/CISIS.2010.73}, 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 \textendash{} a novel paradigm of model\textendash 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\textendash class citizen.}, isbn = {978-1-4244-5917-9}, langid = {english} } @article{BRIGUEZ20146467, title = {Argument-Based Mixed Recommenders and Their Application to Movie Suggestion}, author = {Briguez, Cristian E. and Bud{\'a}n, Maximiliano C.D. and Deagustini, Cristhian A.D. and Maguitman, Ana G. and Capobianco, Marcela and Simari, Guillermo R.}, year = {2014}, journal = {Expert Systems with Applications}, volume = {41}, number = {14}, pages = {6467--6482}, issn = {0957-4174}, url = {http://www.sciencedirect.com/science/article/pii/S0957417414001845}, nodoi = {https://doi.org/10.1016/j.eswa.2014.03.046}, keywords = {Defeasible argumentation,Qualitative vs quantitative recommendations} } @article{broringEnablingIoTEcosystems2017, title = {Enabling {{IoT Ecosystems}} through {{Platform Interoperability}}}, author = {Broring, Arne and Schmid, Stefan and Schindhelm, Corina-Kim and Khelil, Abdelmajid and Kabisch, Sebastian and Kramer, Denis and Phuoc, Danh Le and Mitic, Jelena and Anicic, Darko and Teniente, Ernest}, year = {2017}, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {54--61}, issn = {0740-7459}, 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.}, keywords = {internet of things,software engineering} } @article{broyYesterdayTodayTomorrow2018, title = {Yesterday, {{Today}}, and {{Tomorrow}}: 50 {{Years}} of {{Software Engineering}}}, shorttitle = {Yesterday, {{Today}}, and {{Tomorrow}}}, author = {Broy, M.}, year = {2018}, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {38--43}, issn = {0740-7459}, doi = {10.1109/MS.2018.290111138}, 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.}, keywords = {software engineering} } @inproceedings{bruchEvaluatingRecommenderSystems2008, title = {On Evaluating Recommender Systems for {{API}} Usages}, booktitle = {Proceedings of the 2008 International Workshop on Recommendation Systems for Software Engineering}, author = {Bruch, Marcel and Sch{\"a}fer, Thorsten and Mezini, Mira}, year = {2008}, series = {{{RSSE}} '08}, pages = {16--20}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/1454247.1454254}, acmid = {1454254}, isbn = {978-1-60558-228-3}, nodoi = {10.1145/1454247.1454254}, numpages = {5} } @article{bruelModelTransformationReuse, title = {Model {{Transformation Reuse}} across {{Metamodels}}}, author = {Bruel, Jean-Michel and Combemale, Benoit and Guerra, Esther and J{\'e}z{\'e}quel, Jean-Marc}, pages = {15}, 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 \textendash{} model types, concepts, a-posteriori typing, multilevel modeling, and design patterns for MTs \textendash{} 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.}, langid = {english} } @article{brun2007emf, title = {{{EMF}} Compare}, author = {Brun, Cedric and Musset, Jonathan and Toulme, Antoine}, year = {2007} } @article{bruneliere2014modisco, title = {Modisco: {{A}} Model Driven Reverse Engineering Framework}, author = {Bruneliere, Hugo and Cabot, Jordi and Dup{\'e}, Gr{\'e}goire and Madiot, Fr{\'e}d{\'e}ric}, year = {2014}, journal = {Information and Software Technology}, volume = {56}, number = {8}, pages = {1012--1032}, publisher = {{Elsevier}} } @inproceedings{bruneliereIndustrializationResearchTools2010, title = {Industrialization of Research Tools: {{The ATL}} Case}, shorttitle = {Industrialization of Research Tools}, booktitle = {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)}, author = {Bruneliere, Hugo and Cabot, Jordi and Jouault, Fr{\'e}d{\'e}ric and Tisi, Massimo and B{\'e}zivin, Jean}, year = {2010}, url = {https://hal.inria.fr/hal-00539173/}, urldate = {2016-10-10} } @inproceedings{bruneliereLightweightMetamodelExtension, title = {On {{Lightweight Metamodel Extension}} to {{Support Modeling Tools Agility}}}, booktitle = {11th {{European Conference}} on {{Modelling Foundations}} and {{Applications}} ({{ECMFA}} 2015)(a {{STAF}} 2015 Conference)}, author = {Bruneliere, Hugo and Garcia, Jokin and Desfray, Philippe and Khelladi, Djamel Eddine and Hebig, Regina and Bendraou, Reda and Cabot, Jordi}, url = {https://hal.inria.fr/hal-01146802/}, urldate = {2015-06-24} } @incollection{brunEngineeringSelfadaptiveSystems2009, title = {Engineering Self-Adaptive Systems through Feedback Loops}, booktitle = {Software Engineering for Self-Adaptive Systems}, author = {Brun, Yuriy and Serugendo, Giovanna Di Marzo and Gacek, Cristina and Giese, Holger and Kienle, Holger and Litoiu, Marin and M{\"u}ller, Hausi and Pezz{\`e}, Mauro and Shaw, Mary}, year = {2009}, pages = {48--70}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-02161-9_3}, urldate = {2016-11-03} } @article{brunschwigModellingMobileDevices, title = {Modelling on Mobile Devices}, author = {Brunschwig, Lea and Guerra, Esther and {de Lara}, Juan}, pages = {27}, 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.}, langid = {english} } @article{brynjulfsenXAITextDomainSpecificLanguage, title = {{{XAIText}}: {{A Domain-Specific Language}} for {{Developing}} an {{AI Pipeline}}}, author = {Brynjulfsen, H{\aa}vard and Rabbi, Fazle}, pages = {105}, langid = {english}, keywords = {GOAL_MDE4AI} } @article{bucchiaroneAutonomousShuttleasaServiceASaaS2020, title = {Autonomous {{Shuttle-as-a-Service}} ({{ASaaS}}): {{Challenges}}, {{Opportunities}}, and {{Social Implications}}}, shorttitle = {Autonomous {{Shuttle-as-a-Service}} ({{ASaaS}})}, author = {Bucchiarone, Antonio and Battisti, Sandro and Marconi, Annapaola and Maldacea, Roberto and Ponce, Diego Cardona}, year = {2020}, journal = {IEEE Transactions on Intelligent Transportation Systems}, pages = {1--10}, issn = {1524-9050, 1558-0016}, doi = {10.1109/TITS.2020.3025670}, 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.}, langid = {english} } @article{bucchiaroneRequirementsCodeArchitecturecentric2009, title = {From {{Requirements}} to Code: An {{Architecture-centric Approach}} for Producing {{Quality Systems}}}, author = {Bucchiarone, Antonio and Ruscio, Davide Di and Muccini, Henry and Pelliccione, Patrizio}, year = {2009}, journal = {CoRR}, volume = {abs/0910.0493}, url = {http://arxiv.org/abs/0910.0493} } @article{bucchiaroneWhatFutureModeling2021, title = {What {{Is}} the {{Future}} of {{Modeling}}?}, author = {Bucchiarone, Antonio and Ciccozzi, Federico and Lambers, Leen and Pierantonio, Alfonso and Tichy, Matthias and Tisi, Massimo and Wortmann, Andreas and Zaytsev, Vadim}, year = {2021}, month = mar, journal = {IEEE Software}, volume = {38}, number = {2}, pages = {119--127}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2020.3041522}, langid = {english} } @article{buczakSurveyDataMining2016, title = {A {{Survey}} of {{Data Mining}} and {{Machine Learning Methods}} for {{Cyber Security Intrusion Detection}}}, author = {Buczak, Anna L. and Guven, Erhan}, year = 2016, journal = {IEEE Communications Surveys \& Tutorials}, volume = {18}, number = {2}, pages = {1153--1176}, issn = {1553-877X, 2373-745X}, doi = {10.1109/COMST.2015.2494502} } @book{budinskyEclipseModelingFramework2003, title = {Eclipse {{Modeling Framework}}}, author = {Budinsky, F. and Steinberg, D. and Merks, E. and Ellersick, R. and {T.J. Grose}}, year = {2003}, publisher = {{Addison Wesley}} } @article{bugiottiComparisonDataModels, title = {A {{Comparison}} of {{Data Models}} and {{APIs}} of {{NoSQL Datastores}}}, author = {Bugiotti, Francesca and Cabibbo, Luca}, pages = {12}, 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 \textemdash{} 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.}, langid = {english} } @incollection{bugiottiDatabaseDesignNoSQL2014, ids = {bugiottiDatabaseDesignNoSQL2014a}, title = {Database {{Design}} for {{NoSQL Systems}}}, booktitle = {Conceptual {{Modeling}}}, author = {Bugiotti, Francesca and Cabibbo, Luca and Atzeni, Paolo and Torlone, Riccardo}, editor = {Yu, Eric and Dobbie, Gillian and Jarke, Matthias and Purao, Sandeep}, year = {2014}, volume = {8824}, pages = {223--231}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-319-12206-9_18}, 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.}, isbn = {978-3-319-12205-2 978-3-319-12206-9}, langid = {english} } @misc{BuildingAutomatedMachine, title = {Building an {{Automated Machine Learning Pipeline}}: {{Part One}} | by {{Ceren Iyim}} | {{Towards Data Science}}}, url = {https://towardsdatascience.com/building-an-automated-machine-learning-pipeline-part-one-5c70ae682f35}, urldate = {2021-04-21} } @misc{BuildingIoTOntologies, title = {Building {{IoT}} Ontologies and Integrating Them with {{Eclipse}} Projects | {{EclipseCon Europe}} 2016}, url = {https://www.eclipsecon.org/europe2016/session/building-iot-ontologies-and-integrating-them-eclipse-projects}, urldate = {2016-09-27} } @misc{BuildingRaspberryPi, title = {Building {{A Raspberry Pi VPN Part One}}: {{How And Why To Build A Server}} - {{ReadWrite}}}, url = {http://readwrite.com/2014/04/10/raspberry-pi-vpn-tutorial-server-secure-web-browsing}, urldate = {2015-04-17} } @misc{BuildingSmarterEclipse, title = {Building a {{Smarter Eclipse IoT Greenhouse}} with {{Eclipse Vorto}}, {{Kura}}, {{Californium}} and {{Paho}} | {{EclipseCon Europe}} 2016}, url = {https://www.eclipsecon.org/europe2016/session/building-smarter-eclipse-iot-greenhouse-eclipse-vorto-kura-californium-and-paho}, urldate = {2016-09-27} } @article{Burattin2018322, title = {Who Is behind the Model? Classifying Modelers Based on Pragmatic Model Features}, author = {Burattin, A. and Soffer, P. and Fahland, D. and Mendling, J. and Reijers, H.A. and Vanderfeesten, I. and Weidlich, M. and Weber, B.}, editor = {Montali M., Weber I., vom Brocke J., Weske M.}, year = {2018}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {11080 LNCS}, pages = {322--338}, publisher = {{Springer Verlag}}, issn = {03029743}, doi = {10.1007/978-3-319-98648-7_19}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Technical University of Denmark, Kgs. Lyngby, Denmark; University of Haifa, Haifa, Israel; Eindhoven University of Technology, Eindhoven, Netherlands; Vienna University of Economics and Business, Vienna, Austria; Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Humboldt-University, Berlin, Germany; University of Innsbruck, Innsbruck, Austria}, correspondence_address1 = {Burattin, A.; Technical University of DenmarkDenmark; email: andbur@dtu.dk}, document_type = {Conference Paper}, isbn = {9783319986470}, langid = {english}, source = {Scopus}, keywords = {notion} } @article{buresSoftwareEngineeringSmart2015, title = {Software {{Engineering}} for {{Smart Cyber-Physical Systems}} -- {{Towards}} a {{Research Agenda}}: {{Report}} on the {{First International Workshop}} on {{Software Engineering}} for {{Smart CPS}}}, shorttitle = {Software {{Engineering}} for {{Smart Cyber-Physical Systems}} -- {{Towards}} a {{Research Agenda}}}, author = {Bures, Tomas and Krikava, Filip and Mordinyi, Richard and Pronios, Nikos and Weyns, Danny and Berger, Christian and Biffl, Stefan and Daun, Marian and Gabor, Thomas and Garlan, David and Gerostathopoulos, Ilias and Julien, Christine}, year = {2015}, month = nov, journal = {ACM SIGSOFT Software Engineering Notes}, volume = {40}, number = {6}, pages = {28--32}, issn = {01635948}, doi = {10.1145/2830719.2830736}, langid = {english} } @article{Burgueño20191, title = {The Future of Model Transformation Languages: {{An}} Open Community Discussion}, author = {Burgue{\~n}o, L. and Cabot, J. and G{\'e}rard, S.}, year = {2019}, journal = {Journal of Object Technology}, volume = {18}, number = {3}, pages = {1--11}, publisher = {{Association Internationale pour les Technologies Objets}}, issn = {16601769}, doi = {10.5381/JOT.2019.18.3.A7}, abbrev_source_title = {J. Object Technol.}, affiliation = {IN3, Open University of Catalonia, Spain; Institut LIST, CEA, Universit\'e Paris-Saclay, France; ICREA, Spain}, art_number = {A7}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Transformation-Development,notion} } @inproceedings{Burgueno2019168, title = {Preface to {{MDE}} Intelligence 2019: 1st Workshop on Artificial Intelligence and Model-Driven Engineering}, author = {Burgueno, L. and Burdusel, A. and Gerard, S. and Wimmer, M.}, editor = {Burgueno L., Burgueno L., Voss S., Chaudron M., Kienzle J., Volter M., Gerard S., Zahedi M., Bousse E., Rensink A., Polack F., Engels G., Kappel G., Pretschner A.}, year = {2019}, series = {Proceedings - 2019 {{ACM}}/{{IEEE}} 22nd {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems Companion}}, {{MODELS-C}} 2019}, pages = {168--169}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS-C.2019.00028}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst. Companion, MODELS-C}, affiliation = {IN3, Open University of Catalonia, Spain; Institut LIST, CEA, Universit\'e Paris-Saclay, France; King's College London, United Kingdom; CDL-MINT, Johannes Kepler Universit t Linz, Austria}, art_number = {8904820}, document_type = {Conference Paper}, isbn = {978-1-72815-125-0}, langid = {english}, source = {Scopus} } @inproceedings{Burgueño2019294, title = {An {{LSTM-Based}} Neural Network Architecture for Model Transformations}, author = {Burgue{\~n}o, L. and Cabot, J. and G{\'e}rard, S.}, editor = {Kessentini M., Yue T., Pretschner A., Voss S., Burgueno L., Burgueno L., Yue T.}, year = {2019}, series = {Proceedings - 2019 {{ACM}}/{{IEEE}} 22nd {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS}} 2019}, pages = {294--299}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS.2019.00013}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS}, affiliation = {IN3, Open University of Catalonia, Institut LIST, CEA, Universit\'e Paris-Saclay, France; ICREA, IN3, Open University of Catalonia, Spain}, art_number = {8906971}, document_type = {Conference Paper}, isbn = {978-1-72812-535-0}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Transformation-Development,notion} } @inproceedings{Burgueno2021148, title = {{{MDE}} Intelligence 2021: {{3rdWorkshop}} on Artificial Intelligence and Model-Driven Engineering}, author = {Burgueno, L. and Kessentini, M. and Wimmer, M. and Zschaler, S.}, year = {2021}, series = {Companion {{Proceedings}} - 24th {{International Conference}} on {{Model-Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2021}, pages = {148--149}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS-C53483.2021.00026}, abbrev_source_title = {Companion Proc. - Int. Conf. Model-Driven Eng. Lang. Syst., MODELS-C}, affiliation = {IN3, Open University of Catalonia, Spain; University of Michigan, Dearborn, United States; CDL-MINT, Johannes Kepler Universit\"at Linz, Austria; King's College, London, United Kingdom}, document_type = {Conference Paper}, isbn = {978-1-66542-484-4}, langid = {english}, source = {Scopus} } @article{burguenoContentsModelBasedSoftware2019, title = {Contents for a {{Model-Based Software Engineering Body}} of {{Knowledge}}}, author = {Burgue{\~n}o, Loli and Ciccozzi, Federico and Famelis, Michalis and Kappel, Gerti and Lambers, Leen and Mosser, Sebastien and Paige, Richard F. and Pierantonio, Alfonso and Rensink, Arend and Salay, Rick and Taentzer, Gabriele and Vallecillo, Antonio and Wimmer, Manuel}, year = {2019}, month = dec, journal = {Software and Systems Modeling}, volume = {18}, number = {6}, pages = {3193--3205}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-019-00746-9}, 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.}, langid = {english} } @article{burguenoGuestEditorialTheme2022, title = {Guest Editorial to the Theme Section on {{AI-enhanced}} Model-Driven Engineering}, author = {Burgue{\~n}o, Lola and Cabot, Jordi and Wimmer, Manuel and Zschaler, Steffen}, year = {2022}, month = jun, journal = {Software and Systems Modeling}, volume = {21}, number = {3}, pages = {963--965}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-022-00988-0}, 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\textemdash 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\textemdash 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.}, langid = {english} } @article{burguenoNLPbasedArchitectureAutocompletion, title = {A {{NLP-based}} Architecture for the Autocompletion of Partial Domain Models}, author = {Burgue{\~n}o, Loli and Claris{\'o}, Robert and Li, Shuai and G{\'e}rard, S{\'e}bastien and Cabot, Jordi}, pages = {16}, 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.}, langid = {english} } @book{burguenoProceedingsMODELS20172017, 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}, editor = {Burgue{\~n}o, Loli and Corley, Jonathan and Bencomo, Nelly and Clarke, Peter J. and Collet, Philippe and Famelis, Michalis and Ghosh, Sudipto and Gogolla, Martin and Greenyer, Joel and Guerra, Esther and Kokaly, Sahar and Pierantonio, Alfonso and Rubin, Julia and Ruscio, Davide Di}, year = {2017}, series = {{{CEUR Workshop Proceedings}}}, volume = {2019}, publisher = {{CEUR-WS.org}}, url = {http://ceur-ws.org/Vol-2019} } @article{burguenoStaticFaultLocalization2015, title = {Static {{Fault Localization}} in {{Model Transformations}}}, author = {Burgueno, Loli and Troya, Javier and Wimmer, Manuel and Vallecillo, Antonio}, year = {2015}, month = may, journal = {IEEE Transactions on Software Engineering}, volume = {41}, number = {5}, pages = {490--506}, issn = {0098-5589, 1939-3520}, doi = {10.1109/TSE.2014.2375201} } @article{buttingModelingReusablePlatformIndependent2016, title = {Modeling {{Reusable}}, {{Platform-Independent Robot Assembly Processes}}}, author = {Butting, Arvid and Rumpe, Bernhard and Schulze, Christoph and Thomas, Ulrike and Wortmann, Andreas}, year = {2016}, journal = {arXiv preprint arXiv:1601.02452}, eprint = {1601.02452}, eprinttype = {arxiv}, url = {http://arxiv.org/abs/1601.02452}, urldate = {2016-01-18}, archiveprefix = {arXiv} } @book{buyyaInternetThingsPrinciples2016, title = {Internet of {{Things}}: Principles and Paradigms}, shorttitle = {Internet of {{Things}}}, author = {Buyya, Rajkumar and Dastjerdi, Amir Vahid}, year = {2016}, publisher = {{Morgan Kaufmann}}, address = {{Amsterdam Boston Heidelberg}}, isbn = {978-0-12-805395-9}, langid = {english} } @article{CA7F13858DC9A0A2F2B68A7CEA562E672, title = {{{CA7F13858DC9A0A2F2B68A7CEA562E67-2}}} } @article{Cacheda:2011:CCF:1921591.1921593, title = {Comparison of Collaborative Filtering Algorithms: {{Limitations}} of Current Techniques and Proposals for Scalable, High-Performance Recommender Systems}, author = {Cacheda, Fidel and Carneiro, V{\'i}ctor and Fern{\'a}ndez, Diego and Formoso, Vreixo}, year = {2011}, month = feb, journal = {ACM Transactions on the Web}, volume = {5}, number = {1}, pages = {2:1-2:33}, publisher = {{ACM}}, address = {{New York, NY, USA}}, issn = {1559-1131}, url = {http://doi.acm.org/10.1145/1921591.1921593}, acmid = {1921593}, articleno = {2}, issue_date = {February 2011}, nodoi = {10.1145/1921591.1921593}, numpages = {33}, keywords = {Collaborative filtering,recommender systems} } @article{cadavidAnalysisMetamodelingPractices2015, title = {An Analysis of Metamodeling Practices for {{MOF}} and {{OCL}}}, author = {Cadavid, Juan Jos{\'e} and Combemale, Benoit and Baudry, Benoit}, year = {2015}, month = mar, journal = {Computer Languages, Systems \& Structures}, issn = {14778424}, doi = {10.1016/j.cl.2015.02.002}, langid = {english} } @article{calegariVerificationModelTransformations2013, title = {Verification of {{Model Transformations}}: {{A Survey}} of the {{State-of-the-Art}}}, shorttitle = {Verification of {{Model Transformations}}}, author = {Calegari, Daniel and Szasz, Nora}, year = {2013}, month = mar, journal = {Electronic Notes in Theoretical Computer Science}, series = {Proceedings of the {{XXXVIII Latin American Conference}} in {{Informatics}} ({{CLEI}})}, volume = {292}, pages = {5--25}, issn = {1571-0661}, doi = {10.1016/j.entcs.2013.02.002}, 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.} } @inproceedings{callowAddressingSystemsVerification2011, title = {Addressing Systems Verification of Autonomous Systems through {{Bi-directional}} Model Transformations: {{A}} Systems Model Driven Architecture Approach}, shorttitle = {Addressing Systems Verification of Autonomous Systems through {{Bi-directional}} Model Transformations}, booktitle = {System of {{Systems Engineering}} ({{SoSE}}), 2011 6th {{International Conference}} On}, author = {Callow, Glenn and Kalawsky, Roy and Watson, Graham and Okuda, Yuki}, year = {2011}, pages = {311--316}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5966616}, urldate = {2016-08-21} } @inproceedings{Çam2018372, title = {Supporting Simulation Experiments with Megamodeling}, author = {{\c C}am, S. and Dayiba{\c s}, O. and G{\"o}r{\"u}r, B.K. and O{\v g}uzt{\"u}z{\"u}n, H. and Yilmaz, L. and Chakladar, S. and Doud, K. and Smith, A.E. and {Teran-Somohano}, A.}, editor = {Hammoudi S., Pires L.F., Selic B.}, year = {2018}, series = {{{MODELSWARD}} 2018 - {{Proceedings}} of the 6th {{International Conference}} on {{Model-Driven Engineering}} and {{Software Development}}}, volume = {2018-January}, pages = {372--378}, publisher = {{SciTePress}}, doi = {10.5220/0006586703720378}, 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 \textcopyright{} 2018 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.}, document_type = {Conference Paper}, isbn = {978-989-758-283-7}, source = {Scopus} } @article{camaraBridgingGapControl2020, title = {Towards {{Bridging}} the {{Gap}} between {{Control}} and {{Self-Adaptive System Properties}}}, author = {C{\'a}mara, Javier and Papadopoulos, Alessandro V. and Vogel, Thomas and Weyns, Danny and Garlan, David and Huang, Shihong and Tei, Kenji}, year = {2020}, month = jun, journal = {Proceedings of the IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems}, eprint = {2004.11846}, eprinttype = {arxiv}, pages = {78--84}, doi = {10.1145/3387939.3391568}, 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.}, archiveprefix = {arXiv} } @article{Can2012424, title = {A Comparison of Genetic Programming and Artificial Neural Networks in Metamodeling of Discrete-Event Simulation Models}, author = {Can, B. and Heavey, C.}, year = {2012}, journal = {Computers and Operations Research}, volume = {39}, number = {2}, pages = {424--436}, issn = {03050548}, doi = {10.1016/j.cor.2011.05.004}, abbrev_source_title = {Comp. Oper. Res.}, affiliation = {Enterprise Research Centre, ERB, University of Limerick, Limerick, Ireland}, coden = {CMORA}, correspondence_address1 = {Can, B.; Enterprise Research Centre, , Limerick, Ireland; email: birkan.can@ul.ie}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{canComparisonGeneticProgramming2012, title = {A Comparison of Genetic Programming and Artificial Neural Networks in Metamodeling of Discrete-Event Simulation Models}, author = {Can, B. and Heavey, C.}, year = {2012}, journal = {Computers and Operations Research}, volume = {39}, number = {2}, pages = {424--436}, issn = {03050548}, doi = {10.1016/j.cor.2011.05.004}, abstract = {Genetic programming (GP) and artificial neural networks (ANNs) can be used in the development of surrogate models of complex systems. The purpose of this paper is to provide a comparative analysis of GP and ANNs for metamodeling of discrete-event simulation (DES) models. Three stochastic industrial systems are empirically studied: an automated material handling system (AMHS) in semiconductor manufacturing, an (s,S) inventory model and a serial production line. The results of the study show that GP provides greater accuracy in validation tests, demonstrating a better generalization capability than ANN. However, GP when compared to ANN requires more computation in metamodel development. Even given this increased computational requirement, the results presented indicate that GP is very competitive in metamodeling of DES models. \textcopyright{} 2011 Elsevier Ltd.}, keywords = {Automated material handling systems,Comparative analysis,Computational requirements,Computer simulation,Decision support systems,Decision support tools,Design of experiments,Discrete events,Discrete-event simulation model,Generalization capability,Genetic programming,Industrial systems,Inventory models,Manufacture,Materials handling,Materials handling equipment,Meta model,Metamodel development,Metamodeling,Neural networks,Semiconductor device manufacture,Semiconductor manufacturing,Serial production line,Stochastic models,Surrogate model,Symbolic regression,Validation test} } @article{candelUnifiedMetamodelNoSQL2021, title = {A {{Unified Metamodel}} for {{NoSQL}} and {{Relational Databases}}}, author = {Candel, Carlos J. Fern{\'a}ndez and Ruiz, Diego Sevilla and {Garc{\'i}a-Molina}, Jes{\'u}s J.}, year = {2021}, month = may, journal = {arXiv:2105.06494 [cs]}, eprint = {2105.06494}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2105.06494}, urldate = {2021-06-27}, 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.}, archiveprefix = {arXiv}, keywords = {Computer Science - Databases} } @article{canoHybridRecommenderSystems2017, title = {Hybrid Recommender Systems: {{A}} Systematic Literature Review}, shorttitle = {Hybrid Recommender Systems}, author = {{\c C}ano, Erion and Morisio, Maurizio}, year = {2017}, month = nov, journal = {Intelligent Data Analysis}, volume = {21}, number = {6}, pages = {1487--1524}, issn = {1088467X, 15714128}, doi = {10.3233/IDA-163209}, 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.}, langid = {english} } @misc{CanYouTrust, title = {Can You Trust {{AutoML}}?. {{Is}} Overfitting Really Significant, Or\ldots{} | by {{Ioannis Tsamardinos}} | {{Analytics Vidhya}} | {{Medium}}}, url = {https://medium.com/analytics-vidhya/can-you-trust-automl-3a02332e66a0}, urldate = {2021-05-14} } @inproceedings{Cao2020, title = {Network-Level System Performance Prediction Using Deep Neural Networks with Cross-Layer Information}, author = {Cao, Q. and Zeng, S. and Pun, M.-O. and Chen, Y.}, year = {2020}, series = {{{IEEE International Conference}} on {{Communications}}}, volume = {2020-June}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15503607}, doi = {10.1109/ICC40277.2020.9149189}, 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. \textcopyright{} 2020 IEEE.}, art_number = {9149189}, document_type = {Conference Paper}, isbn = {978-1-72815-089-5}, source = {Scopus} } @inproceedings{Cao2021, title = {Simulation Optimization for a Digital Twin Using a Multi-Fidelity Framework}, author = {Cao, Y. and Currie, C. and Onggo, B.S. and Higgins, M.}, year = {2021}, series = {Proceedings - {{Winter Simulation Conference}}}, volume = {2021-December}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {08917736}, doi = {10.1109/WSC52266.2021.9715498}, abbrev_source_title = {Proc. Winter Simul. Conf.}, affiliation = {Centre for Operational Research, Management Science and Information Systems, University of Southampton, Southampton, SO17 1BJ, United Kingdom; PowerTrain Manufacturing Engineering, Ford Motor Company, Dunton Research Centre, Basildon, SS15 6EE, United Kingdom}, coden = {WSCPD}, document_type = {Conference Paper}, isbn = {978-1-66543-311-2}, langid = {english}, source = {Scopus} } @article{Capiluppi:2019:JSS:Clustering, title = {The {{Effects}} of {{Clustering}} on the {{Characteristics}} of {{Java Software}} - Manuscript under Revision}, author = {Capiluppi, Andrea and Di Ruscio, Davide and Di Rocco, Juri and Nguyen, Phuong T. and Ajienka, Nemitari}, year = {2019}, journal = {Journal of Systems and Software} } @article{capiluppiDetectingJavaSoftware2019, title = {Detecting {{Java Software Similarities}} by Using {{Different Clustering Techniques}}}, author = {Capiluppi, Andrea and Di Ruscio, Davide and Di Rocco, Juri and Nguyen, Phuong T and Ajienka, Nemitari}, year = {2019}, journal = {Elsevier Information and Software Technology (IST) Journal}, pages = {40}, 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.}, langid = {english} } @article{capiluppiDetectingJavaSoftware2020, title = {Detecting {{Java Software Similarities}} by Using {{Different Clustering Techniques}}}, author = {Capiluppi, Andrea and Di Ruscio, Davide and Di Rocco, Juri and Nguyen, Phuong T. and Ajienka, Nemitari}, year = {2020}, journal = {INFORMATION AND SOFTWARE TECHNOLOGY}, doi = {10.1016/j.infsof.2020.106279} } @article{cardelliUnderstandingTypesData1985, title = {On Understanding Types, Data Abstraction, and Polymorphism}, author = {Cardelli, Luca and Wegner, Peter}, year = {1985}, month = dec, journal = {ACM Computing Surveys}, volume = {17}, number = {4}, pages = {471--523}, issn = {0360-0300, 1557-7341}, doi = {10.1145/6041.6042}, 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.}, langid = {english} } @article{carletonAIEffectWorking2020, ids = {carletonAIEffectWorking}, title = {The {{AI Effect}}: {{Working}} at the {{Intersection}} of {{AI}} and {{SE}}}, shorttitle = {The {{AI Effect}}}, author = {Carleton, Anita D. and Harper, Erin and Menzies, Tim and Xie, Tao and Eldh, Sigrid and Lyu, Michael R.}, year = {2020}, month = jul, journal = {IEEE Software}, volume = {37}, number = {4}, pages = {26--35}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2020.2987666}, langid = {english} } @incollection{carpenterHandbookBrainTheory1998, title = {The Handbook of Brain Theory and Neural Networks}, author = {Carpenter, Gail A. and Grossberg, Stephen}, editor = {Arbib, Michael A.}, year = {1998}, pages = {79--82}, publisher = {{MIT Press}}, address = {{Cambridge, MA, USA}}, url = {http://dl.acm.org/citation.cfm?id=303568.303586}, acmid = {303586}, chapter = {Adaptive Resonance Theory (ART)}, isbn = {0-262-51102-9}, numpages = {4} } @article{carverExtractingRequirementsModeling2021, title = {Extracting {{Requirements}} and {{Modeling Information}} and {{Controlling Risk}}}, author = {Carver, Jeffrey C. and Abrahao, Silvia and Penzenstadler, Birgit}, year = {2021}, month = may, journal = {IEEE Software}, volume = {38}, number = {03}, pages = {121--124}, publisher = {{IEEE Computer Society}}, issn = {0740-7459}, doi = {10.1109/MS.2021.3056989}, 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).}, langid = {english} } @article{carverIndustryAcademiaCollaboration2018, title = {Industry\textendash{{Academia Collaboration}} in {{Software Engineering}}}, author = {Carver, J. C. and Prikladnicki, R.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {120--124}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571250}, abstract = {This article aims to encourage more industry\textendash 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.}, keywords = {software engineering} } @article{Casalaro202219, title = {Model-Driven Engineering for Mobile Robotic Systems: A Systematic Mapping Study}, author = {Casalaro, G.L. and Cattivera, G. and Ciccozzi, F. and Malavolta, I. and Wortmann, A. and Pelliccione, P.}, year = {2022}, journal = {Software and Systems Modeling}, volume = {21}, number = {1}, pages = {19--49}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {16191366}, doi = {10.1007/s10270-021-00908-8}, abbrev_source_title = {Softw. Syst. Model.}, affiliation = {Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila, Italy; School of Innovation, Design and Engineering, M\"alardalen University, V\"aster\aa s, Sweden; Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Chair of Software Engineering, RWTH Aachen University, Aachen, Germany; Chalmers | University of Gothenburg, Gothenburg, Sweden; University of L'Aquila, L'Aquila, Italy}, correspondence_address1 = {Ciccozzi, F.; School of Innovation, Sweden; email: federico.ciccozzi@mdh.se}, document_type = {Article}, langid = {english}, source = {Scopus} } @inproceedings{Castells_noveltyand, ids = {Castells_novelty}, title = {Novelty and Diversity Metrics for Recommender Systems: {{Choice}}, Discovery and Relevance}, booktitle = {In Proceedings of International Workshop on Diversity in Document Retrieval ({{DDR}}}, author = {Castells, Pablo and Vargas, Sa{\'u}l}, pages = {29--37}, citeulike-article-id = {9136077}, posted-at = {2011-04-11 15:46:44}, priority = {2}, keywords = {diversity,novelty,project-mavir,recommender,uam} } @misc{CatedrasaesumuNoSQLDataEngineeringNoSQL, title = {Catedrasaes-Umu/{{NoSQLDataEngineering}}: {{NoSQL Data Engineering}}}, url = {https://github.com/catedrasaes-umu/NoSQLDataEngineering#schema-models}, urldate = {2018-05-07} } @article{cedeno-mielesDataAnalysisModeling2020, title = {Data Analysis and Modeling Pipelines for Controlled Networked Social Science Experiments}, author = {{Cedeno-Mieles}, Vanessa and Hu, Zhihao and Ren, Yihui and Deng, Xinwei and Contractor, Noshir and Ekanayake, Saliya and Epstein, Joshua M. and Goode, Brian J. and Korkmaz, Gizem and Kuhlman, Chris J. and Machi, Dustin and Macy, Michael and Marathe, Madhav V. and Ramakrishnan, Naren and Saraf, Parang and Self, Nathan}, editor = {Cai, Ning}, year = {2020}, month = nov, journal = {PLOS ONE}, volume = {15}, number = {11}, pages = {e0242453}, issn = {1932-6203}, doi = {10.1371/journal.pone.0242453}, 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.}, langid = {english} } @article{celebiFAIRProtocolsWorkflows2020, title = {Towards {{FAIR}} Protocols and Workflows: The {{OpenPREDICT}} Use Case}, shorttitle = {Towards {{FAIR}} Protocols and Workflows}, author = {Celebi, Remzi and Rebelo Moreira, Joao and Hassan, Ahmed A. and Ayyar, Sandeep and Ridder, Lars and Kuhn, Tobias and Dumontier, Michel}, year = {2020}, month = sep, journal = {PeerJ Computer Science}, volume = {6}, pages = {e281}, issn = {2376-5992}, doi = {10.7717/peerj-cs.281}, 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.}, langid = {english} } @article{celisseOptimalCrossvalidationDensity2014, title = {Optimal Cross-Validation in Density Estimation with the \${{L}}\^\{2\}\$-Loss}, author = {Celisse, Alain}, year = {2014}, month = oct, journal = {The Annals of Statistics}, volume = {42}, number = {5}, issn = {0090-5364}, doi = {10.1214/14-AOS1240} } @article{Celms2020205, title = {Towards Dsl for Dl Lifecycle Data Management}, author = {Celms, E. and Barzdins, J. and Kalnins, A. and Sprogis, A. and Grasmanis, M. and Rikacovs, S. and Barzdins, P.}, editor = {{Robal T., Haav H.-M.}, Matulevicius R., Penjam J.}, year = {2020}, journal = {Communications in Computer and Information Science}, volume = {1243 CCIS}, pages = {205--218}, publisher = {{Springer}}, issn = {18650929}, doi = {10.1007/978-3-030-57672-1_16}, abbrev_source_title = {Commun. Comput. Info. Sci.}, affiliation = {Institute of Mathematics and Computer Science, University of Latvia, Riga, Latvia; Innovation Labs LETA, Riga, Latvia}, correspondence_address1 = {Celms, E.; Institute of Mathematics and Computer Science, Latvia; email: edgars.celms@lumii.lv}, document_type = {Conference Paper}, isbn = {9783030576714}, langid = {english}, source = {Scopus} } @article{Celms2021597, title = {{{DSL}} Approach to Deep Learning Lifecycle Data Management}, author = {Celms, E. and Barzdins, J. and Kalnins, A. and Barzdins, P. and Sprogis, A. and Grasmanis, M. and Rikacovs, S.}, year = {2021}, journal = {Baltic Journal of Modern Computing}, volume = {8}, number = {4}, pages = {597--617}, publisher = {{University of Latvia}}, issn = {22558942}, doi = {10.22364/BJMC.2020.8.4.09}, abbrev_source_title = {Baltic J. Mod. Comp.}, affiliation = {Institute of Mathematics and Computer Science, University of Latvia, Rai\c{n}a bulvaris 29, Riga, LV-1459, Latvia; Innovation Labs LETA, Latvia, Riga, Marijas iela 2, Riga, LV 1050, Latvia}, document_type = {Article}, langid = {english}, source = {Scopus} } @book{CEURWorkshopProceedings2015, title = {{{CEUR Workshop Proceedings}}}, year = {2015}, journal = {CEUR Workshop Proceedings}, volume = {1406}, publisher = {{CEUR-WS}} } @misc{CEURWorkshopProceedings2015a, title = {{{CEUR Workshop Proceedings}}}, year = {2015}, journal = {CEUR Workshop Proceedings}, volume = {1406}, publisher = {{CEUR-WS}} } @article{Chabanet2021, title = {Coupling Digital Simulation and Machine Learning Metamodel through an Active Learning Approach in {{Industry}} 4.0 Context}, author = {Chabanet, S. and {Bril El-Haouzi}, H. and Thomas, P.}, year = {2021}, journal = {Computers in Industry}, volume = {133}, publisher = {{Elsevier B.V.}}, issn = {01663615}, doi = {10.1016/j.compind.2021.103529}, abbrev_source_title = {Comput Ind}, affiliation = {Universit\'e de Lorraine, CNRS, CRAN, Epinal, F-88000, France}, art_number = {103529}, coden = {CINUD}, correspondence_address1 = {Chabanet, S.; Universit\'e de Lorraine, France; email: sylvain.chabanet@univ-lorraine.fr}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {notion} } @article{Chabanet2021573, title = {Dissimilarity to Class Medoids as Features for {{3D}} Point Cloud Classification}, author = {Chabanet, S. and Chazelle, V. and Thomas, P. and {El-Haouzi}, H.B.}, editor = {Dolgui A., Bernard A., von Cieminski G., Romero D., Lemoine D.}, year = {2021}, journal = {IFIP Advances in Information and Communication Technology}, volume = {632 IFIP}, pages = {573--581}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {18684238}, doi = {10.1007/978-3-030-85906-0_62}, abbrev_source_title = {IFIP Advances in Information and Communication Technology}, affiliation = {Universit\'e de Lorraine, CNRS, CRAN, Epinal, 88000, France}, correspondence_address1 = {Chabanet, S.; Universit\'e de Lorraine, France; email: sylvain.chabanet@univ-lorraine.fr}, document_type = {Conference Paper}, isbn = {9783030859053}, langid = {english}, source = {Scopus} } @article{Chai2022, title = {Data Management for Machine Learning: {{A}} Survey}, author = {Chai, C. and Wang, J. and Luo, Y. and Niu, Z. and Li, G.}, year = {2022}, journal = {IEEE Transactions on Knowledge and Data Engineering}, publisher = {{IEEE Computer Society}}, issn = {10414347}, doi = {10.1109/TKDE.2022.3148237}, 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}, coden = {ITKEE}, document_type = {Article}, source = {Scopus}, keywords = {GOAL_MDE4AI} } @inproceedings{Chatzimparmpas20211, title = {Empirical Study: {{Visual}} Analytics for Comparing Stacking to Blending Ensemble Learning}, author = {Chatzimparmpas, A. and Martins, R.M. and Kucher, K. and Kerren, A.}, year = {2021}, series = {Proceedings - 2021 23rd {{International Conference}} on {{Control Systems}} and {{Computer Science Technologies}}, {{CSCS}} 2021}, pages = {1--8}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/CSCS52396.2021.00008}, abbrev_source_title = {Proc. - Int. Conf. Control Syst. Comput. Sci. Technol., CSCS}, affiliation = {Linnaeus University, Department of Computer Science and Media Technology, V\"axj\"o, Sweden; Department of Computer Science and Media Technology, Linnaeus University, V\"axj\"o, Sweden; Department of Science and Technology, Link\"oping University, Norrk\"oping, Sweden}, art_number = {9481023}, document_type = {Conference Paper}, isbn = {978-1-66543-939-8}, langid = {english}, source = {Scopus} } @article{Chatzimparmpas20211547, title = {{{StackGenVis}}: {{Alignment}} of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics}, author = {Chatzimparmpas, A. and Martins, R.M. and Kucher, K. and Kerren, A.}, year = {2021}, journal = {IEEE Transactions on Visualization and Computer Graphics}, volume = {27}, number = {2}, pages = {1547--1557}, publisher = {{IEEE Computer Society}}, issn = {10772626}, doi = {10.1109/TVCG.2020.3030352}, abbrev_source_title = {IEEE Trans Visual Comput Graphics}, affiliation = {Linnaeus University, V\"axj\"o, Sweden}, art_number = {9222343}, coden = {ITVGE}, document_type = {Article}, langid = {english}, pubmed_id = {33048687}, source = {Scopus} } @inproceedings{Chatzimparmpas2022161, title = {Evaluating {{StackGenVis}} with a Comparative User Study}, author = {Chatzimparmpas, A. and Park, V. and Kerren, A.}, year = {2022}, series = {{{IEEE Pacific Visualization Symposium}}}, volume = {2022-April}, pages = {161--165}, publisher = {{IEEE Computer Society}}, issn = {21658765}, doi = {10.1109/PacificVis53943.2022.00025}, abbrev_source_title = {IEEE Pacific Visual. Symp.}, affiliation = {Linnaeus University, Link\"oping University, Sweden}, correspondence_address1 = {Chatzimparmpas, A.; Linnaeus University, Sweden; email: angelos.chatzimparmpas@lnu.se}, document_type = {Conference Paper}, isbn = {978-1-66542-335-9}, langid = {english}, source = {Scopus} } @book{chaudronProceedings40thInternational2018, title = {Proceedings of the 40th International Conference on Software Engineering, {{ICSE}} 2018, Gothenburg, Sweden, May 27 - June 03, 2018}, editor = {Chaudron, Michel and Crnkovic, Ivica and Chechik, Marsha and Harman, Mark}, year = {2018}, publisher = {{ACM}}, doi = {10.1145/3180155}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/icse/2018}, isbn = {978-1-4503-5638-1}, timestamp = {Wed, 21 Nov 2018 12:43:58 +0100} } @inproceedings{Chen:2005:CCF:2154509.2154540, title = {Context-Aware Collaborative Filtering System: {{Predicting}} the User's Preference in the Ubiquitous Computing Environment}, booktitle = {Proceedings of the First International Conference on Location- and Context-Awareness}, author = {Chen, Annie}, year = {2005}, series = {{{LoCA}}'05}, pages = {244--253}, publisher = {{Springer-Verlag}}, address = {{Berlin, Heidelberg}}, url = {http://dx.doi.org/10.1007/11426646_23}, acmid = {2154540}, isbn = {3-540-25896-5 978-3-540-25896-4}, nodoi = {10.1007/11426646{$_2$}3}, numpages = {10} } @article{Cheng2019472, title = {Deep Neighbor Embedding for Evaluation of Large Portfolios of Variable Annuities}, author = {Cheng, X. and Luo, W. and Gan, G. and Li, G.}, editor = {Douligeris C., Apostolou D., Karagiannis D.}, year = {2019}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {11775 LNAI}, pages = {472--480}, publisher = {{Springer}}, issn = {03029743}, doi = {10.1007/978-3-030-29551-6_42}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Xi'an Shiyou University, Shaanxi, China; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China; Deakin University, Geelong, Australia; University of Connecticut, Storrs, CT, United States}, correspondence_address1 = {Luo, W.; Deakin UniversityAustralia; email: wei.luo@deakin.edu.au}, document_type = {Conference Paper}, isbn = {9783030295509}, langid = {english}, source = {Scopus} } @book{chengSoftwareEngineeringSelfadaptive2009, title = {Software Engineering for Self-Adaptive Systems}, editor = {Cheng, Betty H. C.}, year = {2009}, series = {Lecture Notes in Computer Science}, number = {5525}, publisher = {{Springer}}, address = {{Berlin ; New York}}, isbn = {978-3-642-02160-2}, lccn = {QA76.76.S375 S64 2009} } @article{chenouardAutomaticallyDiscoveringHidden2009, title = {Automatically {{Discovering Hidden Transformation Chaining Constraints}}}, author = {Chenouard, Rapha{\"e}l and Jouault, Fr{\'e}d{\'e}ric}, year = {2009}, journal = {Model Driven Engineering Languages and Systems}, volume = {5795}, pages = {92--106}, doi = {10.1007/978-3-642-04425-0_8} } @inproceedings{chenSimAppFrameworkDetecting2015, ids = {Chen:2015:SFD:2684822.2685305}, title = {{{SimApp}}: {{A Framework}} for {{Detecting Similar Mobile Applications}} by {{Online Kernel Learning}}}, shorttitle = {{{SimApp}}}, author = {Chen, Ning and Hoi, Steven C.H. and Li, Shaohua and Xiao, Xiaokui}, year = {2015}, pages = {305--314}, publisher = {{ACM Press}}, address = {{Shanghai, China}}, doi = {10.1145/2684822.2685305}, acmid = {2685305}, isbn = {978-1-4503-3317-7}, langid = {english}, nodoi = {10.1145/2684822.2685305}, numpages = {10}, keywords = {mobile applications,multi-modal data,multiple kernels,online kernel learning,similarity function} } @inproceedings{Chowdhury2017, title = {Optimal Metamodeling to Interpret Activity-Based Health Sensor Data}, author = {Chowdhury, S. and Mehmani, A.}, year = {2017}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {3}, publisher = {{American Society of Mechanical Engineers (ASME)}}, doi = {10.1115/DETC2017-68385}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {University at Buffalo, Buffalo, NY 14260, United States; Columbia University, New York, NY 10027, United States; Department of Mechanical and Aerospace Engineering, ASME, United States}, correspondence_address1 = {Chowdhury, S.; Department of Mechanical and Aerospace Engineering, United States; email: soumacho@buffalo.edu}, document_type = {Conference Paper}, isbn = {978-0-7918-5815-8}, langid = {english}, source = {Scopus} } @inproceedings{Christen20194124, title = {Cybernetical Concepts for Cellular Automaton and Artificial Neural Network Modelling and Implementation}, author = {Christen, P. and Fabbro, O.D.}, year = {2019}, series = {Conference {{Proceedings}} - {{IEEE International Conference}} on {{Systems}}, {{Man}} and {{Cybernetics}}}, volume = {2019-October}, pages = {4124--4130}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {1062922X}, doi = {10.1109/SMC.2019.8913839}, abbrev_source_title = {Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.}, affiliation = {Institute for Information Systems, FHNW, Olten, Switzerland; ETH Zurich, Zurich, Switzerland}, art_number = {8913839}, coden = {PICYE}, correspondence_address1 = {Christen, P.; Institute for Information Systems, Switzerland; email: patrik.christen@fhnw.ch}, document_type = {Conference Paper}, isbn = {978-1-72814-569-3}, langid = {english}, source = {Scopus} } @article{chughSurveyHandlingComputationally2019, title = {A Survey on Handling Computationally Expensive Multiobjective Optimization Problems with Evolutionary Algorithms}, author = {Chugh, T. and Sindhya, K. and Hakanen, J. and Miettinen, K.}, year = {2019}, journal = {Soft Computing}, volume = {23}, number = {9}, pages = {3137--3166}, publisher = {{Springer Verlag}}, issn = {14327643}, doi = {10.1007/s00500-017-2965-0}, abstract = {Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008 and 2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approximation-based algorithms. We also compare these algorithms based on different criteria such as metamodeling technique and evolutionary algorithm used, type and dimensions of the problem solved, handling constraints, training time and the type of evolution control. Furthermore, we identify and discuss some promising elements and major issues among algorithms in the literature related to using an approximation and numerical settings used. In addition, we discuss selecting an algorithm to solve a given computationally expensive multiobjective optimization problem based on the dimensions in both objective and decision spaces and the computation budget available. \textcopyright{} 2017, Springer-Verlag GmbH Germany, part of Springer Nature.}, keywords = {Approximation algorithms,Budget control,Computational costs,Evolutionary algorithms,Learning systems,Meta model,Multicriteria optimization,Multiobjective optimization,Pareto principle,Pareto-optimality,Problem solving,Response surface approximation,Surrogate,Surveys} } @article{chuiInternetThings2010, title = {The Internet of Things}, author = {Chui, Michael and L{\"o}ffler, Markus and Roberts, Roger}, year = {2010}, journal = {McKinsey Quarterly}, volume = {2}, number = {2010}, pages = {1--9}, url = {https://realyze.in/downloads/TheInternetofThings.pdf}, urldate = {2016-08-21} } @inproceedings{cicchettiAutomatingCoevolutionModelDriven2008, title = {Automating {{Co-evolution}} in {{Model-Driven Engineering}}}, booktitle = {12th {{International IEEE Enterprise Distributed Object Computing Conference}}, {{EDOC}} 2008}, author = {Cicchetti, A and DI RUSCIO, Davide and Eramo, R and Pierantonio, Alfonso}, year = {2008}, pages = {222--231}, publisher = {{IEEE Computer Society}}, doi = {10.1109/EDOC.2008.44} } @inproceedings{cicchettiManagingDependentChanges2009, title = {Managing Dependent Changes in Coupled Evolution}, booktitle = {Theory and {{Practice}} of {{Model Transformations}}, {{Second International Conference}}, {{ICMT}} 2009}, author = {Cicchetti, A and DI RUSCIO, Davide and Pierantonio, Alfonso}, year = {2009}, volume = {5563}, pages = {35--51}, doi = {10.1007/978-3-642-02408-5_4}, isbn = {978-3-642-02407-8} } @article{cicchettiMetamodelIndependentApproach2007, title = {A Metamodel Independent Approach to Difference Representation}, author = {Cicchetti, A and DI RUSCIO, Davide and Pierantonio, Alfonso}, year = {2007}, journal = {JOURNAL OF OBJECT TECHNOLOGY}, volume = {6}, pages = {165--185} } @article{ciccozziAdoptingMDESpecifying2016, title = {Adopting {{MDE}} for {{Specifying}} and {{Executing Civilian Missions}} of {{Mobile Multi-Robot Systems}}}, author = {Ciccozzi, Federico and DI RUSCIO, Davide and Malavolta, Ivano and Pelliccione, Patrizio}, year = {2016}, journal = {IEEE ACCESS}, volume = {4}, pages = {6451--6466}, doi = {10.1109/ACCESS.2016.2613642}, 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.} } @article{ciccozziAdoptingMDESpecifying2016a, title = {Adopting {{MDE}} for {{Specifying}} and {{Executing Civilian Missions}} of {{Mobile Multi-Robot Systems}}}, author = {Ciccozzi, Federico and DI RUSCIO, Davide and Malavolta, Ivano and Pelliccione, Patrizio}, year = {2016}, journal = {IEEE ACCESS}, volume = {4}, pages = {6451--6466}, doi = {10.1109/ACCESS.2016.2613642}, 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.} } @inproceedings{ciccozziAutomaticSynthesisHeterogeneous2013, title = {Automatic Synthesis of Heterogeneous {{CPU-GPU}} Embedded Applications from a {{UML}} Profile}, booktitle = {{{CEUR Workshop Proceedings}}}, author = {Ciccozzi, F.}, editor = {Graf S., Ober I., Noyrit F., Karsai G.}, year = {2013}, volume = {1084}, publisher = {{CEUR-WS}}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924051199&partnerID=40&md5=70ef85d012e33a637026195a4787b475}, abstract = {Modern embedded systems present an ever increasing complexity and model-driven engineering has been shown to be helpful in mitigating it. In our previous works we exploited the power of model-driven engineering to develop a round-trip approach for aiding the evaluation and assessment of extra-functional properties preservation from models to code. In addition, we showed how the round-trip approach could be employed to evaluate different deployment strategies, and the focus was on homogeneous CPUbased platforms. Due to the fact that the assortment of target-platforms in the embedded domain is inevitably shifting to heterogeneous solutions, our goal is to broaden the scope of the round-trip approach towards mixed CPU-GPU configurations. In this work we focus on the modelling of heterogeneous deployment and the enhancement of the current automatic code generator to synthesize code targeting such heterogeneous configurations.}, keywords = {ALF,CHESS-ML,Code synthesis,Codes (symbols),Computational linguistics,Embedded systems,Heterogeneous systems,MARTE,Model-driven Engineering,UML} } @inproceedings{ciccozziBodyKnowledgeModelbased2018, title = {Towards a Body of Knowledge for Model-Based Software Engineering}, booktitle = {Proceedings of the 21st {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}: {{Companion Proceedings}}}, author = {Ciccozzi, Federico and Famelis, Michalis and Kappel, Gerti and Lambers, Leen and Mosser, Sebastien and Paige, Richard F. and Pierantonio, Alfonso and Rensink, Arend and Salay, Rick and Taentzer, Gabi and Vallecillo, Antonio and Wimmer, Manuel}, year = {2018}, month = oct, pages = {82--89}, publisher = {{ACM}}, address = {{Copenhagen Denmark}}, doi = {10.1145/3270112.3270121}, 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 \textemdash{} which constitutes the Body of Knowledge of a discipline \textemdash{} 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.}, isbn = {978-1-4503-5965-8}, langid = {english} } @book{ciccozziProceedings1stInternational2018, title = {Proceedings of the 1st {{International Workshop}} on {{Robotics Software Engineering}}, {{RoSE}}@{{ICSE}} 2018, {{Gothenburg}}, {{Sweden}}, {{May}} 28, 2018}, editor = {Ciccozzi, Federico and Ruscio, Davide Di and Malavolta, Ivano and Pelliccione, Patrizio and Wortmann, Andreas}, year = {2018}, publisher = {{ACM}}, url = {http://dl.acm.org/citation.cfm?id=3196558}, isbn = {978-1-4503-5760-9} } @article{cioffiArtificialIntelligenceMachine2020, title = {Artificial {{Intelligence}} and {{Machine Learning Applications}} in {{Smart Production}}: {{Progress}}, {{Trends}}, and {{Directions}}}, shorttitle = {Artificial {{Intelligence}} and {{Machine Learning Applications}} in {{Smart Production}}}, author = {Cioffi, Raffaele and Travaglioni, Marta and Piscitelli, Giuseppina and Petrillo, Antonella and De Felice, Fabio}, year = {2020}, month = jan, journal = {Sustainability}, volume = {12}, number = {2}, pages = {492}, issn = {2071-1050}, doi = {10.3390/su12020492}, 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.}, langid = {english}, keywords = {artificial intelligence,DONE,machine learning,smart manufacturing} } @inproceedings{citoInteractiveProductionPerformance2019, title = {Interactive {{Production Performance Feedback}} in the {{IDE}}}, booktitle = {2019 {{IEEE}}/{{ACM}} 41st {{International Conference}} on {{Software Engineering}} ({{ICSE}})}, author = {Cito, Jurgen and Leitner, Philipp and Rinard, Martin and Gall, Harald C.}, year = {2019}, month = may, pages = {971--981}, publisher = {{IEEE}}, address = {{Montreal, QC, Canada}}, doi = {10.1109/ICSE.2019.00102}, isbn = {978-1-72810-869-8} } @inproceedings{Clarisó20181, title = {Applying Graph Kernels to Model-Driven Engineering Problems}, author = {Claris{\'o}, R. and Cabot, J.}, editor = {Perrouin G., Acher M., Cordy M., Cordy M., Devroey X.}, year = {2018}, series = {{{MASES}} 2018 - {{Proceedings}} of the 1st {{International Workshop}} on {{Machine Learning}} and {{Software Engineering}} in {{Symbiosis}}, Co-Located with {{ASE}} 2018}, pages = {1--5}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3243127.3243128}, abbrev_source_title = {MASES - Proc. Int. Workshop Mach. Learn. Soft. Eng. Symbiosis, co-located ASE}, affiliation = {Multimedia and Telecommunication Dept. Barcelona, Universitat Oberta de Catalunya (UOC) IT, Spain; SOM Research Lab, ICREA, Barcelona, Spain}, document_type = {Conference Paper}, isbn = {978-1-4503-5972-6}, langid = {english}, source = {Scopus} } @inproceedings{clarisoApplyingGraphKernels2018, title = {Applying Graph Kernels to Model-Driven Engineering Problems}, booktitle = {{{MASES}} 2018 - {{Proceedings}} of the 1st {{International Workshop}} on {{Machine Learning}} and {{Software Engineering}} in {{Symbiosis}}, Co-Located with {{ASE}} 2018}, author = {Claris{\'o}, R. and Cabot, J.}, editor = {Perrouin G., Acher M., Devroey X., Cordy M., Cordy M.}, year = {2018}, pages = {1--5}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3243127.3243128}, abstract = {Machine Learning (ML) can be used to analyze and classify large collections of graph-based information, e.g. images, location information, the structure of molecules and proteins, . . . Graph kernels is one of the ML techniques typically used for such tasks. In a software engineering context, models of a system such as structural or architectural diagrams can be viewed as labeled graphs. Thus, in this paper we propose to employ graph kernels for clustering software modeling artifacts. Among other benefits, this would improve the efficiency and usability of a variety of software modeling activities, e.g., design space exploration, testing or verification and validation. \textcopyright{} 2018 Association for Computing Machinery.}, isbn = {978-1-4503-5972-6}, keywords = {Artificial intelligence,Classification (of information),Clustering,Design space exploration,Graph kernels,Graphic methods,Labeled graphs,Learning systems,Location information,Model-driven Engineering,Software testing,Structure of molecules,Systems analysis,Verification,Verification-and-validation} } @article{clarisoBackwardsReasoningModel2015, title = {Backwards Reasoning for Model Transformations: {{Method}} and Applications}, shorttitle = {Backwards Reasoning for Model Transformations}, author = {Claris{\'o}, Robert and Cabot, Jordi and Guerra, Esther and {de Lara}, Juan}, year = {2015}, month = aug, journal = {Journal of Systems and Software}, issn = {01641212}, doi = {10.1016/j.jss.2015.08.017}, langid = {english} } @misc{ClosedloopSystemClosedloop, title = {Closed-Loop {{System}} and {{Closed-loop Control Systems}}}, url = {http://www.electronics-tutorials.ws/systems/closed-loop-system.html}, urldate = {2016-11-01} } @misc{ClusteringIntroduction, title = {Clustering - {{Introduction}}}, url = {http://home.deib.polimi.it/matteucc/Clustering/tutorial_html/}, urldate = {2015-04-23} } @misc{ClusteringValidationTechniques, title = {On {{Clustering Validation Techniques}} - {{Springer}}}, url = {http://link.springer.com/article/10.1023/A:1012801612483}, urldate = {2015-05-07} } @misc{ClusterSummarizationDense, title = {Cluster {{Summarization}} with {{Dense Region Detection}} - {{Springer}}}, url = {http://link.springer.com/chapter/10.1007/978-3-319-25840-9_5?wt_mc=alerts.TOCseries}, urldate = {2015-11-02} } @inproceedings{Cohen:1995:FER:3091622.3091637, title = {Fast Effective Rule Induction}, booktitle = {Proceedings of the Twelfth International Conference on International Conference on Machine Learning}, author = {Cohen, William W.}, year = {1995}, series = {{{ICML}}'95}, pages = {115--123}, publisher = {{Morgan Kaufmann Publishers Inc.}}, address = {{San Francisco, CA, USA}}, url = {http://dl.acm.org/citation.cfm?id=3091622.3091637}, acmid = {3091637}, isbn = {1-55860-377-8}, numpages = {9} } @article{cohenFourPillarsResearch2021, title = {The {{Four Pillars}} of {{Research Software Engineering}}}, author = {Cohen, Jeremy and Katz, Daniel S. and Barker, Michelle and Chue Hong, Neil and Haines, Robert and Jay, Caroline}, year = {2021}, month = jan, journal = {IEEE Software}, volume = {38}, number = {1}, pages = {97--105}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2020.2973362} } @article{colinaInternetThingsIoT, title = {Internet of {{Things}} ({{IoT}}) in 5 Days}, author = {Colina, Antonio Li{\~n}{\'a}n and Vives, Alvaro and Bagula, Antoine and Zennaro, Marco and Pietrosemoli, Ermanno}, pages = {227} } @article{collobertNaturalLanguageProcessing2011, title = {Natural Language Processing (Almost) from Scratch}, author = {Collobert, Ronan and Weston, Jason and Bottou, L{\'e}on and Karlen, Michael and Kavukcuoglu, Koray and Kuksa, Pavel}, year = {2011}, month = nov, journal = {Journal of Machine Learning Research}, volume = {12}, pages = {2493--2537}, publisher = {{JMLR.org}}, issn = {1532-4435}, url = {http://dl.acm.org/citation.cfm?id=1953048.2078186}, acmid = {2078186}, issue_date = {2/1/2011}, numpages = {45} } @inproceedings{colnagoInformingDesignPersonalized2020, title = {Informing the {{Design}} of a {{Personalized Privacy Assistant}} for the {{Internet}} of {{Things}}}, booktitle = {Proceedings of the 2020 {{CHI Conference}} on {{Human Factors}} in {{Computing Systems}}}, author = {Colnago, Jessica and Feng, Yuanyuan and Palanivel, Tharangini and Pearman, Sarah and Ung, Megan and Acquisti, Alessandro and Cranor, Lorrie Faith and Sadeh, Norman}, year = {2020}, month = apr, pages = {1--13}, publisher = {{ACM}}, address = {{Honolulu HI USA}}, doi = {10.1145/3313831.3376389}, isbn = {978-1-4503-6708-0}, langid = {english} } @article{Combemale202171, title = {A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems}, author = {Combemale, B. and Kienzle, J. and Mussbacher, G. and Ali, H. and Amyot, D. and Bagherzadeh, M. and Batot, E. and Bencomo, N. and Benni, B. and Bruel, J.-M. and Cabot, J. and Cheng, B.H.C. and Collet, P. and Engels, G. and Heinrich, R. and Jezequel, J.-M. and Koziolek, A. and Mosser, S. and Reussner, R. and Sahraoui, H. and Saini, R. and Sallou, J. and Stinckwich, S. and Syriani, E. and Wimmer, M.}, year = {2021}, journal = {IEEE Software}, volume = {38}, number = {4}, pages = {71--84}, publisher = {{IEEE Computer Society}}, issn = {07407459}, doi = {10.1109/MS.2020.2995125}, abbrev_source_title = {IEEE Software}, affiliation = {University of Toulouse, Toulouse, F-35042, France; McGill University, Montr\'eal, QC H3A 0E9, Canada; University of Ottawa, Ottawa, ON K1N 5N5, Canada; Open University of Catalonia, Barcelona, H3C 3J7, Spain; Aston University, Birmingham, B4 7ET, United Kingdom; Concordia University, Montr\'eal, QC F-06103, Canada; Department of Business Informatics-Software Engineering, Johannes Kepler University Linz, Linz, Austria; Michigan State University, East Lansing, MI 48824, United States; University C\^ote d'Azur, Biot, F-06103, France; Paderborn University, Paderborn, D-33100, Germany; Karlsruhe Institute of Technology, Karlsruhe, D-76128, Germany; University of Rennes, Rennes, F-35042, France; University of Qu\'ebec Montr\'eal, Montr\'eal, QC H3C 3P8, Canada; Montr\'eal University, Montr\'eal, QC H3C 3J7, Canada; United Nations University Institute, Macau, 150-8925, Macau}, art_number = {9094197}, coden = {IESOE}, document_type = {Article}, langid = {english}, source = {Scopus} } @incollection{combemaleFormallyDefiningIterating2012, title = {Formally {{Defining}} and {{Iterating Infinite Models}}}, booktitle = {Model {{Driven Engineering Languages}} and {{Systems}}}, author = {Combemale, Benoit and Thirioux, Xavier and Baudry, Benoit}, editor = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Doug and Vardi, Moshe Y. and Weikum, Gerhard and France, Robert B. and Kazmeier, J{\"u}rgen and Breu, Ruth and Atkinson, Colin}, year = {2012}, volume = {7590}, pages = {119--133}, publisher = {{Springer Berlin Heidelberg}}, address = {{Berlin, Heidelberg}}, doi = {10.1007/978-3-642-33666-9_9}, isbn = {978-3-642-33665-2 978-3-642-33666-9}, langid = {english} } @book{combemaleGlobalizingDomainSpecificLanguages2015, title = {Globalizing {{Domain-Specific Languages}}}, editor = {Combemale, Benoit and Cheng, Betty H.C. and France, Robert B. and J{\'e}z{\'e}quel, Jean-Marc and Rumpe, Bernhard}, year = {2015}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {9400}, publisher = {{Springer International Publishing}}, address = {{Cham}}, url = {http://link.springer.com/10.1007/978-3-319-26172-0}, urldate = {2016-01-26}, isbn = {978-3-319-26171-3 978-3-319-26172-0} } @article{combemaleGlobalizingModelingLanguages2014, title = {Globalizing Modeling Languages}, author = {Combemale, Benoit and Deantoni, Julien and Baudry, Benoit and France, Robert B. and J{\'e}z{\'e}quel, Jean-Marc and Gray, Jordan}, year = {2014}, journal = {Computer}, volume = {47}, number = {6}, pages = {68--71}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6839148}, urldate = {2015-09-23} } @phdthesis{combemaleLanguageOrientedModeling2015, title = {Towards {{Language-Oriented Modeling}}}, author = {Combemale, Benoit}, year = {2015}, url = {https://hal.inria.fr/tel-01238817/}, urldate = {2016-01-26}, school = {Universit\'e de Rennes 1} } @article{combemaleSLEBOKSoftwareLanguage2018, title = {{{SLEBOK}}: {{The Software Language Engineering Body}} of {{Knowledge}} ({{Dagstuhl Seminar}} 17342)}, shorttitle = {{{SLEBOK}}}, author = {Combemale, Beno{\^i}t and L{\"a}mmel, Ralf and Van Wyk, Eric}, year = {2018}, pages = {10 pages}, publisher = {{Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany}}, doi = {10.4230/DAGREP.7.8.45}, 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.}, collaborator = {Herbstritt, Marc}, langid = {english}, keywords = {000 Computer science; knowledge; general works,Computer Science} } @article{CommunicationsACMFebruary, title = {Communications of the {{ACM}} - {{February}} 2022}, pages = {124}, langid = {english} } @article{CommunicationsACMJuly, title = {Communications of the {{ACM}} - {{July}} 2020}, pages = {116}, langid = {english} } @article{CommunicationsACMJulya, title = {Communications of the {{ACM}} - {{July}} 2021}, pages = {116}, langid = {english} } @article{CommunicationsACMJune, title = {Communications of the {{ACM}} - {{June}} 2020}, pages = {100}, langid = {english} } @article{CommunicationsACMJunea, title = {Communications of the {{ACM}} - {{June}} 2021}, pages = {124}, langid = {english} } @article{CommunicationsACMMay, title = {Communications of the {{ACM}} - {{May}} 2020}, pages = {116}, langid = {english} } @article{CommunicationsACMOctober, title = {Communications of the {{ACM}} - {{October}} 2020}, pages = {112}, langid = {english} } @misc{ComparisonModelMigration, title = {A {{Comparison}} of {{Model Migration Tools}} - {{Springer}}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-16145-2_5}, urldate = {2015-03-24} } @misc{ComplexNatureMDE, title = {On the Complex Nature of {{MDE}} Evolution and Its Impact on Changeability - {{Online First}} - {{Springer}}}, url = {http://link.springer.com/article/10.1007%2Fs10270-015-0464-2}, urldate = {2015-12-08} } @inproceedings{conf:iscis:MadylovaO09, title = {A Taxonomy Based Semantic Similarity of Documents Using the Cosine Measure.}, booktitle = {{{ISCIS}}}, author = {Madylova, Ainura and {\"O}g{\"u}duc{\"u}, Sule G{\"u}nd{\"u}z}, year = {2009-12-30, 2009}, pages = {129--134}, publisher = {{IEEE}}, url = {http://dblp.uni-trier.de/db/conf/iscis/iscis2009.html#MadylovaO09}, added-at = {2009-12-30T00:00:00.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/2b6f37cbf44daa243cf2b91e00181806f/dblp}, description = {dblp}, ee = {http://dx.doi.org/10.1109/ISCIS.2009.5291865}, interhash = {17227ba19186316517f52ffa39fa8568}, intrahash = {b6f37cbf44daa243cf2b91e00181806f}, keywords = {dblp}, timestamp = {2009-12-31T11:34:52.000+0100} } @inproceedings{conf/stids/OlssonPSP11, title = {Finding and Explaining Similarities in Linked Data.}, booktitle = {{{STIDS}}}, author = {Olsson, Catherine and Petrov, Plamen and Sherman, Jeff and {Perez-Lopez}, Andrew}, editor = {{da Costa}, Paulo Cesar G. and Laskey, Kathryn B.}, year = {2011}, series = {{{CEUR}} Workshop Proceedings}, volume = {808}, pages = {52--59}, publisher = {{CEUR-WS.org}}, url = {http://dblp.uni-trier.de/db/conf/stids/stids2011.html#OlssonPSP11}, added-at = {2013-07-22T00:00:00.000+0200}, biburl = {http://www.bibsonomy.org/bibtex/29536bc182f305da5dad27f51b28a779e/dblp}, ee = {http://ceur-ws.org/Vol-808/STIDS2011\textsubscript{C}R\textsubscript{T}7\textsubscript{O}lssonEtAl.pdf}, interhash = {a078f5c01f90adea0e5067f8a3d88106}, intrahash = {9536bc182f305da5dad27f51b28a779e}, keywords = {dblp}, timestamp = {2013-07-22T00:00:00.000+0200} } @article{connollyWhyComputingBelongs2020a, title = {Why Computing Belongs within the Social Sciences}, author = {Connolly, Randy}, year = {2020}, month = jul, journal = {Communications of the ACM}, volume = {63}, number = {8}, pages = {54--59}, issn = {0001-0782, 1557-7317}, doi = {10.1145/3383444}, abstract = {Fully appreciating the overarching scope of CS requires weaving more than ethics into the reigning curricula.}, langid = {english} } @article{conselInternetThingsChallenge, title = {Internet of {{Things}}: {{A Challenge}} for {{Software Engineering}}}, author = {Consel, Charles and Kab{\'a}{\v c}, Milan}, pages = {3}, langid = {english} } @misc{ConstructingAutonomousSystems, title = {Constructing {{Autonomous Systems}}}, url = {http://aosgrp.com/featured-research/autonomy_and_agents/autonomous_systems/constructing_autonomous_sys.html}, urldate = {2016-08-24} } @misc{ContinuousDeliveryMap, title = {Continuous {{Delivery Map}} | {{Continuous Delivery Map}}}, url = {https://assessment-tools.ca.com/tools/continuous-delivery-tools/en?embed}, urldate = {2018-04-30} } @misc{ControlSystemsFeedback, title = {Control {{Systems}}/{{Feedback Loops}} - {{Wikibooks}}, Open Books for an Open World}, url = {https://en.wikibooks.org/wiki/Control_Systems/Feedback_Loops}, urldate = {2016-11-01} } @misc{ControlTheory101, title = {Control {{Theory}} 101 for {{Beginners}} | {{Nuvation}}}, url = {http://www.nuvation.com/blog/electronic-design-services/control-theory-101-beginners}, urldate = {2016-09-20} } @misc{ControlTheory1012013, title = {Control {{Theory}} 101 for {{Beginners}}}, year = {2013}, month = sep, journal = {Nuvation}, url = {http://www.nuvation.com/blog/electronic-design-services/control-theory-101-beginners}, urldate = {2016-09-20}, abstract = {While not as ubiquitous as electric power or microelectronics, control theory is applied everywhere in our daily lives but it is rarely noticed.} } @article{corbelliniPersistingBigdataNoSQL2017, title = {Persisting Big-Data: {{The NoSQL}} Landscape}, shorttitle = {Persisting Big-Data}, author = {Corbellini, Alejandro and Mateos, Cristian and Zunino, Alejandro and Godoy, Daniela and Schiaffino, Silvia}, year = {2017}, month = jan, journal = {Information Systems}, volume = {63}, pages = {1--23}, issn = {03064379}, doi = {10.1016/j.is.2016.07.009}, 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.}, langid = {english} } @article{correaCoupledEvolutionMetamodels2013, title = {Towards {{Coupled Evolution}} of {{Metamodels}}, {{Models}}, {{Graph-Based Transformations}} and {{Traceability Links}}}, author = {Correa, Chessman and Toacy, Oliveira and Claudia, Werner}, year = {2013} } @article{cosentinoSystematicMappingStudy2017, title = {A {{Systematic Mapping Study}} of {{Software Development With GitHub}}}, author = {Cosentino, Valerio and Canovas Izquierdo, Javier L. and Cabot, Jordi}, year = {2017}, journal = {IEEE Access}, volume = {5}, pages = {7173--7192}, issn = {2169-3536}, doi = {10.1109/ACCESS.2017.2682323} } @article{cosmoSoftwareHeritageWhy, title = {Software {{Heritage}}: {{Why}} and {{How}} to {{Preserve Software Source Code}}}, author = {Cosmo, Roberto Di and Zacchiroli, Stefano}, pages = {10}, 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\textemdash the only representation of software that contains human readable knowledge\textemdash 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.}, langid = {english} } @inproceedings{costaModelingIoTApplications2016, title = {Modeling {{IoT Applications}} with {{SysML4IoT}}}, booktitle = {2016 42th {{Euromicro Conference}} on {{Software Engineering}} and {{Advanced Applications}} ({{SEAA}})}, author = {Costa, Bruno and Pires, Paulo F. and Delicato, Flavia C.}, year = {2016}, month = aug, pages = {157--164}, publisher = {{IEEE}}, address = {{Limassol, Cyprus}}, doi = {10.1109/SEAA.2016.19}, isbn = {978-1-5090-2820-7} } @article{Coutinho2014AnalysisOD, title = {Analysis of Distance Functions for Similarity-Based Test Suite Reduction in the Context of Model-Based Testing}, author = {Coutinho, Ana Em{\'i}lia Victor Barbosa and Cartaxo, Emanuela Gadelha and {de Lima Machado}, Patr{\'i}cia Duarte}, year = {2014}, journal = {Software Quality Journal}, volume = {24}, pages = {407--445} } @inproceedings{Covington:2016:DNN:2959100.2959190, title = {Deep Neural Networks for {{YouTube}} Recommendations}, booktitle = {Proceedings of the 10th {{ACM}} Conference on Recommender Systems}, author = {Covington, Paul and Adams, Jay and Sargin, Emre}, year = {2016}, series = {{{RecSys}} '16}, pages = {191--198}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/2959100.2959190}, acmid = {2959190}, isbn = {978-1-4503-4035-9}, nodoi = {10.1145/2959100.2959190}, numpages = {8}, keywords = {deep learning,recommender system,scalability} } @article{coyleEthicalConcernsUnmanned, title = {Ethical {{Concerns}} of {{Unmanned}} and {{Autonomous Systems}} in {{Engineering Programs}}}, author = {Coyle, Eric Joe}, journal = {age}, volume = {24}, pages = {1}, url = {https://www.asee.org/file_server/papers/attachment/file/0004/3811/ASEE-2014-UNMANNED-ETHICS-final.pdf}, urldate = {2016-08-21} } @inproceedings{Cremonesi:2008:EMC:1468165.1468327, title = {An Evaluation Methodology for Collaborative Recommender Systems}, booktitle = {Proceedings of the 2008 International Conference on Automated Solutions for Cross Media Content and Multi-Channel Distribution}, author = {Cremonesi, Paolo and Turrin, Roberto and Lentini, Eugenio and Matteucci, Matteo}, year = {2008}, series = {{{AXMEDIS}} '08}, pages = {224--231}, publisher = {{IEEE Computer Society}}, address = {{Washington, DC, USA}}, url = {https://doi.org/10.1109/AXMEDIS.2008.13}, acmid = {1468327}, isbn = {978-0-7695-3406-0}, nodoi = {10.1109/AXMEDIS.2008.13}, numpages = {8}, keywords = {Collaborative,evaluation,knowledge discovery,methodology,naive bayesian networks,recommender systems,svd} } @inproceedings{cremonesiPerformanceRecommenderAlgorithms2010, title = {Performance of Recommender Algorithms on Top-n Recommendation Tasks}, booktitle = {Proceedings of the Fourth {{ACM}} Conference on Recommender Systems}, author = {Cremonesi, Paolo and Koren, Yehuda and Turrin, Roberto}, year = {2010}, series = {{{RecSys}} '10}, pages = {39--46}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/1864708.1864721}, acmid = {1864721}, isbn = {978-1-60558-906-0}, nodoi = {10.1145/1864708.1864721}, numpages = {8}, keywords = {evaluation,precision,recall,top-n recommendations} } @article{criadoEnablingReuseStored, title = {Enabling the Reuse of Stored Model Transformations through Annotations}, author = {Criado, Javier and Mart{\i}nez, Salvador and Iribarne, Luis and Cabot, Jordi}, url = {http://modeling-languages.com/wp-content/uploads/2015/04/icmt2015.pdf}, urldate = {2015-05-26} } @misc{CROSSMETERQuestionsBegel, title = {{{CROSSMETER}} - {{Questions}} from {{Begel}}/{{Zimmermann}}'s {{ICSE}} 2014 Paper - {{Google Docs}}}, url = {https://docs.google.com/document/d/1jyZJE4xIUsRLHqMqsGjpZDGt9RBGmZAZLh5S_ueTsQQ/edit}, urldate = {2016-01-22} } @article{CROSSREC-DATA, title = {{{CrossRec}} Tool and Evaluation Data}, author = {Di Rocco, Juri and Nguyen, Phuong T. and Di Ruscio, Davide}, year = {2018} } @article{Crussell2015AnDarwinSD, title = {{{AnDarwin}}: {{Scalable}} Detection of Android Application Clones Based on Semantics}, author = {Crussell, Jonathan and Gibler, Clint and Chen, Hao}, year = {2015}, journal = {IEEE Transactions on Mobile Computing}, volume = {14}, pages = {2007--2019} } @inproceedings{crussellAndarwinScalableDetection2013, ids = {Crussell2013}, title = {Andarwin: {{Scalable}} Detection of Semantically Similar Android Applications}, shorttitle = {Andarwin}, booktitle = {European {{Symposium}} on {{Research}} in {{Computer Security}}}, author = {Crussell, Jonathan and Gibler, Clint and Chen, Hao}, year = {2013}, pages = {182--199}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-40203-6_11}, urldate = {2017-09-25} } @misc{CSGSSISEAI, ids = {CSGSSISEAIa,CSGSSISEAIb}, title = {{{CS}}@{{GSSI}} - {{SE-AI Course}} 2021}, url = {https://sites.google.com/gssi.it/csgssi/ph-d-program/se-ai-course-2021}, urldate = {2021-05-07} } @article{cuadradoModelFindingEMF2020, title = {Model {{Finding}} in the {{EMF Ecosystem}}.}, author = {Cuadrado, Jes{\'u}s S{\'a}nchez and Gogolla, Martin}, year = {2020}, journal = {The Journal of Object Technology}, volume = {19}, number = {2}, pages = {10:1}, issn = {1660-1769}, doi = {10.5381/jot.2020.19.2.a10}, 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.}, langid = {english} } @article{cuadradoVerifiedCatalogueOCL2019, title = {A Verified Catalogue of {{OCL}} Optimisations}, author = {Cuadrado, Jes{\'u}s S{\'a}nchez}, year = {2019}, month = jul, journal = {Software \& Systems Modeling}, issn = {1619-1374}, doi = {10.1007/s10270-019-00740-1}, 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 bent\=o, 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.}, langid = {english} } @article{Cui20211223, title = {{Recommending best suitable metaheuristic based on landmarking feature and meta-learning approach [基于地标特征和元学习方法推荐最适用优化算法]}}, author = {Cui, J.-S. and Lyu, Y. and Xu, Z.-H.}, year = {2021}, journal = {Kongzhi yu Juece/Control and Decision}, volume = {36}, number = {5}, pages = {1223--1231}, publisher = {{Northeast University}}, issn = {10010920}, doi = {10.13195/j.kzyjc.2019.0993}, abbrev_source_title = {Kongzhi yu Juece Control Decis}, affiliation = {Dolinks School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China}, coden = {KYJUE}, correspondence_address1 = {Cui, J.-S.; Dolinks School of Economics and Management, China; email: cuijs@manage.ustb.edu.cn}, document_type = {Article}, langid = {chinese}, source = {Scopus} } @article{Cui2021788, title = {Jointly Sparse Signal Recovery and Support Recovery via Deep Learning with Applications in {{MIMO-Based}} Grant-Free Random Access}, author = {Cui, Y. and Li, S. and Zhang, W.}, year = {2021}, journal = {IEEE Journal on Selected Areas in Communications}, volume = {39}, number = {3}, pages = {788--803}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {07338716}, doi = {10.1109/JSAC.2020.3018802}, 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. \textcopyright{} 1983-2012 IEEE.}, art_number = {9174792}, coden = {ISACE}, document_type = {Article}, source = {Scopus} } @inproceedings{Cunningham2018, title = {A Validation Neural Network ({{VNN}}) Metamodel for Predicting the Performance of Deep Generative Designs}, author = {Cunningham, J. and Tucker, C.S.}, year = {2018}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {2B-2018}, publisher = {{American Society of Mechanical Engineers (ASME)}}, doi = {10.1115/DETC201886299}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {Department of Computer Science and Engineering, Penn State University University, Park, PA 16802, United States; Department of Engineering Design and Industrial and Manufacturing Engineering, Penn State University University, Park, PA 16802, United States}, document_type = {Conference Paper}, isbn = {978-0-7918-5176-0}, langid = {english}, source = {Scopus} } @article{cusumanoSelfdrivingVehicleTechnology2020, title = {Self-Driving Vehicle Technology: Progress and Promises}, shorttitle = {Self-Driving Vehicle Technology}, author = {Cusumano, Michael A.}, year = {2020}, month = sep, journal = {Communications of the ACM}, volume = {63}, number = {10}, pages = {20--22}, issn = {0001-0782, 1557-7317}, doi = {10.1145/3417074}, langid = {english} } @misc{CyberPhysicalSystemsConcept, title = {Cyber-{{Physical Systems}} - a {{Concept Map}}}, url = {http://cyberphysicalsystems.org/}, urldate = {2015-10-09} } @inproceedings{D'Aloisio2022291, title = {Quality-Driven Machine Learning-Based Data Science Pipeline Realization: A Software Engineering Approach}, author = {D'Aloisio, G.}, year = {2022}, series = {Proceedings - {{International Conference}} on {{Software Engineering}}}, pages = {291--293}, publisher = {{IEEE Computer Society}}, issn = {02705257}, doi = {10.1109/ICSE-Companion55297.2022.9793779}, abbrev_source_title = {Proc Int Conf Software Eng}, affiliation = {University of l'Aquila, Italy}, coden = {PCSED}, correspondence_address1 = {D'aloisio, G.; University of l'AquilaItaly; email: giordano.daloisio@graduate.univaq.it}, document_type = {Conference Paper}, isbn = {978-1-66549-598-1}, langid = {english}, source = {Scopus} } @inproceedings{daFonseca2017950, title = {The Potential of Artificial Neural Networks to Model Daylight Harvesting in Buildings Located in Different Climate Zones}, author = {{da Fonseca}, R.W. and Pereira, F.O.R. and Papamichael, K.}, editor = {Barnaby C.S., Wetter M.}, year = {2017}, series = {Building {{Simulation Conference Proceedings}}}, volume = {2}, pages = {950--959}, publisher = {{International Building Performance Simulation Association}}, issn = {25222708}, doi = {10.26868/25222708.2017.570}, abbrev_source_title = {Build. Simul. Conf. Proc.}, affiliation = {Environmental Comfort Laboratory, Federal University of Santa Catarina, Brazil; California Lighting Technology Center, UC Davis, United States}, document_type = {Conference Paper}, isbn = {978-1-5108-7067-3}, langid = {english}, source = {Scopus} } @inproceedings{dagenaisMovingNewSoftware2010, title = {Moving into a New Software Project Landscape}, booktitle = {Proceedings of the {{32Nd ACM}}/{{IEEE}} International Conference on Software Engineering - Volume 1}, author = {Dagenais, Barth{\'e}l{\'e}my and Ossher, Harold and Bellamy, Rachel K. E. and Robillard, Martin P. and {de Vries}, Jacqueline P.}, year = {2010}, series = {{{ICSE}} '10}, pages = {275--284}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/1806799.1806842}, acmid = {1806842}, isbn = {978-1-60558-719-6}, nodoi = {10.1145/1806799.1806842}, numpages = {10} } @article{Dai2017344, title = {A Wavelet Support Vector Machine-Based Neural Network Metamodel for Structural Reliability Assessment}, author = {Dai, H. and Cao, Z.}, year = {2017}, journal = {Computer-Aided Civil and Infrastructure Engineering}, volume = {32}, number = {4}, pages = {344--357}, publisher = {{Blackwell Publishing Inc.}}, issn = {10939687}, doi = {10.1111/mice.12257}, abbrev_source_title = {Comput.-Aided Civ. Infrastruct. Eng.}, affiliation = {School of Civil Engineering, Harbin Institute of Technology, Harbin, China and, Key Lab of Structures Dynamic Behavior and Control (Harbin Institute of Technology), Ministry of Education, Harbin, 150090, China}, coden = {CCIEF}, correspondence_address1 = {Dai, H.; School of Civil Engineering, China; email: hzdai@hit.edu.cn}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{dalpiazNaturalLanguageProcessing2018, title = {Natural {{Language Processing}} for {{Requirements Engineering}}: {{The Best Is Yet}} to {{Come}}}, shorttitle = {Natural {{Language Processing}} for {{Requirements Engineering}}}, author = {Dalpiaz, F. and Ferrari, A. and Franch, X. and Palomares, C.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {115--119}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571242}, 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.}, keywords = {software engineering} } @article{damevskiMiningSequencesDeveloper2017, title = {Mining {{Sequences}} of {{Developer Interactions}} in {{Visual Studio}} for {{Usage Smells}}}, author = {Damevski, Kostadin and Shepherd, David C. and Schneider, Johannes and Pollock, Lori}, year = {2017}, month = apr, journal = {IEEE Transactions on Software Engineering}, volume = {43}, number = {4}, pages = {359--371}, issn = {0098-5589, 1939-3520}, doi = {10.1109/TSE.2016.2592905} } @article{Dang201415, title = {General Frameworks for Optimization of Plastic Injection Molding Process Parameters}, author = {Dang, X.-P.}, year = {2014}, journal = {Simulation Modelling Practice and Theory}, volume = {41}, pages = {15--27}, issn = {1569190X}, doi = {10.1016/j.simpat.2013.11.003}, abbrev_source_title = {Simul. Model. Pract. Theory}, affiliation = {Faculty of Mechanical Engineering, Nha Trang University, 2 Nguyen Dinh Chieu Street, Nha Trang City, Khanh Hoa 57000, Viet Nam}, correspondence_address1 = {Dang, X.-P.; Faculty of Mechanical Engineering, 2 Nguyen Dinh Chieu Street, Nha Trang City, Khanh Hoa 57000, Viet Nam; email: phuongdx@ntu.edu.vn}, document_type = {Article}, langid = {english}, source = {Scopus} } @incollection{danielUMLtoGraphDBMappingConceptual2016, title = {{{UMLtoGraphDB}}: {{Mapping Conceptual Schemas}} to {{Graph Databases}}}, shorttitle = {{{UMLtoGraphDB}}}, booktitle = {Conceptual {{Modeling}}}, author = {Daniel, Gwendal and Suny{\'e}, Gerson and Cabot, Jordi}, editor = {{Comyn-Wattiau}, Isabelle and Tanaka, Katsumi and Song, Il-Yeol and Yamamoto, Shuichiro and Saeki, Motoshi}, year = {2016}, volume = {9974}, pages = {430--444}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-319-46397-1_33}, 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.}, isbn = {978-3-319-46396-4 978-3-319-46397-1}, langid = {english} } @inproceedings{Danvin2019, title = {Laminar to Turbulent Transition Prediction in Hypersonic Flows with Neural Networks Committee}, author = {Danvin, F. and {Olazabal-Loume}, M. and Pinna, F.}, year = {2019}, series = {{{AIAA Aviation}} 2019 {{Forum}}}, publisher = {{American Institute of Aeronautics and Astronautics Inc, AIAA}}, doi = {10.2514/6.2019-2837}, abbrev_source_title = {AIAA Aviation Forum}, affiliation = {CEA-CESTA, 15 avenue des Sabli\`eres, CS 60001, Le Barp Cedex, 33114, France; Von Karman Institute for Fluid Dynamics, 72 Chauss\'ee de Waterloo B-1640, Rhode-Saint-Gen\`ese, Belgium}, document_type = {Conference Paper}, isbn = {978-1-62410-589-0}, langid = {english}, page_count = {14}, source = {Scopus} } @article{Daosabah2021324, title = {Dynamic Composition of Services: An Approach Driven by the User's Intention and Context}, author = {Daosabah, A. and Guermah, H. and Nassar, M.}, year = {2021}, journal = {International Journal of Web Engineering and Technology}, volume = {16}, number = {4}, pages = {324--354}, publisher = {{Inderscience Publishers}}, issn = {14761289}, doi = {10.1504/IJWET.2021.122768}, abbrev_source_title = {Int. J. Web Eng. Technol.}, affiliation = {IMS Team, ADMIR Laboratory, Rabat IT Centre, ENSIAS, Mohammed V University, Rabat, Morocco}, correspondence_address1 = {Daosabah, A.; IMS Team, Morocco; email: a.daosabah@gmail.com}, document_type = {Article}, langid = {english}, source = {Scopus} } @inproceedings{daSilva201915, title = {A Conceptual Vision toward the Management of Machine Learning Models}, author = {{da Silva}, D.N.R. and Sim{\~o}es, A. and Cardoso, C. and {de Oliveira}, D.E.M. and Rittmeyer, J.N. and Wehmuth, K. and Lustosa, H. and Pereira, R.S. and Souto, Y. and Vignoli, L.E.G. and Salles, R. and {de Heleno, S.C.}, Jr. and Ziviani, A. and Ogasawara, E. and Delicato, F.C. and {de Pires}, P.F. and {da Pinto}, H.L.C.P. and Maia, L. and Porto, F.}, editor = {Panach J.I., Guizzardi R., Claro D.B.}, year = {2019}, series = {{{CEUR Workshop Proceedings}}}, volume = {2469}, pages = {15--27}, publisher = {{CEUR-WS}}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074090321&partnerID=40&md5=71c78c0900e41656c1b6f88703cb4f35}, 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 \textcopyright{} 2019 for this paper by its authors.}, document_type = {Conference Paper}, source = {Scopus} } @misc{DataDistributionService, title = {Data {{Distribution Service}}\texttrademark{} ({{DDS}}\texttrademark )}, url = {https://www.youtube.com/watch?v=6iICap5G7rw}, urldate = {2019-10-20}, abstract = {The Data Distribution Service\texttrademark{} (DDS\texttrademark ) is a middleware protocol and API standard for data-centric connectivity from the OMG\textregistered. 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. In 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. Presenter: Dr. Gerardo Pardo-Castellote, Co-Chair OMG DDS Special Interest Group, OMG Board of Directors, and CTO, RTI} } @misc{DataistsTaxonomyData, title = {Dataists \guillemotright{} {{A Taxonomy}} of {{Data Science}}}, url = {http://www.dataists.com/2010/09/a-taxonomy-of-data-science/}, urldate = {2021-03-18}, langid = {american} } @misc{DataMiningCluster, title = {Data {{Mining Cluster Analysis}}}, url = {http://www.tutorialspoint.com/data_mining/dm_cluster_analysis.htm}, urldate = {2015-04-22} } @misc{DataModelDesign, title = {Data {{Model Design}} and {{Best Practices}} ({{Part}} 1) - {{Talend}}}, url = {https://www.talend.com/blog/2017/05/05/data-model-design-best-practices-part-1/}, urldate = {2018-04-30} } @misc{DataModelingAge, title = {Data {{Modeling In The Age Of NoSQL And Big Data}}}, journal = {DATAVERSITY}, url = {http://www.dataversity.net/data-modeling-age-nosql-big-data/}, urldate = {2015-03-26}, 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 \textendash{} needs for better scalability, lower latency, greater flexibility, and a better price/performance ratio in an age of Big Data and Cloud computing.} } @misc{DataModelingDead, title = {Data {{Modeling}} Is {{Dead}}...{{Long Live Schema Design}}! - {{DATAVERSITY}}}, url = {https://www.dataversity.net/data-modeling-dead-long-live-schema-design/}, urldate = {2019-11-11} } @misc{DataModelingGuidelines, title = {Data {{Modeling Guidelines}} for {{NoSQL JSON Document Databases}} | {{MapR}}}, url = {https://mapr.com/blog/data-modeling-guidelines-nosql-json-document-databases/}, urldate = {2018-05-07} } @misc{DataModelingKey, title = {Data {{Modeling}} with {{Key Value NoSQL Data Stores}} \textendash{} {{Interview}} with {{Casey Rosenthal}}}, journal = {InfoQ}, url = {http://www.infoq.com/articles/data-modeling-with-key-value-nosql-data-stores}, urldate = {2015-03-26}, abstract = {In Key Value data stores, data is represented as a collection of key\textendash value pairs. The key\textendash 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.} } @misc{DataModelsInternet, title = {Data Models for the {{Internet}} of {{Things}}}, url = {http://iot-datamodels.blogspot.it/}, urldate = {2016-09-27} } @misc{DataStreamingIoT, title = {Data {{Streaming}} in {{IoT}} and {{Big Data Analytics}}}, 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}, urldate = {2021-01-05}, keywords = {Data analysis,data streaming,DONE,internet of things} } @inproceedings{davidediruscioManagingEvolutionFree2011, title = {Managing the {{Evolution}} of {{Free}} and {{Open Source Software Complex Systems}}}, booktitle = {V {{Conferenza Italiana}} Sul {{Software Libero}} - {{Milano}} 23-24 {{Giugno}} 2011}, author = {Davide Di Ruscio and Pelliccione, P}, year = {2011} } @article{davidEvaluatingCapabilitiesEnterprise2015, title = {Evaluating the Capabilities of {{Enterprise Architecture}} Modeling Tools for {{Visual Analysis}}.}, author = {David, Naranjo and S{\'a}nchez, Mario and Villalobos, Jorge}, year = {2015}, journal = {The Journal of Object Technology}, volume = {14}, number = {1}, pages = {3:1}, issn = {1660-1769}, doi = {10.5381/jot.2015.14.1.a3}, langid = {english} } @incollection{davidStreamingModelTransformations2014, title = {Streaming {{Model Transformations By Complex Event Processing}}}, booktitle = {Model-{{Driven Engineering Languages}} and {{Systems}}}, author = {D{\'a}vid, Istv{\'a}n and R{\'a}th, Istv{\'a}n and Varr{\'o}, D{\'a}niel}, editor = {Dingel, Juergen and Schulte, Wolfram and Ramos, Isidro and Abrah{\~a}o, Silvia and Insfran, Emilio}, year = {2014}, volume = {8767}, pages = {68--83}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-319-11653-2_5}, 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.}, isbn = {978-3-319-11652-5 978-3-319-11653-2}, langid = {english} } @inproceedings{davisRelationshipPrecisionrecallROC2006, title = {The Relationship between Precision-Recall and {{ROC}} Curves}, booktitle = {Proceedings of the 23rd International Conference on Machine Learning}, author = {Davis, Jesse and Goadrich, Mark}, year = {2006}, series = {{{ICML}} '06}, pages = {233--240}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/1143844.1143874}, acmid = {1143874}, isbn = {1-59593-383-2}, nodoi = {10.1145/1143844.1143874}, numpages = {8} } @inproceedings{DBLP:conf/icse/RigbyR13, title = {Discovering Essential Code Elements in Informal Documentation}, booktitle = {35th International Conference on Software Engineering, {{ICSE}} '13, San Francisco, {{CA}}, {{USA}}, May 18-26, 2013}, author = {Rigby, Peter C. and Robillard, Martin P.}, year = {2013}, pages = {832--841}, doi = {10.1109/ICSE.2013.6606629}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/icse/RigbyR13}, timestamp = {Tue, 23 May 2017 01:11:52 +0200} } @inproceedings{DBLP:conf/models/Stevens18, title = {Towards Sound, Optimal, and Flexible Building from Megamodels}, booktitle = {Proceedings of the 21th {{ACM}}/{{IEEE}} International Conference on Model Driven Engineering Languages and Systems, {{MODELS}} 2018, Copenhagen, Denmark, October 14-19, 2018}, author = {Stevens, Perdita}, year = {2018}, pages = {301--311}, doi = {10.1145/3239372.3239378}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/models/Stevens18}, timestamp = {Wed, 21 Nov 2018 12:44:12 +0100} } @inproceedings{DBLP:conf/recsys/WuSCTP14, title = {The Browsemaps: {{Collaborative}} Filtering at {{LinkedIn}}}, booktitle = {{{RSWeb}}@{{RecSys}}}, author = {Wu, Lili and Shah, Sam and Choi, Sean and Tiwari, Mitul and Posse, Christian}, year = {2014}, series = {{{CEUR}} Workshop Proceedings}, volume = {1271}, publisher = {{CEUR-WS.org}} } @article{DBLP:journals/corr/abs-0911-5046, title = {Integrating the Probabilistic Models {{BM25}}/{{BM25F}} into Lucene}, author = {{P{\'e}rez-Iglesias}, Joaqu{\'i}n and {P{\'e}rez-Ag{\"u}era}, Jos{\'e} R. and Fresno, V{\'i}ctor and Feinstein, Yuval Z.}, year = {2009}, journal = {CoRR}, volume = {abs/0911.5046}, eprint = {0911.5046}, eprinttype = {arxiv}, url = {http://arxiv.org/abs/0911.5046}, archiveprefix = {arXiv}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-0911-5046}, timestamp = {Mon, 13 Aug 2018 16:46:38 +0200} } @article{DBLP:journals/corr/abs-1812-04894, title = {A4: {{Automatically}} Assisting Android {{API}} Migrations Using Code Examples}, author = {Lamothe, Maxime and Shang, Weiyi and Chen, Tse-Hsun}, year = {2018}, journal = {CoRR}, volume = {abs/1812.04894}, eprint = {1812.04894}, eprinttype = {arxiv}, url = {http://arxiv.org/abs/1812.04894}, archiveprefix = {arXiv}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1812-04894}, timestamp = {Tue, 01 Jan 2019 15:01:25 +0100} } @article{DBLP:journals/corr/IzmaylovaKSV13, title = {M3: {{An}} Open Model for Measuring Code Artifacts}, author = {Izmaylova, Anastasia and Klint, Paul and Shahi, Ashim and Vinju, Jurgen J.}, year = {2013}, journal = {CoRR}, volume = {abs/1312.1188}, eprint = {1312.1188}, eprinttype = {arxiv}, url = {http://arxiv.org/abs/1312.1188}, archiveprefix = {arXiv}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/corr/IzmaylovaKSV13}, timestamp = {Wed, 07 Jun 2017 14:42:09 +0200} } @article{DBLP:journals/ijswis/HliaoutakisVVPM06, title = {Information Retrieval by Semantic Similarity}, author = {Hliaoutakis, Angelos and Varelas, Giannis and Voutsakis, Epimenidis and Petrakis, Euripides G. M. and Milios, Evangelos E.}, year = {2006}, journal = {Int. J. Semantic Web Inf. Syst.}, volume = {2}, number = {3}, pages = {55--73}, doi = {10.4018/jswis.2006070104}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/ijswis/HliaoutakisVVPM06}, timestamp = {Sat, 20 May 2017 00:24:06 +0200} } @article{DBLP:journals/sigmobile/Shannon01, title = {A Mathematical Theory of Communication}, author = {Shannon, Claude E.}, year = {2001}, journal = {Mobile Computing and Communications Review}, volume = {5}, number = {1}, pages = {3--55}, doi = {10.1145/584091.584093}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/sigmobile/Shannon01}, timestamp = {Wed, 28 Nov 2018 12:57:16 +0100} } @inproceedings{Dean:2012:LSD:2999134.2999271, title = {Large Scale Distributed Deep Networks}, booktitle = {Proceedings of the 25th Int. {{Conf}}. on Neural Information Processing Systems - Volume 1}, author = {Dean, Jeffrey and Corrado, Greg S. and Monga, Rajat and Chen, Kai and Devin, Matthieu and Le, Quoc V. and Mao, Mark Z. and Ranzato, Marc'Aurelio and Senior, Andrew and Tucker, Paul and Yang, Ke and Ng, Andrew Y.}, year = {2012}, series = {{{NIPS}}'12}, pages = {1223--1231}, publisher = {{Curran Associates Inc.}}, address = {{USA}}, acmid = {2999271}, numpages = {9} } @article{Deb:2002:FEM:2221359.2221582, title = {A Fast and Elitist Multiobjective Genetic Algorithm: {{NSGA-II}}}, author = {Deb, K. and Pratap, A. and Agarwal, S. and Meyarivan, T.}, year = {2002}, month = apr, journal = {Trans. Evol. Comp}, volume = {6}, number = {2}, pages = {182--197}, publisher = {{IEEE Press}}, address = {{Piscataway, NJ, USA}}, issn = {1089-778X}, url = {http://dx.doi.org/10.1109/4235.996017}, acmid = {2221582}, issue_date = {April 2002}, nodoi = {10.1109/4235.996017}, numpages = {16} } @article{debieAutomatingDataScience2022, title = {Automating Data Science}, author = {De Bie, Tijl and De Raedt, Luc and {Hern{\'a}ndez-Orallo}, Jos{\'e} and Hoos, Holger H. and Smyth, Padhraic and Williams, Christopher K. I.}, year = {2022}, month = mar, journal = {Communications of the ACM}, volume = {65}, number = {3}, pages = {76--87}, issn = {0001-0782, 1557-7317}, doi = {10.1145/3495256}, abstract = {Given the complexity of data science projects and related demand for human expertise, automation has the potential to transform the data science process.}, langid = {english}, keywords = {STARRED} } @book{degyurkyAutonomousSystemFoundational2014, title = {The Autonomous System a Foundational Synthesis of the Sciences of the Mind}, author = {De Gyurky, Szabolcs Michael and Tarbell, Mark A}, year = {2014}, publisher = {{Wiley}}, address = {{Hoboken. N.J.}}, url = {http://public.eblib.com/choice/publicfullrecord.aspx?p=1465945}, urldate = {2016-08-21}, 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.}, isbn = {978-1-118-75749-9 978-1-118-75995-0 978-1-118-29424-6 978-1-299-98883-5}, langid = {english} } @article{dehghaniFacilitatingMigrationMicroservice2022, title = {Facilitating the Migration to the Microservice Architecture via Model-Driven Reverse Engineering and Reinforcement Learning}, author = {Dehghani, MohammadHadi and {Kolahdouz-Rahimi}, Shekoufeh and Tisi, Massimo and Tamzalit, Dalila}, year = {2022}, month = jun, journal = {Software and Systems Modeling}, volume = {21}, number = {3}, pages = {1115--1133}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-022-00977-3}, langid = {english}, keywords = {GOAL_System-Modernization,TECHNIQUE_Reinforcement-Learning} } @article{dehuryTOSCAdataModelingData2022, title = {{{TOSCAdata}}: {{Modeling}} Data Pipeline Applications in {{TOSCA}}}, shorttitle = {{{TOSCAdata}}}, author = {Dehury, Chinmaya Kumar and Jakovits, Pelle and Srirama, Satish Narayana and Giotis, Giorgos and Garg, Gaurav}, year = {2022}, month = apr, journal = {Journal of Systems and Software}, volume = {186}, pages = {111164}, issn = {01641212}, doi = {10.1016/j.jss.2021.111164}, 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.}, langid = {english} } @article{delaraReusableAbstractionsModeling2013, title = {Reusable Abstractions for Modeling Languages}, author = {{de Lara}, Juan and Guerra, Esther and S{\'a}nchez Cuadrado, Jes{\'u}s}, year = {2013}, month = nov, journal = {Information Systems}, volume = {38}, number = {8}, pages = {1128--1149}, issn = {03064379}, doi = {10.1016/j.is.2013.06.001}, langid = {english} } @inproceedings{delaraReusingModelTransformations2017, title = {Reusing Model Transformations through Typing Requirements Models}, booktitle = {Fundamental {{Approaches}} to {{Software Engineering}} - 20th {{International Conference}}, {{FASE}} 2017, {{Held}} as {{Part}} of the {{European Joint Conferences}} on {{Theory}} and {{Practice}} of {{Software}}, {{ETAPS}} 2017, {{Uppsala}}, {{Sweden}}, {{April}} 22-29, 2017, {{Proceedings}}.}, author = {{de Lara}, Juan and DI ROCCO, Juri and DI RUSCIO, Davide and Guerra, Esther and Iovino, Ludovico and Pierantonio, Alfonso and Cuadrado, Jes{\'u}s S{\'a}nchez}, year = {2017}, volume = {10202}, pages = {264--282}, publisher = {{Springer Verlag}}, doi = {10.1007/978-3-662-54494-5_15}, isbn = {978-3-662-54494-5}, keywords = {Computer Science (all),Theoretical Computer Science} } @article{delavegaLavoisierDSLIncreasing2020, title = {Lavoisier: {{A DSL}} for Increasing the Level of Abstraction of Data Selection and Formatting in Data Mining}, shorttitle = {Lavoisier}, author = {{de la Vega}, Alfonso and {Garc{\'i}a-Saiz}, Diego and Zorrilla, Marta and S{\'a}nchez, Pablo}, year = {2020}, month = oct, journal = {Journal of Computer Languages}, volume = {60}, pages = {100987}, issn = {25901184}, doi = {10.1016/j.cola.2020.100987}, langid = {english} } @article{deldjooAdversarialMachineLearning, title = {Adversarial {{Machine Learning}} in {{Recommender Systems}}: {{State}} of the Art and {{Challenges}}}, author = {Deldjoo, Yashar and Noia, Tommaso Di and Merra, Felice Antonio}, pages = {35}, langid = {english}, keywords = {adversarial machine learning} } @article{deldjooSurveyAdversarialRecommender2021, title = {A {{Survey}} on {{Adversarial Recommender Systems}}: {{From Attack}}/{{Defense Strategies}} to {{Generative Adversarial Networks}}}, shorttitle = {A {{Survey}} on {{Adversarial Recommender Systems}}}, author = {Deldjoo, Yashar and Noia, Tommaso Di and Merra, Felice Antonio}, year = {2021}, month = mar, journal = {ACM Computing Surveys}, volume = {54}, number = {2}, pages = {1--38}, issn = {0360-0300, 1557-7341}, doi = {10.1145/3439729}, 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: Many applications of machine learning (ML) are adversarial in nature [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. 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.}, langid = {english} } @incollection{delemosSoftwareEngineeringSelfadaptive2013, title = {Software Engineering for Self-Adaptive Systems: {{A}} Second Research Roadmap}, shorttitle = {Software Engineering for Self-Adaptive Systems}, booktitle = {Software {{Engineering}} for {{Self-Adaptive Systems II}}}, author = {De Lemos, Rog{\'e}rio and Giese, Holger and M{\"u}ller, Hausi A. and Shaw, Mary and Andersson, Jesper and Litoiu, Marin and Schmerl, Bradley and Tamura, Gabriel and Villegas, Norha M. and Vogel, Thomas and others}, year = {2013}, pages = {1--32}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-35813-5_1}, urldate = {2016-08-21} } @inproceedings{delimaWorkloaddrivenLogicalDesign2015, title = {A Workload-Driven Logical Design Approach for {{NoSQL}} Document Databases}, booktitle = {Proceedings of the 17th {{International Conference}} on {{Information Integration}} and {{Web-based Applications}} \& {{Services}}}, author = {{de Lima}, Claudio and {dos Santos Mello}, Ronaldo}, year = {2015}, month = dec, pages = {1--10}, publisher = {{ACM}}, address = {{Brussels Belgium}}, doi = {10.1145/2837185.2837218}, isbn = {978-1-4503-3491-4}, langid = {english} } @incollection{demuthSupportingCoevolutionMetamodels2013, title = {Supporting the {{Co-evolution}} of {{Metamodels}} and {{Constraints}} through {{Incremental Constraint Management}}}, booktitle = {Model-{{Driven Engineering Languages}} and {{Systems}}}, author = {Demuth, Andreas and {Lopez-Herrejon}, Roberto E. and Egyed, Alexander}, editor = {Moreira, Ana and Sch{\"a}tz, Bernhard and Gray, Jeff and Vallecillo, Antonio and Clarke, Peter}, year = {2013}, series = {Lecture {{Notes}} in {{Computer Science}}}, number = {8107}, pages = {287--303}, publisher = {{Springer Berlin Heidelberg}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-41533-3_18}, urldate = {2015-03-24}, 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.}, copyright = {\textcopyright 2013 Springer-Verlag Berlin Heidelberg}, isbn = {978-3-642-41532-6 978-3-642-41533-3}, langid = {english}, keywords = {software engineering} } @article{derakhshanmaneshModelintegratingDevelopmentSoftware2018, title = {Model-Integrating Development of Software Systems: A Flexible Component-Based Approach}, shorttitle = {Model-Integrating Development of Software Systems}, author = {Derakhshanmanesh, Mahdi and Ebert, J{\"u}rgen and Grieger, Marvin and Engels, Gregor}, year = {2018}, month = jun, journal = {Software \& Systems Modeling}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-018-0682-5}, langid = {english} } @article{derlerModelingCyberPhysical2012, title = {Modeling {{Cyber}}\&\#x2013;{{Physical Systems}}}, author = {Derler, P. and Lee, E. A. and Vincentelli, A. S.}, year = {2012}, month = jan, journal = {Proceedings of the IEEE}, volume = {100}, number = {1}, pages = {13--28}, issn = {0018-9219, 1558-2256}, doi = {10.1109/JPROC.2011.2160929} } @inproceedings{deServiceModellingInternet2011, title = {Service Modelling for the {{Internet}} of {{Things}}}, booktitle = {Computer {{Science}} and {{Information Systems}} ({{FedCSIS}}), 2011 {{Federated Conference}} On}, author = {De, Suparna and Barnaghi, Payam and Bauer, Martin and Meissner, Stefan}, year = {2011}, pages = {949--955}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6078180}, urldate = {2016-02-09} } @inproceedings{desouzaRankingCrowdKnowledge2014, title = {Ranking Crowd Knowledge to Assist Software Development}, booktitle = {Proceedings of the {{22Nd}} International Conference on Program Comprehension}, author = {{de Souza}, Lucas B. L. and Campos, Eduardo C. and Maia, Marcelo de A.}, year = {2014}, series = {{{ICPC}} 2014}, pages = {72--82}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/2597008.2597146}, acmid = {2597146}, isbn = {978-1-4503-2879-1}, nodoi = {10.1145/2597008.2597146}, numpages = {11}, keywords = {crowd knowledge,Q\&A services,recommendation systems} } @inproceedings{dhouibRobotmlDomainspecificLanguage2012, title = {Robotml, a Domain-Specific Language to Design, Simulate and Deploy Robotic Applications}, booktitle = {International {{Conference}} on {{Simulation}}, {{Modeling}}, and {{Programming}} for {{Autonomous Robots}}}, author = {Dhouib, Saadia and Kchir, Selma and Stinckwich, Serge and Ziadi, Tewfik and Ziane, Mikal}, year = {2012}, pages = {149--160}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-34327-8_16}, urldate = {2016-08-21} } @article{Díaz-Manríquez20175647, title = {Comparison of Metamodeling Techniques in Evolutionary Algorithms}, author = {{D{\'i}az-Manr{\'i}quez}, A. and Toscano, G. and Coello Coello, C.A.}, year = {2017}, journal = {Soft Computing}, volume = {21}, number = {19}, pages = {5647--5663}, publisher = {{Springer Verlag}}, issn = {14327643}, doi = {10.1007/s00500-016-2140-z}, abbrev_source_title = {Soft Comput.}, affiliation = {Facultad de Ingenier\'ia y Ciencias, Centro Universitario Victoria, Universidad Aut\'onoma de Tamaulipas, Cd. Victoria, Tamaulipas 87000, Mexico; CINVESTAV-IPN, Unidad Tamaulipas, Parque Cient\'ifico y Tecnol\'ogico TECNOTAM, Km. 5.5 carretera Cd. Victoria-Soto La Marina, Cd. Victoria, Tamaulipas 87130, Mexico; Departamento de Computaci\'on, CINVESTAV-IPN, Av. IPN No. 2508, Col. San Pedro Zacatenco, Mexico, DF 07360, Mexico}, correspondence_address1 = {D\'iaz-Manr\'iquez, A.; Facultad de Ingenier\'ia y Ciencias, Mexico; email: amanriquez@uat.edu.mx}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{diceMeasuresAmountEcologic1945, title = {Measures of the Amount of Ecologic Association between Species}, author = {Dice, Lee R}, year = {1945}, journal = {Ecology}, volume = {26}, number = {3}, pages = {297--302}, publisher = {{Wiley Online Library}} } @article{dicosmoSupportingSoftwareEvolution2011, title = {Supporting Software Evolution in Component-Based {{FOSS}} Systems}, author = {Di Cosmo, R and DI RUSCIO, Davide and Pelliccione, Patrizio and Pierantonio, Alfonso and Zacchiroli, S.}, year = {2011}, journal = {SCIENCE OF COMPUTER PROGRAMMING}, volume = {76}, pages = {1144--1160}, doi = {10.1016/j.scico.2010.11.001} } @article{Did32Waterfall2017, title = {Did 32\% {{Waterfall Surprise You}}?}, year = {2017}, month = jan, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {7--7}, issn = {0740-7459}, url = {http://ieeexplore.ieee.org/document/7819417/}, langid = {english} } @article{dimartinoInternetThingsReference2018, title = {Internet of Things Reference Architectures, Security and Interoperability: {{A}} Survey}, shorttitle = {Internet of Things Reference Architectures, Security and Interoperability}, author = {Di Martino, B. and Rak, M. and Ficco, M. and Esposito, A. and Maisto, S.A. and Nacchia, S.}, year = {2018}, month = sep, journal = {Internet of Things}, volume = {1--2}, pages = {99--112}, issn = {25426605}, doi = {10.1016/j.iot.2018.08.008}, 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.}, langid = {english} } @inproceedings{dingSwoogleSearchMetadata2004, title = {Swoogle: {{A}} Search and Metadata Engine for the Semantic Web}, booktitle = {Proceedings of the Thirteenth {{ACM}} International Conference on Information and Knowledge Management}, author = {Ding, Li and Finin, Tim and Joshi, Anupam and Pan, Rong and Cost, R. Scott and Peng, Yun and Reddivari, Pavan and Doshi, Vishal and Sachs, Joel}, year = {2004}, series = {{{CIKM}} '04}, pages = {652--659}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/1031171.1031289}, acmid = {1031289}, isbn = {1-58113-874-1}, nodoi = {10.1145/1031171.1031289}, numpages = {8}, keywords = {crawler,metadata,rank,search,semantic web} } @inproceedings{DiNoia:2012:LOD:2362499.2362501, title = {Linked Open Data to Support Content-Based Recommender Systems}, booktitle = {Proceedings of the 8th International Conference on Semantic Systems}, author = {Di Noia, Tommaso and Mirizzi, Roberto and Ostuni, Vito Claudio and Romito, Davide and Zanker, Markus}, year = {2012}, series = {I-Semantics '12}, pages = {1--8}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/2362499.2362501}, acmid = {2362501}, isbn = {978-1-4503-1112-0}, nodoi = {10.1145/2362499.2362501}, numpages = {8}, keywords = {content-based recommender systems,DBpedia,freebase,linked data,LinkedMDB,movielens,precision,recall,semantic web,vector space model} } @incollection{DiNoia2014, title = {Linked Open Data-Enabled Recommender Systems: {{ESWC}} 2014 Challenge on Book Recommendation}, booktitle = {Semantic Web Evaluation Challenge: {{SemWebEval}} 2014 at {{ESWC}} 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers}, author = {Di Noia, Tommaso and Cantador, Iv{\'a}n and Ostuni, Vito Claudio}, editor = {Presutti, Valentina and Stankovic, Milan and Cambria, Erik and Cantador, Iv{\'a}n and Di Iorio, Angelo and Di Noia, Tommaso and Lange, Christoph and Reforgiato Recupero, Diego and Tordai, Anna}, year = {2014}, pages = {129--143}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-319-12024-9₁7}, 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.}, isbn = {978-3-319-12024-9} } @inproceedings{DiRocco202170, title = {A {{GNN-based}} Recommender System to Assist the Specification of Metamodels and Models}, author = {Di Rocco, J. and Di Sipio, C. and Di Ruscio, D. and Nguyen, P.T.}, year = {2021}, series = {Proceedings - 24th {{International Conference}} on {{Model-Driven Engineering Languages}} and {{Systems}}, {{MODELS}} 2021}, pages = {70--81}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS50736.2021.00016}, abbrev_source_title = {Proc. - Int. Conf. Model-Driven Eng. Lang. Syst., MODELS}, affiliation = {Universit\`a degli Studi dell'Aquila, L'Aquila, Italy}, document_type = {Conference Paper}, isbn = {978-1-66543-495-9}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Classification,notion} } @inproceedings{diroccoDealingCoupledEvolution2014, title = {Dealing with the Coupled Evolution of Metamodels and Model-to-Text Transformations}, booktitle = {Proceedings of the {{Workshop}} on {{Models}} and {{Evolution}} Co-Located with {{ACM}}/{{IEEE}} 17th {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}} ({{MoDELS}} 2014), {{Valencia}}, {{Spain}}, {{Sept}} 28, 2014}, author = {DI ROCCO, Juri and DI RUSCIO, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2014}, volume = {1331}, pages = {22--31}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @article{diroccoMemoRecRecommenderSystem2022, title = {{{MemoRec}}: A Recommender System for Assisting Modelers in Specifying Metamodels}, author = {DI ROCCO, Juri and DI RUSCIO, Davide and DI SIPIO, Claudio and Nguyen, Phuong T. and Pierantonio, Alfonso}, year = {2022}, journal = {SOFTWARE AND SYSTEMS MODELING}, pages = {1--21}, doi = {10.1007/s10270-022-00994-2} } @article{diroccoMemoRecRecommenderSystem2022a, title = {{{MemoRec}}: A Recommender System for Assisting Modelers in Specifying Metamodels}, author = {Di Rocco, Juri and Di Ruscio, Davide and Di Sipio, Claudio and Nguyen, Phuong Thanh and Pierantonio, Alfonso}, year = {2022}, journal = {SOFTWARE AND SYSTEMS MODELING}, volume = {1}, doi = {10.1007/s10270-022-00994-2} } @inproceedings{diroccoResilienceSiriusEditors2018, title = {Resilience in Sirius Editors: {{Understanding}} the Impact of Meta-Model Changes}, booktitle = {{{CEUR Workshop Proceedings}}}, author = {Di Rocco, Juri and Di Ruscio, Davide and Narayanankutty, Hrishikesh and Pierantonio, Alfonso}, year = {2018}, volume = {2192}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Co-evolution,Computer Science (all),Model-Driven Engineering,Sirius Editors} } @inproceedings{diroccoSystematicRecoveryMDE2018, title = {Systematic Recovery of {{MDE}} Technology Usage}, booktitle = {Lecture {{Notes}} in {{Computer Science}} (Including Subseries {{Lecture Notes}} in {{Artificial Intelligence}} and {{Lecture Notes}} in {{Bioinformatics}})}, author = {Di Rocco, Juri and Di Ruscio, Davide and H{\"a}rtel, Johannes and Iovino, Ludovico and L{\"a}mmel, Ralf and Pierantonio, Alfonso}, year = {2018}, volume = {10888}, pages = {110--126}, publisher = {{Springer Verlag}}, doi = {10.1007/978-3-319-93317-7_5}, isbn = {978-3-319-93316-0}, keywords = {Computer Science (all),Theoretical Computer Science} } @incollection{diroccoUsingATLTransformation2016, title = {Using {{ATL Transformation Services}} in the {{MDEForge Collaborative Modeling Platform}}}, booktitle = {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}, author = {Di Rocco, Juri and Di Ruscio, Davide and Pierantonio, Alfonso and Cuadrado, Jes{\'u}s S{\'a}nchez and {de Lara}, Juan and Guerra, Esther}, editor = {Van Gorp, Pieter and Engels, Gregor}, year = {2016}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {9765}, pages = {70--78}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-319-42064-6_5}, 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.}, isbn = {978-3-319-42063-9 978-3-319-42064-6}, langid = {english}, keywords = {Application programs,Cloud-based architectures,Collaborative model,Configuration management,Integration testing,Model management,Model-driven Engineering,Remote execution,Static analysis Automated testing,Tools and techniques} } @incollection{diruscio9thWorkshopModelling2017, title = {9th {{Workshop}} on {{Modelling}} in {{Software Engineering}} ({{MiSE}} 2017)}, booktitle = {Proceedings - 2017 {{IEEE}}/{{ACM}} 9th {{International Workshop}} on {{Modelling}} in {{Software Engineering}}, {{MiSE}} 2017}, author = {Di Ruscio, Davide and Chechik, Marsha and Rumpe, Bernhard}, year = {2017}, pages = {1--1}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MiSE.2017.15}, isbn = {978-1-5386-0426-7}, keywords = {Modeling and Simulation,Software} } @incollection{diruscio9thWorkshopModelling2017a, title = {9th {{Workshop}} on {{Modelling}} in {{Software Engineering}} ({{MiSE}} 2017)}, booktitle = {Proceedings - 2017 {{IEEE}}/{{ACM}} 9th {{International Workshop}} on {{Modelling}} in {{Software Engineering}}, {{MiSE}} 2017}, author = {Di Ruscio, Davide and Chechik, Marsha and Rumpe, Bernhard}, year = {2017}, pages = {1--1}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MiSE.2017.15}, isbn = {978-1-5386-0426-7}, keywords = {Modeling and Simulation,Software} } @inproceedings{diruscioACadeMicsToolingEclipse2013, title = {{{ACadeMics}} Tooling with {{Eclipse}}: {{ACME}}'13 Workshop Summmary}, booktitle = {{{ACadeMics Tooling}} with {{Eclipse}}, {{ACME}} 2013 - {{A Joint ECMFA}}/{{ECSA}}/{{ECOOP Workshop}}}, author = {Di Ruscio, Davide and Kolovos, Dimitrios and Rose, Louis and {Al-Hilank}, Samir}, year = {2013}, pages = {1--2}, doi = {10.1145/2491279.2491280}, isbn = {978-1-4503-2036-8}, keywords = {Software} } @inproceedings{diruscioACMStudentResearch2013, title = {{{ACM}} Student Research Competition at {{MoDELS}} 2013}, booktitle = {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)}, author = {Di Ruscio, Davide and Jackson, Ethan}, year = {2013}, volume = {1115}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{diruscioAutomaticGenerationDetailed2016, title = {Automatic {{Generation}} of Detailed {{Flight Plans}} from {{High-level Mission Descriptions}}}, booktitle = {Proceedings of the {{ACM}}/{{IEEE}} 19th {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}} ({{MODELS}} '16)}, author = {DI RUSCIO, Davide and Malavolta, Ivano and Pelliccione, Patrizio and Tivoli, Massimo}, year = {2016}, pages = {45--55}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/2976767.2976794}, isbn = {978-1-4503-4321-3}, keywords = {Software} } @inproceedings{diruscioCollaborativeModeldrivenSoftware2018, title = {Collaborative Model-Driven Software Engineering}, booktitle = {40th {{International Conference}} on {{Software Engineering}}}, author = {Di Ruscio, Davide and Franzago, Mirco and Muccini, Henry and Malavolta, Ivano}, year = {2018}, pages = {535--535}, doi = {10.1145/3180155.3182543}, isbn = {978-1-4503-5638-1} } @article{diruscioDataModelingApproach2004, title = {A {{Data Modeling Approach}} to {{Web Application Synthesis}}}, author = {DI RUSCIO, Davide and Muccini, Henry and Pierantonio, Alfonso}, year = {2004}, journal = {INTERNATIONAL JOURNAL OF WEB ENGINEERING AND TECHNOLOGY}, volume = {1}, pages = {320--337}, doi = {10.1504/IJWET.2004.005236}, abstract = {Most web applications are data-intensive, i.e. they rely heavily on dynamic contents usually stored in databases. Website design and maintenance can greatly benefit from conceptual descriptions of both data and hypermedia aspects, i.e. those design dimensions which distinguish this application class: the data upon which the content is based, the way dynamic contents are composed together to form pages, and how pages are linked together in order to move across the application content. The paper proposes Webile, a visual Domain-Specific Language based on UML, which enables a model-driven approach to high-level specification of web applications. In contrast with other approaches, Webile exploits the UML meta-model architecture by serialising the specifications in the XMI interchange format. This representation provides interoperability amongst different operative platforms and enables an XSL transformation-based automatic generation of the applications that are being designed.} } @article{diruscioDatamodellingApproachWeb2004, title = {A Data-Modelling Approach to Web Application Synthesis}, author = {DI RUSCIO, Davide and Muccini, H and Pierantonio, Alfonso}, year = {2004}, journal = {INTERNATIONAL JOURNAL OF WEB ENGINEERING AND TECHNOLOGY}, volume = {1}, pages = {320--337} } @article{diruscioLowcodeDevelopmentModeldriven2022, title = {Low-Code Development and Model-Driven Engineering: {{Two}} Sides of the Same Coin?}, author = {DI RUSCIO, Davide and Kolovos, D. and {de Lara}, J. and Pierantonio, A. and Tisi, M. and Wimmer, M.}, year = {2022}, journal = {SOFTWARE AND SYSTEMS MODELING}, doi = {10.1007/s10270-021-00970-2}, keywords = {Low-code development,Model-driven engineering,No-code development} } @article{diruscioLowcodeDevelopmentModeldriven2022a, title = {Low-Code Development and Model-Driven Engineering: {{Two}} Sides of the Same Coin?}, author = {Di Ruscio, D. and Kolovos, D. and {de Lara}, J. and Pierantonio, A. and Tisi, M. and Wimmer, M.}, year = {2022}, journal = {SOFTWARE AND SYSTEMS MODELING}, doi = {10.1007/s10270-021-00970-2}, keywords = {Low-code development,Model-driven engineering,No-code development} } @inproceedings{diruscioMethodologicalApproachCoupled2013, title = {A {{Methodological Approach}} for the {{Coupled Evolution}} of {{Metamodels}} and {{ATL Transformations}}}, booktitle = {6th {{International Conference}} on {{Theory}} and {{Practice}} of {{Model Transformations}}, {{ICMT}} 2013;}, author = {DI RUSCIO, Davide and Iovino, L and Pierantonio, Alfonso}, year = {2013}, volume = {7909}, pages = {60--75}, publisher = {{Springer-Verlag}}, address = {{BERLIN HEIDELBERG}}, doi = {10.1007/978-3-642-38883-5_9}, 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.}, isbn = {978-3-642-38882-8} } @inproceedings{diruscioPreface2014a, title = {Preface}, booktitle = {{{SLE}} 2015 - {{Proceedings}} of the 2015 {{ACM SIGPLAN International Conference}} on {{Software Language Engineering}}}, author = {DI RUSCIO, Davide and Varro, Daniel}, year = {2014}, volume = {8568}, pages = {VII--VIII}, publisher = {{Association for Computing Machinery, Inc}}, url = {http://springerlink.com/content/0302-9743/copyright/2005/}, isbn = {978-3-319-08788-7}, keywords = {Computer Science (all),Computer Science Applications1707 Computer Vision and Pattern Recognition,Software,Theoretical Computer Science} } @inproceedings{diruscioProceedings2ndWorkshop2014, title = {Proceedings of the 2nd {{Workshop}} on {{Scalability}} in {{Model Driven Engineering}} Co-Located with the {{Software Technologies}}: {{Applications}} and {{Foundations Conference}}, {{BigMDE}}@{{STAF2014}}}, booktitle = {{{CEUR Workshop Proceedings}}}, author = {Di Ruscio, Davide and De Lara, Juan and Kolovos, Dimitris and Matragkas, Nicholas and Rath, Istvan and Tisi, Massimo}, year = {2014}, volume = {1206}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{diruscioWeavingSoftwareArchitecture2006, title = {Towards Weaving Software Architecture Models}, booktitle = {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}}}, author = {DI RUSCIO, Davide and Muccini, Henry and Pierantonio, Alfonso and Pelliccione, Patrizio}, year = {2006}, publisher = {{IEEE Computer Society}}, address = {{NEW YORK}}, doi = {10.1109/MBD-MOMPES.2006.24}, isbn = {0-7695-2538-5} } @inproceedings{disipioMultinomialNaiveBayesian2020, title = {A {{Multinomial Naive Bayesian}} ({{MNB}}) Network to Automatically Recommend Topics for {{GitHub}} Repositories}, booktitle = {24th {{International Conference}} on {{Evaluation}} and {{Assessment}} in {{Software Engineering}} ({{EASE}} 2020)}, author = {Di Sipio, Claudio and Di Ruscio, Davide and Rubei, Riccardo and Nguyen, Phuong T}, year = {2020}, doi = {10.1145/3383219.3383227}, 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.}, langid = {english}, keywords = {GitHub topics,Multinomial Naïve Bayesian network} } @incollection{diskinTraceabilityMappingsFundamental2017, title = {Traceability {{Mappings}} as a {{Fundamental Instrument}} in {{Model Transformations}}}, booktitle = {Fundamental {{Approaches}} to {{Software Engineering}}}, author = {Diskin, Zinovy and G{\'o}mez, Abel and Cabot, Jordi}, editor = {Huisman, Marieke and Rubin, Julia}, year = {2017}, volume = {10202}, pages = {247--263}, publisher = {{Springer Berlin Heidelberg}}, address = {{Berlin, Heidelberg}}, doi = {10.1007/978-3-662-54494-5_14}, isbn = {978-3-662-54493-8 978-3-662-54494-5} } @article{doAmaral20225205, title = {Metamodeling-Based Simulation Optimization in Manufacturing Problems: A Comparative Study}, author = {{do Amaral}, J.V.S. and {de Carvalho Miranda}, R. and Montevechi, J.A.B. and {dos Santos}, C.H. and Gabriel, G.T.}, year = {2022}, journal = {International Journal of Advanced Manufacturing Technology}, volume = {120}, number = {7-8}, pages = {5205--5224}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {02683768}, doi = {10.1007/s00170-022-09072-9}, 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 \texttimes{} sample size expressed the better performance to metamodels' development. Furthermore, the hyperparameter optimization step reduced the metamodels' error in about 32.83\%. \textcopyright{} 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.}, coden = {IJATE}, document_type = {Article}, source = {Scopus} } @article{doi:10.1080/21693277.2016.1192517, title = {Machine Learning in Manufacturing: Advantages, Challenges, and Applications}, author = {Wuest, Thorsten and Weimer, Daniel and Irgens, Christopher and Thoben, Klaus-Dieter}, year = {2016}, journal = {Production \& Manufacturing Research}, volume = {4}, number = {1}, pages = {23--45}, publisher = {{Taylor \& Francis}}, nodoi = {10.1080/21693277.2016.1192517} } @article{Domingos:2012:FUT:2347736.2347755, title = {A Few Useful Things to Know about Machine Learning}, author = {Domingos, Pedro}, year = {2012}, month = oct, journal = {Communications of the ACM}, volume = {55}, number = {10}, pages = {78--87}, publisher = {{ACM}}, address = {{New York, NY, USA}}, issn = {0001-0782}, acmid = {2347755}, issue_date = {October 2012}, nodoi = {10.1145/2347736.2347755}, numpages = {10} } @article{dornenburgPathDevOps2018, title = {The {{Path}} to {{DevOps}}}, author = {D{\"o}rnenburg, E.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {71--75}, issn = {0740-7459}, doi = {10.1109/MS.2018.290110337}, 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.}, keywords = {software engineering} } @inproceedings{Dorodnykh202160, title = {End-User Development of Knowledge Bases for Semi-Automated Formation of Task Cards}, author = {Dorodnykh, N.O. and Kotlov, Y.V. and Nikolaychuk, O.A. and Popov, V.M. and Yurin, A.Y.}, editor = {Bychkov I., Tchernykh A., Feoktistov A.}, year = {2021}, series = {{{CEUR Workshop Proceedings}}}, volume = {2913}, pages = {60--73}, publisher = {{CEUR-WS}}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111360450&partnerID=40&md5=0dfb7b7b307c3f0687f5911003a61f5b}, abbrev_source_title = {CEUR Workshop Proc.}, affiliation = {Matrosov Institute for System Dynamics and Control Theory, Siberian Branch of Russian Academy of Sciences (ISDCT SB RAS), 134, Lermontov str., Irkutsk, 664033, Russian Federation; Moscow State Technical University of Civil Aviation, Irkutsk Branch (MSTUCA), 3, Kommunarov str., Irkutsk, 664003, Russian Federation}, correspondence_address1 = {Yurin, A.Y.; Matrosov Institute for System Dynamics and Control Theory, 134, Lermontov str., Russian Federation; email: iskander@icc.ru}, document_type = {Conference Paper}, langid = {english}, source = {Scopus} } @article{dsouzaWorkshopSoftwareArchitectures, title = {Workshop on {{Software Architectures}} for {{Adaptive Autonomous Systems}} ({{SAAAS}})}, author = {D'Souza, Meenakshi and Mohalik, Swarup Kumar and Jayaraman, Mahesh Babu}, url = {https://pdfs.semanticscholar.org/b1d6/f9387fdefc8d4eb0054162cb1c040de8d69f.pdf}, urldate = {2016-08-21} } @inproceedings{Duala-Ekoko:2012:AAQ:2337223.2337255, title = {Asking and Answering Questions about Unfamiliar {{APIs}}: {{An}} Exploratory Study}, booktitle = {Proceedings of the 34th International Conference on Software Engineering}, author = {{Duala-Ekoko}, Ekwa and Robillard, Martin P.}, year = {2012}, series = {{{ICSE}} '12}, pages = {266--276}, publisher = {{IEEE Press}}, address = {{Piscataway, NJ, USA}}, url = {http://dl.acm.org/citation.cfm?id=2337223.2337255}, acmid = {2337255}, isbn = {978-1-4673-1067-3}, numpages = {11} } @inproceedings{Dubey2020, title = {{{HACO}}: {{A}} Framework for Developing Human-{{AI}} Teaming}, author = {Dubey, A. and Abhinav, K. and Jain, S. and Arora, V. and Puttaveerana, A.}, year = {2020}, series = {{{ACM International Conference Proceeding Series}}}, publisher = {{Association for Computing Machinery}}, doi = {10.1145/3385032.3385044}, abbrev_source_title = {ACM Int. Conf. Proc. Ser.}, affiliation = {Accenture Labs, Bangalore, India}, art_number = {3385044}, document_type = {Conference Paper}, isbn = {978-1-4503-7594-8}, langid = {english}, source = {Scopus} } @article{Dunke2020, title = {Neural Networks for the Metamodeling of Simulation Models with Online Decision Making}, author = {Dunke, F. and Nickel, S.}, year = {2020}, journal = {Simulation Modelling Practice and Theory}, volume = {99}, publisher = {{Elsevier B.V.}}, issn = {1569190X}, doi = {10.1016/j.simpat.2019.102016}, abbrev_source_title = {Simul. Model. Pract. Theory}, affiliation = {Karlsruhe Institute of Technology, Institute of Operations Research, Discrete Optimization and Logistics, Kaiserstr. 12, Karlsruhe, 76131, Germany}, art_number = {102016}, correspondence_address1 = {Dunke, F.; Karlsruhe Institute of Technology, Kaiserstr. 12, Germany; email: fabian.dunke@kit.edu}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-simulation,notion} } @inproceedings{dunlapStudyApplicationSandbox2022, title = {A {{Study}} of {{Application Sandbox Policies}} in {{Linux}}}, booktitle = {Proceedings of the 27th {{ACM}} on {{Symposium}} on {{Access Control Models}} and {{Technologies}}}, author = {Dunlap, Trevor and Enck, William and Reaves, Bradley}, year = {2022}, month = jun, pages = {19--30}, publisher = {{ACM}}, address = {{New York NY USA}}, doi = {10.1145/3532105.3535016}, abstract = {Desktop operating systems, including macOS, Windows 10, and Linux, are adopting the application-based security model pervasive in mobile platforms. In Linux, this transition is part of the movement towards two distribution-independent application platforms: Flatpak and Snap. This paper provides the first analysis of sandbox policies defined for Flatpak and Snap applications, covering 283 applications contained in both platforms. First, we find that 90.1\% of Snaps and 58.3\% of Flatpak applications studied are contained by tamperproof sandboxes. Further, we find evidence that package maintainers actively attempt to define least-privilege application policies. However, defining policy is difficult and error-prone. When studying the set of matching applications that appear in both Flatpak and Snap app stores, we frequently found policy mismatches: e.g., the Flatpak version has a broad privilege (e.g., file access) that the Snap version does not, or vice versa. This work provides confidence that Flatpak and Snap improve Linux platform security while highlighting opportunities for improvement.}, isbn = {978-1-4503-9357-7}, langid = {english} } @article{duongAutomatedFruitRecognition2020, title = {Automated Fruit Recognition Using {{EfficientNet}} and {{MixNet}}}, author = {Duong, L. T. and Nguyen, P. T. and Di Sipio, C. and Di Ruscio, D.}, year = {2020}, journal = {COMPUTERS AND ELECTRONICS IN AGRICULTURE}, volume = {171}, doi = {10.1016/j.compag.2020.105326} } @inproceedings{duttSelfAwarenessCyberPhysicalSystems2016, title = {Self-{{Awareness}} in {{Cyber-Physical Systems}}}, author = {Dutt, Nikil and TaheriNejad, Nima}, year = {2016}, month = jan, pages = {5--6}, publisher = {{IEEE}}, doi = {10.1109/VLSID.2016.129}, isbn = {978-1-4673-8700-2} } @article{ebert50YearsSoftware2018, title = {50 {{Years}} of {{Software Engineering}}: {{Progress}} and {{Perils}}}, shorttitle = {50 {{Years}} of {{Software Engineering}}}, author = {Ebert, C.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {94--101}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571228}, 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.} } @book{ebertGlobalSoftwareIT2011, title = {Global Software and {{IT}}: A Guide to Distributed Development, Projects, and Outsourcing}, shorttitle = {Global Software and {{IT}}}, author = {Ebert, Christof}, year = {2011}, publisher = {{John Wiley \& Sons}}, 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}, urldate = {2017-06-23} } @misc{echelonBuildingIoTIndustrial, title = {Building an {{IoT}} for Industrial Control: {{Part}} 1 \textendash{} {{What}} Is {{Industrial IoT}}?}, shorttitle = {Building an {{IoT}} for Industrial Control}, author = {Echelon, Robert Dolin}, journal = {Embedded}, url = {http://www.embedded.com/design/real-world-applications/4426952/1/Building-an-IoT-for-industrial-control--Part-1--What-is-Industrial-IoT-}, urldate = {2016-11-01}, 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.} } @inproceedings{ECL, ids = {10.1007/978-3-642-02674-4_11}, title = {Establishing Correspondences between Models with the Epsilon Comparison Language}, booktitle = {Model Driven Architecture - Foundations and Applications}, author = {Kolovos, Dimitrios S.}, editor = {Paige, Richard F. and Hartman, Alan and Rensink, Arend}, year = {2009}, pages = {146--157}, publisher = {{Springer Berlin Heidelberg}}, address = {{Berlin, Heidelberg}}, isbn = {978-3-642-02674-4} } @misc{EclipseSmartHomeFlexible, title = {Eclipse {{SmartHome}} - {{A Flexible Framework}} for the {{Smart Home}} - {{Binding}} Development}, url = {http://www.eclipse.org/smarthome/documentation/development/bindings/how-to.html}, urldate = {2016-12-08} } @misc{EclipseZoneGettingStarted, title = {{{EclipseZone}} - {{Getting}} Started with {{OSGi}}: {{Interacting}} ...}, url = {http://www.eclipsezone.com/eclipse/forums/m92131032.html}, urldate = {2016-12-04} } @article{EditorialBoard2018, title = {Editorial {{Board}}}, year = {2018}, month = sep, journal = {Internet of Things}, volume = {1--2}, pages = {ii}, issn = {25426605}, doi = {10.1016/S2542-6605(18)30096-9}, langid = {english} } @article{efremovIntegratedApproachCommon2015, title = {An {{Integrated Approach}} to {{Common Problems}} in the {{Internet}} of {{Things}}}, author = {Efremov, Sergey and Pilipenko, Nikolay and Voskov, Leonid}, year = {2015}, journal = {Procedia Engineering}, volume = {100}, pages = {1215--1223}, issn = {18777058}, doi = {10.1016/j.proeng.2015.01.486}, 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.}, langid = {english} } @article{Eichhoff2015333, title = {A Survey on Automating Configuration and Parameterization in Evolutionary Design Exploration}, author = {Eichhoff, J.R. and Roller, D.}, year = {2015}, journal = {Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM}, volume = {29}, number = {4}, pages = {333--350}, publisher = {{Cambridge University Press}}, issn = {08900604}, doi = {10.1017/S0890060415000372}, abbrev_source_title = {Artif Intell Eng Des Anal Manuf}, affiliation = {Institute of Computer-Aided Product Development Systems, University of Stuttgart, Stuttgart, Germany}, coden = {AIEME}, correspondence_address1 = {Eichhoff, J.R.; Institut f\"ur Rechnergest\"utzte Ingenieursysteme, Universit\"atsstrasse 38, Germany; email: julian.eichhoff@informatik.uni-stuttgart.de}, document_type = {Conference Paper}, langid = {english}, source = {Scopus} } @inproceedings{einarssonSmartHomeMLDomainSpecificModeling2017, title = {{{SmartHomeML}}: {{Towards}} a {{Domain-Specific Modeling Language}} for {{Creating Smart Home Applications}}}, shorttitle = {{{SmartHomeML}}}, author = {Einarsson, Atli F. and Patreksson, Patrekur and Hamdaqa, Mohammad and {Hamou-Lhadj}, Abdelwahab}, year = {2017}, month = jun, pages = {82--88}, publisher = {{IEEE}}, doi = {10.1109/IEEE.ICIOT.2017.35}, isbn = {978-1-5386-2011-3} } @article{eisenbergSearchingModelsHybrid, title = {Searching for {{Models}} with {{Hybrid AI Techniques}}}, author = {Eisenberg, Martin and Pichler, Hans-Peter and Garmendia, Antonio}, pages = {2}, 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\textendash 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.}, langid = {english}, keywords = {GOAL-Model_Search} } @incollection{ekaputraOntologyChangeOntologyBased2015, title = {Ontology {{Change}} in {{Ontology-Based Information Integration Systems}}}, booktitle = {The {{Semantic Web}}. {{Latest Advances}} and {{New Domains}}}, author = {Ekaputra, Fajar Juang}, editor = {Gandon, Fabien and Sabou, Marta and Sack, Harald and {d'Amato}, Claudia and {Cudr{\'e}-Mauroux}, Philippe and Zimmermann, Antoine}, year = {2015}, volume = {9088}, pages = {711--720}, publisher = {{Springer International Publishing}}, address = {{Cham}}, url = {http://link.springer.com/10.1007/978-3-319-18818-8_44}, urldate = {2015-06-24}, isbn = {978-3-319-18817-1 978-3-319-18818-8} } @inproceedings{ekstrandLensKitPythonNextGeneration2020, title = {{{LensKit}} for {{Python}}: {{Next-Generation Software}} for {{Recommender Systems Experiments}}}, shorttitle = {{{LensKit}} for {{Python}}}, booktitle = {Proceedings of the 29th {{ACM International Conference}} on {{Information}} \& {{Knowledge Management}}}, author = {Ekstrand, Michael D.}, year = {2020}, month = oct, pages = {2999--3006}, publisher = {{ACM}}, address = {{Virtual Event Ireland}}, doi = {10.1145/3340531.3412778}, isbn = {978-1-4503-6859-9}, langid = {english} } @article{Elnagar2020383, title = {Towards Applying Deep Learning to the Internet of Things: {{A}} Model and a Framework}, author = {Elnagar, S. and {Osei-Bryson}, K.-M.}, editor = {Themistocleous M., Papadaki M., Kamal M.M.}, year = {2020}, journal = {Lecture Notes in Business Information Processing}, volume = {402}, pages = {383--398}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {18651348}, doi = {10.1007/978-3-030-63396-7_26}, 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. \textcopyright{} 2020, Springer Nature Switzerland AG.}, document_type = {Conference Paper}, isbn = {9783030633950}, source = {Scopus} } @misc{EMFFacet, title = {{{EMF Facet}}}, url = {http://www.eclipse.org/facet/}, urldate = {2015-09-24} } @misc{EnablingAutonomousApplications, title = {Enabling {{Autonomous Applications}} for {{IoT}} - {{Alta Devices Alta Devices}}}, url = {http://www.altadevices.com/energy-harvesting/enabling-autonomous-applications-for-iot/}, urldate = {2016-09-03} } @article{enriquezUnifiedModelRepresentation2020, title = {A Unified Model Representation of Machine Learning Knowledge}, author = {Enr{\'i}quez, J.G. and {Mart{\'i}nez-Rojas}, A. and Lizcano, D. and {Jim{\'e}nez-Ram{\'i}rez}, A.}, year = {2020}, journal = {Journal of Web Engineering}, volume = {19}, number = {2}, pages = {319--340}, publisher = {{River Publishers}}, issn = {15409589}, doi = {10.13052/jwe1540-9589.1929}, abstract = {Nowadays, Machine Learning (ML) algorithms are being widely applied in virtually all possible scenarios. However, developing a ML project entails the effort of many ML experts who have to select and configure the appropriate algorithm to process the data to learn from, between other things. Since there exist thousands of algorithms, it becomes a time-consuming and challenging task. To this end, recently, AutoML emerged to provide mechanisms to automate parts of this process. However, most of the efforts focus on applying brute force procedures to try different algorithms or configuration and select the one which gives better results. To make a smarter and more efficient selection, a repository of knowledge is necessary. To this end, this paper proposes (1) an approach towards a common language to consolidate the current distributed knowledge sources related the algorithm selection in ML, and (2) a method to join the knowledge gathered through this language in a unified store that can be exploited later on, and (3) a traceability links maintenance. The preliminary evaluations of this approach allow to create a unified store collecting the knowledge of 13 different sources and to identify a bunch of research lines to conduct. \textcopyright{} 2020 River Publishers.} } @inproceedings{Eramo2021303, title = {{{AIdoArt}}: {{AI-augmented}} Automation for {{DevOps}}, a Model-Based Framework for Continuous Development in Cyber-Physical Systems}, author = {Eramo, R. and Muttillo, V. and Berardinelli, L. and Bruneliere, H. and Gomez, A. and Bagnato, A. and Sadovykh, A. and Cicchetti, A.}, editor = {Leporati F., Vitabile S., Skavhaug A.}, year = {2021}, series = {Proceedings - 2021 24th {{Euromicro Conference}} on {{Digital System Design}}, {{DSD}} 2021}, pages = {303--310}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/DSD53832.2021.00053}, abbrev_source_title = {Proc. - Euromicro Conf. Digit. Syst. Des., DSD}, affiliation = {University of L'Aquila, L'Aquila, Italy; Johannes Kepler University, Linz, Austria; IMT Atlantique, LS2N (UMR CNRS 6004), Nantes, France; Universitat Oberta de Catalunya, Barcelona, Spain; Softeam, Paris, France; M\"alardalen University, V\"aster\aa s, Sweden}, document_type = {Conference Paper}, isbn = {978-1-66542-703-6}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Assistance,notion} } @article{eramoModeldrivenDesignRuntimeInteraction2019, ids = {eramoModeldrivenDesignRuntimeInteraction2019b}, title = {Model-Driven {{Design-Runtime Interaction}} in {{Safety Critical System Development}}: An {{Experience Report}}.}, shorttitle = {Model-Driven {{Design-Runtime Interaction}} in {{Safety Critical System Development}}}, author = {Eramo, Romina and {Marchand de Kerchove}, Florent and Colange, Maximilien and Tucci, Michele and Ouy, Julien and Bruneliere, Hugo and Di Ruscio, Davide}, year = {2019}, journal = {The Journal of Object Technology}, volume = {18}, number = {2}, pages = {1:1}, issn = {1660-1769}, doi = {10.5381/jot.2019.18.2.a1}, langid = {english}, keywords = {Critical systems,Design,Interactions,Model-driven engineering,Runtime,Traceability} } @article{erdogmus50YearsSoftware2018, title = {50 {{Years}} of {{Software Engineering}}}, author = {Erdogmus, H. and Medvidovi{\'c}, N. and Paulisch, F.}, year = {2018}, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {20--24}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571240}, abstract = {This theme issue on software engineering's 50th anniversary presents a range of contributions\textemdash 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.}, keywords = {software engineering} } @incollection{erginLanguageGraphBasedModel2014, title = {Towards a {{Language}} for {{Graph-Based Model Transformation Design Patterns}}}, booktitle = {Theory and {{Practice}} of {{Model Transformations}}}, author = {Ergin, H{\"u}seyin and Syriani, Eugene}, year = {2014}, pages = {91--105}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/978-3-319-08789-4_7}, urldate = {2015-09-15} } @inproceedings{erlenhovCurrentFutureBots2019, title = {Current and {{Future Bots}} in {{Software Development}}}, booktitle = {2019 {{IEEE}}/{{ACM}} 1st {{International Workshop}} on {{Bots}} in {{Software Engineering}} ({{BotSE}})}, author = {Erlenhov, Linda and {Gomes de Oliveira Neto}, Francisco and Scandariato, Riccardo and Leitner, Philipp}, year = {2019}, month = may, pages = {7--11}, publisher = {{IEEE}}, address = {{Montreal, QC, Canada}}, doi = {10.1109/BotSE.2019.00009}, isbn = {978-1-72812-262-5} } @article{ernstAIDrivenDevelopmentHere2022, title = {{{AI-Driven Development Is Here}}: {{Should You Worry}}?}, shorttitle = {{{AI-Driven Development Is Here}}}, author = {Ernst, Neil A. and Bavota, Gabriele and Menzies, Tim}, year = {2022}, month = mar, journal = {IEEE Software}, volume = {39}, number = {2}, pages = {106--110}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2021.3133805}, langid = {english} } @inproceedings{escobar-avilaUnsupervisedSoftwareCategorization2015, title = {Unsupervised Software Categorization Using Bytecode}, booktitle = {2015 {{IEEE}} 23rd International Conference on Program Comprehension}, author = {{Escobar-Avila}, J. and {Linares-V{\'a}squez}, M. and Haiduc, S.}, year = {2015}, month = may, pages = {229--239}, issn = {1092-8138}, doi = {10.1109/ICPC.2015.33}, keywords = {Accuracy,Apache foundation repository,automatic software categorization,bytecode,clustering,Clustering algorithms,Data mining,dirichlet process,Java,Java libraries,learning (artificial intelligence),program compilers,semantic information,Software,software categorization,Software libraries,software profiles,source code,source code (software),supervised machine learning,training data,unsupervised algorithm,unsupervised software categorization} } @article{escottContinuousModernisationPlaybook, title = {The {{Continuous Modernisation Playbook}}}, author = {Escott, Eban and Tansey, Indi}, pages = {96}, langid = {english} } @misc{EserciziDiMemoria, title = {Esercizi Di Memoria | {{CIMEC}} - {{Centro Interdipartimentale Mente}}/{{Cervello}}}, url = {https://www.cimec.unitn.it/1172/esercizi-di-memoria}, urldate = {2022-07-15} } @article{espinazopaganQueryingLargeModels2014, title = {Querying Large Models Efficiently}, author = {Espinazo Pag{\'a}n, Javier and Garc{\'i}a Molina, Jes{\'u}s}, year = {2014}, month = jun, journal = {Information and Software Technology}, volume = {56}, number = {6}, pages = {586--622}, issn = {09505849}, doi = {10.1016/j.infsof.2014.01.005}, langid = {english} } @book{Essaidi2013240, title = {Model-Driven Data Warehouse Automation: {{A}} Dependent-Concept Learning Approach}, author = {Essaidi, M. and Osmani, A. and Rouveirol, C.}, year = {2013}, pages = {240--267}, publisher = {{IGI Global}}, doi = {10.4018/978-1-4666-4494-6.ch011}, 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. \textcopyright{} 2014 by IGI Global. All rights reserved.}, document_type = {Book Chapter}, isbn = {978-1-4666-4495-3 1-4666-4494-X 978-1-4666-4494-6}, source = {Scopus} } @book{Essaidi2014151, title = {Learning Dependent-Concepts in {{ILP}}: {{Application}} to Model-Driven Data Warehouses}, author = {Essaidi, M. and Osmani, A. and Rouveirol, C.}, year = {2014}, pages = {151--172}, publisher = {{Imperial College Press}}, doi = {10.1142/9781783265091_0017}, 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. \textcopyright{} 2015 Imperial College Press. All rights reserved.}, document_type = {Book Chapter}, isbn = {978-1-78326-509-1 978-1-78326-508-4}, source = {Scopus} } @book{Essaidi20162730, title = {Model-Driven Data Warehouse Automation: {{A}} Dependent-Concept Learning Approach}, author = {Essaidi, M. and Osmani, A. and Rouveirol, C.}, year = {2016}, volume = {4}, pages = {2730--2758}, publisher = {{IGI Global}}, doi = {10.4018/978-1-5225-1759-7.ch113}, 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. \textcopyright{} 2017 by IGI Global. All rights reserved.}, document_type = {Book Chapter}, isbn = {978-1-5225-1760-3 1-5225-1759-6 978-1-5225-1759-7}, source = {Scopus} } @article{etienChainingModelTransformations2012, title = {Chaining Model Transformations}, author = {Etien, Anne and Aranega, Vincent and Blanc, Xavier and Paige, Richard F.}, year = {2012}, journal = {Proceedings of the First Workshop on the Analysis of Model Transformations - AMT '12}, pages = {9--14}, doi = {10.1145/2432497.2432500} } @inproceedings{etienCombiningIndependentModel2010, title = {Combining {{Independent Model Transformations}}}, booktitle = {Proceedings of the 2010 {{ACM Symposium}} on {{Applied Computing}}}, author = {Etien, Anne and Muller, Alexis and Legrand, Thomas and Blanc, Xavier}, year = {2010}, series = {{{SAC}} '10}, pages = {2237--2243}, publisher = {{ACM}}, address = {{New York, NY, USA}}, doi = {10.1145/1774088.1774557}, 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.}, isbn = {978-1-60558-639-7} } @article{etienLocalizedModelTransformations2013, title = {Localized Model Transformations for Building Large-Scale Transformations}, author = {Etien, Anne and Muller, Alexis and Legrand, Thomas and Paige, Richard F.}, year = {2013}, journal = {Software \& Systems Modeling}, doi = {10.1007/s10270-013-0379-8}, keywords = {Model transformation,Reusable transformation,software engineering,Transformation chaining} } @inproceedings{etzlstorferEvolutionModelingEcosystems2017, title = {On the {{Evolution}} of {{Modeling Ecosystems}}: {{An Evaluation}} of {{Co-Evolution Approaches}}:}, shorttitle = {On the {{Evolution}} of {{Modeling Ecosystems}}}, author = {Etzlstorfer, Juergen and Kapsammer, Elisabeth and Schwinger, Wieland}, year = {2017}, pages = {90--99}, publisher = {{SCITEPRESS - Science and Technology Publications}}, doi = {10.5220/0006167900900099}, isbn = {978-989-758-210-3} } @article{Evans2009, title = {Clone Detection via Structural Abstraction}, author = {Evans, William S. and Fraser, Christopher W. and Ma, Fei}, year = {2009}, month = dec, journal = {Software Quality Journal}, volume = {17}, number = {4}, pages = {309--330}, issn = {1573-1367}, doi = {10.1007/s11219-009-9074-y}, 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\textendash 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.} } @misc{ExemplarsSoftwareEngineering, title = {Exemplars | {{Software Engineering}} for {{Self-Adaptive Systems}}}, url = {https://www.hpi.uni-potsdam.de/giese/public/selfadapt/exemplars/}, urldate = {2016-09-24} } @misc{ExploreEclipseOSGi, title = {Explore {{Eclipse}}'s {{OSGi}} Console}, url = {https://www.ibm.com/developerworks/library/os-ecl-osgiconsole/}, urldate = {2016-12-04} } @inproceedings{ExtremeDataManagement2019, title = {Extreme {{Data Management Analysis}} and {{Visualization}} for {{Exascale Supercomputers}} and {{Experimental Facilities}}}, booktitle = {2019 {{Amity International Conference}} on {{Artificial Intelligence}} ({{AICAI}})}, year = {2019}, month = feb, pages = {i-i}, publisher = {{IEEE}}, address = {{Dubai, United Arab Emirates}}, doi = {10.1109/AICAI.2019.8701403}, isbn = {978-1-5386-9346-9} } @misc{fabiofumarolaDataModelingNoSQL14:24:10UTC, type = {Data \& {{Analytics}}}, title = {5 {{Data Modeling}} for {{NoSQL}} 1/2}, author = {Fabio Fumarola}, year = {14:24:10 UTC}, url = {https://www.slideshare.net/fabiofumarola1/data-modeling-for-nosql-12}, urldate = {2018-04-30}, abstract = {The Information Technology have led us into an era where the production,} } @misc{FacebookOpenSourced, title = {Facebook {{Open Sourced}} This {{Architecture}} for {{Personalized Neural Recommendation Systems}} | by {{Jesus Rodriguez}} | {{DataSeries}} | {{May}}, 2021 | {{Medium}}}, url = {https://medium.com/dataseries/facebook-open-sourced-this-architecture-for-personalized-neural-recommendation-systems-97db3fef35bb}, urldate = {2021-06-07} } @article{falessiApplyingEmpiricalSoftware2010, title = {Applying Empirical Software Engineering to Software Architecture: Challenges and Lessons Learned}, shorttitle = {Applying Empirical Software Engineering to Software Architecture}, author = {Falessi, Davide and Babar, Muhammad Ali and Cantone, Giovanni and Kruchten, Philippe}, year = {2010}, month = jun, journal = {Empirical Software Engineering}, volume = {15}, number = {3}, pages = {250--276}, issn = {1382-3256, 1573-7616}, doi = {10.1007/s10664-009-9121-0}, 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.}, langid = {english} } @incollection{falzoneModelBasedRapid2018, title = {Model {{Based Rapid Prototyping}} and {{Evolution}} of {{Web Application}}}, booktitle = {Web {{Engineering}}}, author = {Falzone, Emanuele and Bernaschina, Carlo}, editor = {Mikkonen, Tommi and Klamma, Ralf and Hern{\'a}ndez, Juan}, year = {2018}, volume = {10845}, pages = {496--500}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-319-91662-0_43}, isbn = {978-3-319-91661-3 978-3-319-91662-0} } @article{families2persons, title = {A Simple Illustration of Model to Model Transformation}, author = {Allilaire, Freddy and Jouault, Fr{\dbend}d{\dbend}ric}, year = {2007}, url = {https://www.eclipse.org/atl/documentation/old/ATLUseCase_Families2Persons.pdf} } @inproceedings{Fard2020755, title = {Vertica-{{ML}}: {{Distributed}} Machine Learning in Vertica Database}, author = {Fard, A. and Le, A. and Larionov, G. and Dhillon, W. and Bear, C.}, year = {2020}, series = {Proceedings of the {{ACM SIGMOD International Conference}} on {{Management}} of {{Data}}}, pages = {755--768}, publisher = {{Association for Computing Machinery}}, issn = {07308078}, doi = {10.1145/3318464.3386137}, 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. \textcopyright{} 2020 Association for Computing Machinery.}, document_type = {Conference Paper}, isbn = {978-1-4503-6735-6}, source = {Scopus} } @inproceedings{Fayyad201731, title = {Benchmarks and Process Management in Data Science: {{Will}} We Ever Get over the Mess?}, author = {Fayyad, U.M. and Candel, A. and De La Rubia, E.A. and Pafka, S. and Chong, A. and Lee, J.-Y.}, year = {2017}, series = {Proceedings of the {{ACM SIGKDD International Conference}} on {{Knowledge Discovery}} and {{Data Mining}}}, volume = {Part F129685}, pages = {31--32}, publisher = {{Association for Computing Machinery}}, doi = {10.1145/3097983.3120998}, 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{$\cdots$} 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. \textcopyright{} 2017 Copyright held by the owner/author(s).}, document_type = {Conference Paper}, isbn = {978-1-4503-4887-4}, source = {Scopus} } @inproceedings{fearyMultipleViewsSafetyCritical2016, title = {Multiple {{Views}} on {{Safety-Critical Automation}}: {{Aircrafts}}, {{Autonomous Vehicles}}, {{Air Traffic Management}} and {{Satellite Ground Segments Perspectives}}}, shorttitle = {Multiple {{Views}} on {{Safety-Critical Automation}}}, author = {Feary, Michael and Martinie, C{\'e}lia and Palanque, Philippe and Tscheligi, Manfred}, year = {2016}, pages = {1069--1072}, publisher = {{ACM Press}}, doi = {10.1145/2851581.2886430}, isbn = {978-1-4503-4082-3}, langid = {english} } @inproceedings{Febbo2016, title = {A Combined Plant/Controller Optimization Framework for Hybrid Vehicles with {{MPG}}, Emissions and Drivability Considerations}, author = {Febbo, H. and Ersal, T. and Stein, J.L.}, year = {2016}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {3}, publisher = {{American Society of Mechanical Engineers (ASME)}}, doi = {10.1115/DETC2016-60335}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, United States}, document_type = {Conference Paper}, isbn = {978-0-7918-5013-8}, langid = {english}, source = {Scopus} } @article{felfernigOverviewRecommenderSystems2019, title = {An Overview of Recommender Systems in the Internet of Things}, author = {Felfernig, Alexander and {Polat-Erdeniz}, Seda and Uran, Christoph and Reiterer, Stefan and Atas, Muesluem and Tran, Thi Ngoc Trang and Azzoni, Paolo and Kiraly, Csaba and Dolui, Koustabh}, year = {2019}, month = apr, journal = {Journal of Intelligent Information Systems}, volume = {52}, number = {2}, pages = {285--309}, issn = {0925-9902, 1573-7675}, doi = {10.1007/s10844-018-0530-7}, 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.}, langid = {english}, keywords = {internet of things,recommendation systems} } @article{Feng2019368, title = {Dynamic Reliability Analysis Using the Extended Support Vector Regression ({{X-SVR}})}, author = {Feng, J. and Liu, L. and Wu, D. and Li, G. and Beer, M. and Gao, W.}, year = {2019}, journal = {Mechanical Systems and Signal Processing}, volume = {126}, pages = {368--391}, publisher = {{Academic Press}}, issn = {08883270}, doi = {10.1016/j.ymssp.2019.02.027}, 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 {$\epsilon$}-insensitive support vector regression ({$\epsilon$}-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. \textcopyright{} 2019 Elsevier Ltd}, coden = {MSSPE}, document_type = {Article}, source = {Scopus} } @inproceedings{Ferdjoukh20131044, title = {A {{CSP}} Approach for Metamodel Instantiation}, author = {Ferdjoukh, A. and Baert, A.-E. and Chateau, A. and Coletta, R. and Nebut, C.}, year = {2013}, series = {Proceedings - {{International Conference}} on {{Tools}} with {{Artificial Intelligence}}, {{ICTAI}}}, pages = {1044--1051}, issn = {10823409}, doi = {10.1109/ICTAI.2013.156}, abbrev_source_title = {Proc. Int. Conf. Tools Artif. Intell. ICTAI}, affiliation = {LIRMM, Universi{\'t}e Montpellier 2, CNRS, Montpellier, France}, art_number = {6735367}, coden = {PCTIF}, correspondence_address1 = {LIRMM, , Montpellier, France}, document_type = {Conference Paper}, isbn = {978-1-4799-2971-9}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-synthesis,notion} } @article{Fernández-Ceniceros2014151, title = {The Application of Metamodels Based on Soft Computing to Reproduce the Behaviour of Bolted Lap Joints in Steel Structures}, author = {{Fern{\'a}ndez-Ceniceros}, J. and {Urraca-Valle}, R. and {Anto{\~n}anzas-Torres}, J. and {Sanz-Garcia}, A.}, editor = {Abraham A., Corchado E., Zelinka I., Bringas P.G., Herrero A., Baruque B., Quintian H., Corchado E., de Carvalho A.C.P.L.F., Klett F., Zelinka I., Snasel V.}, year = {2014}, journal = {Advances in Intelligent Systems and Computing}, volume = {239}, pages = {151--160}, publisher = {{Springer Verlag}}, issn = {21945357}, doi = {10.1007/978-3-319-01854-6_16}, abbrev_source_title = {Adv. Intell. Sys. Comput.}, affiliation = {EDMANS Research Group, University of La Rioja, Logrono, Spain}, document_type = {Conference Paper}, isbn = {9783319018539}, langid = {english}, source = {Scopus} } @inproceedings{ferryCloudMFApplying2014, title = {Cloud {{MF}}: {{Applying MDE}} to Tame the Complexity of Managing Multi-Cloud Applications}, booktitle = {Proceedings - 2014 {{IEEE}}/{{ACM}} 7th {{International Conference}} on {{Utility}} and {{Cloud Computing}}, {{UCC}} 2014}, author = {Ferry, N. and Song, H. and Rossini, A. and Chauvel, F. and Solberg, A.}, year = {2014}, pages = {269--277}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/UCC.2014.36}, abstract = {The market of cloud computing encompasses an ever-growing number of cloud providers offering a multitude of infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) solutions. The heterogeneity of these solutions hinders the proper exploitation of cloud computing since it prevents interoperability and promotes vendor lock-in, which increases the complexity of executing and managing multi-cloud applications (i.e., Applications that can be deployed across multiple cloud infrastructures and platforms). Providers of multi-cloud applications seek to exploit the peculiarities of each cloud solution and to combine the delivery models of IaaS and PaaS in order to optimise performance, availability, and cost. In this paper, we show how the Cloud Modelling Framework leverages upon model-driven engineering to tame this complexity by providing: (i) a tool-supported domain-specific language for specifying the provisioning and deployment of multi-cloud applications, and (ii) a models@run-time environment for enacting the provisioning, deployment, and adaptation of these applications. \textcopyright{} 2014 IEEE.}, isbn = {978-1-4799-7881-6}, keywords = {Cloud computing,Cloud infrastructures,Cloud providers,Computer programming languages,Delivery models,Domain specific languages,Embedded systems,Infrastructure as a service (IaaS),Model-driven Engineering,Modeling languages,Modelling framework,Multi-clouds,Platform as a Service (PaaS),Problem oriented languages,Runtime environments} } @article{ferryCloudMFModelDrivenManagement2018, title = {{{CloudMF}}: {{Model-Driven Management}} of {{Multi-Cloud Applications}}}, shorttitle = {{{CloudMF}}}, author = {Ferry, Nicolas and Chauvel, Franck and Song, Hui and Rossini, Alessandro and Lushpenko, Maksym and Solberg, Arnor}, year = {2018}, month = jan, journal = {ACM Transactions on Internet Technology}, volume = {18}, number = {2}, pages = {1--24}, issn = {15335399}, doi = {10.1145/3125621}, langid = {english} } @article{Feth2017135, title = {A Conceptual Safety Supervisor Definition and Evaluation Framework for Autonomous {{Systems}}}, author = {Feth, P. and Schneider, D. and Adler, R.}, editor = {Bitsch F., Tonetta S., Schoitsch E.}, year = {2017}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {10488 LNCS}, pages = {135--148}, publisher = {{Springer Verlag}}, issn = {03029743}, doi = {10.1007/978-3-319-66266-4_9}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern, Germany}, correspondence_address1 = {Feth, P.; Fraunhofer Institute for Experimental Software EngineeringGermany; email: patrik.feth@iese.fraunhofer.de}, document_type = {Conference Paper}, isbn = {9783319662657}, langid = {english}, source = {Scopus} } @article{fischerStackOverflowConsidered2017, title = {Stack {{Overflow Considered Harmful}}? {{The Impact}} of {{Copy}}\&{{Paste}} on {{Android Application Security}}}, shorttitle = {Stack {{Overflow Considered Harmful}}?}, author = {Fischer, Felix and B{\"o}ttinger, Konstantin and Xiao, Huang and Stransky, Christian and Acar, Yasemin and Backes, Michael and Fahl, Sascha}, year = {2017}, month = oct, journal = {arXiv:1710.03135 [cs]}, eprint = {1710.03135}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/1710.03135}, urldate = {2021-06-18}, 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.}, archiveprefix = {arXiv}, keywords = {Computer Science - Cryptography and Security} } @article{fleckModelTransformationModularization2017, title = {Model {{Transformation Modularization}} as a {{Many-Objective Optimization Problem}}}, author = {Fleck, Martin and Troya, Javier and Kessentini, Marouane and Wimmer, Manuel and Alkhazi, Bader}, year = {2017}, journal = {IEEE Transactions on Software Engineering}, pages = {1--1}, issn = {0098-5589, 1939-3520}, doi = {10.1109/TSE.2017.2654255}, keywords = {Model transformation,modularization,multi-objective problem,optimization problem} } @article{fleder2009blockbuster, title = {Blockbuster Culture's next Rise or Fall: {{The}} Impact of Recommender Systems on Sales Diversity}, author = {Fleder, Daniel and Hosanagar, Kartik}, year = {2009}, journal = {Management science}, volume = {55}, number = {5}, pages = {697--712}, publisher = {{INFORMS}} } @article{fleureyQualifyingInputTest2007, title = {Qualifying Input Test Data for Model Transformations}, author = {Fleurey, Franck and Baudry, Benoit and Muller, Pierre-Alain and Traon, Yves Le}, year = {2007}, journal = {Software \& Systems Modeling}, volume = {8}, number = {2}, pages = {185--203}, doi = {10.1007/s10270-007-0074-8}, keywords = {software engineering} } @inproceedings{fogarasScalingLinkbasedSimilarity2005, title = {Scaling Link-Based Similarity Search}, booktitle = {Proceedings of the 14th International Conference on World Wide Web}, author = {Fogaras, D{\'a}niel and R{\'a}cz, Bal{\'a}zs}, year = {2005}, series = {{{WWW}} '05}, pages = {641--650}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/1060745.1060839}, acmid = {1060839}, isbn = {1-59593-046-9}, nodoi = {10.1145/1060745.1060839}, numpages = {10}, keywords = {fingerprint,link-analysis,scalability,similarity search} } @book{Foreward2011, title = {Foreward}, year = {2011}, journal = {ACM International Conference Proceeding Series} } @misc{Foreward2011a, title = {Foreward}, year = {2011}, journal = {ACM International Conference Proceeding Series} } @misc{FOSDEM2016OSCAR, title = {{{FOSDEM}} 2016 - {{OSCAR}}: {{Address}} the New Challenges of Open-Source Software Quality}, url = {https://fosdem.org/2016/schedule/event/oscar/}, urldate = {2016-02-09} } @inproceedings{fowkesParameterfreeProbabilisticAPI2016, title = {Parameter-Free Probabilistic {{API}} Mining across {{GitHub}}}, booktitle = {Proceedings of the 2016 24th {{ACM SIGSOFT}} International Symposium on Foundations of Software Engineering}, author = {Fowkes, Jaroslav and Sutton, Charles}, year = {2016}, series = {{{FSE}} 2016}, pages = {254--265}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/2950290.2950319}, acmid = {2950319}, isbn = {978-1-4503-4218-6}, nodoi = {10.1145/2950290.2950319}, numpages = {12}, keywords = {API mining,sequential pattern mining} } @inproceedings{frakesTermConflationInformation1984, title = {Term Conflation for Information Retrieval}, booktitle = {Proceedings of the 7th Annual International {{ACM SIGIR}} Conference on {{Research}} and Development in Information Retrieval}, author = {Frakes, William B}, year = {1984}, pages = {383--389}, organization = {{British Computer Society}} } @article{FrameworkVerificationModel, title = {A Framework for Verification of Model Transformations} } @book{franceModelDrivenEngineering2012, title = {Model Driven Engineering Languages and Systems: 15th {{International Conference}}, {{MODELS}} 2012, {{Innsbruck}}, {{Austria}}, {{September}} 30-{{October}} 5, 2012: Proceedings}, shorttitle = {Model Driven Engineering Languages and Systems}, editor = {France, Robert}, year = {2012}, series = {Lecture Notes in Computer Science}, number = {7590}, publisher = {{Springer}}, address = {{Berlin ; New York}}, isbn = {978-3-642-33665-2}, lccn = {QA76.76.D47 M6258 2012}, keywords = {Computer software,Conference proceedings,Congresses,Development,Model-driven software architecture,Model-integrated computing} } @inproceedings{franceProvidingSupportModel2007, title = {Providing Support for Model Composition in Metamodels}, booktitle = {Enterprise {{Distributed Object Computing Conference}}, 2007. {{EDOC}} 2007. 11th {{IEEE International}}}, author = {France, Robert and Fleurey, Franck and Reddy, Raghu and Baudry, Benoit and Ghosh, Sudipto}, year = {2007}, pages = {253--253}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4383998}, urldate = {2015-09-24} } @article{franceRepositoryModelDriven2007, title = {Repository for {{Model Driven Development}} ({{ReMoDD}})}, author = {France, Robert and Bieman, Jim and Cheng, Betty H. C.}, year = {2007}, journal = {Models in Software Engineering}, volume = {4364}, pages = {311--317}, doi = {10.1007/978-3-540-69489-2_38} } @article{franchUsingQualityModels2003, title = {Using Quality Models in Software Package Selection}, author = {Franch, Xavier and Carvallo, Juan Pablo}, year = {2003}, journal = {IEEE software}, volume = {20}, number = {1}, pages = {34--41}, url = {http://ieeexplore.ieee.org/abstract/document/1159027/}, urldate = {2017-02-25} } @article{franzagoCollaborativeModelDrivenSoftware2018, title = {Collaborative {{Model-Driven Software Engineering}}: A {{Classification Framework}} and a {{Research Map}}}, author = {Franzago, Mirco and Ruscio, Davide Di and Malavolta, Ivano and Muccini, Henry}, year = {2018}, journal = {IEEE TRANSACTIONS ON SOFTWARE ENGINEERING}, pages = {1--1}, doi = {10.1109/TSE.2017.2755039}, keywords = {Collaborative Software Engineering,Model-Driven Engineering,Systematic Mapping study} } @book{franzagoProtocolSystematicMapping2016, title = {Protocol for a {{Systematic Mapping Study}} on {{Collaborative Model-Driven Software Engineering}}}, author = {Franzago, MIRCO GIOVANNI UMBERTO and DI RUSCIO, Davide and Malavolta, Ivano and Muccini, Henry}, year = {2016}, publisher = {{arXiv}}, url = {http://arxiv.org/abs/1611.02619v1}, 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.}, keywords = {Computer Science - Software Engineering} } @article{fredericksPlanningOptimizationDynamically2019, title = {Planning as {{Optimization}}: {{Dynamically Discovering Optimal Configurations}} for {{Runtime Situations}}}, shorttitle = {Planning as {{Optimization}}}, author = {Fredericks, Erik M. and Gerostathopoulos, Ilias and Krupitzer, Christian and Vogel, Thomas}, year = {2019}, month = jun, journal = {2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)}, eprint = {1905.01071}, eprinttype = {arxiv}, pages = {1--10}, doi = {10.1109/SASO.2019.00010}, 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.}, archiveprefix = {arXiv} } @book{fredKnowledgeDiscoveryKnowledge2015, title = {Knowledge {{Discovery}}, {{Knowledge Engineering}} and {{Knowledge Management}}}, editor = {Fred, Ana and Dietz, Jan L. G. and Aveiro, David and Liu, Kecheng and Filipe, Joaquim}, year = {2015}, series = {Communications in {{Computer}} and {{Information Science}}}, volume = {553}, publisher = {{Springer International Publishing}}, address = {{Cham}}, url = {http://link.springer.com/10.1007/978-3-319-25840-9}, urldate = {2015-11-10}, isbn = {978-3-319-25839-3 978-3-319-25840-9} } @inproceedings{freitasQueryingLinkedData2011, title = {Querying Linked Data Using Semantic Relatedness: {{A}} Vocabulary Independent Approach}, booktitle = {Proceedings of the 16th International Conference on Natural Language Processing and Information Systems}, author = {Freitas, Andr{\'e} and Oliveira, Jo{\~a}o Gabriel and O'Riain, Se{\'a}n and Curry, Edward and Da Silva, Jo{\~a}o Carlos Pereira}, year = {2011}, series = {{{NLDB}}'11}, pages = {40--51}, publisher = {{Springer-Verlag}}, address = {{Berlin, Heidelberg}}, url = {http://dl.acm.org/citation.cfm?id=2026011.2026017}, acmid = {2026017}, isbn = {978-3-642-22326-6}, numpages = {12}, keywords = {linked data,natural language queries} } @inproceedings{freitasTreoBesteffortNatural2011, title = {Treo: {{Best-effort}} Natural Language Queries over Linked Data}, booktitle = {Proceedings of the 16th International Conference on Applications of Natural Language to Information Systems, {{NLDB}} 2011 (Poster)}, author = {Freitas, Andr{\'e} and Oliveira, Jo{\~a}o and O'Riain, Se{\'a}n and Curry, Edward and {Pereira da Silva}, Jo{\~a}o}, editor = {Mu{\~n}oz, Rafael and Montoyo, Andr{\'e}s and M{\'e}tais, Elisabeth}, year = {2011}, series = {Lecture Notes in Computer Science}, volume = {6716}, pages = {286--289}, publisher = {{Springer Berlin Heidelberg}}, address = {{Berlin, Heidelberg}}, url = {http://www.edwardcurry.org/publications/Freitas_Treo_NLDB_2011.pdf}, 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.}, isbn = {978-3-642-22326-6}, mendeley-tags = {LEIdataspace,Linked Data,Natural Language Queries,Treo}, nodoi = {10.1007/978-3-642-22327-3}, keywords = {LEIdataspace,Linked Data,Natural Language Queries,Treo} } @article{fritsche2020avoiding, title = {Avoiding Unnecessary Information Loss: Correct and Efficient Model Synchronization Based on Triple Graph Grammars}, author = {Fritsche, Lars and Kosiol, Jens and Sch{\"u}rr, Andy and Taentzer, Gabriele}, year = {2020}, journal = {International Journal on Software Tools for Technology Transfer}, pages = {1--34}, publisher = {{Springer}} } @article{Froger201932, title = {Generating Personalized and Certifiable Workflow Designs: {{A}} Prototype}, author = {Froger, M. and B{\'e}naben, F. and Truptil, S. and {Boissel-Dallier}, N.}, editor = {Ferreira J.E., Musaev A., Zhang L.-J.}, year = {2019}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {11515 LNCS}, pages = {32--47}, publisher = {{Springer Verlag}}, issn = {03029743}, doi = {10.1007/978-3-030-23554-3_3}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Ecole des Mines d'Albi, Campus Jarlard, Albi Cedex 09, 81013, France; Iterop, 1B rue Antoine Lavoisier, Colomiers, 31770, France}, correspondence_address1 = {Froger, M.; Ecole des Mines d'Albi, Campus Jarlard, France; email: manon.froger@mines-albi.fr}, document_type = {Conference Paper}, isbn = {9783030235536}, langid = {english}, source = {Scopus} } @inproceedings{frostChallengesOpportunitiesAutonomous2010, title = {Challenges and Opportunities for Autonomous Systems in Space}, booktitle = {Frontiers of {{Engineering}}: {{Reports}} on {{Leading-Edge Engineering}} from the 2010 {{Symposium}}}, author = {Frost, C.}, year = {2010}, 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}, urldate = {2016-08-21} } @article{Fu2021, title = {A Model-Driven Deep Unfolding Method for {{JPEG}} Artifacts Removal}, author = {Fu, X. and Wang, M. and Cao, X. and Ding, X. and Zha, Z.}, year = {2021}, journal = {IEEE Transactions on Neural Networks and Learning Systems}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {2162237X}, doi = {10.1109/TNNLS.2021.3083504}, 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}, document_type = {Article}, source = {Scopus}, keywords = {\#duplicate-citation-key} } @inproceedings{Fukas202219, title = {The Management of Artificial Intelligence: {{Developing}} a Framework Based on the Artificial Intelligence Maturity Principle}, author = {Fukas, P.}, editor = {Looy A.V., Weber B., Rosemann M.}, year = {2022}, series = {{{CEUR Workshop Proceedings}}}, volume = {3139}, pages = {19--27}, publisher = {{CEUR-WS}}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130763237&partnerID=40&md5=0470ee8a2332eeaf51d2d7d78d596809}, abbrev_source_title = {CEUR Workshop Proc.}, affiliation = {Osnabr\"uck University, Lower Saxony, Osnabr\"uck, Germany; German Research Center for Artificial Intelligence, Lower Saxony, Osnabr\"uck, Germany; Strategion GmbH, Lower Saxony, Osnabr\"uck, Germany}, correspondence_address1 = {Fukas, P.; Osnabr\"uck University, Lower Saxony, Germany; email: philipp.fukas@uni-osnabrueck.de}, document_type = {Conference Paper}, langid = {english}, source = {Scopus}, keywords = {notion} } @article{fumarolaDataModelingRelationships, title = {Data {{Modeling}} for {{Relationships Handling}} and {{Data Distribution}}}, author = {Fumarola, Dr Fabio}, pages = {45}, langid = {english} } @misc{FundingTenders, title = {Funding \& Tenders}, url = {https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/myarea/project/813884/program/31045243/organisation/999859511/roles/edit?name=Lowcomote}, urldate = {2019-10-30} } @article{gadepallyBigDAWGManagingHeterogenous, title = {{{BigDAWG}}: {{Managing Heterogenous Data}} and {{Streaming}}}, author = {Gadepally, Dr Vijay}, pages = {48}, langid = {english} } @article{gainLowcodeAutoMLaugmentedData2021, title = {Low-Code {{AutoML-augmented Data Pipeline}} \textendash{} {{A Review}} and {{Experiments}}}, author = {Gain, Ulla and Hotti, Virpi}, year = {2021}, month = feb, journal = {Journal of Physics: Conference Series}, volume = {1828}, number = {1}, pages = {012015}, issn = {1742-6588, 1742-6596}, doi = {10.1088/1742-6596/1828/1/012015}, 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.}, langid = {english} } @article{galassoCodeSophisticationCode2022, ids = {galassoCodeSophisticationCode2022a}, title = {Code {{Sophistication}}: {{From Code Recommendation}} to {{Logic Recommendation}}}, shorttitle = {Code {{Sophistication}}}, author = {Galasso, Jessie and Famelis, Michalis and Sahraoui, Houari}, year = {2022}, month = jan, journal = {arXiv:2201.07674 [cs]}, eprint = {2201.07674}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2201.07674}, urldate = {2022-01-25}, 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.}, archiveprefix = {arXiv}, langid = {english}, keywords = {Computer Science - Machine Learning,Computer Science - Programming Languages,Computer Science - Software Engineering,GOAL_Model-Assistance} } @article{gallardoModelingCollaborationProtocols2013, title = {Modeling Collaboration Protocols for Collaborative Modeling Tools: {{Experiences}} and Applications}, author = {Gallardo, Jes{\'u}s and Bravo, Crescencio and Redondo, Miguel A. and {de Lara}, Juan}, year = {2013}, journal = {Journal of Visual Languages \& Computing}, volume = {24}, number = {1}, pages = {10--23}, doi = {10.1016/j.jvlc.2012.10.006} } @article{ganserStagedModelEvolution2015, title = {Staged Model Evolution and Proactive Quality Guidance for Model Libraries}, author = {Ganser, Andreas and Lichter, Horst and Roth, Alexander and Rumpe, Bernhard}, year = {2015}, month = nov, journal = {Software Quality Journal}, issn = {0963-9314, 1573-1367}, doi = {10.1007/s11219-015-9298-y}, langid = {english} } @article{Gao20182627, title = {{{ComNet}}: {{Combination}} of Deep Learning and Expert Knowledge in {{OFDM}} Receivers}, author = {Gao, X. and Jin, S. and Wen, C.-K. and Li, G.Y.}, year = {2018}, journal = {IEEE Communications Letters}, volume = {22}, number = {12}, pages = {2627--2630}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10897798}, doi = {10.1109/LCOMM.2018.2877965}, 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. \textcopyright{} 1997-2012 IEEE.}, art_number = {8509622}, coden = {ICLEF}, document_type = {Article}, source = {Scopus} } @article{gaoCollaborativeFilteringRecommendation2019, title = {Collaborative {{Filtering Recommendation Algorithm}} for {{Heterogeneous Data Mining}} in the {{Internet}} of {{Things}}}, author = {Gao, Ying and Ran, Lingxi}, year = {2019}, journal = {IEEE Access}, volume = {7}, pages = {123583--123591}, issn = {2169-3536}, doi = {10.1109/ACCESS.2019.2935224}, 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.}, langid = {english} } @article{gaoCollaborativeLearningBasedIndustrial2020, title = {Collaborative {{Learning-Based Industrial IoT API Recommendation}} for {{Software-Defined Devices}}: {{The Implicit Knowledge Discovery Perspective}}}, shorttitle = {Collaborative {{Learning-Based Industrial IoT API Recommendation}} for {{Software-Defined Devices}}}, author = {Gao, Honghao and Qin, Xi and Barroso, Ramon J. Duran and Hussain, Walayat and Xu, Yueshen and Yin, Yuyu}, year = {2020}, journal = {IEEE Transactions on Emerging Topics in Computational Intelligence}, pages = {1--11}, issn = {2471-285X}, doi = {10.1109/TETCI.2020.3023155}, 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.}, langid = {english} } @article{garcesEndtoendFinegrainedTraceability, title = {End-to-End {{Fine-grained Traceability Analysis}} in {{Model Transformations}} and {{Transformation Chains}}}, author = {Garces, Victor Guana}, pages = {155}, langid = {english} } @article{garcia-dominguezStresstestingRemoteModel2017, title = {Stress-Testing Remote Model Querying {{APIs}} for Relational and Graph-Based Stores}, author = {{Garcia-Dominguez}, Antonio and Barmpis, Konstantinos and Kolovos, Dimitrios S. and Wei, Ran and Paige, Richard F.}, year = {2017}, month = jun, journal = {Software \& Systems Modeling}, pages = {1--29}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-017-0606-9}, 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.}, langid = {english} } @article{gargMUDABlueAutomaticCategorization2004, title = {{{MUDABlue}}: {{An}} Automatic Categorization System for Open Source Repositories}, author = {Garg, Pankaj K. and Kawaguchi, Shinji and Matsushita, Makoto and Inoue, Katsuro}, year = {2004}, journal = {2013 20th Asia-Pacific Software Engineering Conference (APSEC)}, pages = {184--193}, publisher = {{IEEE Computer Society}}, address = {{Los Alamitos, CA, USA}}, issn = {1530-1362}, nodoi = {doi.ieeecomputersociety.org/10.1109/APSEC.2004.69} } @inproceedings{Garitselov2012316, title = {Fast-Accurate Non-Polynomial Metamodeling for Nano-{{CMOS PLL}} Design Optimization}, author = {Garitselov, O. and Mohanty, S.P. and Kougianos, E.}, year = {2012}, series = {Proceedings of the {{IEEE International Conference}} on {{VLSI Design}}}, pages = {316--321}, issn = {10639667}, doi = {10.1109/VLSID.2012.90}, abbrev_source_title = {Proc IEEE Int Conf VLSI Des}, affiliation = {Nano-Systems Design Laboratory, University of North Texas, Denton, TX, United States; Department of Computer Science and Engineering, University of North Texas, Denton, TX, United States; Department of Engineering Technology, University of North Texas, Denton, TX, United States}, art_number = {6167771}, coden = {PIVDE}, correspondence_address1 = {Garitselov, O.; Nano-Systems Design Laboratory, , Denton, TX, United States; email: omg0006@unt.edu}, document_type = {Conference Paper}, isbn = {978-0-7695-4638-4}, langid = {english}, source = {Scopus} } @inproceedings{Garitselov2012580, title = {Metamodel-Assisted Ultra-Fast Memetic Optimization of a {{PLL}} for {{WiMax}} and {{MMDS}} Applications}, author = {Garitselov, O. and Mohanty, S.P. and Kougianos, E. and Okobiah, O.}, year = {2012}, series = {Proceedings - {{International Symposium}} on {{Quality Electronic Design}}, {{ISQED}}}, pages = {580--585}, issn = {19483287}, doi = {10.1109/ISQED.2012.6187552}, abbrev_source_title = {Proc. - Int. Symp. Qual. Electron. Des., ISQED}, affiliation = {NanoSystems Design Laboratory, University of North Texas, Denton, TX, United States; Department of Computer Science and Engineering, University of North Texas, Denton, TX, United States; Department of Engineering Technology, University of North Texas, Denton, TX, United States}, art_number = {6187552}, correspondence_address1 = {Garitselov, O.; NanoSystems Design Laboratory, , Denton, TX, United States; email: omg0006@unt.edu}, document_type = {Conference Paper}, isbn = {978-1-4673-1036-9}, langid = {english}, source = {Scopus} } @inproceedings{Garitselov2014221, title = {Metamodel-Assisted Disciplining Algorithm for Detecting Spoofed {{GNSS}} Time Signals}, author = {Garitselov, O. and Sohn, D.}, year = {2014}, series = {Proceedings of the {{Annual Precise Time}} and {{Time Interval Systems}} and {{Applications Meeting}}, {{PTTI}}}, volume = {2014-January}, pages = {221--227}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {23332085}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943552646&partnerID=40&md5=25e8f3479adee4ec76849112cfa3446f}, abbrev_source_title = {Proc. Annu. Precis Time Time Interval Syst. Appl. Meet., PTTI}, affiliation = {Spectracom, Rochester, NY, United States}, document_type = {Conference Paper}, isbn = {978-1-63439-794-0}, langid = {english}, source = {Scopus} } @article{garousiGuidelinesIncludingGrey2019, ids = {garousiGuidelinesIncludingGrey2019a}, title = {Guidelines for Including Grey Literature and Conducting Multivocal Literature Reviews in Software Engineering}, author = {Garousi, Vahid and Felderer, Michael and M{\"a}ntyl{\"a}, Mika V.}, year = {2019}, month = feb, journal = {Information and Software Technology}, volume = {106}, pages = {101--121}, issn = {09505849}, doi = {10.1016/j.infsof.2018.09.006}, langid = {english} } @article{gasparicWhatRecommendationSystems2016, title = {What Recommendation Systems for Software Engineering Recommend}, author = {Gasparic, Marko and Janes, Andrea}, year = {2016}, month = mar, journal = {J. Syst. Softw.}, volume = {113}, number = {C}, pages = {101--113}, publisher = {{Elsevier Science Inc.}}, address = {{New York, NY, USA}}, issn = {0164-1212}, url = {http://dx.doi.org/10.1016/j.jss.2015.11.036}, acmid = {2896211}, issue_date = {March 2016}, nodoi = {10.1016/j.jss.2015.11.036}, numpages = {13}, keywords = {recommendation systems,Systematic literature review} } @article{gasparicWhatRecommendationSystems2016a, title = {What Recommendation Systems for Software Engineering Recommend: {{A}} Systematic Literature Review}, shorttitle = {What Recommendation Systems for Software Engineering Recommend}, author = {Gasparic, Marko and Janes, Andrea}, year = {2016}, month = mar, journal = {Journal of Systems and Software}, volume = {113}, pages = {101--113}, issn = {01641212}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0164121215002605}, urldate = {2019-06-13}, 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.}, langid = {english}, nodoi = {10.1016/j.jss.2015.11.036} } @inproceedings{Ge:2010_catalog_coverage, title = {Beyond Accuracy: {{Evaluating}} Recommender Systems by Coverage and Serendipity}, booktitle = {Proceedings of the Fourth {{ACM}} Conference on Recommender Systems}, author = {Ge, Mouzhi and {Delgado-Battenfeld}, Carla and Jannach, Dietmar}, year = {2010}, series = {{{RecSys}} '10}, pages = {257--260}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/1864708.1864761}, acmid = {1864761}, isbn = {978-1-60558-906-0}, nodoi = {10.1145/1864708.1864761}, numpages = {4}, keywords = {coverage,evaluation metric,recommender system,serendipity} } @article{geDataMiningAnalytics2017, title = {Data {{Mining}} and {{Analytics}} in the {{Process Industry}}: {{The Role}} of {{Machine Learning}}}, shorttitle = {Data {{Mining}} and {{Analytics}} in the {{Process Industry}}}, author = {Ge, Zhiqiang and Song, Zhihuan and Ding, Steven X. and Huang, Biao}, year = {2017}, journal = {IEEE Access}, volume = {5}, pages = {20590--20616}, issn = {2169-3536}, doi = {10.1109/ACCESS.2017.2756872}, 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.}, langid = {english} } @misc{GeneralizedAutomaticClustering, title = {A Generalized Automatic Clustering Algorithm in a Multiobjective Framework}, url = {http://www.sciencedirect.com/science/article/pii/S1568494612003493}, urldate = {2015-05-07} } @article{generoBuildingMeasurebasedPrediction2007, title = {Building Measure-Based Prediction Models for {{UML}} Class Diagram Maintainability}, author = {Genero, Marcela and Manso, Esperanza and Visaggio, Aaron and Canfora, Gerardo and Piattini, Mario}, year = {2007}, journal = {Empirical Software Engineering}, volume = {12}, number = {5}, pages = {517--549}, doi = {10.1007/s10664-007-9038-4} } @article{generoSurveyMetricsUML2005, title = {A {{Survey}} of {{Metrics}} for {{UML Class Diagrams}}.}, author = {Genero, Marcela and Piattini, Mario and Calero, Coral}, year = {2005}, journal = {The Journal of Object Technology}, volume = {4}, number = {9}, pages = {59}, doi = {10.5381/jot.2005.4.9.a1} } @inproceedings{gerostathopoulosTRAPPedTrafficSelfAdaptive2019, title = {{{TRAPPed}} in {{Traffic}}? {{A Self-Adaptive Framework}} for {{Decentralized Traffic Optimization}}}, shorttitle = {{{TRAPPed}} in {{Traffic}}?}, booktitle = {2019 {{IEEE}}/{{ACM}} 14th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}} ({{SEAMS}})}, author = {Gerostathopoulos, Ilias and Pournaras, Evangelos}, year = {2019}, month = may, pages = {32--38}, publisher = {{IEEE}}, address = {{Montreal, QC, Canada}}, doi = {10.1109/SEAMS.2019.00014}, 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 \textendash{} 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.}, isbn = {978-1-72813-368-3}, langid = {english} } @article{gessertNoSQLDatabaseSystems2017, title = {{{NoSQL}} Database Systems: A Survey and Decision Guidance}, shorttitle = {{{NoSQL}} Database Systems}, author = {Gessert, Felix and Wingerath, Wolfram and Friedrich, Steffen and Ritter, Norbert}, year = {2017}, month = jul, journal = {Computer Science - Research and Development}, volume = {32}, number = {3-4}, pages = {353--365}, issn = {1865-2034, 1865-2042}, doi = {10.1007/s00450-016-0334-3}, 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.}, langid = {english}, keywords = {TYPHONML} } @misc{GettingStarted, title = {0\_{{Getting Started}} |}, url = {http://self-star.imag.fr/?page_id=63}, urldate = {2016-12-02} } @inproceedings{Gharibi2019, title = {Automated Management of Deep Learning Experiments}, author = {Gharibi, G. and Walunj, V. and Alanazi, R. and Rella, S. and Lee, Y.}, year = {2019}, series = {Proceedings of the {{ACM SIGMOD International Conference}} on {{Management}} of {{Data}}}, publisher = {{Association for Computing Machinery}}, issn = {07308078}, doi = {10.1145/3329486.3329495}, 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. \textcopyright{} 2019 ACM.}, art_number = {3329495}, document_type = {Conference Paper}, isbn = {978-1-4503-6797-4}, source = {Scopus} } @inproceedings{Gharibi201928, title = {{{ModelKB}}: {{Towards}} Automated Management of the Modeling Lifecycle in Deep Learning}, author = {Gharibi, G. and Walunj, V. and Rella, S. and Lee, Y.}, year = {2019}, series = {Proceedings - 2019 {{IEEE}}/{{ACM}} 7th {{International Workshop}} on {{Realizing Artificial Intelligence Synergies}} in {{Software Engineering}}, {{RAISE}} 2019}, pages = {28--34}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/RAISE.2019.00013}, 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. \textcopyright{} 2019 IEEE.}, art_number = {8823655}, document_type = {Conference Paper}, isbn = {978-1-72812-272-4}, source = {Scopus} } @article{Gharibi2021, title = {Automated End-to-End Management of the Modeling Lifecycle in Deep Learning}, author = {Gharibi, G. and Walunj, V. and Nekadi, R. and Marri, R. and Lee, Y.}, year = {2021}, journal = {Empirical Software Engineering}, volume = {26}, number = {2}, publisher = {{Springer}}, issn = {13823256}, doi = {10.1007/s10664-020-09894-9}, 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\textendash 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. \textcopyright{} 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.}, art_number = {17}, coden = {ESENF}, document_type = {Article}, source = {Scopus} } @article{Ghasemi2021, title = {Evolutionary Learning Based Simulation Optimization for Stochastic Job Shop Scheduling Problems}, author = {Ghasemi, A. and Ashoori, A. and Heavey, C.}, year = {2021}, journal = {Applied Soft Computing}, volume = {106}, publisher = {{Elsevier Ltd}}, issn = {15684946}, doi = {10.1016/j.asoc.2021.107309}, 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. \textcopyright{} 2021 Elsevier B.V.}, art_number = {107309}, document_type = {Article}, source = {Scopus} } @article{Ghiasi2018101, title = {Comparative Studies of Metamodeling and {{AI-Based}} Techniques in Damage Detection of Structures}, author = {Ghiasi, R. and Ghasemi, M.R. and Noori, M.}, year = {2018}, journal = {Advances in Engineering Software}, volume = {125}, pages = {101--112}, publisher = {{Elsevier Ltd}}, issn = {09659978}, doi = {10.1016/j.advengsoft.2018.02.006}, abbrev_source_title = {Adv Eng Software}, affiliation = {Department of Civil Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran; Mechanical Engineering Department, California Polytechnic State University, One Grand Avenue San Luis ObispoCA 93405, United States; Distinguished Visiting Professor, International Institute for Urban Systems Engineering, Southeast University, Nanjing, 210096, China}, coden = {AESOD}, correspondence_address1 = {Noori, M.; Mechanical Engineering Department, One Grand Avenue San Luis Obispo, United States; email: contact@mohammadnoori.com}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{Ghiasi2018561, title = {Optimization-Based Method for Structural Damage Detection with Consideration of Uncertainties- a Comparative Study}, author = {Ghiasi, R. and Ghasemi, M.R.}, year = {2018}, journal = {Smart Structures and Systems}, volume = {22}, number = {5}, pages = {561--574}, publisher = {{Techno-Press}}, issn = {17381584}, doi = {10.12989/sss.2018.22.5.561}, abbrev_source_title = {Smart Struct. Syst.}, affiliation = {Department of Civil Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran}, correspondence_address1 = {Ghasemi, M.R.; Department of Civil Engineering, Iran; email: mrghasemi@eng.usb.ac.ir}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{Ghose2001, title = {Taste Tests: {{Impacts}} of Consumer Perceptions and Preferences on Brand Positioning Strategies}, author = {Ghose, Sanjoy and Lowengart, Oded}, year = {2001}, month = aug, journal = {Journal of Targeting, Measurement and Analysis for Marketing}, volume = {10}, number = {1}, pages = {26--41}, issn = {1479-1862}, doi = {10.1057/palgrave.jt.5740031}, 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.} } @article{giacobbeImplementationInfluxDBMonitoring2020, title = {An {{Implementation}} of {{InfluxDB}} for {{Monitoring}} and {{Analytics}} in {{Distributed IoT Environments}}}, author = {Giacobbe, Maurizio and Chaouch, Chakib and Scarpa, Marco and Puliafito, Antonio}, editor = {Bouhlel, Med Salim and Rovetta, Stefano}, year = {2020}, journal = {Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT'18), Vol.1}, volume = {146}, pages = {155--162}, doi = {10.1007/978-3-030-21005-2_15}, langid = {english} } @article{giannottiEXplainableMachineLearning, title = {{{eXplainable}} Machine Learning for {{Trustworthy AI}}}, author = {Giannotti, Fosca}, pages = {44}, langid = {english} } @book{gibaldiMLAHandbookWriters2009, title = {{{MLA}} Handbook for Writers of Research Papers}, editor = {Gibaldi, Joseph and {Modern Language Association of America}}, year = {2009}, edition = {7th ed}, publisher = {{Modern Language Association of America}}, address = {{New York}}, isbn = {978-1-60329-024-1 978-1-60329-025-8}, langid = {english}, lccn = {LB2369 .G53 2009} } @inproceedings{Giese2011, title = {How Can Metamodels Be Used Flexibly}, booktitle = {{{ICSE}} 2011 Workshop on Flexible Modeling Tools}, author = {{Gabrysiak} and Gregor, Holger Giese, Alexander L{\"u}ders and Seibel, Andreas}, year = {2011}, volume = {22} } @article{gilWingsIntelligentWorkflowBased2011, title = {Wings: {{Intelligent Workflow-Based Design}} of {{Computational Experiments}}}, shorttitle = {Wings}, author = {Gil, Yolanda and Ratnakar, Varun and Kim, Jihie and {Gonzalez-Calero}, Pedro and Groth, Paul and Moody, Joshua and Deelman, Ewa}, year = {2011}, month = jan, journal = {IEEE Intelligent Systems}, volume = {26}, number = {1}, pages = {62--72}, issn = {1541-1672}, doi = {10.1109/MIS.2010.9} } @article{Girum2019119, title = {Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy}, author = {Girum, K.B. and Cr{\'e}hange, G. and Hussain, R. and Walker, P.M. and Lalande, A.}, editor = {Nguyen D., Jiang S., Xing L.}, year = {2019}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {11850 LNCS}, pages = {119--127}, publisher = {{Springer}}, issn = {03029743}, doi = {10.1007/978-3-030-32486-5_15}, 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. \textcopyright{} Springer Nature Switzerland AG 2019.}, document_type = {Conference Paper}, isbn = {9783030324858}, source = {Scopus} } @misc{GitHubFacebookresearchDlrm, title = {{{GitHub}} - Facebookresearch/Dlrm: {{An}} Implementation of a Deep Learning Recommendation Model ({{DLRM}})}, url = {https://github.com/facebookresearch/dlrm}, urldate = {2021-06-07} } @article{glauberCollaborativeFilteringVs2019, title = {Collaborative {{Filtering}} vs. {{Content-Based Filtering}}: Differences and Similarities}, shorttitle = {Collaborative {{Filtering}} vs. {{Content-Based Filtering}}}, author = {Glauber, Rafael and Loula, Angelo}, year = {2019}, month = dec, journal = {arXiv:1912.08932 [cs]}, eprint = {1912.08932}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/1912.08932}, urldate = {2020-01-11}, 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.}, archiveprefix = {arXiv} } @incollection{gleitzeFindingUniversalExecution2021, title = {Finding a {{Universal Execution Strategy}} for {{Model Transformation Networks}}}, booktitle = {Fundamental {{Approaches}} to {{Software Engineering}}}, author = {Gleitze, Joshua and Klare, Heiko and Burger, Erik}, editor = {Guerra, Esther and Stoelinga, Mari{\"e}lle}, year = {2021}, volume = {12649}, pages = {87--107}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-030-71500-7_5}, 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.}, isbn = {978-3-030-71499-4 978-3-030-71500-7}, langid = {english} } @article{gloriaDesignImplementationIoT2017, title = {Design and Implementation of an {{IoT}} Gateway to Create Smart Environments}, author = {Gl{\'o}ria, Andr{\'e} and Cercas, Francisco and Souto, Nuno}, year = {2017}, journal = {Procedia Computer Science}, volume = {109}, pages = {568--575}, issn = {18770509}, doi = {10.1016/j.procs.2017.05.343}, langid = {english} } @misc{GmailConcettoDi, title = {Gmail - {{Concetto}} Di "Scenario Misto"}, url = {https://mail.google.com/mail/u/0/?ui=2&ik=c6f0013e0f&view=pt&search=inbox&type=14ce63765de5b01c&msg=14c64feb393c287b&siml=14c64feb393c287b}, urldate = {2015-04-24} } @article{gobertConceptualModelingManipulation, title = {Conceptual {{Modeling}} and {{Manipulation}} of {{Hybrid Polystores}}}, author = {Gobert, Maxime and Meurice, Loup and Cleve, Anthony}, pages = {14}, 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.}, langid = {english} } @article{gomez-abajoSystematicEngineeringMutation2020, title = {Systematic {{Engineering}} of {{Mutation Operators}}.}, author = {{G{\'o}mez-Abajo}, Pablo and Guerra, Esther and {de Lara}, Juan and Merayo, Mercedes G.}, year = {2020}, journal = {The Journal of Object Technology}, volume = {19}, number = {3}, pages = {3:1}, issn = {1660-1769}, doi = {10.5381/jot.2020.19.3.a5}, langid = {english} } @article{gomez-uribeNetflixRecommenderSystem2015, title = {The Netflix Recommender System: {{Algorithms}}, Business Value, and Innovation}, author = {{Gomez-Uribe}, Carlos A. and Hunt, Neil}, year = {2015}, month = dec, journal = {ACM Transactions on Management Information Systems}, volume = {6}, number = {4}, pages = {13:1-13:19}, publisher = {{ACM}}, address = {{New York, NY, USA}}, issn = {2158-656X}, url = {http://doi.acm.org/10.1145/2843948}, acmid = {2843948}, articleno = {13}, issue_date = {January 2016}, nodoi = {10.1145/2843948}, numpages = {19} } @incollection{gomezMapBasedTransparentPersistence2015, title = {Map-{{Based Transparent Persistence}} for {{Very Large Models}}}, booktitle = {Fundamental {{Approaches}} to {{Software Engineering}}}, author = {G{\'o}mez, Abel and Tisi, Massimo and Suny{\'e}, Gerson and Cabot, Jordi}, editor = {Egyed, Alexander and Schaefer, Ina}, year = {2015}, month = apr, series = {Lecture {{Notes}} in {{Computer Science}}}, number = {9033}, pages = {19--34}, publisher = {{Springer Berlin Heidelberg}}, url = {http://link.springer.com/chapter/10.1007/978-3-662-46675-9_2}, urldate = {2015-04-07}, 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.}, copyright = {\textcopyright 2015 Springer-Verlag Berlin Heidelberg}, isbn = {978-3-662-46674-2 978-3-662-46675-9}, langid = {english}, keywords = {software engineering} } @incollection{gomezTemporalEMFTemporalMetamodeling2018, title = {{{TemporalEMF}}: {{A Temporal Metamodeling Framework}}}, shorttitle = {{{TemporalEMF}}}, booktitle = {Conceptual {{Modeling}}}, author = {G{\'o}mez, Abel and Cabot, Jordi and Wimmer, Manuel}, editor = {Trujillo, Juan C. and Davis, Karen C. and Du, Xiaoyong and Li, Zhanhuai and Ling, Tok Wang and Li, Guoliang and Lee, Mong Li}, year = {2018}, volume = {11157}, pages = {365--381}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-030-00847-5_26}, 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.}, isbn = {978-3-030-00846-8 978-3-030-00847-5}, langid = {english} } @article{gonzalezATLTestWhiteBoxTest2012, title = {{{ATLTest}}: {{A White-Box Test Generation Approach}} for {{ATL Transformations}}}, author = {Gonz{\'a}lez, Carlos A. and Cabot, Jordi}, year = {2012}, journal = {Model Driven Engineering Languages and Systems}, volume = {7590}, pages = {449--464}, doi = {10.1007/978-3-642-33666-9_29} } @article{Gorodetsky2015765, title = {The Reference Ontology of Collective Behavior of Autonomous Agents and Its Extensions}, author = {Gorodetsky, V.I. and Samoylov, V.V. and Trotskii, D.V.}, year = {2015}, journal = {Journal of Computer and Systems Sciences International}, volume = {54}, number = {5}, pages = {765--782}, publisher = {{Maik Nauka-Interperiodica Publishing}}, issn = {10642307}, doi = {10.1134/S1064230715030089}, abbrev_source_title = {J. Comput. Syst. Sci. Int.}, affiliation = {St. Petersburg Institute for Informatics and Automation, Russian Academy of Sciences, 14 Liniya 39, St. Petersburg, Russian Federation; St. Petersburg State Polytechnic University, Polytechnicheskaya ul. 29, St. Petersburg, Russian Federation}, coden = {JSSIE}, correspondence_address1 = {Gorodetsky, V.I.; St. Petersburg Institute for Informatics and Automation, Russian Academy of Sciences, 14 Liniya 39, Russian Federation}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{gorrepotuSub1GHzMiniatureWireless2018, title = {Sub-{{1GHz}} Miniature Wireless Sensor Node for {{IoT}} Applications}, author = {Gorrepotu, Ramesh and Korivi, Narendra Swaroop and Chandu, Kavitha and Deb, Subimal}, year = {2018}, month = sep, journal = {Internet of Things}, volume = {1--2}, pages = {27--39}, issn = {25426605}, doi = {10.1016/j.iot.2018.08.002}, 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.}, langid = {english} } @article{gouesBridgingGapResearch2018, title = {Bridging the {{Gap}}: {{From Research}} to {{Practical Advice}}}, shorttitle = {Bridging the {{Gap}}}, author = {Goues, C. L. and Jaspan, C. and Ozkaya, I. and Shaw, M. and Stolee, K. T.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {50--57}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571235}, 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.}, keywords = {software engineering} } @misc{GrahamjensonListRecommender, title = {Grahamjenson/List\_of\_recommender\_systems: {{A List}} of {{Recommender Systems}} and {{Resources}}}, url = {https://github.com/grahamjenson/list_of_recommender_systems}, urldate = {2017-03-10} } @article{grayExplicitImplicitModels2022, title = {Explicit versus Implicit Models: {{What}} Are Good Languages for Modeling?}, shorttitle = {Explicit versus Implicit Models}, author = {Gray, Jeff and Rumpe, Bernhard}, year = {2022}, month = apr, journal = {Software and Systems Modeling}, pages = {s10270-022-01001-4}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-022-01001-4}, langid = {english} } @article{greifenbergEngineeringTaggingLanguages, title = {Engineering {{Tagging Languages}} for {{DSLs}}}, author = {Greifenberg, Timo and Look, Markus and Roidl, Sebastian and Rumpe, Bernhard}, url = {http://www.se-rwth.de/publications/Engineering-Tagging-Languages-for-DSLs.pdf}, urldate = {2015-09-24} } @article{grigorevMLOps10Minutes, title = {{{MLOps}} in 10 {{Minutes}}\_ by {{Alexey Grigorev}} \_ {{Towards Data Science}}}, author = {Grigorev, Alexey}, pages = {13}, langid = {english} } @article{Grossberg:2013:ART:2405841.2405958, title = {Adaptive Resonance Theory: {{How}} a Brain Learns to Consciously Attend, Learn, and Recognize a Changing World}, author = {Grossberg, Stephen}, year = {2013}, month = jan, journal = {Neural Netw.}, volume = {37}, pages = {1--47}, publisher = {{Elsevier Science Ltd.}}, address = {{Oxford, UK, UK}}, issn = {0893-6080}, url = {http://dx.doi.org/10.1016/j.neunet.2012.09.017}, acmid = {2405958}, issue_date = {January, 2013}, nodoi = {10.1016/j.neunet.2012.09.017}, numpages = {47}, keywords = {Adaptive Resonance Theory,Adaptive timing,Amygdala,Attention,Basal ganglia,Consciousness,Entorhinal cortex,Expectation,Gamma and beta oscillations,Hippocampal cortex,Inferotemporal cortex,Learning,Parietal cortex,Prefrontal cortex,Recognition,reinforcement learning,Speech perception,Synchrony,Working memory} } @inproceedings{Gu2016DeepAPI, title = {Deep {{API}} Learning}, booktitle = {24th {{ACM SIGSOFT}} International Symposium on Foundations of Software Engineering}, author = {Gu, Xiaodong and Zhang, Hongyu and Zhang, Dongmei and Kim, Sunghun}, year = {2016}, pages = {631--642}, publisher = {{ACM}}, address = {{New York}}, isbn = {978-1-4503-4218-6}, nodoi = {10.1145/2950290.2950334} } @inproceedings{Gu2018DeepCode, title = {Deep Code Search}, booktitle = {40th International Conference on Software Engineering}, author = {Gu, Xiaodong and Zhang, Hongyu and Kim, Sunghun}, year = {2018}, pages = {933--944}, publisher = {{ACM}}, address = {{New York}}, isbn = {978-1-4503-5638-1}, nodoi = {10.1145/3180155.3180167} } @article{guanaChainTrackerModelTransformationTrace2014, title = {{{ChainTracker}}, a {{Model-Transformation Trace Analysis Tool}} for {{Code-Generation Environments}}}, author = {Guana, Victor and Stroulia, Eleni}, year = {2014}, journal = {Theory and Practice of Model Transformations}, volume = {8568}, pages = {146--153}, doi = {10.1007/978-3-319-08789-4_11} } @article{guerraAutomatedVerificationModel2012, title = {Automated Verification of Model Transformations Based on Visual Contracts}, author = {Guerra, Esther and Lara, Juan and Wimmer, Manuel and Kappel, Gerti and Kusel, Angelika and Retschitzegger, Werner and Sch{\"o}nb{\"o}ck, Johannes and Schwinger, Wieland}, year = {2012}, journal = {Automated Software Engineering}, volume = {20}, number = {1}, pages = {5--46}, doi = {10.1007/s10515-012-0102-y} } @misc{GuestEditorialSpecial, title = {Guest {{Editorial}}: {{Special}} Issue on Constrained Decision-Making in Robotics - {{Online First}} - {{Springer}}}, url = {http://link.springer.com/article/10.1007/s10514-015-9489-1}, urldate = {2015-08-19} } @article{Guha:1998:CEC:276305.276312, title = {{{CURE}}: {{An}} Efficient Clustering Algorithm for Large Databases}, author = {Guha, Sudipto and Rastogi, Rajeev and Shim, Kyuseok}, year = {1998}, month = jun, journal = {SIGMOD Rec.}, volume = {27}, number = {2}, pages = {73--84}, publisher = {{ACM}}, address = {{New York, NY, USA}}, issn = {0163-5808}, url = {http://doi.acm.org/10.1145/276305.276312}, acmid = {276312}, issue_date = {June 1998}, nodoi = {10.1145/276305.276312}, numpages = {12} } @misc{GuideIntelligentCode, title = {A {{Guide}} to {{Intelligent Code Completion Using Eclipse Code Recommenders}}}, url = {https://medium.com/codetrails/insert-knowledge-here-a2f71c2862d2} } @misc{GuideLowcodePlatforms, title = {A {{Guide}} to {{Low-code Platforms}} - {{Federico Tomassetti}} - {{Software Architect}}}, url = {https://tomassetti.me/a-guide-to-low-code-platforms/}, urldate = {2020-04-08}, keywords = {lowcode} } @inproceedings{Gunawardana:2009:UAB:1639714.1639735, title = {A Unified Approach to Building Hybrid Recommender Systems}, booktitle = {Proceedings of the Third {{ACM}} Conference on Recommender Systems}, author = {Gunawardana, Asela and Meek, Christopher}, year = {2009}, series = {{{RecSys}} '09}, pages = {117--124}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/1639714.1639735}, acmid = {1639735}, isbn = {978-1-60558-435-5}, nodoi = {10.1145/1639714.1639735}, numpages = {8}, keywords = {boltzmann machines,cold start,collaborative filtering,content-based filtering,recommender systems} } @inproceedings{Guo:2013:NBS:2540128.2540506, title = {A Novel Bayesian Similarity Measure for Recommender Systems}, booktitle = {Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence}, author = {Guo, Guibing and Zhang, Jie and {Yorke-Smith}, Neil}, year = {2013}, series = {{{IJCAI}} '13}, pages = {2619--2625}, publisher = {{AAAI Press}}, address = {{Beijing, China}}, url = {http://dl.acm.org/citation.cfm?id=2540128.2540506}, acmid = {2540506}, isbn = {978-1-57735-633-2}, numpages = {7} } @article{Guo2015, title = {Robust Design Space Modeling}, author = {Guo, Q. and Chen, T. and Zhou, Z.-H. and Temam, O. and Li, L. and Qian, D. and Chen, Y.}, year = {2015}, journal = {ACM Transactions on Design Automation of Electronic Systems}, volume = {20}, number = {2}, publisher = {{Association for Computing Machinery}}, issn = {10844309}, doi = {10.1145/2668118}, abbrev_source_title = {ACM Trans. Design Autom. Electron. Syst.}, affiliation = {Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, United States; State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China; National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China; Inria, France; Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Beihang University, Beijing, 100191, China}, art_number = {18}, correspondence_address1 = {Chen, Y.; State Key Laboratory of Computer Architecture, China}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{Guo2022547, title = {{{CSI}} Feedback with Model-Driven Deep Learning of Massive {{MIMO}} Systems}, author = {Guo, J. and Wang, L. and Li, F. and Xue, J.}, year = {2022}, journal = {IEEE Communications Letters}, volume = {26}, number = {3}, pages = {547--551}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10897798}, doi = {10.1109/LCOMM.2021.3138927}, 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. \textcopyright{} 1997-2012 IEEE.}, coden = {ICLEF}, document_type = {Article}, source = {Scopus} } @incollection{guthDetailedAnalysisIoT2018, title = {A {{Detailed Analysis}} of {{IoT Platform Architectures}}: {{Concepts}}, {{Similarities}}, and {{Differences}}}, shorttitle = {A {{Detailed Analysis}} of {{IoT Platform Architectures}}}, booktitle = {Internet of {{Everything}}}, author = {Guth, Jasmin and Breitenb{\"u}cher, Uwe and Falkenthal, Michael and Fremantle, Paul and Kopp, Oliver and Leymann, Frank and Reinfurt, Lukas}, editor = {Di Martino, Beniamino and Li, Kuan-Ching and Yang, Laurence T. and Esposito, Antonio}, year = {2018}, pages = {81--101}, publisher = {{Springer Singapore}}, address = {{Singapore}}, doi = {10.1007/978-981-10-5861-5_4}, isbn = {978-981-10-5860-8 978-981-10-5861-5}, langid = {english} } @book{haddadProceedings2005ACM2005, title = {Proceedings of the 2005 {{ACM Symposium}} on {{Applied Computing}} ({{SAC}}), {{Santa Fe}}, {{New Mexico}}, {{USA}}, {{March}} 13-17, 2005}, editor = {Haddad, Hisham and Liebrock, Lorie M. and Omicini, Andrea and Wainwright, Roger L.}, year = {2005}, publisher = {{ACM}}, doi = {10.1145/1066677}, isbn = {1-58113-964-0} } @article{hadipourAutomaticWashingSystem2018, title = {Automatic Washing System of {{LED}} Street Lighting via {{Internet}} of {{Things}}}, author = {Hadipour, Morteza and Derakhshandeh, Javad Farrokhi and Shiran, Mohsen Aghazadeh and Rezaei, Reza}, year = {2018}, month = sep, journal = {Internet of Things}, volume = {1--2}, pages = {74--80}, issn = {25426605}, doi = {10.1016/j.iot.2018.08.006}, 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. \textcopyright{} 2018 Elsevier B.V. All rights reserved.}, langid = {english} } @article{HaertelHHLV17, title = {Interconnected Linguistic Architecture}, author = {H{\"a}rtel, Johannes and H{\"a}rtel, Lukas and Heinz, Marcel and L{\"a}mmel, Ralf and Varanovich, Andrei}, year = {2017}, journal = {The Art, Science, and Engineering of Programming Journal}, volume = {1}, number = {1} } @article{Halkidi01onclustering, title = {On Clustering Validation Techniques}, author = {Halkidi, Maria and Batistakis, Yannis and Vazirgiannis, Michalis}, year = {2001}, journal = {Journal of Intelligent Information Systems}, volume = {17}, pages = {107--145} } @article{hallWEKADataMining2009, title = {The {{WEKA}} Data Mining Software: {{An}} Update}, author = {Hall, Mark and Frank, Eibe and Holmes, Geoffrey and Pfahringer, Bernhard and Reutemann, Peter and Witten, Ian H.}, year = {2009}, month = nov, journal = {SIGKDD Explor. Newsl.}, volume = {11}, number = {1}, pages = {10--18}, publisher = {{ACM}}, address = {{New York, NY, USA}}, issn = {1931-0145}, url = {http://doi.acm.org/10.1145/1656274.1656278}, acmid = {1656278}, issue_date = {June 2009}, nodoi = {10.1145/1656274.1656278}, numpages = {9} } @incollection{hamidModelDrivenMethodologyApproach2014, title = {A {{Model-Driven Methodology Approach}} for {{Developing}} a {{Repository}} of {{Models}}}, booktitle = {Model and {{Data Engineering}}}, author = {Hamid, Brahim}, year = {2014}, pages = {29--44}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/978-3-319-11587-0_5}, urldate = {2015-10-29} } @article{hamiltonWhatErrorsTell2018, title = {What the {{Errors Tell Us}}}, author = {Hamilton, M. H.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {32--37}, issn = {0740-7459}, doi = {10.1109/MS.2018.290110447}, 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.}, keywords = {software engineering} } @article{hammadDeepCloneModelingClones2020, title = {{{DeepClone}}: {{Modeling Clones}} to {{Generate Code Predictions}}}, shorttitle = {{{DeepClone}}}, author = {Hammad, Muhammad and Babur, {\"O}nder and Basit, Hamid Abdul and van den Brand, Mark}, year = {2020}, month = dec, journal = {arXiv:2007.11671 [cs]}, eprint = {2007.11671}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2007.11671}, urldate = {2021-02-02}, 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.}, archiveprefix = {arXiv}, keywords = {Computer Science - Software Engineering} } @inproceedings{HammoudehGarcia2019329, title = {Bootstrapping {{MDE}} Development from {{ROS}} Manual Code - Part 1: {{Metamodeling}}}, author = {Hammoudeh Garcia, N. and Ludtke, M. and Kortik, S. and Kahl, B. and Bordignon, M.}, year = {2019}, series = {Proceedings - 3rd {{IEEE International Conference}} on {{Robotic Computing}}, {{IRC}} 2019}, pages = {329--336}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/IRC.2019.00060}, abbrev_source_title = {Proc. - IEEE Int. Conf. Robot. Comput., IRC}, affiliation = {Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Nobelstr. 12, Stuttgart, 70569, Germany}, art_number = {8675668}, document_type = {Conference Paper}, isbn = {978-1-5386-9245-5}, langid = {english}, source = {Scopus} } @article{Hamrani2021, title = {Machine Learning Surrogate Modeling for Meshless Methods: {{Leveraging}} Universal Approximation}, author = {Hamrani, A. and Akbarzadeh, A. and Madramootoo, C.A. and Bouarab, F.Z.}, year = {2021}, journal = {International Journal of Computational Methods}, publisher = {{World Scientific}}, issn = {02198762}, doi = {10.1142/S021987622141022X}, 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. \textcopyright{} 2021 World Scientific Publishing Company.}, art_number = {2141022}, document_type = {Article}, source = {Scopus} } @article{Hamrani2021, title = {Machine Learning Surrogate Modeling for Meshless Methods: {{Leveraging}} Universal Approximation}, author = {Hamrani, A. and Akbarzadeh, A. and Madramootoo, C.A. and Bouarab, F.Z.}, year = {2021}, journal = {International Journal of Computational Methods}, publisher = {{World Scientific}}, issn = {02198762}, doi = {10.1142/S021987622141022X}, abbrev_source_title = {Int. J. Comput. Methods}, affiliation = {Department of Mechanical and Materials Engineering, Florida International University, Miami, FL, United States; Department of Bioresource Engineering, McGill University, Montreal, QC H9X3V9, Canada; Department of Mechanical Engineering, McGill University, Montreal, QC H3A 0C3, Canada; UR-MPE Universit\'e m'Hamed Bougara, rue de la libert\'e, Boumerd\`es, 35000, Algeria}, art_number = {2141022}, correspondence_address1 = {Hamrani, A.; Department of Mechanical and Materials Engineering, United States; email: hamrani.abderrachid@gmail.com}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{Han20201980, title = {Deep Learning-Based {{FDD}} Non-Stationary Massive {{MIMO}} Downlink Channel Reconstruction}, author = {Han, Y. and Li, M. and Jin, S. and Wen, C.-K. and Ma, X.}, year = {2020}, journal = {IEEE Journal on Selected Areas in Communications}, volume = {38}, number = {9}, pages = {1980--1993}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {07338716}, doi = {10.1109/JSAC.2020.3000836}, 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. \textcopyright{} 1983-2012 IEEE.}, art_number = {9110882}, coden = {ISACE}, document_type = {Conference Paper}, source = {Scopus} } @misc{HandsonManageYour, title = {Hands-on: {{Manage}} Your Devices with {{Lightweight M2M}} and Connect Them to Your Cloud | {{EclipseCon Europe}} 2016}, url = {https://www.eclipsecon.org/europe2016/session/hands-manage-your-devices-lightweight-m2m-and-connect-them-your-cloud}, urldate = {2016-09-27} } @inproceedings{happelPotentialsChallengesRecommendation2008, title = {Potentials and Challenges of Recommendation Systems for Software Development}, booktitle = {Proceedings of the 2008 International Workshop on {{Recommendation}} Systems for Software Engineering - {{RSSE}} '08}, author = {Happel, Hans-J{\"o}rg and Maalej, Walid}, year = {2008}, pages = {11}, publisher = {{ACM Press}}, address = {{Atlanta, Georgia}}, url = {http://portal.acm.org/citation.cfm?doid=1454247.1454251}, urldate = {2019-06-13}, 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.}, isbn = {978-1-60558-228-3}, langid = {english}, nodoi = {10.1145/1454247.1454251} } @article{Hart201228, title = {An Advanced Cost Estimation Methodology for Engineering Systems}, author = {Hart, C.G. and He, Z. and Sbragio, R. and Vlahopoulos, N.}, year = {2012}, journal = {Systems Engineering}, volume = {15}, number = {1}, pages = {28--40}, issn = {10981241}, doi = {10.1002/sys.20192}, abbrev_source_title = {Syst. Eng.}, affiliation = {Naval Architecture and Marine Engineering Department, College of Engineering, University of Michigan, Ann Arbor, MI 48105, United States; Michigan Engineering Services, LLC, Ann Arbor, MI 48105, United States}, correspondence_address1 = {Hart, C.G.3293 Taney Lane, Falls Church, VA 22042, United States; email: hartcg@umich.edu}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{hartelClassificationAPIsHierarchical2018, title = {Classification of {{APIs}} by {{Hierarchical Clustering}}}, author = {H{\"a}rtel, Johannes}, year = {2018}, pages = {11}, 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.}, langid = {english} } @inproceedings{Hartmann2019300, title = {Meta-Modelling Meta-Learning}, author = {Hartmann, T. and Moawad, A. and Schockaert, C. and Fouquet, F. and Le Traon, Y.}, editor = {Kessentini M., Yue T., Pretschner A., Voss S., Burgueno L., Burgueno L., Yue T.}, year = {2019}, series = {Proceedings - 2019 {{ACM}}/{{IEEE}} 22nd {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS}} 2019}, pages = {300--305}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS.2019.00014}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS}, affiliation = {DataThings, University of Luxembourg, Luxembourg, Luxembourg; Paul Wurth, Luxembourg, Luxembourg; University of Luxembourg, Luxembourg, Luxembourg}, art_number = {8906948}, document_type = {Conference Paper}, isbn = {978-1-72812-535-0}, langid = {english}, source = {Scopus} } @article{hartmannNextEvolutionMDE2017, title = {The next Evolution of {{MDE}}: A Seamless Integration of Machine Learning into Domain Modeling}, shorttitle = {The next Evolution of {{MDE}}}, author = {Hartmann, Thomas and Moawad, Assaad and Fouquet, Francois and Le Traon, Yves}, year = {2017}, month = may, journal = {Software \& Systems Modeling}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-017-0600-2}, langid = {english} } @article{hassamAssistanceSystemOCL2011, title = {Assistance {{System}} for {{OCL Constraints Adaptation}} during {{Metamodel Evolution}}}, author = {Hassam, Kahina and Sadou, Salah and Gloahec, Vincent Le and Fleurquin, Regis}, year = {2011}, journal = {2011 15th European Conference on Software Maintenance and Reengineering}, pages = {151--160}, doi = {10.1109/CSMR.2011.21} } @article{haugeAdoptionOpenSource2010, title = {Adoption of Open Source Software in Software-Intensive Organizations \textendash{} {{A}} Systematic Literature Review}, author = {Hauge, {\O}yvind and Ayala, Claudia and Conradi, Reidar}, year = {2010}, month = nov, journal = {Information and Software Technology}, volume = {52}, number = {11}, pages = {1133--1154}, issn = {09505849}, doi = {10.1016/j.infsof.2010.05.008}, langid = {english} } @inproceedings{haugeEmpiricalStudySelection2009, title = {An Empirical Study on Selection of {{Open Source Software-Preliminary}} Results}, booktitle = {Emerging {{Trends}} in {{Free}}/{{Libre}}/{{Open Source Software Research}} and {{Development}}, 2009. {{FLOSS}}'09. {{ICSE Workshop}} On}, author = {Hauge, Oyvind and Osterlie, Thomas and Sorensen, Carl-Fredrik and Gerea, Marinela}, year = {2009}, pages = {42--47}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/abstract/document/5071359/}, urldate = {2017-02-25} } @article{haugheyNOSQLDataLake2017, title = {{{NOSQL}} and {{Data Lake Architecture}}}, author = {Haughey, Tom}, year = {2017}, pages = {28}, langid = {english} } @inproceedings{haveliwalaTopicsensitivePageRank2002, title = {Topic-Sensitive {{PageRank}}}, booktitle = {Proceedings of the 11th International Conference on World Wide Web}, author = {Haveliwala, Taher H.}, year = {2002}, series = {{{WWW}} '02}, pages = {517--526}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/511446.511513}, acmid = {511513}, isbn = {1-58113-449-5}, nodoi = {10.1145/511446.511513}, numpages = {10}, keywords = {link structure,PageRank,personalized search,search,search in context,web graph} } @article{HCI-009, title = {Collaborative Filtering Recommender Systems}, author = {Ekstrand, Michael D. and Riedl, John T. and Konstan, Joseph A.}, year = {2011}, journal = {Foundations and Trends\textregistered{} in Human\textendash Computer Interaction}, volume = {4}, number = {2}, pages = {81--173}, issn = {1551-3955}, url = {http://dx.doi.org/10.1561/1100000009}, nodoi = {10.1561/1100000009} } @article{He201977, title = {Model-Driven Deep Learning for Physical Layer Communications}, author = {He, H. and Jin, S. and Wen, C.-K. and Gao, F. and Li, G.Y. and Xu, Z.}, year = {2019}, journal = {IEEE Wireless Communications}, volume = {26}, number = {5}, pages = {77--83}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15361284}, doi = {10.1109/MWC.2019.1800447}, 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. \textcopyright{} 2002-2012 IEEE.}, art_number = {8715338}, coden = {IWCEA}, document_type = {Article}, source = {Scopus} } @article{He20201702, title = {Model-Driven Deep Learning for Mimo Detection}, author = {He, H. and Wen, C.-K. and Jin, S. and Li, G.Y.}, year = {2020}, journal = {IEEE Transactions on Signal Processing}, volume = {68}, pages = {1702--1715}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {1053587X}, doi = {10.1109/TSP.2020.2976585}, 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. \textcopyright{} 1991-2012 IEEE.}, art_number = {9018199}, coden = {ITPRE}, document_type = {Article}, source = {Scopus} } @article{He20202216, title = {Model-Driven Deep Learning for Massive {{MU-MIMO}} with Finite-Alphabet Precoding}, author = {He, H. and Zhang, M. and Jin, S. and Wen, C.-K. and Li, G.Y.}, year = {2020}, journal = {IEEE Communications Letters}, volume = {24}, number = {10}, pages = {2216--2220}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10897798}, doi = {10.1109/LCOMM.2020.3002082}, 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. \textcopyright{} 1997-2012 IEEE.}, art_number = {9115718}, coden = {ICLEF}, document_type = {Article}, source = {Scopus} } @article{He2021, title = {Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution}, author = {He, J. and Li, J. and Yuan, Q. and Shen, H. and Zhang, L.}, year = {2021}, journal = {IEEE Transactions on Neural Networks and Learning Systems}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {2162237X}, doi = {10.1109/TNNLS.2021.3056181}, 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}, document_type = {Article}, source = {Scopus} } @article{hearstSupportVectorMachines1998, title = {Support Vector Machines}, author = {Hearst, Marti A.}, year = {1998}, month = jul, journal = {IEEE Intelligent Systems}, volume = {13}, number = {4}, pages = {18--28}, publisher = {{IEEE Educational Activities Department}}, address = {{Piscataway, NJ, USA}}, issn = {1541-1672}, url = {http://dx.doi.org/10.1109/5254.708428}, acmid = {630387}, issue_date = {July 1998}, nodoi = {10.1109/5254.708428}, numpages = {11} } @article{heAutoMLSurveyStateoftheart2021, title = {{{AutoML}}: {{A}} Survey of the State-of-the-Art}, shorttitle = {{{AutoML}}}, author = {He, Xin and Zhao, Kaiyong and Chu, Xiaowen}, year = {2021}, month = jan, journal = {Knowledge-Based Systems}, volume = {212}, pages = {106622}, issn = {09507051}, doi = {10.1016/j.knosys.2020.106622}, langid = {english} } @inproceedings{hein2009model, title = {Model-Driven Tool Integration with Modelbus}, booktitle = {Workshop Future Trends of Model-Driven Development}, author = {Hein, Christian and Ritter, Tom and Wagner, Michael}, year = {2009}, pages = {50--52} } @article{heitmannUsingLinkedData2010, title = {Using Linked Data to Build Open, Collaborative Recommender Systems}, author = {Heitmann, Benjamin and Hayes, Conor}, year = {2010}, journal = {Artificial Intelligence}, pages = {76--81}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.174.2755 http://www.aaai.org/ocs/index.php/SSS/SSS10/paper/viewPDFInterstitial/1067/1452}, 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.}, mendeley-tags = {SML-LIB-BIBLIO,lang:ENG}, keywords = {lang:ENG,SML-LIB-BIBLIO,technical report ss 10 07} } @article{henderson-sellersMultiLevelMetaModellingUnderpin2013, title = {Multi-{{Level Meta-Modelling}} to {{Underpin}} the {{Abstract}} and {{Concrete Syntax}} for {{Domain-Specific Modelling Languages}}}, author = {{Henderson-Sellers}, Brian and {Gonzalez-Perez}, Cesar}, year = {2013}, journal = {Domain Engineering}, pages = {291--316}, doi = {10.1007/978-3-642-36654-3_12} } @inproceedings{Henkel:2005:CCR:1062455.1062512, title = {{{CatchUp}}!: {{Capturing}} and Replaying Refactorings to Support {{API}} Evolution}, booktitle = {Proceedings of the 27th International Conference on Software Engineering}, author = {Henkel, Johannes and Diwan, Amer}, year = {2005}, series = {{{ICSE}} '05}, pages = {274--283}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/1062455.1062512}, acmid = {1062512}, isbn = {1-58113-963-2}, nodoi = {10.1145/1062455.1062512}, numpages = {10}, keywords = {application programming interfaces,refactoring,software evolution} } @inproceedings{henningenRetrievingSoftwareObjects1991, title = {Retrieving Software Objects in an Example-Based Programming Environment}, booktitle = {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).}, author = {Henningen, Scott}, year = {1991}, pages = {251--260}, doi = {10.1145/122860.122886}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/sigir/Henningen91}, timestamp = {Tue, 06 Nov 2018 11:07:25 +0100} } @misc{HereWhatVoicecontrolled, title = {Here's What Voice-Controlled {{Android}} Home Automation Looks like [Video]}, url = {http://www.androidauthority.com/voice-controlled-android-home-automation-video-205316/}, urldate = {2015-04-16} } @inproceedings{Herrmann201979, title = {Motion Data and Model Management for Applied Statistical Motion Synthesis}, author = {Herrmann, E. and Du, H. and Antakli, A. and Rubinstein, D. and Schubotz, R. and Sprenger, J. and Hosseini, S. and Cheema, N. and Zinnikus, I. and Manns, M. and Fischer, K. and Slusallek, P.}, editor = {Agus M., Corsini M., Pintus R.}, year = {2019}, series = {Italian {{Chapter Conference}} 2019 - {{Smart Tools}} and {{Apps}} in Computer {{Graphics}}, {{STAG}} 2019}, pages = {79--88}, publisher = {{Eurographics Association}}, doi = {10.2312/stag.20191366}, 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. \textcopyright{} 2019 The Author(s) Eurographics Proceedings \textcopyright{} 2019 The Eurographics Association.}, document_type = {Conference Paper}, isbn = {978-3-03868-100-7}, source = {Scopus} } @article{hidasiSessionbasedRecommendationsRecurrent2015, title = {Session-Based Recommendations with Recurrent Neural Networks}, author = {Hidasi, Bal{\'a}zs and Karatzoglou, Alexandros and Baltrunas, Linas and Tikk, Domonkos}, year = {2015}, journal = {CoRR}, volume = {abs/1511.06939}, eprint = {1511.06939}, eprinttype = {arxiv}, url = {http://arxiv.org/abs/1511.06939}, archiveprefix = {arXiv}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/corr/HidasiKBT15}, timestamp = {Wed, 07 Jun 2017 14:42:57 +0200} } @inproceedings{Hildebrandt2017128, title = {Metamodeling Lightweight Data Compression Algorithms and Its Application Scenarios}, author = {Hildebrandt, J. and Habich, D. and Kuhn, T. and Damme, P. and Lehner, W.}, editor = {Cabanillas C., Farshidi S., Espana S.}, year = {2017}, series = {{{CEUR Workshop Proceedings}}}, volume = {1979}, pages = {128--141}, publisher = {{CEUR-WS}}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034960838&partnerID=40&md5=33294c448edfceb4dead3c958f0f06de}, abbrev_source_title = {CEUR Workshop Proc.}, affiliation = {Technische Universitat Dresden, Database Systems Group, Dresden, Germany; Technische Universitat Dresden, Software Technology Group, Dresden, Germany}, document_type = {Conference Paper}, langid = {english}, source = {Scopus} } @article{hintzeViolinPlotsBox1998, title = {Violin Plots: {{A}} Box Plot-Density Trace Synergism}, author = {Hintze, Jerry L. and {Ray D. Nelson}}, year = {1998}, journal = {The American Statistician}, volume = {52}, number = {2}, eprint = {https://amstat.tandfonline.com/doi/pdf/10.1080/00031305.1998.10480559}, pages = {181--184}, publisher = {{Taylor \& Francis}}, url = {https://amstat.tandfonline.com/doi/abs/10.1080/00031305.1998.10480559}, nodoi = {10.1080/00031305.1998.10480559} } @article{Hirschberg:1977:ALC:322033.322044, title = {Algorithms for the Longest Common Subsequence Problem}, author = {Hirschberg, Daniel S.}, year = {1977}, month = oct, journal = {Journal of the ACM}, volume = {24}, number = {4}, pages = {664--675}, publisher = {{ACM}}, address = {{New York, NY, USA}}, issn = {0004-5411}, url = {http://doi.acm.org/10.1145/322033.322044}, acmid = {322044}, issue_date = {Oct. 1977}, nodoi = {10.1145/322033.322044}, numpages = {12} } @misc{HitchhikerGuideIoT, title = {Hitchhiker's {{Guide}} to {{IoT Standards}} and {{Protocols}} - {{DZone IoT}}}, 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}, urldate = {2016-09-27} } @inproceedings{hnetynkaUsingComponentEnsembles2020, title = {Using Component Ensembles for Modeling Autonomic Component Collaboration in Smart Farming}, booktitle = {Proceedings of the {{IEEE}}/{{ACM}} 15th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}}}, author = {Hnetynka, Petr and Bures, Tomas and Gerostathopoulos, Ilias and Pacovsky, Jan}, year = {2020}, month = jun, pages = {156--162}, publisher = {{ACM}}, address = {{Seoul Republic of Korea}}, doi = {10.1145/3387939.3391599}, isbn = {978-1-4503-7962-5}, langid = {english} } @inproceedings{hoareRoleFormalTechniques1996, title = {The Role of Formal Techniques: Past, Current and Future or How Did Software Get so Reliable without Proof?}, shorttitle = {The Role of Formal Techniques}, booktitle = {Proceedings of the 18th International Conference on {{Software}} Engineering}, author = {Hoare, C. A. R.}, year = {1996}, pages = {233--234}, publisher = {{IEEE Computer Society}}, url = {http://dl.acm.org/citation.cfm?id=227765}, urldate = {2016-11-20} } @article{hodaRiseEvolutionAgile2018, title = {The {{Rise}} and {{Evolution}} of {{Agile Software Development}}}, author = {Hoda, R. and Salleh, N. and Grundy, J.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {58--63}, issn = {0740-7459}, doi = {10.1109/MS.2018.290111318}, 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.}, keywords = {software engineering} } @article{hoislCatalogReusableDesign, title = {A {{Catalog}} of {{Reusable Design Decisions}} for {{Developing UML-}} and {{MOF-based Domain-Specific Modeling Languages}}}, author = {Hoisl, Bernhard and Sobernig, Stefan and {Schefer-Wenzl}, Sigrid and Strembeck, Mark and Baumgrass, Anne}, pages = {24}, 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.}, langid = {english} } @book{hollerMachinetomachineInternetThings2014, title = {From Machine-to-Machine to the {{Internet}} of Things: Introduction to a New Age of Intelligence}, shorttitle = {From Machine-to-Machine to the {{Internet}} of Things}, editor = {H{\"o}ller, Jan}, year = {2014}, publisher = {{Elsevier Academic Press}}, address = {{Amsterdam}}, 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}, isbn = {978-0-12-407684-6 978-0-08-099401-7}, langid = {english}, lccn = {TK5105.875.I57 F76 2014}, keywords = {internet of things} } @article{holmes_strathcona_nodate, title = {Strathcona {{Example Recommendation Tool}}}, author = {Holmes, Reid and Walker, Robert J and Murphy, Gail C}, year = {2015}, pages = {4}, 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.}, langid = {english} } @inproceedings{holmesStrathconaExampleRecommendation2005, title = {Strathcona Example Recommendation Tool}, booktitle = {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}, author = {Holmes, Reid and Walker, Robert J. and Murphy, Gail C.}, year = {2005}, pages = {237--240}, doi = {10.1145/1081706.1081744}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/sigsoft/HolmesWM05}, timestamp = {Tue, 06 Nov 2018 16:59:23 +0100} } @article{holzmannCodeVault2018, title = {Code {{Vault}}}, author = {Holzmann, G. J.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {85--87}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571225}, 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.} } @misc{HomeServerNoob, title = {Home Server Noob. {{Can}}'t Get {{CouchPotato}} to Communicate with {{Deluge}}. : {{HomeServer}}}, url = {http://www.reddit.com/r/HomeServer/comments/2r15vh/home_server_noob_cant_get_couchpotato_to/}, urldate = {2015-04-22} } @misc{HomeSystemsConsulting, title = {Home {{Systems Consulting}}}, url = {http://www.hsyco.com/}, urldate = {2015-04-08} } @misc{HORIZON2020, title = {{{HORIZON}} 2020}, url = {http://een.unioncamerepuglia.it/Italiano/News/HORIZON-2020/}, urldate = {2015-04-08} } @article{hossainIEEEPressEditorial, title = {{{IEEE Press Editorial Board}}}, author = {Hossain, Ekram and Fortino, Giancarlo and Grier, David Alan and Heirman, Donald and Li, Xiaoou and Molisch, Andreas and Nahavandi, Saeid and Perez, Ray and Reed, Jeffrey and Shafer, Linda and Shahidehpour, Mohammad and Spurgeon, Sarah and Tekalp, Ahmet Murat}, pages = {693}, langid = {english} } @inproceedings{Hou:2013:CCA:2550526.2550556, title = {Content Categorization of {{API}} Discussions}, booktitle = {Proceedings of the 2013 {{IEEE}} International Conference on Software Maintenance}, author = {Hou, Daqing and Mo, Lingfeng}, year = {2013}, series = {{{ICSM}} '13}, pages = {60--69}, publisher = {{IEEE Computer Society}}, address = {{Washington, DC, USA}}, url = {http://dx.doi.org/10.1109/ICSM.2013.17}, acmid = {2550556}, isbn = {978-0-7695-4981-1}, nodoi = {10.1109/ICSM.2013.17}, numpages = {10}, keywords = {APIs,AWT/Swing,MALLET,Naive Bayes,Online Forums,Text Categorization} } @misc{HowCanUse, title = {How {{Can I Use This Method}}? - {{IEEE Conference Publication}}}, url = {https://ieeexplore.ieee.org/document/7194634/}, urldate = {2018-07-27} } @misc{HowExactlyMachine, title = {How Exactly Is Machine Learning Used in Recommendation Engines? - {{Quora}}}, url = {https://www.quora.com/How-exactly-is-machine-learning-used-in-recommendation-engines}, urldate = {2017-03-10} } @misc{HttpPdmaidsDibris, title = {{{http://pdm-aids.dibris.unige.it/questionnaire.php}}} } @article{Huang:2012:LCD:2343876.2343884, title = {Learning a Concept-Based Document Similarity Measure}, author = {Huang, Lan and Milne, David and Frank, Eibe and Witten, Ian H.}, year = {2012}, month = aug, journal = {J. Am. Soc. Inf. Sci. Technol.}, volume = {63}, number = {8}, pages = {1593--1608}, publisher = {{John Wiley \& Sons, Inc.}}, address = {{New York, NY, USA}}, issn = {1532-2882}, url = {http://dx.doi.org/10.1002/asi.22689}, acmid = {2343884}, issue_date = {August 2012}, nodoi = {10.1002/asi.22689}, numpages = {16}, keywords = {content analysis,semantic analysis,text mining} } @article{Huang20221457, title = {Noncoherent Massive Random Access for Inhomogeneous Networks: {{From}} Message Passing to Deep Learning}, author = {Huang, J. and Zhang, H. and Huang, C. and Yang, L. and Zhang, W.}, year = {2022}, journal = {IEEE Journal on Selected Areas in Communications}, volume = {40}, number = {5}, pages = {1457--1472}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {07338716}, doi = {10.1109/JSAC.2022.3143260}, 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. \textcopyright{} 1983-2012 IEEE.}, coden = {ISACE}, document_type = {Article}, source = {Scopus} } @inproceedings{huangSimilarityMeasuresText2008, title = {Similarity Measures for Text Document Clustering}, booktitle = {Proceedings of the Sixth New Zealand Computer Science Research Student Conference ({{NZCSRSC2008}}), Christchurch, New Zealand}, author = {Huang, A.}, year = {2008}, pages = {49--56}, 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}, added-at = {2010-01-10T13:17:50.000+0100}, biburl = {http://www.bibsonomy.org/bibtex/22731fc9ee66915a56f9ee14f1436aabf/cdevries}, interhash = {b05294a51336b00c449ecaeb25940212}, intrahash = {2731fc9ee66915a56f9ee14f1436aabf}, keywords = {clustering measures}, timestamp = {2010-01-10T13:17:50.000+0100} } @article{huebscherSurveyAutonomicComputing2008, title = {A Survey of Autonomic Computing\textemdash Degrees, Models, and Applications}, author = {Huebscher, Markus C. and McCann, Julie A.}, year = {2008}, journal = {ACM Computing Surveys (CSUR)}, volume = {40}, number = {3}, pages = {7}, url = {http://dl.acm.org/citation.cfm?id=1380585}, urldate = {2016-08-29} } @incollection{hulsbuschShowingFullSemantics2010, title = {Showing {{Full Semantics Preservation}} in {{Model Transformation}} - {{A Comparison}} of {{Techniques}}}, booktitle = {Integrated {{Formal Methods}}}, author = {H{\"u}lsbusch, Mathias and K{\"o}nig, Barbara and Rensink, Arend and Semenyak, Maria and Soltenborn, Christian and Wehrheim, Heike}, editor = {M{\'e}ry, Dominique and Merz, Stephan}, year = {2010}, series = {Lecture {{Notes}} in {{Computer Science}}}, number = {6396}, pages = {183--198}, publisher = {{Springer Berlin Heidelberg}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-16265-7_14}, urldate = {2015-03-24}, 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.}, copyright = {\textcopyright 2010 Springer Berlin Heidelberg}, isbn = {978-3-642-16264-0 978-3-642-16265-7}, langid = {english}, keywords = {software engineering} } @inproceedings{Hundt20191797, title = {The {{CoSTAR}} Block Stacking Dataset: {{Learning}} with Workspace Constraints}, author = {Hundt, A. and Jain, V. and Lin, C.-H. and Paxton, C. and Hager, G.D.}, year = {2019}, series = {{{IEEE International Conference}} on {{Intelligent Robots}} and {{Systems}}}, pages = {1797--1804}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {21530858}, doi = {10.1109/IROS40897.2019.8967784}, abbrev_source_title = {IEEE Int Conf Intell Rob Syst}, affiliation = {Johns Hopkins University, Department of Computer Science, United States; NVIDIA, United States}, art_number = {8967784}, coden = {85RBA}, document_type = {Conference Paper}, isbn = {978-1-72814-004-9}, langid = {english}, source = {Scopus} } @misc{hurleySimplifyMachineLearning2020, title = {Simplify {{Machine Learning Workflows}}}, author = {Hurley, David}, year = {2020}, month = jul, journal = {Medium}, url = {https://towardsdatascience.com/simplify-machine-learning-workflows-e9d4f404aaeb}, urldate = {2021-03-18}, abstract = {How to use Pipelines to standardize data preprocessing, data transformation, and modeling steps of a machine learning workflow}, langid = {english} } @article{husarAutonomousSystemsModeling2013, title = {Autonomous {{Systems Modeling During Early Architecture Development}}}, author = {Husar, Rosteslaw M. and Stracener, Jerrell}, year = {2013}, journal = {Procedia Computer Science}, volume = {20}, pages = {242--247}, issn = {18770509}, doi = {10.1016/j.procs.2013.09.268}, langid = {english} } @inproceedings{hutchinsonEmpiricalAssessmentMDE2011, title = {Empirical Assessment of {{MDE}} in Industry}, booktitle = {Proceedings of the 33rd {{International Conference}} on {{Software Engineering}}}, author = {Hutchinson, John and Whittle, Jon and Rouncefield, Mark and Kristoffersen, Steinar}, year = {2011}, pages = {471--480}, publisher = {{ACM}}, url = {http://dl.acm.org/citation.cfm?id=1985858}, urldate = {2015-10-29} } @article{hutchinsonModeldrivenEngineeringPractices2013, title = {Model-Driven Engineering Practices in Industry: {{Social}}, Organizational and Managerial Factors That Lead to Success or Failure}, author = {Hutchinson, John and Whittle, Jon and Rouncefield, Mark}, year = {2013}, journal = {Science of Computer Programming}, doi = {10.1016/j.scico.2013.03.017} } @misc{HybridApproachMetamodel, title = {Hybrid {{Approach}} for {{Metamodel}} and {{Model Co-evolution}} - {{Springer}}}, url = {http://link.springer.com/chapter/10.1007%2F978-3-319-19578-0_46}, urldate = {2015-07-19} } @inproceedings{Ickin202072, title = {Ensemble-Based Synthetic Data Synthesis for Federated {{QoE}} Modeling}, author = {Ickin, S. and Vandikas, K. and Moradi, F. and Taghia, J. and Hu, W.}, editor = {De Turck F., Chemouil P., Zhani M.F., Cerroni W., Pasquini R., Zhu Z., Wauters T.}, year = {2020}, series = {Proceedings of the 2020 {{IEEE Conference}} on {{Network Softwarization}}: {{Bridging}} the {{Gap Between AI}} and {{Network Softwarization}}, {{NetSoft}} 2020}, pages = {72--76}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/NetSoft48620.2020.9165379}, 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\%. \textcopyright{} 2020 IEEE.}, art_number = {9165379}, document_type = {Conference Paper}, isbn = {978-1-72815-684-2}, source = {Scopus} } @misc{IEEESoftwareBlog, title = {{{IEEE Software Blog}}: {{Autonomous Computing Systems}}: {{The Convergence}} of {{Control Theory}} and {{Computing Systems}}}, url = {http://blog.ieeesoftware.org/2019/07/autonomous-computing-systems.html}, urldate = {2020-10-05} } @article{iglesiaMAPEKFormalTemplates2015, title = {{{MAPE-K}} Formal Templates to Rigorously Design Behaviors for Self-Adaptive Systems}, author = {Iglesia, Didac Gil De La and Weyns, Danny}, year = {2015}, journal = {ACM Transactions on Autonomous and Adaptive Systems (TAAS)}, volume = {10}, number = {3}, pages = {15}, url = {http://dl.acm.org/citation.cfm?id=2724719}, urldate = {2016-09-19} } @article{ilahiChallengesCountermeasuresAdversarial2020, title = {Challenges and {{Countermeasures}} for {{Adversarial Attacks}} on {{Deep Reinforcement Learning}}}, author = {Ilahi, Inaam and Usama, Muhammad and Qadir, Junaid and Janjua, Muhammad Umar and {Al-Fuqaha}, Ala and Hoang, Dinh Thai and Niyato, Dusit}, year = {2020}, month = jan, journal = {arXiv:2001.09684 [cs]}, eprint = {2001.09684}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2001.09684}, urldate = {2021-04-02}, 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.}, archiveprefix = {arXiv}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Cryptography and Security,Computer Science - Machine Learning} } @article{imamDataModelingGuidelines2018, title = {Data {{Modeling Guidelines}} for {{NoSQL Document-Store Databases}}}, author = {Imam, Abdullahi Abubakar and Basri, Shuib and Ahmad, Rohiza and Watada, Junzo and T., Maria and Ahmad, Malek}, year = {2018}, journal = {International Journal of Advanced Computer Science and Applications}, volume = {9}, number = {10}, issn = {21565570, 2158107X}, doi = {10.14569/IJACSA.2018.091066}, 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.}, langid = {english} } @misc{incLowCodePlatformRapidly2020, title = {Low-{{Code Platform}}: {{Rapidly Build Enterprise-Grade Analytics Apps}}}, shorttitle = {Low-{{Code Platform}}}, author = {Inc, Gramener}, year = {2020}, month = oct, journal = {Gramener Blog}, url = {https://blog.gramener.com/low-code-platform-for-enterprise-analytics-applications/}, urldate = {2021-03-18}, abstract = {This article talks about the importance of low-code platform in the analytics world and Low code vs traditional application development.}, langid = {american} } @misc{IndustrialCyberPhysicalSystems, title = {Industrial {{Cyber-Physical Systems Center}} ({{iCyPhy}})}, url = {http://www.icyphy.org/}, urldate = {2016-01-26} } @misc{IndustryEclipseKura, title = {Industry 4.0 with {{Eclipse Kura}} | {{EclipseCon Europe}} 2016}, url = {https://www.eclipsecon.org/europe2016/session/industry-40-eclipse-kura}, urldate = {2016-09-27} } @misc{InternetThingsCS, title = {Internet of {{Things}} [{{CS Open CourseWare}}]}, url = {http://ocw.cs.pub.ro/courses/iot}, urldate = {2016-09-11} } @misc{InternetThingsRoad, title = {The {{Internet}} of {{Things}} Is on the {{Road}} to {{Autonomous Driving}}}, url = {http://www.intel.com/content/www/us/en/internet-of-things/infographics/iot-autonomous-driving-infographic.html}, urldate = {2016-09-03} } @misc{IntocpsAuDk, title = {Into-Cps.Au.Dk}, url = {http://into-cps.au.dk/}, urldate = {2016-02-09} } @misc{IntroductionBuildingMachine, title = {1. {{Introduction}} - {{Building Machine Learning Pipelines}} [{{Book}}]}, url = {https://www.oreilly.com/library/view/building-machine-learning/9781492053187/ch01.html}, urldate = {2021-03-18}, 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 \ldots{} - Selection from Building Machine Learning Pipelines [Book]}, langid = {english} } @misc{IntroductionControlSystems, title = {An {{Introduction To Control Systems}}}, url = {https://www.facstaff.bucknell.edu/mastascu/eControlHTML/Intro/Intro1.html}, urldate = {2016-11-01} } @misc{IntroductionParallelComputing, title = {Introduction to {{Parallel Computing}}}, url = {https://computing.llnl.gov/tutorials/parallel_comp/#Whatis}, urldate = {2017-02-23} } @misc{IntroduzioneScuolaCultura, title = {Introduzione - {{Scuola}} e Cultura}, url = {https://www.scuola-e-cultura.it/manuale-della-cultura/introduzione.htm}, urldate = {2022-07-16} } @inproceedings{inverardiProducingSoftwareIntegration2013, title = {Producing Software by Integration: Challenges and Research Directions (Keynote)}, booktitle = {Proceeding {{ESEC}}/{{FSE}} 2013 {{Proceedings}} of the 2013 9th {{Joint Meeting}} on {{Foundations}} of {{Software Engineering}}}, author = {Inverardi, P and Autili, M and Di Ruscio, D and Pelliccione, P and Tivoli, M}, year = {2013}, pages = {2--12}, publisher = {{ACM, Association for Computing Machinery}}, doi = {10.1145/2491411.2505428}, 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.}, isbn = {978-1-4503-2237-9}, keywords = {Automated synthesis,Dependable software systems,Model elicitation} } @misc{IoTSecurityAction, title = {{{IoT Security}} in Action! | {{EclipseCon Europe}} 2016}, url = {https://www.eclipsecon.org/europe2016/session/iot-security-action}, urldate = {2016-09-27} } @misc{IoTVsM2M, title = {{{IoT}} vs. {{M2M}}, {{CPS}}, {{WoT}}....: {{Are}} These Terms Synonyms? | {{John Soldatos}} | {{Pulse}} | {{LinkedIn}}}, url = {https://www.linkedin.com/pulse/iot-vs-m2m-cps-wot-terms-synonyms-john-soldatos}, urldate = {2016-08-21} } @article{Iovino2012OnTI, title = {On the Impact Significance of Metamodel Evolution in {{MDE}}}, author = {Iovino, Ludovico and Pierantonio, Alfonso and Malavolta, Ivano}, year = {2012}, journal = {Journal of Object Technology}, volume = {11}, pages = {3: 1-33} } @inproceedings{iovinoMetamodelDeprecationManage2020, title = {Metamodel Deprecation to Manage Technical Debt in Model Co-Evolution}, booktitle = {Proceedings - 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2020 - {{Companion Proceedings}}}, author = {Iovino, L. and Di Salle, A. and Di Ruscio, D. and Pierantonio, A.}, year = {2020}, pages = {306--315}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3417990.3419625}, isbn = {978-1-4503-8135-2}, keywords = {Deprecation,MDE,Metamodeling,Technical debt} } @article{ISINKAYE2015261, title = {Recommendation Systems: {{Principles}}, Methods and Evaluation}, author = {Isinkaye, F.O. and Folajimi, Y.O. and Ojokoh, B.A.}, year = {2015}, journal = {Egyptian Informatics Journal}, volume = {16}, number = {3}, pages = {261--273}, issn = {1110-8665}, url = {http://www.sciencedirect.com/science/article/pii/S1110866515000341}, nodoi = {https://doi.org/10.1016/j.eij.2015.06.005}, keywords = {Collaborative filtering,Content-based filtering,Evaluation,Hybrid filtering technique,recommendation systems} } @inproceedings{islamLeveragingAutomatedSentiment2017, title = {Leveraging {{Automated Sentiment Analysis}} in {{Software Engineering}}}, author = {Islam, Md Rakibul and Zibran, Minhaz F.}, year = {2017}, month = may, pages = {203--214}, publisher = {{IEEE}}, doi = {10.1109/MSR.2017.9}, isbn = {978-1-5386-1544-7} } @article{islamSemanticTextSimilarity2008, title = {Semantic Text Similarity Using Corpus-Based Word Similarity and String Similarity}, author = {Islam, Aminul and Inkpen, Diana}, year = {2008}, month = jul, journal = {ACM Trans. Knowl. Discov. Data}, volume = {2}, number = {2}, pages = {10:1-10:25}, publisher = {{ACM}}, address = {{New York, NY, USA}}, issn = {1556-4681}, url = {http://doi.acm.org/10.1145/1376815.1376819}, acmid = {1376819}, articleno = {10}, issue_date = {July 2008}, nodoi = {10.1145/1376815.1376819}, numpages = {25}, keywords = {corpus-based measures,Semantic similarity of words,similarity of short texts} } @article{jaccardDistributionFloraAlpine1912, title = {The Distribution of the Flora in the Alpine Zone}, author = {Jaccard, Paul}, year = {1912}, journal = {New Phytologist}, volume = {11}, number = {2}, pages = {37--50}, nodoi = {10.1111/j.1469-8137.1912.tb05611.x} } @article{Jackson202090, title = {Neuroevolutionary Approach to Metamodeling of Production-Inventory Systems with Lost-Sales and Markovian Demand}, author = {Jackson, I.}, year = {2020}, journal = {Lecture Notes in Networks and Systems}, volume = {117}, pages = {90--99}, publisher = {{Springer}}, issn = {23673370}, doi = {10.1007/978-3-030-44610-9_10}, abbrev_source_title = {Lect. Notes Networks Syst.}, affiliation = {Transport and Telecommunication Institute (TTI), Lomonosova iela 1, Riga, Latvia}, correspondence_address1 = {Jackson, I.; Transport and Telecommunication Institute (TTI), Lomonosova iela 1, Latvia; email: jackson.i@tsi.lv}, document_type = {Book Chapter}, langid = {english}, source = {Scopus} } @article{Jackson202184, title = {Neuroevolutionary Approach to Metamodel-Based Optimization in Production and Logistics}, author = {Jackson, I.}, editor = {Kabashkin I., Yatskiv I., Prentkovskis O.}, year = {2021}, journal = {Lecture Notes in Networks and Systems}, volume = {195}, pages = {84--93}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {23673370}, doi = {10.1007/978-3-030-68476-1_8}, abbrev_source_title = {Lect. Notes Networks Syst.}, affiliation = {Transport and Telecommunication Institute (TTI), Lomonosova Iela 1, Riga, Latvia}, correspondence_address1 = {Jackson, I.; Transport and Telecommunication Institute (TTI), Lomonosova Iela 1, Latvia; email: jackson.i@tsi.lv}, document_type = {Conference Paper}, isbn = {9783030684754}, langid = {english}, source = {Scopus} } @article{jacksonAutomaticallyReasoningMetamodeling2015, title = {Automatically Reasoning about Metamodeling}, author = {Jackson, Ethan K. and Levendovszky, Tihamer and Balasubramanian, Daniel}, year = {2015}, month = feb, journal = {Software \& Systems Modeling}, volume = {14}, number = {1}, pages = {271--285}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-013-0315-y}, langid = {english} } @article{jainDataClusteringReview1999, ids = {jain1999data}, title = {Data Clustering: A Review}, shorttitle = {Data Clustering}, author = {Jain, Anil K. and Murty, M. Narasimha and Flynn, Patrick J.}, year = {1999}, journal = {ACM computing surveys (CSUR)}, volume = {31}, number = {3}, pages = {264--323}, publisher = {{Acm}}, url = {http://dl.acm.org/citation.cfm?id=331504}, urldate = {2015-04-24} } @article{Jamei2015133, title = {Security Breach Possibility with {{RSS-Based}} Localization of Smart Meters Incorporating Maximum Likelihood Estimator}, author = {Jamei, M. and Sarwat, A.I. and Iyengar, S.S. and Kaleem, F.}, year = {2015}, journal = {Advances in Intelligent Systems and Computing}, volume = {1089}, pages = {133--139}, publisher = {{Springer Verlag}}, issn = {21945357}, doi = {10.1007/978-3-319-08422-0_20}, 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). \textcopyright{} Springer International Publishing Switzerland 2015.}, document_type = {Conference Paper}, isbn = {9783319084213}, source = {Scopus} } @article{Javidan2019602, title = {Variance-Based Global Sensitivity Analysis for Fuzzy Random Structural Systems}, author = {Javidan, M.M. and Kim, J.}, year = {2019}, journal = {Computer-Aided Civil and Infrastructure Engineering}, volume = {34}, number = {7}, pages = {602--615}, publisher = {{Blackwell Publishing Inc.}}, issn = {10939687}, doi = {10.1111/mice.12436}, abbrev_source_title = {Comput.-Aided Civ. Infrastruct. Eng.}, affiliation = {Department of Civil \& Architectural Engineering, Sungkyunkwan University, Suwon, South Korea}, coden = {CCIEF}, correspondence_address1 = {Kim, J.; Department of Civil \& Architectural Engineering, South Korea; email: jkim12@skku.edu}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{jeanjeanIDECodeReifying2021, title = {{{IDE}} as {{Code}}: {{Reifying Language Protocols}} as {{First-Class Citizens}}}, author = {Jeanjean, Pierre and Combemale, Benoit and Barais, Olivier}, year = {2021}, pages = {6}, 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.}, langid = {english} } @inproceedings{jehSimRankMeasureStructuralcontext2002, title = {{{SimRank}}: {{A}} Measure of Structural-Context Similarity}, booktitle = {Proceedings of the Eighth {{ACM SIGKDD}} International Conference on Knowledge Discovery and Data Mining}, author = {Jeh, Glen and Widom, Jennifer}, year = {2002}, series = {{{KDD}} '02}, pages = {538--543}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/775047.775126}, acmid = {775126}, isbn = {1-58113-567-X}, nodoi = {10.1145/775047.775126}, numpages = {6} } @article{Jeon2022, title = {Artificial Intelligence for Physical-Layer Design of {{MIMO}} Communications with One-Bit {{ADCs}}}, author = {Jeon, Y. and Kim, D. and Hong, S. and Lee, N. and Heath, R.W.}, year = {2022}, journal = {IEEE Communications Magazine}, pages = {1--7}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {01636804}, doi = {10.1109/MCOM.007.2200002}, 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}, coden = {ICOMD}, document_type = {Article}, source = {Scopus} } @article{Jha20212374, title = {Online Downlink Multi-User Channel Estimation for {{mmWave}} Systems Using Bayesian Neural Network}, author = {Jha, N.K. and Lau, V.K.N.}, year = {2021}, journal = {IEEE Journal on Selected Areas in Communications}, volume = {39}, number = {8}, pages = {2374--2387}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {07338716}, doi = {10.1109/JSAC.2021.3087249}, 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. \textcopyright{} 1983-2012 IEEE.}, art_number = {9448139}, coden = {ISACE}, document_type = {Article}, source = {Scopus} } @article{jhaAdversarialMachineLearning, title = {Adversarial {{Machine Learning}} ({{AML}})}, author = {Jha, Somesh}, pages = {71}, langid = {english}, keywords = {adversarial machine learning} } @inproceedings{Jia2021, title = {Estimation of Room-Level Cooling Energy in Hot/Arid Climate by Machine Learning-Based Approaches}, author = {Jia, B. and Hou, D. and Wang, L.L. and Hassan, I.G.}, year = {2021}, series = {Proceedings of the 2021 {{ASME Verification}} and {{Validation Symposium}}, {{VVS}} 2021}, publisher = {{American Society of Mechanical Engineers (ASME)}}, doi = {10.1115/VVS2021-65272}, abbrev_source_title = {Proc. ASME Verif. Valid. Symp., VVS}, affiliation = {Concordia University, Montreal, Canada; Texas AandM University at Qatar, Doha, Qatar}, art_number = {VVS2021-65272}, document_type = {Conference Paper}, isbn = {978-0-7918-8478-2}, langid = {english}, source = {Scopus} } @article{Jiang20217655, title = {{{AI-Aided}} Online Adaptive {{OFDM}} Receiver: {{Design}} and Experimental Results}, author = {Jiang, P. and Wang, T. and Han, B. and Gao, X. and Zhang, J. and Wen, C.-K. and Jin, S. and Li, G.Y.}, year = {2021}, journal = {IEEE Transactions on Wireless Communications}, volume = {20}, number = {11}, pages = {7655--7668}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15361276}, doi = {10.1109/TWC.2021.3087191}, 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. \textcopyright{} 2002-2012 IEEE.}, document_type = {Article}, source = {Scopus} } @inproceedings{jiangSemanticSimilarityBased1997, title = {Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy}, booktitle = {Proc. of the Int'l. {{Conf}}. on Research in Computational Linguistics}, author = {Jiang, J.J. and Conrath, D.W.}, year = {1997}, pages = {19--33}, url = {http://www.cse.iitb.ac.in/~cs626-449/Papers/WordSimilarity/4.pdf}, 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.}, added-at = {2010-03-12T16:18:27.000+0100}, biburl = {http://www.bibsonomy.org/bibtex/2c4ffc507dafc908eab62fde53f7e4f7a/sdo}, description = {Jiang Conrath Ma\ss}, interhash = {175ec03ee8c47d4b2d0a083609a78e05}, intrahash = {c4ffc507dafc908eab62fde53f7e4f7a}, keywords = {1997 Conrath Jiang JiangConrath folksonomy lexical measure semantic similarity taxonomy}, timestamp = {2010-03-12T16:18:27.000+0100} } @article{jiangWhyHowDevelopers2017, title = {Why and How Developers Fork What from Whom in {{GitHub}}}, author = {Jiang, Jing and Lo, David and He, Jiahuan and Xia, Xin and Kochhar, Pavneet Singh and Zhang, Li}, year = {2017}, month = feb, journal = {Empirical Softw. Engg.}, volume = {22}, number = {1}, pages = {547--578}, publisher = {{Kluwer Academic Publishers}}, address = {{Hingham, MA, USA}}, issn = {1382-3256}, url = {https://doi.org/10.1007/s10664-016-9436-6}, acmid = {3042043}, issue_date = {February 2017}, nodoi = {10.1007/s10664-016-9436-6}, numpages = {32}, keywords = {Fork,GitHub,Open source software} } @article{Jindal20213202, title = {Machine Learning for Cloud Data Systems: {{The}} Progress so Far and the Path Forward}, author = {Jindal, A. and Interlandi, M.}, editor = {Dong X.L., Naumann F.}, year = {2021}, journal = {Proceedings of the VLDB Endowment}, volume = {14}, number = {12}, pages = {3202--3205}, publisher = {{VLDB Endowment}}, issn = {21508097}, doi = {10.14778/3476311.3476408}, 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. \textcopyright{} The authors.}, document_type = {Conference Paper}, source = {Scopus} } @inproceedings{jinghanSurveyNoSQLDatabase2011, title = {Survey on {{NoSQL}} Database}, booktitle = {2011 6th {{International Conference}} on {{Pervasive Computing}} and {{Applications}}}, author = {{Jing Han} and {Haihong E} and {Guan Le} and {Jian Du}}, year = {2011}, month = oct, pages = {363--366}, doi = {10.1109/ICPCA.2011.6106531}, 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.}, keywords = {Big Data,Blogs,CAP theorem,cloud computing,column-oriented,Computational modeling,data model,database,Databases,document,Facebook,high-concurrency applications,Internet,key-value,NoSQL,NoSQL database,query dynamic user data,query processing,relational database,relational databases,Reliability,search engines,SNS,SQL} } @article{johannKiefMorrisInfrastructure2017, title = {Kief {{Morris}} on {{Infrastructure}} as {{Code}}}, author = {Johann, Sven}, year = {2017}, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {117--120}, issn = {0740-7459}, 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.}, keywords = {software engineering} } @misc{JointICTPIAEASchool, title = {Joint {{ICTP-IAEA School}} on {{LoRa Enabled Radiation}} and {{Environmental Monitoring Sensors}}}, url = {http://wireless.ictp.it/school_2018/}, urldate = {2021-01-05}, keywords = {grafana,influxdb,internet of things} } @article{josebaAutomaticImpactAnalysis, title = {Automatic Impact Analysis of Software Architecture Migration on {{Model Driven Software Development}}}, author = {Joseba, Agirre and Leire, Etxeberria and Goiuria, Sagardui}, journal = {AMT @MoDELS 2013} } @article{journals/bmcbi/SchlickerDRL06, title = {A New Measure for Functional Similarity of Gene Products Based on {{Gene Ontology}}.}, author = {Schlicker, Andreas and Domingues, Francisco S. and Rahnenf{\"u}hrer, J{\"o}rg and Lengauer, Thomas}, year = {2009-11-10, 2006}, journal = {BMC Bioinformatics}, volume = {7}, pages = {302}, url = {http://dblp.uni-trier.de/db/journals/bmcbi/bmcbi7.html#SchlickerDRL06}, added-at = {2009-11-10T00:00:00.000+0100}, biburl = {http://www.bibsonomy.org/bibtex/209c4c56514b6a72f7b855ebea6cdacd0/dblp}, description = {dblp}, ee = {http://dx.doi.org/10.1186/1471-2105-7-302}, interhash = {799547cf798d57975c427f4f389a5e0b}, intrahash = {09c4c56514b6a72f7b855ebea6cdacd0}, keywords = {dblp}, timestamp = {2009-11-10T00:00:00.000+0100} } @inproceedings{jungBuildingAutomationSmart2013, title = {Building {{Automation}} and {{Smart Cities}}: {{An Integration Approach Based}} on a {{Service-Oriented Architecture}}}, shorttitle = {Building {{Automation}} and {{Smart Cities}}}, author = {Jung, Markus and Weidinger, J. and Kastner, W. and Olivieri, A.}, year = {2013}, month = mar, pages = {1361--1367}, publisher = {{IEEE}}, doi = {10.1109/WAINA.2013.200}, isbn = {978-1-4673-6239-9 978-0-7695-4952-1} } @article{Jurgelaitis202163, title = {Smart Contract Code Generation from Platform Specific Model for Hyperledger Go}, author = {Jurgelaitis, M. and Drungilas, V. and {\v C}eponien{\.e}, L. and Vai{\v c}iukynas, E. and Butkien{\.e}, R. and {\v C}eponis, J.}, editor = {Rocha A., Adeli H., Moreira F., Correia A.M.R., Dzemyda G.}, year = {2021}, journal = {Advances in Intelligent Systems and Computing}, volume = {1368 AISC}, pages = {63--73}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {21945357}, doi = {10.1007/978-3-030-72654-6_7}, abbrev_source_title = {Adv. Intell. Sys. Comput.}, affiliation = {Informatics Faculty, Kaunas University of Technology, Kaunas, Lithuania}, correspondence_address1 = {Jurgelaitis, M.; Informatics Faculty, Lithuania; email: mantas.jurgelaitis@ktu.lt}, document_type = {Conference Paper}, isbn = {9783030726539}, langid = {english}, source = {Scopus} } @misc{JustAddData, title = {Just {{Add Data}}: {{Automated Predictive Modeling}} and {{BioSignature Discovery}} | {{bioRxiv}}}, url = {https://www.biorxiv.org/content/10.1101/2020.05.04.075747v1.full}, urldate = {2021-05-14} } @article{Kanetaki2021V, title = {Creating a Metamodel for Predicting Learners Satisfaction by Utilizing an Educational Information System during {{COVID-19}} Pandemic}, author = {Kanetaki, Z. and Stergiou, C. and Bekas, G. and Troussas, C. and Sgouropoulou, C.}, editor = {Frasson C., Kabassi K., Voulodimos A.}, year = {2021}, journal = {Frontiers in Artificial Intelligence and Applications}, volume = {338}, pages = {V-VI}, publisher = {{IOS Press BV}}, issn = {09226389}, doi = {10.3233/FAIA210085}, abbrev_source_title = {Front. Artif. Intell. Appl.}, affiliation = {University of West Attica, Athens, Greece}, art_number = {127-136}, document_type = {Conference Paper}, isbn = {9781643682044}, langid = {english}, source = {Scopus} } @inproceedings{karasneh2013online, title = {Online {{Img2UML}} Repository: {{An}} Online Repository for {{UML}} Models.}, booktitle = {{{EESSMOD}}@ {{MoDELS}}}, author = {Karasneh, Bilal and Chaudron, Michel RV}, year = {2013}, pages = {61--66} } @inproceedings{Karatzoglou:2017:DLR:3109859.3109933, title = {Deep Learning for Recommender Systems}, booktitle = {Proceedings of the Eleventh {{ACM}} Conference on Recommender Systems}, author = {Karatzoglou, Alexandros and Hidasi, Bal{\'a}zs}, year = {2017}, series = {{{RecSys}} '17}, pages = {396--397}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/3109859.3109933}, acmid = {3109933}, isbn = {978-1-4503-4652-8}, nodoi = {10.1145/3109859.3109933}, numpages = {2}, keywords = {deep learning,recommender systems} } @inproceedings{karsaiDistributedManagedResearch2014, title = {Distributed and {{Managed}}: {{Research Challenges}} and {{Opportunities}} of the {{Next Generation Cyber-Physical Systems}}}, shorttitle = {Distributed and {{Managed}}}, author = {Karsai, Gabor and Balasubramanian, Daniel and Dubey, Abhishek and Otte, William R.}, year = {2014}, month = jun, pages = {1--8}, publisher = {{IEEE}}, doi = {10.1109/ISORC.2014.36}, isbn = {978-1-4799-4430-9} } @incollection{karsaiModelintegratedDevelopmentCyberphysical2008, title = {Model-Integrated Development of Cyber-Physical Systems}, booktitle = {Software {{Technologies}} for {{Embedded}} and {{Ubiquitous Systems}}}, author = {Karsai, Gabor and Sztipanovits, Janos}, year = {2008}, pages = {46--54}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/978-3-540-87785-1_5}, urldate = {2016-03-10} } @article{Karypis:1999:CHC:619043.621303, title = {Chameleon: {{Hierarchical}} Clustering Using Dynamic Modeling}, author = {Karypis, George and Han, Eui-Hong (Sam) and Kumar, Vipin}, year = {1999}, month = aug, journal = {Computer}, volume = {32}, number = {8}, pages = {68--75}, publisher = {{IEEE Computer Society Press}}, address = {{Los Alamitos, CA, USA}}, issn = {0018-9162}, url = {http://dx.doi.org/10.1109/2.781637}, acmid = {621303}, issue_date = {August 1999}, nodoi = {10.1109/2.781637}, numpages = {8} } @inproceedings{Karypis:2001:EIT:502585.502627, title = {Evaluation of Item-Based Top-n Recommendation Algorithms}, booktitle = {Procs. of the Tenth International Conf. on Information and Knowledge Management}, author = {Karypis, George}, year = {2001}, series = {{{CIKM}} '01}, pages = {247--254}, publisher = {{ACM}}, address = {{New York, NY, USA}}, acmid = {502627}, isbn = {1-58113-436-3}, numpages = {8}, keywords = {collaborative filtering,recommender system} } @article{Kasrin202176, title = {Data-Sharing Markets for Integrating {{IoT}} Data Processing Functionalities}, author = {Kasrin, N. and Benabbas, A. and Elmamooz, G. and Nicklas, D. and Steuer, S. and S{\"u}nkel, M.}, year = {2021}, journal = {CCF Transactions on Pervasive Computing and Interaction}, volume = {3}, number = {1}, pages = {76--93}, publisher = {{Springer}}, issn = {2524521X}, doi = {10.1007/s42486-020-00054-y}, 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. \textcopyright{} 2021, The Author(s).}, document_type = {Article}, source = {Scopus} } @article{katirtzisSummarizingSoftwareAPI, title = {Summarizing {{Software API Usage Examples}} Using {{Clustering Techniques}}}, author = {Katirtzis, Nikolaos and Diamantopoulos, Themistoklis and Sutton, Charles}, pages = {17}, 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.}, langid = {english} } @inproceedings{katsamakasWhyMostOpen2007, title = {Why Most Open Source Development Projects Do Not Succeed?}, booktitle = {Emerging {{Trends}} in {{FLOSS Research}} and {{Development}}, 2007. {{FLOSS}}'07. {{First International Workshop}} On}, author = {Katsamakas, Evangelos and Georgantzas, Nicholas}, year = {2007}, pages = {3--3}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/abstract/document/4273074/}, urldate = {2017-06-23} } @book{kaufman:clustering1990, ids = {opac-b1087461}, title = {Finding {{Groups}} in {{Data}}: An Introduction to Cluster Analysis}, author = {Kaufman, L. and Rousseeuw, P.J.}, year = {1990}, publisher = {{Wiley}}, added-at = {2017-11-14T13:30:05.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/254cc9fb0fc88d6057dc9b1ce3feb1293/tomhanika}, description = {Dissertation}, foo = {bar}, interhash = {119bf8c432712ad3bbc1612759e0b7b4}, intrahash = {54cc9fb0fc88d6057dc9b1ce3feb1293}, keywords = {clustering kdd17}, timestamp = {2017-11-24T16:26:04.000+0100} } @book{kaufman2009finding, title = {Finding Groups in Data: An Introduction to Cluster Analysis}, author = {Kaufman, Leonard and Rousseeuw, Peter J}, year = {2009}, volume = {344}, publisher = {{John Wiley \& Sons}} } @incollection{KaufmanL1987Cbmo, title = {Clustering by Means of Medoids}, booktitle = {Statistical Data Analysis Based on the {{L1}} Norm and Related Methods}, author = {Kaufman, L and Rousseeuw, Peter}, year = {1987}, pages = {405--416}, publisher = {{North-Holland; Amsterdam}}, url = {$$Uhttps://lirias.kuleuven.be/retrieve/377090$$DKaufmanRousseeuw_ClusteringByMedoids_L1Norm_1987.pdf [Available for KU Leuven users]}, isbn = {0-444-70273-3}, langid = {english}, organization = {{Dodge, Y}} } @inproceedings{Kaur2021671, title = {Improved Skin Cancer Detection Classification Residual Network Feature Engineering}, author = {Kaur, R. and Kaur, N.}, editor = {Paul S., Verma J.K.}, year = {2021}, series = {2021 {{International Conference}} on {{Computational Performance Evaluation}}, {{ComPE}} 2021}, pages = {671--675}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ComPE53109.2021.9751930}, 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. \textcopyright{} 2021 IEEE.}, document_type = {Conference Paper}, isbn = {978-1-66543-656-4}, source = {Scopus} } @article{kazmanManagingEnergyConsumption2018, title = {Managing {{Energy Consumption}} as an {{Architectural Quality Attribute}}}, author = {Kazman, R. and Haziyev, S. and Yakuba, A. and Tamburri, D. A.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {102--107}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571227}, 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.}, keywords = {internet of things} } @inproceedings{Kazmi2017449, title = {Generic Framework to Predict Repeat Behavior of Customers Using Their Transaction History}, author = {Kazmi, A.H. and Shroff, G. and Agarwal, P.}, year = {2017}, series = {Proceedings - 2016 {{IEEE}}/{{WIC}}/{{ACM International Conference}} on {{Web Intelligence}}, {{WI}} 2016}, pages = {449--452}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/WI.2016.0072}, abbrev_source_title = {Proc. - IEEE/WIC/ACM Int. Conf. Web Intell., WI}, affiliation = {TCS Research, India}, art_number = {7817089}, document_type = {Conference Paper}, isbn = {978-1-5090-4470-2}, langid = {english}, source = {Scopus} } @misc{KDMWelcome, title = {{{KDM}} - {{Welcome}}}, url = {http://kdm.dataview.org/}, urldate = {2018-04-30} } @misc{KeepAllYour, title = {Keep All Your Packages up to Date with {{Dependabot}} - {{The GitHub Blog}}}, url = {https://github.blog/2020-06-01-keep-all-your-packages-up-to-date-with-dependabot/}, urldate = {2021-01-11} } @article{kehrerUnderstandComplexChanges, title = {Understand Complex Changes and Improve the Quality of Your {{UML}} and Domain-Specific Models}, author = {Kehrer, Timo}, pages = {79}, langid = {english} } @article{kephartVisionAutonomicComputing2003, title = {The Vision of Autonomic Computing}, author = {Kephart, Jeffrey O. and Chess, David M.}, year = {2003}, journal = {Computer}, volume = {36}, number = {1}, pages = {41--50}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1160055}, urldate = {2016-08-26} } @article{kerstenFivePredictionsComing2018, title = {Five {{Predictions}} for the {{Coming Decades}} of {{Software}}}, author = {Kersten, M.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {7--9}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571232}, 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.}, keywords = {artificial intelligence,software engineering} } @article{KESSENTINI201949, title = {Automated Metamodel/Model Co-Evolution: {{A}} Search-Based Approach}, author = {Kessentini, Wael and Sahraoui, Houari and Wimmer, Manuel}, year = {2019}, journal = {Information and Software Technology}, volume = {106}, pages = {49--67}, issn = {0950-5849}, doi = {10.1016/j.infsof.2018.09.003}, keywords = {Coupled evolution,Metamodel/model co-evolution,Model migration,Search based software engineering} } @incollection{kessentiniAutomatedCoevolutionMetamodels2018, title = {Automated {{Co-evolution}} of {{Metamodels}} and {{Transformation Rules}}: {{A Search-Based Approach}}}, shorttitle = {Automated {{Co-evolution}} of {{Metamodels}} and {{Transformation Rules}}}, booktitle = {Search-{{Based Software Engineering}}}, author = {Kessentini, Wael and Sahraoui, Houari and Wimmer, Manuel}, editor = {Colanzi, Thelma Elita and McMinn, Phil}, year = {2018}, volume = {11036}, pages = {229--245}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-319-99241-9_12}, isbn = {978-3-319-99240-2 978-3-319-99241-9} } @inproceedings{kessentiniIntegratingDesignerIntheloop2018, title = {Integrating the {{Designer}} In-the-Loop for {{Metamodel}}/{{Model Co-Evolution}} via {{Interactive Computational Search}}}, booktitle = {Proceedings of the 21th {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}}, author = {Kessentini, Wael and Wimmer, Manuel and Sahraoui, Houari}, year = {2018}, month = oct, pages = {101--111}, publisher = {{ACM}}, address = {{Copenhagen Denmark}}, doi = {10.1145/3239372.3239375}, isbn = {978-1-4503-4949-9}, langid = {english} } @article{kessentiniSearchbasedMetamodelMatching2014, title = {Search-Based Metamodel Matching with Structural and Syntactic Measures}, author = {Kessentini, Marouane and Ouni, Ali and Langer, Philip and Wimmer, Manuel and Bechikh, Slim}, year = {2014}, month = nov, journal = {Journal of Systems and Software}, volume = {97}, pages = {1--14}, issn = {01641212}, doi = {10.1016/j.jss.2014.06.040}, 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.}, langid = {english} } @article{khakpourFormalModelingEvolving2012, title = {Formal Modeling of Evolving Self-Adaptive Systems}, author = {Khakpour, Narges and Jalili, Saeed and Talcott, Carolyn and Sirjani, Marjan and Mousavi, MohammadReza}, year = {2012}, month = nov, journal = {Science of Computer Programming}, volume = {78}, number = {1}, pages = {3--26}, issn = {01676423}, doi = {10.1016/j.scico.2011.09.004}, langid = {english} } @techreport{khalilSupportingEvolutionUML2013, title = {Supporting the Evolution of {{UML}} Models in Model Driven Software Developmeny: {{A Survey}}}, shorttitle = {Supporting the Evolution of {{UML}} Models in Model Driven Software Developmeny}, author = {Khalil, Amal and Dingel, Juergen}, year = {2013}, institution = {{Technical Report, School of Computing. Queens University, Canada}}, url = {http://research.cs.queensu.ca/TechReports/Reports/2013-602.pdf}, urldate = {2015-04-02} } @article{Khan:2016:STS:3004996.3005218, title = {A Survey on Test Suite Reduction Frameworks and Tools}, author = {Khan, Saif Ur Rehman and Lee, Sai Peck and Ahmad, Raja Wasim and Akhunzada, Adnan and Chang, Victor}, year = {2016}, month = dec, journal = {Int. J. Inf. Manag.}, volume = {36}, number = {6}, pages = {963--975}, publisher = {{Elsevier Science Publishers B. V.}}, address = {{Amsterdam, The Netherlands, The Netherlands}}, issn = {0268-4012}, url = {https://doi.org/10.1016/j.ijinfomgt.2016.05.025}, acmid = {3005218}, issue_date = {December 2016}, nodoi = {10.1016/j.ijinfomgt.2016.05.025}, numpages = {13}, keywords = {Fault localization,Frameworks,Regression testing,Test suite optimization,Test Suite Reduction} } @article{Khan2021130, title = {Robustness of {{AI-based}} Prognostic and Systems Health Management}, author = {Khan, S. and Tsutsumi, S. and Yairi, T. and Nakasuka, S.}, year = {2021}, journal = {Annual Reviews in Control}, volume = {51}, pages = {130--152}, publisher = {{Elsevier Ltd}}, issn = {13675788}, doi = {10.1016/j.arcontrol.2021.04.001}, abbrev_source_title = {Annu Rev Control}, affiliation = {University of Tokyo, Department of Aeronautics and Astronautics, Tokyo, 113-8654, Japan; Japan Aerospace Exploration Agency, Research and Development Directorate, Kanagawa, 252-5210, Japan}, coden = {ARCOF}, correspondence_address1 = {Khan, S.; University of Tokyo, Department of Aeronautics and Astronautics, Japan; email: khan@ailab.t.u-tokyo.ac.jp}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{khanFederatedLearningInternet2020, title = {Federated {{Learning}} for {{Internet}} of {{Things}}: {{Recent Advances}}, {{Taxonomy}}, and {{Open Challenges}}}, shorttitle = {Federated {{Learning}} for {{Internet}} of {{Things}}}, author = {Khan, Latif U. and Saad, Walid and Han, Zhu and Hossain, Ekram and Hong, Choong Seon}, year = {2020}, month = sep, journal = {arXiv:2009.13012 [cs]}, eprint = {2009.13012}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2009.13012}, urldate = {2020-12-22}, 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.}, archiveprefix = {arXiv}, langid = {english}, keywords = {federated learning,internet of things} } @inproceedings{kharlamovSemanticApproachPolystores2016, title = {A Semantic Approach to Polystores}, author = {Kharlamov, E. and Mailis, T. and Bereta, K. and Bilidas, D. and Brandt, S. and {Jimenez-Ruiz}, E. and Lamparter, S. and Neuenstadt, C. and Ozcep, O. and Soylu, A. and Svingos, C. and Xiao, G. and Zheleznyakov, D. and Calvanese, D. and Horrocks, I. and Giese, M. and Ioannidis, Y. and Kotidis, Y. and Moller, R. and Waaler, A.}, year = {2016}, month = dec, pages = {2565--2573}, publisher = {{IEEE}}, doi = {10.1109/BigData.2016.7840898}, isbn = {978-1-4673-9005-7}, langid = {english} } @article{Khaytbaev2020115, title = {Decision Routing Problems in a Wireless Sensor Network Based on a Neural Mechanism}, author = {Khaytbaev, A.F.}, year = {2020}, journal = {Journal of ICT Research and Applications}, volume = {14}, number = {2}, pages = {115--133}, publisher = {{Institute for Research and Community Services, Institut Teknologi Bandung}}, issn = {23375787}, doi = {10.5614/itbj.ict.res.appl.2020.14.2.2}, abbrev_source_title = {J. ICT Res. Appl.}, affiliation = {Department of Telecommunication Engineering, Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, A. Temur St., 108, Tashkent, 100084, Uzbekistan}, correspondence_address1 = {Khaytbaev, A.F.; Department of Telecommunication Engineering, A. Temur St., 108, Uzbekistan; email: a.xaytbayev1981@inbox.ru}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{khelladiDetectingComplexChanges2016, ids = {KHELLADI2016220}, title = {Detecting Complex Changes and Refactorings during ({{Meta}})Model Evolution}, author = {Khelladi, Djamel Eddine and Hebig, Regina and Bendraou, Reda and Robin, Jacques and Gervais, Marie-Pierre}, year = {2016}, month = dec, journal = {Information Systems}, volume = {62}, pages = {220--241}, issn = {03064379}, doi = {10.1016/j.is.2016.05.002}, langid = {english} } @article{khomhSoftwareEngineeringMachineLearning2018, title = {Software {{Engineering}} for {{Machine-Learning Applications}}: {{The Road Ahead}}}, shorttitle = {Software {{Engineering}} for {{Machine-Learning Applications}}}, author = {Khomh, F. and Adams, B. and Cheng, J. and Fokaefs, M. and Antoniol, G.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {81--84}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571224}, 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.}, keywords = {artificial intelligence,DONE,machine learning,software engineering} } @inproceedings{Khrouf:2013:HER:2507157.2507171, title = {Hybrid Event Recommendation Using Linked Data and User Diversity}, booktitle = {Proceedings of the 7th {{ACM}} Conference on Recommender Systems}, author = {Khrouf, Houda and Troncy, Rapha{\"e}l}, year = {2013}, series = {{{RecSys}} '13}, pages = {185--192}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/2507157.2507171}, acmid = {2507171}, isbn = {978-1-4503-2409-0}, nodoi = {10.1145/2507157.2507171}, numpages = {8}, keywords = {event recommendation,linked data,lode ontology,user diversity} } @incollection{khusroRecommenderSystemsIssues2016, title = {Recommender Systems: {{Issues}}, Challenges, and Research Opportunities}, booktitle = {Information Science and Applications ({{ICISA}}) 2016}, author = {Khusro, Shah and Ali, Zafar and Ullah, Irfan}, editor = {Kim, Kuinam J. and Joukov, Nikolai}, year = {2016}, pages = {1179--1189}, publisher = {{Springer Singapore}}, address = {{Singapore}}, doi = {10.1007/978-981-10-0557-2₁12}, 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.}, isbn = {978-981-10-0557-2} } @article{kienzleModeldrivenSustainabilityEvaluation2020, title = {Toward Model-Driven Sustainability Evaluation}, author = {Kienzle, J{\"o}rg and Mussbacher, Gunter and Combemale, Benoit and Bastin, Lucy and Bencomo, Nelly and Bruel, Jean-Michel and Becker, Christoph and Betz, Stefanie and Chitchyan, Ruzanna and Cheng, Betty H. C. and Klingert, Sonja and Paige, Richard F. and Penzenstadler, Birgit and Seyff, Norbert and Syriani, Eugene and Venters, Colin C.}, year = {2020}, month = feb, journal = {Communications of the ACM}, volume = {63}, number = {3}, pages = {80--91}, issn = {0001-0782, 1557-7317}, doi = {10.1145/3371906}, langid = {english} } @inproceedings{kim_f_2018, ids = {DBLP:conf/icse/KimKBC0KT18}, title = {{{FaCoY}}: A Code-to-Code Search Engine}, shorttitle = {F}, booktitle = {Proceedings of the 40th {{International Conference}} on {{Software Engineering}} - {{ICSE}} '18}, author = {Kim, Kisub and Kim, Dongsun and Bissyand{\'e}, Tegawend{\'e} F. and Choi, Eunjong and Li, Li and Klein, Jacques and Traon, Yves Le}, year = {2018}, pages = {946--957}, publisher = {{ACM Press}}, address = {{Gothenburg, Sweden}}, url = {http://dl.acm.org/citation.cfm?doid=3180155.3180187}, urldate = {2019-09-04}, 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.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/icse/KimKBC0KT18}, isbn = {978-1-4503-5638-1}, langid = {english}, nodoi = {10.1145/3180155.3180187}, timestamp = {Wed, 21 Nov 2018 12:43:59 +0100} } @inproceedings{kim2014convolutional, title = {Convolutional Neural Networks for Sentence Classification}, booktitle = {Proceedings of the 2014 Conf. on Empirical Methods in {{NLP}}, {{EMNLP}} 2014, October 25-29, 2014, Doha, Qatar}, author = {Kim, Yoon}, year = {2014}, pages = {1746--1751}, added-at = {2017-01-25T12:34:20.000+0100}, bibsource = {dblp computer science bibliography, http://dblp.org}, biburl = {https://www.bibsonomy.org/bibtex/2ca3690ad4dc124a0e0f30afaa475adb9/albinzehe}, description = {dblp: BibTeX record conf/emnlp/Kim14}, interhash = {5a18dcdef0fe1455c8d7d96cee67e2b6}, intrahash = {ca3690ad4dc124a0e0f30afaa475adb9}, keywords = {cnn gpu kallimachos ma-zehe mlnlp neuralnet sentimentanalysis}, timestamp = {2018-06-08T05:08:41.000+0200} } @article{Kim2022, title = {Instance-Based Transfer Learning Method via Modified Domain-Adversarial Neural Network with Influence Function: {{Applications}} to Design Metamodeling and Fault Diagnosis}, author = {Kim, J. and Lee, J.}, year = {2022}, journal = {Applied Soft Computing}, volume = {123}, publisher = {{Elsevier Ltd}}, issn = {15684946}, doi = {10.1016/j.asoc.2022.108934}, abbrev_source_title = {Appl. Soft Comput.}, affiliation = {School of Mechanical Engineering, Yonsei University, Seoul, 03722, South Korea}, art_number = {108934}, correspondence_address1 = {Lee, J.; School of Mechanical Engineering, South Korea; email: jleej@yonsei.ac.kr}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{KizhakkeKodakkattu2020553, title = {Design Optimization of Helicopter Rotor with Trailing-Edge Flaps Using Genetic Algorithm}, author = {Kizhakke Kodakkattu, S.}, editor = {Venkata Rao R., Taler J.}, year = {2020}, journal = {Advances in Intelligent Systems and Computing}, volume = {949}, pages = {553--562}, publisher = {{Springer Verlag}}, issn = {21945357}, doi = {10.1007/978-981-13-8196-6_48}, abbrev_source_title = {Adv. Intell. Sys. Comput.}, affiliation = {Government Engineering College Kozhikode, West Hill P.O., Kozhikode, Kerala 673008, India}, correspondence_address1 = {Kizhakke Kodakkattu, S.; Government Engineering College Kozhikode, West Hill P.O., India; email: saijalkk@gmail.com}, document_type = {Conference Paper}, isbn = {9789811381959}, langid = {english}, source = {Scopus} } @inproceedings{KlingJWBC12, title = {{{MoScript}}: {{A DSL}} for Querying and Manipulating Model Repositories}, booktitle = {Proc. {{SLE}} 2011}, author = {Kling, Wolfgang and Jouault, Fr{\'e}d{\'e}ric and Wagelaar, Dennis and Brambilla, Marco and Cabot, Jordi}, year = {2012}, series = {{{LNCS}}}, volume = {6940}, pages = {180--200}, publisher = {{Springer}} } @article{Kochovski2021215, title = {Smart Contract for Cross-Border {{AI}} Model Management}, author = {Kochovski, P. and Kum, S. and Moon, J. and Vuji{\'c}, A. and Stankovski, V.}, editor = {Tserpes K., Banares J.A., Tuffin B., Altmann J., Ben-Yehuda O.A., Stankovski V., Djemame K.}, year = {2021}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {13072 LNCS}, pages = {215--222}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-92916-9_20}, 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. \textcopyright{} 2021, Springer Nature Switzerland AG.}, document_type = {Conference Paper}, isbn = {9783030929152}, source = {Scopus} } @inproceedings{koegel2010emfstore, title = {{{EMFStore}}: A Model Repository for {{EMF}} Models}, booktitle = {2010 {{ACM}}/{{IEEE}} 32nd International Conference on Software Engineering}, author = {Koegel, Maximilian and Helming, Jonas}, year = {2010}, volume = {2}, pages = {307--308}, organization = {{IEEE}} } @inproceedings{Kohavi:1995:SCB:1643031.1643047, title = {A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection}, booktitle = {Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 2}, author = {Kohavi, Ron}, year = {1995}, series = {{{IJCAI}}'95}, pages = {1137--1143}, publisher = {{Morgan Kaufmann Publishers Inc.}}, address = {{San Francisco, CA, USA}}, url = {http://dl.acm.org/citation.cfm?id=1643031.1643047}, acmid = {1643047}, isbn = {1-55860-363-8}, numpages = {7} } @article{kolahdouz-rahimiEvaluationModelTransformation2014, title = {Evaluation of Model Transformation Approaches for Model Refactoring}, author = {{Kolahdouz-Rahimi}, S. and Lano, K. and Pillay, S. and Troya, J. and Van Gorp, P.}, year = {2014}, month = jun, journal = {Science of Computer Programming}, volume = {85}, pages = {5--40}, issn = {01676423}, doi = {10.1016/j.scico.2013.07.013}, langid = {english} } @inproceedings{kolovosDifferentModelsModel2009, ids = {10.1109/CVSM.2009.5071714,5071714}, title = {Different Models for Model Matching: {{An}} Analysis of Approaches to Support Model Differencing}, booktitle = {Proceedings of the 2009 {{ICSE}} Workshop on Comparison and Versioning of Software Models, {{CVSM}} 2009}, author = {Kolovos, DS and Di Ruscio, D and Pierantonio, A and Paige, RF}, year = {2009}, issn = {null}, doi = {10.1109/CVSM.2009.5071714}, numpages = {6}, keywords = {Algorithm design and analysis,computational complexity,Computational complexity,Computer science,Conferences,Context modeling,Control system synthesis,Environmental management,graph theory,model differencing process,model driven engineering,Model driven engineering,model matching,simulation languages,Unified modeling language,Visualization} } @inproceedings{kolovosDomainspecificLanguagesDesign2019, title = {Domain-Specific {{Languages}} for the {{Design}}, {{Deployment}} and {{Manipulation}} of {{Heterogeneous Databases}}}, booktitle = {11th {{Workshop}} on {{Modelling}} in {{Software Engineering}} ({{MiSE}}'2019) Hosted by {{ICSE}} 2019}, author = {Kolovos, Dimitris S and Medhat, Fady and Paige, Richard F and Di Ruscio, Davide and {ven der Storm}, Tijs and Scholze, Sebastian and Zolotas, Athanasios}, year = {2019}, url = {http://vps.diruscio.org/nc/s/tCdFXFci6FWeXjw}, 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.}, langid = {english} } @inproceedings{kolovosDomainspecificLanguagesDesign2019a, title = {Domain-Specific Languages for the Design, Deployment and Manipulation of Heterogeneous Databases}, booktitle = {Proceedings - 2019 {{IEEE}}/{{ACM}} 11th {{International Workshop}} on {{Modelling}} in {{Software Engineering}}, {{MiSE}} 2019}, author = {Kolovos, D. and Medhat, F. and Paige, R. and Di Ruscio, D. and Van Der Storm, T. and Scholze, S. and Zolotas, A.}, year = {2019}, pages = {89--92}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MiSE.2019.00021}, isbn = {978-1-72812-231-1}, keywords = {Domain-specific languages,Hybrid persistence,Model-driven engineering,Non-relational databases,Relational databases} } @article{kolovosEugeniaDisciplinedAutomated2015, title = {Eugenia: Towards Disciplined and Automated Development of {{GMF-based}} Graphical Model Editors}, shorttitle = {Eugenia}, author = {Kolovos, Dimitrios S. and {Garc{\'i}a-Dom{\'i}nguez}, Antonio and Rose, Louis M. and Paige, Richard F.}, year = {2015}, month = feb, journal = {Software \& Systems Modeling}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-015-0455-3}, langid = {english} } @book{kolovosProceedings2ndWorkshop2014, 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}, editor = {Kolovos, Dimitris S. and Ruscio, Davide Di and Matragkas, Nicholas Drivalos and de Lara, Juan and R{\'a}th, Istv{\'a}n and Tisi, Massimo}, year = {2014}, series = {{{CEUR Workshop Proceedings}}}, volume = {1206}, publisher = {{CEUR-WS.org}}, url = {http://ceur-ws.org/Vol-1206} } @book{kolovosProceedings3rdWorkshop2015, 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}, editor = {Kolovos, Dimitris S. and Ruscio, Davide Di and Matragkas, Nicholas Drivalos and Cuadrado, Jes{\'u}s S{\'a}nchez and R{\'a}th, Istv{\'a}n and Tisi, Massimo}, year = {2015}, series = {{{CEUR Workshop Proceedings}}}, volume = {1406}, publisher = {{CEUR-WS.org}}, url = {http://ceur-ws.org/Vol-1406} } @book{kolovosProceedings4rdWorkshop2016, 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}, editor = {Kolovos, Dimitris S. and Ruscio, Davide Di and Matragkas, Nicholas Drivalos and Cuadrado, Jes{\'u}s S{\'a}nchez and R{\'a}th, Istv{\'a}n and Tisi, Massimo}, year = {2016}, series = {{{CEUR Workshop Proceedings}}}, volume = {1652}, publisher = {{CEUR-WS.org}}, url = {http://ceur-ws.org/Vol-1652} } @article{koModelTransformationVerification2013, title = {Model Transformation Verification Using Similarity and Graph Comparison Algorithm}, author = {Ko, Jong-Won and Chung, Kyung-Yong and Han, Jung-Soo}, year = {2013}, journal = {Multimedia Tools and Applications}, doi = {10.1007/s11042-013-1581-y} } @article{Kontolatis2013313, title = {Image-Based Part Programming with Process Parameter Selection Guidance for Laser Milling}, author = {Kontolatis, N. and Vosniakos, G.-C. and Gogouvitis, X.V.}, year = {2013}, journal = {International Journal of Manufacturing Research}, volume = {8}, number = {3}, pages = {313--335}, publisher = {{Inderscience Publishers}}, issn = {17500591}, doi = {10.1504/IJMR.2013.055246}, abbrev_source_title = {Int. J. Manuf. Res.}, affiliation = {School of Mechanical Engineering, National Technical University of Athens, Zografou Campus, HeroonPolytechniou 9, 15780 Zografou, Greece}, correspondence_address1 = {Vosniakos, G.-C.; School of Mechanical Engineering, HeroonPolytechniou 9, 15780 Zografou, Greece; email: vosniak@central.ntua.gr}, document_type = {Article}, langid = {english}, source = {Scopus} } @inproceedings{Koseler201915, title = {Realization of a Machine Learning Domain Specific Modeling Language: {{A}} Baseball Analytics Case Study}, author = {Koseler, K. and McGraw, K. and Stephan, M.}, editor = {Hammoudi S., Pires L.F., Selic B.}, year = {2019}, series = {{{MODELSWARD}} 2019 - {{Proceedings}} of the 7th {{International Conference}} on {{Model-Driven Engineering}} and {{Software Development}}}, pages = {15--26}, publisher = {{SciTePress}}, doi = {10.5220/0007245800150026}, abbrev_source_title = {MODELSWARD - Proc. Int. Conf. Model-Driven Eng. Softw. Dev.}, affiliation = {Dept. of Computer Science and Software Engineering, Miami University, 510 East High Street, Oxford Ohio, United States}, document_type = {Conference Paper}, isbn = {978-989-758-358-2}, langid = {english}, source = {Scopus} } @article{koshimaReconciliationFrameworkSupport2013, title = {A {{Reconciliation Framework}} to {{Support Cooperative Work}} with {{DSM}}}, author = {Koshima, Amanuel Alemayehu and Englebert, Vincent and Thiran, Philippe}, year = {2013}, journal = {Domain Engineering}, pages = {239--259}, doi = {10.1007/978-3-642-36654-3_10} } @article{kotsiantis2007supervised, title = {Supervised Machine Learning: {{A}} Review of Classification Techniques}, author = {Kotsiantis, Sotiris B and Zaharakis, I and Pintelas, P}, year = {2007}, journal = {Emerging artificial intelligence applications in computer engineering}, volume = {160}, pages = {3--24} } @inproceedings{Kourouklidis2021160, title = {A Model-Driven Engineering Approach for Monitoring Machine Learning Models}, author = {Kourouklidis, P. and Kolovos, D. and Noppen, J. and Matragkas, N.}, year = {2021}, series = {Companion {{Proceedings}} - 24th {{International Conference}} on {{Model-Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2021}, pages = {160--164}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS-C53483.2021.00028}, abbrev_source_title = {Companion Proc. - Int. Conf. Model-Driven Eng. Lang. Syst., MODELS-C}, affiliation = {University of York, British Telecom, Ipswich, United Kingdom; University of York, York, United Kingdom}, document_type = {Conference Paper}, isbn = {978-1-66542-484-4}, langid = {english}, source = {Scopus} } @inproceedings{kramerSelfManagedSystemsArchitectural2007, title = {Self-{{Managed Systems}}: An {{Architectural Challenge}}}, shorttitle = {Self-{{Managed Systems}}}, author = {Kramer, Jeff and Magee, Jeff}, year = {2007}, month = may, pages = {259--268}, publisher = {{IEEE}}, doi = {10.1109/FOSE.2007.19}, isbn = {978-0-7695-2829-8} } @article{krauseMetamodelSpecificCoupledEvolution2013, title = {Metamodel-{{Specific Coupled Evolution Based}} on {{Dynamically Typed Graph Transformations}}}, author = {Krause, Christian and Dyck, Johannes and Giese, Holger}, year = {2013}, journal = {Theory and Practice of Model Transformations}, volume = {7909}, pages = {76--91}, doi = {10.1007/978-3-642-38883-5_10} } @incollection{Krenker11, title = {Introduction to the Artificial Neural Networks}, booktitle = {Artificial Neural Networks}, author = {Krenker, Andrej and Bester, Janez and Kos, Andrej}, editor = {Suzuki, Kenji}, year = {2011}, publisher = {{IntechOpen}}, address = {{Rijeka}}, chapter = {1}, nodoi = {10.5772/15751} } @inproceedings{Krishnan2017, title = {{{PALM}}: {{Machine}} Learning Explanations for Iterative Debugging}, author = {Krishnan, S. and Wu, E.}, year = {2017}, series = {Proceedings of the 2nd {{Workshop}} on {{Human-In-the-Loop Data Analytics}}, {{HILDA}} 2017}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3077257.3077271}, abbrev_source_title = {Proc. Workshop Hum.-Loop Data Anal.}, affiliation = {UC Berkeley, United States; Columbia University, United States}, art_number = {3077271}, document_type = {Conference Paper}, isbn = {978-1-4503-5029-7}, langid = {english}, source = {Scopus}, keywords = {GOAL_Debugging,notion} } @article{Kroetz2017394, title = {Performance of Global Metamodeling Techniques in Solution of Structural Reliability Problems}, author = {Kroetz, H.M. and Tessari, R.K. and Beck, A.T.}, year = {2017}, journal = {Advances in Engineering Software}, volume = {114}, pages = {394--404}, publisher = {{Elsevier Ltd}}, issn = {09659978}, doi = {10.1016/j.advengsoft.2017.08.001}, abbrev_source_title = {Adv Eng Software}, affiliation = {Department of Structural Engineering, University of S\~ao Paulo, S\~ao Carlos, Brazil}, coden = {AESOD}, correspondence_address1 = {Kroetz, H.M.; Department of Structural Engineering, Brazil; email: henrique.kroetz@usp.br}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{krupitzerSurveyEngineeringApproaches2015, title = {A Survey on Engineering Approaches for Self-Adaptive Systems}, author = {Krupitzer, Christian and Roth, Felix Maximilian and VanSyckel, Sebastian and Schiele, Gregor and Becker, Christian}, year = {2015}, month = feb, journal = {Pervasive and Mobile Computing}, volume = {17}, pages = {184--206}, issn = {15741192}, doi = {10.1016/j.pmcj.2014.09.009}, langid = {english} } @article{kuangNewSearchEngine, title = {A {{New Search Engine Integrating Hierarchical Browsing}} and {{Keyword Search}}}, author = {Kuang, Da and Li, Xiao and Ling, Charles X}, pages = {6}, 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.}, langid = {english} } @inproceedings{kuhn2005enriching, title = {Enriching Reverse Engineering with Semantic Clustering}, booktitle = {12th Working Conference on Reverse Engineering ({{WCRE}}'05)}, author = {Kuhn, Adrian and Ducasse, St{\'e}phane and Girba, Tudor}, year = {2005}, pages = {10-pp}, organization = {{IEEE}} } @article{kulaDevelopersUpdateTheir2018, title = {Do Developers Update Their Library Dependencies?: {{An}} Empirical Study on the Impact of Security Advisories on Library Migration}, shorttitle = {Do Developers Update Their Library Dependencies?}, author = {Kula, Raula Gaikovina and German, Daniel M. and Ouni, Ali and Ishio, Takashi and Inoue, Katsuro}, year = {2018}, month = feb, journal = {Empirical Software Engineering}, volume = {23}, number = {1}, pages = {384--417}, issn = {1382-3256, 1573-7616}, doi = {10.1007/s10664-017-9521-5}, langid = {english} } @article{kullbackInformationSufficiency1951, title = {On Information and Sufficiency}, author = {Kullback, S. and Leibler, R. A.}, year = {1951}, journal = {Ann. Math. Statist.}, volume = {22}, number = {1}, pages = {79--86}, added-at = {2010-10-31T19:59:47.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/2560a5719c537c5c4a496bfebd4a21603/lee\textsubscript{p}eck}, description = {Kullback , Leibler : On Information and Sufficiency}, interhash = {f9d41d76a07383cca4c3a1a94c24d533}, intrahash = {560a5719c537c5c4a496bfebd4a21603}, keywords = {51 Kullback Leibler divergence kl}, timestamp = {2010-10-31T19:59:47.000+0100} } @article{kumarToolRecommenderSystem2021, title = {Tool Recommender System in {{Galaxy}} Using Deep Learning}, author = {Kumar, Anup and Rasche, Helena and Gr{\"u}ning, Bj{\"o}rn and Backofen, Rolf}, year = {2021}, month = jan, journal = {GigaScience}, volume = {10}, number = {1}, pages = {giaa152}, issn = {2047-217X}, doi = {10.1093/gigascience/giaa152}, abstract = {Abstract Background 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. Findings 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. Conclusions 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.}, langid = {english} } @article{kuselRealityCheckModel, title = {Reality {{Check}} for {{Model Transformation Reuse}}: {{The ATL Transformation Zoo Case Study}}}, author = {Kusel, A and Schonbock, J and Wimmer, M and Retschitzegger, W and Schwinger, W and Kappel, G} } @article{kuselReuseModeltomodelTransformation2015, title = {Reuse in Model-to-Model Transformation Languages: Are We There Yet?}, shorttitle = {Reuse in Model-to-Model Transformation Languages}, author = {Kusel, A. and Sch{\"o}nb{\"o}ck, J. and Wimmer, M. and Kappel, G. and Retschitzegger, W. and Schwinger, W.}, year = {2015}, month = may, journal = {Software \& Systems Modeling}, volume = {14}, number = {2}, pages = {537--572}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-013-0343-7}, langid = {english} } @article{Kusiak201590, title = {Effective Strategies of Metamodelling of Industrial Metallurgical Processes}, author = {Kusiak, J. and Sztangret, {\L}. and Pietrzyk, M.}, year = {2015}, journal = {Advances in Engineering Software}, volume = {89}, pages = {90--97}, publisher = {{Elsevier Ltd}}, issn = {09659978}, doi = {10.1016/j.advengsoft.2015.02.002}, abbrev_source_title = {Adv Eng Software}, affiliation = {Department of Applied Computer Science and Modelling, AGH University of Science and Technology, Krak\'ow, Poland}, coden = {AESOD}, correspondence_address1 = {Kusiak, J.; Department of Applied Computer Science and Modelling, Poland; email: kusiak@agh.edu.pl}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{kutsche2008bizycle, title = {Bizycle: {{Model-based}} Interoperability Platform for Software and Data Integration}, author = {Kutsche, Ralf and Milanovic, Nikola and Bauhoff, Gregor and Baum, Timo and Cartsburg, Mario and Kumpe, Daniel and Widiker, J{\"u}rgen}, year = {2008}, journal = {Proceedings of the MDTPI at ECMDA}, volume = {430} } @inproceedings{kuwaharaAutomatedPlanningSystem2019, title = {Automated Planning of System Rollback in Declarative {{IT}} System Update}, booktitle = {2019 {{IFIP}}/{{IEEE Symposium}} on {{Integrated Network}} and {{Service Management}}, {{IM}} 2019}, author = {Kuwahara, T. and Kuroda, T. and Nakanoya, M. and Yakuwa, Y. and Sato, Y. and Matsunaga, Y.}, year = {2019}, pages = {428--434}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067009661&partnerID=40&md5=e12df5fd076b85398777e6937d090a82}, abstract = {The automation of system management has been expanding, and there has been interest lately in an automated workflow generation that automatically generates the workflows of system updates. However, because these automation technologies operate under the assumption that systems work in accordance with their underlying system model, they are not good at handling unexpected behaviors of target systems.In this paper, we propose a way to incorporate unexpected handling into our declarative system update mechanism by automatically generating a "recovery workflow" to roll back a target system in the event of abnormal system stops. We evaluate our tool through a practical three-tier architecture system operating a simple Web service, and found that our method can complete generation of a recovery workflow in one second, and roll back the system from all system states. \textcopyright{} 2019 IFIP.}, isbn = {978-3-903176-15-7}, keywords = {AI planning,Automation,Change management,Client server computer systems,Computer system recovery,Declarative,Fault management,Model-driven,System rollback,Web services} } @article{Kyriacou2014895, title = {Efficient {{PCA-driven EAs}} and Metamodel-Assisted {{EAs}}, with Applications in Turbomachinery}, author = {Kyriacou, S.A. and Asouti, V.G. and Giannakoglou, K.C.}, year = {2014}, journal = {Engineering Optimization}, volume = {46}, number = {7}, pages = {895--911}, publisher = {{Taylor and Francis Ltd.}}, issn = {0305215X}, doi = {10.1080/0305215X.2013.812726}, abbrev_source_title = {Eng Optim}, affiliation = {Parallel CFD and Optimization Unit, National Technical University of Athens, Heroon Polytechniou 9, Athens 15780, Greece; Andritz HYDRO GmbH, Lunzerstrasse 78, 4031 Linz, Austria}, coden = {EGOPA}, correspondence_address1 = {Giannakoglou, K.C.; Parallel CFD and Optimization Unit, National Technical University of Athens, Heroon Polytechniou 9, Athens 15780, Greece; email: kgianna@central.ntua.gr}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{kyriazisSmartAutonomousReliable2013, title = {Smart, {{Autonomous}} and {{Reliable Internet}} of {{Things}}}, author = {Kyriazis, Dimosthenis and Varvarigou, Theodora}, year = {2013}, journal = {Procedia Computer Science}, volume = {21}, pages = {442--448}, issn = {18770509}, doi = {10.1016/j.procs.2013.09.059}, langid = {english} } @article{L04, title = {Detection of Redundant Code Using {{R2D2}}}, author = {Leit{\~a}o, Ant{\'o}nio Menezes}, year = {2004}, month = dec, journal = {Software Quality Journal}, volume = {12}, number = {4}, pages = {361--382}, issn = {1573-1367}, doi = {10.1023/B:SQJO.0000039793.31052.72}, 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.} } @article{lacavaEvaluatingRecommenderSystems2021, title = {Evaluating Recommender Systems for {{AI-driven}} Biomedical Informatics}, author = {La Cava, William and Williams, Heather and Fu, Weixuan and Vitale, Steve and Srivatsan, Durga and Moore, Jason H}, editor = {Wren, Jonathan}, year = {2021}, month = apr, journal = {Bioinformatics}, volume = {37}, number = {2}, pages = {250--256}, issn = {1367-4803, 1460-2059}, doi = {10.1093/bioinformatics/btaa698}, abstract = {Abstract Motivation 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. Results 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{$\pm$}0.02) that performs on par with state-of-the-art deep learning results for this task, with much less computational effort. Availability and implementation PennAI is available free of charge and open-source. It is distributed under the GNU public license (GPL) version 3. Supplementary information Supplementary data are available at Bioinformatics online.}, langid = {english} } @article{laiRobustOnlinePath2016, title = {A Robust Online Path Planning Approach in Cluttered Environments for Micro Rotorcraft Drones}, author = {Lai, Shupeng and Wang, Kangli and Qin, Hailong and Cui, Jin Q. and Chen, Ben M.}, year = {2016}, month = feb, journal = {Control Theory and Technology}, volume = {14}, number = {1}, pages = {83--96}, issn = {2095-6983, 2198-0942}, doi = {10.1007/s11768-016-6007-8}, langid = {english} } @inproceedings{Lakshminarayan20192043, title = {Enterprise-Wide Machine Learning Using Teradata Vantage: {{An}} Integrated Analytics Platform}, author = {Lakshminarayan, C. and Ramakrishnan, T. and {Al-Omari}, A. and Bouaziz, K. and Ahmad, F. and Raghavan, S. and Agarwal, P.}, editor = {Baru C., Huan J., Hu X.T., Ak R., Tian Y., Barga R., Zaniolo C., Lee K., Ye Y.F., Khan L.}, year = {2019}, series = {Proceedings - 2019 {{IEEE International Conference}} on {{Big Data}}, {{Big Data}} 2019}, pages = {2043--2046}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/BigData47090.2019.9006321}, 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. \textcopyright{} 2019 IEEE.}, art_number = {9006321}, document_type = {Conference Paper}, isbn = {978-1-72810-858-2}, source = {Scopus} } @inproceedings{Lakshminarayan20196110, title = {Model Management and Handwritten Character Recognition: {{An}} Enterprise Solution}, author = {Lakshminarayan, C. and Ramakrishnan, T. and {Al-Omari}, A. and Bouaziz, K. and Ahmad, F. and Raghavan, S. and Agarwal, P.}, editor = {Baru C., Huan J., Hu X.T., Ak R., Tian Y., Barga R., Zaniolo C., Lee K., Ye Y.F., Khan L.}, year = {2019}, series = {Proceedings - 2019 {{IEEE International Conference}} on {{Big Data}}, {{Big Data}} 2019}, pages = {6110--6112}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/BigData47090.2019.9005445}, 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 {$\sigma$}programs 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. \textcopyright{} 2019 IEEE.}, art_number = {9005445}, document_type = {Conference Paper}, isbn = {978-1-72810-858-2}, source = {Scopus} } @book{lalandaAutonomicComputing2013, title = {Autonomic {{Computing}}}, author = {Lalanda, Philippe and McCann, Julie A. and Diaconescu, Ada}, year = {2013}, series = {Undergraduate {{Topics}} in {{Computer Science}}}, publisher = {{Springer London}}, address = {{London}}, url = {http://link.springer.com/10.1007/978-1-4471-5007-7}, urldate = {2016-09-29}, isbn = {978-1-4471-5006-0 978-1-4471-5007-7} } @article{Landauer1998, title = {An Introduction to Latent Semantic Analysis}, author = {Landauer, T.K. and Foltz, P.W. and Laham, D.}, year = {1998}, journal = {Discourse processes}, volume = {25}, pages = {259--284}, publisher = {{ABLEX PUBLISHING CO}}, added-at = {2009-11-19T19:28:27.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/2d07817c8e498b282f56f8abc53c156d9/georg.oettl}, interhash = {60c2cae5093c82d65be9f2e516da9b29}, intrahash = {d07817c8e498b282f56f8abc53c156d9}, keywords = {Psycho NLP semantic}, timestamp = {2009-11-19T19:28:27.000+0100} } @book{landauer2006latent, title = {Latent Semantic Analysis}, author = {Landauer, Thomas K}, year = {2006}, publisher = {{Wiley Online Library}} } @article{langerEMFProfilesLightweight2012, title = {{{EMF Profiles}}: {{A Lightweight Extension Approach}} for {{EMF Models}}.}, shorttitle = {{{EMF Profiles}}}, author = {Langer, Philip and Wieland, Konrad and Wimmer, Manuel and Cabot, Jordi}, year = {2012}, journal = {The Journal of Object Technology}, volume = {11}, number = {1}, pages = {8:1}, issn = {1660-1769}, doi = {10.5381/jot.2012.11.1.a8}, langid = {english} } @article{langerPosterioriOperationDetection2013, title = {A Posteriori Operation Detection in Evolving Software Models}, author = {Langer, Philip and Wimmer, Manuel and Brosch, Petra and Herrmannsd{\"o}rfer, Markus and Seidl, Martina and Wieland, Konrad and Kappel, Gerti}, year = {2013}, month = feb, journal = {Journal of Systems and Software}, volume = {86}, number = {2}, pages = {551--566}, issn = {01641212}, doi = {10.1016/j.jss.2012.09.037}, langid = {english} } @inproceedings{Lano2020277, title = {Enhancing Model Transformation Synthesis Using Natural Language Processing}, author = {Lano, K. and Fang, S. and Umar, M.A. and {Yassipour-Tehrani}, S.}, year = {2020}, series = {Proceedings - 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2020 - {{Companion Proceedings}}}, pages = {277--286}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3417990.3421386}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS-C - Companion Proc.}, affiliation = {Dept. of Informatics, King's College London, London, United Kingdom; National Automative Innovation Centre, University of Warwick, United Kingdom}, document_type = {Conference Paper}, isbn = {978-1-4503-8135-2}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Transformation-Development,notion} } @inproceedings{Lano2021173, title = {Automated Requirements Formalisation for Agile {{MDE}}}, author = {Lano, K. and {Yassipour-Tehrani}, S. and Umar, M.A.}, year = {2021}, series = {Companion {{Proceedings}} - 24th {{International Conference}} on {{Model-Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2021}, pages = {173--180}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS-C53483.2021.00030}, abbrev_source_title = {Companion Proc. - Int. Conf. Model-Driven Eng. Lang. Syst., MODELS-C}, affiliation = {Dept. of Informatics, King's College London, London, United Kingdom; Roehampton University, Dept. of Computer Science, London, United Kingdom}, document_type = {Conference Paper}, isbn = {978-1-66542-484-4}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Requirements,notion} } @article{Lano2022, title = {Model Transformation Development Using Automated Requirements Analysis, Metamodel Matching, and Transformation by Example}, author = {Lano, K. and {Kolahdouz-Rahimi}, S. and Fang, S.}, year = {2022}, journal = {ACM Transactions on Software Engineering and Methodology}, volume = {31}, number = {2}, publisher = {{Association for Computing Machinery}}, issn = {1049331X}, doi = {10.1145/3471907}, abbrev_source_title = {ACM Trans. Software Eng. Methodol.}, affiliation = {Dept. of Informatics, King's College London, Strand, London, WC2R 2LS, United Kingdom; Dept. of Software Engineering, University of Isfahan, Isfahan, Iran}, art_number = {18}, coden = {ATSME}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Transformation-Development,notion} } @inproceedings{Lano2022362, title = {Program Translation Using Model-Driven Engineering}, author = {Lano, K.}, year = {2022}, series = {Proceedings - {{International Conference}} on {{Software Engineering}}}, pages = {362--363}, publisher = {{IEEE Computer Society}}, issn = {02705257}, doi = {10.1109/ICSE-Companion55297.2022.9793785}, abbrev_source_title = {Proc Int Conf Software Eng}, affiliation = {King's College London, Dept. of Informatics, London, United Kingdom}, coden = {PCSED}, correspondence_address1 = {Lano, K.; King's College London, United Kingdom; email: kevin.lano@kcl.ac.uk}, document_type = {Conference Paper}, isbn = {978-1-66549-598-1}, langid = {english}, source = {Scopus} } @article{lanoConstraintbasedSpecificationModel2013, title = {Constraint-Based Specification of Model Transformations}, author = {Lano, K. and {Kolahdouz-Rahimi}, S.}, year = {2013}, journal = {Journal of Systems and Software}, volume = {86}, number = {2}, pages = {412--436}, doi = {10.1016/j.jss.2012.09.006} } @article{lanzaPolymetricViewsLightweight2003, title = {Polymetric Views - {{A}} Lightweight Visual Approach to Reverse Engineering}, author = {Lanza, M. and Ducasse, S.}, year = {2003}, journal = {IEEE Transactions on Software Engineering}, volume = {29}, number = {9}, pages = {782--795}, doi = {10.1109/TSE.2003.1232284} } @article{laraAbstractingModellingLanguages2012, title = {Abstracting {{Modelling Languages}}: {{A Reutilization Approach}}}, author = {Lara, Juan and Guerra, Esther and {S{\'a}nchez-Cuadrado}, Jes{\'u}s}, year = {2012}, journal = {Advanced Information Systems Engineering}, volume = {7328}, pages = {127--143}, doi = {10.1007/978-3-642-31095-9_9} } @article{laraAutomatedReuseModel2019, title = {Automated {{Reuse}} of {{Model Transformations}} through {{Typing Requirements Models}}}, author = {Lara, Juan De and Guerra, Esther and Ruscio, Davide Di and Rocco, Juri Di and Cuadrado, Jesus Sanchez and Iovino, Ludovico and Pierantonio, Alfonso}, year = {2019}, journal = {ACM Transactions on Software Engineering and Methodology}, pages = {57}, langid = {english} } @article{laraModeldrivenEngineeringDomainspecific2013, title = {Model-Driven Engineering with Domain-Specific Meta-Modelling Languages}, author = {Lara, Juan and Guerra, Esther and Cuadrado, Jes{\'u}s S{\'a}nchez}, year = {2013}, journal = {Software \& Systems Modeling}, doi = {10.1007/s10270-013-0367-z} } @book{laraProceedingsWorkshopExtreme2013, 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}, editor = {de Lara, Juan and Ruscio, Davide Di and Pierantonio, Alfonso}, year = {2013}, series = {{{CEUR Workshop Proceedings}}}, volume = {1089}, publisher = {{CEUR-WS.org}}, url = {http://ceur-ws.org/Vol-1089} } @article{laraTypesTypeRequirements2011, title = {From Types to Type Requirements: Genericity for Model-Driven Engineering}, author = {Lara, Juan and Guerra, Esther}, year = {2011}, journal = {Software \& Systems Modeling}, volume = {12}, number = {3}, pages = {453--474}, doi = {10.1007/s10270-011-0221-0}, keywords = {software engineering} } @article{larruceaSoftwareEngineeringInternet2017a, title = {Software {{Engineering}} for the {{Internet}} of {{Things}}}, author = {Larrucea, Xabier and Combelles, Annie and Favaro, John and Taneja, Kunal}, year = {2017}, month = jan, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {24--28}, issn = {0740-7459}, doi = {10.1109/MS.2017.28}, langid = {english} } @misc{LatexSource, type = {Latex Source}, title = {Latex Source}, journal = {Latex source}, url = {https://github.com/MDEGroup/ESEM2020} } @misc{LatexSourcePaper, title = {Latex Source of the Paper}, url = {https://www.overleaf.com/project/5e5fdaa1a7e16e00013cf31f}, urldate = {2020-03-10} } @techreport{Lawrence981, type = {Technical Report}, title = {The {{PageRank}} Citation Ranking: {{Bringing}} Order to the Web}, author = {Lawrence, Page and Sergey, Brin and Motwani, Rajeev and Winograd, Terry}, year = {1998}, institution = {{Stanford University}} } @article{LeClair2018AdaptingNT, title = {Adapting Neural Text Classification for Improved Software Categorization}, author = {LeClair, Alexander and Eberhart, Zachary and McMillan, Collin}, year = {2018}, journal = {CoRR}, volume = {abs/1806.01742} } @misc{LectureIoTData, title = {Lecture 6: {{IoT Data Processing}}}, url = {https://www2.slideshare.net/PayamBarnaghi/lecture-6-iot-data-processing?qid=8711baae-0a4e-45df-ba7a-9eb987306850&v=&b=&from_search=14}, urldate = {2021-01-05}, keywords = {data processing,internet of things} } @misc{LecturesSENG371, title = {Lectures {{SENG}} 371 {{Software Evolution}}}, url = {http://www.engr.uvic.ca/~seng371/lectures.html}, urldate = {2016-09-19} } @article{Lee202035, title = {An Approximate Optimization Strategy Using Refined Hybrid Metamodel}, author = {Lee, K.-H. and Jeong, G.-I. and Lee, S.-H.}, year = {2020}, journal = {Computational Intelligence}, volume = {36}, number = {1}, pages = {35--54}, publisher = {{Blackwell Publishing Inc.}}, issn = {08247935}, doi = {10.1111/coin.12237}, abbrev_source_title = {Comput Intell}, affiliation = {Department of Mechanical Engineering, Dong-A University, Busan, South Korea}, coden = {COMIE}, correspondence_address1 = {Lee, K.-H.; Department of Mechanical Engineering, South Korea; email: leekh@dau.ac.kr}, document_type = {Article}, langid = {english}, source = {Scopus} } @inproceedings{leeCyberPhysicalSystems2008, title = {Cyber {{Physical Systems}}: {{Design Challenges}}}, shorttitle = {Cyber {{Physical Systems}}}, author = {Lee, Edward A.}, year = {2008}, month = may, pages = {363--369}, publisher = {{IEEE}}, doi = {10.1109/ISORC.2008.25}, isbn = {978-0-7695-3132-8} } @article{leeInternetThingsIoT2015, title = {The {{Internet}} of {{Things}} ({{IoT}}): {{Applications}}, Investments, and Challenges for Enterprises}, shorttitle = {The {{Internet}} of {{Things}} ({{IoT}})}, author = {Lee, In and Lee, Kyoochun}, year = {2015}, month = jul, journal = {Business Horizons}, volume = {58}, number = {4}, pages = {431--440}, issn = {00076813}, doi = {10.1016/j.bushor.2015.03.008}, langid = {english} } @article{leePresentFutureCyberPhysical2015, title = {The {{Past}}, {{Present}} and {{Future}} of {{Cyber-Physical Systems}}: {{A Focus}} on {{Models}}}, shorttitle = {The {{Past}}, {{Present}} and {{Future}} of {{Cyber-Physical Systems}}}, author = {Lee, Edward}, year = {2015}, month = feb, journal = {Sensors}, volume = {15}, number = {3}, pages = {4837--4869}, issn = {1424-8220}, doi = {10.3390/s150304837}, langid = {english} } @article{leeSelfAdaptiveFrameworkBased2019, title = {Self-{{Adaptive Framework Based}} on {{MAPE Loop}} for {{Internet}} of {{Things}}}, author = {Lee, Euijong and Seo, Young-Duk and Kim, Young-Gab}, year = {2019}, month = jul, journal = {Sensors}, volume = {19}, number = {13}, pages = {2996}, issn = {1424-8220}, doi = {10.3390/s19132996}, 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.}, langid = {english} } @article{Lejeune2021, title = {Geometric Stability Classification: {{Datasets}}, Metamodels, and Adversarial Attacks}, author = {Lejeune, E.}, year = {2021}, journal = {CAD Computer Aided Design}, volume = {131}, publisher = {{Elsevier Ltd}}, issn = {00104485}, doi = {10.1016/j.cad.2020.102948}, abbrev_source_title = {CAD Comput Aided Des}, affiliation = {Department of Mechanical Engineering, Boston University, Boston, MA 02215, United States}, art_number = {102948}, coden = {CAIDA}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Classification,notion} } @article{leopoldTextCategorizationSupport2002, title = {Text Categorization with Support Vector Machines. {{How}} to Represent Texts in Input Space?}, author = {Leopold, Edda and Kindermann, J{\"o}rg}, year = {2002}, journal = {Machine Learning}, volume = {46}, number = {1-3}, pages = {423--444}, publisher = {{Springer}} } @article{lepallecSupportQualityMetrics2013, title = {Support for Quality Metrics in Metamodelling}, author = {Le Pallec, Xavier and {Dupuy-Chessa}, Sophie}, year = {2013}, journal = {Proceedings of the Second Workshop on Graphical Modeling Language Development - GMLD '13}, pages = {23--31}, doi = {10.1145/2489820.2489825} } @article{Leppänen2020308, title = {Service Modeling for Opportunistic Edge Computing Systems with Feature Engineering}, author = {Lepp{\"a}nen, T. and Savaglio, C. and Fortino, G.}, year = {2020}, journal = {Computer Communications}, volume = {157}, pages = {308--319}, publisher = {{Elsevier B.V.}}, issn = {01403664}, doi = {10.1016/j.comcom.2020.04.011}, abbrev_source_title = {Comput Commun}, affiliation = {Center for Ubiquitous Computing, University of Oulu, Finland; Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Italy}, coden = {COCOD}, correspondence_address1 = {Lepp\"anen, T.P.O.Box 4500, FI-90014 University of Oulu, Finland; email: teemu.leppanen@oulu.fi}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{levenshtein1966bcc, ids = {Levenshtein_SPD66}, title = {Binary Codes Capable of Correcting Deletions, Insertions and Reversals}, author = {Levenshtein, VI}, year = {1966}, journal = {Soviet Physics Doklady}, volume = {10}, pages = {707}, added-at = {2008-03-15T10:37:17.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/21a29b294552edb63828d57f3495e2eb2/brightbyte}, interhash = {55f7ad93fcb9ae3ed999afaa6e24937d}, intrahash = {1a29b294552edb63828d57f3495e2eb2}, keywords = {edit-distance,lexicography similarity}, timestamp = {2009-01-23T09:58:50.000+0100} } @article{Li2015122, title = {An Improved Support Vector Regression and Its Modelling of Manoeuvring Performance in Multidisciplinary Ship Design Optimization}, author = {Li, D. and Wilson, P.A. and Jiang, Z.}, year = {2015}, journal = {International Journal of Modelling and Simulation}, volume = {35}, number = {3-4}, pages = {122--128}, publisher = {{Taylor and Francis Ltd.}}, issn = {02286203}, doi = {10.1080/02286203.2015.1111055}, abbrev_source_title = {Int J Modell Simul}, affiliation = {School of Naval Architecture \& Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, China; Faculty of Engineering and the Environment, University of Southampton, Southampton, SO17 1BJ, United Kingdom}, coden = {IMSIE}, correspondence_address1 = {Li, D.; School of Naval Architecture \& Ocean Engineering, China; email: lidq@just.edu.cn}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{Li201928737, 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}, author = {Li, Y. and Zhang, Q. and Wu, Y. and Wang, S.}, year = {2019}, journal = {Multimedia Tools and Applications}, volume = {78}, number = {20}, pages = {28737--28756}, publisher = {{Springer New York LLC}}, issn = {13807501}, doi = {10.1007/s11042-018-6563-7}, abbrev_source_title = {Multimedia Tools Appl}, affiliation = {School of Mechanical and Electrical Engineering, Xuchang University, Xuchang, 461000, China; National CAD Centre, Huazhong University of Science and Technology, Wuhan, China}, coden = {MTAPF}, correspondence_address1 = {Li, Y.; School of Mechanical and Electrical Engineering, China; email: liyaohui@hust.edu.cn}, document_type = {Article}, langid = {english}, source = {Scopus} } @inproceedings{Li2021172, title = {Data-Driven Receiver for {{OTFS}} System with Deep Learning}, author = {Li, Q. and Gong, Y. and Meng, F. and Xu, Z.}, year = {2021}, series = {Proceedings of 2021 7th {{IEEE International Conference}} on {{Network Intelligence}} and {{Digital Content}}, {{IC-NIDC}} 2021}, pages = {172--176}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/IC-NIDC54101.2021.9660432}, 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. \textcopyright{} 2021 IEEE.}, document_type = {Conference Paper}, isbn = {978-1-66540-582-9}, source = {Scopus} } @article{liangModeldrivenClusterResource2022, title = {Model-Driven {{Cluster Resource Management}} for {{AI Workloads}} in {{Edge Clouds}}}, author = {Liang, Qianlin and Hanafy, Walid A. and {Ali-Eldin}, Ahmed and Shenoy, Prashant}, year = {2022}, month = jan, journal = {arXiv:2201.07312 [cs, eess]}, eprint = {2201.07312}, eprinttype = {arxiv}, primaryclass = {cs, eess}, url = {http://arxiv.org/abs/2201.07312}, urldate = {2022-01-25}, 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.}, archiveprefix = {arXiv}, langid = {english}, keywords = {Computer Science - Distributed; Parallel; and Cluster Computing,Electrical Engineering and Systems Science - Systems and Control} } @article{Liao20201724, title = {A Model-Driven Deep Learning Method for Massive {{MIMO}} Detection}, author = {Liao, J. and Zhao, J. and Gao, F. and Li, G.Y.}, year = {2020}, journal = {IEEE Communications Letters}, volume = {24}, number = {8}, pages = {1724--1728}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10897798}, doi = {10.1109/LCOMM.2020.2989672}, 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. \textcopyright{} 1997-2012 IEEE.}, art_number = {9075976}, coden = {ICLEF}, document_type = {Article}, source = {Scopus} } @article{liaoDataAdapterQuerying2016, title = {Data Adapter for Querying and Transformation between {{SQL}} and {{NoSQL}} Database}, author = {Liao, Ying-Ti and Zhou, Jiazheng and Lu, Chia-Hung and Chen, Shih-Chang and Hsu, Ching-Hsien and Chen, Wenguang and Jiang, Mon-Fong and Chung, Yeh-Ching}, year = {2016}, month = dec, journal = {Future Generation Computer Systems}, volume = {65}, pages = {111--121}, issn = {0167739X}, doi = {10.1016/j.future.2016.02.002}, 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.}, langid = {english} } @inproceedings{Lim2019219, title = {Reliability-Based {{MOGA}} Design Optimization Using Probabilistic Response Surface Method and {{Bayesian}} Neural Network}, author = {Lim, J. and Lee, J.}, year = {2019}, series = {{{GECCO}} 2019 {{Companion}} - {{Proceedings}} of the 2019 {{Genetic}} and {{Evolutionary Computation Conference Companion}}}, pages = {219--220}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3319619.3321901}, abbrev_source_title = {GECCO Companion - Proc. Genet. Evolut. Comput. Conf. Companion}, affiliation = {Yonsei University, Seoul, South Korea}, document_type = {Conference Paper}, isbn = {978-1-4503-6748-6}, langid = {english}, source = {Scopus} } @inproceedings{Lin:1998:IDS:645527.657297, title = {An Information-Theoretic Definition of Similarity}, booktitle = {Proceedings of the Fifteenth International Conference on Machine Learning}, author = {Lin, Dekang}, year = {1998}, series = {{{ICML}} '98}, pages = {296--304}, publisher = {{Morgan Kaufmann Publishers Inc.}}, address = {{San Francisco, CA, USA}}, url = {http://dl.acm.org/citation.cfm?id=645527.657297}, acmid = {657297}, isbn = {1-55860-556-8}, numpages = {9} } @inproceedings{Linares-Vasquez:2014:ACT:2597008.2597155, title = {How Do {{API}} Changes Trigger Stack Overflow Discussions? {{A}} Study on the Android {{SDK}}}, booktitle = {Proceedings of the {{22Nd}} International Conference on Program Comprehension}, author = {{Linares-V{\'a}squez}, Mario and Bavota, Gabriele and Di Penta, Massimiliano and Oliveto, Rocco and Poshyvanyk, Denys}, year = {2014}, series = {{{ICPC}} 2014}, pages = {83--94}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/2597008.2597155}, acmid = {2597155}, isbn = {978-1-4503-2879-1}, nodoi = {10.1145/2597008.2597155}, numpages = {12}, keywords = {Android,API changes,Social media,StackOverflow} } @article{Linares-Vasquez:2014:UML:2617668.2617703, title = {On Using Machine Learning to Automatically Classify Software Applications into Domain Categories}, author = {{Linares-V{\'a}squez}, Mario and Mcmillan, Collin and Poshyvanyk, Denys and Grechanik, Mark}, year = {2014}, month = jun, journal = {Empirical Softw. Engg.}, volume = {19}, number = {3}, pages = {582--618}, publisher = {{Kluwer Academic Publishers}}, address = {{Hingham, MA, USA}}, issn = {1382-3256}, url = {http://dx.doi.org/10.1007/s10664-012-9230-z}, acmid = {2617703}, issue_date = {June 2014}, nodoi = {10.1007/s10664-012-9230-z}, numpages = {37}, keywords = {Closed-source,machine learning,Open-source,Software categorization} } @inproceedings{linares-vasquezAPIChangeFault2013, title = {{{API}} Change and Fault Proneness: A Threat to the Success of {{Android}} Apps}, shorttitle = {{{API}} Change and Fault Proneness}, booktitle = {Proceedings of the 2013 9th {{Joint Meeting}} on {{Foundations}} of {{Software Engineering}} - {{ESEC}}/{{FSE}} 2013}, author = {{Linares-V{\'a}squez}, Mario and Bavota, Gabriele and {Bernal-C{\'a}rdenas}, Carlos and Di Penta, Massimiliano and Oliveto, Rocco and Poshyvanyk, Denys}, year = {2013}, pages = {477}, publisher = {{ACM Press}}, address = {{Saint Petersburg, Russia}}, doi = {10.1145/2491411.2491428}, 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.}, isbn = {978-1-4503-2237-9}, langid = {english} } @inproceedings{linares-vasquezAutomaticallyDetectingSimilar2016, title = {On Automatically Detecting Similar Android Apps}, booktitle = {Program {{Comprehension}} ({{ICPC}}), 2016 {{IEEE}} 24th {{International Conference}} On}, author = {{Linares-V{\'a}squez}, Mario and Holtzhauer, Andrew and Poshyvanyk, Denys}, year = {2016}, pages = {1--10}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/abstract/document/7503721/}, urldate = {2017-09-25} } @article{Linden:2003:ARI:642462.642471, title = {Amazon.{{Com}} Recommendations: {{Item-to-item}} Collaborative Filtering}, author = {Linden, Greg and Smith, Brent and York, Jeremy}, year = {2003}, month = jan, journal = {IEEE Internet Computing}, volume = {7}, number = {1}, pages = {76--80}, publisher = {{IEEE Educational Activities Department}}, address = {{Piscataway, NJ, USA}}, issn = {1089-7801}, url = {http://dx.doi.org/10.1109/MIC.2003.1167344}, acmid = {642471}, issue_date = {January 2003}, nodoi = {10.1109/MIC.2003.1167344}, numpages = {5} } @article{linModelingUsersMobile, title = {Modeling {{Users}}' {{Mobile App Privacy Preferences}}: {{Restoring Usability}} in a {{Sea}} of {{Permission Settings}}}, author = {Lin, Jialiu and Liu, Bin and Sadeh, Norman and Hong, Jason I}, pages = {14}, 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.}, langid = {english} } @article{linSentimentAnalysisSoftware2018, title = {Sentiment {{Analysis}} for {{Software Engineering}}: {{How Far Can We Go}}?}, author = {Lin, Bin and Zampetti, Fiorella and Bavota, Gabriele and Penta, Massimiliano Di and Lanza, Michele and Oliveto, Rocco}, year = {2018}, pages = {11}, 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\textemdash on a set of 40k manually labeled sentences/words extracted from Stack Overflow\textemdash 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.}, langid = {english} } @article{linsteadSourcererMiningSearching2009, ids = {DBLP:journals/datamine/LinsteadBNRLB09}, title = {Sourcerer: Mining and Searching Internet-Scale Software Repositories}, shorttitle = {Sourcerer}, author = {Linstead, Erik and Bajracharya, Sushil and Ngo, Trung and Rigor, Paul and Lopes, Cristina and Baldi, Pierre}, year = {2009}, month = apr, journal = {Data Mining and Knowledge Discovery}, volume = {18}, number = {2}, pages = {300--336}, issn = {1384-5810, 1573-756X}, url = {http://link.springer.com/10.1007/s10618-008-0118-x}, urldate = {2019-09-04}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/datamine/LinsteadBNRLB09}, langid = {english}, nodoi = {10.1007/s10618-008-0118-x}, timestamp = {Sat, 20 May 2017 00:25:07 +0200} } @article{liPreprocessingMethodsPipelines, title = {Preprocessing {{Methods}} and {{Pipelines}} of {{Data Mining}}}, shorttitle = {0}, author = {Li, Canchen}, journal = {0}, url = {0}, urldate = {2021-03-18}, abstract = {0}, keywords = {Computer Science - Databases,Computer Science - Machine Learning,DONE,STARRED,Statistics - Machine Learning} } @article{liSystematicMappingStudy2015, title = {A Systematic Mapping Study on Technical Debt and Its Management}, author = {Li, Zengyang and Avgeriou, Paris and Liang, Peng}, year = {2015}, month = mar, journal = {Journal of Systems and Software}, volume = {101}, pages = {193--220}, issn = {01641212}, doi = {10.1016/j.jss.2014.12.027}, langid = {english} } @inproceedings{Liu:2006:GDS:1150402.1150522, title = {{{GPLAG}}: {{Detection}} of Software Plagiarism by Program Dependence Graph Analysis}, booktitle = {Proceedings of the 12th {{ACM SIGKDD}} International Conference on Knowledge Discovery and Data Mining}, author = {Liu, Chao and Chen, Chen and Han, Jiawei and Yu, Philip S.}, year = {2006}, series = {{{KDD}} '06}, pages = {872--881}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/1150402.1150522}, acmid = {1150522}, isbn = {1-59593-339-5}, nodoi = {10.1145/1150402.1150522}, numpages = {10}, keywords = {graph mining,program dependence graph,software plagiarism detection} } @inproceedings{Liu2015, title = {Towards Nonlinear Multimaterial Topology Optimization Using Unsupervised Machine Learning and Metamodel-Based Optimization}, author = {Liu, K. and Tovar, A. and Nutwell, E. and Detwiler, D.}, year = {2015}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {2B-2015}, publisher = {{American Society of Mechanical Engineers (ASME)}}, doi = {10.1115/DETC201546534}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, United States; Dept. of Mechanical Engineering, Indiana Univ.-Purdue Univ. Indpls, Indianapolis, IN 46202, United States; Honda R and D Americas, Raymond, OH 43067, United States}, correspondence_address1 = {Tovar, A.; Dept. of Mechanical Engineering, United States; email: tovara@iupui.edu}, document_type = {Conference Paper}, isbn = {978-0-7918-5708-3}, langid = {english}, source = {Scopus} } @inproceedings{Liu2016, title = {Machine Learning and Metamodel-Based Design Optimization of Nonlinear Multimaterial Structures}, author = {Liu, K. and Detwiler, D. and Tovar, A.}, year = {2016}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {2B-2016}, publisher = {{American Society of Mechanical Engineers (ASME)}}, doi = {10.1115/DETC2016-60471}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {School of Mechanical Engr., Purdue University, West Lafayette, IN, United States; Honda R and D Americas, Inc., Raymond, OH, United States; Dept. of Mechanical Engr., Indiana U - Purdue U Indianapolis, Indianapolis, IN, United States}, correspondence_address1 = {Tovar, A.; Dept. of Mechanical Engr., United States; email: tovara@iupui.edu}, document_type = {Conference Paper}, isbn = {978-0-7918-5011-4}, langid = {english}, source = {Scopus} } @article{Liu2017, title = {Optimal Design of Nonlinear Multimaterial Structures for Crashworthiness Using Cluster Analysis}, author = {Liu, K. and Detwiler, D. and Tovar, A.}, year = {2017}, journal = {Journal of Mechanical Design, Transactions of the ASME}, volume = {139}, number = {10}, publisher = {{American Society of Mechanical Engineers (ASME)}}, issn = {10500472}, doi = {10.1115/1.4037620}, abbrev_source_title = {J Mech Des, Trans ASME}, affiliation = {School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, United States; Honda RandD Americas, Inc., Raymond, OH 43067, United States; Department of Mechanical Engineering, Indiana University-Purdue, University Indianapolis, Indianapolis, IN 46202, United States}, art_number = {101401}, coden = {JMDEE}, correspondence_address1 = {Tovar, A.; Department of Mechanical Engineering, United States; email: tovara@iupui.edu}, document_type = {Article}, langid = {english}, source = {Scopus} } @inproceedings{Liu2019, title = {Deep {{CNN}} for Spectrum Sensing in Cognitive Radio}, author = {Liu, C. and Liu, X. and Liang, Y.-C.}, year = {2019}, series = {{{IEEE International Conference}} on {{Communications}}}, volume = {2019-May}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15503607}, doi = {10.1109/ICC.2019.8761360}, 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. \textcopyright{} 2019 IEEE.}, art_number = {8761360}, document_type = {Conference Paper}, isbn = {978-1-5386-8088-9}, source = {Scopus}, keywords = {EXCLUDED} } @article{Liu20223, title = {Efficient Online Service Based on Go-Tensorflow in the Middle-Station Scenario of Grid Service}, author = {Liu, P. and Lu, Y. and Wang, G. and Zhou, W.}, editor = {Qiu M., Gai K., Qiu H.}, year = {2022}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {13202 LNCS}, pages = {3--13}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-97774-0_1}, 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. \textcopyright{} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.}, document_type = {Conference Paper}, isbn = {9783030977733}, source = {Scopus} } @article{liuFollowMyRecommendations, title = {Follow {{My Recommendations}}: {{A Personalized Privacy Assistant}} for {{Mobile App Permissions}}}, author = {Liu, Bin and Andersen, Mads Schaarup and Schaub, Florian and Almuhimedi, Hazim and Zhang, Shikun and Sadeh, Norman and Acquisti, Alessandro and Agarwal, Yuvraj}, pages = {16}, 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.}, langid = {english} } @book{liuJointProceedingsMODELS2014, 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}, editor = {Liu, Yan and Zschaler, Steffen and Baudry, Benoit and Ghosh, Sudipto and Ruscio, Davide Di and Jackson, Ethan K. and Wimmer, Manuel}, year = {2014}, series = {{{CEUR Workshop Proceedings}}}, volume = {1115}, publisher = {{CEUR-WS.org}}, url = {http://ceur-ws.org/Vol-1115} } @article{Lo2022, title = {Architectural Patterns for the Design of Federated Learning Systems}, author = {Lo, S.K. and Lu, Q. and Zhu, L. and Paik, H.-Y. and Xu, X. and Wang, C.}, year = {2022}, journal = {Journal of Systems and Software}, volume = {191}, publisher = {{Elsevier Inc.}}, issn = {01641212}, doi = {10.1016/j.jss.2022.111357}, 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. \textcopyright{} 2022 Elsevier Inc.}, art_number = {111357}, coden = {JSSOD}, document_type = {Article}, source = {Scopus} } @misc{LondonbasedGyanaRaises, title = {London-Based {{Gyana}} Raises \$3.{{9M}} for a No-Code Approach to Data Science \textendash{} {{TechCrunch}}}, 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}, urldate = {2020-03-02} } @inproceedings{lopez-fernandezAssessingQualityMetamodels2014, title = {Assessing the {{Quality}} of {{Meta-models}}}, booktitle = {11th {{Workshop}} on {{Model Driven Engineering}}, {{Verification}} and {{Validation MoDeVVa}} 2014}, author = {{L{\'o}pez-Fern{\'a}ndez}, Jes{\'u}s J. and Guerra, Esther and {de Lara}, Juan}, year = {2014}, pages = {3}, url = {http://ceur-ws.org/Vol-1235/MoDeVVa2014-complete.pdf#page=9}, urldate = {2015-09-15} } @article{lopez-fernandezExampledrivenMetamodelDevelopment2015, title = {Example-Driven Meta-Model Development}, author = {{L{\'o}pez-Fern{\'a}ndez}, Jes{\'u}s J. and Cuadrado, Jes{\'u}s S{\'a}nchez and Guerra, Esther and {de Lara}, Juan}, year = {2015}, month = oct, journal = {Software \& Systems Modeling}, volume = {14}, number = {4}, pages = {1323--1347}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-013-0392-y}, langid = {english} } @article{López-Quintero20181845, title = {A Personal Knowledge Management Metamodel Based on Semantic Analysis and Social Information}, author = {{L{\'o}pez-Quintero}, J.F. and Cueva Lovelle, J.M. and Gonz{\'a}lez Crespo, R. and {Garc{\'i}a-D{\'i}az}, V.}, year = {2018}, journal = {Soft Computing}, volume = {22}, number = {6}, pages = {1845--1854}, publisher = {{Springer Verlag}}, issn = {14327643}, doi = {10.1007/s00500-016-2437-y}, abbrev_source_title = {Soft Comput.}, affiliation = {Universidad de Oviedo, Oviedo, Spain; Departamento de Inform\'atica, Universidad de Oviedo, Oviedo, Spain; Escuela Superior de Ingenier\'ia y Tecnolog\'ia, Universidad Internacional de La Rioja (UNIR), Logro\~no, Spain}, correspondence_address1 = {Gonz\'alez Crespo, R.; Escuela Superior de Ingenier\'ia y Tecnolog\'ia, Spain; email: ruben.gonzalez@unir.net}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{López2022967, title = {{{ModelSet}}: A Dataset for Machine Learning in Model-Driven Engineering}, author = {L{\'o}pez, J.A.H. and C{\'a}novas Izquierdo, J.L. and Cuadrado, J.S.}, year = {2022}, journal = {Software and Systems Modeling}, volume = {21}, number = {3}, pages = {967--986}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {16191366}, doi = {10.1007/s10270-021-00929-3}, abbrev_source_title = {Softw. Syst. Model.}, affiliation = {Facultad de Inform\'atica, Universidad de Murcia, Murcia, Spain; UOC - IN3, Castelldefels, Spain}, correspondence_address1 = {L\'opez, J.A.H.; Facultad de Inform\'atica, Spain; email: joseantonio.hernandez6@um.es}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {EXCLUDED_NO-AI-ML} } @article{lopezMARStructurebasedSearch2020, title = {Low-Code Engineering for Internet of Things: {{A}} State of Research}, shorttitle = {{{MAR}}}, author = {L{\'o}pez, Jos{\'e} Antonio Hern{\'a}ndez and Cuadrado, Jes{\'u}s S{\'a}nchez}, year = {2020}, month = aug, journal = {Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems}, volume = {abs/2009.01876}, eprint = {2008.11858}, eprinttype = {arxiv}, pages = {57--67}, doi = {10.1145/3365438.3410947}, 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.}, archiveprefix = {arXiv}, langid = {english}, keywords = {IoT,Low-code engineering,Model driven engineering (MDE)} } @incollection{LopsCB, title = {Content-Based Recommender Systems: {{State}} of the Art and Trends.}, booktitle = {Recommender Systems Handbook}, author = {Lops, Pasquale and {de Gemmis}, Marco and Semeraro, Giovanni}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, year = {2011}, pages = {73--105}, publisher = {{Springer}}, url = {http://dblp.uni-trier.de/db/reference/rsh/rsh2011.html#LopsGS11}, isbn = {978-0-387-85819-7}, keywords = {dblp} } @article{lorenzoniMachineLearningModel2021, title = {Machine {{Learning Model Development}} from a {{Software Engineering Perspective}}: {{A Systematic Literature Review}}}, shorttitle = {Machine {{Learning Model Development}} from a {{Software Engineering Perspective}}}, author = {Lorenzoni, Giuliano and Alencar, Paulo and Nascimento, Nathalia and Cowan, Donald}, year = {2021}, month = feb, journal = {arXiv:2102.07574 [cs]}, eprint = {2102.07574}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2102.07574}, urldate = {2021-03-25}, 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.}, archiveprefix = {arXiv}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Computer Science - Software Engineering} } @article{lourencoChoosingRightNoSQL2015, title = {Choosing the Right {{NoSQL}} Database for the Job: A Quality Attribute Evaluation}, shorttitle = {Choosing the Right {{NoSQL}} Database for the Job}, author = {Louren{\c c}o, Jo{\~a}o Ricardo and Cabral, Bruno and Carreiro, Paulo and Vieira, Marco and Bernardino, Jorge}, year = {2015}, month = dec, journal = {Journal of Big Data}, volume = {2}, number = {1}, pages = {18}, issn = {2196-1115}, doi = {10.1186/s40537-015-0025-0}, langid = {english}, keywords = {TYPHONML} } @misc{LowcodeAbstractionLevels, title = {Low-Code and Abstraction Levels - {{Stefan Dreverman}} - {{Medium}}}, url = {https://medium.com/@stefan.dreverman/low-code-and-abstraction-levels-e9412e9e5329}, urldate = {2020-04-08}, keywords = {lowcode} } @misc{LowCodeDevelopment, title = {Low {{Code Development Platforms}}: {{A Complete Guide}} | {{QuickBase}}}, url = {https://www.quickbase.com/resources/articles/low-code-development-platforms}, urldate = {2020-04-08}, keywords = {lowcode} } @misc{LowCodeDevelopmentPlatform, title = {Low-{{Code Development Platform Economic}} ({{Free}}) {{Survey}}}, journal = {Google Docs}, url = {https://docs.google.com/document/d/1F1pLpNudMnth3bxYd1RyfjkxxUYeTbi-qa3BTJmat_8/edit?ts=5e3d9277&usp=embed_facebook}, urldate = {2020-02-11}, 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...}, langid = {british}, keywords = {lowcode} } @misc{LowcodeNocodeDevelopment, ids = {LowcodeNocodeDevelopmenta}, title = {Low-Code and No-Code Development Platforms}, url = {https://www.computerweekly.com/feature/Low-code-and-no-code-development-platforms}, urldate = {2020-03-29}, keywords = {lowcode} } @misc{LowcodePlatformsFuture2020, title = {Low-Code Platforms: {{The Future}} of {{Data Analytics}}}, shorttitle = {Low-Code Platforms}, year = {2020}, month = jul, journal = {Big Data Analytics News}, url = {https://bigdataanalyticsnews.com/low-code-platforms-future-of-data-analytics/}, urldate = {2021-03-18}, 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.}, chapter = {Analytics}, langid = {american} } @misc{LowCodePlatformsSurvey, title = {Low-{{Code Platforms Survey}}\_{{MoDELS}} Conference\_v2}, url = {https://www.overleaf.com/4361461464kxwpjrzcvszz}, urldate = {2020-02-11}, abstract = {An online LaTeX editor that's easy to use. No installation, real-time collaboration, version control, hundreds of LaTeX templates, and more.}, langid = {english}, keywords = {lowcode} } @article{LowcodeWillDigital, ids = {LowcodeWillDigitala}, title = {Low-Code Will Save the {{Digital Transformation}}}, pages = {21}, langid = {english}, keywords = {lowcode} } @incollection{loza14recsys, title = {A Hybrid Multi-Strategy Recommender System Using Linked Open Data}, booktitle = {Semantic Web Evaluation Challenge, Proceedings ({{ESWC}} 2014)}, author = {Ristoski, Petar and Loza Menc{\'i}a, Eneldo and Paulheim, Heiko}, year = {2014}, month = may, series = {Communications in Computer and Information Science}, volume = {475}, pages = {150--156}, publisher = {{Springer}}, url = {http://2014.eswc-conferences.org/sites/default/files/eswc2014-challenges_rs_submission_12.pdf}, 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.}, isbn = {978-3-319-12023-2}, nodoi = {10.1007/978-3-319-12024-9{$_1$}9} } @article{Lu2007, title = {Node Similarity in the Citation Graph}, author = {Lu, Wangzhong and Janssen, J. and Milios, E. and Japkowicz, N. and Zhang, Yongzheng}, year = {2007}, month = jan, journal = {Knowledge and Information Systems}, volume = {11}, number = {1}, pages = {105--129}, issn = {0219-3116}, doi = {10.1007/s10115-006-0023-9}, 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.} } @article{luan_aroma:_2018, title = {Aroma: {{Code Recommendation}} via {{Structural Code Search}}}, shorttitle = {Aroma}, author = {Luan, Sifei and Yang, Di and Barnaby, Celeste and Sen, Koushik and Chandra, Satish}, year = {2018}, month = dec, journal = {arXiv:1812.01158 [cs]}, eprint = {1812.01158}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/1812.01158}, urldate = {2019-06-13}, 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.}, archiveprefix = {arXiv}, langid = {english}, keywords = {Computer Science - Software Engineering} } @article{lucasCollabRDLLanguageCoordinate2017, title = {{{CollabRDL}}: {{A}} Language to Coordinate Collaborative Reuse}, shorttitle = {{{CollabRDL}}}, author = {Lucas, Edson M. and Oliveira, Toacy C. and Farias, Kleinner and Alencar, Paulo S.C.}, year = {2017}, month = feb, journal = {Journal of Systems and Software}, issn = {01641212}, doi = {10.1016/j.jss.2017.01.031}, langid = {english}, keywords = {collavorative modeling} } @article{lucene, title = {Apache Lucene Core}, url = {https://lucene.apache.org/core/} } @incollection{lucioFTGPMIntegrated2013, title = {{{FTG}}+{{PM}}: {{An Integrated Framework}} for {{Investigating Model Transformation Chains}}}, shorttitle = {{{FTG}}+{{PM}}}, booktitle = {{{SDL}} 2013: {{Model-Driven Dependability Engineering}}}, author = {L{\'u}cio, Levi and Mustafiz, Sadaf and Denil, Joachim and Vangheluwe, Hans and Jukss, Maris}, editor = {Khendek, Ferhat and Toeroe, Maria and Gherbi, Abdelouahed and Reed, Rick}, year = {2013}, series = {Lecture {{Notes}} in {{Computer Science}}}, number = {7916}, pages = {182--202}, publisher = {{Springer Berlin Heidelberg}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-38911-5_11}, urldate = {2015-03-24}, 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.}, copyright = {\textcopyright 2013 Springer-Verlag Berlin Heidelberg}, isbn = {978-3-642-38910-8 978-3-642-38911-5}, langid = {english}, keywords = {software engineering} } @article{lucioModelTransformationIntents2014, title = {Model Transformation Intents and Their Properties}, author = {L{\'u}cio, Levi and Amrani, Moussa and Dingel, J{\"u}rgen and Lambers, Leen and Salay, Rick and Selim, Gehan MK and Syriani, Eugene and Wimmer, Manuel}, year = {2014}, journal = {Software \& Systems Modeling}, pages = {1--38}, url = {http://link.springer.com/article/10.1007/s10270-014-0429-x}, urldate = {2015-03-20} } @article{lucioTechniqueAutomaticValidation2010, title = {A {{Technique}} for {{Automatic Validation}} of {{Model Transformations}}}, author = {L{\'u}cio, Levi and Barroca, Bruno and Amaral, Vasco}, year = {2010}, journal = {Model Driven Engineering Languages and Systems}, volume = {6394}, pages = {136--150}, doi = {10.1007/978-3-642-16145-2_10} } @inproceedings{luckeyHighqualitySpecificationSelfadaptive2013, title = {High-Quality Specification of Self-Adaptive Software Systems}, booktitle = {Proceedings of the 8th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}}}, author = {Luckey, Markus and Engels, Gregor}, year = {2013}, pages = {143--152}, publisher = {{IEEE Press}}, url = {http://dl.acm.org/citation.cfm?id=2487359}, urldate = {2016-09-21} } @article{lucredioMOOGLEMetamodelbasedModel2010, title = {{{MOOGLE}}: A Metamodel-Based Model Search Engine}, author = {Lucr{\'e}dio, Daniel and M. Fortes, Renata P. and Whittle, Jon}, year = {2010}, journal = {Software \& Systems Modeling}, volume = {11}, number = {2}, pages = {183--208}, doi = {10.1007/s10270-010-0167-7} } @article{ludovicoModelRepairQualityBased2020, title = {Model {{Repair}} with {{Quality-Based Reinforcement Learning}}.}, author = {Ludovico, Iovino and Barriga, Angela and Rutle, Adrian and Heldal, Rogardt}, year = {2020}, journal = {The Journal of Object Technology}, volume = {19}, number = {2}, pages = {17:1}, issn = {1660-1769}, doi = {10.5381/jot.2020.19.2.a17}, 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.}, langid = {english}, keywords = {GOAL-Model_Repair,TECHNIQUE_Reinforcement-Learning} } @article{LUNG2004227, title = {Applications of Clustering Techniques to Software Partitioning, Recovery and Restructuring}, author = {Lung, Chung-Horng and Zaman, Marzia and Nandi, Amit}, year = {2004}, journal = {Journal of Systems and Software}, volume = {73}, number = {2}, pages = {227--244}, issn = {0164-1212}, url = {http://www.sciencedirect.com/science/article/pii/S0164121203002346}, 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.}, nodoi = {https://doi.org/10.1016/S0164-1212(03)00234-6}, keywords = {Clustering,Cohesion and coupling,Design recovery,Evolution,Restructuring,Reverse engineering,Software partitioning} } @article{Luo2019504, title = {Deep Angular Embedding and Feature Correlation Attention for Breast {{MRI}} Cancer Analysis}, author = {Luo, L. and Chen, H. and Wang, X. and Dou, Q. and Lin, H. and Zhou, J. and Li, G. and Heng, P.-A.}, editor = {{Shen D., Yap P.-T.}, Peters T.M., Khan A., Staib L.H., Essert C., Zhou S., Liu T.}, year = {2019}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {11767 LNCS}, pages = {504--512}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-32251-9_55}, 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. \textcopyright{} Springer Nature Switzerland AG 2019.}, document_type = {Conference Paper}, isbn = {9783030322502}, source = {Scopus} } @article{luongFACOSFindingAPI2021, title = {{{FACOS}}: {{Finding API Relevant Contents}} on {{Stack Overflow}} with {{Semantic}} and {{Syntactic Analysis}}}, shorttitle = {{{FACOS}}}, author = {Luong, Kien and Hadi, Mohammad and Thung, Ferdian and Fard, Fatemeh and Lo, David}, year = {2021}, month = nov, journal = {arXiv:2111.07238 [cs]}, eprint = {2111.07238}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2111.07238}, urldate = {2021-11-21}, 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.}, archiveprefix = {arXiv}, langid = {english}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Programming Languages,Computer Science - Software Engineering} } @inproceedings{Lutz2021583, 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}, author = {Lutz, C.B. and Giabbanelli, P.J. and Fisher, A. and Mago, V.K.}, editor = {Martin C.R., Blas M.J., Psijas A.I.}, year = {2021}, series = {Simulation {{Series}}}, volume = {53}, pages = {583--594}, publisher = {{The Society for Modeling and Simulation International}}, issn = {07359276}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118574816&partnerID=40&md5=9eea6773e6551052e5a35c095cf09935}, abbrev_source_title = {Simul. Ser.}, affiliation = {Dept. of Computer Science and Software Engineering, Miami University, 205 Benton Hall, 510 E. High Street, Oxford, OH, United States; Dept. of Computer Science, Lakehead University, FB 1009B, 955 Oliver Rd, Thunder Bay, ON, Canada}, document_type = {Conference Paper}, langid = {english}, source = {Scopus} } @inproceedings{lv_codehow:_2015, ids = {DBLP:conf/kbse/LvZLWZZ15}, title = {{{CodeHow}}: {{Effective Code Search Based}} on {{API Understanding}} and {{Extended Boolean Model}} ({{E}})}, shorttitle = {{{CodeHow}}}, booktitle = {2015 30th {{IEEE}}/{{ACM International Conference}} on {{Automated Software Engineering}} ({{ASE}})}, author = {Lv, Fei and Zhang, Hongyu and Lou, Jian-guang and Wang, Shaowei and Zhang, Dongmei and Zhao, Jianjun}, year = {2015}, month = nov, pages = {260--270}, publisher = {{IEEE}}, address = {{Lincoln, NE, USA}}, url = {http://ieeexplore.ieee.org/document/7372014/}, urldate = {2019-09-11}, 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.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/kbse/LvZLWZZ15}, isbn = {978-1-5090-0025-8}, langid = {english}, nodoi = {10.1109/ASE.2015.42}, timestamp = {Fri, 01 Mar 2019 13:05:18 +0100} } @article{Ma20212388, title = {Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid {{MIMO}} Systems}, author = {Ma, X. and Gao, Z. and Gao, F. and DI Renzo, M.}, year = {2021}, journal = {IEEE Journal on Selected Areas in Communications}, volume = {39}, number = {8}, pages = {2388--2406}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {07338716}, doi = {10.1109/JSAC.2021.3087269}, 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. \textcopyright{} 1983-2012 IEEE.}, art_number = {9452036}, coden = {ISACE}, document_type = {Article}, source = {Scopus} } @article{Maarek:1991:IRA:126244.126254, title = {An Information Retrieval Approach for Automatically Constructing Software Libraries}, author = {Maarek, Yo{\"e}lle S. and Berry, Daniel M. and Kaiser, Gail E.}, year = {1991}, month = aug, journal = {IEEE Transactions on Software Engineering}, volume = {17}, number = {8}, pages = {800--813}, publisher = {{IEEE Press}}, address = {{Piscataway, NJ, USA}}, issn = {0098-5589}, url = {http://dx.doi.org/10.1109/32.83915}, acmid = {126254}, issue_date = {August 1991}, nodoi = {10.1109/32.83915}, numpages = {14}, keywords = {attributes,automatic programming,browsing,clustering technique,free-style natural language queries,free-text indexing scheme,indexing scheme,information retrieval approach,information retrieval systems,large software libraries,lexical affinities,natural language documentation,natural languages,software reusability,software reuse,subroutines} } @inproceedings{maccioniQUEPAQUeryingExploring2016, title = {{{QUEPA}}: {{QUerying}} and {{Exploring}} a {{Polystore}} by {{Augmentation}}}, shorttitle = {{{QUEPA}}}, author = {Maccioni, Antonio and Basili, Edoardo and Torlone, Riccardo}, year = {2016}, pages = {2133--2136}, publisher = {{ACM Press}}, doi = {10.1145/2882903.2899393}, 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.}, isbn = {978-1-4503-3531-7}, langid = {english}, keywords = {TYPHONML} } @inproceedings{Macdonald2012, title = {Allocation of Simulation Effort for Neural Network vs. Regression Metamodels}, author = {Macdonald, C. and Gunn, E.A.}, year = {2012}, series = {Proceedings - {{Winter Simulation Conference}}}, issn = {08917736}, doi = {10.1109/WSC.2012.6464998}, abbrev_source_title = {Proc. Winter Simul. Conf.}, affiliation = {Dalhousie University, PO Box 15000, Halifax, NS B3H 4R2, Canada}, art_number = {6464998}, coden = {WSCPD}, correspondence_address1 = {Macdonald, C.; Dalhousie University, PO Box 15000, Halifax, NS B3H 4R2, Canada; email: corinne.macdonald@dal.ca}, document_type = {Conference Paper}, isbn = {978-1-4673-4779-2}, langid = {english}, source = {Scopus}, keywords = {notion} } @misc{MachineLearningAutomation, title = {Machine {{Learning Automation}} - {{Run}}:{{AI}}}, url = {https://www.run.ai/guides/machine-learning-operations/machine-learning-automation/}, urldate = {2021-04-21} } @misc{MachineLearningPipelines, title = {Machine {{Learning Pipelines}}: {{Provenance}}, {{Reproducibility}} and {{FAIR Data Principles}}}, shorttitle = {(17) ({{PDF}}) {{Machine Learning Pipelines}}}, journal = {ResearchGate}, url = {https://www.researchgate.net/publication/342377391_Machine_Learning_Pipelines_Provenance_Reproducibility_and_FAIR_Data_Principles}, urldate = {2021-03-18}, abstract = {ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free.}, langid = {english}, keywords = {DONE} } @article{macias-escrivaSelfadaptiveSystemsSurvey2013, title = {Self-Adaptive Systems: {{A}} Survey of Current Approaches, Research Challenges and Applications}, shorttitle = {Self-Adaptive Systems}, author = {{Mac{\'i}as-Escriv{\'a}}, Frank D. and Haber, Rodolfo and {del Toro}, Raul and Hernandez, Vicente}, year = {2013}, month = dec, journal = {Expert Systems with Applications}, volume = {40}, number = {18}, pages = {7267--7279}, issn = {09574174}, doi = {10.1016/j.eswa.2013.07.033}, langid = {english} } @article{mahdavinejadMachineLearningInternet2018, title = {Machine Learning for Internet of Things Data Analysis: A Survey}, shorttitle = {Machine Learning for Internet of Things Data Analysis}, author = {Mahdavinejad, Mohammad Saeid and Rezvan, Mohammadreza and Barekatain, Mohammadamin and Adibi, Peyman and Barnaghi, Payam and Sheth, Amit P.}, year = {2018}, month = aug, journal = {Digital Communications and Networks}, volume = {4}, number = {3}, pages = {161--175}, issn = {23528648}, doi = {10.1016/j.dcan.2017.10.002}, 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.}, langid = {english} } @article{Mahmoudi20161, title = {Benefits of Metamodel-Reduction for Nonlinear Dynamic Response Analysis of Damaged Composite Structures}, author = {Mahmoudi, S. and Trivaudey, F. and Bouhaddi, N.}, year = {2016}, journal = {Finite Elements in Analysis and Design}, volume = {119}, pages = {1--14}, publisher = {{Elsevier B.V.}}, issn = {0168874X}, doi = {10.1016/j.finel.2016.05.001}, abbrev_source_title = {Finite Elem Anal Des}, affiliation = {FEMTO-ST Institute, UMR 6174, Department of Applied Mechanics, University of Franche-Comt\'e, UBFC, 24 rue de l\'Epitaphe, Besan\c{c}on, 25000, France}, coden = {FEADE}, correspondence_address1 = {Mahmoudi, S.; FEMTO-ST Institute, 24 rue de l\'Epitaphe, France; email: saber.mahmoudi@femto-st.fr}, document_type = {Article}, langid = {english}, source = {Scopus} } @inproceedings{maiaDragonflyToolSimulating2019, title = {Dragonfly: A {{Tool}} for {{Simulating Self-Adaptive Drone Behaviours}}}, shorttitle = {Dragonfly}, booktitle = {2019 {{IEEE}}/{{ACM}} 14th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}} ({{SEAMS}})}, author = {Maia, Paulo Henrique and Vieira, Lucas and Chagas, Matheus and Yu, Yijun and Zisman, Andrea and Nuseibeh, Bashar}, year = {2019}, month = may, pages = {107--113}, publisher = {{IEEE}}, address = {{Montreal, QC, Canada}}, doi = {10.1109/SEAMS.2019.00022}, 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.}, isbn = {978-1-72813-368-3}, langid = {english} } @incollection{maiwaldWhatAreReal2019, title = {What {{Are Real JSON Schemas Like}}?: {{An Empirical Analysis}} of {{Structural Properties}}}, shorttitle = {What {{Are Real JSON Schemas Like}}?}, booktitle = {Advances in {{Conceptual Modeling}}}, author = {Maiwald, Benjamin and Riedle, Benjamin and Scherzinger, Stefanie}, editor = {Guizzardi, Giancarlo and Gailly, Frederik and Suzana Pitangueira Maciel, Rita}, year = {2019}, volume = {11787}, pages = {95--105}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-030-34146-6_9}, isbn = {978-3-030-34145-9 978-3-030-34146-6}, langid = {english} } @article{maki_context, title = {Context {{Extraction}} in {{Recommendation Systems}} in {{Software Engineering}}: {{A Preliminary Survey}}}, author = {Maki, Sana and Kpodjedo, S{\`e}gla and Boussaidi, Ghizlane El}, year = {2015}, pages = {10}, 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.}, langid = {english} } @inproceedings{maLibRadarFastAccurate2016, title = {{{LibRadar}}: Fast and Accurate Detection of Third-Party Libraries in {{Android}} Apps}, shorttitle = {{{LibRadar}}}, author = {Ma, Ziang and Wang, Haoyu and Guo, Yao and Chen, Xiangqun}, year = {2016}, pages = {653--656}, publisher = {{ACM Press}}, doi = {10.1145/2889160.2889178}, isbn = {978-1-4503-4205-6}, langid = {english} } @inproceedings{mangharamThreeChallengesCyberphysical2016, title = {Three Challenges in Cyber-Physical Systems}, booktitle = {2016 8th {{International Conference}} on {{Communication Systems}} and {{Networks}} ({{COMSNETS}})}, author = {Mangharam, Rahul and Abbas, Houssam and Behl, Madhur and Jang, Kuk and Pajic, Miroslav and Jiang, Zhihao}, year = {2016}, pages = {1--8}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7440015}, urldate = {2016-08-21} } @book{Manning:2008:IIR:1394399, title = {Introduction to Information Retrieval}, author = {Manning, Christopher D. and Raghavan, Prabhakar and Sch{\"u}tze, Hinrich}, year = {2008}, publisher = {{Cambridge University Press}}, address = {{New York, NY, USA}}, isbn = {0-521-86571-9 978-0-521-86571-5} } @article{mansoNoredundantMetricsUML2003, title = {No-Redundant {{Metrics}} for {{UML Class Diagram Structural Complexity}}}, author = {Manso, Ma Esperanza and Genero, Marcela and Piattini, Mario}, year = {2003}, journal = {Advanced Information Systems Engineering}, volume = {2681}, pages = {127--142}, doi = {10.1007/3-540-45017-3_11} } @article{mansoorMOMMMultiobjectiveModel2015, title = {{{MOMM}}: {{Multi-objective}} Model Merging}, shorttitle = {{{MOMM}}}, author = {Mansoor, Usman and Kessentini, Marouane and Langer, Philip and Wimmer, Manuel and Bechikh, Slim and Deb, Kalyanmoy}, year = {2015}, month = may, journal = {Journal of Systems and Software}, volume = {103}, pages = {423--439}, issn = {01641212}, doi = {10.1016/j.jss.2014.11.043}, langid = {english} } @article{mansouryFeedbackLoopBias2020, title = {Feedback {{Loop}} and {{Bias Amplification}} in {{Recommender Systems}}}, author = {Mansoury, Masoud and Abdollahpouri, Himan and Pechenizkiy, Mykola and Mobasher, Bamshad and Burke, Robin}, year = {2020}, month = jul, journal = {arXiv:2007.13019 [cs]}, eprint = {2007.13019}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2007.13019}, urldate = {2022-03-24}, 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.}, archiveprefix = {arXiv}, keywords = {Computer Science - Information Retrieval} } @article{mantzCoevolvingMetamodelsTheir2015, title = {Co-Evolving Meta-Models and Their Instance Models: {{A}} Formal Approach Based on Graph Transformation}, shorttitle = {Co-Evolving Meta-Models and Their Instance Models}, author = {Mantz, Florian and Taentzer, Gabriele and Lamo, Yngve and Wolter, Uwe}, year = {2015}, month = jun, journal = {Science of Computer Programming}, volume = {104}, pages = {2--43}, issn = {01676423}, doi = {10.1016/j.scico.2015.01.002}, langid = {english} } @article{mantzCustomizingModelMigrations2013, title = {Customizing Model Migrations by Rule Schemes}, author = {Mantz, Florian and Taentzer, Gabriele and Lamo, Yngve}, year = {2013}, journal = {Proceedings of the 2013 International Workshop on Principles of Software Evolution - IWPSE 2013}, pages = {1}, doi = {10.1145/2501543.2501545} } @misc{ManuallyConfigureTelegraf, title = {Manually Configure {{Telegraf}} for {{InfluxDB}} v2.0 | {{InfluxDB OSS}} 2.0 {{Documentation}}}, url = {https://docs.influxdata.com/influxdb/v2.0/write-data/no-code/use-telegraf/manual-config/}, urldate = {2021-01-11} } @article{MAO201757, title = {A Survey of the Use of Crowdsourcing in Software Engineering}, author = {Mao, Ke and Capra, Licia and Harman, Mark and Jia, Yue}, year = {2017}, journal = {Journal of Systems and Software}, volume = {126}, pages = {57--84}, issn = {0164-1212}, url = {http://www.sciencedirect.com/science/article/pii/S0164121216301832}, nodoi = {https://doi.org/10.1016/j.jss.2016.09.015} } @article{Mao20222870, title = {Joint Channel Estimation and Active-User Detection for Massive Access in Internet of Things-{{A}} Deep Learning Approach}, author = {Mao, Z. and Liu, X. and Peng, M. and Chen, Z. and Wei, G.}, year = {2022}, journal = {IEEE Internet of Things Journal}, volume = {9}, number = {4}, pages = {2870--2881}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {23274662}, doi = {10.1109/JIOT.2021.3097133}, 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. \textcopyright{} 2014 IEEE.}, document_type = {Article}, source = {Scopus} } @article{maozFrameworkRelatingSyntactic, title = {A {{Framework}} for {{Relating Syntactic}} and {{Semantic Model Differences}}}, author = {Maoz, Shahar and Ringert, Jan Oliver}, url = {http://www.cs.tau.ac.il/~ringert/publications/MR15synsemdiff.pdf}, urldate = {2015-09-10} } @article{maqbool2007hierarchical, title = {Hierarchical Clustering for Software Architecture Recovery}, author = {Maqbool, Onaiza and Babri, Haroon}, year = {2007}, journal = {IEEE Transactions on Software Engineering}, volume = {33}, number = {11}, pages = {759--780}, publisher = {{IEEE}} } @article{marozzoWorkflowManagementSystem2018, title = {A {{Workflow Management System}} for {{Scalable Data Mining}} on {{Clouds}}}, author = {Marozzo, Fabrizio and Talia, Domenico and Trunfio, Paolo}, year = {2018}, month = may, journal = {IEEE Transactions on Services Computing}, volume = {11}, number = {3}, pages = {480--492}, issn = {1939-1374}, doi = {10.1109/TSC.2016.2589243}, 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.}, langid = {english}, keywords = {STARRED} } @inproceedings{martinezAutomatingExtractionModelBased2015, title = {Automating the {{Extraction}} of {{Model-Based Software Product Lines}} from {{Model Variants}} ({{T}})}, author = {Martinez, Jabier and Ziadi, Tewfik and Bissyande, Tegawende F. and Klein, Jacques and le Traon, Yves}, year = {2015}, month = nov, pages = {396--406}, publisher = {{IEEE}}, doi = {10.1109/ASE.2015.44}, isbn = {978-1-5090-0025-8} } @article{marussySpecificationLanguageConsistent2020, title = {A {{Specification Language}} for {{Consistent Model Generation}} Based on {{Partial Models}}.}, author = {Marussy, Krist{\'o}f and Semer{\'a}th, Oszk{\'a}r and A. Babikian, Aren and Varr{\'o}, D{\'a}niel}, year = {2020}, journal = {The Journal of Object Technology}, volume = {19}, number = {3}, pages = {3:1}, issn = {1660-1769}, doi = {10.5381/jot.2020.19.3.a12}, langid = {english} } @article{Masthead2017, title = {Masthead}, year = {2017}, month = jan, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {8--8}, issn = {0740-7459}, doi = {10.1109/MS.2017.5}, langid = {english} } @article{Masthead2018, title = {Masthead}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {c2-c2}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571248}, abstract = {Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.} } @article{Masthead2020, title = {Masthead}, year = {2020}, month = jul, journal = {IEEE Software}, volume = {37}, number = {4}, pages = {C2-C2}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2020.2972660}, langid = {english} } @article{Masthead2020a, title = {Masthead}, year = {2020}, month = sep, journal = {IEEE Software}, volume = {37}, number = {5}, pages = {C2-C2}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2020.2972672}, langid = {english} } @article{mathewSoftwareEngineeringTop2018, title = {Software {{Engineering}}'s {{Top Topics}}, {{Trends}}, and {{Researchers}}}, author = {Mathew, G. and Menzies, T.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {88--93}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571230}, 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\textemdash 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.}, keywords = {software engineering} } @article{Matic2021, title = {Extensible Chatbot Architecture Using Metamodels of Natural Language Understanding}, author = {Matic, R. and Kabiljo, M. and Zivkovic, M. and Cabarkapa, M.}, year = {2021}, journal = {Electronics (Switzerland)}, volume = {10}, number = {18}, publisher = {{MDPI}}, issn = {20799292}, doi = {10.3390/electronics10182300}, abbrev_source_title = {Electronics (Switzerland)}, affiliation = {Department for Information Systems and Technologies, Belgrade Academy for Business and Arts Applied Studies, Kraljice Marije 73, Belgrade, 11000, Serbia; Faculty of Informatics and Computing, Singidunum University, Danijelova 32, Belgrade, 11000, Serbia; School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, Belgrade, 11000, Serbia}, art_number = {2300}, correspondence_address1 = {Zivkovic, M.; Faculty of Informatics and Computing, Danijelova 32, Serbia; email: mzivkovic@singidunum.ac.rs; Cabarkapa, M.; School of Electrical Engineering, Bulevar Kralja Aleksandra 73, Serbia; email: cabmilan@etf.bg.ac.rs}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Assistance,notion} } @article{mattihalliPlantLeafDiseases2018, title = {Plant Leaf Diseases Detection and Auto-Medicine}, author = {Mattihalli, Channamallikarjuna and Gedefaye, Edemialem and Endalamaw, Fasil and Necho, Adugna}, year = {2018}, month = sep, journal = {Internet of Things}, volume = {1--2}, pages = {67--73}, issn = {25426605}, doi = {10.1016/j.iot.2018.08.007}, 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.}, langid = {english} } @article{mattsonDemonstratingBigDAWGPolystore, title = {Demonstrating the {{BigDAWG Polystore System}} for {{Ocean Metagenomic Analysis}}}, author = {Mattson, Tim and Gadepally, Vijay and She, Zuohao and Dziedzic, Adam and Parkhurst, Jeff}, pages = {9}, 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.}, langid = {english} } @article{mazaheriRecommenderSystemScientific2021, title = {A {{Recommender System}} for {{Scientific Datasets}} and {{Analysis Pipelines}}}, author = {Mazaheri, Mandana and Kiar, Gregory and Glatard, Tristan}, year = {2021}, month = aug, journal = {2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS)}, volume = {7}, pages = {62011--62021}, doi = {10.1109/WORKS54523.2021.00006}, 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.}, langid = {english}, keywords = {Computer Science - Information Retrieval,Computer Science - Machine Learning} } @incollection{mazanekToolDemonstrationTransformation2011, title = {Tool Demonstration of the Transformation Judge}, booktitle = {Applications of {{Graph Transformations}} with {{Industrial Relevance}}}, author = {Mazanek, Steffen and Rutetzki, Christian and Minas, Mark}, year = {2011}, pages = {97--104}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-34176-2_10}, urldate = {2016-02-09} } @article{mccallumBuildingMDaocmhianineLSepaercniincgSTeaecrhchniEqunegsines, title = {{{BuildingMDaocmhianine-LSepaercniincgSTeaecrhchniEqunegsines}} With}, author = {McCallum, Andrew and Nigam, Kamal and Rennie, Jason and Seymore, Kristie}, pages = {12}, 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.}, langid = {english}, keywords = {GOAL-Model_Search} } @book{mcewenDesigningInternetThings2014, title = {Designing the {{Internet}} of Things}, author = {McEwen, Adrian and Cassimally, Hakim}, year = {2014}, edition = {Reprinted with corrections}, publisher = {{Wiley}}, address = {{Chichester}}, isbn = {978-1-118-43062-0 978-1-118-43063-7 978-1-118-43065-1}, langid = {english} } @inproceedings{McMillan:2011:CSA:2117694.2119646, ids = {6080801}, title = {Categorizing Software Applications for Maintenance}, booktitle = {Proceedings of the 2011 27th {{IEEE}} International Conference on Software Maintenance}, author = {McMillan, Collin and {Linares-Vasquez}, Mario and Poshyvanyk, Denys and Grechanik, Mark}, year = {2011}, series = {{{ICSM}} '11}, pages = {343--352}, publisher = {{IEEE Computer Society}}, address = {{Washington, DC, USA}}, issn = {1063-6773}, url = {https://doi.org/10.1109/ICSM.2011.6080801}, acmid = {2119646}, isbn = {978-1-4577-0663-9}, nodoi = {10.1109/ICSM.2011.6080801}, numpages = {10}, keywords = {API calls,application program interfaces,application programming interface,automatic categorization,binary executables,byte-code,categorizing software applications,closed source repository,closed-source,closed-source repository,Companies,Entropy,Java,Java repository,learning (artificial intelligence),legal reasons,Libraries,machine learning,maintenance tasks,open-source,open-source repository,organizational reasons,predict maintenance problems,project management,public domain software,Software,software categorization,software maintenance,software management,software projects,software repository,source code,Support vector machines,third-party library} } @inproceedings{mcmillanDetectingSimilarSoftware2012, ids = {McMillan:2012:DSS:2337223.2337267}, title = {Detecting Similar Software Applications}, booktitle = {Software {{Engineering}} ({{ICSE}}), 2012 34th {{International Conference}} On}, author = {McMillan, Collin and Grechanik, Mark and Poshyvanyk, Denys}, year = {2012}, pages = {364--374}, publisher = {{IEEE}}, address = {{Zurich, Switzerland}}, url = {http://ieeexplore.ieee.org/abstract/document/6227178/}, urldate = {2017-03-14}, acmid = {2337267}, numpages = {11} } @inproceedings{mcmillanRecommendingSourceCode2010, title = {Recommending Source Code Examples via {{API}} Call Usages and Documentation}, booktitle = {Proceedings of the {{2Nd}} International Workshop on Recommendation Systems for Software Engineering}, author = {McMillan, Collin and Poshyvanyk, Denys and Grechanik, Mark}, year = {2010}, series = {{{RSSE}} '10}, pages = {21--25}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/1808920.1808925}, acmid = {1808925}, isbn = {978-1-60558-974-9}, nodoi = {10.1145/1808920.1808925}, numpages = {5} } @misc{MDD4DRESProgram, title = {{{MDD4DRES Program}}}, url = {http://www.mdd4dres.org/program/#JM}, urldate = {2016-03-10} } @article{MDE, title = {Guest Editor's Introduction: {{Model-driven}} Engineering}, author = {Schmidt, D. C.}, year = {2006}, month = feb, journal = {Computer}, volume = {39}, number = {2}, pages = {25--31}, issn = {1558-0814}, doi = {10.1109/MC.2006.58} } @article{meadHalfCenturySoftware2018, title = {Half a {{Century}} of {{Software Engineering Education}}: {{The CMU Exemplar}}}, shorttitle = {Half a {{Century}} of {{Software Engineering Education}}}, author = {Mead, N. R. and Garlan, D. and Shaw, M.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {25--31}, issn = {0740-7459}, doi = {10.1109/MS.2018.290110743}, 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.}, keywords = {software engineering} } @article{meijerCorelationalModelData2011, title = {A Co-Relational Model of Data for Large Shared Data Banks}, author = {Meijer, Erik and Bierman, Gavin}, year = {2011}, month = apr, journal = {Communications of the ACM}, volume = {54}, number = {4}, pages = {49}, issn = {00010782}, doi = {10.1145/1924421.1924436}, langid = {english} } @inproceedings{meloContextAugmentedSoftwareDevelopment2019, title = {Context-{{Augmented Software Development}} in {{Traditional}} and {{Big Data Projects}}: {{Literature Review}} and {{Preliminary Framework}}}, shorttitle = {Context-{{Augmented Software Development}} in {{Traditional}} and {{Big Data Projects}}}, booktitle = {2019 {{IEEE International Conference}} on {{Big Data}} ({{Big Data}})}, author = {Melo, Glaucia and Alencar, Paulo and Cowan, Don}, year = {2019}, month = dec, pages = {3449--3457}, publisher = {{IEEE}}, address = {{Los Angeles, CA, USA}}, doi = {10.1109/BigData47090.2019.9006245}, isbn = {978-1-72810-858-2} } @incollection{melvilleRecommenderSystems2010, title = {Recommender Systems.}, booktitle = {Encyclopedia of Machine Learning}, author = {Melville, Prem and Sindhwani, Vikas}, editor = {Sammut, Claude and Webb, Geoffrey I.}, year = {2010}, pages = {829--838}, publisher = {{Springer}}, url = {http://dblp.uni-trier.de/db/reference/ml/ml2010.html#MelvilleS10}, added-at = {2011-11-25T00:00:00.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/226b0f0d297e0d3302c28cbf58efad665/dblp}, ee = {http://dx.doi.org/10.1007/978-0-387-30164-8{$_7$}05}, interhash = {50b623dcfae0cd344f966cb9e9d7b9c0}, intrahash = {26b0f0d297e0d3302c28cbf58efad665}, isbn = {978-0-387-30768-8}, keywords = {dblp}, timestamp = {2011-11-26T11:38:23.000+0100} } @article{mendoncaDevelopingSelfAdaptiveMicroservice2021, title = {Developing {{Self-Adaptive Microservice Systems}}: {{Challenges}} and {{Directions}}}, shorttitle = {Developing {{Self-Adaptive Microservice Systems}}}, author = {Mendonca, Nabor C. and Jamshidi, Pooyan and Garlan, David and Pahl, Claus}, year = {2021}, month = mar, journal = {IEEE Software}, volume = {38}, number = {2}, pages = {70--79}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2019.2955937}, langid = {english} } @inproceedings{Meng2019, title = {Power Allocation in Multi-User Cellular Networks with Deep {{Q}} Learning Approach}, author = {Meng, F. and Chen, P. and Wu, L.}, year = {2019}, series = {{{IEEE International Conference}} on {{Communications}}}, volume = {2019-May}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15503607}, doi = {10.1109/ICC.2019.8761431}, 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. \textcopyright{} 2019 IEEE.}, art_number = {8761431}, document_type = {Conference Paper}, isbn = {978-1-5386-8088-9}, source = {Scopus} } @article{menziesFiveLawsSE2020, ids = {menziesFiveLawsSE2020a,menziesFiveLawsSE2020b}, title = {The {{Five Laws}} of {{SE}} for {{AI}}}, author = {Menzies, Tim}, year = {2020}, month = jan, journal = {IEEE Software}, volume = {37}, number = {1}, pages = {81--85}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2019.2954841}, langid = {english}, keywords = {DONE} } @article{menziesShockinglySimpleKEYS2021, ids = {menziesShockinglySimpleKEYS2021a}, title = {Shockingly {{Simple}}:"{{KEYS}}" for {{Better AI}} for {{SE}}}, shorttitle = {Shockingly {{Simple}}}, author = {Menzies, Tim}, year = {2021}, month = mar, journal = {IEEE Software}, volume = {38}, number = {2}, pages = {114--118}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2020.3043014}, langid = {english} } @article{menziesSoftwareAnalyticsWhat2018, title = {Software {{Analytics}}: {{What}}'s {{Next}}?}, shorttitle = {Software {{Analytics}}}, author = {Menzies, T. and Zimmermann, T.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {64--70}, issn = {0740-7459}, doi = {10.1109/MS.2018.290111035}, 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.}, keywords = {artificial intelligence,software engineering} } @inproceedings{merilinnaStateArtPractice2006, title = {State of the Art and Practice of Opensource Component Integration}, booktitle = {Software {{Engineering}} and {{Advanced Applications}}, 2006. {{SEAA}}'06. 32nd {{EUROMICRO Conference}} On}, author = {Merilinna, Janne and Matinlassi, Mari}, year = {2006}, pages = {170--177}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/abstract/document/1690138/}, urldate = {2017-02-25} } @book{MessageRoSE20182018, title = {Message from the {{RoSE}} 2018 {{Co-Organizers}}}, year = {2018}, journal = {Proceedings - International Conference on Software Engineering}, volume = {137815}, publisher = {{IEEE Computer Society}} } @misc{MessageRoSE20182018a, title = {Message from the {{RoSE}} 2018 {{Co-Organizers}}}, year = {2018}, journal = {Proceedings - International Conference on Software Engineering}, volume = {137815}, publisher = {{IEEE Computer Society}} } @book{meyerSoftwareEngineering2015, ids = {laser_software_2015}, title = {Software {{Engineering}}}, editor = {Meyer, Bertrand and Nordio, Martin}, year = {2015}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {8987}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-319-28406-4}, isbn = {978-3-319-28405-7 978-3-319-28406-4} } @inproceedings{miEmpiricalCharacterizationIFTTT2017, title = {An Empirical Characterization of {{IFTTT}}: Ecosystem, Usage, and Performance}, shorttitle = {An Empirical Characterization of {{IFTTT}}}, booktitle = {Proceedings of the 2017 {{Internet Measurement Conference}} on - {{IMC}} '17}, author = {Mi, Xianghang and Qian, Feng and Zhang, Ying and Wang, XiaoFeng}, year = {2017}, pages = {398--404}, publisher = {{ACM Press}}, address = {{London, United Kingdom}}, doi = {10.1145/3131365.3131369}, 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.}, isbn = {978-1-4503-5118-8}, langid = {english} } @inproceedings{Mihalcea:2006:CKM:1597538.1597662, title = {Corpus-Based and Knowledge-Based Measures of Text Semantic Similarity}, booktitle = {Proceedings of the 21st National Conference on Artificial Intelligence - Volume 1}, author = {Mihalcea, Rada and Corley, Courtney and Strapparava, Carlo}, year = {2006}, series = {{{AAAI}}'06}, pages = {775--780}, publisher = {{AAAI Press}}, address = {{Boston, Massachusetts}}, url = {http://dl.acm.org/citation.cfm?id=1597538.1597662}, acmid = {1597662}, isbn = {978-1-57735-281-5}, numpages = {6} } @techreport{millerMDAGuideVersion2003, title = {{{MDA Guide Version}} 1.0.1}, author = {Miller, J. and Mukerji, J.}, year = {2003}, institution = {{Object Management Group (OMG)}}, keywords = {architecture model-driven} } @article{millerWordNetLexicalDatabase1995, title = {{{WordNet}}: {{A}} Lexical Database for English}, author = {Miller, George A.}, year = {1995}, month = nov, journal = {Communications of the ACM}, volume = {38}, number = {11}, pages = {39--41}, publisher = {{ACM}}, address = {{New York, NY, USA}}, issn = {0001-0782}, url = {http://doi.acm.org/10.1145/219717.219748}, acmid = {219748}, issue_date = {Nov. 1995}, nodoi = {10.1145/219717.219748}, numpages = {3} } @article{minoliBlockchainMechanismsIoT2018, title = {Blockchain Mechanisms for {{IoT}} Security}, author = {Minoli, Daniel and Occhiogrosso, Benedict}, year = {2018}, month = sep, journal = {Internet of Things}, volume = {1--2}, pages = {1--13}, issn = {25426605}, doi = {10.1016/j.iot.2018.05.002}, 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 \textendash{} or data \textendash{} 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.}, langid = {english} } @article{miorandiInternetThingsVision2012, title = {Internet of Things: {{Vision}}, Applications and Research Challenges}, shorttitle = {Internet of Things}, author = {Miorandi, Daniele and Sicari, Sabrina and De Pellegrini, Francesco and Chlamtac, Imrich}, year = {2012}, month = sep, journal = {Ad Hoc Networks}, volume = {10}, number = {7}, pages = {1497--1516}, issn = {15708705}, doi = {10.1016/j.adhoc.2012.02.016}, 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.}, langid = {english}, keywords = {relevant} } @inproceedings{miorNoSESchemaDesign2016, title = {{{NoSE}}: {{Schema}} Design for {{NoSQL}} Applications}, shorttitle = {{{NoSE}}}, booktitle = {2016 {{IEEE}} 32nd {{International Conference}} on {{Data Engineering}} ({{ICDE}})}, author = {Mior, Michael J. and Salem, Kenneth and Aboulnaga, Ashraf and Liu, Rui}, year = {2016}, month = may, pages = {181--192}, publisher = {{IEEE}}, address = {{Helsinki, Finland}}, doi = {10.1109/ICDE.2016.7498239}, isbn = {978-1-5090-2020-1} } @inproceedings{Miranda:2008:ICF:1486927.1487083, title = {Incremental Collaborative Filtering for Binary Ratings}, booktitle = {Proceedings of the 2008 {{IEEE}}/{{WIC}}/{{ACM}} International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01}, author = {Miranda, Catarina and Jorge, Al{\'i}pio M.}, year = {2008}, series = {{{WI-IAT}} '08}, pages = {389--392}, publisher = {{IEEE Computer Society}}, address = {{Washington, DC, USA}}, url = {http://dx.doi.org/10.1109/WIIAT.2008.263}, acmid = {1487083}, isbn = {978-0-7695-3496-1}, nodoi = {10.1109/WIIAT.2008.263}, numpages = {4}, keywords = {Incremental Collaborative Filtering,Web Recommender Systems} } @misc{MiSE2017ProgramPreparatory, title = {{{MiSE2017}} - {{Program}} and Preparatory Emails}, journal = {Google Docs}, url = {https://docs.google.com/document/d/1FVBtlKZzdkNVYqea-9nB2YCpicTohzntjEcx2ZKcqjA/edit?usp=sharing&usp=embed_facebook}, urldate = {2017-05-08}, abstract = {Program 1 Registrants (33) 3 Preparatory emails 5 To presenters 6 To attendees 6 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] ...} } @inproceedings{misraSoftwareClusteringUnifying2012, title = {Software Clustering: {{Unifying}} Syntactic and Semantic Features}, booktitle = {2012 19th Working Conference on Reverse Engineering}, author = {Misra, J. and Annervaz, K. M. and Kaulgud, V. and Sengupta, S. and Titus, G.}, year = {2012}, month = oct, pages = {113--122}, issn = {2375-5369}, doi = {10.1109/WCRE.2012.21}, keywords = {application portfolios,architectural recovery,automated component labeling,cluster selection,component discovery,distance estimation,feature extraction,feature location problem,graph theory,high level component architecture extraction,information quality enhancement,inter-component interaction generation,latent semantic indexing,lexical analysis,multiobjective global modularity criterion,multiple hierarchical levels,noise reduction,object-oriented programming,pattern clustering,program comprehension,program diagnostics,Reverse engineering,semantic features,software architecture,software clustering,source code elements,syntactic features,vector space model,weighted graph partitioning} } @misc{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}, url = {http://www.ab-log.ru/smart-house/heating-automation/gaz_meter}, urldate = {2015-04-04} } @article{mitchellFAIRDataPipeline2021, title = {{{FAIR Data Pipeline}}: Provenance-Driven Data Management for Traceable Scientific Workflows}, shorttitle = {{{FAIR Data Pipeline}}}, author = {Mitchell, Sonia Natalie and Lahiff, Andrew and Cummings, Nathan and Hollocombe, Jonathan and Boskamp, Bram and Reddyhoff, Dennis and Field, Ryan and Zarebski, Kristian and Wilson, Antony and Burke, Martin and Archibald, Blair and Bessell, Paul and Blackwell, Richard and Boden, Lisa A. and Brett, Alys and Brett, Sam and Dundas, Ruth and Enright, Jessica and {Gonzalez-Beltran}, Alejandra N. and Harris, Claire and Hinder, Ian and Hughes, Christopher David and Knight, Martin and Mano, Vino and McMonagle, Ciaran and Mellor, Dominic and Mohr, Sibylle and Marion, Glenn and Matthews, Louise and McKendrick, Iain J. and Pooley, Christopher Mark and Porphyre, Thibaud and Reeves, Aaron and Townsend, Edward and Turner, Robert and Walton, Jeremy and Reeve, Richard}, year = {2021}, month = oct, journal = {arXiv:2110.07117 [cs, q-bio]}, eprint = {2110.07117}, eprinttype = {arxiv}, primaryclass = {cs, q-bio}, url = {http://arxiv.org/abs/2110.07117}, urldate = {2022-02-24}, 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.}, archiveprefix = {arXiv}, keywords = {Computer Science - Digital Libraries,Quantitative Biology - Quantitative Methods} } @article{mittelmannPersonalKnowledgeManagement2016, title = {Personal {{Knowledge Management}} as {{Basis}} for {{Successful Organizational Knowledge Management}} in the {{Digital Age}}}, author = {Mittelmann, Angelika}, year = {2016}, journal = {Procedia Computer Science}, volume = {99}, pages = {117--124}, issn = {18770509}, doi = {10.1016/j.procs.2016.09.105}, langid = {english} } @inproceedings{MMR02, title = {Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching}, booktitle = {Proceedings. 18th International Conference on Data Engineering, 2002}, author = {Melnik, S. and {Garcia-Molina}, H. and Rahm, E.}, year = {2002}, pages = {117--128}, issn = {1063-6382}, doi = {10.1109/ICDE.2002.994702}, keywords = {accuracy metric,biochemical applications,Biochemistry,Bioinformatics,Catalogs,data handling,data schemas,data structures,Data structures,data warehouses,data warehousing,e-business,filters,fixpoint computation,Floods,graph matching algorithm,high-level operators,Humans,Information management,information models,mappings,Matched filters,pattern matching,schema matching,similarity flooding,Testing,user labor savings,Warehousing} } @incollection{mmsim2, title = {Metamodel Matching for Automatic Model Transformation Generation}, booktitle = {Model Driven Engineering Languages and Systems}, author = {Falleri, Jean-R{\'e}my and Huchard, Marianne and Lafourcade, Mathieu and Nebut, Cl{\'e}mentine}, editor = {Czarnecki, Krzysztof and Ober, Ileana and Bruel, Jean-Michel and Uhl, Axel and V{\"o}lter, Markus}, year = {2008}, series = {Lecture Notes in Computer Science}, volume = {5301}, pages = {326--340}, publisher = {{Springer Berlin Heidelberg}}, doi = {10.1007/978-3-540-87875-9_24}, isbn = {978-3-540-87874-2} } @article{mobasherAttacksRemediesCollaborative2007, title = {Attacks and {{Remedies}} in {{Collaborative Recommendation}}}, author = {Mobasher, Bamshad and Burke, Robin and Bhaumik, Runa and Sandvig, J.J.}, year = {2007}, month = may, journal = {IEEE Intelligent Systems}, volume = {22}, number = {3}, pages = {56--63}, issn = {1541-1672}, doi = {10.1109/MIS.2007.45}, langid = {english} } @misc{MobileAutonomousSystems, title = {Mobile {{Autonomous Systems Laboratory}} | {{Electrical Engineering}} and {{Computer Science}} | {{MIT OpenCourseWare}}}, url = {http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-186-mobile-autonomous-systems-laboratory-january-iap-2005/index.htm}, urldate = {2016-01-23} } @article{mocriiIoTbasedSmartHomes2018, title = {{{IoT-based}} Smart Homes: {{A}} Review of System Architecture, Software, Communications, Privacy and Security}, shorttitle = {{{IoT-based}} Smart Homes}, author = {Mocrii, Dragos and Chen, Yuxiang and Musilek, Petr}, year = {2018}, month = sep, journal = {Internet of Things}, volume = {1--2}, pages = {81--98}, issn = {25426605}, doi = {10.1016/j.iot.2018.08.009}, 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.}, langid = {english} } @misc{ModeldrivenEngineeringScientific, title = {Model-Driven {{Engineering}} of {{Scientific Applications}} - {{Mohamed Almorsy Abdelrazek}}}, url = {https://sites.google.com/site/mohamedalmorsy/home/research/model-driven-engineering-of-scientific-applications}, urldate = {2017-02-23} } @misc{MODELS20, title = {{{MODELS20}}}, url = {https://www.overleaf.com/8979411763njfrwbxdyfcg}, urldate = {2020-02-13}, abstract = {An online LaTeX editor that's easy to use. No installation, real-time collaboration, version control, hundreds of LaTeX templates, and more.}, langid = {english} } @misc{ModelTypingSpringer, title = {On Model Typing - {{Springer}}}, url = {http://link.springer.com/article/10.1007%2Fs10270-006-0036-6}, urldate = {2015-04-01} } @misc{MoDeS3, title = {{{MoDeS3}}}, url = {http://modes3.tumblr.com/}, urldate = {2016-08-21} } @inproceedings{mohagheghiMetamodelSpecifyingQuality2008, title = {A Metamodel for Specifying Quality Models in Model-Driven Engineering}, booktitle = {Proc. {{The Nordic Workshop}} on {{Model Driven Engineering}}}, author = {Mohagheghi, Parastoo and Dehlen, Vegard}, year = {2008}, pages = {51--65}, url = {http://www.sintef-group.com/globalassets/upload/ikt/9012/qualitymetamodel_final.pdf}, urldate = {2015-12-02} } @inproceedings{Mohanty2015239, title = {Ultra-Fast Variability-Aware Optimization of Mixed-Signal Designs Using Bootstrapped Kriging}, author = {Mohanty, S.P. and Kougianos, E. and Yanambaka, V.P.}, year = {2015}, series = {Proceedings - {{International Symposium}} on {{Quality Electronic Design}}, {{ISQED}}}, volume = {2015-April}, pages = {239--242}, publisher = {{IEEE Computer Society}}, issn = {19483287}, doi = {10.1109/ISQED.2015.7085432}, abbrev_source_title = {Proc. - Int. Symp. Qual. Electron. Des., ISQED}, affiliation = {NanoSystem Design Laboratory (NSDL), Department of Computer Science and Engineering, University of North Texas, Denton, TX 76207, United States; NanoSystem Design Laboratory (NSDL), Department of Electrical Engineering Technology, University of North Texas, Denton, TX 76207, United States}, art_number = {7085432}, document_type = {Conference Paper}, isbn = {978-1-4799-7581-5}, langid = {english}, source = {Scopus} } @inproceedings{Moin2022144, title = {{{ML-Quadrat}} \& {{DriotData}}: {{A}} Model-Driven Engineering Tool and a Low-Code Platform for Smart {{IoT}} Services}, author = {Moin, A. and Mituca, A. and Challenger, M. and Badii, A. and Gunnemann, S.}, year = {2022}, series = {Proceedings - {{International Conference}} on {{Software Engineering}}}, pages = {144--148}, publisher = {{IEEE Computer Society}}, issn = {02705257}, doi = {10.1109/ICSE-Companion55297.2022.9793752}, abbrev_source_title = {Proc Int Conf Software Eng}, affiliation = {Technical Univ. of Munich (TUM), Dept. of Informatics, Germany; DriotData Ug, Munich, Germany; Univ. of Antwerp \& Flanders Make, Dept. of Computer Science, Belgium; Univ. of Reading, Dept. of Computer Science, United Kingdom; Tum, Science Institute, Dept. of Informatics \& Munich Data, Germany}, coden = {PCSED}, document_type = {Conference Paper}, isbn = {978-1-66549-598-1}, langid = {english}, source = {Scopus} } @article{moinINVALIDSCITEVALUEINVALID_SCITE_VALUE, title = {{INVALID\_SCITE\_VALUE}}, shorttitle = {{INVALID\_SCITE\_VALUE}}, author = {Moin, Armin and Challenger, Moharram and Badii, Atta and G{\"u}nnemann, Stephan}, year = {INVALID\_SCITE\_VALUE}, journal = {INVALID\_SCITE\_VALUE}, issn = {INVALID\_SCITE\_VALUE}, doi = {INVALID_SCITE_VALUE}, abstract = {INVALID\_SCITE\_VALUE}, langid = {INVALID\_SCITE\_VALUE} } @article{moinMDE4QAIModelDrivenEngineering2021, title = {{{MDE4QAI}}: {{Towards Model-Driven Engineering}} for {{Quantum Artificial Intelligence}}}, shorttitle = {{{MDE4QAI}}}, author = {Moin, Armin and Challenger, Moharram and Badii, Atta and G{\"u}nnemann, Stephan}, year = {2021}, publisher = {{arXiv}}, doi = {10.48550/ARXIV.2107.06708}, 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.}, copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International} } @misc{mongodbSchemaDesignExample10:25:57UTC, title = {Schema {{Design By Example}}}, author = {MongoDB}, year = {10:25:57 UTC}, url = {https://www.slideshare.net/mongodb/schema-design-by-example}, urldate = {2018-04-30}, abstract = {One of the challenges that comes with moving to MongoDB is figuring how to} } @misc{mongodbTransitioningSQLMongoDB10:56:52UTC, type = {Technology}, title = {Transitioning from {{SQL}} to {{MongoDB}}}, author = {MongoDB}, year = {10:56:52 UTC}, url = {https://www.slideshare.net/mongodb/transition-sql2mongo-1?next_slideshow=1}, urldate = {2018-04-30}, abstract = {Learn how to transition from SQL to MongoDB with this presentation.} } @misc{MonitoringYourHome, title = {Monitoring Your Home Network with {{InfluxDB}} on {{Raspberry Pi}} with {{Docker}} | by {{Pete Shima}} | {{Medium}}}, url = {https://medium.com/@petey5000/monitoring-your-home-network-with-influxdb-on-raspberry-pi-with-docker-78a23559ffea}, urldate = {2021-01-07} } @article{monperrusMeasuringModels, title = {Measuring Models}, author = {Monperrus, Martin and Jezequel, Jean-Marc and Champeau, Joel and Hoeltzener, Brigitte} } @misc{MoralEducationSelfManagement, title = {E {{M C I}}: {{Moral Education}}: {{Self-Management}} - {{Lecture}} 5}, url = {http://spu.edu/depts/iccs/emci/courses/lectures/self_management_lec5.htm}, urldate = {2016-09-21} } @incollection{moreno-llorenaFunctionalCharacterizationCollaborative2011, title = {Towards a Functional Characterization of Collaborative Systems}, booktitle = {Cooperative {{Design}}, {{Visualization}}, and {{Engineering}}}, author = {{Moreno-Llorena}, Jaime and Claros, Iv{\'a}n and Mart{\'i}n, Rafael and Cobos, Ruth and {de Lara}, Juan and Guerra, Esther}, year = {2011}, pages = {182--185}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-23734-8_30}, urldate = {2015-04-01} } @inproceedings{Moreno:2015:IUT:2818754.2818860, title = {How Can {{I}} Use This Method?}, booktitle = {37th International Conference on Software Engineering}, author = {Moreno, Laura and Bavota, Gabriele and Di Penta, Massimiliano and Oliveto, Rocco and Marcus, Andrian}, year = {2015}, pages = {880--890}, publisher = {{IEEE}}, address = {{Piscataway}}, acmid = {2818860}, isbn = {978-1-4799-1934-5}, nodoi = {10.1109/ICSE.2015.98}, numpages = {11} } @inproceedings{Morin20162160, title = {Machine Learning-Based Metamodels for Sawing Simulation}, author = {Morin, M. and Paradis, F. and Rolland, A. and Wery, J. and Gaudreault, J. and Laviolette, F.}, year = {2016}, series = {Proceedings - {{Winter Simulation Conference}}}, volume = {2016-February}, pages = {2160--2171}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {08917736}, doi = {10.1109/WSC.2015.7408329}, abbrev_source_title = {Proc. Winter Simul. Conf.}, affiliation = {FORAC Research Consortium, Department of Computer Science, Software Engineering, 1065, avodela Medecine, Quebec, QC G1VOA6, Canada}, art_number = {7408329}, coden = {WSCPD}, document_type = {Conference Paper}, isbn = {978-1-4673-9743-8}, langid = {english}, source = {Scopus} } @article{morinModelBasedSoftwareEngineering2017, title = {Model-{{Based Software Engineering}} to {{Tame}} the {{IoT Jungle}}}, author = {Morin, Brice and Harrand, Nicolas and Fleurey, Franck}, year = {2017}, month = jan, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {30--36}, issn = {0740-7459}, doi = {10.1109/MS.2017.11}, langid = {english} } @book{morrisonSoftwareArchitecture2nd2005, title = {Software {{Architecture}}, 2nd {{European Workshop}}, {{EWSA}} 2005, {{Pisa}}, {{Italy}}, {{June}} 13-14, 2005, {{Proceedings}}}, editor = {Morrison, Ronald and Oquendo, Fl{\'a}vio}, year = {2005}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {3527}, publisher = {{Springer}}, doi = {10.1007/b136986}, isbn = {3-540-26275-X} } @article{mostermanCyberphysicalSystemsChallenges2016, title = {Cyber-Physical Systems Challenges: A Needs Analysis for Collaborating Embedded Software Systems}, shorttitle = {Cyber-Physical Systems Challenges}, author = {Mosterman, Pieter J. and Zander, Justyna}, year = {2016}, month = feb, journal = {Software \& Systems Modeling}, volume = {15}, number = {1}, pages = {5--16}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-015-0469-x}, langid = {english} } @article{mostermanIndustryCyberPhysicalSystem2016, title = {Industry 4.0 as a {{Cyber-Physical System}} Study}, author = {Mosterman, Pieter J. and Zander, Justyna}, year = {2016}, month = feb, journal = {Software \& Systems Modeling}, volume = {15}, number = {1}, pages = {17--29}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-015-0493-x}, langid = {english} } @article{Mozejko201819, title = {Traffic Signal Settings Optimization Using Gradient Descent}, author = {Mozejko, M. and Brzeski, M. and Madry, L. and Skowronek, L. and Gora, P.}, year = {2018}, journal = {Schedae Informaticae}, volume = {27}, pages = {19--30}, publisher = {{Jagiellonian University}}, issn = {17323916}, doi = {10.4467/20838476SI.18.002.10407}, abbrev_source_title = {Schedae Informaticae}, affiliation = {TensorCell; Faculty of Mathematics and Computer Science, Jagiellonian University, Poland; Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Poland}, document_type = {Article}, langid = {english}, source = {Scopus} } @misc{MSCAITN2016, title = {{{MSCA-ITN-2016}}}, url = {https://ec.europa.eu/research/participants/portal/desktop/en/opportunities/h2020/topics/2056-msca-itn-2016.html}, urldate = {2015-11-19} } @inproceedings{mucciniSelfadaptationCyberphysicalSystems2016, title = {Self-Adaptation for Cyber-Physical Systems: A Systematic Literature Review}, shorttitle = {Self-Adaptation for Cyber-Physical Systems}, author = {Muccini, Henry and Sharaf, Mohammad and Weyns, Danny}, year = {2016}, pages = {75--81}, publisher = {{ACM Press}}, doi = {10.1145/2897053.2897069}, isbn = {978-1-4503-4187-5}, langid = {english} } @incollection{mullerAutonomicComputingNow2009, title = {Autonomic {{Computing Now You See It}}, {{Now You Don}}'t}, booktitle = {Software {{Engineering}}}, author = {M{\"u}ller, Hausi A. and Kienle, Holger M. and Stege, Ulrike}, editor = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Doug and Vardi, Moshe Y. and Weikum, Gerhard and De Lucia, Andrea and Ferrucci, Filomena}, year = {2009}, volume = {5413}, pages = {32--54}, publisher = {{Springer Berlin Heidelberg}}, address = {{Berlin, Heidelberg}}, url = {http://link.springer.com/10.1007/978-3-540-95888-8_2}, urldate = {2016-09-29}, isbn = {978-3-540-95887-1 978-3-540-95888-8} } @article{Mumuni2022191, title = {Bayesian Cue Integration of Structure from Motion and {{CNN-based}} Monocular Depth Estimation for Autonomous Robot Navigation}, author = {Mumuni, F. and Mumuni, A.}, year = {2022}, journal = {International Journal of Intelligent Robotics and Applications}, volume = {6}, number = {2}, pages = {191--206}, publisher = {{Springer}}, issn = {23665971}, doi = {10.1007/s41315-022-00226-2}, 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\textemdash 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\textemdash 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. \textcopyright{} 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.}, document_type = {Article}, source = {Scopus} } @incollection{munappyDataPipelineManagement2020, title = {Data {{Pipeline Management}} in {{Practice}}: {{Challenges}} and {{Opportunities}}}, shorttitle = {Data {{Pipeline Management}} in {{Practice}}}, booktitle = {Product-{{Focused Software Process Improvement}}}, author = {Munappy, Aiswarya Raj and Bosch, Jan and Olsson, Helena Homstr{\"o}m}, editor = {Morisio, Maurizio and Torchiano, Marco and Jedlitschka, Andreas}, year = {2020}, volume = {12562}, pages = {168--184}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-030-64148-1_11}, 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.}, isbn = {978-3-030-64147-4 978-3-030-64148-1}, langid = {english}, keywords = {STARRED} } @article{murLineeGuidaIniziative, title = {{Linee Guida per le iniziative di sistema Missione 4: Istruzione e ricerca Componente 2: Dalla ricerca all'impresa}}, author = {Mur, Pnrr}, pages = {47}, langid = {italian} } @article{Murtagh2012, title = {Algorithms for Hierarchical Clustering: {{An}} Overview}, author = {Murtagh, Fionn and Contreras, Pedro}, year = {2012}, month = jan, journal = {Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery}, volume = {2}, pages = {86--97}, nodoi = {10.1002/widm.53} } @inproceedings{mussbacherAssessmentGridIntelligent2020, title = {Towards an Assessment Grid for Intelligent Modeling Assistance}, booktitle = {Proceedings of the 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}: {{Companion Proceedings}}}, author = {Mussbacher, Gunter and Combemale, Benoit and Abrah{\~a}o, Silvia and Bencomo, Nelly and Burgue{\~n}o, Loli and Engels, Gregor and Kienzle, J{\"o}rg and K{\"u}hn, Thomas and Mosser, S{\'e}bastien and Sahraoui, Houari and Weyssow, Martin}, year = {2020}, month = oct, pages = {1--10}, publisher = {{ACM}}, address = {{Virtual Event Canada}}, doi = {10.1145/3417990.3421396}, isbn = {978-1-4503-8135-2}, langid = {english}, keywords = {GOAL_Model-Assistance} } @article{nagornyBigDataAnalysis, title = {Big {{Data Analysis}} in {{Smart Manufacturing}}: {{A Review}}}, author = {Nagorny, Kevin and {Lima-Monteiro}, Pedro and Barata, Jose and Colombo, Armando Walter}, pages = {29}, 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.}, langid = {english}, keywords = {big data,DONE,smart manufacturing} } @article{nairFindingFasterConfigurations, title = {Finding {{Faster Configurations}} Using {{FLASH}}}, author = {Nair, Vivek and Yu, Zhe and Menzies, Tim and Siegmund, Norbert and Apel, Sven}, pages = {17}, 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.}, langid = {english} } @article{nanayakkaraSurveyFindingTrends2021, title = {A {{Survey}} of {{Finding Trends}} in {{Data Mining Techniques}} for {{Social Media Analysis}}}, author = {Nanayakkara, A. C. and Kumara, B. T. G. S. and Rathnayaka, R. M. K. T.}, year = {2021}, month = aug, journal = {Sri Lanka Journal of Social Sciences and Humanities}, volume = {1}, number = {2}, pages = {37}, issn = {2773-692X, 2773-6911}, doi = {10.4038/sljssh.v1i2.36}, 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.}, langid = {english} } @inproceedings{Narayanankutty202192, title = {Self-Adapting Model-Based {{SDSec}} for {{IoT}} Networks Using Machine Learning}, author = {Narayanankutty, H.}, year = {2021}, series = {Proceedings - 2021 {{IEEE}} 18th {{International Conference}} on {{Software Architecture Companion}}, {{ICSA-C}} 2021}, pages = {92--93}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ICSA-C52384.2021.00023}, 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. \textcopyright{} 2021 IEEE.}, art_number = {9425853}, document_type = {Conference Paper}, isbn = {978-1-66543-910-7}, source = {Scopus} } @article{nassifAutomaticallyCategorizingSoftware2018, title = {Automatically {{Categorizing Software Technologies}}}, author = {Nassif, Mathieu and Treude, Christoph and Robillard, Martin}, year = {2018}, journal = {IEEE Transactions on Software Engineering}, pages = {1--1}, issn = {0098-5589, 1939-3520}, doi = {10.1109/TSE.2018.2836450}, 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.}, langid = {english} } @inproceedings{nasticPatRICIANovelProgramming2013, title = {{{PatRICIA}} -- {{A Novel Programming Model}} for {{IoT Applications}} on {{Cloud Platforms}}}, author = {Nastic, Stefan and Sehic, Sanjin and Vogler, Michael and Truong, Hong-Linh and Dustdar, Schahram}, year = {2013}, month = dec, pages = {53--60}, publisher = {{IEEE}}, doi = {10.1109/SOCA.2013.48}, isbn = {978-1-4799-2702-9 978-1-4799-2701-2} } @inproceedings{nasticProvisioningSoftwareDefinedIoT2014, title = {Provisioning {{Software-Defined IoT Cloud Systems}}}, author = {Nastic, Stefan and Sehic, Sanjin and Le, Duc-Hung and Truong, Hong-Linh and Dustdar, Schahram}, year = {2014}, month = aug, pages = {288--295}, publisher = {{IEEE}}, doi = {10.1109/FiCloud.2014.52}, isbn = {978-1-4799-4357-9} } @article{naumovDeepLearningRecommendation2019, title = {Deep {{Learning Recommendation Model}} for {{Personalization}} and {{Recommendation Systems}}}, author = {Naumov, Maxim and Mudigere, Dheevatsa and Shi, Hao-Jun Michael and Huang, Jianyu and Sundaraman, Narayanan and Park, Jongsoo and Wang, Xiaodong and Gupta, Udit and Wu, Carole-Jean and Azzolini, Alisson G. and Dzhulgakov, Dmytro and Mallevich, Andrey and Cherniavskii, Ilia and Lu, Yinghai and Krishnamoorthi, Raghuraman and Yu, Ansha and Kondratenko, Volodymyr and Pereira, Stephanie and Chen, Xianjie and Chen, Wenlin and Rao, Vijay and Jia, Bill and Xiong, Liang and Smelyanskiy, Misha}, year = {2019}, month = may, journal = {arXiv:1906.00091 [cs]}, eprint = {1906.00091}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/1906.00091}, urldate = {2021-06-07}, 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.}, archiveprefix = {arXiv}, langid = {english}, keywords = {68T05,Computer Science - Information Retrieval,Computer Science - Machine Learning,H.3.3,H.3.4,I.2.6,I.5.0} } @article{navarreteIntroducingSubjectiveKnowledge, title = {Introducing {{Subjective Knowledge Graphs}}}, author = {Navarrete, Francisco J and Vallecillo, Antonio}, pages = {10}, 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.}, langid = {english} } @article{nazabalDataEngineeringData2020, title = {Data {{Engineering}} for {{Data Analytics}}: {{A Classification}} of the {{Issues}}, and {{Case Studies}}}, shorttitle = {Data {{Engineering}} for {{Data Analytics}}}, author = {Nazabal, Alfredo and Williams, Christopher K. I. and Colavizza, Giovanni and Smith, Camila Rangel and Williams, Angus}, year = {2020}, month = apr, journal = {arXiv:2004.12929 [cs]}, eprint = {2004.12929}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2004.12929}, urldate = {2020-07-21}, 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.}, archiveprefix = {arXiv}, langid = {english} } @article{nejatiNextGenerationSoftwareVerification2021, title = {Next-{{Generation Software Verification}}: {{An AI Perspective}}}, shorttitle = {Next-{{Generation Software Verification}}}, author = {Nejati, Shiva}, year = {2021}, month = may, journal = {IEEE Software}, volume = {38}, number = {03}, pages = {126--130}, publisher = {{IEEE Computer Society}}, issn = {0740-7459}, doi = {10.1109/MS.2021.3049322}, 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.}, langid = {english} } @article{Neto2017293, title = {A Framework for Data Transformation in {{Credit Behavioral Scoring}} Applications Based on {{Model Driven Development}}}, author = {Neto, R. and Jorge Adeodato, P. and Carolina Salgado, A.}, year = {2017}, journal = {Expert Systems with Applications}, volume = {72}, pages = {293--305}, publisher = {{Elsevier Ltd}}, issn = {09574174}, doi = {10.1016/j.eswa.2016.10.059}, 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\%. \textcopyright{} 2016}, coden = {ESAPE}, document_type = {Article}, source = {Scopus} } @misc{NewSimilarityMeasure, title = {A {{New Similarity Measure}} for an {{Ontology Matching System}} - {{Springer}}}, url = {http://link.springer.com/chapter/10.1007/978-3-319-25840-9_17?wt_mc=alerts.TOCseries}, urldate = {2015-11-02} } @misc{NewSoftRobot, title = {New {{Soft Robot}} Is {{Completely Autonomous}} and {{Has No Electronics}}!}, url = {http://sciencenewsjournal.com/new-soft-robot-completely-autonomous-no-electronics/}, urldate = {2016-08-29} } @article{Ng:2002:CMC:627342.628263, title = {{{CLARANS}}: {{A}} Method for Clustering Objects for Spatial Data Mining}, author = {Ng, Raymond T. and Han, Jiawei}, year = {2002}, month = sep, journal = {IEEE Trans. on Knowl. and Data Eng.}, volume = {14}, number = {5}, pages = {1003--1016}, publisher = {{IEEE Educational Activities Department}}, address = {{Piscataway, NJ, USA}}, issn = {1041-4347}, url = {http://dx.doi.org/10.1109/TKDE.2002.1033770}, acmid = {628263}, issue_date = {September 2002}, nodoi = {10.1109/TKDE.2002.1033770}, numpages = {14}, keywords = {clustering algorithms,computational geometry.,randomized search,Spatial data mining} } @inproceedings{Nguyen:2015:CRV:2942298.2942305, title = {Content-Based Recommendations via {{DBpedia}} and Freebase: {{A}} Case Study in the Music Domain}, booktitle = {Proceedings of the 14th International Conference on the Semantic Web - {{ISWC}} 2015 - Volume 9366}, author = {Nguyen, Phuong T. and Tomeo, Paolo and Di Noia, Tommaso and Di Sciascio, Eugenio}, year = {2015}, pages = {605--621}, publisher = {{Springer-Verlag New York, Inc.}}, address = {{New York, NY, USA}}, url = {http://dx.doi.org/10.1007/978-3-319-25007-6_35}, acmid = {2942305}, isbn = {978-3-319-25006-9}, nodoi = {10.1007/978-3-319-25007-6{$_3$}5}, numpages = {17}, keywords = {Content-based recommender systems,Linked open data,Quality assessment,Semantic similarity} } @inproceedings{Nguyen:2015:ESP:2740908.2742141, title = {An Evaluation of {{SimRank}} and Personalized {{PageRank}} to Build a Recommender System for the Web of Data}, booktitle = {Proceedings of the 24th International Conference on World Wide Web}, author = {Nguyen, Phuong T. and Tomeo, Paolo and Di Noia, Tommaso and Di Sciascio, Eugenio}, year = {2015}, series = {{{WWW}} '15 Companion}, pages = {1477--1482}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/2740908.2742141}, acmid = {2742141}, isbn = {978-1-4503-3473-0}, nodoi = {10.1145/2740908.2742141}, numpages = {6}, keywords = {personalized pagerank,recommender systems,simrank,web of data} } @inproceedings{Nguyen:2016:ACR:2950290.2950333, title = {{{API}} Code Recommendation Using Statistical Learning from Fine-Grained Changes}, booktitle = {Proceedings of the 2016 24th {{ACM SIGSOFT}} International Symposium on Foundations of Software Engineering}, author = {Nguyen, Anh Tuan and Hilton, Michael and Codoban, Mihai and Nguyen, Hoan Anh and Mast, Lily and Rademacher, Eli and Nguyen, Tien N. and Dig, Danny}, year = {2016}, series = {{{FSE}} 2016}, pages = {511--522}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/2950290.2950333}, acmid = {2950333}, isbn = {978-1-4503-4218-6}, nodoi = {10.1145/2950290.2950333}, numpages = {12}, keywords = {API Recommendation,Fine-grained Code Changes,Statistical Learning} } @inproceedings{Nguyen:2017:ACD:3098344.3098399, title = {Automatic Categorization with Deep Neural Network for Open-Source Java Projects}, booktitle = {Proceedings of the 39th International Conference on Software Engineering Companion}, author = {Nguyen, Anh Tuan and Nguyen, Tien N.}, year = {2017}, series = {{{ICSE-C}} '17}, pages = {164--166}, publisher = {{IEEE Press}}, address = {{Piscataway, NJ, USA}}, url = {https://doi.org/10.1109/ICSE-C.2017.118}, acmid = {3098399}, isbn = {978-1-5386-1589-8}, nodoi = {10.1109/ICSE-C.2017.118}, numpages = {3} } @article{Nguyen:2019:JSS:CrossRec, title = {{{CrossRec}}: {{Recommending}} Highly Relevant Third-Party Libraries - Manuscript under Review}, author = {Nguyen, Phuong T. and Di Rocco, Juri and Di Ruscio, Davide and Di Penta, Massimiliano}, year = {2019}, journal = {Journal of Systems and Software} } @article{Nguyen:2019:JSS:CrossSim, title = {An Automated Approach to Assess the Similarity of {{GitHub}} Repositories - Manuscript under Revision}, author = {Nguyen, Phuong T. and Di Rocco, Juri and Rubei, Riccardo and Di Ruscio, Davide}, year = {2019}, journal = {Software Quality Journal} } @inproceedings{Nguyen2019272, title = {Automated Classification of Metamodel Repositories: {{A}} Machine Learning Approach}, author = {Nguyen, P.T. and Di Rocco, J. and Di Ruscio, D. and Pierantonio, A. and Iovino, L.}, editor = {Kessentini M., Yue T., Pretschner A., Voss S., Burgueno L., Burgueno L., Yue T.}, year = {2019}, series = {Proceedings - 2019 {{ACM}}/{{IEEE}} 22nd {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS}} 2019}, pages = {272--282}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS.2019.00011}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS}, affiliation = {Universit\`a degli Studi dell'Aquila, L'Aquila, Italy; Gran Sasso Science Institute, L'Aquila, Italy}, art_number = {8906979}, document_type = {Conference Paper}, isbn = {978-1-72812-535-0}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Classification,notion} } @article{Nguyen2021, title = {Convolutional Neural Networks for Enhanced Classification Mechanisms of Metamodels}, author = {Nguyen, P.T. and Di Ruscio, D. and Pierantonio, A. and Di Rocco, J. and Iovino, L.}, year = {2021}, journal = {Journal of Systems and Software}, volume = {172}, publisher = {{Elsevier Inc.}}, issn = {01641212}, doi = {10.1016/j.jss.2020.110860}, abbrev_source_title = {J Syst Software}, affiliation = {Universit\`a degli studi dell'Aquila, L'Aquila, 67100, Italy; Gran Sasso Science Institute, Italy}, art_number = {110860}, coden = {JSSOD}, correspondence_address1 = {Di Ruscio, D.; Universit\`a degli studi dell'AquilaItaly; email: davide.diruscio@univaq.it}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Classification,notion} } @article{Nguyen20211797, title = {Evaluation of a Machine Learning Classifier for Metamodels}, author = {Nguyen, P.T. and Di Rocco, J. and Iovino, L. and Di Ruscio, D. and Pierantonio, A.}, year = {2021}, journal = {Software and Systems Modeling}, volume = {20}, number = {6}, pages = {1797--1821}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {16191366}, doi = {10.1007/s10270-021-00913-x}, abbrev_source_title = {Softw. Syst. Model.}, affiliation = {Universit\`a degli studi dell'Aquila, L'Aquila, Italy; Gran Sasso Science Institute, L'Aquila, Italy}, correspondence_address1 = {Di Ruscio, D.; Universit\`a degli studi dell'AquilaItaly; email: davide.diruscio@univaq.it}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Classification,notion} } @article{Nguyen20217333, title = {Linear and Deep Neural Network-Based Receivers for Massive {{MIMO}} Systems with One-Bit {{ADCs}}}, author = {Nguyen, L.V. and Swindlehurst, A.L. and Nguyen, D.H.N.}, year = {2021}, journal = {IEEE Transactions on Wireless Communications}, volume = {20}, number = {11}, pages = {7333--7345}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15361276}, doi = {10.1109/TWC.2021.3082844}, 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. \textcopyright{} 2002-2012 IEEE.}, document_type = {Article}, source = {Scopus} } @inproceedings{nguyenAdversarialAttacksAPI2021, title = {Adversarial {{Attacks}} to {{API Recommender Systems}}: {{Time}} to {{Wake Up}} and {{Smell}} the {{Coffee$f$}}}, booktitle = {Proceedings - 2021 36th {{IEEE}}/{{ACM International Conference}} on {{Automated Software Engineering}}, {{ASE}} 2021}, author = {Nguyen, Phuong and Di Sipio, C. and Di Rocco, J. and Di Penta, M. and Di Ruscio, D.}, year = {2021}, pages = {253--265}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ASE51524.2021.9678946}, isbn = {978-1-66540-337-5}, keywords = {Adversarial Attacks,Adversarial Machine Learning,API Mining,Recommender systems} } @inproceedings{nguyenAdversarialMachineLearning2021, title = {Adversarial Machine Learning: {{On}} the Resilience of Third-Party Library Recommender Systems}, booktitle = {{{ACM International Conference Proceeding Series}}}, author = {Nguyen, Phuong and Di Ruscio, D. and Di Rocco, J. and Di Sipio, C. and Di Penta, Massimiliano}, year = {2021}, pages = {247--253}, publisher = {{Association for Computing Machinery}}, doi = {10.1145/3463274.3463809}, isbn = {978-1-4503-9053-8}, keywords = {Adversarial Machine Learning,Recommender systems} } @article{nguyenAutomatedApproachAssess2019, title = {An {{Automated Approach}} to {{Assess}} the {{Similarity}} of {{GitHub Repositories}}}, author = {Nguyen, Phuong T and Di Rocco, Juri and Rubei, Riccardo and Di Ruscio, Davide}, year = {2020}, month = feb, journal = {Software Quality Journal}, doi = {10.1007/978-3-030-21005-2_15} } @article{nguyenAutomatedApproachAssess2020, title = {An Automated Approach to Assess the Similarity of {{GitHub}} Repositories}, author = {Nguyen, Phuong and Di Rocco, J. and Rubei, R. and Di Ruscio, D.}, year = {2020}, journal = {SOFTWARE QUALITY JOURNAL}, volume = {28}, pages = {595--631}, doi = {10.1007/s11219-019-09483-0}, keywords = {Mining software repositories,SimRank,Software quality,Software similarity} } @article{nguyenAutomatedApproachAssess2020a, title = {An Automated Approach to Assess the Similarity of {{GitHub}} Repositories}, author = {Nguyen, Phuong T. and Di Rocco, Juri and Rubei, Riccardo and Di Ruscio, Davide}, year = {2020}, journal = {SOFTWARE QUALITY JOURNAL}, doi = {10.1007/s11219-019-09483-0} } @article{nguyenAutomatedApproachAssess2020b, title = {An Automated Approach to Assess the Similarity of {{GitHub}} Repositories}, author = {Nguyen, Phuong T. and Di Rocco, Juri and Rubei, Riccardo and Di Ruscio, Davide}, year = {2020}, month = jun, journal = {Software Quality Journal}, volume = {28}, number = {2}, pages = {595--631}, issn = {0963-9314, 1573-1367}, doi = {10.1007/s11219-019-09483-0}, langid = {english} } @inproceedings{nguyenAutomatedClassificationMetamodel2019, title = {Automated {{Classification}} of {{Metamodel Repositories}}: {{A Machine Learning Approach}}}, booktitle = {{{IEEE}} / {{ACM}} 22nd {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}} ({{MODELS}})}, author = {Nguyen, Phuong T. and DI ROCCO, Juri and DI RUSCIO, Davide and Pierantonio, Alfonso and Iovino, Ludovico}, year = {2019}, publisher = {{Springer}} } @inproceedings{nguyenBuildingInformationSystems2019, title = {Building Information Systems by Means of Collaborative-Filtering Recommendation Techniques}, booktitle = {2nd {{Workshop}} on {{Flexible Advanced Information Systems}} ({{FAiSE}}) at {{CAiSE}} 2019}, author = {Nguyen, Phuong T and Di Rocco, Juri and Di Ruscio, Davide}, year = {2019}, address = {{Rome (Italy)}}, url = {http://vps.diruscio.org/nc/s/C6eS5s74DyZtSnH} } @article{nguyenConvolutionalNeuralNetworks2021, title = {Convolutional Neural Networks for Enhanced Classification Mechanisms of Metamodels}, author = {Nguyen, Thanh Phuong and Di Ruscio, D. and Pierantonio, A. and Di Rocco, J. and Iovino, L.}, year = {2021}, journal = {THE JOURNAL OF SYSTEMS AND SOFTWARE}, volume = {172}, doi = {10.1016/j.jss.2020.110860} } @article{nguyenCrossRecSupportingSoftware2019, ids = {crossrec}, title = {{{CrossRec}}: {{Supporting Software Developers}} by {{Recommending Third-party Libraries}}}, author = {Nguyen, Phuong T and Rocco, Juri Di and Ruscio, Davide Di and Penta, Massimiliano Di}, year = {2019}, journal = {Journal of Systems and Software - Elsevier}, pages = {54}, publisher = {{Elsevier BV}}, 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.}, langid = {english}, keywords = {Mining software repositories,Open Source software} } @inproceedings{nguyenCrossSimExploitingMutual2018, title = {{{CrossSim}}: {{Exploiting Mutual Relationships}} to {{Detect Similar OSS Projects}}}, booktitle = {44th {{Euromicro Conference}} on {{Software Engineering}} and {{Advanced Applications}}}, author = {Nguyen, Phuong T. and Di Rocco, Juri and Rubei, Riccardo and Di Ruscio, Davide}, year = {2018}, pages = {388--395}, doi = {10.1109/SEAA.2018.00069}, isbn = {978-1-5386-7383-6} } @inproceedings{nguyenCrossSimExploitingMutual2018a, title = {{{CrossSim}}: {{Exploiting}} Mutual Relationships to Detect Similar {{OSS}} Projects}, booktitle = {Proceedings - 44th {{Euromicro Conference}} on {{Software Engineering}} and {{Advanced Applications}}, {{SEAA}} 2018}, author = {Nguyen, Phuong and Di Rocco, J. and Rubei, R. and Di Ruscio, D.}, year = {2018}, pages = {388--395}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/SEAA.2018.00069}, isbn = {978-1-5386-7383-6}, keywords = {Mining software repositories,SimRank,Software similarities} } @article{nguyenDeepLibMachineTranslation2022, title = {{{DeepLib}}: {{Machine}} Translation Techniques to Recommend Upgrades for Third-Party Libraries}, author = {Nguyen, Phuong Thanh and Di Rocco, Juri and Rubei, Riccardo and Di Sipio, Claudio and Di Ruscio, Davide}, year = {2022}, journal = {EXPERT SYSTEMS WITH APPLICATIONS}, doi = {10.1016/j.eswa.2022.117267} } @inproceedings{nguyenEnablingHeterogeneousRecommendations2019, ids = {Nguyen:2019:EHR:3319008.3319353}, title = {Enabling Heterogeneous Recommendations in {{OSS}} Development: What's Done and What's next in {{CROSSMINER}}}, booktitle = {23rd {{Evaluation}} and {{Assessment}} in {{Software Engineering}} ({{EASE}} 2019)}, author = {Nguyen, Phuong T and Rocco, Juri Di and Ruscio, Davide Di}, year = {2019}, pages = {6}, address = {{Copenhagen, Denmark}}, url = {http://vps.diruscio.org/nc/s/gd36SsJm5MBQg68}, 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.}, acmid = {3319353}, langid = {english}, nodoi = {10.1145/3319008.3319353}, numpages = {6}, keywords = {machine learning,recommender systems,software engineering} } @article{nguyenEvaluationMachineLearning, title = {Evaluation of {{Machine Learning Classifiers}} for {{Metamodels}}}, author = {Nguyen, Phuong T and Rocco, Juri Di and Iovino, Ludovico and Ruscio, Davide Di and Pierantonio, Alfonso}, pages = {25}, 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.}, langid = {english} } @inproceedings{nguyenKnowledgeawareRecommenderSystem2018, title = {Knowledge-Aware Recommender System for Software Development}, booktitle = {Proceedings of the 1st Workshop on Knowledge-Aware and Conversational Recommender System}, author = {Nguyen, Phuong T. and Di Rocco, Juri and Di Ruscio, Davide}, year = {2018}, series = {{{KaRS}} 2018}, publisher = {{ACM}}, address = {{New York, NY, USA}}, numpages = {7}, keywords = {Autoencoders,DBpedia,Deep Learning,Knowledge graphs,Linked Open Data,Recommender Systems} } @inproceedings{nguyenKnowledgeawareRecommenderSystem2018a, title = {Knowledge-Aware Recommender System for Software Development}, booktitle = {{{CEUR Workshop Proceedings}}}, author = {Nguyen, P. T. and Di Rocco, J. and Di Ruscio, D.}, year = {2018}, volume = {2290}, pages = {16--22}, publisher = {{CEUR-WS}} } @inproceedings{nguyenKnowledgeawareRecommenderSystem2018b, title = {Knowledge-Aware Recommender System for Software Development}, booktitle = {{{CEUR Workshop Proceedings}}}, author = {Nguyen, P. T. and Di Rocco, J. and Di Ruscio, D.}, year = {2018}, volume = {2290}, pages = {16--22}, publisher = {{CEUR-WS}} } @inproceedings{nguyenMiningSoftwareRepositories2018, ids = {DBLP:conf/iir/NguyenRR18}, title = {Mining Software Repositories to Support {{OSS}} Developers: {{A}} Recommender Systems Approach}, booktitle = {{{CEUR}} Workshop Proceedings}, author = {Nguyen, Phuong and Di Rocco, Juri and Di Ruscio, Davide}, year = {2018}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {2140}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/iir/NguyenRR18}, keywords = {Computer Science (all)}, timestamp = {Tue, 24 Jul 2018 12:47:23 +0200} } @inproceedings{nguyenMiningSoftwareRepositories2018a, title = {Mining Software Repositories to Support {{OSS}} Developers: {{A}} Recommender Systems Approach}, booktitle = {{{CEUR Workshop Proceedings}}}, author = {Nguyen, THANH PHUONG and Di Rocco, Juri and Di Ruscio, Davide}, year = {2018}, volume = {2140}, publisher = {{CEUR-WS}}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @article{nguyenRecommendingAPIFunction2021, title = {Recommending {{API Function Calls}} and {{Code Snippets}} to {{Support Software Development}}}, author = {Nguyen, Phuong T. and Rocco, Juri Di and Sipio, Claudio Di and Ruscio, Davide Di and Penta, Massimiliano Di}, year = {2021}, journal = {CoRR}, volume = {abs/2102.07508}, url = {https://arxiv.org/abs/2102.07508} } @inproceedings{Nicolae2021, title = {Metamodel-Based Prediction of {{On Resistance}} for Microelectronic Power Switches}, author = {Nicolae, G. and Buzo, A. and Feuerbaum, C. and Diaconu, C.V. and Cucu, H. and Pelz, G. and Burileanu, C.}, year = {2021}, series = {{{IEEE Electrical Design}} of {{Advanced Packaging}} and {{Systems Symposium}}}, volume = {2021-December}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {21511225}, doi = {10.1109/EDAPS53774.2021.9656996}, abbrev_source_title = {IEEE Electr. Des. Adv. Packag. Syst. Symp.}, affiliation = {University Politehnica of Bucharest, Romania; Infineon Technologies, Neubiberg, Germany}, correspondence_address1 = {Nicolae, G.; University Politehnica of BucharestRomania; email: georgian.nicolae@ieee.org}, document_type = {Conference Paper}, isbn = {978-1-66546-613-4}, langid = {english}, source = {Scopus} } @article{nielsenNeuralNetworksDeep2018, type = {Misc}, title = {Neural Networks and Deep Learning}, author = {Nielsen, Michael A.}, year = {2018}, publisher = {{Determination Press}}, url = {http://neuralnetworksanddeeplearning.com/}, added-at = {2019-01-15T22:46:49.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/274383acee84241145ff4ffede9658206/slicside}, interhash = {04d527cadd39f888fc3babcad3343362}, intrahash = {74383acee84241145ff4ffede9658206}, keywords = {ba-2018-hahnrico}, timestamp = {2019-01-15T22:46:49.000+0100} } @inproceedings{Niemann:2013:NCF:2487575.2487656, title = {A New Collaborative Filtering Approach for Increasing the Aggregate Diversity of Recommender Systems}, booktitle = {Proceedings of the 19th {{ACM SIGKDD}} International Conference on Knowledge Discovery and Data Mining}, author = {Niemann, Katja and Wolpers, Martin}, year = {2013}, series = {{{KDD}} '13}, pages = {955--963}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/2487575.2487656}, acmid = {2487656}, isbn = {978-1-4503-2174-7}, nodoi = {10.1145/2487575.2487656}, numpages = {9}, keywords = {aggregate diversity,item-item similarity,long tail,niche items,recommender systems,usage context} } @inproceedings{Niggemann201221, title = {Solving Modeling Problems with Machine Learning a Classification Scheme of Model Learning Approaches for Technical Systems}, author = {Niggemann, O. and Stein, B. and Maier, A.}, year = {2012}, series = {Tagungsband - {{Dagstuhl-Workshop MBEES}}: {{Modellbasierte Entwicklung}} Eingebetteter {{Systeme VIII}}, {{MBEES}} 2012}, pages = {21--29}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84873361603&partnerID=40&md5=dd7dfd4d9c468d595a967b9a1c21ce38}, abbrev_source_title = {Tagungsband - Dagstuhl-Workshop MBEES: Model. Entwickl. eingebetteter Systeme, MBEES}, affiliation = {Fraunhofer IOSB - Competence Center Industrial Automation, Lemgo, Germany; IT - Institut Industrial IT, Hochschule Ostwestfalen-Lippe, Lemgo, Germany; Bauhaus-Universit\"at Weimar, Germany}, correspondence_address1 = {Niggemann, O.; Fraunhofer IOSB - Competence Center Industrial Automation, Lemgo, Germany; email: oliver.niggemann@iosb-ina.fraunhofer.de}, document_type = {Conference Paper}, langid = {english}, source = {Scopus} } @article{nikanjamFaultsDeepReinforcement2021, title = {Faults in {{Deep Reinforcement Learning Programs}}: {{A Taxonomy}} and {{A Detection Approach}}}, shorttitle = {Faults in {{Deep Reinforcement Learning Programs}}}, author = {Nikanjam, Amin and Morovati, Mohammad Mehdi and Khomh, Foutse and Braiek, Houssem Ben}, year = {2021}, month = jan, journal = {arXiv:2101.00135 [cs]}, eprint = {2101.00135}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2101.00135}, urldate = {2021-01-09}, 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.}, archiveprefix = {arXiv} } @article{Niknam202046, ids = {niknamFederatedLearningWireless2020a}, title = {Federated Learning for Wireless Communications: {{Motivation}}, Opportunities, and Challenges}, author = {Niknam, S. and Dhillon, H.S. and Reed, J.H.}, year = {2020}, journal = {IEEE Communications Magazine}, volume = {58}, number = {6}, pages = {46--51}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {01636804}, doi = {10.1109/MCOM.001.1900461}, 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. \textcopyright{} 1979-2012 IEEE.}, art_number = {9141214}, coden = {ICOMD}, document_type = {Article}, source = {Scopus}, keywords = {Central-entity,Communication overheads,Data handling,Privacy preserving,Private data,Technical challenges,Traditional models,Wireless application,Wireless communications} } @article{niuAPIUsagePattern2017, ids = {Niu2017API,Niu:2017:AUP:3104915.3104977}, title = {{{API}} Usage Pattern Recommendation for Software Development}, author = {Niu, Haoran and Keivanloo, Iman and Zou, Ying}, year = {2017}, month = jul, journal = {Journal of Systems and Software}, volume = {129}, pages = {127--139}, publisher = {{Elsevier}}, issn = {01641212}, doi = {10.1016/j.jss.2016.07.026}, acmid = {3104977}, issue_date = {July 2017}, langid = {english}, noaddress = {New York, NY, USA}, nodoi = {10.1016/j.jss.2016.07.026}, numpages = {13}, keywords = {Clustering,Object usage,Usage pattern} } @inproceedings{noiaRecommenderSystemsLinked2015, title = {Recommender Systems and Linked Open Data}, booktitle = {Reasoning Web. {{Web}} Logic Rules - 11th International Summer School 2015, Berlin, Germany, July 31 - August 4, 2015, Tutorial Lectures}, author = {Noia, Tommaso Di and Ostuni, Vito Claudio}, year = {2015}, pages = {88--113}, doi = {10.1007/978-3-319-21768-0_4}, bibsource = {dblp computer science bibliography, http://dblp.org}, biburl = {http://dblp.org/rec/bib/conf/rweb/NoiaO15}, timestamp = {Sun, 21 May 2017 00:20:32 +0200} } @book{nonamiAutonomousControlSystems2013, 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]}, shorttitle = {Autonomous Control Systems and Vehicles}, editor = {Nonami, Kenzo and {International Conference on Intelligent Unmanned Systems} and {International Society of Intelligent Unmanned Systems}}, year = {2013}, series = {International Series on Intelligent Systems, Control and Automation: Science and Engineering}, number = {65}, publisher = {{Springer}}, address = {{Tokyo}}, isbn = {978-4-431-54276-6 978-4-431-54275-9}, langid = {english} } @incollection{nordmannSurveyDomainspecificLanguages2014, title = {A Survey on Domain-Specific Languages in Robotics}, booktitle = {Simulation, {{Modeling}}, and {{Programming}} for {{Autonomous Robots}}}, author = {Nordmann, Arne and Hochgeschwender, Nico and Wrede, Sebastian}, year = {2014}, pages = {195--206}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/978-3-319-11900-7_17}, urldate = {2015-04-21} } @book{northropUltralargescaleSystemsSoftware2006, title = {Ultra-Large-Scale Systems: The Software Challenge of the Future}, shorttitle = {Ultra-Large-Scale Systems}, author = {Northrop, Linda and Feiler, Peter H and Pollak, Bill and Pipitone, Daniel}, year = {2006}, publisher = {{Software Engineering Institute, Carnegie Mellon University}}, address = {{Pittsburgh, Pa.}}, isbn = {978-0-9786956-0-6}, langid = {english} } @misc{NoSQLDataModeling, title = {{{NoSQL Data Modeling}} | {{eBay Tech Blog}}}, url = {http://www.ebaytechblog.com/2014/10/10/nosql-data-modeling/#.VRPVNfmJtrc}, urldate = {2015-03-26} } @misc{NoSQLDataModelinga, title = {{{NoSQL Data Modeling Techniques}} \textendash{} {{Highly Scalable Blog}}}, url = {https://highlyscalable.wordpress.com/2012/03/01/nosql-data-modeling-techniques/}, urldate = {2018-05-06} } @misc{NotionAllinoneWorkspace, ids = {NotionAllinoneWorkspacea,NotionAllinoneWorkspaceb,NotionAllinoneWorkspacec}, title = {Notion \textendash{} {{The}} All-in-One Workspace for Your Notes, Tasks, Wikis, and Databases.}, journal = {Notion}, url = {https://www.notion.so}, urldate = {2020-02-11}, abstract = {A new tool that blends your everyday work apps into one. It's the all-in-one workspace for you and your team}, langid = {english} } @misc{NotionNotes, title = {Notion Notes}, url = {https://www.notion.so/Publications-SoSyM-and-Visions-81b70721668c4e5d83b78bac2dbde571} } @article{novielliLoveJoyAnger2020, title = {Love, {{Joy}}, {{Anger}}, {{Sadness}}, {{Fear}}, and {{Surprise}}: {{SE Needs Special Kinds}} of {{AI}}: {{A Case Study}} on {{Text Mining}} and {{SE}}}, shorttitle = {Love, {{Joy}}, {{Anger}}, {{Sadness}}, {{Fear}}, and {{Surprise}}}, author = {Novielli, Nicole and Calefato, Fabio and Lanubile, Filippo}, year = {2020}, month = may, journal = {IEEE Software}, volume = {37}, number = {3}, pages = {86--91}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2020.2968557}, langid = {english} } @article{ntiMinireviewMachineLearning2022, title = {A Mini-Review of Machine Learning in Big Data Analytics: {{Applications}}, Challenges, and Prospects}, shorttitle = {A Mini-Review of Machine Learning in Big Data Analytics}, author = {Nti, Isaac Kofi and Quarcoo, Juanita Ahia and Aning, Justice and Fosu, Godfred Kusi}, year = {2022}, month = jun, journal = {Big Data Mining and Analytics}, volume = {5}, number = {2}, pages = {81--97}, issn = {2096-0654}, doi = {10.26599/BDMA.2021.9020028}, 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.}, langid = {english} } @misc{Numediart, title = {Numediart}, url = {http://www.numediart.org/2015/06/23/hci-seminar-research-advances-in-interactive-systems-modeling-%C2%BB/}, urldate = {2016-01-23} } @article{oakesBuildingDomainSpecificMachine2022, title = {Building {{Domain-Specific Machine Learning Workflows}}: {{A Conceptual Framework}} for the {{State-of-the-Practice}}}, shorttitle = {Building {{Domain-Specific Machine Learning Workflows}}}, author = {Oakes, Bentley James and Famelis, Michalis and Sahraoui, Houari}, year = {2022}, month = mar, journal = {arXiv:2203.08638 [cs]}, eprint = {2203.08638}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2203.08638}, urldate = {2022-03-22}, 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.}, archiveprefix = {arXiv}, keywords = {GOAL_MDE4AI} } @article{obrenovicQuotesIEEESoftware2018, title = {Quotes from {{IEEE Software History}}}, author = {Obrenovi{\'c}, {\v Z}}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {10--13}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571243}, 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\textendash 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.} } @inproceedings{ODMD14a, title = {A Linked Data Recommender System Using a Neighborhood-Based Graph Kernel}, booktitle = {The 15th International Conference on Electronic Commerce and Web Technologies}, author = {Ostuni, Vito Claudio and Di Noia, Tommaso and Mirizzi, Roberto and Di Sciascio, Eugenio}, year = {2014}, series = {Lecture Notes in Business Information Processing}, publisher = {{Springer}}, url = {http://sisinflab.poliba.it/sisinflab/publications/ 2014/ODMD14a} } @article{odonovanIndustrialBigData2015, title = {An Industrial Big Data Pipeline for Data-Driven Analytics Maintenance Applications in Large-Scale Smart Manufacturing Facilities}, author = {O'Donovan, P. and Leahy, K. and Bruton, K. and O'Sullivan, D. T. J.}, year = {2015}, month = dec, journal = {Journal of Big Data}, volume = {2}, number = {1}, pages = {25}, issn = {2196-1115}, doi = {10.1186/s40537-015-0034-z}, 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.}, langid = {english} } @inproceedings{Ogden202151, title = {Many Models at the Edge: {{Scaling}} Deep Inference via Model-Level Caching}, author = {Ogden, S.S. and Gilman, G.R. and Walls, R.J. and Guo, T.}, editor = {{El-Araby E., Kalogeraki V.}, Lassabe F., Porter B., Ghahremani S., Nunes I., Bakhouya M., Tomforde S., Pianini D.}, year = {2021}, series = {Proceedings - 2021 {{IEEE International Conference}} on {{Autonomic Computing}} and {{Self-Organizing Systems}}, {{ACSOS}} 2021}, pages = {51--60}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ACSOS52086.2021.00027}, 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 \textcopyright{} 2021 IEEE.}, document_type = {Conference Paper}, isbn = {978-1-66541-261-2}, source = {Scopus} } @article{Okewu2020273, title = {A Software Engineering Approach to Implementation of {{SDG}} 6 in Adum-Aiona Community of Nigeria}, author = {Okewu, E. and Misra, S. and Lius, F.-S.}, editor = {Gervasi O., Murgante B., Garau C., Blecic I., Taniar D., Apduhan B.O., Rocha A.M.A.C., Tarantino E., Torre C.M., Karaca Y., Misra S.}, year = {2020}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {12254 LNCS}, pages = {273--288}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-58817-5_21}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Centre for Information Technology and Systems, University of Lagos, Lagos, Nigeria; Department of Electrical and Information Engineering, Covenant University, Ota, Nigeria; Department of Computer Sciences, University of Alcala, Henares, Spain}, correspondence_address1 = {Okewu, E.; Centre for Information Technology and Systems, Nigeria; email: eokewu@unilag.edu.ng}, document_type = {Conference Paper}, isbn = {9783030588168}, langid = {english}, source = {Scopus} } @inproceedings{Okobiah2014365, title = {Kriging Bootstrapped Neural Network Training for Fast and Accurate Process Variation Analysis}, author = {Okobiah, O. and Mohanty, S.P. and Kougianos, E.}, year = {2014}, series = {Proceedings - {{International Symposium}} on {{Quality Electronic Design}}, {{ISQED}}}, pages = {365--372}, publisher = {{IEEE Computer Society}}, issn = {19483287}, doi = {10.1109/ISQED.2014.6783349}, abbrev_source_title = {Proc. - Int. Symp. Qual. Electron. Des., ISQED}, affiliation = {NanoSystem Design Laboratory (NSDL), University of North Texas, Denton, TX 76207, United States}, art_number = {6783349}, document_type = {Conference Paper}, isbn = {978-1-4799-3946-6}, langid = {english}, source = {Scopus} } @article{omaEnergyefficientModelFog2018, title = {An Energy-Efficient Model for Fog Computing in the {{Internet}} of {{Things}} ({{IoT}})}, author = {Oma, Ryuji and Nakamura, Shigenari and Duolikun, Dilawaer and Enokido, Tomoya and Takizawa, Makoto}, year = {2018}, month = sep, journal = {Internet of Things}, volume = {1--2}, pages = {14--26}, issn = {25426605}, doi = {10.1016/j.iot.2018.08.003}, 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.}, langid = {english} } @misc{OpenVsClosedloop, title = {Open- vs. Closed-Loop Control~|~{{Control Engineering}}}, url = {http://www.controleng.com/single-article/open-vs-closed-loop-control/f8d8023a15738d0fcfe78d6a2d71dd60.html}, urldate = {2016-11-01} } @article{OrchestratingATLModel, title = {Orchestrating {{ATL Model Transformations}}} } @misc{OSGiModularityTutorial, title = {{{OSGi Modularity}} - {{Tutorial}}}, url = {http://www.vogella.com/tutorials/OSGi/article.html#introduction-into-software-modularity-with-osgi}, urldate = {2016-12-02} } @inproceedings{ostuniTopnRecommendationsImplicit2013, title = {Top-n Recommendations from Implicit Feedback Leveraging Linked Open Data}, booktitle = {Proceedings of the 7th {{ACM}} Conference on Recommender Systems}, author = {Ostuni, Vito Claudio and Di Noia, Tommaso and Di Sciascio, Eugenio and Mirizzi, Roberto}, year = {2013}, series = {{{RecSys}} '13}, pages = {85--92}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/2507157.2507172}, acmid = {2507172}, isbn = {978-1-4503-2409-0}, nodoi = {10.1145/2507157.2507172}, numpages = {8}, keywords = {dbpedia,hybrid recommender system,implicit feedback,learning to rank,linked data,top-n recommendations} } @article{oubelliScalableModelBased2018, title = {A Scalable Model Based Approach for Data Model Evolution: {{Application}} to Space Missions Data Models}, shorttitle = {A Scalable Model Based Approach for Data Model Evolution}, author = {Oubelli, Lynda Ait and A{\"i}t Ameur, Yamine and Bedouet, Judicael and Kervarc, Romain and {Chausserie-Lapr{\'e}e}, Benoit and Larzul, B{\'e}atrice}, year = {2018}, month = aug, journal = {Computer Languages, Systems \& Structures}, issn = {14778424}, doi = {10.1016/j.cl.2018.08.001}, langid = {english} } @inproceedings{ouelletControlSwarmsAutonomous2011, title = {Control of Swarms of Autonomous Robots Using {{Model Driven Development-A}} State-Based Approach}, booktitle = {Systems {{Conference}} ({{SysCon}}), 2011 {{IEEE International}}}, author = {Ouellet, Dany and Givigi, Sidney N. and Beaulieu, Alain JG}, year = {2011}, pages = {512--519}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5929129}, urldate = {2016-08-21} } @article{ouniSearchbasedSoftwareLibrary2017, ids = {Ouni:2017:SSL:3032135.3032325}, title = {Search-Based Software Library Recommendation Using Multi-Objective Optimization}, author = {Ouni, Ali and Kula, Raula Gaikovina and Kessentini, Marouane and Ishio, Takashi and German, Daniel M. and Inoue, Katsuro}, year = {2017}, month = mar, journal = {Information and Software Technology}, volume = {83}, pages = {55--75}, publisher = {{Butterworth-Heinemann}}, address = {{Newton, MA, USA}}, issn = {09505849}, doi = {10.1016/j.infsof.2016.11.007}, acmid = {3032325}, issue_date = {March 2017}, langid = {english}, nodoi = {10.1016/j.infsof.2016.11.007}, numpages = {21}, keywords = {Multi-objective optimization,Search-based software engineering,Software library,Software reuse} } @misc{OverviewAutonomousSystems, title = {Overview of the {{Autonomous Systems Area}} | {{Wallenberg ASP}}}, url = {http://wasp-sweden.org/research/overview-of-autonomous-systems-area/}, urldate = {2016-08-26} } @misc{PaaSword, title = {{{PaaSword}}}, url = {https://sites.google.com/site/paaswordeu/}, urldate = {2015-04-08} } @inproceedings{Padget201435, title = {On Requirements Representation and Reasoning Using Answer Set Programming}, author = {Padget, J. and Elakehal, E.E. and Satoh, K. and Ishikawa, F.}, year = {2014}, series = {2014 {{IEEE}} 1st {{International Workshop}} on {{Artificial Intelligence}} for {{Requirements Engineering}}, {{AIRE}} 2014 - {{Proceedings}}}, pages = {35--42}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/AIRE.2014.6894854}, abbrev_source_title = {IEEE Int. Workshop Artif. Intell. Requir. Eng., AIRE - Proc.}, affiliation = {Department of Computer Science, University of Bath, United Kingdom; National Institute of Informatics and Sokendai, Japan; National Institute of Informatics, Japan}, art_number = {6894854}, correspondence_address1 = {Padget, J.; Department of Computer Science, University of BathUnited Kingdom}, document_type = {Conference Paper}, isbn = {978-1-4799-6355-3}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Requirements,notion} } @inproceedings{pageLearningAutonomousSystems2017, title = {Learning Autonomous Systems \textemdash{} {{An}} Interdisciplinary Project-Based Experience}, booktitle = {2017 {{IEEE Frontiers}} in {{Education Conference}} ({{FIE}})}, author = {Page, Brian R. and Ziaeefard, Saeedeh and Moridian, Barzin and Mahmoudian, Nina}, year = {2017}, month = oct, pages = {1--7}, publisher = {{IEEE}}, address = {{Indianapolis, IN}}, doi = {10.1109/FIE.2017.8190555}, isbn = {978-1-5090-5920-1} } @article{paigeEvolvingModelsModelDriven2015, ids = {paigeEvolvingModelsModelDriven2016,wrro110199}, title = {Evolving {{Models}} in {{Model-Driven Engineering}}: {{State-of-the-Art}} and {{Future Challenges}}}, shorttitle = {Evolving {{Models}} in {{Model-Driven Engineering}}}, author = {Paige, Richard F. and Matragkas, Nicholas and Rose, Louis M.}, year = {2015}, journal = {Journal of Systems and Software}, url = {http://www.sciencedirect.com/science/article/pii/S0164121215001909}, urldate = {2015-10-19} } @book{paigeProceedings2015ACM2015, title = {Proceedings of the 2015 {{ACM SIGPLAN International Conference}} on {{Software Language Engineering}}, {{SLE}} 2015, {{Pittsburgh}}, {{PA}}, {{USA}}, {{October}} 25-27, 2015}, editor = {Paige, Richard F. and Ruscio, Davide Di and V{\"o}lter, Markus}, year = {2015}, publisher = {{ACM}}, url = {http://dl.acm.org/citation.cfm?id=2814251}, isbn = {978-1-4503-3686-4} } @article{paigeRigorousIdentificationEncoding2010, title = {Rigorous Identification and Encoding of Trace-Links in Model-Driven Engineering}, author = {Paige, Richard F. and Drivalos, Nikolaos and Kolovos, Dimitrios S. and Fernandes, Kiran J. and Power, Christopher and Olsen, Goran K. and Zschaler, Steffen}, year = {2010}, journal = {Software \& Systems Modeling}, volume = {10}, number = {4}, pages = {469--487}, doi = {10.1007/s10270-010-0158-8} } @inproceedings{pakdeetrakulwongRecommendationSystemsSoftware2014, title = {Recommendation Systems for Software Engineering: {{A}} Survey from Software Development Life Cycle Phase Perspective}, shorttitle = {Recommendation Systems for Software Engineering}, author = {Pakdeetrakulwong, Udsanee and Wongthongtham, Pornpit and Siricharoen, Waralak V.}, year = {2014}, month = dec, pages = {137--142}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/document/7038793/}, urldate = {2017-06-19}, isbn = {978-1-908320-39-1}, nodoi = {10.1109/ICITST.2014.7038793} } @inproceedings{palyartMDE4HPCApproachUsing2011, title = {{{MDE4HPC}}: An Approach for Using Model-Driven Engineering in High-Performance Computing}, shorttitle = {{{MDE4HPC}}}, booktitle = {International {{SDL Forum}}}, author = {Palyart, Marc and Lugato, David and Ober, Ileana and Bruel, Jean-Michel}, year = {2011}, pages = {247--261}, publisher = {{Springer}}, url = {http://link.springer.com/10.1007%2F978-3-642-25264-8_19}, urldate = {2017-02-23} } @article{PAM, title = {{{PAM}}: {{Probabilistic API}} Miner}, author = {Fowkes, Jaroslav and Sutton, Charles}, url = {https://github.com/mast-group/api-mining} } @article{panachEvaluatingModelDrivenDevelopment2021, title = {Evaluating {{Model-Driven Development Claims}} with {{Respect}} to {{Quality}}: {{A Family}} of {{Experiments}}}, shorttitle = {Evaluating {{Model-Driven Development Claims}} with {{Respect}} to {{Quality}}}, author = {Panach, Jose Ignacio and Dieste, Oscar and Marin, Beatriz and Espana, Sergio and Vegas, Sira and Pastor, Oscar and Juristo, Natalia}, year = {2021}, month = jan, journal = {IEEE Transactions on Software Engineering}, volume = {47}, number = {1}, pages = {130--145}, issn = {0098-5589, 1939-3520, 2326-3881}, doi = {10.1109/TSE.2018.2884706} } @article{panDevelopingHybridIntrusion2015, title = {Developing a {{Hybrid Intrusion Detection System Using Data Mining}} for {{Power Systems}}}, author = {Pan, Shengyi and Morris, Thomas and Adhikari, Uttam}, year = {2015}, month = nov, journal = {IEEE Transactions on Smart Grid}, volume = {6}, number = {6}, pages = {3104--3113}, issn = {1949-3053, 1949-3061}, doi = {10.1109/TSG.2015.2409775}, 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.}, langid = {english} } @inproceedings{Panichella:2013:EUT:2486788.2486857, title = {How to Effectively Use Topic Models for Software Engineering Tasks? {{An}} Approach Based on Genetic Algorithms}, booktitle = {Proceedings of the 2013 International Conference on Software Engineering}, author = {Panichella, Annibale and Dit, Bogdan and Oliveto, Rocco and Di Penta, Massimiliano and Poshyvanyk, Denys and De Lucia, Andrea}, year = {2013}, series = {{{ICSE}} '13}, pages = {522--531}, publisher = {{IEEE Press}}, address = {{Piscataway, NJ, USA}}, url = {http://dl.acm.org.univaq.clas.cineca.it/citation.cfm?id=2486788.2486857}, acmid = {2486857}, isbn = {978-1-4673-3076-3}, numpages = {10} } @article{Pant2020504, title = {Co-Modelling Strategy for Development of Airpath Metamodel on Multi-Physics Simulation Platform}, author = {Pant, G. and Campean, F. and Korsunovs, A. and Neagu, D. and {Garcia-Afonso}, O.}, editor = {Ju Z., Zhou D., Yang L., Yang C., Gegov A.}, year = {2020}, journal = {Advances in Intelligent Systems and Computing}, volume = {1043}, pages = {504--516}, publisher = {{Springer Verlag}}, issn = {21945357}, doi = {10.1007/978-3-030-29933-0_42}, abbrev_source_title = {Adv. Intell. Sys. Comput.}, affiliation = {University of Bradford, Bradford, United Kingdom; University of La Laguna, Tenerife, Spain}, correspondence_address1 = {Pant, G.; University of BradfordUnited Kingdom; email: g.pant@bradford.ac.uk}, document_type = {Conference Paper}, isbn = {9783030299323}, langid = {english}, source = {Scopus} } @misc{PapyrusIoTModeling, title = {Papyrus for {{IoT}} \textendash{} {{A Modeling Solution}} for {{IoT}}}, url = {https://www.eclipse.org/community/eclipse_newsletter/2016/april/article3.php}, urldate = {2016-08-21} } @misc{ParallelProgrammingModel, title = {1.3 {{A Parallel Programming Model}}}, url = {http://www.mcs.anl.gov/~itf/dbpp/text/node9.html}, urldate = {2017-02-23} } @inproceedings{Park2015, title = {A Prediction Modeling Framework: {{Toward}} Integration of Noisy Manufacturing Data and Product Design}, author = {Park, J. and Kim, K.-Y. and Sohmshetty, R.}, year = {2015}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {2A-2015}, publisher = {{American Society of Mechanical Engineers (ASME)}}, doi = {10.1115/DETC201546236}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, United States; Group Leader, Advanced Steel Technology R and A Engineering, Ford Motor Company, Dearborn, MI 48124, United States}, document_type = {Conference Paper}, isbn = {978-0-7918-5707-6}, langid = {english}, source = {Scopus} } @article{Park202083, title = {Designing the Ai Developing System through Ecological Interface Design}, author = {Park, D. and Park, H. and Song, S.}, editor = {Ahram T., Falcao C.}, year = {2020}, journal = {Advances in Intelligent Systems and Computing}, volume = {1217 AISC}, pages = {83--96}, publisher = {{Springer}}, issn = {21945357}, doi = {10.1007/978-3-030-51828-8_12}, 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. \textcopyright{} The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.}, document_type = {Conference Paper}, isbn = {9783030518271}, source = {Scopus} } @inproceedings{parkIoTRoutingArchitecture2014, title = {{{IoT}} Routing Architecture with Autonomous Systems of Things}, booktitle = {Internet of {{Things}} ({{WF-IoT}}), 2014 {{IEEE World Forum}} On}, author = {Park, Soochang and Crespi, Noel and Park, Hosung and Kim, Sang-Ha}, year = {2014}, pages = {442--445}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6803207}, urldate = {2016-11-02} } @inproceedings{parkSelfmanagementSystemBased2006, title = {Self-Management System Based on Self-Healing Mechanism}, booktitle = {Asia-{{Pacific Network Operations}} and {{Management Symposium}}}, author = {Park, Jeongmin and Yoo, Giljong and Jeong, Chulho and Lee, Eunseok}, year = {2006}, pages = {372--382}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/11876601_38}, urldate = {2016-09-21} } @article{parnasCriteriaBeUsed1972, title = {On the {{Criteria To Be Used}} in {{Decomposing Systems}} into {{Modules}}}, author = {Parnas, D L}, year = {1972}, volume = {15}, number = {12}, pages = {6}, 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.}, langid = {english} } @article{parnin2012crowd, title = {Crowd Documentation: {{Exploring}} the Coverage and the Dynamics of {{API}} Discussions on {{Stack Overflow}}}, author = {Parnin, Chris and Treude, Christoph and Grammel, Lars and Storey, Margaret-Anne}, year = {2012}, journal = {Georgia Institute of Technology, Tech. Rep} } @article{parra-ullauriEventdrivenTemporalModels2022, title = {Event-Driven Temporal Models for Explanations - {{ETeMoX}}: Explaining Reinforcement Learning}, shorttitle = {Event-Driven Temporal Models for Explanations - {{ETeMoX}}}, author = {{Parra-Ullauri}, Juan Marcelo and {Garc{\'i}a-Dom{\'i}nguez}, Antonio and Bencomo, Nelly and Zheng, Changgang and Zhen, Chen and {Boubeta-Puig}, Juan and Ortiz, Guadalupe and Yang, Shufan}, year = {2022}, month = jun, journal = {Software and Systems Modeling}, volume = {21}, number = {3}, pages = {1091--1113}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-021-00952-4}, abstract = {Abstract 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 online model-agnostic 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.}, langid = {english}, keywords = {TECHNIQUE_Reinforcement-Learning} } @inproceedings{Passant:2010:DMR:1940334.1940349, title = {Dbrec: {{Music}} Recommendations Using {{DBpedia}}}, booktitle = {Proceedings of the 9th International Semantic Web Conference on the Semantic Web - Volume Part {{II}}}, author = {Passant, Alexandre}, year = {2010}, series = {{{ISWC}}'10}, pages = {209--224}, publisher = {{Springer-Verlag}}, address = {{Berlin, Heidelberg}}, url = {http://dl.acm.org/citation.cfm?id=1940334.1940349}, acmid = {1940349}, isbn = {3-642-17748-4 978-3-642-17748-4}, numpages = {16}, keywords = {DBpedia,linked data,recommendation systems,semantic distance,semantic web applications} } @inproceedings{passantMeasuringSemanticDistance2010, title = {Measuring Semantic Distance on Linking Data and Using It for Resources Recommendations.}, booktitle = {{{AAAI}} Spring Symposium: {{Linked}} Data Meets Artificial Intelligence}, author = {Passant, Alexandre}, year = {2010}, publisher = {{AAAI}}, url = {http://dblp.uni-trier.de/db/conf/aaaiss/aaaiss2010-7.html#Passant10}, added-at = {2012-02-17T00:00:00.000+0100}, biburl = {http://www.bibsonomy.org/bibtex/2cdd5d7e0e615eb104fe56a4c90ceb96a/dblp}, ee = {http://www.aaai.org/ocs/index.php/SSS/SSS10/paper/view/1147}, interhash = {eee04621d061bb7f9143ce6b36b9ded6}, intrahash = {cdd5d7e0e615eb104fe56a4c90ceb96a}, keywords = {dblp}, timestamp = {2012-02-17T00:00:00.000+0100} } @book{pastorAdvancedInformationSystems2005, title = {Advanced {{Information Systems Engineering}}, 17th {{International Conference}}, {{CAiSE}} 2005, {{Porto}}, {{Portugal}}, {{June}} 13-17, 2005, {{Proceedings}}}, editor = {Pastor, Oscar and e Cunha, Jo{\~a}o Falc{\~a}o}, year = {2005}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {3520}, publisher = {{Springer}}, doi = {10.1007/b136788}, isbn = {3-540-26095-1} } @article{patelEnablingHighlevelApplication2015, title = {Enabling High-Level Application Development for the {{Internet}} of {{Things}}}, author = {Patel, Pankesh and Cassou, Damien}, year = {2015}, journal = {Journal of Systems and Software}, volume = {103}, pages = {62--84}, url = {http://www.sciencedirect.com/science/article/pii/S0164121215000187}, urldate = {2016-05-30} } @article{pattersonCarbonEmissionsLarge2021, title = {Carbon {{Emissions}} and {{Large Neural Network Training}}}, author = {Patterson, David and Gonzalez, Joseph and Le, Quoc and Liang, Chen and Munguia, Lluis-Miquel and Rothchild, Daniel and So, David and Texier, Maud and Dean, Jeff}, year = {2021}, month = apr, journal = {arXiv:2104.10350 [cs]}, eprint = {2104.10350}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2104.10350}, urldate = {2022-04-04}, 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 \textasciitilde 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 \textasciitilde 1.4-2X more energy efficient than typical datacenters, and the ML-oriented accelerators inside them can be \textasciitilde 2-5X more effective than off-the-shelf systems. Remarkably, the choice of DNN, datacenter, and processor can reduce the carbon footprint up to \textasciitilde 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.}, archiveprefix = {arXiv}, keywords = {Computer Science - Computers and Society,Computer Science - Machine Learning} } @article{pautassoMicroservicesPracticePart2017, title = {Microservices in {{Practice}}, {{Part}} 1: {{Reality Check}} and {{Service Design}}}, shorttitle = {Microservices in {{Practice}}, {{Part}} 1}, author = {Pautasso, Cesare and Zimmermann, Olaf and Amundsen, Mike and Lewis, James and Josuttis, Nicolai and {undefined} and {undefined} and {undefined} and {undefined}}, year = {2017}, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {91--98}, issn = {0740-7459}, 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.}, keywords = {software development,software engineering} } @incollection{Pazzani2007, title = {Content-Based Recommendation Systems}, booktitle = {The Adaptive Web: {{Methods}} and Strategies of Web Personalization}, author = {Pazzani, Michael J. and Billsus, Daniel}, editor = {Brusilovsky, Peter and Kobsa, Alfred and Nejdl, Wolfgang}, year = {2007}, pages = {325--341}, publisher = {{Springer Berlin Heidelberg}}, address = {{Berlin, Heidelberg}}, doi = {10.1007/978-3-540-72079-9₁0}, isbn = {978-3-540-72079-9} } @article{pelliccioneArtificialIntelligenceSoftware, title = {Artificial {{Intelligence}} and {{Software Engineering}}}, author = {Pelliccione, Patrizio and Ruscio, Davide Di and Begel, Andrew and Crnkovic, Ivica}, pages = {5}, 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.}, langid = {english} } @inproceedings{penaModeldrivenArchitectureApproach2006, title = {A Model-Driven Architecture Approach for Modeling, Specifying and Deploying Policies in Autonomous and Autonomic Systems}, booktitle = {2006 2nd {{IEEE International Symposium}} on {{Dependable}}, {{Autonomic}} and {{Secure Computing}}}, author = {Pena, Joaquin and Hinchey, Michael G. and Sterritt, Roy and {Ruiz-Cortes}, Antonio and Resinas, Manuel}, year = {2006}, pages = {19--30}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4030862}, urldate = {2016-08-21} } @inproceedings{pereiraPlatformEnableSelfadaptive2020, title = {A Platform to Enable Self-Adaptive Cloud Applications Using Trustworthiness Properties}, booktitle = {Proceedings of the {{IEEE}}/{{ACM}} 15th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}}}, author = {Pereira, Jos{\'e} D'Abruzzo and Silva, Rui and Antunes, Nuno and Silva, Jorge L. M. and {de Fran{\c c}a}, Breno and Moraes, Regina and Vieira, Marco}, year = {2020}, month = jun, pages = {71--77}, publisher = {{ACM}}, address = {{Seoul Republic of Korea}}, doi = {10.1145/3387939.3391608}, 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.}, isbn = {978-1-4503-7962-5}, langid = {english}, keywords = {DONE} } @article{perez-sanchezOnlineLearningAlgorithm2013, title = {An Online Learning Algorithm for Adaptable Topologies of Neural Networks}, author = {{P{\'e}rez-S{\'a}nchez}, Beatriz and {Fontenla-Romero}, Oscar and {Guijarro-Berdi{\~n}as}, Bertha and {Mart{\'i}nez-Rego}, David}, year = {2013}, month = dec, journal = {Expert Systems with Applications}, volume = {40}, number = {18}, pages = {7294--7304}, issn = {09574174}, doi = {10.1016/j.eswa.2013.06.066}, langid = {english} } @article{Pérez-Soler2020207, title = {Model-Driven Chatbot Development}, author = {{P{\'e}rez-Soler}, S. and Guerra, E. and {de Lara}, J.}, editor = {Dobbie G., Frank U., Liddle S.W., Mayr H.C., Kappel G.}, year = {2020}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {12400 LNCS}, pages = {207--222}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-62522-1_15}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Universidad Aut\'onoma de Madrid, Madrid, Spain}, correspondence_address1 = {P\'erez-Soler, S.; Universidad Aut\'onoma de MadridSpain; email: Sara.PerezS@uam.es}, document_type = {Conference Paper}, isbn = {9783030625214}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Assistance,notion} } @inproceedings{pervaizExaminingChallengesDevelopment2019, title = {Examining the Challenges in Development Data Pipeline}, booktitle = {Proceedings of the {{Conference}} on {{Computing}} \& {{Sustainable Societies}} - {{COMPASS}} 19}, author = {Pervaiz, Fahad and Vashistha, Aditya and Anderson, Richard}, year = {2019}, pages = {13--21}, publisher = {{ACM Press}}, address = {{Accra, Ghana}}, doi = {10.1145/3314344.3332496}, 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.}, isbn = {978-1-4503-6714-1}, langid = {english} } @inproceedings{pescadorPatternBasedDevelopmentDomainSpecific2015, title = {Pattern-{{Based Development}} of {{Domain-Specific Modelling Languages}}}, author = {Pescador, Ana and Garmendia, Antonio and Guerra, Esther and Cuadrado, Jes{\'u}s S{\'a}nchez and {de Lara}, Juan}, year = {2015}, publisher = {{MODELS}}, url = {http://www.miso.es/pubs/DSLtao.pdf}, urldate = {2015-09-24} } @article{petcuProcessingExtremeData2016, title = {On {{Processing Extreme Data}}}, author = {Petcu, Dana and Iuhasz, Gabriel and Pop, Daniel and Talia, Domenico and Carretero, Jesus and Prodan, Radu and Fahringer, Thomas and Grasso, Ivan and Doallo, Ramon and Martin, Maria J. and Fraguela, Basilio B. and Trobec, Roman and Depolli, Matjaz and Rodriguez, Francisco Almeida and De Sande, Francisco and Da Costa, Georges and Pierson, Jean-Marc and Anastasiadis, Stergios and Bartzokas, Aristides and Lolis, Christos and Goncalves, Pedro and Brito, Fabrice and Brown, Nick}, year = {2016}, month = jan, journal = {Scalable Computing: Practice and Experience}, volume = {16}, number = {4}, pages = {467--490}, issn = {1895-1767}, doi = {10.12694/scpe.v16i4.1134}, keywords = {STARRED} } @inproceedings{Petroll2021, title = {Synthetic Data Generation for Deep Learning Models}, author = {Petroll, C. and Denk, M. and Holtmannsp{\"o}tter, J. and Paetzold, K. and H{\"o}fer, P.}, editor = {Krause D., Paetzold K., Wartzack S.}, year = {2021}, series = {Proceedings of the 32nd {{Symposium Design}} for {{X}}, {{DFX}} 2021}, publisher = {{The Design Society}}, doi = {10.35199/dfx2021.11}, abbrev_source_title = {Proc. Symp. Des. X, DFX}, affiliation = {Bundeswehr Research Institute for Materials, Fuels and Lubricants (WIWeB); Universit\"at der Bundeswehr M\"unchen (UniBwM), Germany; Technische Universit\"at Dresden, Germany}, correspondence_address1 = {Petroll, C.Institutsweg 1, Germany; email: christophpetroll@bundeswehr.org}, document_type = {Conference Paper}, langid = {english}, source = {Scopus} } @article{pettigrewTastingProjectiveTechnique2008, title = {Tasting as a Projective Technique}, author = {Pettigrew, Simone and Charters, Stephen}, year = {2008}, journal = {Qualitative Market Research: An International Journal}, volume = {11}, number = {3}, eprint = {https://doi.org/10.1108/13522750810879048}, pages = {331--343}, url = {https://doi.org/10.1108/13522750810879048}, abstract = {Purpose \textendash{} 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 \textendash{} 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 \textendash{} 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 \textendash{} 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 \textendash{} 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.}, nodoi = {10.1108/13522750810879048} } @inproceedings{pezoaFoundationsJSONSchema2016, title = {Foundations of {{JSON Schema}}}, booktitle = {Proceedings of the 25th {{International Conference}} on {{World Wide Web}}}, author = {Pezoa, Felipe and Reutter, Juan L. and Suarez, Fernando and Ugarte, Mart{\'i}n and Vrgo{\v c}, Domagoj}, year = {2016}, month = apr, pages = {263--273}, publisher = {{International World Wide Web Conferences Steering Committee}}, address = {{Montr\'eal Qu\'ebec Canada}}, doi = {10.1145/2872427.2883029}, isbn = {978-1-4503-4143-1}, langid = {english} } @article{phuong_t_nguyen_2018_1476035, title = {An Automated Approach to Assess the Similarity of {{GitHub}} Repositories - Online Appendix}, author = {Rocco, Di and {Juri} and Nguyen, Phuong T. and Rubei, Riccardo and Di Ruscio, Davide}, year = {2018}, month = oct, doi = {10.5281/zenodo.1476035} } @article{pierantonioalfonsoKeynoteJCRSTAFa, title = {Keynote {{JCR STAF}}}, author = {{Pierantonio, Alfonso}} } @article{pierantonioOpenAccessAll2020, title = {Open {{Access}}: All You Wanted to Know and Never Dared to Ask.}, shorttitle = {Open {{Access}}}, author = {Pierantonio, Alfonso and {van den Brand}, Mark and Combemale, Benoit}, year = {2020}, journal = {The Journal of Object Technology}, volume = {19}, number = {1}, pages = {1}, issn = {1660-1769}, doi = {10.5381/jot.2020.19.1.e1}, langid = {english} } @article{Pinna Puissant2015461, title = {Resolving Model Inconsistencies Using Automated Regression Planning}, author = {Pinna Puissant, J. and Van Der Straeten, R. and Mens, T.}, year = {2015}, journal = {Software and Systems Modeling}, volume = {14}, number = {1}, pages = {461--481}, publisher = {{Springer Verlag}}, issn = {16191366}, doi = {10.1007/s10270-013-0317-9}, abbrev_source_title = {Softw. Syst. Model.}, affiliation = {Service de G\'enie Logiciel, Institut COMPLEXYS, Universit\'e de Mons, Place du Parc 20, Mons, 7000, Belgium; Software Languages Lab, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium}, correspondence_address1 = {Pinna Puissant, J.; Service de G\'enie Logiciel, Place du Parc 20, Belgium}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Repair,notion} } @article{PMID:25142186, title = {Uncovering the Information Core in Recommender Systems}, author = {Zeng, Wei and Zeng, An and Liu, Hao and Shang, Ming-Sheng and Zhou, Tao}, year = {2014}, journal = {Scientific reports}, volume = {4}, pages = {6140}, issn = {2045-2322}, doi = {10.1038/srep06140} } @article{polyakovMachineLearningCybersecurity, title = {Machine {{Learning}} for {{Cybersecurity}} 10}, author = {Polyakov, Alexander}, pages = {23}, langid = {english} } @misc{PolystoreDatabasesBe, title = {Polystore {{Databases}} to Be {{Examined}} at {{IEEE}}, {{CIDR Conferences}} | {{Intel Science}} \& {{Technology Center}} for {{Big Data}}}, url = {http://istc-bigdata.org/index.php/polystore-databases-at-ieee-cidr-conferences/}, urldate = {2018-04-16} } @article{pontaMetadataCodecentricUsagebased2018, title = {Beyond {{Metadata}}: {{Code-centric}} and {{Usage-based Analysis}} of {{Known Vulnerabilities}} in {{Open-source Software}}}, shorttitle = {Beyond {{Metadata}}}, author = {Ponta, Serena E. and Plate, Henrik and Sabetta, Antonino}, year = {2018}, month = jun, journal = {arXiv:1806.05893 [cs]}, eprint = {1806.05893}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/1806.05893}, urldate = {2018-10-08}, 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.}, archiveprefix = {arXiv} } @inproceedings{ponzanelliMiningStackOverflowTurn2014, ids = {Ponzanelli:2014:MST:2597073.2597077,ponzanelliMiningStackOverflowTurn2014a}, title = {Mining {{StackOverflow}} to Turn the {{IDE}} into a Self-Confident Programming Prompter}, author = {Ponzanelli, Luca and Bavota, Gabriele and Di Penta, Massimiliano and Oliveto, Rocco and Lanza, Michele}, year = {2014}, pages = {102--111}, publisher = {{ACM Press}}, address = {{Hyderabad, India}}, doi = {10.1145/2597073.2597077}, acmid = {2597077}, isbn = {978-1-4503-2863-0}, langid = {english}, nodoi = {10.1145/2597073.2597077}, numpages = {10}, keywords = {Developers Support,Empirical Studies,Recommender Systems} } @article{ponzanelliPrompterTurningIDE2016, ids = {DBLP:journals/ese/PonzanelliBPOL16}, title = {Prompter: {{Turning}} the {{IDE}} into a Self-Confident Programming Assistant}, shorttitle = {Prompter}, author = {Ponzanelli, Luca and Bavota, Gabriele and Di Penta, Massimiliano and Oliveto, Rocco and Lanza, Michele}, year = {2016}, month = oct, journal = {Empirical Software Engineering}, volume = {21}, number = {5}, pages = {2190--2231}, issn = {1382-3256, 1573-7616}, url = {http://link.springer.com/10.1007/s10664-015-9397-1}, urldate = {2019-09-04}, 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.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/ese/PonzanelliBPOL16}, langid = {english}, nodoi = {10.1007/s10664-015-9397-1}, timestamp = {Thu, 15 Jun 2017 21:30:27 +0200} } @inproceedings{ponzanelliSeahawkStackOverflow2013, title = {Seahawk: {{Stack}} Overflow in the {{IDE}}}, booktitle = {2013 35th International Conference on Software Engineering ({{ICSE}})}, author = {Ponzanelli, L. and Bacchelli, A. and Lanza, M.}, year = {2013}, month = may, pages = {1295--1298}, issn = {0270-5257}, doi = {10.1109/ICSE.2013.6606701} } @article{poojaraServerlessDataPipeline2022, title = {Serverless Data Pipeline Approaches for {{IoT}} Data in Fog and Cloud Computing}, author = {Poojara, Shivananda R. and Dehury, Chinmaya Kumar and Jakovits, Pelle and Srirama, Satish Narayana}, year = {2022}, month = may, journal = {Future Generation Computer Systems}, volume = {130}, pages = {91--105}, issn = {0167739X}, doi = {10.1016/j.future.2021.12.012}, 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.}, langid = {english} } @article{portugalUseMachineLearning2015, ids = {DBLP:journals/corr/PortugalAC15a}, title = {The {{Use}} of {{Machine Learning Algorithms}} in {{Recommender Systems}}: {{A Systematic Review}}}, shorttitle = {The {{Use}} of {{Machine Learning Algorithms}} in {{Recommender Systems}}}, author = {Portugal, Ivens and Alencar, Paulo and Cowan, Donald}, year = {2015}, journal = {arXiv preprint arXiv:1511.05263}, eprint = {1511.05263}, eprinttype = {arxiv}, url = {https://arxiv.org/abs/1511.05263}, urldate = {2017-03-10}, archiveprefix = {arXiv}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/corr/PortugalAC15a}, timestamp = {Mon, 13 Aug 2018 16:46:19 +0200} } @article{pottsSoftwareengineeringResearchRevisited1993, title = {Software-Engineering Research Revisited}, author = {Potts, Colin}, year = {1993}, journal = {IEEE software}, volume = {10}, number = {5}, pages = {19--28}, url = {http://ieeexplore.ieee.org/abstract/document/232392/}, urldate = {2017-07-03} } @article{potvinWhyGoogleStores2016, title = {Why {{Google}} Stores Billions of Lines of Code in a Single Repository}, author = {Potvin, Rachel and Levenberg, Josh}, year = {2016}, journal = {Communications of the ACM}, volume = {59}, number = {7}, pages = {78--87}, url = {http://dl.acm.org/citation.cfm?id=2854146}, urldate = {2017-05-25} } @article{Pourpanah:2016:HMF:2884077.2884195, title = {A Hybrid Model of Fuzzy {{ARTMAP}} and Genetic Algorithm for Data Classification and Rule Extraction}, author = {Pourpanah, Farhad and Lim, Chee Peng and Saleh, Junita Mohamad}, year = {2016}, month = may, journal = {Expert Systems with Applications}, volume = {49}, number = {C}, pages = {74--85}, publisher = {{Pergamon Press, Inc.}}, address = {{Tarrytown, NY, USA}}, issn = {0957-4174}, url = {https://doi.org/10.1016/j.eswa.2015.11.009}, acmid = {2884195}, issue_date = {May 2016}, nodoi = {10.1016/j.eswa.2015.11.009}, numpages = {12}, keywords = {Data classification,Fuzzy ARTMAP,Genetic algorithm,Q-learning,reinforcement learning,Rule extraction} } @article{prasadConvolutionalNeuralNetworks, title = {Convolutional {{Neural Networks}} with {{Tensor}} Ow}, author = {Prasad, Ashu}, pages = {28}, langid = {english} } @article{prev-93837, title = {{{MDE Adoption}}\textemdash{{A}} Three-Legged Chair}, booktitle = {Grand Challenges in Modeling}, year = {2017} } @misc{Project2ACLAutonomous, title = {Project2 - {{ACL}} - {{Autonomous Systems}} and {{Robotics}} - {{Research Groups}} - {{Research}} - {{ACSE}} - {{The University}} of {{Sheffield}}}, url = {https://www.sheffield.ac.uk/acse/research/groups/asrg/acl/project2}, urldate = {2016-08-26} } @incollection{prokschHowBuildRecommendation2015, title = {How to {{Build}} a {{Recommendation System}} for {{Software Engineering}}}, booktitle = {Software {{Engineering}}}, author = {Proksch, Sebastian and Bauer, Veronika and Murphy, Gail C.}, editor = {Meyer, Bertrand and Nordio, Martin}, year = {2015}, volume = {8987}, pages = {1--42}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-319-28406-4_1}, 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.}, isbn = {978-3-319-28405-7 978-3-319-28406-4}, langid = {english} } @inproceedings{Protasiewicz2020, title = {A Neural Network Toolbox for Electricity Consumption Forecasting}, author = {Protasiewicz, J.}, year = {2020}, series = {Proceedings of the {{International Joint Conference}} on {{Neural Networks}}}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/IJCNN48605.2020.9206996}, abbrev_source_title = {Proc Int Jt Conf Neural Networks}, affiliation = {National Information Processing Institute, Warsaw, Poland}, art_number = {9206996}, coden = {85OFA}, correspondence_address1 = {Protasiewicz, J.; National Information Processing InstitutePoland}, document_type = {Conference Paper}, isbn = {978-1-72816-926-2}, langid = {english}, source = {Scopus}, keywords = {notion} } @inproceedings{provoostDingNetSelfAdaptiveInternetofThings2019, title = {{{DingNet}}: {{A Self-Adaptive Internet-of-Things Exemplar}}}, shorttitle = {{{DingNet}}}, booktitle = {2019 {{IEEE}}/{{ACM}} 14th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}} ({{SEAMS}})}, author = {Provoost, Michiel and Weyns, Danny}, year = {2019}, month = may, pages = {195--201}, publisher = {{IEEE}}, address = {{Montreal, QC, Canada}}, doi = {10.1109/SEAMS.2019.00033}, isbn = {978-1-72813-368-3}, keywords = {DONE} } @misc{PtolemyProjectHome, title = {Ptolemy {{Project Home Page}}}, url = {http://ptolemy.eecs.berkeley.edu/}, urldate = {2016-01-26} } @misc{PublicationsGEMOCInitiative, title = {Publications \guillemotright{} {{The GEMOC Initiative}}}, url = {http://gemoc.org/publications/}, urldate = {2015-09-28} } @article{pulgattiDataMigrationDifferent, title = {Data {{Migration Between Different Data Models}} of {{NoSql Databases}}}, author = {Pulgatti, Leandro Duarte}, pages = {80}, langid = {english} } @article{Pylianidis202145, title = {Location-Specific vs Location-Agnostic Machine Learning Metamodels for Predicting Pasture Nitrogen Response Rate}, author = {Pylianidis, C. and Snow, V. and Holzworth, D. and Bryant, J. and Athanasiadis, I.N.}, editor = {Del Bimbo A., Cucchiara R., Farinella G.M., Mei T., Bertini M., Escalante H.J., Vezzani R., Sclaroff S.}, year = {2021}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {12666 LNCS}, pages = {45--54}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-68780-9_5}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Wageningen University, Wageningen, Netherlands; AgResearch, Christchurch, New Zealand; CSIRO, Brisbane, Australia}, correspondence_address1 = {Pylianidis, C.; Wageningen UniversityNetherlands; email: christos.pylianidis@wur.nl}, document_type = {Conference Paper}, isbn = {9783030687793}, langid = {english}, source = {Scopus} } @article{Qiang20202508, title = {A Model-Driven Deep Learning Algorithm for Joint Activity Detection and Channel Estimation}, author = {Qiang, Y. and Shao, X. and Chen, X.}, year = {2020}, journal = {IEEE Communications Letters}, volume = {24}, number = {11}, pages = {2508--2512}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10897798}, doi = {10.1109/LCOMM.2020.3011571}, 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. \textcopyright{} 1997-2012 IEEE.}, art_number = {9146533}, coden = {ICLEF}, document_type = {Article}, source = {Scopus} } @inproceedings{Qiao2021282, title = {Research on Motion Modeling and Control of Tracking Car Based on Neural Network}, author = {Qiao, J.}, year = {2021}, series = {Proceedings - 2021 2nd {{International Conference}} on {{Computing}} and {{Data Science}}, {{CDS}} 2021}, pages = {282--286}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/CDS52072.2021.00056}, abbrev_source_title = {Proc. - Int. Conf. Comput. Data Sci., CDS}, affiliation = {Research Institute of Micro/Nano Science and Technology, Shanghai Jiao Tong University, Shanghai, China}, art_number = {9463303}, correspondence_address1 = {Qiao, J.; Research Institute of Micro/Nano Science and Technology, China; email: qiao\textsubscript{j}ing\textsubscript{y}ang@163.com}, document_type = {Conference Paper}, isbn = {978-1-66540-428-0}, langid = {english}, source = {Scopus} } @article{Quinlan:1986:IDT:637962.637969, title = {Induction of Decision Trees}, author = {Quinlan, J. R.}, year = {1986}, month = mar, journal = {Machine Learning}, volume = {1}, number = {1}, pages = {81--106}, publisher = {{Kluwer Academic Publishers}}, address = {{Hingham, MA, USA}}, issn = {0885-6125}, url = {http://dx.doi.org/10.1023/A:1022643204877}, acmid = {637969}, nodoi = {10.1023/A:1022643204877}, numpages = {26}, keywords = {classification,decision trees,expert systems,induction,information theory,knowledge acquisition} } @article{Quintero2016219, title = {Knowledge Management Metamodel from Social Analysis of Lessons Learnt Registered in the Cloud}, author = {Quintero, J.L. and Garc{\'i}a, V.H.M. and Garc{\'i}a, C.P.}, editor = {Liberona D., Feldmann B., Uden L.}, year = {2016}, journal = {Communications in Computer and Information Science}, volume = {620}, pages = {219--232}, publisher = {{Springer Verlag}}, issn = {18650929}, doi = {10.1007/978-3-319-42147-6_19}, abbrev_source_title = {Commun. Comput. Info. Sci.}, affiliation = {CUN-Corporaci\'on Unificada Nacional de Educaci\'on Superior, Bogot\'a, Colombia; Universidad Distrital Francisco Jos\'e de Caldas, Bogot\'a, Colombia; Universidad de Oviedo, Oviedo, Spain}, correspondence_address1 = {Garc\'ia, V.H.M.; Universidad Distrital Francisco Jos\'e de CaldasColombia; email: vmedina@udistrital.edu.co}, document_type = {Conference Paper}, isbn = {9783319421469}, langid = {english}, source = {Scopus} } @inproceedings{Rabbah2020, title = {A New Classification Model Based on Stacknet and Deep Learning for Fast Detection of {{COVID}} 19 through {{X}} Rays Images}, author = {Rabbah, J. and Ridouani, M. and Hassouni, L.}, editor = {Oubenaalla Y., Nfaoui E.H., Loqman C., Riffi J., Kozma R., Mestari M., Alippi C., Joumhidi J.}, year = {2020}, series = {4th {{International Conference}} on {{Intelligent Computing}} in {{Data Sciences}}, {{ICDS}} 2020}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ICDS50568.2020.9268777}, abbrev_source_title = {Int. Conf. Intell. Comput. Data Sci., ICDS}, affiliation = {Hassan Ii University, Ritm Laboratory, Ced Engineering Sciences, Casablanca, Morocco}, art_number = {9268777}, document_type = {Conference Paper}, isbn = {978-1-72818-084-7}, langid = {english}, source = {Scopus} } @article{Rabbi201749, title = {An {{MDE}} Approach for Modelling and Reasoning about Multi-Agent Systems}, author = {Rabbi, F. and Lamo, Y. and Kristensen, L.M.}, editor = {Carrascosa C., Julian Inglada V., Osman N., Criado Pacheco N.}, year = {2017}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {10207 LNAI}, pages = {49--57}, publisher = {{Springer Verlag}}, issn = {03029743}, doi = {10.1007/978-3-319-59294-7_5}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Western Norway University of Applied Sciences, Bergen, Norway; University of Oslo, Oslo, Norway}, correspondence_address1 = {Rabbi, F.; Western Norway University of Applied SciencesNorway; email: Fazle.Rabbi@hvl.no}, document_type = {Conference Paper}, isbn = {9783319592930}, langid = {english}, source = {Scopus} } @article{Rafique2018D126, title = {Machine Learning for Network Automation: {{Overview}}, Architecture, and Applications [{{Invited Tutorial}}]}, author = {Rafique, D. and Velasco, L.}, year = {2018}, journal = {Journal of Optical Communications and Networking}, volume = {10}, number = {10}, pages = {D126-D143}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {19430620}, doi = {10.1364/JOCN.10.00D126}, 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. \textcopyright{} 2009-2012 OSA.}, art_number = {8501533}, document_type = {Article}, source = {Scopus} } @article{Ragkhitwetsagul2018, title = {A Comparison of Code Similarity Analysers}, author = {Ragkhitwetsagul, Chaiyong and Krinke, Jens and Clark, David}, year = {2018}, month = aug, journal = {Empirical Software Engineering}, volume = {23}, number = {4}, pages = {2464--2519}, issn = {1573-7616}, doi = {10.1007/s10664-017-9564-7}, 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.} } @inproceedings{ragoneSchemasummarizationLinkeddatabasedFeature2017, title = {Schema-Summarization in Linked-Data-Based Feature Selection for Recommender Systems}, booktitle = {Proceedings of the Symposium on Applied Computing}, author = {Ragone, Azzurra and Tomeo, Paolo and Magarelli, Corrado and Di Noia, Tommaso and Palmonari, Matteo and Maurino, Andrea and Di Sciascio, Eugenio}, year = {2017}, series = {{{SAC}} '17}, pages = {330--335}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/3019612.3019837}, acmid = {3019837}, isbn = {978-1-4503-4486-9}, nodoi = {10.1145/3019612.3019837}, numpages = {6}, keywords = {information gain,linked data,ontology summarization} } @inproceedings{Rajaei2021149, title = {A {{DSL}} for Encoding Models for Graph-Learning Processes}, author = {Rajaei, Z. and {Kolahdouz-Rahimi}, S. and Tisi, M. and Jouault, F.}, editor = {Iovino L., Kristensen L.M.}, year = {2021}, series = {{{CEUR Workshop Proceedings}}}, volume = {2999}, pages = {149--161}, publisher = {{CEUR-WS}}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118923715&partnerID=40&md5=0392ba00df3c85c19cc98510b244e3b9}, abbrev_source_title = {CEUR Workshop Proc.}, affiliation = {MDSE Research Group, Department of Software Engineering, University of Isfahan, Iran; IMT Atlantique, LS2N (UMR CNRS 6004), Nantes, France; ERIS, ESEO-TECH, Angers, France}, correspondence_address1 = {Rajaei, Z.; MDSE Research Group, Iran; email: z.rajaei@eng.ui.ac.ir}, document_type = {Conference Paper}, langid = {english}, source = {Scopus} } @book{rajInternetThingsEnabling2017, title = {The {{Internet}} of Things: Enabling Technologies, Platforms, and Use Cases}, shorttitle = {The {{Internet}} of Things}, author = {Raj, Pethuru and Raman, Anupama C.}, year = {2017}, publisher = {{CRC Press/Taylor \& Francis Group}}, address = {{Boca Raton}}, isbn = {978-1-4987-6128-4}, langid = {english}, lccn = {TK5105.8857 .R35 2017}, keywords = {internet of things} } @inproceedings{Ramaswamy201450, title = {Modeling Non-Functional Properties for Human-Machine Systems}, author = {Ramaswamy, A. and Monsuez, B. and Tapus, A.}, year = {2014}, series = {{{AAAI Spring Symposium}} - {{Technical Report}}}, volume = {SS-14-02}, pages = {50--55}, publisher = {{AI Access Foundation}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904888679&partnerID=40&md5=8255705b33be09a555b7c43d9218c3a7}, abbrev_source_title = {AAAI Spring Symp. Tech. Rep.}, affiliation = {Department of Computer and System Engineering, ENSTA-ParisTech, 828 Blvd Marechaux, Palaiseau, France; VeDeCom Institute, 77 rue des Chantiers, 78000 Versailles, France}, document_type = {Conference Paper}, isbn = {978-1-57735-655-4}, langid = {english}, source = {Scopus} } @inproceedings{ramaswamyModeldrivenSoftwareDevelopment2014, title = {Model-Driven Software Development Approaches in Robotics Research}, author = {Ramaswamy, Arunkumar and Monsuez, Bruno and Tapus, Adriana}, year = {2014}, pages = {43--48}, publisher = {{ACM Press}}, doi = {10.1145/2593770.2593781}, isbn = {978-1-4503-2849-4}, langid = {english} } @article{ramosUsingTFIDFDetermine1999, title = {Using {{TF-IDF}} to Determine Word Relevance in Document Queries}, author = {Ramos, Juan}, year = {1999}, added-at = {2011-08-09T16:33:51.000+0200}, biburl = {https://www.bibsonomy.org/bibtex/2e757decbf13ecf55dee0b5eae56f9ccd/reynares.e}, interhash = {59140639220e47ed6a9636c3ee35ac1a}, intrahash = {e757decbf13ecf55dee0b5eae56f9ccd}, keywords = {ontologyₗearning}, timestamp = {2013-05-16T22:34:47.000+0200} } @article{randObjectiveCriteriaEvaluation1971, title = {Objective Criteria for the Evaluation of Clustering Methods}, author = {Rand, W.M.}, year = {1971}, journal = {Journal of the American Statistical association}, volume = {66}, number = {336}, pages = {846--850}, publisher = {{JSTOR}}, issn = {0162-1459}, added-at = {2011-03-28T19:20:19.000+0200}, biburl = {https://www.bibsonomy.org/bibtex/26967eb207406719bb83669ebbebb2099/dunarel}, interhash = {1afaf0170bc705a9e49b625f67679ee2}, intrahash = {6967eb207406719bb83669ebbebb2099}, owner = {root}, keywords = {imported}, timestamp = {2011-03-28T19:20:21.000+0200} } @article{Rangel-Patino2019733, title = {Post-Silicon Receiver Equalization Metamodeling by Artificial Neural Networks}, author = {{Rangel-Patino}, F.E. and {Rayas-Sanchez}, J.E. and {Viveros-Wacher}, A. and {Chavez-Hurtado}, J.L. and {Vega-Ochoa}, E.A. and Hakim, N.}, year = {2019}, journal = {IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems}, volume = {38}, number = {4}, pages = {733--740}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {02780070}, doi = {10.1109/TCAD.2018.2834403}, abbrev_source_title = {IEEE Trans Comput Aided Des Integr Circuits Syst}, affiliation = {Intel Corporation, Zapopan, 45109, Mexico; Department of Electronics, Systems, and Informatics, ITESO-The Jesuit University of Guadalajara, Tlaquepaque, 45604, Mexico; Intel Corporation, Santa Clara, CA 95052, United States}, art_number = {8355951}, coden = {ITCSD}, correspondence_address1 = {Rayas-Sanchez, J.E.; Department of Electronics, Mexico; email: erayas@iteso.mx}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {notion} } @article{Rasiman202235, title = {How Effective Is Automated Trace Link Recovery in Model-Driven Development?}, author = {Rasiman, R. and Dalpiaz, F. and Espa{\~n}a, S.}, editor = {Gervasi V., Vogelsang A.}, year = {2022}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {13216 LNCS}, pages = {35--51}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-98464-9_4}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Utrecht University, Utrecht, Netherlands}, correspondence_address1 = {Dalpiaz, F.; Utrecht UniversityNetherlands; email: f.dalpiaz@uu.nl}, document_type = {Conference Paper}, isbn = {9783030984632}, langid = {english}, source = {Scopus}, keywords = {GOAL_Trace-link-recovery,notion} } @article{Rausch2021127, title = {Evaluating the Effectiveness of Metamodeling in Emulating Quantitative Models}, author = {Rausch, M. and Sanders, W.H.}, editor = {Abate A., Marin A.}, year = {2021}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {12846 LNCS}, pages = {127--145}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-85172-9_7}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {University of Illinois at Urbana-Champaign, Urbana, IL, United States; Carnegie Mellon University, Pittsburgh, PA, United States}, correspondence_address1 = {Rausch, M.; University of Illinois at Urbana-ChampaignUnited States; email: mjrausc2@illinois.edu}, document_type = {Conference Paper}, isbn = {9783030851712}, langid = {english}, source = {Scopus} } @article{raySurveyInternetThings2018, title = {A Survey on {{Internet}} of {{Things}} Architectures}, author = {Ray, P.P.}, year = {2018}, month = jul, journal = {Journal of King Saud University - Computer and Information Sciences}, volume = {30}, number = {3}, pages = {291--319}, issn = {13191578}, doi = {10.1016/j.jksuci.2016.10.003}, 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.}, langid = {english} } @article{raySurveyIoTCloud2016, title = {A Survey of {{IoT}} Cloud Platforms}, author = {Ray, Partha Pratim}, year = {2016}, month = dec, journal = {Future Computing and Informatics Journal}, volume = {1}, number = {1-2}, pages = {35--46}, issn = {23147288}, doi = {10.1016/j.fcij.2017.02.001}, 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.}, langid = {english} } @misc{RealWorldIoT, title = {Real {{World IoT}}: {{Architectures}} and {{Projects}} with {{Eclipse IoT}} | {{EclipseCon Europe}} 2016}, url = {https://www.eclipsecon.org/europe2016/session/real-world-iot-architectures-and-projects-eclipse-iot}, urldate = {2016-09-27} } @article{REBY199735, title = {Artificial Neural Networks as a Classification Method in the Behavioural Sciences}, author = {Reby, David and Lek, Sovan and Dimopoulos, Ioannis and Joachim, Jean and Lauga, Jacques and Aulagnier, St{\'e}phane}, year = {1997}, journal = {Behavioural Processes}, volume = {40}, number = {1}, pages = {35--43}, issn = {0376-6357}, 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.}, keywords = {Classification,Deer,Mammal,Modelling,Neural network,Vocalization} } @inproceedings{reedTFICFNewTerm2006, title = {{{TF-ICF}}: {{A}} New Term Weighting Scheme for Clustering Dynamic Data Streams}, booktitle = {Proceedings of the 5th International Conference on Machine Learning and Applications}, author = {Reed, Joel W. and Jiao, Yu and Potok, Thomas E. and Klump, Brian A. and Elmore, Mark T. and Hurson, Ali R.}, year = {2006}, series = {{{ICMLA}} '06}, pages = {258--263}, publisher = {{IEEE Computer Society}}, address = {{Washington, DC, USA}}, url = {http://dx.doi.org/10.1109/ICMLA.2006.50}, acmid = {1193734}, isbn = {0-7695-2735-3}, nodoi = {10.1109/ICMLA.2006.50}, numpages = {6} } @article{ReliableDataProcessing2021, title = {Reliable {{Data Processing}} with {{Minimal Toil}}}, year = {2021}, pages = {15}, langid = {english} } @inproceedings{Rendon:2011:CIE:1959666.1959695, title = {A Comparison of Internal and External Cluster Validation Indexes}, booktitle = {Proceedings of the 2011 American Conference on Applied Mathematics and the 5th {{WSEAS}} International Conference on Computer Engineering and Applications}, author = {Rend{\'o}n, Er{\'e}ndira and Abundez, Itzel M. and Gutierrez, Citlalih and Zagal, Sergio D{\'i}az and Arizmendi, Alejandra and Quiroz, Elvia M. and Arzate, H. Elsa}, year = {2011}, series = {{{AMERICAN-MATH}}'11/{{CEA}}'11}, pages = {158--163}, publisher = {{World Scientific and Engineering Academy and Society (WSEAS)}}, address = {{Stevens Point, Wisconsin, USA}}, url = {http://dl.acm.org/citation.cfm?id=1959666.1959695}, acmid = {1959695}, isbn = {978-960-474-270-7}, numpages = {6}, keywords = {cluster validity,clustering algorithm,external indexes,internal indexes,k-means} } @article{Rendon2011, title = {Internal versus {{External}} Cluster Validation Indexes}, author = {Rend{\'o}n, Er{\'e}ndira and Abundez, Itzel and Arizmendi, Alejandra and Quiroz, Elvia M.}, year = {2011}, month = mar, journal = {Int. Journal of Compt. and Comm.}, volume = {5}, number = {1}, pages = {27--34}, address = {{Hingham, MA, USA}}, abstract = {.}, acmid = {1061908}, added-at = {2011-09-18T22:25:48.000+0200}, biburl = {http://www.bibsonomy.org/bibtex/21afe6065cc536f52534a7c15eed599c3/jil}, numpages = {8}, keywords = {cluster clustering entropy evaluation f-score fscore measure measures purity} } @article{RePEc:eee:intfor:v:14:y:1998:i:1:p:35-62, title = {Forecasting with Artificial Neural Networks:: {{The}} State of the Art}, author = {Zhang, Guoqiang and Eddy Patuwo, B. and Y. Hu, Michael}, year = {1998}, journal = {International Journal of Forecasting}, volume = {14}, number = {1}, pages = {35--62} } @misc{RepubblicaItNews, title = {{La Repubblica.it - News in tempo reale - Le notizie e i video di politica, cronaca, economia, sport}}, journal = {Repubblica.it}, url = {http://www.repubblica.it/}, urldate = {2020-01-15}, abstract = {Repubblica \`e il quotidiano online aggiornato 24 ore su 24 su politica, cronaca, economia, sport, esteri, spettacoli, musica, cultura, scienza, tecnologia.}, langid = {italian} } @misc{ResearchInsightsServerless, title = {({{Research}}) {{Insights}} for {{Serverless Application Engineering}}}, url = {https://www.computer.org/csdl/magazine/so/2021/01/09305894/1pNkwYVzrUc}, urldate = {2021-01-17} } @inproceedings{resnikUsingInformationContent1995, title = {Using Information Content to Evaluate Semantic Similarity in a Taxonomy}, booktitle = {Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 1}, author = {Resnik, Philip}, year = {1995}, series = {{{IJCAI}}'95}, pages = {448--453}, publisher = {{Morgan Kaufmann Publishers Inc.}}, address = {{San Francisco, CA, USA}}, url = {http://dl.acm.org/citation.cfm?id=1625855.1625914}, acmid = {1625914}, isbn = {1-55860-363-8 978-1-55860-363-9}, numpages = {6} } @misc{Results1stCall, title = {Results of the 1st Call on {{Smart System Integration}} under {{H2020}}}, journal = {Digital Agenda for Europe}, url = {ec.europa.eu//digital-agenda/en/news/results-1st-call-smart-system-integration-under-h2020}, urldate = {2015-04-08}, abstract = {10 projects were selected for co-financing under the first H2020 call for proposals on Smart System Integration.} } @article{riahisfarRoadmapSecurityChallenges2018, title = {A Roadmap for Security Challenges in the {{Internet}} of {{Things}}}, author = {Riahi Sfar, Arbia and Natalizio, Enrico and Challal, Yacine and Chtourou, Zied}, year = {2018}, month = apr, journal = {Digital Communications and Networks}, volume = {4}, number = {2}, pages = {118--137}, issn = {23528648}, doi = {10.1016/j.dcan.2017.04.003}, 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.}, langid = {english} } @incollection{Ricci2011, title = {Introduction to Recommender Systems Handbook}, booktitle = {Recommender Systems Handbook}, author = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, year = {2011}, pages = {1--35}, publisher = {{Springer US}}, address = {{Boston, MA}}, doi = {10.1007/978-0-387-85820-3₁}, isbn = {978-0-387-85820-3} } @article{richardsonVendorLandscapeFractured2016, title = {Vendor {{Landscape}}: {{The Fractured}}, {{Fertile Terrain Of Low-Code Application Platforms}}}, author = {Richardson, Clay and Rymer, John R}, year = {2016}, pages = {23}, langid = {english} } @inproceedings{Ries202141, title = {An {{MDE}} Method for Improving Deep Learning Dataset Requirements Engineering Using Alloy and {{UML}}}, author = {Ries, B. and Guelfi, N. and Jahi{\'c}, B.}, editor = {Hammoudi S., Pires L.F., Soley R., Seidewitz E.}, year = {2021}, series = {{{MODELSWARD}} 2021 - {{Proceedings}} of the 9th {{International Conference}} on {{Model-Driven Engineering}} and {{Software Development}}}, pages = {41--52}, publisher = {{SciTePress}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103060952&partnerID=40&md5=4945b5c1de311257ad57a8d74cfc36ba}, abbrev_source_title = {MODELSWARD - Proc. Int. Conf. Model-Driven Eng. Softw. Dev.}, affiliation = {University of Luxembourg, Esch-sur-Alzette, Luxembourg}, document_type = {Conference Paper}, isbn = {978-989-758-487-9}, langid = {english}, source = {Scopus} } @inproceedings{Rigou2020, title = {A Sketch of a Deep Learning Approach for Discovering {{UML}} Class Diagrams from System's Textual Specification}, author = {Rigou, Y. and Lamontagne, D. and Khriss, I.}, editor = {Benhala B., Mansouri K., Qbadou M., Raihani A.}, year = {2020}, series = {2020 1st {{International Conference}} on {{Innovative Research}} in {{Applied Science}}, {{Engineering}} and {{Technology}}, {{IRASET}} 2020}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/IRASET48871.2020.9092144}, abbrev_source_title = {Int. Conf. Innov. Res. Appl. Sci., Eng. Technol., IRASET}, affiliation = {D'informatique et de g\'enie, UQAR, D\'epartement de math\'ematiques, Rimouski, Canada}, art_number = {9092144}, document_type = {Conference Paper}, isbn = {978-1-72814-979-0}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Search,notion} } @inproceedings{Rivera2020631, title = {On the Engineering of {{IoT-Intensive}} Digital Twin Software Systems}, author = {Rivera, L.F. and M{\"u}ller, H.A. and Villegas, N.M. and Tamura, G. and Jim{\'e}nez, M.}, year = {2020}, series = {Proceedings - 2020 {{IEEE}}/{{ACM}} 42nd {{International Conference}} on {{Software Engineering Workshops}}, {{ICSEW}} 2020}, pages = {631--638}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3387940.3392195}, abbrev_source_title = {Proc. - IEEE/ACM Int. Conf. Softw. Eng. Workshops, ICSEW}, affiliation = {University of Victoria, Victoria, BC, Canada; Universidad Icesi, Cali, Valle del Cauca, Colombia}, document_type = {Conference Paper}, isbn = {978-1-4503-7963-2}, langid = {english}, source = {Scopus}, keywords = {notion} } @article{Rivolli2021471, title = {A Study of the Correlation of Metafeatures Used for Metalearning}, author = {Rivolli, A. and Garcia, L.P.F. and Lorena, A.C. and {de Carvalho}, A.C.P.L.F.}, editor = {Rojas I., Joya G., Catala A.}, year = {2021}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {12861 LNCS}, pages = {471--483}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-85030-2_39}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Computing Department, Universidade Tecnol\'ogica Federal Do Paran\'a, Av. Alberto Carazzai, 1640, Corn\'elio Proc\'opio, Paran\'a, 86300-000, Brazil; Department of Computer Science, University of Bras\'ilia, Campus Darcy Ribeiro, Asa Norte, Bras\'ilia, 70910-900, Brazil; Aeronautics Institute of Technology, Pra\c{c}a Marechal Eduardo Gomes, 50, S\~ao Jos\'e dos Campos, S\~ao Paulo, 12228-900, Brazil; Institute of Mathematical and Computer Sciences, University of S\~ao Paulo, Av. Trabalhador S\~ao-carlense, 400, S\~ao Carlos, S\~ao Paulo, 13560-970, Brazil}, correspondence_address1 = {Rivolli, A.; Computing Department, Av. Alberto Carazzai, 1640, Brazil; email: rivolli@utfpr.edu.br}, document_type = {Conference Paper}, isbn = {9783030850296}, langid = {english}, source = {Scopus} } @article{robertsonProbabilisticRelevanceFramework2009, title = {The Probabilistic Relevance Framework: {{BM25}} and Beyond}, author = {Robertson, Stephen and Zaragoza, Hugo}, year = {2009}, month = apr, journal = {Found. Trends Inf. Retr.}, volume = {3}, number = {4}, pages = {333--389}, publisher = {{Now Publishers Inc.}}, address = {{Hanover, MA, USA}}, issn = {1554-0669}, url = {http://dx.doi.org/10.1561/1500000019}, acmid = {1704810}, issue_date = {April 2009}, nodoi = {10.1561/1500000019}, numpages = {57} } @article{Robillard:2013:AAP:2498733.2498776, title = {Automated {{API}} Property Inference Techniques}, author = {Robillard, Martin P. and Bodden, Eric and Kawrykow, David and Mezini, Mira and Ratchford, Tristan}, year = {2013}, month = may, journal = {IEEE Transactions on Software Engineering}, volume = {39}, number = {5}, pages = {613--637}, publisher = {{IEEE Press}}, address = {{Piscataway, NJ, USA}}, issn = {0098-5589}, url = {http://dx.doi.org/10.1109/TSE.2012.63}, acmid = {2498776}, issue_date = {May 2013}, nodoi = {10.1109/TSE.2012.63}, numpages = {25}, keywords = {API evolution,API property,API usage pattern,Association rules,Context,data mining,interface,Itemsets,pattern mining,Programming,programming rules,protocols,Protocols,software engineering,specifications} } @incollection{robillardIntroductionRecommendationSystems2014, title = {An Introduction to Recommendation Systems in Software Engineering}, booktitle = {Recommendation {{Systems}} in {{Software Engineering}}}, author = {Robillard, Martin P. and Walker, Robert J.}, year = {2014}, pages = {1--11}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-45135-5_1}, urldate = {2017-03-08} } @article{robillardRecommendationSystemsSoftware2010, ids = {5235134}, title = {Recommendation Systems for Software Engineering}, author = {Robillard, Martin and Walker, Robert and Zimmermann, Thomas}, year = {2010}, journal = {IEEE software}, volume = {27}, number = {4}, pages = {80--86}, url = {http://ieeexplore.ieee.org/abstract/document/5235134/}, urldate = {2017-06-08}, keywords = {bug reports,coding tools and techniques,design tools and techniques,development tools,information space,programming environments,recommendation systems,recommender systems,software construction tools,software development,software engineering,software tool,software tools,time seeking information,value-producing task} } @book{robillardRecommendationSystemsSoftware2014, title = {Recommendation {{Systems}} in {{Software Engineering}}}, editor = {Robillard, Martin P. and Maalej, Walid and Walker, Robert J. and Zimmermann, Thomas}, year = {2014}, publisher = {{Springer Berlin Heidelberg}}, address = {{Berlin, Heidelberg}}, doi = {10.1007/978-3-642-45135-5}, isbn = {978-3-642-45134-8 978-3-642-45135-5}, langid = {english} } @inproceedings{roblesExtensiveDatasetUML2017, title = {An {{Extensive Dataset}} of {{UML Models}} in {{GitHub}}}, booktitle = {2017 {{IEEE}}/{{ACM}} 14th {{International Conference}} on {{Mining Software Repositories}} ({{MSR}})}, author = {Robles, Gregorio and {Ho-Quang}, Truong and Hebig, Regina and Chaudron, Michel R.V. and Fernandez, Miguel Angel}, year = {2017}, month = may, pages = {519--522}, publisher = {{IEEE}}, address = {{Buenos Aires, Argentina}}, doi = {10.1109/MSR.2017.48}, isbn = {978-1-5386-1544-7} } @misc{RoboticsAutonomousSystems, title = {Robotics and Autonomous Systems: Apply for Innovation Funding - {{News}} Stories - {{GOV}}.{{UK}}}, url = {https://www.gov.uk/government/news/robotics-and-autonomous-systems-apply-for-innovation-funding}, urldate = {2016-08-26} } @misc{RoboticsProgrammingLaboratory, title = {Robotics {{Programming Laboratory}}}, url = {http://se.inf.ethz.ch/courses/2013b_fall/rpl/#lectures}, urldate = {2016-01-12} } @inproceedings{roccoResilienceSiriusEditors2018, title = {Resilience in {{Sirius Editors}}: {{Understanding}} the {{Impact}} of {{Metamodel Changes}}}, booktitle = {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}, author = {Rocco, Juri Di and Ruscio, Davide Di and Narayanankutty, Hrishikesh and Pierantonio, Alfonso}, editor = {Hebig, Regina and Berger, Thorsten}, year = {2018}, series = {{{CEUR Workshop Proceedings}}}, volume = {2245}, pages = {620--630}, publisher = {{CEUR-WS.org}}, url = {http://ceur-ws.org/Vol-2245/me_paper_6.pdf} } @inproceedings{roccoTopFilterApproachRecommend2020, title = {{{TopFilter}}: {{An Approach}} to {{Recommend Relevant GitHub Topics}}}, booktitle = {{{ESEM}} '20: {{ACM}} / {{IEEE International Symposium}} on {{Empirical Software Engineering}} and {{Measurement}}, {{Bari}}, {{Italy}}, {{October}} 5-7, 2020}, author = {Rocco, Juri Di and Ruscio, Davide Di and Sipio, Claudio Di and Nguyen, Phuong T. and Rubei, Riccardo}, editor = {Baldassarre, Maria Teresa and Lanubile, Filippo and Kalinowski, Marcos and Sarro, Federica}, year = {2020}, pages = {21:1--21:11}, publisher = {{ACM}}, doi = {10.1145/3382494.3410690} } @inproceedings{ROCKRobustClustering1999, title = {{{ROCK}}: {{A}} Robust Clustering Algorithm for Categorical Attributes}, booktitle = {Proceedings of the 15th International Conference on Data Engineering}, year = {1999}, series = {{{ICDE}} '99}, pages = {512-}, publisher = {{IEEE Computer Society}}, address = {{Washington, DC, USA}}, url = {http://dl.acm.org/citation.cfm?id=846218.847264}, acmid = {847264}, isbn = {0-7695-0071-4}, key = {!!} } @article{rodriguez-graciaCollaborativeTestbedWeb2014, title = {A Collaborative Testbed Web Tool for Learning Model Transformation in Software Engineering Education}, author = {{Rodr{\'i}guez-Gracia}, D. and Criado, J. and Iribarne, L. and Padilla, N.}, year = {2014}, month = dec, journal = {Computers in Human Behavior}, issn = {07475632}, doi = {10.1016/j.chb.2014.11.096}, langid = {english} } @incollection{rodriguezMetamodelDependenciesExecutable2011, title = {Metamodel {{Dependencies}} for {{Executable Models}}}, booktitle = {Objects, {{Models}}, {{Components}}, {{Patterns}}}, author = {Rodr{\'i}guez, Carlos and S{\'a}nchez, Mario and Villalobos, Jorge}, editor = {Bishop, Judith and Vallecillo, Antonio}, year = {2011}, volume = {6705}, pages = {83--98}, publisher = {{Springer Berlin Heidelberg}}, address = {{Berlin, Heidelberg}}, doi = {10.1007/978-3-642-21952-8_8}, isbn = {978-3-642-21951-1 978-3-642-21952-8} } @article{rohrModeldrivenDevelopmentSelfmanaging2006, title = {Model-Driven Development of Self-Managing Software Systems}, author = {Rohr, Matthias and Boskovic, Marko and Giesecke, Simon and Hasselbring, Wilhelm}, year = {2006}, url = {http://eprints.uni-kiel.de/14544/1/MODELS2006.pdf}, urldate = {2016-09-21} } @book{Rojas:1996:NNS:235222, title = {Neural Networks: {{A}} Systematic Introduction}, author = {Rojas, Ra{\'u}l}, year = {1996}, publisher = {{Springer-Verlag}}, address = {{Berlin, Heidelberg}}, isbn = {3-540-60505-3} } @incollection{Rokach2005, title = {Clustering Methods}, booktitle = {Data Mining and Knowledge Discovery Handbook}, editor = {Maimon, Oded and Rokach, Lior}, year = {2005}, pages = {321--352}, publisher = {{Springer US}}, address = {{Boston, MA}}, doi = {10.1007/0-387-25465-X₁5}, isbn = {978-0-387-25465-4} } @article{Roldán2020, title = {Integrating Complex Event Processing and Machine Learning: {{An}} Intelligent Architecture for Detecting {{IoT}} Security Attacks}, author = {Rold{\'a}n, J. and {Boubeta-Puig}, J. and Luis Mart{\'i}nez, J. and Ortiz, G.}, year = {2020}, journal = {Expert Systems with Applications}, volume = {149}, publisher = {{Elsevier Ltd}}, issn = {09574174}, doi = {10.1016/j.eswa.2020.113251}, 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. \textcopyright{} 2020 Elsevier Ltd}, art_number = {113251}, coden = {ESAPE}, document_type = {Article}, source = {Scopus} } @incollection{rosaSelfmanagementDistributedSystems2013, title = {Self-Management of {{Distributed Systems Using High-Level Goal Policies}}}, booktitle = {Software {{Engineering}} for {{Self-Adaptive Systems II}}}, author = {Rosa, Liliana and Rodrigues, Lu{\'i}s and Lopes, Ant{\'o}nia}, year = {2013}, pages = {162--190}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-35813-5_7}, urldate = {2016-09-21} } @incollection{roseComparisonModelMigration2010, title = {A Comparison of Model Migration Tools}, booktitle = {Model {{Driven Engineering Languages}} and {{Systems}}}, author = {Rose, Louis M. and Herrmannsdoerfer, Markus and Williams, James R. and Kolovos, Dimitrios S. and Garc{\'e}s, Kelly and Paige, Richard F. and Polack, Fiona AC}, year = {2010}, pages = {61--75}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-16145-2_5}, urldate = {2015-03-20} } @article{roseGenericityModelManagement2011, title = {Genericity for Model Management Operations}, author = {Rose, Louis and Guerra, Esther and Lara, Juan and Etien, Anne and Kolovos, Dimitris and Paige, Richard}, year = {2011}, journal = {Software \& Systems Modeling}, volume = {12}, number = {1}, pages = {201--219}, doi = {10.1007/s10270-011-0203-2} } @article{roseInternetThingsOverview2015, title = {The Internet of Things: {{An}} Overview}, shorttitle = {The Internet of Things}, author = {Rose, Karen and Eldridge, Scott and Chapin, Lyman}, year = {2015}, journal = {The Internet Society (ISOC)}, url = {http://www.internetsociety.org/sites/default/files/ISOC-IoT-Overview-20151014_0.pdf}, urldate = {2016-05-30} } @inproceedings{Rossi:2012:DPU:2367861.2367871, title = {Dynamic Pagerank Using Evolving Teleportation}, booktitle = {Proceedings of the 9th International Conference on Algorithms and Models for the Web Graph}, author = {Rossi, Ryan A. and Gleich, David F.}, year = {2012}, series = {{{WAW}}'12}, pages = {126--137}, publisher = {{Springer-Verlag}}, address = {{Berlin, Heidelberg}}, url = {http://dx.doi.org/10.1007/978-3-642-30541-2_10}, acmid = {2367871}, isbn = {978-3-642-30540-5}, nodoi = {10.1007/978-3-642-30541-2{$_1$}0}, numpages = {12} } @article{roughan10Lessons102011, title = {10 {{Lessons}} from 10 {{Years}} of {{Measuring}} and {{Modeling}} the {{Internet}}'s {{Autonomous Systems}}}, author = {Roughan, Matthew and Willinger, Walter and Maennel, Olaf and Perouli, Debbie and Bush, Randy}, year = {2011}, month = oct, journal = {IEEE Journal on Selected Areas in Communications}, volume = {29}, number = {9}, pages = {1810--1821}, issn = {0733-8716}, doi = {10.1109/JSAC.2011.111006} } @article{rousseeuwSilhouettesGraphicalAid1987, title = {Silhouettes: {{A}} Graphical Aid to the Interpretation and Validation of Cluster Analysis}, author = {Rousseeuw, Peter J.}, year = {1987}, journal = {Journal of Computational and Applied Mathematics}, volume = {20}, pages = {53--65}, issn = {0377-0427}, url = {http://www.sciencedirect.com/science/article/pii/0377042787901257}, 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.}, nodoi = {https://doi.org/10.1016/0377-0427(87)90125-7}, keywords = {classification,cluster analysis,clustering validity,Graphical display} } @article{roy-hubaraDesignMethodsNew2020, title = {Design Methods for the New Database Era: A Systematic Literature Review}, shorttitle = {Design Methods for the New Database Era}, author = {{Roy-Hubara}, Noa and Sturm, Arnon}, year = {2020}, month = mar, journal = {Software and Systems Modeling}, volume = {19}, number = {2}, pages = {297--312}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-019-00739-8}, langid = {english}, keywords = {DONE,TYPHONML} } @incollection{roy-hubaraMethodDatabaseModel2019, title = {A {{Method}} for {{Database Model Selection}}}, booktitle = {Enterprise, {{Business-Process}} and {{Information Systems Modeling}}}, author = {{Roy-Hubara}, Noa and Shoval, Peretz and Sturm, Arnon}, editor = {{Reinhartz-Berger}, Iris and Zdravkovic, Jelena and Gulden, Jens and Schmidt, Rainer}, year = {2019}, volume = {352}, pages = {261--275}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-030-20618-5_18}, isbn = {978-3-030-20617-8 978-3-030-20618-5}, langid = {english}, keywords = {TYPHONML} } @article{roy-hubaraModelingGraphDatabase2017, title = {Modeling {{Graph Database Schema}}}, author = {{Roy-Hubara}, Noa and Rokach, Lior and Shapira, Bracha and Shoval, Peretz}, year = {2017}, month = nov, journal = {IT Professional}, volume = {19}, number = {6}, pages = {34--43}, issn = {1520-9202}, doi = {10.1109/MITP.2017.4241458} } @article{roy-hubaraQuestDatabaseSelection, title = {The {{Quest}} for a {{Database Selection}} and {{Design Method}}}, author = {{Roy-Hubara}, Noa}, pages = {9}, 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.}, langid = {english}, keywords = {DONE,TYPHONML} } @inproceedings{Rubei:ASE:2019, title = {Recommeding Highly Relevant {{StackOverflow}} Posts with Boosted Multi-Facet Queries - Manuscript under Review}, booktitle = {34th {{IEEE}}/{{ACM}} International Conference on Automated Software Engineering, {{ASE}} 2019, San Diego, California, {{USA}}, 2019}, author = {Rubei, Riccardo and Di Sipio, Claudio and Nguyen, Phuong T. and Di Rocco, Juri and {Di Ruscio}} } @inproceedings{Rubei2021477, title = {A Lightweight Approach for the Automated Classification and Clustering of Metamodels}, author = {Rubei, R. and Rocco, J.D. and Ruscio, D.D. and Nguyen, P.T. and Pierantonio, A.}, year = {2021}, series = {Companion {{Proceedings}} - 24th {{International Conference}} on {{Model-Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2021}, pages = {477--482}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS-C53483.2021.00074}, abbrev_source_title = {Companion Proc. - Int. Conf. Model-Driven Eng. Lang. Syst., MODELS-C}, affiliation = {Universit\`a Degli Studi Dell'Aquila, L'Aquila, 67100, Italy}, document_type = {Conference Paper}, isbn = {978-1-66542-484-4}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Classification,notion} } @article{rubeiPostFinderMiningStack2020, title = {{{PostFinder}}: {{Mining Stack Overflow}} Posts to Support Software Developers}, shorttitle = {{{PostFinder}}}, author = {Rubei, Riccardo and Di Sipio, Claudio and Nguyen, Phuong T. and Di Rocco, Juri and Di Ruscio, Davide}, year = {2020}, month = nov, journal = {Information and Software Technology}, volume = {127}, pages = {106367}, issn = {09505849}, doi = {10.1016/j.infsof.2020.106367}, 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.}, langid = {english}, keywords = {Indexing posts,Mining Stack Overflow posts} } @article{rubeiProvidingUpgradePlans2022, title = {Providing Upgrade Plans for Third-Party Libraries: A Recommender System Using Migration Graphs}, author = {Rubei, R. and Di Ruscio, D. and Di Sipio, C. and Di Rocco, J. and Nguyen, THANH PHUONG}, year = {2022}, journal = {APPLIED INTELLIGENCE}, doi = {10.1007/s10489-021-02911-4}, keywords = {API migration,Data mining,Recommendation systems} } @article{rubeiProvidingUpgradePlans2022a, title = {Providing Upgrade Plans for Third-Party Libraries: A Recommender System Using Migration Graphs}, author = {Rubei, R. and Di Ruscio, D. and Di Sipio, C. and Di Rocco, J. and Nguyen, Phuong}, year = {2022}, journal = {APPLIED INTELLIGENCE}, doi = {10.1007/s10489-021-02911-4}, keywords = {API migration,Data mining,Recommendation systems} } @inproceedings{rubinDeclarativeApproachModel2008, title = {Declarative Approach for Model Composition}, booktitle = {Proceedings of the 2008 International Workshop on {{Models}} in Software Engineering}, author = {Rubin, Julia and Chechik, Marsha and Easterbrook, Steve M.}, year = {2008}, pages = {7--14}, publisher = {{ACM}}, url = {http://dl.acm.org/citation.cfm?id=1370734}, urldate = {2015-09-24} } @article{ruscioDatamodellingApproachWeb2004, ids = {11697_13349,11697_9328}, title = {A Data-Modelling Approach to Web Application Synthesis}, author = {Ruscio, Davide Di and Muccini, Henry and Pierantonio, Alfonso}, year = {2004}, journal = {Int. J. Web Eng. Technol.}, volume = {1}, number = {3}, pages = {320--337}, doi = {10.1504/IJWET.2004.005236}, copyright = {All rights reserved} } @article{ruscioExtremeModellingXM2014, title = {Extreme {{Modelling}} ({{XM}}) 2012 {{Special Section}}}, author = {Ruscio, Davide Di and Pierantonio, Alfonso and de Lara, Juan}, year = {2014}, journal = {J. Object Technol.}, volume = {13}, number = {3}, doi = {10.5381/jot.2014.13.3.e1} } @article{ruscioInternationalWorkshopModel2012, title = {International {{Workshop}} on {{Model Comparison}}}, author = {Ruscio, Davide Di and Kolovos, Dimitris S.}, year = {2012}, journal = {J. Object Technol.}, volume = {11}, number = {3}, doi = {10.5381/jot.2012.11.3.e1} } @book{ruscioPostproceedingsSeventhSeminar2015, title = {Post-Proceedings of the {{Seventh Seminar}} on {{Advanced Techniques}} and {{Tools}} for {{Software Evolution}}, {{SATToSE}} 2014, {{L}}'{{Aquila}}, {{Italy}}, 9-11 {{July}} 2014}, editor = {Ruscio, Davide Di and Zaytsev, Vadim}, year = {2015}, series = {{{CEUR Workshop Proceedings}}}, volume = {1354}, publisher = {{CEUR-WS.org}}, url = {http://ceur-ws.org/Vol-1354} } @book{ruscioProceedings2ndWorkshop2016, 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}, editor = {Ruscio, Davide Di and de Lara, Juan and Pierantonio, Alfonso}, year = {2016}, series = {{{CEUR Workshop Proceedings}}}, volume = {1694}, publisher = {{CEUR-WS.org}}, url = {http://ceur-ws.org/Vol-1694} } @book{ruscioProceedings3rdWorkshop2014, 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}, editor = {Ruscio, Davide Di and de Lara, Juan and Pierantonio, Alfonso}, year = {2014}, series = {{{CEUR Workshop Proceedings}}}, volume = {1239}, publisher = {{CEUR-WS.org}}, url = {http://ceur-ws.org/Vol-1239} } @book{ruscioProceedingsWorkshopACadeMics2013, title = {Proceedings of the Workshop on {{ACadeMics Tooling}} with {{Eclipse}}, {{ACME}}@{{ECOOP}} 2013, {{Montpellier}}, {{France}}, {{July}} 2, 2013}, editor = {Ruscio, Davide Di and Kolovos, Dimitris S. and Rose, Louis M. and {Al-Hilank}, Samir}, year = {2013}, publisher = {{ACM}}, doi = {10.1145/2491279}, isbn = {978-1-4503-2036-8} } @book{ruscioProceedingsWorkshopFlexible2015, 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}, editor = {Ruscio, Davide Di and de Lara, Juan and Pierantonio, Alfonso}, year = {2015}, series = {{{CEUR Workshop Proceedings}}}, volume = {1470}, publisher = {{CEUR-WS.org}}, url = {http://ceur-ws.org/Vol-1470} } @book{ruscioProceedingsWorkshopScalability2013, title = {Proceedings of the {{Workshop}} on {{Scalability}} in {{Model Driven Engineering}}, {{Budapest}}, {{Hungary}}, {{June}} 17, 2013}, editor = {Ruscio, Davide Di and Kolovos, Dimitris S. and Matragkas, Nicholas}, year = {2013}, publisher = {{ACM}}, url = {http://dl.acm.org/citation.cfm?id=2487766}, isbn = {978-1-4503-2165-5} } @book{ruscioTheoryPracticeModel2014, title = {Theory and {{Practice}} of {{Model Transformations}} - 7th {{International Conference}}, {{ICMT}}@{{STAF}} 2014, {{York}}, {{UK}}, {{July}} 21-22, 2014. {{Proceedings}}}, editor = {Ruscio, Davide Di and Varr{\'o}, D{\'a}niel}, year = {2014}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {8568}, publisher = {{Springer}}, doi = {10.1007/978-3-319-08789-4}, isbn = {978-3-319-08788-7} } @inproceedings{Růžička2021105, title = {Combining Gaussian Processes with Neural Networks for Active Learning in Optimization}, author = {R{\r{u}}{\v z}i{\v c}ka, J. and Koza, J. and Tumpach, J. and Pitra, Z. and Hole{\v n}a, M.}, editor = {Krempl G., Lemaire V., Holzinger A., Hammer B., Kottke D.}, year = {2021}, series = {{{CEUR Workshop Proceedings}}}, volume = {3079}, pages = {105--120}, publisher = {{CEUR-WS}}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124088084&partnerID=40&md5=489c84189b9e10b74826f211a50decf3}, abbrev_source_title = {CEUR Workshop Proc.}, affiliation = {Czech Technical University, Prague, Czech Republic; Charles University, Prague, Czech Republic; Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic}, document_type = {Conference Paper}, langid = {english}, source = {Scopus} } @article{rymerForresterWaveLowCode2019, title = {The {{Forrester Wave}}\texttrademark : {{Low-Code Development Platforms For AD}}\&{{D Professionals}}, {{Q1}} 2019}, author = {Rymer, John R and Koplowitz, Rob}, year = {2019}, pages = {17}, langid = {english}, keywords = {lowcode} } @inproceedings{sahayUnderstandingRoleModel2020, title = {Understanding the Role of Model Transformation Compositions in Low-Code Development Platforms}, booktitle = {Proceedings - 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2020 - {{Companion Proceedings}}}, author = {Sahay, A. and Di Ruscio, D. and Pierantonio, A.}, year = {2020}, pages = {431--435}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3417990.3420197}, isbn = {978-1-4503-8135-2}, keywords = {Low-code development platform,Model driven engineering,Model transformation,Model transformation composition} } @inproceedings{saidComparativeRecommenderSystem2014, title = {Comparative Recommender System Evaluation: Benchmarking Recommendation Frameworks}, shorttitle = {Comparative Recommender System Evaluation}, booktitle = {Proceedings of the 8th {{ACM Conference}} on {{Recommender}} Systems - {{RecSys}} '14}, author = {Said, Alan and Bellog{\'i}n, Alejandro}, year = {2014}, pages = {129--136}, publisher = {{ACM Press}}, address = {{Foster City, Silicon Valley, California, USA}}, doi = {10.1145/2645710.2645746}, isbn = {978-1-4503-2668-1}, langid = {english} } @inproceedings{Saied2015Could, title = {Could We Infer Unordered {{API}} Usage Patterns Only Using the Library Source Code?}, booktitle = {23rd International Conference on Program Comprehension}, author = {Saied, Mohamed Aymen and Abdeen, Hani and Benomar, Omar and Sahraoui, Houari}, year = {2015}, pages = {71--81}, publisher = {{IEEE}}, address = {{Piscataway}}, nodoi = {10.1109/ICPC.2015.16} } @article{SAIED2018164, title = {Improving Reusability of Software Libraries through Usage Pattern Mining}, author = {Saied, Mohamed Aymen and Ouni, Ali and Sahraoui, Houari and Kula, Raula Gaikovina and Inoue, Katsuro and Lo, David}, year = {2018}, journal = {Journal of Systems and Software}, volume = {145}, pages = {164--179}, issn = {0164-1212}, url = {http://www.sciencedirect.com/science/article/pii/S0164121218301699}, 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.}, nodoi = {https://doi.org/10.1016/j.jss.2018.08.032}, keywords = {Clustering,Software libraries,Software reuse,Usage patterns} } @inproceedings{saiedMiningMultilevelAPI2015, title = {Mining Multi-Level {{API}} Usage Patterns}, booktitle = {22nd International Conference on Software Analysis, Evolution, and Reengineering}, author = {Saied, M. A. and Benomar, O. and Abdeen, H. and Sahraoui, H.}, year = {2015}, pages = {23--32}, publisher = {{IEEE}}, address = {{Piscataway}}, issn = {1534-5351}, keywords = {API Documentation,API Usage,application program interfaces,application programming interface,Clustering algorithms,Context,data mining,Documentation,Graphical user interfaces,Java,Layout,MLUP,multilevel API usage pattern mining,Security,Software Clustering,software libraries,Usage Pattern} } @inproceedings{Saini2019714, title = {Teaching Modelling Literacy: {{An}} Artificial Intelligence Approach}, author = {Saini, R. and Mussbacher, G. and Guo, J.L.C. and Kienzle, J.}, editor = {Burgueno L., Burgueno L., Voss S., Chaudron M., Kienzle J., Volter M., Gerard S., Zahedi M., Bousse E., Rensink A., Polack F., Engels G., Kappel G., Pretschner A.}, year = {2019}, series = {Proceedings - 2019 {{ACM}}/{{IEEE}} 22nd {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems Companion}}, {{MODELS-C}} 2019}, pages = {714--719}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS-C.2019.00108}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst. Companion, MODELS-C}, affiliation = {Dept. of ECE, McGill University, Montr\'eal, QC, Canada; School of Computer Science, McGill University, Montr\'eal, QC, Canada}, art_number = {8904688}, document_type = {Conference Paper}, isbn = {978-1-72815-125-0}, langid = {english}, source = {Scopus} } @inproceedings{Saini2020136, title = {Artificial Intelligence Empowered Domain Modelling Bot}, author = {Saini, R.}, year = {2020}, series = {Proceedings - 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2020 - {{Companion Proceedings}}}, pages = {136--141}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3417990.3419486}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS-C - Companion Proc.}, affiliation = {McGill University, Montre\'al, QC, Canada}, correspondence_address1 = {Saini, R.; McGill UniversityCanada; email: rijul.saini@mail.mcgill.ca}, document_type = {Conference Paper}, isbn = {978-1-4503-8135-2}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Assistance,notion} } @article{Saini20221015, title = {Automated, Interactive, and Traceable Domain Modelling Empowered by Artificial Intelligence}, author = {Saini, R. and Mussbacher, G. and Guo, J.L.C. and Kienzle, J.}, year = {2022}, journal = {Software and Systems Modeling}, volume = {21}, number = {3}, pages = {1015--1045}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {16191366}, doi = {10.1007/s10270-021-00942-6}, abbrev_source_title = {Softw. Syst. Model.}, affiliation = {Department of Electrical and Computer Engineering, McGill University, Montr\'eal, Canada; School of Computer Science, McGill University, Montr\'eal, Canada}, correspondence_address1 = {Saini, R.; Department of Electrical and Computer Engineering, Canada; email: rijul.saini@mail.mcgill.ca}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Assistance,notion} } @article{sainiAutomatedInteractiveTraceable2022, title = {Automated, Interactive, and Traceable Domain Modelling Empowered by Artificial Intelligence}, author = {Saini, Rijul and Mussbacher, Gunter and Guo, Jin L. C. and Kienzle, J{\"o}rg}, year = {2022}, month = jun, journal = {Software and Systems Modeling}, volume = {21}, number = {3}, pages = {1015--1045}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-021-00942-6}, langid = {english} } @article{salehieSelfadaptiveSoftwareLandscape2009, title = {Self-Adaptive Software: {{Landscape}} and Research Challenges}, shorttitle = {Self-Adaptive Software}, author = {Salehie, Mazeiar and Tahvildari, Ladan}, year = {2009}, journal = {ACM Transactions on Autonomous and Adaptive Systems (TAAS)}, volume = {4}, number = {2}, pages = {14}, url = {http://dl.acm.org/citation.cfm?id=1516538}, urldate = {2016-01-12} } @article{Salemi2016, title = {Moving Least Squares Regression for High-Dimensional Stochastic Simulation Metamodeling}, author = {Salemi, P. and Nelson, B.L. and Staum, J.}, year = {2016}, journal = {ACM Transactions on Modeling and Computer Simulation}, volume = {26}, number = {3}, publisher = {{Association for Computing Machinery}}, issn = {10493301}, doi = {10.1145/2724708}, 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. \textcopyright{} 2016 ACM.}, art_number = {16}, coden = {ATMCE}, document_type = {Article}, source = {Scopus} } @article{samadControlSystemsInternet2016, title = {Control {{Systems}} and the {{Internet}} of {{Things}} [{{Technical Activities}}]}, author = {Samad, Tariq}, year = {2016}, month = feb, journal = {IEEE Control Systems}, volume = {36}, number = {1}, pages = {13--16}, issn = {1066-033X}, doi = {10.1109/MCS.2015.2495022} } @phdthesis{Samuel_2019, title = {A Provenance-Based Semantic Approach to Support Understandability, Reproducibility, and Reuse of Scientific Experiments}, author = {Samuel, Sheeba}, year = {2019}, address = {{Jena}}, doi = {10.22032/dbt.40396}, 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.} } @article{sanchez-cuadradoBottomUpMetaModellingInteractive2012, title = {Bottom-{{Up Meta-Modelling}}: {{An Interactive Approach}}}, author = {{S{\'a}nchez-Cuadrado}, Jes{\'u}s and Lara, Juan and Guerra, Esther}, year = {2012}, journal = {Model Driven Engineering Languages and Systems}, volume = {7590}, pages = {3--19}, doi = {10.1007/978-3-642-33666-9_2} } @article{sanchezBuildingModularYAWL2012, title = {Building a Modular {{YAWL}} Engine with {{Cumbia}}}, author = {Sanchez, Mario and Puentes, Diana and Villalobos, Jorge}, year = {2012}, journal = {International Journal of Business Process Integration and Management}, volume = {6}, number = {1}, pages = {41}, issn = {1741-8763, 1741-8771}, doi = {10.1504/IJBPIM.2012.047912}, 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.}, langid = {english} } @article{sanchezcuadradoApproachesModelTransformation2008, title = {Approaches for {{Model Transformation Reuse}}: {{Factorization}} and {{Composition}}}, author = {S{\'a}nchez Cuadrado, Jes{\'u}s and Garc{\'i}a Molina, Jes{\'u}s}, year = {2008}, journal = {Theory and Practice of Model Transformations}, volume = {5063}, pages = {168--182}, doi = {10.1007/978-3-540-69927-9_12} } @article{sanchezcuadradoComponentModelModel2014, title = {A {{Component Model}} for {{Model Transformations}}}, author = {Sanchez Cuadrado, J. and Guerra, E. and De Lara, J.}, year = {2014}, month = nov, journal = {IEEE Transactions on Software Engineering}, volume = {40}, number = {11}, pages = {1042--1060}, issn = {0098-5589}, doi = {10.1109/TSE.2014.2339852}, 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.} } @article{sanchezSemanticbasedPrivacySettings2020, title = {Semantic-Based Privacy Settings Negotiation and Management}, author = {Sanchez, Odnan Ref and Torre, Ilaria and Knijnenburg, Bart P.}, year = {2020}, month = oct, journal = {Future Generation Computer Systems}, volume = {111}, pages = {879--898}, issn = {0167739X}, doi = {10.1016/j.future.2019.10.024}, 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.}, langid = {english} } @article{sandhuBigDataCloud2022, title = {Big {{Data}} with {{Cloud Computing}}: {{Discussions}} and {{Challenges}}}, author = {Sandhu, Amanpreet Kaur}, year = {2022}, journal = {Big Data Mining and Analytics}, pages = {9}, 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.}, langid = {english} } @inproceedings{sandhuIntegrationArtificialIntelligence2021, title = {Integration of {{Artificial Intelligence}} into Software Reuse: {{An}} Overview of {{Software Intelligence}}}, shorttitle = {Integration of {{Artificial Intelligence}} into Software Reuse}, booktitle = {2021 2nd {{International Conference}} on {{Computation}}, {{Automation}} and {{Knowledge Management}} ({{ICCAKM}})}, author = {Sandhu, Amandeep Kaur and Batth, Ranbir Singh}, year = {2021}, month = jan, pages = {357--362}, publisher = {{IEEE}}, address = {{Dubai, United Arab Emirates}}, doi = {10.1109/ICCAKM50778.2021.9357738}, isbn = {978-1-72819-491-2} } @article{santhanamBotsSoftwareEngineering2022, title = {Bots in Software Engineering: A Systematic Mapping Study}, shorttitle = {Bots in Software Engineering}, author = {Santhanam, Sivasurya and Hecking, Tobias and Schreiber, Andreas and Wagner, Stefan}, year = {2022}, month = feb, journal = {PeerJ Computer Science}, volume = {8}, pages = {e866}, issn = {2376-5992}, doi = {10.7717/peerj-cs.866}, 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.}, langid = {english} } @inproceedings{Saracevic:1995:EEI:215206.215351, title = {Evaluation of Evaluation in Information Retrieval}, booktitle = {Proceedings of the 18th Annual International {{ACM SIGIR}} Conference on Research and Development in Information Retrieval}, author = {Saracevic, Tefko}, year = {1995}, series = {{{SIGIR}} '95}, pages = {138--146}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/215206.215351}, acmid = {215351}, isbn = {0-89791-714-6}, nodoi = {10.1145/215206.215351}, numpages = {9} } @article{sarkarAllYouNeed, title = {All You Need to Know about `{{Attention}}' and `{{Transformers}}' \textemdash{} {{In-depth Understanding}} \textemdash{} {{Part}} 1}, author = {Sarkar, Arjun}, pages = {17}, langid = {english} } @inproceedings{Sarwar:2001:ICF:371920.372071, ids = {IB-CF-2011}, title = {Item-Based Collaborative Filtering Recommendation Algorithms}, booktitle = {10th International Conference on World Wide Web}, author = {Sarwar, Badrul and Karypis, George and Konstan, Joseph and Riedl, John}, year = {2001}, pages = {285--295}, publisher = {{ACM}}, address = {{New York}}, acmid = {372071}, isbn = {1-58113-348-0}, nodoi = {10.1145/371920.372071}, numpages = {11} } @article{sasPerilsPitfallsClassifying, title = {The {{Perils}} and {{Pitfalls}} of {{Classifying Software Systems}}}, author = {Sas, Cezar and Capiluppi, Andrea}, pages = {11}, 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.}, langid = {english} } @book{SATToSE2017Postproceedings2017, title = {{{SATToSE}} 2017: {{The}} Post-Proceedings Editorial}, year = {2017}, journal = {CEUR Workshop Proceedings}, volume = {2070}, publisher = {{CEUR-WS}} } @misc{SATToSE2017Postproceedings2017a, title = {{{SATToSE}} 2017: {{The}} Post-Proceedings Editorial}, year = {2017}, journal = {CEUR Workshop Proceedings}, volume = {2070}, publisher = {{CEUR-WS}} } @inproceedings{Sauer201749, title = {{Use of deep learning on the locally resolved description of part properties [Einsatz von Deep Learning zur ortsaufgel\"osten Beschreibung von Bauteileigenschaften]}}, author = {Sauer, C. and K{\"o}stner, C. and Schleich, B. and Wartzack, S.}, editor = {Krause D., Wartzack S., Paetzold K.}, year = {2017}, series = {{DFX 2017: Proceedings of the 28th Symposium Design for X}}, pages = {49--60}, publisher = {{TuTech Innovation Verlag}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035053178&partnerID=40&md5=ef20b0fdc9966435df7632d5dc9badb7}, abbrev_source_title = {DFX: Proc. Symp. Des. X}, affiliation = {Lehrstuhl fur Konstruktionstechnik (KTmfk), Friedrich-Alexander-Universit\"at Erlangen-N\"ornberg, Germany}, document_type = {Conference Paper}, isbn = {978-3-946094-20-3}, langid = {English; German}, source = {Scopus} } @inproceedings{Sauer20182999, title = {Deep Learning in Sheet-Bulk Metal Forming Part Design}, author = {Sauer, C. and Schleich, B. and Wartzack, S.}, editor = {Bojcetic N., Storga M., Skec S., Pavkovic N., Marjanovic D.}, year = {2018}, series = {Proceedings of {{International Design Conference}}, {{DESIGN}}}, volume = {6}, pages = {2999--3010}, publisher = {{Faculty of Mechanical Engineering and Naval Architecture}}, issn = {18479073}, doi = {10.21278/idc.2018.0147}, abbrev_source_title = {Proc. Int. Des. Conf., DESIGN}, affiliation = {Friedrich-Alexander Universit\"at Erlangen-N\"urnberg, Engineering Design (KTmfk), Martensstra\ss e 9, Erlangen, 91058, Germany}, document_type = {Conference Paper}, isbn = {978-953-7738-59-4}, langid = {english}, source = {Scopus} } @article{savolainenSELECTIONLOWCODEPLATFORMS, title = {{{SELECTION OF LOW-CODE PLATFORMS BASED ON ORGANIZATION AND APPLICATION TYPE}}}, author = {Savolainen, Paula}, pages = {86}, langid = {english}, keywords = {lowcode} } @inproceedings{scavuzzoInteroperableDataMigration2014, title = {{Interoperable Data Migration between NoSQL Columnar Databases}}, author = {Scavuzzo, Marco and Nitto, Elisabetta Di and Ceri, Stefano}, year = {2014}, month = sep, pages = {154--162}, publisher = {{IEEE}}, doi = {10.1109/EDOCW.2014.32}, isbn = {978-1-4799-5467-4}, langid = {italian} } @article{schaarschmidtAutomatedPolyglotPersistence, title = {Towards {{Automated Polyglot Persistence}}}, author = {Schaarschmidt, Michael and Gessert, Felix and Ritter, Norbert}, pages = {10}, 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.}, langid = {english} } @article{schaefferSurveyGraphClustering2007, title = {Survey: {{Graph}} Clustering}, author = {Schaeffer, Satu Elisa}, year = {2007}, month = aug, journal = {Computer Science Review}, volume = {1}, number = {1}, pages = {27--64}, publisher = {{Elsevier Science Publishers B. V.}}, address = {{Amsterdam, The Netherlands, The Netherlands}}, issn = {1574-0137}, url = {http://dx.doi.org/10.1016/j.cosrev.2007.05.001}, acmid = {2296057}, issue_date = {August, 2007}, nodoi = {10.1016/j.cosrev.2007.05.001}, numpages = {38} } @incollection{schaferAdaptiveWeb2007, title = {The Adaptive Web}, author = {Schafer, J. Ben and Frankowski, Dan and Herlocker, Jon and Sen, Shilad}, editor = {Brusilovsky, Peter and Kobsa, Alfred and Nejdl, Wolfgang}, year = {2007}, pages = {291--324}, publisher = {{Springer-Verlag}}, address = {{Berlin, Heidelberg}}, acmid = {1768208}, chapter = {Collaborative Filtering Recommender Systems}, isbn = {978-3-540-72078-2}, numpages = {34} } @article{schaferDyadRankingUsing2018, title = {Dyad Ranking Using {{Plackett}}\textendash{{Luce}} Models Based on Joint Feature Representations}, author = {Sch{\"a}fer, Dirk and H{\"u}llermeier, Eyke}, year = {2018}, month = may, journal = {Machine Learning}, volume = {107}, number = {5}, pages = {903--941}, issn = {0885-6125, 1573-0565}, doi = {10.1007/s10994-017-5694-9}, langid = {english} } @article{Schatten2017359, title = {Automated {{MMORPG}} Testing \textendash{} {{An}} Agent-Based Approach}, author = {Schatten, M. and Okrea{\v s}a {\DH}uri{\'c}, B. and Tomi{\v c}i{\v c}, I. and Ivkovi{\v c}, N.}, editor = {Demazeau Y., Davidsson P., Bajo J., Vale Z.}, year = {2017}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {10349 LNCS}, pages = {359--363}, publisher = {{Springer Verlag}}, issn = {03029743}, doi = {10.1007/978-3-319-59930-4_38}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Artificial Intelligence Laboratory, Faculty of Organization and Informatics, University of Zagreb, Zagreb, Croatia}, correspondence_address1 = {Schatten, M.; Artificial Intelligence Laboratory, Croatia; email: markus.schatten@foi.hr}, document_type = {Conference Paper}, isbn = {9783319599298}, langid = {english}, source = {Scopus} } @article{schatzDesignSpaceExplorationConstraintBased2010, title = {Design-{{Space Exploration}} through {{Constraint-Based Model-Transformation}}}, author = {Sch{\"a}tz, Bernhard and H{\"o}lzl, Florian and Lundkvist, Torbj{\"o}rn}, year = {2010}, journal = {2010 17th IEEE International Conference and Workshops on Engineering of Computer Based Systems}, pages = {173--182}, doi = {10.1109/ECBS.2010.25} } @article{schelterDeequDataQuality, title = {Deequ - {{Data Quality Validation}} for {{Machine Learning Pipelines}}}, author = {Schelter, Sebastian and Grafberger, Stefan and Schmidt, Philipp and Rukat, Tammo and Kiessling, Mario and Taptunov, Andrey and Biessmann, Felix and Lange, Dustin}, pages = {3}, 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.}, langid = {english} } @inproceedings{schlegelDesignAbstractionProcesses2010, title = {Design Abstraction and Processes in Robotics: From Code-Driven to Model-Driven Engineering}, shorttitle = {Design Abstraction and Processes in Robotics}, booktitle = {International {{Conference}} on {{Simulation}}, {{Modeling}}, and {{Programming}} for {{Autonomous Robots}}}, author = {Schlegel, Christian and Steck, Andreas and Brugali, Davide and Knoll, Alois}, year = {2010}, pages = {324--335}, publisher = {{Springer}}, url = {http://link.springer.com/content/pdf/10.1007/978-3-642-17319-6_31.pdf}, urldate = {2016-08-21} } @inproceedings{schonbockModelDrivenCoevolutionAgile2015, title = {Model-{{Driven Co-evolution}} for {{Agile Development}}}, author = {Schonbock, J. and Etzlstorfer, J. and Kapsammer, E. and Kusel, A. and Retschitzegger, W. and Schwinger, W.}, year = {2015}, month = jan, pages = {5094--5103}, publisher = {{IEEE}}, doi = {10.1109/HICSS.2015.603}, isbn = {978-1-4799-7367-5} } @article{sculleyHiddenTechnicalDebt, title = {Hidden {{Technical Debt}} in {{Machine Learning Systems}}}, author = {Sculley, D and Holt, Gary and Golovin, Daniel and Davydov, Eugene and Phillips, Todd and Ebner, Dietmar and Chaudhary, Vinay and Young, Michael and Crespo, Jean-Fran{\c c}ois and Dennison, Dan}, pages = {9}, 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.}, langid = {english}, keywords = {DONE,machine learning} } @article{SEfSAS3challenges, title = {{{SEfSAS3-challenges}}} } @article{Segundo2017301, title = {Improvement of Newborn Screening Using a Fuzzy Inference System}, author = {Segundo, U. and {Ald{\'a}miz-Echevarr{\'i}a}, L. and {L{\'o}pez-Cuadrado}, J. and Buenestado, D. and Andrade, F. and P{\'e}rez, T.A. and Barrena, R. and {P{\'e}rez-Yarza}, E.G. and Pikatza, J.M.}, year = {2017}, journal = {Expert Systems with Applications}, volume = {78}, pages = {301--318}, publisher = {{Elsevier Ltd}}, issn = {09574174}, doi = {10.1016/j.eswa.2017.02.022}, 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. \textcopyright{} 2017 Elsevier Ltd}, coden = {ESAPE}, document_type = {Article}, source = {Scopus} } @article{sehrawatDataMiningIoT2018, title = {Data {{Mining}} in {{IoT}} and Its {{Challenges}}}, author = {Sehrawat, Deepti and {Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, Haryana, India} and Gill, Nasib Singh and {Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, Haryana, India}}, year = {2018}, month = apr, journal = {International Journal of Computer Sciences and Engineering}, volume = {6}, number = {4}, pages = {289--295}, issn = {23472693}, doi = {10.26438/ijcse/v6i4.289295}, 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.}, langid = {english} } @incollection{seibelDedicatedLanguageContext2012, title = {A {{Dedicated Language}} for {{Context Composition}} and {{Execution}} of {{True Black-Box Model Transformations}}}, booktitle = {Software {{Language Engineering}}}, author = {Seibel, Andreas and Hebig, Regina and Neumann, Stefan and Giese, Holger}, editor = {Sloane, Anthony and A{\ss}mann, Uwe}, year = {2012}, series = {Lecture {{Notes}} in {{Computer Science}}}, number = {6940}, pages = {19--39}, publisher = {{Springer Berlin Heidelberg}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-28830-2_2}, urldate = {2015-03-24}, 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.}, copyright = {\textcopyright 2012 Springer-Verlag Berlin Heidelberg}, isbn = {978-3-642-28829-6 978-3-642-28830-2}, langid = {english}, keywords = {software engineering} } @misc{SelfmanagingInformationSystems, title = {Self-Managing Information Systems}, url = {http://www.inf.u-szeged.hu/~jelasity/selfstar05.html}, urldate = {2016-09-24} } @article{selimAutomatedVerificationModel2013, title = {Automated {{Verification}} of {{Model Transformations}} in the {{Automotive Industry}}}, author = {Selim, Gehan M. K. and B{\"u}ttner, Fabian and Cordy, James R. and Dingel, Juergen and Wang, Shige}, year = {2013}, journal = {Model-Driven Engineering Languages and Systems}, volume = {8107}, pages = {690--706}, doi = {10.1007/978-3-642-41533-3_42} } @article{selimModelTransformationsMigrating2012, title = {Model {{Transformations}} for {{Migrating Legacy Models}}: {{An Industrial Case Study}}}, author = {Selim, Gehan M. K. and Wang, Shige and Cordy, James R. and Dingel, Juergen}, year = {2012}, journal = {Modelling Foundations and Applications}, volume = {7349}, pages = {90--101}, doi = {10.1007/978-3-642-31491-9_9} } @article{Sen2013236, title = {A Modified Differential Evolution for Symbol Detection in {{MIMO-OFDM}} System}, author = {Sen, A. and Roy, S. and Das, S.}, year = {2013}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {8297 LNCS}, number = {PART 1}, pages = {236--247}, issn = {03029743}, doi = {10.1007/978-3-319-03753-0_22}, 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. \textcopyright{} 2013 Springer International Publishing.}, document_type = {Conference Paper}, isbn = {9783319037523}, source = {Scopus} } @inproceedings{serbanAdoptionEffectsSoftware2020, ids = {serbanAdoptionEffectsSoftware2020a}, title = {Adoption and {{Effects}} of {{Software Engineering Best Practices}} in {{Machine Learning}}}, booktitle = {Proceedings of the 14th {{ACM}} / {{IEEE International Symposium}} on {{Empirical Software Engineering}} and {{Measurement}} ({{ESEM}})}, author = {Serban, Alex and {van der Blom}, Koen and Hoos, Holger and Visser, Joost}, year = {2020}, month = oct, pages = {1--12}, publisher = {{ACM}}, address = {{Bari Italy}}, doi = {10.1145/3382494.3410681}, 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. Method. 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. Results. 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. Conclusion. 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.}, isbn = {978-1-4503-7580-1}, langid = {english}, keywords = {best practices,machine learning,software engineering} } @article{serbanSurveyIntelligentAssistants2013, title = {A Survey of Intelligent Assistants for Data Analysis}, author = {Serban, Floarea and Vanschoren, Joaquin and Kietz, J{\"o}rg-Uwe and Bernstein, Abraham}, year = {2013}, month = jun, journal = {ACM Computing Surveys}, volume = {45}, number = {3}, pages = {1--35}, issn = {0360-0300, 1557-7341}, doi = {10.1145/2480741.2480748}, 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. 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.}, langid = {english} } @misc{ServerlessApplicationsWhy, title = {Serverless {{Applications}}: {{Why}}, {{When}}, and {{How}}?}, url = {https://www.computer.org/csdl/magazine/so/2021/01/09190031/1mYZaiUIVhu}, urldate = {2021-01-17} } @incollection{sevillaruizInferringVersionedSchemas2015, title = {Inferring {{Versioned Schemas}} from {{NoSQL Databases}} and {{Its Applications}}}, booktitle = {Conceptual {{Modeling}}}, author = {Sevilla Ruiz, Diego and Morales, Severino Feliciano and Garc{\'i}a Molina, Jes{\'u}s}, editor = {Johannesson, Paul and Lee, Mong Li and Liddle, Stephen W. and Opdahl, Andreas L. and Pastor L{\'o}pez, {\'O}scar}, year = {2015}, volume = {9381}, pages = {467--480}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-319-25264-3_35}, 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.}, isbn = {978-3-319-25263-6 978-3-319-25264-3}, langid = {english} } @article{shafiqMachineLearningSoftware2020, title = {Machine {{Learning}} for {{Software Engineering}}: {{A Systematic Mapping}}}, shorttitle = {Machine {{Learning}} for {{Software Engineering}}}, author = {Shafiq, Saad and Mashkoor, Atif and {Mayr-Dorn}, Christoph and Egyed, Alexander}, year = {2020}, month = may, journal = {arXiv:2005.13299 [cs]}, eprint = {2005.13299}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2005.13299}, urldate = {2020-10-26}, 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. Method: We conduct a systematic mapping study on applications of machine learning to software engineering following the standard guidelines and principles of empirical software engineering. Results: 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. Conclusion: 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.}, archiveprefix = {arXiv}, langid = {english}, keywords = {machine learning} } @article{shahrivarBusinessModelCommercial2018, title = {A Business Model for Commercial Open Source Software: {{A}} Systematic Literature Review}, shorttitle = {A Business Model for Commercial Open Source Software}, author = {Shahrivar, Shahrokh and Elahi, Shaban and Hassanzadeh, Alireza and Montazer, Gholamali}, year = {2018}, month = nov, journal = {Information and Software Technology}, volume = {103}, pages = {202--214}, issn = {0950-5849}, doi = {10.1016/j.infsof.2018.06.018}, abstract = {Context Commercial 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. Objective The purpose of this research is to describe and present the COSS business model with all its components. Method A 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. Results The 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. Conclusion This 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.} } @inproceedings{shaoQuantifyMusicArtist2008, title = {Quantify Music Artist Similarity Based on Style and Mood}, booktitle = {Proceedings of the 10th {{ACM}} Workshop on Web Information and Data Management}, author = {Shao, Bo and Li, Tao and Ogihara, Mitsunori}, year = {2008}, series = {{{WIDM}} '08}, pages = {119--124}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/1458502.1458522}, acmid = {1458522}, isbn = {978-1-60558-260-3}, nodoi = {10.1145/1458502.1458522}, numpages = {6}, keywords = {hierarchical co-clustering,music artist similarity,similarity quantification} } @article{Sharifnia2021, title = {Robust Simulation Optimization for Supply Chain Problem under Uncertainty via Neural Network Metamodeling}, author = {Sharifnia, S.M.E. and Amrollahi Biyouki, S. and Sawhney, R. and Hwangbo, H.}, year = {2021}, journal = {Computers and Industrial Engineering}, volume = {162}, publisher = {{Elsevier Ltd}}, issn = {03608352}, doi = {10.1016/j.cie.2021.107693}, abbrev_source_title = {Comput Ind Eng}, affiliation = {Department of Industrial and Systems Engineering, The University of Tennessee, Knoxville, United States}, art_number = {107693}, coden = {CINDD}, correspondence_address1 = {Sharifnia, S.M.E.; Department of Industrial and Systems Engineering, United States; email: ssharifn@vols.utk.edu}, document_type = {Article}, langid = {english}, source = {Scopus} } @inproceedings{Sharma:2017:CGR:3084226.3084287, title = {Cataloging {{GitHub}} Repositories}, booktitle = {Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering}, author = {Sharma, Abhishek and Thung, Ferdian and Kochhar, Pavneet Singh and Sulistya, Agus and Lo, David}, year = {2017}, series = {{{EASE}}'17}, pages = {314--319}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org.univaq.clas.cineca.it/10.1145/3084226.3084287}, acmid = {3084287}, isbn = {978-1-4503-4804-1}, nodoi = {10.1145/3084226.3084287}, numpages = {6}, keywords = {Genetic Algorithm,GitHub,Latent Dirichlet Allocation} } @article{shevtsovControlTheoreticalSoftwareAdaptation2018, title = {Control-{{Theoretical Software Adaptation}}: {{A Systematic Literature Review}}}, shorttitle = {Control-{{Theoretical Software Adaptation}}}, author = {Shevtsov, Stepan and Berekmeri, Mihaly and Weyns, Danny and Maggio, Martina}, year = {2018}, month = aug, journal = {IEEE Transactions on Software Engineering}, volume = {44}, number = {8}, pages = {784--810}, issn = {0098-5589, 1939-3520, 2326-3881}, doi = {10.1109/TSE.2017.2704579}, 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.}, langid = {english} } @article{Shi:2014:CFB:2620784.2556270, title = {Collaborative Filtering beyond the User-Item Matrix: {{A}} Survey of the State of the Art and Future Challenges}, author = {Shi, Yue and Larson, Martha and Hanjalic, Alan}, year = {2014}, month = may, journal = {ACM Computing Surveys}, volume = {47}, number = {1}, pages = {3:1-3:45}, publisher = {{ACM}}, address = {{New York, NY, USA}}, issn = {0360-0300}, url = {http://doi.acm.org/10.1145/2556270}, acmid = {2556270}, articleno = {3}, issue_date = {July 2014}, nodoi = {10.1145/2556270}, numpages = {45}, keywords = {Algorithms,applications,challenges,collaborative filtering,recommender systems,social networks,survey} } @article{Shi2021, title = {A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation}, author = {Shi, R. and Mo, Z. and Huang, K. and Di, X. and Du, Q.}, year = {2021}, journal = {IEEE Transactions on Intelligent Transportation Systems}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15249050}, doi = {10.1109/TITS.2021.3106259}, 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}, document_type = {Article}, source = {Scopus} } @inproceedings{shinDynamicAdaptationSoftwaredefined2020, title = {Dynamic Adaptation of Software-Defined Networks for {{IoT}} Systems: A Search-Based Approach}, shorttitle = {Dynamic Adaptation of Software-Defined Networks for {{IoT}} Systems}, booktitle = {Proceedings of the {{IEEE}}/{{ACM}} 15th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}}}, author = {Shin, Seung Yeob and Nejati, Shiva and Sabetzadeh, Mehrdad and Briand, Lionel C. and Arora, Chetan and Zimmer, Frank}, year = {2020}, month = jun, pages = {137--148}, publisher = {{ACM}}, address = {{Seoul Republic of Korea}}, doi = {10.1145/3387939.3391603}, isbn = {978-1-4503-7962-5}, langid = {english}, keywords = {DONE} } @article{shinNoSQLDatabaseDesign2017, ids = {shinNoSQLDatabaseDesign2017a}, title = {{{NoSQL Database Design Using UML Conceptual Data Model Based}} on {{Peter Chen}}'s {{Framework}}}, author = {Shin, Kwangchul and Hwang, Chulhyun and Jung, Hoekyung}, year = {2017}, volume = {12}, number = {5}, pages = {5}, 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.}, langid = {english} } @inproceedings{shresthaAutomaticGenerationSimulink2020, title = {Automatic {{Generation}} of {{Simulink Models}} to {{Find Bugs}} in a {{Cyber-Physical System Tool Chain}} Using {{Deep Learning}}}, booktitle = {Proceedings - 2020 {{ACM}}/{{IEEE}} 42nd {{International Conference}} on {{Software Engineering}}: {{Companion}}, {{ICSE-Companion}} 2020}, author = {Shrestha, S.L.}, year = {2020}, pages = {110--112}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1145/3377812.3382163}, abstract = {Testing cyber-physical system (CPS) development tools such as MathWorks' Simulink is very important as they are widely used in design, simulation, and verification of CPS data-flow models. Existing randomized differential testing frameworks such as SLforge leverages semi-formal Simulink specifications to guide random model generation which requires significant research and engineering investment along with the need to manually update the tool, whenever MathWorks updates model validity rules. To address the limitations, we propose to learn validity rules automatically by learning a language model using our framework DeepFuzzSL from existing corpus of Simulink models. In our experiments, DeepFuz-zSL consistently generate over 90\% valid Simulink models and also found 2 confirmed bugs by MathWorks Support. \textcopyright{} 2020 ACM.}, isbn = {978-1-4503-7122-3}, keywords = {Automatic Generation,Cyber Physical System,Cyber-physical systems (CPS),Data flow analysis,Dataflow model,Deep learning,Development tools,Differential testing,Embedded systems,Language model,Learning systems,Model validity,Program debugging,Simulink models,Software engineering} } @article{Sidhu2022166, title = {A Machine Learning Approach to Software Model Refactoring}, author = {Sidhu, B.K. and Singh, K. and Sharma, N.}, year = {2022}, journal = {International Journal of Computers and Applications}, volume = {44}, number = {2}, pages = {166--177}, publisher = {{Taylor and Francis Ltd.}}, issn = {1206212X}, doi = {10.1080/1206212X.2020.1711616}, abbrev_source_title = {Int J Comput Appl}, affiliation = {Department of Computer Science and Engineering, Punjabi University, Patiala, India; University Computer Centre, Punjabi University, Patiala, India; Department of Computer Science, Punjabi University, Patiala, India}, coden = {IJCAF}, correspondence_address1 = {Sidhu, B.K.; Department of Computer Science and Engineering, Punjab, India; email: brahmaleen.ce@pbi.ac.in}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Refactoring,notion} } @book{siegwartIntroductionAutonomousMobile2004, title = {Introduction to Autonomous Mobile Robots}, author = {Siegwart, Roland and Nourbakhsh, Illah Reza}, year = {2004}, series = {Intelligent Robots and Autonomous Agents}, publisher = {{MIT Press}}, address = {{Cambridge, Mass}}, isbn = {978-0-262-19502-7}, lccn = {TJ211.415 .S54 2004} } @article{sierraSurveySelfadmittedTechnical2019, title = {A Survey of Self-Admitted Technical Debt}, author = {Sierra, Giancarlo and Shihab, Emad and Kamei, Yasutaka}, year = {2019}, month = jun, journal = {Journal of Systems and Software}, volume = {152}, pages = {70--82}, issn = {01641212}, doi = {10.1016/j.jss.2019.02.056}, langid = {english} } @article{Sikdar2014225, title = {Modified Differential Evolution for Biochemical Name Recognizer}, author = {Sikdar, U.K. and Ekbal, A. and Saha, S.}, year = {2014}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {8403 LNCS}, number = {PART 1}, pages = {225--236}, publisher = {{Springer Verlag}}, issn = {03029743}, doi = {10.1007/978-3-642-54906-9_18}, 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. \textcopyright{} 2014 Springer-Verlag Berlin Heidelberg.}, document_type = {Conference Paper}, isbn = {9783642549052}, source = {Scopus} } @article{simGettingWholeStory2011, title = {Getting the Whole Story: An Experience Report on Analyzing Data Elicited Using the War Stories Procedure}, shorttitle = {Getting the Whole Story}, author = {Sim, Susan Elliott and Alspaugh, Thomas A.}, year = {2011}, month = aug, journal = {Empirical Software Engineering}, volume = {16}, number = {4}, pages = {460--486}, issn = {1382-3256, 1573-7616}, doi = {10.1007/s10664-011-9157-9}, abstract = {When analyzing data elicited using the ``war stories'' technique, previously introduced by Lutters and Seaman (Inf Softw Technol 49(6):576\textendash 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.}, langid = {english} } @misc{SimilarityMatrix, title = {Similarity Matrix}, journal = {Google Docs}, url = {https://docs.google.com/spreadsheets/d/1jJ7FGuN1I7cWJZw4J6dO-KYaU118AFtpbvNzC01re0c/edit?usp=sharing&usp=embed_facebook}, urldate = {2020-02-11}, abstract = {Petrinet Subject/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 petrinet2.ecore,100,33,33,66,33,0,25,33,33,50 PetriNet.ecore,20,100,64,20,37,0,20,4...}, langid = {british} } @misc{SimplifyingModelTransformation, title = {Simplifying {{Model Transformation Chains}} by {{Rule Composition}} - {{Springer}}}, url = {http://link.springer.com/chapter/10.1007%2F978-3-642-21210-9_28}, urldate = {2015-03-24} } @inproceedings{Singaravel20171497, title = {Component-Based Machine Learning Modelling Approach for Design Stage Building Energy Prediction: {{Weather}} Conditions and Size}, author = {Singaravel, S. and Geyer, P. and Suykens, J.}, editor = {Barnaby C.S., Wetter M.}, year = {2017}, series = {Building {{Simulation Conference Proceedings}}}, volume = {3}, pages = {1497--1506}, publisher = {{International Building Performance Simulation Association}}, issn = {25222708}, doi = {10.26868/25222708.2017.059}, abbrev_source_title = {Build. Simul. Conf. Proc.}, affiliation = {Architectural Engineering Division, KU Leuven, Belgium; ESAT-STADIUS, KU Leuven, Belgium}, correspondence_address1 = {Singaravel, S.; Architectural Engineering Division, Belgium; email: sundar.singaravel@kuleuven.be}, document_type = {Conference Paper}, isbn = {978-1-5108-7067-3}, langid = {english}, source = {Scopus} } @article{sinnapoluIntegratingWearablesCloudbased2018, title = {Integrating Wearables with Cloud-Based Communication for Health Monitoring and Emergency Assistance}, author = {Sinnapolu, GiriBabu and Alawneh, Shadi}, year = {2018}, month = sep, journal = {Internet of Things}, volume = {1--2}, pages = {40--54}, issn = {25426605}, doi = {10.1016/j.iot.2018.08.004}, 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.}, langid = {english} } @article{Sirres2018, title = {Augmenting and Structuring User Queries to Support Efficient Free-Form Code Search}, author = {Sirres, Raphael and Bissyand{\'e}, Tegawend{\'e} F. and Kim, Dongsun and Lo, David and Klein, Jacques and Kim, Kisub and Traon, Yves Le}, year = {2018}, month = oct, journal = {Proceedings of the 40th international conference on software engineering}, series = {{{ICSE}} '18}, volume = {23}, number = {5}, pages = {2622--2654}, address = {{Gothenburg, Sweden}}, issn = {1573-7616}, doi = {10.1007/s10664-017-9544-y}, 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.}, acmid = {3182513}, nodoi = {10.1145/3180155.3182513}, numpages = {1} } @article{sivieriBuildingInternetThings2016, title = {Building {{Internet}} of {{Things}} Software with {{ELIoT}}}, author = {Sivieri, Alessandro and Mottola, Luca and Cugola, Gianpaolo}, year = {2016}, month = feb, journal = {Computer Communications}, issn = {01403664}, doi = {10.1016/j.comcom.2016.02.004}, langid = {english} } @misc{SmartAnythingEverywhere, title = {Smart {{Anything Everywhere}} | {{EU H2020}}}, url = {https://smartanythingeverywhere.eu/}, urldate = {2015-04-08} } @article{SMR:SMR567, title = {Feature Location in Source Code: A Taxonomy and Survey}, author = {Dit, Bogdan and Revelle, Meghan and Gethers, Malcom and Poshyvanyk, Denys}, year = {2013}, journal = {Journal of Software: Evolution and Process}, volume = {25}, number = {1}, pages = {53--95}, publisher = {{John Wiley \& Sons, Ltd}}, issn = {2047-7481}, url = {http://dx.doi.org/10.1002/smr.567}, nodoi = {10.1002/smr.567}, keywords = {concept location,Feature location,program comprehension,software maintenance and evolution} } @misc{SoftwareEngineeringSelfAdaptive, title = {Software {{Engineering}} for {{Self-Adaptive Systems}}: {{A Second Research Roadmap}} - {{Springer}}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-35813-5_1}, urldate = {2016-02-17} } @incollection{soldatosBuildingBlocksIoT2016, title = {Building {{Blocks}} for {{IoT Analytics}}}, author = {Soldatos, John}, year = {2016}, pages = {1--294}, doi = {10.13052/rp-9788793519046}, langid = {english}, keywords = {Data analysis,DONE,internet of things} } @article{spearmanProofMeasurementAssociation1904, title = {The Proof and Measurement of Association between Two Things}, author = {Spearman, Charles}, year = {1904}, journal = {The American journal of psychology}, volume = {15}, number = {1}, pages = {72--101}, publisher = {{JSTOR}} } @article{spinellisSoftwareEngineeringInternetThings2017a, ids = {spinellisSoftwareEngineeringInternetThings2017}, title = {Software-{{Engineering}} the {{Internet}} of {{Things}}}, author = {Spinellis, Diomidis}, year = {2017}, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {4--6}, url = {http://ieeexplore.ieee.org/abstract/document/7819398/}, urldate = {2017-02-27}, keywords = {internet of things,iot} } @article{spinellisSuccessHeavenlyMarriage2018, title = {The {{Success}} of a {{Heavenly Marriage}}}, author = {Spinellis, D.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {3--6}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571251}, 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.} } @inproceedings{Spruegel2018, title = {Methodology for Plausibility Checking of Structural Mechanics Simulations Using {{Deep Learning}} on Existing Simulation Data}, author = {Spruegel, T.C. and Rothfelder, R. and Bickel, S. and Grauf, A. and Sauer, C. and Schleich, B. and Wartzack, S.}, year = {2018}, series = {Proceedings of {{NordDesign}}: {{Design}} in the {{Era}} of {{Digitalization}}, {{NordDesign}} 2018}, publisher = {{The Design Society}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057150108&partnerID=40&md5=3fa21ccaf4f28ff72326348e85cbde09}, abbrev_source_title = {Proc. NordDesign: Des. Era Digitalization, NordDesign}, affiliation = {Friedrich-Alexander-Universit\"at Erlangen-N\"urnberg, Lehrstuhl f\"ur Konstruktionstechnik, Germany}, document_type = {Conference Paper}, isbn = {978-91-7685-185-2}, langid = {english}, source = {Scopus} } @inproceedings{Sridhar2020351, title = {Model Governance: {{Reducing}} the Anarchy of Production {{ML}}}, author = {Sridhar, V. and Subramanian, S. and Arteaga, D. and Sundararaman, S. and Roselli, D. and Talagala, N.}, year = {2020}, series = {Proceedings of the 2018 {{USENIX Annual Technical Conference}}, {{USENIX ATC}} 2018}, pages = {351--357}, publisher = {{USENIX Association}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075761598&partnerID=40&md5=393aa3aa505e90fb80de134aad651b4f}, 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. \textcopyright{} Proceedings of the 2018 USENIX Annual Technical Conference, USENIX ATC 2018. All rights reserved.}, document_type = {Conference Paper}, isbn = {978-1-939133-02-1}, source = {Scopus} } @article{srinivasanWebAppSecurity2017, title = {Web {{App Security}}: {{A Comparison}} and {{Categorization}} of {{Testing Frameworks}}}, shorttitle = {Web {{App Security}}}, author = {Srinivasan, Satish M. and Sangwan, Raghvinder S. and {undefined} and {undefined} and {undefined} and {undefined}}, year = {2017}, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {99--102}, issn = {0740-7459}, 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.}, keywords = {software engineering} } @article{sriramInternetThingsPerspectives2015, title = {Internet of {{Things Perspectives}}}, author = {Sriram, Ram D. and Sheth, Amit}, year = {2015}, month = may, journal = {IT Professional}, volume = {17}, number = {3}, pages = {60--63}, issn = {1520-9202}, doi = {10.1109/MITP.2015.43} } @article{SS04, title = {How Is Open Source Affecting Software Development?}, author = {Spinellis, D. and Szyperski, C.}, year = {2004}, month = jan, journal = {IEEE Software}, volume = {21}, number = {1}, pages = {28--33}, issn = {0740-7459}, keywords = {Automation,Business communication,Law,Licenses,Open source software,Operating systems,Packaging,Programming,Software libraries,Software packages} } @book{stahlModeldrivenSoftwareDevelopment2006, title = {Model-Driven Software Development: Technology, Engineering, Management}, shorttitle = {Model-Driven Software Development}, author = {Stahl, Thomas and V{\"o}lter, Markus}, year = {2006}, publisher = {{John Wiley}}, address = {{Chichester, England ; Hoboken, NJ}}, isbn = {978-0-470-02570-3}, langid = {english}, lccn = {QA76.76.D47 S69713 2006}, keywords = {Computer software,Development,Model-driven software architecture} } @article{stankovicResearchDirectionsInternet2014, ids = {stankovicResearchDirectionsInternet2014a}, title = {Research {{Directions}} for the {{Internet}} of {{Things}}}, author = {Stankovic, John A.}, year = {2014}, month = feb, journal = {IEEE Internet of Things Journal}, volume = {1}, number = {1}, pages = {3--9}, issn = {2327-4662}, doi = {10.1109/JIOT.2014.2312291} } @article{stansburyGraduateProgramUnmanned2015, title = {A {{Graduate Program}} in {{Unmanned}} and {{Autonomous Systems Engineering}}}, author = {Stansbury, Richard S. and Moncayo, Hever and Currier, Patrick}, year = {2015}, url = {http://se.asee.org/proceedings/ASEE2015/papers2015/79.pdf}, urldate = {2016-08-21} } @inproceedings{steinbach00comparison, ids = {Steinbach00}, title = {A Comparison of Document Clustering Techniques}, booktitle = {{{KDD}} Workshop on Text Mining}, author = {Steinbach, M. and Karypis, G. and Kumar, V.}, year = {2000}, url = {http://citeseer.ist.psu.edu/steinbach00comparison.html}, added-at = {2007-01-09T09:03:22.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/210e5c1e3ff54d9dce505a231f8ae7b32/hotho}, description = {A Comparison of Document Clustering Techniques}, interhash = {3340fbf75ada2ccb45a50dd5832f5f07}, intrahash = {10e5c1e3ff54d9dce505a231f8ae7b32}, keywords = {imported,kmeans clustering bisec text ***** document hac}, timestamp = {2007-01-09T09:03:22.000+0100} } @inproceedings{Stephan201921, title = {Towards a Cognizant Virtual Software Modeling Assistant Using Model Clones}, author = {Stephan, M.}, year = {2019}, series = {Proceedings - 2019 {{IEEE}}/{{ACM}} 41st {{International Conference}} on {{Software Engineering}}: {{New Ideas}} and {{Emerging Results}}, {{ICSE-NIER}} 2019}, pages = {21--24}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ICSE-NIER.2019.00014}, abbrev_source_title = {Proc. - IEEE/ACM Int. Conf. Softw. Eng.: New Ideas Emerg. Results, ICSE-NIER}, affiliation = {Department of Computer Science and Software Engineering, Miami University, Oxford, OH, United States}, art_number = {8805738}, correspondence_address1 = {Stephan, M.; Department of Computer Science and Software Engineering, United States; email: stephamd@miamioh.edu}, document_type = {Conference Paper}, isbn = {978-1-72811-758-4}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Assistance,notion} } @inproceedings{stereotypes2004, title = {On the Classification of Uml's Meta Model Extension Mechanism}, booktitle = {International Conference on the Unified Modeling Language}, author = {{Jiang} and Yanbing, Weizhong Shao, Zhiyi Ma, Xiangwen Meng, Lu Zhang and Ma., Haohai}, year = {2004}, pages = {54--68} } @inproceedings{Stevens201754, title = {On Ontologology}, author = {Stevens, P. and Gibbons, J.}, editor = {Johnson M., Eramo R.}, year = {2017}, series = {{{CEUR Workshop Proceedings}}}, volume = {1827}, pages = {54--58}, publisher = {{CEUR-WS}}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019246553&partnerID=40&md5=bf8dd48d63d77a58878a1fc67b9f12fe}, abbrev_source_title = {CEUR Workshop Proc.}, affiliation = {School of Informatics, University of Edinburgh, United Kingdom; Department of Computer Science, University of Oxford, United Kingdom}, document_type = {Conference Paper}, langid = {english}, source = {Scopus} } @article{stilgoeMachineLearningSocial2018, title = {Machine Learning, Social Learning and the Governance of Self-Driving Cars}, author = {Stilgoe, Jack}, year = {2018}, journal = {Social Studies of Science}, volume = {48}, number = {1}, pages = {25--56}, 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.}, nodoi = {10.1177/0306312717741687} } @article{stolABCSoftwareEngineering2018, title = {The {{ABC}} of {{Software Engineering Research}}}, author = {Stol, Klaas-Jan and Fitzgerald, Brian}, year = {2018}, month = sep, journal = {ACM Transactions on Software Engineering and Methodology}, volume = {27}, number = {3}, pages = {1--51}, issn = {1049331X}, doi = {10.1145/3241743}, langid = {english} } @article{storzAnnotateTrainEvaluate2013, title = {Annotate. {{Train}}. {{Evaluate}}. {{A}} Unified Tool for the Analysis and Visualization of Workflows in Machine Learning Applied to Object Detection}, author = {Storz, M. and Ritter, M. and Manthey, R. and Lietz, H. and Eibl, M.}, year = {2013}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {8008 LNCS}, number = {PART 5}, pages = {196--205}, issn = {03029743}, doi = {10.1007/978-3-642-39342-6_22}, abstract = {The development of classifiers for object detection in images is a complex task that comprises the creation of representative and potentially large datasets from a target object by repetitive and time-consuming intellectual annotations, followed by a sequence of methods to train, evaluate and optimize the generated classifier. This is conventionally achieved by the usage and combination of many different tools. Here, we present a holistic approach to this scenario by providing a unified tool that covers the single development stages in one solution to facilitate the development process. We prove this concept by the example of creating a face detection classifier. \textcopyright{} 2013 Springer-Verlag.}, isbn = {9783642393419}, keywords = {Development process,Development stages,Holistic approach,Human computer interaction,Image processing,Large datasets,Learning systems,Model-driven,Object Detection,Object recognition,Target object,Tools,Workflow analysis} } @article{strittmatter2016challenges, title = {Challenges for {{Addressing Quality Factors}} in {{Model Transformation}}}, author = {Syriani, Eugene and Gray, Jeff}, year = {2016}, journal = {CEUR workshop proceedings}, volume = {1706}, pages = {30--39}, doi = {10.1109/ICST.2012.198}, keywords = {\#duplicate-citation-key} } @article{strittmatter2016challenges, title = {Challenges in the {{Evolution}} of {{Metamodels}}: {{Smells}} and {{Anti-Patterns}} of a {{Historically-Grown Metamodel}}}, author = {Strittmatter, Misha and Hinkel, Georg and Langhammer, Michael}, year = {2016}, journal = {CEUR workshop proceedings}, volume = {1706}, pages = {30--39}, 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.}, langid = {english}, keywords = {\#duplicate-citation-key} } @article{strittmatterChallengesEvolvingMetamodels, title = {Challenges in Evolving {{Metamodels}}}, author = {Strittmatter, Misha and Heinrich, Robert}, pages = {4}, 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.}, langid = {english} } @misc{studerCRISPMLMachineLearning2021, title = {Towards {{CRISP-ML}}({{Q}}): {{A Machine Learning Process Model}} with {{Quality Assurance Methodology}}}, shorttitle = {Towards {{CRISP-ML}}({{Q}})}, author = {Studer, Stefan and Bui, Thanh Binh and Drescher, Christian and Hanuschkin, Alexander and Winkler, Ludwig and Peters, Steven and Mueller, Klaus-Robert}, year = {2021}, month = feb, number = {arXiv:2003.05155}, eprint = {2003.05155}, eprinttype = {arxiv}, primaryclass = {cs, stat}, publisher = {{arXiv}}, url = {http://arxiv.org/abs/2003.05155}, urldate = {2022-08-04}, abstract = {Machine learning is an established and frequently used technique in industry and academia but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners have a need for guidance throughout the life cycle of a machine learning application to meet business expectations. We therefore propose a process model for the development of machine learning applications, that covers six phases from defining the scope to maintaining the deployed machine learning application. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications, as the risk of model degradation in a changing environment is eminent. With each task of the process, we propose quality assurance methodology that is suitable to adress challenges in machine learning development that we identify in form of risks. The methodology is drawn from practical experience and scientific literature and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support but lacks to address machine learning specific tasks. Our work proposes an industry and application neutral process model tailored for machine learning applications with focus on technical tasks for quality assurance.}, archiveprefix = {arXiv}, keywords = {Computer Science - Machine Learning,Computer Science - Software Engineering,Statistics - Machine Learning} } @article{Subahi2020, title = {Cognification of Program Synthesis\textemdash a Systematic Feature-Oriented Analysis and Future Direction}, author = {Subahi, A.F.}, year = {2020}, journal = {Computers}, volume = {9}, number = {2}, publisher = {{MDPI AG}}, issn = {2073431X}, doi = {10.3390/computers9020027}, abbrev_source_title = {Comput.}, affiliation = {Department of Computer Science, University College of Al Jamoum, Umm Al Qura University, P.O.Box 715, Mecca, Saudi Arabia}, art_number = {27}, correspondence_address1 = {Subahi, A.F.; Department of Computer Science, P.O.Box 715, Saudi Arabia; email: AFSubahi@uqu.edu.sa}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {notion} } @inproceedings{Suchänek202074, title = {Bi-Directional Transformation between Normalized Systems Elements and Domain Ontologies in {{OWL}}}, author = {Such{\"a}nek, M. and Mannaert, H. and Uhn{\"a}k, P. and Pergl, R.}, editor = {Ali R., Kaindl H., Maciaszek L., Maciaszek L.}, year = {2020}, series = {{{ENASE}} 2020 - {{Proceedings}} of the 15th {{International Conference}} on {{Evaluation}} of {{Novel Approaches}} to {{Software Engineering}}}, pages = {74--85}, publisher = {{SciTePress}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088385688&partnerID=40&md5=df36b44338dd85b1c2007e585a204b4d}, abbrev_source_title = {ENASE - Proc. Int. Conf. Eval. Novel Approaches Softw. Eng.}, affiliation = {Faculty of Information Technology, Czech Technical University in Prague, Th\"akurova 9, Prague, Czech Republic; Normalized Systems Institute, University of Antwerp, Prinsstraat 13, Antwerp, Belgium; NSX Bvba, Wetenschapspark Universiteit Antwerpen, Galileilaan 15, Niel, 2845, Belgium}, document_type = {Conference Paper}, isbn = {978-989-758-421-3}, langid = {english}, source = {Scopus} } @inproceedings{Sun:2014:ESN:2627508.2627514, title = {Empirical Studies on the {{NLP}} Techniques for Source Code Data Preprocessing}, booktitle = {Proceedings of the 2014 3rd Int. {{Workshop}} on Evidential Assessment of Soft. {{Tech}}.}, author = {Sun, Xiaobing and Liu, Xiangyue and Hu, Jiajun and Zhu, Junwu}, year = {2014}, series = {{{EAST}} 2014}, pages = {32--39}, publisher = {{ACM}}, address = {{New York, NY, USA}}, acmid = {2627514}, isbn = {978-1-4503-2965-1}, nodoi = {10.1145/2627508.2627514}, numpages = {8}, keywords = {empirical studies,NLP techniques,Program comprehension,source code preprocessing} } @article{sunAIEnhancedOffloadingEdge2019, title = {{{AI-Enhanced Offloading}} in {{Edge Computing}}: {{When Machine Learning Meets Industrial IoT}}}, shorttitle = {{{AI-Enhanced Offloading}} in {{Edge Computing}}}, author = {Sun, Wen and Liu, Jiajia and Yue, Yanlin}, year = {2019}, month = sep, journal = {IEEE Network}, volume = {33}, number = {5}, pages = {68--74}, issn = {0890-8044, 1558-156X}, doi = {10.1109/MNET.001.1800510}, 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.}, langid = {english}, keywords = {DONE,machine learning} } @article{sunConvergenceRecommenderSystems2020, title = {Convergence of {{Recommender Systems}} and {{Edge Computing}}: {{A Comprehensive Survey}}}, shorttitle = {Convergence of {{Recommender Systems}} and {{Edge Computing}}}, author = {Sun, Chuan and Li, Hui and Li, Xiuhua and Wen, Junhao and Xiong, Qingyu and Zhou, Wei}, year = {2020}, journal = {IEEE Access}, volume = {8}, pages = {47118--47132}, issn = {2169-3536}, doi = {10.1109/ACCESS.2020.2978896}, keywords = {internet of things,recommendation systems} } @article{sunhareInternetThingsData2020, title = {Internet of Things and Data Mining: {{An}} Application Oriented Survey}, shorttitle = {Internet of Things and Data Mining}, author = {Sunhare, Priyank and Chowdhary, Rameez R. and Chattopadhyay, Manju K.}, year = {2020}, month = jul, journal = {Journal of King Saud University - Computer and Information Sciences}, pages = {S131915782030416X}, issn = {13191578}, doi = {10.1016/j.jksuci.2020.07.002}, 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.}, langid = {english} } @inproceedings{suriModelbasedDevelopmentModular2017, title = {Model-Based {{Development}} of {{Modular Complex Systems}} for {{Accomplishing System Integration}} for {{Industry}} 4.0:}, shorttitle = {Model-Based {{Development}} of {{Modular Complex Systems}} for {{Accomplishing System Integration}} for {{Industry}} 4.0}, author = {Suri, Kunal and Cuccuru, Arnaud and Cadavid, Juan and Gerard, Sebastien and Gaaloul, Walid and Tata, Samir}, year = {2017}, pages = {487--495}, publisher = {{SCITEPRESS - Science and Technology Publications}}, doi = {10.5220/0006210504870495}, isbn = {978-989-758-210-3} } @misc{SurveyClusteringData, title = {A {{Survey}} of {{Clustering Data Mining Techniques}} - {{Springer}}}, url = {http://link.springer.com/chapter/10.1007%2F3-540-28349-8_2}, urldate = {2015-04-16} } @misc{SurveyNoSQLDatabase, title = {Survey on {{NoSQL}} Database}, url = {http://ieeexplore.ieee.org/abstract/document/6106531/?casa_token=skk-O-EQilsAAAAA:E0LtNJ8JtgHBiTRq54qaAudBrRo6Iz4BFciGElfCEkBSW7ZVSzK8lyjhT-MGt35cwpStASMZ}, urldate = {2021-03-22}, 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.}, langid = {american} } @article{sutskeverSequenceSequenceLearning2014, title = {Sequence to {{Sequence Learning}} with {{Neural Networks}}}, author = {Sutskever, Ilya and Vinyals, Oriol and Le, Quoc V.}, year = {2014}, month = dec, journal = {arXiv:1409.3215 [cs]}, eprint = {1409.3215}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/1409.3215}, urldate = {2021-03-31}, 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.}, archiveprefix = {arXiv}, keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning} } @article{Svozil1997, title = {Introduction to Multi-Layer Feed-Forward Neural Networks}, author = {Svozil, Daniel and Kvasnicka, Vladimir and Posp{\'i}chal, Ji{\v r}{\'i}}, year = {1997}, month = nov, journal = {Chemometrics and Intelligent Laboratory Systems}, volume = {39}, pages = {43--62}, nodoi = {10.1016/S0169-7439(97)00061-0} } @article{svyatkovskiyPythiaAIassistedCode2019, title = {Pythia: {{AI-assisted Code Completion System}}}, shorttitle = {Pythia}, author = {Svyatkovskiy, Alexey and Zhao, Ying and Fu, Shengyu and Sundaresan, Neel}, year = {2019}, month = jul, journal = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining}, eprint = {1912.00742}, eprinttype = {arxiv}, pages = {2727--2735}, doi = {10.1145/3292500.3330699}, 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.}, archiveprefix = {arXiv}, langid = {english}, keywords = {Computer Science - Machine Learning,Computer Science - Software Engineering} } @misc{SwarmRobotsCan, title = {Swarm Robots Can Learn by Simply Observing -- {{ScienceDaily}}}, url = {https://www.sciencedaily.com/releases/2016/08/160830083653.htm}, urldate = {2016-08-30} } @article{Symonds2016606, title = {Development of an {{England-wide}} Indoor Overheating and Air Pollution Model Using Artificial Neural Networks}, author = {Symonds, P. and Taylor, J. and Chalabi, Z. and Mavrogianni, A. and Davies, M. and Hamilton, I. and Vardoulakis, S. and Heaviside, C. and Macintyre, H.}, year = {2016}, journal = {Journal of Building Performance Simulation}, volume = {9}, number = {6}, pages = {606--619}, publisher = {{Taylor and Francis Ltd.}}, issn = {19401493}, doi = {10.1080/19401493.2016.1166265}, abbrev_source_title = {J. Build. Perform. Simul.}, affiliation = {Institute of Environmental Design and Engineering, University College London, Central House, 14 Woburn Place, London, WC1H 0NN, United Kingdom; London School of Hygiene and Tropical Medicine, 15\textendash 17 Tavistock Place, London, WC1H 9SH, United Kingdom; Centre for Radiation, Chemical and Environmental Hazards, Public Health England, Didcot, United Kingdom}, correspondence_address1 = {Symonds, P.; Institute of Environmental Design and Engineering, Central House, 14 Woburn Place, United Kingdom; email: p.symonds@ucl.ac.uk}, document_type = {Article}, langid = {english}, source = {Scopus} } @inproceedings{syrianiAToMPMWebbasedModeling2013, title = {{{AToMPM}}: {{A Web-based Modeling Environment}}.}, shorttitle = {{{AToMPM}}}, booktitle = {Demos/{{Posters}}/{{StudentResearch}}@ {{MoDELS}}}, author = {Syriani, Eugene and Vangheluwe, Hans and Mannadiar, Raphael and Hansen, Conner and Van Mierlo, Simon and Ergin, H{\"u}seyin}, year = {2013}, pages = {21--25}, publisher = {{Citeseer}}, url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.407.6965&rep=rep1&type=pdf}, urldate = {2015-06-24} } @article{syrianiModelingModelTransformation2013, title = {Modeling a {{Model Transformation Language}}}, author = {Syriani, Eugene and Gray, Jeff and Vangheluwe, Hans}, year = {2013}, journal = {Domain Engineering}, pages = {211--237}, doi = {10.1007/978-3-642-36654-3_9} } @book{syrianiProceedings23rdACM2020, title = {Proceedings of the 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}.}, author = {Syriani, Eugene and {Association for Computing Machinery} and {Special Interest Group on Software Engineering}}, year = {2020}, url = {https://dl.acm.org/action/showBook?doi=10.1145/3365438}, urldate = {2021-01-07}, isbn = {978-1-4503-7019-6}, langid = {english} } @article{syrianiTCoreFrameworkCustombuilt2013, title = {T-{{Core}}: A Framework for Custom-Built Model Transformation Engines}, author = {Syriani, Eugene and Vangheluwe, Hans and LaShomb, Brian}, year = {2013}, journal = {Software \& Systems Modeling}, doi = {10.1007/s10270-013-0370-4} } @article{szvetitsSystematicLiteratureReview2013, title = {Systematic Literature Review of the Objectives, Techniques, Kinds, and Architectures of Models at Runtime}, author = {Szvetits, Michael and Zdun, Uwe}, year = {2013}, month = dec, journal = {Software \& Systems Modeling}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-013-0394-9}, langid = {english} } @article{TableContents2017, title = {Table of Contents}, year = {2017}, month = jan, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {2--3}, issn = {0740-7459}, doi = {10.1109/MS.2017.23}, langid = {english} } @article{Tadejko2020169, title = {Cloud Cognitive Services Based on Machine Learning Methods in Architecture of Modern Knowledge Management Solutions}, author = {Tadejko, P.}, year = {2020}, journal = {Lecture Notes on Data Engineering and Communications Technologies}, volume = {40}, pages = {169--190}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {23674512}, doi = {10.1007/978-3-030-34706-2_9}, 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\textemdash 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. \textcopyright{} Springer Nature Switzerland AG 2020.}, document_type = {Book Chapter}, source = {Scopus} } @article{taghaviNewInsightsDeveloping2018, title = {New {{Insights Towards Developing Recommender Systems}}}, author = {Taghavi, Mona and Bentahar, Jamal and Bakhtiyari, Kaveh and Hanachi, Chihab}, year = {2018}, month = mar, journal = {The Computer Journal}, volume = {61}, number = {3}, pages = {319--348}, issn = {0010-4620, 1460-2067}, doi = {10.1093/comjnl/bxx056}, langid = {english} } @article{tahaModelingBasicAspects2013, title = {Modeling Basic Aspects of Cyber-Physical Systems}, author = {Taha, Walid and Philippsen, Roland}, year = {2013}, journal = {arXiv preprint arXiv:1303.2792}, eprint = {1303.2792}, eprinttype = {arxiv}, url = {http://arxiv.org/abs/1303.2792}, urldate = {2016-02-05}, archiveprefix = {arXiv} } @article{tairasCorpusbasedAnalysisDomainspecific2015, title = {Corpus-Based Analysis of Domain-Specific Languages}, author = {Tairas, Robert and Cabot, Jordi}, year = {2015}, month = may, journal = {Software \& Systems Modeling}, volume = {14}, number = {2}, pages = {889--904}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-013-0352-6}, langid = {english} } @article{taivalsaariRoadmapProgrammableWorld2017, title = {A {{Roadmap}} to the {{Programmable World}}: {{Software Challenges}} in the {{IoT Era}}}, shorttitle = {A {{Roadmap}} to the {{Programmable World}}}, author = {Taivalsaari, Antero and Mikkonen, Tommi and {undefined} and {undefined} and {undefined} and {undefined}}, year = {2017}, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {72--80}, issn = {0740-7459}, 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.}, keywords = {internet of things,software engineering} } @article{Tan2020566, 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]}, author = {Tan, Y. and Chen, W. and Guo, Z. and Lin, E. and Lin, P. and Zhou, M. and Li, J.}, year = {2020}, journal = {Qinghua Daxue Xuebao/Journal of Tsinghua University}, volume = {60}, number = {7}, pages = {566--574}, publisher = {{Press of Tsinghua University}}, issn = {10000054}, doi = {10.16511/j.cnki.qhdxxb.2020.26.004}, 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. \textcopyright{} 2020, Tsinghua University Press. All right reserved.}, coden = {QDXKE}, document_type = {Article}, source = {Scopus} } @inproceedings{Tang2019385, title = {Improving Code Generation from Descriptive Text by Combining Deep Learning and Syntax Rules}, author = {Tang, X. and Wang, Z. and Qi, J. and Li, Z.}, year = {2019}, series = {Proceedings of the {{International Conference}} on {{Software Engineering}} and {{Knowledge Engineering}}, {{SEKE}}}, volume = {2019-July}, pages = {385--390}, publisher = {{Knowledge Systems Institute Graduate School}}, issn = {23259000}, doi = {10.18293/SEKE2019-170}, abbrev_source_title = {Proc. Int. Conf. Softw. Eng. Knowl. Eng., SEKE}, affiliation = {School of Computer Science, Central China Normal University, Wuhan, China; Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China; School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China}, correspondence_address1 = {Li, Z.; School of Computer Science, China; email: zengyangli@mail.ccnu.edu.cn}, document_type = {Conference Paper}, isbn = {1-891706-48-9}, langid = {english}, source = {Scopus}, keywords = {GOAL_Code-generation,notion} } @inproceedings{tangBridgingGapRequirement2015, title = {Bridging the Gap between Requirement Analysis and Architecture Design of Self-Adaptive Systems}, booktitle = {Software {{Engineering}} and {{Service Science}} ({{ICSESS}}), 2015 6th {{IEEE International Conference}} On}, author = {Tang, Shan and Li, Liping and Cao, Xiaoxia and Tan, Wenan}, year = {2015}, pages = {1102--1105}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7339244}, urldate = {2016-08-21} } @article{Tayarani-Najaran2022, title = {A Novel Ensemble Machine Learning and an Evolutionary Algorithm in Modeling the {{COVID-19}} Epidemic and Optimizing Government Policies}, author = {{Tayarani-Najaran}, M.}, year = {2022}, journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {21682216}, doi = {10.1109/TSMC.2022.3143955}, abbrev_source_title = {IEEE Trans. Syst. Man Cybern. Syst.}, affiliation = {School of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, U.K. (e-mail: tayaranm@herts.ac.uk)}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{Teh:EtAl:06, title = {Hierarchical Dirichlet Processes}, author = {Teh, Yee Whye and Jordan, Michael I. and Beal, Matthew J. and Blei, David M.}, year = {2006}, journal = {Journal of the American Statistical Association}, volume = {101}, number = {476}, pages = {1566--1581}, url = {http://www.gatsby.ucl.ac.uk/~ywteh/research/npbayes/jasa2006.pdf}, 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.}, added-at = {2007-03-02T00:36:19.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/2306a104860208c7a3c9be306ef709008/seandalai}, interhash = {34e30f6d1538ed136344f6a9cf8a791b}, intrahash = {306a104860208c7a3c9be306ef709008}, keywords = {2006 dirichlet bayesian}, timestamp = {2007-03-02T00:36:19.000+0100} } @misc{TemporalEMFTemporalMeta, title = {{{TemporalEMF}}: {{A Temporal}} (Meta) Modeling {{Framework}}}, url = {https://modeling-languages.com/temporal-modeling-framework-emf/}, urldate = {2018-08-10} } @inproceedings{thummalapentaParsewebProgrammerAssistant2007, title = {Parseweb: {{A}} Programmer Assistant for Reusing Open Source Code on the Web}, booktitle = {Proceedings of the Twenty-Second {{IEEE}}/{{ACM}} International Conference on Automated Software Engineering}, author = {Thummalapenta, Suresh and Xie, Tao}, year = {2007}, series = {{{ASE}} '07}, pages = {204--213}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/1321631.1321663}, acmid = {1321663}, isbn = {978-1-59593-882-4}, nodoi = {10.1145/1321631.1321663}, numpages = {10}, keywords = {code examples,code reuse,code search engine,ranking code samples} } @article{Thung2013Automated, title = {Thung et al. - 2013 - {{Automated}} Library Recommendation} } @inproceedings{thungAPIRecommendationSystem2016, title = {{{API}} Recommendation System for Software Development}, booktitle = {Automated {{Software Engineering}} ({{ASE}}), 2016 31st {{IEEE}}/{{ACM International Conference}} On}, author = {Thung, Ferdian}, year = {2016}, pages = {896--899}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/abstract/document/7582836/}, urldate = {2017-06-19} } @inproceedings{thungAutomaticRecommendationAPI2013, ids = {Thung:2013:ARA:3107656.3107694}, title = {Automatic Recommendation of {{API}} Methods from Feature Requests}, booktitle = {Proceedings of the 28th {{IEEE}}/{{ACM International Conference}} on {{Automated Software Engineering}}}, author = {Thung, Ferdian and Wang, Shaowei and Lo, David and Lawall, Julia}, year = {2013}, pages = {290--300}, publisher = {{IEEE Press}}, address = {{Silicon Valley, CA, USA}}, acmid = {3107694}, nodoi = {10.1109/ASE.2013.6693088}, numpages = {11} } @inproceedings{thungDetectingSimilarApplications2012, ids = {Lo:2012:DSA:2473496.2473616}, title = {Detecting Similar Applications with Collaborative Tagging}, booktitle = {Software {{Maintenance}} ({{ICSM}}), 2012 28th {{IEEE International Conference}} On}, author = {Thung, Ferdian and Lo, David and Jiang, Lingxiao}, year = {2012}, pages = {600--603}, publisher = {{IEEE}}, url = {http://ieeexplore.ieee.org/abstract/document/6405331/}, urldate = {2017-03-14}, acmid = {2473616}, nodoi = {10.1109/ICSM.2012.6405331}, numpages = {4}, keywords = {Cloning,Collaboration,Java,Search engines,Software systems,Tagging} } @article{thurimellaGuidelinesManagingRequirements2017, title = {Guidelines for {{Managing Requirements Rationales}}}, author = {Thurimella, Anil Kumar and Schubanz, Mathias and Pleuss, Andreas and Botterweck, Goetz and {undefined} and {undefined} and {undefined} and {undefined}}, year = {2017}, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {82--90}, issn = {0740-7459}, 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.}, keywords = {software engineering} } @book{ThWorkshopFlexible2018, title = {4 Th Workshop on Flexible Model-Driven Engineering ({{FlexMDE}} 2018)}, year = {2018}, journal = {CEUR Workshop Proceedings}, volume = {2245}, publisher = {{CEUR-WS}} } @misc{ThWorkshopFlexible2018a, title = {4 Th Workshop on Flexible Model-Driven Engineering ({{FlexMDE}} 2018)}, year = {2018}, journal = {CEUR Workshop Proceedings}, volume = {2245}, publisher = {{CEUR-WS}} } @article{Tiarks2011, title = {An Extended Assessment of Type-3 Clones as Detected by State-of-the-Art Tools}, author = {Tiarks, Rebecca and Koschke, Rainer and Falke, Raimar}, year = {2011}, month = jun, journal = {Software Quality Journal}, volume = {19}, number = {2}, pages = {295--331}, issn = {1573-1367}, doi = {10.1007/s11219-010-9115-6}, 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.} } @article{tichyEmpiricalSoftwareResearch2011, title = {Empirical Software Research: An Interview with {{Dag Sj\o berg}}, {{University}} of {{Oslo}}, {{Norway}}}, shorttitle = {Empirical Software Research}, author = {Tichy, Walter}, year = {2011}, journal = {Ubiquity}, volume = {2011}, number = {June}, pages = {2}, url = {http://dl.acm.org/citation.cfm?id=1998374}, urldate = {2017-02-25} } @article{Tijskens202120, title = {The Impact of a Reduced Training Subspace on the Prediction Accuracy of Neural Networks for Hygrothermal Predictions}, author = {Tijskens, A. and Janssen, H. and Roels, S.}, year = {2021}, journal = {Journal of Building Performance Simulation}, volume = {14}, number = {1}, pages = {20--37}, publisher = {{Taylor and Francis Ltd.}}, issn = {19401493}, doi = {10.1080/19401493.2020.1832148}, abbrev_source_title = {J. Build. Perform. Simul.}, affiliation = {Department of Civil Engineering, Building Physics Section, KU Leuven, Kasteelpark Arenberg 40 Bus 2447, Heverlee, 3001, Belgium}, correspondence_address1 = {Tijskens, A.; Department of Civil Engineering, Kasteelpark Arenberg 40 Bus 2447, Belgium; email: astrid.tijskens@kuleuven.be}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{Tong2021298, title = {Design of Hotel Marketing Information Management Model Based on Deep Learning}, author = {Tong, L. and Wang, F.}, editor = {Fu W., Xu Y., Zhang Y., Wang S.}, year = {2021}, journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST}, volume = {387}, pages = {298--310}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {18678211}, doi = {10.1007/978-3-030-82562-1_28}, 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. \textcopyright{} 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.}, document_type = {Conference Paper}, isbn = {9783030825614}, source = {Scopus} } @inproceedings{Toscano20164358, title = {Study of the Approximation of the Fitness Landscape and the Ranking Process of Scalarizing Functions for Many-Objective Problems}, author = {Toscano, G. and Deb, K.}, year = {2016}, series = {2016 {{IEEE Congress}} on {{Evolutionary Computation}}, {{CEC}} 2016}, pages = {4358--4365}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/CEC.2016.7744344}, abbrev_source_title = {IEEE Congr. Evol. Comput., CEC}, affiliation = {CINVESTAV-Tamaulipas, Cd. Victoria, Tamaulipas, 87130, Mexico; Michigan State University, East LansingMI, United States}, art_number = {7744344}, document_type = {Conference Paper}, isbn = {978-1-5090-0622-9}, langid = {english}, source = {Scopus} } @article{totterdaleCASESTUDYUTILIZATION2018, title = {{{CASE STUDY}}: {{THE UTILIZATION OF LOW-CODE DEVELOPMENT TECHNOLOGY TO SUPPORT RESEARCH DATA COLLECTION}}}, author = {Totterdale, Robert L}, year = {2018}, volume = {19}, number = {2}, pages = {8}, 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.}, langid = {english} } @inproceedings{Toutiaee20201097, title = {Gaussian Function on Response Surface Estimation}, author = {Toutiaee, M. and Miller, J.A.}, editor = {Wu X., Jermaine C., Hu X.T., Kotevska O., Lu S., Xu W., Aluru S., Zhai C., Al-Masri E., Chen Z., Saltz J., Xiong L.}, year = {2020}, series = {Proceedings - 2020 {{IEEE International Conference}} on {{Big Data}}, {{Big Data}} 2020}, pages = {1097--1102}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/BigData50022.2020.9378132}, abbrev_source_title = {Proc. - IEEE Int. Conf. Big Data, Big Data}, affiliation = {University of Georgia, Computer Science Department, Athens, GA, United States}, art_number = {9378132}, document_type = {Conference Paper}, isbn = {978-1-72816-251-5}, langid = {english}, source = {Scopus} } @article{trakadasArtificialIntelligenceBasedCollaboration2020, title = {An {{Artificial Intelligence-Based Collaboration Approach}} in {{Industrial IoT Manufacturing}}: {{Key Concepts}}, {{Architectural Extensions}} and {{Potential Applications}}}, shorttitle = {An {{Artificial Intelligence-Based Collaboration Approach}} in {{Industrial IoT Manufacturing}}}, author = {Trakadas, Panagiotis and Simoens, Pieter and Gkonis, Panagiotis and Sarakis, Lambros and Angelopoulos, Angelos and {Ramallo-Gonz{\'a}lez}, Alfonso P. and Skarmeta, Antonio and Trochoutsos, Christos and Calv{$o$}, Daniel and Pariente, Tomas and Chintamani, Keshav and Fernandez, Izaskun and Irigaray, Aitor Arnaiz and Parreira, Josiane Xavier and Petrali, Pierluigi and Leligou, Nelly and Karkazis, Panagiotis}, year = {2020}, month = jan, journal = {Sensors}, volume = {20}, number = {19}, pages = {5480}, publisher = {{Multidisciplinary Digital Publishing Institute}}, doi = {10.3390/s20195480}, 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.}, copyright = {http://creativecommons.org/licenses/by/3.0/}, langid = {english}, keywords = {DONE} } @article{tranDependableControlSystems2015, title = {Dependable Control Systems with {{Internet}} of {{Things}}}, author = {Tran, Tri and Ha, Q.P.}, year = {2015}, month = nov, journal = {ISA Transactions}, volume = {59}, pages = {303--313}, issn = {00190578}, doi = {10.1016/j.isatra.2015.08.008}, langid = {english} } @incollection{tranMultiBackEndsModel2013, title = {Multi {{Back-Ends}} for a {{Model Library Abstraction Layer}}}, booktitle = {Computational {{Science}} and {{Its Applications}}\textendash{{ICCSA}} 2013}, author = {Tran, Ngoc Viet and Ganser, Andreas and Lichter, Horst}, year = {2013}, pages = {160--174}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-39646-5_12}, urldate = {2015-06-24} } @article{tsamardinosBootstrappingOutofsamplePredictions2018, title = {Bootstrapping the Out-of-Sample Predictions for Efficient and Accurate Cross-Validation}, author = {Tsamardinos, Ioannis and Greasidou, Elissavet and Borboudakis, Giorgos}, year = {2018}, month = dec, journal = {Machine Learning}, volume = {107}, number = {12}, pages = {1895--1922}, issn = {0885-6125, 1573-0565}, doi = {10.1007/s10994-018-5714-4}, langid = {english} } @incollection{tsamardinosPerformanceEstimationPropertiesCrossValidationBased2014, title = {Performance-{{Estimation Properties}} of {{Cross-Validation-Based Protocols}} with {{Simultaneous Hyper-Parameter Optimization}}}, booktitle = {Artificial {{Intelligence}}: {{Methods}} and {{Applications}}}, author = {Tsamardinos, Ioannis and Rakhshani, Amin and Lagani, Vincenzo}, editor = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Kobsa, Alfred and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Terzopoulos, Demetri and Tygar, Doug and Weikum, Gerhard and Likas, Aristidis and Blekas, Konstantinos and Kalles, Dimitris}, year = {2014}, volume = {8445}, pages = {1--14}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-319-07064-3_1}, isbn = {978-3-319-07063-6 978-3-319-07064-3} } @inproceedings{Tsantalis:2018:AER:3180155.3180206, title = {Accurate and Efficient Refactoring Detection in Commit History}, booktitle = {Proceedings of the 40th International Conference on Software Engineering}, author = {Tsantalis, Nikolaos and Mansouri, Matin and Eshkevari, Laleh M. and Mazinanian, Davood and Dig, Danny}, year = {2018}, series = {{{ICSE}} '18}, pages = {483--494}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/3180155.3180206}, acmid = {3180206}, isbn = {978-1-4503-5638-1}, nodoi = {10.1145/3180155.3180206}, numpages = {12}, keywords = {abstract syntax tree,accuracy,commit,Git,Oracle,refactoring} } @misc{TSEFOCUSJournalPaper, title = {{{TSE-FOCUS Journal Paper}}}, journal = {Google Docs}, url = {https://docs.google.com/document/d/1_40QPw-9Ddk7yZ2fQy1HRaPtK5I1dI_TxOSuOWcHKkU/edit?usp=embed_facebook}, urldate = {2020-02-11}, 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...}, langid = {british} } @inproceedings{Tun202113, title = {Goal-Centralized Metamodel Based Requirements Integration for Machine Learning Systems}, author = {Tun, H.T. and Husen, J.H. and Yoshioka, N. and Washizaki, H. and Fukazawa, Y.}, year = {2021}, series = {Proceedings - {{Asia-Pacific Software Engineering Conference}}, {{APSEC}}}, pages = {13--16}, publisher = {{IEEE Computer Society}}, issn = {15301362}, doi = {10.1109/APSECW53869.2021.00013}, abbrev_source_title = {Proc. Asia Pac. Softw. Eng. Conf. APSEC}, affiliation = {Waseda University, Tokyo, Japan}, document_type = {Conference Paper}, isbn = {978-1-66543-813-1}, langid = {english}, source = {Scopus} } @article{Turney:2010:FMV:1861751.1861756, title = {From Frequency to Meaning: {{Vector}} Space Models of Semantics}, author = {Turney, Peter D. and Pantel, Patrick}, year = {2010}, month = jan, journal = {J. Artif. Int. Res.}, volume = {37}, number = {1}, pages = {141--188}, publisher = {{AI Access Foundation}}, address = {{USA}}, issn = {1076-9757}, url = {http://dl.acm.org/citation.cfm?id=1861751.1861756}, acmid = {1861756}, issue_date = {January 2010}, numpages = {48} } @article{tversky1977features, title = {Features of Similarity}, author = {Tversky, Amos}, year = {1977}, journal = {Psychological Review}, volume = {84}, number = {4}, pages = {327--352}, publisher = {{American Psychological Association}}, address = {{US}}, issn = {19391471}, 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)}, added-at = {2014-02-04T10:50:37.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/26213352d60fe7cf406596d8f1db71f8a/jaeschke}, interhash = {03a061a2e7ecca2b5e8d900655596144}, intrahash = {6213352d60fe7cf406596d8f1db71f8a}, nodoi = {10.1037/0033-295X.84.4.327}, refid = {1978-09287-001}, keywords = {psychology similarity toread}, timestamp = {2014-07-28T15:57:31.000+0200} } @inproceedings{ugurelWhatCodeAutomatic2002, title = {What's the Code?: {{Automatic}} Classification of Source Code Archives}, booktitle = {Proceedings of the Eighth {{ACM SIGKDD}} International Conference on Knowledge Discovery and Data Mining}, author = {Ugurel, Secil and Krovetz, Robert and Giles, C. Lee}, year = {2002}, series = {{{KDD}} '02}, pages = {632--638}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/775047.775141}, acmid = {775141}, isbn = {1-58113-567-X}, nodoi = {10.1145/775047.775141}, numpages = {7} } @article{undefinedDarkitectureRealitySkirted2017, title = {Darkitecture: {{The Reality Skirted}} by {{Architecture}}}, shorttitle = {Darkitecture}, author = {{undefined} and {undefined} and {undefined} and {undefined}}, year = {2017}, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {103--105}, issn = {0740-7459}, 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.} } @article{undefinedKeyAbstractionsIoTOriented2017, title = {Key {{Abstractions}} for {{IoT-Oriented Software Engineering}}}, author = {{undefined} and {undefined} and {undefined} and {undefined}}, year = {2017}, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {38--45}, issn = {0740-7459}, 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.}, keywords = {internet of things,software development,software engineering} } @article{undefinedPracticesTechnologiesComputer2017, title = {Practices and {{Technologies}} in {{Computer Game Software Engineering}}}, author = {{undefined} and {undefined} and {undefined} and {undefined}}, year = {2017}, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {110--116}, issn = {0740-7459}, 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.}, keywords = {software engineering} } @article{undefinedSoftwareEngineeringInternetThings2017, title = {Software-{{Engineering}} the {{Internet}} of {{Things}}}, author = {{undefined} and {undefined} and {undefined} and {undefined}}, year = {2017}, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {4--6}, issn = {0740-7459} } @article{undefinedValueDoubt2017, title = {The {{Value}} of {{Doubt}}}, author = {{undefined} and {undefined} and {undefined} and {undefined}}, year = {2017}, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {106--109}, issn = {0740-7459}, 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.} } @book{ungerAutonomousSystemsDevelopments2012, title = {Autonomous {{Systems}}: {{Developments}} and {{Trends}}}, shorttitle = {Autonomous {{Systems}}}, editor = {Unger, Herwig and Kyamaky, Kyandoghere and Kacprzyk, Janusz}, year = {2012}, series = {Studies in {{Computational Intelligence}}}, volume = {391}, publisher = {{Springer Berlin Heidelberg}}, address = {{Berlin, Heidelberg}}, url = {http://link.springer.com/10.1007/978-3-642-24806-1}, urldate = {2016-08-21}, isbn = {978-3-642-24805-4 978-3-642-24806-1} } @misc{UniversitySouthAustralia, title = {University of {{South Australia}} {$>$} {{Course}}}, url = {http://programs.unisa.edu.au/public/pcms/course.aspx?pageid=101801&y=2016}, urldate = {2016-08-21} } @article{UsingRecommenderSystems, title = {Using {{Recommender Systems}} to {{Improve Proactive Modeling}}} } @misc{UsingTorPrivoxy, title = {Using {{Tor}}, {{Privoxy}} and {{Polipo}} \textasciitilde{} {{A}} Little Bit of Everything}, url = {http://teebeenator.blogspot.it/2014/03/using-tor-privoxy-and-polipo.html}, urldate = {2015-03-30} } @article{Vakil-Baghmisheh2003, title = {A Fast Simplified Fuzzy {{ARTMAP}} Network}, author = {{Vakil-Baghmisheh}, Mohammad-Taghi and Pave{\v s}i{\'c}, Nikola}, year = {2003}, month = jun, journal = {Neural Processing Letters}, volume = {17}, number = {3}, pages = {273--316}, issn = {1573-773X}, doi = {10.1023/A:1026004816362}, 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.} } @article{vallecilloTypingModelTransformations2012, title = {Typing {{Model Transformations Using Tracts}}}, author = {Vallecillo, Antonio and Gogolla, Martin}, year = {2012}, journal = {Theory and Practice of Model Transformations}, volume = {7307}, pages = {56--71}, doi = {10.1007/978-3-642-30476-7_4} } @article{vanamstelUsingMetricsAssessing, title = {Using {{Metrics}} for {{Assessing}} the {{Quality}} of {{ATL Model Transformations}}}, author = {{van Amstel} and {van den Brand}} } @inproceedings{vanderdoncktApplyingDeepLearning2020, title = {Applying Deep Learning to Reduce Large Adaptation Spaces of Self-Adaptive Systems with Multiple Types of Goals}, booktitle = {Proceedings of the {{IEEE}}/{{ACM}} 15th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}}}, author = {Van Der Donckt, Jeroen and Weyns, Danny and Quin, Federico and Van Der Donckt, Jonas and Michiels, Sam}, year = {2020}, month = jun, pages = {20--30}, publisher = {{ACM}}, address = {{Seoul Republic of Korea}}, doi = {10.1145/3387939.3391605}, 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.}, isbn = {978-1-4503-7962-5}, langid = {english} } @inproceedings{VanDerWaa2018, title = {The Design and Validation of an Intuitive Confidence Measure}, author = {Van Der Waa, J. and Van DIggelen, J. and Neerincx, M.}, editor = {Said A., Komatsu T.}, year = {2018}, series = {{{CEUR Workshop Proceedings}}}, volume = {2068}, publisher = {{CEUR-WS}}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044532830&partnerID=40&md5=2ed349cd86e9fe55b202cfbb8c2a5f7d}, 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. \textcopyright{} 2018 Copyright for the individual papers remains with the authors.}, document_type = {Conference Paper}, source = {Scopus} } @article{VanGelder2014245, title = {Comparative Study of Metamodelling Techniques in Building Energy Simulation: {{Guidelines}} for Practitioners}, author = {Van Gelder, L. and Das, P. and Janssen, H. and Roels, S.}, year = {2014}, journal = {Simulation Modelling Practice and Theory}, volume = {49}, pages = {245--257}, publisher = {{Elsevier}}, issn = {1569190X}, doi = {10.1016/j.simpat.2014.10.004}, abbrev_source_title = {Simul. Model. Pract. Theory}, affiliation = {Building Physics Section, Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40 Bus 2447, Heverlee, 3001, Belgium; Bartlett School of Graduate Studies, University College London, Central House, 14 Upper Woburn Place, London, WC1H 0NN, United Kingdom}, correspondence_address1 = {Van Gelder, L.; Building Physics Section, Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40 Bus 2447, Belgium}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{vanhooffFrameworkTransformationChain2006, title = {A {{Framework}} for {{Transformation Chain Development Processes}}}, author = {Vanhooff, Bert and Ayed, Dhouha and Berbers, Yolande}, year = {2006}, pages = {3--8} } @article{vanhooffTransformationChainModeling2006, title = {Towards a {{Transformation Chain Modeling Language}}}, author = {Vanhooff, Bert and Baelen, Stefan and Hovsepyan, Aram and Joosen, Wouter and Berbers, Yolande}, year = {2006}, journal = {Embedded Computer Systems: Architectures, Modeling, and Simulation}, volume = {4017}, pages = {39--48}, doi = {10.1007/11796435_6} } @inproceedings{Vargas_sales_diversity_14, title = {Improving Sales Diversity by Recommending Users to Items}, booktitle = {Eighth {{ACM}} Conference on Recommender Systems, {{RecSys}} '14, Foster City, Silicon Valley, {{CA}}, {{USA}} - October 06 - 10, 2014}, author = {Vargas, Sa{\'u}l and Castells, Pablo}, year = {2014}, pages = {145--152}, doi = {10.1145/2645710.2645744}, bibsource = {dblp computer science bibliography, http://dblp.org}, biburl = {http://dblp.uni-trier.de/rec/bib/conf/recsys/VargasC14}, timestamp = {Thu, 02 Oct 2014 08:41:01 +0200} } @inproceedings{vargasRankRelevanceNovelty2011, title = {Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems}, booktitle = {Proceedings of the Fifth {{ACM}} Conference on Recommender Systems}, author = {Vargas, Sa{\'u}l and Castells, Pablo}, year = {2011}, series = {{{RecSys}} '11}, pages = {109--116}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/2043932.2043955}, acmid = {2043955}, isbn = {978-1-4503-0683-6}, nodoi = {10.1145/2043932.2043955}, numpages = {8}, keywords = {diversity,evaluation,metrics,novelty,recommender systems} } @article{vasilescuHowHealthyAre2014, title = {How Healthy Are Software Engineering Conferences?}, author = {Vasilescu, Bogdan and Serebrenik, Alexander and Mens, Tom and {van den Brand}, Mark G.J. and Pek, Ekaterina}, year = {2014}, month = sep, journal = {Science of Computer Programming}, volume = {89}, pages = {251--272}, issn = {01676423}, doi = {10.1016/j.scico.2014.01.016}, langid = {english} } @inproceedings{vassevAutonomyRequirementsEngineering2013, title = {Autonomy Requirements Engineering: A Case Study on the {{BepiColombo}} Mission}, shorttitle = {Autonomy Requirements Engineering}, booktitle = {Proceedings of the {{International C}}* {{Conference}} on {{Computer Science}} and {{Software Engineering}}}, author = {Vassev, Emil and Hinchey, Mike}, year = {2013}, pages = {31--41}, publisher = {{ACM}}, url = {http://dl.acm.org/citation.cfm?id=2494472}, urldate = {2016-08-21} } @article{vathy-fogarassyUniformDataAccess2017, title = {Uniform Data Access Platform for {{SQL}} and {{NoSQL}} Database Systems}, author = {{Vathy-Fogarassy}, {\'A}gnes and Hugy{\'a}k, Tam{\'a}s}, year = {2017}, month = sep, journal = {Information Systems}, volume = {69}, pages = {93--105}, issn = {03064379}, doi = {10.1016/j.is.2017.04.002}, 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.}, langid = {english} } @inproceedings{vazEmpiricalStudyTask2019, title = {An {{Empirical Study}} on {{Task Documentation}} in {{Software Crowdsourcing}} on {{TopCoder}}}, booktitle = {2019 {{ACM}}/{{IEEE}} 14th {{International Conference}} on {{Global Software Engineering}} ({{ICGSE}})}, author = {Vaz, Luis and Steinmacher, Igor and Marczak, Sabrina}, year = {2019}, month = may, pages = {48--57}, publisher = {{IEEE}}, address = {{Montreal, QC, Canada}}, doi = {10.1109/ICGSE.2019.00041}, isbn = {978-1-5386-9196-0} } @article{venChallengesStrategiesUse2008, title = {Challenges and Strategies in the Use of {{Open Source Software}} by {{Independent Software Vendors}}}, author = {Ven, Kris and Mannaert, Herwig}, year = {2008}, month = aug, journal = {Information and Software Technology}, volume = {50}, number = {9-10}, pages = {991--1002}, issn = {09505849}, doi = {10.1016/j.infsof.2007.09.001}, langid = {english} } @article{venkateshScalableApplicationDesignIoT2017, title = {Scalable-{{Application Design}} for the {{IoT}}}, author = {Venkatesh, Jagannathan and Aksanli, Baris and Chan, Christine S. and Akyurek, Alper S. and Rosing, Tajana S. and {undefined} and {undefined} and {undefined} and {undefined}}, year = {2017}, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {62--70}, issn = {0740-7459}, 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.}, keywords = {internet of things,software engineering} } @book{vermesanInternetThingsApplications2014, title = {Internet of Things Applications - from Research and Innovation to Market Deployment.}, author = {Vermesan, Ovidiu}, year = {2014}, publisher = {{River Publishers}}, address = {{Place of publication not identified}}, isbn = {978-87-93102-94-1}, langid = {english} } @book{vermesanInternetThingsConverging2013, title = {Internet of {{Things}}: {{Converging Technologies}} for {{Smart Environments}}}, shorttitle = {Internet of {{Things}}}, author = {Vermesan, Ovidiu}, year = {2013}, publisher = {{River Publishers}}, langid = {english} } @article{vermesanInternetThingsStrategic2011, title = {Internet of Things Strategic Research Roadmap}, author = {Vermesan, Ovidiu and Friess, Peter and Guillemin, Patrick and Gusmeroli, Sergio and Sundmaeker, Harald and Bassi, Alessandro and Jubert, Ignacio Soler and Mazura, Margaretha and Harrison, Mark and Eisenhauer, M. and others}, year = {2011}, journal = {Internet of Things-Global Technological and Societal Trends}, pages = {9--52}, 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}, urldate = {2016-06-03} } @incollection{vermolenReconstructingComplexMetamodel2012, title = {Reconstructing {{Complex Metamodel Evolution}}}, booktitle = {Software {{Language Engineering}}}, author = {Vermolen, Sander D. and Wachsmuth, Guido and Visser, Eelco}, year = {2012}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {6940}, pages = {201--221} } @incollection{vieiraMetricsMeasureChange2014, title = {Metrics to {{Measure}} the {{Change Impact}} in {{ATL Model Transformations}}}, booktitle = {Product-{{Focused Software Process Improvement}}}, author = {Vieira, Andreza and Ramalho, Franklin}, year = {2014}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {8892}, pages = {254--268} } @article{vignagaTypingArtifactsMegamodeling2011, title = {Typing Artifacts in Megamodeling}, author = {Vignaga, Andr{\'e}s and Jouault, Fr{\'e}d{\'e}ric and Bastarrica, Mar{\'i}a Cecilia and Bruneli{\`e}re, Hugo}, year = {2011}, journal = {Software \& Systems Modeling}, volume = {12}, number = {1}, pages = {105--119}, doi = {10.1007/s10270-011-0191-2} } @incollection{vignagaTypingModelManagement2009, title = {Typing in {{Model Management}}}, booktitle = {Theory and {{Practice}} of {{Model Transformations}}}, author = {Vignaga, Andr{\'e}s and Jouault, Fr{\'e}d{\'e}ric and Bastarrica, Mar{\'i}a Cecilia and Bruneli{\`e}re, Hugo}, editor = {Paige, Richard F.}, year = {2009}, series = {Lecture {{Notes}} in {{Computer Science}}}, number = {5563}, pages = {197--212}, publisher = {{Springer Berlin Heidelberg}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-02408-5_14}, urldate = {2015-04-01}, 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.}, copyright = {\textcopyright 2009 Springer Berlin Heidelberg}, isbn = {978-3-642-02407-8 978-3-642-02408-5}, langid = {english}, keywords = {software engineering} } @article{viroliSASO2014Selected2016, title = {{{SASO}} 2014: {{Selected}}, {{Revised}}, and {{Extended Best Papers}}}, shorttitle = {{{SASO}} 2014}, author = {Viroli, Mirko and Diaconescu, Ada and Kandasamy, Nagarajan}, year = {2016}, month = jul, journal = {ACM Transactions on Autonomous and Adaptive Systems}, volume = {11}, number = {2}, pages = {1--2}, issn = {15564665}, doi = {10.1145/2939206}, langid = {english} } @article{vladHypersonicModelAnalysis, title = {Hypersonic: {{Model Analysis}} and {{Checking}} in the {{Cloud}}}, author = {Vlad, Acretoaie and Harald, Storrle} } @inproceedings{vogelsangRequirementsEngineeringMachine2017, title = {Requirements {{Engineering}} for {{Machine Learning}}: {{Perspectives}} from {{Data Scientists}}}, shorttitle = {Requirements {{Engineering}} for {{Machine Learning}}}, booktitle = {2019 {{IEEE}} 27th {{International Requirements Engineering Conference Workshops}} ({{REW}})}, author = {Vogelsang, Andreas and Borg, Markus}, year = {2017}, month = jan, eprint = {1908.04674}, eprinttype = {arxiv}, pages = {245--251}, publisher = {{IEEE}}, address = {{Jeju Island, Korea (South)}}, doi = {10.1109/REW.2019.00050}, 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.}, archiveprefix = {arXiv}, isbn = {978-1-72815-165-6}, langid = {english} } @phdthesis{voigtStructuralGraphbasedMetamodel2011, ids = {V11}, title = {Structural {{Graph-based Metamodel Matching}}}, author = {Voigt, Konrad}, year = {2011} } @book{volter2013model, title = {Model-Driven Software Development: Technology, Engineering, Management}, author = {V{\"o}lter, Markus and Stahl, Thomas and Bettin, Jorn and Haase, Arno and Helsen, Simon}, year = {2013}, publisher = {{John Wiley \& Sons}} } @misc{VWMonDataLogger, title = {{{VWMon}}: {{Data}} Logger and Remote Control for the {{Vaillant}} Heat Pump | Construction Blog by {{Katja}} \& {{Alexey}}}, shorttitle = {{{VWMon}}}, url = {http://baublog.ozerov.de/waermepumpe/vwmon-datenlogger-fuer-die-vaillant-waermepumpe/}, urldate = {2015-03-27} } @inproceedings{walensteinSimilarityPrograms2006, title = {Similarity in Programs}, booktitle = {Duplication, {{Redundancy}}, and {{Similarity}} in {{Software}}, 23.07. - 26.07.2006}, author = {Walenstein, Andrew and {El-Ramly}, Mohammad and Cordy, James R. and Evans, William S. and Mahdavi, Kiarash and Pizka, Markus and Ramalingam, Ganesan and {von Gudenberg}, J{\"u}rgen Wolff}, year = {2006}, url = {http://drops.dagstuhl.de/opus/volltexte/2007/968}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/dagstuhl/WalensteinECEMPRG06}, timestamp = {Thu, 23 Aug 2018 15:56:31 +0200} } @article{Wan2022423, title = {A Variational Bayesian Inference-Inspired Unrolled Deep Network for {{MIMO}} Detection}, author = {Wan, Q. and Fang, J. and Huang, Y. and Duan, H. and Li, H.}, year = {2022}, journal = {IEEE Transactions on Signal Processing}, volume = {70}, pages = {423--437}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {1053587X}, doi = {10.1109/TSP.2022.3140926}, 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. \textcopyright{} 1991-2012 IEEE.}, coden = {ITPRE}, document_type = {Article}, source = {Scopus} } @inproceedings{Wang:2004:BEM:977401.978142, title = {{{BIDE}}: {{Efficient}} Mining of Frequent Closed Sequences}, booktitle = {Proceedings of the 20th International Conference on Data Engineering}, author = {Wang, Jianyong and Han, Jiawei}, year = {2004}, series = {{{ICDE}} '04}, pages = {79-}, publisher = {{IEEE Computer Society}}, address = {{Washington, DC, USA}}, url = {http://dl.acm.org/citation.cfm?id=977401.978142}, acmid = {978142}, isbn = {0-7695-2065-0} } @inproceedings{Wang2015689, title = {An Accurate {{ACOSSO}} Metamodeling Technique for Processor Architecture Design Space Exploration}, author = {Wang, H. and Zhu, Z. and Shi, J. and Su, Y.}, year = {2015}, series = {20th {{Asia}} and {{South Pacific Design Automation Conference}}, {{ASP-DAC}} 2015}, pages = {689--694}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ASPDAC.2015.7059090}, abbrev_source_title = {Asia South Pac. Des. Autom. Conf., ASP-DAC}, affiliation = {Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing, 100190, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China}, art_number = {7059090}, document_type = {Conference Paper}, isbn = {978-1-4799-7792-5}, langid = {english}, source = {Scopus} } @article{Wang2021107, title = {{{InDuDoNet}}: {{An}} Interpretable Dual Domain Network for {{CT}} Metal Artifact Reduction}, author = {Wang, H. and Li, Y. and Zhang, H. and Chen, J. and Ma, K. and Meng, D. and Zheng, Y.}, editor = {{de Bruijne M., de Bruijne M.}, Cotin S., Padoy N., Speidel S., Zheng Y., Essert C., Cattin P.C.}, year = {2021}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {12906 LNCS}, pages = {107--118}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-87231-1_11}, 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. \textcopyright{} 2021, Springer Nature Switzerland AG.}, document_type = {Conference Paper}, isbn = {9783030872304}, source = {Scopus} } @article{Wang20212270, title = {A Model-Driven {{DL}} Algorithm for {{PAPR}} Reduction in {{OFDM}} System}, author = {Wang, X. and Jin, N. and Wei, J.}, year = {2021}, journal = {IEEE Communications Letters}, volume = {25}, number = {7}, pages = {2270--2274}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10897798}, doi = {10.1109/LCOMM.2021.3076605}, 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. \textcopyright{} 2021 IEEE.}, art_number = {9419069}, coden = {ICLEF}, document_type = {Article}, source = {Scopus} } @article{Wang20212385, title = {Pilot-Assisted {{SIMO-NOMA}} Signal Detection with Learnable Successive Interference Cancellation}, author = {Wang, X. and Zhu, P. and Li, D. and Xu, Y. and You, X.}, year = {2021}, journal = {IEEE Communications Letters}, volume = {25}, number = {7}, pages = {2385--2389}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10897798}, doi = {10.1109/LCOMM.2021.3070705}, 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. \textcopyright{} 2021 IEEE.}, art_number = {9393981}, coden = {ICLEF}, document_type = {Article}, source = {Scopus} } @inproceedings{Wang2021639, title = {Morphling: {{Fast}}, near-Optimal Auto-Configuration for Cloud-Native Model Serving}, author = {Wang, L. and Yang, L. and Yu, Y. and Wang, W. and Li, B. and Sun, X. and He, J. and Zhang, L.}, year = {2021}, series = {{{SoCC}} 2021 - {{Proceedings}} of the 2021 {{ACM Symposium}} on {{Cloud Computing}}}, pages = {639--653}, publisher = {{Association for Computing Machinery, Inc}}, doi = {10.1145/3472883.3486987}, abbrev_source_title = {SoCC - Proc. ACM Symp. Cloud Comput.}, affiliation = {HKUST, Alibaba Group, Hong Kong}, document_type = {Conference Paper}, isbn = {978-1-4503-8638-8}, langid = {english}, source = {Scopus} } @article{wangCoCoSumContextualCode2021, title = {{{CoCoSum}}: {{Contextual Code Summarization}} with {{Multi-Relational Graph Neural Network}}}, shorttitle = {{{CoCoSum}}}, author = {Wang, Yanlin and Shi, Ensheng and Du, Lun and Yang, Xiaodi and Hu, Yuxuan and Han, Shi and Zhang, Hongyu and Zhang, Dongmei}, year = {2021}, month = jul, journal = {arXiv:2107.01933 [cs]}, eprint = {2107.01933}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2107.01933}, urldate = {2022-01-28}, 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.}, archiveprefix = {arXiv}, langid = {english}, keywords = {Computer Science - Software Engineering} } @inproceedings{wangMiningSuccinctHighcoverage2013, title = {Mining Succinct and High-Coverage {{API}} Usage Patterns from Source Code}, booktitle = {10th Working Conference on Mining Software Repositories}, author = {Wang, J. and Dang, Y. and Zhang, H. and Chen, K. and Xie, T. and Zhang, D.}, year = {2013}, pages = {319--328}, publisher = {{IEEE}}, address = {{Piscataway}}, issn = {2160-1852}, keywords = {API usage,application program interfaces,application programming interface,client code mining,Clustering algorithms,Context,data mining,Data mining,high-coverage API usage pattern mining,Indexes,large-scale Microsoft codebase,MAPO,Measurement,mining software repositories,Probabilistic logic,Redundancy,sequence mining,software development,software reusability,software reuse,source code,succinct API usage pattern mining,UP-miner,usage pattern,usage pattern discovery,usage pattern miner} } @article{wangPersonalizingLabelPrediction2022, title = {Personalizing Label Prediction for {{GitHub}} Issues}, author = {Wang, Jun and Zhang, Xiaofang and Chen, Lin and Xie, Xiaoyuan}, year = {2022}, month = jan, journal = {Information and Software Technology}, pages = {106845}, issn = {09505849}, doi = {10.1016/j.infsof.2022.106845}, 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. Method: 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. Result: 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. Conclusion: The experimental results show that our method PLPI can improve label prediction performance and provide personalized label recommendation results for different open-source projects.}, langid = {english} } @inproceedings{wangVerifyingMetamodelCoverage2006, title = {Verifying Metamodel Coverage of Model Transformations}, author = {Wang, J. and Kim, S.-K. and Carrington, D.}, year = {2006}, pages = {10 pp.-282}, publisher = {{IEEE}}, doi = {10.1109/ASWEC.2006.55}, isbn = {978-0-7695-2551-8} } @inproceedings{wangWuKongScalableAccurate2015, title = {{{WuKong}}: {{A}} Scalable and Accurate Two-Phase Approach to Android App Clone Detection}, booktitle = {Proceedings of the 2015 International Symposium on Software Testing and Analysis}, author = {Wang, Haoyu and Guo, Yao and Ma, Ziang and Chen, Xiangqun}, year = {2015}, series = {{{ISSTA}} 2015}, pages = {71--82}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/2771783.2771795}, acmid = {2771795}, isbn = {978-1-4503-3620-8}, nodoi = {10.1145/2771783.2771795}, numpages = {12}, keywords = {Android,Clone detection,mobile applications,repackaging,third-party library} } @article{waszkowskiLowcodePlatformAutomating2019, title = {Low-Code Platform for Automating Business Processes in Manufacturing}, author = {Waszkowski, Robert}, year = {2019}, journal = {IFAC-PapersOnLine}, volume = {52}, number = {10}, pages = {376--381}, issn = {24058963}, doi = {10.1016/j.ifacol.2019.10.060}, langid = {english} } @book{watzoldtModelingCollaborationsSelfadaptive2015, title = {Modeling Collaborations in Self-Adaptive Systems of Systems: Terms, Characteristics, Requirements, and Scenarios}, shorttitle = {Modeling Collaborations in Self-Adaptive Systems of Systems}, author = {W{\"a}tzoldt, Sebastian and Giese, Holger}, year = {2015}, series = {Technische {{Berichte}} Des {{Hasso-Plattner-Instituts}} F\"ur {{Softwaresystemtechnik}} an Der {{Universit\"at Potsdam}}}, number = {96}, publisher = {{Univ.-Verl}}, address = {{Potsdam}}, collaborator = {{Hasso-Plattner-Institut f\"ur Softwaresystemtechnik}}, isbn = {978-3-86956-324-4}, langid = {english} } @article{Weber2020403, title = {A Model Management Platform for Industry 4.0 \textendash{} Enabling Management of Machine Learning Models in Manufacturing Environments}, author = {Weber, C. and Hirmer, P. and Reimann, P.}, editor = {Abramowicz W., Klein G.}, year = {2020}, journal = {Lecture Notes in Business Information Processing}, volume = {389 LNBIP}, pages = {403--417}, publisher = {{Springer}}, issn = {18651348}, doi = {10.1007/978-3-030-53337-3_30}, 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. \textcopyright{} Springer Nature Switzerland AG 2020.}, document_type = {Conference Paper}, isbn = {9783030533366}, source = {Scopus} } @inproceedings{Weber202091, title = {{{MMP}} - {{A}} Platform to Manage Machine Learning Models in Industry 4.0 Environments}, author = {Weber, C. and Reimann, P.}, year = {2020}, series = {Proceedings - {{IEEE International Enterprise Distributed Object Computing Workshop}}, {{EDOCW}}}, volume = {2020-October}, pages = {91--94}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15417719}, doi = {10.1109/EDOCW49879.2020.00025}, 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. \textcopyright{} 2020 IEEE.}, art_number = {9233284}, document_type = {Conference Paper}, isbn = {978-1-72816-471-7}, source = {Scopus} } @inproceedings{Wei2020, title = {Learned Conjugate Gradient Descent Network for Massive {{MIMO}} Detection}, author = {Wei, Y. and Zhao, M.-M. and Hong, M. and Zhao, M.-J. and Lei, M.}, year = {2020}, series = {{{IEEE International Conference}} on {{Communications}}}, volume = {2020-June}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15503607}, doi = {10.1109/ICC40277.2020.9149227}, 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. \textcopyright{} 2020 IEEE.}, art_number = {9149227}, document_type = {Conference Paper}, isbn = {978-1-72815-089-5}, source = {Scopus} } @article{Wei20206336, title = {Learned Conjugate Gradient Descent Network for Massive {{MIMO}} Detection}, author = {Wei, Y. and Zhao, M.-M. and Hong, M. and Zhao, M.-J. and Lei, M.}, year = {2020}, journal = {IEEE Transactions on Signal Processing}, volume = {68}, pages = {6336--6349}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {1053587X}, doi = {10.1109/TSP.2020.3035832}, 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. \textcopyright{} 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.}, art_number = {3035832}, coden = {ITPRE}, document_type = {Article}, source = {Scopus} } @inproceedings{weissModelDrivenDevelopmentSelfDescribing2011, title = {Model-{{Driven Development}} of {{Self-Describing Components}} for {{Self-Adaptive Distributed Embedded Systems}}}, author = {Weiss, Gereon and Becker, Klaus and Kamphausen, Benjamin and Radermacher, Ansgar and Gerard, Sebastien}, year = {2011}, month = aug, pages = {477--484}, publisher = {{IEEE}}, doi = {10.1109/SEAA.2011.78}, isbn = {978-1-4577-1027-8} } @inproceedings{weynsApplyingArchitectureBasedAdaptation2018, title = {Applying {{Architecture-Based Adaptation}} to {{Automate}} the {{Management}} of {{Internet-of-Things}}}, booktitle = {Software {{Architecture}}}, author = {Weyns, Danny and Iftikhar, M. Usman and Hughes, Danny and Matthys, Nelson}, editor = {Cuesta, Carlos E. and Garlan, David and P{\'e}rez, Jennifer}, year = {2018}, series = {Lecture {{Notes}} in {{Computer Science}}}, pages = {49--67}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-030-00761-4_4}, 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.}, isbn = {978-3-030-00761-4}, langid = {english}, keywords = {DONE} } @article{Weyssow20221071, title = {Recommending Metamodel Concepts during Modeling Activities with Pre-Trained Language Models}, author = {Weyssow, M. and Sahraoui, H. and Syriani, E.}, year = {2022}, journal = {Software and Systems Modeling}, volume = {21}, number = {3}, pages = {1071--1089}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {16191366}, doi = {10.1007/s10270-022-00975-5}, abbrev_source_title = {Softw. Syst. Model.}, affiliation = {DIRO, Universit\'e de Montr\'eal, Montreal, Canada}, correspondence_address1 = {Weyssow, M.; DIRO, Canada; email: martin.weyssow@umontreal.ca}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Assistance,notion} } @article{whalenRequirementsArchitecturesSecure2016, title = {Requirements and {{Architectures}} for {{Secure Vehicles}}}, author = {Whalen, Michael W. and Cofer, Darren and Gacek, Andrew}, year = {2016}, journal = {IEEE Software}, volume = {33}, number = {4}, pages = {22--25}, url = {http://ieeexplore.ieee.org/abstract/document/7498541/}, urldate = {2016-09-28} } @misc{WhatDifferenceAutonomous, title = {What's the Difference between Autonomous Systems, {{ISPs}} and {{RIRs}}? - {{Network Engineering Stack Exchange}}}, url = {http://networkengineering.stackexchange.com/questions/25951/whats-the-difference-between-autonomous-systems-isps-and-rirs}, urldate = {2016-08-26} } @misc{WhatDifferenceEvolution, title = {What Is the Difference between Evolution and Change? | {{WikiDiff}}}, url = {https://wikidiff.com/evolution/change}, urldate = {2020-02-10} } @misc{WhatLowCode2020, title = {What {{Is Low-Code}}? [2020 {{Update}}]}, url = {https://www.outsystems.com/blog/what-is-low-code.html}, urldate = {2020-04-08}, keywords = {lowcode} } @misc{WhenHowUse, title = {When and {{How}} to {{Use Multi-Level Modelling}}.Pdf} } @article{whitmoreInternetThingsSurvey2015, title = {The {{Internet}} of {{Things}}\textemdash{{A}} Survey of Topics and Trends}, author = {Whitmore, Andrew and Agarwal, Anurag and Da Xu, Li}, year = {2015}, month = apr, journal = {Information Systems Frontiers}, volume = {17}, number = {2}, pages = {261--274}, issn = {1387-3326, 1572-9419}, doi = {10.1007/s10796-014-9489-2}, langid = {english} } @inproceedings{whittleIndustrialAdoptionModeldriven2013, title = {Industrial Adoption of Model-Driven Engineering: {{Are}} the Tools Really the Problem?}, shorttitle = {Industrial Adoption of Model-Driven Engineering}, booktitle = {International {{Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}}, author = {Whittle, Jon and Hutchinson, John and Rouncefield, Mark and akan Burden, H{\textbackslash}a and Heldal, Rogardt}, year = {2013}, pages = {1--17}, publisher = {{Springer}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-41533-3_1}, urldate = {2017-02-22} } @incollection{Wilcoxon1992, title = {Individual Comparisons by Ranking Methods}, booktitle = {Breakthroughs in Statistics: {{Methodology}} and Distribution}, author = {Wilcoxon, Frank}, editor = {Kotz, Samuel and Johnson, Norman L.}, year = {1992}, pages = {196--202}, publisher = {{Springer New York}}, address = {{New York, NY}}, doi = {10.1007/978-1-4612-4380-9₁6}, 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.}, isbn = {978-1-4612-4380-9} } @misc{WileyAutonomousSystem, title = {Wiley: {{The Autonomous System}}: {{A Foundational Synthesis}} of the {{Sciences}} of the {{Mind}} - {{Szabolcs Michael}} de {{Gyurky}}, {{Mark A}}. {{Tarbell}}}, url = {http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1118294246,subjectCd-EE79.html}, urldate = {2016-08-22} } @inproceedings{Williams2020, title = {Deriving Metamodels to Relate Machine Learning Quality to Design Repository Characteristics in the Context of Additive Manufacturing}, author = {Williams, G. and Meisel, N.A. and Simpson, T.W. and McComb, C.}, year = {2020}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {11A-2020}, publisher = {{American Society of Mechanical Engineers (ASME)}}, doi = {10.1115/DETC2020-22518}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {Pennsylvania State University, University Park, PA, United States}, art_number = {V11AT11A006}, document_type = {Conference Paper}, isbn = {978-0-7918-8400-3}, langid = {english}, source = {Scopus} } @article{williamsEngineeringSecurityVulnerability2018, title = {Engineering {{Security Vulnerability Prevention}}, {{Detection}}, and {{Response}}}, author = {Williams, L. and McGraw, G. and Migues, S.}, year = {2018}, month = sep, journal = {IEEE Software}, volume = {35}, number = {5}, pages = {76--80}, issn = {0740-7459}, doi = {10.1109/MS.2018.290110854}, 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\textemdash 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.}, keywords = {software engineering} } @inproceedings{williamsModelBasedAutonomousSystems1996, title = {Model-{{Based Autonomous Systems}} in the {{New Millenium}}.}, booktitle = {{{AIPS}}}, author = {Williams, Brian C.}, year = {1996}, pages = {275--282}, url = {http://www.aaai.org/Papers/AIPS/1996/AIPS96-035.pdf}, urldate = {2016-08-21} } @article{Wills06googlespagerank, title = {Google's {{PageRank}}: {{The}} Math behind the Search Engine}, author = {Wills, Rebecca S.}, year = {2006}, journal = {The Mathematical Intelligencer}, pages = {6--10} } @article{wimmerCatalogueRefactoringsModeltoModel2012, title = {A {{Catalogue}} of {{Refactorings}} for {{Model-to-Model Transformations}}.}, author = {Wimmer, Manuel and Mart{\'i}nez, Salvador and Jouault, Fr{\'e}d{\'e}ric and Cabot, Jordi}, year = {2012}, journal = {The Journal of Object Technology}, volume = {11}, number = {2}, pages = {2:1}, doi = {10.5381/jot.2012.11.2.a2} } @article{wimmerHowWebCan2008, title = {How {{Web}} 2.0 Can Leverage {{Model Engineering}} in {{Practice}}}, author = {Wimmer, Manuel and Schauerhuber, Andrea and Michael, Strommer and J{\"u}rgen, Flandorfer and Gerti, Kappel}, year = {2008}, journal = {Workshop Dom\"anspezifische Modellierungssprachen} } @inproceedings{wimmerPlugPlayModel2010, title = {Plug \& Play Model Transformations: A {{DSL}} for Resolving Structural Metamodel Heterogeneities}, shorttitle = {Plug \& Play Model Transformations}, booktitle = {Proceedings of the 10th {{Workshop}} on {{Domain-Specific Modeling}}}, author = {Wimmer, Manuel and Retschitzegger, W. and Kappel, G. and Schoenboeck, J. and Kusel, A. and Schwinger, Wieland}, year = {2010}, pages = {7}, publisher = {{ACM}}, url = {http://dl.acm.org/citation.cfm?id=2060348}, urldate = {2015-06-24} } @article{wimmerReusingModelTransformations2011, title = {Reusing {{Model Transformations}} across {{Heterogeneous Metamodels}}}, author = {Wimmer, Manuel}, year = {2011} } @inproceedings{winterMonitoringawareIDEs2019, title = {Monitoring-Aware {{IDEs}}}, booktitle = {Proceedings of the 2019 27th {{ACM Joint Meeting}} on {{European Software Engineering Conference}} and {{Symposium}} on the {{Foundations}} of {{Software Engineering}}}, author = {Winter, Jos and Aniche, Maur{\'i}cio and Cito, J{\"u}rgen and van Deursen, Arie}, year = {2019}, month = aug, pages = {420--431}, publisher = {{ACM}}, address = {{Tallinn Estonia}}, doi = {10.1145/3338906.3338926}, isbn = {978-1-4503-5572-8}, langid = {english} } @inproceedings{winterMonitoringawareIDEs2019a, title = {Monitoring-Aware {{IDEs}}}, booktitle = {Proceedings of the 2019 27th {{ACM Joint Meeting}} on {{European Software Engineering Conference}} and {{Symposium}} on the {{Foundations}} of {{Software Engineering}}}, author = {Winter, Jos and Aniche, Maur{\'i}cio and Cito, J{\"u}rgen and van Deursen, Arie}, year = {2019}, month = aug, series = {{{ESEC}}/{{FSE}} 2019}, pages = {420--431}, publisher = {{Association for Computing Machinery}}, address = {{New York, NY, USA}}, doi = {10.1145/3338906.3338926}, abstract = {Engineering modern large-scale software requires software developers to not solely focus on writing code, but also to continuously examine monitoring data to reason about the dynamic behavior of their systems. These additional monitoring responsibilities for developers have only emerged recently, in the light of DevOps culture. Interestingly, software development activities happen mainly in the IDE, while reasoning about production monitoring happens in separate monitoring tools. We propose an approach that integrates monitoring signals into the development environment and workflow. We conjecture that an IDE with such capability improves the performance of developers as time spent continuously context switching from development to monitoring would be eliminated. This paper takes a first step towards understanding the benefits of a possible monitoring-aware IDE. We implemented a prototype of a Monitoring-Aware IDE, connected to the monitoring systems of Adyen, a large-scale payment company that performs intense monitoring in their software systems. Given our results, we firmly believe that monitoring-aware IDEs can play an essential role in improving how developers perform monitoring.}, isbn = {978-1-4503-5572-8}, keywords = {devops,IDE,Integrated Development Environment,runtime monitoring,software engineering,systems monitoring} } @article{wintersSoftwareEngineeringGoogle, title = {Software {{Engineering}} at {{Google}}}, author = {Winters, Titus and Manschreck, Tom and Wright, Hyrum}, pages = {602}, langid = {english} } @article{Wollstadt2022, title = {{{CarHoods10k}}: {{An}} Industry-Grade Data Set for Representation Learning and Design Optimization in Engineering Applications}, author = {Wollstadt, P. and Bujny, M. and Ramnath, S. and Shah, J.J. and Detwiler, D. and Menzel, S.}, year = {2022}, journal = {IEEE Transactions on Evolutionary Computation}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {1089778X}, doi = {10.1109/TEVC.2022.3147013}, abbrev_source_title = {IEEE Trans Evol Comput}, affiliation = {Honda Research Institute Europe, Offenbach/Main, Germany. (e-mail: patricia.wollstadt@honda-ri.de); Honda Research Institute Europe, Offenbach/Main, Germany.; Simulation Innovation \&\#x0026; Modeling Center, The Ohio State University Columbus, Ohio, USA.; Digital Design \&\#x0026; Manufacturing Lab, The Ohio State University Columbus, Ohio, USA.; Honda Development \&\#x0026; Manufacturing of America, Raymond, Ohio, USA.}, coden = {ITEVF}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{wongPerformanceEvaluationClassification2015, title = {Performance Evaluation of Classification Algorithms by K-Fold and Leave-One-out Cross Validation}, author = {Wong, Tzu-Tsung}, year = {2015}, journal = {Pattern Recognition}, volume = {48}, number = {9}, pages = {2839--2846}, publisher = {{Elsevier}}, address = {{New York}}, issn = {0031-3203}, nodoi = {10.1016/j.patcog.2015.03.009}, numpages = {8} } @inproceedings{Worsey2021265, title = {Observations from Using a Portable {{LIDAR}} Scanner to Capture {{RF}} Propagation Modelling Environments}, author = {Worsey, J. and Hindmarch, I. and Armour, S. and Bull, D.}, year = {2021}, series = {Proceedings - 2021 {{IEEE}} 4th {{5G World Forum}}, {{5GWF}} 2021}, pages = {265--268}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/5GWF52925.2021.00053}, abbrev_source_title = {Proc. - IEEE 5G World Forum, 5GWF}, affiliation = {University of Bristol, Department of Electrical And Electronic Engineering, Bristol, United Kingdom}, document_type = {Conference Paper}, isbn = {978-1-66544-308-1}, langid = {english}, source = {Scopus} } @article{wortmannModelingLanguagesIndustry2019, title = {Modeling Languages in {{Industry}} 4.0: An Extended Systematic Mapping Study}, shorttitle = {Modeling Languages in {{Industry}} 4.0}, author = {Wortmann, Andreas and Barais, Olivier and Combemale, Benoit and Wimmer, Manuel}, year = {2019}, month = sep, journal = {Software and Systems Modeling}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-019-00757-6}, 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\textendash 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.}, langid = {english} } @article{Wu20202230, title = {Ensemble Generalized Multiclass Support-Vector-Machine-Based Health Evaluation of Complex Degradation Systems}, author = {Wu, J. and Guo, P. and Cheng, Y. and Zhu, H. and Wang, X.-B. and Shao, X.}, year = {2020}, journal = {IEEE/ASME Transactions on Mechatronics}, volume = {25}, number = {5}, pages = {2230--2240}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10834435}, doi = {10.1109/TMECH.2020.3009449}, abbrev_source_title = {IEEE ASME Trans Mechatron}, affiliation = {School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; College of Electrical Engineering, Henan University of Technology, Zhengzhou, 450001, China}, art_number = {9141518}, coden = {IATEF}, correspondence_address1 = {Wang, X.-B.; College of Electrical Engineering, China; email: xb\textsubscript{w}ang@live.com}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{Wu2021, title = {Causal Artificial Neural Network and Its Applications in Engineering Design}, author = {Wu, D. and Wang, G.G.}, year = {2021}, journal = {Engineering Applications of Artificial Intelligence}, volume = {97}, publisher = {{Elsevier Ltd}}, issn = {09521976}, doi = {10.1016/j.engappai.2020.104089}, abbrev_source_title = {Eng Appl Artif Intell}, affiliation = {Product Design and Optimization Laboratory, Mechatronic System Engineering Department, Simon Fraser University, Surrey, BCV3T 0A3, Canada}, art_number = {104089}, coden = {EAAIE}, correspondence_address1 = {Wu, D.; Product Design and Optimization Laboratory, Surrey, BC, Canada; email: dwa88@sfu.ca}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{Wu20211915, title = {Channel Prediction in High-Mobility Massive {{MIMO}}: {{From}} Spatio-Temporal Autoregression to Deep Learning}, author = {Wu, C. and Yi, X. and Zhu, Y. and Wang, W. and You, L. and Gao, X.}, year = {2021}, journal = {IEEE Journal on Selected Areas in Communications}, volume = {39}, number = {7}, pages = {1915--1930}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {07338716}, doi = {10.1109/JSAC.2021.3078503}, 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. \textcopyright{} 1983-2012 IEEE.}, art_number = {9427230}, coden = {ISACE}, document_type = {Article}, source = {Scopus} } @article{wuGraphNeuralNetworks2021, title = {Graph {{Neural Networks}} in {{Recommender Systems}}: {{A Survey}}}, shorttitle = {Graph {{Neural Networks}} in {{Recommender Systems}}}, author = {Wu, Shiwen and Sun, Fei and Zhang, Wentao and Cui, Bin}, year = {2021}, month = apr, journal = {arXiv:2011.02260 [cs]}, eprint = {2011.02260}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2011.02260}, urldate = {2021-10-19}, 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.}, archiveprefix = {arXiv}, keywords = {Computer Science - Information Retrieval,Computer Science - Machine Learning} } @inproceedings{wuLowComplexityModelDriven2021, title = {A {{Low Complexity Model-Driven Deep Learning LDPC Decoding Algorithm}}}, booktitle = {2021 {{IEEE}} 6th {{International Conference}} on {{Computer}} and {{Communication Systems}}, {{ICCCS}} 2021}, author = {Wu, Q. and Tang, S.-K. and Liang, Y. and Lam, C.T. and Ma, Y.}, year = {2021}, pages = {558--563}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ICCCS52626.2021.9449266}, abstract = {A novel Neural Offset Min-Sum(NOMS) Belief Propagation(BP) decoding algorithm based on model-driven is proposed which applied to LDPC decoding. NOMS is improved multiplication in Neural Normalized Min-Sum(NNMS) into addition operation to reduce the complexity of calculation., a better Bit Error Rate (BER) performance is simultaneously achieved in the same condition. Secondly, considering that there are still many multiplication operations in NOMS, we propose a novel Shared Offset Min-Sum(SNOMS) to reduce the number of weights in the network by sharing parameters. Finally, codebook-based quantization is used to further reduce the memory consumption. Simulation experimental results show that the proposed method has a better BER performance, and the decoding accuracy of the decoder is 0.65dB higher than that of the NNMS after 5 iterations. In addition, SNOMS decoding method achieves almost the same decoding performance comparable to that of NOMS, but requires less complex calculation. Proposed quantization of code-book method reduces memory requirement significantly with slight performance loss. \textcopyright{} 2021 IEEE.}, isbn = {978-0-7381-2604-3}, keywords = {Belief propagation decoding algorithms,Bit error rate,Bit error rate (BER) performance,Complex networks,Complexity modeling,Decoding performance,Deep learning,Iterative decoding,Memory consumption,Memory requirements,Multiplication operations,Performance loss} } @article{Xanthopoulos201869, title = {Cluster Analysis and Neural Network-Based Metamodeling of Priority Rules for Dynamic Sequencing}, author = {Xanthopoulos, A.S. and Koulouriotis, D.E.}, year = {2018}, journal = {Journal of Intelligent Manufacturing}, volume = {29}, number = {1}, pages = {69--91}, publisher = {{Springer New York LLC}}, issn = {09565515}, doi = {10.1007/s10845-015-1090-0}, abbrev_source_title = {J Intell Manuf}, affiliation = {Department of Production and Management Engineering, Democritus University of Thrace, V. Sofias 12, Xanthi, 67100, Greece}, coden = {JIMNE}, correspondence_address1 = {Xanthopoulos, A.S.; Department of Production and Management Engineering, V. Sofias 12, Greece; email: axanthop@pme.duth.gr}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{xhafaInternetThingsEngineering2018, title = {Internet of {{Things}}: {{Engineering Cyber Physical Human Systems}}}, shorttitle = {Internet of {{Things}}}, author = {Xhafa, Fatos}, year = {2018}, month = sep, journal = {Internet of Things}, volume = {1--2}, pages = {iii}, issn = {25426605}, doi = {10.1016/S2542-6605(18)30099-4}, langid = {english} } @inproceedings{xia:tag:2013, title = {Tag Recommendation in Software Information Sites}, booktitle = {Proceedings of the 10th Working Conference on Mining Software Repositories}, author = {Xia, Xin and Lo, David and Wang, Xinyu and Zhou, Bo}, year = {2013}, series = {{{MSR}} '13}, pages = {287--296}, publisher = {{IEEE Press}}, address = {{Piscataway, NJ, USA}}, url = {http://dl.acm.org/citation.cfm?id=2487085.2487140}, isbn = {978-1-4673-2936-1} } @article{xieSystematicMappingStudy2021, title = {Systematic {{Mapping Study}} on the {{Machine Learning Lifecycle}}}, author = {Xie, Yuanhao and Cruz, Lu{\'i}s and Heck, Petra and Rellermeyer, Jan S.}, year = {2021}, month = mar, journal = {arXiv:2103.10248 [cs]}, eprint = {2103.10248}, eprinttype = {arxiv}, primaryclass = {cs}, url = {http://arxiv.org/abs/2103.10248}, urldate = {2021-03-23}, 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.}, archiveprefix = {arXiv}, keywords = {68T01 (Primary),Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Computer Science - Software Engineering,D.2.9,I.2.5} } @article{Xiong2022, title = {Towards a Robust and Trustworthy Machine Learning System Development: {{An}} Engineering Perspective}, author = {Xiong, P. and Buffett, S. and Iqbal, S. and Lamontagne, P. and Mamun, M. and Molyneaux, H.}, year = {2022}, journal = {Journal of Information Security and Applications}, volume = {65}, publisher = {{Elsevier Ltd}}, issn = {22142134}, doi = {10.1016/j.jisa.2022.103121}, 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. \textcopyright{} 2022}, art_number = {103121}, document_type = {Article}, source = {Scopus}, keywords = {GOAL_ML-System-Development} } @article{xiuExploratoryStudyMachine2021, title = {An {{Exploratory Study}} of {{Machine Learning Model Stores}}}, author = {Xiu, Minke and Jiang, Zhen Ming Jack and Adams, Bram}, year = {2021}, month = jan, journal = {IEEE Software}, volume = {38}, number = {1}, pages = {114--122}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2020.2975159} } @article{Xu2013653, title = {Crashworthiness Design of Multi-Component Tailor-Welded Blank ({{TWB}}) Structures}, author = {Xu, F. and Sun, G. and Li, G. and Li, Q.}, year = {2013}, journal = {Structural and Multidisciplinary Optimization}, volume = {48}, number = {3}, pages = {653--667}, publisher = {{Springer Verlag}}, issn = {1615147X}, doi = {10.1007/s00158-013-0916-7}, abbrev_source_title = {Struct. Mutltidiscip. Opt.}, affiliation = {State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China; Key Laboratory of Manufacture and Test Techniques for Automobile Parts, Ministry of Education, Chongqing 400054, China; School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney, Sydney NSW 2006, Australia}, coden = {SMOTB}, correspondence_address1 = {Li, G.; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, , Changsha 410082, China; email: gyli@hnu.edu.cn}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{Xu2015, title = {A Comprehensive Survey of Clustering Algorithms}, author = {Xu, Dongkuan and Tian, Yingjie}, year = {2015}, month = jun, journal = {Annals of Data Science}, volume = {2}, number = {2}, pages = {165--193}, issn = {2198-5812}, doi = {10.1007/s40745-015-0040-1}, 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.} } @inproceedings{Xu2016, title = {Mixed-Variable Metamodeling Methods for Designing Multimaterial Structures}, author = {Xu, H. and Chuang, C.-H. and Yang, R.-J.}, year = {2016}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {2B-2016}, publisher = {{American Society of Mechanical Engineers (ASME)}}, doi = {10.1115/DETC2016-59176}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {Research and Advanced Engineering, Ford Motor Company, Dearborn, MI, United States}, correspondence_address1 = {Xu, H.; Research and Advanced Engineering, United States; email: hxu41@ford.com}, document_type = {Conference Paper}, isbn = {978-0-7918-5011-4}, langid = {english}, source = {Scopus} } @inproceedings{xuREPERSPRecommendingPersonalized2017, title = {{{REPERSP}}: {{Recommending Personalized Software Projects}} on {{GitHub}}}, shorttitle = {{{REPERSP}}}, author = {Xu, Wenyuan and Sun, Xiaobing and Hu, Jiajun and Li, Bin}, year = {2017}, month = sep, pages = {648--652}, publisher = {{IEEE}}, doi = {10.1109/ICSME.2017.20}, isbn = {978-1-5386-0992-7} } @article{Yan2018343, title = {Data-Driven Prediction of Mechanical Properties in Support of Rapid Certification of Additively Manufactured Alloys}, author = {Yan, F. and Chan, Y.-C. and Saboo, A. and Shah, J. and Olson, G.B. and Chen, W.}, year = {2018}, journal = {CMES - Computer Modeling in Engineering and Sciences}, volume = {117}, number = {3}, pages = {343--366}, publisher = {{Tech Science Press}}, issn = {15261492}, doi = {10.31614/cmes.2018.04452}, abbrev_source_title = {CMES Comput. Model. Eng. Sci.}, affiliation = {Department of Materials Science and Engineering, Northwestern University, 2220 Campus Dr, Evanston, IL 60208, United States; Department of Mechanical Engineering, Northwestern University, 2145 Sheridan Rd, Evanston, IL 60208, United States; QuesTek Innovations LLC, 1820 Ridge Ave, Evanston, IL 60201, United States; Product Development and Analysis LLC, 1776 Legacy Circle, Suite \#115, Naperville, IL 60563, United States}, correspondence_address1 = {Chen, W.; Department of Mechanical Engineering, 2145 Sheridan Rd, United States; email: weichen@northwestern.edu}, document_type = {Article}, langid = {english}, source = {Scopus} } @inproceedings{Yang2017, title = {A Domain-Driven Approach to Metamodeling in Additive Manufacturing}, author = {Yang, Z. and Hagedorn, T. and Eddy, D. and Krishnamurty, S. and Grosse, I. and Denno, P. and Lu, Y. and Witherell, P.}, year = {2017}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {1}, publisher = {{American Society of Mechanical Engineers (ASME)}}, doi = {10.1115/DETC2017-67807}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst, Amherst, MA 01003, United States; Engineering Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, United States}, art_number = {67807}, document_type = {Conference Paper}, isbn = {978-0-7918-5811-0}, langid = {english}, source = {Scopus} } @inproceedings{Yang2018, title = {A Super-Metamodeling Framework to Optimize System Predictability}, author = {Yang, Z. and Eddy, D. and Krishnamurty, S. and Grosse, I. and Lu, Y.}, year = {2018}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {1A-2018}, publisher = {{American Society of Mechanical Engineers (ASME)}}, doi = {10.1115/DETC201886055}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {University of Massachusetts Amherst, Department of Mechanical and Industrial Engineering, Amherst, MA 01003, United States; National Institute of Standards and Technology Engineering Laboratory, Gaithersburg, MD 20899, United States}, correspondence_address1 = {Eddy, D.; University of Massachusetts Amherst, United States; email: dceddy@engin.umass.edu}, document_type = {Conference Paper}, isbn = {978-0-7918-5172-2}, langid = {english}, source = {Scopus} } @inproceedings{Yang2021, title = {Deep Joint Source Channel Coding for Wireless Image Transmission with {{OFDM}}}, author = {Yang, M. and Bian, C. and Kim, H.-S.}, year = {2021}, series = {{{IEEE International Conference}} on {{Communications}}}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15503607}, doi = {10.1109/ICC42927.2021.9500996}, 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. \textcopyright{} 2021 IEEE.}, document_type = {Conference Paper}, isbn = {978-1-72817-122-7}, source = {Scopus} } @article{yangActionableAnalyticsSoftware2018, title = {Actionable {{Analytics}} for {{Software Engineering}}}, author = {Yang, Ye and Falessi, Davide and Menzies, Tim and Hihn, Jairus}, year = {2018}, journal = {IEEE Software}, volume = {35}, number = {1}, pages = {51--53} } @article{yangIoTStreamProcessing2017, title = {{{IoT Stream Processing}} and {{Analytics}} in the {{Fog}}}, author = {Yang, Shusen}, year = {2017}, month = aug, journal = {IEEE Communications Magazine}, volume = {55}, number = {8}, pages = {21--27}, issn = {0163-6804, 1558-1896}, doi = {10.1109/MCOM.2017.1600840}, keywords = {Data analysis,DONE,internet of things} } @article{yangNaturalAttackPretrained2022, title = {Natural {{Attack}} for {{Pre-trained Models}} of {{Code}}}, author = {Yang, Zhou and Shi, Jieke and He, Junda and Lo, David}, year = {2022}, month = jan, journal = {arXiv:2201.08698 [cs]}, eprint = {2201.08698}, eprinttype = {arxiv}, primaryclass = {cs}, doi = {10.1145/3510003.3510146}, 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.}, archiveprefix = {arXiv}, langid = {english}, keywords = {Computer Science - Software Engineering} } @inproceedings{yangStackOverflowGithub2017, title = {Stack Overflow in Github: Any Snippets There?}, shorttitle = {Stack Overflow in Github}, booktitle = {Mining {{Software Repositories}} ({{MSR}}), 2017 {{IEEE}}/{{ACM}} 14th {{International Conference}} On}, author = {Yang, Di and Martins, Pedro and Saini, Vaibhav and Lopes, Cristina}, year = {2017}, pages = {280--290}, publisher = {{IEEE}} } @inproceedings{yaoIntelligentManufacturingSmart2017, title = {From {{Intelligent Manufacturing}} to {{Smart Manufacturing}} for {{Industry}} 4.0 {{Driven}} by {{Next Generation Artificial Intelligence}} and {{Further On}}}, booktitle = {2017 5th {{International Conference}} on {{Enterprise Systems}} ({{ES}})}, author = {Yao, Xifan and Zhou, Jiajun and Zhang, Jiangming and Boer, Claudio R.}, year = {2017}, month = sep, pages = {311--318}, publisher = {{IEEE}}, address = {{Beijing}}, doi = {10.1109/ES.2017.58}, 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.}, isbn = {978-1-5386-0936-1}, langid = {english} } @inproceedings{Yarom2020725, title = {Artificial Neural Networks and Reinforcement Learning for Model-Based Design of an Automated Vehicle Guidance System}, author = {Yarom, O.A. and Scherler, S. and Goellner, M. and {Liu-Henke}, X.}, editor = {Rocha A., Steels L., van den Herik J.}, year = {2020}, series = {{{ICAART}} 2020 - {{Proceedings}} of the 12th {{International Conference}} on {{Agents}} and {{Artificial Intelligence}}}, volume = {2}, pages = {725--733}, publisher = {{SciTePress}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083099118&partnerID=40&md5=7022f6c914439fde6f66fb48b414ff4f}, abbrev_source_title = {ICAART - Proc. Int. Conf. Agents Artif. Intell.}, affiliation = {Ostfalia University of Applied Sciences, Salzdahulmer Str. 46/48, Wolfenbuettel, 38302, Germany}, document_type = {Conference Paper}, isbn = {978-989-758-395-7}, langid = {english}, source = {Scopus}, keywords = {GOAL_Model-Assistance,notion} } @inproceedings{Yavanoglu20172186, title = {A Review on Cyber Security Datasets for Machine Learning Algorithms}, author = {Yavanoglu, O. and Aydos, M.}, editor = {{Nie J.-Y., Obradovic Z.}, Ghosh R., Nambiar R., Wang C., Zang H., Baeza-Yates R., Baeza-Yates R., Hu X., Kepner J., Cuzzocrea A., Tang J., Toyoda M., Suzumura T.}, year = {2017}, series = {Proceedings - 2017 {{IEEE International Conference}} on {{Big Data}}, {{Big Data}} 2017}, volume = {2018-January}, pages = {2186--2193}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/BigData.2017.8258167}, 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. \textcopyright{} 2017 IEEE.}, document_type = {Conference Paper}, isbn = {978-1-5386-2714-3}, source = {Scopus} } @article{Ye202166, title = {Deep-Learning-Enhanced {{NOMA}} Transceiver Design for Massive {{MTC}}: {{Challenges}}, State of the Art, and Future Directions}, author = {Ye, N. and An, J. and Yu, J.}, year = {2021}, journal = {IEEE Wireless Communications}, volume = {28}, number = {4}, pages = {66--73}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15361284}, doi = {10.1109/MWC.001.2000472}, 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. \textcopyright{} 2002-2012 IEEE.}, art_number = {9535455}, coden = {IWCEA}, document_type = {Article}, source = {Scopus} } @inproceedings{yeSupportingReuseDelivering2002, title = {Supporting Reuse by Delivering Task-Relevant and Personalized Information}, booktitle = {Proceedings of the 24th International Conference on Software Engineering}, author = {Ye, Yunwen and Fischer, Gerhard}, year = {2002}, series = {{{ICSE}} '02}, pages = {513--523}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/581339.581402}, acmid = {581402}, isbn = {1-58113-472-X}, nodoi = {10.1145/581339.581402}, numpages = {11} } @article{Yin2022, title = {Deep {{CSI}} Compression for Massive {{MIMO}}: {{A}} Self-Information Model-Driven Neural Network}, author = {Yin, Z. and Xu, W. and Xie, R. and Zhang, S. and Ng, D.W.K. and You, X.}, year = {2022}, journal = {IEEE Transactions on Wireless Communications}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15361276}, doi = {10.1109/TWC.2022.3170576}, 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}, document_type = {Article}, source = {Scopus} } @article{yinDynamicDataMining2020, title = {Dynamic {{Data Mining}} of {{Sensor Data}}}, author = {Yin, Yunfei and Long, Lianjie and Deng, Xiyu}, year = {2020}, journal = {IEEE Access}, volume = {8}, pages = {41637--41648}, issn = {2169-3536}, doi = {10.1109/ACCESS.2020.2976699}, 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.}, langid = {english} } @article{yingSelectionPresentationPractices, title = {Selection and {{Presentation Practices}} for {{Code Example Summarization}}}, author = {Ying, Annie T T and Robillard, Martin P}, pages = {12}, 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.}, langid = {english} } @article{Yılmaz20214305, title = {Artificial Neural Network Metamodeling-Based Design Optimization of a Continuous Motorcyclists Protection Barrier System}, author = {Y{\i}lmaz, {\.I}. and Yelek, {\.I}. and {\"O}zcanan, S. and Atahan, A.O. and Hiekmann, J.M.}, year = {2021}, journal = {Structural and Multidisciplinary Optimization}, volume = {64}, number = {6}, pages = {4305--4323}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {1615147X}, doi = {10.1007/s00158-021-03080-1}, abbrev_source_title = {Struct. Mutltidiscip. Opt.}, affiliation = {Bor\c{c}elik \c{C}elik San. ve Tic. A.\c{S}., Ata Mh. 125. Sk. No:1, Gemlik, Bursa, 16601, Turkey; Department of Civil Engineering, Sirnak University, M. Emin Acar Campus, Sirnak, 73000, Turkey; Department of Civil Engineering, \.Istanbul Technical University, Ayaza\u{g}a Campus, \.Istanbul, 34469, Turkey; Pass+Co Road Restraint Systems, Seigen, Germany}, coden = {SMOTB}, correspondence_address1 = {Y\i lmaz, \.I.; Bor\c{c}elik \c{C}elik San. ve Tic. A.\c{S}., Ata Mh. 125. Sk. No:1, Gemlik, Turkey; email: ilyilmaz@borcelik.com}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{Yoo2022, title = {Deep Learning-Based Efficient Metamodeling via Domain Knowledge-Integrated Designable Data Augmentation with Transfer Learning: Application to Vehicle Crash Safety}, author = {Yoo, Y. and Park, C.-K. and Lee, J.}, year = {2022}, journal = {Structural and Multidisciplinary Optimization}, volume = {65}, number = {7}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {1615147X}, doi = {10.1007/s00158-022-03290-1}, abbrev_source_title = {Struct. Mutltidiscip. Opt.}, affiliation = {School of Mechanical Engineering, Yonsei University, Seoul, 03722, South Korea; Department of Automotive Engineering, Gwangju University, Gwangju, 61743, South Korea}, art_number = {189}, coden = {SMOTB}, correspondence_address1 = {Lee, J.; School of Mechanical Engineering, South Korea; email: jleej@yonsei.ac.kr}, document_type = {Article}, langid = {english}, source = {Scopus} } @inproceedings{Yoon202010468, title = {Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via {{KISS-GP}}}, author = {Yoon, S. and Kim, A.}, year = {2020}, series = {{{IEEE International Conference}} on {{Intelligent Robots}} and {{Systems}}}, pages = {10468--10475}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {21530858}, doi = {10.1109/IROS45743.2020.9341769}, 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. \textcopyright{} 2020 IEEE.}, art_number = {9341769}, coden = {85RBA}, document_type = {Conference Paper}, isbn = {978-1-72816-212-6}, source = {Scopus} } @inproceedings{yuAPIBookEffectiveApproach2016, title = {{{APIBook}}: An Effective Approach for Finding {{APIs}}}, shorttitle = {{{APIBook}}}, author = {Yu, Haibo and Song, Wenhao and Mine, Tsunenori}, year = {2016}, pages = {45--53}, publisher = {{ACM Press}}, doi = {10.1145/2993717.2993727}, isbn = {978-1-4503-4829-4}, langid = {english} } @inproceedings{yuEfficientSimRankComputation2013, title = {Towards Efficient {{SimRank}} Computation on Large Networks.}, booktitle = {{{ICDE}}}, author = {Yu, Weiren and Lin, Xuemin and Zhang, Wenjie}, editor = {Jensen, Christian S. and Jermaine, Christopher M. and Zhou, Xiaofang}, year = {2013}, pages = {601--612}, publisher = {{IEEE Computer Society}}, url = {http://dblp.uni-trier.de/db/conf/icde/icde2013.html#YuLZ13}, added-at = {2013-06-27T00:00:00.000+0200}, biburl = {http://www.bibsonomy.org/bibtex/2026a998396ad1c9c6555439949e04747/dblp}, ee = {http://doi.ieeecomputersociety.org/10.1109/ICDE.2013.6544859}, interhash = {22d1f058c9d3b87edb546d64328413e8}, intrahash = {026a998396ad1c9c6555439949e04747}, isbn = {978-1-4673-4909-3}, keywords = {dblp}, timestamp = {2013-06-27T00:00:00.000+0200} } @article{yuRapidApplicationDevelopment2011, title = {Towards the {{Rapid Application Development Based}} on {{Predefined Frameworks}}}, author = {Yu, Dongjin}, year = {2011}, month = aug, journal = {Journal of Software}, volume = {6}, number = {9}, pages = {1795--1804}, issn = {1796-217X}, doi = {10.4304/jsw.6.9.1795-1804}, 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.}, langid = {english} } @inproceedings{Zahoor2020198, title = {An Investigation of Smart Parking Tools, Technologies, \& Challenges}, author = {Zahoor, T. and Azam, F. and Anwar, M.W. and Tariq, A. and Javaid, H.A.}, year = {2020}, series = {{{ACM International Conference Proceeding Series}}}, pages = {198--203}, publisher = {{Association for Computing Machinery}}, doi = {10.1145/3436829.3436851}, abbrev_source_title = {ACM Int. Conf. Proc. Ser.}, affiliation = {Department of Computer and Software Engineering, College of EandME, National University of Sciences and Technology (NUST), H-12 Islamabad, Pakistan}, document_type = {Conference Paper}, isbn = {978-1-4503-7721-8}, langid = {english}, source = {Scopus} } @article{zambonelliKeyAbstractionsIoTOriented2017, title = {Key {{Abstractions}} for {{IoT-Oriented Software Engineering}}}, author = {Zambonelli, Franco}, year = {2017}, month = jan, journal = {IEEE Software}, volume = {34}, number = {1}, pages = {38--45}, issn = {0740-7459}, doi = {10.1109/MS.2017.3}, langid = {english} } @inproceedings{zambonelliSelfAdaptationSelfExpressionSelfAwareness2011, title = {On {{Self-Adaptation}}, {{Self-Expression}}, and {{Self-Awareness}} in {{Autonomic Service Component Ensembles}}}, author = {Zambonelli, Franco and Bicocchi, Nicola and Cabri, Giacomo and Leonardi, Letizia and Puviani, Mariachiara}, year = {2011}, month = oct, pages = {108--113}, publisher = {{IEEE}}, doi = {10.1109/SASOW.2011.24}, isbn = {978-1-4577-2029-1 978-0-7695-4545-5} } @inproceedings{zampettiHowOpenSource2017, title = {How {{Open Source Projects Use Static Code Analysis Tools}} in {{Continuous Integration Pipelines}}}, author = {Zampetti, Fiorella and Scalabrino, Simone and Oliveto, Rocco and Canfora, Gerardo and Di Penta, Massimiliano}, year = {2017}, month = may, pages = {334--344}, publisher = {{IEEE}}, doi = {10.1109/MSR.2017.2}, isbn = {978-1-5386-1544-7} } @inproceedings{zayanEffectsUsingExamples2014, title = {Effects of Using Examples on Structural Model Comprehension: A Controlled Experiment}, shorttitle = {Effects of Using Examples on Structural Model Comprehension}, booktitle = {Proceedings of the 36th {{International Conference}} on {{Software Engineering}}}, author = {Zayan, Dina and Antkiewicz, Micha{\textbackslash}l and Czarnecki, Krzysztof}, year = {2014}, pages = {955--966}, publisher = {{ACM}}, url = {http://dl.acm.org/citation.cfm?id=2568270}, urldate = {2015-05-17} } @article{zelkowitzExperimentalModelsValidating1998, title = {Experimental Models for Validating Technology}, author = {Zelkowitz, M.V. and Wallace, D.R.}, year = {1998}, month = may, journal = {Computer}, volume = {31}, number = {5}, pages = {23--31}, issn = {00189162}, doi = {10.1109/2.675630}, langid = {english} } @inproceedings{Zeng2017982, title = {Metamodel of the Two-Dimensional Magnet-Rail Relationship Based on a {{BP}} Neural Network}, author = {Zeng, S. and Liu, Y. and Li, J. and Zhou, S.}, year = {2017}, series = {Proceedings - 2017 {{Chinese Automation Congress}}, {{CAC}} 2017}, volume = {2017-January}, pages = {982--987}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/CAC.2017.8242909}, abbrev_source_title = {Proc. - Chin. Autom. Congr., CAC}, affiliation = {College of Mechatronics Engineering and Automation, National University of Defense Technology, Changsha, China}, document_type = {Conference Paper}, isbn = {978-1-5386-3524-7}, langid = {english}, source = {Scopus}, keywords = {notion} } @inproceedings{zennaroMachineLearningApproach2018, title = {A Machine Learning Approach for Area Prediction of Hardware Designs from Abstract Specifications}, booktitle = {Proceedings - 21st {{Euromicro Conference}} on {{Digital System Design}}, {{DSD}} 2018}, author = {Zennaro, E. and Servadei, L. and Devarajegowda, K. and Ecker, W.}, editor = {Konofaos N., Novotny M., Skavhaug A.}, year = {2018}, pages = {413--420}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/DSD.2018.00076}, abstract = {Advancements of Machine Learning (ML) in the field of computer vision have paved the way for its potential application in many other fields. Researchers and hardware domain experts are exploring possible applications of Machine Learning in optimizing many aspects of hardware development process. In this paper, we propose a novel approach for predicting the area of hardware components from specifications. The flow uses an existing RTL generation framework, for generating valid data samples that enable ML algorithms to train the learning models. The approach has been successfully employed to predict the area of real-life hardware components such as Control and Status Register (CSR) interfaces that are ubiquitous in embedded systems. With this approach we are able to predict the area with more than 98\% accuracy and 600x faster than the existing methods. In addition, we are able to rank the features according to their importance in final area estimations. \textcopyright{} 2018 IEEE.}, isbn = {978-1-5386-7376-8}, keywords = {Area estimation,Artificial intelligence,Code Generation,Computer hardware,Design productivity,Embedded systems,Forecasting,Hardware,Learning systems,Meta model,Model driven architectures,Software architecture,Software design,Specifications,Systems analysis} } @article{Zhang:1997:BND:593415.593443, title = {{{BIRCH}}: {{A}} New Data Clustering Algorithm and Its Applications}, author = {Zhang, Tian and Ramakrishnan, Raghu and Livny, Miron}, year = {1997}, month = jan, journal = {Data Mining and Knowledge Discovery}, volume = {1}, number = {2}, pages = {141--182}, publisher = {{Kluwer Academic Publishers}}, address = {{Hingham, MA, USA}}, issn = {1384-5810}, url = {https://doi.org/10.1023/A:1009783824328}, acmid = {593443}, issue_date = {1997}, nodoi = {10.1023/A:1009783824328}, numpages = {42}, keywords = {Data Classification and Compression,Data Clustering,Incremental Algorithm,Very Large Databases} } @article{Zhang2017, title = {Performing Global Uncertainty and Sensitivity Analysis from given Data in Tunnel Construction}, author = {Zhang, L. and Wu, X. and Zhu, H. and Abourizk, S.M.}, year = {2017}, journal = {Journal of Computing in Civil Engineering}, volume = {31}, number = {6}, publisher = {{American Society of Civil Engineers (ASCE)}}, issn = {08873801}, doi = {10.1061/(ASCE)CP.1943-5487.0000714}, abbrev_source_title = {J. Comput. Civ. Eng.}, affiliation = {College of Design, School of Building Construction, Georgia Institute of Technology, 280 Ferst Dr., Atlanta, GA 30332-0680, United States; School of Civil Engineering and Mechanics, Huazhong Univ. of Science and Technology, Wuhan, Hubei, 430074, China; Dept. of Civil and Environmental Engineering, Hole School of Construction Engineering, Univ. of Alberta, 5-047 Markin/Canadian Natural Resources Limited, Natural Resources Engineering Facility, Edmonton, AB T6G 2W2, Canada}, art_number = {04017065}, coden = {JCCEE}, correspondence_address1 = {Zhu, H.; School of Civil Engineering and Mechanics, China; email: hpzhu@mail.hust.edu.cn}, document_type = {Article}, langid = {english}, source = {Scopus} } @inproceedings{Zhang2019, title = {Fingerprint-Based Localization Using Commercial {{LTE}} Signals: {{A}} Field-Trial Study}, author = {Zhang, H. and Zhang, Z. and Zhang, S. and Xu, S. and Cao, S.}, year = {2019}, series = {{{IEEE Vehicular Technology Conference}}}, volume = {2019-September}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15502252}, doi = {10.1109/VTCFall.2019.8891257}, 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. \textcopyright{} 2019 IEEE.}, art_number = {8891257}, coden = {IVTCD}, document_type = {Conference Paper}, isbn = {978-1-72811-220-6}, source = {Scopus} } @inproceedings{Zhang2019376, title = {{{PS2}}: {{Parameter}} Server on Spark}, author = {Zhang, Z. and Cui, B. and Shao, Y. and Yu, L. and Jiang, J. and Miao, X.}, year = {2019}, series = {Proceedings of the {{ACM SIGMOD International Conference}} on {{Management}} of {{Data}}}, pages = {376--388}, publisher = {{Association for Computing Machinery}}, issn = {07308078}, doi = {10.1145/3299869.3314038}, 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\texttimes{} and specialized ML systems like Petuum by up to 3.7\texttimes. \textcopyright{} 2019 Association for Computing Machinery.}, document_type = {Conference Paper}, isbn = {978-1-4503-5643-5}, source = {Scopus} } @inproceedings{Zhang20201513, title = {{{ColumnSGD}}: {{A}} Column-Oriented Framework for Distributed Stochastic Gradient Descent}, author = {Zhang, Z. and Wu, W. and Jiang, J. and Yu, L. and Cui, B. and Zhang, C.}, year = {2020}, series = {Proceedings - {{International Conference}} on {{Data Engineering}}}, volume = {2020-April}, pages = {1513--1524}, publisher = {{IEEE Computer Society}}, issn = {10844627}, doi = {10.1109/ICDE48307.2020.00134}, 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. \textcopyright{} 2020 IEEE.}, art_number = {9101731}, document_type = {Conference Paper}, isbn = {978-1-72812-903-7}, source = {Scopus} } @article{Zhang2020903, 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]}, author = {Zhang, Y. and Ma, L. and Liu, R. and Cheng, S. and Fan, X. and Luo, Z.}, year = {2020}, journal = {Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics}, volume = {32}, number = {6}, pages = {903--910}, publisher = {{Institute of Computing Technology}}, issn = {10039775}, doi = {10.3724/SP.J.1089.2020.17999}, 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. \textcopyright{} 2020, Beijing China Science Journal Publishing Co. Ltd. All right reserved.}, coden = {JFTXF}, document_type = {Article}, source = {Scopus} } @article{Zhang2021141, title = {Deep Learning Techniques for Advancing {{6G}} Communications in the Physical Layer}, author = {Zhang, S. and Liu, J. and Rodrigues, T.K. and Kato, N.}, year = {2021}, journal = {IEEE Wireless Communications}, volume = {28}, number = {5}, pages = {141--147}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15361284}, doi = {10.1109/MWC.001.2000516}, 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. \textcopyright{} 2021 IEEE.}, coden = {IWCEA}, document_type = {Article}, source = {Scopus} } @article{Zhang2022, title = {An Adaptive Dendrite-{{HDMR}} Metamodeling Technique for High-Dimensional Problems}, author = {Zhang, Q. and Wu, Y. and Lu, L. and Qiao, P.}, year = {2022}, journal = {Journal of Mechanical Design, Transactions of the ASME}, volume = {144}, number = {8}, publisher = {{American Society of Mechanical Engineers (ASME)}}, issn = {10500472}, doi = {10.1115/1.4053526}, abbrev_source_title = {J Mech Des, Trans ASME}, affiliation = {National CAD Supported Software Engineering Centre, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China}, art_number = {081701}, coden = {JMDEE}, correspondence_address1 = {Wu, Y.; National CAD Supported Software Engineering Centre, China; email: cad.wyz@hust.edu.cn}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{Zhang20221037, title = {Data Augmentation Empowered Neural Precoding for Multiuser {{MIMO}} with {{MMSE}} Model}, author = {Zhang, S. and Xu, J. and Xu, W. and Wang, N. and Ng, D.W.K. and You, X.}, year = {2022}, journal = {IEEE Communications Letters}, volume = {26}, number = {5}, pages = {1037--1041}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10897798}, doi = {10.1109/LCOMM.2022.3156946}, 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. \textcopyright{} 1997-2012 IEEE.}, coden = {ICLEF}, document_type = {Article}, source = {Scopus} } @article{Zhang2022190, title = {{Meta-Learning Based Multi-Fidelity Deep Neural Networks Metamodel Method [基于元学习的多可信度深度神经网络代理模型]}}, author = {Zhang, L. and Chen, J. and Xiong, F. and Ren, C. and Li, C.}, year = {2022}, journal = {Jixie Gongcheng Xuebao/Journal of Mechanical Engineering}, volume = {58}, number = {1}, pages = {190--200}, publisher = {{Chinese Mechanical Engineering Society}}, issn = {05776686}, doi = {10.3901/JME.2022.01.190}, abbrev_source_title = {Jixie Gongcheng Xuebao}, affiliation = {School of Astronautics, Beijing Institute of Technology, Beijing, 100081, China; China Aerodynamic Research and Development Center, Mianyang, 621000, China}, coden = {CHHKA}, correspondence_address1 = {Xiong, F.; School of Astronautics, China; email: fenfenx@bit.edu.cn}, document_type = {Article}, langid = {chinese}, source = {Scopus} } @article{Zhang20222368, title = {Model-Driven Learning for Generic {{MIMO}} Downlink Beamforming with Uplink Channel Information}, author = {Zhang, J. and You, M. and Zheng, G. and Krikidis, I. and Zhao, L.}, year = {2022}, journal = {IEEE Transactions on Wireless Communications}, volume = {21}, number = {4}, pages = {2368--2382}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15361276}, doi = {10.1109/TWC.2021.3111843}, 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. \textcopyright{} 2002-2012 IEEE.}, document_type = {Article}, source = {Scopus} } @inproceedings{zhangOptimalityNaiveBayes2004, title = {The Optimality of Naive Bayes}, booktitle = {Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference ({{FLAIRS}} 2004)}, author = {Zhang, Harry}, editor = {Barr, Valerie and Markov, Zdravko}, year = {2004}, publisher = {{AAAI Press}}, 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.}, added-at = {2011-03-27T19:35:34.000+0200}, biburl = {https://www.bibsonomy.org/bibtex/29288cd3adf6e5273ce7f8b74beb4c6e2/cocus}, booktitleaddon = {May 17-19, 2004}, interhash = {a8e31b4197a90abcb0bdb2b93504acda}, intrahash = {9288cd3adf6e5273ce7f8b74beb4c6e2}, owner = {CK}, venue = {Miami Beach, Florida, USA}, keywords = {bayesian,naive-bayes}, timestamp = {2011-03-27T19:35:45.000+0200} } @article{zhangTPPFAMUseThreshold2014, title = {{{TPPFAM}}: {{Use}} of Threshold and Posterior Probability for Category Reduction in Fuzzy {{ARTMAP}}}, author = {Zhang, Yongquan and Ji, Hongbing and Zhang, Wenbo}, year = {2014}, journal = {Neurocomputing}, volume = {124}, pages = {63--71}, issn = {0925-2312}, url = {http://www.sciencedirect.com/science/article/pii/S0925231213008151}, 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.}, nodoi = {https://doi.org/10.1016/j.neucom.2013.07.042}, keywords = {Category proliferation,Fuzzy ARTMAP (FAM),Overlapping classes,Posterior probability,Threshold adjustment parameter} } @article{zhao2005, title = {Hierarchical Clustering Algorithms for Document Datasets}, author = {Zhao, Ying and Karypis, George and Fayyad, Usama}, year = {2005}, month = mar, journal = {Data Mining and Knowledge Discovery}, volume = {10}, number = {2}, pages = {141--168}, publisher = {{Kluwer Academic Publishers}}, address = {{Hingham, MA, USA}}, issn = {1384-5810}, url = {http://dl.acm.org/citation.cfm?id=1061897.1061908}, 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.}, acmid = {1061908}, added-at = {2011-09-18T22:25:48.000+0200}, biburl = {http://www.bibsonomy.org/bibtex/21afe6065cc536f52534a7c15eed599c3/jil}, interhash = {fdb19684fd849b0c44ac0fb996b7888e}, intrahash = {1afe6065cc536f52534a7c15eed599c3}, nodoi = {10.1007/s10618-005-0361-3}, numpages = {28}, keywords = {cluster clustering entropy evaluation f-score fscore measure measures purity}, timestamp = {2013-11-23T20:11:51.000+0100} } @article{Zhao2019S3, title = {An On-Demand Service Aggregation and Service Recommendation Method Based on {{RGPS}}}, author = {Zhao, Y. and Guo, J. and He, K.}, year = {2019}, journal = {Intelligent Data Analysis}, volume = {23}, number = {S1}, pages = {S3-S23}, publisher = {{IOS Press}}, issn = {1088467X}, doi = {10.3233/IDA-192628}, abbrev_source_title = {Intell. Data Anal.}, affiliation = {Department School of Computer Science, Wuhan University, Wuhan, China; Department School of Data Science and Software Engineering, Qingdao University, Qingdao, China}, correspondence_address1 = {Zhao, Y.; Department School of Computer Science, China; email: ivwepriu@sina.com}, document_type = {Conference Paper}, langid = {english}, source = {Scopus} } @article{Zhao2022, title = {Interaction Design System for Artificial Intelligence User Interfaces Based on {{UML}} Extension Mechanisms}, author = {Zhao, Y.}, year = {2022}, journal = {Mobile Information Systems}, volume = {2022}, publisher = {{Hindawi Limited}}, issn = {1574017X}, doi = {10.1155/2022/3534167}, abbrev_source_title = {Mob. Inf. Sys.}, affiliation = {Department of Art and Design, Shijiazhuang University of Applied Technology, Shijiazhuang, 050081, China}, art_number = {3534167}, correspondence_address1 = {Zhao, Y.; Department of Art and Design, China; email: 2013010678@sjzpt.edu.cn}, document_type = {Article}, langid = {english}, source = {Scopus} } @article{zhaoOndemandServiceAggregation2019, title = {An On-Demand Service Aggregation and Service Recommendation Method Based on {{RGPS}}}, author = {Zhao, Y. and Guo, J. and He, K.}, year = {2019}, journal = {Intelligent Data Analysis}, volume = {23}, number = {S1}, pages = {S3-S23}, publisher = {{IOS Press}}, issn = {1088467X}, doi = {10.3233/IDA-192628}, abstract = {'Internet plus' application service recommendation is challenged by two issues: One is the increase in service volume and the disorderliness of the service organizations. A second is the diversification of user requirements. The research focus of this study was to investigate how to achieve more ordered aggregation and recommend services that meet the individualized requirements of users. This paper addresses the disorderliness of conventional service aggregation and considers the aggregation requirements of QoS weights with non-functional targets. Based on semantic relevance using the role (R), goal (G), process (P), service (S) demand metamodel, an RGPS association is proposed that is a weighted network for ordered QoS service aggregation. An individualized service recommendation method then is provided, based on an LSTM neural network with role and target backstepping using RGPS association network, that can achieve a high-quality precision service. Finally, a simulation experiment was carried out on service recommendations in the tourism domain, which verified the precision, effectiveness and application value of the service recommendation method. \textcopyright{} 2019 - IOS Press and the authors. All rights reserved.}, keywords = {Application services,Conventional services,Long short-term memory,Meta model,nonfunctional target requirement,On-demand services,Semantic relevance,Semantics,Service organizations,Service recommendations} } @inproceedings{zhaoUserbasedCollaborativefilteringRecommendation2010, title = {User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop}, booktitle = {Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining}, author = {Zhao, Zhi-Dan and Shang, Ming-sheng}, year = {2010}, series = {{{WKDD}} '10}, pages = {478--481}, publisher = {{IEEE Computer Society}}, address = {{Washington, DC, USA}}, url = {https://doi.org/10.1109/WKDD.2010.54}, acmid = {1749278}, isbn = {978-0-7695-3923-2}, nodoi = {10.1109/WKDD.2010.54}, numpages = {4}, keywords = {cloud computing,collaborative filtering,hadoop,Map-Reduce,recommender systems} } @article{Zheng201210, title = {Model-Driven Centerline Extraction for Severely Occluded Major Coronary Arteries}, author = {Zheng, Y. and Shen, J. and Tek, H. and {Funka-Lea}, G.}, year = {2012}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {7588 LNCS}, pages = {10--18}, issn = {03029743}, doi = {10.1007/978-3-642-35428-1_2}, 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. \textcopyright{} 2012 Springer-Verlag.}, document_type = {Conference Paper}, isbn = {9783642354274}, source = {Scopus} } @inproceedings{Zheng201375, title = {{{IVAMS}}: {{Intelligent}} Metamodel-Integrated {{Verilog-AMS}} for Circuit-Accurate System-Level Mixed-Signal Design Exploration}, author = {Zheng, G. and Mohanty, S.P. and Kougianos, E. and Okobiah, O.}, year = {2013}, series = {Proceedings of the {{International Conference}} on {{Application-Specific Systems}}, {{Architectures}} and {{Processors}}}, pages = {75--78}, issn = {10636862}, doi = {10.1109/ASAP.2013.6567553}, abbrev_source_title = {Proc Int Conf Appl Spec Syst Arcitec Process Proc}, affiliation = {NanoSystem Design Laboratory, University of North Texas, Denton, TX 76203, United States}, art_number = {6567553}, coden = {PIAAF}, correspondence_address1 = {NanoSystem Design Laboratory, , Denton, TX 76203, United States}, document_type = {Conference Paper}, isbn = {978-1-4799-0492-1}, langid = {english}, source = {Scopus} } @inproceedings{zhengCodEXSourceCode2018, title = {{{CodEX}}: {{Source}} Code Plagiarism Detection Based on Abstract Syntax Tree}, booktitle = {Proceedings for the 26th {{AIAI}} Irish Conference on Artificial Intelligence and Cognitive Science Trinity College Dublin, Dublin, Ireland, December 6-7th, 2018.}, author = {Zheng, Mengya and Pan, Xingyu and Lillis, David}, year = {2018}, pages = {362--373}, url = {http://ceur-ws.org/Vol-2259/aics_33.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/aics/ZhengPL18}, timestamp = {Tue, 08 Jan 2019 17:34:19 +0100} } @inproceedings{zhouMoreAccurateContent2014, title = {Towards More Accurate Content Categorization of {{API}} Discussions}, booktitle = {Proceedings of the {{22Nd}} International Conference on Program Comprehension}, author = {Zhou, Bo and Xia, Xin and Lo, David and Tian, Cong and Wang, Xinyu}, year = {2014}, series = {{{ICPC}} 2014}, pages = {95--105}, publisher = {{ACM}}, address = {{New York, NY, USA}}, url = {http://doi.acm.org/10.1145/2597008.2597142}, acmid = {2597142}, isbn = {978-1-4503-2879-1}, nodoi = {10.1145/2597008.2597142}, numpages = {11}, keywords = {API Discussion,Cache-Based Method,Composite Method,Text Categorization} } @article{Zhu20215434, title = {Deep-Learned Approximate Message Passing for Asynchronous Massive Connectivity}, author = {Zhu, W. and Tao, M. and Yuan, X. and Guan, Y.}, year = {2021}, journal = {IEEE Transactions on Wireless Communications}, volume = {20}, number = {8}, pages = {5434--5448}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15361276}, doi = {10.1109/TWC.2021.3067903}, 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. \textcopyright{} 2002-2012 IEEE.}, art_number = {9390399}, document_type = {Article}, source = {Scopus} } @inproceedings{zhuMiningAPIUsage2014, title = {Mining {{API Usage Examples}} from {{Test Code}}}, author = {Zhu, Zixiao and Zou, Yanzhen and Xie, Bing and Jin, Yong and Lin, Zeqi and Zhang, Lu}, year = {2014}, month = sep, pages = {301--310}, publisher = {{IEEE}}, doi = {10.1109/ICSME.2014.52}, 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.}, isbn = {978-1-4799-6146-7}, langid = {english} } @article{zolotasRESTsecLowcodePlatform2018, title = {{{RESTsec}}: A Low-Code Platform for Generating Secure by Design Enterprise Services}, shorttitle = {{{RESTsec}}}, author = {Zolotas, Christoforos and Chatzidimitriou, Kyriakos C. and Symeonidis, Andreas L.}, year = {2018}, month = oct, journal = {Enterprise Information Systems}, volume = {12}, number = {8-9}, pages = {1007--1033}, issn = {1751-7575, 1751-7583}, doi = {10.1080/17517575.2018.1462403}, 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.}, langid = {english}, keywords = {lowcode} } @article{zolotasTypeInferenceFlexible2018, title = {Type Inference in Flexible Model-Driven Engineering Using Classification Algorithms}, author = {Zolotas, Athanasios and Matragkas, Nicholas and Devlin, Sam and Kolovos, Dimitrios S. and Paige, Richard F.}, year = {2018}, month = jan, journal = {Software \& Systems Modeling}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-018-0658-5}, langid = {english} } @misc{ZoomingPanningHTML5, title = {Zooming \& {{Panning}} in {{HTML5}} \& {{JavaScript Chart}}}, journal = {CanvasJS}, url = {http://canvasjs.com/docs/charts/basics-of-creating-html5-chart/zooming-panning/}, urldate = {2015-04-02}, 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.} } @inproceedings{Zou2018174, title = {Improved Sliding-Mode on-Line Adaptive Position Control for {{AMT}} Clutch Systems Based on Neural Networks}, author = {Zou, J. and Huang, H. and G{\"u}hmann, C.}, year = {2018}, series = {{{IEEE Intelligent Vehicles Symposium}}, {{Proceedings}}}, volume = {2018-June}, pages = {174--178}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/IVS.2018.8500691}, abbrev_source_title = {IEEE Intell Veh Symp Proc}, affiliation = {Center for Automotive Electronics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China; Schaeffler Technologies AG and Co. KG, Germany; Electronic Measurement and Diagnostic Technology, Technische Universit\"at, Berlin, Germany}, art_number = {8500691}, document_type = {Conference Paper}, isbn = {978-1-5386-4452-2}, langid = {english}, source = {Scopus} }