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Davide Di Ruscio
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assets/storages/logseq-citation-manager/Biblioteca-zotero.bib
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@@ -2972,17 +2972,23 @@ This paper investigates the conceptual and technical feasibility of a new softwa
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@@ -5153,6 +5167,18 @@ A universal metamodel aimed at the representation of schemas in a way that is at
The definitions, architecture, fundamental technologies, and applications of IoT are systematically reviewed and the major challenges which need addressing by the research community and corresponding potential solutions are investigated.}
}
@book{auffarthGenerativeAILangChain2025,
title = {Generative {{AI}} with {{LangChain}}: Build Production-Ready {{LLM}} Applications and Advanced Agents Using {{Python}}, {{LangChain}}, and {{LangGraph}}},
shorttitle = {Generative {{AI}} with {{LangChain}}},
author = {Auffarth, Ben and Kuligin, Leonid},
date = {2025},
edition = {Second edition},
publisher = {Packt Publishing},
location = {Birmingham},
abstract = {Go beyond foundational LangChain documentation with detailed coverage of LangGraph interfaces, design patterns for building AI agents, and scalable architectures used in production--ideal for Python developers building GenAI applications Key Features Bridge the gap between prototype and production with robust LangGraph agent architectures Apply enterprise-grade practices for testing, observability, and monitoring Build specialized agents for software development and data analysis Purchase of the print or Kindle book includes a free PDF eBook Book Description This second edition tackles the biggest challenge facing companies in AI today: moving from prototypes to production. Fully updated to reflect the latest developments in the LangChain ecosystem, it captures how modern AI systems are developed, deployed, and scaled in enterprise environments. This edition places a strong focus on multi-agent architectures, robust LangGraph workflows, and advanced retrieval-augmented generation (RAG) pipelines. You'll explore design patterns for building agentic systems, with practical implementations of multi-agent setups for complex tasks. The book guides you through reasoning techniques such as Tree-of -Thoughts, structured generation, and agent handoffs--complete with error handling examples. Expanded chapters on testing, evaluation, and deployment address the demands of modern LLM applications, showing you how to design secure, compliant AI systems with built-in safeguards and responsible development principles. This edition also expands RAG coverage with guidance on hybrid search, re-ranking, and fact-checking pipelines to enhance output accuracy. Whether you're extending existing workflows or architecting multi-agent systems from scratch, this book provides the technical depth and practical instruction needed to design LLM applications ready for success in production environments. What you will learn Design and implement multi-agent systems using LangGraph Implement testing strategies that identify issues before deployment Deploy observability and monitoring solutions for production environments Build agentic RAG systems with re-ranking capabilities Architect scalable, production-ready AI agents using LangGraph and MCP Work with the latest LLMs and providers like Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI's o3-mini Design secure, compliant AI systems aligned with modern ethical practices Who this book is for This book is for developers, researchers, and anyone looking to learn more about LangChain and LangGraph. With a strong emphasis on enterprise deployment patterns, it's especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this updated edition expands its reach to support engineering teams and decision-makers working on enterprise-scale LLM strategies. A basic understanding of Python is required, and familiarity with machine learning will help you get the most out of this book},
langid = {english}
}
@article{augusteijnNeuralNetworkClassification2002,
title = {Neural Network Classification and Novelty Detection},
author = {Augusteijn, M. F. and {B. A. Folkert}},
@@ -11273,6 +11299,11 @@ The Internet of Things (IoT) has become a reality with the emergence of Smart Ci
organization = {Nuvation}
}
@misc{ConvenzioneGSSIINFNUnivaq,
title = {Convenzione {{GSSI-INFN}} Univaq},
keywords = {SERVICES/PHDICT}
}
@online{conwayEnterprisesOnboardAI2025,
title = {Enterprises Onboard {{AI}} Teammates Faster with {{NVIDIA NeMo}} Tools to Scale Employee Productivity},
author = {Conway, Joey},
@@ -16976,6 +17007,13 @@ This paper developed the Cross-Layer Modeler XLM approach which relies on increm
langid = {english}
}
@online{DerivaRivisteScientifiche,
title = {La Deriva Delle Riviste Scientifiche},
url = {https://www.ilfoglio.it/scienza/2025/04/25/news/la-deriva-delle-riviste-scientifiche-7650974/},
urldate = {2025-05-28},
abstract = {Il mercato delle pubblicazioni del\ settore \è vittima di vere e proprie\ strategie predatorie organizzate, in cui\ si elimina ogni freno editoriale che possa rallentare il flusso di denaro. Per limitare\ i rischi per le istituzioni accademiche, bisogna\ tornare a\ una comunicazione responsabile}
}
@article{derlerModelingCyberx2013PhysicalSystems2012,
title = {Modeling {{Cyber}}\&\#x2013;{{Physical Systems}}},
author = {Derler, P. and Lee, E. A. and Vincentelli, A. S.},
@@ -22852,6 +22890,13 @@ TL;DR
Fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level component that can be mixed and matched to build new approaches.}
}
@online{HowBuildAI,
title = {How to Build {{AI}} Agents with {{Microsoft Azure}} | {{Alex Wang}} Ha Pubblicato Un Post Sull’argomento | {{LinkedIn}}},
url = {https://www.linkedin.com/posts/alexwang2911_aiagents-aiengineering-enterpriseai-activity-7334292535135096834-Q_Y6},
urldate = {2025-06-01},
abstract = {Building AI agents \&\#61; 5\% AI + 95\% software engineering Not exactly accurate, but you get the point. Instead of saying we “build” an AI agent, it’s more like we architect a system, and let the AI sit inside it. This diagram from Microsoft Azure shows what real, enterprise-ready AI infrastructure actually looks like. It’s software architecture, system design, and reliability engineering: ✅ Identity management ✅ Document filtering (ACLs, redaction, PII masking) ✅ Schema mapping ✅ Human-in-the-loop escalation ✅ Infra that scales across both vector + SQL Think of AI agents like APIs that reason - not magic. They still need: - Fine-grained access control - Storage that separates structured vs unstructured knowledge - Observability, fallback routing, and data traceability - A flow that connects document pipelines with model orchestration ... Before you tune the prompts, build the foundations. Also, if you\&\#39;re building agents, copilots, or just trying to level up in the AI space - we’ve started a free AI community where people learn, share, and build together: 🔗 https://lnkd.in/gAE32Fae Workshops, challenges, courses, and a lot of smart folks trying to figure it out like you. Happy exploring!🐣 \_\_\_\_\_\_\_\_\_\_ For more on AI and learning materials, plz check my previous posts. I share my journey here. Join me and let\&\#39;s grow together. Alex Wang \#AIagents \#AIengineering \#EnterpriseAI~ | 176 commenti su LinkedIn}
}
@online{HowCanUse,
title = {How {{Can I Use This Method}}? - {{IEEE Conference Publication}}},
url = {https://ieeexplore.ieee.org/document/7194634/},
@@ -25450,13 +25495,6 @@ This paper proposes the integration of an automatic evaluation of domain models
timestamp = {Sat, 09 Apr 2022 12:28:30 +0200}
}
@online{IscrivitiLinkedIn,
title = {Iscriviti | {{LinkedIn}}},
url = {https://www.linkedin.com/},
urldate = {2025-04-21},
abstract = {Oltre 500 milioni di utenti | Gestisci la tua identità professionale. Crea la tua rete professionale e interagisci con essa. Accedi al sapere, a informazioni importanti e a tante opportunità.}
}
@article{ISINKAYE2015261,
title = {Recommendation Systems: {{Principles}}, Methods and Evaluation},
author = {Isinkaye, F.O. and Folajimi, Y.O. and Ojokoh, B.A.},
@@ -29230,6 +29268,12 @@ The results show that using Android-specific semantic anchors are useful for det
This work compares three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods, and their algorithm, which is called item-to-item collaborative filtering.}
}
@article{LINEEGUIDALassicurAzione,
title = {LINEE GUIDA PER L’assicurAzione della qualita’ dei corsi di dottorato},
langid = {italian},
keywords = {LOGSEQ}
}
@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},
@@ -38612,6 +38656,11 @@ This surveying paper’s central aspect is its comprehensive summary of the gove
urldate = {2016-09-27}
}
@article{RebuttalICSE2026,
title = {Rebuttal for {{ICSE}} 2026 {{Submission}} \#50},
langid = {english}
}
@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, Stéphane},
@@ -38813,6 +38862,247 @@ This surveying paper’s central aspect is its comprehensive summary of the gove
\mkbibemph{I'm not sure this can be done. }}
}
@misc{RecSys_2025_Reproducibility_689,
title = {{{RecSys}}\_2025\_{{Reproducibility}}\_689}
}
@misc{RecSys_2025_Reproducibility_711,
title = {{{RecSys}}\_2025\_{{Reproducibility}}\_711},
note = {I'm reviewing a research paper and I took the following notes:
\par
\section{Annotazioni\\
(9/6/2025, 16:24:56)}
\par
- “environmental footprint of recommender systems has received growing attention in the research community” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#5fb236
\par
- “comprehensive evaluation should also account for the emissions produced during inference time,” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#5fb236
\par
- “In this study, we build upon and extend the analysis of Spillo et al. by incorporating the inference phase into the carbon footprint assessment and exploring how variations in training configurations affect emissions.” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#a28ae5
\par
- “Our findings reveal that models with higher training emissions can, in some cases, offer lower environmental costs at inference time” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#e56eee
\par
- “Moreover, we show that minimizing the number of validation metrics computed during training can lead to significant reductions in overall carbon footprint, highlighting the importance of thoughtful experimental design in sustainable machine learning.” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#e56eee
\par
- “Between 2012 and 2018, the compute used in state-of-the-art training runs increased by over 300,000x” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#a28ae5
\par
- “global data center electricity consumption could nearly double by 2030” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#a28ae5
\par
- “Green AI initiative” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#2ea8e5
\par
- “Recommender Systems (RS)” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#5fb236
\par
- “have received comparatively less attention in terms of sustainability” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#a28ae5
\par
- “Also, given that RS are commonly retrained at daily and weekly intervals, their environmental footprint can accumulate rapidly over time, magnifying even small inefficiencies in model or infrastructure choices.” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#e56eee
\par
- “complex deep and graph-based models often provide only marginal accuracy improvements while significantly increasing carbon emissions.” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#a28ae5
\par
- “their analysis focused only the training phase, and neglecting the energy cost of routine validation and model inference, both critical components in real-world deployment.” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#a28ae5
\par
- “In this work, we revisit and extend Spillo et al. [18]’s study with the goal of broadening the environmental lens through which RS are evaluated.” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#e56eee
\par
- “RQ1: Training setup” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#2ea8e5
\par
- “How do variations in the training setup affect the reproducibility of performance and energy footprint measurements for RS training?” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#e56eee
\par
- “RQ2: Inference.” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#2ea8e5
\par
- “Do models that incur in higher energy footprints during training also exhibit higher footprints at inference time?” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#e56eee
\par
- “Validation overhead.” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#2ea8e5
\par
- “computing validations metrics” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#a28ae5
\par
- “Life-cycle perspective” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#2ea8e5
\par
- “inference cost,” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#a28ae5
\par
- “Reproducibility conditions.” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#2ea8e5
\par
- “how observations shift even with modest changes to the execution environment.” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#a28ae5
\par
- “sustainable RS research and offers actionable guidance for researchers and practitioners working towards low-carbon RS.” (“RecSys\_2025\_Reproducibility\_711”, p. 1) \#f19837
\par
- “Carbon Intensity (CI) of 475 gCO2/kWh, based on the 2019 International Energy Agency Global Energy and CO2 Status Report” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#a28ae5
\par
- “This simplified approach treats all computational activity as equally carbon-intensive, regardless of location, energy source or infrastructure.” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#5fb236
\par
- “direct energy tracking.” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#f19837
\par
- “By reporting raw energy rather than emissions” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#a28ae5
\par
- “Amazon Books” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#2ea8e5
\par
- “MovieLens 1M” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#2ea8e5
\par
- “Mind” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#2ea8e5
\par
- “we adopt a mobile laptop environment that reflects increasingly common deployment and experimentation scenarios in real-world contexts (such as academic research, early-stage prototyping, and low-budget settings).” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#ffd400\\
\mkbibemph{It's not clear what they want to do with this configuration. What's the external validity of it?}
\par
- “While our CPU is newer and has more cores, it is also a mobile processor with a focus on energy efficiency” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#5fb236
\par
- “The original experiments ran on Ubuntu 20.04 with Linux kernel 5.15.0. Our experiments were conducted on Windows 11 24H2. We conducted our experiments on Windows to reflect a broader range of practical usage scenarios, particularly in environments where it remains the dominant OS.” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#ffd400\\
\mkbibemph{a completely different setup. How can you get a fair comparison? This is another dimension that differentiates the original setup with that of this paper.}
\par
- “we anticipate its impact to be secondary compared to hardware/software stack differences” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#e56eee
\par
- “Model Training Methodology” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#5fb236
\par
- “RecBole implementations and kept most hyper-parameters at their default values.” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#5fb236
\par
- “First, we enabled RecBole’s reproducibility mode, which ensures the training, validation and test splits remain consistent across runs” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#5fb236
\par
- “epochs, setting it to 50 for MovieLens and Mind,” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#2ea8e5
\par
- “20 for Amazon Books.” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#2ea8e5
\par
- “Model evaluation was performed after training each epoch” (“RecSys\_2025\_Reproducibility\_711”, p. 2) \#5fb236
\par
- “This entire process was repeated 10 times per configuration, and we report the mean and standard deviation across these runs.” (“RecSys\_2025\_Reproducibility\_711”, p. 3) \#5fb236
\par
- “Relative efficiency ranking is largely stable” (“RecSys\_2025\_Reproducibility\_711”, p. 3) \#2ea8e5
\par
- “Absolute energy use decreases in most, but not all cases” (“RecSys\_2025\_Reproducibility\_711”, p. 3) \#2ea8e5
\par
- “Our mobile hardware setup reduces training energy consumption in 31 of the 47 model/dataset combinations compared to the original desktop environment” (“RecSys\_2025\_Reproducibility\_711”, p. 3) \#ffd400\\
\mkbibemph{What can we say about this? It seems you are considering modern hardware devices, and consequently this was quite expected, isn't it? This seems to be obvious..}
\par
- “They reinforce the point that absolute energy figures are not easily generalizable across environments.” (“RecSys\_2025\_Reproducibility\_711”, p. 3) \#ffd400\\
\mkbibemph{I think this is quite obvious, isn't it?}
\par
- “Validation metric computation carries notable overhead.” (“RecSys\_2025\_Reproducibility\_711”, p. 3) \#2ea8e5
\par
- “1.03\% to 71.71\%,” (“RecSys\_2025\_Reproducibility\_711”, p. 3) \#ffd400\\
\mkbibemph{Thisi is a high variance!!!!!}
\par
- “We observe that quantitative energy values and model rankings are sensitive to changes in hardware, dataset, and evaluation setup.” (“RecSys\_2025\_Reproducibility\_711”, p. 3) \#ffd400\\
\mkbibemph{This seems obvious to me}
\par
- “Our results show that training and inference efficiencies are not strongly aligned.” (“RecSys\_2025\_Reproducibility\_711”, p. 3) \#5fb236\\
\mkbibemph{This is interesting instead.}
\par
- “By jointly analyzing training and inference phases, and highlighting the interplay between model complexity and hardware efficiency, our study offers a more granular view of sustainable recommendation and supports more informed, context-aware decisions” (“RecSys\_2025\_Reproducibility\_711”, p. 4) \#5fb236
\par
COnsider that those that are tagget with \#5fb236 are just highlights, those that are tagged with \#e56eee and \#a28ae5 are imporant sentences. Please pay attention instead to the notes that are tagged with \#ffd400. Those that are tagged with \#ff6666 are typos or errors. Could you please draft a review by organizing it as follows:
\par
\textbf{Overall Presentation.}*~Please reflect on the overall quality of the presentation of this contribution. Poor – The paper is difficult to follow due to unclear writing, lack of structure, or numerous grammatical and typographical errors. Figures, tables, and explanations are inadequate. Below Average – The main ideas are present but not clearly communicated. Writing may be inconsistent, and the structure could be improved. Some figures or tables may be unclear. Adequate – The paper is understandable but could benefit from better clarity, organization, or formatting. Some sections may be overly complex or underdeveloped. Well-Presented – The paper is clearly written, well-structured, and easy to follow. Figures, tables, and explanations support the content. Minor improvements could enhance readability. Excellent – The paper is exceptionally well-written, logically structured, and highly engaging. All figures, tables, and explanations are clear, precise, and enhance comprehension significantly.
\par
5: Excellent
\par
4: Well-Presented
\par
3: Adequate
\par
2: Below Average
\par
1: Poor
\par
\textbf{Technical Soundness.}*~How technically sound is the research presented in this contribution? Fundamentally Flawed – The research contains critical errors, invalid assumptions, or methodological weaknesses that undermine its conclusions. Weak Technical Rigor – Some aspects of the methodology are questionable. Adequate Technical Soundness – The approach is generally valid, but there may be minor methodological weaknesses, limited evaluation, or room for improvement in execution. Strong Technical Soundness – The research is well-executed, with a solid methodology, and appropriate evaluation. Any limitations are acknowledged and justified. Excellent – This contribution is methodologically impeccable.
\par
5: Excellent
\par
4: Strong Technical Soundness
\par
3: Adequate Technical Soundness
\par
2: Weak Technical Rigor
\par
1: Fundamentally Flawed
\par
\textbf{Reproducibility of Methods.}*~Please reflect on the reproducibility of the work presented in this contribution. Not Reproducible – Key details of the methodology are missing or unclear, making it impossible for others to replicate the work. No code/data/protocols are provided. Weak Reproducibility – Some methodological details are provided, but important steps, parameters, or implementation details are missing. Limited access to code/data/protocols. Moderate Reproducibility – The methodology is described sufficiently for partial replication, but some ambiguity remains. code/data/protocols may be available but not fully documented. Highly Reproducible – The methodology is well-documented, and replication is feasible with the provided details. code/data/protocols are available and reasonably well-organized. Fully Reproducible – The study is meticulously documented with complete details, ensuring full reproducibility. code/data/protocols are openly available and well-structured for easy replication.
\par
5: Fully Reproducible
\par
4: Highly Reproducible
\par
3: Moderate
\par
2: Weak
\par
1: Not Reproducible
\par
\textbf{Impact.}*~Please reflect on whether the ideas and/or results presented will have an impact on the community. Minimal Impact – The ideas or results have little to no influence on the field. They may be too incremental, lack practical relevance, or fail to offer meaningful insights. Limited Impact – The work provides some useful findings but is unlikely to influence future research or applications. Moderate Impact – The contribution is valuable and may inspire further research or applications in certain areas, though its broader significance remains limited. High Impact – The ideas or results have strong potential to shape future research, influence practices, or contribute meaningfully to advancements in the field. Transformative Impact – The work introduces groundbreaking ideas or results that could redefine current understanding, open new research directions, or have significant real-world applications.
\par
5: Transformative
\par
4: High
\par
3: Moderate
\par
2: Limited
\par
1: Minimal
\par
\textbf{Artifacts- . Are the paper artifacts (source code, data) available and well documented?”.}*~Consider paper artifacts (source code, data) and state whether they are available and well documented
\par
\textbf{Artifacts. Are the artifacts executable by following the instructions provided in the documentation?.}~Consider whether the artifacts are executable by following the instructions provided in the documentation. Note that we do not expect the reviewers to run the full set of experiments, especially if they are expensive or rely on specialized hardware, rather we ask you to assess if the experimental pipeline can be started and is executable. Minor issues are acceptable if they can be fixed easily.
\par
\textbf{Review.}*~Please provide a review of the paper, offering general comments to authors, along with justification from the different aspects you have rated this contribution (relevance, originality, technical soundness, impact, overall presentation, coverage of related work, and reproducibility). In other words, when reviewing, consider how well the paper builds on prior work, the soundness of its methodology, the strength of its evidence and claims, and the clarity of its presentation. Also, assess its practical relevance, ethical considerations, and whether it provides enough detail for replication.
\par
\textbf{Showstopper.}*~To streamline the review process, please list any key issues where a response from the authors during the rebuttal phase would be essential for your evaluation of this contribution.
\par
\textbf{Overall Evaluation.}*~You have given detailed feedback to the authors. Now, we ask for your final recommendation along with a brief justification. Both the recommendation score and the review text are required. Remember that when making your final recommendation, you should focus primarily on whether this work makes a strong and original contribution to the field of Recommender Systems. You should also consider the impact of the presented outcomes and the rigor of the research methodology.
\par
3: Strong accept
\par
2: Accept
\par
1: Weak accept
\par
-1: Weak reject
\par
-2: Reject
\par
-3: Strong reject}
}
@misc{Recsys2024workshops_paper_130,
title = {Recsys2024-Workshops\_paper\_130},
keywords = {LOGSEQ},
@@ -38965,6 +39255,15 @@ This surveying paper’s central aspect is its comprehensive summary of the gove
\mkbibemph{An illustrative example would help here.}}
}
@online{RecSysACMRecommender,
title = {{{RecSys}} – {{ACM Recommender Systems}}},
url = {https://recsys.acm.org/recsys25/call/},
urldate = {2025-06-05},
abstract = {Call for Contributions},
langid = {english},
organization = {RecSys}
}
@online{redazioneScritturaManoMemorizzazione2025,
title = {La Scrittura a Mano, La Memorizzazione e La Lettura Contro Il "Marciume Cerebrale" Causato Dagli Smartphone. {{Così}} Le Nuove Indicazioni Nazionali Combattono Gli Effetti Negativi Della Tecnologia},
author = {{redazione}},
@@ -39071,7 +39370,7 @@ Per quanto riguarda la \textbf{calligrafia}, questa non solo migliora la qualit
title = {Regolamento per Il Conferimento Di Contratti Di Ricerca},
url = {https://www.univaq.it/include/utilities/blob.php?table=regolamento&id=200&item=file},
urldate = {2025-04-17},
keywords = {/unread}
keywords = {/unread,LOGSEQ}
}
@article{ReliableDataProcessing2021,
@@ -41112,6 +41411,13 @@ Scikit-learn is a Python module integrating a wide range of state-of-the-art mac
"design patterns" (\href{zotero://open-pdf/library/items/TJI3FLRY?page=8}{Sculley et al :16})}
}
@online{ScuolaSuperioreSantAnna,
title = {Scuola {{Superiore Sant}}’{{Anna Pisa}}: Talenti e Futuro a {{Pisa}} - {{Focus}}.It},
url = {https://www.focus.it/comportamento/scuola-e-universita/scuola-superiore-sant-anna-pisa-eccellenza-universitaria-e-internazionalita},
urldate = {2025-06-01},
keywords = {SERVICES/PHDICT}
}
@article{SEfSAS3challenges,
title = {{{SEfSAS3-challenges}}}
}
@@ -44286,6 +44592,13 @@ This work considers problems involving groups of data where each observation wit
urldate = {2018-08-10}
}
@online{temporary-citekey-31112,
title = {𝗬𝗼𝘂 𝗰𝗮𝗻𝗻𝗼𝘁 𝗯𝘂𝗶𝗹𝗱 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀, 𝘄𝗶𝘁𝗵𝗼𝘂𝘁… | {{Om Nalinde}} | 72 Commenti},
url = {https://www.linkedin.com/posts/that-aum_%F0%9D%97%AC%F0%9D%97%BC%F0%9D%98%82-%F0%9D%97%B0%F0%9D%97%AE%F0%9D%97%BB%F0%9D%97%BB%F0%9D%97%BC%F0%9D%98%81-%F0%9D%97%AF%F0%9D%98%82%F0%9D%97%B6%F0%9D%97%B9%F0%9D%97%B1-%F0%9D%97%94%F0%9D%97%9C-%F0%9D%97%94%F0%9D%97%B4%F0%9D%97%B2%F0%9D%97%BB%F0%9D%98%81%F0%9D%98%80-activity-7326212339916062722-h6sq},
urldate = {2025-06-04},
abstract = {𝗬𝗼𝘂 𝗰𝗮𝗻𝗻𝗼𝘁 𝗯𝘂𝗶𝗹𝗱 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀, 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲𝘀𝗲 6 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗱𝗲𝘀𝗶𝗴𝗻 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 ⬇️ Let\&\#39;s dive deeper: 1. 𝗥𝗲𝗔𝗰𝘁 𝗔𝗴𝗲𝗻𝘁 ➜Thinks, takes action, looks at the result, repeats. ➜Classic loop: “Should I Google this?” → Does it → Adjusts. ➜ Used in most AI products today (like basic chat assistants). 2. 𝗖𝗼𝗱𝗲𝗔𝗰𝘁 𝗔𝗴𝗲𝗻𝘁: ➜ Runs real code, not just JSON. ➜ So instead of saying “call API X,” it writes and runs a Python script. ➜ More powerful. Used in research agents and dev assistants. 3. 𝗠𝗼𝗱𝗲𝗿𝗻 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: ➜ Sends tasks to smart APIs (search, cloud, data), and lets them do the heavy lifting. ➜ The agent mostly routes and formats info. ➜ Think: a smart middleman between you and powerful services. 4. 𝗦𝗲𝗹𝗳-𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻: ➜ Agent checks its own work. ➜ Did it make a mistake? It catches it, critiques it, and tries again. ➜ Most AI errors happen **because this step is missing.** 5. 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄: ➜ One agent isn’t doing everything. ➜ You have a planner, a researcher, and a writer — all working together. ➜ Like a mini team of AIs. More accurate. Less chaos. 6. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚: ➜ This is what powers apps like Perplexity. ➜ The agent looks stuff up (retrieval), thinks about it, uses tools, and gives you a smarter answer. ➜ Works with real-time data, not just model memory. If you\&\#39;re building AI Agents, check out my profile for free resources 👋 Kudos to Rakesh Gohel Sir for this great infographic! | 72 commenti su LinkedIn}
}
@inproceedings{teyton_mining_2012,
title = {Mining {{Library Migration Graphs}}},
booktitle = {2012 19th {{Work}}. {{Conf Reverse Eng}}.},
@@ -47655,6 +47968,13 @@ This paper investigates the interplay between Stack Overflow activities and the
isbn = {978-1-5386-9196-0}
}
@online{VediLattivitaDi,
title = {Vedi l’attività Di {{Uma Maheswar Nethi}} Su {{LinkedIn}}},
url = {https://www.linkedin.com/posts/uma-maheswar-nethi_activity-7333808301928783873-D5ij},
urldate = {2025-05-29},
abstract = {Accedi o iscriviti subito per vedere post come questo e tanto altro.}
}
@thesis{velazquez-rodriguezUncoveringLibraryFeatures,
title = {Uncovering {{Library Features}} from {{Incomplete Information}} on {{Stack Overflow}}},
author = {Velázquez-Rodríguez, Camilo},
@@ -49088,6 +49408,15 @@ This paper introduces CodeUltraFeedback, a preference dataset of 10,000 complex
keywords = {lowcode}
}
@online{WhatPhDInteresting,
title = {What Is a {{PhD}}? {{An}} Interesting, Insightful Read. {{I}} Have Seen Huge… | {{Lionel Briand}}},
shorttitle = {What Is a {{PhD}}?},
url = {https://www.linkedin.com/posts/lionel-briand-5082b1_what-is-a-phd-an-interesting-insightful-activity-7333079820467138560-oX6E},
urldate = {2025-05-28},
abstract = {What is a PhD? An interesting, insightful read. I have seen huge differences across systems and universities in my career.},
keywords = {SERVICES/PHDICT}
}
@online{WhatsDifferenceAutonomous,
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},