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[null,"1.","Record Nr.",null,"UNINA9910983491303321"]
[null,null,"Autore",null,"Meo Rosa"]
[null,null,"Titolo",null,"Machine Learning and Principles and Practice of Knowledge Discovery in Databases : International Workshops of ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Revised Selected Papers, Part II / / edited by Rosa Meo, Fabrizio Silvestri"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"9783031746277"]
[{"$nulls":4},"3031746279"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (550 pages)"]
[null,null,"Collana",null,"Communications in Computer and Information Science, , 1865-0937 ; ; 2134"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Image processing - Digital techniques"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Data structures (Computer science)"]
[{"$nulls":4},"Information theory"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Education - Data processing"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer Imaging, Vision, Pattern Recognition and Graphics"]
[{"$nulls":4},"Data Structures and Information Theory"]
[{"$nulls":4},"Computer Engineering and Networks"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[{"$nulls":4},"Computers and Education"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"-- RKDE 2023: 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education.  -- PICA: A Data-driven Synthesis of Peer Instruction and Continuous Assessment.  -- The ChatGPT and Education Tweets Dataset.  -- A Fair Post-Processing Method based on the MADD Metric for Predictive Student Models.  -- Distractor generation for multiple-choice questions with predictive prompting and "]
[{"$nulls":4},"large language models.  -- Towards Personalized Educational Materials: Mapping Student Knowledge through Natural Language Processing.  -- A 2-step methodology for XAI in education.  -- Consolidation and Transmission of Multiple xAPI Data Sources from Virtual Learning Environments to Different Learning Record Stores .  -- SoGood 2023 – 8th Workshop on Data Science for Social Good.  -- Efficient and general text classification: An Active Learning approach.  -- Identifying Features of Constructive Journalism in News Articles: An Explainable ML Approach.  -- Anomaly Detection in Pet Behavioral Data.  -- Detecting sexually explicit content in the context of the child sexual abuse materials (CSAM): end-to-end classifiers and region-based networks.  -- PrivateCTGAN: Adapting GAN for Privacy-aware Tabular Data Sharing.  -- Data Science for Fighting Environmental Crime.  -- Fairness Analysis in Causal Models: An Application to Public Procurement.  -- Exploring the Generalizability of Transfer Learning for Camera Trap Animal Image Classification.  -- Towards Hybrid Human-Machine Learning and Decision Making (HLDM).  -- Towards a hybrid human-machine discovery of complex movement patterns.  -- Trustworthy Hybrid Decision Making.  -- Optimizing delegation between human and AI collaborative agents.  -- Exploring the Risks of General-Purpose AI: The Role of Nearsighted Goals and the Brain's Reward Mechanism in Processes of Decision Makings.  -- Towards synergistic human-AI collaboration in hybrid decision-making systems.  -- On the Challenges and Practices of Reinforcement Learning from Real Human Feedback.  -- Conversational XAI: Formalizing its Basic Design Principles.  -- TCuPGAN: A novel framework developed for optimizing human-machine interactions in citizen science.  -- A Crossroads for Hybrid Human-Machine decision-making.  -- Enhancing Fairness, Justice and Accuracy of Hybrid Human AI Decisions by Shifting Epistemological Stances.  -- Interpreting Dynamic Causal Model Policies.  -- Uncertainty meets explainability in machine learning.  -- Relation of Activity and Confidence when Training Deep Neural Networks.  -- Explaining an image classifier with a GAN conditioned by uncertainty.  -- Identifying Trends in Feature Attributions during Training of Neural Networks.  -- Using Stochastic Methods to Setup High Precision Experiments.  -- Designing a Method to Identify Explainability Requirements in Cancer Research.  -- Explainable Learning with Hierarchical Online Deterministic Annealing.  -- Explaining uncertainty in AI for clinical decision support systems.  -- Towards Explainability in Monocular Depth Estimation.  -- Using Part-based Representations for Explainable Deep Reinforcement Learning.  -- Regionally Additive Models: Explainable-by-design models minimizing feature interactions.  -- FALE: Fairness aware ALE plots for auditing bias in subgroups.  -- Workshop: Deep Learning and Multimedia Forensics. Combating fake media and misinformation.  -- Tracing Videos to their Social Network with Robust DCT Analysis.  -- All-for-One and One-For-All: Deep learning-based feature fusion for Synthetic Speech Detection.  -- Improving Tiled Evolutionary Adversarial Attack.  -- Adversarial Magnification to Deceive Deepfake Detection through Super Resolution.  -- DivNoise: A Data Collection for Source Identification on Diverse Camera Sensors.  -- Detecting Face Synthesis Using a Concealed Fusion Model.  -- Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It.  -- Towards a Fine-Grained Threat Model for Video-Based Remote Identity Proofing."]
[null,null,null,"Sommario/riassunto","The five-volume set CCIS 2133-2137 constitutes the refereed proceedings of the workshops held in conjunction with the Joint European Conference on Machine Learning and Knowledge Discovery in "]
[{"$nulls":4},"Databases, ECML PKDD 2023, which took place in Turin, Italy, during September 18-22, 2023. The 200 full papers presented in these proceedings were carefully reviewed and selected from 515 submissions. The papers have been organized in the following tracks: Part I: Advances in Interpretable Machine Learning and Artificial Intelligence -- Joint Workshop and Tutorial; BIAS 2023 - 3rd Workshop on Bias and Fairness in AI; Biased Data in Conversational Agents; Explainable Artificial Intelligence: From Static to Dynamic; ML, Law and Society; Part II: RKDE 2023: 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education; SoGood 2023 – 8th Workshop on Data Science for Social Good; Towards Hybrid Human-Machine Learning and Decision Making (HLDM); Uncertainty meets explainability in machine learning; Workshop: Deep Learning and Multimedia Forensics. Combating fake media and misinformation; Part III: XAI-TS: Explainable AI for Time Series: Advances and Applications; XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining; Deep Learning for Sustainable Precision Agriculture; Knowledge Guided Machine Learning; MACLEAN: MAChine Learning for EArth ObservatioN; MLG: Mining and Learning with Graphs; Neuro Explicit AI and Expert Informed ML for Engineering and Physical Sciences; New Frontiers in Mining Complex Patterns; Part IV: PharML, Machine Learning for Pharma and Healthcare Applications; Simplification, Compression, Efficiency and Frugality for Artificial intelligence; Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making; 6th Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living (ARIAL); Adapting to Change: Reliable Multimodal Learning Across Domains; AI4M: AI for Manufacturing; Part V: Challenges and Opportunities of Large Language Models in Real-World Machine Learning Applications; Deep learning meets Neuromorphic Hardware; Discovery challenge; ITEM: IoT, Edge, and Mobile for Embedded Machine Learning; LIMBO - LearnIng and Mining for BlOckchains; Machine Learning for Cybersecurity (MLCS 2023); MIDAS - The 8th Workshop on MIning DAta for financial applicationS; Workshop on Advancements in Federated Learning."]