{"product_id":"machine-learning-and-principles-and-practice-of-knowledge-discovery-in-databases-9783031746260","title":"Machine Learning and Principles and Practice of Knowledge Discovery in Databases","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003e.- RKDE 2023: 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e.- PICA: A Data-driven Synthesis of Peer Instruction and Continuous Assessment.\u003c\/p\u003e\u003cp\u003e.- The ChatGPT and Education Tweets Dataset.\u003c\/p\u003e\u003cp\u003e.- A Fair Post-Processing Method based on the MADD Metric for Predictive Student Models.\u003c\/p\u003e\u003cp\u003e.- Distractor generation for multiple-choice questions with predictive prompting and large language models.\u003c\/p\u003e\u003cp\u003e.- Towards Personalized Educational Materials: Mapping Student Knowledge through Natural Language Processing.\u003c\/p\u003e\u003cp\u003e.-  A 2-step methodology for XAI in education.\u003c\/p\u003e\u003cp\u003e.- Consolidation and Transmission of Multiple xAPI Data Sources from Virtual Learning Environments to Different Learning Record Stores .\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e.- SoGood 2023  8th Workshop on Data Science for Social Good.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e.- Efficient and general text classification: An Active Learning approach.\u003c\/p\u003e\u003cp\u003e.- Identifying Features of Constructive Journalism in News Articles: An Explainable ML Approach.\u003c\/p\u003e\u003cp\u003e.- Anomaly Detection in Pet Behavioral Data.\u003c\/p\u003e\u003cp\u003e.- Detecting sexually explicit content in the context of the child sexual abuse materials (CSAM): end-to-end classifiers and region-based networks.\u003c\/p\u003e\u003cp\u003e.- PrivateCTGAN: Adapting GAN for Privacy-aware Tabular Data Sharing.\u003c\/p\u003e\u003cp\u003e.- Data Science for Fighting Environmental Crime.\u003c\/p\u003e\u003cp\u003e.- Fairness Analysis in Causal Models: An Application to Public Procurement.\u003c\/p\u003e\u003cp\u003e.- Exploring the Generalizability of Transfer Learning for Camera Trap Animal Image Classification.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e.- Towards Hybrid Human-Machine Learning and Decision Making (HLDM).\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e.- Towards a hybrid human-machine discovery of complex movement patterns.\u003c\/p\u003e\u003cp\u003e.- Trustworthy Hybrid Decision Making.\u003c\/p\u003e\u003cp\u003e.- Optimizing delegation between human and AI collaborative agents.\u003c\/p\u003e\u003cp\u003e.- Exploring the Risks of General-Purpose AI: The Role of Nearsighted Goals and the Brain's Reward Mechanism in Processes of Decision Makings.\u003c\/p\u003e\u003cp\u003e.- Towards synergistic human-AI collaboration in hybrid decision-making systems.\u003c\/p\u003e\u003cp\u003e.- On the Challenges and Practices of Reinforcement Learning from Real Human Feedback.\u003c\/p\u003e\u003cp\u003e.- Conversational XAI: Formalizing its Basic Design Principles.\u003c\/p\u003e\u003cp\u003e.- TCuPGAN: A novel framework developed for optimizing human-machine interactions in citizen science.\u003c\/p\u003e\u003cp\u003e.- A Crossroads for Hybrid Human-Machine decision-making.\u003c\/p\u003e\u003cp\u003e.- Enhancing Fairness, Justice and Accuracy of Hybrid Human AI Decisions by Shifting Epistemological Stances.\u003c\/p\u003e\u003cp\u003e.- Interpreting Dynamic Causal Model Policies.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e.- Uncertainty meets explainability in machine learning.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e.- Relation of Activity and Confidence when Training Deep Neural Networks.\u003c\/p\u003e\u003cp\u003e.- Explaining an image classifier with a GAN conditioned by uncertainty.\u003c\/p\u003e\u003cp\u003e.- Identifying Trends in Feature Attributions during Training of Neural Networks.\u003c\/p\u003e\u003cp\u003e.- Using Stochastic Methods to Setup High Precision Experiments.\u003c\/p\u003e\u003cp\u003e.- Designing a Method to Identify Explainability Requirements in Cancer Research.\u003c\/p\u003e\u003cp\u003e.- Explainable Learning with Hierarchical Online Deterministic Annealing.\u003c\/p\u003e\u003cp\u003e.- Explaining uncertainty in AI for clinical decision support systems.\u003c\/p\u003e\u003cp\u003e.- Towards Explainability in Monocular Depth Estimation.\u003c\/p\u003e\u003cp\u003e.- Using Part-based Representations for Explainable Deep Reinforcement Learning.\u003c\/p\u003e\u003cp\u003e.- Regionally Additive Models: Explainable-by-design models minimizing feature interactions.\u003c\/p\u003e\u003cp\u003e.- FALE: Fairness aware ALE plots for auditing bias in subgroups.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e.- Workshop: Deep Learning and Multimedia Forensics. Combating fake media and misinformation.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e.- Tracing Videos to their Social Network with Robust DCT Analysis.\u003c\/p\u003e\u003cp\u003e.- All-for-One and One-For-All: Deep learning-based feature fusion for Synthetic Speech Detection.\u003c\/p\u003e\u003cp\u003e.- Improving Tiled Evolutionary Adversarial Attack.\u003c\/p\u003e\u003cp\u003e.- Adversarial Magnification to Deceive Deepfake Detection through Super Resolution.\u003c\/p\u003e\u003cp\u003e.- DivNoise: A Data Collection for Source Identification on Diverse Camera Sensors.\u003c\/p\u003e\u003cp\u003e.- Detecting Face Synthesis Using a Concealed Fusion Model.\u003c\/p\u003e\u003cp\u003e.- Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It.\u003c\/p\u003e\u003cp\u003e.- Towards a Fine-Grained Threat Model for Video-Based Remote Identity Proofing.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53195416600919,"sku":"9783031746260","price":75.99,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/machine-learning-and-principles-and-practice-of-knowledge-discovery-in-databases-9783031746260","provider":"Book Curl","version":"1.0","type":"link"}