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Machine Learning and Principles and Practice of Knowledge Discovery in Databases : International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part I - Damian Roqueiro
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Machine Learning and Principles and Practice of Knowledge Discovery in Databases

International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part I

By: Damian Roqueiro (Editor), Szymon Jaroszewicz (Editor), Slawomir Nowaczyk (Editor), Riccardo Guidotti (Editor), Pedro M. Ferreira (Editor)

Paperback | 31 January 2023

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Workshop on Data Science for Social Good (SoGood 2022).- Preface from the workshop organisers.- Gender Stereotyping Impact on Facial Expression Recognition.- A Social Media Tool for Domain-Specific Information Retrieval - A Case Study in Human Trafficking.- A Unified Framework for Assessing Energy Efficiency of Machine Learning.- Fault Detection in Wastewater Treatment Plants: Application of Autoencoders Models with Streaming Data.- A Temporal Fusion Transformer for Long-term Explainable Prediction of Emergency Department Overcrowding.- Exploitation and Merge of Information Sources for Public Procurement Improvement.- Geovisualisation tools for reporting and monitoring Transthyretin-associated Familial Amyloid Polyneuropathy disease.- Evaluation of Group Fairness Measures in Student Performance Prediction Problems.- Combining Image Enhancement Techniques and Deep Learning for Shallow Water Benthic Marine Litter Detection.- Ethical and Technological AI Risks Classification: A Human vs Machine Approach.- A Reinforcement Learning Algorithm for Fair Electoral Redistricting in Parliamentary Systems.- Study on Correlation Between Vehicle Emissions and Air Quality in Porto.- Intelligently Detecting Information Online-weaponisation Trends (IDIOT).- Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2022).- Preface from the workshop organisers.- Multi-Modal Terminology Management: Corpora, Data Models and Implementations in TermSTAR.- Cluster algorithm for social choice.- Sentimental Analysis of COVID-19 Vaccine Tweets using BERT+NBSVM.- Rules, subgroups and redescriptions as features in classification tasks.- Bitpaths: compressing datasets without decreasing predictive performance.- Anomaly Detection for Physical Threat Intelligence.- Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2022).- Preface from the workshop organisers.- Is Attention Interpretation? A Quantitative Assessment on Sets.- From Disentangled Representation to Concept Ranking: Interpreting Deep Representations in Image Classification tasks.- RangeGrad: Explaining Neural Networks by Measuring Uncertainty through Bound Propagation.- An Empirical Evaluation of Predicted Outcomes as Explanations in Human-AI Decision-Making.- Local Multi-Label Explanations for Random Forest.- Interpretable and Reliable Rule Classification based on Conformal Prediction.- Measuring the Burden of (Un)fairness Using Counterfactuals.- Are SHAP values biased towards high-entropy features?.- Simple explanations to summarise Subgroup Discovery outcomes: a case study concerning patient phenotyping.- Limits of XAI task performance evaluation: an e-sport prediction example.- Improving the quality of rule-based GNN explanations.- Exposing Racial Dialect Bias in Abusive Language Detection: Can Explainability Play a Role?.- On the Granularity of Explanations in Model Agnostic NLP Interpretability.- Workshop on Uplift Modeling (UMOD 2022).- Preface from the workshop organisers.- Estimating the impact of coupon non-usage.- Shrinkage estimators for uplift regression.- Workshop on IoT, Edge and Mobile for Embedded Machine Learning (ITEM 2022).- Preface from the workshop organisers.- Hierarchical Design Space Exploration for Distributed CNN Inference at the Edge.- Automated Search for Deep Neural Network Inference Partitioning on Embedded FPGA.- Framework to Evaluate Deep Learning Algorithms for Edge Inference and Training.- Hardware Execution Time Prediction for Neural Network Layers.- Enhancing Energy-efficiency by Solving the Throughput Bottleneck of LSTM Cells for Embedded FPGAs.- Accelerating RNN-based Speech Enhancement on a Multi-Core MCU with Mixed FP16-INT8 Post-Training Quantization.

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