Discovery Science 26th International Conference, DS 2023, Porto, Portugal, October 9–11, 2023, Proceedings

This book constitutes the proceedings of the 26th International Conference on Discovery Science, DS 2023, which took place in Porto, Portugal, in October 2023. The 37 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 133 submissions. They were organized in topical sections as follows: Machine learning methods and applications; natural language processing and social media analysis; interpretability and explainability in AI; data analysis and optimization; fairness, privacy and security in AI; control and spatio-temporal modeling; graph theory and network analysis; time series and forecasting; healthcare and biological data analysis; anomaly, outlier and novelty detection.

Ensembles of classifiers and quantifiers with data fusion for Quantification Learning.- Exploring the Intricacies of Neural Network Optimization.- Exploring the Reduction of Configuration Spaces of Workflows.- iSOUP-SymRF: Symbolic feature ranking with random forests in online multi-target regression.- Knowledge-Guided Additive Modeling For Supervised Regression.- Audience Prediction for Game Streaming Channels Based on Vectorization of User Comments.- From Tweets to Stance: An Unsupervised Framework for User Stance Detection on Twitter.- GLORIA: A Graph Convolutional Network-based Approach for Review Spam Detection.- Unmasking COVID-19 False Information on Twitter: a Topic-based Approach with BERT.- Unsupervised Key-Phrase Extraction from Long Texts with Multilingual Sentence Transformers.- Counterfactuals Explanations for Outliers via Subspaces Density Contrastive Loss.- Explainable Spatio-Temporal Graph Modeling.- ProbabilisticScoring Lists for Interpretable Machine Learning.- Refining Temporal Visualizations Using the Directional Coherence Loss.- Semantic enrichment of explanations of AI models for healthcare.- Text to Time Series Representations: Towards Interpretable Predictive Models.- Enhancing intra-modal similarity in a cross-modal triplet loss.- Exploring the Potential of Optimal Active Learning via a Non-myopic Oracle Policy.- Extrapolation is Not the Same as Interpolation.- Gene Interactions in Survival Data Analysis: A Data-driven Approach Using Restricted Mean Survival Time and Literature Mining.- Joining Imputation and Active Feature Acquisition for Cost Saving on Data Streams with Missing Features.- EXPHLOT: EXplainable Privacy assessment for Human LOcation Trajectories.- Fairness-aware Mixture of Experts with Interpretability Budgets.- GenFair: A Genetic Fairness-Enhancing Data Generation Framework.- Privacy-Preserving Learning of Random Forests Without Revealing the Trees.- Unlearning Spurious Correlations in Chest X-ray Classification.- Explaining the Chronological Attribution of Greek Papyri Images.- Leveraging the Spatiotemporal Analysis of Meisho-e Landscapes.- Predictive Inference Model of the Physical Environment that emulates Predictive Coding.- Transferring a Learned Qualitative Cart-Pole Control Model to Uneven Terrains.- Which Way to Go - Finding Frequent Trajectories Through Clustering.- Boosting-based Construction of BDDs for Linear Threshold Functions and  Its Application to Verification of Neural Networks.- Interpretable Data Partitioning through Tree-based Clustering Methods.- Jaccard-constrained dense subgraph discovery.- RIMBO - an ontology for model revision databases.- Unsupervised Graph Neural Networks for Source Code Similarity Detection.- A Universal Approach for Post-Correcting Time Series.- Forecasts: Reducing Long-term ErrorsIn Multistep Scenarios.- Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting.- Pseudo Session-Based Recommendation with Hierarchical Embedding and Session Attributes.- Chance and the predictive limit in  basketball (both college and professional).- Exploring Label Correlations for Quantification of ICD Codes.- LGEM+: a first-order logic framework for automated improvement of metabolic network models through abduction.- Predicting age from human lung tissue through multi-modal data integration.- Error Analysis on Industry Data:Using Weak Segment Detection for Local Model Agnostic Prediction Intervals.- HEART: Heterogeneous Log Anomaly Detection using Robust Transformers.- Multi-Kernel Time Series Outlier Detection.- Toward Streamlining the Evaluation of Novelty Detection in Data Streams.