.- XAI-TS: Explainable AI for Time Series: Advances and Applications. .- Introducing the Attribution Stability Indicator: a Measure for Time Series XAI Attributions. .- LMFD: Latent Monotonic Feature Discovery. .- LinC: Explaining Time Series Clusterings with User-Provided Constraints. .- Explainable Long- and Short-term Pattern Detection in Projected Sequential Data. .- XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining. .- Matching the expert’s knowledge via a counterfactual-based feature importance measure. .- Explaining Fatigue in Runners Using Time Series Analysis on Wearable Sensor Data. .- Wave Top-k Random-d Family Search: How to Guide an Expert in a Structured Pattern Space. .- Diffusion-based Visual Counterfactual Explanations - Towards Systematic Quantitative Evaluation. .- Exploring gender bias in misclassification with clustering and local explanations. .- Are Generative-based Graph Counterfactual Explainers Worth It?. .- FIPER: a Visual-based Explanation Combining Rules and Feature Importance. .- Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem. .- Using Graph Neural Networks for the Detection and Explanation of Network Intrusions. .- Game Theoretic Explanations for Graph Neural Networks. .- From Black Box to Glass Box: Evaluating the Faithfulness of Process Predictions with GCNNs. .- A New Class of Intelligible Models for Tabular Learning. .- Deep Learning for Sustainable Precision Agriculture. .- Plant Disease Detection using Deep Learning: A. .- Proof of Concept on Pear Leaf Disease Detection. .- Modelling Solar PV Adoption in Irish Dairy Farms using Agent-Based Modelling. .- Deep Networks based Approach for Automatic Counting Panicles on UAV captured Paddy RGB Imagery. .- The ACRE Crop-Weed Dataset for Benchmarking Weed Detection Models on Maize and Beans Fields. .- Integrating Renewable Energy in Agriculture: A Deep Reinforcement Learning-based Approach. .- Knowledge Guided Machine Learning. .- Unsupervised Ontology- and Taxonomy Construction through Hyperbolic Relational Domains and Ranges. .- A Filter-based Neural ODE Approach for Modelling Natural Systems with Prior Knowledge Constraints. .- Towards Automatically Refining Low-Quality Domain Knowledge: A Case Study in Healthcare. .- Lorentz-invariant augmentation for high-energy physics deep learning models. .- Discovering SpatioTemporal Warning Contexts from Non-Emergency Call Reports. .- SEEDOT: Tool for Enhancing Sentiment Lexicon with Machine Learning. .- MACLEAN: MAChine Learning for EArth ObservatioN. .- Detection and semantic description of changes in Earth Observation Time Series data. .- Low-rank hierarchical clustering of PRISMA hyperspectral images to identify burned areas. .- Next day fire prediction via semantic segmentation. .- Robust Burned Area Delineation through Multitask Learning. .- Burnt area extraction from high-resolution satellite images based on anomaly detection. .- Seasonal average temperature forecast with the AutoGluonTS modern autoML tool. .- MLG: Mining and Learning with Graphs. .- Curvature-based Pooling within Graph Neural Networks. .- Finding coherent node groups in directed graphs. .- Neuro Explicit AI and Expert Informed ML for Engineering and Physical Sciences. .- Constructing Neural Forms for Hard-Constraint PINNs with Complex Dirichlet Boundaries. .- Enhancing generability: AutoML for robust denoising of strong gravitational lens systems. .- Data-Efficient Interactive Multi-Objective Optimization Using ParEGO. .- New Frontiers in Mining Complex Patterns. .- Striving for Simplicity in Deep Neural Models Trained for Malware Detection. .- On the Effectiveness of Non-negative Matrix Factorization for Text Open-set Recognition. .- Real-time Anomaly Prediction from Cryptocurrency Time Series. .- A Joint Analysis of Trajectory Mining and Process Mining for Smartphone User Behaviour. .- Towards Automation of Pollen Monitoring - Dealing with the Background in Pollen Monitoring Images.

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