Foundations of Intelligent Systems

Social Media and Recommendation.- Granular Emotion Detection in Social Media Using Multi-DisciplineEnsembles.- Sentiment Polarity and Emotion Detection from Tweets Using Distant Supervision and Deep Learning Models.- Disruptive Event Identification in Online Social Network.- Modeling Polarization on Social Media Posts: A Heuristic Approach Using Media Bias.- Sarcasm detection in Tunisian social media comments: Case of COVID-19.- Multimodal Deep Learning and Fast Retrieval for Recommendation.- Natural Language Processing.- Mining news articles dealing with Food Security.- Identification of Paragraph Regularities in Legal Judgements through Clustering and Textual Embedding.- Aspect term extraction improvement based on a hybrid method.- Exploring the Impact of Gender Bias Mitigation Approaches on a Downstream Classification Task.- A semi-automatic data generator for Query Answering.- Explainability,.- XAI to explore robustness of features in adversarial training for cybersecurity.- Impact of Feedback Type on Explanatory Interactive Learning.- Learning and Explanation of Extreme Multi-Label Deep Classification Models for Media Content.- An Interpretable Machine Learning Approach to Prioritizing Factors Contributing to Clinician Burnout.- A general-purpose method for applying Explainable AI for Anomaly Detection.- More Sanity Checks for Saliency Maps.- Intelligent Systems.- Deep Reinforcement Learning for Automated Stock Trading: Inclusion of Short Selling.- Scaling Posterior Distributions over Differently-Curated Datasets: A Bayesian-Neural-Networks Methodology.- Ensembling Sparse Autoencoders for Network Covert Channel Detection in IoT Ecosystems.- Towards Automation of Pollen Monitoring: Image-Based Tree Pollen Recognition.- Rough Sets for Intelligence on Embedded Systems.- Context as a Distance Function in ConSQL.- Classification and Clustering.- Detecting Anomalies with LatentOut: Novel Scores, Architectures, and Settings.- Richness Fallacy.- Adapting loss functions to learning progress improves accuracy of classification in neural networks.- Multiscale and multivariate time series clustering: A new approach.- Improve Calibration Robustness of Temperature Scaling by Penalizing Output Entropy.- Understanding Negative Calibration from Entropy Perspective.- A New Clustering Preserving Transformation for $k$-Means Algorithm Output.- Complex Data.- A Transformer-Based Framework for Geomagnetic Activity Prediction.- AS-SIM: an approach to Action-State Process Model Discovery.- Combining Active Learning and Fast DNN Ensembles for Process Deviance Discovery.- Temporal Graph-based CNNs (TG-CNNs) for Online Course Dropout Prediction.- Graph Convolutional Networks Using Node Addition and Edge Reweighting.- Audio Super-Resolution via Vision Transformer.- Similarity embedded temporal Transformers: Enhancing stock predictions with historically similar trends.- Investigating noise interference on speech towards applying the Lombard effect automatically.- Medical Applications.- Towards Polynomial Adaptive Local Explanations for Healthcare Classifiers.- Towards Tailored Intervention in Medicine Using Patients' Segmentation.- Application of association rules to classify IBD patients.- Unsupervised Learning Based Rule Generating System with Temporal Features Extractions Tuned for Tinnitus Retraining Therapy.- Industrial Applications.- TrueDetective 4.0: a Big data architetture for real time anomaly detection.- Optimising the Machine Translation Workflow: Analysis, Development, Benchmarking, Testing and Maintenance.- Classification vs Recommendation methods for Therapeutics Recommendation.- Document LayoutAnalysis with Variational Autoencoders : an Industrial Application.