We introduce AI on the Pulse, a real-world-ready anomaly detection system that continuously monitors patients using a fusion of wearable sensors, ambient intelligence, and advanced AI models. Powered by UniTS, a state-of-the-art (SoTA) universal time-series model, our framework autonomously learns each patient's unique physiological and behavioral patterns, detecting subtle deviations that signal potential health risks. Unlike classification methods that require impractical, continuous labeling in real-world scenarios, our approach uses anomaly detection to provide real-time, personalized alerts for reactive home-care interventions. Our approach outperforms 12 SoTA anomaly detection methods, demonstrating robustness across both high-fidelity medical devices (ECG) and consumer wearables, with a ~22% improvement in F1 score. However, the true impact of AI on the Pulse lies in @HOME, where it has been successfully deployed for continuous, real-world patient monitoring. By operating with non-invasive, lightweight devices like smartwatches, our system proves that high-quality health monitoring is possible without clinical-grade equipment. Beyond detection, we enhance interpretability by integrating LLMs, translating anomaly scores into clinically meaningful insights for healthcare professionals.

Automated systems must adapt to evolving environments, yet many struggle with drift phenomena affecting healthcare, finance, and cybersecurity domains.The DELTA workshop addresses this by distinguishing between data and concept drift, aiming to create a practical, human-centric framework for managing drift.The workshop seeks innovative drift detection, prediction, and analysis solutions by uniting researchers and practitioners.DELTA fosters collaboration to advance the understanding and management of drift in dynamic data landscapes by featuring keynotes, paper presentations, interactive sessions, and discussions.

We present GRACIE (Graph Recalibration and Adaptive Counterfactual Inspection and Explanation), a novel approach for generative classification and counterfactual explanations of dynamically changing graph data. We study graph classification problems through the lens of generative classifiers. We propose a dynamic, self-supervised latent variable model that updates by identifying plausible counterfactuals for input graphs and recalibrating decision boundaries through contrastive optimization. Unlike prior work, we do not rely on linear separability between the learned graph representations to find plausible counterfactuals. Moreover, GRACIE eliminates the need for stochastic sampling in latent spaces and graph-matching heuristics. Our work distills the implicit link between generative classification and loss functions in the latent space, a key insight to understanding recent successes with this architecture. We further observe the inherent trade-off between validity and pulling explainee instances towards the central region of the latent space, empirically demonstrating our theoretical findings. In extensive experiments on synthetic and real-world graph data, we attain considerable improvements, reaching ~99% validity when sampling sets of counterfactuals even in the challenging setting of dynamic data landscapes.

Real-time monitoring of human behaviours, especially in e-Health applications, has been an active area of research in the past decades. On top of IoT-based sensing environments, anomaly detection algorithms have been proposed for the early detection of abnormalities. Gradual change procedures, commonly referred to as drift anomalies, have received much less attention in the literature because they represent a much more challenging scenario than sudden temporary changes (point anomalies). In this article, we propose, for the first time, a fully unsupervised real-time drift detection algorithm named DynAmo, which can identify drift periods as they are happening. DynAmo comprises a dynamic clustering component to capture the overall trends of monitored behaviours and a trajectory generation component, which extracts features from the densest cluster centroids. Finally, we apply an ensemble of divergence tests on sliding reference and detection windows to detect drift periods in the behavioural sequence.

The black-box nature and the lack of interpretability detract from constant improvements in Graph Neural Networks (GNNs) performance in social network tasks like friendship prediction and community detection. Graph Counterfactual Explanation (GCE) methods aid in understanding the prediction of GNNs by generating counterfactual examples that promote trustworthiness, debiasing, and privacy in social networks. Alas, the literature on GCE lacks standardised definitions, explainers, datasets, and evaluation metrics. To bridge the gap between the performance and interpretability of GNNs in social networks, we discuss GRETEL, a unified framework for GCE methods development and evaluation. We demonstrate how GRETEL comes with fully extensible built-in components that allow users to define ad-hoc explainer methods, generate synthetic datasets, implement custom evaluation metrics, and integrate state-of-the-art prediction models.

Online courses and e-degrees, although present since the mid-1990, have received enormous attention only in the last decade. Moreover, the new Coronavirus disease (COVID-19) outbreak forced many nations (e.g. Italy, the US, and other countries) to massively push their education system towards an online environment. Academics now are also looking at the crisis as an opportunity for universities to adopt digital technologies for teaching more broadly. But they will have to understand what possible ways of evaluating and effectively teaching will be in this new scenario. The depicted overview, in conjunction with the utility and ubiquitous access to the educational platforms of online courses, entails a vast amount of enrolments. Nevertheless, a high enrolment rate usually translates into a significant dropout (or withdrawal) rate of students (40-80% of online students drop out). Student dropout prediction (SDP) consists of modelling and fore-casting student behaviour when interacting with e-learning platforms. It is a significant phenomenon that has repercussions on online institutions, the involved students and professors. Early approaches tended to perform manual analytic examinations to devise retention strategies. Recent research has adopted automated policies to thoroughly exploit the advantages of student activities(hereafter e-tivities) in the e-platforms and identify at-risk students. These approaches include machine learning and deep learning techniques to predict the student dropout status. Therefore, being able to cope with the trend shifting of student interactions with the course platforms in real-time has become of paramount importance. In this tutorial, we comprehensively overview the SDP problem in the literature. We provide mathematical formalisation to the different definitions proposed, and we introduce simple and complex predictive methods adhering to the following: Student dropout definition, Input modelling, Underlying machine and deep learning techniques, Evaluation measures, Datasets, and privacy concerns.

In this paper, we compare several deep and surface state-of-the-art machine learning methods for risk prediction in problems that can be modelled as a trajectory of events separated by irregular time intervals. Trajectories are the abstract representation of many real-life data, such as patient records, student e-tivities, online financial transactions, and many others. Given the continuously increasing number of machine learning methods to predict future high-risk events in these contexts, we aim to provide more insight into reproducibility and applicability of these methods when changing datasets, parameters, and evaluation measures. As an additional contribution, we release to the community the implementations of all compared methods.