Lear (Spain)

We present MobiPhysio, a 2D video-based dataset designed to support AI-driven physiotherapy assessment and monitoring. The dataset contains 3686 segmented videos of 9 Active Range of Motion physiotherapy exercises performed by 58 male and female participants. The recordings are done under the variations in lighting, camera angles, occlusion, and jitter in order to mimic real-world conditions. Data collection occurred in two phases: first from non-expert participants at Stamford University Bangladesh, and later from expert participants at the Department of Physiotherapy and Rehabilitation, Jashore University of Science and Technology. The entire process was conducted under the guidance of certified physiotherapists. Each video is further annotated with assessment scores derived from the exercise-specific Exercise Accuracy Assessment Questionnaire (EAAQ), developed under expert guidance. This dataset would enable researchers to build and test AI-powered physiotherapy and rehabilitation systems, examine human motion, and create exercise monitoring solutions using available 2D camera devices like mobile phones without the need of external body-reliant sensors.

Στην παρούσα εργασία προτείνεται ένα ανθρωποκεντρικό μοντέλο χρήσης της ΔΤΝ για το σχεδιασμόεξατομικευμένων μαθησιακών εμπειριών. Ο/η εκπαιδευτικός λειτουργεί ως "σκηνοθέτης" μαθησιακώνεμπειριών, αξιοποιώντας μαθησιακά υλικά και παρέχοντας παιδαγωγική καθοδήγηση στην ΔΤΝ. Ηεμπλοκή της ΔΤΝ οργανώνεται σε τέσσερα επίπεδα: χρονική επέκταση της μάθησης, πλαισίωση στοπροσωπικό πλαίσιο του εκπαιδευόμενου, ενίσχυση διαδραστικότητας σε πραγματικό χρόνο καιδιαβαθμίσεις ανθρωπομορφισμού της προσωπικότητας της ΔΤΝ. Το μοντέλο ενισχύει τη βιωματική,προσαρμοσμένη στις ανάγκες και συναισθηματικά υποστηρικτική μάθηση, συνδυάζοντας σαφήπαιδαγωγική καθοδήγηση με τη δυναμική υποστήριξη της ΔΤΝ.

Cardiovascular disease (CVD) and mental health issues are leading causes of poor health and mortality worldwide, significantly affecting both the quality and longevity of life. The relationship between cardiovascular and mental health has been recognised for decades and research suggests the contributing factors are multifaceted, but still not fully understood. Mental health issues increase the risk of developing CVD, while CVD can trigger or exacerbate mental health conditions such as depression and anxiety.This article explores the relationship between cardiovascular and mental health and their effects on individuals. It also reviews evidence-based treatment options, including psychological therapies, medicines, exercise and cardiac rehabilitation. The author uses a case-study approach to provide practical information and help clinicians better support patients affected by these commonly coexisting conditions and thereby improve clinical outcomes.

The rapid evolution of Artificial Intelligence (AI) has expanded access to large-scale online adaptive learning systems. Such AI-powered systems strive to deliver personalized learning experiences, often by means of advanced algorithms that continuously model learner behavior. Ensuring the reliability of these systems is fundamental, otherwise their ability to optimize individual learning paths and inform decision-making is undermined. In order to trust such systems, learners with identical profiles should follow highly similar learning trajectories. But how can we evaluate the reliability of these dynamic learning environments, especially in systems that are continuously developed and updated? This paper demonstrates the effectiveness of A/A testing --- large-scale double-blind experiments with identical conditions --- in systematically evaluating the reliability of AI-powered learning environments. We illustrate this by assessing the reliability of student model parameters in a large-scale online arithmetic learning platform that is driven by a well-studied and powerful explainable AI algorithm. We duplicated the item bank of a newly developed game and randomly assigned 50% of the players to one of two identical versions, which were launched simultaneously in the live environment. We then analyzed the reliability of item difficulty convergence, the stability of student ability estimates in the new game, and their relationship to ability estimates from other arithmetic games, as well as patterns in student errors. Our results indicate that the student model parameters are stable across the two variants, highlighting A/A testing as a valuable tool for assessing the reliability of large-scale AI-powered learning systems. We discuss its advantages and suggest future directions for adapting the approach, while considering its relevance in dynamic learning environments.

Cloud computing has become crucial for the commercial world due to its computational capacity, storage capabilities, scalability, software integration, and billing convenience. Initially, clouds were relatively homogeneous, but now diverse machine configurations in heterogeneous clouds are recognized for their improved application performance and energy efficiency. This shift is driven by the integration of various hardware to accommodate diverse user applications. However, alongside these advancements, security threats like micro-architectural attacks are increasing concerns for cloud providers and users. Studies like Repttack and Cloak & Co-locate highlight the vulnerability of heterogeneous clouds to co-location attacks, where attacker and victim instances are placed together. The ease of these attacks isn’t solely linked to heterogeneity but also correlates with how heterogeneous the target systems are. Despite this, no numerical metrics exist to quantify cloud heterogeneity. This paper introduces the Heterogeneity Score (HeteroScore) to evaluate server setups and instances. HeteroScore significantly correlates with co-location attack security. The paper also proposes strategies to balance diversity and security. This study pioneers the quantitative analysis connecting cloud heterogeneity and infrastructure security.