Connectivity Fund

Alzheimer’s Diagnosis: Multimodal Explainable AI for Early Detection and Personalized Care

Nadeem Qazi Senior Lecturer in AI machine learning at University of East London,UK Alzheimer’s disease (AD) is becoming more common, emphasizing the need for early detection and prediction to improve patient outcomes. Current diagnostic methods are often too late, missing the opportunity for early intervention. This research seeks to develop advanced explainable AI models that

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Exploring Prosocial Dynamics in Child-Robot Interactions: Adaptation, Measurement, and Trust

Ana Isabel Caniço Neto Assistant Researcher at the University of Lisbon Social robots are increasingly finding application in diverse settings, including our homes and schools, thus exposing children to interactions with multiple robots individually or in groups. Understanding how to design robots that can effectively interact and cooperate with children in these hybrid groups, in

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Types of Contamination in AI Evaluation: Reasoning and Triangulation

Behzad Mehrbakhsh PhD student at Universitat Politècnica de València A comprehensive and accurate evaluation of AI systems is indispensable for advancing the field and fostering a trustworthy AI ecosystem. AI evaluation results have a significant impact on both academic research and industrial applications, ultimately determining which products or services are deemed effective, safe and reliable

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CLAIRE | Rising Research Network: AI Research and Mental Well-Being Workshop 2nd edition

Marie Anastacio PhD candidate at Leiden University, RWTH Aachen After the successful execution of our 2023 workshop in collaboration with the TAILOR-ESSAI Summer School, we propose to organise a second edition at ESSAI2024. The event will focus on fostering a community of young AI researchers in Europe, supporting AI researchers and promoting mental well-being for

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Machine Learning Modalities for Materials Science

Milica Todorovic Associate professor at University of Turku In the past decade, artificial intelligence algorithms have demonstrated a tremendous potential and impact in speeding up the processing, optimisation, and discovery of new materials. The objective of the workshop and school “Machine Learning Modalities for Materials Science” (MLM4MS 2024) was to bring together the community of

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Evaluating the Trustworthiness of Human-like Robotic Motion

Filipa Correia Assistant Researcher at Interactive Technologies Institute, University of Lisbon The research project will explore the trustworthiness of an embodied AI, such as a social robot. Specifically, it will investigate whether the performance of humanlike motions of a non-humanoid robot enhances the perceived trustworthiness of that robot. Beyond the scientific contribution to the current

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Explanations and Reasoning: Proofs and Models of Intuitionistic Modal Logics

Philippe Balbiani CNRS researcher at Toulouse Institute of Computer Science Research (Toulouse, France) Rooted in Intuitionistic and Constructive Reasoning, Intermediate Logics have found important applications through the Curry-Howard correspondence. Nowadays, there is an Intuitionistic Modal Logics renaissance in Computer Science and Artificial Intelligence. Connections between, on one hand, Intuitionistic and Constructive Mathematics and, on the

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Neuro-symbolic integration for graph data

Manfred Jaeger Associate Professor at Aalborg University Learning and reasoning with graph and network data has developed as an area of increasing importance over recent years. Social networks, knowledge graphs, sensor and traffic networks are only some of the examples where graph-structured data arises in important applications. Much of the attention currently focuses on graph

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