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For Bruno Dumas, computer science fits in with the principles of applied psychology Artificial intelligence (AI) is interfering in our professional as well as our private lives. It both seduces and worries us. On a global scale, it is at the heart of major strategic, societal or economic issues, still being debated in mid-February 2025, at the AI World Summit in Paris. But how can we, as users, avoid being subjected to it? How can we gain access to the necessary transparency of its workings? By placing his research prism on the user's side, Bruno Dumas is something of a "computer psychologist". An expert in human-computer interaction, co-president of the NaDI Institute (Namur Digital Institute), he defends the idea of a reasoned and enlightened use of emerging technologies.

A lifelong video game enthusiast, Fanny Barnabé, a researcher at the CRIDS research center (Namur Digital Institute) and lecturer at the University of Namur, explores behind the scenes of a major cultural phenomenon: video game streaming on Twitch. Between humor, irony and toxic discourse, she deciphers the issues at stake in a digital space in the throes of change.

Abstract Deep learning has become an extremely important technology in numerous domains such as computer vision, natural language processing, and autonomous systems. As neural networks grow in size and complexity to meet the demands of these applications, the cost of designing and training efficient models continues to rise in computation and energy consumption. Neural Architecture Search (NAS) has emerged as a promising solution to automate the design of performant neural networks. However, conventional NAS methods often require evaluating thousands of architectures, making them extremely resource-intensive and environmentally costly.This thesis introduces a novel, energy-aware NAS pipeline that operates at the intersection of Software Engineering and Machine Learning. We present CNNGen, a domain-specific generator for convolutional architectures, combined with performance and energy predictors to drastically reduce the number of architectures that need full training. These predictors are integrated into a multi-objective genetic algorithm (NSGA-II), enabling an efficient search for architectures that balance accuracy and energy consumption.Our approach explores a variety of prediction strategies, including sequence-based models, image-based representations, and deep metric learning, to estimate model quality from partial or symbolic representations. We validate our framework across three benchmark datasets, CIFAR-10, CIFAR-100, and Fashion-MNIST, demonstrating that it can produce results comparable to state-of-the-art architectures with significantly lower computational cost. By reducing the environmental footprint of NAS while maintaining high performance, this work contributes to the growing field of Green AI and highlights the value of predictive modelling in scalable and sustainable deep learning workflows. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Gilles Perrouin - University of Namur, BelgiumProf. Benoit Frénay - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Clément Quinton - University of Lille, FranceProf. Paul Temple- University of Rennes, FranceProf. Schin'ichi Satoh - National Institute of Informatics, Japan

Research seminar co-organized by Louis Carré and Sabina Tortorella as part of the activities of the Esphin Institute, the Department of Philosophy, and the Arcadie Center as well as the Marie Skłodowska-Curie SOCIAL project This seminar sets out to explore contemporary uses of Hegel's practical thought as well as to question its relevance and legacy. Themes central to recent philosophical debates, such as globalization, race, feminism and the Anthropocene, as well as contemporary challenges facing philosophical reflection - such as social justice and ecological transition, state sovereignty in the face of international markets and the emergence of supranational subjects, or the crisis of democracy in the face of the rise of populism and the return of war - may call for a mobilization of Hegelian thought. The aim of this seminar is not necessarily to propose a strictly historical-philosophical reading of Hegelian thought, but rather to seek to take Hegel beyond Hegel himself, by engaging in a reflection on problematics that find their first formulation in him, but that have developed far beyond his conceptual framework, or by questioning his concepts from perspectives that do not necessarily lay claim to Hegelianism. The aim of this approach is to bring Hegel's thought into dialogue with other philosophical traditions and currents of political philosophy, in order to question its ability to shed light on some of the major issues of our time. By questioning its topicality and limitations, this seminar aims to examine what practical Hegelian philosophy can still offer us today, and how it enables us to question our own problems from a renewed angle. While it is unlikely to provide ready-made solutions, it can perhaps help us to ask the right questions and think differently about the tensions of our time.Chiara Magni (Università degli Studi Roma Tre) will speak on the theme: "What rights for the accused and the convicted? Criminal prosecution and human dignity in the light of Hegel's practical philosophy"Link to attend the online seminar Contact: sabina.tortorella@unamur.beThis project has received funding from the European Union's Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101150961.

Abstract Deep learning has become an extremely important technology in numerous domains such as computer vision, natural language processing, and autonomous systems. As neural networks grow in size and complexity to meet the demands of these applications, the cost of designing and training efficient models continues to rise in computation and energy consumption. Neural Architecture Search (NAS) has emerged as a promising solution to automate the design of performant neural networks. However, conventional NAS methods often require evaluating thousands of architectures, making them extremely resource-intensive and environmentally costly.This thesis introduces a novel, energy-aware NAS pipeline that operates at the intersection of Software Engineering and Machine Learning. We present CNNGen, a domain-specific generator for convolutional architectures, combined with performance and energy predictors to drastically reduce the number of architectures that need full training. These predictors are integrated into a multi-objective genetic algorithm (NSGA-II), enabling an efficient search for architectures that balance accuracy and energy consumption.Our approach explores a variety of prediction strategies, including sequence-based models, image-based representations, and deep metric learning, to estimate model quality from partial or symbolic representations. We validate our framework across three benchmark datasets, CIFAR-10, CIFAR-100, and Fashion-MNIST, demonstrating that it can produce results comparable to state-of-the-art architectures with significantly lower computational cost. By reducing the environmental footprint of NAS while maintaining high performance, this work contributes to the growing field of Green AI and highlights the value of predictive modelling in scalable and sustainable deep learning workflows. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Gilles Perrouin - University of Namur, BelgiumProf. Benoit Frénay - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Clément Quinton - University of Lille, FranceProf. Paul Temple- University of Rennes, FranceProf. Schin'ichi Satoh - National Institute of Informatics, Japan

How can teleworking and face-to-face work be reconciled? How can these professional practices be framed to reinforce the innovative and sustainable dimensions of hybrid work? These are the questions that Michel Ajzen, a specialist in organizational management, is tackling as part of his teaching assignments in the Department of Management Sciences at UNamur. His research focuses on hybrid work and organizational innovation, with a transdisciplinary approach aimed at reinventing managerial practices to meet contemporary challenges.