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Gabriel Dias De Carvalho Junior adds his expertise in didactics and cognitive psychology to the physics didactics research unit of UNamur's Department of Physics. With almost two decades of teaching experience in Brazil, he works to improve teaching practices. His innovative projects aim to make teaching more inclusive and accessible, while establishing international collaborations.

The F.R.S.-FNRS has just published the results of its various 2024 calls. Equipment calls, research credits and projects, FRIA doctoral grants and Mandant d'Impulsion Scientifique (MIS), there are many instruments to support fundamental research. Find out more about UNamur's results.

The impact of NaDI-CeRCLe, our third pillar, is multidimensional. Indeed, we aim to make an impact through research and education, influencing both community and society.

NaDI-CeRCLe, as an internationally recognized center, collaborates with renowned universities in Belgium and abroad, as well as with practitioners from various industries and public and non-profit organizations. These collaborations foster a rich exchange of ideas and advance innovative research in the fields of marketing and service management.

At the NaDI-CeRCLe research laboratory, we are committed to producing high-quality, high-impact research. Our members regularly publish in internationally renowned scientific journals, subject to a rigorous peer-review process. Our work actively contributes to the understanding and resolution of current issues through theoretically, managerially and socially relevant research.

Can a computer learn a language like a child? A recent study published in the leading journal Computational Linguistics by Professors Katrien Beuls (Université de Namur) and Paul Van Eecke (AI-lab, Vrije Universiteit Brussel) sheds new light on this question. The researchers argue for a fundamental revision of the way artificial intelligence acquires and processes language.

Katrien Beuls is undoubtedly a fine example of the growing number of women in STEM (science, technology, engineering, and mathematics) careers. After a rather literary career, guided by her curiosity, she began studying computer science and became interested in computational methods for processing human language with the help of Artificial Intelligence (AI).

The annual conference of Trail, the structure that brings together all artificial intelligence researchers in the Wallonia-Brussels Federation, and entitled "Inclusion, Parcimony and Plurality: the Future of AI?", was held at UNamur on May 14. 150 participants came to listen to a particularly rich and varied program.

Increasingly sophisticated technologies are invading our spheres of activity without our prior consultation as citizens. Shouldn't the new digital tools, artificial intelligence or technologies resulting from progress in neuroscience, which are transforming our identity and social relationships, be the subject of broad and sufficiently informed democratic debates? This question is at the heart of the international "research-action" project "A new humanism in the age of neuroscience and artificial intelligence" in which UNamur is participating.

TEF-Health: Testing and Experimentation Facilities for Health AI and Robotics is a major European project aimed at the rapid adoption of solutions based on artificial intelligence and robotics in the medical sector. UNamur, with the expertise of its Centre de Recherche Information Droit et Société (CRIDS), is a partner in this project.

The program 2:00 pm | Keynote lecture on the use of AI in research - Hugues BERSINI, Professor at the Université libre de Bruxelles: "Can science be just data driven?" 3:00 pm | Presentations by UNamur researchers3:00 pm | Catherine Guirkinger: Use of AI in an economic history project3:15 pm | Nicolas Roy (PI: Alexandre Mayer): AI at the service of innovation in photonics and optics: revealing the secrets of scrolls through the classification of animal species15:25 | Nemanja Antonic (PI: Elio Tuci): An in silico representation of C. elegans collective behaviour<15h35 | Nicolas Franco : The benefits and dangers of "predicting the future" with covid-like machine learning models 15h45 | Michel Ajzen : Managerial and human implications of AI in organizations <15h55 | Robin Ghyselinck (PI : Bruno Dumas) : Deep Learning for endoscopy: towards next generation computer-aided diagnosis4:05 pm | Auguste Debroise (PI : Guilhem Cassan) : LLMs to measure the importance of stereotypes within gender representations in Hollywood films16h15 | Gabriel Dias De Carvalho : Learning practices in physics using generative AI16h25 | Sébastien Dujardin (PI : Catherine Linard) : Where Geography meets AI: A case study on mapping online flood conversations16h35 | Jeremy Dodeigne : LLMs in SHS: revolutionary tools in a Wild West Territory? Reflections on costs, transparency and open science16h45 | Antoinette Rouvroy : Governing AI in Democracy17h00 | Keynote lecture on ethics and guidelines to consider when using AI in research projects and writing research articles - Bettina BERENDT, Professor at KU Leuven18h00 | Benoît Frenay and Michaël Lobet : Creation of an IA scientific committee at UNamur18:10 | DrinkA certificate of attendance, worth 0.5 cross-disciplinary doctoral training credits, will be issued on request. Contact: secretariat.adre@unamur.beThis event is free of charge, but registration is required. I want to register

SynopsisA honeypot is a security tool deliberately designed to be vulnerable, thereby enticing attackers to probe, exploit, and compromise it. Since their introduction in the early 1990s, honeypots have remained among the most widely used tools for capturing cyberattacks, complementing traditional defenses such as firewalls and intrusion detection systems. They serve both as early warning systems and as sources of valuable attack data, enabling security professionals to study the techniques and behaviors of threat actors.While conventional honeypots have achieved significant success, they remain deterministic in their responses to attacks. This is where adaptive or intelligent honeypots come into play. An adaptive honeypot leverages Machine Learning techniques, such as Reinforcement Learning, to interact with attackers. These systems learn to take actions that can disrupt the normal execution flow of an attack, potentially forcing attackers to alter their techniques. As a result, attackers must find alternative routes or tools to achieve their objectives, ultimately leading to the collection of more attack data.Despite their advantages, traditional honeypots face two main challenges. First, emulation-based honeypots (also known as low- and medium-interaction honeypots) are increasingly susceptible to detection, which undermines their effectiveness in collecting meaningful attack data. Second, real-system-based honeypots (also known as high-interaction honeypots) pose security risks to the hosting organization if not properly isolated and protected. Since adaptive honeypots rely on the same underlying systems, they also inherit these challenges.This thesis investigates whether it is possible to design a honeypot system that mitigates these challenges while still fulfilling its primary objective of collecting attack data. To this end, it proposes a new abstract model for adaptive self-guarded honeypots, designed to balance attack data collection, detection evasion, and security preservation, ensuring that it does not pose a risk to the rest of the network.Jury membersProf. Wim VANHOOF, President, University of NamurProf. Jean-Noël COLIN, Promoter, University of NamurProf. Florentin ROCHET, Internal Member, University of NamurProf. Benoît FRENAY, Internal Member, University of NamurProf. Ramin SADRE, External Member, Catholic University of LeuvenDr. Jérôme FRANCOIS, External Member, University of LuxembourgYou are cordially invited to a drink, which will follow the public defense. For good organization, please give your answer by Tuesday, May 20, 2025. I want to register