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The NaDI-CeRCLe research center has distinguished itself brilliantly on the international scene in recent weeks. Three young researchers from the EMCP Faculty have received prestigious recognition at leading international events for their research in service management: they are Floriane Goosse, Victor Sluÿters and Florence Nizette. This summer, let's discover the work of these PhD students and their significant contributions to the advancement of knowledge and practice in this field.

Law students at UNamur, Marilyn Emmerechts and Delphine Blomme are part of the MUN Society Belgium (MSB), an organization that offers debate simulations in UN committees. In March, their team travelled to Manila, Philippines, to compete in the World Student Diplomacy Championships, organized by Harvard University. For five days, they debated alongside over 1,000 students. Interview.

Instantly obtain a translation in sign language (LSFB) of a word written in French: that's what MOSI (Du mot au signe) makes possible. This new tool is the fruit of a collaboration between the University of Namur, the asbl École et Surdité and the asbl LSFB, supported by the King Baudouin Foundation.

In a fast-changing hospital sector, with ever-increasing demands for performance and innovation, project management plays a key role. Kevin Lejeune, Program Manager at CHU UCL Namur, is tackling these challenges as part of a management thesis at the University of Namur, within the EMCP Faculty (Economics, Management, Communication and SciencesPo), under the supervision of Professor Corentin Burnay. His ambition: to understand and structure the human and technological dynamics shaping hospital governance, and propose concrete levers to support its transformation.

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.

For the past ten years, Professor Guilhem Cassan has been working on the question of the link between the media and political life in collaboration with Professor Julia Cagé, who has just been awarded the highly prestigious Yrjö Jahnsson Prize, which recognizes the best European economist under the age of 45. The UNamur Department of Economics (EMCP Faculty) and the DeFiPP Institute (CRED Centre) have a network and recognized international expertise in development economics and environmental economics.

Synopsis The spreading of internet-based technologies since the mid-90s has led to a paradigm shift from monolithic centralized information systems to distributed information systems based upon the composition of software components, interacting with each other and of heterogeneous natures. The popularity of these systems is nowadays such that our everyday life is touched by them.Classically concurrent and distributed systems are coded by using the message passing paradigm-according to which components exchange information by sending and receiving messages. In the aim of clearly separating computational and interactional aspects of computations, Gelernter and Carriero have proposed an alternative framework in which components interact through the availability of information placed on a shared space. Their framework has been concretized in a language called Linda. A series of languages, referred to nowadays as coordination languages, have been developed afterwards. In addition to providing a more declarative framework, such languages nicely fit applications like Facebook, LinkedIn and Twitter, in which users share information by adding it or consulting it in a common place. Such systems are in fact particular cases of so-called socio-technical systems in which humans interact with machines and their environments through complex dependencies. As coordination languages nicely meet social networks, the question naturally arises whether they can also nicely code socio-technical systems. However, answering this question first requires to see how well programs written in coordination languages can reflect what they are assumed to model.This thesis aims at addressing these two questions. To that end, we shall use the Bach coordination language developed at the University of Namur as a representative of Linda-like languages. We shall extend it in a language named Multi-Bach to be able to code and reason on socio-technical systems. We will also introduce a workbench Anemone to support the modelling of such systems. Finally, we will evidence the interest of our approach through the coding of several social-technical systems. The Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Jean-Marie Jacquet - University of Namur, BelgiumProf. Katrien Beuls - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Laura Bocchi - University of Kent, United KingdomProf. Stefano Mariani - UNIMORE University, Italy Participation upon registration. Register here

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

Synopsis deep learning methods have become increasingly popular for building intelligent systems. Currently, many deep learning architectures constitute the state of the art in their respective domains, such as image recognition, text generation, speech recognition, etc. The availability of mature libraries and frameworks to develop such systems is also a key factor in this success.This work explores the use of these architectures to build intelligent systems for sign languages. The creation of large sign language data corpora has made it possible to train deep learning architectures from scratch. The contributions presented in this work cover all aspects of the development of an intelligent system based on deep learning. A first contribution is the creation of a database for the Langue des Signes de Belgique Francophone (LSFB). This is derived from an existing corpus and has been adapted to the needs of deep learning methods. The possibility of using crowdsourcing methods to collect more data is also explored.The second contribution is the development or adaptation of architectures for automatic sign language recognition. The use of contrastive methods to learn better representations is explored, and the transferability of these representations to other sign languages is assessed.Finally, the last contribution is the integration of models into software for the general public. This led to a reflection on the challenges of integrating an intelligent module into the software development life cycle.Jury membersProf. Wim VANHOOF, President, University of NamurProf. Benoît FRENAY, Promoter, University of NamurProf. Anthony CLEVE, Co-promoter, University of NamurProf. Laurence MEURANT, Internal Member, University of NamurProf. Lorenzo BARALDI, External Member, University of ModenaProf. Annelies BRAFFORT, External Member, University of Paris-SaclayProf. Joni DAMBRE, External Member, University of GhentYou are cordially invited to a drink, which will follow the public defense. For a good organization, please give your answer by Friday June 6. I want to register

Essay topic Essays on the macroeconomic effects of cross-country and time heterogeneity in a monetary union Composition of the Jury PromotersProfessor Jean-Yves Gnabo, University of NamurProfessor Hamza Bennani (University of Nantes), Co-promoterOther Jury membersProfessor Sophie Béreau, Université de NamurProfessor Pauline Gandré, Université Paris NanterreProfessor Yuliya Rychalovska, Université de NamurPresident of the JuryProfessor Oscar Bernal, Université de Namur

Measuring poverty and well-being, to better understand development inequalities between countries and better assess development policies. This is the theme on which Benoit Decerf, assistant professor in the Department of Economics and researcher at UNamur's Development Economics Research Center, is working. He has been involved in improving the poverty indicators used by the World Bank.

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 The public defense will be followed by a reception.Registration required. I want to register