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The morning will be devoted to presenting the detailed results of the study and putting them into perspective in the broader context of current economic, social, and environmental issues.The afternoon will give the floor to stakeholders in the field through testimonials and round tables, illustrating how the tourism sector is adapting to the challenges posed by climate change. Registration and detailed program here: Walloon tourism in the face of climate change: between risks and opportunities (January 26, 2026): Home · UNamur Event (Indico) 

Program: 5:00 p.m.: Welcome & presentation of the film5:15 p.m.: Screening of the film I, Daniel Blake6:45 p.m.: Discussion: "Offline, out of the game? Let's fight the digital divide"7:15 p.m.: EndFollowing the screening, three experts from the Namur Digital Institute (NADI), Simon Dechamps (MINDIT Research Center), Alix Gobert (CRIDS Research Center), and Floriane Goose (CeRCLe Research Center) will discuss the following questions: What is the digital divide? How does it conflict with the digitization of government agencies? How can we take users into account? Is digital inclusion a solution?  Register

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.

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.

The program Interventions by experts and researchers in the field of data science, , AI, digital twins, digital law and participatory processes.Registrations on the Vivre la Ville... website.

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

The workshop will feature:A keynote presentation on public value and AI implementation at VRT.Sessions on discoverability, user agency, and explainability.Discussions on regulation, including perspectives on the AI Act and transparency in media.An interactive session showingcasing AI-driven prototypes.The event will also highlight our project's latest findings. Join us for a day of thought-provoking discussions, knowledge exchange, and networking opportunities!Would you like to attend? Places are limited and will be allocated on a first-come, first-served basis, so register as soon as possible. Registration will close on April 11, 2025. More information here

Researchers, graduate students, artists, designers, teachers, and creatives from all walks of life: this is a unique opportunity to take your ideas beyond the usual framework and bring them to life in a new way. The goal of this first edition: to collaboratively design an educational treasure hunt in Namur, centered on the theme of misinformation, aimed at young people aged 15 to 18.Practical detailsWhen? June 1, 2, and/or 3, 2026 (9 a.m.–4 p.m.), flexible participation (a few hours, half a day, a full day, or all three days)Where? The TRAKK in NamurLunch provided by NADI + unlimited coffee/tea in a collaborativeatmosphere Everything is set up for experimenting, testing, and creatingRegistration: free, but please send an email to digifactory.asbl@gmail.comA chance to bridge disciplines, break out of silos…and invent new ways to tell stories, convey information, and share.Note: For doctoral students, participation counts toward your doctoral program (course credits).

Abstract Predictive maintenance (PdM) is increasingly being used to improve system reliability, reduce downtime, and optimize operating costs in complex industrial environments through anomaly detection. As industrial systems become more complex, with components interacting with one another and monitored by ever-expanding sensor networks, anomaly detection faces new methodological challenges. In particular, there is a gap in the literature regarding anomaly detection using traditional machine learning (ML) methods based on a Single Label Classification (SLC) approach, which is unsuitable when multiple failures can occur simultaneously.Traditional anomaly detection approaches based on ML methods using an SLC formulation assume that each observation belongs to a single anomaly category. This assumption does not hold when multiple failures occur simultaneously on the same equipment, resulting in a loss of information. We begin by illustrating this limitation through a concrete example showing that using an SLC approach instead of a multi-label formulation degrades the classification of simultaneous anomalies. To address this issue, anomaly detection is reformulated as a Multi-Label Classification (MLC) problem, enabling the detection of multiple failures within a single time step. Following this reformulation, the issue of selecting multi-label classifiers suited to the context of Predictive Maintenance (PdM) must be examined. The general conclusion from the literature is that the performance of classifiers depends heavily on the context and that while there are many detection methods, none is universally dominant. Although in-depth comparative studies exist in the field of MLC, datasets from complex systems are absent from these comparisons, even though they exhibit specific characteristics such as multivariate time-series structures, significant label imbalance, and complex interactions between components.To address this gap, a structured and reproducible evaluation protocol is proposed to evaluate eight state-of-the-art ML methods in an MLC framework across three public industrial datasets. The results confirm that the performance of classifiers depends heavily on the characteristics of the dataset and that no single method consistently outperforms others across all scenarios. However, conducting this type of comparison for each new industrial context is costly in terms of time, computational resources, and data requirements, which limits its feasibility for real-world industrial deployment. The thesis also addresses this issue through dimensionality reduction and variable selection methods. PM systems generate large multivariate datasets in which only a subset of the variables is actually relevant for anomaly detection. A self-adaptive evolutionary strategy is proposed to perform wrapper-style variable selection and obtain a subset containing only the most informative variables. Experimental results show that reducing the variable space improves computational efficiency and, in most cases, anomaly prediction performance. A comparison of the proposed method with state-of-the-art metaheuristic approaches on three PdM datasets and five anomaly detection methods confirms its competitiveness in terms of predictive performance and the optimal size of the selected feature subset. Finally, feature space reduction not only optimizes the resources required for detection but also reduces the number of signals that technicians must analyze during root cause investigations. This simplification aims to strengthen confidence in the system. Finally, the thesis addresses the practical aspects of deploying detection methods in complex systems. Among these, there is a lack of trust and transparency among maintenance technicians and decision-makers. An analysis of the interpretability and robustness of anomaly detection methods is provided to directly contribute to their deployment in real-world conditions. First, the proposed variable selection strategy is evaluated on a public PdM dataset using SHAP-based explanations to verify the consistency between the selected variables and those identified as the most important by tools dedicated to interpretability. The results show that the selected variables are consistent with those identified as the most important by these tools. Furthermore, visual analysis of the most influential variables reveals that anomalies are associated with a limited and well-defined subset of sensor signals, which helps improve transparency and strengthen confidence in automatic detection systems. At the same time, it is necessary to conduct a study of the robustness of multi-label classifiers in controlled sensor degradation scenarios, including different types of degradation and various levels of severity. The results show that the performance of classifiers can deteriorate significantly, particularly in the presence of gradual drifts. To mitigate this effect, introducing sensor degradation during training appears to be a relevant strategy. Finally, this thesis proposes a structured framework for multi-label anomaly detection in complex systems, covering classification methods, feature space optimization, interpretability, and robustness. For each of these aspects, the thesis adopts an industrial perspective and takes into account the differences between controlled laboratory experimental conditions and the additional constraints of real-world complex environments. The jury Prof. Katrien Beuls - University of Namur, BelgiumProf. Elio Tuci - University of Namur, BelgiumProf. Patrick Heymans - University of Namur, BelgiumProf. Jenni Raitoharju - University of Jyväskylä, FinlandMr. Fabio Pinna - Telespazio, BelgiumMr. Vito Trianni - ISTC-CNR, Italy Sign me up

AbstractThe increasing regulatory pressure on Cyber-Physical Systems (CPS), particularly in Europe, has made compliance a critical yet complex challenge for industry stakeholders. Many of these CPS are often long-lived systems with legacy code and limited (or no) access to representative documentation. This poses a problem when these systems must comply with newer regulatory frameworks. Indeed, current conformity assessment practices rely predominantly on documentation review and operational observations, while the analysis of software artifacts remains underutilized. This situation is compounded by a persistent communication gap between legal experts and engineers, and a lack of systematic traceability between high-level regulatory obligations and low-level technical implementations. Such a disconnect frequently leads to inconsistencies detected late in the development process, increasing correction costs and delaying time-to-market. Furthermore, the growing complexity of regulations, such as the Medical Device Regulation (MDR), contributes to the perception that compliance hinders innovation.Transitioning from theoretical frameworks to industrial implementation presents significant challenges. During this research, efforts to validate the approach in real-world production environments were hampered by restricted access to sensitive data and architectures, as well as the inherent risks of intrusive analysis in highly interconnected systems. Consequently, this thesis emphasizes a conceptual framework validated through modular prototypes and isolated CPS categories (e.g., medical devices), demonstrating the effectiveness of the proposed methods where full-scale production deployment is not yet feasible.This thesis addresses these challenges by investigating how regulatory requirements can be transformed into structured, traceable, and partially automatable elements directly linked to software artifacts. The research adopts an industry-grounded exploratory approach structured around three main axes: (1) the formalization of regulatory requirements to bridge the semantic gap between legal and technical domains; (2) the extraction of system functionalities from source code using Large Language Models; and (3) a unified framework, supported by tool prototypes, to bridge expected system behavior and actual implementation through evidence-based assessment.By combining regulatory analysis, software engineering, and AI-based methods, this work contributes to redefining compliance as a continuous, traceability-driven process. Ultimately, it aims to support a paradigm shift toward “compliant-by-design” systems, enabling earlier detection of discrepancies and better alignment between regulatory intent and technical implementation in complex CPS environments.JuryPhD, P.Eng. Jun YAN, Concordia University, CanadaProf., Ph.D., Fuyuki ISHIKAWA, National Institute of Informatics, JapanPh.D., Paolo ARCAINI, National Institute of Informatics, JapanProf., Ph.D., Benoît VANDEROSE, University of Namur, BelgiumProf., PhD, Anthony CLEVE, University of Namur, BelgiumProf., PhD, Xavier DEVROEY, University of Namur, Belgium

What happens when AI knows a great deal about you? This fifth session of the “What If AI?” series explores the profiling and data collection mechanisms that enable artificial intelligence systems to predict behaviors, preferences, and decisions.Drawing on well-known examples such as the Cambridge Analytica scandal, the speakers will highlight the issues of surveillance, manipulation, and personalization. This session aims to encourage critical reflection on our tolerance for profiling and to provide tools for better protecting our privacy in an increasingly data-driven world. Je m'inscris