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English versionIn the context of an international Chaire Francqui 2024-2025, the Faculty of Computer Science (UNamur) has the honor to receive Professor Arie Van Deursen (TU Delft, Netherlands ) from the 24th to the 27st of March, for a series of lectures on the theme of: “ Explainable Software Engineering ”.The inaugural lecture, entitled “Explainable Software Engineering in the Public Sector”, will be held on the 25th of March 2025, at 6pm at PA02 (Sentier Thomas, 5000 – Namur).French versionAs part of an international Francqui Chair 2024-2025, the Faculty of Computer Science (UNamur) will have the pleasure of welcoming Professor Arie Van Deursen (TU Delft, Netherlands) from March 24 to 27, 2025, for a series of lessons on the theme: "Explainable Software Engineering".The inaugural lesson, entitled "Explainable Software Engineering in the Public Sector", will take place on Tuesday March 25, 2025 at 6:00 pm at PA02 (Sentier Thomas, 5000 - Namur). Explainable Software Engineering in the Public Sector The field of software engineering seeks to devise theories, methods, tools, and techniques that support the development, operation, and evolution of the digital infrastructure modern society relies on. While the software engineering capabilities have advanced substantially over the past decades, it remains challenging to deliver high quality systems in a timely and cost-effective manner. Government system in particular have a weak reputation in this respect.To better understand why, we analyze 125 complex software projects in the public sector in The Netherlands. The projects are described in public reports published by the Advisory Council on IT Assessments (AcICT), which advises the Dutch parliament and cabinet on riks and chances of success in complex Information Technology (IT) projects. The projects span a time period of 10 years, represent a total budget of over 14 billion Euros, and cover such areas as tax collection, social security, pensions, health, traffic control, defense, or water management.We study these reports through the lens of "explainability", focusing on supporting decision making. Furthermore, we reflect on current advances in software engineering, including modern software testing and large language models, in addressing current software engineering challenges. Program INAUGURAL LECTURE: Explainable Software Engineering in the Public Sector - Tuesday 25/03 - 18:00-19:00 - PA02 SESSION 1: Architectural decision making in software engineering - Monday 24/03 - 10:30-12:30 - I33SESSION 2 : Tests as executable explanations - Tuesday 25/03 - 14:00-16:00 - I33SESSION 3: Technical debt, test smells, legacy systems - Wednesday 26/03 - 14:00-16:00 - I33SESSION 4: Artificial Intelligence for Software Engineering - Thursday 27/03 - 10:30-12:30 - I33 About the speaker Arie van Deursen is a professor at Delft University of Technology, where he leads the Software Engineering Research Group. His research interests include software testing, language models for code, trustworthy artificial intelligence, and human aspects of software engineering. He presently serves as chair of the Steering Commmittee of the ACM/IEEE International Conference on Software Engineering (ICSE).He is a member of the Dutch Advisory Council on IT Assessments (AcICT), as well as a member of the Advisory Board of ING Bank The Netherlands. In 2023, he was elected fellow of the Netherlands Academy of Engineering (NAE). Read more Register for the event

In late November 2024, UNamur hosted the 23rd BENEVOL Congress, an annual research seminar that provides an opportunity for international researchers to meet and discuss new ideas, important issues and cutting-edge research in the field of software engineering, maintenance and evolution.

Detecting a failure before it occurs: that is the goal of the research being conducted by Antoine Hubermont, a doctoral student at UNamur. This project, named Monsater, is funded by SPW Research as part of the Win4Doc program in collaboration with the space company Telespazio Belgium. It addresses a key strategic challenge: ensuring the reliability of complex systems, particularly in the space sector. 

A 24-hour live stream, about ten computers and video game consoles, around fifteen participants… and one goal: to raise as much money as possible for Télévie. Building on the success of its first event, the UNamur Computer Club embarked on the adventure once again with a new charity marathon, which took place from April 7 to 8. In total, the event raised €1,831.91 for Télévie.

The past decade has seen the release of numerous mixed reality headsets. Some are aimed at casual recreational use (for example, the Meta Quest 3), while others are marketed as next-generation computing platforms (for example, the Apple Vision Pro). As these devices become integrated into our daily lives, they are redefining the way we—as human beings—interact with them.Their nature differs significantly from traditional computing devices (e.g., computers or smartphones), introducing multiple paradigm shifts driven by several factors. On the one hand, they integrate and democratize various sensors that enable the use of gaze, hand gestures, and speech as means of interaction, thereby serving as effective vectors for the adoption of multimodal user interfaces. On the other hand, their portable nature implies continuous contextual changes that fundamentally alter interface design and redefine human-computer interaction as their use becomes ubiquitous.To better understand such systems, this research is divided into three areas. First, we immerse users in a deliberately constructed pervasive environment to explore their perceptions while examining their attitudes toward managing intrusive content through manual interventions, thereby highlighting needs emerging from such contexts. Second, we explore new communication channels by leveraging metaphors and designing interaction techniques that use multiple modalities to enable more natural communication, thereby addressing the needs arising from pervasive use. Finally, after designing multimodal interaction techniques, we examine the technical requirements and review existing tools that support the development of multimodal user interfaces, identify the limitations of some of these tools, and address them by introducing a new tool called Ummi.Through these three complementary perspectives, this thesis addresses six research questions and contributes to the fields of mixed reality and multimodal interaction.The juryProf. Vincent Englebert - University of Namur, BelgiumProf. Bruno Dumas - University of Namur, BelgiumProf. Xavier Devroey - University of Namur, BelgiumProf. Marcos Serrano - University of Toulouse, FranceProf. Denis Lalanne - University of Fribourg, SwitzerlandFree event; registration required. Sign me up

Abstract Since its emergence in 1996, the Asian H5 Goose/Guangdong (Gs/Gd) lineage has circulated widely in poultry in southern China, spilling over to wild birds by 2002. Wild bird infections facilitated global dissemination via migratory waterfowl and repeated spillback into poultry, challenging the view that HPAI primarily arises from LPAI mutation. Subclade 2.3.4.4b emerged in Asia in 2013, reached Europe in 2016, caused recurrent epizootics, diversified into multiple genotypes, became dominant in wild birds, and shows zoonotic potential.This thesis investigates critical knowledge gaps regarding H5Nx subclade 2.3.4.4b in poultry: (1) early within-flock spread after punctual introduction in chickens, particularly during the first European epizootics; (2) influence of pre-existing immunity on silent circulation; (3) limitations in diagnostic throughput during epizootic peaks; (4) potential of environmental surveillance, including air and dust sampling; and (5) impact on egg contamination and the reproductive tract, relevant for food safety and zoonotic risk.Four main objectives were addressed: (1) development of an experimental model simulating punctual introductions and spread, comparing 2017 and 2020 strains and assessing pre-existing immunity; (2) enhancement of diagnostic capacity via alternative sampling, semi-automated RNA extraction, and high-throughput processing; (3) evaluation of air and dust sampling for virus monitoring under experimental and field conditions; and (4) assessment of egg contamination risk. Alternative sampling and environmental monitoring were also applied to Newcastle disease virus as a comparative notifiable pathogen. Jury Prof. Tuci Elio - University of Namur, BelgiumProf. Anthony Cleve - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Alvaro Gutierrez - Universidad Politecnica de Madrid, EspagneMr. Fabian Duchesne - Qualitics SPRLProf. Anders Lyhne Christensen - SDU, Denmark Evènement public et gratuit - Inscription obligatoire Je m'inscris

Abstract Deep learning has revolutionized computer vision in recent years and has been applied to many fields. This thesis focuses on medical endoscopy, where deep learning can assist physicians in many tasks, such as navigating the lungs during bronchoscopy, assisting in the detection of lung diseases, detecting Crohn's disease from capsule endoscopy (PillCam), or automating the detection of polyps during colonoscopy procedures.This thesis, entitled From Pixels to Practice: Deep Learning for Endoscopy, explores how modern neural networks and learning paradigms can improve visual understanding in endoscopy, with the aim of contributing to computer-aided detection (CAD) systems that can be integrated into clinical workflows.This work follows an article-based structure and links methodological advances in geometric and temporal modeling to techniques for handling data scarcity and imbalance, as well as to the practical and clinical implications of deep learning for lung tumor detection, both from a clinical and practitioner perspective. The first part of the manuscript provides a common foundation for all subsequent parts. First, we present a general introduction to the field of machine learning in Chapter 1, explaining concepts such as classification, loss functions, and artificial neural networks. Next, Chapter 2 focuses on the field of deep learning for computer vision, detailing the main vision tasks, the concept of convolutional neural networks, ResNet, and U-Net. Finally, Chapter 3 describes medical imaging, with a focus on computed tomography (CT) scans and optical imaging. The second part of the thesis focuses on learning spatio-temporal representations. In Chapter 4, we use deep neural networks combining spatial features and temporal recurrence to address the problem of detecting the bronchial carina, an anatomical landmark that helps doctors navigate the lungs. By evaluating classification (ResNet-50), segmentation (nnU-Net), and recurrent (GRU) models on a bronchoscopy dataset we created, the study highlights the benefits of combining information from segmentation masks and temporal features. Chapter 5 continues the segmentation task by analyzing the extent to which rotation-equivariant U-Nets, based on E(2)-CNNs with C4, C8, and D4 symmetry groups, can improve performance when the orientation of objects in the image is arbitrary. Together, these chapters show how temporal and geometric modeling capture complementary aspects of visual structure. They further highlight that data imbalance and scarcity are recurring problems in deep learning. The third part studies learning in situations of data scarcity and imbalance. First, Chapter 6 explores supervised contrastive pre-training [1] on large, domain-close endoscopic datasets (Hyper-Kvasir [2], LDPolyp [3]), which is then transferred to smaller, disease-specific data (Crohn-IPI [4]). This methodology performs better than pre-training on ImageNet or based on cross-entropy, highlighting the value of domain-specific contrastive representations. Next, Chapter 7 introduces Mask-Aware Cropping (MAC), a new data augmentation technique that mitigates pixel-level imbalance in segmentation. On various datasets with varying imbalance regimes (URDE [5], Kvasir-SEG [6], HAM10000 [7]), MAC consistently improves Dice and IoU metrics under conditions of extreme imbalance. Together, these methods form a data-centric framework for effective learning when annotations are scarce or unevenly distributed. The fourth part of the thesis focuses on deep learning in the operating room. Chapter 8 proposes a first model (ResNet-50) for the visual detection of lung cancer in bronchoscopy, trained on real, in-vivo data. The model outperforms junior physicians, while remaining inferior to experts. This result shows that CAD systems for lung cancer detection are promising. Chapter 9 extends this work by evaluating the usability of a CAD system based on a deep learning model. Combining probability indices, temporal graphs, and saliency map overlays, a multicenter evaluation with 10 physicians is conducted. The tool received favorable feedback, with high usability (SUS score of 80.5 [8]) and strong clinical acceptance. Beyond endoscopy, the results concerning rotation equivariance and pixel imbalance can be generalized to other fields such as microscopy, dermatology, and aerial imaging. This shows that the proposed methods are applicable to visual learning under structured variability and limited data constraints.Keywords: machine learning, computer vision, medicine, endoscopy, convolutional neural networks, segmentation, recurrent models, equivariance.  Jury Prof. Bruno Dumas - University of NamurProf. Frénay Benoit - University of NamurProf. Schobbens P-Y. - University of NamurProf. Beuls Katrien - University of Namur,Dr. Benjamin Mertens - Lys MédicalProf. Oramas Mogrojevo José Antonio - University of AntwerpDr. Mancas Matei - University of Mons Register

JuryProf. Guillaume BERIONNI (UNamur), PresidentProf. Stéphane VINCENT (UNamur), SecretaryProf. Carmen GALAN (University of Bristol)Dr. Louis FENSTERBANK (Collège de France)Prof. Raphaël ROBIETTE (Université catholique de Louvain)AbstractCarboxylic acids are ubiquitous in nature and inexpensive compounds. Decarboxylation has become a key chemical transformation and has been widely reported in organic chemistry except for carbohydrates. This reaction can be catalyzed by transition metal and can also be induced by light, thermal activation, or photocatalysis. Borylated compounds have stimulated the pharmaceutical industry's interest (Boromycin, Bortezomib or boron neutron capture therapy). Recent methodologies have been developed to transform carboxylic acids to boronate esters by metal-catalyzed or light-promoted or photocatalyzed reactions. In this thesis, we explored the synthesis of borylated carbohydrates through a decarboxylation pathway. More specifically, sialic acids being among the most important carbohydrates in glycobiology, we addressed the problem of the synthesis of borylated sialic acids. On the other hand, organophosphates play an important role in diverse fields: in materials chemistry, in agriculture, in organic chemistry, and in biochemistry. Phosphorylation is a key reaction in biological processes such as signal transduction and cell activity regulation. The formation of phosphorylated carbohydrates has been widely described through two-electron mechanisms. However, radical phosphorylation of carbohydrates remains unexplored. This Ph.D. thesis describes the development of new methodologies for the decarboxylative functionalization of carbohydrates, focusing on borylation and phosphorylation..

Terminator to talk about AI? Wall-E to talk about technological dependence? The Truman Show to discuss social media? In a new book, two professors from UNamur, Anthony Simonofski (digital transformation—EMCP Faculty—NaDI Institute) and Benoît Vanderose (software engineering—Faculty of Computer Science—NaDI Institute), take readers on a journey at the crossroads of digital technology and cinematic imagination. 

A Hackathon is a short event where teams reflect on a particular theme. They attempt to find solutions by developing innovative projects. At the end of the event, a jury determines which projects have caught their attention and thus won the competition. A CSLabs initiative The Computer Science Labs is a junior enterprise that grew out of the Faculty of Computer Science at the University of Namur. In practical terms, its actions revolve around carrying out projects, training members on IT-related topics and organizing events. Read more

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.