Résultats de recherche

Abstract Detecting semantic code clones in logic programs is a longstanding challenge, due to the lack of a unified definition of semantic similarity and the diversity of syntactic expressions that can represent similar behaviours. This thesis introduces a formal and flexible framework for semantic clone detection based on Constrained Horn Clauses (CHC). The approach considers two predicates as semantic clones if they can be independently transformed, via semantics-preserving program transformations, into a common third predicate. At the core of the method lies anti-unification, a process that computes the most specific generalisation of two predicates by identifying their shared structural patterns. The framework is parametric in regard with the allowed program transformations, the notion of generality, and the so-called quality estimators that steer the anti-unification process. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Katrien Beuls - University of Namur, BelgiumProf. Jean-Marie Jacquet - University of Namur, BelgiumProf. Temur Kutsia - Johannes Kepler University, AustriaProf. Frédéric Mesnard - University of the Reunion, Reunion IslandProf. Paul Van Eecke - Free University of Brussels, Belgium

Abstract In recent decades, the volume of data generated worldwide has grown exponentially, significantly accelerating advancements in machine learning. This explosion of data has led to an increased need for effective data exploration techniques, giving rise to a specialized field known as dimensionality reduction. Dimensionality reduction methods are used to transform high-dimensional data into a low-dimensional space (typically 2D or 3D), so that it can be easily visualized and understood by humans. Algorithms such as Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and t-distributed Stochastic Neighbor Embedding (t-SNE) have become essential tools for visualizing complex datasets. These techniques play a critical role in exploratory data analysis and in interpreting complex models like Convolutional Neural Networks (CNNs). Despite their widespread adoption, dimensionality reduction techniques, particularly non-linear ones, often lack interpretability. This opacity makes it difficult for users to understand the meaning of the visualizations or the rationale behind specific low-dimensional representations. In contrast, the field of supervised machine learning has seen significant progress in explainable AI (XAI), which aims to clarify model decisions, especially in high-stakes scenarios. While many post-hoc explanation tools have been developed to interpret the outputs of supervised models, there is still a notable gap in methods for explaining the results of dimensionality reduction techniques. This research investigates how post-hoc explanation techniques can be integrated into dimensionality reduction algorithms to improve user understanding of the resulting visualizations. Specifically, it explores how interpretability methods originally developed for supervised learning can be adapted to explain the behavior of non-linear dimensionality reduction algorithms. Additionally, this work examines whether the integration of post-hoc explanations can enhance the overall effectiveness of data exploration. As these tools are intended for end-users, we also design and evaluate an interactive system that incorporates explanatory mechanisms. We argue that combining interpretability with interactivity significantly improves users' understanding of embeddings produced by non-linear dimensionality reduction techniques. In this research, we propose enhancements to an existing post-hoc explanation method that adapts LIME for t-SNE. We introduce a globally-local framework for fast and scalable explanations of t-SNE embeddings. Furthermore, we present a completely new approach that adapts saliency map-based explanations to locally interpret non-linear dimensionality reduction results. Lastly, we introduce our interactive tool, Insight-SNE, which integrates our gradient-based explanation method and enables users to explore low-dimensional embeddings through direct interaction with the explanations. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Benoit Frénay - University of Namur, BelgiumProf. Bruno Dumas - University of Namur, BelgiumProf. John Lee - University of Louvain, BelgiumProf. Luis Galarraga - University of Rennes, France

Comment concilier télétravail et travail en présentiel ? Comment encadrer ces pratiques professionnelles pour renforcer les dimensions innovantes et durables du travail hybride ? C’est à toutes ces questions que Michel Ajzen, spécialiste en management des organisations, s’intéresse dans le cadre de ses missions d’enseignement au sein du département des sciences de gestion de l’UNamur. Ses recherches se concentrent sur le travail hybride et l'innovation organisationnelle, avec une approche transdisciplinaire visant à réinventer les pratiques managériales pour relever les défis contemporains. 

Floriane Goosse, doctorante à l’Université de Namur, au sein du centre de recherche NaDI-CeRCLe, a reçu le prestigieux prix « Best Research Paper Award » pour son papier de thèse mené en collaboration avec Wafa Hammedi, professeure au Département de gestion de l’UNamur, et Dominik Mahr, de l’Université de Maastricht. 

Notre société vit une révolution numérique qui a des conséquences sur son organisation, ses pratiques, et même ses valeurs. La plupart des secteurs de notre société sont concernés par cette révolution, que ce soit la santé, le gouvernement, et les services et l’économie collaborative. La résolution de ces défis nécessite une approche pluridisciplinaire permettant le dialogue entre experts technologiques, scientifiques, mais aussi sociétaux, éthiques, juridiques et économiques. Le centre de recherche NaDI a pour mission de fédérer tous les chercheurs de l'UNamur travaillant sur les défis suivants dans 7 domaines de recherche.

S'appuyant sur une tradition de recherche en informatique à l'Université de Namur, NaDI fédère six centres de recherche axés sur différents aspects de la société numérique.

Contacts Co-President Bruno Dumas bruno.dumas@unamur.be Co-President Alexandre de Streel alexandre.destreel@unamur.be Organisation Découvrir les membres