Weakly Supervised Models for Computational Language Documentation
2023-12-19 14:00 - 17:00 (Europe/Paris) | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
Thesis commitee
Claire Gardent, senior researcher at LORIA (CNRS, Université de Lorraine) ;
Alexis Nasr, professor at LIS (CNRS, UTLN, Université Aix Marseille) ;
Roland Kuhn, Principal Research Officer at the National Research Council Canada ;
François Pellegrino, senior researcher at DDL – ISH (CNRS, Université de Lyon 2) ;
Agata Savary, professor at LISN (CNRS, Université Paris-Saclay) ;
Laurent Besacier, Principal Scientist at Naver Labs Europe and professor at Université Grenoble-Alpes ;
François Yvon, doctoral advisor, senior researcher at ISIR (CNRS, Sorbonne Université).
Keywords
Computational language documentation, word segmentation, Bayesian non-parametric model, interlinear gloss generation, weak supervision, field linguistics
Abstract
In the wake of the threat of extinction of half of the languages spoken today by the end of the century, language documentation is a field of linguistics notably dedicated to the recording, annotation, and archiving of data. In this context, computational language documentation aims to devise tools for linguists to ease several documentation steps through natural language processing approaches. As part of the CLD2025 computational language documentation project, this thesis focuses mainly on two tasks: word segmentation to identify word boundaries in an unsegmented transcription of a recorded sentence and automatic interlinear glossing to predict linguistic annotations for each sentence unit. For the first task, we improve the performance of the Bayesian non-parametric models used until now through weak supervision. For this purpose, we leverage realistically available resources during documentation, such as already-segmented sentences or dictionaries. Since we still observe an over-segmenting tendency in our models, we introduce a second segmentation level: morphemes. Our experiments with various types of two-level segmentation models indicate a slight improvement in the segmentation quality. Still, we also face the limitations in differentiating words from morphemes, using statistical cues only The second task concerns the generation of either grammatical or lexical glosses. As the latter cannot be predicted using training data solely, our statistical sequence-labelling model adapts the set of possible labels for each sentence and provides a competitive alternative to the most recent neural models.

Spotting expressivity bottlenecks in neural networks and fixing them by optimal architecture growth
2025-03-28 10:30 - 13:30 | LISN Site Plaine – Digitéo, 1 rue René THOM 91190 Gif-sur-Yvette, France
Abstract
We aim at adapting a neural network architecture during training, by detecting expressivity bottlenecks. This would allow to start from tiny architectures and make them grow as necessary, which would gain considerable training time and computational ressources compared to the standard approach where over-sized models are trained and then reduced. A promising mathematical formulation of the problem yields a way to directly precisely locate expressivity bottlenecks, at low computational cost, to the opposite of standard Auto-Deep-Learning techniques which proceed by trial and error via random architecture modifications (which is very costly).
Jury
Rémi Gribonval, Directeur de recherche Inria, ENS Lyon, rapporteur
Hervé Luga, Professeur Université Toulouse Jean Jaurès, rapporteur
David Picard, Directeur de recherche École des Ponts ParisTech
Aurélie Névéol, Directrice de recherche, CNRS, examinatrice
Guillaume Lecué, Professeur ESSEC, CY Cergy Paris Université, examinateur
Keywords
Neural networks,expressive power,low-rank approximation,optimization,

Mini-workshop on the sense of agency in collaborative contexts
2025-03-06 10:00 - 12:00 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
The sense of agency refers to the feeling of making things happen and is constitutive of our sense of selfhood. Social factors can lead to a fundamental change in the nature of the sense of agency, from self-agency to joint agency. The sense of agency is considered a key element when it comes to distinguishing our actions from those of others and, as such, it is a necessary condition to personal liability. If the sense of agency can indeed shift from an individual to a collective identity, what are the consequences for individual (and collective) responsibility? Furthermore, the notion of we-agency is also timely in light of current trends in digital technology, the result of which is a rapid change in the nature and scope of human-machine interaction. Indeed, recent technological developments allow users to interact with autonomous agents that can potentially become actual teammates. How we experience agency when we cooperate with artificial agents thus becomes critical.
Presentations and speakers
“Determinants of the sense of joint agency”Elisabeth Pacherie, Directrice de Recherche CNRS, Institut Jean Nicod, ENS Paris
"From Obedience to Resistance: Neuro-Cognitive and Social Mechanisms in Laboratory Settings and Post-Genocide Contexts"Emily Caspar, Associate Professor, Ghent University, Belgium
“From Human-Machine Cooperation to Human-Technology Symbiosis: Towards a Deeper Integration”Jordan Navarro, Professor Université Lumière Lyon 2, Laboratoire EMC - Étude des Mécanismes Cognitifs
Title to comeLionel Brunel, Professeur Université Paul-Valery Montpellier, Laboratoire Epsylon

Optimizing workflows in Machine Learning
2025-05-23 14:00 - 16:00 | LISN Site Plaine – Digitéo, 1 rue René THOM 91190 Gif-sur-Yvette, France
We're thrilled to welcome two visitors who will tell us about open source initiatives to better collaborate and share data science analysis tools:
Croissant: A metadata format for AI-ready datasets par Omar Benjelloun (https://mlcommons.org/working-groups/data/croissant/, Google)
Understanding and automating Machine Learning with OpenML par Joaquin Vanschoren (https://www.openml.org/, TU Eindhoven)

Sur les méthodes de contrôle par apprentissage des systèmes dynamiques: application en dynamique des fluides
2025-04-10 15:00 - 18:00 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
Jury
Ana BUŠIĆ, Chargée de recherche, Inria DYOGENE, ENS-PSL (Rapporteuse & Examinatrice)
Tristan CAZENAVE, Professeur, LAMSADE - CNRS - Université Paris Dauphine-PSL (Rapporteur & Examinateur)
Michèle SEBAG, Directrice de recherche, LISN - CNRS - Université Paris-Saclay, Inria TAU (Examinatrice)
Emmanuel RACHELSON, Professeur, ISAE-SUPAERO (Examinateur)
Laurent CORDIER, Directeur de recherche, Institut P' - CNRS - ISAE-ENSMA - Université de Poitiers (Examinateur)
Résumé
Les impératifs environnementaux suscitent un regain d'intérêt pour la recherche sur le contrôle de l'écoulement des fluides afin de réduire la consommation d'énergie dans diverses applications telles que l'aéronautique et l'automobile. Les stratégies de contrôle des fluides peuvent optimiser le système en temps réel, en tirant parti des mesures des capteurs et des modèles physiques. Ces stratégies visent à manipuler le comportement d'un système pour atteindre un état souhaité (stabilité, performance, consommation d'énergie). Dans le même temps, le développement d'approches de contrôle pilotées par les données dans des domaines concurrents tels que les jeux et la robotique a ouvert de nouvelles perspectives pour le contrôle des fluides. Cependant, l'intégration du contrôle basé sur l'apprentissage en dynamique des fluides présente de nombreux défis, notamment en ce qui concerne la robustesse de la stratégie de contrôle, l'efficacité de l'échantillon de l'algorithme d'apprentissage, et la présence de retards de toute nature dans le système.Ainsi, cette thèse vise à étudier et à développer des stratégies de contrôle basées sur l'apprentissage en tenant compte de ces défis, dans lesquels deux classes principales de stratégies de contrôle basées sur les données sont considérées : l'apprentissage par renforcement (RL) et la commande prédictive basée sur l'apprentissage (LB-MPC).Tout d'abord, un développement étendu sur la connexion entre les domaines du contrôle stochastique (temps continu) et du processus de décision de Markov (temps discret) est fourni pour unifier les deux approches.Deuxièmement, des preuves empiriques sur les propriétés de régularisation de l'algorithme d'apprentissage par renforcement par maximum d'entropie sont présentées à travers des concepts d'apprentissage statistique pour mieux comprendre la caractéristique de robustesse de l'approche par maximum d'entropie.Troisièmement, la notion d'abstraction temporelle est utilisée pour améliorer l'efficacité de l'échantillonnage d'un algorithme de commande prédictive par modèle basé sur l'apprentissage et piloté par une règle d'échantillonnage de la théorie de l'information.Enfin, les modèles différentiels neuronaux sont introduits à travers le concept d'équations différentielles neuronales à retard pour modéliser des systèmes à temps continu avec des retards pour des applications en commande prédictive.Les différentes études sont développées à l'aide de simulations numériques appliquées à des systèmes minimalistes issus des théories des systèmes dynamiques et du contrôle afin d'illustrer les résultats théoriques. Les expériences de la dernière partie sont également menées sur des simulations d'écoulement de fluides en 2D.
Publications
Les publications de Rémy Hosseinkhan-Boucher sont visibles sur HAL : https://universite-paris-saclay.hal.science/LISN/search/index?q=remy+hosseinkhan

Scheduling and graph-exploration with deadlines
2025-06-20 14:00 | LISN Site Plaine, 1 rue Raimond Castaing 91190 Gif-sur-Yvette, France
Consider a graph with weights on its edges and nodes. A node weight represents a (time) deadline for this node and an edge weight represents the time a robot needs to traverse this edge. A robot needs to visit each node of the graph within its deadline. Can we decide in polynomial time whether such a `successful’ exploration is possible?
We survey this area of research first focusing on a clique topology where each node must be visited not just once but periodically and the maximum allowed time-period between any two consecutive visits of the same node should not exceed its deadline. This case has been introduced as a scheduling problem known as ‘Pinwheel’ and quite a few questions remain open for more than 30 years. We compare the computational complexity of this case with the complexity of the case where each node must be visited at least once within its deadline (and not periodically). We also present some results in other selected topologies.
The above exploration-scheduling problems find applications in areas like mobile monitoring and facility service and maintenance.
Biography
Euripides Markou is a Professor at the Department of Computer Science & Engineering of the University of Ioannina, Greece, and an affiliated researcher of Archimedes, Athena Research Center. He received his B.Sc. degree (in Physics) from the University of Ioannina and his Ph.D. degree (in Theoretical Computer Science) from the National Technical University of Athens. He has been a postdoctoral researcher at the Universite du Quebec en Outaouais, Gatineau, Canada, at the National and Kapodistrian University of Athens, at the Laboratoire Bordelais de Recherche en Informatique, Bordeaux, France and at McMaster University, Hamilton, Canada. Before joining the University of Ioannina in 2024, he had held a faculty position at the Department of Computer Science and Biomedical Informatics (2008 - 2024), University of Thessaly. His research interests include the design of algorithms and the study of computational complexity for problems particularly in the areas of distributed computing, algorithmic game theory, computational geometry and computational biology.
Place
LISN, 1 rue Raimond Castaing, 91190 Gif-sur-Yvette - Room 445.

Data Integration: A perpetually evolving challenge for new research perspectives
2025-09-16 14:30 - 16:30
Jury
Amel Bouzeghoub, Professor, Telecom SudParis, Samovar Laboratory
Zoubida Kedad, Professor, Versailles Saint-Quentin-en-Yvelines University, Paris-Saclay University, DAVID laboratory
Mohan C. Mohan, Distinguished Professor, IBM/Hong Kong Baptist University.
Paolo Papotti, Associate Professor, Eurecom.
Pascal Poncelet, Professor, Montpellier University, LIRRM Laboratory.
Genoveva Vargas-Solar, Full-time researcher, CNRS, LIRIS Laboratory.
Abstract
Data is everywhere and can be produced and stored from everyday scenarios. The structures used for storing data are heterogeneous, continuously evolving, and consumed by applications according to different patterns. It is demonstrated that most of the value of any application is in the ability to access useful and reliable data. From data processing, we can extract value and insights. In addition, the more different and heterogeneous the data are, the more challenging it is to analyze, understand, and make predictions on top of them. In my work, I summarize part of my research contributions to the field of heterogeneous data integration. First, I present the classical data integration scenarios on NoSQL databases. Then I address the challenge of developing new integration techniques conceived for feeding Artificial Intelligence algorithms. The illustration will focus on applications arising from multiple domains used as case studies during the development of the research ideas: smart cities, carbon storage, user profiling, music recognition, and medical data integration. Finally, I investigate the application of data integration techniques to new challenges and the development of new prospective oriented in data for AI.

Virtual Reality Forest Bathing: Beyond Optimal Stress Reduction
2025-07-07 14:00 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
Forest bathing is practice of sensory immersion in a nature environment for stress relief and mental resource restoration. Forest bathing offers many positive effects that can help people’s mental health; however, many people who are in the most need of stress reduction benefits do not have access to nature, such as people in hospitals, nursing homes, and large cities. Virtual reality (VR) has the potential to increase accessibility to the benefits of nature by delivering immersive nature simulations that provide similar benefits. To create an effective virtual nature environment (VNE), research needs to be conducted on the components of a nature environment that make forest bathing effective. Additionally, a key issue with creating VNE’s is making them deployable on accessible, everyday VR headsets like the Oculus Quest 2. This talk will discuss the results from our experiments showing the potential of biomass, or living green nature, as an essential component for restorative effect. I will also include current findings and what's to come!
Biography
Francisco R. Ortega is an Associate Professor at Colorado State University (CSU) and has been Director of the Natural User Interaction Lab (NUILAB) since Fall 2018. Dr. Ortega earned his Ph.D. in Computer Science (CS) with a focus on Human-Computer Interaction (HCI) and 3D User Interfaces (3DUI) from Florida International University (FIU). He also held the Postdoctoral and visiting assistant professor positions at FIU between February 2015 and July 2018. His research has focused on (1) multimodal and unimodal interaction (gesture-centric), which includes gesture elicitation (e.g., a form of participatory design), (2) information access effort in augmented reality (e.g., visual cues and automation bias), (3) AR notifications, and (4) stress reduction using virtual reality forest bathing. For multimodal interaction research, Dr. Ortega focuses on enhancing user interaction through (a) multimodal elicitation, (b) developing interactive techniques, and (c) refining augmented reality visualization techniques. The primary domains for interaction include general environments, immersive analytics, and VR sketching. His research has resulted in over 90 peer-reviewed publications, including books, journals, conferences, workshops, and magazine articles, in venues such as ACM CHI, ACM VRST, IEEE VR, IEEE TVCG, IEEE ISMAR, ACM PACMHCI, ACM ISS, ACM SUI, IEEE 3DUI, HFES, and Human Factor Journals, among others. Dr. Ortega has experience with multiple government-funded projects. For example, Dr. Ortega served as a co-Principal Investigator for the DARPA Communicating with Computers project. He is a principal investigator (PI) for a 3-year effort funded by ONR, titled "Perceptual/Cognitive Aspects of Augmented Reality: Experimental Research and a Computational Model." He was recently awarded a new ONR grant titled “Assessing Cognitive Load and Managing Extraneous Load to Optimize Training.” The National Science Foundation and other agencies and companies have also funded him. This includes the NSF CAREER 2023 for microgestures and multimodal interaction. Since his tenure-track appointment at CSU in August 2018, Dr. Ortega has brought over 4.2 million dollars in external funding (with 3.7 million as principal investigator).
His lab website is https://nuilab.org

Social Performance in Collaborative Problem Solving: Influence of Self-Awareness and Individual Characteristics
2025-09-22 14:00 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
Jury
Estelle MICHINOV (Université de Rennes 1, LP3C), Rapporteure
Olivier LUMINET (Université catholique de Louvain, IPSY), Rapporteur
Alice NORMAND (Université Clermont Auvergne, LAPSCO), Examinatrice
Yann COELLO (Université de Lille, SCALab) — Examinateur
Abstract
Social skills, which are socially learned behaviors, enable individuals to adapt to various social situations. Among these, collaborative problem-solving (CPS) skills play an important role in academic and professional success. They are defined as a set of skills for solving problems and progressing toward a common goal while collaborating with group members. We perceive their mobilization through social performance, an indicator of the adequacy of an individual's responses to the expectations of the situation. This performance results from a dynamic between endogenous (person-related) and exogenous (environment-related) factors. This thesis examines how CPS performance is modulated by social anxiety and alexithymia on the one hand, and by the induction of self-focused attention and the behavior of a virtual partner on the other. In order to investigate this, we built a multimodal corpus of dyads engaged in collaborative tasks with manipulated visual feedback (feedback on oneself, on the other, or combined). Based on this corpus, we validated two French-language scales for assessing social skills specific to CPS. Our results show that self-focused attention can impair social performance, especially in alexithymic individuals; however, in highly anxious participants, performance was preserved or even improved when attention was focused on the self rather than divided between the self and the partner. To isolate the effect of the partner in CPS tasks, we designed virtual agents (high-performing vs. low-performing profiles). Interacting with a high-performing agent increases the user's social performance, while a low-performing agent decreases it. The results of this second study suggest that these effects are moderated by anxiety and alexithymia. The contributions of this thesis provide new insights into the relationship between endogenous and exogenous factors of social performance in CPS, provide robust measurement tools (SPRS, SSC), a shareable corpus, and open up application prospects via adaptive virtual agents for personalized training of social skills.
Publications
Publications of Jennifer Hamet are available on line: https://hal.science/search/index?q=jennifer+hamet+bagnou

Reexamining automation surprise: a design-driven investigation of flaws
2025-09-24 16:00 | LISN Site Plaine - Digitéo, France
Jury
James HOLLAN - Professor Emeritus, UC San Diego (Reviewer/Examiner)
Wendy JU - Associate Professor, Cornell Tech (Reviewer/Examiner)
Elena MUGELLINI - Professor, Haute école d'ingénierie et d'architecture de Fribourg (Examiner)
Nicolas SABOURET - Professor, Université Paris-Saclay (Examiner)
Martine GODFROY-COOPER - PhD, AIRBUS Operations (Invited Member)
Résumé
This thesis investigates how specific design choices in aircraft autopilot systems contribute to automation surprise—when the aircraft behaves differently from the pilot’s expectations. It focuses on two key interfaces: the Flight Mode Annunciator (FMA) and the Flight Control Unit (FCU). The FMA displays autopilot modes in use and has remained unchanged since the 1970s, with most manufacturers adopting the same design despite longstanding concerns raised in the literature. In contrast, the FCU, which allows pilots to select these modes, varies across aircraft manufacturers in both layout and interaction design. While the FMA is difficult to interpret and monitor, the FCU raises concerns because of overloaded controls. Across both interfaces, the industry has historically relied on training and procedural workarounds to address design flaws rather than rethinking interaction design. Grounded in Human-Computer Interaction (HCI) theories, this thesis adopts a mixed-methods, primarily qualitative approach, combining empirical studies with pilots and a participatory design process to explore the roots of automation surprise and propose design alternatives to better support mode awareness. This thesis contributes to ongoing research on automation in safety-critical systems and argues for a shift toward co-designing cockpit interfaces with pilots, rather than merely for them.

Technical, Economic and Environmental Evaluation of Vehicular and Edge Computing
2025-11-04 14:00 | LISN Site Plaine, 1 rue Raimond Castaing 91190 Gif-sur-Yvette, France
Jury
M. Marcelo DIAS DE AMORIM, CNRS, LIP6, Sorbonne Université, Reporter
M. Riadh DHAOU, Institut National Polytechnique de Toulouse, Reporter
Mme Kinda KHAWAM, Université Paris-Saclay, Examiner
M. Claudio Enrico PALAZZI, Università degli Studi di Padova, Examiner
M. Laurent ROULLET, Nokia Bell Labs France – Paris-Saclay, Invited member
Keywords
Cloud véhiculaire,cloud computing,réseaux véhiculaires,edge computing,investissement au edge,5G
Abstract
This thesis investigates the technical, economic, and environmental feasibility of computing architectures supporting delay-sensitive applications such as Augmented Reality (AR) and Autonomous Driving (AD). While Cloud Computing (CC) is the prevalent paradigm today, it cannot offer sufficiently low latency. This limitation is addressed by Edge Computing (EC), which deploys computational capability at the edge of the access network. However, EC entails high infrastructure costs and raises environmental concerns due to the short lifecycle of electronic devices (around four years) and increased energy consumption. Meanwhile, the number of connected vehicles is steadily increasing. These vehicles already carry onboard computing and communication resources that can be opportunistically exploited not only for driving-related tasks but also for offloading computation from external devices such as smartphones, laptops, or wearable health devices. These resources can be managed under the Vehicular Cloud Computing (VCC) paradigm. In this thesis, we first analyze the economic feasibility of EC deployment through a game-theoretic model, showing how multi-tenant cooperation can mitigate high deployment costs. Then, we evaluate under which conditions VCC can replace EC, that is, whether offloading tasks to vehicles can provide performance comparable to EC. Results are obtained via high fidelity network and mobility simulations in an urban mobile network scenario. We find that VCC can achieve ultra low latency, around 10 ms, even when vehicles are sparsely distributed. A comparative cost analysis shows that replacing EC with VCC can reduce infrastructure expenditure by about 10 percent over five years. Finally, we propose a VCC management scheme that optimizes energy consumption, carbon emissions, and computes a fair allocation of revenues generated by end user tasks. The scheme is based on mathematical programming and coalitional game theory. Through Monte Carlo simulations, we show that the energy consumption attributable to VCC is below 0.1 percent of total vehicle consumption in realistic scenarios, and that vehicle owners receive substantial incentives to participate in task execution. Overall, this thesis demonstrates the feasibility of next generation mobile network architectures such as Edge Computing and Vehicular Cloud Computing for supporting ultra low latency applications.

Méthodes de prédiction structurée pour l'analyse sémantiqueStructured prediction methods for semantic parsingMéthodes de prédiction structurée pour l'analyse sémantique
2024-02-09 14:00 - 17:00 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
The defence will be in English. You can attend it remotely via the following link: https://bbb.lisn.upsaclay.fr/b/alb-fxj-85m-4b1.
Thesis committee
- Maxime AMBLARD, professor at LORIA (CNRS, Université de Lorraine) ;
- Marie CANDITO, assistant professor at LLF (Université Paris-Cité) ;
- Pascal DENIS, researcher at INRIA (CNRS, Université de Lille) ;
- Vincent GUIGUE, professor at AgroParisTech (Université Paris-Saclay) ;
- Laura KALLMEYER, professor (University of Düsseldorf) ;
- François YVON, doctoral advisor, senior researcher at ISIR (CNRS, Sorbonne Université);
- Caio CORRO, doctoral co-advisor, associate professor at ISIR (CNRS, Sorbonne Université).
Keywords
Semantic parsing, compositional generalization, natural language processing
Abstract
Semantic parsing is the task of mapping a natural language utterance into a formal representation that can be manipulated by a computer program. It is a major task in Natural Language Processing with several applications, including the development of questions answers systems or code generation among others.
In recent years, neural-based approaches and particularly sequence-to-sequence architectures have demonstrated strong performances on this task. However, several works have put forward the limitations of neural-based parsers on out-of-distribution examples. In particular, they fail when compositional generalization is required. It is thus essential to develop parsers that exhibit better compositional abilities.
The representation of the semantic content is another concern when tackling semantic parsing. As different syntactic structures can be used to represent the same semantic content, one should focus on structures that can both accurately represent the semantic content and align well with natural language.
In that regard, this thesis relies on graph-based representations for semantic parsing and focuses on two tasks.
The first one deals with the training of graph-based semantic parsers. They need to learn a correspondence between the parts of the semantic graph and the natural language utterance. As this information is usually absent in the training data, we propose training algorithms that treat this correspondence as a latent variable.
The second task focuses on improving the compositional abilities of graph-based semantic parsers in two different settings. Note that in graph prediction, the traditional pipeline is to first predict the nodes and then the arcs of the graph. In the first setting, we assume that the graphs that must be predicted are trees and propose an optimization algorithm based on constraint smoothing and conditional gradient that allows to predict the entire graph jointly. In the second setting, we do not make any assumption regarding the nature of the semantic graphs. In that case, we propose to introduce an intermediate supertagging step in the inference pipeline that constrains the arc prediction step. In both settings, our contributions can be viewed as introducing additional local constraints to ensure the well-formedness the overall prediction. Experimentally, our contributions significantly improve the compositional abilities of graph-based semantic parsers and outperform comparable baselines on several datasets designed to evaluate compositional generalization.

Advancing Anomaly Detection in Tabular Data: A Case-Study on Credit Card Fraud Identification
2024-09-30 14:00 - 17:00 | LISN Site Plaine, 1 rue Raimond Castaing 91190 Gif-sur-Yvette, France
Recent advances in the field of Machine Learning has enabled banks to rely on this class of algorithms to build or augment their detection systems. Nevertheless, applying machine learning methods to identify frauds still remains challenging due to (i) the inherent imbalance in the available datasets and (ii) the possibility of distribution shift. Weakly-supervised anomaly detection (AD) methods appear as a possible solution as they should be robust to both challenges. In this work, we propose two novel weakly-supervised AD methods targeted for tabular data. We then test AD methods on a private online credit card payment dataset and compare their performance to Gradient Boosted Decision Trees (GBDT).
We observe a significant performance gap between GBDT and AD methods, in favor of GBDT. Our experiments supports the idea that although promising, weakly-supervised AD method need further improvements to compete with GBDT for the task of fraud detection.
Jury
Alain CELISSE, rapporteur.
Marius KLOFT, rapporteur.
Mazen ALAMIR, examinateur.
Louise TRAVÉ-MASSUYÈS, examinatrice.
Gaël VAROQUAUX, examinateur.

A language to support UX design – Proposition of a lexicon, a chronicle model and a grammar
2024-09-17 14:00 - 17:00 | France
Jury
Stéphanie BUISINE, Directrice de recherche, CESI, Rapporteure & Examinatrice
Emilie POIRSON, Professeure des universités, Ecole Centrale de Nantes, Rapporteure & Examinatrice
Fabrice MANTELET, Maître de conférences HDR, Arts et Métiers ParisTech, Examinateur
Pascal SALEMBIER, Professeur, Université de Technologie de Troyes, Examinateur
Abstract
The importance of user experience (UX) in design is increasingly recognized. This thesis aims to improve UX design practices carried out by multidisciplinary teams. These design activities involve actors with diverse specialties. The literature highlights that dialogues and languages play a central role in these design processes. The scientific literature on UX design resembles a Tower of Babel, with a multitude of definitions, models, and tools, but lacking a common language for design. We propose to improve UX design practices of design teams through a new UX design language. In multidisciplinary teams, this language should facilitate the establishment of a common framework among designers, both on the object of their design and on the areas for improvement in their practices. This language consists of three elements. The first is a UX lexicon, providing referential values to enable designers who conceptualize things differently or in a conflicting manner to discuss and understand each other. The second element is a model of UX chronicles, which allows for the graphical representation of UX design activities throughout a project. This model enables designers to adopt a reflective perspective on their practices through a shared graphical representation. The final element is a UX grammar, allowing designers to specify, imagine, and evaluate a UX through natural language statements organized and connected in diagram form. This tool enables designers to create shared objects for discussing design problems and their solutions, complementing other supports specific to their practices. These tools have been tested in an experiment, showing promising results in terms of usability and perceived interest. However, additional experiments on industrial cases are necessary to confirm the concrete benefits of these tools in practice.
Keywords
User Experience; Design activities; Design tools; Design language

Role of energy transfers in the dynamo effect: application to the von Kármán flow
2023-12-21 14:00 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
Jury
Yannick PONTY, Directeur de recherche, CNRS, Laboratoire Lagrange Observatoire de la Côte d'Azur
Nathanaël SCHAEFFER, Chargé de recherche, HDR, CNRS, Laboratoire ISTerre
Sébastien GALTIER, Professeur à l'Universite Paris-Saclay, Laboratoire de Physique des Plasmas, Ecole polytechnique
Francky LUDDENS, Maître de conférences à l’Université de Rouen Normandie, Laboratoire de Mathématiques Raphaël Salem
Abstract
In this thesis, the aim is to better understand the dynamo effect that takes place in conducting fluids by studying the various energy transfers in magnetohydrodynamics (MHD). Motivated by the observation that dynamo is a conversion mechanism between magnetic and kinetic energy, we develop a new approach to unravel the dynamo mechanism based on local (in space, scale, and time) energy budgets describing dissipation and scale-by-scale energy transfers. Our approach is based upon a new filtering approach that can be used effectively for any type of meshes,including unstructured ones.In order to simulate the MHD, we use the SFEMaNS code which is a hybrid method using both a finite element and spectral decomposition. We first present some developments in SFEMaNS: a better repartition of the unstructured mesh on the processors and a study on solving the Navier-Stokes equations using preconditioning matrix methods instead of the current prediction-correction method. Then, we explain how various post-processing tools have been implemented in order to compute, visualize, and study the energy transfers.Finally, these tools are used to study the von Kármán Sodium setup, showing dynamo action for two types of impellers (steel or soft iron) in the magnetic field growth and saturation phases. Although the two types of dynamo hardly differ from the mean-field theory point of view (the velocity fields are the same in both cases), the locality of our formalism allows us to trace the origin of the differences between these two types of dynamo: for steel impellers, the dynamo is due to the transfer of kinetic energy both in the bulk and in the vicinity of the impellers, whereas for soft iron impellers, the dynamo effect comes from the impellers. We also discuss possible signatures of precursors to anomalous dissipation and fast dynamo, that could become relevant in the inviscid limit.

Sustainable ICT summerschool
2024-09-09 - 2024-09-13 | Bruxelles, France
Utopia or dystopia: perspectives & choices in Information and Communications Technology (ICT)
Informations et inscriptions :
https://www.sictdoctoralschool.com/
Setting the scene
In 2023, six out of nine planetary boundaries have been breached. We are facing multiple ecological and environmental crises while we continuously ignore planetary boundaries. Digitalisation and the use of Information and Communication Technologies (ICTs) are proposed as ways to face these crises. But is this techno-solutionism, i.e. the hope that more technology can solve the problems of earlier technology, not just an utopia? ICT can provide some benefits, such as reducing the need for travel or improving energy management. However, their ubiquitous deployment and use, and their constant renewal cause severe socio-environmental damage and fall short of equity throughout their life cycle. We question the relevance of continuously introducing new digital systems, and the relevance of propositions solely focused on improving efficiency without addressing the socio-environmental damage these systems cause. As is, these systems and their optimizations are “band-aid” solutions that fall short of developing broader perspectives and focusing on systemic change – revising our current models and considering alternative systems.
What we are going to explore
The Sustainable ICT (SICT) Summer School is an interdisciplinary event that emphasizes learning as much as doing, and involves a series of lectures as well as workshops. We will start the week by exploring the current sustainability challenges and opportunities within the ICT industry. In this context, we will also explore the evolving labour market dynamics influenced by advances in ICT and AI. The focus will be on the 'hidden' workers along the ICT supply chain as well as, the challenges faced by researchers working in this discourse in navigating issues of justice and sustainability. Recognising the urgency of the sustainability challenges in ICT, the Summer School will look at initiatives taken by both official bodies, such as policy makers, and unofficial entities, such as grass-root organisations and activists. At the end of the week, the Summer School aims to give participants a sense of agency by demonstrating that we not only have choices, but also by providing clear action steps. By highlighting actions rather than just choices, the program aims to provide clear strategies for tackling these challenges head-on. SICT 2024 aims to empower participants with knowledge, facilitate critical discussions and inspire concrete actions towards a more sustainable and equitable ICT landscape.
How we will proceed
The aim of SICT 2024 is to provide participants with a comprehensive insight into the multifaceted landscape of socio-economic harms along the supply chain and life-cycle of products, as well as the efforts being made to promote sustainability issues, and to foster a systemic understanding of the collective action required to shape a more sustainable future in the ICT sector. Throughout the week, voices from academia, politics, artists, activists and organisations will come together to discuss and analyse the sustainability challenges facing the ICT sector. The summer school will aim to move away from neo-colonialist and extractivist debates on how much pollution is acceptable for progress, to an approach that is making room for the most impacted to be heard.
We invite people from diverse communities, academic fields, the industry and arts to come together for a five-day summer school in early September 2024, in Brussels, Belgium. The event will be held in English. Places are available to master/PhD students, researchers, senior academics and people working in the industry and arts. We may have a limited number of scholarships available, details on this will be announced in the coming months.

Granular Insights into Financial Discourse: Fine-Grained Opinion Analysis of Expert Texts
2023-12-19 14:30 - 17:30 | Rue Noetzlin 91190 Gif-sur-Yvette, France
Jury
Véronique MORICEAU, MdC IRIT - Université Paul Sabatier - Toulouse 3
Serge DAROLLES, Prof. Université Paris Dauphine – PSL
Philippe GILLET, MdC RITM - Faculté Jean Monnet - Université Paris-Saclay
Elena CABRIO, Prof. Université Côte d’Azur, Inria, CNRS, I3S
Bich-Liên DOAN Prof. CentraleSupelec, PhD supervisor
Dirk SCHUMACHER Dr, Natixis, PhD supervisor
Patrick PAROUBEK Dr/HDR, LISN, CNRS, Université Paris-Saclay, PhD supervisor
Abstract
This PhD research is about analyzing automatically texts written by financial professionals. As companies listed on stock markets have to communicate about their business activities every quarter, we leverage the recent progress of Artificial Intelligence to analyze the texts they publish. We want to know which topics business leaders are the most worried about and which topics they are optimistic / pessimistic about, referred to repectively as “aspect” and “polarity”. Currently, the most effective technology in Computation Linguistics to program computers to "understand" human languages are neural networks models, often called “black-box” algorithms. Thanks to examples labeled by humans and the effective design of algorithms, the computer learns how to extract the intended aspects and classify them into predefined polarities. The performance evaluation is enabled by reference annotated labels. My experiments show that information labeled by algorithms can also help to improve the accuracy in predicting the right opinions.

Augmented Reality for Human Intelligence Amplification
2024-09-09 11:00 - 12:00 | LISN Site Belvédère, France
With the recent proliferation of artificial intelligence (AI) into many aspects of our life, there is an increased interest in human-centric AO, which puts the human in control rather than seeking universal automation. An important aspect of this human-friendly line of thinking is intelligence amplification, which seeks to provide the human with computer-supported tools for performing cognitive tasks. For example, "AI copilots" already support us in our everyday desktop work. However, away from the desktop, intelligence amplification becomes scarce, since we are lacking sufficiently rich and simple-to-use interfaces. Here, augmented reality (AR) can help to fill the gap. By augmentating human visual perception with computer-generated information, we can provide cognitive support when and where it is needed. This talk will present selected AR techniques from over two decades of research, and outline how AR and AI can be used in new synergistic ways.
Biography
Dieter Schmalstieg is Alexander von Humboldt Professor of Visual Computing at the University of Stuttgart, Germany. He is also an adjunct professor at the Institute of Computer Graphics and Vision at Graz University of Technology, Austria. His current research interests are augmented reality, virtual reality, computer graphics, visualization and human-computer interaction. He received Dipl.-Ing. (1993), Dr. techn. (1997) and Habilitation (2001) from Vienna University of Technology. He is author and co-author of over 400 peer-reviewed scientific publications with over 20,000 citations and over twenty best paper awards and nominations. His organizational roles include associate editor in chief of IEEE Transactions on Visualization and Computer Graphics, associate editor of Frontiers in Robotics and AI, member of the steering committee of the IEEE International Symposium on Mixed and Augmented Reality, chair of the EUROGRAPHICS working group on Virtual Environments (1999-2010), key researcher of the K-Plus Competence Center for Virtual Reality and Visualization in Vienna and key researcher of the Know-Center in Graz. In 2002, he received the START career award presented by the Austrian Science Fund. In 2008, he founded the Christian Doppler Laboratory for Handheld Augmented Reality. In 2012, he received the IEEE Virtual Reality technical achievement award, and, in 2020, the IEEE ISMAR Career Impact Award. He was elected as Fellow of IEEE, as a member of the Austrian Academy of Sciences, the Academia Europaea, and the IEEE VGTC Virtual Reality Academy.

Projecting Computer Languages for a Protean Interaction
2024-03-18 15:30 - 18:30 | LISN Site Plaine – Digitéo, 1 rue René THOM 91190 Gif-sur-Yvette, France
Jury
Amy KO Professor, University of Washington Rapporteure & Examinatrice
Stéphane CONVERSY Professor, ENAC-LII & Université de Toulouse Rapporteur & Examinateur
Alan DIX Professor, Swansea University Examinateur
Anastasia BEZERIANOS Professor, Université Paris-Saclay Examinatrice
Tomas PETRICEK Assistant Professor, Charles University Examinateur
Abstract
To interact with computers, we often rely on computer languages such as Python for creating programs and LaTeX for writing technical documents. In the past few decades, these languages have become increasingly used in a variety of fields ranging from science to arts. They are now being taught to millions of pupils and have become a staple skill in the job market, widening the gap between those who are computer literates and the others. However, for the most part, our interaction with these languages has remained similar to what it used to be fifty years ago: reading and writing code as plain text. Although various alternatives have been introduced, they have often been developed with a modernist approach, in isolation from popular languages and widespread workflows, resulting in systems that hardly cross the borders of academic circles and niche communities. As a result, our interaction with computer languages is now hampered by essentialist views that encourage us to move away from them, at the risk of losing some of the agency they give us, as if dealing with code was nothing but a burden from the past compared to ever more “simple” user interfaces and “intelligent” code synthesis. In this thesis, I argue against this view that I consider harmful to human-computer interaction research and computer-driven democracies. To do so, I introduce a number of arguments that show that it is indeed possible—and even beneficial—to make our interaction with computer languages more pluralist, including with the many languages that already exist that are inherent to the diversity of computing in the 21st century. I first develop a new theory of interaction with computer languages that shows that no such language is inherently bound to a specific representation or type of interaction. To that end, I deconstruct the notion of computer language into five fundamental aspects to isolate interaction from the other constituents of these languages, yielding a more holistic model than those that already exist. I then use this model to identify different levels of interaction with computer languages, which can be hybridised, and show that a single piece of code can very well be projected onto several representations to let end-users decide which representation supports the form of interaction most appropriate for them. I then apply this view of computer languages to two research problems using user-centred design methodologies: helping users author documents written in LaTeX and helping programmers appropriate their text editors by crafting their own projections. For each problem, I assess the limitations of existing solutions with the help of a formative study; I develop a prototype of a text editor equipped with additional projections; and I evaluate it with both qualitative and quantitative user studies. The results show that that complementing text with other representations helps us understand and modify code faster and with a lower workload and that these representations can be created by recomposing existing parts that can be reused from one projection to another. In conclusion, I show that considering interaction with computer languages as projections makes it more protean, an approach which, according to this thesis, is theoretically grounded, technically possible and empirically desirable. It lends itself to the urgent task of equipping an ever-growing public of citizens with new intellectual and technical tools to help them understand and appropriate the computer languages that rule the societies we live in.

Designing Motivational, Adaptive and Mobile Interactions for Behavior Change: Application to Low Back Pain
2024-10-22 09:30 - 12:30 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
Jury
Yann LAURILLAU, Maître de conférences-HdR, LIG-CNRS, Université Grenoble Alpes, Rapporteur & Examinateur
Elena MUGELLINI, Professeure des Universités, Institut HumanTech, Haute école d'ingénierie et d'architecture de Fribourg, Rapporteuse et Examinatrice
Gaëlle CALVARY, Professeure des Universités, LIG-CNRS, Université Grenoble Alpes, Examinatrice,
Christophe KOLSKI, Professeur des Universités, Université Polytechnique Hauts-de-France, Examinateur,
Audrey SERNA, Maîtresse de conférences, LIRIS-CNRS, Institut National des Sciences Appliquées de Lyon, Examinatrice.Christelle Nguyen, Professeure des Universités - Praticienne Hospitalière, INSERM UMRS-1124, Université de Paris, Hôpital Cochin-AP-HP, Invitée
Abstract
Today's technologies are known as persuasive or motivational, referring to technologies, applications or services designed to induce changes in the attitudes and behaviors of those who use them. This is the subject of research in Human-Computer Interaction, in conjunction with theories from psychology linked, for example, to behavior change or motivation. Research on these technologies suggests that, in order to encourage long-term adherence, these “virtual coaches” need to be personalized and/or adapted according to users' individual characteristics (stage of behavior change, motivations, preferences, barriers). For example, according to Self-Determination Theory (SDT), an individual may present different forms of motivation, more or less effective. The aim is to identify the forms of motivation present in users, in order to offer services and interactions that reinforce or develop them. The use of such systems in the healthcare field has the potential to induce and reinforce health behaviors that are sometimes difficult for caregivers to establish. Offering personalized daily care represents a considerable human and financial cost for healthcare professionals. A mobile application has the advantage of being able to cope with these constraints. The aim of this thesis is to design this computer coach with and for patients suffering from chronic low back pain, to help them manage their condition, particularly with regard to their pain and the practice of regular physical activity. The major challenges of this thesis include the selection and coordination of different existing methodologies, as well as the evaluation of the application's quality and effectiveness. Key issues address a thorough understanding of patients in terms of psychological characteristics, the implementation of a user-centered and participatory approach to meet patients' needs, the tailoring of the intervention to foster the behavior change process, and the evaluation of this intervention on patients' engagement and behavior change process. The contributions are structured around three axes: (1) the proposal of a design and evaluation metamodel for adaptive digital interventions to support behavior change, (2) the identification of low back pain patient profiles based on psychological characteristics related to the behavior change process, and (3) the design of a mobile application for low back pain patients. Thus, this thesis proposes a useful approach to guide the design of adaptive human-machine interventions and interactions, based on theories in the psychology of behavior change and involving professionals and patients in the design process.

Towards multimodal assessment of L2 level: speech and eye tracking features in a cross-cultural settingVers l'évaluation multimodale du niveau L2: caractéristiques de la parole et du mouvement des yeux dans un cadre multimodal
2024-12-19 14:00 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
Committee
Véronique DELVAUX, Professor, Université de Mons
Sophie HERMENT, Professor, Université d’Aix-Marseille
Ralph ROSE, Professor, Waseda University
Sébastien LALLÉ, Associate Professor, Sorbonne Université
Nicolas AUDIBERT, Associate Professor, Université Sorbonne Nouvelle
Cédric Gendrot, Professor, Université Sorbonne Nouvelle
Abstract
In recent years, the world of education has undergone critical changes, especially with the system's massive digitalization in 2020, as well as the advancement of generative AI technologies. LeCycl (Learning Cyclotron) [Vargo et al., 2023] project is a part of this scientific trend and its aim is to accelerate the knowledge flow. It takes into consideration 3 main processes of learning: perception, mastering and transfer. This thesis, as part of the LeCycl project, focuses on exploring second language (L2) oral reading strategies and analyzing difficulties faced by representatives of different cultures and their techniques of coping with them. For this purpose, we are relying on multimodal cues including speech and eye tracking, as well as an original protocol that introduces nudges (represented by comic books) for the cultural adaptation [Hutin et al., 2023]. For this purpose, we developed a protocol involving the collection of reading aloud data from both native and non-native English speakers (French and Japanese speakers) [Kobylyanskaya, 2022]. We analyzed speakers’ performance throughacoustic and linguistic measures (phoneme realization, prosody and disfluencies such as pauses, hesitations, truncations), as well as eye movement measures [El Baha et al. 2022; Kobylyanskaya et al., 2023]. Then, we usedmachine learning methods to define the speaker’s L2 level based on the extracted measures. Finally, we evaluate the contribution of comic books images on speakers’ oral reading performance. The results highlight that the representatives of different cultures face different challenges when reading in a foreign language and employ different strategies to overcome them, which are translated both at verbal and ocular levels. Our results underline the need for culturally adapted learning tools and the challenges involved in developing them.

The socio-affective dimension in digital innovations : toward a new understanding of Human-Machine Interactions in health and education
2024-12-13 14:00 - 17:00 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
Jury
M. Jacques Fischer Lokou, Université Bretagne Sud IUT de Vannes, Reviewer.
M. Thierry Meyer, Université Paris Nanterre, Reviewer
Mme Estelle Michinov, Université de Rennes 2, Reviewer
Mme Beatrice Cahour, Institut Interdisciplinaire de l'Innovation, Examiner
M. Jean-Claude Martin, Université Paris Saclay, Examiner
Abstract
Modern lifestyles, characterized by inadequate dietary habits and a lack of physical activity, contribute to the rise of non-communicable diseases, exacerbating public health issues while negatively impacting the environment. In response to these challenges, sustainable behavioral changes are essential to promoting healthier and more environmentally friendly lifestyles.
Psychology has long been concerned with the mechanisms of behavior change and the factors that facilitate it. Emerging technologies, particularly virtual agents, present promising perspectives for studying and influencing these dynamics. These agents, more than mere interfaces, are perceived by users as social entities capable of fostering rich interactions, where mental states, for instance, may be attributed to them. The primary aim of this research is to enhance the theoretical understanding of socio-emotional interactions between humans and machines while guiding the development of interactive technologies that meet the emotional and social needs of users.
Our research highlights the importance of congruent multimodal cues and context in recognizing emotions expressed by virtual agents. These findings underscore the necessity for accurate modeling of interactions to optimize the human-machine relationship. Additionally, personalizing interactions, combined with the integration of socio-affective competencies in virtual agents tailored to user profiles, is crucial for improving engagement, the quality of exchanges, and the adoption of technologies in complex environments.
Moreover, our work on multisensory interactions demonstrates that targeted improvements in virtual environments and assistive technologies can significantly enhance user experience, increase accessibility, and maximize the efficiency of these devices.
Our research project advances these contributions by focusing on three main areas: (1) emotional dynamics in human-machine interactions, (2) collaborative processes between humans and virtual agents, and (3) long-term user engagement in digital interventions. We believe that addressing the socio-affective dimension of Human-Machine Interaction and personalizing design are key elements in creating effective and widely adopted digital interventions, thereby addressing current challenges in education and public health.

Augmented Reality for Human Intelligence Amplification
2024-09-09 11:00 - 12:00 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
With the recent proliferation of artificial intelligence (AI) into many aspects of our life, there is an increased interest in human-centric AO, which puts the human in control rather than seeking universal automation.An important aspect of this human-friendly line of thinking is intelligence amplification, which seeks to provide the human with computer-supported tools for performing cognitive tasks. For example, "AI copilots" alreadysupport us in our everyday desktop work. However, away from the desktop, intelligence amplification becomes scarce, since we are lacking sufficiently rich and simple-to-use interfaces. Here, augmented reality (AR) can help to fill the gap. By augmentating human visual perception with computer-generated information, we can provide cognitive support when and where it is needed. This talk will present selected AR techniques from over two decades of research, and outline how AR and AI can be used in new synergistic ways.

Collaborative Immersive Analytics for Meteorological and Climatological Data
2024-10-02 14:00 - 17:00 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
Jury
Krzysztof Walczak, Professeur à l'Université d'Économie et de Commerce de Poznań, Pologne, Rapporteur
Charles Pontonnier, Maître de conférences HDR à l'École Normale Supérieure de Rennes, France, Rapporteur
Samir Otmane, Professeur à l'Université d'Évry Paris-Saclay, France, Examinateur
Christophe Hurter, Professeur à l'École Nationale de l'Aviation Civile, France, Examinateur
Valérie Gouranton, Maîtresse de conférences HDR à l'INSA Rennes, France, Examinatrice
Nicolas Viltard, Maître de conférences à l'Université de Versailles Saint-Quentin-en-Yvelines, France, Examinateur
Résumé
Recent advancements in immersive technologies, such as Head-Mounted Displays (HMDs) in Augmented Reality (AR), have opened new possibilities for collaborative visual analytics. However, leveraging these technologies to enhance data analytics remains challenging due to the complexity and density of data, particularly in fields like meteorology. In this work, we propose novel interaction techniques aimed at improving the selection and analysis of dense data point clouds in AR environments. We also introduce a data integration approach based on Ontology and Linked Data, facilitating efficient querying and filtering during analysis. We evaluated these techniques through a user experimental study using storm and cyclone datasets, which showed promising results for collaborative climate data analysis. Additionally, we proposed and evaluated a spatial partitioning technique for co-located collaborative AR setups. Our findings demonstrate the potential of AR spatial partitioning in immersive analytics for complex data exploration.

Developing the Framed Multi30k (FM30k) multimodal-multilingual dataset 2/2
2025-09-30 11:00 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
Résumé
Les modèles de langage transforment la manière dont on analyse les contenus politiques, mais ils peuvent aussi refléter des biais implicites qui influencent leurs réponses. Nous verrons comment, pour une tâche simple, le seul fait de changer le nom d’un politicien dans une phrase peut faire varier le jugement du modèle.Pour aller plus loin, nous explorerons ces biais à travers plusieurs langues (anglais, français, arabe, russe…) et différents modèles (Mistral, Meta, OpenAI…). Nous essayerons de comprendre comment des facteurs comme le pays d’origine, l’orientation politique, le genre ou la langue d'entrainement influencent ces décalages, avec des biais parfois surprenants.Nous analyserons les différences entre langues, questionnerons l’origine de ces biais, et réfléchirons à leurs conséquences pour les futurs développements.Enfin, nous proposerons une méthode simple pouvant déjà aider à limiter ces effets, tout en discutant des défis éthiques majeurs liés à l’utilisation responsable des LLMs dans des contextes politiques de plus en plus polarisés
Référence
https://arxiv.org/pdf/2505.19776

Reinforcement Learning and Federated Learning-based Multi-Band Assignment for IoT Short Packet Communications
2025-09-17 09:00
Jury members
André-Luc BEYLOT, Professor, Toulouse INP, Reviewer & Examiner
Kandaraj PIAMRAT, Maitre de Conferences, Nantes Université, Reviewer & Examiner
Nadjib AIT-SAADI, Professor, Université Paris-Saclay, Examiner
Fatiha ZAIDI, Professor, Université Paris-Saclay, Examiner
Steven MARTIN, Professor, Université Paris-Saclay, Examiner
Yusheng JI, Professor, National Institute of Informatics, Examiner
Kensuke FUKUDA, Professor, National Institute of Informatics, Examiner
Takashi KURIMOTO, Professor, National Institute of Informatics, Examiner
Abstract
5G services are classified into Ultra-Reliable Low-Latency Communications (URLLC), enhanced Mobile Broadband communications (eMBB), and massive Machine Type Communications (mMTC). Beyond 5G (B5G) and 6G communications are expected to cover extreme demands, jointly in terms of rate, delay, reliability. Emergent applications such as Extended Reality (XR) or autonomous driving are expected to require Terabits data rate, extreme reliability and/or delay. Moreover, given the tremendous amount of wireless devices, and especially IoT devices, meeting the requirements of future networks appears to be extremely challenging. Many recent studies have focused on the use of the high millimeter Wave (mmWave) frequencies due to their large bandwidth. However, mmWaves suffer of a severe sensitivity to obstacles and a high path loss, restricting their applicability. To overcome these frailties, many works proposed to jointly use the conventional Sub-6GHz frequencies and the mmWave. Most IoT applications, for example in industrial control, can be characterized by their generation of small packets (Short-Packet Communications (SPC)). For such traffic, Shannon’s capacity theorem becomes inapplicable due to the size and nature of SPC. To better model the achievable rates, we adopt the Finite Blocklength theory, which accounts for packet size and reliability constraints. This thesis investigates the problem of band association and radio resource allocation of IoT devices to maximize the system sum-rate under heterogeneous Quality of Service (QoS) demands. We first propose a unifying framework devoted to SPC that jointly optimizes the user partitioning, and the radio resource scheduling within each band. Leveraging centralized Deep Reinforcement Learning (DRL), the proposed method enables to better tackle the challenges imposed by dynamically varying mobile environments. Regarding the DRL-based user partitioning, we have investigated three different types of actions to obtain a high network performance and rapid convergence. Regarding the resource allocation, we designed two optimization methods, one that leverages Difference of Convex Programming (DCP), and the second that accelerates convergence. Numerical evaluations show that the proposed methods outperform conventional approaches. While our centralized framework efficiently optimizes users' band associations, centralization entails important communication costs and increasing complexity. Recently, Multi-Agent DRL (MADRL) approaches attracted attention, as solving the problem at the edge avoids communication burden. However, efficient solutions must be provided to allow efficient coordination of distributed devices. To enhance coordination while avoiding heavy communication costs, Federated MADRL (F-MADRL) has been studied where each device trains its model locally while a periodic model aggregation done at a central node improves cooperation. Since each IoT device faces a unique mobile environment, personalized F-MADRL was introduced to improve local decision making. We propose two personalized F-MADRL methods that enable each user to adapt their model to the mobile environment, thereby improving the overall network performance and reducing signaling costs. Simulations show that our methods outperform benchmark MADRL schemes. Finally, we focus our work on denser and more dynamic network scenarios. We evaluate a third personalization strategy to improve robustness in heterogeneous environments. To reduce the complexity of the DCP-based resource allocation, we explore low-complexity algorithm, as the mathematical optimization we used until now requires a computationally intensive process. Results show promising performance of our proposed solution in denser networks, outperforming conventional algorithm in terms of outage probabilities or sum-rate. Finally, we evaluate robustness under user mobility and blockage, showing the fast adaptivity of our personalized F-MADRL framework.

Towards More Natural Dialogues: Integrating Chitchat Capabilities into Task-oriented Dialogue Agents
2025-10-08 09:30 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
Rapporteurs
François Portet, Professeur, Université Grenoble-Alpes
Yves Lepage, Professeur, Waseda University
Examinateurs
Sakriani Sakti, Professeure, Nara Institute of Science and Technology (NAIST)
Sophie Rosset, Directrice de Recherche CNRS, Université Paris-Saclay
Ondřej Dušek, Assistant Professor, Charles University, Prague
Abstract
The ability to hold natural, human-like conversations has long been regarded as a hallmark of intelligence. Contemporary dialogue systems are typically divided into two categories: task-oriented dialogue (TOD), focused on efficiency and goal completion, and open-domain chitchat, centered on engagement and social connection. Yet human conversations rarely conform to this dichotomy. Everyday dialogue interleaves transactional and social goals, suggesting that dialogue systems should likewise integrate both modes to achieve more natural interactions.
This thesis investigates the integration of chitchat into task-oriented dialogue, advancing the argument that these modes are not competing but complementary. The work is structured in two parts. The first part provides a state-of-the-art overview of dialogue systems, tracing the historical evolution of TOD and chitchat agents, the datasets and evaluation paradigms that shaped them, and the emergence of integrated “inter-mode” approaches. It also introduces relevant natural language processing and machine learning foundations, positioning recent advances in large language models as critical enablers of hybrid dialogue systems.
The second part presents original research contributions that explore how chitchat can enhance task-oriented dialogue in practice. Four studies are reported. First, a comparative study evaluates different strategies for injecting chitchat into TOD, revealing distinct lexical patterns and showing how certain forms of social talk improve diversity and engagement. Second, a unified modeling approach demonstrates that incorporating user emotion detection into an end-to-end TOD system improves both task success and conversational naturalness, underscoring the value of affective signals in practical interactions. Third, we augment TODs with synthetic user backstories, simulating inter-mode scenarios by embedding elements of a user narrative into task requests. This training data supports both the evaluation and improvement of model robustness to such common inter-mode inputs. Finally, a few-shot prompting approach shows that large language models can flexibly combine task-oriented and chitchat capabilities without architectural modifications, highlighting the practicality of LLM-based methods for building unified dialogue agents.
Together, these contributions advance the development of inter-mode dialogue systems by addressing key challenges such as dataset limitations, unified modeling of social and task signals, and robustness to blended user input. The thesis demonstrates that chitchat and TOD can be coherently integrated without necessarily sacrificing task performance. Ultimately, it argues that moving beyond the artificial separation of TOD and chitchat is essential for creating dialogue systems that are not only effective and reliable, but also more natural, empathetic, and enjoyable.
Lien visio conférence : ici

Construction of Model Behavior by Novices through Interaction with Machine Learning
2025-10-02 14:00 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
Jury
Genoveva Vargas-Solar – Researcher (HDR), Université Claude Bernard Lyon 1 – Reviewer & Examiner
Simone Stumpf – Professor, University of Glasgow – Reviewer & Examiner
Marianela Ciolfi Felice – Assistant Professor, KTH Royal Institute of Technology – Examiner
Anastasia Bezerianos – Professor, Université Paris-Saclay – Examiner
Abstract
Machine learning (ML) systems increasingly mediate decisions across domains such as hiring, healthcare, criminal justice, and content moderation. While often framed as neutral or objective, these systems frequently reproduce the very inequalities they claim to address. Such harms are not isolated technical failures, but reflections of broader sociotechnical dynamics: models encode dominant norms shaped by the data, assumptions, and institutional practices that underlie their design. Addressing these dynamics requires moving beyond internal performance metrics and attending to how model behavior unfolds in context, including how models operate in real-world settings and how people interpret and respond to their outputs.
This thesis investigates how model behavior is not only examined but constructed through human interaction with ML models in real-world settings. It centers novice users who are typically excluded from formal development pipelines but directly impacted by model decisions, exploring how they interpret, evaluate, and potentially shape model behavior through interaction. Grounded in Human–Computer Interaction (HCI) and informed by Science and Technology Studies (STS) and AI ethics, this thesis adopts a human-centered lens to present two empirical studies that engage novice users in the development and post-deployment phases of the ML lifecycle. These studies examine how user interaction makes model behavior accessible for reflection, negotiation, critique, and, if possible, change.
The first study introduces Collaborative Interactive Machine Teaching (CIMT), where small groups of novice users train an image classifier using a web-based platform called TeachTOK. It examines how participants collaboratively plan teaching strategies, curate training data, and reflect on model behavior using real-time feedback. Findings show that collaboration was central to negotiating teaching objectives, such as representational diversity, and building a shared understanding of model behavior.
The second study investigates user-driven algorithm auditing as a post-deployment practice, in which non-experts evaluate a pretrained image captioning model for gender bias. Drawing on cognitive models of sensemaking, we designed an auditing interface with two additional tools: one that supports hypothesis testing through input manipulation, and another that enables grouping outputs to reveal patterns. A between-subjects online study examined how interface conditions shaped users’ sensemaking outcomes. Findings show that different tools influenced which harms users could detect, how they formed hypotheses, and how they developed confidence in their interpretations.
Across both development and post-deployment phases, this thesis demonstrates how novices’ interaction with the ML model contributes to the construction of model behavior. In CIMT, participants shaped behavior through teaching and negotiation. In auditing, they surfaced harms and evaluated model behavior through iterative sensemaking. In both cases, users brought their own perspectives to bear on how models should behave, perspectives often excluded from conventional ML development and evaluation.
This work contributes empirical, technical, and methodological insights that have the potential to support participatory and reflexive approaches to machine learning within human-centered AI, and calls for future research grounded in sustained, real-world, and community-led practices.

Conformal Predictions and Risk Control in Machine Learning Models to Improve Performance and Human Decision-Making
2025-12-11 09:30 | LISN Site Plaine – Digitéo, 1 rue René THOM 91190 Gif-sur-Yvette, France
Keywords
Computer vision, uncertainty quantification, conformal prediction, risk control, deep learning
Abstract
Machine Learning (ML) and Deep Learning (DL) models are increasingly deployed in critical applications where errors may lead to severe consequences. Despite their strong predictive performance, their inability to reliably express uncertainty limits trust, safety, and compliance. Uncertainty Quantification (UQ) addresses this challenge by estimating confidence levels in predictions, enabling safer decision-making and improved performance.In this thesis, I introduce post-hoc, model-agnostic, and black-box UQ methods applicable to both classical tasks (regression, classification) and structured outputs such as segmentation and object detection. The contributions include: (1) new conformal prediction techniques integrated into the MAPIE library, including extensions to Gaussian processes, and (2) risk-control approaches based on accuracy for biomedical detection, as well as adaptive risk control for segmentation. Experiments on various real-world datasets demonstrate that these methods enhance the reliability and effectiveness of machine learning systems.
Jury Composition
Mr. Mathieu SERRURIER, Université Toulouse II Jean Jaurès – Reviewer
Mr. Lionel BOMBRUN, Laboratory for Integration from Material to System, Université de Bordeaux – Reviewer
Ms. Soundouss MESSOUDI, Université de Technologie de Compiègne – Examiner
Mr. Sébastien GERCHINOVITZ, IRT Saint Exupéry – Examiner
Mr. Antoine MANZANERA, ENSTA-Paris, IP Paris – Examiner
Mr. Gilles BLANCHARD, Université Paris-Saclay – Examiner
Publications
Access to the open Publications published on HAL : https://universite-paris-saclay.hal.science/LISN/search/index?q=blot

Méthodes computationnelles basées sur l’apprentissage profond pour la prédiction des structures 3D d’ARN
2025-10-06 14:00 | France
Jury
Mr. Frédéric Cazals, Research Director, Inria Centre at Université Côte d’Azur, Reviewer
Mr. Pierre Geurts, Full professor, Liège University, Reviewer
Mr. Sebastian Kmiecik, Full professor, University of Warsaw, Reviewer
Ms. Florence D’Alche-Buc, Professor, Institut Polytechnique de Paris, Telecom Paris, Examiner,
Mr. Alain Denise, Professeur, Paris-Saclay University, LISN, Examinor
Ms. Elena Rivas, Senior Research Fellow, Harvard University, Examinor
Mr. Tomasz Zok, Associate professor, Poznan University of Technology, Examinor
Ms. Marta Szachniuk, Full professor, Poznan University of Technology, Invited member
Abstract
RNAs are, like proteins, biological molecules that play essential roles at various stages in the life of an organism and are involved in various diseases. Determining their structure, especially 3D, is essential to understand their function better. Recently, Google DeepMind proposed a method called AlphaFold, for the prediction of the 3D structure of proteins based on deep learning, which revolutionized the field by showing a high outperformance compared to the state-of-art. However, RNA and protein molecules differ significantly in structure and dynamics, making it non-trivial to apply protein- based methods directly to RNA. AlphaFold, AlphaFold 2, as well as AlphaFold 3, its new version that also predicts RNA 3D structure, rely heavily on multiple sequence alignments (MSAs) as input, which are expensive to compute and not always available, especially for RNAs.
In this thesis, we aim to get ride of the MSA information for the prediction of RNA 3D structures. We seek to develop methods to predict RNA 3D structures from sequence information only. For this, we leverage deep learning methods and particularly language-based models
to map sequences to structure features. By using language-based models pretrained on a large set of RNA sequences, we can learn RNA structural features and then predict the 3D structure.
The work in this thesis is separated into three main contributions. The first, called RNAdvisor, is a tool that wraps the state-of-the-art RNA 3D structure assessment tools to comprehensively evaluate RNA 3D structures, both with and without experimental references. The second contribution, State-of-the-RNArt, is a benchmark of the state-of-the-art RNA 3D structure prediction methods, highlighting current methods’ limitations and challenges. It is followed by a more detailed analysis of the limitations of AlphaFold 3. The third contribution, RNA-TorsionBERT, is a deep learning method that predicts the torsion angles of RNA 3D structures from the sequence, which are an important feature of RNA 3D structures. It leverages a language-based model to map sequences to structure features. It is extended to a new scoring function, TorsionBERT-MCQ, that can assess the quality of RNA 3D structures in torsional space. This work is a step towards the development of deep learning methods for RNA 3D structure prediction, using only sequence information and not relying on costly multiple-sequence alignments.

Forum Codalab / Codabench
2025-04-02 13:30 - 16:00 | LISN Site Plaine – Digitéo, 1 rue René THOM 91190 Gif-sur-Yvette, France
Pour la deuxième année consécutive, en 2024, Codabench a été classée première plateforme dans le « State of Competitive Machine Learning » de MLConstests (voir l'actualité sur le site du LISN en 2023).
Programme
13h30 Café d'accueil
14h00 Présentation des plateformes Codalab et Codabench
Introduction : Anne-Catherine Letournel, Ingénieure LISN, équipe ASARD
La science avec Codalab et Codabench : Adrien Pavao, Contributeur, Docteur en informatique
Infrastructure de production : Obada Haddad-Soussac, Ingénieur, LISN, équipe ASARD
Building Together in Open Source: the Github community : Ihsan Ullah, Contributeur, Ingénieur
14h40 Retours d'expérience d'organisateurs de compétitions
"Organisation des campagnes d'évaluation multilingues en traitement automatique des langues avec Codabench - réseaux PARSEME et UniDive": Agata Savary, Professeure, LISN, équipe SEME
"NeurIPS'24 experiment: LLM as an Author Checklist Assistant": Hieu Khuong, chercheur doctorant, LISN équipes GALAC-A&O
"Évaluation du module Algorithmique Avancée de la dominante Info&Num à CentraleSupélec": Joanna Tomasik, professeur, LISN équipe GALaC
15h30 Questions / réponses et conclusion
Accès au site de Codabench
Lien visio
Joanna Tomasik
Joanna Tomasik
Agata Savary
Pause
Isabelle Guyon
Anne-Catherine Letournel
Obada Haddad-Soussac
Obada Haddad-Soussac
A lire sur le site du LISN, pour en savoir plusCODALAB classée première plateforme (mars 2024)CODALAB : des défis scientifiques pour accélérer l’innovation en IA (mai 2024

Statistical Learning for Climate Models
2024-12-04 10:00 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
Climate models face challenges in accurately representing atmospheric circulation patterns related to extreme weather events, especially regarding regional variability. This thesis explores how Latent Dirichlet Allocation (LDA), a statistical learning method originating from natural language processing, can be adapted to evaluate the ability of climate models to represent data such as SeaLevel Pressure (SLP). LDA identifies a set of local synoptic-scale structures, physically interpretable as cyclones and anticyclones, referred to as motifs. A common basis of motifs can be used to describe reanalysis and model data so that any SLP map can be represented as a sparse combination of these motifs. The motif weights provide local information on the synoptic configuration of circulation. By analyzing the weights, we can characterize circulation patterns in both reanalysis data and models, allowing us to identify local biases, both in general data and during extreme events. A global dynamic error can be defined for each model run based on the differences between the average weights of the run and reanalysis data. This methodology was applied to four CMIP6 models. While large-scale circulation is well predicted by all models on average, higher errors are found for heatwaves and cold spells. In general, a major source of error is found to be associated with Mediterranean motifs, for all models. Additional evaluation criteria were considered: one was based on the frequency of motifs in the sparse map representation. Another one involved combining the global dynamic error with the temperature error, thus making it possible to discriminate between models. These results show the potential of LDA for model evaluation and preselection.
The defense will take place in the presence of the following jury :
Marc BOCQUET, Professeur, CEREA, école des ponts
Corentin HERBERT, Chargé de recherche CNRS, ENS de Lyon
Mathieu VRAC, Directeur de recherche CNRS, LSCE
François YVON, Directeur de recherche CNRS, ISIR, Sorbonne Université

Efficient generation of rectangulations and elimination trees via permutation languages
2021-12-13 15:00 - 16:00 | France
In this talk we apply the Hartung-Hoang-Mütze-Williams permutation language framework to derive exhaustive generation algorithms for two further classes of combinatorial objects, as well as Hamilton paths and cycles on the corresponding polytopes: (3) different classes of rectangulations, which are subdivisions of a rectangle into smaller rectangles (see www.combos.org/rect); (4) elimination trees of chordal graphs, which encode several interesting combinatorial objects such as permutations, binary trees and bitstrings (see www.combos.org/elim). This talk is based on joint work with Torsten Mütze and Jean Cardinal (SoCG 2021 + SODA 2022).

Soutenance de Léon-Paul Schaub
2022-02-12 14:30 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
Le principe de l’IRM, qui a valu en 2003 le prix Nobel de médecine aux chercheurs américain Paul Lauterbur et britannique Peter Mansfield, repose sur le phénomène de résonance magnétique lié aux propriétés magnétiques de certains atomes, principalement le noyau d’hydrogène. Placés dans un champ magnétique généré par de puissants aimants supraconducteurs, ces noyaux s’orientent dans la même direction. Des impulsions de radiofréquences viennent alors les perturber, comme une pichenette déstabiliserait une toupie en rotation, sans la faire chuter. Ils accumulent de l’énergie, restituée à l’arrêt de la stimulation lorsqu’ils reviennent à leur alignement initial. Capté par des antennes réceptrices et interprété, ce signal donne naissance à des images en deux ou trois dimensions.

Séminaire critique de l'intelligence artificielle : Environmental crisis and digitalization, the case of AI
2022-03-16 16:00 - 17:30 | France
Ce séminaire du Centre Borelli (animé par l'ENS Paris-Saclay) souhaite insuffler une réflexion épistémologique à propos de l’intelligence artificielle, à l’heure où son déploiement semble totalisant, pose les questions suivantes : quelles sont ses limites; qui produit le savoir, dans quelles conditions ; qu’est-ce qui, dans l’imaginaire collectif, relève du mythe ou de l’idéologie; quels sont les rapports de pouvoir, les structures sociales entre les différents acteurs (entreprises, universités); quelle est sa pertinence sociale notamment à l’heure du défi écologique etc.
La séance du 16 mars recevra Gauthier Roussilhe et Anne-Laure Ligozat (LISN) pour une présentation intitulée «Environmental crisis and digitalization, the case of AI», sur les impacts environnementaux de l'intelligence artificielle.
Lien Zoom : https://us02web.zoom.us/j/86425346031#success

Dimensions mémorielles de l’interaction écrite homme-machine
2022-03-22 00:00
La thèse a été dirigée par Patrick Paroubek et co-encadrée par Gil Francopoulo puis par Samuel Rumeur. Le jury est composé de :
Frédéric Landragin (rapporteur)Chloé Clavel (rapporteure)Yves Lepage (examinateur) Frédéric Béchet (examinateur) Magalie Ochs (examinatrice) Gil Francopoulo (examinateur)Samuel Rumeur, directeur produit de la société Akio (invité)
Il s'agit d'une thèse CIFRE réalisée, en partenariat avec le LISN (anciennement LIMSI), chez Akio, une entreprise éditrice de logiciels pour l'interaction client omnicananux (SMS, mail, tchat, appels téléphoniques...). L'objectif de cette thèse était d'opérer un transfert industriel des dernières avancées en système de dialogue pour l'implémentation et l'intégration d'un chatbot dans le logiciel d'interaction client appelé Unified afin d'automatiser certaines de ces interactions et faciliter le travail des agents. J'ai résumé la contribution scientifique de la manière suivante :"Dans ce travail, nous nous intéressons à la place des systèmes de dialogue orientés-tâche à la fois dans le traitement automatique des langues, et dans l’interaction homme-machine. Nous nous concentrons plus particulièrement sur la différence de traitement de l’information et de l’utilisation de la mémoire, d’un tour de parole à l’autre, par l’humain et la machine, pendant une conversation écrite de type clavardage. Après avoir étudié les mécanismes de rétention et de rappel mémoriels chez l’humain durant un dialogue, en particulier dans l’accomplissement d’une tâche, nous émettons l’hypothèse qu’un des éléments susceptible d’expliquer que les performances des machines demeurent en deçà de celles des humains, est la capacité à posséder non seulement une image de l’utilisateur, mais également une image de soi, explicitement convoquée pendant les inférences liées à la poursuite du dialogue. Cela se traduit pour le système par les trois axes suivants. Tout d’abord, par l’anticipation, à un tour de parole donné, du tour suivant de l’utilisateur. Ensuite, par la détection d’une incohérence dans son propre énoncé, facilitée, comme nous le démontrons, par l’anticipation du tour suivant de l’utilisateur en tant qu’indice supplémentaire. Enfin, par la prévision du nombre de tours de paroles restants dans le dialogue afin d’avoir une meilleure vision de la progression du dialogue. C’est que nous appelons le double modèle du système, qui, en prenant en compte la potentielle présence d’une incohérence dans son propre énoncé, représente à la fois l’utilisateur et l’image que le système renvoie à l’utilisateur. Pour mettre en place ces fonctionnalités, nous exploitons les réseaux de mémoire de bout-en-bout, un modèle de réseau de neurones récurrent qui possède la spécificité non seulement de traiter des historiques de dialogue longs (comme un RNN ou un LSTM) mais également de créer des sauts de réflexion, permettant de filtrer l’information contenue à la fois dans l’énoncé de l’utilisateur et dans celui de l’historique de dialogue. De plus, ces sauts de réflexion servent de mécanisme d’attention “naturel” pour le réseau de mémoire, à la manière d’un décodeur de transformeur. Pour notre étude, nous améliorons, en y ajoutant nos trois fonctionnalités, un type de réseau de mémoire appelé WMM2Seq (réseau de mémoire de travail par séquence). Ce modèle s’inspire des modèles cognitifs de la mémoire, en présentant les concepts de mémoire épisodique, de mémoire sémantique et de mémoire de travail. Il obtient des résultats performants sur des tâches de génération de réponse de dialogue sur les corpus DSTC2 (homme-machine dans le domaine du restaurant) et MultiWOZ (multi-domaine créé avec Magicien d’Oz); ce sont les corpus que nous utilisons pour nos expériences. Le système obtenu grâce à ce modèle se nomme Bi-WMM2Seq. Les trois axes mentionnés précédemment apportent deux contributions principales à l’existant. En premier lieu, ceci complexifie l’intelligence du système de dialogue en le dotant d’un garde-fou (incohérences détectées). En second lieu, cela optimise à la fois le traitement des informations dans le dialogue (réponses plus précises ou plus riches) et la durée de celui-ci. Nous évaluons les performances de notre système avec premièrement les mesures de f-mesure pour les entités détectées à chaque tour de parole, deuxièmement de score BLEU pour la fluidité de l’énoncé du système et troisièmement de taux d’exactitude jointe pour la réussite du dialogue. Les résultats obtenus (+ 7% de F-mesure sur les entités détectées) montrent qu’il serait intéressant d’orienter les recherches vers des modèles de gestion de la mémoire plus cognitifs afin de réduire l’écart de performance dans un dialogue entre l’homme et la machine."

Data-driven and equation informed tools for turbulent reconstruction and classification
2022-03-24 14:00 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France

Ecole thématique Masses de Données 2022
2022-06-19 - 2022-06-24 | Bastia, France
Les thèmes de l’école 2022
Quatre thèmes seront abordés lors de l’école MDD 2022 pour toucher une large palette de chercheurs :
Gestion de données hétérogènes, revisité : Des médiateurs aux polystores modernesBlockchain du point de vue de la distribution et des bases de donnéesComment mener une campagne de mesures expérimentalesAugmenter la gestion des données avec l’IA et l’apprentissage automatique
Une semaine de rencontres et d’échanges
Un objectif fort de l’école MDD est de favoriser les échanges entre participants et de donner à chacun(e) l’occasion de s’exprimer, et de rencontrer d’autres chercheurs ou futurs chercheurs dans une ambiance conviviale et détendue.
Informations et inscriptions
https://cedric.cnam.fr/lab/confs/mdd2022/

LREC 2022
2022-06-20 - 2022-06-25
LREC aims to provide an overview of the state-of-the-art, explore new R&D directions and emerging trends, exchange information regarding LRs and their applications, evaluation methodologies and tools, on-going and planned activities, industrial uses and needs, requirements coming from e-science and e-society, with respect to both scientific/technological issues as well as policy and organisational ones.
LREC provides a unique forum for researchers, industrials and funding agencies from across a wide spectrum of areas to discuss issues and opportunities, find new synergies and promote initiatives for international cooperation, in support of investigations in language sciences, progress in language technologies (LT) and development of corresponding products, services and applications, and standards.
Programme et inscriptions : https://lrec2022.lrec-conf.org/en/

Journée scientifique Joffroy Beauquier
2022-07-11 00:00 | LISN Site Plaine, 1 rue Raimond Castaing 91190 Gif-sur-Yvette, France
Programme
9h45 - 10h30 : petit-déjeuner dans le hall (à côté de l’amphithéâtre)
10h30 : Bernadette Charron-Bost : "Distributed Computation of the Average"
11h15 : Colette Johnen : "Silent self-stabilizing schema for spanning tree-like construction"
12h00 : Luis Rodrigues : "Kauri: BFT Consensus with Pipelined Tree-Based Dissemination and Aggregation"
13h : cocktail dans le hall

Spécification d'un logiciel de traduction assistée par ordinateur à destination des langues signées
2022-09-26 14:00 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
Composition du jury
Annelies Braffort (Directrice de recherche, LISN-CNRS, Université Paris-Saclay) - Directrice de thèseMichael Filhol (Chargé de recherche, LISN-CNRS, Université Paris-Saclay) - Co-encadrant de thèseLaurence Meurant (Chercheuse qualifiée FNRS, Institut NaLTT, LSFB-Lab, Université de Namur) - apportricePierrette Bouillon (Professeure, Faculté de Traduction et d'Interprétation, Université de Genève) - RapportriceFrançois Yvon (Directeur de recherche, LISN-CNRS, Université Paris-Saclay) - ExaminateurFlorence Encrevé (Maîtresse de conférence, SFL-CNRS, Université Paris 8) - Examinatrice
Mots-clés
Traduction assistée par ordinateur - Traduction - Langue des signes
Résumé de la thèse
La traduction tient une place importante dans notre société, où la communication et le partage de l’information sont centraux. Pour aider les traducteurs dans leur tâche, les logiciels de traduction assistée par ordinateur (TAO) ont vu le jour dans les années 70, et fournissent une assistance au cours du processus de traduction. Ces logiciels se sont développés et améliorés au fil des années, au point qu’aujourd’hui, leur maîtrise est un pré-requis dans le domaine de la traduction professionnelle. Ces outils ne sont cependant pas en mesure de prendre en charge la traduction des langues des signes (LS), et ce malgré un nombre non-négligeable de locuteurs d’une part, et les encouragements sinon les obligations de la législation d’autre part. En effet, en France depuis 2005, la langue des signes française (LSF) est reconnue comme langue à part entière, et la loi stipule depuis lors le droit pour tout citoyen de jouir d’un accès total à l’information du domaine public Comme la plupart des outils numériques actuels, les logiciels de TAO reposent exclusivement sur les langues écrites. Si ces logiciels ont réellement révolutionné la traduction de l’écrit, alors pourquoi pas celle des langues signées, pour laquelle la demande est croissante ? Comment pourrait-on adapter la TAO aux langues des signes ? Quels sont les noeuds lorsqu’il est question de passer de la TAO de l’écrit à la TAO des LS ?
Cette thèse se propose de (1) identifier les fondamentaux de l’assistance informatique à la traduction, c'est-à-dire les points clés sur lesquels reposent ces logiciels, et les innovations importantes qu’ils ont pu apporter ; (2) comprendre le processus de traduction des langues des signes : quelles étapes, sont-elles systématiques, dans quel ordre ? ; (3) faire le lien avec les besoins des professionnels du domaine, proposer une première spécification logicielle pour la TAO des langues des signes ; (4) développer de premiers prototypes d’outils, et proposer un protocole pour leur évaluation. Sur la base d’un corpus vidéo de traducteurs à l’oeuvre, d’un corpus bilingue français/LSF de brèves journalistiques, ainsi que les résultats de plusieurs consultations avec des professionnels de la traduction, cette thèse apporte trois contributions.
Dans un premier temps, nous avons identifié les tâches constitutives du processus de traduction des LS en analysant des vidéos de traducteurs au travail. Nous avons également pu faire émerger non pas un ordre strict, mais malgré tout une forme de précédence dans l’exécution de ses différentes tâches. En partenariat avec d’autres professionnels de la traduction, nous avons fait émerger une liste de besoins propres au métier, et auxquels la TAO serait à même de répondre. La seconde contribution de cette thèse correspond à l’élaboration d’un cahier des charges relatif à la création d’un outil de TAO destiné à la traduction des LS. En reprenant les fondamentaux du fonctionnement des logiciels de TAO, ainsi que les nécessités induites par l’utilisation de la LSF, nous avons proposé une première spécification pour le développement d’une assistance informatisée adaptée. Nous proposons également deux prototypes qui suivent ces recommandations. Le premier est un concordancier français-langue des signes française. Le second est un environnement de travail intégré, à l’image des logiciels de TAO existants, et dans lequel le concordancier est intégré. La troisième contribution réside dans les méthodes d’évaluation et les évaluations elles-mêmes des outils développés. Cette évaluation se divise en deux aspects : un retour subjectif sur l’utilisation de l’outil, et un retour objectif quant à ses performances, au moyen de l’évaluation de la qualité de la traduction. Elle permet de mettre en avant les modifications à apporter aux prototypes, mais également au protocole d’évaluation lui-même.

Inférence logique de réseaux booléens à partir de connaissances et d’observations de processus de différenciation cellulaire
2022-09-29 14:00 - 17:00 | LISN Site Plaine, 1 rue Raimond Castaing 91190 Gif-sur-Yvette, France
La soutenance aura lieu le jeudi 29 septembre à 14h, dans la salle des thèses (salle 435) du bâtiment 650. Vous pouvez également y assister en visioconférence via le lien : https://eu.bbcollab.com/guest/7158d4f5c7a64ef1bf65b2f399daa165La soutenance se déroulera en français.
Le jury est composé de :
• Simon DE GIVRY - Chargé de recherche, INRAE, MIAT - Rapporteur
Élisabeth REMY - Directrice de recherche, CNRS, I2M - Rapportrice Franck DELAPLACE - Professeur, Université d’Évry Paris-Saclay, IBISC - Examinateur Carito GUZIOLOWSKI - Maîtresse de conférence, Centrale Nantes, LS2N - Examinatrice Fatiha SAÏS - Professeure, Université Paris-Saclay, LISN - ExaminatriceChristine FROIDEVAUX - Professeure émérite, Université Paris-Saclay, LISN - Directrice de thèse Loïc PAULEVÉ - Chargé de recherche, CNRS, LaBRI - Co-encadrant, membre invitéAndrei ZINOVYEV - Ingénieur de recherche, Institut Curie, U900 - Co-encadrant, membre invité
Résumé de la thèse
Les modèles dynamiques sont des outils importants pour l'exploration des mécanismes de régulation en biologie. Les travaux de cette thèse ont été guidés par le besoin exprimé en cancérologie et embryologie d'inférer automatiquement des réseaux booléens reproduisant des processus de différenciation cellulaire. En considérant les observations et les connaissances que les modélisateurs ont à disposition, ce mémoire de thèse présente une approche qui permet de modéliser la richesse de ce comportement cellulaire en inférant l’ensemble des réseaux booléens compatibles et qui passe à l’échelle des réseaux de régulation couramment considérés en biologie. Les travaux de thèse effectués dans le but de développer cette méthode apportent trois contributions principales.
La première contribution est la proposition d'un cadre formel sur les propriétés des données collectées pour étudier la différenciation cellulaire. Ce cadre permet de raisonner sur les propriétés dynamiques souhaitées au sein des réseaux booléens pour qu’ils soient compatibles avec ce comportement cellulaire. La deuxième contribution porte sur l'encodage du problème d’inférence de modèles comme un problème de satisfiabilité booléenne dont les solutions sont les réseaux booléens compatibles avec les données biologiques. Pour cela, des contraintes sur la dynamique des réseaux booléens correspondant aux propriétés précédemment formalisées ont été implémentées en programmation logique. La dernière contribution est l’application à des problématiques biologiques réelles de la méthode d’inférence de modèles, nommée BoNesis, qui a été développée grâce aux contraintes créées. Ces applications ont montré l’apport de l’inférence d’ensemble de modèles pour l’analyse de processus et illustré la méthodologie de modélisation, de la préparation des données biologiques à l’analyse des modèles inférés.

Soutenance de Sylvain Famié
2022-10-07 14:00 - 18:00
Jury
Robin Baurès, Maître de Conférences HDR, Université Toulouse 3 - Paul Sabatier (Rapporteur)Frédéric Danion, Chargé de Recherche CNRS, Université de Poitiers (Rapporteur)Sylvie Vernazza-Martin, Professeure des Universités, Université Paris Nanterre (Examinatrice)Laurence Mouchnino, Maître de Conférences HDR, Université Aix-Marseille (Examinatrice)Michel-Ange Amorim, Professeur des Universités, Université Paris-Saclay (Directeur)Mehdi Ammi, Professeur des Universités, Université Paris 8 (Co-Directeur)Vincent Bourdin, Ingénieur de Recherche CNRS, Université Paris-Saclay (Invité)
Résumé
La friction est une force avec laquelle nous devons composer dans la vie de tous les jours, lorsque nous manipulons des objets ou que nous les faisons glisser. Dans la présente thèse, les participants ont effectué un mouvement de frappe (avec l'index) pour faire glisser un objet plastique de forme cubique dans une gouttière jusqu’à une distance cible. Selon les conditions, les facteurs expérimentaux manipulés sont : le matériau de la surface sur laquelle l'objet glisse, l’inclinaison de la surface (-10°, 0°, ou +10°) et la distance cible, pour un nombre d'essais donné.
L'analyse de l'erreur spatiale (entre l’objet et la cible) ainsi que la vitesse initiale de l'objet après impact avec le doigt suggèrent qu‘au fil des essais, le mouvement est paramétré pour réduire l'erreur spatiale afin de calibrer un modèle interne du coefficient de friction (μ subjectif) pour une surface donnée. Par la suite, ce μ subjectif peut être généralisé lors d’un changement de condition d’inclinaison et/ou de distance, ou bien faire l'objet d’une recalibration. L’analyse biomécanique montre que dans les conditions les plus simples, les participants adoptent la rotation d’un seul segment (la main) afin de fournir l’énergie cinétique nécessaire pour atteindre la cible. Ce n’est qu’avec l’augmentation des contraintes de la tâche que certains participants décident d’adopter une nouvelle stratégie motrice en ajoutant un degré de liberté (rotation de l’avant-bras), avec un plus grand succès dans les conditions les plus difficiles. Nos résultats montrent également que certains participants préfèrent garder un paramètre spatiotemporel invariant (amplitude ou durée de la frappe) et faire varier l’autre pour contrôler la vitesse à l’impact ; tandis que d’autres participants font varier les deux paramètres privilégiant le contrôle de l’accélération avant l’impact.
En conclusion, nous apportons la preuve, qu’au travers de la répétition des essais, le cerveau se construit un modèle interne de la friction qui permet de paramétrer le mouvement balistique de frappe afin de réguler la quantité d'énergie cinétique nécessaire à l’impact, pour transmettre à l’objet la vitesse initiale appropriée afin qu’il glisse jusqu'à une distance cible.

Une journée en hommage à Yannis Manoussakis
2022-10-10 | LISN Site Plaine – Digitéo, 1 rue René THOM 91190 Gif-sur-Yvette, France
Programme détaillé : https://yannis.sciencesconf.org/
Conférenciers invités
Yandong Bai, Northwestern Polytechnical University, China
Jørgen Bang-Jensen, University of Southern Denmark
François Pirot, Université Paris-Saclay
Zsolt Tuza, MTA Rényi Institute, Budapest & University of Pannonia, Veszprém
Equipe organisatrice
Nathalie AubrunJohanne CohenBenjamin Hellouin de MénibusHao LiAntoine LobsteinFrançois PirotSonia Vanot
Le CNRS rend hommage à Yannis Manoussakishttps://www.ins2i.cnrs.fr/fr/cnrsinfo/disparition-de-yannis-manoussakis

Soutenance de Coline Gianfrotta
2022-10-10 14:30 - 17:30 | France
La soutenance aura lieu le lundi 10 octobre à 14h30, à l'UFR des Sciences de l'UVSQ (45, avenue des Etats-Unis, 78000, Versailles), au 3e étage du bâtiment Descartes (salle 301).
Jury
Christine GASPIN, Directrice de recherche, INRAE-MIAT, Castanet-Tolosan - RapportriceGuillaume FERTIN, Professeur, LS2N, Université de Nantes - RapporteurAlessandra CARBONE, Professeure, LGQB, Sorbonne Université - ExaminatriceSarah COHEN-BOULAKIA, Professeure, LISN, Université Paris-Saclay - ExaminatriceDominique BARTH, Professeur, DAVID, UVSQ, Université Paris-Saclay - Directeur de thèseAlain DENISE, Professeur, LISN & I2BC, Université Paris-Saclay - Co-encadrant, membre invitéVladimir REINHARZ, Professeur, Department of Computer Science, Université du Québec à Montréal - Membre invité
Résumé
Les molécules d'ARN sont des molécules biologiques impliquées dans de nombreuses fonctions au sein de la cellule, allant de la régulation de l'expression génétique, à la protection de la cellule. La fonction qu'assure une molécule d'ARN est intrinsèquement liée à la structure tridimensionnelle qu'adopte cette molécule dans l'espace. Cependant, la structure tridimensionnelle d'une molécule d'ARN est difficile à obtenir. C'est pourquoi l'un des enjeux liés à l'étude de ces molécules est d'être capable de prédire la structure tridimensionnelle à partir d'informations telles que la séquence en nucléotides. Dans cette thèse, nous étudions le contexte structural de motifs structuraux d’ARN dans le but de progresser vers leur prédiction. Les motifs structuraux d'ARN sont des sous-structures apparaissant de manière récurrente dans les structures d'ARN, et certains d'entre eux sont impossibles à prédire actuellement. Nous modélisons par des graphes le contexte structural topologique de ces motifs, et comparons les contextes des différentes occurrences en utilisant plusieurs algorithmes de graphes. Nous classifions ensuite les occurrences de motif selon leurs similarités de contexte topologique et selon leurs similarités de contexte 3D, à l’aide d’un algorithme de clustering recouvrant. Dans un premier temps, nous montrons sur un jeu de données de trois motifs structuraux que les similarités observées entre les contextes topologiques sont cohérentes avec les similarités entre les contextes 3D. Cela indique que le contexte topologique peut être suffisant pour déterminer le contexte 3D pour ces trois motifs. Dans un deuxième temps, nous étudions plusieurs classifications d’occurrences du motif A-minor de type I/II, selon des similarités de contexte 3D. Nous y observons que des similarités de contexte 3D existent entre occurrences non homologues, ce qui pourrait être le signe d’un phénomène de convergence évolutive. De plus, nous observons que certaines parties du contexte 3D semblent mieux conservées que d’autres entre occurrences non homologues. Dans un troisième temps, nous étudions la capacité de prédiction du contexte topologique commun à des occurrences de motif A-minor, partageant des contextes 3D similaires, ainsi que la capacité de prédiction d'un signal de séquence sur ces mêmes occurrences. Pour cela, nous étudions la fréquence d'apparition de cette topologie et de ces séquences dans des structures d'ARN en l'absence de motifs A-minor. Nous en concluons que la topologie et la séquence associées représentent un bon signal pour la majorité des classes d'occurrences homologues étudiées.

Journées communes DOR/REST
2022-10-12 - 2022-10-13 | LISN Site Plaine – Digitéo, 1 rue René THOM 91190 Gif-sur-Yvette, France
L'action transverse DOR (Décision et Optimisation Robuste) et l'axe de recherche REST (Réseaux, Energie, Santé et Transports) du GDR RO organisent un séminaire commun les mercredi 12 et jeudi 13 octobre 2022.
Programme et inscription
http://gdrro.lip6.fr/?q=node/275

Statistical learning and inverse uncertainty quantification in nuclear thermal-hydraulic simulation: application to the condensation modelling at the safety injection
2022-11-03 14:30 - 17:30 | France
Composition du Jury
Tomasz Kozlowski, Associate Professor, University of Illinois, RapporteurNicola Pedroni, Associate Professor, Polytechnic of Turin, RapporteurPierre Barbillon, Professeur, AgroParisTech, ExaminateurJordi Freixa, Fellow Professor, Technical University of Catalonia, ExaminateurJean-Luc Vacher, Ingénieur en thermohydraulique, EDF, ExaminateurEnrico Zio, Full Professor, Mines ParisTech et Polytechnic of Milan, ExaminateurDidier Lucor, Directeur de these, LISN – CNRS
Résumé
Dans le contexte industriel actuel, un effort considérable est consacré à la Vérification et à la Validation (V&V) des outils de calcul scientifiques et des modélisations physiques sous-jacentes. Plus précisément, concernant les simulations thermiques et thermohydrauliques, la méthodologie Best Estimate Plus Uncertainty (BEPU) est de plus en plus utilisée. Cette méthodologie a pour objectif, d'une part de simuler aussi fidèlement que possible les phénomènes physiques d'intérêt, et d'autre part de quantifier l'incertitude de prédiction associée. Pour ce faire, des tests expérimentaux à effets séparés (SET - Separate Effect Test) sont utilisés pour la validation car ils permettent d'évaluer aussi individuellement que possible l'impact des modèles physiques sur les quantités d'intérêt (température, pression, etc). Cependant dans certains cas, seuls des tests à effets combinés (CET  Coupled Effect Test) sont disponibles. L'effet du modèle que l'on veut valider est alors en interaction avec au moins un autre modèle, qui lui aussi est incertain et doit être validé. Pour traiter ces cas où le risque de compensation d'erreur est accru, aucune méthodologie n'a à ce stade été formalisée. L'objectif de cette thèse est de développer/établir une nouvelle démarche de validation en présence de CET et d'élaborer des méthodes statistiques avancées pour la quantification des incertitudes associées. Enfin, la faisabilité d'extrapoler hors de leur domaine de validation les modèles utilisées sera testée (évaluation des effets d'échelle). Pour développer ces nouvelles méthodes, nous proposons d'étudier un cas pratique : la validation et la quantification des incertitudes des modèles physiques utilisées pour la prédiction de la température de paroi de la cuve d'un réacteur nucléaire au cours des transitoires accidentels de type APRP (Accident de Perte de Réfrigérant Primaire). Cet accident déclenche une injection de sécurité à basse température pouvant être à l'origine d'un choc thermique pressurisé (« Choc froid » ou « Pressurized Thermal Shock  PTS ») risquant d'endommager la cuve du réacteur. Les modèles parmi les plus influents pour ce type de transitoire concernent la condensation de la vapeur lors de l'injection d'eau froide.

Journées des GDR LIFT et TAL
2022-11-14 - 2022-11-15 | France
Thèmes des journées : “Ressources” et “Langues et Tâches peu dotées”
Thèmes des GdR LIFT et TAL
Retours d’expérience concernant l’emploi et/ou le développement d’outils informatiques pour l’analyse linguistiqueLinguistique informatique et Science ouverte : perspectives ouvertes par le partage des données, des outils et des publicationsModélisation informatique et linguistique formelle (théorie des langages formels, grammaires d’unification, théorie de la preuve…)Mise en dialogue des modèles linguistiques et des modèles d’apprentissage automatique (de tous types : approches générativeset discriminantes, approches statistiques neuronales, approches de type encodeurs-décodeurs…)Méthodes non supervisées ou faiblement supervisées pour l’analyse des langues peu dotées, peu écrites ou non documentéesRéflexions au sujet de l’automatisation des processus d’analyse et de validationModèles et méthodes pour le TAL : modèles formels, apprentissage automatique supervisé, non-supervisié, et auto-supervisé, zero-shot, promptingTraitement du langage multimodalité et intermodal : parole, langue des signes, interactions, TAL conversationnel, grounding, images, vidéosMultilinguisme, Muliplicité des langues : traduction automatique, modèles multilingues et cross-lingues, traitement de nouvelles langues, langues peu dotéesAccès à l’information et fouille de textes : modélisation et découverte des connaissances, prise en compte des utilisateurs, passage à l’échelleMéthodologie, explicabilité, biais et considérations éthiques

Problématiques fondamentales et pratiques dans la distillation de l'air
2022-11-17 14:00 - 16:00 | France
La distillation de l'air est le procédé industriel le plus utilisé pour la production des gaz de l'air (oxygène, azote, argon, etc.). Nous proposons de présenter quelques problématiques actuelles autour de ce procédé, en nousattardant sur les verrous scientifiques "fondamentaux" qui subsistent dans ce domaine. Cela permettra également d'illustrer certaines différences entre recherche académique fondamentale et recherche appliquée, et d'insister sur la complémentarité de ces deux approches.
Salle des séminaires FAST-LPTMS (Bât. 530, salle C.120, 1er)Accès : http://www.fast.u-psud.fr/fr/plan.php

La détection d’opinion et la mesure de sa fiabilité
2022-11-29 11:30 - 13:30 | LISN Site Belvédère, Rue du Belvédère Campus universitaire bât 507 91405 Orsay, France
La présence d’information de nature subjective (opinion) apparaît de manière très visible sur différentes plate-formes. Ces opinions ont une grande importance dans plusieurs domaines (politique, commercial, ou industriel). Elles jouent un rôle essentiel dans la prise de décision des internautes. Une opinion non fiable peut mener à des décisions désastreuses. De ce fait, déterminer l’opinion fiable est d’une importance primordiale.Mes travaux de recherche portent sur cette problématique. La première tâche est de pouvoir modéliser et déterminer l’opinion dans un document. Pour ce faire, nous proposons une approche lexicale afin de déterminer les documents porteurs d’opinion. Nous exploitons différentes ressources subjectives, ouvertes, disponibles telles que IMDb, ROTTEN, CHESLY et MPQA qui constituent la source d’opinions. Nous supposons que si un document est similaire à cette source, il est vraisemblablement porteur d’opinions. Pour estimer cette vraisemblance, nous proposons de modéliser le document à tester et la source d’opinion par des modèles de langue (des modèles probabilistes) et de mesurer la similarité des deux modèles. Plus cette similarité est grande et plus le document est vraisemblablement subjectif. La deuxième tâche est de pouvoir déterminer les documents porteurs d’opinions fiables. A cet effet, nous introduisons le concept de score de confiance qui mesure la fiabilité de l’opinion du document par rapport au sujet en question. Le calcul de ce score se base sur différentes informations telles que le nombre de commentaires postés, le nombre de commentaires utiles et d’autres informations liées au commentaire et à l’utilisateur. Ce score de confiance est combiné au score d’opinion pour l’obtention d’un score final permettant de retourner les documents porteurs d’opinions fiables.
