Publications

2025

• Graphically Speaking: Unmasking Abuse in Social Media with Conversation Insights
  
  • Nouri Célia
  • Cointet Jean-Philippe
  • Clavel Chloé
  , 2025. Detecting abusive language in social media conversations poses significant challenges, as identifying abusiveness often depends on the conversational context, characterized by the content and topology of preceding comments. Traditional Abusive Language Detection (ALD) models often overlook this context, which can lead to unreliable performance metrics. Recent Natural Language Processing (NLP) approaches that incorporate conversational context often rely on limited or overly simplified representations of this context, leading to inconsistent and sometimes inconclusive results. In this paper, we propose a novel approach that utilizes graph neural networks (GNNs) to model social media conversations as graphs, where nodes represent comments, and edges capture reply structures. We systematically investigate various graph representations and context windows to identify the optimal configurations for ALD. Our GNN model outperforms both context-agnostic baselines and linear context-aware methods, achieving significant improvements in F1 scores. These findings demonstrate the critical role of structured conversational context and establish GNNs as a robust framework for advancing context-aware ALD. Our code is available at https://github.com/celia-nouri/ConversationALD/.
• StreamMLOps: Online Learning in Practice from Big Data Streams & Real-Time Applications
  
  • Barry Mariam
  • Montiel Jacob
  • Bifet Albert
  • Manchev Nikolay
  • Wadkar Sameer
  • Halford Max
  • Chiky Raja
  • El Jaouhari Saad
  • Shakman Katherine B
  • Al Fehaily Joudi
  • Le Deit Fabrice
  • Tran Vinh-Thuy
  • Guerizec Eric
  , 2025. <div><p>Learning and serving from evolving streaming data to real-time inference in production is a challenging problem. Traditionally, data is partitioned and processed in batches to train machine learning models. In dynamic environments, models' performance drops over time (model degradation), requiring new models to be trained and deployed in their place. This paper deals with the MLOps aspects of deploying online and continual learning models addressing the requirements in the production of real-time applications. We have demonstrated that Online Learning methods can be scaled horizontally in production to meet the high-velocity streaming feature pipeline. The design is based on open platforms and the paper demonstrates an MLOps strategy to execute Online Learning and Predictions, perform Online Learning on a stream and deploy an online learning model version without stream interruption. The approach is suitable for highly regulated industries like banking which also have high throughput requirements. Experiments on high-dimensional and feature-evolving data streams (Malicious URL detection) demonstrate the effectiveness and efficiency of online learning models in terms of time, space and F1-score. Finally, we provide some best practices for using architectural design to deploy these dynamic models on a stream and perform Online Learning and deploy them without stopping the streaming pipeline using open-source technology such as Kafka, Flink, MLflow and river.</p></div> (10.1109/ICDE55515.2023.00272)
  DOI : 10.1109/ICDE55515.2023.00272
• Survey on forecasting for electric vehicle charging-power demand
  
  • Yang Wen
  • Laurenty Ignacio
  • Fontaine Mathieu
  • d'Alché-Buc Florence
  , 2025.
• Investigating Raman backscattering decay and the perspective of time-multiplexed quantum communications
  
  • Verdier Pierre-Enguerrand
  • Alléaume Romain
  • Rivera Thomas
  Optics Express, Optical Society of America - OSA Publishing, 2025, 33 (15), pp.31029-31041. We have studied the temporal dynamics of Raman scattering caused by classical power in optical fiber and its impact on counter-propagating quantum signals. We investigated, on the entire telecom bands, the duration during which the quantum channel cannot be used in a time-division multiplexing context. Thereby, we estimated performance in terms of secure key rates within the framework of time-division multiplexing. By applying our model to the discrete variable quantum key distribution (DV-QKD) protocol BB84 in different optical communication contexts, we demonstrate the feasibility of counter-propagating time-multiplexing classical and quantum communications. Our results highlight a better preservation of the maximum communication distance for quantum channels compared to other multiplexing schemes. (10.1364/OE.561961)
  DOI : 10.1364/OE.561961
• Don’t Forget Your Inverse DDIM for Image Editing
  
  • Gomez-Trenado Guillermo
  • Mesejo Pablo
  • Cordón Oscar
  • Lathuilière Stéphane
  IEEE Computational Intelligence Magazine, Institute of Electrical and Electronics Engineers, 2025, 20 (3), pp.10-18. The field of text-to-image generation has undergone significant advancements with the introduction of diffusion models. Nevertheless, the challenge of editing real images persists, as most methods are either computationally intensive or produce poor reconstructions. This paper introduces SAGE (Self-Attention Guidance for image Editing) - a novel technique leveraging pre-trained diffusion models for image editing. SAGE builds upon the DDIM algorithm and incorporates a novel guidance mechanism utilizing the self-attention layers of the diffusion U-Net. This mechanism computes a reconstruction objective based on attention maps generated during the inverse DDIM process, enabling efficient reconstruction of unedited regions without the need to precisely reconstruct the entire input image. Thus, SAGE directly addresses the key challenges in image editing. The superiority of SAGE over other methods is demonstrated through quantitative and qualitative evaluations and confirmed by a statistically validated comprehensive user study, in which all 47 surveyed users preferred SAGE over competing methods. Additionally, SAGE ranks as the top-performing method in seven out of 10 quantitative analyses and secures second and third places in the remaining three. (10.1109/MCI.2025.3563859)
  DOI : 10.1109/MCI.2025.3563859
• Assessing the Vulnerabilities of RISC-V using the gem5 Simulator
  
  • Khan Mahreen
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2025. <div><p>Microarchitectural timing side-channel attacks exploit variations in execution times caused by the underlying hardware to extract sensitive information. These attacks leverage architectural features like caches, branch predictors, and speculative execution.</p></div>
• Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
  
  • Cortés Adrien
  • Rehm Rémi
  • Letzelter Victor
  , 2025. We introduce TimeMCL, a method leveraging the Multiple Choice Learning (MCL) paradigm to forecast multiple plausible time series futures. Our approach employs a neural network with multiple heads and utilizes the Winner-Takes-All (WTA) loss to promote diversity among predictions. MCL has recently gained attention due to its simplicity and ability to address ill-posed and ambiguous tasks. We propose an adaptation of this framework for time-series forecasting, presenting it as an efficient method to predict diverse futures, which we relate to its implicit quantization objective. We provide insights into our approach using synthetic data and evaluate it on real-world time series, demonstrating its promising performance at a light computational cost.
• Two-Stage MIMO Equalization for Long Haul Coupled Multi-Core Fiber Systems
  
  • Darweesh Jamal
  • Abouseif Akram
  • Rekaya Ben Othman Ghaya
  • Jaouën Yves
  • Klaimi Rami
  , 2025.
• Beyond Low-rank Decomposition: A Shortcut Approach for Efficient On-Device Learning
  
  • Nguyen Le-Trung
  • Quélennec Aël
  • Nguyen van Tam
  • Tartaglione Enzo
  , 2025, Forty-second International Conference on Machine Learning. On-device learning has emerged as a promising direction for AI development, particularly because of its potential to reduce latency issues and mitigate privacy risks associated with device-server communication, while improving energy efficiency. Despite these advantages, significant memory and computational constraints still represent major challenges for its deployment. Drawing on previous studies on low-rank decomposition methods that address activation memory bottlenecks in backpropagation, we propose a novel shortcut approach as an alternative. Our analysis and experiments demonstrate that our method can reduce activation memory usage, even up to 120.09× compared to vanilla training, while also reducing overall training FLOPs up to 1.86× when evaluated on traditional benchmarks.
• To Each Metric Its Decoding: Post-Hoc Optimal Decision Rules of Probabilistic Hierarchical Classifiers
  
  • Plaud Roman
  • Perez-Lebel Alexandre
  • Labeau Matthieu
  • Saillenfest Antoine
  • Bonald Thomas
  , 2025. Hierarchical classification offers an approach to incorporate the concept of mistake severity by leveraging a structured, labeled hierarchy. However, decoding in such settings frequently relies on heuristic decision rules, which may not align with task-specific evaluation metrics. In this work, we propose a framework for the optimal decoding of an output probability distribution with respect to a target metric. We derive optimal decision rules for increasingly complex prediction settings, providing universal algorithms when candidates are limited to the set of nodes. In the most general case of predicting a subset of nodes, we focus on rules dedicated to the hierarchical hF β scores, tailored to hierarchical settings. To demonstrate the practical utility of our approach, we conduct extensive empirical evaluations, showcasing the superiority of our proposed optimal strategies, particularly in underdetermined scenarios. These results highlight the potential of our methods to enhance the performance and reliability of hierarchical classifiers in real-world applications. The code is available at https://github.com/RomanPlaud/ hierarchical decision rules Sketch of the proof. Key elements of the decoding strategy are displayed in Algorithm 1. We give here some general insights on how the algorithm is derived.
• Assessing the Vulnerabilities of RISC-V using the gem5 Simulator (Access-Retired)
  
  • Khan Mahreen
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2025. <div><p>Emerging RISC-V processors require rigorous security evaluation to address microarchitectural vulnerabilities inherent in their rapidly evolving ecosystem. A recent paper [Gea23] implemented both known and novel side-channel attacks targeting commercial RISC-V CPUs (U74 and C906). While this hardware-based research confirmed vulnerabilities, it could not provide detailed insights into attack dynamics. We bridge this gap using the gem5 simulation framework to systematically analyze side-channel attacks on RISC-V architectures. Our paper focuses on the accessretired attack, which exploits the unprivileged rdinstret instruction to infer protected filesystem data. By tracking retired instruction counts, attackers detect microarchitectural state differences caused by directory access checks. We utilize the gem5 simulator in full-system (FS) mode to capture kernel-level behaviors, allowing us to analyze critical performance metrics including instruction retirement, cache performance, and branch prediction statistics. This detailed simulationbased analysis is essential for understanding the behavior of the attack and for developing effective countermeasures. Advancing RISC-V security research with simulation tools like gem5 is thus a promising direction for mitigating future side-channel vulnerabilities.</p></div>
• U-DREAM: Unsupervised Dereverberation guided by a Reverberation Model
  
  • Bahrman Louis
  • Fontaine Mathieu
  • Richard Gaël
  , 2025. This paper explores the outcome of training state-ofthe-art dereverberation models with supervision settings ranging from weakly-supervised to fully unsupervised, relying solely on reverberant signals and an acoustic model for training. Most of the existing deep learning approaches typically require paired dry and reverberant data, which are difficult to obtain in practice. We develop instead a sequential learning strategy motivated by a bayesian formulation of the dereverberation problem, wherein acoustic parameters and dry signals are estimated from reverberant inputs using deep neural networks, guided by a reverberation matching loss. Our most data-efficient variant requires only 100 reverberation-parameter-labelled samples to outperform an unsupervised baseline, demonstrating the effectiveness and practicality of the proposed method in low-resource scenarios.
• DRST: a Non-Intrusive Framework for Performance Analysis in Softwarized Networks
  
  • Liu Qiong
  • Lin Jianke
  • Zhang Tianzhu
  • Linguaglossa Leonardo
  , 2025. <div><p>The last decade has witnessed the proliferation of network function virtualization (NFV) in the telco industry, thanks to its unparalleled flexibility, scalability, and cost-effectiveness. However, as the NFV infrastructure is shared by virtual network functions (VNFs), sporadic resource contentions are inevitable. Such contention makes it extremely challenging to guarantee the performance of the provisioned network services, especially in high-speed regimes (e.g., Gigabit Ethernet). Existing solutions typically rely on direct traffic analysis (e.g., packet-or flow-level measurements) to detect performance degradation and identify bottlenecks, which is not always applicable due to significant integration overhead and systemlevel constraints. This paper complements existing solutions with a lightweight, non-intrusive framework for online performance inference that easily adapts to drift (i.e., a change over time of the actual state of our system). Instead of direct data-plane collection, we reuse hardware features in the underlying NFV infrastructure, introducing negligible interference in the data-plane. Our Drift-Resilient and Self-Tuning (DRST) framework can be integrated into existing NFV systems with minimal engineering effort and operates without the need for predefined traffic models or VNF-specific customization. DRST is deployed via a lightweight MLOps pipeline that automates the adaptation under runtime drift. We show how DRST can deliver accurate performance inference or diagnose run-time bottleneck diagnose, as demonstrated through comprehensive evaluation across diverse NFV scenarios.</p></div>
• Défi du GDR GPL ADaptation DYnamique et ConTinue ADDYCT
  
  • Laval Jannik
  • Philippe Jolan
  • Cariou Eric
  • Ameur-Boulifa Rabéa
  • Guérin Sylvain
  • Kouchnarenko Olga
  • Guermouche Nawal
  , 2025. L'adaptabilité est un enjeu majeur des systèmes complexes dans des environnements dynamiques. Ces environnements regroupent les architectures distribuées composées de systèmes "component-based" et les infrastructures déployées sur des plateformes hétérogènes à différentes échelles : Cloud, Fog, Edge, ou IoT. Tous ces systèmes doivent être capables d'ajuster leur configuration de manière autonome pour répondre à des évènements exogènes et/ou endogènes. Les systèmes logiciels doivent être considérés dès leur conception comme des systèmes durables en termes de temporalité (Système temps long, Cycle de vie, Couplage), de scalabilité (Granularité, Interfaces, Gestion massive de données) et d'hétérogénéité (Intégration, Interopérabilité). Ces challenges sont d'autant plus importants lorsque la taille du système est grande et couplée avec des artefacts matériels (IoT, CPS, Jumeaux numériques, Cloud...) L'objectif de ce défi est de modéliser, analyser et d'implémenter des moyens et des politiques d'adaptation pour des systèmes logiciels complexes (distribués, componentisés etc.). L'approche adoptée repose sur les boucles de contrôle MAPE-K, un modèle d'auto-adaptation autonome capable de s'ajuster dynamiquement à un environnement permettant de répondre à la nécessité d'intégrer les données collectées et leurs modèles de traitement, le système opérant et sa connexion avec son environnement, l'évolution dynamique et la nécessité de maintenir une représentation fidèle du comportement attendu.
• Deep Learning for Embedded Cybersecurity
  
  • Varillon Arnaud
  , 2025. Public-key cryptography is one of the core pillars of cybersecurity, in particular thanks to the authentication schemes it enables. It is embedded in many ubiquitous objects, such as hardware wallets. Side-channel attacks are one of the major threats to such devices. In particular, the advances in machine learning, and more specifically deep learning, which have marked the last ten years, seem likely to make such attacks remarkably effective. In such a hostile environment, assessing the true security level of devices intended for cryptographic use is of utmost importance: indeed, it has now even become vital to the smooth running of information systems.In this thesis, we have appraised the security, of implementations, reputed to be the more secure ones in the face of such attacks (“power” and “EM” channels), which manipulate the secret key bit by bit. Numerous contributions, which mainly use deep learning, have been published on this subject. Unfortunately, none of them provides any guarantee regarding the robustness of the device under study in the face of such attacks: each time, the method being described does not allow one to state with certainty that it is not possible to find a more powerful attack than that being presented. The security level of the latter devices is therefore potentially underestimated. More specifically, fundamental aspects of the classification task associated with any attack, such as the shape of its decision boundary or the optimality - in terms of attack performance - of the features derived from the sampling of the side-channel under consideration, are never addressed. Therefore, we have sought to find methodologies that are as close to optimality as possible given the conditions imposed by the exercise (for example, the possibility of configuring the key used by the target for in-depth analyses).Initially, assuming that an attacker can control the key that is parameterized in the target, only vertical attacks are considered. In this context, the optimal effectiveness of the joint use of NICV, for feature selection, and the perceptron, for classification, is highlighted from a theoretical point of view. In particular, the security of a cryptographic library hitherto considered robust (libecc) is called into question. Secondly, assuming that an attacker cannot set the key as he wishes in the target, yet still has a functionally perfect clone, another procedure is proposed for carrying out the security evaluation, this time using horizontal (collision-based) attacks based on an unsupervised learning technique which, because it requires (by definition) minimal training at most, is better suited to such a scenario. Compared to the state of the art, the approach followed is closer to optimality without however achieving it, but avenues are suggested to get there in the near future. Last, to validate these findings, experimental verifications have been carried out on a board (STM32F407) which features a Cortex-M4 processor that can be found in many hardware wallets (e.g. Trezor Model T).
• Planning Socio-Emotional Response Generation for Conversational Agents
  
  • Vanel Lorraine
  , 2025. Conversational systems are now capable of producing impressive and generally relevant responses. However, we have no visibility or control over the socio-emotional strategies behind state-of-the-art large language models (LLMs). This poses a problem in terms of their transparency and thus their trustworthiness for critical applications, such as industrial customer service virtual agents. This thesis aims to develop socially intelligent conversational systems that can model the conversational dynamics of user and agent social behaviours. To train models in the role of customer service agents, we need to capture the various nuances of their speech patterns, which often combine emotional support and problem-solving skills. We design a multi-label approach, modelling the behaviours expressed in one speaker as a chronologically ordered sequence of socio-emotional labels, including emotions and conversational actions.We propose a two-module architecture. The planning module uses the dialogue's history to generate the best sequence of social and emotional strategies for the response. The generative module conditions the response generation according to the predicted sequence of labels to improve the quality of the final answer while allowing for transparency and controllability. To efficiently adapt the system to our use case, we require adequate datasets; however, no publicly available corpus meets all our criteria, including multi-label annotations and French task-oriented conversations. To address this, we introduce DATA-SERGE, a new corpus composed of French customer service conversations, annotated following a comprehensive multi-label scheme. Additionally, we observe that existing automatic metrics are lacking to properly evaluate such a system. We present a human evaluation protocol and new scores to fill this gap.We evaluate this architecture on both DailyDialog, an English open-domain dataset, and DATA-SERGe. The findings support our primary research hypothesis and demonstrate that, even in highly domain-specific contexts like French customer service data, planning sequences of labels has a positive impact on response generation.
• RESCUE: Multi-Robot Planning Under Resource Uncertainty and Objective Criticality
  
  • Cordeiro Franco
  • Tardieu Samuel
  • Pautet Laurent
  , 2025, 37th Euromicro Conference on Real-Time Systems (ECRTS 2025), pp.5:1-5:23. Robot planning in distributed systems, such as drone fleets performing active perception missions, presents complex challenges. These missions require cooperation to achieve objectives like collecting sensor data or capturing images. Multi-robot systems offer significant advantages, including faster execution and increased robustness, as robots can compensate for individual failures. However, resource costs, affected by environmental factors such as wind or terrain, are highly uncertain, impacting battery consumption and overall performance. Mission objectives are often prioritized by criticality, such as retrieving data from low-battery sensors to prevent data loss. Addressing these priorities requires sophisticated scheduling to navigate high-dimensional state-action spaces. While heuristics are useful for approximating solutions, few approaches extend to multi-robot systems or adequately address cost uncertainty and criticality, particularly during replanning. The Mixed-Criticality (MC) paradigm, extensively studied in real-time scheduling, provides a framework for handling cost uncertainty by ensuring the completion of high-critical tasks. Despite its potential, the application of MC in distributed systems remains limited. To address the decision-making challenges faced by distributed robots operating under cost uncertainty and objective criticality, we propose four contributions: a comprehensive model integrating criticality, uncertainty, and robustness; distributed synchronization and replanning mechanisms; the incorporation of mixed-criticality principles into multi-robot systems; and enhanced resilience against robot failures. We evaluated our solution, named RESCUE, in a simulated scenario and show how it increases the robustness by reducing the oversizing of the system and completing up to 40% more objectives. We found an increase in resilience of the multi-robot system as our solution not only guaranteed the safe return of every non-faulty robot, but also reduced the effects of a faulty robot by up to 14%. We also computed the performance gain compared to using MCTS in a single robot of up to 2.31 for 5 robots. (10.4230/LIPIcs.ECRTS.2025.5)
  DOI : 10.4230/LIPIcs.ECRTS.2025.5
• Side-Channel Attack Detection using gem5 and Machine Learning: A Case Study on Fault-based Attacks in RISC-V
  
  • Khan Mahreen
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2025. <div><p>Microarchitectural side-channel attacks pose a significant threat to modern computing architectures. This paper presents a machine learning-based methodology for detecting these attacks using the gem5 simulator, focusing on the recently discovered Flush+Fault attack [6] on RISC-V. Our approach follows a three-phase process. The first phase is data collection, where we simulate attack and non-attack scenarios in gem5 and extract microarchitectural features indicative of side-channel activity. The second phase is the training phase, where we utilize machine learning (ML) techniques to build a classification model capable of distinguishing between normal execution and attack patterns. The last phase is the testing phase, where we evaluate the trained model using various performance metrics to validate its accuracy and precision. To the best of our knowledge, this is the first detection framework for Flush+Fault attacks [6] on RISC-V, showcasing its effectiveness in mitigating emerging threats. Our results indicate that gem5 metrics combined with machine learning models can reliably detect Flush+Fault attacks, achieving 0.99 accuracy with random forest (RF), 0.96 with support vector machine (SVM), and 0.95 with naïve bayes (NB). Moreover, this methodology is adaptable to different side-channel attacks and architectures, making it a promising approach for strengthening microarchitectural security.</p></div>
• Side-Channel Attack Detection using gem5 and ML: A Case Study on Fault-based Attacks in RISC-V
  
  • Khan Mahreen
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2025. <div><p>Microarchitectural timing side-channel attacks exploit variations in execution times caused by the underlying hardware to extract sensitive information. These attacks leverage architectural features like caches and branch predictors. For thorough analysis, we use Gem5 simulations to analyze Flush+Fault attack behavior on RISC-V.</p></div>
• Neural networks for solition prediction in optical communication
  
  • Mukhopadhyay Meghna
  • Thedrez Bruno
  , 2025. <div><p>We study a neural network approach for predicting the number of solitons in WDM signals, extending it to the computation of the nonlinear Fourier transform eigenvalues in the case of Satsuma-Yajima signals.</p></div>
• Quantum-aware network planning and integration
  
  • Ware Cédric
  • Lourdiane Mounia
  , 2025. In order to broaden the adoption of highly-demanded quantum functionalities such as QKD, there is a need for having quantum signals coexist with classical traffic over the same physical medium, typically optical fibers in already-deployed networks. Beyond the experimental point-to-point demonstrations of the past few years, efforts are now underway to integrate QKD at the network level: developing interfaces with the software-defined-network ecosystem; but also network planning tools that satisfy physical-layer contraints jointly on the classical and quantum signals. We have found that in certain situations, naïve network planning prioritizing quantum traffic drastically degrades classical capacity, whereas a quantum-aware wavelength assignment heuristic allows coexistence with minimal impact on both capacities. More such techniques will be required to enable widespread deployment of QKD and other future quantum functionalities. (10.1109/ICTON67126.2025.11125476)
  DOI : 10.1109/ICTON67126.2025.11125476
• First Order Logic with Fuzzy Semantics for Describing and Recognizing Nerves in Medical Images
  
  • Bloch Isabelle
  • Bonnot Enzo
  • Gori Pietro
  • La Barbera Giammarco
  • Sarnacki Sabine
  , 2025. This article deals with the description and recognition of fiber bundles, in particular nerves, in medical images, based on the anatomical description of the fiber trajectories. To this end, we propose a logical formalization of this anatomical knowledge. The intrinsically imprecise description of nerves, as found in anatomical textbooks, leads us to propose fuzzy semantics combined with first-order logic. We define a language representing spatial entities, relations between these entities and quantifiers. A formula in this language is then a formalization of the natural language description. The semantics are given by fuzzy representations in a concrete domain and satisfaction degrees of relations. Based on this formalization, a spatial reasoning algorithm is proposed for segmentation and recognition of nerves from anatomical and diffusion magnetic resonance images, which is illustrated on pelvic nerves in pediatric imaging, enabling surgeons to plan surgery.
• Stein's method for max-stable random vectors
  
  • Costacèque Bruno
  • Decreusefond Laurent
  , 2025. Motivated by the omnipresence of extreme value distributions in limit theorems involving extremes of random processes, we adapt Stein's method to include these laws as possible target distributions. We do so by using the generator approach of Stein's method, which is possible thanks to a recently introduced family of semi-groups. We study the corresponding Stein solution and its properties when the working distance is either the smooth Wasserstein distance or the Kolmogorov distance. We make use of those results to bound the distance between two max-stable random vectors, as well as to get a rate of convergence for the de Haan-LePage series in smooth Wasserstein distance. (10.48550/arXiv.2507.00463)
  DOI : 10.48550/arXiv.2507.00463
• Arithmétisation de la partie entière et applications à la cryptographie
  
  • Berthet Pierre-Augustin
  • Tavernier Cédric
  , 2025. Dans ce travail nous proposons une méthodologie pour calculer la partie entière à l'aide uniquement d'additions et de multiplications, la rendant ainsi compatible avec le chiffrement homomorphe.
• Cartographier l'intelligence artificielle : collecte et analyse d'un corpus de chartes et manifestes sur l'éthique de l'IA
  
  • Viard Tiphaine
  • Delarue Simon
  • Gornet Mélanie
  • Boritchev Maria
  , 2025. Nous présentons ici un corpus de 436 chartes et manifestes au sujet de l'éthique de l'intelligence artificielle, ainsi qu'une analyse discursive de celui-ci, permettant de comprendre les enjeux autour desquels le monde social de l'éthique de l'IA se structure. Nous mettons à disposition le corpus, sa documentation, et le code permettant son analyse.