Publications

2022

• Towards Globally Optimized Hybrid Homomorphic Encryption - Featuring the Elisabeth Stream Cipher
  
  • Cosseron Orel
  • Hoffmann Clément
  • Méaux Pierrick
  • Standaert François-Xavier
  , 2022.
• DNN-FREE LOW-LATENCY ADAPTIVE SPEECH ENHANCEMENT BASED ON FRAME-ONLINE BEAMFORMING POWERED BY BLOCK-ONLINE FASTMNMF
  
  • Nugraha Aditya Arie
  • Sekiguchi Kouhei
  • Fontaine Mathieu
  • Bando Yoshiaki
  • Yoshii Kazuyoshi
  , 2022. This paper describes a practical dual-process speech enhancement system that adapts environment-sensitive frame-online beamforming (front-end) with help from environment-free block-online source separation (back-end). To use minimum variance distortionless response (MVDR) beamforming, one may train a deep neural network (DNN) that estimates timefrequency masks used for computing the covariance matrices of sources (speech and noise). Backpropagation-based runtime adaptation of the DNN was proposed for dealing with the mismatched training-test conditions. Instead, one may try to directly estimate the source covariance matrices with a state-ofthe-art blind source separation method called fast multichannel non-negative matrix factorization (FastMNMF). In practice, however, neither the DNN nor the FastMNMF can be updated in a frame-online manner due to its computationally-expensive iterative nature. Our DNN-free system leverages the posteriors of the latest source spectrograms given by block-online FastMNMF to derive the current source covariance matrices for frame-online beamforming. The evaluation shows that our frame-online system can quickly respond to scene changes caused by interfering speaker movements and outperformed an existing block-online system with DNN-based beamforming by 5.0 points in terms of the word error rate.
• Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF
  
  • Jayneel Parekh
  • Sanjeel Parekh
  • Pavlo Mozharovskyi
  • Florence d'Alché-Buc
  • Richard Gael
  , 2022. This paper tackles post-hoc interpretability for audio processing networks. Our goal is to interpret decisions of a trained network in terms of high-level audio objects that are also listenable for the end-user. To this end, we propose a novel interpreter design that incorporates non-negative matrix factorization (NMF). In particular, a regularized interpreter module is trained to take hidden layer representations of the targeted network as input and produce time activations of pre-learnt NMF components as intermediate outputs. Our methodology allows us to generate intuitive audio-based interpretations that explicitly enhance parts of the input signal most relevant for a network's decision. We demonstrate our method's applicability on popular benchmarks, including a real-world multi-label classification task.
• Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts
  
  • Gonthier Nicolas
  • Ladjal Saïd
  • Gousseau Yann
  Computer Vision and Image Understanding, Elsevier, 2022, 214. Weakly supervised object detection (WSOD) using only image-level annotations has attracted a growing attention over the past few years. Whereas such task is typically addressed with a domain-specific solution focused on natural images, we show that a simple multiple instance approach applied on pre-trained deep features yields excellent performances on non-photographic datasets, possibly including new classes. The approach does not include any fine-tuning or cross-domain learning and is therefore efficient and possibly applicable to arbitrary datasets and classes. We investigate several flavors of the proposed approach, some including multi-layers perceptron and polyhedral classifiers. Despite its simplicity, our method shows competitive results on a range of publicly available datasets, including paintings (People-Art, IconArt), watercolors, cliparts and comics and allows to quickly learn unseen visual categories. (10.1016/j.cviu.2021.103299)
  DOI : 10.1016/j.cviu.2021.103299
• Statistical learning from biased training samples
  
  • Clémençon Stéphan
  • Laforgue Pierre
  Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2022, 16 (2), pp.6086-6134. With the deluge of digitized information in the Big Data era, massive datasets are becoming increasingly available for learning predictive models. However, in many practical situations, the poor control of the data acquisition processes may naturally jeopardize the outputs of machine learning algorithms, and selection bias issues are now the subject of much attention in the literature. The present article investigates how to extend Empirical Risk Minimization, the principal paradigm in statistical learning, when training observations are generated from biased models, i.e., from distributions that are different from that in the test/prediction stage, and absolutely continuous with respect to the latter. Precisely, we show how to build a “nearly debiased” training statistical population from biased samples and the related biasing functions, following in the footsteps of the approach originally proposed in [46]. Furthermore, we study from a nonasymptotic perspective the performance of minimizers of an empirical version of the risk computed from the statistical population thus created. Remarkably, the learning rate achieved by this procedure is of the same order as that attained in absence of selection bias. Beyond the theoretical guarantees, we also present experimental results supporting the relevance of the algorithmic approach promoted in this paper. (10.1214/22-EJS2084)
  DOI : 10.1214/22-EJS2084
• Cybersecurity in Smart Homes: Architectures, Solutions and Technologies
  
  • Khatoun Rida
  , 2022. Smart homes use Internet-connected devices, artificial intelligence, protocols and numerous technologies to enable people to remotely monitor their home, as well as manage various systems within it via the Internet using a smartphone or a computer. A smart home is programmed to act autonomously to improve comfort levels, save energy and potentially ensure safety; the result is a better way of life. Innovative solutions continue to be developed by researchers and engineers and thus smart home technologies are constantly evolving. By the same token, cybercrime is also becoming more prevalent. Indeed, a smart home system is made up of connected devices that cybercriminals can infiltrate to access private information, commit cyber vandalism or infect devices using botnets. This book addresses cyber attacks such as sniffing, port scanning, address spoofing, session hijacking, ransomware and denial of service. It presents, analyzes and discusses the various aspects of cybersecurity as well as solutions proposed by the research community to counter the risks. Cybersecurity in Smart Homes is intended for people who wish to understand the architectures, protocols and different technologies used in smart homes.
• Participation de l’équipe TGV à DEFT 2022 : Prédiction automatique de notes d’étudiants à des questionnaires en fonction du type de question
  
  • Gaudray Bouju Vanessa
  • Guettier Margot
  • Lerus Gwennola
  • Guibon Gaël
  • Labeau Matthieu
  • Lefeuvre Luce
  , 2022, pp.23-35. Cet article présente l’approche de l’équipe TGV lors de sa participation à la tâche de base de DEFT 2022, dont l’objectif était de prédire automatiquement les notes obtenues par des étudiants sur la base de leurs réponses à des questionnaires. Notre stratégie s’est focalisée sur la mise au point d’une méthode de classification des questions en fonction du type de réponse qu’elles attendent, de manière à pouvoir mener une approche différenciée pour chaque type. Nos trois runs ont consisté en une approche non différenciée, servant de référence, et deux approches différenciées, la première se basant sur la constitution d’un jeu de caractéristiques et la seconde sur le calcul de TF-IDF et de la fonction de hashage. Notre objectif premier était ainsi de vérifier si des approches dédiées à chaque type de questions sont préférables à une approche globale.
• Survey of Exposure to RF Electromagnetic Fields in the Connected Car
  
  • Tognola Gabriella
  • Bonato Marta
  • Benini Martina
  • Aerts Sam
  • Gallucci Silvia
  • Chiaramello Emma
  • Fiocchi Serena
  • Parazzini Marta
  • Masini Barbara
  • Joseph Wout
  • Wiart Joe
  • Ravazzani Paolo
  IEEE Access, IEEE, 2022, 10, pp.47764-47781. Future vehicles will be increasingly connected to enable new applications and improve safety, traffic efficiency and comfort, through the use of several wireless access technologies, ranging from vehicle-to-everything (V2X) connectivity to automotive radar sensing and Internet of Things (IoT) technologies for intra-car wireless sensor networks. These technologies span the radiofrequency (RF) range, from a few hundred MHz as in intra-car network of sensors to hundreds of GHz as in automotive radars used for in-vehicle occupant detection and advanced driver assistance systems. Vehicle occupants and road users in the vicinity of the connected vehicle are thus daily immersed in a multi-source and multi-band electromagnetic field (EMF) generated by such technologies. This paper is the first comprehensive and specific survey about EMF exposure generated by the whole ensemble of connectivity technologies in cars. For each technology we describe the main characteristics, relevant standards, the application domain, and the typical deployment in modern cars. We then extensively describe the EMF exposure scenarios resulting from such technologies by resuming and comparing the outcomes from past studies on the exposure in the car. Results from past studies suggested that in no case EMF exposure was above the safe limits for the general population. Finally, open challenges for a more realistic characterization of the EMF exposure scenario in the connected car are discussed. (10.1109/ACCESS.2022.3170035)
  DOI : 10.1109/ACCESS.2022.3170035
• Video-to-Music Recommendation using Temporal Alignment of Segments
  
  • Prétet Laure
  • Richard Gael
  • Souchier Clément
  • Peeters Geoffroy
  IEEE Transactions on Multimedia, Institute of Electrical and Electronics Engineers, 2022.
• A User Centric Blockage Model for Wireless Networks
  
  • Baccelli François
  • Liu Bin
  • Decreusefond Laurent
  • Song Rongfang
  IEEE Transactions on Wireless Communications, Institute of Electrical and Electronics Engineers, 2022, 21 (10), pp.10 p.. This paper proposes a cascade blockage model for analyzing the vision that a user has of a wireless network. This model, inspired by the classical multiplicative cascade models, has a radial structure meant to analyze blockages seen by the receiver at the origin in different angular sectors. The main novelty is that it is based on the geometry of obstacles and takes the joint blockage phenomenon into account. We show on a couple of simple instances that the Laplace transforms of total interference satisfies a functional equation that can be solved efficiently by an iterative scheme. This is used to analyze the coverage probability of the receiver and the effect of blockage correlation and penetration loss in both dense and sparse blockage environments. Furthermore, this model is used to investigate the effect of blockage correlation on user beamforming techniques. Another functional equation and its associated iterative algorithm are proposed to derive the coverage performance of the best beam selection in this context. In addition, the conditional coverage probability is also derived to evaluate the effect of beam switching. The results not only show that beam selection is quite efficient for multi-beam terminals, but also show how the correlation brought by blockages can be leveraged to accelerate beam sweeping and pairing. (10.1109/TWC.2022.3166211)
  DOI : 10.1109/TWC.2022.3166211
• Unifying conditional and unconditional semantic image synthesis with OCO-GAN
  
  • Careil Marlène
  • Lathuilière Stéphane
  • Couprie Camille
  • Verbeek Jakob
  , 2022. Generative image models have been extensively studied in recent years. In the unconditional setting, they model the marginal distribution from unlabelled images. To allow for more control, image synthesis can be conditioned on semantic segmentation maps that instruct the generator the position of objects in the image. While these two tasks are intimately related, they are generally studied in isolation. We propose OCO-GAN, for Optionally COnditioned GAN, which addresses both tasks in a unified manner, with a shared image synthesis network that can be conditioned either on semantic maps or directly on latents. Trained adversarially in an end-to-end approach with a shared discriminator, we are able to leverage the synergy between both tasks. We experiment with Cityscapes, COCO-Stuff, ADE20K datasets in a limited data, semi-supervised and full data regime and obtain excellent performance, improving over existing hybrid models that can generate both with and without conditioning in all settings. Moreover, our results are competitive or better than state-of-the art specialised unconditional and conditional models.
• Approximate Bayesian computation with the sliced-Wasserstein distance
  
  • Nadjahi Kimia
  • de Bortoli Valentin
  • Durmus Alain
  • Badeau Roland
  • Şimşekli Umut
  , 2022. Approximate Bayesian Computation (ABC) is a popular method for approximate inference in generative models with intractable but easy-to-sample likelihood. It constructs an approximate posterior distribution by finding parameters for which the simulated data are close to the observations in terms of summary statistics. These statistics are defined beforehand and might induce a loss of information, which has been shown to deteriorate the quality of the approximation. To overcome this problem, Wasserstein-ABC has been recently proposed, and compares the datasets via the Wasserstein distance between their empirical distributions, but does not scale well to the dimension or the number of samples. We propose a new ABC technique, called Sliced-Wasserstein ABC and based on the Sliced-Wasserstein distance, which has better computational and statistical properties. We derive two theoretical results showing the asymptotical consistency of our approach, and we illustrate its advantages on synthetic data and an image denoising task. (10.1109/icassp40776.2020.9054735)
  DOI : 10.1109/icassp40776.2020.9054735
• Custom Structure Preservation in Face Aging
  
  • Gomez-Trenado Guillermo
  • Lathuilière Stéphane
  • Mesejo Pablo
  • Cordón Óscar
  , 2022. In this work, we propose a novel architecture for face age editing that can produce structural modifications while maintaining relevant details present in the original image. We disentangle the style and content of the input image and propose a new decoder network that adopts a style-based strategy to combine the style and content representations of the input image while conditioning the output on the target age. We go beyond existing aging methods allowing users to adjust the degree of structure preservation in the input image during inference. To this purpose, we introduce a masking mechanism, the CUstom Structure Preservation module, that distinguishes relevant regions in the input image from those that should be discarded. CUSP requires no additional supervision. Finally, our quantitative and qualitative analysis which include a user study, show that our method outperforms prior art and demonstrates the effectiveness of our strategy regarding image editing and adjustable structure preservation. Code and pretrained models are available at https://github.com/guillermogotre/CUSP.
• Dominating, Locating-Dominating and Identifying Codes in the q-ary Lee Hypercube
  
  • Hudry Olivier
  • Charon Irene
  • Lobstein Antoine
  , 2022.
• Negative Sampling Strategies for Contrastive Self-Supervised Learning of Graph Representations
  
  • Hafidi Hakim
  • Ghogho Mounir
  • Ciblat Philippe
  • Swami Ananthram
  Signal Processing, Elsevier, 2022, 190 (4). Contrastive learning has become a successful approach for learning powerful text and image representations in a self-supervised manner. Contrastive frameworks learn to distinguish between representations coming from augmentations of the same data point (positive pairs) and those of other (negative) examples. Recent studies aim at extending methods from contrastive learning to graph data. In this work, we propose a general framework for learning node representations in a self supervised manner called Graph Constrastive Learning (GraphCL). It learns node embeddings by maximizing the similarity between the nodes representations of two randomly perturbed versions of the same graph. We use graph neural networks to produce two representations of the same node and leverage a contrastive learning loss to maximize agreement between them. We investigate different standard and new negative sampling strategies as well as a comparison without negative sampling approach. We demonstrate that our approach significantly outperforms the state-of-the-art in unsupervised learning on a number of node classification benchmarks in both transductive and inductive learning setups.
• A general sample complexity analysis of vanilla policy gradient
  
  • Yuan Rui
  • Gower Robert M
  • Lazaric Alessandro
  , 2022. We adapt recent tools developed for the analysis of Stochastic Gradient Descent (SGD) in non-convex optimization to obtain convergence and sample complexity guarantees for the vanilla policy gradient (PG). Our only assumptions are that the expected return is smooth w.r.t. the policy parameters, that its H-step truncated gradient is close to the exact gradient, and a certain ABC assumption. This assumption requires the second moment of the estimated gradient to be bounded by A ≥ 0 times the suboptimality gap, B ≥ 0 times the norm of the full batch gradient and an additive constant C ≥ 0, or any combination of aforementioned. We show that the ABC assumption is more general than the commonly used assumptions on the policy space to prove convergence to a stationary point. We provide a single convergence theorem that recovers the O(−4) sample complexity of PG. Our results also affords greater flexibility in the choice of hyper parameters such as the step size and places no restriction on the batch size m, including the single trajectory case (i.e., m = 1). We then instantiate our theorem in different settings, where we both recover existing results and obtained improved sample complexity, e.g., for convergence to the global optimum for Fisher-nondegenerated parameterized policies.
• FAST STRATEGIES FOR MULTI-TEMPORAL SPECKLE REDUCTION OF SENTINEL-1 GRD IMAGES
  
  • Meraoumia Inès
  • Dalsasso Emanuele
  • Denis Loïc
  • Tupin Florence
  , 2022. Reducing speckle and limiting the variations of the physical parameters in Synthetic Aperture Radar (SAR) images is often a key-step to fully exploit the potential of such data. Nowadays, deep learning approaches produce state of the art results in single-image SAR restoration. Nevertheless, huge multi-temporal stacks are now often available and could be efficiently exploited to further improve image quality. This paper explores two fast strategies employing a singleimage despeckling algorithm, namely SAR2SAR [1], in a multi-temporal framework. The first one is based on Quegan filter [2] and replaces the local reflectivity pre-estimation by SAR2SAR. The second one uses SAR2SAR to suppress speckle from a ratio image encoding the multi-temporal information under the form of a "super-image", i.e. the temporal arithmetic mean of a time series. Experimental results on Sentinel-1 GRD data show that these two multi-temporal strategies provide improved filtering results while adding a limited computational cost. (10.1109/IGARSS46834.2022.9883448)
  DOI : 10.1109/IGARSS46834.2022.9883448
• A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation
  
  • Mina Rayan
  • Jabbour Chadi
  • Sakr George E
  Electronics, MDPI, 2022, 11 (3), pp.435. Analog integrated circuit design is widely considered a time-consuming task due to the acute dependence of analog performance on the transistors’ and passives’ dimensions. An important research effort has been conducted in the past decade to reduce the front-end design cycles of analog circuits by means of various automation approaches. On the other hand, the significant progress in high-performance computing hardware has made machine learning an attractive and accessible solution for everyone. The objectives of this paper were: (1) to provide a comprehensive overview of the existing state-of-the-art machine learning techniques used in analog circuit sizing and analyze their effectiveness in achieving the desired goals; (2) to point out the remaining open challenges, as well as the most relevant research directions to be explored. Finally, the different analog circuits on which machine learning techniques were applied are also presented and their results discussed from a circuit designer perspective. (10.3390/electronics11030435)
  DOI : 10.3390/electronics11030435
• Curves on Frobenius classical surfaces in $\mathbb{P}^{3}$ over finite fields
  
  • Berardini Elena
  • Nardi Jade
  Acta Arithmetica, Instytut Matematyczny PAN, 2022, 205 (4), pp.323-340. (10.4064/aa211118-12-9)
  DOI : 10.4064/aa211118-12-9
• Codes in the q-ary Lee Hypercube
  
  • Hudry Olivier
  • Charon Irène
  • Lobstein Antoine
  WSEAS Transactions on Mathematics, World Scientific and Engineering Academy and Society (WSEAS), 2022, 21, pp.173-186. (10.37394/23206.2022.21.24)
  DOI : 10.37394/23206.2022.21.24
• SELF ATTENTION DEEP GRAPH CNN CLASSIFICATION OF TIMES SERIES IMAGES FOR LAND COVER MONITORING
  
  • Chaabane Ferdaous
  • Réjichi Safa
  • Tupin Florence
  , 2022. Time Series of Satellite Imagery (SITS) acquired by recent Earth observation systems represent an important source of information that supports several remote sensing applications related to monitoring the dynamics of the Earth's surface over large areas. A major challenge then is to design new deep learning models that can take into account intelligently the complementarity between temporal and spatial contexts that characterize these data structures. In this work, we propose to use an adapted self-attention convolutional neural network for spatio-temporal graphs classification that exploits both spatial and temporal dimensions. The graphs will be generated from a series of temporal images that are segmented into different regions. Those graphs are then classified using the Self-Attention Deep Graph CNN (DGCNN) model to highlight the temporal evolution of land cover areas through the construction of a spatio-temporal Map.
• What are the best systems? New perspectives on NLP Benchmarking
  
  • Irurozki Ekhine
  • Colombo Pierre
  • Noiry Nathan
  • Clémençon Stéphan
  , 2022. In Machine Learning, a benchmark refers to an ensemble of datasets associated with one or multiple metrics together with a way to aggregate different systems performances. They are instrumental in (i) assessing the progress of new methods along different axes and (ii) selecting the best systems for practical use. This is particularly the case for NLP with the development of large pre-trained models (e.g. GPT, BERT) that are expected to generalize well on a variety of tasks. While the community mainly focused on developing new datasets and metrics, there has been little interest in the aggregation procedure, which is often reduced to a simple average over various performance measures. However, this procedure can be problematic when the metrics are on a different scale, which may lead to spurious conclusions. This paper proposes a new procedure to rank systems based on their performance across different tasks. Motivated by the social choice theory, the final system ordering is obtained through aggregating the rankings induced by each task and is theoretically grounded. We conduct extensive numerical experiments (on over 270k scores) to assess the soundness of our approach both on synthetic and real scores (e.g. GLUE, EXTREM, SEVAL, TAC, FLICKR). In particular, we show that our method yields different conclusions on state-of-the-art systems than the mean-aggregation procedure while being both more reliable and robust
• Comparing Deep Models and Evaluation Strategies for Multi-Pitch Estimation in Music Recordings
  
  • Weis Christof
  • Peeters Geoffroy
  IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2022, 30, pp.2814-2827. (10.1109/TASLP.2022.3200547)
  DOI : 10.1109/TASLP.2022.3200547
• Information Leakage in Code-based Masking: A Systematic Evaluation by Higher-Order Attacks
  
  • Cheng Wei
  • Guilley Sylvain
  • Danger Jean-Luc
  IEEE Transactions on Information Forensics and Security, Institute of Electrical and Electronics Engineers, 2022, 17, pp.1624-1638. Code-based masking is a recent line of research on masking schemes aiming at provably counteracting side-channel attacks. It generalizes and unifies many masking schemes within a coding-theoretic formalization. In code-based masking schemes, the tuning parameters are the underlying linear codes, whose choice significantly affects the side-channel resilience. In this paper, we investigate the exploitability of the information leakage in code-based masking and present attack-based evaluation results of higher-order optimal distinguisher (HOOD). Particularly, we consider two representative instances of code-based masking, namely inner product masking (IPM) and Shamir’s secret sharing (SSS) based masking. Our results do confirm the state-of-the-art theoretical derivatives in an empirical manner with numerically simulated measurements. Specifically, theoretical results are based on quantifying information leakage; we further complete the panorama with attack-based evaluations by investigating the exploitability of the leakage. Moreover, we classify all possible candidates of linear codes in IPM with 2 and 3 shares and (3, 1)-SSS based masking, and highlight both optimal and worst codes for them. Relying on our empirical evaluations, we therefore recommend investigating the coding-theoretic properties to find the best linear codes in strengthening instances of code-based masking. As for applications, our attack-based evaluation directly empowers designers, by employing optimal linear codes, to enhance the protection of code-based masking. Our framework leverages simulated leakage traces, hence allowing for source code validation or patching in case it is found to be attackable. (10.1109/TIFS.2022.3167914)
  DOI : 10.1109/TIFS.2022.3167914
• Delaunay Painting: Perceptual image coloring from raster contours with gaps
  
  • Parakkat Amal Dev
  • Memari Pooran
  • Cani Marie-Paule
  Computer Graphics Forum, Wiley, 2022. We introduce Delaunay Painting, a novel and easy-to-use method to flat-color contour-sketches with gaps. Starting from a Delaunay triangulation of the input contours, triangles are iteratively filled with the appropriate colors, thanks to the dynamic update of flow values calculated from color hints. Aesthetic finish is then achieved, through energy minimisation of contour curves and further heuristics enforcing the appropriate sharp corners. To be more efficient, the user can also make use of our color diffusion framework which automatically extends coloring to small, internal regions such as those delimited by hatches. The resulting method robustly handles input contours with strong gaps. As an interactive tool, it minimizes user's efforts and enables any coloring strategy, as the result does not depend on the order of interactions. We also provide an automatized version of the coloring strategy for quick segmentation of contours images, that we illustrate with an application to medical imaging.