Publications

2020

• Storage-Computation-Communication Tradeoff in Distributed Computing: Fundamental Limits and Complexity
  
  • Yan Qifa
  • Yang Sheng
  • Wigger Michèle
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2020. Distributed computing has become one of the most important frameworks in dealing with large computation tasks. In this paper, we propose a systematic construction of coded computing schemes for MapReduce-type distributed systems. The construction builds upon placement delivery arrays (PDA), originally proposed by Yan et al. for coded caching schemes. The main contributions of our work are threefold. First, we identify a class of PDAs, called Comp-PDAs, and show how to obtain a coded computing scheme from any Comp-PDA. We also characterize the normalized number of stored files (storage load), computed intermediate values (computation load), and communicated bits (communication load), of the obtained schemes in terms of the Comp-PDA parameters. Then, we show that the performance achieved by Comp-PDAs describing Maddah-Ali and Niesen's coded caching schemes matches a new information-theoretic converse, thus establishing the fundamental region of all achievable performance triples. In particular, we characterize all the Comp-PDAs achieving the pareto-optimal storage, computation, and communication (SCC) loads of the fundamental region. Finally, we investigate the file complexity of the proposed schemes, i.e., the smallest number of files required for implementation. In particular, we describe Comp-PDAs that achieve pareto-optimal SCC triples with significantly lower file complexity than the originally proposed Comp-PDAs.
• Degrees-of-Freedom in Multi-Cloud Based Sectored Cellular Networks
  
  • Gelincik Samet
  • Rekaya-Ben Othman Ghaya
  Entropy, MDPI, 2020.
• Combined Vector Perturbation for Adaptive Modulation in MU-MIMO
  
  • Askri Aymen
  • Rekaya-Ben Othman Ghaya
  , 2020.
• Space-Time Coding Performance Analysis for CDL-impaired Multi-Core Fiber Transmission
  
  • Abouseif Akram
  • Rekaya-Ben Othman Ghaya
  • Jaouën Yves
  , 2020.
• An analysis of the transfer learning of convolutional neural networks for artistic images
  
  • Gonthier Nicolas
  • Gousseau Yann
  • Ladjal Saïd
  , 2020. Transfer learning from huge natural image datasets, fine-tuning of deep neural networks and the use of the corresponding pre-trained networks have become de facto the core of art analysis applications. Nevertheless, the effects of transfer learning are still poorly understood. In this paper, we first use techniques for visualizing the network internal representations in order to provide clues to the understanding of what the network has learned on artistic images. Then, we provide a quantitative analysis of the changes introduced by the learning process thanks to metrics in both the feature and parameter spaces, as well as metrics computed on the set of maximal activation images. These analyses are performed on several variations of the transfer learning procedure. In particular, we observed that the network could specialize some pre-trained filters to the new image modality and also that higher layers tend to concentrate classes. Finally, we have shown that a double fine-tuning involving a medium-size artistic dataset can improve the classification on smaller datasets, even when the task changes. (10.1007/978-3-030-68796-0_39)
  DOI : 10.1007/978-3-030-68796-0_39
• Fidelity metrics between curves and surfaces: currents, varifolds, and normal cycles
  
  • Charon Nicolas
  • Charlier Benjamin
  • Glaunès Joan
  • Gori Pietro
  • Roussillon Pierre
  , 2020, pp.441-477. This chapter provides an overview of some mathematical and computational models that have been proposed over the past few years for defining data attachment terms on shape spaces of curves or surfaces. In all these models shapes are seen as elements of a space of generalized distributions, such as currents or varifolds. Then norms are defined through reproducing kernel Hilbert spaces (RKHS), which lead to shape distances that can be conveniently computed in practice. These were originally introduced in conjunction with diffeomorphic methods in computational anatomy and have indeed proved to be very efficient in this field. We provide a basic description of these different models and their practical implementation, then discuss the respective properties and potential advantages or downsides of each of them in diffeomorphic registration problems. (10.1016/B978-0-12-814725-2.00021-2)
  DOI : 10.1016/B978-0-12-814725-2.00021-2
• Uncovering reflection insensitive semiconductor lasers for silicon photonic integration (invited)
  
  • Grillot Frédéric
  , 2020, pp.M4H.4. (10.1364/OFC.2020.M4H.4)
  DOI : 10.1364/OFC.2020.M4H.4
• Delayed labelling evaluation for data streams
  
  • Grzenda Maciej
  • Gomes Heitor Murilo
  • Bifet Albert
  Data Mining and Knowledge Discovery, Springer, 2020, 34 (5), pp.1237--1266. A large portion of the stream mining studies on classification rely on the availability of true labels immediately after making predictions. This approach is well exemplified by the test-then-train evaluation, where predictions immediately precede true label arrival. However, in many real scenarios, labels arrive with non-negligible latency. This raises the question of how to evaluate classifiers trained in such circumstances. This question is of particular importance when stream mining models are expected to refine their predictions between acquiring instance data and receiving its true label. In this work, we propose a novel evaluation methodology for data streams when verification latency takes place, namely continuous re-evaluation. It is applied to reference data streams and it is used to differentiate between stream mining techniques in terms of their ability to refine predictions based on newly arriving instances. Our study points out, discusses and shows empirically the importance of considering the delay of instance labels when evaluating classifiers for data streams. (10.1007/S10618-019-00654-Y)
  DOI : 10.1007/S10618-019-00654-Y
• Anticanonical codes from del Pezzo surfaces with Picard rank one
  
  • Blache Régis
  • Couvreur Alain
  • Hallouin Emmanuel
  • Madore David
  • Nardi Jade
  • Rambaud Matthieu
  • Randriambololona Hugues
  Transactions of the American Mathematical Society, American Mathematical Society, 2020. We construct algebraic geometric codes from del Pezzo surfaces and focus on the ones having Picard rank one and the codes associated to the anticanonical class. We give explicit constructions of del Pezzo surfaces of degree 4, 5 and 6, compute the parameters of the associated anticanonical codes and study their isomorphisms arising from the automorphisms of the surface. We obtain codes with excellent parameters and some of them turn out to beat the best known codes listed on the database codetable. (10.1090/tran/8119)
  DOI : 10.1090/tran/8119
• Training CNNs on speckled optical dataset for edge detection in SAR images
  
  • Liu Chenguang
  • Tupin Florence
  • Gousseau Yann
  ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, 2020. Edge detection in SAR images is a difficult task due to the strong multiplicative noise. Many researches have been dedicated to edge detection in SAR images but very few try to address the most challenging 1-look situations. Motivated by the success of CNNs for the analysis of natural images, we develop a CNN edge detector for 1-look SAR images. We propose to simulate a SAR dataset using the optical dataset BSDS500 to avoid the tedious job of edge labeling, and we propose a framework, a hand-crafted layer followed by learnable layers, to enable the model trained on simulated SAR images to work in real SAR images. The hypothesis behind these two propositions is that both optical and SAR images can be divided into piecewise constant areas and edges are boundaries between two homogeneous areas. The hand-crafted layer, which is defined by a ratio based gradient computation method, helps to tackle the gap between training and testing images, because the gradient distribution will not be influenced by the mean intensity values of homogeneous areas. The gradient computation step is done by Gradient by Ratio (GR) and the learnable layers are identical to those in HED. The proposed edge detector, GRHED, outperforms concurrent approaches in all our simulations especially in two 1-look real SAR images. The source code of GRHED is available at https://github.com/ChenguangTelecom/GRHED .
• Separation of Alpha-Stable Random Vectors
  
  • Fontaine Mathieu
  • Badeau Roland
  • Liutkus Antoine
  Signal Processing, Elsevier, 2020, pp.107465. Source separation aims at decomposing a vector into additive components. This is often done by first estimating source parameters before feeding them into a filtering method, often based on ratios of covariances. The whole pipeline is traditionally rooted in some probabilistic framework providing both the likelihood for parameter estimation and the separation method. While Gaussians are ubiquitous for this purpose, many studies showed the benefit of heavy-tailed models for estimation. However, there is no counterpart filtering method to date exploiting such formalism, so that related studies revert to covariance-based filtering after estimation is finished. Here, we introduce a new multivariate separation technique, that fully exploits the flexibility of α-stable heavy-tailed distributions. We show how a spatial representation can be exploited, which decomposes the observation as an infinite sum of contributions originating from all directions. Two methods for separation are derived. The first one is non-linear and similar to a beamforming technique, while the second one is linear, but minimizes a covariation criterion, which is the counterpart of the covariance for α-stable vectors. We evaluate the proposed techniques in a large number of challenging and adverse situations on synthetic experiments, demonstrating their performance for the extraction of signals from strong interferences. (10.1016/j.sigpro.2020.107465)
  DOI : 10.1016/j.sigpro.2020.107465
• Construction and Random Generation of Hypergraphs with Prescribed Degree and Dimension Sequences
  
  • Arafat Naheed Anjum
  • Basu Debabrota
  • Decreusefond Laurent
  • Bressan Stéphane
  , 2020. We propose algorithms for construction and random generation of hy-pergraphs without loops and with prescribed degree and dimension sequences. The objective is to provide a starting point for as well as an alternative to Markov chain Monte Carlo approaches. Our algorithms leverage the transposition of properties and algorithms devised for matrices constituted of zeros and ones with prescribed row-and column-sums to hypergraphs. The construction algorithm extends the applicability of Markov chain Monte Carlo approaches when the initial hypergraph is not provided. The random generation algorithm allows the development of a self-normalised importance sampling estimator for hypergraph properties such as the average clustering coefficient. We prove the correctness of the proposed algorithms. We also prove that the random generation algorithm generates any hypergraph following the prescribed degree and dimension sequences with a non-zero probability. We empirically and comparatively evaluate the effectiveness and efficiency of the random generation algorithm. Experiments show that the random generation algorithm provides stable and accurate estimates of average clustering coefficient, and also demonstrates a better effective sample size in comparison with the Markov chain Monte Carlo approaches.
• Approximating morphological operators with part-based representations learned by asymmetric auto-encoders
  
  • Blusseau Samy
  • Ponchon Bastien
  • Velasco-Forero Santiago
  • Angulo Jesus
  • Bloch Isabelle
  Mathematical Morphology - Theory and Applications, De Gruyter, 2020, 4 (1), pp.64 - 86. This paper addresses the issue of building a part-based representation of a dataset of images. More precisely, we look for a non-negative, sparse decomposition of the images on a reduced set of atoms, in order to unveil a morphological and explainable structure of the data. Additionally, we want this decomposition to be computed online for any new sample that is not part of the initial dataset. Therefore, our solution relies on a sparse, non-negative auto-encoder, where the encoder is deep (for accuracy) and the decoder shallow (for explainability). This method compares favorably to the state-of-the-art online methods on two benchmark datasets (MNIST and Fashion MNIST) and on a hyperspectral image, according to classical evaluation measures and to a new one we introduce, based on the equivariance of the representation to morphological operators. (10.1515/mathm-2020-0102)
  DOI : 10.1515/mathm-2020-0102
• Age of Information Aware Cache Updating with File-and Age-Dependent Update Durations
  
  • Tang Haoyue
  • Ciblat Philippe
  • Wang Jintao
  • Wigger Michèle
  • Yates Roy
  , 2020. We consider a system consisting of a library of time-varying files, a server that at all times observes the current version of all files, and a cache that at the beginning stores the current versions of all files but afterwards has to update these files from the server. Unlike previous works, the update duration is not constant but depends on the file and its Age of Information (AoI), i.e., of the time elapsed since it was last updated. The goal of this work is to design an update policy that minimizes the average AoI of all files with respect to a given popularity distribution. Actually a relaxed problem, close to the original optimization problem, is solved and a practical update policy is derived. The update policy relies on the file popularity and on the functions that characterize the update durations of the files depending on their AoI. Numerical simulations show a significant improvement of this new update policy compared to the so-called square-root policy that is optimal under file-independent and constant update durations.
• Extracting Complex Information from Natural Language Text: A Survey
  
  • Mechket Emna
  • Suchanek Fabian
  CEUR Workshop Proceedings, CEUR-WS.org, 2020. Information Extraction is the art of extracting structured information from natural language text, and it has come a long way in recent years. Many systems focus on binary relationships between two entities-a subject and an object. However, most natural language text contains complex information such as beliefs, causality, anteriority, or relationships that span several sentences. In this paper, we survey existing approaches at this frontier, and outline promising directions of future work.
• A class of narrow-sense BCH codes over $\mathbb{F}_q$ of length $\frac{q^m-1}{2}$
  
  • Ling X.
  • Mesnager Sihem
  • Qi Y.
  • Tang C.
  Journal of Designs, Codes, and Cryptography, 2020.
• On the Menezes-Teske-Weng conjecture
  
  • Mesnager Sihem
  • Kim K. H.
  • Choe J.
  • Tang C.
  Cryptography and Communications–Discrete Structures, Boolean Functions, and Sequences, 2020.
• Algorithmes : Biais, Discrimination et Équité
  
  • Bertail Patrice
  • Bounie David
  • Clémençon Stéphan
  • Waelbroeck Patrick
  HR Today, ALMA Medien SA, 2020 (58). Les algorithmes s’immiscent de plus en plus dans notre quotidien à l’image des algorithmes d’aide à la décision (algorithme de recommandation ou de scoring), ou bien des algorithmes autonomes embarqués dans des machines intelligentes (véhicules autonomes). Déployés dans de nombreux secteurs et industries pour leur efficacité, leurs résultats sont de plus en plus discutés et contestés. En particulier, ils sont accusés d’être des boites noires et de conduire à des pratiques discriminatoires liées au genre ou à l’origine ethnique. L’objectif de cet article est de décrire les biais liés aux algorithmes et d’esquisser des pistes pour y remédier. Nous nous intéressons en particulier aux résultats des algorithmes en rapport avec des objectifs d’équité, et à leurs conséquences en termes de discrimination. Trois questions motivent cet article : Par quels mécanismes les biais des algorithmes peuvent-ils se produire ? Peut-on les éviter ? Et, enfin, peut-on les corriger ou bien les limiter ? Dans une première partie, nous décrivons comment fonctionne un algorithme d’apprentissage statistique. Dans une deuxième partie nous nous intéressons à l’origine de ces biais qui peuvent être de nature cognitive, statistique ou économique. Dans une troisième partie, nous présentons quelques approches statistiques ou algorithmiques prometteuses qui permettent de corriger les biais. Nous concluons l’article en discutant des principaux enjeux de société soulevés par les algorithmes d’apprentissage statistique tels que l’interprétabilité, l’explicabilité, la transparence, et la responsabilité.
• Méthodes et dispositifs de codage et de décodage de données
  
  • Baccouch Hana
  • Boukhatem Nadia
  , 2020.
• Simulation Framework for Misbehavior Detection in Vehicular Networks
  
  • Kamel Joseph
  • Raashid Ansari Mohammad
  • Petit Jonathan
  • Kaiser Arnaud
  • Ben Jemaa Ines
  • Urien Pascal
  IEEE Transactions on Vehicular Technology, Institute of Electrical and Electronics Engineers, 2020, 69 (6), pp.6631-6643. Cooperative Intelligent Transport Systems (C-ITS) is an ongoing technology that will change our driving experience in the near future. In such systems, vehicles and RoadSide Unit (RSU) cooperate by broadcasting V2X messages over the vehicular network. Safety applications use these data to detect and avoid dangerous situations on time. MisBehavior Detection (MBD) in C-ITS is an active research topic which consists of monitoring data semantics of the exchanged Vehicle-to-X communication (V2X) messages to detect and identify potential misbehaving entities. The detection process consists of performing plausibility and consistency checks on the received V2X messages. If an anomaly is detected, the entity may report it by sending a Misbehavior Report (MBR) to the Misbehavior Authority (MA). The MA will then investigate the event and decide to revoke the sender or not. In this paper, we present a MisBehavior Detection (MBD) simulation framework that enables the research community to develop, test, and compare MBD algorithms. We also demonstrate its capabilities by running example scenarios and discuss their results. Framework For Misbehavior Detection (F 2 MD) is open source and available for free on our github. (10.1109/TVT.2020.2984878)
  DOI : 10.1109/TVT.2020.2984878
• La mathématique migrante
  
  • Zayana Karim
  • Jadiba Sami
  • Kraiem Selma
  • Fayala Abdelwahid
  Les Cahiers Pédagogiques, 2020, 558. La route de l'inconnu(e) : histoire de quelques concepts mathématiques à travers les grandes migrations en méditerranée
• Optimizing Inner Product Masking Scheme by A Coding Theory Approach
  
  • Cheng Wei
  • Guilley Sylvain
  • Carlet Claude
  • Mesnager Sihem
  • Danger Jean-Luc
  IEEE Transactions on Information Forensics and Security, Institute of Electrical and Electronics Engineers, 2020, 16, pp.220-235. Masking is one of the most popular countermeasures to protect cryptographic implementations against side-channel analysis since it is provably secure and can be deployed at the algorithm level. To strengthen the original Boolean masking scheme, several works have suggested using schemes with high algebraic complexity. The Inner Product Masking (IPM) is one of those. In this paper, we propose a unified framework to quantitatively assess the side-channel security of the IPM in a coding-theoretic approach. Specifically, starting from the expression of IPM in a coded form, we use two defining parameters of the code to characterize its side-channel resistance. In order to validate the framework, we then connect it to two leakage metrics (namely signal-to-noise ratio and mutual information, from an information-theoretic aspect) and one typical attack metric (success rate, from a practical aspect) to build a firm foundation for our framework. As an application, our results provide ultimate explanations on the observations made by Balasch et al. at EUROCRYPT'15 and at ASIACRYPT'17, Wang et al. at CARDIS'16 and Poussier et al. at CARDIS'17 regarding the parameter effects in IPM, like higher security order in bounded moment model. Furthermore, we show how to systematically choose optimal codes (in the sense of a concrete security level) to optimize IPM by using this framework. Eventually, we present a simple but effective algorithm for choosing optimal codes for IPM, which is of special interest for designers when selecting optimal parameters for IPM. (10.1109/TIFS.2020.3009609)
  DOI : 10.1109/TIFS.2020.3009609
• Groove2Groove: One-Shot Music Style Transfer with Supervision from Synthetic Data
  
  • Cífka Ondřej
  • Şimşekli Umut
  • Richard Gael
  IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2020, 28, pp.2638-2650. Style transfer is the process of changing the style of an image, video, audio clip or musical piece so as to match the style of a given example. Even though the task has interesting practical applications within the music industry, it has so far received little attention from the audio and music processing community. In this paper, we present Groove2Groove, a one-shot style transfer method for symbolic music, focusing on the case of accompaniment styles in popular music and jazz. We propose an encoder-decoder neural network for the task, along with a synthetic data generation scheme to supply it with parallel training examples. This synthetic parallel data allows us to tackle the style transfer problem using end-to-end supervised learning, employing powerful techniques used in natural language processing. We experimentally demonstrate the performance of the model on style transfer using existing and newly proposed metrics, and also explore the possibility of style interpolation. (10.1109/TASLP.2020.3019642)
  DOI : 10.1109/TASLP.2020.3019642
• CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer
  
  • Kips R.
  • Perrot M.
  • Gori P.
  • Bloch Isabelle
  , 2020.
• Artifical Intelligence and Pattern Recognition, Vision, Learning
  
  • Bloch Isabelle
  • Clouard Régis
  • Revenu Marinette
  • Sigaud Olivier
  , 2020, III, pp.337-364.