Publications

2021

• Distributed Resource Allocation Algorithms for Multi-Operator Cognitive Communication Systems
  
  • Tohidi Ehsan
  • Gesbert David
  • Ciblat Philippe
  , 2021. We address the problem of resource allocation (RA) in a cognitive radio (CR) communication system with multiple secondary operators sharing spectrum with an incumbent primary operator. The key challenge of the RA problem is the inter-operator coordination arising in the optimization problem so that the aggregated interference at the primary users (PUs) does not exceed the target threshold. While this problem is easily solvable if a centralized unit could access information of all secondary operators, it becomes challenging in a realistic scenario. In this paper, considering a satellite setting, we alleviate this problem by proposing two approaches to reduce the information exchange level among the secondary operators. In the first approach, we formulate an RA scheme based on a partial information sharing method which enables distributed optimization across secondary operators. In the second approach, instead of exchanging secondary users (SUs) information, the operators only exchange their contributions of the interference-level and RA is performed locally across secondary operators. These two approaches, for the first time in this context, provide a trade-off between performance and level of inter-operator information exchange. Through the numerical simulations, we explain this trade-off and illustrate the penalty resulting from partial information exchange.
• The Deep Learning Revolution in MIR: The Pros and Cons, the Needs and the Challenges 2021
  
  • Peeters Geoffroy
  , 2021.
• Convergence and Dynamical Behavior of the Adam Algorithm for Non Convex Stochastic Optimization
  
  • Barakat Anas
  • Bianchi Pascal
  SIAM Journal on Optimization, Society for Industrial and Applied Mathematics, 2021. Adam is a popular variant of the stochastic gradient descent for finding a local minimizer of a function. The objective function is unknown but a random estimate of the current gradient vector is observed at each round of the algorithm. Assuming that the objective function is differentiable and non-convex, we establish the convergence in the long run of the iterates to a stationary point. The key ingredient is the introduction of a continuous-time version of Adam, under the form of a non-autonomous ordinary differential equation. The existence and the uniqueness of the solution are established, as well as the convergence of the solution towards the stationary points of the objective function. The continuous-time system is a relevant approximation of the Adam iterates, in the sense that the interpolated Adam process converges weakly to the solution to the ODE.
• Anomalies Detection Using Isolation in Concept-Drifting Data Streams
  
  • Togbe Maurras Ulbricht
  • Chabchoub Yousra
  • Boly Aliou
  • Barry Mariam
  • Chiky Raja
  • Bahri Maroua
  Computers, MDPI, 2021, 10 (1), pp.13. Detecting anomalies in streaming data is an important issue for many application domains, such as cybersecurity, natural disasters, or bank frauds. Different approaches have been designed in order to detect anomalies: statistics-based, isolation-based, clustering-based, etc. In this paper, we present a structured survey of the existing anomaly detection methods for data streams with a deep view on Isolation Forest (iForest). We first provide an implementation of Isolation Forest Anomalies detection in Stream Data (IForestASD), a variant of iForest for data streams. This implementation is built on top of scikit-multiflow (River), which is an open source machine learning framework for data streams containing a single anomaly detection algorithm in data streams, called Streaming half-space trees. We performed experiments on different real and well known data sets in order to compare the performance of our implementation of IForestASD and half-space trees. Moreover, we extended the IForestASD algorithm to handle drifting data by proposing three algorithms that involve two main well known drift detection methods: ADWIN and KSWIN. ADWIN is an adaptive sliding window algorithm for detecting change in a data stream. KSWIN is a more recent method and it refers to the Kolmogorov–Smirnov Windowing method for concept drift detection. More precisely, we extended KSWIN to be able to deal with n-dimensional data streams. We validated and compared all of the proposed methods on both real and synthetic data sets. In particular, we evaluated the F1-score, the execution time, and the memory consumption. The experiments show that our extensions have lower resource consumption than the original version of IForestASD with a similar or better detection efficiency. (10.3390/computers10010013)
  DOI : 10.3390/computers10010013
• SAR2SAR: a semi-supervised despeckling algorithm for SAR images
  
  • Dalsasso Emanuele
  • Denis Loïc
  • Tupin Florence
  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2021, pp.1-1. Speckle reduction is a key step in many remote sensing applications. By strongly affecting synthetic aperture radar (SAR) images, it makes them difficult to analyse. Due to the difficulty to model the spatial correlation of speckle, a deep learning algorithm with self-supervision is proposed in this paper: SAR2SAR. Multi-temporal time series are leveraged and the neural network learns to restore SAR images by only looking at noisy acquisitions. To this purpose, the recently proposed noise2noise framework has been employed. The strategy to adapt it to SAR despeckling is presented, based on a compensation of temporal changes and a loss function adapted to the statistics of speckle. A study with synthetic speckle noise is presented to compare the performances of the proposed method with other state-of-the-art filters. Then, results on real images are discussed, to show the potential of the proposed algorithm. The code is made available to allow testing and reproducible research in this field. (10.1109/JSTARS.2021.3071864)
  DOI : 10.1109/JSTARS.2021.3071864
• An ontology for multi-paradigm modelling
  
  • Giese Holger
  • Blouin Dominique
  • Al-Ali Rima
  • Mkaouar Hana
  • Bandyopadhyay Soumyadip
  • Iacono Mauro
  • Amrani Moussa
  • Klikovits Stefan
  • Erata Ferhat
  , 2021, pp.67-122. (10.1016/B978-0-12-819105-7.00009-X)
  DOI : 10.1016/B978-0-12-819105-7.00009-X
• Data&Musée : de nouveaux usages sémantiques du big data culturel en France
  
  • Moissinac Jean-Claude Jc
  • Wadhera Piyush
  Histoire de l'art, Association des professeurs d'archéologie et d'histoire de l'art des universités – APAHAU [1988-....], 2021.
• Learnable Descriptors for Visual Search
  
  • Migliorati Andrea
  • Fiandrotti Attilio
  • Francini Gianluca
  • Leonardi Riccardo
  IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2021, 30, pp.80 - 91. This work proposes LDVS, a learnable binary local descriptor devised for matching natural images within the MPEG CDVS framework. LDVS descriptors are learned so that they can be sign-quantized and compared using the Hamming distance. The underlying convolutional architecture enjoys a moderate parameters count for operations on mobile devices. Our experiments show that LDVS descriptors perform favorably over comparable learned binary descriptors at patch matching on two different datasets. A complete pair-wise image matching pipeline is then designed around LDVS descriptors, integrating them in the reference CDVS evaluation framework. Experiments show that LDVS descriptors outperform the compressed CDVS SIFT-like descriptors at pair-wise image matching over the challenging CDVS image dataset. (10.1109/tip.2020.3031216)
  DOI : 10.1109/tip.2020.3031216
• An ontological foundation for multi-paradigm modelling for cyber-physical systems
  
  • Blouin Dominique
  • Al-Ali Rima
  • Iacono Mauro
  • Tekinerdogan Bedir
  • Giese Holger
  , 2021, pp.9-43. (10.1016/B978-0-12-819105-7.00007-6)
  DOI : 10.1016/B978-0-12-819105-7.00007-6
• MUSIC GENRE DESCRIPTOR FOR CLASSIFICATION BASED ON TONNETZ TRAJECTORIES
  
  • Karystinaios Emmanouil
  • Guichaoua Corentin
  • Andreatta Moreno
  • Bigo Louis
  • Bloch Isabelle
  , 2021. Dans cet article, nous présentons un nouveau descripteur pour la classification automatique du style musical. Notre méthode consiste à définir une trajectoire harmonique dans un espace géométrique, le Tonnetz, puis à la résumer à ses valeurs de centralité, qui constituent les descripteurs. Ceux-ci, associés à des descripteurs classiques, sont utilisés comme caractéristiques pour la classification. Les résultats montrent des scores F 1 supérieurs à 0,8 avec une méthode classique de forêts aléatoires pour 8 classes (une par compositeur), et supérieurs à 0,9 pour une classification en 4 classes de style ou période de composition.
• Playable Video Generation
  
  • Menapace Willi
  • Lathuilière Stéphane
  • Tulyakov Sergey
  • Siarohin Aliaksandr
  • Ricci Elisa
  , 2021. This paper introduces the unsupervised learning problem of playable video generation (PVG). In PVG, we aim at allowing a user to control the generated video by selecting a discrete action at every time step as when playing a video game. The difficulty of the task lies both in learning semantically consistent actions and in generating realistic videos conditioned on the user input. We propose a novel framework for PVG that is trained in a self-supervised manner on a large dataset of unlabelled videos. We employ an encoder-decoder architecture where the predicted action labels act as bottleneck. The network is constrained to learn a rich action space using, as main driving loss, a reconstruction loss on the generated video. We demonstrate the effectiveness of the proposed approach on several datasets with wide environment variety. Further details, code and examples are available on our project page willi-menapace.github.io/playable-video-generation-website.
• Information theoretic distinguishers for timing attacks with partial profiles: Solving the empty bin issue
  
  • de Chérisey Eloi
  • Guilley Sylvain
  • Rioul Olivier
  • Jayasinghe Darshana
  Journal of Information Security, Scientific Research Publishing (SCIRP), 2021, 12 (1). In any side-channel attack, it is desirable to exploit all the available leakage data to compute the distinguisher’s values. The profiling phase is essential to obtain an accurate leakage model, yet it may not be exhaustive. As a result, information theoretic distinguishers may come up on previously unseen data, a phenomenon yielding empty bins. A strict application of the maximum likelihood method yields a distinguisher that is not even sound. Ignoring empty bins reestablishes soundness, but seriously limits its performance in terms of success rate. The purpose of this paper is to remedy this situation. In this research, we propose six different techniques to improve the performance of information theoretic distinguishers. We study them thoroughly by applying them to timing attacks, both with synthetic and real leakages. Namely, we compare them in terms of success rate, and show that their performance depends on the amount of profiling, and can be explained by a bias-variance analysis. The result of our work is that there exist use-cases, especially when measurements are noisy, where our novel information theoretic distinguishers (typically the soft-drop distinguisher) perform the best compared to known side-channel distinguishers, despite the empty bin situation. (10.4236/jis.2021.121001)
  DOI : 10.4236/jis.2021.121001
• Annotations sémantiques de textes liés à l’héritage culturel français
  
  • Moissinac Jean-Claude Jc
  , 2021.
• Enriching Wikidata with Semantified Wikipedia Hyperlinks
  
  • Boschin Armand
  • Bonald Thomas
  , 2021. We propose a novel approach to enrich Wikidata with the textual content of Wikipedia. Specifically, we leverage knowledge graph (KG) embedding models to classify the hyperlinks between Wikipedia articles and predict the corresponding facts. For instance, we would like to complete the triple (Berlin, *, Germany) with the relation capital of, given a hyperlink from Berlin to Germany in Wikipedia. While existing KG embedding models can be used for this task of relation prediction, they were not explicitly designed for it and their performance is not satisfactory. In this paper, we propose two methods that greatly improve the performance of these models on this task: first, a new negative sampling method that balances the roles of entities and relations during training; second, a method to exploit the types of entities in the selection of candidate relations. We obtain accuracy scores as high as 94% on the popular FB15k237 dataset and 75% on WDV5, an extraction of Wikidata. The efficiency of the approach is illustrated on some Wikipedia pages, where new facts unknown to Wikidata are predicted by our method.
• Smart energy: A collaborative demand response solution for smart neighborhood
  
  • Al Zahr Sawsan
  • Doumith Elias
  • Forestier Philippe
  , 2021.
• A construction method of balanced rotation symmetric Boolean functions on arbitrary even number of variables with optimal algebraic immunity
  
  • Mesnager Sihem
  • Su Sihong
  • Zhang Hui
  Designs, Codes and Cryptography, Springer Verlag, 2021, 89 (1), pp.1-17. (10.1007/s10623-020-00806-y)
  DOI : 10.1007/s10623-020-00806-y
• ``{Readers} digest of'' 17-year achievements on {Boolean} and vectorial functions and open problems.
  
  • Mesnager Sihem
  , 2022.
• Analysis and simulation of the relative intensity noise in a Fabry-Perot interband cascade laser highlights relaxation oscillations around GHz
  
  • Spitz O
  • Herdt A
  • Wu J
  • Maisons G
  • Carras M
  • Wong C.-W
  • Elsässer W
  • Grillot F
  , 2021.
• High-speed transmissions with direct-modulation room-temperature semiconductor lasers emitting in the transparency window around 4 µm
  
  • Spitz O
  • Durupt Lauréline
  • Didier P
  • Díaz-Thomas D A
  • Cerutti Laurent
  • Baranov A. N. N
  • Carras M
  • Grillot F
  , 2021. We experimentally realize a free-space transmission over one meter with roomtemperature quantum cascade lasers and interband cascade lasers. With direct electrical modulation and raw analysis, the data-rate of the real-time transmission outperforms similar reported schemes.
• Dynamic performance and reflection sensitivity of quantum dot distributed feedback lasers with large optical mismatch
  
  • Dong Bozhang
  • Duan Jianan
  • Huang Heming
  • Norman Justin
  • Nishi Kenichi
  • Takemasa Keizo
  • Sugawara Mitsuru
  • Bowers John
  • Grillot Frédéric
  Photonics research, Optical Society of America, 2021, 9 (8), pp.1550. (10.1364/PRJ.421285)
  DOI : 10.1364/PRJ.421285
• Progressive Discrete Domains for Implicit Surface Reconstruction
  
  • Zhao Tong
  • Alliez Pierre
  • Boubekeur Tamy
  • Busé Laurent
  • Thiery Jean-Marc
  Computer Graphics Forum, Wiley, 2021, 40 (5), pp.143-156. Many global implicit surface reconstruction algorithms formulate the problem as a volumetric energy minimization, trading data fitting for geometric regularization. As a result, the output surfaces may be located arbitrarily far away from the input samples. This is amplified when considering i) strong regularization terms, ii) sparsely distributed samples or iii) missing data. This breaks the strong assumption commonly used by popular octree-based and triangulation-based approaches that the output surface should be located near the input samples. As these approaches refine during a pre-process, their cells near the input samples, the implicit solver deals with a domain discretization not fully adapted to the final isosurface.We relax this assumption and propose a progressive coarse-to-fine approach that jointly refines the implicit function and its representation domain, through iterating solver, optimization and refinement steps applied to a 3D Delaunay triangulation. There are several advantages to this approach: the discretized domain is adapted near the isosurface and optimized to improve both the solver conditioning and the quality of the output surface mesh contoured via marching tetrahedra. (10.1111/cgf.14363)
  DOI : 10.1111/cgf.14363
• L'éthique en radiologie : quand, comment ? Premiers éléments
  
  • Israel-Jost Vincent
  • Weil-Dubuc Paul-Loup
  • Adamsbaum Catherine
  • Bloch Isabelle
  Journal d'imagerie diagnostique et interventionnelle, Elsevier, 2021, 4, pp.238-240. (10.1016/j.jidi.2021.07.004)
  DOI : 10.1016/j.jidi.2021.07.004