Publications

2017

• Sparsity Analysis using a Mixed Approach with Greedy and LS Algorithms on Channel Estimation
  
  • Maciel Nilson
  • Crespo Marques Elaine
  • Naviner Lirida
  , 2017, pp.91-95.
• Subject-specific time-frequency selection for multi-class motor imagery-based BCIs using few Laplacian EEG channels
  
  • Yang Yuan
  • Chevallier Sylvain
  • Wiart Joe
  • Bloch Isabelle
  Biomedical Signal Processing and Control, Elsevier, 2017, 38, pp.302-311. The essential task of a motor imagery brain–computer interface (BCI) is to extract the motor imagery-related features from electroencephalogram (EEG) signals for classifying motor intentions. However, the optimal frequency band and time segment for extracting such features differ from subject to subject. In this work, we aim to improve the multi-class classification and to reduce the required EEG channel in motor imagery-based BCI by subject-specific time-frequency selection. Our method is based on a criterion namely Fisher discriminant analysis-type F-score to simultaneously select the optimal frequency band and time segment for multi-class classification. The proposed method uses only few Laplacian EEG channels (C3, Cz and C4) located around the sensorimotor area for classification. Applied to a standard multi-class BCI dataset (BCI competition III dataset IIIa), our method leads to better classification performance and smaller standard deviation across subjects compared to the state-of-art methods. Moreover , adding artifacts contaminated trials to the training dataset does not necessarily deteriorate our classification results, indicating that our method is tolerant to artifacts. (10.1016/j.bspc.2017.06.016)
  DOI : 10.1016/j.bspc.2017.06.016
• Paysage, la carte postale de la consommation énergétique
  
  • Lacroix Samuel
  • Huron Samuel
  • Detienne Françoise
  • Foissac G
  , 2017, pp.8 p.. Nous présentons Paysage, une représentation de données de consommation d'énergie adaptée pour les usagers non experts dans le contexte domestique. Nous avons identifié trois causes de désintérêt : la complexité des unités énergétiques, la spécificité des comportements dans le contexte domestique, des représentations inadaptées aux non-experts. Nous avons conçu et implémenté une application différente des visualisations classiques. Inspirée par l'analogie des cartes postales, Paysage représente des indicateurs clés de consommation de façon esthétique et synthétique. Ce poster présente la conception de ces indicateurs de consommation, la métaphore de la carte postale pour les illustrer et leurs représentations graphiques. (10.1145/3132129.3132159)
  DOI : 10.1145/3132129.3132159
• Revue et Perspectives du Toucher Social en IHM
  
  • Teyssier Marc
  • Bailly Gilles
  • Lecolinet Éric
  • Pelachaud Catherine
  , 2017, pp.12 p.. Le toucher est l’un des principaux canaux intervenant dans la communication non verbale. Il permet de transmettre des émotions et de renforcer les liens affectifs entre les personnes. Son utilisation a déjà été envisagé en Interaction Homme-Machine comme un moyen d’interaction avec des dispositifs, mais rarement pour une communication émotionnelle directe entre individus. Cet article présente un état de l’art du toucher social en IHM. Considérant les travaux en psychologie, en IHM, en haptique et de informatique affective, nous expliquons en premier lieu le rôle et l’importance du toucher social dans la communication et la transmission des émotions. Nous évoquons ensuite les technologies existantes et émergentes pour effectuer un toucher social et enfin nous présentons de nouvelles perspectives pour les interfaces en IHMs. (10.1145/3132129.3132135)
  DOI : 10.1145/3132129.3132135
• Démonstration de MarkPad : Augmentation du pavé tactile pour la sélection de commandes
  
  • Fruchard Bruno
  • Lecolinet Eric
  • Chapuis Olivier
  , 2017, pp.2 pages. MarkPad est une technique prenant avantage du touchpad pour permettre la création d’un grand nombre de gestes dépendants de leur taille. Elle se base sur l’idée d’utiliser des marques visuelles ou visuo-tactiles sur le touchpad ou une combinaison des deux. Les gestes démarrent d’une marque sur le bord et nissent sur une autre n’importe où. MarkPad ne rentre pas en con it avec le pointage et propose un mode novice qui agit comme un mode d’entraînement pour le mode expert. Nous présentons un prototype fonctionnel qui permet de spéci er des raccourcis spatialement organisés selon le souhait de l’utilisateur. Des associations entre des actions et des gestes mènent à la création de menus gestuels, permettant de les regrouper sémantiquement.
• Lightweight Ciphers and their Side-channel Resilience
  
  • Heuser Annelie
  • Picek Stjepan
  • Guilley Sylvain
  • Mentens Nele
  IEEE Transactions on Computers, Institute of Electrical and Electronics Engineers, 2017, pp.1-16. Side-channel attacks represent a powerful category of attacks against cryptographic devices. Still, side-channel analysis for lightweight ciphers is much less investigated than for instance for AES. Although intuition may lead to the conclusion that lightweight ciphers are weaker in terms of side-channel resistance, that remains to be confirmed and quantified. In this paper, we consider various side-channel analysis metrics which should provide an insight on the resistance of lightweight ciphers against side-channel attacks. In particular, for the non-profiled scenario we use the theoretical confusion coefficient and empirical optimal distinguisher. Our study considers side-channel attacks on the first, the last, or both rounds simultaneously. Furthermore, we conduct a profiled side-channel analysis using various machine learning attacks to recover 4-bit and 8-bit intermediate states of the cipher. Our results show that the difference between AES and lightweight ciphers is smaller than one would expect, and even find scenarios in which lightweight ciphers may be more resistant. Interestingly, we observe that the studied 4-bit S-boxes have a different side-channel resilience, while the difference in the 8-bit ones is only theoretically present. (10.1109/TC.2017.2757921)
  DOI : 10.1109/TC.2017.2757921
• Interaction Techniques Exploiting Memory to Faciliate Command Activation
  
  • Fruchard Bruno
  , 2017. The goal of this Ph.D. is to propose a new category of interactive techniques based on methods that augment the human memory to allow, through gestural interaction, easy and fast access to a large set of commands or data items. This project has two contributions : 1) to improve the understanding of phenomenons involved in learning gestures and memorizing commands ; 2) to propose new gestural interaction techniques helping memorization by taking advantage of previous results and knowledge of mnemonic methods.
• Explorer le potentiel des interactions tangibles rotatives pour les Smart Watches
  
  • Brulé Emeline
  • Bailly Gilles
  • Serrano Marcos
  • Teyssier Marc
  • Huron Samuel
  , 2017, pp.8 p.. Watches benefit from a long design history. Designers and engineers have successfully built devices using rotary physical inputs such as crowns, bezels, and wheels, separately or combined. Smart watch designers have explored the use of some of these inputs for interactions. However, a systematic exploration of their combinations has yet to be done. We investigate the design space of interactions with multiple rotary inputs through a three stages exploration. (1) We build upon observations of a collection of 113 traditional or electronic watches to propose a typology of physical rotary inputs for watches. (2) We conduct two focus groups to explore combination of physical rotary inputs. (3) We then build upon the output of these focus groups to design a low fidelity prototype, and further discuss the potential and challenges of rotary inputs combinations during a third focus group. (10.1145/3132129.3132139)
  DOI : 10.1145/3132129.3132139
• EMOEEG: A new multimodal dataset for dynamic EEG-based emotion recognition with audiovisual elicitation
  
  • Conneau Anne-Claire
  • Hajlaoui Ayoub
  • Chetouani Mohamed
  • Essid Slim
  , 2017, pp.738-742. (10.23919/EUSIPCO.2017.8081305)
  DOI : 10.23919/EUSIPCO.2017.8081305
• Amplitude and Phase Dereverberation of Monocomponent Signals
  
  • Belhomme Arthur
  • Badeau Roland
  • Grenier Yves
  • Humbert Eric
  , 2017, pp.1320-1324. While most dereverberation methods focus on how to estimate the amplitude of an anechoic signal, we propose a method which also takes the phase into account. By applying a sinusoidal model to the anechoic signal, we derive a formulation to compute the amplitude and phase of each sinusoid. These parameters are then estimated by our method in the reverberant case. As we jointly estimate the amplitude and phase of the clean signal, we achieve a very strong dereverberation, resulting in a significant improvement of dereverberation objective measures over the state-of-the-art.
• A Concurrency-Optimal Binary Search Tree
  
  • Aksenov Vitalii
  • Gramoli Vincent
  • Kuznetsov Petr
  • Malova Anna
  • Ravi Srivatsan
  , 2017. The paper presents the first \emph{concurrency-optimal} implementation of a binary search tree (BST). The implementation, based on a standard sequential implementation of an internal tree, ensures that every \emph{schedule} is accepted, i.e., interleaving of steps of the sequential code, unless linearizability is violated. To ensure this property, we use a novel read-write locking scheme that protects tree \emph{edges} in addition to nodes. Our implementation outperforms the state-of-the art BSTs on most basic workloads, which suggests that optimizing the set of accepted schedules of the sequential code can be an adequate design principle for efficient concurrent data structures.
• Semi-Blind Student's t Source Separation for Multichannel Audio Convolutive Mixtures
  
  • Leglaive Simon
  • Badeau Roland
  • Richard Gael
  , 2017, pp.2323-2327. This paper addresses the problem of multichannel audio source separation in under-determined convolutive mixtures. We target a semi-blind scenario assuming that the mixing filters are known. The convolutive mixing process is exactly modeled using the time-domain impulse responses of the mixing filters. We propose a Student's t time-frequency source model based on non-negative matrix factorization (NMF). The Student's t distribution being heavy-tailed with respect to the Gaussian, it provides some flexibility in the modeling of the sources. We also study a simpler Student's t sparse source model within the same general source separation framework. The inference procedure relies on a variational expectation-maximization algorithm. Experiments show the advantage of using an NMF model compared with the sparse source model. While the Student's t NMF source model leads to slightly better results than our previous Gaussian one, we demonstrate the superiority of our method over two other approaches from the literature.
• Distributed Approach for Deblurring Large Images with Shift-Variant Blur
  
  • Mourya Rahul
  • Ferrari André
  • Flamary Rémi
  • Bianchi Pascal
  • Richard Cédric
  , 2017. Image deblurring techniques are effective tools to obtain high quality image from acquired image degraded by blur and noise. In applications such as astronomy and satellite imaging, size of acquired images can be extremely large (up to gigapixels) covering a wide field-of-view suffering from shift-variant blur. Most of the existing deblurring techniques are designed to be cost effective on a centralized computing system having a shared memory and possibly multicore processor. The largest image they can handle is then conditioned by the memory capacity of the system. In this paper, we propose a distributed shift-variant image deblurring algorithm in which several connected processing units (each with reasonable computational resources) can deblur simultaneously different portions of a large image while maintaining a certain coherency among them to finally obtain a single crisp image. The proposed algorithm is based on a distributed Douglas-Rachford splitting algorithm with a specific structure of the penalty parameters used in the proximity operator. Numerical experiments show that the proposed algorithm produces images of similar quality as the existing centralized techniques while being distributed and being cost effective for extremely large images. (10.23919/EUSIPCO.2017.8081653)
  DOI : 10.23919/EUSIPCO.2017.8081653
• Hyperparameter Estimation in Maximum a Posteriori Regression Using Group Sparsity with an Application to Brain Imaging
  
  • Bekhti Yousra
  • Badeau Roland
  • Gramfort Alexandre
  , 2017, pp.256-260. Setting hyperparameters is a recurrent problem in the statistics literature. Popular strategies are cross-validation or Bayesian inference, yet it remains an active topic of research in order to offer better or faster algorithms. The models considered in this work are sparse regression models with convex or non-convex group-Lasso-like penalties. Starting from a recent work of Pereyra et al. [1], where they give an analytical expression to estimate the regularization parameter, we show that their framework used as such is may be suitable for an analysis prior, but can not work for a synthesis prior. The main contribution of this paper is to overcome this issue. Second, we demonstrate how one can estimate one regularization parameter per group of coefficients to improve both the support and the amplitude bias in the convex group-Lasso problem. This approach is compared with an alternative method that uses a single parameter but a non-convex penalty. Results are presented on simulations and on a brain source localization problem using magneto/electroencephalography.
• Scalable Source Localization with Multichannel Alpha-Stable Distributions
  
  • Fontaine Mathieu
  • Vanwynsberghe Charles
  • Liutkus Antoine
  • Badeau Roland
  , 2017, pp.11-15. In this paper, we focus on the problem of sound source localization and we propose a technique that exploits the known and arbitrary geometry of the microphone array. While most probabilistic techniques presented in the past rely on Gaussian models, we go further in this direction and detail a method for source localization that is based on the recently proposed alpha-stable harmonizable processes. They include Cauchy and Gaussian as special cases and their remarkable feature is to allow a simple modeling of impulsive and real world sounds with few parameters. The approach we present builds on the classical convolutive mixing model and has the particularities of requiring going through the data only once, to also work in the underdetermined case of more sources than microphones and to allow massively parallelizable implementations operating in the time-frequency domain. We show that the method yields interesting performance for acoustic imaging in realistic simulations.
• Vectorized Point based Global Illumination on Intel MIC Architecture
  
  • Xu Xiang
  • Wang Beibei
  • Wang Lu
  • Yanning Xu
  • Boubekeur Tamy
  Computers and Graphics, Elsevier, 2017. Point-Based Global Illumination (PBGI) is a popular rendering method in visual special effects and motion picture productions. This rendering algorithm models the 3D scene as a dense point cloud, which acts as caching records for light transport simulation. Structured in a tree,this cache supports the image synthesis stage through massive adaptive tree cut searches, together with the projection of these cuts on the many receiver shading points i.e., unprojected pixels in 3D space, where visibility is solved with receiver-specific z-buffer. These two operations are both time consuming in this algorithm, but they can be formulated for efficient parallel execution, in particular regarding wide-SIMD hardware. During the PBGI tree traversal procedure, we introduce a single-receiver traversal scheme for incoherent receivers, a packet traversal scheme for coherent receivers, as well as logic for dynamically switching between these methods at run-time. During the per-receiver rasterization procedure, we propose three different vectorization strategies for near-,mid- and far-distance points separately. We conducted experiments on an Intel Many Integrated Core (MIC) architecture and report results on several scenes, showing that up to a 9×speedup can be achieved when compared with non-vectorized execution during the traversal step, and nearly 2.5× speedup during rasterization step without quality degradation
• Real-Time Selective Encryption Solution based on ROI for MPEG-A Visual Identity Management AF
  
  • Bergeron Cyril
  • Sidaty Naty
  • Hamidouche Wassim
  • Boyadjis Benoit
  • Le Feuvre Jean
  • Youngkwon Lim
  , 2017. As part of a new MPEG-A standardization activity, called Visual Identity Management Application Format (VIMAF), this paper presents an end-to-end encryption solution of Region of Interest (ROI) in both AVC and HEVC encoded streams for privacy protection applications. This solution uses a selective encryption method that encrypts only the most sensitive information of the video and proposes a new adapted syntax in order to facilitate interoperability between equipments. Objective video quality measurements have shown the robustness of the proposed selective encryption solution with only a slight bitrate increase. (10.1109/icdsp.2017.8096144)
  DOI : 10.1109/icdsp.2017.8096144
• Localization of multiple jamming attackers in vehicular ad hoc network
  
  • Pang Liang
  • Chen Xiao
  • Shi Yong
  • Xue Zhi
  • Khatoun Rida
  International Journal of Distributed Sensor Networks, Hindawi Publishing Corporation, 2017, 13 (8), pp.155014771772569. (10.1177/1550147717725698)
  DOI : 10.1177/1550147717725698
• Automatic Measures to Characterise Verbal Alignment in Human-Agent Interaction
  
  • Dubuisson Duplessis Guillaume
  • Clavel Chloé
  • Landragin Frédéric
  , 2017, pp.71–81. This work aims at characterising verbal alignment processes for improving virtual agent communicative capabilities. We propose computationally inexpensive measures of verbal alignment based on expression repetition in dyadic textual dialogues. Using these measures, we present a contrastive study between Human-Human and Human-Agent dialogues on a negotiation task. We exhibit quantitative differences in the strength and orientation of verbal alignment showing the ability of our approach to characterise important aspects of verbal alignment.
• Stochastic Bandit Models for Delayed Conversions
  
  • Vernade Claire
  • Cappé Olivier
  • Perchet Vianney
  , 2017. Online advertising and product recommendation are important domains of applications for multi-armed bandit methods. In these fields, the reward that is immediately available is most often only a proxy for the actual outcome of interest, which we refer to as a conversion. For instance, in web advertising, clicks can be observed within a few seconds after an ad display but the corresponding sale –if any– will take hours, if not days to happen. This paper proposes and investigates a new stochas-tic multi-armed bandit model in the framework proposed by Chapelle (2014) –based on empirical studies in the field of web advertising– in which each action may trigger a future reward that will then happen with a stochas-tic delay. We assume that the probability of conversion associated with each action is unknown while the distribution of the conversion delay is known, distinguishing between the (idealized) case where the conversion events may be observed whatever their delay and the more realistic setting in which late conversions are censored. We provide performance lower bounds as well as two simple but efficient algorithms based on the UCB and KLUCB frameworks. The latter algorithm, which is preferable when conversion rates are low, is based on a Poissonization argument, of independent interest in other settings where aggregation of Bernoulli observations with different success probabilities is required.
• Analytic wavelets for multivariate time series analysis
  
  • Gannaz Irène
  • Achard Sophie
  • Clausel Marianne
  • Roueff François
  , 2017, 10394, pp.1-8. Many applications fields deal with multivariate long-memory time series. A challenge is to estimate the long-memory properties together with the coupling between the time series. Real wavelets procedures present some limitations due to the presence of phase phenomenons. A perspective is to use analytic wavelets to recover jointly long-memory properties, modulus of long-run covariance between time series and phases. Approximate wavelets Hilbert pairs of Selesnick (2002) fullfilled some of the required properties. As an extension of Selesnick (2002)’s work, we present some results about existence and quality of these approximately analytic wavelets. (10.1117/12.2272928)
  DOI : 10.1117/12.2272928
• Fractional Langevin Monte Carlo: Exploring L\'{e}vy Driven Stochastic Differential Equations for Markov Chain Monte Carlo
  
  • Şimşekli Umut
  , 2017. Along with the recent advances in scalable Markov Chain Monte Carlo methods, sampling techniques that are based on Langevin diffusions have started receiving increasing attention. These so called Langevin Monte Carlo (LMC) methods are based on diffusions driven by a Brownian motion, which gives rise to Gaussian proposal distributions in the resulting algorithms. Even though these approaches have proven successful in many applications, their performance can be limited by the light-tailed nature of the Gaussian proposals. In this study, we extend classical LMC and develop a novel Fractional LMC (FLMC) framework that is based on a family of heavy-tailed distributions, called $\alpha$-stable L\'{e}vy distributions. As opposed to classical approaches, the proposed approach can possess large jumps while targeting the correct distribution, which would be beneficial for efficient exploration of the state space. We develop novel computational methods that can scale up to large-scale problems and we provide formal convergence analysis of the proposed scheme. Our experiments support our theory: FLMC can provide superior performance in multi-modal settings, improved convergence rates, and robustness to algorithm parameters.
• An Asynchronous Computability Theorem for Fair Adversaries
  
  • Kuznetsov Petr
  • Rieutord Thibault
  • He Yuan
  , 2017. This paper proposes a simple topological characterization of a large class of fair adversarial models via affine tasks: sub-complexes of the second iteration of the standard chromatic subdivision. We show that the task computability of a model in the class is precisely captured by iterations of the corresponding affine task. Fair adversaries include, but are not restricted to, the models of wait-freedom, t-resilience, and k-concurrency. Our results generalize and improve all previously derived topological characterizations of the ability of a model to solve distributed tasks.
• Propagation modelling towards the design of drone-borne GPR for humanitarian applications
  
  • Tanzi Tullio
  • Chandra Madhu
  , 2017.
• Double Hierarchies for Directional Importance Sampling in Monte Carlo Rendering
  
  • Bus Norbert
  • Boubekeur Tamy
  Journal of Computer Graphics Techniques, Williams College, 2017, 6 (3), pp.35-37. We describe a novel representation of the light field tailored to improve importance sampling for Monte Carlo rendering. The domain of the light field i.e., the product space of spatial positions and directions is hierarchically subdivided into subsets on which local models characterize the light transport. The data structure, that is based on double trees, approximates the exact light field and enables very efficient queries for importance sampling and easy tracing of photons in the scene. The framework is simple yet flexible, enabling the usage of any type of local model for representing the light field, provided it can be efficiently importance sampled. The method also supports progressive refinement with an arbitrary number of photons. We provide a reference open source implementation.