Publications

2018

• “My autonomous car is an elephant”: A Machine Learning based Detector for Implausible Dimension
  
  • Monteuuis Jean-Philippe
  • Petit Jonathan
  • Zhang Jun
  • Labiod Houda
  • Mafrica Stefano
  • Servel Alain
  , 2018, pp.1-8. (10.1109/SSIC.2018.8556651)
  DOI : 10.1109/SSIC.2018.8556651
• An Incentive Framework for Collaborative Sensing in Fog Networks
  
  • Zhang Chongchong
  • Shen Fei
  • Zhang Guowei
  • Yan Feng
  • Martins Philippe
  , 2018, pp.1-6. As the big data era arrives, massive data traffic and applications generated by various terminal devices need to be processed in real time. To relieve the pressure of cloud computing on link congestion, delay, and energy consumption caused by the long distance between terminals and cloud server, the promising fog computing has been proposed. The fog network consisting of several fog clusters is considered, in which a fog controller(FC) collects all the resource information of all its fog nodes (FNs). In order to better serve the terminal nodes, different FCs are willing to exchange the information of their FNs and share their services to some extent. Therefore, in this paper, we propose a novel incentive framework for collaborative sensing to motivate the fog cluster to provide service for other fog clusters. The SRs use the computation reward prices to motivate the SP to provide more computational capability to complete the tasks. The utility functions of the SRs and the SP are proposed, considering the payment for task computation, the task delay and the computation cost. The existences of the global optimums of both the utilities for the SRs the SP are proved. Numerous simulations verify our theoretical analyses and indicate the importance of our proposed incentive framework for collaborative sensing between fog clusters subscribed to different mobile providers in the fog network. (10.1109/WCSP.2018.8555644)
  DOI : 10.1109/WCSP.2018.8555644
• Online Offloading in Dense Wireless Networks: An Adversary Multi-armed Bandit Approach
  
  • Hua Cunqing
  • Wang Lingzhi
  • Gu Pengwenlong
  , 2018, pp.1-6. (10.1109/WCSP.2018.8555671)
  DOI : 10.1109/WCSP.2018.8555671
• Total Eclipse: How To Completely Isolate a Bitcoin Peer
  
  • Adja Elloh Yves Christian
  • Hammi Badis
  • Serhrouchni Ahmed
  • Labiod Houda
  , 2018, pp.1-7. In the cryptocurrency world, Bitcoin holds the first place in terms of market cap and currency price, which makes it the first target and victim of attack attempts. Indeed, there are various attacks against cryptocurrencies in general and Bitcoin in particular, e.g., block withholding, transaction malleability and the Eclipse attack. The latter, allows an attacker to completely isolate a peer and to monopolize all permanent connections from/to the victim. However, in this attack, the non permanent connections, remains non monopolized by the attacker, which can disturb the attack success. In this paper, we propose (1) a characterization of the misbehavior mechanism applied by Bitcoin and its weaknesses; and (2) a new method to realize the Eclipse attack which monopolizes all the peer's connections, even the non permanent ones, with a minimal number of IP addresses. Our characterization and attack realization, were performed on the main Bitcoin network and on a real client. (10.1109/SSIC.2018.8556790)
  DOI : 10.1109/SSIC.2018.8556790
• An adaptive authentication and authorization scheme for IoT’s gateways: a blockchain based approach
  
  • Fayad Achraf
  • Hammi Badis
  • Khatoun Rida
  , 2018, pp.1-7. Security of the Internet of Things represents a field that strongly attracts academia and industry since it represents one of the main obstacles in its adoption. In this area, authentication and authorization methods holds a golden place in priority rank. Indeed, current approaches suffers from numerous limits. Moreover, generally, deployment systems use separately two methods one dedicated to the authentication and the other to the authorization, while the number of methods that combine both requirements is limited. In this work we propose an adaptive blockchain based authentication and authorization approach for IoT use cases. We provided a real implementation of our approach using Java language. The extensive evaluation provided, shows clearly the ability of our scheme in meeting the different requirements, as well as its ability in ensuring a very lightweight cost. (10.1109/SSIC.2018.8556668)
  DOI : 10.1109/SSIC.2018.8556668
• Detecting User's Likes and Dislikes for a Virtual Negotiating Agent
  
  • Langlet Caroline
  • Clavel Chloé
  , 2018, pp.103-110. This article tackles the issue of the detection of the user's likes and dislikes in a negotiation with a virtual agent for helping the creation of a model of user's preferences. We introduce a linguistic model of user's likes and dislikes as they are expressed in a negotiation context. The identification of syntactic and semantic features enables the design of formal grammars embedded in a bottom-up and rule-based system. It deals with conversational context by considering simple and collaborative likes and dislikes within adjacency pairs. We present the annotation campaign we conduct by recruiting annotators on CrowdFlower and using a dedicated annotation platform. Finally, we measure agreement between our system and the human reference. The obtained scores show substantial agreement (10.1145/3242969.3243024)
  DOI : 10.1145/3242969.3243024
• The Tamed Unadjusted Langevin Algorithm
  
  • Brosse Nicolas
  • Durmus Alain
  • Moulines Éric
  • Sabanis Sotirios
  Stochastic Processes and their Applications, Elsevier, 2018. In this article, we consider the problem of sampling from a probability measure π having a density on R d known up to a normalizing constant, $x → e −U (x) / R d e −U (y) dy$. The Euler discretization of the Langevin stochastic differential equation (SDE) is known to be unstable in a precise sense, when the potential U is superlinear, i.e. lim inf $x→+∞ U (x) / x = +∞$. Based on previous works on the taming of superlinear drift coefficients for SDEs, we introduce the Tamed Unadjusted Langevin Algorithm (TULA) and obtain non-asymptotic bounds in V-total variation norm and Wasserstein distance of order 2 between the iterates of TULA and π, as well as weak error bounds. Numerical experiments are presented which support our findings. (10.1016/j.spa.2018.10.002)
  DOI : 10.1016/j.spa.2018.10.002
• Non-uniformity induced distinguishability of nonlinearly generated photon pairs
  
  • Harlé Thibault
  • Cordier Martin
  • Zaquine Isabelle
  • Delaye Philippe
  , 2018.
• Engineering four-wave mixing spectral entanglement in hollow-core fibers
  
  • Cordier Martin
  • Orieux Adeline
  • Debord Benoît
  • Gérôme Frédéric
  • Gorse Alexandre
  • Chafer Matthieu
  • Diamanti Eleni
  • Delaye Philippe
  • Benabid Fetah
  • Zaquine Isabelle
  , 2018.
• CDBE: A cooperative way to improve end-to-end congestion control in mobile network
  
  • Zhong Zhenzhe
  • Hamchaoui Isabelle
  • Ferrieux Alexandre
  • Khatoun Rida
  • Serhrouchni Ahmed
  , 2018, pp.216-223. (10.1109/WiMOB.2018.8589175)
  DOI : 10.1109/WiMOB.2018.8589175
• MobiLimb: Augmenting Mobile Devices with a Robotic Limb
  
  • Teyssier Marc
  • Bailly Gilles
  • Pelachaud Catherine
  • Lecolinet Eric
  , 2018, 18. In this paper, we explore the interaction space of MobiLimb, a small 5-DOF serial robotic manipulator attached to a mobile device. It (1) overcomes some limitations of mobile devices (static, passive, motionless); (2) preserves their form factor and I/O capabilities; (3) can be easily attached to or removed from the device; (4) offers additional I/O capabilities such as physical deformation and (5) can support various modular elements such as sensors, lights or shells. We illustrate its potential through three classes of applications: As a tool, MobiLimb offers tangible affordances and an expressive controller that can be manipulated to control virtual and physical objects. As a partner, it reacts expressively to users' actions to foster curiosity and engagement or assist users. As a medium, it provides rich haptic feedback such as strokes, pat and other tactile stimuli on the hand or the wrist to convey emotions during mediated multimodal communications. (10.1145/3242587.3242626)
  DOI : 10.1145/3242587.3242626
• Combined complexity of probabilistic query evaluation
  
  • Monet Mikaël
  , 2018. Query evaluation over probabilistic databases (probabilistic queryevaluation, or PQE) is known to be intractable inmany cases, even in data complexity, i.e., when the query is fixed. Althoughsome restrictions of the queries and instances have been proposed tolower the complexity, these known tractable cases usually do not apply tocombined complexity, i.e., when the query is not fixed. My thesis investigates thequestion of which queries and instances ensure the tractability ofPQE in combined complexity.My first contribution is to study PQE of conjunctive queries on binary signatures, which we rephraseas a probabilistic graph homomorphism problem. We restrict the query and instance graphs to be trees and show the impact on the combined complexity of diverse features such as edge labels, branching,or connectedness. While the restrictions imposed in this setting are quite severe, my second contribution shows that,if we are ready to increase the complexity in the query, then we can evaluate a much more expressive language on more general instances. Specifically, I show that PQE for a particular class of Datalog queries on instances of bounded treewidth can be solved with linear complexity in the instance and doubly exponential complexity in the query.To prove this result, we use techniques from tree automata and knowledge compilation. The third contribution is to show the limits of some of these techniques by proving general lower bounds on knowledge compilation and tree automata formalisms.
• Some advances in patch-based image denoising
  
  • Houdard Antoine
  , 2018. This thesis studies non-local methods for image processing, and their application to various tasks such as denoising. Natural images contain redundant structures, and this property can be used for restoration purposes. A common way to consider this self-similarity is to separate the image into "patches". These patches can then be grouped, compared and filtered together.In the first chapter, "global denoising" is reframed in the classical formalism of diagonal estimation and its asymptotic behaviour is studied in the oracle case. Precise conditions on both the image and the global filter are introduced to ensure and quantify convergence.The second chapter is dedicated to the study of Gaussian priors for patch-based image denoising. Such priors are widely used for image restoration. We propose some ideas to answer the following questions: Why are Gaussian priors so widely used? What information do they encode about the image?The third chapter proposes a probabilistic high-dimensional mixture model on the noisy patches. This model adopts a sparse modeling which assumes that the data lie on group-specific subspaces of low dimensionalities. This yields a denoising algorithm that demonstrates state-of-the-art performance.The last chapter explores different way of aggregating the patches together. A framework that expresses the patch aggregation in the form of a least squares problem is proposed.
• Thermohaline Contribution of the Caspian Sea Water Dynamic
  
  • Moradi Ayoub
  • de Viron Olivier
  • Métivier Laurent
  • Homayouni Saeid
  Journal of Geography, Environment and Earth Science International, Sciencedomain International, 2018, 17 (3), pp.1-10. (10.9734/JGEESI/2018/44294)
  DOI : 10.9734/JGEESI/2018/44294
• Relative intensity noise properties of quantum dot lasers
  
  • Duan Jianan
  • Wang Xing-Guang
  • Zhou Yue-Guang
  • Wang Cheng
  • Grillot Frederic
  , 2018, pp.31. (10.1117/12.2500796)
  DOI : 10.1117/12.2500796
• BelMan: Bayesian Bandits on the Belief--Reward Manifold
  
  • Basu Debabrota
  • Senellart Pierre
  • Bressan Stéphane
  , 2018. We propose a generic, Bayesian, information geometric approach to the exploration--exploitation trade-off in multi-armed bandit problems. Our approach, BelMan, uniformly supports pure exploration, exploration--exploitation, and two-phase bandit problems. The knowledge on bandit arms and their reward distributions is summarised by the barycentre of the joint distributions of beliefs and rewards of the arms, the \emph{pseudobelief-reward}, within the beliefs-rewards manifold. BelMan alternates \emph{information projection} and \emph{reverse information projection}, i.e., projection of the pseudobelief-reward onto beliefs-rewards to choose the arm to play, and projection of the resulting beliefs-rewards onto the pseudobelief-reward. It introduces a mechanism that infuses an exploitative bias by means of a \emph{focal distribution}, i.e., a reward distribution that gradually concentrates on higher rewards. Comparative performance evaluation with state-of-the-art algorithms shows that BelMan is not only competitive but can also outperform other approaches in specific setups, for instance involving many arms and continuous rewards.
• Distributed Function Chaining with Anycast Routing
  
  • Wion Adrien
  • Bouet Mathieu
  • Iannone Luigi
  • Conan Vania
  , 2018. Current networks more and more rely on virtualized middleboxes to flexibly provide security, protocol optimization, and policy compliance functionalities. As such, delivering these services requires that the traffic be steered through the desired sequence of virtual appliances. Current solutions introduce a new logically centralized entity, often called orchestrator, needing to build its own holistic view of the whole network so to decide where to direct the traffic. We advocate that such a centralized orchestration is not necessary and that, on the contrary, the same objectives can be achieved by augmenting the network layer routing so to include the notion of service and its chaining. In this paper, we support our claim by designing such a system. We also present an implementation and an early evaluation, showing that we can easily steer traffic through available resources. This approach also offers promising features such as incremental deployability, multi-domain service chaining, failure resiliency, and easy maintenance.
• Directing the Photography: Combining Cinematic Rules, Indirect Light Controls and Lighting-by-Example
  
  • Galvane Quentin
  • Lino Christophe
  • Christie Marc
  • Cozot Rémi
  Computer Graphics Forum, Wiley, 2018, 37 (7), pp.45-53. The placement of lights in a 3D scene is a technical and artistic task that requires time and trained skills. Most 3D modelling tools only provide a direct control of light sources, through the manipulation of parameters such as size, location, flux (the perceived power of light) or opening angle (the light frustum). Approaches have been relying on automated or semi-automated techniques to relieve users from such low-level manipulations at the expense of an important computational cost. In this paper, guided by discussions with experts in scene and object lighting, we propose an indirect control of area light sources. We first formalize the classical 3-point lighting design principle (key-light, fill-lights and back/rim-lights) in a parametric model. Given a key-light placed in the scene, we then provide a computational approach to (i) automatically compute the position and size of fill-lights and back/rim-lights by analyzing the geometry of 3D character, and (ii) automatically compute the flux and size of key, fill and back/rim lights, given a sample reference image in a computationally efficient way. Results demonstrate the benefits of the approach on the quick lighting of 3D characters, and further demonstrate the feasibility of interactive control of multiple lights through image features. (10.1111/cgf.13546)
  DOI : 10.1111/cgf.13546
• Non-reprocity of photon pair emission in non-uniform nonlinear medium
  
  • Harlé Thibault
  • Cordier Martin
  • Zaquine Isabelle
  • Delaye Philippe
  , 2018.
• TROPICAL AND MORPHOLOGICAL OPERATORS FOR SIGNALS ON GRAPHS
  
  • Blusseau Samy
  • Velasco-Forero Santiago
  • Angulo Jesus
  • Bloch Isabelle
  , 2018. We extend recent work on mathematical morphology for signal processing on weighted graphs, based on discrete tropical algebra. The framework is general and can be applied to any scalar function defined on a graph. We show applications in structure tensors analysis and the regularization of grayscale images. (10.1109/ICIP.2018.8451395)
  DOI : 10.1109/ICIP.2018.8451395
• Fully Convolutional Siamese Networks for Change Detection
  
  • Daudt Rodrigo Caye
  • Le Saux Bertrand
  • Boulch Alexandre
  , 2018. This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images. Most notably, we propose two Siamese extensions of fully convolutional networks which use heuris-tics about the current problem to achieve the best results in our tests on two open change detection datasets, using both RGB and multispectral images. We show that our system is able to learn from scratch using annotated change detection images. Our architectures achieve better performance than previously proposed methods, while being at least 500 times faster than related systems. This work is a step towards efficient processing of data from large scale Earth observation systems such as Copernicus or Landsat.
• Information-Theoretic Analysis of the Speed-Accuracy Tradeoff with Feedback
  
  • Gori Julien
  • Rioul Olivier
  , 2018, pp.3452-3457. Human movements are inherently variable and involve some feedback mechanism. A study of the positional variance in a tapping task reveals that the variance profiles are unimodal in time. In the variance-decreasing phase, the aiming task can be modeled by a Shannon-like communication scheme where information is transmitted from a "source"-determined by the distance to the target at maximum variance-to a "destination"-the movement endpoint-over a "channel" with feedback perturbed by Gaussian noise. Thanks to the feedback link, we show that the variance decreases exponentially at a rate given by the channel capacity. This is confirmed on real data. The proposed information-theoretic model has promise to improve our understanding of human aimed movements.
• Safe optimization algorithms for variable selection and hyperparameter tuning
  
  • Ndiaye Eugene
  , 2018. Massive and automatic data processing requires the development of techniques able to filter the most important information. Among these methods, those with sparse structures have been shown to improve the statistical and computational efficiency of estimators in a context of large dimension. They can often be expressed as a solution of regularized empirical risk minimization and generally lead to non differentiable optimization problems in the form of a sum of a smooth term, measuring the quality of the fit, and a non-smooth term, penalizing complex solutions. Although it has considerable advantages, such a way of including prior information, unfortunately introduces many numerical difficulties both for solving the underlying optimization problem and to calibrate the level of regularization. Solving these issues has been at the heart of this thesis. A recently introduced technique, called "Screening Rules", proposes to ignore some variables during the optimization process by benefiting from the expected sparsity of the solutions. These elimination rules are said to be safe when the procedure guarantees to not reject any variable wrongly. In this work, we propose a unified framework for identifying important structures in these convex optimization problems and we introduce the "Gap Safe Screening Rules". They allows to obtain significant gains in computational time thanks to the dimensionality reduction induced by this method. In addition, they can be easily inserted into iterative algorithms and apply to a large number of problems.To find a good compromise between minimizing risk and introducing a learning bias, (exact) homotopy continuation algorithms offer the possibility of tracking the curve of the solutions as a function of the regularization parameters. However, they exhibit numerical instabilities due to several matrix inversions and are often expensive in large dimension. Another weakness is that a worst-case analysis shows that they have exact complexities that are exponential in the dimension of the model parameter. Allowing approximated solutions makes possible to circumvent the aforementioned drawbacks by approximating the curve of the solutions. In this thesis, we revisit the approximation techniques of the regularization paths given a predefined tolerance and we propose an in-depth analysis of their complexity w.r.t. the regularity of the loss functions involved. Hence, we propose optimal algorithms as well as various strategies for exploring the parameters space. We also provide calibration method (for the regularization parameter) that enjoys globalconvergence guarantees for the minimization of the empirical risk on the validation data.Among sparse regularization methods, the Lasso is one of the most celebrated and studied. Its statistical theory suggests choosing the level of regularization according to the amount of variance in the observations, which is difficult to use in practice because the variance of the model is oftenan unknown quantity. In such case, it is possible to jointly optimize the regression parameter as well as the level of noise. These concomitant estimates, appeared in the literature under the names of Scaled Lasso or Square-Root Lasso, and provide theoretical results as sharp as that of theLasso while being independent of the actual noise level of the observations. Although presenting important advances, these methods are numerically unstable and the currently available algorithms are expensive in computation time. We illustrate these difficulties and we propose modifications based on smoothing techniques to increase stability of these estimators as well as to introduce a faster algorithm.
• Short Review of Sentiment-Based Recommender Systems
  
  • Barrière Valentin
  • Kembellec Gérald
  , 2018, pp.1-4. (10.1145/3240117.3240120)
  DOI : 10.1145/3240117.3240120
• Temporal clustering analysis of endothelial cell gene expression following exposure to a conventional radiotherapy dose fraction using Gaussian process clustering
  
  • Heinonen M.
  • Milliat Fabien
  • Benadjaoud Mohamed‐amine
  • Francois Agnes
  • Buard Valerie
  • d'Alché-Buc Florence
  • Guipaud O.
  • Tarlet Georges
  PLoS ONE, Public Library of Science, 2018, 13 (10), pp.e0204960. The vascular endothelium is considered as a key cell compartment for the response to ionizing radiation of normal tissues and tumors, and as a promising target to improve the differential effect of radiotherapy in the future. Following radiation exposure, the global endothelial cell response covers a wide range of gene, miRNA, protein and metabolite expression modifications. Changes occur at the transcriptional, translational and post-translational levels and impact cell phenotype as well as the microenvironment by the production and secretion of soluble factors such as reactive oxygen species, chemokines, cytokines and growth factors. These radiation-induced dynamic modifications of molecular networks may control the endothelial cell phenotype and govern recruitment of immune cells, stressing the importance of clearly understanding the mechanisms which underlie these temporal processes. A wide variety of time series data is commonly used in bioinformatics studies, including gene expression, protein concentrations and metabolomics data. The use of clustering of these data is still an unclear problem. Here, we introduce kernels between Gaussian processes modeling time series, and subsequently introduce a spectral clustering algorithm. We apply the methods to the study of human primary endothelial cells (HUVECs) exposed to a radiotherapy dose fraction (2 Gy). Time windows of differential expressions of 301 genes involved in key cellular processes such as angiogenesis, inflammation, apoptosis, immune response and protein kinase were determined from 12 hours to 3 weeks post-irradiation. Then, 43 temporal clusters corresponding to profiles of similar expressions, including 49 genes out of 301 initially measured, were generated according to the proposed method. Forty-seven transcription factors (TFs) responsible for the expression of clusters of genes were predicted from sequence regulatory elements using the MotifMap system. Their temporal profiles of occurrences were established and clustered. Dynamic network interactions and molecular pathways of TFs and differential genes were finally explored, revealing key node genes and putative important cellular processes involved in tissue infiltration by immune cells following exposure to a radiotherapy dose fraction. (10.1371/journal.pone.0204960)
  DOI : 10.1371/journal.pone.0204960