Publications

2018

• Preliminary study of CEDBT and CESM performances using simulated analytical contrast uptakes
  
  • Sanchez de La Rosa Ruben
  • Carton A.-K.
  • Milioni de Carvalho P.
  • Li Z.
  • Muller S.
  • Bloch Isabelle
  , 2018, pp.792-795.
• Large-signal capabilities of an optically injection-locked semiconductor laser using gain lever
  
  • Sarraute Jean-Maxime
  • Schires Kevin
  • Larochelle Sophie
  • Grillot Frédéric
  , 2018.
• On the optimality and practicability of mutual information analysis in some scenarios
  
  • de Chèrisey Èloi
  • Guilley Sylvain
  • Heuser Annelie
  • Rioul Olivier
  Cryptography and Communications - Discrete Structures, Boolean Functions and Sequences, Springer, 2018, 10 (1), pp.101-121. The best possible side-channel attack maximizes the success rate and would correspond to a maximum likelihood (ML) distinguisher if the leakage probabilities were totally known or accurately estimated in a profiling phase. When profiling is unavailable, however, it is not clear whether Mutual Information Analysis (MIA), Correlation Power Analysis (CPA), or Linear Regression Analysis (LRA) would be the most successful in a given scenario. In this paper, we show that MIA coincides with the maximum likelihood expression when leakage probabilities are replaced by online estimated probabilities. Moreover, we show that the calculation of MIA is lighter that the computation of the maximum likelihood. We then exhibit two case-studies where MIA outperforms CPA. One case is when the leakage model is known but the noise is not Gaussian. The second case is when the leakage model is partially unknown and the noise is Gaussian. In the latter scenario MIA is more efficient than LRA of any order. (10.1007/s12095-017-0241-x)
  DOI : 10.1007/s12095-017-0241-x
• Gaussian Priors for Image denoising
  
  • Delon Julie
  • Houdard Antoine
  , 2018. This chapter is dedicated to the study of Gaussian priors for patch-based image denoising. In the last twelve years, patch priors have been widely used for image restoration. In a Bayesian framework, such priors on patches can be used for instance to estimate a clean patch from its noisy version, via classical estimators such as the conditional expectation or the maximum a posteriori. As we will recall, in the case of Gaussian white noise, simply assuming Gaussian (or Mixture of Gaussians) priors on patches leads to very simple closed-form expressions for some of these estimators. Nevertheless, the convenience of such models should not prevail over their relevance. For this reason, we also discuss how these models represent patches and what kind of information they encode. The end of the chapter focuses on the different ways in which these models can be learned on real data. This stage is particularly challenging because of the curse of dimensionality. Through these different questions, we compare and connect several denoising methods using this framework.
• Prediction of weakly locally stationary processes by auto-regression
  
  • Roueff François
  • Sanchez-Perez Andres
  ALEA : Latin American Journal of Probability and Mathematical Statistics, Instituto Nacional de Matemática Pura e Aplicada (Rio de Janeiro, Brasil) [2006-....], 2018, 15, pp.1215–1239. In this contribution we introduce weakly locally stationary time series through the local approximation of the non-stationary covariance structure by a stationary one. This allows us to define autoregression coefficients in a non-stationary context, which, in the particular case of a locally stationary Time Varying Autoregressive (TVAR) process, coincide with the generating coefficients. We provide and study an estimator of the time varying autoregression coefficients in a general setting. The proposed estimator of these coefficients enjoys an optimal minimax convergence rate under limited smoothness conditions. In a second step, using a bias reduction technique, we derive a minimax-rate estimator for arbitrarily smooth time-evolving coefficients, which outperforms the previous one for large data sets. In turn, for TVAR processes, the predictor derived from the estimator exhibits an optimal minimax prediction rate. (10.30757/ALEA.v15-45)
  DOI : 10.30757/ALEA.v15-45
• Adaptive random forests for data stream regression
  
  • Gomes Heitor Murilo
  • Barddal Jean Paul
  • Ferreira Luis Eduardo Boiko
  • Bifet Albert
  , 2018.
• Online Learning with Reoccurring Drifts: The Perspective of Case-Based Reasoning
  
  • Al-Ghossein Marie
  • Murena Pierre-Alexandre
  • Cornuéjols Antoine
  • Abdessalem Talel
  , 2018.
• Pas de probas, pas de chocolat !
  
  • Zayana Karim
  Au fil des maths, APMEP, 2018. Expériences aléatoires, lois discrètes et continues, approximation des unes par les autres, intervalles de confiance, fluctuations d’échantillonnage, tests statistiques, paradoxes probabilistes
• Introduction to the issue on physics and applications of laser dynamics (IS-PALD 2017)
  
  • Grillot F.
  • Sciamanna Marc
  • Chan S.-C.
  Optics Express, Optical Society of America - OSA Publishing, 2018, 26 (16), pp.21375-21378. In this paper, we introduce the Optics Express feature issue of the 7th International Symposium on Physics and Applications of Laser Dynamics (IS-PALD). This issue consists of expanded papers related to oral and poster presentations. Selected papers represent the best of IS-PALD 2017. © 2018 Optical Society of America (10.1364/OE.26.021375)
  DOI : 10.1364/OE.26.021375
• A contrario comparison of local descriptors for change detection in Very High spatial Resolution (VHR) satellite images of urban areas
  
  • Tupin Florence
  • Liu Gang
  • Gousseau Yann
  IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2018. Change detection is a key problem for many remote sensing applications. In this paper, we present a novel unsupervised method for change detection between two high resolution remote sensing images possibly acquired by two different sensors. This method is based on keypoints matching, evaluation and grouping, and does not require any image co-registration. It consists of two main steps. First, global and local mapping functions are estimated through keypoints extraction and matching. Secondly, based on these mappings, keypoint matchings are used to detect changes and then grouped to extract regions of changes. Both steps are defined through an {\it a contrario} framework, simplifying the parameter setting and providing a robust pipeline. The proposed approach is evaluated on synthetic and real data from different optic sensors with different resolutions, incidence angles and illumination conditions. (10.1109/TGRS.2018.2888985)
  DOI : 10.1109/TGRS.2018.2888985
• High-Dimensional Mixture Models For Unsupervised Image Denoising (HDMI)
  
  • Houdard Antoine
  • Bouveyron Charles
  • Delon Julie
  SIAM Journal on Imaging Sciences, Society for Industrial and Applied Mathematics, 2018. This work addresses the problem of patch-based image denoising through the unsupervised learning of a probabilistic high-dimensional mixture models on the noisy patches. The model, named hereafter HDMI, proposes a full modeling of the process that is supposed to have generated the noisy patches. To overcome the potential estimation problems due to the high dimension of the patches, the HDMI model adopts a parsimonious modeling which assumes that the data live in group-specific subspaces of low dimension-alities. This parsimonious modeling allows in turn to get a numerically stable computation of the conditional expectation of the image which is applied for denoising. The use of such a model also permits to rely on model selection tools, such as BIC, to automatically determine the intrinsic dimensions of the subspaces and the variance of the noise. This yields a blind denoising algorithm that demonstrates state-of-the-art performance, both when the noise level is known and unknown.
• Caching Encrypted Content via Stochastic Cache Partitioning
  
  • Araldo Andrea
  • Dan Gyorgy
  • Rossi Dario
  IEEE/ACM Transactions on Networking, IEEE/ACM, 2018, 26 (1), pp.548-561. In-network caching is an appealing solution to cope with the increasing bandwidth demand of video, audio and data transfer over the Internet. Nonetheless, in order to protect consumer privacy and their own business, Content Providers (CPs) increasingly deliver encrypted content, thereby preventing Internet Service Providers (ISPs) from employing traditional caching strategies, which require the knowledge of the objects being transmitted. To overcome this emerging tussle between security and effi- ciency, in this paper we propose an architecture in which the ISP partitions the cache space into slices, assigns each slice to a different CP, and lets the CPs remotely manage their slices. This architecture enables transparent caching of encrypted content, and can be deployed in the very edge of the ISP’s network (i.e., base stations, femtocells), while allowing CPs to maintain exclusive control over their content. We propose an algorithm, called SDCP, for partitioning the cache storage into slices so as to maximize the bandwidth savings provided by the cache. A distinctive feature of our algorithm is that ISPs only need to measure the aggregated miss rates of each CP, but they need not know of the individual objects that are requested. We prove that the SDCP algorithm converges to a partitioning that is close to the optimal, and we bound its optimality gap. We use simulations to evaluate SDCP’s convergence rate under stationary and non-stationary content popularity. Finally, we show that SDCP significantly outperforms traditional reactive caching techniques, considering both CPs with perfect and with imperfect knowledge of their content popularity.
• Building a coverage hole-free communication tree
  
  • Vergne Anais
  • Decreusefond Laurent
  • Martins Philippe
  , 2018. Wireless networks are present everywhere but their management can be tricky since their coverage may contain holes even if the network is fully connected. In this paper we propose an algorithm that can build a communication tree between nodes of a wireless network with guarantee that there is no coverage hole in the tree. We use simplicial homology to compute mathematically the coverage, and Prim's algorithm principle to build the communication tree. Some simulation results are given to study the performance of the algorithm and compare different metrics. In the end, we show that our algorithm can be used to create coverage hole-free communication groups with a limited number of hops.
• Practical Random Linear Coding for MultiPath TCP: MPC-TCP
  
  • Paul-Louis Ageneau
  • Boukhatem Nadia
  • Gerla Mario
  , 2018. MPTCP is a TCP extension that enables transparent multipath for multihomed hosts. However, MPTCP is subject to head-of-line blocking, a problem that degrades delay and throughput. This problem is especially critical when used in wireless environments. On wireless, unreliable links, for example, traffic can get stalled on one path, slowing down the entire flow. A related problem is rescheduling the packets in other subflows too early, which could result in increased overhead. Random linear network coding is a potential approach to solve this problem among others, and we choose to focus in its practical capability to attenuate performance drops caused by blocking while guaranteeing full network compatibility. We have developed a version of MPTCP with network coding, MPC-TCP (MultiPath Coded TCP) and implemented it in the Linux kernel. This scheme offers a simple, practical implementation of network coding across subflows, requires minimal changes to MPTCP and preserves the TCP subflows compatibility with middleboxes. We then use our implementation to investigate the network scenarios where efficiency gains are the highest compared to vanilla MPTCP.
• «Informathique»
  
  • Zayana Karim
  • Croix Edwige
  Au fil des maths, APMEP, 2018. Essai sur la didactique de l'informatique, en lien avec les mathématiques
• Integral estimation based on Markovian design
  
  • Azaïs Romain
  • Delyon Bernard
  • Portier François
  Advances in Applied Probability, Applied Probability Trust, 2018, 50 (3), pp.833-857. Suppose that a mobile sensor describes a Markovian trajectory in the ambient space. At each time the sensor measures an attribute of interest, e.g., the temperature. Using only the location history of the sensor and the associated measurements, the aim is to estimate the average value of the attribute over the space. In contrast to classical probabilistic integration methods, e.g., Monte Carlo, the proposed approach does not require any knowledge on the distribution of the sensor trajectory. Probabilistic bounds on the convergence rates of the estimator are established. These rates are better than the traditional "root n"-rate, where n is the sample size, attached to other probabilistic integration methods. For finite sample sizes, the good behaviour of the procedure is demonstrated through simulations and an application to the evaluation of the average temperature of oceans is considered. (10.1017/apr.2018.38)
  DOI : 10.1017/apr.2018.38
• Semiconductor quantum dot lasers epitaxially grown on silicon with low linewidth enhancement factor
  
  • Duan J.
  • Huang H.
  • Jung D.
  • Zhang Z.
  • Norman J.
  • Bowers J. E.
  • Grillot F.
  Applied Physics Letters, American Institute of Physics, 2018, 112 (25), pp.251111. This work reports on the ultra-low linewidth enhancement factor (αH-factor) of semiconductor quantum dot lasers epitaxially grown on silicon. Owing to the low density of threading dislocations and resultant high gain, an αH value of 0.13 that is rather independent of the temperature range (288 K–308 K) is measured. Above the laser threshold, the linewidth enhancement factor does not increase extensively with the bias current which is very promising for the realization of future integrated circuits including high performance laser sources. (10.1063/1.5025879)
  DOI : 10.1063/1.5025879
• Ultrafast and nonlinear dynamics of InAs/GaAs semiconductor quantum dot lasers
  
  • Grillot Frédéric
  • Arsenijevic Dejan
  • Huang Heming
  • Bimberg Dieter
  , 2018.
• High-speed per-flow software monitoring with limited resources
  
  • Zhang Tianzhu
  • Linguaglossa Leonardo
  • Gallo Massimo
  • Giaccone Paolo
  • Rossi Dario
  , 2018.
• Weakly Supervised Representation Learning for Unsynchronized Audio-Visual Events
  
  • Parekh Sanjeel
  • Essid Slim
  • Ozerov Alexey
  • Duong Ngoc Q K
  • Pérez Patrick
  • Richard Gael
  , 2018. Audiovisual representation learning is an important task from the perspective of designing machines with the ability to understand complex events. To this end, we propose a novel multimodal framework that instantiates multiple instance learning. We show that the learnt representations are useful for classifying events and localizing their characteristic audiovisual elements. The system is trained using only video-level event labels without any timing information. An important feature of our method is its capacity to learn from unsynchronized audiovisual events. We achieve state-of-the-art results on a large-scale dataset of weakly-labeled audio event videos. Visualizations of localized visual regions and audio segments substantiate our system's efficacy, especially when dealing with noisy situations where modality-specific cues appear asynchronously.
• Behaviour Driven Development for Hardware Design
  
  • Diepenbeck Melanie
  • Kühne Ulrich
  • Soeken Mathias
  • Grosse Daniel
  • Drechsler Rolf
  IPSJ Transactions on System LSI Design Methodology, 2018, 11, pp.29-45. (10.2197/ipsjtsldm.11.29)
  DOI : 10.2197/ipsjtsldm.11.29
• Nonnegative Matrix Factorization
  
  • Badeau Roland
  • Virtanen Tuomas
  , 2018, pp.131-160.
• Remembered events are unexpected (Commentary on Mahr \& Csibra: Why do we remember? The communicative function of episodic memory)
  
  • Dessalles Jean-Louis
  Behavioral and Brain Sciences, Cambridge University Press (CUP), 2018, 41, pp.22. We remember a small proportion of our experiences as events. Are these events selected because they are useful and can be proven true, or rather because they are unexpected? (10.1017/S0140525X17001315)
  DOI : 10.1017/S0140525X17001315
• A Safe Communication Protocol for IoT Devices
  
  • Hammi Mohamed T.
  • Livolant Erwan
  • Bellot Patrick
  • Minet Pascale
  • Serhrouchni Ahmed
  Annals of Telecommunications - annales des télécommunications, Springer, 2018, pp.15. The Internet of Things (IoT) has overturned the information technology world. This new phenomenon is becoming inescapable and already covers almost all fields, from watchmaking to automated factories. IoT simplifies our everyday life and creates value for people and businesses. Things, also called entities, are very heterogeneous, use different communication technologies and, generally, are limited capacity devices. Therefore securing such systems raises many challenges. Communicating entities should authenticate each other and protect the integrity and the confidentiality of the data they exchange while using lightweight, fast and energy-efficient algorithms. In this paper, we propose a robust security protocol, designed especially for constrained IoT devices. We carried out a real implementation and the obtained results prove the efficiency of our protocol.
• Bubbles of Trust: a decentralized Blockchain-based authentication system for IoT
  
  • Hammi Mohamed T.
  • Hammi Badis
  • Bellot Patrick
  • Serhrouchni Ahmed
  Computers & Security, Elsevier, 2018, pp.15. Internet of Things becomes a major part of our lives, billions of autonomous devices are connected and communicate with each other. This revolutionary paradigm creates a new dimension that removes the boundaries between the real and the virtual worlds. The Wireless Sensor Networks are a masterpiece of the success of this technology, using limited capacity sensors and actuators, industrial, medical, agricultural and many other environments can be covered and managed automatically. This autonomous interacting things should authenticate each other, and communicate securely. Otherwise malicious users can cause serious damages on such systems. In this paper we propose a robust, transparent, flexible and energy efficient blockchain-based authentication mechanism called BCTrust, which is designed especially for devices with computational, storage and energy consumption constraints. In order to evaluate our approach, we realized a real implementation with C programming language, and Ethereum Blockchain.