Publications

2019

• All-Or-Nothing data protection for ubiquitous communication: Challenges and perspectives
  
  • Qiu Han
  • Kapusta Katarzyna
  • Lu Zhihui
  • Qiu Meikang
  • Memmi Gérard
  Information Sciences, Elsevier, 2019, 502, pp.434-445. (10.1016/j.ins.2019.06.031)
  DOI : 10.1016/j.ins.2019.06.031
• High frequency dynamics in quantum cascade lasers : a roadmap to free-space communications in the mid-infrared
  
  • Spitz O
  • Herdt A
  • Maisons G.
  • Carras M.
  • Elsässer W
  • Grillot F.
  , 2019. Quantum cascade lasers, which can emit deterministic chaotic patterns, are found to exhibit improved chaos properties when using optical injection instead of feedback. These findings pave a way for high-speed secure communications in the mid-infrared
• Optimal and suboptimal channel precoding and decoding matrices for linear video coding
  
  • Zheng Shuo
  • Cagnazzo Marco
  • Kieffer Michel
  Signal Processing: Image Communication, Elsevier, 2019, 78, pp.135-151. (10.1016/j.image.2019.06.011)
  DOI : 10.1016/j.image.2019.06.011
• 25 Gb/s Colorless Transmitter Based on Reflective Electroabsorption Modulator for Ultra-dense WDM-PON Application
  
  • Atra Kebede
  • Cerulo Giancarlo
  • Provost Jean-Guy
  • Mekhazni Karim
  • Calo Cosimo
  • Pommereau Frédéric
  • Gomez Carmen
  • Fortin Catherine
  • Decobert Jean
  • Martin Florence
  • Derouin Estelle
  • Caillaud Christophe
  • Erasme Didier
  • Ware Cédric
  • Mallecot Franck
  • Achouche Mohand
  , 2019.
• Multitask learning for large-scale semantic change detection
  
  • Daudt Rodrigo Caye
  • Le Saux Bertrand
  • Boulch Alexandre
  • Gousseau Yann
  Computer Vision and Image Understanding, Elsevier, 2019, 187, pp.102783. Change detection is one of the main problems in remote sensing, and is essential to the accurate processing and understanding of the large scale Earth observation data available. Most of the recently proposed change detection methods bring deep learning to this context, but change detection labelled datasets which are openly available are still very scarce, which limits the methods that can be proposed and tested. In this paper we present the first large scale very high resolution semantic change detection dataset, which enables the usage of deep supervised learning methods for semantic change detection with very high resolution images. The dataset contains coregistered RGB image pairs, pixel-wise change information and land cover information. We then propose several supervised learning methods using fully convolutional neural networks to perform semantic change detection. Most notably, we present a network architecture that performs change detection and land cover mapping simultaneously, while using the predicted land cover information to help to predict changes. We also describe a sequential training scheme that allows this network to be trained without setting a hyperparameter that balances different loss functions and achieves the best overall results. (10.1016/j.cviu.2019.07.003)
  DOI : 10.1016/j.cviu.2019.07.003
• Data Center's Energy Savings for Data Transport via TCP on Hybrid Optoelectronic Switches
  
  • Minakhmetov Artur
  • Ware Cédric
  • Iannone Luigi
  , 2019 (TuC3.3). We report on possible 75% lower energy consumption for packet transport in data center networks replacing Electronic with Hybrid Optical Packet Switching (optical switches with a shared electronic buffer) combined with enhanced Transmission Control Protocol.
• Secure PUF: Physically Unclonable Function based on Arbiter with Enhanced Resistance against Machine Learning (ML) Attacks
  
  • El Hajj Mohammad
  • Fadlallah Ahmad
  • Chamoun Maroun
  • Serhrouchni Ahmed
  , 2019. Latest cryptographic algorithms are based on the storage of secret information(stored in RAM)which is prone to a number of attacks like reverse-engineering, cold-boot, side channel, device tampering, etc. Therefore new methods should be developed to enable secure transmission of data and authentication especially in IoT sectors.In the past few years promising hardware security primitive is used Physical Unclonable Functions(PUFs)which is a lightweight one-way function to extract a unique identity to each end device based on physical factors introduced during manufacturing. In this paper, we introduced a new scheme of Arbiter PUF to create an identity for each device. We have provided a scheme that is prone to machine learning attacks. Our scheme avoids the shortcoming of several previously proposed related PUF based authentication protocols that is summarized in a benchmark done in this paper.
• Resource management in computer clusters : algorithm design and performance analysis
  
  • Comte Céline
  , 2019. The growing demand for cloud-based services encourages operators to maximize resource efficiency within computer clusters. This motivates the development of new technologies that make resource management more flexible. However, exploiting this flexibility to reduce the number of computers also requires efficient resource-management algorithms that have a predictable performance under stochastic demand. In this thesis, we design and analyze such algorithms using the framework of queueing theory.Our abstraction of the problem is a multi-server queue with several customer classes. Servers have heterogeneous capacities and the customers of each class enter the queue according to an independent Poisson process. Each customer can be processed in parallel by several servers, depending on compatibility constraints described by a bipartite graph between classes and servers, and each server applies first-come-first-served policy to its compatible customers. We first prove that, if the service requirements are independent and exponentially distributed with unit mean, this simple policy yields the same average performance as balanced fairness, an extension to processor-sharing known to be insensitive to the distribution of the service requirements. A more general form of this result, relating order-independent queues to Whittle networks, is also proved. Lastly, we derive new formulas to compute performance metrics.These theoretical results are then put into practice. We first propose a scheduling algorithm that extends the principle of round-robin to a cluster where each incoming job is assigned to a pool of computers by which it can subsequently be processed in parallel. Our second proposal is a load-balancing algorithm based on tokens for clusters where jobs have assignment constraints. Both algorithms are approximately insensitive to the job size distribution and adapt dynamically to demand. Their performance can be predicted by applying the formulas derived for the multi-server queue.
• Deep Learning Approaches for Sparse Recovery in Compressive Sensing
  
  • Marques E.
  • Maciel N.
  • Naviner Lirida
  • Cai H.
  • Yang J.
  , 2019, pp.129-134. Compressive sensing enables sparse signals recovery by less measurements than required by the Nyquist rate, so leading to energy and processing saving. Accuracy and complexity improvements can be achieved applying neural network to sparse linear inverse problem. This work focuses on sparse recovery with deep network. Improvements to the Learned Iterative Shrinkage-Thresholding Algorithm (LISTA) and a novel neural network are proposed. Results show that these propositions can decrease up to 10.8 dB the NMSE value and require fewer layers than if only LISTA is used to estimate the signal. (10.1109/ISPA.2019.8868841)
  DOI : 10.1109/ISPA.2019.8868841
• Advanced Robot 3D Simulation Interface for Disaster Management
  
  • Bertolino Matteo
  • Tanzi Tullio Joseph
  , 2019.
• Scalable Coding Framework for a View-Dependent Streaming of Digital Holograms
  
  • Rhammad Anas El
  • Gioia Patrick
  • Gilles Antonin
  • Cagnazzo Marco
  , 2019, pp.146-150. (10.1109/ICIP.2019.8802950)
  DOI : 10.1109/ICIP.2019.8802950
• Knowledge Base Completion With Analogical Inference on Context Graphs
  
  • Mimouni Nada
  • Moissinac Jean-Claude Jc
  • Vu Anh Tuan
  , 2019. Knowledge base completion refers to the task of adding new, missing, links between entities. In this work we are interested in the problem of knowledge Graph (KG) incompleteness in general purpose knowledge bases like DBpedia and Wikidata. We propose an approach for discovering implicit triples using observed ones in the incomplete graph leveraging analogy structures deducted from a KG embedding model. We use a language modelling approach where semantic regularities between words are preserved which we adapt to entities and relations. We consider excerpts from large input graphs as a reduced and meaningful context for a set of entities of a given domain. The first results show that analogical inferences in the projected vector space is relevant to a link prediction task.
• Vibration identification over 50km SSMF with Pol-Mux coded phase-OTDR
  
  • Dorize C.
  • Awwad Elie
  • Guerrier Sterenn
  • Renaudier J.
  , 2019, pp.164 (4 pp.)-164 (4 pp.). (10.1049/cp.2019.0898)
  DOI : 10.1049/cp.2019.0898
• Visual Representation of Online Handwriting Time Series for Deep Learning Parkinson's Disease Detection
  
  • Taleb Catherine
  • Khachab Maha
  • Mokbel Chafic
  • Likforman-Sulem Laurence
  , 2019, pp.25-30. Parkinson's disease (PD) is a neurological disorder associated with a progressive decline in motor skills, speech, and cognitive processes. Since the diagnosis of Parkinson's disease is difficult, researchers have worked to develop a support tool based on algorithms to separate healthy controls from PD patients. Online handwriting dynamic signals can provide more detailed and complex information for PD detection task. Existing techniques often depended on hand-crafted features that required expert knowledge of the field. In this paper, it is suggested to learn pen-based features by means of deep learning for automatic classification of PD. For this purpose, a visual representation of the time series can be computed and used at the input of a convolutional neural network (CNN) as in [4]. Classically, the time series is transformed into a fixed dimension image applying normalization on the time dimension. In this work we have experimented several visual representations, including the spectrogram where normalization of the time scale is applied after short term information has been extracted locally. We have been able to show that considering the local short term information allows the deep learning models to provide better classification results compared to a globally normalized fixed dimension visual representation. For validation purpose, a CNN-BLSTM was directly applied on the time series, without any normalization of the time scale which led to best performance equivalent to the one obtained on spectrogram representation (10.1109/ICDARW.2019.50111)
  DOI : 10.1109/ICDARW.2019.50111
• OFDM Based System Radio Resources Dimensioning Approach: A Comparison Between Cox Process and Poisson Point Process
  
  • Rachad Jalal
  • Nasri Ridha
  • Decreusefond Laurent
  , 2019. The upcoming fifth generation (5G) New Radio (NR) interface inherits many concepts and techniques from 4G systems such as the Orthogonal Frequency Division Multiplex (OFDM) based waveform and multiple access. Dimensioning 5G NR interface will likely follow the same principles as in 4G networks. It aims at finding the number of radio resources required to carry a forecast data traffic at a target users Quality of Services (QoS). The present paper attempts to provide a new approach of radio resources dimensioning considering the congestion probability, qualified as a relevant metric for QoS evaluation. We distinguish between the spatial random distribution of indoor users, modeled by a spatial Poisson Point Process (spatial PPP) in a typical area covered by a 5G cell, and the distribution of outdoor users modeled by a linear PPP generated in a random system of roads modeled according to a Poisson Line Process (PLP). Moreover, we show that the total requested Physical Resource Blocks (PRBs) follows a compound Poisson distribution and we attempt to derive the explicit expression of the congestion probability by introducing a mathematical tool from combinatorial analysis called the exponential Bell polynomials. Finally we show how to dimension radio resources, for a given target congestion probability, by solving an implicit relation between the necessary resources and the forecast data traffic expressed in terms of cell throughput. Different numerical results are presented to justify this dimensioning approach.
• SEGMENTATION OF RETINAL ARTERIAL BIFURCATIONS IN 2D ADAPTIVE OPTICS OPHTHALMOSCOPY IMAGES
  
  • Trimeche Iyed
  • Rossant Florence
  • Bloch Isabelle
  • Pâques Michel
  , 2019. The study of vascular morphometry requires segmenting vessels with high precision. Of particular clinical interest is the morpho-metric analysis of arterial bifurcations in Adaptive Optics Ophthal-moscopy (AOO) images of eye fundus. In this paper, we extend our previous approach for segmenting retinal vessel branches to the segmentation of bifurcations. This enables us to recover the mi-crovascular tree and extract biomarkers that charactarize the blood flow. Segmentation results are shown to be within the range of intra-and inter-user variability, allowing a preliminary study on biomark-ers derived from vessel diameter estimates at arterial bifurcations. (10.1109/ICIP.2019.8803076)
  DOI : 10.1109/ICIP.2019.8803076
• Provenance in Databases: Principles and Applications
  
  • Senellart Pierre
  , 2019, pp.104-109. Data provenance is extra information computed during query evaluation over databases, which provides additional context about query results. Several formal frameworks for data provenance have been proposed , in particular based on provenance semirings. The provenance of a query can be computed in these frameworks for a variety of query languages. Provenance has applications in various settings, such as prob-abilistic databases, view maintenance, or explanation of query results. Though the theory of provenance semirings has mostly been developed in the setting of relational databases, it can also apply to other data representations, such as XML, graph, and triple-store databases. (10.1007/978-3-030-31423-1_3)
  DOI : 10.1007/978-3-030-31423-1_3
• Trends in cerebral organisation in Upper Palaeolithic and recent humans.
  
  • Albessard-Ball Lou
  • Balzeau Antoine
  • Durrleman​ Stanley
  • Gori Pietro
  • Grimaud-Hervé Dominique
  , 2019.
• Crowd Behavior Characterization for Scene Tracking
  
  • Franchi Gianni
  • Aldea Emanuel
  • Dubuisson Séverine
  • Bloch Isabelle
  , 2019, pp.1-8. In this work, we perform an in-depth analysis of the specific difficulties a crowded scene dataset raises for tracking algorithms. Starting from the standard characteristics depicting the crowd and their limitations, we introduce six en-tropy measures related to the motion patterns and to the appearance variability of the individuals forming the crowd, and one appearance measure based on Principal Component Analysis. The proposed measures are discussed on synthetic configurations and on multiple real datasets. These criteria are able to characterize the crowd behavior at a more detailed level and may be helpful for evaluating the tracking difficulty of different datasets. The results are in agreement with the perceived difficulty of the scenes. (10.1109/AVSS.2019.8909893)
  DOI : 10.1109/AVSS.2019.8909893
• Requirements to Models of Automotive Software: Application to the Automatic Park Assist function
  
  • Assioua Yasmine
  • Ameur-Boulifa Rabéa
  • Guitton-Ouhamou Patricia
  , 2019. In the software development lifecycle, errors and flaws can be introduced in the different phases and lead to failures. Establishing a set of functional requirements helps producing safe software. However, ensuring that the (being) developed software is compliant with those requirements is a challenging task due to the lack of automatic and formal means to lead this verification. In this paper, we present our approach that aims at analysing a collection of automotive requirements by using formal methods. The proposed approach for formal verification is evaluated by the application to the Automatic Park Assist (APA) function.
• Implementation of Sources in an Energy-Stress Tensor Based Diffuse Sound Field Model
  
  • Meacham Aidan
  • Badeau Roland
  • Polack Jean-Dominique
  , 2019. An implementation of acoustic sources is developed in the context of an energetic wave equation derived from the energy-stress tensor, examined in the one-dimensional case [Dujourdy et al, Acta Acustica united with Acustica 103:480-491, 2017]. The method efficiently models diffuse sound fields that dominate reverberation at higher frequencies and larger distances. Monopole and dipole electroacoustical sources are considered. Using loudspeaker models rather than idealized distributions of sound energy allows for a convenient structure to evaluate directional dependence and frequency dependence for a variety of source types. Compared to initial condition formulations, an explicit source term enables realistic modeling of complex sound sources with the possibility of spatial changes in time. A finite volume time domain (FVTD) approach is utilized to lay the groundwork for future extensions to three dimensions. The spatially invariant model parameters are determined iteratively by comparison with in situ measurements of a long hallway for both the monopole and dipole case in order to verify the validity of the framework. (10.18154/RWTH-CONV-240108)
  DOI : 10.18154/RWTH-CONV-240108
• Knowledge Representation and Rule Mining in Entity-Centric Knowledge Bases
  
  • Suchanek Fabian
  • Lajus Jonathan
  • Boschin Armand
  • Weikum Gerhard
  , 2019. Entity-centric knowledge bases are large collections of facts about entities of public interest, such as countries, politicians, or movies. They find applications in search engines, chatbots, and semantic data mining systems. In this paper, we first discuss the knowledge representation that has emerged as a pragmatic consensus in the research community of entity-centric knowledge bases. Then, we describe how these knowledge bases can be mined for logical rules. Finally, we discuss how entities can be represented alternatively as vectors in a vector space, by help of neural networks.
• Recommendation System-based Upper Confidence Bound for Online Advertising
  
  • Nguyen-Thanh Nhan
  • Marinca Dana
  • Khawam Kinda
  • Rohde David
  • Vasile Flavian
  • Lohan Elena Simona
  • Martin Steven
  • Quadri Dominique
  , 2019. In this paper, the method UCB-RS, which resorts to recommendation system (RS) for enhancing the upper-confidence bound algorithm UCB, is presented. The proposed method is used for dealing with non-stationary and large-state spaces multi-armed bandit problems. The proposed method has been targeted to the problem of the product recommendation in the online advertising. Through extensive testing with RecoGym, an OpenAI Gym-based reinforcement learning environment for the product recommendation in online advertising, the proposed method outperforms the widespread reinforcement learning schemes such as Epsilon-Greedy, Upper Confidence (UCB1) and Exponential Weights for Exploration and Exploitation (EXP3).
• BelMan: An Information-Geometric Approach to Stochastic Bandits
  
  • Basu Debabrota
  • Senellart Pierre
  • Bressan Stéphane
  , 2019. We propose a Bayesian information-geometric approach to the exploration-exploitation trade-off in stochastic multi-armed bandits. The uncertainty on reward generation and belief is represented using the manifold of joint distributions of rewards and beliefs. Accumulated information is summarised by the barycentre of joint distributions, the pseudobelief-reward. While the pseudobelief-reward facilitates information accumulation through exploration, another mechanism is needed to increase exploitation by gradually focusing on higher rewards, the pseudobelief-focal-reward. Our resulting algorithm, BelMan, alternates between projection of the pseudobelief-focal-reward onto belief-reward distributions to choose the arm to play, and projection of the updated belief-reward distributions onto the pseudobelief-focal-reward. We theoretically prove BelMan to be asymptotically optimal and to incur a sublinear regret growth. We instantiate BelMan to stochastic bandits with Bernoulli and exponential rewards, and to a real-life application of scheduling queueing bandits. Comparative evaluation with the state of the art shows that BelMan is not only competitive for Bernoulli bandits but in many cases also outperforms other approaches for exponential and queueing bandits.
• Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning
  
  • Vogel Robin
  • Bellet Aurélien
  • Clémençon Stéphan
  • Jelassi Ons
  • Papa Guillaume
  , 2020, 11907, pp.229–245. The development of cluster computing frameworks has allowed practitioners to scale out various statistical estimation and machine learning algorithms with minimal programming effort. This is especially true for machine learning problems whose objective function is nicely separable across individual data points, such as classification and regression. In contrast, statistical learning tasks involving pairs (or more generally tuples) of data points-such as metric learning, clustering or ranking-do not lend themselves as easily to data-parallelism and in-memory computing. In this paper, we investigate how to balance between statistical performance and computational efficiency in such distributed tuplewise statistical problems. We first propose a simple strategy based on occasionally repartitioning data across workers between parallel computation stages, where the number of repartition-ing steps rules the trade-off between accuracy and runtime. We then present some theoretical results highlighting the benefits brought by the proposed method in terms of variance reduction, and extend our results to design distributed stochastic gradient descent algorithms for tuplewise empirical risk minimization. Our results are supported by numerical experiments in pairwise statistical estimation and learning on synthetic and real-world datasets.