Publications

2018

• Belief revision, minimal change and relaxation: A general framework based on satisfaction systems, and applications to description logics
  
  • Aiguier Marc
  • Atif Jamal
  • Bloch Isabelle
  • Hudelot Céline
  Artificial Intelligence (AIJ), Elsevier, 2018, 256, pp.160 - 180. Belief revision of knowledge bases represented by a set of sentences in a given logic has been extensively studied but for specific logics, mainly propositional, and also recently Horn and description logics. Here, we propose to generalize this operation from a model-theoretic point of view, by defining revision in the abstract model theory of satisfaction systems. In this framework, we generalize to any satisfaction system the characterization of the AGM postulates given by Katsuno and Mendelzon for propositional logic in terms of minimal change among interpretations. In this generalization, the constraint on syntax independence is partially relaxed. Moreover, we study how to define revision, satisfying these weakened AGM postulates, from relaxation notions that have been first introduced in description logics to define dissimilarity measures between concepts, and the consequence of which is to relax the set of models of the old belief until it becomes consistent with the new pieces of knowledge. We show how the proposed general framework can be instantiated in different logics such as propositional, first-order, description and Horn logics. In particular for description logics, we introduce several concrete relaxation operators tailored for the description logic ALC and its fragments EL and ELU, discuss their properties and provide some illustrative examples. (10.1016/j.artint.2017.12.002)
  DOI : 10.1016/j.artint.2017.12.002
• Training and Compensation of Class-conditioned NMF Bases for Speech Enhancement
  
  • Chung Hanwook
  • Badeau Roland
  • Plourde Eric
  • Champagne Benoît
  Neurocomputing, Elsevier, 2018. In this paper, we introduce a training and compensation algorithm of the class-conditioned basis vectors in the non-negative matrix factorization (NMF) model for single-channel speech enhancement. The main goal is to estimate the basis vectors of different signal sources in a way that prevents them from representing other sources, in order to reduce the residual noise components that have features similar to the speech signal. During the proposed training stage, the basis matrices for the clean speech and noises are estimated jointly by constraining them to belong to different classes. To this end, we employ the probabilistic generative model (PGM) of classification, specified by class-conditional densities, as an a priori distribution for the basis vectors. The update rules of the NMF and the PGM parameters of classification are jointly obtained by using the variational Bayesian expectation-maximization (VBEM) algorithm, which guarantees convergence to a stationary point. Another goal of the proposed algorithm is to handle a mismatch between the characteristics of the training and test data. This is accomplished during the proposed enhancement stage, where we implement a basis compensation scheme. Specifically, we use extra free basis vectors to capture the features which are not included in the training data. Objective experimental results for different combination of speaker and noise types show that the proposed algorithm can provide better speech enhancement performance than the benchmark algorithms under various conditions.
• IoT technologies for smart cities
  
  • Hammi Badis
  • Khatoun Rida
  • Zeadally Sherali
  • Fayad Achraf
  • Khoukhi Lyes
  IET Networks, John Wiley & Sons Inc., 2018, 7 (1), pp.1-13. The large deployment of Internet of things (IoT) is actually enabling smart city projects and initiatives all over the world. Objects used in daily life are being equipped with electronic devices and protocol suites in order to make them interconnected and connected to the Internet. According to a recent Gartner study, 50 billion connected objects will be deployed in smart cities by 2020. These connected objects will make the authors’ cities smart. However, they will also open up risks and privacy issues. As various smart city initiatives and projects have been launched in recent years, theyhave witnessed not only the expected benefits, but the risks introduced. They describe the current and future trends of smart city and IoT. They also discuss the interaction between smart cities and IoT and explain some of the drivers behind the evolution and development of IoT and smart city. Finally, they discuss some of the IoT weaknesses and how they can be addressed when used for smart cities. (10.1049/iet-net.2017.0163)
  DOI : 10.1049/iet-net.2017.0163
• Technologies d’antennes- De l’antenne élémentaire aux grandes antennes
  
  • Begaud Xavier
  , 2018.
• Platypus – A Multilingual Question Answering Platform for Wikidata
  
  • Tanon Thomas Pellissier
  • Dias de Assuncao Marcos
  • Caron Eddy
  • Suchanek Fabian
  , 2018. In this paper we present Platypus, a natural language question answering system. Our objective is to provide the research community with a production-ready multilingual question answering platform that targets Wiki-data, the largest general-purpose knowledge base on the Semantic Web. Our platform can answer complex queries in several languages, using hybrid grammatical and template based techniques.
• Perceived dynamic range of HDR images with no semantic information
  
  • Hulusic Vedad
  • Valenzise Giuseppe
  • Dufaux Frédéric
  , 2018, 30 (14), pp.1-6. Computing dynamic range of high dynamic range (HDR) content is an important procedure when selecting the test material , designing and validating algorithms, or analyzing aesthetic attributes of HDR content. It can be computed on a pixel-based level, measured through subjective tests or predicted using a mathematical model. However, all these methods have certain limitations. This paper investigates whether dynamic range of modeled images with no semantic information, but with the same first order statistics as the original, natural content, is perceived the same as for the corresponding natural images. If so, it would be possible to improve the perceived dynamic range (PDR) pre-dictor model by using additional objective metrics, more suitable for such synthetic content. Within the subjective study, three experiments were conducted with 43 participants. The results show significant correlation between the mean opinion scores for the two image groups. Nevertheless, natural images still seem to provide better cues for evaluation of PDR. (10.2352/ISSN.2470-1173.2018.14.HVEI-507)
  DOI : 10.2352/ISSN.2470-1173.2018.14.HVEI-507
• Task Computability in Unreliable Anonymous Networks
  
  • Kuznetsov Petr
  • Yanagisawa Nayuta
  , 2018, pp.23:1-23:13. (10.4230/LIPIcs.OPODIS.2018.23)
  DOI : 10.4230/LIPIcs.OPODIS.2018.23
• Parallel Combining: Benefits of Explicit Synchronization
  
  • Aksenov Vitaly
  • Kuznetsov Petr
  • Shalyto Anatoly
  , 2018, pp.11:1-11:16. (10.4230/LIPIcs.OPODIS.2018.11)
  DOI : 10.4230/LIPIcs.OPODIS.2018.11
• Finding events in temporal networks: Segmentation meets densest-subgraph discovery
  
  • Rozenshtein Polina
  • Bonchi Francesco
  • Gionis Aristides
  • Sozio Mauro
  • Tatti Nikolaj
  , 2018. In this paper we study the problem of discovering a timeline of events in a temporal network. We model events as dense subgraphs that occur within intervals of network activity. We formulate the event-discovery task as an optimization problem, where we search for a partition of the network timeline into k non-overlapping intervals, such that the intervals span subgraphs with maximum total density. The output is a sequence of dense subgraphs along with corresponding time intervals, capturing the most interesting events during the network lifetime. A naïve solution to our optimization problem has polynomial but prohibitively high running time complexity. We adapt existing recent work on dynamic densest-subgraph discovery and approximate dynamic programming to design a fast approximation algorithm. Next, to ensure richer structure, we adjust the problem formulation to encourage coverage of a larger set of nodes. This problem is NP-hard even for static graphs. However, on static graphs a simple greedy algorithm leads to approximate solution due to submodularity. We extended this greedy approach for the case of temporal networks. However, the approximation guarantee does not hold. Nevertheless, according to the experiments, the algorithm finds good quality solutions. (10.1109/ICDM.2018.00055)
  DOI : 10.1109/ICDM.2018.00055
• EviDense: a Graph-based Method for Finding Unique High-impact Events with Succinct Keyword-based Descriptions
  
  • Balalau Oana
  • Castillo Carlos
  • Sozio Mauro
  , 2018. Despite the significant efforts made by the research community in recent years, automatically acquiring valuable information about high impact-events from social media remains challenging. We present EVIDENSE, a graph-based approach for finding high-impact events (such as disaster events) in social media. Our evaluation shows that our method outper-forms state-of-the-art approaches for the same problem, in terms of having higher precision, lower number of duplicates, while providing a keyword-based description that is succinct and informative.
• Biometric Systems Private by Design: Reasoning about privacy properties of biometric system architectures
  
  • Bringer Julien
  • Chabanne Hervé
  • Le Métayer Daniel
  • Lescuyer Roch
  Transactions on Data Privacy, IIIA-CSIC, 2018, 11 (2), pp.111-137. The goal of the work presented in this paper is to show the applicability of the privacy by design approach to biometric systems and the benefit of using formal methods to this end. We build on a general framework for the definition and verification of privacy architectures introduced at STM 2014 and show how it can be adapted to biometrics. The choice of particular techniques and the role of the components (central server, secure module, biometric terminal, smart card, etc.) in the architecture have a strong impact on the privacy guarantees provided by a biometric system. Some architectures have already been analysed but on a case by case basis, which makes it difficult to draw comparisons and to provide a rationale for the choice of specific options. In this paper, we describe the application of a general privacy architecture framework to specify different design options for biometric systems and to reason about them in a formal way.
• A Simple and Exact Algorithm to Solve l1 Linear Problems: Application to the Compressive Sensing Method
  
  • Ciril Igor
  • Darbon Jérôme
  • Tendero Yohann
  , 2018, 4, pp.54-62. This paper considers l1-regularized linear inverse problems that frequently arise in applications. One striking example is the so called compressive sensing method that proposes to reconstruct a high dimensional signal u from low dimensional measurements b=Au. The basis pursuit is another example. For most of these problems the number of unknowns is very large. The recovered signal is obtained as the solution to an optimization problem and the quality of the recovered signal directly depends on the quality of the solver. Theoretical works predict a sharp transition phase for the exact recovery of sparse signals. However, to the best of our knowledge, other state-of-the-art algorithms are not effective enough to accurately observe this transition phase. This paper proposes a simple algorithm that computes an exact l1 minimizer under the constraints Au=b. This algorithm can be employed in many problems: as soon as A has full row rank. In addition, a numerical comparison with standard algorithms available in the iterature is exhibited. These comparisons illustrate that our algorithm compares advantageously: the aforementioned transition phase is empirically observed with a much better quality. (10.5220/0006624600540062)
  DOI : 10.5220/0006624600540062
• Depiction of the perfusion components’ volume fraction distribution in generalized intravoxel incoherent motion by using Gaussian mixture model
  
  • Wang Shunli
  • Liu Wanyu
  • Kuai Zixiang
  • Zhu Yuemin
  Concepts in Magnetic Resonance Part B: Magnetic Resonance Engineering, Wiley, 2018, 48 (3), pp.e21399. Gaussian mixture model (GMM) was proposed to depict the perfusion volume fraction distribution in the generalized intravoxel incoherent motion model (GIVIM) to improve GIVIM's ability of describing complex perfusion conditions and their changes. Different hepatic perfusion conditions were accounted for by performing different combinations of imaging sequence and diffusion time on six normal livers. In order to evaluate GIVIM-GMM's reliability in perfusion condition analysis, the fitting to diffusion-weighted (DW) data and the consistency between diffusion-related parameters' change and the data's change were tested and the recent GIVIM and the triexponential models were chosen for comparison. The difference of the fitting results was evaluated by performing the extra-sum-of-squares F test and information criteria on normal human DW data. The difference of the consistency was assessed by using two-tailed paired Student's t test. In the extra-sum-of-squares F test, the relative difference ratio F values derived from theGIVIM and GIVIM-GMM and that derived from the triexponential model and the GIVIM-GMM are respectively 25.334 and 27.976, which indicates that significant difference existed and that the GIVIM-GMM provides better fit to the normal human liver DW data. In information criteria test, the evidence ratio values were determined by dividing the GIVIM's or triexponential model's correct probability by the GIVIM-GMM's. Both evidence ratio values (2.3942x10(-10), 8.6167x10(-9), respectively) are much smaller than 1, which also expresses that the best model used to fit the normal human liver DW data was the GIVIM-GMM. In two-tailed paired student's t test, the GIVIM-GMM provides more parameters to give a finer description of perfusion than the triexponential model or GIVIM. In short, all the results demonstrated that the GIVIM-GMM provides better performance than the existing IVIM models for depicting the signal attenuation in DW imaging. (10.1002/cmr.b.21399)
  DOI : 10.1002/cmr.b.21399
• Chromatic dispersion, nonlinear parameter and modulation formats monitoring based on Godard’s error for coherent optical transmission systems
  
  • Jiang Lin
  • Yan Lianshan
  • Yi Anlin
  • Pan Yan
  • Hao Ming
  • Pan Wei
  • Luo Bin
  • Jaouën Yves
  IEEE Photonics Journal, Institute of Electrical and Electronics Engineers (IEEE), 2018, 10 (1), pp.790051. This paper considers Godard's error as signal quality metric to monitor chromatic dispersion (CD), nonlinear parameter and modulation format in the DSP module of the coherent receivers. We first review a CD monitoring based on Godard's error that can be able to accurately monitor arbitrarily large dispersion values in uncompensated transmission links in combination with frequency domain equalizer, then extend the previous nonlinear parameter monitoring method based on Godard's error by blindly obtaining the optimized value γξp to significantly improve the adaptive capability, and present a simple and effective modulation format monitoring based on Godard's error. Meanwhile, the effectiveness has been experimentally verified in 128-Gb/s PDM-QPSK, 192-Gb/s PDM-8QAM, and 256-Gb/s PDM-16QAM systems. (10.1109/JPHOT.2017.2786697)
  DOI : 10.1109/JPHOT.2017.2786697
• From Transductive to Inductive Semi-Supervised Attributes for Ship Category Recognition
  
  • Oliveau Quentin
  • Sahbi Hichem
  , 2018, pp.4827-4830. Fine-grained ship category recognition is a data-hungry learning task that requires a lot of labeled data which are usually scarce. Alternative models, as transductive attributes, bypass this limitation by considering not only labeled data but also abundant unlabeled ones. However, these transductive methods are basically designed for observed data, and their extension to unobserved sets requires retraining the whole models. In this paper, we introduce a novel ship category recognition method based on semi-supervised learning; the strength of our method resides in its ability to leverage labeled and unlabeled observed data while being highly effective and efficient in order to handle unobserved ones. We consider two variants of our method, the first one is non-parametric and based on support vector regression while the second one is parametric and based on deep neural networks. Experiments conducted on the challenging fine-grained ship category recognition show that our semi-supervised method is highly effective and generalizes well across unobserved sets. (10.1109/IGARSS.2018.8518265)
  DOI : 10.1109/IGARSS.2018.8518265
• Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau.
  
  • Durmus Alain
  • Moulines Éric
  • Pereyra Marcelo
  SIAM Journal on Imaging Sciences, Society for Industrial and Applied Mathematics, 2018, 11 (1). In this paper, two new algorithms to sample from possibly non-smooth log-concave probability measures are introduced. These algorithms use Moreau-Yosida envelope combined with the Euler-Maruyama discretization of Langevin diffusions. They are applied to a de-convolution problem in image processing, which shows that they can be practically used in a high dimensional setting. Finally, non-asymptotic bounds for one of the proposed methods are derived. These bounds follow from non-asymptotic results for ULA applied to probability measures with a convex continuously differentiable log-density with respect to the Lebesgue measure. (10.1137/16M110834)
  DOI : 10.1137/16M110834
• An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI
  
  • Tor-Díez Carlos
  • Passat Nicolas
  • Bloch Isabelle
  • Faisan Sylvain
  • Bednarek Nathalie
  • Rousseau François
  Computerized Medical Imaging and Graphics, Elsevier [1988-....], 2018, 70, pp.73-82. Brain structure analysis in the newborn is a major health issue. This is especially the case for premature neonates, in order to obtain predictive information related to the child development. In particular, the cortex is a structure of interest, that can be observed in MRI (magnetic resonance imaging). However, neonatal MRI data present specific properties that make them challenging to process. In this context, multi-atlas approaches constitute an efficient strategy, taking advantage of images processed beforehand. The method proposed in this article relies on such multi-atlas strategy. More precisely, it uses two paradigms: first, a non-local model based on patches; second, an iterative optimization scheme. Coupling both concepts allows us to consider patches related not only to the image information, but also to the current segmentation. This strategy is compared to other multi-atlas methods proposed in the literature. Experiments show that the proposed approach provides robust cortex segmentation results. (10.1016/j.compmedimag.2018.09.003)
  DOI : 10.1016/j.compmedimag.2018.09.003
• An Improved Path Optimum Algorithm for Container Relocation Problems in Port Terminals Worldwide
  
  • Wang A.
  • Mehmood Fahad
  • Mohmand Y.T.
  • Zheng S.
  Journal of Coastal Research, Coastal Education and Research Foundation, 2018, 34 (3), pp.752--765. The container relocation problem (CRP) for ports worldwide has been a highly significant research topic because of its contribution to the improvement of yard-running efficiency. It can be defined as a sequence that allows each container to be extracted with the least number of relocations when identical containers in a cluster have been stacked in a block. This study differs from previous research mainly in five aspects: (1) a two-level goal programming model for CRP is presented that can help in understanding of this problem and provide a solid theoretical ground for this research; (2) because of the NP time hardness of the CRP, heuristic rules are proposed to reduce hunting space by the way of dividing solution space; (3) an improved path optimum algorithm (I-POA) is proposed to reduce unfeasible solutions and find a high-quality solution for any three-dimensional case in a shorter running time; (4) the numerical experiments show that the algorithm proposed in this research achieves better performance than similar algorithms because of its higher levels of efficiency and more robust property; and (5) a general expression for the utilization level of the storage area and the number of relocations is proposed to check the reliability of the result by conducting a sensitivity analysis. Based on this research, the following conclusion can be obtained: I-POA possesses significant practical value in the improvement of intelligent resource scheduling standards of coastal container ports worldwide. \textcopyright 2018 Coastal Education and Research Foundation, Inc. (10.2112/JCOASTRES-D-17-00056.1)
  DOI : 10.2112/JCOASTRES-D-17-00056.1
• Defining services and service orchestrators acting on shared sensors and actuators
  
  • Baghli Rayhana
  • Najm Elie
  • Traverson Bruno
  , 2018.
• Scalable Model-Based Cascaded Imputation of Missing Data
  
  • Montiel Jacob
  • Read Jesse
  • Bifet Albert
  • Abdessalem Talel
  , 2018, 10939, pp.64-76.
• Unsupervised real-time detection of BGP anomalies leveraging high-rate and fine-grained telemetry data
  
  • Putina Andrian
  • Barth Steven
  • Bifet Albert
  • Pletcher Drew
  • Precup Cristina
  • Nivaggioli Patrice
  • Rossi Dario
  , 2018, pp.1-2. (10.1109/INFCOMW.2018.8406838)
  DOI : 10.1109/INFCOMW.2018.8406838
• Mass Volume Curves and Anomaly Ranking
  
  • Clémençon Stéphan
  • Thomas Albert
  Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2018, 12 (2). This paper aims at formulating the issue of ranking multivariate unlabeled observations depending on their degree of abnormality as an unsupervised statistical learning task. In the 1-d situation, this problem is usually tackled by means of tail estimation techniques: univariate observations are viewed as all the more `abnormal' as they are located far in the tail(s) of the underlying probability distribution. It would be desirable as well to dispose of a scalar valued `scoring' function allowing for comparing the degree of abnormality of multivariate observations. Here we formulate the issue of scoring anomalies as a M-estimation problem by means of a novel functional performance criterion, referred to as the Mass Volume curve (MV curve in short), whose optimal elements are strictly increasing transforms of the density almost everywhere on the support of the density. We first study the statistical estimation of the MV curve of a given scoring function and we provide a strategy to build confidence regions using a smoothed bootstrap approach. Optimization of this functional criterion over the set of piecewise constant scoring functions is next tackled. This boils down to estimating a sequence of empirical minimum volume sets whose levels are chosen adaptively from the data, so as to adjust to the variations of the optimal MV curve, while controling the bias of its approximation by a stepwise curve. Generalization bounds are then established for the difference in sup norm between the MV curve of the empirical scoring function thus obtained and the optimal MV curve. (10.1214/18-EJS1474)
  DOI : 10.1214/18-EJS1474
• Subsampling for big data : some recent advances
  
  • Bertail Patrice
  • Jelassi Ons
  • Tressou Jessica
  • Zetlaoui Mélanie
  , 2018.
• Evolving Attacker Perspectives for Secure Embedded System Design
  
  • Li Letitia W.
  • Lugou Florian
  • Apvrille Ludovic
  , 2018. In our increasingly connected world, security is a growing concern for embedded systems. A systematic design and verification methodology could help detect vulnerabilities before mass production. While Attack Trees help a designer consider the attacks a system will face during a preliminary analysis phase, they can be further integrated into the design phases. We demonstrate that explicitly modeling attacker actions within a system model helps us to evaluate its impact and possible countermeasures. This paper describes how we evolved the SysML-Sec Methodology with ``Attacker Scenarios'' for the improved design of secure embedded systems.
• Planets, candidates, and binaries from the CoRoT/Exoplanet programme
  
  • Deleuil Magali
  • Aigrain Suzanne
  • Moutou Claire
  • Cabrera Juan
  • Bouchy François
  • Deeg Hans-Jörg
  • Almenara J. M.
  • Hébrard Guillaume
  • Santerne Alexandre
  • Alonso Rafael
  • Bonomo Aldo Stefano
  • Bordé Pascal
  • Csizmadia Szilard
  • Diaz Rodrigo Fernando
  • Erikson Anders
  • Fridlund Malcolm
  • Gandolfi Davide
  • Guenther Eike
  • Guillot Tristan
  • Guterman Pascal
  • Grziwa Sascha
  • Hatzes Artie
  • Léger Alain
  • Mazeh Tsevi
  • Ofir Aviv
  • Ollivier Marc
  • Pätzold Martin
  • Parviainen Hannu
  • Rauer Heike
  • Rouan Daniel
  • Schneider Jean
  • Titz-Weider Ruth
  • Tingley Brandon
  • Weingrill Jörg
  Astronomy & Astrophysics - A&A, EDP Sciences, 2018, 619, pp.A97. The CoRoT space mission observed 163 665 stars over 26 stellar fields in the faint star channel. The exoplanet teams detected a total of 4123 transit-like features in the 177 454 light curves. We present the complete re-analysis of all these detections carried out with the same softwares so that to ensure their homogeneous analysis. Although the vetting process involves some human evaluation, it also involves a simple binary flag system over basic tests: detection significance, presence of a secondary, difference between odd and even depths, colour dependence, V-shape transit, and duration of the transit. We also gathered the information from the large accompanying ground-based programme carried out on the planet candidates and checked how useful the flag system could have been at the vetting stage of the candidates. From the initial list of transit-like features, we identified and separated 824 false alarms of various kind, 2269 eclipsing binaries among which 616 are contact binaries and 1653 are detached ones, 37 planets and brown dwarfs, and 557 planet candidates. We provide the catalogue of all these transit-like features, including false alarms. For the planet candidates, the catalogue gives not only their transit parameters but also the products of their light curve modelling: reduced radius, reduced semi-major axis, and impact parameter, together with a summary of the outcome of follow-up observations when carried out and their current status. For the detached eclipsing binaries, the catalogue provides, in addition to their transit parameters, a simple visual classification. Among the planet candidates whose nature remains unresolved, we estimate that eight (within an error of three) planets are still to be identified. After correcting for geometric and sensitivity biases, we derived planet and brown dwarf occurrences and confirm disagreements with Kepler estimates, as previously reported by other authors from the analysis of the first runs: small-size planets with orbital period less than ten days are underabundant by a factor of three in the CoRoT fields whereas giant planets are overabundant by a factor of two. These preliminary results would however deserve further investigations using the recently released CoRoT light curves that are corrected of the various instrumental effects and a homogeneous analysis of the stellar populations observed by the two missions. (10.1051/0004-6361/201731068)
  DOI : 10.1051/0004-6361/201731068