Publications

2021

• Information theoretic distinguishers for timing attacks with partial profiles: Solving the empty bin issue
  
  • de Chérisey Eloi
  • Guilley Sylvain
  • Rioul Olivier
  • Jayasinghe Darshana
  Journal of Information Security, Scientific Research Publishing (SCIRP), 2021, 12 (1). In any side-channel attack, it is desirable to exploit all the available leakage data to compute the distinguisher’s values. The profiling phase is essential to obtain an accurate leakage model, yet it may not be exhaustive. As a result, information theoretic distinguishers may come up on previously unseen data, a phenomenon yielding empty bins. A strict application of the maximum likelihood method yields a distinguisher that is not even sound. Ignoring empty bins reestablishes soundness, but seriously limits its performance in terms of success rate. The purpose of this paper is to remedy this situation. In this research, we propose six different techniques to improve the performance of information theoretic distinguishers. We study them thoroughly by applying them to timing attacks, both with synthetic and real leakages. Namely, we compare them in terms of success rate, and show that their performance depends on the amount of profiling, and can be explained by a bias-variance analysis. The result of our work is that there exist use-cases, especially when measurements are noisy, where our novel information theoretic distinguishers (typically the soft-drop distinguisher) perform the best compared to known side-channel distinguishers, despite the empty bin situation. (10.4236/jis.2021.121001)
  DOI : 10.4236/jis.2021.121001
• Progressive Discrete Domains for Implicit Surface Reconstruction
  
  • Zhao Tong
  • Alliez Pierre
  • Boubekeur Tamy
  • Busé Laurent
  • Thiery Jean-Marc
  Computer Graphics Forum, Wiley, 2021, 40 (5), pp.143-156. Many global implicit surface reconstruction algorithms formulate the problem as a volumetric energy minimization, trading data fitting for geometric regularization. As a result, the output surfaces may be located arbitrarily far away from the input samples. This is amplified when considering i) strong regularization terms, ii) sparsely distributed samples or iii) missing data. This breaks the strong assumption commonly used by popular octree-based and triangulation-based approaches that the output surface should be located near the input samples. As these approaches refine during a pre-process, their cells near the input samples, the implicit solver deals with a domain discretization not fully adapted to the final isosurface.We relax this assumption and propose a progressive coarse-to-fine approach that jointly refines the implicit function and its representation domain, through iterating solver, optimization and refinement steps applied to a 3D Delaunay triangulation. There are several advantages to this approach: the discretized domain is adapted near the isosurface and optimized to improve both the solver conditioning and the quality of the output surface mesh contoured via marching tetrahedra. (10.1111/cgf.14363)
  DOI : 10.1111/cgf.14363
• Annotations sémantiques de textes liés à l’héritage culturel français
  
  • Moissinac Jean-Claude Jc
  , 2021.
• L'éthique en radiologie : quand, comment ? Premiers éléments
  
  • Israel-Jost Vincent
  • Weil-Dubuc Paul-Loup
  • Adamsbaum Catherine
  • Bloch Isabelle
  Journal d'imagerie diagnostique et interventionnelle, Elsevier, 2021, 4, pp.238-240. (10.1016/j.jidi.2021.07.004)
  DOI : 10.1016/j.jidi.2021.07.004
• Enriching Wikidata with Semantified Wikipedia Hyperlinks
  
  • Boschin Armand
  • Bonald Thomas
  , 2021. We propose a novel approach to enrich Wikidata with the textual content of Wikipedia. Specifically, we leverage knowledge graph (KG) embedding models to classify the hyperlinks between Wikipedia articles and predict the corresponding facts. For instance, we would like to complete the triple (Berlin, *, Germany) with the relation capital of, given a hyperlink from Berlin to Germany in Wikipedia. While existing KG embedding models can be used for this task of relation prediction, they were not explicitly designed for it and their performance is not satisfactory. In this paper, we propose two methods that greatly improve the performance of these models on this task: first, a new negative sampling method that balances the roles of entities and relations during training; second, a method to exploit the types of entities in the selection of candidate relations. We obtain accuracy scores as high as 94% on the popular FB15k237 dataset and 75% on WDV5, an extraction of Wikidata. The efficiency of the approach is illustrated on some Wikipedia pages, where new facts unknown to Wikidata are predicted by our method.
• Smart energy: A collaborative demand response solution for smart neighborhood
  
  • Al Zahr Sawsan
  • Doumith Elias
  • Forestier Philippe
  , 2021.
• A construction method of balanced rotation symmetric Boolean functions on arbitrary even number of variables with optimal algebraic immunity
  
  • Mesnager Sihem
  • Su Sihong
  • Zhang Hui
  Designs, Codes and Cryptography, Springer Verlag, 2021, 89 (1), pp.1-17. (10.1007/s10623-020-00806-y)
  DOI : 10.1007/s10623-020-00806-y
• ``{Readers} digest of'' 17-year achievements on {Boolean} and vectorial functions and open problems.
  
  • Mesnager Sihem
  , 2022.
• Analysis and simulation of the relative intensity noise in a Fabry-Perot interband cascade laser highlights relaxation oscillations around GHz
  
  • Spitz O
  • Herdt A
  • Wu J
  • Maisons G
  • Carras M
  • Wong C.-W
  • Elsässer W
  • Grillot F
  , 2021.
• Dynamic performance and reflection sensitivity of quantum dot distributed feedback lasers with large optical mismatch
  
  • Dong Bozhang
  • Duan Jianan
  • Huang Heming
  • Norman Justin
  • Nishi Kenichi
  • Takemasa Keizo
  • Sugawara Mitsuru
  • Bowers John
  • Grillot Frédéric
  Photonics research, Optical Society of America, 2021, 9 (8), pp.1550. (10.1364/PRJ.421285)
  DOI : 10.1364/PRJ.421285
• High-speed transmissions with direct-modulation room-temperature semiconductor lasers emitting in the transparency window around 4 µm
  
  • Spitz O
  • Durupt Lauréline
  • Didier P
  • Díaz-Thomas D A
  • Cerutti Laurent
  • Baranov A. N. N
  • Carras M
  • Grillot F
  , 2021. We experimentally realize a free-space transmission over one meter with roomtemperature quantum cascade lasers and interband cascade lasers. With direct electrical modulation and raw analysis, the data-rate of the real-time transmission outperforms similar reported schemes.
• Scalable Semidefinite Programming
  
  • Yurtsever Alp
  • Tropp Joel A.
  • Fercoq Olivier
  • Udell Madeleine
  • Cevher Volkan
  SIAM Journal on Mathematics of Data Science, Society for Industrial and Applied Mathematics, 2021. Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking potential for data science applications. This paper develops a provably correct algorithm for solving large SDP problems by economizing on both the storage and the arithmetic costs. Numerical evidence shows that the method is effective for a range of applications, including relaxations of MaxCut, abstract phase retrieval, and quadratic assignment. Running on a laptop, the algorithm can handle SDP instances where the matrix variable has over $10^{13}$ entries. (10.1137/19M1305045)
  DOI : 10.1137/19M1305045
• Free-space video broadcasting with a packaged, air-cooled, mid-infrared quantum cascade laser
  
  • Didier Pierre
  • Spitz Olivier
  • Grillot Frédéric
  , 2021.
• Deep Learning for Audio and Music
  
  • Peeters Geoffroy
  • Richard Gael
  , 2021.
• Attention-Based Neural Network Equalization in Fiber-Optic Communications
  
  • Shahkarami Abtin
  • Yousefi Mansoor
  • Jaouën Yves
  Asia Communications and Photonics Conference 2021, 2021. An attention mechanism is integrated into neural network-based equalizers to prune the fully-connected output layer. For a 100 GBd 16-QAM 20 × 100 km SMF transmission, this approach reduces the computational complexity by ∼15% in a CNN+LSTM model. (10.1364/acpc.2021.m5h.3)
  DOI : 10.1364/acpc.2021.m5h.3
• Solving analogies on words based on minimal complexity transformation
  
  • Murena Pierre Alexandre
  • Al-Ghossein Marie
  • Dessalles Jean-Louis
  • Cornuéjols Antoine
  , 2020, pp.1848-1854. Analogies are 4-ary relations of the form “A is to B as C is to D”. When A, B and C are fixed, we call analogical equation the problem of finding the correct D. A direct applicative domain is Natural Language Processing, in which it has been shown successful on word inflections, such as conjugation or declension. If most approaches rely on the axioms of proportional analogy to solve these equations, these axioms are known to have limitations, in particular in the nature of the considered flections. In this paper, we propose an alternative approach, based on the assumption that optimal word inflections are transformations of minimal complexity. We propose a rough estimation of complexity for word analogies and an algorithm to find the optimal transformations. We illustrate our method on a large-scale benchmark dataset and compare with state-of-the-art approaches to demonstrate the interest of using complexity to solve analogies on words.
• Intrinsic Resiliency of S-boxes Against Side-Channel Attacks -Best And Worst Scenarios
  
  • Carlet Claude
  • de Chérisey Eloi
  • Guilley Sylvain
  • Kavut Selçuk
  • Tang Deng
  IEEE Transactions on Information Forensics and Security, Institute of Electrical and Electronics Engineers, 2021, 16, pp.203-218. Constructing S-boxes that are inherently resistant against side-channel attacks is an important problem in cryptography. By using an optimal distinguisher under an additive Gaussian noise assumption, we clarify how a defender (resp., an attacker) can make side-channel attacks as difficult (resp., easy) as possible, in relation with the auto-correlation spectrum of Boolean functions. We then construct balanced Boolean functions that are optimal for each of these two scenarios. Generalizing the objectives for an S-box, we analyze the auto-correlation spectra of some well-known S-box constructions in dimensions at most 8 and compare their intrinsic resiliency against side-channel attacks. Finally, we perform several simulations of side-channel attacks against the aforementioned constructions, which confirm our theoretical approach. (10.1109/TIFS.2020.3006399)
  DOI : 10.1109/TIFS.2020.3006399
• On the use and denoising of the temporal geometric mean for SAR time series
  
  • Gasnier Nicolas
  • Denis Loïc
  • Tupin Florence
  IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2021. The increasing availability of SAR time series creates many opportunities for remote sensing applications, but it can be challenging in terms of amount of data to process. This letter discusses the interest of the geometric mean to average SAR time series. First, the properties of the geometric mean and of the arithmetic mean are compared. Then, a speckle-reduction method specifically designed to improve images obtained with the geometric mean is presented. This method is based on an adaptation of the MuLoG framework to take into account the specific distribution of the geometric mean. Finally, applications of this denoised geometric-mean image are presented. (10.1109/LGRS.2021.3051936)
  DOI : 10.1109/LGRS.2021.3051936
• Data Physicalization: Introduction to the Special Issue on Data Physicalization (part 2)
  
  • Hogan Trevor
  • Hinrichs Uta
  • Huron Samuel
  • Alexander Jason
  • Jansen Yvonne
  IEEE Computer Graphics and Applications, Institute of Electrical and Electronics Engineers, 2021, 41 (1), pp.63-64. (10.1109/MCG.2020.3043983)
  DOI : 10.1109/MCG.2020.3043983
• An integrated ontology for multi-paradigm modelling for cyber-physical systems
  
  • Blouin Dominique
  • Al-Ali Rima
  • Giese Holger
  • Klikovits Stefan
  • Bandyopadhyay Soumyadip
  • Barisic Ankica
  • Erata Ferhat
  , 2021, pp.123-145. This chapter presents the Multi-Paradigm Modelling for Cyber-Physical Systems (MPM4CPS) ontology. This ontology integrates the Shared, MPM and CPS ontologies respectively introduced in Chapters 2, 3 and 4. It includes cross-cutting notions such as viewpoints, model-based development processes and modelling paradigms that together relate the formalisms and workflows (and their paradigms) to the part of CPSs developed with these formalisms. A brief state of the art on these notions is first presented, on which the MPM4CPS ontology builds. An overview of the ontology is then developed by introducing its main classes and properties. The validation of the ontology is finally presented by showing how it can adequately model the two case studies briefly introduced in Chapter 2. The chapter also discusses perspectives and future work on this integrated ontological framework, which can serve as a basis to develop model management solutions to relate and combine modelling languages and tools, in order to better develop cyber-physical systems with appropriate formalismes and workflows. (10.1016/B978-0-12-819105-7.00010-6)
  DOI : 10.1016/B978-0-12-819105-7.00010-6
• Multi-Paradigm Modeling for Cyber-Physical Systems: A Systematic Mapping Review
  
  • Barisic Ankica
  • Ruchkin Ivan
  • Savić Dušan
  • Abshir Mohamed Mustafa
  • Al-Ali Rima
  • Li Letitia W
  • Mkaouar Hana
  • Eslampanah Raheleh
  • Challenger Moharram
  • Blouin Dominique
  • Nikiforova Oksana
  • Cicchetti Antonio
  Journal of Systems and Software, Elsevier, 2021. Cyber-Physical Systems (CPS) are heterogeneous and require cross-domain expertise to model. The complexity of these systems leads to questions about prevalent modeling approaches, their ability to integrate heterogeneous models, and their relevance to the application domains and stakeholders. The methodology for Multi-Paradigm Modeling (MPM) of CPS is not yet fully established and standardized, and researchers apply existing methods for modeling of complex systems and introducing their own. No systematic review has been previously performed to create an overview of the field on the methods used for MPM of CPS. In this paper, we present a systematic mapping study that determines the models, formalisms, and development processes used over the last decade. Additionally, to determine the knowledge necessary for developing CPS, our review studied the background of actors involved in modeling and authors of surveyed studies. The results of the survey show a tendency to reuse multiple existing formalisms and their associated paradigms, in addition to a tendency towards applying transformations between models. These findings suggest that MPM is becoming a more popular approach to model CPS, and highlight the importance of future integration of models, standardization of development process and education. (10.1016/j.jss.2021.111081)
  DOI : 10.1016/j.jss.2021.111081
• Narrow River Extraction from SAR Images Using Exogenous Information
  
  • Gasnier Nicolas
  • Denis Loïc
  • Fjørtoft Roger
  • Liege Frédéric
  • Tupin Florence
  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2021, 14, pp.5720 - 5734. Monitoring of rivers is of major scientific and societal importance due to the crucial resource they provide to human activities and the threats caused by flood events. Rapid revisit synthetic aperture radar (SAR) sensors such as Sentinel-1 or the future surface water and ocean topography (SWOT) mission are indispensable tools to achieve all-weather monitoring of water bodies at the global scale. Unfortunately, at the spatial resolution of these sensors, the extraction of narrow rivers is extremely difficult without resorting to exogenous knowledge. This article introduces an innovative river segmentation method from SAR images using a priori databases such as the global river widths from Landsat (GRWL). First, a recently proposed linear structure detector is used to produce a map of likely line structures. Then, a limited number of nodes along the prior river centerline are extracted from the exogenous database and used to reconstruct the full river centerline from the detection map. Finally, an innovative conditional random field approach is used to delineate accurately the river extent around its centerline. The proposed method has been tested on several Sentinel-1 images and on simulated SWOT data. Both visual and qualitative evaluations demonstrate its efficiency. (10.1109/JSTARS.2021.3083413)
  DOI : 10.1109/JSTARS.2021.3083413