Publications

2022

• Storage-Computation-Communication Tradeoff in Distributed Computing: Fundamental Limits and Complexity
  
  • Yan Qifa
  • Yang Sheng
  • Wigger Michèle
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2022, 68 (8), pp.5496-5512. Distributed computing has become one of the most important frameworks in dealing with large computation tasks. In this paper, we propose a systematic construction of coded computing schemes for MapReduce-type distributed systems. The construction builds upon placement delivery arrays (PDA), originally proposed by Yan et al. for coded caching schemes. The main contributions of our work are three-fold. First, we identify a class of PDAs, called Comp-PDAs , and show how to obtain a coded computing scheme from any Comp-PDA. We also characterize the normalized number of stored files ( storage load ), computed intermediate values ( computation load ), and communicated bits ( communication load ), of the obtained schemes in terms of the Comp-PDA parameters. Then, we show that the performance achieved by Comp-PDAs describing Maddah-Ali and Niesen’s coded caching schemes matches a new information-theoretic converse, thus establishing the fundamental region of all achievable performance triples. In particular, we characterize all the Comp-PDAs achieving the pareto-optimal storage, computation, and communication (SCC) loads of the fundamental region. Finally, we investigate the file complexity of the proposed schemes, i.e., the smallest number of files required for implementation. In particular, we describe Comp-PDAs that achieve pareto-optimal SCC triples with significantly lower file complexity than the originally proposed Comp-PDAs (10.1109/TIT.2022.3158828)
  DOI : 10.1109/TIT.2022.3158828
• Analysis of the Spontaneous Emission Limited Linewidth of an Integrated III–V/SiN Laser
  
  • Chow Weng W
  • Wan Yating
  • Bowers John E
  • Grillot Frédéric
  Laser and Photonics Reviews, Wiley-VCH Verlag, 2022, 16 (6), pp.2100620:1-2100620:10. This article describes a calculation of the spontaneous emission limited linewidth of a semiconductor laser consisting of hybrid or heterogeneously integrated, silicon and III–V intracavity components. Central to the approach are a) description of the multi-element laser cavity in terms of composite laser/free-space eigenmodes, b) use of multimode laser theory to treat mode competition and multiwave mixing, and c) incorporation of quantum-optical contributions to account for spontaneous emission effects. Application of the model is illustrated for the case of linewidth narrowing in an InAs quantum-dot laser coupled to a high-Q SiN cavity. (10.1002/lpor.202100620)
  DOI : 10.1002/lpor.202100620
• Detecting Communities in Complex Networks Using Formal Concept Analysis
  
  • Missaoui Rokia
  • Messaoudi Abir
  • Ibrahim Mohamed Hamza
  • Abdessalem Talel
  , 2022, 1004, pp.77-105. The complex nature of many real-world networks is motivating researchers to investigate or extend network analysis methods such as centrality computation, link prediction, and community detection. One of these complex structures is the multilayer network in which each layer contains a network. Multilayer networks frequently possess complex local structures of multimodal data and interlinked relations. Thus, efficient detection of local communities in such networks often remains a key challenge. In this paper, we propose a community detection strategy, called CoDeBi, which leverages Formal Concept Analysis (FCA) to find possibly overlapping and nested communities in multilayer networks. At the preprocessing stage, we exploit operations such as apposition, subposition and composition on formal contexts---associated with individual layers---to generate a global formal context representing the whole multilayer network. At the first step of CoDeBi, we extract the formal concepts that capture groups in the global formal context while in the second step, we filter the extracted formal concepts to keep only the ones that have a high harmonic mean of stability and separation indices. Such groups represent core communities. In the third step, we detect final communities by refining the core groups using Silhouette Analysis. Our validation study shows that CoDeBi can accurately identify communities in bipartite graphs, and hence can be exploited for community detection in multilayer networks. Another contribution of this paper is the application of the attractive features of Triadic Concept Analysis and the adaptation of our approach to the analysis of tridimensional networks represented by a tridimensional adjacency matrix. (10.1007/978-3-030-90287-2_5)
  DOI : 10.1007/978-3-030-90287-2_5
• Leakage Power Analysis in Different S-Box Masking Protection Schemes
  
  • Bahrami Javad
  • Ebrahimabadi Mohammad
  • Danger Jean-Luc
  • Guilley Sylvain
  • Karimi Naghmeh
  , 2022, pp.1263-1268. Internet-of- Things (IoT) devices are natural targets for side-channel attacks. Still, side-channel leakage can be com-plex: its modeling can be assisted by statistical tools. Projection of the leakage into an orthonormal basis allows to understand its structure, typically linear (1st-order leakage) or non-linear (sometimes referred to as glitches). In order to ensure cryptosystems protection, several masking methods have been published. Unfortunately, they follow different strategies; thus it is hard to compare them. Namely, ISW is constructive, GLUT is systematic, RSM is a low-entropy version of GLUT, RSM-ROM is a further optimization aiming at balancing the leakage further, and TI aims at avoiding, by design, the leakage arising from the glitches. In practice, no study has compared these styles on an equal basis. Accordingly, in this paper, we present a consistent methodology relying on a Walsh-Hadamard transform in this respect. We consider different masked implementations of substitution boxes of PRESENT algorithm, as this function is the most leaking in symmetric cryptography. We show that ISW is the most secure among the considered masking implementations. For sure, it takes strong advantage of the knowledge of the PRESENT substitution box equation. Tabulated masking schemes appear as providing a lesser amount of security compared to unprotected counterparts. The leakage is assessed over time, i.e., considering device aging which contributes to mitigate the leakage differently according to the masking style (10.23919/DATE54114.2022.9774763)
  DOI : 10.23919/DATE54114.2022.9774763
• Asynchronous reconfiguration with Byzantine failures
  
  • Kuznetsov Petr
  • Tonkikh Andrei
  Distributed Computing, Springer Verlag, 2022, 35 (6), pp.477-502. Replicated services are inherently vulnerable to failures and security breaches. In a long-running system, it is, therefore, indispensable to maintain a reconfiguration mechanism that would replace faulty replicas with correct ones. An important challenge is to enable reconfiguration without affecting the availability and consistency of the replicated data: the clients should be able to get correct service even when the set of service replicas is being updated. In this paper, we address the problem of reconfiguration in the presence of Byzantine failures: faulty replicas or clients may arbitrarily deviate from their expected behavior. We describe a generic technique for building asynchronous and Byzantine fault-tolerant reconfigurable objects: clients can manipulate the object data and issue reconfiguration calls without reaching consensus on the current configuration. With the help of forward-secure digital signatures, our solution makes sure that superseded and possibly compromised configurations are harmless, that slow clients cannot be fooled into reading stale data, and that Byzantine clients cannot cause a denial of service by flooding the system with reconfiguration requests. Our approach is modular and based on dynamic Byzantine lattice agreement abstraction, and we discuss how to extend it to enable Byzantine fault-tolerant implementations of a large class of reconfigurable replicated services. (10.1007/S00446-022-00421-1)
  DOI : 10.1007/S00446-022-00421-1
• Exploring generative adversarial networks for controllable musical audio synthesis
  
  • Nistal Hurlé Javier
  , 2022. Audio synthesizers are electronic musical instruments that generate artificial sounds under some parametric control. While synthesizers have evolved since they were popularized in the 70s, two fundamental challenges are still unresolved: 1) the development of synthesis systems responding to semantically intuitive parameters; 2) the design of "universal," source-agnostic synthesis techniques. This thesis researches the use of Generative Adversarial Networks (GAN) towards building such systems. The main goal is to research and develop novel tools for music production that afford intuitive and expressive means of sound manipulation, e.g., by controlling parameters that respond to perceptual properties of the sound and other high-level features. Our first work studies the performance of GANs when trained on various common audio signal representations (e.g., waveform, time-frequency representations). These experiments compare different forms of audio data in the context of tonal sound synthesis. Results show that the Magnitude and Instantaneous Frequency of the phase and the complex-valued Short-Time Fourier Transform achieve the best results. Building on this, our following work presents DrumGAN, a controllable adversarial audio synthesizer of percussive sounds. By conditioning the model on perceptual features describing high-level timbre properties, we demonstrate that intuitive control can be gained over the generation process. This work results in the development of a VST plugin generating full-resolution audio and compatible with any Digital Audio Workstation (DAW). We show extensive musical material produced by professional artists from Sony ATV using DrumGAN. The scarcity of annotations in musical audio datasets challenges the application of supervised methods to conditional generation settings. Our third contribution employs a knowledge distillation approach to extract such annotations from a pre-trained audio tagging system. DarkGAN is an adversarial synthesizer of tonal sounds that employs the output probabilities of such a system (so-called “soft labels”) as conditional information. Results show that DarkGAN can respond moderately to many intuitive attributes, even with out-of-distribution input conditioning. Applications of GANs to audio synthesis typically learn from fixed-size two-dimensional spectrogram data analogously to the "image data" in computer vision; thus, they cannot generate sounds with variable duration. In our fourth paper, we address this limitation by exploiting a self-supervised method for learning discrete features from sequential data. Such features are used as conditional input to provide step-wise time-dependent information to the model. Global consistency is ensured by fixing the input noise z (characteristic in adversarial settings). Results show that, while models trained on a fixed-size scheme obtain better audio quality and diversity, ours can competently generate audio of any duration. One interesting direction for research is the generation of audio conditioned on preexisting musical material, e.g., the generation of some drum pattern given the recording of a bass line. Our fifth paper explores a simple pretext task tailored at learning such types of complex musical relationships. Concretely, we study whether a GAN generator, conditioned on highly compressed MP3 musical audio signals, can generate outputs resembling the original uncompressed audio. Results show that the GAN can improve the quality of the audio signals over the MP3 versions for very high compression rates (16 and 32 kbit/s). As a direct consequence of applying artificial intelligence techniques in musical contexts, we ask how AI-based technology can foster innovation in musical practice. Therefore, we conclude this thesis by providing a broad perspective on the development of AI tools for music production, informed by theoretical considerations and reports from real-world AI tool usage by professional artists.
• Deep learning for SAR imagery : from denoising to scene understanding
  
  • Dalsasso Emanuele
  , 2022. Synthetic Aperture Radars (SARs) can collect data for Earth Observation purposes regardless of the daylight or cloud cover. Nowadays, thanks to the Copernicus program of the European Space Agency, a huge amount of SAR data is freely available. However, the exploitation of satellite SAR images is limited by the presence of strong fluctuations in the backscattered signal. Indeed, SAR images are corrupted by speckle, a phenomenon inherent to coherent imaging systems. In this Ph.D thesis, we aim to improve the interpretation of SAR images by resorting to speckle reduction techniques. Existing approaches are based on Goodman’s model, which describes the speckle component as a spatially uncorrelated multiplicative noise. In the computer vision field, denoising methods relying on Convolutional Neural Networks (deep learning approaches) have led to great improvements and provide nowadays state-of-the-art results. We propose to use deep learning-based denoising techniques to reduce speckle from SAR images (despeckling methods). At first, we study the adaptation of supervised techniques that minimize a certain distance between the estimation provided by the CNN and a reference image, also called “groundtruth”. We propose to create a dataset of reference images by averaging multi-temporal images acquired over the same area. Pairs of reference and corrupted images can be generated by synthetizing speckle following Goodman’s model. However, in real images the speckle component is spatially correlated which typically requires subsampling these images by a factor 2 to reduce the spatial correlations, which also degrades the spatial resolution. Given the limits of supervised approaches and inspired by noise2noise, a self-supervised denoising method, we propose to train our networks directly on actual SAR images. The principle of self-supervised denoising methods is the following: if a signal contains a deterministic component and a random component, then a network trained to predict a new signal realization from a first independent signal realization will only predict the deterministic component, i.e., the underlying scene, thereby suppressing the speckle. In the method we have developed, SAR2SAR, we leverage multi-temporal SAR series to obtain independent realizations of the same scene, under the hypothesis of temporally decorrelated speckle. Changes are compensated by devising an iterative training strategy. SAR2SAR is thus trained directly on images with spatially correlated speckle and can readily be applied on SAR images without subsampling, providing high-quality results. The training of SAR2SAR is quite heavy: it is articulated in several steps to compensate changes and a dataset comprising stacks of images must be built. With our approach “MERLIN”, we alleviate the training by proposing a single-image learning strategy. Indeed, in single-look-complex SAR images, real and imaginary parts are mutually independent and can benaturally exploited to train CNNs with self-supervision. We show the potential of this training framework for three imaging modalities, different in terms of spatial resolution, textures, and speckle spatial correlation. For the sake of open science, the code associated to each algorithm developed is made freely available.
• Method for generating a reduced-blur digital image
  
  • Gousseau Yann
  • Ladjal Saïd
  • Ocampo-Blandon Cristian Felipe
  , 2022. A method for generating a reduced-blur digital image representing a scene, the method being computer-implemented and comprising the following successive steps: i) providing at least two digital source images, a same element of the scene being represented in at least two source images, ii) selecting a reference image among the source images, iii) for at least one source image different from the reference image, and for at least one pixel of the reference image, a) defining a pattern in the reference image comprising pixels of the reference image, the element being represented in said pattern, b) constructing a map of coordinates that associates coordinates of the pattern in the reference image with the coordinates of the most similar pattern in the source image, c) optionally, filtering of the map of coordinates, and d) generating a corrected image by assigning to a pixel of the corrected image, the position of the pixel of the reference image and a color extracted from the source image point which position is defined by the, optionally filtered, map of coordinates, iv) generating the reduced-blur image by processing, with a multifocus image fusion technique, the corrected image(s) and the reference image.
• A review of recent results of mid-infrared quantum cascade photonic devices operating under external optical control
  
  • Spitz Olivier
  • Grillot Frédéric
  Journal of Physics: Photonics, IOP Science, 2022, 4, pp.1-21. The purpose of this article is to gather recent findings about the non-linear dynamics of distributed feedback quantum cascade lasers (QCLs), with a view on practical applications in a near future. As opposed to other semiconductor lasers, usually emitting in the visible or the near-infrared region, QCL technology takes advantage of intersubband transitions and quantum engineering to emit in the mid-infrared and far-infrared domain. This peculiarity and its physical consequences were long considered as a detrimental characteristic to generate non-linear dynamics under external optical control. However, we show that a wide diversity of phenomena, from high-dimensional chaos to giant pulses can be observed when the QCL is under external optical feedback or under optical injection and with a continuous current bias. Most of these phenomena have already been observed in other semiconductor lasers under optical feedback or under optical injection, which allows us to compare QCLs with their interband counterparts. (10.1088/2515-7647/ac5494)
  DOI : 10.1088/2515-7647/ac5494
• Variational bounds on the relative entropy and their applications
  
  • Fawzi Omar
  • Brown Peter
  • Fawzi Hamza
  , 2022.
• Symbiotic joint operation of quantum and classical coherent communications
  
  • Aymeric Raphaël
  • Jaouën Yves
  • Ware Cédric
  • Alléaume Romain
  , 2022 (W2A.37). We report successful joint operation of quantum and classical communications with shared hardware. Leveraging information learned from the classical DSP, low-noise quantum communications (0.009 SNU at 15 km) compatible with 15 Mbit/s QKD is demonstrated. (10.1364/OFC.2022.W2A.37)
  DOI : 10.1364/OFC.2022.W2A.37
• Deep learning for radar data exploitation of autonomous vehicle
  
  • Ouaknine Arthur
  , 2022. Autonomous driving requires a detailed understanding of complex driving scenes. The redundancy and complementarity of the vehicle’s sensors provide an accurate and robust comprehension of the environment, thereby increasing the level of performance and safety. This thesis focuses the on automotive RADAR, which is a low-cost active sensor measuring properties of surrounding objects, including their relative speed, and has the key advantage of not being impacted by adverse weather conditions.With the rapid progress of deep learning and the availability of public driving datasets, the perception ability of vision-based driving systems (e.g., detection of objects or trajectory prediction) has considerably improved. The RADAR sensor is seldom used for scene understanding due to its poor angular resolution, the size, noise, and complexity of RADAR raw data as well as the lack of available datasets. This thesis proposes an extensive study of RADAR scene understanding, from the construction of an annotated dataset to the conception of adapted deep learning architectures.First, this thesis details approaches to tackle the current lack of data. A simple simulation as well as generative methods for creating annotated data will be presented. It will also describe the CARRADA dataset, composed of synchronised camera and RADAR data with a semi-automatic method generating annotations on the RADAR representations.This thesis will then present a proposed set of deep learning architectures with their associated loss functions for RADAR semantic segmentation. The proposed architecture with the best results outperforms alternative models, derived either from the semantic segmentation of natural images or from RADAR scene understanding,while requiring significantly fewer parameters. It will also introduce a method to open up research into the fusion of LiDAR and RADAR sensors for scene understanding.Finally, this thesis exposes a collaborative contribution, the RADIal dataset with synchronised High-Definition (HD) RADAR, LiDAR and camera. A deep learning architecture is also proposed to estimate the RADAR signal processing pipeline while performing multitask learning for object detection and free driving space segmentation simultaneously.
• The Interplay of Modal Dispersion with Nonlinear Impairments on Mode Division Multiplexed Fibers
  
  • Rading Reinhardt
  , 2022. Space division multiplexed fibers represent a promising solution to the upcoming capacity crunch in singlemode fibers, but such fibers introduce new challenges due to the interactions among the propagating modes. In ideal optical fibers, birefringence does not exist, and thus in a multi-mode fiber, random birefringence leads to spatial mode dispersion among the different spatial modes. This paper investigates the impact of spatial mode dispersion on optical fiber transmissions focusing on its interaction with the nonlinear interference arising during the signal propagation. The obtained results show that modal dispersion can be beneficial in reducing the cross-phase modulation in various optical transmission scenarios.
• Riemannian space tessellation with polyhedral room images
  
  • Polack Jean-Dominique
  • Meacham Aidan
  • Badeau Roland
  • Valière Jean-Christophe
  , 2022. We show that Riemannian geometry is the natural setting for developing polyhedral rooms of arbitrary shapes into their image rooms, and therefore counting the image sources. This new setting makes it also possible to account for scattering on particular edges, called hinges, characterized by negative deficit dihedral angles created by reflections on the adjacent faces. Using energy conservation, we show that sound rays are then deviated by the hinges, depending on their frequencies and the distances they pass by.
• Nos racines
  
  • Zayana Karim
  CultureMath, ENS, 2022. Sketch sur la racine carrée interprété par les élèves du collège Elsa Triolet (Paris 13).
• ABR-aware prefetching methods in P2P
  
  • Yousef Hiba
  • Feuvre Jean Le
  • Storelli Alexandre
  , 2022, pp.92-93. (10.1145/3510450.3517272)
  DOI : 10.1145/3510450.3517272
• Linear codes from support designs of ternary cyclic codes
  
  • Tan Pan
  • Fan Cuiling
  • Mesnager Sihem
  • Guo Wei
  Designs, Codes and Cryptography, Springer Verlag, 2022, 90 (3), pp.681-693. (10.1007/s10623-021-01001-3)
  DOI : 10.1007/s10623-021-01001-3
• Speckle reduction in matrix-log domain for synthetic aperture radar imaging
  
  • Deledalle Charles-Alban A
  • Denis Loïc
  • Tupin Florence
  Journal of Mathematical Imaging and Vision, Springer Verlag, 2022, 64, pp.298-320. Synthetic aperture radar (SAR) images are widely used for Earth observation to complement optical imaging. By combining information on the polarization and the phase shift of the radar echos, SAR images offer high sensitivity to the geometry and materials that compose a scene. This information richness comes with a drawback inherent to all coherent imaging modalities: a strong signal-dependent noise called "speckle". This paper addresses the mathematical issues of performing speckle reduction in a transformed domain: the matrix-log domain. Rather than directly estimating noiseless covariance matrices, recasting the denoising problem in terms of the matrix-log of the covariance matrices stabilizes noise fluctuations and makes it possible to apply off-the-shelf denoising algorithms. We refine the method MuLoG by replacing heuristic procedures with exact expressions and improving the estimation strategy. This corrects a bias of the original method and should facilitate and encourage the adaptation of general-purpose processing methods to SAR imaging. (10.1007/s10851-022-01067-1)
  DOI : 10.1007/s10851-022-01067-1
• Y'a moyen de moyenner ?
  
  • Zayana Karim
  • Bernard Jean-Noël
  • Boyer Ivan
  • Rabiet Victor
  CultureMath, ENS, 2022. Une moyenne... Oui mais laquelle ? Il y a bien des façons de moyenner deux nombres : harmoniquement, géométriquement, arithmétiquement (la plus habituelle), quadratiquement -- la liste n'est pas exhaustive. Cette variété répond à autant de situations qu'un zeste de Physique a la vertu d'éclairer, jusqu'aux inégalités algébriques (majorations, minorations) qu'elles induisent. Structuré en courts paragraphes largement indépendants, aux modèles volontairement simples associés chacun à une problématique donnée, ce texte est exploitable à loisir dès les classes de collège et de lycée, en cours de mathématiques ou de sciences comme en séances de Travaux Dirigés et de Travaux Pratiques.
• Multi-buffer AVX-512 accelerated parallelization of CBCS common encryption mode
  
  • Cornu Marcel
  • Jewett Mark
  • Mohan Sumit
  • Kantecki Tomasz
  • Kelly Gordon
  • Bouqueau Romain
  • Feuvre Jean Le
  • Giladi Alex
  , 2022, pp.90-90. (10.1145/3510450.3517299)
  DOI : 10.1145/3510450.3517299
• Estimated all-day and evening whole-brain radiofrequency electromagnetic fields doses, and sleep in preadolescents
  
  • Cabré-Riera Alba
  • van Wel Luuk
  • Liorni Ilaria
  • Koopman-Verhoeff M. Elisabeth
  • Imaz Liher
  • Ibarluzea Jesús
  • Huss Anke
  • Wiart Joe
  • Vermeulen Roel
  • Joseph Wout
  • Capstick Myles
  • Vrijheid Martine
  • Cardis Elisabeth
  • Röösli Martin
  • Eeftens Marloes
  • Thielens Arno
  • Tiemeier Henning
  • Guxens Mònica
  Environmental Research, Elsevier, 2022, 204, pp.112291. (10.1016/j.envres.2021.112291)
  DOI : 10.1016/j.envres.2021.112291
• RandSolomon: Optimally Resilient Random Number Generator with Deterministic Termination
  
  • Freitas de Souza Luciano
  • Tonkikh Andrei
  • Tucci-Piergiovanni Sara
  • Sirdey Renaud
  • Stan Oana
  • Quero Nicolas
  • Kuznetsov Petr
  , 2022, 217, pp.23:1-23:16. Multi-party random number generation is a key building-block in many practical protocols. While straightforward to solve when all parties are trusted to behave correctly, the problem becomes much more difficult in the presence of faults. This paper presents RandSolomon, a partially synchronous protocol that allows a system of N processes to produce an unpredictable common random number shared by correct participants. The protocol is optimally resilient, as it allows up to f = ⌊(N-1)/3⌋ of the processes to behave arbitrarily, ensures deterministic termination and, contrary to prior solutions, does not, at any point, expect faulty processes to be responsive. (10.4230/LIPIcs.OPODIS.2021.23)
  DOI : 10.4230/LIPIcs.OPODIS.2021.23
• What Should I Notice? Using Algorithmic Information Theory to Evaluate the Memorability of Events in Smart Homes
  
  • Houzé Étienne
  • Dessalles Jean-Louis
  • Diaconescu Ada
  • Menga David
  Entropy, MDPI, 2022, 24 (3), pp.346. With the increasing number of connected devices, complex systems such as smart homes record a multitude of events of various types, magnitude and characteristics. Current systems struggle to identify which events can be considered more memorable than others. In contrast, humans are able to quickly categorize some events as being more “memorable” than others. They do so without relying on knowledge of the system’s inner working or large previous datasets. Having this ability would allow the system to: (i) identify and summarize a situation to the user by presenting only memorable events; (ii) suggest the most memorable events as possible hypotheses in an abductive inference process. Our proposal is to use Algorithmic Information Theory to define a “memorability” score by retrieving events using predicative filters. We use smart-home examples to illustrate how our theoretical approach can be implemented in practice. (10.3390/e24030346)
  DOI : 10.3390/e24030346
• Internet Of Secure Elements Concepts And Applications. : Invited Paper
  
  • Urien Pascal
  , 2022, pp.1-6. This paper introduces internet of secure elements paradigm and some applications. The main idea is to integrate TLS1.3 server in secure element (TLS-SE). These servers are identified by their server name (SN) attributes. On the client side authentication procedures are optionally hosted in dedicated identity module (TLS-IM). In internet of things context, TLS-SE stacks enhance object security. For example TLS-SE provides cryptographic resources (keystore), or interacts with sensors and actuators. We present grids of secure elements, optionally associated to secure element processors (SEP), called internet of secure elements servers (IOSE). Such server supports on-demand TLS-SE deployment; it also connect TLS-SE devices to internet. Finally we detail a procedure for transferring exclusive property of TLS-SE server to its user. (10.1109/MobiSecServ50855.2022.9727207)
  DOI : 10.1109/MobiSecServ50855.2022.9727207
• Complexité du problème de l'unicité d'un transversal minimum dans un graphe
  
  • Hudry Olivier
  • Lobstein Antoine
  , 2022. Complexité du problème de l'unicité d'un transversal minimum dans un graphe