Publications

2021

• APT: Efficient Side-Channel Analysis Framework against Inner Product Masking Scheme
  
  • Ming Jingdian
  • Cheng Wei
  • Zhou Yongbin
  • Li Huizhong
  , 2021, pp.575-582. (10.1109/ICCD53106.2021.00093)
  DOI : 10.1109/ICCD53106.2021.00093
• Vers une formulation floue des explications par contraste
  
  • Bloch Isabelle
  • Lesot Marie-Jeanne
  , 2021.
• Enhancing the Resiliency of Multi-bit Parallel Arbiter-PUF and Its Derivatives Against Power Attacks
  
  • Danger Jean-Luc
  • Kroeger Trevor
  • Cheng Wei
  • Guilley Sylvain
  • Karimi Naghmeh
  , 2021, 12910, pp.303-321. (10.1007/978-3-030-89915-8_14)
  DOI : 10.1007/978-3-030-89915-8_14
• HEMP: High-order entropy minimization for neural network compression
  
  • Tartaglione Enzo
  • Lathuilière Stéphane
  • Fiandrotti Attilio
  • Cagnazzo Marco
  • Grangetto Marco
  Neurocomputing, Elsevier, 2021, 461, pp.244-253. We formulate the entropy of a quantized artificial neural network as a differentiable function that can be plugged as a regularization term into the cost function minimized by gradient descent. Our formulation scales efficiently beyond the first order and is agnostic of the quantization scheme. The network can then be trained to minimize the entropy of the quantized parameters, so that they can be optimally compressed via entropy coding. We experiment with our entropy formulation at quantizing and compressing well-known network architectures over multiple datasets. Our approach compares favorably over similar methods, enjoying the benefits of higher order entropy estimate, showing flexibility towards non-uniform quantization (we use Lloyd-max quantization), scalability towards any entropy order to be minimized and efficiency in terms of compression. We show that HEMP is able to work in synergy with other approaches aiming at pruning or quantizing the model itself, delivering significant benefits in terms of storage size compressibility without harming the model’s performance. (10.1016/j.neucom.2021.07.022)
  DOI : 10.1016/j.neucom.2021.07.022
• Analysis and Protection of the Two-Metric Helper Data Scheme
  
  • Tebelmann Lars
  • Kühne Ulrich
  • Danger Jean-Luc
  • Pehl Michael
  , 2021, 12910, pp.279-302. To compensate for the poor reliability of Physical Unclonable Function (PUF) primitives, some low complexity solutions not requiring error-correcting codes (ECC) have been proposed. One simple method is to discard less reliable bits, which are indicated in the helper data stored inside the PUF. To avoid discarding bits, the Two-metric Helper Data (TMH) method, which particularly applies to oscillation-based PUFs, allows to keep all bits by using different metrics when deriving the PUF response. However, oscillation-based PUFs are sensitive to side-channel analysis (SCA) since the frequencies of the oscillations can be observed by current or electromagnetic measurements. This paper studies the security of PUFs using TMH in order to obtain both reliable and robust PUF responses. We show that PUFs using TMH are sensitive to SCA, but can be greatly improved by using temporal masking and adapted extraction metrics. In case of public helper data, an efficient protection requires the randomization of the measurement order. We study two different solutions, providing interesting insights into trade-offs between security and complexity. (10.1007/978-3-030-89915-8_13)
  DOI : 10.1007/978-3-030-89915-8_13
• A Complete End to End Open Source Toolchain for the Versatile Video Coding (VVC) Standard
  
  • Wieckowski Adam
  • Lehmann Christian
  • Bross Benjamin
  • Marpe Detlev
  • Biatek Thibaud
  • Raulet Mikael
  • Le Feuvre Jean
  , 2021, pp.3795-3798. (10.1145/3474085.3478320)
  DOI : 10.1145/3474085.3478320
• A Hitchhiker’s Guide towards Transactive Memory System Modeling in Small Group Interactions
  
  • Tartaglione Enzo
  • Biancardi Beatrice
  • Mancini Maurizio
  • Varni Giovanna
  , 2021, pp.254-262. (10.1145/3461615.3485414)
  DOI : 10.1145/3461615.3485414
• Analog-to-feature converter optimization through power-aware feature selection
  
  • Back Antoine
  • Chollet Paul
  • Fercoq Olivier
  • Desgreys Patricia
  , 2021. Analog-to-feature (A2F) conversion is an acquisition method thought for IoT devices in order to increase wireless sensor's battery life. The operating principle of A2F is to perform classification tasks at sub-Nyquist rate, by extracting relevant features in the analog domain and then performing the classification step in the digital domain. We propose to use non-uniform wavelet sampling (NUWS) combined with feature selection to find and extract from the signal, a small set of relevant features for electrocardiogram (ECG) anomalies detection. A CMOS 0.18 µm m mixed architecture for NUWS feature extraction is proposed, to obtain a power consumption model for A2F. This model can be taken into account in the feature selection step by evaluating the energy cost of each wavelet and then try to maximize classification accuracy while minimizing the energy needed for extraction. We demonstrate the benefits of A2F showing that the energy needed can be divided by 16 compared to classical approach.
• An Exploratory Computational Study on the Effect of Emergent Leadership on Social and Task Cohesion
  
  • Sabry Soumaya
  • Maman Lucien
  • Varni Giovanna
  , 2021, Companion Publication of the 2021 23rd ACM International Conference on Multimodal Interaction (ICMI), pp.263-272. Leadership is a complex and dynamic phenomenon that has received a lot of attention from psychologists over the last 50 years, primarily due to its relationships with team effectiveness and performances. Depending on the group (e.g., size, relationships among members) and the context (e.g., solving a task under pressure), various styles of leadership could emerge. These styles can either be formally decided or manifest informally. Among the informal types of leadership, emergent leadership is one of the most studied. It is an emergent state that develops over time in a group and that interplays with other emergent states such as cohesion. Only a few computational studies focusing on predicting emergent leadership take advantage of the relationships with other phenomena to improve their models' performances. These approaches, however, only apply to their models aimed at predicting emergent leadership. There is, to the best of our knowledge, no approach that integrates emergent leadership into computational models of cohesion. In this study, we take a first step towards bridging this gap by introducing 2 families of approaches inspired by Social Sciences' insights to integrate emergent leadership into computational models of cohesion. The first family consists of amplifying the differences between leaders' and followers' features while the second one focuses on adding leadership representation directly into the computational model's architecture. In particular, for each family, we describe 2 approaches that are applied to a Deep Neural Network model aimed at predicting the dynamics of cohesion across various tasks over time. This study explores whether and how applying our approaches improves the prediction of the dynamics of the Social and Task dimensions of cohesion. Therefore, the performance of a computational model of cohesion that does not integrate the interplay between cohesion and emergent leadership is compared with the same computational models that apply our approaches. Results show that approaches from both families significantly improved the prediction of the Task cohesion dynamics, confirming the benefits of integrating emergent leadership following Social Psychology's insights to enforce computational models of cohesion at both feature and architecture levels. (10.1145/3461615.3485415)
  DOI : 10.1145/3461615.3485415
• Exploiting the Interplay between Social and Task Dimensions of Cohesion to Predict its Dynamics Leveraging Social Sciences
  
  • Maman Lucien
  • Likforman-Sulem Laurence
  • Chetouani Mohamed
  • Varni Giovanna
  , 2021. (10.1145/3462244.3479940)
  DOI : 10.1145/3462244.3479940
• Automatic Segmentation of Posterior Fossa Structures in Pediatric Brain MRIs
  
  • Oliveira Hugo
  • Dias Larissa
  • Ferraciolli Suely
  • Takahashi Marcelo
  • Maciel José
  • Bloch Isabelle
  • Cesar-Jr Roberto M
  , 2021.
• Get Together in the Middle-earth: a First Step Towards Hybrid Intelligence Systems
  
  • Varni Giovanna
  • Pez André-Marie
  • Mancini Maurizio
  , 2021.
• A Tooled Method for Developing Knowledge-Based Activity Recognizers
  
  • Belloum Rafik
  • Riche Antoine
  • Volanschi Nic
  • Consel Charles
  , 2021. Monitoring the daily activities of older adults is a key enabler for aging in place because it reliably indicates whether autonomy is preserved and it prevents unwanted situations (e.g., lack of activity during daytime). To fulfill its promises, activity monitoring requires development methods capable of systematically delivering activity recognizers that are accurate enough to be trusted and accepted by users and their caregivers. This paper presents a systematic approach to developing accurate activity recognizers, based on a tooled method. To achieve accuracy, our strategy is twofold: 1) to encompass the main variations of a target activity by abstracting over descriptions reported by users; 2) to ensure proper customization with respect to user specificities using a dedicated tool. This development method is iterative, allowing to adjust the parameters of an activity recognizer to maximize its accuracy. We validated our approach by applying it to a case study. Specifically, we applied our tooled method to the development of 6 generic activity recognizers, which were then customized with respect to the specificities of 5 older adults, and deployed in their homes during 5 days. Once deployed, the results produced by these activity recognizers were checked daily against activities self-reported by our participants. This experiment shows that 80% of the outputs of our activity detectors were confirmed by the user reports. The accuracy of our approach goes up to 88% when considering the four, more routinized participants.
• CATS2021: International Workshop on Corpora And Tools for Social skills annotation
  
  • Biancardi Beatrice
  • Ceccaldi Eleonora
  • Clavel Chloé
  • Chollet Mathieu
  • Dinkar Tanvi
  , 2021, pp.857-859. This Workshop aims at stimulating multidisciplinary discussions about the challenges related to corpus creation and annotation for social skills behavior analysis. Contributions from computational, psychological and psychometrics perspectives, as well as applications including platforms to share corpora and annotations, are welcomed. The main challenges related to corpus creation include the choice of the best setup and sensors, finding a trade-off between eliciting natural interactions, limiting invasiveness and collecting precise information. The second issue in this context regards the process of annotation. The choice of the type of annotators (experts vs. nonexperts), the type of annotations (automatic vs. manual, continue vs. discrete), the temporal segmentation (windowed vs. holistic) is crucial for a correct measure of the phenomenon of interest and getting significant results. The topics of CATS2021 will have a strong impact on researchers and stakeholders across different disciplines, such as Computer Science, Social Signal Processing, Psychology, Statistics. Leveraging the opportunities offered by such a multidisciplinary environment, the participants could enrich their perspective, strengthen their practices and methodologies and draw together a research roadmap tackling the discussed challenges, which might be taken up in future collaborations. (10.1145/3462244.3480977)
  DOI : 10.1145/3462244.3480977
• Optimal exponents in cascaded hypothesis testing under expected rate constraints
  
  • Hamad Mustapha
  • Wigger Michèle
  • Sarkiss Mireille
  , 2021, pp.1-6. Cascaded binary hypothesis testing is studied in this paper with two decision centers at the relay and the receiver. All terminals have their own observations, where we assume that the observations at the transmitter, the relay, and the receiver form a Markov chain in this order. The communication occurs over two hops, from the transmitter to the relay, and from the relay to the receiver. Expected rate constraints are imposed on both communication links. In this work, we characterize the optimal type-II error exponents at the two decision centers under constraints on the allowed type-I error probabilities. Our recent work characterized the optimal type-II error exponents in the special case when the two decision centers have same type-I error constraints and provided an achievability scheme for the general setup. To obtain the exact characterization for the general case, in this paper we provide a new converse proof as well as a new matching achievability scheme. Our results indicate that under unequal type-I error constraints at the relay and the receiver, a tradeoff arises between the maximum type-II error probabilities at these two terminals. Previous results showed that such a tradeoff does not exist under equal type-I error constraints or under general type-I error constraints when a maximum rate constraint is imposed on the communication links. (10.1109/ITW48936.2021.9611470)
  DOI : 10.1109/ITW48936.2021.9611470
• Cooperative Encoding and Decoding of Mixed Delay Traffic under Random-User Activity
  
  • Nikbakht Homa
  • Wigger Michèle
  • Shamai Shlomo
  • Gorce Jean-Marie
  , 2021, ITW 2021 - IEEE Information Theory Workshop, pp.1-6. This paper analyses the multiplexing gain (MG) achievable over Wyner's symmetric network with random user activity and random arrival of mixed-delay traffic. The mixeddelay traffic is composed of delay-tolerant traffic and delaysensitive traffic where only the former can benefit from transmitter and receiver cooperation since the latter is subject to stringent decoding delays. The total number of cooperation rounds at transmitter and receiver sides is limited to D rounds. We derive inner and outer bounds on the MG region. In the limit as D → ∞, the bounds coincide and the results show that transmitting delaysensitive messages does not cause any penalty on the sum MG. For finite D our bounds are still close and prove that the penalty caused by delay-sensitive transmissions is small. (10.1109/ITW48936.2021.9611402)
  DOI : 10.1109/ITW48936.2021.9611402
• User-guided one-shot deep model adaptation for music source separation
  
  • Cantisani Giorgia
  • Ozerov Alexey
  • Essid Slim
  • Richard Gael
  , 2021. Music source separation is the task of isolating individual instruments which are mixed in a musical piece. This task is particularly challenging, and even state-of-the-art models can hardly generalize to unseen test data. Nevertheless, prior knowledge about individual sources can be used to better adapt a generic source separation model to the observed signal. In this work, we propose to exploit a temporal segmentation provided by the user, that indicates when each instrument is active, in order to fine-tune a pre-trained deep model for source separation and adapt it to one specific mixture. This paradigm can be referred to as user-guided one-shot deep model adaptation for music source separation, as the adaptation acts on the target song instance only. Our results are promising and show that state-of-the-art source separation models have large margins of improvement especially for those instruments which are underrepresented in the training data.
• Learning Multi-Pitch Estimation From Weakly Aligned Score-Audio Pairs Using a Multi-Label CTC Loss
  
  • Weiss Christof
  • Peeters Geoffroy
  , 2021. Detecting the simultaneous activity of pitches in music audio recordings is a central task within music processing, commonly known as multi-pitch estimation or frame-wise polyphonic music transcription. Deep-learning approaches recently achieved major improvements for this task, but the lack of annotated, large-size datasets beyond the piano solo scenario is still a limitation for fully exploiting their potential. In this paper, we propose a strategy for training a CNN-based multi-pitch estimator on weakly aligned score--audio pairs of pieces in different instrumentations. To this end, we make use of a multi-label variant of the connectionist temporal classification loss (MCTC), recently proposed for image recognition tasks. We re-formalize the MCTC loss to be applicable for multi-pitch estimation and perform several systematic experiments to analyze its behavior and robustness to training conditions. Finally, we report on multi-pitch estimation results for common datasets using weakly aligned training with MCTC, which performs similar than systems trained on strongly aligned scores.
• Linear Programming Bounds on the Kissing Number of q-ary Codes
  
  • Solé Patrick
  • Liu Yi
  • Cheng Wei
  • Guilley Sylvain
  • Riou Olivier
  , 2021, pp.1-5. We use linear programming (LP) to derive upper and lower bounds on the "kissing number" A d of any q-ary code C with distance distribution frequencies Ai, in terms of the given parameters (n, M, d). In particular, a polynomial method gives explicit analytic bounds in a certain range of parameters, which are sharp for some low-rate codes like the first-order Reed-Muller codes. The general LP bounds are more suited to numerical estimates. Besides the classical estimation of the probability of decoding error and of undetected error, we outline recent applications in hardware protection against side-channel attacks using code-based masking countermeasures, where the protection is all the more efficient as the kissing number is low. (10.1109/ITW48936.2021.9611478)
  DOI : 10.1109/ITW48936.2021.9611478
• On the topic of frequency dependent exponential decay matrices and Lie groups
  
  • Aknin Achille
  • Badeau Roland
  , 2021. This document is an annex of the paper [1], providing mathematical proofs of theorems that we use in the paper. More specifically, we will prove that the set P, of which we recall the definition in Section 1, is a matrix Lie group in Section 2 and that we can swap matrices of P with lower triangular Toeplitz matrices in Section 3.
• On Conditional alpha-Information and its Application to Side-Channel Analysis
  
  • Liu Yi
  • Cheng Wei
  • Guilley Sylvain
  • Rioul Olivier
  , 2021. A conditional version of Sibson's α-information is defined using a simple closed-form "log-expectation" expression, which satisfies important properties such as consistency, uniform expansion, and data processing inequalities. This definition is compared to previous ones, which in contrast do not satisfy all of these properties. Based on our proposal and on a generalized Fano inequality, we extend the case α = 1 of previous works to obtain sharp universal upper bounds for the probability of success of any type side-channel attack, particularly when α = 2.
• Adaptative Monte-Carlo methods for complex models
  
  • Daudel Kamélia
  , 2021. This thesis lies in the field of Statistical Inference and more precisely in Bayesian Inference, where the goal is to model a phenomenon given some data while taking into account prior knowledge on the model parameters.The availability of large datasets sparked the interest in using complex models for Bayesian Inference tasks that are able to capture potentially complicated structures inside the data. Such a context requires the development and study of adaptive algorithms that can efficiently process large volumes of data when the dimension of the model parameters is high.Two main classes of methods attempt to fulfil this role: sampling-based Monte Carlo methods and optimisation-based Variational Inference methods. By relying on the optimisation literature and more recently on Monte Carlo methods, the latter have made it possible to construct fast algorithms that overcome some of the computational hurdles encountered in Bayesian Inference.Yet, the theoretical results and empirical performances of Variational Inference methods are often impacted by two factors: one, an inappropriate choice of the objective function appearing in the optimisation problem and two, a search space that is too restrictive to match the target at the end of the optimisation procedure.This thesis explores how we can remedy the two issues mentioned above in order to build improved adaptive algorithms for complex models at the intersection of Monte Carlo and Variational Inference methods.In our work, we suggest selecting the alpha-divergence as a more general class of objective functions and we propose several ways to enlarge the search space beyond the traditional framework used in Variational Inference. The specificity of our approach in this thesis is then that it derives numerically advantageous adaptive algorithms with strong theoretical foundations, in the sense that they provably ensure a systematic decrease in the alpha-divergence at each step. In addition, we unravel important connections between the sampling-based and the optimisation-based methodologies.
• Privacy in Advanced Cryptographic Protocols: Prototypical Examples
  
  • Phan Duong Hieu
  • Yung Moti
  Journal of Computer Science and Cybernetics, Vietnamese Academy of Science and Technology, 2021, 37 (4), pp.429-451. Cryptography is the fundamental cornerstone of cybersecurity employed for achieving data confidentiality, integrity, and authenticity. However, when cryptographic protocols are deployed for emerging applications such as cloud services or big data, the demand for security grows beyond these basic requirements. Data nowadays are being extensively stored in the cloud, users also need to trust the cloud servers/authorities that run powerful applications. Collecting user data, combined with powerful machine learning tools, can come with a huge risk of mass surveillance or undesirable data-driven strategies for making profits rather than for serving the user. Privacy, therefore, becomes more and more important, and new techniques should be developed to protect personal information and to reduce trust requirements on the authorities or the Big Tech providers. In a general sense, privacy is ``the right to be left alone'' and privacy protection allows individuals to have control over how their personal information is collected and used. In this survey, we discuss the privacy protection methods of various cryptographic protocols, in particular we review: - Privacy in electronic voting systems. This may be, perhaps, the most important real-world application where privacy plays a fundamental role. %classical authentication with group, ring signatures, anonymous credentials. - Private computation. This may be the widest domain in the new era of modern technologies with cloud computing and big data, where users delegate the storage of their data and the computation to the cloud. In such a situation, ``how can we preserve privacy?'' is one of the most important questions in cryptography nowadays. - Privacy in contact tracing. This is a typical example of a concrete study on a contemporary scenario where one should deal with the unexpected social problem but needs not pay the cost of weakening the privacy of users. Finally, we will discuss some notions which aim at reinforcing privacy by masking the type of protocol that we execute, we call it the covert cryptographic primitives and protocols. (10.15625/1813-9663/37/4/16104)
  DOI : 10.15625/1813-9663/37/4/16104
• A fully digital MIMO-OFDM scheme for fading mitigation in coherent Δϕ-OTDR
  
  • Guerrier Sterenn
  • Dorize Christian
  • Awwad Elie
  • Renaudier Jérémie
  Optics Express, Optical Society of America - OSA Publishing, 2021, 29 (22), pp.35149-35160. (10.1364/OE.436146)
  DOI : 10.1364/OE.436146
• Innovative Countermeasures to Defeat Cyber Attacks Against Blockchain Wallets: A Crypto Terminal Use Case
  
  • Urien Pascal
  , 2021, pp.49-54. Blockchain transactions are signed by private keys. Secure key storage and tamper-proof computers are essential requirements for deploying a trusted infrastructure. In this paper, we identify some threats against blockchain wallets and propose a set of physical and logical countermeasures to thwart them. We present the crypto terminal device, operating with a removable secure element, built on open software and hardware architectures, capable of detecting a cloned device or corrupted software. These technologies are based on tamper-resistant computing (javacard), smart card anti-cloning, smart card content attestation, application firewall, bare-metal architecture, remote attestation, dynamic Physical Unclonable Function (dPUF), and programming tokens as a root of trust. This paper is an extended version of the paper "Innovative Countermeasures to Defeat Cyber Attacks Against Blockchain Wallets," 2021 5th Cyber Security in Networking Conference (CSNet), 2021, pp. 49-54, doi: 10.1109/CSNet52717.2021.9614649 (10.1109/csnet52717.2021.9614649)
  DOI : 10.1109/csnet52717.2021.9614649