Publications

2021

• Making Obfuscated PUFs Secure Against Power Side-Channel Based Modeling Attacks
  
  • Danger Jean-Luc
  • Kroeger Trevor
  • Cheng Wei
  • Guilley Sylvain
  • Karimi Nazhmeh
  , 2021, pp.1000-1005. (10.23919/DATE51398.2021.9474137)
  DOI : 10.23919/DATE51398.2021.9474137
• Security Analysis of Out-of-Band Device Pairing Protocols: A Survey
  
  • Khalfaoui Sameh
  • Leneutre Jean
  • Villard Arthur
  • Ma Jingxuan
  • Urien Pascal
  Wireless Communications and Mobile Computing, Hindawi Publishing Corporation, 2021, 2021, pp.1-30. Numerous secure device pairing (SDP) protocols have been proposed to establish a secure communication between unidentified IoT devices that have no preshared security parameters due to the scalability requirements imposed by the ubiquitous nature of the IoT devices. In order to provide the most user-friendly IoT services, the usability assessment has become the main requirement. Thus, the complete security analysis has been replaced by a sketch of a proof to partially validate the robustness of the proposal. The few existing formal or computational security verifications on the SDP schemes have been conducted based on the assessment of a wide variety of uniquely defined security properties. Therefore, the security comparison between these protocols is not feasible and there is a lack of a unified security analysis framework to assess these pairing techniques. In this paper, we survey a selection of secure device pairing proposals that have been formally or computationally verified. We present a systematic description of the protocol assumptions, the adopted verification model, and an assessment of the verification results. In addition, we normalize the used taxonomy in order to enhance the understanding of these security validations. Furthermore, we refine the adversary capabilities on the out-of-band channel by redefining the replay capability and by introducing a new notion of delay that is dependent on the protocol structure that is more adequate for the ad hoc pairing context. Also, we propose a classification of a number of out-of-band channels based on their security properties and under our refined adversary model. Our work motivates the future SDP protocol designer to conduct a formal or a computational security assessment to allow the comparability between these pairing techniques. Furthermore, it provides a realistic abstraction of the adversary capabilities on the out-of-band channel which improves the modeling of their security characteristics in the protocol verification tools. (10.1155/2021/8887472)
  DOI : 10.1155/2021/8887472
• Energy modeling of Hoeffding tree ensembles
  
  • García-Martín Eva
  • Bifet Albert
  • Lavesson Niklas
  Intelligent Data Analysis, IOS Press, 2021, 25 (1), pp.81--104. Energy consumption reduction has been an increasing trend in machine learning over the past few years due to its socio-ecological importance. In new challenging areas such as edge computing, energy consumption and predictive accuracy are key variables during algorithm design and implementation. State-of-the-art ensemble stream mining algorithms are able to create highly accurate predictions at a substantial energy cost. This paper introduces the nmin adaptation method to ensembles of Hoeffding tree algorithms, to further reduce their energy consumption without sacrificing accuracy. We also present extensive theoretical energy models of such algorithms, detailing their energy patterns and how nmin adaptation affects their energy consumption. We have evaluated the energy efficiency and accuracy of the nmin adaptation method on five different ensembles of Hoeffding trees under 11 publicly available datasets. The results show that we are able to reduce the energy consumption significantly, by 21% on average, affecting accuracy by less than one percent on average. (10.3233/IDA-194890)
  DOI : 10.3233/IDA-194890
• Efficient scheduling of applications onto cloud FPGAs
  
  • Bertolino Matteo
  , 2021. This thesis has been realized in Télécom Paris and it has been financed by Nokia Bell Labs France. It founds its motivations in the increasing usage of hardware accelerators such as FPGAs and their recent integration in modern cloud data center [1][2]. In some cases, servers and FPGAs are rented to users and the cost is related to the utilization time. Thus, offering a better sharing of FPGA pools would interest all stakeholders, namely cloud providers and users. We focus on scheduling and, in particular, we focus on makespan minimization of applications. The latter are assumed to be composed of several dependent tasks, whose features (i.e., dependencies, execution time, resource requirements, and so on) are known prior to their execution. With respect to the state of the art, we have sought to design an approach which is, at the same time, (i) general, (ii) fast and (iii) of high-quality. Indeed, several related works represent the applications and the architecture through simple models (e.g., the FPGA is often represented only with the amount of reconfigurable logic). We retain that such simple models may lead to unfeasible scheduling. Moreover, the vast majority of them is either based on slow and precise algorithms or on fast heuristics whose quality is far from the optimum. We therefore propose a scheduling solution [3] characterized by a good quality in terms of makespan while keeping the decision time in the order of tens of milliseconds for common applications. The main contributions of the thesis are a modelling proposal for FPGAs, the design of a heuristic which targets the makespan minimization and the evaluation of this heuristic on a synthetic benchmark of pseudo-randomly generated applications. Additionally, we have integrated this method to a model-driven engineering (MDE) tool to better support the early design of embedded systems. Finally, we propose several extensions to extend the approach to different architectures.References:[1] A. M. Caulfield et al., "A cloud-scale acceleration architecture," 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), Taipei, 2016, pp. 1-13, doi: 10.1109/MICRO.2016.7783710[2] Amazon Web Services Elastic Compute Cloud, https://docs.aws.amazon.com/ec2/index.html?nc2=h_ql_doc_ec2[3] Matteo Bertolino, Andrea Enrici, Renaud Pacalet, Ludovic Apvrille. Efficient Scheduling of FPGAs for Cloud Data Center Infrastructures. Euromicro DSD 2020, Aug 2020, Portorož, Slovenia. Proceedings will be published on IEEExplore, paper available on HAL of Télécom Paris: https://hal.telecom-paris.fr/hal-02894662v1
• Heavy-tailed nature of stochastic gradient descent in deep learning : theoretical and empirical analysis
  
  • Nguyen Thanh Huy
  , 2021. In this thesis, we are concerned with the Stochastic Gradient Descent (SGD) algorithm. Specifically, we perform theoretical and empirical analysis of the behavior of the stochastic gradient noise (GN), which is defined as the difference between the true gradient and the stochastic gradient, in deep neural networks. Based on these results, we bring an alternative perspective to the existing approaches for investigating SGD. The GN in SGD is often considered to be Gaussian for mathematical convenience. This assumption enables SGD to be studied as a stochastic differential equation (SDE) driven by a Brownian motion. We argue that the Gaussianity assumption might fail to hold in deep learning settings and hence render the Brownian motion-based analyses inappropriate. Inspired by non-Gaussian natural phenomena, we consider the GN in a more general context that suggests that the GN is better approximated by a "heavy-tailed" alpha-stable random vector. Accordingly, we propose to analyze SGD as a discretization of an SDE driven by a Lévy motion. Firstly, to justify the alpha-stable assumption, we conduct experiments on common deep learning scenarios and show that in all settings, the GN is highly non-Gaussian and exhibits heavy-tails. Secondly, under the heavy-tailed GN assumption, we provide a non-asymptotic analysis for the discrete-time dynamics SGD to converge to the global minimum in terms of suboptimality. Finally, we investigate the metastability nature of the SDE driven by Lévy motion that can then be exploited for clarifying the behavior of SGD, especially in terms of `preferring wide minima'. More precisely, we provide formal theoretical analysis where we derive explicit conditions for the step-size such that the metastability behavior of SGD, viewed as a discrete-time SDE, is similar to its continuous-time limit. We show that the behaviors of the two systems are indeed similar for small step-sizes and we describe how the error depends on the algorithm and problem parameters. We illustrate our metastability results with simulations on a synthetic model and neural networks. Our results open up a different perspective and shed more light on the view that SGD prefers wide minima.
• Comparing Representations for Audio Synthesis Using Generative Adversarial Networks
  
  • Richard Gaël
  • Nistal Javier
  • Plattner Stefan
  , 2021, pp.161-165. —In this paper, we compare different audio signal representations, including the raw audio waveform and a variety of time-frequency representations, for the task of audio synthesis with Generative Adversarial Networks (GANs). We conduct the experiments on a subset of the NSynth dataset. The architecture follows the benchmark Progressive Growing Wasserstein GAN. We perform experiments both in a fully non-conditional manner as well as conditioning the network on the pitch information. We quantitatively evaluate the generated material utilizing standard metrics for assessing generative models, and compare training and sampling times. We show that complex-valued as well as the magnitude and Instantaneous Frequency of the ShortTime Fourier Transform achieve the best results, and yield fast generation and inversion times. The code for feature extraction, training and evaluating the model is available online. (10.23919/Eusipco47968.2020.9287799)
  DOI : 10.23919/Eusipco47968.2020.9287799
• Multi-Domain Image-to-Image Translation with Adaptive Inference Graph
  
  • Nguyen The-Phuc
  • Lathuilière Stéphane
  • Ricci Elisa
  , 2021. In this work, we address the problem of multi-domain image-to-image translation with particular attention paid to computational cost. In particular, current state of the art models require a large and deep model in order to handle the visual diversity of multiple domains. In a context of limited computational resources, increasing the network size may not be possible. Therefore, we propose to increase the network capacity by using an adaptive graph structure. At inference time, the network estimates its own graph by selecting specific sub-networks. Sub-network selection is implemented using Gumbel-Softmax in order to allow end-to-end training. This approach leads to an adjustable increase in number of parameters while preserving an almost constant computational cost. Our evaluation on two publicly available datasets of facial and painting images shows that our adaptive strategy generates better images with fewer artifacts than literature methods
• DR2S : Deep Regression with Region Selection for Camera Quality Evaluation
  
  • Tworski Marcelin
  • Lathuilière Stéphane
  • Belkarfa Salim
  • Fiandrotti Attilio
  • Cagnazzo Marco
  , 2020. In this work, we tackle the problem of estimating a camera capability to preserve fine texture details at a given lighting condition. Importantly, our texture preservation measurement should coincide with human perception. Consequently, we formulate our problem as a regression one and we introduce a deep convolutional network to estimate texture quality score. At training time, we use ground-truth quality scores provided by expert human annotators in order to obtain a subjective quality measure. In addition, we propose a region selection method to identify the image regions that are better suited at measuring perceptual quality. Finally, our experimental evaluation shows that our learning-based approach outperforms existing methods and that our region selection algorithm consistently improves the quality estimation.
• CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations
  
  • Ouaknine Arthur
  • Newson Alasdair
  • Rebut Julien
  • Tupin Florence
  • Perez Patrick
  2020 25th International Conference on Pattern Recognition (ICPR), 2021. High quality perception is essential for autonomous driving (AD) systems. To reach the accuracy and robustness that are required by such systems, several types of sensors must be combined. Currently, mostly cameras and laser scanners (lidar) are deployed to build a representation of the world around the vehicle. While radar sensors have been used for a long time in the automotive industry, they are still under-used for AD despite their appealing characteristics (notably, their ability to measure the relative speed of obstacles and to operate even in adverse weather conditions). To a large extent, this situation is due to the relative lack of automotive datasets with real radar signals that are both raw and annotated. In this work, we introduce CARRADA, a dataset of synchronized camera and radar recordings with rangeangle-Doppler annotations. We also present a semi-automatic annotation approach, which was used to annotate the dataset, and a radar semantic segmentation baseline, which we evaluate on several metrics. Both our code and dataset are available online. (10.1109/icpr48806.2021.9413181)
  DOI : 10.1109/icpr48806.2021.9413181
• CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification
  
  • Delorme Guillaume
  • Xu Yihong
  • Lathuilière Stéphane
  • Horaud Radu
  • Alameda-Pineda Xavier
  , 2021, pp.4428-4435. Unsupervised person re-ID is the task of identifying people on a target data set for which the ID labels are unavailable during training. In this paper, we propose to unify two trends in unsupervised person re-ID: clustering & fine-tuning and adversarial learning. On one side, clustering groups training images into pseudo-ID labels, and uses them to fine-tune the feature extractor. On the other side, adversarial learning is used, inspired by domain adaptation, to match distributions from different domains. Since target data is distributed across different camera viewpoints, we propose to model each camera as an independent domain, and aim to learn domain-independent features. Straightforward adversarial learning yields negative transfer, we thus introduce a conditioning vector to mitigate this undesirable effect. In our framework, the centroid of the cluster to which the visual sample belongs is used as conditioning vector of our conditional adversarial network, where the vector is permutation invariant (clusters ordering does not matter) and its size is independent of the number of clusters. To our knowledge, we are the first to propose the use of conditional adversar-ial networks for unsupervised person re-ID. We evaluate the proposed architecture on top of two state-of-the-art clustering-based unsupervised person re-identification (re-ID) methods on four different experimental settings with three different data sets and set the new state-of-the-art performance on all four of them. Our code and model will be made publicly available at https://team.inria.fr/perception/canu-reid/. (10.1109/ICPR48806.2021.9412431)
  DOI : 10.1109/ICPR48806.2021.9412431
• Learning Visual Voice Activity Detection with an Automatically Annotated Dataset
  
  • Guy Sylvain
  • Lathuilière Stéphane
  • Mesejo Pablo
  • Horaud Radu
  , 2021, pp.4851-4856. Visual voice activity detection (V-VAD) uses visual features to predict whether a person is speaking or not. VVAD is useful whenever audio VAD (A-VAD) is inefficient either because the acoustic signal is difficult to analyze or because it is simply missing. We propose two deep architectures for V-VAD, one based on facial landmarks and one based on optical flow. Moreover, available datasets, used for learning and for testing VVAD, lack content variability. We introduce a novel methodology to automatically create and annotate very large datasets inthe-wild – WildVVAD – based on combining A-VAD with face detection and tracking. A thorough empirical evaluation showsthe advantage of training the proposed deep V-VAD models with this dataset. (10.1109/ICPR48806.2021.9412884)
  DOI : 10.1109/ICPR48806.2021.9412884
• A New IoT Trust Model Based on TLS-SE and TLS-IM Secure Elements: A Blockchain Use Case
  
  • Urien Pascal
  , 2021, pp.1-2. (10.1109/CCNC49032.2021.9369485)
  DOI : 10.1109/CCNC49032.2021.9369485
• Machine Learning Detection for SMiShing Frauds
  
  • Msahli Mounira
  • Boukari Badr Eddine
  • Ravi Akshaya
  , 2021, pp.1-2. (10.1109/CCNC49032.2021.9369640)
  DOI : 10.1109/CCNC49032.2021.9369640
• Innovative TLS 1.3 Identity Module for Trusted IoT Device
  
  • Urien Pascal
  , 2021, pp.1-4. (10.1109/CCNC49032.2021.9369656)
  DOI : 10.1109/CCNC49032.2021.9369656
• A Survey on the Current Security Landscape of Intelligent Transportation Systems
  
  • Lamssaggad Ayyoub
  • Benamar Nabil
  • Hafid Abdelhakim Senhaji
  • Msahli Mounira
  IEEE Access, IEEE, 2021, pp.1-1. (10.1109/ACCESS.2021.3050038)
  DOI : 10.1109/ACCESS.2021.3050038
• Survey on Feature Transformation Techniques for Data Streams
  
  • Bahri Maroua
  • Bifet Albert
  • Maniu Silviu
  • Gomes Heitor Murilo
  , 2021, pp.4796-4802. Mining high-dimensional data streams poses a fundamental challenge to machine learning as the presence of high numbers of attributes can remarkably degrade any mining task’s performance. In the past several years, dimension reduction (DR) approaches have been successfully applied for different purposes (e.g., visualization). Due to their high-computational costs and numerous passes overlarge data, these approaches pose a hindrance when processing infinite data streams that are potentially high-dimensional. The latter increases the resource-usage of algorithms that could suffer from the curse of dimensionality. To cope with these issues, some techniques for incremental DR have been proposed. In this paper, we provide a survey on reduction approaches designed to handle data streams and highlight the key benefits of using these approaches for stream mining algorithms. (10.24963/ijcai.2020/668)
  DOI : 10.24963/ijcai.2020/668
• DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arrays
  
  • Furnon Nicolas
  • Serizel Romain
  • Essid Slim
  • Illina Irina
  IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2021, 29, pp.2310 - 2323. Deep neural network (DNN)-based speech enhancement algorithms in microphone arrays have now proven to be efficient solutions to speech understanding and speech recognition in noisy environments. However, in the context of ad-hoc microphone arrays, many challenges remain and raise the need for distributed processing. In this paper, we propose to extend a previously introduced distributed DNN-based time-frequency mask estimation scheme that can efficiently use spatial information in form of so-called compressed signals which are pre-filtered target estimations. We study the performance of this algorithm named Tango under realistic acoustic conditions and investigate practical aspects of its optimal application. We show that the nodes in the microphone array cooperate by taking profit of their spatial coverage in the room. We also propose to use the compressed signals not only to convey the target estimation but also the noise estimation in order to exploit the acoustic diversity recorded throughout the microphone array. (10.1109/TASLP.2021.3092838)
  DOI : 10.1109/TASLP.2021.3092838
• Les mégadonnées et l'essor de l'intelligence artificielle
  
  • Clémençon Stéphan
  Les Cahiers français : documents d'actualité, La Documentation Française, 2021 (419), pp.68.
• Computer-aided diagnosis tool for cervical cancer screening with weakly supervised localization and detection of abnormalities using adaptable and explainable classifier
  
  • Pirovano A.
  • Almeida Leandro G
  • Ladjal Saïd
  • Bloch Isabelle
  • Berlemont S.
  Medical Image Analysis, Elsevier, 2021, 73, pp.102167. While pap test is the most common diagnosis methods for cervical cancer, their results are highly dependent on the ability of the cytotechnicians to detect abnormal cells on the smears using brightfield microscopy. In this paper, we propose an explainable region classifier in whole slide images that could be used by cyto-pathologists to handle efficiently these big images (100,000x100,000 pixels). We create a dataset that simulates pap smears regions and uses a loss, we call classification under regression constraint, to train an efficient region classifier (about 66.8% accuracy on severity classification, 95.2% accuracy on normal/abnormal classification and 0.870 KAPPA score). We explain how we benefit from this loss to obtain a model focused on sensitivity and, then, we show that it can be used to perform weakly supervised localization (accuracy of 80.4%) of the cell that is mostly responsible for the malignancy of regions of whole slide images. We extend our method to perform a more general detection of abnormal cells (66.1% accuracy) and ensure that at least one abnormal cell will be detected if malignancy is present. Finally, we experiment our solution on a small real clinical slide dataset, highlighting the relevance of our proposed solution, adapting it to be as easily integrated in a pathology laboratory workflow as possible, and extending it to make a slide-level prediction. (10.1016/j.media.2021.102167)
  DOI : 10.1016/j.media.2021.102167
• Optimal transport between determinantal point processes and application to fast simulation
  
  • Decreusefond Laurent
  • Moroz Guillaume
  Modern Stochastics: Theory and Applications, VTEX, 2021, 8 (2), pp.209--237. We analyze several optimal transportation problems between de-terminantal point processes. We show how to estimate some of the distances between distributions of DPP they induce. We then apply these results to evaluate the accuracy of a new and fast DPP simulation algorithm. We can now simulate in a reasonable amount of time more than ten thousands points. (10.15559/21-VMSTA180)
  DOI : 10.15559/21-VMSTA180
• Phoneme Level Lyrics Alignment and Text-Informed Singing Voice Separation
  
  • Schulze-Forster Kilian
  • Doire Clement S J
  • Richard Gael
  • Badeau Roland
  IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2021. The goal of singing voice separation is to recover the vocals signal from music mixtures. State-of-the-art performance is achieved by deep neural networks trained in a supervised fashion. Since training data are scarce and music signals are extremely diverse, it remains challenging to achieve high separation quality across various recording and mixing conditions as well as music styles. In this paper, we investigate to which extent the separation can be improved when lyrics transcripts are used as additional information. To this end, we propose a joint approach to phoneme level lyrics alignment and text-informed singing voice separation. It is based on DTW-attention, a new monotonic attention mechanism including a differentiable approximation of dynamic time warping. Experimental results show that the method can align phonemes with mixed singing voice with high precision given accurate transcripts. It also achieves competitive results on challenging word level alignment test sets using less training data than state-of-the-art methods. Sequential alignment and informed separation lead to improved separation quality according to objective measures. Text information helps preserving spectral phoneme properties in the separated voice signals. (10.1109/TASLP.2021.3091817)
  DOI : 10.1109/TASLP.2021.3091817
• The Vagueness of Vagueness in Noun Phrases
  
  • Paris Pierre-Henri
  • El Aoud Syrine
  • Suchanek Fabian
  , 2021. Natural language text has a great potential to feed knowledge bases. However, natural language is not always precise-and sometimes intentionally so. In this position paper, we study vagueness in noun phrases. We manually analyze the frequency of vague noun phrases in a Wikipedia corpus, and find that 1/4 of noun phrases exhibit some form of vagueness. We report on their nature and propose a categorization. We then conduct a literature review and present different definitions of vagueness, and different existing methods to deal with the detection and modeling of vagueness. We find that, despite its frequency, vagueness has not yet be addressed in its entirety.
• Self-healing Networks via Self-organising Mobile Agents
  
  • Rodriguez Arles
  • Gomez Jonatan
  • Diaconescu Ada
  Journal of Autonomous Agents and Multi-agent Systems (JAAMAS), 2021.
• Automatic Feature Selection for Improved Interpretability on Whole Slide Imaging
  
  • Pirovano A.
  • Heuberger H.
  • Berlemont S.
  • Ladjal Saïd
  • Bloch Isabelle
  Machine Learning and Knowledge Extraction, MDPI, 2021, 3 (1), pp.243-262. Deep learning methods are widely used for medical applications to assist medical doctors in their daily routine. While performances reach expert's level, interpretability (highlighting how and what a trained model learned and why it makes a specific decision) is the next important challenge that deep learning methods need to answer to be fully integrated in the medical field. In this paper, we address the question of interpretability in the context of whole slide images (WSI) classification with the formalization of the design of WSI classification architectures and propose a piece-wise interpretability approach, relying on gradient-based methods, feature visualization and multiple instance learning context. After training two WSI classification architectures on Camelyon-16 WSI dataset, highlighting discriminative features learned, and validating our approach with pathologists, we propose a novel manner of computing interpretability slide-level heat-maps, based on the extracted features, that improves tile-level classification performances. We measure the improvement using the tile-level AUC that we called Localization AUC, and show an improvement of more than 0.2. We also validate our results with a RemOve And Retrain (ROAR) measure. Then, after studying the impact of the number of features used for heat-map computation, we propose a corrective approach, relying on activation colocalization of selected features, that improves the performances and the stability of our proposed method. (10.3390/make3010012)
  DOI : 10.3390/make3010012
• Power Allocation for Uplink Multiband Satellite Communications with Nonlinear Impairments
  
  • Louchart Arthur
  • Ciblat Philippe
  • Poulliat Charly
  IEEE Communications Letters, Institute of Electrical and Electronics Engineers, 2021, 25 (8), pp.2713-2717. In this letter, we develop some generic power allocation strategies in an uplink multiband satellite communications system when nonlinear impairments on the High-Power Amplifier onboard satellite occur. Based on the capacity closed-form expression related to receivers seeing nonlinear interference as a noise, we propose practical and scalable algorithms for three power allocation problems: i) sum-power minimization, ii) maximization of minimum per-user data rate, iii) sum-rate maximization. We show that the solutions mainly rely on Geometric Programming and/or Successive Convex Approximation approaches. The proposed solutions outperform naive approaches while enabling user scalability contrary to optimal brute-force grid search algorithms. (10.1109/LCOMM.2021.3087408)
  DOI : 10.1109/LCOMM.2021.3087408