Publications

2021

• Triple Sensing Current Margin for Maintainable MRAM Yield at Sub-100% Tunnel Magnetoresistance Ratio
  
  • Cai Hao
  • Han Menglin
  • Zhou Yongliang
  • Liu Bo
  • Naviner Lirida
  IEEE Transactions on Magnetics, Institute of Electrical and Electronics Engineers, 2021, 57 (2), pp.1-5. Spin transfer torque magnetic random access memory (STT-MRAM) creates significant breakthroughs as a proper candidate of next-generation non-volatile memory (NVM). Although STT-MRAM achieves high endurance, low access latency, and power consumption, the yield issue remains one of the critical concerns in high-density and large-scale MRAM array design. In this article, a novel triple-current margin sensing amplifier (TM-SA) is proposed for maintainable MRAM yield based on the current-mode SA and transmission gate switches. The sensing current margin of the proposed TM-SA is three times enlarged compared to traditional current mean (CM)-SA and resistance mean (RM)-SA. With a seriously degraded tunnel magnetoresistance (TMR) ratio (sub-100%, as low as 10%), the maximum voltage margin is 4.6 times of conventional CM-SA and five times of RM-SA. Monte-Carlo simulation shows that sensing failure probability can be greatly alleviated with the proposed TM-SA. The performance of TM-SA with respect to voltage margin can be further improved than that of CM-SA and RM-SA. (10.1109/TMAG.2020.3011614)
  DOI : 10.1109/TMAG.2020.3011614
• 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 Thus, the complete security analysis has been replaced by a sketch of a proof to partially validate the robustness of the proposal.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
• Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases
  
  • Weikum Gerhard
  • Dong Xin Luna
  • Razniewski Simon
  • Suchanek Fabian M.
  , 2021, 10 (2-4), pp.108-490. Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods. (10.1561/1900000064)
  DOI : 10.1561/1900000064
• Automated neurosurgical stereotactic planning for intraoperative use: a comprehensive review of the literature and perspectives
  
  • Zanello Marc
  • Carron Romain
  • Peeters Sophie
  • Gori Pietro
  • Roux Alexandre
  • Bloch Isabelle
  • Oppenheim Catherine
  • Pallud Johan
  Neurosurgical Review, 2021, 44, pp.867-888.
• A Stochastic Geometry Approach to EMF Exposure Modeling
  
  • Gontier Quentin
  • Petrillo Lucas
  • Rottenberg Francois
  • Horlin Francois
  • Wiart Joe
  • Oestges Claude
  • de Doncker Philippe
  IEEE Access, IEEE, 2021, 9, pp.91777-91787. (10.1109/ACCESS.2021.3091804)
  DOI : 10.1109/ACCESS.2021.3091804
• Optimization of wireless sensor networks deployment with coverage and connectivity constraints
  
  • Elloumi Sourour
  • Hudry Olivier
  • Marie Estel
  • Martin Agathe
  • Plateau Agnès
  • Rovedakis Stephane
  Annals of Operations Research, Springer Verlag, 2021, 298 (1-2), pp.183-206. Wireless sensor networks have been widely deployed in the last decades to provide various services, like environmental monitoring or object tracking. Such a network is composed of a set of sensor nodes which are used to sense and transmit collected information to a base station. To achieve this goal, two properties have to be guaranteed: (i) the sensor nodes must be placed such that the whole environment of interest (represented by a set of targets) is covered, and (ii) every sensor node can transmit its data to the base station (through other sensor nodes). In this paper, we consider the Minimum Connected k-Coverage (MCkC) problem, where a positive integer k ≥ 1 defines the coverage multiplicity of the targets. We propose two mathematical programming formulations for the MCkC problem on square grid graphs and random graphs. We compare them to a recent model proposed by (Rebai et al 2015). We use a standard mixed integer linear programming solver to solve several instances with different formulations. In our results, we point out the quality of the LP-bound of each formulation as well as the total CPU time or the proportion of solved instances to optimality within a given CPU time. (10.1007/s10479-018-2943-7)
  DOI : 10.1007/s10479-018-2943-7
• Self-improving system integration: Mastering continuouschange
  
  • Bellman Kirstie
  • Botev Jean F
  • Diaconescu Ada
  • Esterle Lukas
  • Gruhl Christian
  • Landauer Christopher
  • Lewis Peter R.
  • Nelson Phyllis
  • Pournaras Evangelos
  • Stein Anthony
  • Tomforde Sven
  Future Generation Computer Systems, Elsevier, 2021.
• Association between estimated whole-brain radiofrequency electromagnetic fields dose and cognitive function in preadolescents and adolescents
  
  • Cabré-Riera Alba
  • van Wel Luuk
  • Liorni Ilaria
  • Thielens Arno
  • Birks Laura Ellen
  • Pierotti Livia
  • Joseph Wout
  • González-Safont Llúcia
  • Ibarluzea Jesús
  • Ferrero Amparo
  • Huss Anke
  • Wiart Joe
  • Santa-Marina Loreto
  • Torrent Maties
  • Vrijkotte Tanja
  • Capstick Myles
  • Vermeulen Roel
  • Vrijheid Martine
  • Cardis Elisabeth
  • Röösli Martin
  • Guxens Mònica
  International Journal of Hygiene and Environmental Health, Elsevier, 2021, 231, pp.113659. (10.1016/j.ijheh.2020.113659)
  DOI : 10.1016/j.ijheh.2020.113659
• Maximizing the Number of Scheduled Lightpath Demands in Optical Networks by Conflict Graphs
  
  • Hudry Olivier
  International Journal of Mathematics, Statistics and Operations Research, Academic Research Foundations, 2021.
• Optical injection of mid-infrared extreme events in unilaterally coupled quantum cascade lasers
  
  • Spitz Olivier
  • Herdt Andreas
  • Elsassaer Wolfgang
  • Grillot Frédéric
  , 2021.
• Depth for Curve Data and Applications
  
  • de Micheaux Pierre Lafaye
  • Mozharovskyi Pavlo
  • Vimond Myriam
  Journal of the American Statistical Association, Taylor & Francis, 2021, 116 (536), pp.1881-1897. In 1975, John W. Tukey defined statistical data depth as a function that determines the centrality of an arbitrary point with respect to a data cloud or to a probability measure. During the last decades, this seminal idea of data depth evolved into a powerful tool proving to be useful in various fields of science. Recently, extending the notion of data depth to the functional setting attracted a lot of attention among theoretical and applied statisticians. We go further and suggest a notion of data depth suitable for data represented as curves, or trajectories, which is independent of the parameterization. We show that our curve depth satisfies theoretical requirements of general depth functions that are meaningful for trajectories. We apply our methodology to diffusion tensor brain images and also to pattern recognition of handwritten digits and letters. Supplementary materials for this article are available online. (10.1080/01621459.2020.1745815)
  DOI : 10.1080/01621459.2020.1745815