Publications

2019

• On-the-fly Detection of User Engagement Decrease in Spontaneous Human-Robot Interaction
  
  • Ben Youssef Atef
  • Varni Giovanna
  • Essid Slim
  • Clavel Chloé
  International Journal of Social Robotics, 2019. In this paper, we address the detection of engagement decrease of users spontaneously interacting with a socially assistive robot in a public space. We first describe the UE-HRI dataset that collects spontaneous Human-Robot Interactions following the guidelines provided by the Affective Computing research community to collect data "in-the-wild". We then analyze the users' behaviors focusing on proxemics, gaze, head motion, facial expressions and speech during interactions with the robot. Engaged behaviors versus signs of engagement decrease exhibited by the users were annotated and analyzed. Finally, we investigate the use of deep leaning techniques (Recurrent and Deep Neural Networks) to detect user engagement decrease in real-time. The results of this work particularly highlight the relevance of taking into account temporal dynamics of the user's behavior. Allowing 1 to 2 seconds as buffer delay improves the performance of taking a decision on user engagement.
• The $f$-divergence expectation iteration scheme
  
  • Daudel Kamélia
  • Douc Randal
  • Portier François
  • Roueff François
  , 2019. This paper introduces the $f$-EI$(\phi)$ algorithm, a novel iterative algorithm which operates on measures and performs $f$-divergence minimisation in a Bayesian framework. We prove that for a rich family of values of $(f,\phi)$ this algorithm leads at each step to a systematic decrease in the $f$-divergence and show that we achieve an optimum. In the particular case where we consider a weighted sum of Dirac measures and the $\alpha$-divergence, we obtain that the calculations involved in the $f$-EI$(\phi)$ algorithm simplify to gradient-based computations. Empirical results support the claim that the $f$-EI$(\phi)$ algorithm serves as a powerful tool to assist Variational methods.
• Merit-guided dynamic feature selection filter for data streams
  
  • Barddal Jean Paul
  • Enembreck Fabrício
  • Gomes Heitor Murilo
  • Bifet Albert
  • Pfahringer Bernhard
  Expert Syst. Appl., 2019, 116, pp.227-242. (10.1016/j.eswa.2018.09.031)
  DOI : 10.1016/j.eswa.2018.09.031
• On the Capacity of MIMO Optical Wireless Channels
  
  • Li Longguang
  • Moser Stefan M
  • Wang Ligong
  • Wigger Michèle
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2019. This paper studies the capacity of a general multiple-input multiple-output (MIMO) free-space optical intensity channel under a per-input-antenna peak-power constraint and a total average-power constraint over all input antennas. The main focus is on the scenario with more transmit than receive antennas. In this scenario, different input vectors can yield identical distributions at the output, when they result in the same image vector under multiplication by the channel matrix. We first determine the most energy-efficient input vectors that attain each of these image vectors. Based on this, we derive an equivalent capacity expression in terms of the image vector, and establish new lower and upper bounds on the capacity of this channel. The bounds match when the signal-to-noise ratio (SNR) tends to infinity, establishing the high-SNR asymptotic capacity. We also characterize the low-SNR slope of the capacity of this channel. (10.1109/ITW.2018.8613496)
  DOI : 10.1109/ITW.2018.8613496
• Medical imaging and AI
  
  • Bloch Isabelle
  , 2019.
• A Conditional Gradient-Based Augmented Lagrangian Framework
  
  • Yurtsever Alp
  • Fercoq Olivier
  • Cevher Volkan
  , 2019, 97. This paper considers a generic convex minimization template with affine constraints over a compact domain, which covers key semidefinite programming applications. The existing conditional gradient methods either do not apply to our template or are too slow in practice. To this end, we propose a new conditional gradient method, based on a unified treatment of smoothing and augmented Lagrangian frameworks. The proposed method maintains favorable properties of the classical conditional gradient method, such as cheap linear minimization oracle calls and sparse representation of the decision variable. We prove $\mathcal{O}(1/\sqrt{k})$ convergence rate of our method in the objective residual and the feasibility gap. This rate is essentially the same as the state of the art CG-type methods for our problem template, but the proposed method is significantly superior to existing methods in various semidefinite programming applications.
• A 3D Beamforming Scheme Based on The Spatial Distribution of User Locations
  
  • Rachad Jalal
  • Nasri Ridha
  • Decreusefond Laurent
  , 2019. Multi-antenna technologies such as massive Multiple-Input Multiple-Output (massive MIMO) and beamforming are key features to enhance performance, in terms of capacity and coverage, by using a large number of antennas intelligently. With the upcoming 5G New Radio (NR), FD-MIMO (Full Dimension MIMO) will play a major key role. FD-MIMO consists in arranging a large number of antennas in a 2D array, which enables to use 3D beamforming i.e., beamforming in both horizontal and vertical dimensions. The present paper provides a 3D beamforming model where beam steering depends on the random spatial distribution of users. We attempt to derive some analytical results regarding the probability distribution of antenna beamforming radiation pattern. Also, through system level simulations, we show how 3D beamforming can reduce interference impact, compared to the traditional 2D beamforming, and enhances system performance in terms of the coverage probability and users throughput.
• Evaluation of cortical segmentation pipelines on clinical neonatal MRI data
  
  • Tor-Díez Carlos
  • Pham Chi-Hieu
  • Meunier Hélène
  • Faisan Sylvain
  • Bloch Isabelle
  • Bednarek Nathalie
  • Passat Nicolas
  • Rousseau François
  , 2019, pp.6553-6556. Magnetic Resonance Imaging (MRI) can provide 3D morphological information on brain structures. Such information is particularly relevant for carrying out morphometric brain analysis, especially in the newborn and in the case of prematurity. However, 3D neonatal MRI acquired in clinical environments are low-resolution, anisotropic images, making segmentation a challenging task. In this context, preprocessing techniques aim to increase the image resolution. Interpolation techniques were classically used; super-resolution (SR) techniques have recently appeared as an emerging alternative. In this paper, we evaluate the performance of different SR methods against the classical interpolation in the application of neonatal cortex segmentation. Additionally, we assess the robustness of different segmentation methods for each estimation of high resolution MRI input. Results are evaluated both qualitatively and quantitatively with neonatal clinical MRI. (10.1109/EMBC.2019.8856795)
  DOI : 10.1109/EMBC.2019.8856795
• New characterization and parametrization of LCD codes.
  
  • Mesnager Sihem
  • Carlet C.
  • Tang C.
  • Qi Y.
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2019.
• New Bernstein and Hoeffding type inequalities for regenerative Markov chains
  
  • Bertail Patrice
  • Ciolek Gabriela
  ALEA : Latin American Journal of Probability and Mathematical Statistics, Instituto Nacional de Matemática Pura e Aplicada (Rio de Janeiro, Brasil) [2006-....], 2019, 16 (1), pp.259. (10.30757/ALEA.v16-09)
  DOI : 10.30757/ALEA.v16-09
• Procédé de gestion de contenus multimédia et dispositif pour la mise en œuvre du procédé
  
  • Lucas Gregory
  • Le Feuvre J.
  • Toullec Eric
  , 2019.
• Procédé de gestion de contenus multimédia et dispositif pour la mise en œuvre du procédé
  
  • Grégory Lucas
  • Le Feuvre J.
  • Toullec Eric
  , 2019.
• On q-ary plateaued functions over Fq and their explicit characterizations.
  
  • Mesnager Sihem
  • Özbudak Ferruh
  • Sinak A.
  • Cohen Gerard
  European Journal of Combinatorics, Elsevier, 2019.
• Three-Weight Minimal Linear Codes and Their Applications
  
  • Mesnager Sihem
  • Sinak A.
  • Yayla O.
  , 2019.
• Further study of 2-to-1 mappings over $F_2^n$
  
  • Mesnager Sihem
  • Li K.
  • Qu L.
  , 2019.
• Construction of Efficient Codes for High-Order Direct Sum Masking
  
  • Mesnager Sihem
  • Carlet Claude
  • Guilley Sylvain
  • Guneri Cem
  • Özbudak Ferruh
  , 2019, 27, pp.108-128.
• Mixed Delay Constraints on a Fading C-RAN Uplink
  
  • Nikbakht Homa
  • Wigger Michèle
  • Hachem Walid
  • Shamai Shitz Shlomo
  , 2019. A cloud radio access network (C-RAN) is considered where the first hop from the user equipments (UEs) to the basestations (BSs) is modeled by the fading Wyner soft-handoff model. The focus is on mixed-delay constraints where a set of messages (so called "slow" messages) are jointly decoded in the cloud unit (CU), whereas the remaining messages (called "fast" messages) have to be decoded immediately at the BSs. This paper presents inner and outer bounds on the capacity region for such a setup. Moreover, the multiplexing gain region is characterized exactly. The presented results show that for small fronthaul capacity it is beneficial to send both "fast" and "slow" messages. However, when the rate of "fast" messages is already large, then increasing it further, deteriorates the sum-rate of the system. In this regime, the stringent decoding delay on the "fast" messages penalizes the overall performance. Our results indicate that this penalty is larger at moderate SNR than at high SNR and it is also larger for random time-varying fading coefficients than for static ones. (10.1109/itw44776.2019.8989156)
  DOI : 10.1109/itw44776.2019.8989156
• Diversity and Struggles in Critical Performativity. The Case of French Community-Supported Agriculture
  
  • Ouahab Alban
  • Maclouf Etienne
  M@n@gement, AIMS (Association internationale de management stratégique), 2019, 22 (4), pp.537-558. This article contributes to the debates about critical performativity (CP), a research program aimed at reorienting critical management studies toward affirmative and transformative research. While some scholars explain how CP can be engineered to create alternative organizations, others remain skeptical, exposing its potential for failure. We examine alternative organizations with a particular focus on the struggles in which they are entangled, such as competition with other performative programs and normative agendas. These struggles cause permanent reconfigurations to agencements and make the future effects of performative engines uncertain. To understand these reconfigurations, we look at the transformation of already established alternative organizations. We conducted a case study on French Community-Supported Agriculture (CSA), which is illustrative of CP "in the field," looking at how the CSA network can engineer local organizations. We show how the struggles between competing performative programs produce diversity, in time and space, of organizational settings and goals within the French CSA movement. Our contributions are twofold. Firstly, because of the struggles in which it is entangled, a performative engine can create diverse and potentially competing normative content rather than a single stable agenda. Secondly, deviations from the initial normative content are not neutral and may undermine the subversive potential of those agencements. Ultimately, we call for a research agenda which would look beyond the implementation of subversive practices to question the way subversive agencements develop, and which would acknowledge that CP is also about struggles between competing engines. (10.3917/mana.224.0537)
  DOI : 10.3917/mana.224.0537
• Improving Deep Learning Parkinson’s Disease Detection Through Data Augmentation Training
  
  • Taleb Catherine
  • Likforman-Sulem Laurence
  • Mokbel Chafic
  , 2020, 1144, pp.79-93. Deep learning has been successfully applied to different classification applications where large data are available. However, the lack of data makes it more difficult to predict Parkinson’s disease (PD) with the deep models, which requires enough number of training data. Online handwriting dynamic signals can provide more detailed and complex information for PD detection task. In our previous work [1], two different deep models were studied for time series classification; the convolutional neural network (CNN) and the convolutional neural network- bidirectional long short term memory network (CNN-BLSTM). Different approaches were applied to encode pen-based signals into images for the CNN model while the raw time series are used directly with the CNN-BLSTM model. We have showed that both CNN model with spectrogram images as input and CNN-BLSTM model, improve the performance of time series classification applied for early PD stage detection. However, these approaches did not outperform classical support vector machine (SVM) classification applied on pre-engineered features. In this paper we investigate transfer learning and data augmentation approaches in order to train these models for PD detection on large-scale data. Various data augmentation methods for pen-based signals are proposed. Our experimental results show that the CNN-BLSTM model used with the combination of Jittering and Synthetic data augmentation methods provides promising results in the context of early PD detection, with accuracy reaching 97.62%. We have illustrated that deep architecture can surpass the models trained on pre-engineered features even though the available data is small. (10.1007/978-3-030-37548-5_7)
  DOI : 10.1007/978-3-030-37548-5_7
• On σ-LCD codes
  
  • Mesnager Sihem
  • Carlet C.
  • Tang C.
  • Qi Y.
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2019.
• Reconstruction 3D en IRM du pelvis de l'enfant : segmentation des structures osseuses par intelligence artificielle
  
  • Peyrot Q.
  • Muller C.
  • Virzi A.
  • Delmonte A.
  • Meignan P.
  • Berteloot L.
  • Grevent D.
  • Blanc T.
  • Gori P.
  • Boddaert N.
  • Bloch Isabelle
  • Sarnacki S.
  , 2019.
• From Pairwise Comparisons and Rating to a Unified Quality Scale
  
  • Perez-Ortiz Maria
  • Mikhailiuk Aliaksei
  • Zerman Emin
  • Hulusic Vedad
  • Valenzise Giuseppe
  • Mantiuk Rafal
  IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2019, 29, pp.1139-1151. The goal of psychometric scaling is the quantifi-cation of perceptual experiences, understanding the relationship between an external stimulus, the internal representation and the response. In this paper, we propose a probabilistic framework to fuse the outcome of different psychophysical experimental protocols , namely rating and pairwise comparisons experiments. Such a method can be used for merging existing datasets of subjective nature and for experiments in which both measurements are collected. We analyze and compare the outcomes of both types of experimental protocols in terms of time and accuracy in a set of simulations and experiments with benchmark and real-world image quality assessment datasets, showing the necessity of scaling and the advantages of each protocol and mixing. Although most of our examples focus on image quality assessment, our findings generalize to any other subjective quality-of-experience task. (10.1109/TIP.2019.2936103)
  DOI : 10.1109/TIP.2019.2936103
• Mathematical Morphology on a Few Discrete Structures
  
  • Bloch Isabelle
  , 2019.
• Model-Based Programming for Multi-Processor Platforms with TTool/DIPLODOCUS and OMC
  
  • Enrici Andrea
  • Lallet Julien
  • Pacalet Renaud
  • Apvrille Ludovic
  • Desnos Karol
  • Latif Imran
  , 2019, pp.56_81. Abstract. The complexity of today's multi-processor architectures raises the need to increase the level of abstraction of software development paradigms above third-generation programming languages (e.g., C/C++). Code generation from model-based specifications is considered to be more efficient with respect to traditional paradigms where software is mainly developed from code. However, existing model-based approaches typically generate application software in SoC-programming languages (e.g., C/C++, OpenCL, Verilog/VHDL) without considering the optimization of non-functional properties (e.g., memory footprint, scheduling). This paper proposes a novel approach and tools where system-level models are compiled into standard C code while optimizing the systems memory footprint. We show the effectiveness of our approach with the model-based programming of UML/SysML diagrams for a 5G decoder. From the compiled C code, we generate both a software implementation for a Digital Signal Processor platform and a hardware-software implementation for a platform based on hardware Intellectual Property (IP) blocks. Overall, our optimizations achieve a memory footprint reduction of 80.07% in the first case and 88.93% in the second case. (10.1007/978-3-030-11030-7_4)
  DOI : 10.1007/978-3-030-11030-7_4
• Game Theory for Networking Applications
  
  • Song Ju Bin
  • Li Husheng
  • Coupechoux Marceau
  , 2019, pp.229.