Publications

2018

• Subsampling for big data : some recent advances
  
  • Bertail Patrice
  • Jelassi Ons
  • Tressou Jessica
  • Zetlaoui Mélanie
  , 2018.
• An In-depth Comparison of Group Betweenness Centrality Estimation Algorithms
  
  • Chehreghani Mostafa Haghir
  • Bifet Albert
  • Abdessalem Talel
  , 2018, pp.2104-2113.
• A Survey on Data-driven Dictionary-based Methods for 3D Modeling
  
  • Lescoat Thibault
  • Ovsjanikov Maks
  • Memari Pooran
  • Thiery Jean-Marc
  • Boubekeur Tamy
  Computer Graphics Forum, Wiley, 2018. Dictionaries are very useful objects for data analysis, as they enable a compact representation of large sets of objects through the combination of atoms. Dictionary-based techniques have also particularly benefited from the recent advances in machine learning, which has allowed for data-driven algorithms to take advantage of the redundancy in the input dataset and discover relations between objects without human supervision or hard-coded rules. Despite the success of dictionary-based techniques on a wide range of tasks in geometric modeling and geometry processing, the literature is missing a principled state-of-the-art of the current knowledge in this field. To fill this gap, we provide in this survey an overview of data-driven dictionary-based methods in geometric modeling. We structure our discussion by application domain: surface reconstruction, compression, and synthesis. Contrary to previous surveys, we place special emphasis on dictionary-based methods suitable for 3D data synthesis, with applications in geometric modeling and design. Our ultimate goal is to enlight the fact that these techniques can be used to combine the data-driven paradigm with design intent to synthesize new plausible objects with minimal human intervention. This is the main motivation to restrict the scope of the present survey to techniques handling point clouds and meshes, making use of dictionaries whose definition depends on the input data, and enabling shape reconstruction or synthesis through the combination of atoms.
• Identifier Randomization: An Efficient Protection Against CAN-Bus Attacks
  
  • Danger Jean-Luc
  • Karray Khaled
  • Guilley Sylvain
  • Elaabid M. Abdelaziz
  , 2018, pp.219-254. (10.1007/978-3-319-98935-8_11)
  DOI : 10.1007/978-3-319-98935-8_11
• Assessing Locator/Identifier Separation Protocol interworking performance through RIPE Atlas
  
  • Li Yue
  • Iannone Luigi
  , 2018.
• Ultra-low noise dual-frequency VECSEL at telecom wavelength using fully correlated pumping
  
  • Liu Hui
  • Gredat Grégory
  • De Syamsundar
  • Fsaifes Ihsan
  • Ly Aliou
  • Vatré Rémy
  • Baili Ghaya
  • Bouchoule Sophie
  • Goldfarb Fabienne
  • Bretenaker Fabien
  Optics Letters, Optical Society of America - OSA Publishing, 2018, 43 (8), pp.1794. An ultra-low intensity and beatnote phase noise dual-frequency vertical-external-cavity surface-emitting laser is built at telecom wavelength. The pump laser is realized by polarization combining two single-mode fibered laser diodes in a single-mode fiber, leading to a 100% in-phase correlation of the pump noises for the two modes. The relative intensity noise is lower than −140 dB∕Hz, and the beatnote phase noise is suppressed by 30 dB, getting close to the spontaneous emission limit. The role of the imperfect cancellation of the thermal effect resulting from unbalanced pumping of the two modes in the residual phase noise is evidenced. (10.1364/OL.43.001794)
  DOI : 10.1364/OL.43.001794
• Perception of Emotions and Body Movement in the Emilya Database
  
  • Fourati Nesrine
  • Pelachaud Catherine
  IEEE Transactions on Affective Computing, Institute of Electrical and Electronics Engineers, 2018, 9 (1), pp.90-101. In this paper, we examine the perception of emotions as well as the characterization and the classification of emotional body expressions based on perceptual body cues ratings. Emilya (EMotional body expression In daILY Actions), a database of body expressions of 8 emotions (including Neutral) in 7 daily actions performed by 11 actors, is used for these purposes. A perceptual study is conducted to explore four issues: 1) how expressed emotions are perceived by humans, 2) how emotion recognition by humans differs across daily actions, 3) how expressed emotions are characterized by humans through body cues, and 4) how emotions are automatically classified based on human rating of body cues. Across all the actions, most of the expressed emotions were correctly identified, but some were confused (e.g. Shame and Sadness). Confusions occurring at the level of emotion perception may be due to a lack of contextual factors (Emilya contains body movement of daily actions without reference to a context), to a similarity of bodily expressions, but also to the lack of other modalities that may contribute to a better recognition of bodily expression of these emotions (e.g. facial expressions). In the paper, we detail and discuss the results from these different studies. (10.1109/TAFFC.2016.2591039)
  DOI : 10.1109/TAFFC.2016.2591039
• Operations research and voting theory
  
  • Hudry Olivier
  , 2018, pp.20-41.
• Audio-Visual Analysis of Music Performances
  
  • Duan Zhiyao
  • Essid Slim
  • Liem Cynthia
  • Richard Gael
  • Sharma Gaurav
  IEEE Signal Processing Magazine, Institute of Electrical and Electronics Engineers, 2018.
• Adding Missing Words to Regular Expressions
  
  • Rebele Thomas
  • Tzompanaki Aikaterini
  • Suchanek Fabian M.
  , 2018.
• Statistical Inference with Ensemble of Clustered Desparsified Lasso
  
  • Chevalier Jérôme-Alexis
  • Salmon Joseph
  • Thirion Bertrand
  , 2018. Medical imaging involves high-dimensional data, yet their acquisition is obtained for limited samples. Multivariate predictive models have become popular in the last decades to fit some external variables from imaging data, and standard algorithms yield point estimates of the model parameters. It is however challenging to attribute confidence to these parameter estimates, which makes solutions hardly trustworthy. In this paper we present a new algorithm that assesses parameters statistical significance and that can scale even when the number of predictors p ≥ 10^5 is much higher than the number of samples n ≤ 10^3 , by lever-aging structure among features. Our algorithm combines three main ingredients: a powerful inference procedure for linear models –the so-called Desparsified Lasso– feature clustering and an ensembling step. We first establish that Desparsified Lasso alone cannot handle n p regimes; then we demonstrate that the combination of clustering and ensembling provides an accurate solution, whose specificity is controlled. We also demonstrate stability improvements on two neuroimaging datasets.