CentraleSupélec

About: CentraleSupélec is a based out in . It is known for research contribution in the topics: MIMO & Wireless network. The organization has 1330 authors who have published 2344 publications receiving 30533 citations. The organization is also known as: CentraleSupelec & CentraleSupelec of the Paris-Saclay University.

Privacy-Enabled Smart Home Framework with Voice Assistant

Abstract: Smart home environment plays a prominent role in improving the quality of life of the residents by enabling home automation, health care and safety through various Internet of Things (IoT) devices. However, a large amount of data generated by sensors in a smart home environment heighten security and privacy concerns among potential users. Some of the data can be sensitive as it contains information about users’ private activities, location, behavioural patterns and health status. Other concerns of the users are towards the distribution and sharing of data to third parties. In this chapter, we propose privacy-enabled smart home framework consisting of three major components: activity recognition and occupancy detection, privacy-preserving data management and voice assistant. The proposed platform includes unobtrusive sensors for multiple occupancy detection and activity recognition. The privacy-enabled voice assistant performs interaction with smart home. We also present a detailed description of system architecture with service middleware.

Communication-Aware Energy Efficient Trajectory Planning With Limited Channel Knowledge

Abstract: Wireless communications is nowadays an important aspect of robotics. There are many applications in which a robot must move to a certain goal point while transmitting information through a wireless channel which depends on the particular trajectory chosen by the robot to reach the goal point. In this context, we develop a method to generate optimum trajectories which allow the robot to reach the goal point using little mechanical energy while transmitting as much data as possible. This is done by optimizing the trajectory (path and velocity profile) so that the robot consumes less energy while also offering good wireless channel conditions. In this article, we consider a realistic wireless channel model as well as a realistic dynamic model for the mobile robot (considered here to be a drone). Simulations results illustrate the merits of the proposed method.

Risk-sensitive safety analysis using Conditional Value-at-Risk

Abstract: This paper develops a safety analysis method for stochastic systems that is sensitive to the possibility and severity of rare harmful outcomes. We define risk-sensitive safe sets as sub-level sets of the solution to a non-standard optimal control problem, where a random maximum cost is assessed via Conditional Value-at-Risk (CVaR). The objective function represents the maximum extent of constraint violation of the state trajectory, averaged over a given percentage of worst cases. This problem is well-motivated but difficult to solve tractably because the temporal decomposition for CVaR is history-dependent. Our primary theoretical contribution is to derive computationally tractable under-approximations to risk-sensitive safe sets. Our method provides a novel, theoretically guaranteed, parameter-dependent upper bound to the CVaR of a maximum cost without the need to augment the state space. For a fixed parameter value, the solution to only one Markov decision process problem is required to obtain the under-approximations for any family of risk-sensitivity levels. In addition, we propose a second definition for risk-sensitive safe sets and provide a tractable method.

Spotting Micro-Expressions on Long Videos Sequences

Abstract: This paper presents two methods for the first Micro-Expression Spotting Challenge 2019 by evaluating local temporal pattern (LTP) and local binary pattern (LBP) on two most recent databases, i.e. SAMM and CAS(ME)2. First we propose LTP-ML method as the baseline results for the challenge and then we compare the results with the LBP-χ2-distance method. The LTP patterns are extracted by applying PCA in a temporal window on several facial local regions. The micro-expression sequences are then spotted by a local classification of LTP and a global fusion. The LBP-χ2-distance method is to compare the feature difference by calculating χ2 distance of LBP in a time window, the facial movements are then detected with a threshold. The performance is evaluated by Leave-One-Subject-Out cross validation. The overlap frames are used to determine the True Positives and the metric F1-score is used to compare the spotting performance of the databases. The F1-score of LTP-ML result for SAMM and CAS(ME)2 are 0.0316 and 0.0179, respectively. The results show our proposed LTP-ML method outperformed LBP-χ2-distance method in terms of F1-score on both databases.

Neural Network-based Word Alignment through Score Aggregation

Abstract: We present a simple neural network for word alignment that builds source and target word window representations to compute alignment scores for sentence pairs. To enable unsupervised training, we use an aggregation operation that summarizes the alignment scores for a given target word. A soft-margin objective increases scores for true target words while decreasing scores for target words that are not present. Compared to the popular Fast Align model, our approach improves alignment accuracy by 7 AER on English-Czech, by 6 AER on Romanian-English and by 1.7 AER on English-French alignment.