Search results for "machine learning."
showing 10 items of 1455 documents
Assessing the Relationship Between Centrality and Hierarchy in Complex Networks
2020
International audience
Hierarchy and Centrality: Two Sides of The Same Coin?
2020
International audience
Classical versus Community-aware Centrality Measures: An Empirical Study
2021
International audience
Concevoir pour le développement dans une approche non-curriculaire en formation des adultes
2016
International audience; La formation des adultes est principalement structurée selon une approche curriculaire et séquentielle ayant montré son efficacité, et qui suppose notamment i) la didactisation préalable d'objets d'apprentissage, ii) l'identification des situations susceptibles de favoriser l'acquisition de ces objets et iii) la répétition et la graduation de ces situations. Cette approche montre aussi ses limites i) vis-à-vis de certains publics (en échec à l'université, évitant la formation continue, décrochant des institutions), et ii) vis-à-vis d'enjeux sociétaux majeurs (catastrophes industrielles et environnementales, atteintes graves à la santé…). Cette inadéquation est due à …
Discovering human mobility from mobile data : probabilistic models and learning algorithms
2020
Smartphone usage data can be used to study human indoor and outdoor mobility. In our work, we investigate both aspects in proposing machine learning-based algorithms adapted to the different information sources that can be collected.In terms of outdoor mobility, we use the collected GPS coordinate data to discover the daily mobility patterns of the users. To this end, we propose an automatic clustering algorithm using the Dirichlet process Gaussian mixture model (DPGMM) so as to cluster the daily GPS trajectories. This clustering method is based on estimating probability densities of the trajectories, which alleviate the problems caused by the data noise.By contrast, we utilize the collecte…
A comparison of community-aware centrality measures in online social networks
2021
An empirical study on classical and community-aware centrality measures in complex networks
2021
Community structure is a ubiquitous feature in natural and artificial systems. Identifying key nodes is a fundamental task to speed up or mitigate any diffusive processes in these systems. Centrality measures aim to do so by selecting a small set of critical nodes. Classical centrality measures are agnostic to community structure, while community-aware centrality measures exploit this property. Several works study the relationship between classical centrality measures, but the relationship between classical and community-aware centrality measures is almost unexplored. In this work [1], we answer two questions: (1) How do classical and community-aware centrality measures relate? (2) What is …
Perspective-n-Learned-Point: Pose Estimation from Relative Depth
2019
International audience; In this paper we present an online camera pose estimation method that combines Content-Based Image Retrieval (CBIR) and pose refinement based on a learned representation of the scene geometry extracted from monocular images. Our pose estimation method is two-step, we first retrieve an initial 6 Degrees of Freedom (DoF) location of an unknown-pose query by retrieving the most similar candidate in a pool of geo-referenced images. In a second time, we refine the query pose with a Perspective-n-Point (PnP) algorithm where the 3D points are obtained thanks to a generated depth map from the retrieved image candidate. We make our method fast and lightweight by using a commo…
Temporal Semantic Centrality for the Analysis of Communication Networks
2012
National audience; De nos jours, la compréhension des communautés en ligne devient un enjeu majeur du Web. Dans cet article nous proposons une nouvelle mesure, la Probabilité de Propagation Sémantique (SPP), qui caractérise la capacité de l'utilisateur à propager un concept sémantique à d'autres utilisateurs, d'une manière rapide et ciblée. La sémantique des messages est analysée selon une ontologie donnée. Nous utilisons cette mesure pour obtenir la Centralité Sémantique Temporelle (TSC) d'un utilisateur dans une communauté. Nous proposons et évaluons une expérimentation de cette mesure, en utilisant une ontologie et des données réelles issues du Web.
Nonlinear sculpturing of optical pulses with normally dispersive fiber-based devices
2018
International audience; We present a general method to determine the parameters of nonlinear pulse shaping systems based on pulse propagation in a normally dispersive fiber that are required to achieve the generation of pulses with various specified temporal properties. The nonlinear shaping process is reduced to a numerical optimization problem over a three-dimensional space, where the intersections of different surfaces provide the means to quickly identify the sets of parameters of interest. We also show that the implementation of a machine-learning strategy can efficiently address the multi-parameter optimization problem being studied.