Search results for "cs.LG"

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…

Modern Multispectral Sensors Help Track Explosive Eruptions

2013

Due to its massive air traffic impact, the 2010 eruption of Eyjafjallajokull was felt by millions of people and cost airlines more than U.S. $1.7 billion. The event has, thus, become widely cited in renewed efforts to improve real-time tracking of volcanic plumes, as witnessed by special sections published last year in Journal of Geophysical Research, (117, issues D20 and B9).

Large-scale nonlinear dimensionality reduction for network intrusion detection

2017

International audience; Network intrusion detection (NID) is a complex classification problem. In this paper, we combine classification with recent and scalable nonlinear dimensionality reduction (NLDR) methods. Classification and DR are not necessarily adversarial, provided adequate cluster magnification occurring in NLDR methods like $t$-SNE: DR mitigates the curse of dimensionality, while cluster magnification can maintain class separability. We demonstrate experimentally the effectiveness of the approach by analyzing and comparing results on the big KDD99 dataset, using both NLDR quality assessment and classification rate for SVMs and random forests. Since data involves features of mixe…

Enhancing identification of causal effects by pruning

2018

Causal models communicate our assumptions about causes and effects in real-world phe- nomena. Often the interest lies in the identification of the effect of an action which means deriving an expression from the observed probability distribution for the interventional distribution resulting from the action. In many cases an identifiability algorithm may return a complicated expression that contains variables that are in fact unnecessary. In practice this can lead to additional computational burden and increased bias or inefficiency of estimates when dealing with measurement error or missing data. We present graphical criteria to detect variables which are redundant in identifying causal effe…

SEMANTIC AND CONTEXTUAL APPROACH FOR THE RECOMMENDATION OF LEARNING MODULES IN MOBILITY

2012

International audience; Many researchers argue that mobile learning is just an adaptation of e-learning on mobile technology, but far from a simple extension of e-learning, m-learning raises original issues in technological and pedagogical terms. M-learning is usually based on the consideration of a context rich on information and interactions. The challenge of m-learning is therefore, not simply to transfer on mobile content designed primarily for e-learning. This concept implies that we must rethink the entire process of the learning experience in mobility to maximize its efficiency.

Recommender system for combination of learning elements in mobile environment

2012

5 pages; International audience; The paper presents an ongoing research about the development of a new recommender system dedicated to m-learning. This system is an extension of content based recommender system proposals. It's made of three levels architecture: 1/ a domain model describing the knowledge of teaching, 2/ a user model defining learner's profile and learning's context, 3/ an adaptation model containing rules and metaheuristics, which aims at combining learning modules. Our system takes into account the spatio-temporal context of the learners, the evolution of learner's profile and the dynamic adaptation of modules during the learning process in a mobile environment. The result …