Ha shoah

Sermon preached at Waterloo Lutheran Seminary, April 9, 1997

Information filtering via preferential diffusion

Recommender systems have shown great potential to address information overload problem, namely to help users in finding interesting and relevant objects within a huge information space. Some physical dynamics, including heat conduction process and mass or energy diffusion on networks, have recently found applications in personalized recommendation. Most of the previous studies focus overwhelmingly on recommendation accuracy as the only important factor, while overlook the significance of diversity and novelty which indeed provide the vitality of the system. In this paper, we propose a recommendation algorithm based on the preferential diffusion process on user-object bipartite network. Numerical analyses on two benchmark datasets, MovieLens and Netflix, indicate that our method outperforms the state-of-the-art methods. Specifically, it can not only provide more accurate recommendations, but also generate more diverse and novel recommendations by accurately recommending unpopular objects.Comment: 12 pages, 10 figures, 2 table

L'accompagnement du petit enfant dans le processus d'apprentissage des limites et des règles

L'apprentissage des limites et des règles est un processus essentiel au développement de l’enfant, car il va l’amener progressivement à s'autonomiser et à se socialiser. Ce travail a donc pour but d'identifier des outils pédagogiques qui vont favoriser l’accompagnement du petit enfant, de un à trois ans, sur le chemin du « vivre ensemble ». Ces outils vont être pensés à partir de différents axes - la connaissance du développement de l'enfant, la valeur de la relation et l’environnement - afin qu’ils s'ajustent au mieux à ses besoins et au milieu dans lequel il grandit. Au terme de cette recherche, je peux affirmer qu’un encadrement stable, cohérent et constant ainsi qu’un regard bienveillant et un langage empathique, vont générer un sentiment de sécurité auprès du petit enfant. Il va pouvoir s’engager vers le monde extérieur avec confiance et se construire en affirmant sa propre personne

Location privacy-preserving task allocation for mobile crowdsensing with differential geo-obfuscation

In traditional mobile crowdsensing applications, organizers need participants' precise locations for optimal task allocation, e.g., minimizing selected workers' travel distance to task locations. However, the exposure of their locations raises privacy concerns. Especially for those who are not eventually selected for any task, their location privacy is sacrificed in vain. Hence, in this paper, we propose a location privacy-preserving task allocation framework with geo-obfuscation to protect users' locations during task assignments. Specifically, we make participants obfuscate their reported locations under the guarantee of differential privacy, which can provide privacy protection regardless of adversaries' prior knowledge and without the involvement of any third- part entity. In order to achieve optimal task allocation with such differential geo- obfuscation, we formulate a mixed-integer non-linear programming problem to minimize the expected travel distance of the selected workers under the constraint of differential privacy. Evaluation results on both simulation and real-world user mobility traces show the effectiveness of our proposed framework. Particularly, our framework outperforms Laplace obfuscation, a state-of-the-art differential geo-obfuscation mechanism, by achieving 45% less average travel distance on the real-world data