Manuel Ramos-Cabrer

TL;DR: A novel technology-enhanced pedagogical approach aimed at engaging groups of people into immersive experiences to improve their learning about historical battles and wars from the points of view of reenactors and historians, named REENACT.

TL;DR: In this paper, a recommender system of personalized TV contents, named AVATAR, is presented, whose main novelty is the semantic reasoning about user preferences and historical logs, to improve the traditional syntactic content search.

TL;DR: A system that automatically infers the users' preferences from their TV viewing histories, i.e., the tourism resources the users might appreciate are selected by considering the TV contents they enjoyed in the past are proposed.

TL;DR: Several enhancements to the reference implementations of the VNLayer and the adaptation of AODV to work with VNs are presented, proving by means of mathematical analysis and simulation experiments that the solutions achieve better performance in terms of overhead, packet delivery fraction, and latencies in VANET scenarios.

TL;DR: This paper presents a recommender system of personalized TV contents, called AVATAR, for which a modular multiagent architecture is proposed, that combines different knowledge inference strategies (such as Bayesian techniques, profiles matching and semantic reasoning), and focuses on the description of one of these strategies, the naive Bayesian classifiers.