Abstract
Scalability and topological stability are two of the most challenging issues in current wireless mesh networks (WMNs) deployments. In the literature, both the scalability and the topological stability of WMNs are described as likely to suffer from poor performance due to the ad hoc nature of the underlying IEEE 802.11 mechanisms. The main contribution of this article is a comprehensive review of the main topological stability and scalability-related issues in IEEE 802.11s-based networks. Moreover, the most relevant proposed solutions are surveyed, where both the drawbacks and the merits of each proposal are highlighted. At the end of the article, some open research challenges are presented and discussed. It is expected that this work may serve as motivation for more and deeper research on these issues to allow the design of future more stable and scalable IEEE 802.11s mesh networks deployments. Copyright (c) 2015 John Wiley & Sons, Ltd.

Abstract
Nearest neighbour collaborative filtering (NNCF) algorithms are commonly used in multimedia recommender systems to suggest media items based on the ratings of users with similar preferences. However, the prediction accuracy of NNCF algorithms is affected by the reduced number of items - the subset of items co-rated by both users typically used to determine the similarity between pairs of users. In this paper, we propose a different approach, which substantially enhances the accuracy of the neighbour selection process - a user-based CF (UbCF) with semantic neighbour discovery (SND). Our neighbour discovery methodology, which assesses pairs of users by taking into account all the items rated at least by one of the users instead of just the set of co-rated items, semantically enriches this enlarged set of items using linked data and, finally, applies the Collinearity and Proximity Similarity metric (CPS), which combines the cosine similarity with Chebyschev distance dissimilarity metric. We tested the proposed SND against the Pearson Correlation neighbour discovery algorithm off-line, using the HetRec data set, and the results show a clear improvement in terms of accuracy and execution time for the predicted recommendations.

Abstract
This paper presents a PhD thesis proposal in Informatics Engineering, scheduled for completion in July 2018. This PhD thesis is part of Spatio-Temporal Information Systems, with applicability in technological communication tools and visual representation of knowledge, for Digital Media (newspapers, radio and television). It is intended to maximize the efficiency and effectiveness of the value of heterogeneous, multivariate, multidimensional information characteristic of this context, produced and shared by different sources, in different formats. It is hoped that participation in this Doctoral Symposium will enrich and update the work in progress and help the preparation of the PhD thesis proposal.

Abstract
Here we propose a simplified model for the path planning of an Autonomous Underwater Vehicle (AUV) in an horizontal plane when ocean currents are considered. The model includes kinematic equations and a simple dynamic equation. Our problem of interest is a minimum time problem with state constraints where the control appears linearly. This problem is solved numerically using the direct method. We extract various tests from the Maximum Principle that are then used to validate the numerical solution. In contrast to many other literature we apply the Maximum Principle as defined in [9].

Abstract
We develop a general framework for compressive linear-projection measurements with side information. Side information is an additional signal correlated with the signal of interest. We investigate the impact of side information on classification and signal recovery from low-dimensional measurements. Motivated by real applications, two special cases of the general model are studied. In the first, a joint Gaussian mixture model is manifested on the signal and side information. The second example again employs a Gaussian mixture model for the signal, with side information drawn from a mixture in the exponential family. Theoretical results on recovery and classification accuracy are derived. The presence of side information is shown to yield improved performance, both theoretically and experimentally. © 2016 IEEE.

Abstract