Abstract
TourismShare is a context-aware recommendation platform that allows tourists to share private locations and videos and obtain recommendations regarding potential Points of Interest (POI), including complementary articles and videos. The user experience is enhanced with the addition audio immersion during video playback and automatic recommendation features. The developed system consists of a distributed application comprising a front-end client module (Android application), which provides the user interface and consumes directly external support services, and the back-end server module, which includes the central database and recommendation service. The communication between the client and server modules is implemented by a dedicated application level protocol. The recommendations, which are based on the user context (user position, date and current time, past ratings and user activity level), are provide on request or automatically, whenever POI of great relevance to the user are found. The recommended POI are presented on a map, showing the timetable together with complementary articles and videos. The audio immersion at video playback time takes into account the weather conditions of the video recording and the user activity level.

Abstract

Abstract
In this work we address the development of a fast solver of the ray-tracing equations based on heterogeneous supercomputing using PyOpenCL. We apply this solver to the study of gravitational lensing and light propagation in optical systems.

Abstract
Nowadays, the increase of robotic technology application to industry scenarios is notorious. Proposals for new effective solutions are in continuous development once industry needs a constantly improvement in time as well as in production quality and efficiency. The EuRoC research project proposes a scientific competition in which research and industry manufacturers joint teams are encouraged to develop and test solutions that can solve several issues as well as be useful in manufacturing improvement. This paper presents the TIMAIRIS architecture and approach used in the Challenge 2 - Stage II - Benchmarking phase, namely regarding the perception, manipulation and planning strategy that was applied to achieve the tasks objectives. The used approach proved to be quite robust and efficient, which allowed us to rank first in the Benchmarking phase. © 2017 IEEE.

Abstract

Abstract
Matrix and data manipulation programming languages are an essential tool for data analysts. However, these languages are often unstructured and lack modularity mechanisms. This paper presents a business intelligence approach for studying the manifestations of lack of modularity support in that kind of languages. The study is focused on MATLAB as a well established representative of those languages. We present a technique for the automatic detection and quantification of concerns in MATLAB, as well as their exploration in a code base. Ubiquitous Self Organizing Map (UbiSOM) is used based on direct usage of indicators representing different sets of tokens in the code. UbiSOM is quite effective to detect patterns of co-occurrence between multiple concerns. To illustrate, a repository comprising over 35, 000 MATLAB files is analyzed using the technique and relevant conclusions are drawn.