Abstract
People in modern societies have increasingly sedentary lifestyles. They usually do not have time to take part in physical activity on a regular basis. Additionally, due to time constraints, people are consuming more processed and junk foods. This behavior may lead to health issues, such as obesity or cardiovascular disease. On the other hand people are becoming more aware and more interested in doing physical activities, which has resulted in an increase of memberships at gymnasiums. People usually obtain better results in training by having a personal trainer, especially in the beginning, because personal trainers can recommend safer and more effective exercises, as well as provide motivation. Moreover, personal trainers can also play the roles of life coaches or nutritionists. Despite the benefits, having a personal trainer can be difficult. Due to time constraints, it might not be simple to combine both schedules of the personal trainer and the client. In this paper we present a novel health training platform to maximize the personal trainer and client relationship and, therefore, increase the client's well-being. The health training platform allows clients to have sensors connected to their smartphones and send their exercise data to their personal trainer. It also allows personal trainers to observe their clients' evolution and provide feedback. The health training platform has an architecture that allows multiple configurations involving personal trainers, clients and gymnasiums. We have built and tested a prototype of a health training platform.

Abstract
The increasing number of users and data generated is leading the Social Networks to a scenario where the information cannot be verified. Social networks developers have faced problems about unreliable information sharing and availability of content intentionally prepared to confuse or mislead users. This paper presents a solution to add audit capacity in social networks. It is based on a catalog that organizes characteristics and operationalizations which support auditability of information in social networks and a guide that can help developers to build software that allow evaluation of information reliability. © Springer International Publishing Switzerland 2016.

Abstract
In this paper we present a 12-dimensional tonal space in the context of the Tonnetz, Chew's Spiral Array, and Harte's 6-dimensional Tonal Centroid Space. The proposed Tonal Interval Space is calculated as the weighted Discrete Fourier Transform of normalized 12-element chroma vectors, which we represent as six circles covering the set of all possible pitch intervals in the chroma space. By weighting the contribution of each circle (and hence pitch interval) independently, we can create a space in which angular and Euclidean distances among pitches, chords, and regions concur with music theory principles. Furthermore, the Euclidean distance of pitch configurations from the centre of the space acts as an indicator of consonance.

Abstract
A long-period grating (LPG) written on a standard single mode fiber is investigated as a curvature and vibration sensor. It is demonstrated a high sensitivity to applied curvature and the possibility to monitor vibration in a wide range of frequencies from 30 Hz to 2000 Hz. The system was tested using an intensity based interrogation scheme with the LPG sensor operating in the curvature regime. Results have shown a reproducible frequency discrimination in the 30 Hz to 2000 Hz, with resolutions between 11 mHz and 913 mHz. Frequency retrieval could be perfouned independent of temperature up to 86 degrees C.

Abstract
A methodology for estimating the destination of passenger journeys from automated fare collection (AFC) system data is described. It proposes new spatial validation features to increase the accuracy of destination inference results and to verify key assumptions present in previous origin-destination estimation literature. The methodology applies to entry-only system configurations combined with distance-based fare structures, and it aims to enhance raw AFC system data with the destination of individual journeys. This paper describes an algorithm developed to implement the methodology and the results from its application to bus service data from Porto. The data relate to an AFC system integrated with an automatic vehicle location system that records a transaction for each passenger boarding a bus, containing attributes regarding the route, the vehicle, and the travel card used, along with the time and the location where the journey began. Some of these are recorded for the purpose of allowing onboard ticket inspection but additionally enable innovative spatial validation features introduced by the methodology. The results led to the conclusion that the methodology is effective for estimating journey destinations at the disaggregate level and identifies false positives reliably.

Abstract
The collection, storage, management, and anticipation of contextual information about the user to support decision-making constitute some of the key operations in most Ambient Intelligent (AmI) systems. When the instructor has a computer-based class it is often difficult to confirm if the students are working in the proposed activities. In order to mitigate problems that might occur in an environment with learning technologies we suggest an AmI system aimed at capturing, measuring, and supervising the students’ level of attentiveness in real scenarios and dynamically provide recommendations to the instructor. With this system it is possible to assess both individual and group attention, in real-time, providing a measure of the level of engagement of each student in the proposed activities and allowing the instructor to better steer teaching methodologies.