Abstract
Over the last few years consumption of news articles has shifted more and more from the written versions towards the web. Mobile devices, which became more powerful, with larger screens and connected to the Internet, have had a great influence on this paradigm change. A critical problem associated to online news is related to the fact that the large number of daily articles can be overwhelming to the users. Recommendation services can largely improve the efficiency and accuracy of acquired information. These systems are designed to filter critical news, key events and meaningful items that might be of interest to a reader. In this paper, a news recommendation system in a mobility scenario is presented. The implemented recommendation system combines content-based and georeferenced recommendation techniques. Recommendations are supported by short-term and long-term user profiles created implicitly and considering also the mobile device geolocation. The final recommendation list is obtained by combining recommendations provided by the different recommendation approaches. To evaluate the performance of the solution, a user study was conducted. Results indicate that the quality of the recommendations is acknowledged by the test users. The system was integrated in a mobile application of a Portuguese newspaper (Público) in the context of the project Pglobal. © 2016 ACM.

Abstract
Multimedia content has the potential for significant impact on users’ emotions, their sense of presence and engagement experiencing the service, application or information being provided, in immersive environments. The evolution of technology, user expectations and results from research activities have led to an enormous increase in the amount of content delivered in different formats, via a number of heterogeneous communication networks, to a range of devices, many of them portable and offering tremendous opportunities for immersion, user participation and personalization. New paradigms for media production, distribution and consumption have been emerging, introducing different sensory modalities and audio-visual surround effects, for an increased sense of presence, and also enabling participation and social interaction in the media chain, thus increasing the sense of belonging and contributing to the success of the services being provided

Abstract
The design, manufacture and operational characteristics (e.g., yield, performance, and reliability) of modern electronic integrated systems exhibit extreme levels of complexity that cannot be easily modelled or predicted. Different mathematical methodologies have been explored to address this issue. Monte Carlo simulation is the most widely employed and straightforward approach to evaluate the circuits' performance statistics. However, the high number of trial cases and the long simulations times required to obtain results for complex circuits with a ppm resolution, lead to very long analysis times. The present work addresses the evaluation of alternative statistical inference methodologies which allow obtaining similar results departing from a smaller dimension data set of Monte Carlo simulations from which the overall population is estimated. These methodologies include the use of Bayesian inference, Expectation-inimization, and Kolmogorov-Smirnov tests. Results are presented which show the validity of these approaches.

Abstract
A new methodology for fault detection on wearable medical devices is proposed. The basic strategy relies on correctly classifying the captured physiological signals, in order to identify whether the actual cause is a wearer health abnormality or a system functional flaw. Data fusion techniques, namely fuzzy logic, are employed to process the physiological signals, like the electrocardiogram (ECG) and blood pressure (BP), to increase the trust levels of the captured data after rejecting or correcting distorted vital signals from each sensor, and to provide additional information on the patient's condition by classifying the set of signals into normal or abnormal condition (e.g. arrhythmia, chest angina, and stroke). Once an abnormal situation is detected in one or several sensors the monitoring system runs a set of tests in a fast and energy efficient way to check if the wearer shows a degradation of his health condition or the system is reporting erroneous values.

Abstract
This article addresses the design, development, and evaluation of T-shirt prototypes that embed novel textile sensors for the capture of cardio and respiratory signals. The sensors are connected through textile interconnects to either an embedded custom-designed data acquisition and transmission unit or to snap fastener terminals for connection to external monitoring devices. The performance of the T-shirt prototype is evaluated in terms of signal-to-noise ratio amplitude and signal interference caused by baseline wander and motion artefacts, through laboratory tests with subjects in standing and walking conditions. Performance tests were also conducted in a hospital environment using a T-shirt prototype connected to a commercial three-channel Holter monitoring device. The textile sensors and interconnects were realized with the assistance of an industrial six-needle digital embroidery tool and their resistance to wear addressed with normalized tests of laundering and abrasion. The performance of these wearable systems is discussed, and pathways and methods for their optimization are highlighted.

Abstract
High levels of dependability are required to promote the adherence by public and medical communities to wearable medical devices. The study presented herein addresses fault detection and diagnosis in these systems. The main objective resides on correctly classifying the captured physiological signals, in order to distinguish whether the actual cause of a detected anomaly is a wearer health condition or a system functional flaw. Data fusion techniques, namely fuzzy logic, artificial neural networks, decision trees and naive Bayes classifiers are employed to process the captured data to increase the trust levels with which diagnostics are made. Concerning the wearer condition, additional information is provided after classifying the set of signals into normal or abnormal (e.g., arrhythmia, tachycardia and bradycardia). As for the monitoring system, once an abnormal situation is detected in its operation or in the sensors, a set of tests is run to check if actually the wearer shows a degradation of his health condition or if the system is reporting erroneous values. Selected features from the vital signals and from quantities that characterize the system performance serve as inputs to the data fusion algorithms for Patient and System Status diagnosis purposes. The algorithms performance was evaluated based on their sensitivity, specificity and accuracy. Based on these criteria the naive Bayes classifier presented the best performance.