Abstract
Social media is rapidly becoming the main source of news consumption for users, raising significant challenges to news aggregation and recommendation tasks. One of these challenges concerns the recommendation of very recent news. To tackle this problem, approaches to the prediction of news popularity have been proposed. In this paper, we study the task of predicting news popularity upon their publication, when social feedback is unavailable or scarce, and to use such predictions to produce news rankings. Unlike previous work, we focus on accurately predicting highly popular news. Such cases are rare, causing known issues for standard prediction models and evaluation metrics. To overcome such issues we propose the use of resampling strategies to bias learners towards these rare cases of highly popular news, and a utility-based framework for evaluating their performance. An experimental evaluation is performed using real-world data to test our proposal in distinct scenarios. Results show that our proposed approaches improve the ability of predicting and recommending highly popular news upon publication, in comparison to previous work.

Abstract
Electrocardiogram signals acquired through a steering wheel could be the key to seamless, highly comfortable, and continuous human recognition in driving settings. This paper focuses on the enhancement of the unprecedented lesser quality of such signals, through the combination of Savitzky-Golay and moving average filters, followed by outlier detection and removal based on normalised cross-correlation and clustering, which was able to render ensemble heartbeats of significantly higher quality. Discrete Cosine Transform (DCT) and Haar transform features were extracted and fed to decision methods based on Support Vector Machines (SVM), k-Nearest Neighbours (kNN), Multilayer Perceptrons (MLP), and Gaussian Mixture Models - Universal Background Models (GMM-UBM) classifiers, for both identification and authentication tasks. Additional techniques of user-tuned authentication and past score weighting were also studied. The method's performance was comparable to some of the best recent state-of-the-art methods (94.9% identification rate (IDR) and 2.66% authentication equal error rate (EER)), despite lesser results with scarce train data (70.9% IDR and 11.8% EER). It was concluded that the method was suitable for biometric recognition with driving electrocardiogram signals, and could, with future developments, be used on a continuous system in seamless and highly noisy settings.

Abstract
Informal learning can have an important role in today’s Education but, to be effective, it should be contextualized individually for each learner, and situated to enhance experience. Museums have invaluable collections of assets that are in display and curators use their knowledge to engage the audience. Museums are places where informal learning can be fostered to engage the students and provide opportunities for situated learning. Pervasive systems, that take into account context, from both the learner and the location, have a good potential to promote this effectiveness in a gamified process that transforms the regular museum exploration into an engaging experience that provides learning opportunities at the appropriate time and place. In this paper, we propose a gamified approach based on the concept of sticker album collection and its integration in an Augmented Reality (AR) mobile application. The concept of sticker album collection is quite familiar to most people, mainly from their youth, and is the main dynamic of the gamification design, engaging the learner to collect more stickers and progress in the exploration of the museum. As a pervasive solution, we do not use physical support, but instead, a mobile application to provide the learning experiences by uncovering the stickers using AR over the museum collection, in order to enhance the knowledge transfer and rewarding. We present a prototype developed for a boat museum where, digital stickers are obtained by overcoming challenges in the context of the exploration of the boats in the museum. Furthermore, we provide two evaluations fromexperts: a preliminary evaluation of user experienceand a gamification evaluation using the Octalsysframework. © Springer International Publishing AG 2017.

Abstract
The operation of Wireless Sensor Networks (WSNs) is subject to multiple constraints, among which one of the most critical is available energy. Sensor nodes are typically powered by electrochemical batteries. The stored energy in battery devices is easily influenced by the operating temperature and the discharge current values. Therefore, it becomes difficult to estimate their voltage/charge behavior over time, which are relevant variables for the implementation of energy-aware policies. Nowadays, there are hardware and/or software approaches that can provide information about the battery operating conditions. However, this type of hardware-based approach increases the battery production cost, which may impair its use for sensor node implementations. The objective of this work is to propose a software-based approach to estimate both the state of charge and the voltage of batteries in WSN nodes based on the use of a temperature-dependent analytical battery model. The achieved results demonstrate the feasibility of using embedded analytical battery models to estimate the lifetime of batteries, without affecting the tasks performed by the WSN nodes.

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