Abstract
The ever-increasing demand for higher quality live streams is driving the need for better networking infrastructures, specially when disseminating content over highly congested areas, such as stadiums, concerts and museums. Traditional approaches to handle this type of scenario relies on a combination of cellular data, through 4G distributed antenna arrays (DAS), with a high count of WiFi (802.11) access points. This obvious requires a substantial upfront cost for equipment, planning and deployment. Recently, new efforts have been introduced to securely leverage the capabilities of wireless multipath, including WiFi multicast, 4G, and device-to-device communications. In order to solve these issues, we propose an approach that lessens the requirements imposed on the wireless infrastructures while potentially expanding wireless coverage through the crowd-sourcing of mobile devices. In order to achieve this, we propose a novel pervasive approach that combines secure distributed systems, WiFi multicast, erasure coding, source coding and opportunistic offloading that makes use of hyperlocal mobile edge clouds. We empirically show that our solution is able to offer a 11 fold reduction on the infrastructural WiFi bandwidth usage without having to modify any existing software or firmware stacks while ensuring stream integrity, authorization and authentication.

Abstract
High dynamic range (HDR) displays are capable of displaying a wider dynamic range of values than conventional displays. As HDR content becomes more ubiquitous, the use of these displays is likely to accelerate. As HDR displays can present a wider range of values, traditional strategies for mapping HDR content to low dynamic range (LDR) displays can be replaced with either directly displaying values, or using a simple shift mapping (exposure adjustment). The latter approach is especially important when considering ambient lighting, as content viewed in a dark environment may appear substantially different to a bright one. This paper seeks to identify an exposure value which is suitable for displaying specific HDR content on an HDR display under a range of ambient lighting levels. Based on data captured with human participants, this paper establishes user preferred exposure values for a variety of maximum display brightnesses, content and ambient lighting levels. These are then used to develop two models to predict preferred exposure. The first is based on linear regression using straightforward image statistics which require minimal computation and memory to be computed, making this method suitable to be directly used in display hardware. The second is a model based on convolutional neural networks (CNN) to learn image features which best predict exposure values. The CNN model generates better results than the first model at the cost of memory and computation time.

Abstract
For the context of a mobile tracking system, an underwater acoustic positioning system was developed, using three hydrophones to compute the direction of an acoustic source relative to an Autonomous Surface Vehicle (ASV). The paper presents an algorithm for the Direction of Arrival (DoA) of an acoustic source, which allows to estimate its position. Preliminary results will be shown in this paper relative to the detection and identification (ID) of the acoustic sources, as well as an analysis of the proposed algorithm. The solution allows the position estimation of an acoustic source, which can be used in tracking solutions. The system can be applied in an ASV or fixed buoys, as long as the baseline's hydrophones are at equal angular distances. The main objective is to track targets with the DoA algorithm as well to estimate their position, improving what was done in [1].

Abstract
Digital media expanded people's creative horizons by placing knowledge, tools, design procedures and its practices within reach, yet it also called for new literacies. Games can encourage reflection and interaction in alternative ways, and ease learning and the articulation of knowledge between individuals, thus they may be valuable for such requirements. In spite of this, games are still struggling to find their way into classrooms and workplaces as tools for creativity, as educators are not prepared to design them for such purposes, which limits their use in scope and in substance. With this in mind, we developed Bully Who?, an analogue game prototype for players to learn to deal with bullying in a creative way, by acting as a stage for embodying the roles of aggressors and victims, making players aware of their emotions and consequences involved. To check for viability, usability and potential, we resorted to play-testing sessions involving a small sample of twenty-five, socially-diverse individuals. This study helped us draw several conclusions: 1) simulating embodied, dramatized actions increases awareness of one's emotions and helps speculating on the state of others' - something valuable to cultivate emotional intelligence; 2) theatrical gameplay can help establish an attitude favourable for game-based learning; 3) games can act as ground to bridge intergenerational exchange for problem-solving; 4) according to participants the prototype has the potential to be adapted to stimulate creative discussion on similar social concerns.

Abstract
When dealing with electrocardiography (ECG) the main focus relies on the classification of the heart's electric activity and deep learning has been proving its value over the years classifying the heartbeats, exhibiting great performance when doing so. Following these assumptions, we propose a deep learning model based on a ResNet architecture with convolutional ID layers to classes the beats into one of the 4 classes: normal, atrial premature contraction, premature ventricular contraction and others. Experimental results with MIT-BIH Arrhythmia Database confirmed that the model is able to perform well, obtaining an accuracy of 96% when using stochastic gradient descent (SGD) and 83% when using adaptive moment estimation (Adam), SGD also obtained F1-scores over 90% for the four classes proposed. A larger dataset was created and tested as unforeseen data for the trained model, proving that new tests should be done to improve the accuracy of it.

Abstract
This study has the objective of select the best service at image processing and recognition, running in the cloud, and best suited for usage in systems to aid and improve the daily lives of blind people. To accomplish this purpose, a set of candidate services was built, including Microsoft Cognitive Services and Google Cloud Vision. A test mobile app was developed to automatically take pictures, which are sent to the online cloud services for processing. The results and the functionalities were evaluated with the aim to measure their accuracy and relevance. The following variables were registered: relative accuracy, represented by the ratio of the number of accurate results vs. the number of results shown; confidence degree, representing the service accuracy (when provided by the service); and relevance, identifying situations that can be useful in the daily lives of the blind people. The results have shown that these two services, Microsoft Cognitive Services and Google Cloud Vision, provided good accuracy and significance, in supporting systems to help blind people in their daily tasks. It was chosen some functionalities in two APIs of services running in the cloud like face identification, image description, objects, and text recognition. © BEIESP.