Abstract
Over the last decades, Serious Games have gained increased importance, mainly due to the evolution and expansion of video games and its application in multiple areas. Integration in the world of sport is one of the solutions that individuals with disabilities or motor disorders develop to feel more socially integrated, more independent and confident with themselves. Boccia is a Paraly-mpic sport that is increasingly getting more attention around the world. This has contributed for the objectives of this project since it attracts these patients a lot more easily and including it in the Serious Games category enables them to develop and rehabilitate their cognitive capabilities. It will allow the users being dynamic, holding their attention and motivating them instead of the traditional cognitive rehabilitation processes that quickly become repetitive and discouraging. This paper describes a realistic Boccia game simulator adapted for people with disabilities or motor disorders still on development that aims to integrate a set of features that include real physics, multimodal user interface and social features (diversion, rehabilitation, competition and improvement). These features can be used to enhance the interest of non-practitioners of the sport and to improve the training conditions of Boccia athletes. Results observed in an experiment with real Boccia game components indicate that the simulator offers great similarity to the reality with the maximum difference between the measures obtained in both being 10 cm.

Abstract
How to improve the performance of a simulated soccer team using final game statistics? This is the question this research aims to answer using model-based collaborative techniques and a robotic team - FC Portugal - as a case study. After developing a framework capable of automatically calculating the final game statistics through the RoboCup log files, a feature selection algorithm was used to select the variables that most influence the final game result. In the next stage, given the statistics of the current game, we rank the strategies that obtained the maximum average of goal difference in similar past games. This is done by splitting offline past games into different k-clusters. Then, for each cluster, the expected best strategy was assigned. The online phase consists in the selection of the expected best strategy for the cluster in which the current game best fits. Regarding the final results, our approach proved that it is possible to improve the performance of a robotic team by more than 35%, even in a competitive environment such as the RoboCup 2D simulation league.

Abstract
In soccer, like in other collective sports, although players try to hide their strategy, it is always possible, with a careful analysis, to detect it and to construct a model that characterizes their behavior throughout the game phases. These findings are extremely relevant for a soccer coach, in order not only to evaluate the performance of his athletes, but also for the construction of the opponent team model for the next match. During a soccer match, due to the presence of a complex set of intercorrelated variables, the detection of a small set of factors that directly influence the final result becomes almost an impossible task for a human being. In consequence of that, a huge number of software packages for analysis capable of calculating a vast set of game statistics appeared over the years. However, all of them need a soccer expert in order to interpret the produced data and select which are the most relevant variables. Having as a base a set of statistics extracted from the RoboCup 2D Simulation League log files and using a multivariable analysis, the aim of this research project is to identify which are the variables that most influence the final game result and create prediction models capable of automatically detecting soccer team behaviors. For those purposes, more than two hundred games (from 2006-2009 competition years) were analyzed according to a set of variables defined by a soccer experts board, and using the MARS and RReliefF algorithms. The obtained results show that the MARS algorithm presents a lower error value, when compared to RReliefF (from a pairwire t-test for a significance level of 5%). The p-value for this test was 2.2e-16 which means these two techniques present a significant statistical difference for this data. In the future, this work will be used in an offline analysis module, with the goal of detecting which is the team strategy that will maximize the final game result against a specific opponent.

Abstract
Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign languages are not standard and universal and the grammars differ from country to country. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of features and an accuracy of 99.6% with a second dataset of features. Although the implemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.

Abstract
Online Peer-To-Peer lending has seen some growing media attention since its recent creation. Nonetheless, the systems which provide deal brokerage in this context have yet to be given significant consideration within the scientific community. This paper is part of a broader effort to setup a Peer-to-Peer lending community in Portugal. This work focuses on solving the infrastructural problem of combining investment offers from potential lenders with loan requests from potential borrowers. The combination process must strive for an optimal result, which pleases lenders and borrowers alike, despite their opposing agendas. Simultaneously the combination result should also benefit the platform's business model, so as to keep it sustainable and profitable. Several optimization metaheuristics, powered by a constraint programming module, were applied to efficiently explore the problem's solution space and to find optimal solutions. The results achieved with this approach show how metaheuristic-driven optimization can be successfully applied to Peer-to-Peer lending combination problems. © 2013, IGI Global.

Abstract
This paper describes the development of a graphical interface for a new soccer scouting software that enables to integrate data and information from distinct sources, including other software and analog data. The advantages of a web platform and of the aggregation system implemented when compared with the traditional approach are also presented. The solution proposed is based on web programming languages such as HTML5, CSS3, Ruby On Rails, BootStrapp, enabling to achieve the quality patterns expected by the users. The system developed was based on a detailed related work analysis, analysis of commercial and scientific software available and a survey and joint design of the interface together with soccer professionals.