Abstract
Text Categorization (TC) has attracted the attention of the research community in the last decade. Algorithms like Support Vector Machines, Naïve Bayes or k Nearest Neighbors have been used with good performance, confirmed by several comparative studies. Recently, several ensemble classifiers were also introduced in TC. However, many of those can only provide a category for a given new sample. Instead, in this paper, we propose a methodology - MECAC - to build an ensemble of classifiers that has two advantages to other ensemble methods: 1) it can be run using parallel computing, saving processing time and 2) it can extract important statistics from the obtained clusters. It uses the mean co-association matrix to solve binary TC problems. Our experiments revealed that our framework performed, on average, 2.04% better than the best individual classifier on the tested datasets. These results were statistically validated for a significance level of 0.05 using the Friedman Test. © 2012 Springer-Verlag.

Abstract
Location-based games have become more popular thanks to the growth of mobile device's technology. This paper presents a framework for the development of location-based augmented reality games and wARms, an augmented-reality location-based mobile game prototype based on said framework that uses the player's real position and orientation in order to play against others. The game shows how modern mobile device's sensors can be used for providing new and unusual gaming experiences. (C) 2012 The Authors. Published by Elsevier B. V. Selection and/or peer-review under responsibility of the scientific programme committee of VS-Games 2012

Abstract
This paper considers the information theoretic secrecy rates that are achievable by an orthogonal frequency-division multiplexing (OFDM) transmitter/receiver pair in the presence of an eavesdropper that might either use an OFDM structure or choose a more complex receiver architecture. The analysis is made possible by modeling the system as a particular instance of a high dimensional multiple-input multiple-output wiretap channel. The secrecy capacity is formulated as a maximization problem under a trace constraint, and simple expressions are given for its high signal-to-noise (SNR) limit. The low rate limit of the secrecy outage probability is also evaluated under a fading channel model. As for the finite SNR case, the secrecy rates that can be achieved with particular inputs are considered. Numerical results are provided under a Rayleigh fading channel model and under dependence of the main and eavesdropper channels. The secrecy loss due to the OFDM structure constraints, and the information gain for an eavesdropper that uses amore complex receiver, are also considered.

Abstract
This article presents an image processing application for hand detection and tracking using the 4-connected skeleton of the segmentation mask. The system has been designed to be used with techniques of virtual reality to develop an interactive application for phantom limb pain reduction in therapeutic treatments. One of the major contributions is the design of a fast and accurate skeleton extractor, that has proven to be faster than those available in the literature. The skeleton allows the system to precisely detect the position of all the interest points of the hand (namely the fingers and the hand center). The system, composed of both the hand detector and tracker, and the virtual reality application, can work in real-time, allowing the patient to watch the virtual image of his own hand on a screen.

Abstract
In recent years, both companies and researchers have been exploring intelligent data analysis to increase the profitability of the taxi industry. Intelligent systems for online taxi dispatching and time saving route finding have been built to do so. In this paper, we propose a novel methodology to produce online predictions regarding the spatial distribution of passenger demand throughout taxi stand networks. We have done so by assembling two well-known time series short-term forecast models: the time-varying Poisson models and ARIMA models. Our tests were performed using data gathered over a period of 6 months and collected from 63 taxi stands within the city of Porto, Portugal. Our results demonstrate that this model is a true major contribution to the driver mobility intelligence: 78% of the 253745 demanded taxi services were correctly forecasted in a 30 minutes horizon. © Springer-Verlag Berlin Heidelberg 2012.

Abstract
From small farms to electricity markets the interest and importance of wind power production is continuously increasing. This interest is mainly caused by the fact that wind is a continuous resource of clean energy. To take full advantage of the potential of wind power production it is crucial to have tools that accurately forecast the expected wind speed. However, forecasting the wind speed is not a trivial task. Wind speed is characterised by a random behaviour as well as several other intermittent characteristics. This paper proposes a new approach to the task of wind speed forecasting. The main distinguishing feature of this proposal is its reliance on both temporal and spatial characteristics to produce a forecast of the future wind speed. We have experimentally tested the proposed method with historical data concerning wind speed on the eastern region of the US. Nevertheless, the methodology that is described in the paper can be seen as a general approach to spatio-temporal prediction. We have compared our proposal to other standard approaches in the task of forecasting 2 hours ahead wind speed. Our extensive experiments show that our proposal has clear advantages in most setups.