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.

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Abstract
Image quality assessment as perceived by humans is of crucial importance in numerous fields of image processing. Transmission and storage of digital media require efficient methods to reduce the large number of bits to store an image, while maintaining sufficiently high quality compared to the original image. Since subjective evaluations cannot be performed in various scenarios, it is necessary to have objective metrics that predict image quality consistent with human perception. However, objective metrics that considers high levels of the human visual system are still limited. In this paper, we investigate the possibility of automatically predict, based on saliency maps, the minimum image quality threshold from which humans can perceive the elements on a compressed image. We conducted a series of experimental subjective tests where human observers have been exposed to compressed images with decreasing compression rates. To measure the difference between the saliency maps of the compressed and the original image it was used the normalized absolute error metric. Our results indicate that the elements on the image are only perceived by most of the human subjects not at a specific compressed image quality level, but depending on a saliency map difference threshold.

Abstract
The aim of this work is to describe an exploratory study on the use of a SAX-based Multiresolution Motif Discovery method for Heart Sound Classification. The idea of our work is to discover relevant frequent motifs in the audio signals and use the discovered motifs and their frequency as characterizing attributes. We also describe different configurations of motif discovery for defining attributes and compare the use of a decision tree based algorithm with random forests on this kind of data. Experiments were performed with a dataset obtained from a clinic trial in hospitals using the digital stethoscope DigiScope. This exploratory study suggests that motifs contain valuable information that can be further exploited for Heart Sound Classification. © 2013 ACM.

Abstract