Abstract
In this work we aim at increasing the utility of a preelection poll, by improving the quality of the vote share estimates, both at macro and micro level. Three different methodologies are applied with that purpose: (1) polls aggregation, using existing auxiliary polling; (2) application of multilevel regression methods, using the multilevel structure of the data; and (3) methods of small area estimation, making use of auxiliary information through the application of the Empirical Best Linear Unbiased Prediction (EBLUP). These methods are applied to real data collected from a survey with the aim of estimating the vote share in the Portuguese legislative elections. When auxiliary information is required, we concluded that polls aggregations and EBLUP have to be applied with caution, since this information is extremely important for a good application of these models to the data set and to obtain good reliable forecasts. On the other hand, if auxiliary information is not available or if it is not of good quality, then multilevel regression can and should be seen as a safe alternative to obtain more precise estimates, either at the micro or macro level. Besides, this is the method which further improves the precision of the estimates. In the presence of good auxiliary information, EBLUP proved to be the method with greater proximity with real values. © Springer-Verlag Berlin Heidelberg 2013.

Abstract
This paper presents an approach to infer UI patterns existent in a web application. This reverse engineering process is performed in two steps. First, execution traces are collected from user interactions using the Selenium software. Second, the existing UI patterns within those traces are identified using Machine Learning inference with the Aleph ILP system. The paper describes and illustrates the proposed methodology on a case study over the Amazon web site.

Abstract
We present r..eal , a library that integrates the R statistical environment with Prolog. Due to R's functional programming affinity the interface introduced has a minimalistic feel. Programs utilising the library syntax are elegant and succinct with intuitive semantics and clear integration. In effect, the library enhances logic programming with the ability to tap into the vast wealth of statistical and probabilistic reasoning available in R. The software is a useful addition to the efforts towards the integration of statistical reasoning and knowledge representation within an AI context. Furthermore it can be used to open up new application areas for logic programming and AI techniques such as bioinformatics, computational biology, text mining, psychology and neuro sciences, where R has particularly strong presence. © 2013 Springer-Verlag.

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
The recognition of human activities through sensors embedded in smart-phone devices, such as iPhone, is attracting researchers due to its relevance. The advances of this kind of technology are making possible the widespread and pervasiveness of sensing technology to take advantage of multiple sources of sensing to enrich users experience or to achieve proactive, context-aware applications and services. Human activity recognition and monitoring involves a continuing analysis of large amounts of data so, any increase or decrease in accuracy results in a wide variation in the number of activities correctly classied and incorrectly classied, so it is very important to increase the rate of correct classication. We have researched on a vector with 159 different features and on the vector subsets in order to improve the human activities recognition. We extracted features from the Magnitude of the Signal, the raw signal data, the vertical acceleration, the Horizontal acceleration, and the ltered Raw data. In the evaluation process we used the classiers: Naive Bayes, K-Nearest Neighbor and Random Forest. The features were extracted using the java programming language and the evaluation was done with WEKA. The maximum accuracy was obtained, as expected, with Random Forest using all the 159 features. The best subset found has twelve features: the Pearson correlation between vertical acceleration and horizontal acceleration, the Pearson correlation between x and y, the Pearson correlation between x and z, the STD of acceleration z, the STD of digital compass y, the STD of digital compass z, the STD of digital compass x, the mean between axis, the energy of digital compass x, the mean of acceleration x, the mean of acceleration z, the median of acceleration z.

Abstract
In this work we address the Single-Source Uncapacitated Minimum Cost Network Flow Problem with concave cost functions. This problem is NP-Hard, therefore we propose a hybrid heuristic to solve it. Our goal is not only to apply an ant colony optimization (ACO) algorithm to such a problem, but also to provide an insight on the behaviour of the parameters in the performance of the algorithm. The performance of the ACO algorithm is improved with the hybridization of a local search (LS) procedure. The core ACO procedure is used to mainly deal with the exploration of the search space, while the LS is incorporated to further cope with the exploitation of the best solutions found. The method we have developed has proven to be very efficient while solving both small and large size problem instances. The problems we have used to test the algorithm were previously solved by other authors using other population based heuristics. Our algorithm was able to improve upon some of their results in terms of solution quality, proving that the HACO algorithm is a very good alternative approach to solve these problems. In addition, our algorithm is substantially faster at achieving these improved solutions. Furthermore, the magnitude of the reduction of the computational requirements grows with problem size.