Abstract

Abstract

Abstract
Intelligence is increasingly emerging in our ambients. Evidences of this emergence are the existence of smart homes, smart vehicles, intelligent manufacturing systems and most importantly, the appearance of the concept of intelligent cities. Humans are presently surrounded by technology that is intended to increase their quality of life and simplify their daily activities. Multi-Agent Systems are an example of technology that can be used in these activities. The concept of ubiquitous computing is implicit in these technologies and can generate an invisible ambient of interactivity. This paper presents a survey and a comparative analysis of some of the research projects concerning Ambient Intelligence (AmI). The main objective of this work was to understand the current necessities, devices and the main results in the development of these projects. By analysing these projects using several evaluation criteria one of the main conclusions is that most projects do not explore the potential of human profiles in the context of ambient adaptation. Thus, this may be a very intersting research area for future work.

Abstract
In this paper we propose an audio beat tracking system, IBT, for multiple applications. The proposed system integrates an automatic monitoring and state recovery mechanism, that applies (re-)inductions of tempo and beats, on a multi-agent-based beat tracking architecture. This system sequentially processes a continuous onset detection function while propagating parallel hypotheses of tempo and beats. Beats can be predicted in a causal or in a non-causal usage mode, which makes the system suitable for diverse applications. We evaluate the performance of the system in both modes on two application scenarios: standard (using a relatively large database of audio clips) and streaming (using long audio streams made up of concatenated clips). We show experimental evidence of the usefulness of the automatic monitoring and state recovery mechanism in the streaming scenario (i.e., improvements in beat tracking accuracy and reaction time). We also show that the system performs efficiently and at a level comparable to state-of-the-art algorithms in the standard scenario. IBT is multi-platform, open-source and freely available, and it includes plugins for different popular audio analysis, synthesis and visualization platforms.

Abstract
Soccer is a team sport in which the performances of all team members are important for the outcome of a match. Even though the analysis of game events can be used to measure the team's performance, their perception, especially during the match, is extremely difficult, even for the involved agents. Soccer has been used as a simulation environment in many studies, mainly in the area of robotics. The RoboCup is an international robotics competition with an ambitious goal: in 2050 a robotics team will be capable of defeating the human world champion at the time. In this context, we compared technical similarities between human and robotics soccer. Based on an off-line automatic event detection tool, game statistics for the finals of both human and robotics soccer tournaments were collected and compared using the Wilcoxon test. The results show that the most frequent event in both forms of soccer is successful passes. Analysing the two types of passes considered (successful and missed), we conclude that there are significant differences between the two forms (W = 2, P = 0.000354), with human soccer presenting a higher percentage of successful passes (77.89% vs. 66.97%). Of restart events (W = 0, P = 0.00048965), the most frequent one, in both forms, is the throw-in (human 59.91%, robotics 66.4%), and the least frequent is the corner (human 13.7%, robotics 14.09%). Regarding the frequency of shots, in the robotics environment "shots" were the most predominant type (43.27%), whereas in human soccer "shots on target" predominated (71.25%; W = 64, P = 0.000085641). Finally, the number of faults is minor in robotics soccer.

Abstract
To get the most out of powerful tools, expert knowledge is often required. Experts are the ones with the suitable knowledge to tune the tools' parameters. In this paper we assess several techniques which can automatically fine-tune ANN parameters. Those techniques include the use of GA and stratified sampling. The fine-tuning includes the choice of the best ANN structure and the best network biases and their weights. Empirical results achieved in experiments performed using nine heterogeneous data sets show that the use of the proposed Stratified Sampling technique is advantageous. © 2009 Springer Science+Business Media, LLC.