Abstract
Given the large amount of data mining algorithms, their combinations (e.g. ensembles) and possible parameter settings, finding the most adequate method to analyze a new dataset becomes an ever more challenging task. This is because in many cases testing all possibly useful alternatives quickly becomes prohibitively expensive. In this paper we propose a novel technique, called active testing, that intelligently selects the most useful cross-validation tests. It proceeds in a tournament-style fashion, in each round selecting and testing the algorithm that is most likely to outperform the best algorithm of the previous round on the new dataset. This 'most promising' competitor is chosen based on a history of prior duels between both algorithms on similar datasets. Each new cross-validation test will contribute information to a better estimate of dataset similarity, and thus better predict which algorithms are most promising on the new dataset. We also follow a different path to estimate dataset similarity based on data characteristics. We have evaluated this approach using a set of 292 algorithm-parameter combinations on 76 UCI datasets for classification. The results show that active testing will quickly yield an algorithm whose performance is very close to the optimum, after relatively few tests. It also provides a better solution than previously proposed methods. The variants of our method that rely on crossvalidation tests to estimate dataset similarity provides better solutions than those that rely on data characteristics.

Abstract
Even though we now witness a popular use of location-based mobile games, the player experience in these applications is always limited by the errors of common location technologies, especially in indoor scenarios. This paper describes the way we minimize this problem in our game development platform, by levering the potential behind smartphone sensors to estimate players' trajectories. Our approach is based on a Pedestrian Dead Reckoning (PDR) algorithm that combines methods to determine orientation, detect steps and estimate their length. Other typical multiplayer mobile games problems, like network latency, are also briefly addressed. © 2012 Springer-Verlag Berlin Heidelberg.

Abstract
CMMI practices can be poorly implemented leading to weak performance gain. SCAMPI verifies model compliance but not performance. Hence, a framework to evaluate the quality of implementation of each practice, based on compliance and performance results, will prevent poor implementation, locate and fix problems, and ultimately achieve better results. In this paper we propose such a framework, based on a combination of leading and lagging indicators measuring compliance, efficiency and efficacy. © 2012 Springer-Verlag.

Abstract
In a deregulated profit-based environment, consumers and producers need short-term intelligent prediction tools to predict their bid strategies in the energy market. Also, all markets players need accurate forecasting tools with lower uncertainty, allowing to maximizing their profits. Hence, this manuscript presents a new intelligent approach based on a combination of Wavelet Transform (WT), Evolutionary Particle Swarm Optimization (EPSO) and Adaptive Network and Fuzzy Inference System (ANFIS) for forecasting in power engineering systems. The results of two real-world case studies are shown, regarding energy prices and wind power, which show the proficiency of the proposed intelligent approach. © 2012 IEEE.

Abstract
The increasing number of players that operate in power systems leads to a more complex management. In this paper a new multi-agent platform is proposed, which simulates the real operation of power system players. MASGriP - A Multi-Agent Smart Grid Simulation Platform is presented. Several consumer and producer agents are implemented and simulated, considering real characteristics and different goals and actuation strategies. Aggregator entities, such as Virtual Power Players and Curtailment Service Providers are also included. The integration of MASGriP agents in MASCEM (Multi-Agent System for Competitive Electricity Markets) simulator allows the simulation of technical and economical activities of several players. An energy resources management architecture used in microgrids is also explained.

Abstract
Hostile environments, shrinking feature sizes and processor aging elicit a need for resilient computing. Coarse-grained hardware approaches, such as Triple Modular Redundancy (TMR) and Temporal Redundancy (TR), while exhibiting acceptable levels of fault coverage [1], are often wasteful of resources such as time, device/chip area and power. A TMR-hardened computation can exhibit poor performance relative to a non-TMR hardware configuration with similar area. This is because the resources that are used to replicate functional units in parallel (in the case of TMR) can only execute one operation at a time. Conversely, in an equivalent non-TMR configuration, those same resources could execute three different operations concurrently (albeit with no resiliency coverage). In short, TMR is very rigid in its allocation of resources, using them only for resiliency. © 2012 IEEE.