Abstract
It is necessary to study how to improve the reuse of learning objects stored in repositories, otherwise they will serve merely as archives, neither assisting learner interested in some subjects nor educators developing courses. In this paper we deal with a number of questions related to a project committed to develop and evaluate a model for repositories that can make feasible the reuse of learning objects, beneficiating learners, educators and stakeholders. We argue that the pragmatic characterization of learning objects is a valid approach to reach this goal.

Abstract
Music information retrieval encompasses a complex and diverse set of problems. Some recent work has focused on automatic textual annotation of audio data, paralleling work in image retrieval. Here we take a narrower approach to the automatic textual annotation of music signals and focus on rhythmic style. Training data for rhythmic styles are derived from simple, precisely labeled drum loops intended for content creation. These loops are already textually annotated with the rhythmic style they represent. The training loops are then compared against a database of music content to apply textual annotations of rhythmic style to unheard music signals. Three distinct methods of rhythmic analysis are explored. These methods are tested on a small collection of electronic dance music resulting in a labeling accuracy of 73%.

Abstract
The concept of microgrid (mu grid) has been emerging as a way to integrate microgeneration (mu G) in LV networks and simultaneously improve its potential benefits. Technical requirements to connect mu grids to LV networks have been studied in order to make this concept technologically feasible and safe to operate. However, the regulatory framework for economic integration of mu G and mu grids on distribution systems, despite being crucial, is still an open issue. The main purpose of this paper is to contribute for the development of an appropriate economic regulation framework that removes the barriers to mu G and mu grid development To do so, the relevant costs and benefits resulting from the establishment of mu G and mu grid are identified and a methodology for sharing those costs and benefits among the involved economic agents is presented. The only pre-requisite of such a methodology is that a net benefit to all economic agents exists, which is the case most of the times. An illustrative example is included

Abstract
In a competitive energy market environment, the procedure for fair loss allocation constitutes a matter of considerable importance. This task is often based on rough principles, given the difficulties on the practical implementation of a fairest process. This paper proposes a methodology based on neural networks for the distribution of power distribution losses among the loads. The process is based on the knowledge of load profiles and on the usual consumption measures. Simulations ere carried out for a typical MV network, with an extensive variety of load scenarios. For each scenario, losses were calculated and distributed by the consumers. The allocation criterion is established assuming a distribution proportional to the squared power. Finally, a neural network is trained in order to obtain a fast and accurate losses allocation. Illustrative results support the feasibility of the proposed methodology.

Abstract
In this paper we address the problem of automatically computing a set of views over a simulated three dimensional enviromnent. The viewing system aims at, for each moment, supplying the user with the most pertinent information in order to allow a good understanding of the evolving environment. Our approach relies on an innovative optimization architecture that enables intelligent optimization techniques based on simulated annealing and genetic algorithms. Reported experiments were performed in urban rescue scenarios from the RoboCup Rescue Domain. We outline the possible extension of the proposed architecture to other visualization problems and argue on how several problems within the fields of Visualization and Rendering can benefit from it.

Abstract
This paper presents an artificial neural network approach for short-term electricity prices forecasting. In the new deregulated framework, producers and consumers require short-term. price forecasting to derive their bidding strategies to the electricity market. Accurate forecasting tools are required for producers to maximize their profits and for consumers to maximize their utilities. A three-layered feedforward artificial neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting the next 168 hour electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed approach, reporting the numerical results from a real-world case study based on an electricity market.