Abstract
Recent work highlights advantages in decomposing multiclass decision problems into multiple binary problems. Several strategies have been proposed for this decomposition. The most frequently investigated are All-vs-All, One-vs-All and the Error correction output codes (ECOC). ECOC are binary words (codewords) and can be adapted to be used in classifications problems. They must, however, comply with some specific constraints. The codewords can have several dimensions for each number of classes to be represented. These dimensions grow exponentially with the number of classes of the multiclass problem. Two methods to choose the dimension of a ECOC, which assure a good trade-off between redundancy and error correction capacity, are proposed in this paper. The methods are evaluated in a set of benchmark classification problems. Experimental results show that they are competitive against conventional multiclass decomposition methods. Copyright

Abstract
Aspect-Oriented Programming is an emerging programming paradigm providing novel constructs that eliminate code scattering and tangling by modularizing crosscutting concerns in their own aspect modules. Many current aspect-oriented languages are backwards compatible extensions to existing popular languages, which opens the way to aspectize systems written in those languages. This paper contributes with the beginnings of a pattern language for refactoring existing systems into aspect-oriented versions of those systems. The pattern language covers the early assessment and decision stages: identifying latent aspects in existing systems, knowing when it is feasible to refactor to aspects and assessment of the necessary prerequisites for the refactoring process. © 2007 Copyright is held by the authors.

Abstract
The widening use of Information Systems, which allow the collection, extraction, storage, management and search of information, is increasing the need for information security. After a user is successfully identified and authenticated to a system, he needs to be authorised to access the resources he/she requested. Access control is part of this last process that checks if a user can access those resources. This is particularly important in the healthcare environment where there is the need to control access to Electronic Medical Records (EMR). Although EMR can be an important support tool for the healthcare professional there are some barriers that prevent its successful integration. These barriers include the fact that healthcare professionals do not participate in the development of access control to access the EMR imposing them extra effort in its use. New access control policies to be implemented should focus on human processes and needs. The main objective of this project is to reduce EMR barriers by including healthcare professionals and patients in the definition and improvement of access control policies and models. If this can be achieved, we hypothesize that the EMR can be more successfully integrated into the healthcare practice and provide for better patient treatment.

Abstract
Dermoscopy is a non-invasive diagnostic technique for the in vivo observation of pigmented skin lesions used in dermatology. There is currently a great interest in the prospects of automatic image analysis methods for dermoscopy, both to provide quantitative information about a lesion, which can be of relevance for the clinician, and as a stand alone early warning tool. The effective implementation of such a tool could lead to a reduction in the number of cases selected for exeresis, with obvious benefits both to the patients and to the health care system. The standard approach in automatic dermoscopic image analysis has usually three stages: (i) image segmentation, (ii) feature extraction and feature selection, (iii) lesion classification. This paper presents a comparison of segmentation methods applied to 50 dermoscopic image analysis, along with a clinical evaluation of each segmentation result performed by an experienced dermatologist.

Abstract
In this paper, we address the problem of tracking feature points along image sequences efficiently. Thus, to estimate the undergoing movement we use an approach based on Kalman filtering, which performs the prediction and correction of the features' movement in every image frame. Measured data is incorporated by optimizing the global association set built on efficient approximations of the Mahalanobis distance (MD). We analyze the difference between the usage in the tracking results of the original MD formulation and its more efficient approximation, as well as the related computational costs. Experimental results which validate our approach are presented.

Abstract
Large deployment of MicroGrids will have a considerable impact on the future operation of the electrical networks and will greatly influence the power system dynamics mainly at the Medium Voltage (MV) level whenever the upstream system has been lost. In dynamic studies the whole power system cannot be represented in a detailed manner because the huge system dimension would require a very large computational effort Therefore dynamic equivalents for MicroGrids need to be derived. The proposed approach is based on system identification theory for developing dynamic equivalents for MicroGrids, which are able to retain the relevant dynamics with respect to the existing MV network.