Abstract
Increasing environmental concerns have motivated efforts for the modernization of the power system recently. As a result, the distribution system (DS) has been given specific importance. Especially, residential end-users have been rendered active trough the introduction of several assets such as electric vehicles, energy storage systems and self-production units. Although many opportunities emerge from the spreading of these so-called "smart" households, the DS may be negatively affected if appropriate coordination techniques are not developed. At this point, the distribution system operators (DSO) that are responsible for the operation of the DS need to intervene. The smart households struggle to minimize their daily electricity procurement cost by exploiting dynamic pricing tariff schemes. On the other hand, the DSO aims to minimize the active power losses of the DS. DS reconfiguration is a core element of the daily operational practice of DSO and should be considered in the development of coordination strategies. In this study, a two-side interaction framework is proposed in order to guarantee that both the smart households and the DSO sufficiently achieve their individual goals. State-of-the art computing techniques are also used in order to render the proposed formulation tractable for real-life applications.

Abstract
This paper addresses the simultaneous estimation of state variables and network topology in the context of power system real-time modeling. The proposed method assumes that selected substations are modeled at the bus section level, and circuit breakers and disconnects are explicitly represented. Available information on the statuses of such switching branches are then treated as a priori topology information to be processed by a specialized estimator. The presumed topology will eventually be either validated or corrected by using the information conveyed by real-time measurements. An algorithm based on a fast version of orthogonal Givens rotations is employed to solve the integrated state & topology estimation problem. The proposed method preserves the bad data processing capabilities of weighted least-squares state estimators. The performance of the integrated state & topology estimator is assessed through its application to test systems derived from IEEE benchmark networks. © 2015 IEEE.

Abstract
This paper addresses the problem of decision making in the context of financial markets. More specifically, the problem of forecasting the correct trading action for a certain future horizon. We study and compare two different alternative ways of addressing these forecasting tasks: i) using standard numeric prediction models to forecast the variation on the prices of the target asset and on a second stage transform these numeric predictions into a decision according to some pre-defined decision rules; and ii) use models that directly forecast the right decision thus ignoring the intermediate numeric forecasting task. The objective of our study is to determine if both strategies provide identical results or if there is any particular advantage worth being considered that may distinguish each alternative in the context of financial markets.

Abstract
Crohn's disease is one type of inflammatory bowel disease whose incidence is currently increasing, and may affect any part of both the small and large intestine, possibly irritating deeper layers of the organs. Being a chronic disease, neither treatment nor surgery actually heals the patients. Thus, focus has been given to identifying good prognostic models based on clinical factors since they are more easily included in daily practice. The aim of this work is to provide an initial study on the adequacy of a Bayesian network model to enhance the prognosis prediction for patients with Crohn's disease. Multicentric study data of patients with surgery or immunosuppression in the six month after diagnosis was used to derive a Bayesian network, focusing on the prognosis and the analysis of factors interaction, including clinical features, disease course, treatment, follow-up plan, and adverse events. Two models were evaluated (naive Bayes and Tree-Augmented Naive Bayes) and also compared with logistic regression, using cross-validation and ROC curve analysis. Preliminary results showed competitive accuracy (above 75%) and discriminative power (above 70%). The generated models presented interesting insights on factor interaction and predictive ability for the prognosis, supporting their use in future clinical decision support systems.

Abstract
Understanding the underlying differences between groups or classes in certain contexts can be of the utmost importance. Contrast set mining relies on discovering significant patterns by contrasting two or more groups. A contrast set is a conjunction of attribute-value pairs that differ meaningfully in its distribution across groups. A previously proposed technique is rules for contrast sets, which seeks to express each contrast set found in terms of rules. This work extends rules for contrast sets to a temporal data mining task. We define a set of temporal patterns in order to capture the significant changes in the contrasts discovered along the considered time line. To evaluate the proposal accuracy and ability to discover relevant information, two different real-life data sets were studied using this approach.

Abstract
Humans perform and rely on face recognition routinely and effortlessly throughout their daily lives. Multiple works in recent years have sought to replicate this process in a robust and automatic way. However, it is known that the performance of face recognition algorithms is severely compromised in non-ideal image acquisition scenarios. In an attempt to deal with conditions, such as occlusion and heterogeneous illumination, we propose a new approach motivated by the global precedent hypothesis of the human brain's cognitive mechanisms of perception. An automatic modeling of SIFT keypoint descriptors using a Gaussian mixture model (GMM)-based universal background model method is proposed. A decision is, then, made in an innovative hierarchical sense, with holistic information gaining precedence over a more detailed local analysis. The algorithm was tested on the ORL, ARand Extended Yale B Face databases and presented state-of-the-art performance for a variety of experimental setups.