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.

Abstract
We propose a new paradigm for public participation in urban planning, a field which presents significant challenges for public understanding and participation. Our approach is based on leveraging the rich diversity of meaning associated with cultural gestures, traditions, folklore, and rituals, and using them in augmented reality systems, in order for citizens' to explore, understand, and communicate the complex, systemic ideas and concepts associated with urban planning. At an immediate level, this approach holds the potential for enabling increased public awareness of what is at stake in urban planning - both on the part of citizens and on the part of public officials, policy-makers, and decision-makers - and consequently enhancing understanding and improving participation in public life and citizenship. It may also open up a new field of research and development in human-computer interaction, to leverage the richness of meaning and modes of expression which exist in various cultures and societies, rather than ignoring them and imposing dumbed-down or prescribed command methods. Thus, it aims to facilitate new levels of empowerment of users in the use of digital systems and data. The active utilization of cultural meaning in gestures, rituals, and social practices may also support and promote better inclusion and participation of minority groups and migrant communities in contemporary, technology-rich life. (c) 2015 The Authors. Published by Elsevier B.V.

Abstract
Assessing firms' Eco-efficiency is important to ensure they succeed in creating wealth without compromising the needs of future generations. This work aims to extend the Eco-efficiency concept by including in the assessment new features related to environmental benefits. Eco-efficiency is evaluated using a DEA model specified with a Directional Distance Function. The new methodology proposed in this paper is illustrated with an application to world-class mining companies, whose results and managerial implications are discussed.