Abstract
More and more technological advances offer new paradigms for training, allowing novel forms of teaching and learning to be devised. A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. This forecast turns out to be an opportunity for human-computer interaction as a way to monitor and assess the user’s stress levels during high-risk tasks. The main effects of stress are increased physiological arousal, somatic complaints, mood disturbances (anxiety, fear and anger) and diminished quality of working life (e.g. reduced job satisfaction). To mitigate these problems, it is necessary to detect stressful users and apply coping measures to manage stress. Human-computer interaction could be improved by having machines naturally monitor their users’ stress, in a non-invasive and non-intrusive way. This article discusses the development of a random forest classifier with the goal of enabling the assessment of high school students’ stress during academic exams, through the analysis of mouse behaviour and decision-making patterns.

Abstract
The comprehension of social network phenomena is closely related to data visualization. However, even with only hundreds of nodes, the visualization of dense networks is usually difficult. The strategy adopted in this work is data reduction using communities. Community detection in social network analysis is a very important issue and in particular detection of community overlapping. In this approach, the information extracted from social networks transcends cohesive groups, enabling the discovery of brokers that interact among communities. To find admissible solutions in hard problems, relaxed approaches are used. Quasi-cliques are generated, and partition is found using a partial set-covering heuristic. The proposed method allows the identification of communities and actors that link two or more groups. In the visualization process, the user can choose different dimension reduction approaches for the condensed graph. For each condensed structure, a hypergraph can be drawn, identifying communities and brokers.

Abstract
The top management view of organizations tends not to reach consensus on the prioritization of investments in Information Systems, particularly when they must prioritize their impact on overall performance and budget constraints. This paper presents the results of applying CRUDi Framework to a bank. This allows to obtain new indicators to support the decision and alignment of investment priorities in the processes that support the business strategy. The Framework introduces a new method and tools that allow us to gauge the relative importance of Information Systems to the organizations’ businesses.

Abstract
The uncertainty and variability of renewable energy sources, wind energy in particular, poses serious challenges for the optimal operation and planning of power systems. In this paper, in order to obtain flexible market conditions while power generated by renewable units is short and supply and demand are imbalanced, a Demand Response (DR) strategy is studied to provide network requirements, because Demand Response Programs (DRPs) improve demand potential and increase security, stability and economic performance. The proposed hybrid model created by the integration of wind energy and DR using Time of Use (ToU) or Emergency DRP (EDRP) improves supply and demand. The problem is solved considering the Independent System Operator (ISO) and using a stochastic multiple-objective (MO) method. The objective is to simultaneously minimize the operation costs and the environmental pollution while assuring compliance of network security constraints and considering multiple economical and technical indexes.

Abstract
In bioacoustic recognition approaches, a flat classifier is usually trained to recognize several species of anurans, where the number of classes is equal to the number of species. Consequently, the complexity of the classification function increases proportionally with the number of species. To avoid this issue, we propose a hierarchical approach that decomposes the problem into three taxonomic levels: the family, the genus, and the species. To accomplish this, we transform the original single-labelled problem into a multi-output problem (multi-label and multi-class) considering the biological taxonomy of the species. We then develop a top-down method using a set of classifiers organized as a hierarchical tree. We test and compare two hierarchical methods, using (1) one classifier per parent node and (2) one classifier per level, against a flat approach. Thus, we conclude that it is possible to predict the same set of species as a flat classifier, and additionally obtain new information about the samples and their taxonomic relationship. This helps us to better understand the problem and achieve additional conclusions by the inspection of the confusion matrices at the three classification levels. In addition, we propose a soft decision rule based on the joint probabilities of hierarchy pathways. With this we are able to identify and reject confusing cases. We carry out our experiments using cross-validation performed by individuals. This form of CV avoids mixing syllables that belong to the same specimens in the testing and training sets, preventing an overestimate of the accuracy and generalizing the predictive capabilities of the system. We tested our methods in a dataset with sixty individual frogs, from ten different species, eight genera, and four families, achieving a final Macro-Fscore of 80 and 70% with and without applying the rejection rule, respectively.

Abstract
With the aim of disseminating the potential and the capacity of Educational Data Mining (EDM) as an instrument of investigation and analysis in the support to the management of Higher Education Institutions, this paper presents a brief description of some of the most relevant studies in the area. The analysis carried out allows to highlight the innovations that EDM has been promoting, as well as current and future research trends.