Abstract
Electricity has been acquiring a more significant presence in our lives, and it is estimated that the future will be increasingly electric. Nowadays, we have access to enormous amounts of data that do not have much-added value if they cannot support decision-making or plan systems in advance and correctly. Forecasts are vital tools to support decision-making. We believe it is possible to resort to open data available on the Internet to make electricity price forecasts that - decision-makers can use in the sector. In this work, we study the multi-attribute hourly forecast of the electricity price in MIBEL (Iberian electricity market) for the 24 h of the following day, using open data. The realization of the multi-attribute predictions fell on the TIM (‘Tangent Information Modeler’) tool with AutoML (‘Auto Machine Learning’) capabilities. The TOPSIS (‘technique for order of preference by similarity to ideal solution’) decision support technique was used to analyze the results. © 2021, Springer Nature Switzerland AG.

Abstract
This research aims to identify the corruption of the civil servants in the Federal District, Brazilian Public Administration. For this purpose, a predictive model was created integrating data from eight different systems and applying logistic regression to real datasets that, by their nature, present a low percentage of examples of interest in identifying patterns for machine learning, a situation defined as a class imbalance. In this study, the imbalance of classeswas considered extreme at a ratio of 1:707 or, in percentage terms, 0.14% of the interest class to the population. Two possible approaches were used, balancing with resampling techniques using synthetic minority oversampling techniqueSMOTEand applying algorithms with specific parameterization to obtain the desired standards of the minority classwithout generating bias from the dominant class. The best modeling resultwas obtained by applying it to the second approach, generating an area value on the ROC curve of around 0.69. Based on sixty-eight features, the respective coefficients that correspond to the risk factors for corruption were found. A subset of twenty features is discussed in order to find practical utility after the discovery process.

Abstract
This article describes a dataset of different services acquired by users during the period in which they are active in a sports facility as well as their behavior in terms of frequency of the sport facility itself and the type of classes they prefer to attend. Each observation in the dataset corresponds to one user, including the features of subscriptions and frequency. Data were collected between June 1st 2014 and October 31st 2019 from a database of an ERP solution operating in a sports facility in Lisbon, Portugal. From this database, it was possible to perform operations of extraction, transformation and loading into the dataset. The dataset with real data can be useful for research in areas such as customer retention, machine learning, marketing, actionable knowledge and others. Although we present real data from users of a sports facility, in order to comply the GDPR legislation, the attributes that could identify the users were removed making the data anonymized. (C) 2021 The Author(s). Published by Elsevier Inc.

Abstract
An integrated view of the information system has been an objective to deal with complexity. However, bibliography proposes many solutions with many synonyms depending on the layer, methodology, framework or tool used, that does not allow a broad view of the system. In this work we chose three basic elements of the information systems and we demonstrate how they are enough to integrate a set of essential UML diagrams. The proposed model firstly defines a set of UML diagrams for each layer of the Enterprise Architecture, and then heuristic rules are detailed in order to ensure vertical and horizontal alignment. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The advances in mobile computing and the market demand for new products which meet an increasingly public represent the importance to assure the quality of mobile applications. In this context, automated GUI testing has become highlighted in research. However, studies indicate that there are still limitations to achieve a large number of possible combinations of operations, transitions, functionality coverage, and failures reproduction. In this paper, a Deep Q-Network-based android application GUI testing tool (DeepGUIT) is proposed to test case generation for android mobile apps, guiding the exploration by code coverage value and new activities. The tool was evaluated with 15 open-source mobile applications. The obtained results showed higher code coverage than the state-of-the-art tools Monkey (61% average higher) and Q-testing (47% average higher), in addition, a greater number of failures.

Abstract