José Luís Borges

Abstract
Intelligent Transportation Systems are an important enabler for the smart cities paradigm. Currently, such systems generate massive amounts of granular data that can be analyzed to better understand people’s dynamics. To address the multivariate nature of spatiotemporal urban mobility data, researchers and practitioners have developed an extensive body of research and interactive visualization tools. Data visualization provides multiple perspectives on data and supports the analytical tasks of domain experts. This article surveys related studies to analyze which topics of urban mobility were addressed and their related phenomena, and to identify the adopted visualization techniques and sensors data types. We highlight research opportunities based on our findings. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

Abstract
The personal characteristics of entrepreneurs can be importantly related to entrepreneurial startup intentions and behaviors. A country-moderated hypothesis including the relationship between an individual's risk-taking propensity and entrepreneurship (behaviors or intentions of the person) was conceptually developed and empirically tested in this study. The data collection was performed through a structured questionnaire. Multinominal logistic regression was used for analyzing data obtained from 1,414 students in six countries. The crucial contribution of this research is the clarification of the character of risk-taking propensity in entrepreneurship and the indication that the risk-taking propensity-entrepreneurship relationship can be moderated contingent on power distance.

Abstract
Resumo O abandono escolar é um problema complexo que afeta a maioria dos programas de graduação pós-secundária, em todo o mundo. O curso de engenharia industrial do Instituto ISVOUGA, localizado em Santa Maria da Feira, Portugal, não é exceção. Este estudo usou um conjunto de dados contendo informações gerais dos estudantes e suas notas para as unidades curriculares já avaliadas. A partir deste conjunto de dados, foram selecionados dezessete preditores potenciais: cinco intrínsecos (gênero, estado civil, situação profissional, idade e regime de dedicação aos estudos – integral ou parcial) e doze extrínsecos (as notas em todas as doze unidades curriculares ministradas durante os dois primeiros semestres do curso). O objetivo principal desta investigação foi prever a probabilidade de um estudante abandonar o curso com base nos referidos preditores. Foi usada uma regressão logística binária para classificar os estudantes como tendo uma probabilidade alta ou baixa de não se reinscreverem no curso. Para validar se a metodologia utilizada é apropriada para o estudo em causa, a precisão obtida com o modelo de regressão logística foi comparada, por via de uma validação cruzada com cinco partições, com a precisão obtida pela utilização de três métodos muito utilizados em data mining: One R, K Nearest Neighbors e Naive Bayes. O modelo de regressão logística identificou quatro variáveis significativas na previsão do abandono escolar (as classificações nas unidades curriculares de ciência dos materiais, eletricidade, cálculo 1 e química). Os dois preditores mais influentes do abandono dos estudantes são não conseguir aprovação nas unidades curriculares menos exigentes: ciência dos materiais e eletricidade. Ao contrário do que seria de supor antes desta investigação, descobrimos que a não aprovação em unidades curriculares mais exigentes, como física ou estatística, não tem influência significativa no abandono escolar.

Abstract
Air temperature data from many locations worldwide are only available as series of daily minima and maxima temperatures. Historically, several different approaches have been used to estimate the actual daily mean temperature, as only in the last two or three decades automatic thermometers are able to compute its actual value. The most common approach is to estimate it by averaging the daily minima and maxima. When only daily minima and maxima are available, an alternative approach, proposed by Dall'Amico and Hornsteiner in 2006, uses the two daily extremes together with next day minima temperature and a coefficient related to the local daily astronomical sunset time. Additionally, the method uses two optimizable coefficients related to the region's temperature profile. In order to use this approach it is necessary to optimize the region's unknown parameters. For this optimization, it is necessary a dataset containing the maxima, minima, and the actual daily mean temperatures for at least one year. In this research, for the period 2007-2014, we used three datasets of minima, maxima and actual mean temperatures obtained at three automatic meteorological stations located in the Douro Valley to optimize the two unknown parameters in the Dall'Amico and Hornsteiner approach. Moreover, we compared the actual mean daily temperatures available from the three datasets with the correspondent values estimated by using i) the usual approach of averaging the daily maxima and minima temperatures and ii) the Dall'Amico and Hornsteiner approach. Results show that the former approach overestimates, on average, the daily mean temperatures by 0.5 degrees C. The Dall'Amico and Hornsteiner approach showed to be a better approximation of mean temperatures for the three meteorological stations used in this research, being unbiased relative to the actual mean values of daily temperatures. In conclusion, this research confirms that the Dall'Amico and Hornsteiner is a better approach to estimate the mean daily temperatures and provides the optimized parameters for three sites located at each of the three sub-regions of the Douro Valley (Baixo Corgo, Cima Corgo and Douro Superior).

Abstract
Customers' response is an important topic in direct marketing. This study proposes a data mining response model supported by random forests to support the definition of target customers for banking campaigns. Class imbalance is a typical problem in telemarketing that can affect the performance of the data mining techniques. This study also contributes to the literature by exploring the use of class imbalance methods in the banking context. The performance of an undersampling method (the EasyEnsemble algorithm) is compared with that of an oversampling method (the Synthetic Minority Oversampling Technique) in order to determine the most appropriate specification. The importance of the attribute features included in the response model is also explored. In particular, discriminative performance was enhanced by the inclusion of demographic information, contact details and socio-economic features. Random forests, supported by an undersampling algorithm, presented very high prediction performance, outperforming the other techniques explored.