Abstract
Low performance of nontechnical engineering students in programming courses is a problem that remains unsolved. Over the years, many authors have tried to identify the multiple causes for that failure, but there is unanimity on the fact that motivation is a key factor for the acquisition of knowledge by students. To better understand motivation, a new evaluation strategy has been adopted in a second programming course of a nontechnical degree, consisting of 91 students. The goals of the study were to identify if those students felt more motivated to answer multiple-choice questions in comparison to development questions, and what type of question better allows for testing student knowledge acquisition. Possibilities around the motivational qualities of multiple-choice questions in programming courses will be discussed in light of the results. In conclusion, it seems clear that student performance varies according to the type of question. Our study points out that multiple-choice questions can be seen as a motivational factor for engineering students and it might also be a good way to test acquired programming concepts. Therefore, this type of question could be further explored in the evaluation points.

Abstract
Automatic Identification System data has been used in several studies with different directions like traffic forecasting, pollution control or anomalous behavior detection in vessels trajectories. Considering this last subject, the intersection between vessels is often related with abnormal behaviors, but this topic has not been exploited yet. In this paper an approach to assist the domain experts in the task of analyzing these intersections is introduced, based on data processing and visualization. The work was experimented with a proprietary dataset that covers the Portuguese maritime zone, containing an average of 6460 intersections by day. The results show that several intersections were only noticeable with the visualization strategies here proposed. Copyright

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Denial of Service attacks, which have become commonplace on the Information and Communications Technologies domain, constitute a class of threats whose main objective is to degrade or disable a service or functionality on a target. The increasing reliance of Cyber-Physical Systems upon these technologies, together with their progressive interconnection with other infrastructure and/or organizational domains, has contributed to increase their exposure to these attacks, with potentially catastrophic consequences. Despite the potential impact of such attacks, the lack of generality regarding the related works in the attack prevention and detection fields has prevented its application in real-world scenarios. This paper aims at reducing that effect by analyzing the behavior of classification algorithms with different dataset characteristics. © 2019, Springer Nature Switzerland AG.

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No abstract available.

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