Abstract
Healthcare agents, in particular in the oncology field, are currently collecting vast amounts of diverse patient data. In this context, some decision-support systems, mostly based on deep learning techniques, have already been approved for clinical purposes. Despite all the efforts in introducing artificial intelligence methods in the workflow of clinicians, its lack of interpretability - understand how the methods make decisions - still inhibits their dissemination in clinical practice. The aim of this article is to present an easy guide for oncologists explaining how these methods make decisions and illustrating the strategies to explain them. Theoretical concepts were illustrated based on oncological examples and a literature review of research works was performed from PubMed between January 2014 to September 2020, using deep learning techniques, interpretability and oncology as keywords. Overall, more than 60% are related to breast, skin or brain cancers and the majority focused on explaining the importance of tumor characteristics (e.g. dimension, shape) in the predictions. The most used computational methods are multilayer perceptrons and convolutional neural networks. Nevertheless, despite being successfully applied in different cancers scenarios, endowing deep learning techniques with interpretability, while maintaining their performance, continues to be one of the greatest challenges of artificial intelligence.

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
Background: Recurrence is an important cornerstone in breast cancer behavior, intrinsically related to mortality. In spite of its relevance, it is rarely recorded in the majority of breast cancer datasets, which makes research in its prediction more difficult. Objectives: To evaluate the performance of machine learning techniques applied to the prediction of breast cancer recurrence. Material and Methods: Revision of published works that used machine learning techniques in local and open source databases between 1997 and 2014. Results: The revision showed that it is difficult to obtain a representative dataset for breast cancer recurrence and there is no consensus on the best set of predictors for this disease. High accuracy results are often achieved, yet compromising sensitivity. The missing data and class imbalance problems are rarely addressed and most often the chosen performance metrics are inappropriate for the context. Discussion and Conclusions: Although different techniques have been used, prediction of breast cancer recurrence is still an open problem. The combination of different machine learning techniques, along with the definition of standard predictors for breast cancer recurrence seem to be the main future directions to obtain better results.

Abstract
Breast cancer is the most frequently diagnosed cancer in women. Using historical patient information stored in clinical datasets, data mining and machine learning approaches can be applied to predict the survival of breast cancer patients. A common drawback is the absence of information, i.e., missing data, in certain clinical trials. However, most standard prediction methods are not able to handle incomplete samples and, then, missing data imputation is a widely applied approach for solving this inconvenience. Therefore, and taking into account the characteristics of each breast cancer dataset, it is required to perform a detailed analysis to determine the most appropriate imputation and prediction methods in each clinical environment This research work analyzes a real breast cancer dataset from Institute Portuguese of Oncology of Porto with a high percentage of unknown categorical information (most clinical data of the patients are incomplete), which is a challenge in terms of complexity. Four scenarios are evaluated: (I) 5-year survival prediction without imputation and 5-year survival prediction from cleaned dataset with (II) Mode imputation, (Ill) Expectation-Maximization imputation and (IV) K-Nearest Neighbors imputation. Prediction models for breast cancer survivability are constructed using four different methods: K-Nearest Neighbors, Classification Trees, Logistic Regression and Support Vector Machines. Experiments are performed in a nested ten-fold cross-validation procedure and, according to the obtained results, the best results are provided by the K-Nearest Neighbors algorithm: more than 81% of accuracy and more than 0.78 of area under the Receiver Operator Characteristic curve, which constitutes very good results in this complex scenario.

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

Abstract
High failure and dropout rates are common in higher education institutions with introductory programming courses. Some researchers advocate that sometimes teachers don't use correct methods of assessment and that many students pass in programming without knowing how to program. In this paper authors describe the assessment methodology applied to a first year, first semester, Biomedical Engineering programming course (2015/2016). Students' programming skills were tested by playing a game in the first class, then they were assessed with three tests and a final exam, each with topics the authors considered fundamental for the students to master. A correlation analyses between the different types of tests and exam questions is done, to evaluate the most suitable, for assessing programming knowledge, showing that it is possible to use different question types as a pedagogical strategy, to assess student difficulty levels and programming skills, that help students acquire abstract, reasoning and algorithm thinking in an acceptable level. Also, it is shown that different forms of questions are equivalent to assess equal knowledge and that it is possible to predict the ability of a student to program at an early stage.