Abstract

Abstract
This paper introduces the Disturbance Description Language (DDL), an XML dialect intended to describe a number of anomalous elements that can occur in a given scenario (including people, vehicles, fire or focus of pollution) and their respective properties, such as temporal availability, location, motion pattern and details for individual components, such as growth pattern and detectability. This dialect is part of a framework to support the execution of cooperative missions by a group of vehicles, in a simulated, augmented or real environment. An interface was incorporated into the framework, for creating and editing XML files following the defined schema. Once the information is correctly specified, it can be used in the framework, thus facilitating the process of environment disturbances specification and deployment. A survey answered by both practitioners and researchers shows that the degree of satisfaction with DDL is elevated (the overall evaluation of DDL achieved a 4.14 score (out of 5), with 81.1% of the answers being equal to or above 4); also, the usability of the interface was evaluated, having achieved a score of 83.6 in the SUS scale. These results imply that DDL is flexible enough to represent several types of disturbances, through a user-friendly interface.

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
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.

Abstract
Climate change has raised consciousness of the need to use cleaner energy instead of fossil fuels. Meanwhile, a change of consciousness regarding resource use has to be achieved, which can be triggered by energy consumption monitoring studies that also provide useful recommendations for energy saving. Over time, researchers have identified behaviors by monitoring energy consumption in households, but these studies are usually limited to the number of monitored households and/or to the geographical region in which the monitoring takes place. In this research work, a study with a global reach is proposed to mitigate these limitations. Using a hierarchical clustering algorithm, three distinct groups were identified using the collected data, representative of distinct behaviors. The results illustrate several behaviors regarding energy consumption, like cold temperatures, seasonal behaviors, wake up hour, stay-at-home periods, and standby device consumption.

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.