Dropout is a chronic problem that affects education in Brazil at all levels. When considering higher education, Brazil has one of the highest university dropout rates in public and private institutions. Such a problem is considered a social loss as well as a misuse of resources. This paper aims to identify academic and social performance factors that influence the dropout of undergraduate students from Computer Science major at the University of Brasilia (UnB). The data set consists of 879 observations and 16 variables with aspects of the students enrolled in Computer Science from the first semester of 2014 to the second semester of 2019. The survival analysis methodology was employed, more specifically, the Log-Normal regression model, in which the response variable was the time (number of semesters) until dropout (known as failure time) or the last follow-up of the student (either the completion of the course or the student is still enrolled by 2019/2). The model proved to be robust in the residual analysis and in presenting results consistent with the dropout literature. This work can assist in the development of policies and educational strategies to decrease the number of dropouts in the computer science undergraduate major.

Increasing gender diversity in STEM graduate programs is a challenge. In Brazil, the National Council for Scientific and Technological Development (CNPq) has classified knowledge into different "broad areas", one of which is Exact and Earth Sciences (EES). This area includes the STEM subjects: Physics, Computer Science, Mathematics, Statistics and Chemistry. These EES areas have a low representation of women. The University of Brasília, one of the top 10 universities in Brazil, has graduate programs (master’s and doctoral degrees) in all these subjects. In this context, this paper has the main research question: What is the level of gender diversity in each EES area at the University of Brasília in master’s and doctoral programs? This research question was analyzed with the indicators of student enrollment, number of graduations, and retention rates in the programs. The data used for analysis were the available Brazilian open public data of graduate programs for 11 years, 2007-2017. The findings include that women are in the minority in the total number of graduates in Computer Science and Physics. Despite the low number of women overall in EES, the Chemistry program stands out with the highest female participation, reaching more women than men at the doctorate level. The program that has the fewest women is Computer Science. This paper presents all the results of this study.

Full Paper - The field of Computer Science (CS) has been of little interest to women straight out of high school when considering undergraduate majors in Brazil. At the University of Brasília, a top-ten university in Brazil, female undergraduate students account for less than 15% of the students in the Department of Computer Science. According to Stout and Blaney, a sense of intellectual belonging is “the sense that one is believed to be a competent member of the community”. This perception may be especially challenging for members of underrepresented minority groups, such as female undergraduate students in CS majors. In this context, this paper addresses two research questions: i) “How does the intellectual sense of belonging of female students compare to the male students' in introduction to computer science courses?”; ii) Is it similar for female undergraduate students in both CS and non-CS majors?”. We devised a questionnaire for students in the introduction to computer science courses for different majors. We analyzed the responses and, in general, introductory programming courses are challenging for all students, however, female students feel worse about their computing competencies than male ones.

The recent development of a new kind of public transportation system relies on a particular double-steering kinematic structure enhancing maneuverability in cluttered environments such as downtown areas. We call bi-steerable car a vehicle showing this kind of kinematics. Endowed with autonomy capacities, the bi-steerable car ought to combine suitably and safely a set of abilities: simultaneous localisation and environment modelling, motion planning and motion execution amidst moderately dynamic obstacles. In this paper we address the integration of these four essential autonomy abilities into a single application. Specifically, we aim at reactive execution of planned motion. We address the fusion of controls issued from the control law and the obstacle avoidance module using probabilistic techniques.

Evolve safely in an unchanged environment and possibly following an optimal trajectory is one big challenge presented by situated robotics research field. Collision avoidance is a basic security requirement and this paper proposes a solution based on a probabilistic approach called Bayesian Programming. This approach aims to deal with the uncertainty, imprecision and incompleteness of the information handled. Some examples illustrate the process of embodying the programmer preliminary knowledge into a Bayesian program and experimental results of these examples implementation in an electrical vehicle are described and commented. Some videos illustrating these experiments can be found at http://www-laplace.imag.fr.