Sign language is the primary means of communication for many people who are deaf or hard of hearing. The advancement of automatic sign language recognition systems is essential to enhance accessibility and reduce communication barriers. However, the lack of robust and accessible technological solutions still poses a significant obstacle to the full social inclusion of this population. In this context, video signal recognition systems have gained prominence, especially with the advancement of computer vision techniques based on deep learning. This study presents a multimodal recognition approach that combines RGB and depth video data through an attention-based fusion mechanism. The proposed architecture employs two pre-trained Multiscale Vision Transformers (MViT) to extract spatiotemporal features from each modality. These features are then integrated using an attention module that dynamically adjusts the contribution of each input stream. Experiments were conducted using the LIBRAS-UFOP dataset, which contains 56 signs performed by five different individuals, grouped into four linguistic categories. To evaluate the model’s performance, two evaluation protocols were employed: a random data split and a leave-one-signer-out strategy to assess the model’s ability to generalize to unseen users. The results show that while multimodal fusion provided modest improvements in the random split scenario, it achieved significantly higher accuracy when evaluated on unseen signers. These findings demonstrate the value of combining multiple modalities and leveraging attention mechanisms to address user variability, ultimately contributing to more robust and reliable sign language recognition in real-world applications.

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.

Skin cancer is one of the cancers that most aggravates the problem in public health. Among the types of cancer, melanoma is the most aggressive type. Its early diagnosis is essential to increase the possibility of adequate treatment, aiming to reduce the mortality rate. Dermatologists generally use manual methods to diagnose skin lesions. These methods, in addition to being time-consuming, as they are performed manually, can present different results for the same lesion when analyzed by different specialists. Therefore, an automated diagnosis may be necessary to deal with this issue as well as avoid invasive tests. For this, the task of segmenting the skin lesion in the dermoscopic image can be fundamental, as it is a basic task in the image analysis process. In the present work, a Convolutional Neural Network (CNN) model, based on the U-Net, is used to segment the lesion in dermoscopic images. This proposal achieved an accuracy of 0.949 and Jaccard of 0.833 for the 2017 ISIC base, and an accuracy of 0.954 and Jaccard of 0.850 for the 2018 ISIC base. The proposed model has a simpler architecture, in addition to requiring less computational resources. The experiments made it possible to observe that the proposed model results are promising compared with other CNN models presented in the literature.

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.

Research Full Paper Most top universities in Brazil are public government institutions and tuition free. However, until recently, access to these institutions has been limited by extremely difficult entrance exams. The high standards at public universities are in contrast to the k-12 educational system, where public schools fail to prepare students for the exams, with only some of the private schools offering adequate preparation. In 2012, the Higher Education System in Brazil changed: the Quota Law was implemented for all 59 federal public government universities. This law reserves 50% of the enrollments for the public high-school students with the best grades in the entrance exams. Also, from this 50% allocation of places for students from the public high-school system, half are allocated to students from low-income families (up to one and a half times the minimum monthly salary), black and indigenous students. In this context, this paper addresses the research question: "How does the intellectual sense of belonging and self-efficacy of the quota students compare to that the non-quota students' taking Introduction to Computer Science courses?" We devised a questionnaire for students enrolled in the first programming course of different majors at a top-10 Brazilian university. This paper presents an analysis of the responses that indicates some differences in self-efficacy perceptions between the students admitted through the quota system and the ones admitted exclusively by their placement in entrance exams.