The Constitution of Brazil and its subsequent laws have established various rights and protections for Indigenous peoples, among them the right to Indigenous schools where their culture and native language must be taught, learned, and preserved as something alive and essential to their well-being. Brazil's National Digital Education Policy, which mandates the teaching of computing in K-12 education, is a recent development not yet implemented in indigenous schools, where access to computers and the Internet is still quite limited. To promote the inclusion of indigenous populations in higher education, the University of Brasília (UnB), in collaboration with Brazil's National Foundation for Indigenous Peoples, has created an admission pathway dedicated to students from these populations. In 2022, UNB's Computer Science Department welcomed its first three Indigenous students from the Ticuna community in the Amazon region of Brazil. The Ticuna people represent the largest indigenous ethnic group in Brazil. Ticuna students, computer science professors, and computer science students at UnB have collaborated to address the gap in the K-12 teaching of computing in Ticuna communities. This work describes the materials created by the indigenous students for teaching computing in their communities within the context of their culture and language.

Summary Software defined network (SDN) has emerged as a new paradigm in terms of network architecture, providing flexibility, agility, and programmability to network management. These benefits boosted the SDN adoption, bringing new challenges mainly related to security, in particular, those related to Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. The detection, prevention, and mitigation of these attacks are important since they can affect the entire network. Many current security measures use statistical techniques, as entropy, or machine learning (ML) algorithms to detect DoS and DDoS attacks. While the definition of a threshold to determine whether a traffic is an attack is not trivial in statistical techniques, ML solutions may provide better accuracy but require considerable computational resources and time to converge to a model able to detect these attacks. Trying to circumvent these limitations, current hybrid approaches either use the results from entropy as input in ML algorithms (EntropyML) or use entropy as a filter and ML algorithms to identify attacks. This work goes one step ahead and combines these techniques in a three‐step approach (EntropyMLEntropy), called ML‐Entropy, which inherits the intelligence of ML algorithms to adjust the threshold used by entropy. The proposed solution was implemented and evaluated in two datasets, the well‐known synthetic DARPA dataset and a dataset composed by traffic collected from a real‐corporate environment. Experimental results show that, in general, ML‐Entropy presents an accuracy above 99%, similar to support vector machine (SVC) and random forest (RF) algorithms, being able to converge to a detection model up to and faster than RF and SVC, respectively.

This full paper proposes an innovative framework for teaching computer science topics to undergraduate students in Computer Science and Teacher Training (CSTT) for k12 majors. A Computer Science major is required by k12 teachers in Computer Science. However, there has been a concern in Brazil over the lack of explicit pedagogical methods included in CSTT majors. In this context, our research question is: how to teach a CSTT major, so that knowledge of computer science is learned by learning how to teach it through the application of a learning theory? To address this research question, this paper presents the LLL framework, a practical approach in which undergraduate students design and plan computer science lessons using a learning theory approach with a focus on teaching computer science to schools. The acronym LLL comes from “Learn computer science and Learn how to teach computer science by applying a Learning theory”. The LLL framework was tested in two consecutive courses in a year-long study on the operating systems topic in the Department of Computer Science at the University of Brasilia (UnB). UnB was a pioneer, founding the first CSTT major for basic education in Brazil in 1997. The undergraduate students were asked to plan a lesson using this approach and teach the content to their peers, being assessed by the instructors and their peers. The findings showed that the LLL framework is effective for preparing future k12 teachers to teach this content to school students. By adopting the LLL framework, CSTT undergraduate students (teacher candidates) will be able to develop solid pedagogical skills and provide a more meaningful learning experience for k12 students. This methodology has the potential to improve the quality of computer science teaching in schools, contributing to the training of undergraduate students who will be more prepared and engaged in this field.

Teaching programming in the early years of undergraduate courses has been a challenge for students, institutions, and professors. In view of this, Learning Management Systems (LMSs) and other teaching platforms have emerged to address some of the difficulties in this process. In this context, the present work intends to answer the following research question (RQ): What does the literature tell us about the use of automatic feedback in teaching programming in undergraduate courses? To answer this question, a literature mapping was conducted based on 119 articles published between 2017 and 2021. The mapping showed that the research area is expanding and has related studies from all over the world. The papers have different origins, and 37 countries are represented in this survey. The main programming languages used are Java, Python, C and C++. Another finding was that it is common practice to develop specific platforms for automatic feedback in programming courses. This paper presents the findings and results obtained.

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.

Semantics is still a challenge to automation and to the improvement of the relationship between humans and machines. Content management systems have a lot of improvement possibilities using semantics. A news writing process incorporating a content management system that provides semantic search, semantic relationships between articles and communication with external semantic systems can improve the productivity of the writers and make the reader's task easier. This work presents a functional prototype of a content management system which focuses on the construction of semantic annotations based on domain ontology, reuse annotations in search and on relationship construction between stored texts, providing a semantic interface for external systems. We present in this paper the annotation algorithm, the use cases of annotation, article creation and editing, as well as an approach for doing a semantic search and creating semantic relationships between texts. The system enables users to create semantic annotations quickly and allows them to remove and add annotations, including those suggested by the annotation algorithm. The two approaches for semantic relationships between texts are accurate and useful, while the search tool is versatile because it allows users to search in semantic and non semantic fields at the same time and it also uses logic operators in all fields.

The Brazilian Government has made available on the Web a massive volume of public data. This data may be structured, semi-structured or non-structured in order to turn the administration as transparent as possible. Thus, we notice the great challenge in providing applications capable enough to handle this Big Data environment, and to make information available for decision making. In this environment, data processing is done via new approaches from Information Science and Computer Science areas, by involving Technologies and processes for collecting, representing, storing and disseminating information. This paper presents a conceptual model, the technical architecture and the prototype implementation of a tool DBgoldbr, designed to classify government public information with the help of ontologies, by transforming open data into open linked data. To fulfill the purpose of the solution, we used Soft System Methodology to identify problems, to collect users needs and to design solutions that fit the purpose of specific groups. The DBgoldbr tool was designed to ease up the search for open data made available by many Brazilian Government institutions, so that this data can be reused to support the evaluation and monitoring of social programs, in order to support the design and management of public policies.

In this paper we introduce a scalable Video-on-Demand (VoD) system called GloVE (Global Video Environment) in which active clients cooperate to create a shareable video cache that is used as the primary source of video content for subsequent client requests. In this way, GloVE server's bandwidth does not limit the number of simultaneous clients that can watch a video since once its content is in the cooperative video cache (CVC) it can be directly transmitted from the cache rather than the VoD server Also, GloVE follows the peer-to-peer approach, allowing the use of low-cost PCs as video servers. In addition, GloVE supports video servers without multicast capability and videos in any stored format. We analyze preliminary performance results of GloVE implemented in a PC server using a Fast Ethernet interconnect and small video buffers at the clients. Our results confirm that while the GloVE-based server uses only a single video channel to deliver a highly popular video simultaneously to N clients, conventional VoD servers require as much as N times more channels.