O Computer on the Beach é um evento técnico-científico que visa reunir profissionais, pesquisadores e acadêmicos da área de Computação, a fim de discutir as tendências de pesquisa e mercado da computação em suas mais diversas áreas.
Fundação Universidade do Vale do Itajaí - UNIVALI
Ferramenta para qualificar a Complexidade Cognitiva do código utilizando a técnica Cognitive Driven Development
ABSTRACT This work proposed the development of an extension for Visual Studio Code, which supports the PHP programming language, using the Cognitive Driven Development (CDD) method to identify the cognitive complexity of the source code. The extension helps the developer to locate code snippets with unnecessary complexity, encouraging the application of refactoring techniques and good code design practices. For this purpose, research related to CDD was analyzed and the necessary parameters for the creation of a configuration file in JSON were defined. Due to the limitation of the VS Code API, which does not provide the AST (Abstract Syntax Tree) of the code, it was decided to integrate the Php-parser library, written in JavaScript, which proved to be compatible with the Typescript development environment. The tool was validated through tests on a real project, comparing its performance before and after refactoring. In the initial analysis, the extension correctly identified the critical points that needed improvement. After refactoring, a reduction in the identified complexity was observed, evidencing the effectiveness of the tool, even when applied to a small project. In summary, the extension proves to be an effective solution for automating the identification of cognitive complexity, contributing to more efficient software development and continuous maintenance of source code.
Conectando Ideias: Relato de um Encontro de Projetos Parceiros do Programa Meninas Digitais no Amazonas
ABSTRACT This paper reports a collaborative meeting between university projects focused on the inclusion of women in computing, held in a region with limited initiatives in this field. The event aimed to foster support networks and share effective strategies for promoting gender equity in technology. The program featured discussions on academic and professional advancement, highlighting practices to enhance the participation of women in computing. Additionally, the presentation of a well-established project with nearly 10 years of experience served as inspiration for a recently launched local initiative. The exchange of experiences demonstrated the transformative potential of such events, emphasizing the importance of expanding similar initiatives to foster diversity, strengthen networks, and promote equitable opportunities in computing.
Filtro de Kalman Modificado (FKM) para o Controle Ótimo de Tremores Parkinsonianos
ABSTRACT Parkinson’s disease and other neurological disorders often manifest with involuntary tremors, significantly impacting quality of life. This study proposes the Modified Kalman Filter for tremor attenuation, proposing a novel modification to enhance its performance in hand vibration control. Despite the advancements in adaptive algorithms and deep learning models for tremor mitigation, challenges such as data acquisition and implementation in wearable devices persist. By leveraging the Kalman Filter’s ability to estimate system states, we aim to develop a more effective and accessible solution for individuals with tremors. Our findings demonstrate that the Modified Kalman Filter can achieve significant tremor reduction, when compared to Kalman Filter, paving the way for future research in wearable assistive technologies and brain-computer interfaces.
Pensamento computacional e jogos educativos Proposta de atividade para a Educação Infantil
ABSTRACT This article presents an activity that uses games as educational tools to promote computational thinking in early childhood education children. Based on the principles of constructivist pedagogy and the guidelines of the National Common Curricular Base, the study highlights the importance of play-based learning. Conducted in a preschool in northern Paraná, it demonstrates how games can be used to develop logical reasoning and problem-solving skills in 5-year-old children. The study involved games designed with difficulty levels, challenging children to think critically and sequentially. The results show that most children were able to complete the games at the simplest level, while more complex tasks required additional support. The authors argue that incorporating games into the preschool curriculum can improve children's overall development, including their cognitive, social, and emotional skills.
O uso do software Scratch no ensino de Computação e desenvolvimento do Pensamento Computacional
ABSTRACT This study aimed to investigate the application of programming logic using Scratch software in the context of teaching Computing and developing Computational Thinking. This is a qualitative, exploratory study conducted with first-year high school students from a public school. The results indicated that students developed skills related to programming logic using Scratch, recognizing and applying the pillars of Computational Thinking – decomposition, pattern recognition, abstraction and algorithms – through the creation of a digital game. The analysis of the research corpus revealed that the construction of the digital game, based on Scratch programming logic, contributed to the development of skills such as identifying and selecting strategies for formulating algorithms. This approach provided students with opportunities to think, discuss, recognize and improve programming logic, making the learning process more meaningful and effective.
Sistema de Localização Baseado em Comunicação por Luz Visível Utilizando a Técnica de Impressão Digital
ABSTRACT This paper proposes an indoor localization system for Visible Light Communication (VLC) systems, using LED lamps as light sources to determine the position of a receiver. The proposed method is based on the fingerprinting technique, which uses prior measurements of patterns in the environment and compares them with real-time measurements to determine the location of a receiver. Specifically, the proposal uses multiple light irradiation patterns, one for each lamp, to improve accuracy and reduce ambiguities in position determination. The preliminary results presented in this paper demonstrate the effectiveness of the method, while the proposed implementation reduces the computational complexity involved in the search process.
«LLAB UX: Construindo Um Laboratório de Ensino Prático e Mentoria sobre Experiência do Usuário e Design de Interfaces.
ABSTRACT User Experience (UX) aims to understand the human factors influencing interaction, making it a prominent area in software projects. In this context, the LearningLab project established a laboratory dedicated to UX studies (LLAB UX), combining lectures with interface development to explore the UX process. A leveling survey identified profiles ranging from beginners to intermediate users and experienced members proficient in Figma (a tool used for interface design). A carefully planned approach to address these varied needs was implemented, ensuring inclusivity and fostering learning and satisfaction among all students.
ANTi-Mafia: Desenvolvimento de Jogo Digital na Graduação com Ferramentas Abertas e Acessíveis
Resumo This article shares the experience of undergraduate students in technology who developed a digital game called "ANTi- Mafia"while exploring accessible game development tools and methodologies. Despite challenges such as time constraints, lack of experience, and no financial backing, the project demonstrated that high-quality games can be produced with minimal investment using free and open-source tools. The process emphasized the importance of the right methodology for successful learning and project development. The game currently features 6 levels and uses the top down shooter genre mechanics.
Atendimento Inteligente: Solução Automatizada de Suporte Acadêmico para Alunos de uma Instituição de Ensino
Resumo This paper presents partial results from the development of a chatbot designed to support students within an educational institution. To evaluate the system under development, a survey was conducted with 36 potential users to test and validate the proposed solution. Through this initiative, the project aims not only to automate student services but also to promote technological innovation in academic management.
Manipulação vetorial e cálculos matemáticos para a quantificação dos movimentos das mãos e interação manual
Resumo The development of interactive and accessible computational technologies for treating patients, elderly individuals, and people with disabilities includes the stimulation of manual movements, especially when related to routine activities and the maintenance of autonomy. Equipment that algebraically quantifies and provides precise data on human body kinematics, including hand movements, enables the reconstruction of biomechanical movement and allows interaction with virtual environments, made possible through computer vision techniques. In this context, a study and application of mathematical vector manipulation calculations are proposed to quantify hand movements and highlight their applications in healthcare.
Acesso Aberto e Recursos Educacionais Abertos A Experiência de Redesign de um Repositório Educacional Aberto
ABSTRACT Access to information is one of the main topics covered in education, and the idea of introducing community participation in the creation, development and propagation of knowledge has increased mainly with the movement in defense of open access and free information. In this context, UNESCO also began to establish educational dissemination strategies, such as Open Educational Resources (REA). These materials encourage teaching and research in the public domain, in addition to encouraging the use, production and adaptation by third parties and also providing the possibility of collaboration between institutions. The Arcaz repository, developed by the PET-CoCE group, is based on the group's research into the growing importance of disseminating knowledge and information in Open Access format. This article presents a heuristic evaluation developed in Arcaz based on Interaction Design Principles. This evaluation, with the participation of experts, resulted in the redesign of the repository. The next steps must involve community participation for the participatory construction of new features.
Explorando o Redirecionamento de Streaming de Vídeo em Infraestruturas de Borda-Nuvem para Usuários Móveis
Resumo Video streaming has become one of the most popular applications in recent years. With the exponential increase in demand for content, the development of adaptive solutions that balance the efficient use of computational resources while maintaining a high quality of experience for users, especially mobile users, has become essential. In this context, the integration between edge and cloud infrastructures emerges as a promising approach for delivering high-quality video as the user moves. This integration encompasses a wide range of devices, from mobile equipment to servers in data centers, including intermediary servers based on fog computing, many of which are deployed near 4G/5G base stations. This work proposes to investigate rule-based autonomic computing strategies for the dynamic adaptation of streaming video services to better utilize computational resources in edge and cloud infrastructures. Specifically, it explores the content-steering architecture, implemented by the Dynamic Adaptive Streaming over HTTP (DASH) protocol, as a solution to optimize streaming video, focusing on enhancing the quality of experience for mobile users.
EduMap: Uma Ferramenta de Analítica Acadêmica para o Mapeamento Geográfico da Origem dos Estudantes
ABSTRACT This article presents the development and application of an academic analytics tool called EduMap, focused on the mapping and geographic analysis of university students. The tool enables data visualization to facilitate the identification of geographic aspects related to the students’ origins, aiming to promote data-driven decision-making. An educational analysis is presented based on the proposed tool.
Aplicação da Arquitetura YOLOv11 Nano na Classificação de Raças de Cães
Abstract This study evaluates the effectiveness of the YOLOv11 Nano neural network architecture for dog breed classification. YOLOv11 Nano is a small convolutional neural network (CNN) model specifically designed for image classification task, particularly suited for objects with similar characteristics, such as dog breeds. The proposed architecture presents results comparable to previously applied networks, such as NASNet-A Mobile, and slightly underperforms in comparison to Inception-ResNet-V2, despite its substantially smaller model size. A comprehensive evaluation was conducted, incorporating pre-trained models alongside optimization strategies, including data augmentation and K-Fold cross-validation. The results highlight the potential of YOLOv11 Nano for the dog breed classification task, offering a balance between accuracy and size.
Meninas Digitais UTFPR-CP: Relato de Experiência do Projeto de Extensão
Abstract This report presents the actions promoted by the Meninas Digitais UTFPR-CP extension project. The project, created within the Federal Technological University of Paraná, Cornélio Procópio Campus, aims to increase the participation of girls in computing and STEM, encouraging and helping elementary and high school girls from public schools in the cities of Cornélio Procópio and the surrounding area, in the interior of Paraná, to conquer their space in these areas. To this end, the project promotes actions such as programming workshops, mentoring for the Technovation Girls global competition, and discussion groups, including in these actions the resolution of challenges of the Sustainable Development Goals (SDGs).
Predição de Diabetes Utilizando Modelos de Machine Learning
Abstract This study investigates the application of supervised learning algorithms to predict the incidence of diabetes, leveraging easily accessible data such as demographic information and health indicators to identify the most effective approaches for early disease detection. The primary goal is to compare the performance of different classification models in this task. The experimental results considered three classification methods, namely: K-Nearest Neighbors (KNN), Logistic Regression, and Support Vector Machine (SVM). Due to the imbalanced distribution of the classes (presence or absence of diabetes), besides obtaining decent accuracy values, the recall of all methods was highly impacted. The continuation of this work will include: i) adding more classification methods to the experiment, such as neural networks and ensemble-based methods; ii) compare the obtained results with the literature; and iii) consider the impact of data pre-processing steps to mitigate the class imbalance.
Controle de Acesso com Reconhecimento Facial: Uma Revisão Sistemática da Literatura
ABSTRACT With the aim of identifying how facial recognition is used in monitoring and attendance control systems, this study presents a systematic literature review (SLR) on the topic. It was found that the most employed Artificial Intelligence technique was Convolutional Neural Networks (CNN). Regarding algorithmic approaches, the Haar Cascade Classifier was used in most studies. Concerning the datasets, proprietary databases were the most frequent. In terms of the number of images in the datasets, the analyzed studies ranged from small experimental sets to databases with millions of images, and the employed hardware ranged from affordable devices to highperformance GPUs. The test scenarios explored both controlled environments, with variations in lighting and angles, and practical applications in real-world settings. Finally, accuracy rates ranged from 78.40% to 100%, with an average of 96.02%, while precision rates varied between 80% and 100%, with an average of 94.98%. The main contributions of this study lie in identifying the most effective techniques, the challenges faced, and the gaps that may guide future studies.
Reconhecimento de Voz Aplicado ao Jogo de Xadrez Usando a Plataforma Arduino
Abstract Chess is a strategic game that enhances cognitive skills, yet its physical demands can exclude players with motor disabilities. This study presents a voice recognition system that enables chess piece movement through verbal commands, enhancing accessibility. The proposed solution integrates the VoiceRecognitionV3 module with the Arduino Uno to recognize spoken commands related to game actions. The methodology involves training the system to interpret a structured set of voice commands, organizing the chessboard into quadrants for efficient processing. Initial tests confirm the feasibility of voice-controlled chess interactions, demonstrating potential benefits for accessibility. However, limitations include the module’s constraint of recognizing only seven commands simultaneously and the necessity for user-specific voice training. Future improvements may involve integrating advanced speech recognition algorithms and machine learning to expand command recognition and adaptability. This research highlights the potential of voice recognition in interactive and inclusive gaming environments.
FlipUFSC Estratégia de Gamificação como Alternativa Pedagógica para o Período Pré-vestibular
ABSTRACT The Federal University of Santa Catarina’s entrance exam is the primary means of admission to the institution. This period often induces anxiety, fear, insecurity, and distress among students. Reflecting on the potential of teaching methodologies to ensure the necessary competencies and mitigate tensions during exam preparation, the inclusion of digital games in education has proven to be a positive approach. This work presents the development process of the educational game FlipUFSC — modeled after an arcade game and employing the Design Science Research Methodology (DSRM) — which integrates questions sourced from UFSC’s entrance exams. Thus, it aims to contribute to ensuring inclusive, equitable, and quality education, as established in Goal 4 (Quality Education) of the United Nations Sustainable Development Goals (SDGs), by making this preparatory period more engaging in order to support access to higher education.
Robótica Artística e Sustentável: Fortalecendo a Participação Feminina na Tecnologia
ABSTRACT Este trabalho relata a participação de três alunas do curso técnico integrado em Informática ao Ensino Médio, em um projeto de Robótica Aplicada. As alunas competiram na fase estadual da Olimpíada Brasileira de Robótica em 2024, na modalidade artística, e avançaram para a etapa nacional. O principal objetivo do projeto foi desenvolver robôs sustentáveis por meio da reutilização criativa de materiais recicláveis e componentes eletrônicos, visando reduzir os impactos ambientais associados ao descarte inadequado desses itens. A iniciativa adotou a metodologia de aprendizado baseado em projetos, que promoveu o desenvolvimento de habilidades práticas em eletrônica e programação. Além de fomentar a sustentabilidade, o projeto também incentivou a participação feminina nas inovações tecnológicas. Como resultado, as alunas não apenas aprimoraram suas habilidades técnicas e criativas, mas também conquistaram o primeiro lugar na etapa estadual da OBR. Além disso, desempenharam um papel crucial na promoção da conscientização ambiental e na ampliação da visibilidade feminina no campo da tecnologia
Amostragem Esparsa e Codificação VVC: Uma proposta para Compressão de Dados de Light Fields
ABSTRACT Light field imaging has increasingly gained attention in the research community for its ability to provide a richer and more complete representation of visual scenes by capturing both spatial and angular information. However, the massive data volume generated by light field images poses significant challenges in terms of storage and transmission, particularly in bandwidth-limited or real-time environments. This work proposes a novel approach for compressing light field data by integrating Sparse Sampling techniques with the Versatile Video Coding (VVC) standard. The proposed method leverages the Discrete Cosine Transform (DCT) to generate a sparse representation of light field sub-aperture images, followed by random sparse sampling to reduce the dimensionality of the data. A reconstruction process using orthogonal matching pursuit ensures high-fidelity recovery of the light field prior to VVC encoding. The performance of the proposed solution is evaluated using samples of the HCI Dataset, and results demonstrate significant reductions in data size while maintaining visual quality in up to 38db (PSNR) and 0,95 SSIM. The integration of sparse sampling and VVC coding demonstrates promising results for light field compression, enabling its deployment in high-speed communication networks.
Diálogos sobre Mulheres em STEAM Uma Perspectiva Crítica
ABSTRACT Women participation in technology is one of the most important topics that leads to the problem of gender equality in a group where male presence clearly prevails. It is also a matter that appears in the metrics of ONU of Sustainable Development Goals (SDGs) proposed in 2015 proving it as a real society issue. The modern computation world was built through numerous important feminine efforts and discoveries throughout history. Even so, the majority of men in the field expose a strange phenomenon, where women are the minority in the area nowadays. One of the ways to mitigate that is to empower and encourage girls and women of all ages to enter and explore more subjects in technology and computer science and show them they’re capable of doing everything despite their gender.
Redes pré-treinadas na Odontologia: Classificação de imagens de radiografias panorâmicas dentárias fora do domínio
ABSTRACT Este trabalho explora uma metodologia para a classificação de imagens radiográficas panorâmicas em odontologia, aplicando técnicas de transferência de aprendizado. Incorpora-se a expertise de um especialista na classificação das imagens, especificamente para procedimentos endodônticos, ampliando assim o escopo e a relevância das bases de dados utilizada. Para a tarefa de classificação, foram empregadas duas redes neurais convolucionais pré-treinadas: VGG19 e EfficientNetB0. A análise de desempenho dessas redes revelou uma pontuação F1 de 65,00% para VGG19 e 96,24% para Efficient- NetB0, indicando uma eficácia significativa na segunda arquitetura que consegue convergir e fazer classificação precisa das imagens radiográficas odontológicas com foco em endodontia.
Um Sistema para Monitoramento Remoto de Sinais de Eletrocardiograma em Ambientes Hospitalares e de Home Care
ABSTRACT Annually, about 17.9 million people in the world die from some form of cardiovascular disease, and most of these deaths could be prevented with a simple ECG (electrocardiogram) exam, followed by an adequate medical treatment. The ECG is an exam that collects the heart’s electrical impulses. These data can be visualized in graphs that allow the identification of possible alterations or behaviors out of the known parameters, that may cause harm to the patients. The proposed systems enables medical team to collect ECG signals and detect some anomaly in patients allocated in hospitals and home care environments. The data collected are transmitted through a Wi-Fi network to a server at the internet cloud, where they are propperly storaged. This way, the medical team can visualize the necessary parameters in real-time using an App designed to mobile devices. Real experiments were performed, in a case study, showing the feasibility of the proposed system.
Recicla.me: A tecnologia em prol de um futuro mais limpo
Resumo The project "Recicla.me" aims to raise awareness among children and adolescents about the importance of recycling through an arcade-style educational game. In the game, players must correctly dispose of different types of waste into colored bins, promoting sustainable practices and environmental education. The game, featuring 2D graphics in 8-bit style and original soundtrack, is responsive and can be played online, with the possibility of a mobile app version. In addition to providing an enjoyable and informative experience, the game supports the United Nations’ Sustainable Development Goals (SDGs), particularly regarding responsible consumption and waste management.
Fogo Virtual, Impacto Real: Educação Ambiental e Preservação Amazônica com Realidade Aumentada
ABSTRACT The fires in the Amazon represent an environmental crisis that threatens biodiversity and global climate balance, affecting local communities and contributing to climate change. Environmental awareness, especially among young people, is crucial to tackling this issue. Therefore, an innovative project is being developed to raise awareness among elementary school students about the fires in a region. The initiative involves creating an educational book with information on forest fires, using augmented reality (AR) to simulate fires in 3D and display videos in the cultural settings of a specific locality. This approach aims to educate and sensitize children, promoting the preservation of the forest and encouraging environmental responsibility from an early age.
Elaboração de Documentos Técnicos Acessíveis Para Pessoas Com Dislexia
Resumo The concept of accessibility has recently emerged as a prominent topic of discourse within our society, particularly concerning digital accessibility, which exerts a growing influence on our lives and the operations of businesses. This article delves into the salient issues associated with the creation of PDF technical documents pertinent to automation products, with a focus on ensuring accessibility for individuals afflicted with dyslexia, a specific learning disorder that manifests as impaired reading. The study encompasses an examination of the primary challenges encountered by these individuals in comprehending and interpreting information, along with the methodologies employed in the creation of accessible documents and the prevailing standards that govern their development. To illustrate the concepts discussed, a technical document on variable frequency drives available on the market is analyzed. The accessibility of a specific PDF technical document is evaluated based on recommendations from the "Adobe Acrobat Pro"[2] and "Common Look PDF Validador"[4] software.The document is modified to improve accessibility, and the article concludes with an analysis of the results of the modified PDF document and future perspectives.
Decodificação de Atenção Auditiva com Ear-EEG: Uma Revisão Sistemática da Literatura
ABSTRACT Ear-EEG technology has emerged as a promising solution for discreet and comfortable brain monitoring, with potential applications in areas such as auditory attention decoding (AAD), brain-computer interfaces, and smart assistive devices. This work presents a systematic literature review focused on the application of ear-EEG for auditory attention detection, investigating methods and technologies developed. In addition, we have discussed the main technical limitations and gaps in the literature, highlighting the challenges associated with accurate AAD. The results indicate a growing interest in the area, so future research should focus on standardizing preprocessing techniques and exploring low-cost, efficient devices and algorithms for signal acquisition and analysis.
JOGO EM REALIDADE VIRTUAL DE TREINAMENTO PARA COMPENSAÇÃO DE PERDA DO CAMPO VISUAL PARA PACIENTES PÓS-AVC
Abstract Stroke, or Cerebrovascular Accident, affects thousands of individuals annually and remains a leading cause of death in Brazil. Among survivors, many experience lasting sequelae, such as hemianopsia— a partial loss of the visual field. The standard treatment for this condition typically involves visual field compensation exercises, which, while effective, are often repetitive and demotivating for patients. To address these challenges, we developed AeroVR, an innovative game designed to support patient rehabilitation. AeroVR combines virtual reality technology, which facilitates the stimulation of a broader visual field, with the principles of serious games to enhance patient engagement and adherence to therapy. Developed iteratively in partnership with the Occupational Therapy School-Clinic, the game is highly customizable, ensuring it can cater to a diverse range of patients. By merging advanced technology with user-centered design, AeroVR offers a promising solution for improving the effectiveness and accessibility of hemianopia rehabilitation.
LLMs e o Raciocínio Lógico: Um Estudo de Caso com Desafios de Lógica
ABSTRACT This paper evaluates the ability of seven Large Language Models (LLMs) to solve logic challenges. The models tested include GPT- 4, Claude 3.5 (Sonnet and Haiku), Gemini 1.5, Llama 3.1, Grok, and Mistral 7B. Four challenges were proposed, and the results demonstrate that none of the LLMs were able to solve all challenges correctly. The study highlights the current limitations of LLMs in logical reasoning tasks, despite advancements in other areas of natural language processing.
Assessing the Feasibility of a Spatio-Temporal Approach for Recognizing Microorganisms in Image Sequences
ABSTRACT Data classification is one of the main challenges in data mining and knowledge discovery. It is widely explored in multiple applications, such as identifying microorganisms by analyzing images of cultures grown in Petri dishes. This work proposes to chronologically organize images of bacteria on solid media, captured at equidistant intervals over several days, treating them as video frames to improve the recognition of these microorganisms. To this end, we develop an approach that chains classification models, integrating spatial features from pre-trained convolutional neural networks with temporal information propagated through target meta-features, i.e., classifier outputs.We experimentally compared our proposal with two intentionally designed baseline methods using a dataset with 240 images, 48 per class, and considering the macro-averaged F1 score. Results demonstrate that addressing chronological relations enhances the identification models’ performance, even though baseline strategies may benefit from fewer examples.
Fortalecendo a Presença Feminina na Computação: Análise do Impacto do projeto Meninas Digitais Vale do Itajaí no Desenvolvimento Acadêmico
ABSTRACT Despite increasing career opportunities in computing, women remain underrepresented in this field due to societal stereotypes and systemic challenges. The Meninas Digitais Vale do Itajaí project, aims to address gender disparities by promoting initiatives that encourage women to pursue and persist in computing careers. This study evaluates the impact of the project's activities on female students in Information Systems and Computer Science at FURB University.
CASULO: DESENVOLVIMENTO DE APLICATIVO MÓVEL DE INCENTIVO A PERMANÊNCIA NA REABILITAÇÃO DE DEPENDENTES DE DROGAS LÍCITAS
ABSTRACT The consumption of legal drugs (tobacco, alcohol and medications) is constantly increasing and is considered a social problem. It can be influenced by social, economic and cultural factors, as well as the availability of drugs and other environmental factors, as family relationships and influences. These substances are responsible for altering the functionality of the central nervous system, mood and perception of the environment, directly affecting health and causing dependency. For this reason, interventions are necessary through treatments and relapses prevention, in addition to raising awareness about addiction and its triggers. The project proposes the development of a mobile application that, using gamification resources, encourages persistence in the treatment for chemical dependency, aiming to reduce relapses.
Dispositivos portáteis para medição dos níveis de açúcar em bebidas: uma revisão sistemática da literatura
ABSTRACT Knowing the levels of sugar ingested in foods and beverages is essential for health, given its relationship with various diseases such as obesity and diabetes, among others. Using portable devices for measuring sugar in beverages aims to raise awareness and alert people about excessive sugar consumption. This work presents a systematic literature review (SLR) conducted using the PICO methodology and managed with the Parsifal tool to identify new methods and portable devices for measuring sugar in beverages commonly consumed by the population. Seven papers were selected for a more in-depth analysis, showing that the most commonly used techniques for this application are electrochemical techniques using immersion electrodes and non-visible light through Near-Infrared Spectroscopy. Only one study briefly addressed using the Internet of Medical Things to share results. The study aims to present the current academic scenario by mapping technologies and opening new research possibilities.
Revisão Sistemática sobre o Uso de Radares FMCW para Monitoramento Sem Fio de Sinais Vitais para Aplicações em Internet das Coisas Médicas
ABSTRACT This work presents a Systematic Literature Review (SLR) for contactless vital signs monitoring based on the Internet of Medical Things (IoMT). This SLR aims to search for relevant studies that use Frequency-Modulated Continuous Wave (FMCW) radars to estimate the breath and heart rate of a target while having Internet of Things (IoT) connectability. We utilized the PICO methodology to retrieve 34 articles, and through inclusion and exclusion criterias, we filtered this result to 5 articles. From these articles, the two main points observed are the continuous growth of studies that use contactless methods for vital signs monitoring in recent years and the surprising lack of devices with IoT or IoMT compatibility, in which several articles only cite the technology but don’t elaborate concrete steps for its implementation. This made us realize the opportunity for future works and research to create and implement IoMT technologies with contactless vital signs monitoring.
Criação de uma rede neural para classificação de amostras d’água conforme normativa CONAMA 357/05 a partir de um dataset da SEMASA
ABSTRACT This work presents the creation of a neural network trained using a dataset from SEMASA (Serviço Municipal de Água, Saneamento Básico e Infraestrutura) from Itajaí, containing pH, turbidity, and apparent color data of water bodies from various neighborhoods in the municipality, capable of classifying water samples according to the water types defined by the CONAMA (Conselho Nacional do Meio Ambiente) 357/05 standard for embedding in an IoT device. The dataset is composed of 8,928 records from 2018 to 2023, covering neighborhoods São Roque I and II, Arapongas, and Limoeiro. The selected variables are critical for water quality assessment, where pH indicates the acidity or alkalinity of the water, turbidity measures the presence of suspended particles, and apparent color is a visual indicator of dissolved and suspended materials.
Explorando Padrões de Captura e Comprimento do Tubarão azul (Prionace glauca) por meio da Visualização de Dados no Sudoeste do Oceano Atlântico
ABSTRACT The present study developed a dashboard to analyze the patterns of the spatial and temporal distribution of catches and lengths of marine species, with a particular focus on the blue shark (Prionace glauca), in the southwestern Atlantic Ocean, a region considered critical for the species due to the presence of parturition areas and juvenile nursery grounds. The data were collected from commercial pelagic longline fisheries between 2017 and 2024, through onboard monitoring and landing records. Additionally, biometric data for blue sharks were obtained during field expeditions conducted by UNIVALI/LEMA professionals or provided by companies during their site visits. The results of this study are presented in real time through a dashboard, offering an interactive platform to explore and visualize dynamic graphs of blue shark.
Escape Programado: Um jogo para ensinar pensamento computacional em ambiente de realidade virtual
ABSTRACT Given the recent popularity of escape rooms, dynamics like these can be used in the classroom to stimulate teamwork, and their implementation in virtual reality (VR) environments can bring benefits such as increased student engagement. Therefore, this article proposes a game titled Escape Programado, which utilizes a VR environment to build an escape room, with challenges that stimulate computational logical thinking in players. As results, a prototype of the first phase and the structuring of the game’s dynamics were presented.
Proposta de Metodologia de Análise de Redes Wi-Fi Prediais
ABSTRACT The present work proposes a methodology for evaluating building Wi-Fi networks using a spectrum analyzer alongside Wi-Fi network analysis tools. The Agilent CSA Spectrum Analyzer, model N1996A-506, and the tools ManageEngine OpManager and PRTG Professional Wi-Fi Analyzer will be used to measure the Wi-Fi signal of an academic building. The collected Received Signal Strength Indicator (RSSI) data will be imported and modeled in Python code to generate heatmaps that visually represent the quality of the Wi-Fi network coverage. The results obtained from the tools will be compared with those obtained from the analyzer to verify the accuracy and reliability of the reports generated by the tools.
Aplicação de Visão Computacional para Identificação de Códigos de Contêineres
RESUMO A evolução tecnológica tem desempenhado um papel essencial na otimização do setor logístico, promovendo a automação de procedimentos e impulsionando a competitividade no mercado. Um dos setores que se destaca no mercado é o de gestão da cadeia de suprimentos, porém, a dificuldade em controlar a entrada e saída de contêineres devido ao alto tráfego de caminhões nos pátios de armazenamento resulta em filas e atrasos. Com o desenvolvimento de uma ferramenta que facilite o controle de contêineres, é possível superar esse desafio. A aplicação de um software capaz de realizar a leitura automática e precisa do código identificador do contêiner em tempo real traz ganhos operacionais significativos. Neste contexto, este trabalho apresenta uma aplicação destinada à extração do código identificador de contêineres por meio de vídeos em tempo real com requisito de processamento de 30 quadros por segundo. Utilizando uma abordagem de pesquisa aplicada, foi elaborado um software que emprega métodos de reconhecimento de objetos e técnicas de processamento de imagens. Após uma análise da literatura, o método mais apropriado para o reconhecimento de objetos, o YOLOv8, foi selecionado. Adicionalmente, uma pesquisa de métodos de processamento de imagens foi conduzida para aprimorar a precisão da biblioteca de OCR Tesseract durante a etapa de reconhecimento de caracteres. Um terminal foi construído e equipado com duas câmeras para a realização e validação do software desenvolvido. Os resultados obtidos apresentaram uma taxa de precisão de 80% com apenas uma das câmeras, junto a análise dos casos de erro visando identificar melhorias futuras.
Inteligência Artificial na Execução Fiscal transformação tecnológica e governança regulatória
ABSTRACT Tax enforcement is one of the biggest challenges facing the Brazilian Judiciary due to the large number of processes and the complexity of credit recovery. This study explores how Artificial Intelligence (AI), by automating repetitive tasks and assisting in decision making, can increase the efficiency and speed of tax executions in the context of Brazil, while at the same time presenting risks and limitations, such as the possibility of algorithmic biases and lack of transparency in automated decisions. The methodology adopted in this work was an integrative literature review with the analysis of scientific articles and reports that investigate the impact of Artificial Intelligence techniques on tax enforcement. The results reveal that, although Artificial Intelligence has the potential to transform tax enforcement, increasing effectiveness in credit recovery and optimizing procedural management, its implementation still faces obstacles, such as the need for regulation and technological integration with judicial systems. existing. It is concluded that, for AI to be applied effectively and ethically, it is essential to develop a robust regulatory framework that ensures continuous human supervision, transparency of algorithms and the protection of rights. Thus, the study suggests that the combination of technological innovation with adequate governance is essential to maximize the benefits of AI and mitigate its restrictions in the context of tax executions.
Classificação de Aves Predadoras: Fine-tuning Progressivo em Redes Neurais Convolucionais
Abstract This paper presents a process for classifying predatory birds by family and species. The motivation for this study arises from the high variability observed among birds of different species and the importance of performing classification efficiently and in a timely manner. Additionally, this work aims to analyze the impact of using RGB channels in comparison to grayscale images on classification performance, as well as the effect of applying data augmentation techniques during training. The dataset contains 42,475 images, distributed across 6 families and 41 species. The process employs fine-tuning, using the ResNet-50 model. Early stopping was applied to control overfitting and obtain the best model. The test results highlight the effectiveness of the proposed process in classification tasks, with performance varying across different input configurations. For species classification, the model trained with grayscale channels achieved an F1-Score of 0.80. Using RGB channels improved the performance significantly, resulting in an F1-Score of 0,86. Further applying data augmentation techniques to the RGB slightly improved the metrics, achieving an F1-Score of 0.87. These results demonstrate the benefits of incorporating color information and data augmentation in enhancing classification accuracy.
Regularização em Redes Neurais com Reamostragem de Dados para Monitoramento Urbano Inteligente
Abstract The advancement of smart cities has led to a growing demand for efficient urban management systems, such as parking space management. In this context, regularization has proven to be an essential technique for improving the generalization of deep learning models. This paper proposes a regularization method based on the use of smaller data subsets across multiple epochs with resampling, aiming to balance model learning and reduce the risk of overfitting. Using the MobileNetV3-Small, four experiments were conducted with the PKLot and CNRPark-EXT datasets. The results indicate that the proposed method achieved competitive results while utilizing only a fraction of the original data during training. By employing these strategies, it was possible to reduce the data required per epoch by up to 97%, while maintaining an average accuracy close to 89%. Furthermore, training with the PKLot dataset highlighted the positive impact on model robustness when using datasets with greater diversity and quantity. This study underscores the importance of new regularization approaches to enhance the efficiency and generalization of deep learning models in urban applications.
NPCs Interativos Utilizando LLMs: uma Revisão Sistemática da Literatura
Avaliação In-Domain e Cross-Domain em Restauração de Pontuação utilizando Processamento de Linguagem Natural
ABSTRACT Punctuation plays a fundamental role in conveying the correct meaning in written texts. As a result, punctuation errors can occur, significantly impairing the way a message is interpreted, whether in formal or informal contexts. In this sense, the use of machine learning, combined with recent techniques in natural language processing, has been widely used in the task of punctuation restoration, in languages such as English. However, despite the wide application of this task in other languages, its use in Portuguese is still quite limited. In this work, we propose to adapt a punctuation restoration model for its application in formal texts in the Portuguese language, in addition to evaluating the model’s behavior in informal texts. The Portuguese Legal Sentences v3 dataset was used to train the model, which was also used for the in-domain evaluation. Regarding the cross-domain evaluation, the IWSLT (International Workshop on Spoken Language Translation) database was used, consisting of transcripts of lectures known as TED Talks. The results indicate that the model with the largest amount of training data and that mapped all question marks to full stops performed satisfactorily in the formal context, suggesting that the methodology adopted was adequate for the proposed task. Furthermore, it was found that the scarcity of question marks negatively impacts the model’s performance and that, in the informal context, the results were unsatisfactory in the evaluation metrics, suggesting that formal and informal sentences have their own structures, which the model was unable to generalize adequately in the informal context.
Avaliação de Abordagens para Classificação de Malware Bancário sob a presença de Concept Drift
Abstract Financial malware is an increasing threat due to the potential of profit for cybercriminals. Year after year, the arms race between malware developers and security professionals foster the release of millions of malware variants. Although machine learning techniques have been successfully applied to malware classification tasks, the occurrence of concept drifts requires constant updates (or even retraining) of these models. In this work, we evaluate how distinct machine learning training approaches for malware classification when we consider the arrival flow of samples as a data stream. We also observe the manifestation of concept drift in an actual dataset comprised of thousands of financial malware samples, discussing different incremental learning approaches to deal with a custom dataset and how concept drift can arise in malware data.
Classificação de Cenários Acústicos em Dispositivos Auditivos: Uma Revisão Sistemática da Literatura sobre Desafios, Avanços e Perspectivas
ABSTRACT Acoustic Scene Classification (ASC) is an area of growing relevance, with applications ranging from assistive devices, such as hearing aids, to advanced wearable technologies (hearables). This paper presents a Systematic Literature Review (SLR) that analyzes the main adaptive and machine learning-based methods used in ASC, with a focus on hearing devices. The challenges related to computational resource limitations, energy consumption and real-time operation, especially in dynamic environments, are discussed. The review highlights recent advances, such as the use of generative probabilistic models and convolutional neural networks, as well as hybrid approaches that combine cloud computing and edge computing for greater efficiency. The results show that, despite significant progress, there are still important technical barriers, such as the need for more efficient, customizable and robust algorithms to operate in real conditions. This study contributes by identifying gaps in the literature and suggesting future directions to improve the integration of ASC in hearing devices.
Diferenciando o Não Diferenciável: Investigando problemas na diferenciação da ReLU no treinamento de modelos de Aprendizado Profundo
ABSTRACT Activation functions are necessary for deep learning models to be able to represent non-linear relationships. Their derivative is required during training, however, many non differentiable activation functions are commonly used in neural networks, such as the Rectified Linear Unit (ReLU) and its variants. This paper discusses the impact of non differentiability on activation functions during the training phase, and how these functions compare to differentiable alternatives. To analyse this problem, we trained neural networks in an image classification problem using various activation functions. We showed that non-differentiable points occur rarely during training, especially in deep models, and have little to no negative impact in a model’s performance.
Análise de Escalabilidade para Armazenamento e Processamento de Arquivos de Áudio Utilizando Transformers
ABSTRACT In a continental-sized country like Brazil, collecting feedback on governmental services such as education, healthcare, and security is challenging and impractical to perform manually, except through sampling techniques. With advancements in machine learning, particularly models based on transformers, it is now possible to automate this process on a large scale, enabling, for instance, the dissemination of health campaign information or the collection of citizen opinions on recently used services. This paper focuses on speech-to-text transcription, a crucial step for enabling largescale voice-based responses.We explored scalability challenges and evaluated combinations of transcription models and audio formats (WAV, FLAC, and MP3), aiming to balance the computational cost and transcription quality. Our results showed that MP3 files sampled at 14 kHz provide transcription quality comparable to WAV files sampled at 16 kHz while requiring only 11% of the storage size. Furthermore, we demonstrated that smaller models, such as Wav2Vec2-XLSR-53 with 3.17 × 108 parameters, can achieve results similar to larger models, such as Seamless M4T, which has approximately an order of magnitude more parameters.
Detecção e Classificação de Documentos contendo Macros Maliciosas com base em Processamento de Linguagem Natural
Abstract Macros are functions written in Visual Basic for automation within MS Office documents. On the one hand, macros bring many facilities to home users and organizations. On the other hand, they have also stimulated the arise of macro viruses (malware that exploit macros to infect users loading compromised documents, spreadsheets, and presentations). Those viruses may delete data and steal information, and have been causing losses of billions of dollars in global attacks. Although more prevalent in the 1990s and 2000s, macro viruses resurged in the last decade and continue to threat current MS Windows/Office users. In this article, we present a natural language processing-based pipeline to detect macros in MS Office documents and classify them in malicious or benign. Using byte2vec as document representation, we outperform the state-ofthe- art in Macro detection, reaching over 99% of Precision-Recall Area Under Curve (PRAUC) metric for four out of seven evaluated classifiers (and over 98% PRAUC in the remaining three classifiers).
Machine Learning-based Method to Label Signals from People with Neurological Injuries
ABSTRACT Interfaces that use sEMG signals face the challenge of correctly identifying the signal while distinguishing it from noise or interference. Although classical techniques like visual inspection and machine learning methods exist, most studies focus on signals from healthy individuals. There is a lack of data and methods suitable for signals from individuals with neurological conditions, such as cerebral palsy and post-stroke. This study analyzes sEMG data from individuals with neurological injuries, using machine learning methods to identify muscle contractions and rest without pre-processing. The data were acquired from people with neurological diseases, such as cerebral palsy and post-stroke. They were extracted using sEMG from triceps brachii and extensor carpi radialis muscles. The signals were not preprocessed and were input as segmented time windows to three proposed classifiers: Support Vector Machine, Random Forest and an Ensemble Voting classifier. All three classifiers reached around 99% accuracy and F1-Score on typical sEMG data, but the results on abnormal data were inconclusive.
Predição da Energia Não Suprida para Estudos de Resiliência de Sistemas de Distribuição Elétrica usando Algoritmos de Aprendizado de Máquina
Agrupamento de usuários e organizações para verificação de viabilidade de distinção comportamental de uso de sistemas com base em aplicações e tempo
Abstract Authentication mechanisms are still the standard way to allow access to systems and devices within an organization. Through credentials (login and password) and other associated methods (multi-factor authentication, such as tokens, biometrics, or onetime passwords sent to additional devices), access control is implemented, and user activity across different necessary systems is recorded. However, organizations are concerned that access control may be bypassed due to the loss or theft of authentication information/ devices, potentially leading to intellectual property breaches through industrial espionage. In this context, User and Entity Behavior Analytics (UEBA) has been studied and applied to profile users and identify anomalous patterns that could, for example, block a user from accessing another account. However, achieving this level of protection in real-world systems may be unfeasible. This article examines the feasibility of distinguishing user behavior in organizations based on the most frequently used applications and their usage time. To this end, a real dataset was collected, consisting of data from over 700 organizations and nearly 60,000 users between March and September 2024. The results discuss the techniques used, the possibility of detecting real intruder users, and the false alarm rates observed in the dataset, paving the way for future research in the field.
Synthetic Images Impact in Parking Spot Classification using Neural Networks
Abstract Urban growth and the increasing number of vehicles have intensified the demand for efficient parking management solutions. In this context, machine learning-based image monitoring systems have gained prominence due to their low cost and ease of installation compared to traditional methods, such as physical sensors. These systems achieve an average accuracy of 95% in cross-validation scenarios using well-known datasets like PKLot and CNRPark-EXT. However, despite the availability of extensive datasets, challenges remain regarding the accessibility and diversity of training data. This is especially critical when aiming to improve the accuracy of generalist models or specialize them for specific scenarios, where each application requires a substantial effort to collect, segment, and label new images for optimal performance. This study proposes the use of synthetic images, generated with the Unity 5 graphics engine and the Unity Perception package, to complement or replace real data in training parking classification models. A synthetic image generation protocol was developed to reduce costs compared to the collection, segmentation, and labeling of real images. The images generated through this protocol are referred to as low-fidelity due to their lower quality and reduced capacity to simulate specific environments. Using MobileNetV3 and transfer learning, experiments were conducted in three scenarios: total replacement of real data, supplementation of diverse datasets, and specialization for specific scenarios. The results showed that synthetic images could improve model generalization by up to 2% in datasets with limited real data (e.g., CNRPark-EXT). However, synthetic images alone could not fully replace real data due to their limited fidelity in replicating real-world conditions, reinforcing the need for combinations with real data or more realistic synthetic data for better results.
Diversidade e Inclusão de Mulheres na Engenharia da Computação: uma relação em construção
Resumo This work was motivated by the need for women’s inclusion and the desire for diversity in Computer Engineering. The project focused on reference articles, surveys based on questionnaires, data analysis, and periodic meetings with female students in the program. The main goal of the project is to foster a more welcoming environment for women in the field and to make the course more appealing to those interested in joining, as well as to understand and address some of the factors contributing to the underrepresentation of women in this area. Additionally, the study highlights some implemented actions and proposes future interventions to promote diversity in this degree program. The actions already implemented, such as support groups and an increase in the number of female scholarship recipients, have shown positive results and emphasize the importance of continuing efforts to promote diversity and inclusion. The study concludes that, although progress has been made, it remains urgent to intensify efforts to ensure the retention and success of women in the field, fostering a more inclusive and diverse environment.
Integrating Computer Vision with Embedded Intelligent Agents
Abstract In the current technological landscape, we are transitioning to a lifestyle shaped and supported by advanced artificial intelligence systems. In this context, there is a growing tendency to decentralize such systems, moving them to the edge, and placing more responsibility on the devices that compose these systems. The field of Multi- Agent Systems offers a powerful paradigm for distributed artificial intelligence, where intelligent agents can incorporate a variety of AI techniques to make autonomous decisions. In this work, we propose an approach for implementing embedded autonomous intelligent agents. We propose an architecture to implement autonomous intelligent agents embedded in simple microcontrollers, and as a case study, we describe the integration of computer vision, in which agents are capable of extracting information from computer vision components and utilizing this data in their decision-making processes.
Uma análise de múltiplos critérios do Problema de Roteamento de Veículos usando o AEMMT.
Abstract The following work presents a resolution of the Vehicle Routing Problem, focusing on fuel distribution, using Evolutionary Algorithms, which is a bio-inspired Artificial Intelligence technique. The study examines the behavior of a monoobjective version compared to the ‘MultiObjective Evolutionary Algorithm with Many Tables’ (AEMMT, in portuguese), which operates simultaneously with different subpopulations, where each one is dedicated to minimizing a specific objective function. Therefore, the objective is to determine which of the two produces the best results when considering several factors such as distance, time and fuel. As a result, in this work the version algorithm with many objectives proved to be superior to the single-objective version in relation to the value of the improvement brought, especially analyzing the evolutionary behavior of each criterion.
Conjunto de Dados para Envenenamento de Modelos Baseados em Self-Training em Fluxo de Dados
Abstract In problems with a large volume of unlabeled data, semi-supervised learning techniques, such as self-training, are attractive because they make full use of the data and do not require extensive labeling of the data, since it is an expensive process. However, using pseudolabels to train a model indiscriminately can lead to undue changes in the model’s decision boundary, which can happen unintentionally or intentionally, such as in malware classification, where attackers want to classify malicious software as benign. In this paper, we propose a dataset for poisoning models based on self-training that simulates a data stream, intending to evaluate the robustness of these models against intentional or unintentional poisoning by unlabeled instances. Our experiments use models from the MOA-SS framework, and show that models that use incremental training and prediction confidence as a criterion for using the unlabeled instance in training are more susceptible to poisoning.
André Ricardo Abed Grégio Ricardo Abed Grégio
Análise de Classificadores para Predição de Evasão em um Curso Superior de Tecnologia da Informação de um Instituto Federal
Abstract School dropout has been a long-standing issue in Higher Education Institutions (HEIs), raising concerns and prompting mitigation efforts. This paper examines dropout in a Technology course at a Federal Institute, addressing the research question: "How can machine learning classifiers assist in predicting student dropout?". The study applies machine learning classifiers using data from a Federal Institute, including demographic (age group, income, city, special needs, ethnicity, gender) and academic variables (admission method, vacancy type, enrollment status). The objective is to develop analyses ranging from descriptive statistics to predictive modeling. The classifiers used include Support Vector Machine, Random Forest, Logistic Regression, Gradient Boosting, AdaBoost, and K-Nearest Neighbors. The goal is to support teachers, coordinators, and administrators in implementing preventive measures such as personalized mentoring, continuous monitoring, and institutional policies to improve academic infrastructure and inclusivity. The results show that Support Vector Machine, Gradient Boosting, and AdaBoost achieved the best performance, with F1-measure values between 0.6 and 0.8, demonstrating their predictive capability. This highlights the potential of machine learning in addressing student dropout in higher education.
Bird Apprehension Forecasting with XGBoost
ABSTRACT This article applied exploratory data analysis (EDA) and time series forecasting using extreme gradient boosting (XGBoost) to bird apprehension data from the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA) covering 2010 to 2024, the dataset processed included 150,000 records, filtered to focus on significant patterns across two distinct periods: a COVID-19 pre-pandemic period (2010-2020) and a period that includes the COVID-19 pandemic (2014-2024). This analysis identified shifts in apprehension patterns with an overall decrease of 69.17% in bird apprehensions during the pandemic. The XGBoost model demonstrated satisfactory root mean squared error (RMSE) on most of the species except on Zenaida auriculata and Zenaida auriculata noronha where the RMSE values were 37.73 and 47.80, respectively, for the (2010–2020) period, while for the (2014–2024) period, the RMSE values were 42.93 and 48.76, which are higher values when compared to other results. The most apprehended bird species prepandemic was Sicalis flaveola with 163, 437 apprehensions, while Zenaida auriculata and Zenaida auriculata noronha remained highly apprehended throughout both periods. The northeast regions of Brazil, with states like Rio Grande do Norte, Ceará and Paraíba, showed the highest apprehension rates. Overall, the model achieved satisfactory performance in understanding the pattern of apprehension numbers in the tested periods, with significantly low RMSE values for the ten most apprehended species.
Determinação de Riscos de Sobrevoo por Visão Computacional
Abstract In the 20th century, aviation has proven itself as one of the most important techno-social revolutions of modern human history. Now, Unmanned Aerial Systems (UAS) are on the verge of leading the next techno-social revolution, remodelling several areas and moving a global market that does not stop growing. The establishment of UAV Traffic Management (UTM) systems is necessary to make this revolution happen, and one of the main obstacles is the risks involved in UAS flight, especially in urban environments. A methodology to estimate risks on the overflown environment was carried out by the usage of convolutional neural networks on the imagery of such environments, allowing the definition of safer routes and their management on the fly. Development and experimentation processes were carried out, with promising results including a convolutional neural network for pixel-wise domain that was capable of estimate risks from satellite imagery and return overflight risks heat maps.
Algoritmo para a Minimização do uso de Recipientes no Problema do Empacotamento Bidimensional com Restrições de Guilhotina
ABSTRACT Cutting and packing problems are prevalent in various industries, logistics, and the construction sector. Therefore, the study of this class of problems is justified due to its potential to reduce material wastage and associated costs, among other benefits. This paper presents a study aimed at minimizing the number of bins required to pack all items in the rectangular two-dimensional variable-sized bin packing problem. This problem involves guillotine constraints without rotations. The heuristics ILS and a modified BCSA were implemented. After conducting preliminary tests, the use of ILS was discarded, and only the proposed modified BCSA was employed to obtain results. To validate the performance and quality of the developed algorithm, various experiments were conducted on benchmark instances from the literature, including the dataset of [1], dataset of [2], and dataset of [3], and compared with several other heuristics. Consequently, it was found that the modified BCSA applied in this work achieved excellent results in most classes, with an average improvement of 14.6% over the compared heuristics in the first dataset and 14.08% in the second.
Análise Temporal e Predição de Nutricionistas no Ceará: Abordagens em Ciência de Dados para Suporte à Gestão em Saúde
ABSTRACT A ciência da nutrição ganhou destaque no início do século XX, sendo considerada um fenômeno recente no Brasil. A formação de profissionais de nutrição iniciou-se na década de 1940, com a criação dos primeiros cursos de graduação profissionalizante e o surgimento dos médicos nutricionistas. Em outubro de 2024, o Ceará contabilizava 1.623 nutricionistas registrados na base de dados do CNES, totalizando 2.028 vínculos, este trabalho apresenta uma análise temporal com o objetivo de quantificar o estado atual da distribuição dos profissionais de nutrição no estado do Ceará e suas perspectivas futuras, considerando especialidades e áreas de atuação. Os dados foram organizados em tabelas em que foi possível avaliar as flutuações dos vínculos empregatícios dos profissionais de saúde no estado em diferentes períodos. Adicionalmente, foram testados diferentes modelos de aprendizado de máquina para prever a evolução dos vínculos de nutricionistas ao longo do tempo. O modelo ExponentialSmoothing destacou-se como a abordagem mais precisa, apresentando os menores valores de erro, como MAE de 30,87 e MAPE de 2,1%. Esses resultados demonstram a eficácia desse modelo para capturar padrões temporais e sazonais na força de trabalho. Os achados reforçam a relevância de ferramentas preditivas no suporte à tomada de decisão estratégica, permitindo aos gestores de saúde otimizar a alocação de recursos, reduzir desigualdades regionais e planejar políticas públicas fundamentadas em evidências.
Reconhecimento e personalização de gestos das mãos para interação com páginas web
Resumo The Hand Mouse browser extension presented in this paper enables mouse control on web pages by means of the user’s hand gestures. This tool aims to enhance flexibility and ergonomics, offering a natural alternative to the traditional use of the mouse, potentially reducing the risk of repetitive strain injuries. The main innovations of this paper are the usability studies within the extension‘s user interfaces and the development of the gesture customization feature. This allows the user to define and train the AI model used in the software with new gestures to perform actions such as moving the cursor, clicking, scrolling up, scrolling down, going back a page, going forward a page and free movement.
Avaliação de Interface em Aplicativo para Medição de Nível de Atividade Física Baseado no IPAQ da OMS
Abstract Due to globalization and digitalization, there has been a huge increase in the number of mobile applications being developed. This makes the user experience and usability of apps a topic of interest. This study evaluates an app based on the World Health Organization’s International Physical Activity Questionnaire using Jakob Nielsen’s heuristics as a framework, aiming to evaluate the final users perception of usability. The following ten criteria were considered: visibility of system statuses; correspondence between the real world and the system; user control and freedom; consistency and standards; error prevention; recognition rather than recall; flexibility, and efficiency of use; aesthetic and minimalist design; helping users to recognize, diagnose, and recover from errors; and finally, help and documentation. Therefore, the metology used in this article made possible to evaluate the system - a scientific iniciation’s product - using Nielsen’s heurístics as a baseline of study. In conclusion, this article makes recommendations for changes to the system en route to improve its usability as well the used metodology itself.
Aprimoramento da Classificação de Linfócitos e Monócitos em Imagens Médicas: O Impacto do CLAHE em Redes Neurais Convolucionais
ABSTRACT The human immune system plays a critical role in defending against infections and diseases, with white blood cells (WBCs) being pivotal in these processes. Automated classification of agranulocyte cells, specifically lymphocytes, and monocytes, is essential for accurate diagnostics and treatment monitoring in hematology and oncology. This study evaluates the performance of a convolutional neural network (CNN) model, previously proposed for WBC classification, on public datasets, with and without the use of Contrast Limited Adaptive Histogram Equalization (CLAHE) for image preprocessing. The results show that CLAHE improved classification metrics, achieving up to 82.16% test accuracy on the Paul Mooney dataset and maintaining a high test accuracy of 98.72% on the Uncle Samulus dataset. Metrics such as precision, recall, and F1-score also exhibited notable improvements, reaching up to 98% for lymphocytes and monocytes in the best-performing dataset. These findings highlight CLAHE’s potential to enhance CNN-based classification under varying image conditions.
Impact of Image Enhancement Filters on Road Defect Detection Using Deep Learning
ABSTRACT The maintenance of road pavements is essential to ensuring the safety and efficiency of land transportation. With advancements in artificial intelligence and deep learning, new possibilities have emerged for automating pavement defect detection. However, the quality of the images used to train these models plays a crucial role in their performance, as variations in lighting or high contrast can compromise the precise identification of defects. In this study, we analyzed the impact of image preprocessing techniques on defect detection using a deep learning model. The CLAHE (Contrast Limited Adaptive Histogram Equalization), LIME (Low-Light Image Enhancement), and gamma adjustment methods were applied, along with unprocessed images. The experiments revealed that the LIME method achieved the best performance, with 78,8% mAP, 91,1% in Precision, 86% in Recall, and an F1 Score of 74%. This study highlights that preprocessing techniques are promising tools for improving the accuracy and reliability of deep learning models applied to the automated detection of defects in asphalt pavement surfaces, significantly contributing to the development of more efficient and accessible pavement management systems.
Proposta de Modelos Leves para Classificação de Vagas de Estacionamentos em Cidades Inteligentes
ABSTRACT In smart cities, a common problem is the parking spots classification into empty and occupied. It may seem simple, but a large number of Deep Learning approaches rely on CNNs (Convolutional Neural Networks). These solutions are commonly expensive, demanding high computational power and specialized hardware to run properly, making them unsuitable for large-scale deployments, such as in smart cities. In this work, we propose two lightweight CNN architectures, built upon existing solutions by enhancing their efficiency and robustness. We used a cross-dataset scenario, where a model is trained and validated in two datasets and tested in another, applying three robust state-of-the-art datasets: PKLot, CNRPark-EXT and PLds. This process improves generalization across different contexts and sets a more realistic scenario when compared to real urban environments. Also, we compared our models to state-ofthe- art networks, such as MobileNetV3 Large and Small to ensure consistency and validate the results with well-explored models in the literature. Our results showed that our models, with up to 34× and 88× fewer parameters than the MobileNetV3 Large, reach less than 2% lower accuracy when compared to the MobileNetV3 networks. Furthermore, by using grayscale images, the results were slightly better and also decreased processing and storage costs.
Exploring Multi-armed Bandits Learning Strategies in the Emergent Web Server
Abstract Self-adaptive systems are designed to modify their architecture or behavior to uphold high-level objectives despite changes in their operating environments. A critical aspect of developing such systems involves creating strategies to handle unexpected events in the operating environments. While this remains an active area of research within the autonomic computing and self-adaptive systems community, one commonly adopted approach is leveraging machine learning techniques, particularly reinforcement learning, to address unforeseen challenges. In this paper, we conduct experiments using the EmergentWeb Server exemplar, a publicly available self-adaptive web server, to investigate various monitoring metrics and implement a multi-armed bandit reinforcement learning approach. This approach enables the system to identify the optimal web server configuration for maximizing performance under varying workload patterns and operating conditions, enabling the system to react to unexpected events that rises from the operating environment with minimum human interference.
Explorando o Pensamento Computacional nos Anos Iniciais: Uma Jornada de Ensino e Extensão no Ensino Fundamental
ABSTRACT This study reports the actions of an extension project that applied Computational Thinking and robotics concepts in a municipal elementary school. The project, aligned with the Common National Curriculum Base (BNCC), aimed to foster logical reasoning through practical and playful activities. Unplugged methodologies were employed, including translating binary numbers, simulating classification networks, and exploring a computer maze. These activities introduced students to the core principles of computational thinking, focusing on algorithmic thinking, task decomposition, and problem-solving. The results demonstrated the project's effectiveness, as students engaged actively and developed essential cognitive skills. This experience highlights the value of incorporating computational thinking into early basic education through playful and adaptable teaching approaches tailored to diverse school contexts.
Automated Extraction of Body Measurements Using Computer Vision
Abstract Extracting body measurements manually is a time-consuming and challenging process that can be impractical without the appropriate tools. This paper proposes a method to obtain body measurements, which can then be used in applications such as finding the perfect clothing fit. By leveraging computer vision, the developed system calculates body measurements from a photo submitted by the user, along with their height. Utilizing essential libraries and detailed digital image processing techniques, we successfully performed the calculations to return the user’s body measurements. Test results demonstrate that the model achieves high accuracy, underscoring the efficiency of this approach and offering a swift alternative for obtaining body measurements. The developed method can be employed in numerous other applications that require precise body measurements.
Análise Espacial e Temporal do Uso e Cobertura da Terra em Joinville/SC de 1985 a 2022
ABSTRACT This research analyzes land use and land cover dynamics in Joinville through remote sensing images. The methodology consists of three main steps: defining the study area, acquiring and processing images and spatial data, and reclassifying the images using a geographic information system. The images used are from the Landsat series, specifically Collection 8.0 of the MapBiomas Project, covering the period from 1985 to 2022. These images represent five primary classes: forest, non-forest natural formation, agricultural land, non-vegetated areas, and water bodies. The results indicate land use and land cover change rates ranging from 5 to 167% for increases, and from 5 to 53% for decreases. In conclusion, the approach presented significantly contributes to generating territorial indicators, which are important resources for territorial planning and management.
OptiCore: Scalable Greedy Coreset Optimization Method for Efficient Deep Learning
Abstract The growing use of Deep Learning in various domains has amplified the challenges of training models due to high computational costs and the need for large volumes of data. To address these limitations, this study presents the OptiCore method, a new dataset optimization approach based on the Greedy Coreset technique. OptiCore strategically reduces the size of datasets while preserving their representativeness and diversity, integrating computational cost analyses through the Relative Cost Normalized metric. This method balances data efficiency and model performance, offering a scalable solution for practical applications. The methodology is designed for generalization and reproducibility, extending its usefulness to different Deep Learnig contexts. In the case study, Deep Learning models were applied for the classification of three-dimensional shapes, with the ResNet-50 architecture showing the best results. OptiCore reduced the dataset by up to 90%, maintaining competitive accuracy while significantly reducing computational demands.
Super-Resolução de Imagens em Tomografia Computadorizada de Baixa Dosagem: Comparação entre Métodos de Aprendizado Profundo
Abstract The acquisition of high-resolution medical images is essential for the accurate diagnosis and effective treatment of many diseases. However, obtaining high-resolution images can be limited by factors such as device limitations and patient exposure to radiation. To solve this problem, this study proposes the use of super-resolution techniques based on deep learning to improve the resolution of computerized tomography images without increasing the patient’s exposure to radiation. The LoDoPaB-CT image dataset was used. Five deep learning-based super-resolution techniques - SRCNN, ESRGAN, SwinIR, HAT and DAT - and the traditional FBP method were compared. The evaluation metrics included PSNR, SSIM, LPIPS, NIQE, NRQM and PI. In the tests carried out, the ESRGAN model obtained the best results, outperforming the other techniques in metrics such as SSIM, NIQE and PI. On the other hand, the FBP method showed comparable performance in PSNR, LPIPS and NRQM. These findings underscore the need to fine-tune the models and highlight the potential benefit of involving experts in the subjective analysis process to obtain the best results.
Bruno Légora Souza da Silva Légora Souza da Silva
Relato de Experiência: Aprendizagem Ativa no Ensino de Visão Computacional com Cascade e HOG
ABSTRACT Computer vision, a growing field in artificial intelligence, is crucial in sectors such as healthcare and security. This article describes the application of active teaching methodology in a short course on computer vision, involving 26 Software Engineering students. The course covered the techniques of Cascade Classifier and Histogram of Oriented Gradients (HOG), integrating theory and practice over 20 hours of activities. The effectiveness of the course was evaluated through quantitative and qualitative analyses. Quantitative results showed a significant improvement in students’ performance, while qualitative analysis indicated an increase in practical skills and understanding of the techniques, despite challenges related to theoretical workload and activity support. The practical approach was praised, with suggestions to improve the teaching materials and support during activities.
Simulador Interativo para Programação na Arquitetura de Processadores RISC-V
Abstract The RISC-V architecture, recognized for its modularity and efficiency, faces challenges due to the limited availability of educational resources. To address this issue, an interactive web simulator was developed based on the RV32I instruction set, using the Angular framework, with an interface integrated into a core comprising a code parser, assembler, and processor, enabling both continuous and step-by-step execution. The simulator’s validation confirmed the proper functioning of essential features, including code editing, assembling, execution, and visualization of registers and memory. This simulator is intended for students, educators, and embedded systems designers in projects involving the RISC-V architecture, serving as a teaching tool in computer architecture courses.
Araucárias: um corpus de árvores sintáticas para o ensino da sintaxe do português brasileiro
ABSTRACT Automatic syntactic parsers can support the teaching of Brazilian Portuguese. However, existing corpora that underpin such parsers are primarily designed for industrial applications rather than for the instruction of the vernacular. In this context, we propose the Araucárias corpus, a collection of syntactic trees derived from well-formed Portuguese sentences. The aim is to demonstrate its relevance for educational purposes in comparison to other existing corpora. Due to its relatively intuitive, simple, and evocative nature—attributes that are absent in the evaluated existing corpora—Araucárias is expected to offer a more viable approach to teaching Portuguese syntax.
Panorama das Pesquisas sobre Pensamento Computacional e Ensino de Computação no Ensino Fundamental: Um Mapeamento Sistemático
ABSTRACT This work is being developed within the scope of Manna_Team. Computational Thinking (CT) has become an increasingly important skill in education, particularly in Elementary Education, due to the growing digitalization of society. This systematic mapping provides an overview of research on computer science education and the development of CT at the elementary school level, focusing on methodologies, technological tools, application contexts, and results obtained between 2012 and 2024. The review covers 49 articles and highlights current trends such as the use of visual programming platforms, educational robotics, unplugged teaching, and gamification. The study also emphasizes the challenges and opportunities for integrating CT into school curricula, analyzes the pedagogical practices adopted, and proposes directions for future research and improvements in teacher training.
O Papel do Pensamento Computacional na Formação de Professores para a Educação Básica Uma Revisão Sistemática de Literatura
ABSTRACT This work is being developed within the scope of Manna_Team. It investigates the role of Computational Thinking (CT) in the training of teachers for Basic Education through a systematic literature review. CT is recognized as an essential competency in the contemporary educational context, encompassing skills such as abstraction, decomposition, and pattern recognition. Despite the inclusion of CT in the National Common Curricular Base (BNCC) as a transversal competency, its implementation faces significant challenges, particularly regarding teacher training. This research analyzes methodologies, resources, and obstacles encountered in both initial and continuing education for teachers, highlighting the scarcity of teaching materials and the lack of adequate training. The results indicate that active methodologies, such as experiential learning and the use of tools like Scratch and MIT App Inventor, are fundamental for integrating CT into the curriculum. The study concludes that coordinated actions are essential to overcome existing barriers, ensuring teacher training empowers educators to effectively apply CT in their pedagogical practices.
Análise do Desenvolvimento de Pensamento Computacional por meio de Ações de Programação Aplicada na Educação Básica
ABSTRACT Computational Thinking (CT) has been increasingly recognized as an essential paradigm for the development of crucial skills and competencies for a contemporary society. In view of the current legislation to incorporate the teaching of Computing from Basic Education, the importance of this approach in improving school learning and in the more effective use of technologies for the benefit of society is highlighted. This paper analyzes CT learning in an applied programming course conducted with 395 public school students from the final grades of elementary school in Vale do São Patrício. Only 18.4% of the students were aware of the term CP and after the course, 88% declared that they were confident in using CP to solve problems.
Viral goritmos: jogo educacional para ensino e aprendizagem de algoritmos
This article presents the development of an educational game aimed at teaching and assisting higher education students with learning algorithms and programming. Due to the significant problem-solving difficulties faced by students, the low pass rates in algorithms and programming courses, and a gap in games that cover this content, a game was developed to provide an interactive environment to experience different ways of teaching abstract concepts of algorithms, maintaining its focus on Portugol Studio’s syntax, as well as to serve as a tool to support teaching and learning of algorithms and programming courses. The assessment that determined the relevance of this game was conducted following game evaluation models proposed in scientific studies, the model focused on evaluating criteria such as motivation, UX and learning value. The educational game was positively evaluated by university students who make part of the target audience and was considered relevant with its proposal.
AlimentAção: A Jornada Nutritiva - Educação e Nutrição Alimentar na Infância
ABSTRACT Childhood obesity is a growing concern worldwide, with significant impacts on public health. This article presents the development of an educational game entitled AlimentAção: The Nutritional Journey, designed to promote nutritional education among children in a playful and interactive way. Based on learning, the game aims to teach nutrition concepts in an engaging way, promoting healthy eating habits. The project includes everything from the initial design of the game to its pedagogical evaluation with groups of children.
Jogo digital acessível para crianças com TEA: aprendizagem de matemática com interface tangível e inclusiva
ABSTRACT This article discusses the development of an accessible digital game for children with ASD, aimed at learning mathematics through a natural interface and computer vision. The methodology adopted is Design Science Research, which allows problems to be solved through the creation of artifacts. The game was designed, developed and empirically analyzed by a teacher and 11 students with ASD and one student without disabilities, aged between 8 and 10, from an elementary school in the municipality of Itajaí, considering accessibility criteria for digital games. The results showed the students' interest and involvement, favored by the game's tangible approach, and contributed to research into digital educational games, promoting discussion about differentiated interfaces for the target audience of inclusive education.
Do Brincar ao Aprender: Como o LEGO MINDSTORM Fomenta Competências STEAM em Diversas Faixas Etárias.
ABSTRACT This paper presents a mentoring project in computing and robotics that introduces students to robotics fundamentals using LEGO MINDSTORMS. The project fosters STEAM skills through hands-on activities, enhancing logical reasoning and creativity. A structured methodology was applied, covering robot assembly, programming, and problem-solving. Over four years, the initiative expanded, training educators and organizing competitions to consolidate learning. Results showed significant improvements in technical and collaborative skills, increasing student interest in technology careers.
Gamifying Electronic Trading Through a Minecraft Commodities Exchange
This paper explores the integration of a virtual commodities exchange within Minecraft, transforming in-game items into tradable digital assets. By implementing a dynamic electronic trading system, including a limit order book and market-making algorithms, the platform replicates real-world financial market operations in a familiar and interactive virtual environment. Players engage with the exchange by buying and selling assets, fostering a player-driven economy governed by supply and demand. The integration leverages Minecraft’s modding capabilities through a custom plugin, facilitating seamless interaction between the game and the trading system. Beyond enhancing gameplay, this approach demonstrates significant educational potential, offering players and learners insights into market dynamics, trading strategies, and economic principles in an engaging and risk-free context. Observations of player interactions reveal authentic economic behaviors, further emphasizing the platform’s value as a tool for both financial education and entertainment.
Representatividade Feminina na Produção Científica: Análise da Universidade de Maior Destaque no Brasil
ABSTRACT The present study investigated scientific production at the State University of Maringá (UEM), based on the gender of its authors. UEM stands out in scientific research with the presence of women in Brazil and South America, with 53.3% of scientific article authorships featuring female participation, according to the 2023 Leiden Ranking. This prominence contrasts with the prevailing gender inequality in academia in general, as the most recent data from UNESCO, published in 2024, reports that the proportion of women in the field of academic research totals worldwide. This work was motivated by this contrast and analyzed the female presence in UEM’s publications and their representation in productivity grants across fields of knowledge. To this end, the methodology of the Leiden Ranking was reproduced to gather metadata of research produced by the institution during the period considered by the 2023 Leiden Ranking and compare it with data on CNPq productivity grants, categorized by gender and field of knowledge. The data obtained in this study are consistent with the findings of the Leiden Ranking, as 58.8% of the authorships in the considered publications are female. On other hand, only 35.8% of women at UEM held productivity grants during the analyzed period, highlighting a significant gender disparity in scientific production at UEM.
Gamificando e Explorando as Soft Skills em um Projeto de Ensino, Pesquisa, Extensão e Desenvolvimento: Análise das Percepções dos Estudantes Membros
ABSTRACT The computer professional’s resume goes beyond technical skills to stay active. Therefore, the project LearningLab, which covers several sub-areas of computing, investigates which soft skills were developed and how they will be improved with gamification. The methodology includes: (i) Literature review; (ii) Application of questionnaire; (iii) Preliminary analysis of results; (iv) Creation of a gamified system; and (v) Collection and analysis of data from the gamified system. Preliminary data, collected quantitatively via an online questionnaire, identified the main soft skills by sector and those most desired by members. Gamification is expected to promote the exciting development of these skills.
Sim, Nós Criamos: Empoderamento Feminino na Computação Através da Cultura Maker e do Pensamento Computacional
ABSTRACT This article presents an experience report on the activities conducted by a project aimed at deconstructing stereotypes and empowering girls and women in the field of computing. The initiatives were carried out within a study group composed of women from computing courses, leveraging maker culture and mentorships as primary tools. Using Design Thinking (DT), the project developed solutions tailored to the specific needs of the students. The study highlights the importance of empowering women in the technology field by providing a safe space for experience sharing and the strengthening of support networks. The DT methodology enabled an empathetic and contextualized approach, offering a structured set of steps that can be replicated by other projects and educational institutions seeking to address similar challenges, thus making a significant contribution to the field.
Lost Knowledge: uma jornada de diversão e conhecimento rumo aos estilos de aprendizagem de Felder-Silverman
Resumo This article presents the development of the educational game called Lost Knowledge, which aims to instruct university students in identifying their learning styles. Inspired by the Felder and Silverman model, the game employs a questionnaire to assess learning preferences across the dimensions of sensory/intuitive, visual/verbal, active/reflective, and sequential/global. The game development used the Short Game Design Document (SGDD). The game was evaluated by undergraduate students in Computing courses, using an evaluation model that integrates aspects from student reaction, motivation, user experience and impact on learning. The evaluation results demonstrate that the game met its objectives and represents a proposal to integrate technology and education, contributing to promoting more engaging and personalized learning in the digital age.
Do laboratório à sala de aula: avaliação de protótipos de jogos com Arduino no ensino médio
Abstract The application of games in the classroom promotes a fun environment, and when we associate educational concepts with games, we awaken students’ interest through the challenges that the games propose. In Computer Science, various theoretical concepts can be applied in different projects using the Arduino. This article presents the design and development of three game prototypes using the Arduino platform: a memory game, an infinite runner-style game, and a stacking game. The prototypes were evaluated by 59 high school students, indicating a great acceptance of the games, entertaining and motivating the students to study the concepts covered.
NIEP eXperience: Aprimorando a Usabilidade do Emulador de Redes com Suporte à NFV NIEP
Abstract New networking paradigms have been developed to provide greater flexibility and enable finer-grained management of computer networks. One of the most prominent new network paradigm is the Network Function Virtualization (NFV), which can leverage the NFV Infrastructure Emulation Platform (NIEP) as an environment for prototyping and testing virtualized network functions and services. Despite its versatility and comprehensiveness, NIEP poses a steep learning curve for new users, particularly in the process of specifying virtualized network topologies. To address this challenge, this work proposes NIEPx, an assistive interface designed to simplify topology specification within NIEP while minimizing common errors in the process. The effectiveness of NIEPx was evaluated through a qualitative-quantitative model, which demonstrated its ability to reduce human errors during topology specification. Furthermore, the interface was tested for communication overhead with the NIEP emulator, showing negligible impact in the analyzed scenarios. Thus, NIEPx emerges as an effective solution to facilitate the onboarding of new users to NIEP, while also supporting experienced users in handling more complex network topology specification tasks.
Uma experiência extensionista: Tecnologia e Envelhecimento
Resumo Este artigo traz um relato de experiência extensionista de realização de um curso de capacitação digital voltado para mulheres com 60 anos ou mais. O foco do curso foi promover a inclusão digital e aumentar a autonomia dessas mulheres no uso de tecnologias. A metodologia utilizada, que envolveu atividades práticas e teóricas, resultou em avanços significativos nas habilidades digitais das participantes. O artigo também discute a importância da educação continuada para essa faixa etária e os benefícios de promover a inclusão digital entre as mulheres 60+.
Da Robótica à Inclusão Digital: Fortalecendo a Presença Feminina na Computação com Pesquisa e Extensão
ABSTRACT This paper presents an experience report of a scientific initiation project that integrates research and extension activities with the goal of increasing female participation in computing and promoting digital inclusion. Based on the principles of experiential learning, the project organized a series of practical and interactive activities, including educational robotics workshops using LEGO Mindstorms, unplugged computing dynamics, and discussion circles about women's presence in technology. The initiatives also involved career mentorships conducted in partnership with influential professionals and educational institutions, as well as a workshop aimed at the digital inclusion of indigenous women. The results were evaluated qualitatively and quantitatively, highlighting the positive impact of the activities on increasing confidence, participants' interest in technological areas, and the promotion of a more inclusive environment. Finally, this work reinforces the importance of connecting research and extension to foster diversity and strengthen female leadership in technology, presenting a replicable model for other educational initiatives.
O Cenário Atual de Golpes em Redes Sociais: Uma Revisão da Literatura
Abstract Social networks were conceived as platforms for interaction, content creation and engagement between users. Over time, browsing these networks has become an integral part of everyday life, culminating in billions of profiles created and accessed regularly on various platforms. However, the ease of access and limited control over content by the maintainers have turned social networks into a favorable environment for fraudulent activities, allowing malicious users to act as scammers in search of illicit benefits. Faced with this scenario, academia and industry have dedicated themselves to analyzing and developing methods to identify, detect, prevent and mitigate scams. However, the diversity of scams, many with unique characteristics, requires specific analysis and solutions for each case. In this context, this paper proposes a systematization of social media scams addressed in the literature between 2014 and 2024, defining categories and subcategories based on their methodological characteristics and objectives. The main result is a taxonomy made up of 5 categories and 19 subcategories.
A Dynamic Programming Approach for the Brachistochrone Problem
Abstract In this paper, we address the brachistochrone problem using Dynamic Programming and an extension that accounts for forbidden regions or obstacles. The brachistochrone problem seeks the curve of fastest descent under gravity between two points, minimizing travel time. While traditional approaches derive the cycloidal curve as the solution using calculus of variations, this work employs a discrete, grid-based Dynamic Programming formulation to approximate the optimal trajectory. Computational experiments showcase the method’s flexibility, particularly in adapting to constraints such as forbidden regions, and its ability to dynamically recalculate paths. Despite some limitations in angular resolution due to discretization, the proposed approach demonstrates robustness and scalability in addressing constrained trajectory optimization problems. Moreover, this project lays the groundwork for extending the proposed methodology to more complex scenarios, such as incorporating randomness into the trajectory, where the adaptability of this approach can be effectively leveraged.
DeMONS++: Utilizando Técnicas de Modelagem de Tráfego no Combate de DDoS via Serviço DeMONS
Abstract With the increasing use and popularity of computer networks, data security and online service availability have become priority concerns for academia and industry. One of the significant threats to be prevented, detected, and mitigated is the Distributed Denial of Service (DDoS) attack. With the advancement of new technologies and network paradigms, these attacks have been tackled by innovative solutions such as mitigation services based on Network Function Virtualization (NFV), which was utilized to develop the DeMONS service, a solution for DDoS mitigation. Despite its effectiveness, DeMONS’ results can be enhanced by introducing new traffic engineering modules. In this way, this paper presents DeMONS++, an extension of DeMONS that incorporates a strategically positioned traffic shaping module within its service topology. Experiments conducted in simulated environments demonstrated that DeMONS++ improves DDoS mitigation, achieving better performance in metrics such as benign traffic acceptance and user satisfaction in the tested scenarios.
Ferramenta Interativa para Elaboração de Sistemas Integrados Baseados em Múltiplos Núcleos
Abstract Currently, most computers adopt the System-on-Chip (SoC) approach, integrating various components into a single chip. Traditionally, the interconnection of these components is achieved through buses, which are essential for effective communication. However, as system complexity and performance demands increase, Networks-on-Chip (NoCs) have emerged as a more efficient alternative, offering greater scalability and bandwidth. The integration of SoC components with buses or NoCs remains a challenge, requiring specific adaptations that make the process labor-intensive and prone to errors. This work aims to simplify this integration by developing a graphical interface tool that automates the integration of AXI bus-based components into the XINA NoC. The tool, developed in Python with a Qt interface, was validated using traffic generators and monitors and integrated into the AMD/Xilinx Vivado development environment.
Sistema de Monitoramento Remoto da Temperatura de Ninhos de uma Ave Símbolo da Zona Costeira do Sul do Brasil
ABSTRACT This paper details the design and development of a bird monitoring system, focusing on the study of bird breeding. The project encompasses the entire development lifecycle: selecting components, assembling the hardware, programming the ESP32 microcontroller, and developing a web-based data visualization interface. The methodology begins with component selection, including the design of a custom temperature sensor, as well as considerations for connectivity and energy efficiency essential for remote deployments. Programming the ESP32 involves configuring modules, implementing time-stamped data logging, and optimizing for low-power operation. The final phase focuses on creating a user-friendly web interface using HTML, CSS, and JavaScript, providing a seamless experience for data access and visualization. This system offers a practical, end-to-end solution for ecological monitoring, enabling researchers to track environmental parameters in real-time and over extended periods to support habitat preservation effort
Aplicação Prática das Heurísticas de Nielsen na Avaliação de um Sistema de Análise da Não Conformidade de Softwares com a LGPD
ABSTRACT In recent years, technological advancements have markedly intensified the collection, processing, and storage of personal data, making it essential for current systems to conform to Brazil’s General Data Protection Law (Lei Geral de Proteção de Dados — LGPD) to ensure legal compliance and effectively safeguard user privacy. This paper explores the practical application of Nielsen’s usability heuristics in evaluating the Adequa 2.0 system, a tool developed to identify software products’ non-compliance with the LGPD. Adequa 2.0 seeks to automate the assessment process through a dynamic questionnaire that fosters effective and intuitive user interaction. Usability was measured using Nielsen’s 10 heuristics. Tests were carried out with students in the technical Informatics program at the Federal Center for Technological Education of Minas Gerais (CEFET-MG), Leopoldina campus, who used the tool to evaluate their projects’ compliance with the LGPD. The findings underscore the importance of uniting regulatory requirements with best practices in interface design to ensure LGPD conformity without compromising user experience.
ABSTRACT This paper presents the development and implementation of NKE-Arduino, an educational multiprogrammable operating system specifically designed for the Arduino platform. This project involved porting the existing NKE system, originally developed for ARM-based platforms, to the Arduino environment. NKE-Arduino supports multiple concurrent tasks, scheduling and timing mechanisms provided by the TimerOne library. The system incorporates task create operations, semaphore-based synchronization to manage access to shared resources, preventing race conditions and ensuring data integrity. Additional functionalities include control of LEDs, serial input reading analogous to scanf, and serial output printing similar to printf. Designed to operate on the resource-limited hardware of the Arduino Uno, NKEArduino offers a practical and accessible tool for educational purposes. The system provides students with the opportunity to understand and experiment with fundamental operating system concepts. The development of NKE-Arduino not only serves as an instructional tool but also showcases the feasibility of implementing a multitasking operating system on constrained hardware.
PROV-SwSystem: AWeb System to Capture and Analyze Provenance Data from Software Development Processes
ABSTRACT Data provenance encompasses the description of the origin and historical events of data processing. Although models such as W3C PROV allow capturing and analyzing provenance data, additional methods are required to address software development processes’ specific uses and complexities. In this context, the PROV-SwProcess provenance model was developed to extend W3C PROV. However, the PROV-SwProcess provenance model does not provide a specific tool to enable its instantiation. In this vein, this paper aims to present the PROV-SwSystem - a web-based system to capture, store, and analyze software process data along with their provenance, adhering to the PROV-SwProcess model. To achieve this, the Design Science Research (DSR) methodology was adopted, supporting the system creation, construction, and validation. PROV-SwSystem was implemented using Javascript, Bootstrap, Node.js, and Jquery, encompassing all predefined use cases. Unit tests and system tests were performed, identifying six redundant variables and five redundant functions. In addition, an exploratory usability test was performed with an external user, revealing fifteen navigation issues, eight attempts to register data in inappropriate sections, six registration confirmation failures, and data integration problems. Actions for data entry validation and interface improvements were implemented, enabling the tool’s usage and paving the way for future testing on a larger scale.
Sistema para Verificação Da Não Conformidade De Produtos de Software Com A Lei Geral De Proteção De Dados Pessoais (LGPD)
ABSTRACT The Adequa 2.0 system is a tool designed to assist in the process of verifying software products to ensure compliance with Brazil’s General Data Protection Law (LGPD). With the growing need for systems to adapt to data protection regulations, the system was developed to offer a solution for organizations seeking to adapt their processes and software systems to legal requirements. This article describes the development steps of Adequa 2.0, from requirements gathering and system architecture to modeling and prototyping, emphasizing the use of UML techniques and prototyping tools to ensure a functional design suited to user needs. The third phase of the project, involving software development, is ongoing as planned. With an interface that aims to be intuitive, the system aims to assist in compliance with the LGPD and raise awareness about the importance of data protection.
Análise de Desempenho de Frameworks para Aplicações Distribuídas
Abstract As distributed systems become increasingly pervasive in everyday life, performance analyses that provide quantifiable insights into the efficiency of these systems are essential. This study proposes an analysis of the performance of backend frameworks that enable the development of such distributed systems. It is expected that the findings of this project will guide better technology choices for the development of future distributed applications. The results indicate that the choice of the ideal framework depends on the specific needs of the application. The Flask framework stands out as a robust and efficient option for scenarios requiring a high number of requests and handling of lightweight data. Actix is an interesting choice for applications that prioritize low resource consumption. ASP.NET can be a good option for handling large volumes of data, despite its tendency for high CPU usage.
Avaliação de Desempenho de Compressão de Páginas Utilizando o ZRAM
ABSTRACT With computers processing an ever-increasing amount of data, expanding DRAM capacity is not always feasible due to economic and structural issues. In this context, memory compression emerges as an alternative provided by the ZRAM module, which expands the DRAM’s data capacity by using a reserved area to store compressed pages directly into the main memory. Therefore, reducing secondary storage accesses caused by page swaps and potentially reducing the program’s execution time. This paper evaluates the ZRAM performance, comparing the speedup obtained when running NAS Parallel Benchmarks under different disksize values for ZRAM, in systems with 8 GB, 4 GB, and 1 GB of DRAM. Also, we compared our baseline system with an older one, to study the effect of the systems improvement over the ZRAM usage impacts. Our results indicate that the impact of ZRAM on the application’s execution time increases with the reduction of the available main memory space. We show no variations in speedup with 8 GB of DRAM, and up to 10% improvement in scenarios with 4 GB of memory. In the 1 GB scenario, compression allowed certain benchmarks to achieve speed increases of up to 39%, even with a low memory footprint application, although most others did not benefit. We conclude that ZRAM has a more pronounced impact in memoryconstrained environments, and can have a significant impact even in applications with a low memory footprint. The code is available at https://github.com/Guilherme-Eduardo/ZRAM.
HardCore: Detalhando um HIDS por Assinatura em Hardware
Abstract Most Host Intrusion Detection Systems (HIDS) do not count with memory evaluation capabilities, being that the reason why they are not able to detect Advanced Volatile Threats, such as Fileless Malware, which can only be detected via memory scans. In this work, we propose HardCore, a signature based HIDS in hardware, able to perform signature matching with no overhead to the endpoint system. HardCore receives as input, at every memory write, a cache line, outputting any matches against known fileless malware present in the signature base. HardCore uses bloom filters and malware clustering to operate, having its signature matrices divided to obtain the gains originating from those implementation choices.
Komisan Aplicação web para interação de artistas digitais
RESUMO O mercado dos artistas digitais autônomos é composto pela informalidade do pequeno empreendedor, que se empenha para a divulgação de seu trabalho sem nenhuma garantia de se popularizar diante de ambientes em que o foco é outro, como as redes sociais. Nesse contexto, o projeto Komisan visa uma democratização do acesso às artes digitais, por meio do desenvolvimento de uma aplicação web com a função de ser uma rede social de divulgação de trabalhos de artistas autônomos à procura de ofertas de trabalho. Ainda objetiva permitir a integração entre artistas e usuários interessados na temática.
Uma experiência extensionista: Democratização do Acesso a Serviços Públicos por Meio de Aplicativo Mobile
Resumo O uso da informática tem se tornado cada vez mais relevante na sociedade atual. A partir dos problemas municipais relatados em conversas, foi elaborado um protótipo de um aplicativo que inclui grande parte das soluções dos problemas citados: Alerta de Cheia, Coleta Seletiva, Alerta de Assédio e Aviso de Furto ou Perda. Este protótipo tem como objetivo facilitar o acesso a informações e serviços públicos da cidade de Bom Jesus do Itabapoana. A metodologia envolveu reuniões de levantamento de problemas e a utilização do software de design Canva para o desenvolvimento do protótipo. Como resultado, o aplicativo permitirá que os usuários tenham acesso aos serviços públicos com maior qualidade e facilidade, democratizando o acesso a esses serviços.
Anna Carolina Rodrigues Boldrini do Nascimento
Cartilhas de Atividades para Promoção de Igualdade de Gênero e Cultura digital
Abstract Recent studies has shown that women have been historically underrepresented in STEAM. The main goal of this project was to disseminate the contributions of women in Computing whilst promoting Computational Thinking, Digital Culture and Digital Technology – related to the 5th General Competence from BNCC. In order to achieve this goal, four booklets were developed to help to disseminate in a ludic manner the female contributions and the computational concepts previously mentioned. This same material was presented to educators in workshops and it was also made available in a digital in the project’s website.
Protótipo de Aplicativo para Alerta de Cheias: Enchentes SOS
Resumo As enchentes são desastres naturais recorrentes que causam danos e colocam vidas em risco, especialmente em regiões vulneráveis. No entanto, muitas vezes faltam soluções tecnológicas acessíveis para fornecer informações em tempo real à população afetada. Este estudo apresenta o desenvolvimento de um protótipo de aplicativo móvel de alerta de enchentes como parte de um projeto educacional multidisciplinar em um curso técnico em informática. A metodologia envolveu a identificação do problema, o design do sistema e a implementação utilizando tecnologias de desenvolvimento móvel. Os resultados inicias demonstraram a viabilidade do aplicativo em fornecer atualizações sobre os níveis dos rios e notícias recentes. O projeto destaca o potencial das iniciativas educacionais na criação de soluções tecnológicas para problemas locais.
Conectando Culturas: Estratégias Digitais para Mulheres Indígenas Empreendedoras nas Redes Sociais
ABSTRACT This study aims to promote digital inclusion and promote empowerment indigenous women entrepreneurs through a practical workshop focused on leveraging technology to expand their businesses. The project was developed as part of an extension initiative, combining active learning methodologies with hands-on activities to make participants protagonists of their learning process. The workshop had a total duration of 40 hours, conducted over five days, with 20 women voluntarily participating in activities designed to teach essential digital skills. Each day addressed a specific topic, such as optimizing mobile devices, exploring communication tools, using social media (Facebook and Instagram), producing visual content, and planning future digital strategies. The results demonstrated a significant improvement in the participants' digital skills, self-confidence, and motivation to use technology for business promotion. Qualitative feedback highlighted the positive impact of collaborative learning and the relevance of content tailored to their cultural and entrepreneurial contexts. By connecting traditional knowledge with modern technological tools, the project not only enhanced their capacity to engage with digital platforms but also valued and preserved their cultural identity.
ConectaMães: Rede social de apoio que auxilia a troca de experiências parentais
Resumo Na atualidade, as redes sociais digitais possuem impacto significativo no modo como o indivíduo enxerga e se apresenta na sociedade. Nesse contexto, a mídia digital possui efeito sobre as relações familiares, podendo repercutir ideias patriarcais, que geram uma pressão pela busca da maternidade perfeita, ou desafiá-las em busca de uma sociedade mais igualitária que não se prenda a padrões de gênero nessas relações. Assim, o presente projeto possui como proposta um sistema web que atue como uma rede social oferecendo um espaço virtual adequado para que mães e outros responsáveis legais possam compartilhar experiências ou atender a pedidos de auxílio relacionados à parentalidade. Inspirado pelo feminismo matricentrico, que destaca narrativas maternas online relacionadas a ressignificar papéis sociais e expor desigualdades estruturais na maternidade patriarcal, o sistema busca promover um ambiente acolhedor. Portanto, o objetivo do sistema é contribuir para a criação de uma rede de apoio virtual significativa, onde aqueles que praticam cuidados parentais possam se sentir acolhidos e confortáveis para compartilhar experiências e encontrar suporte de forma colaborativa.
Avaliação da acessibilidade de um recurso plugado para a promoção do Pensamento Computacional
RESUMO O pensamento computacional (PC) tem sido amplamente empregado em contextos educacionais, especialmente em salas de aula com crianças e adolescentes, com o objetivo de desenvolver habilidades essenciais. Com o propósito de verificar se um recurso plugado de PC pode ser utilizado por todos os estudantes, visto que a acessibilidade digital se apresenta como uma lacuna a ser preenchida, o presente trabalho avaliou a acessibilidade do Blockly Games. O processo de avaliação foi realizado em três etapas: avaliação manual com as diretrizes da WCAG, avaliação automática e avaliação heurística. Com base nos resultados obtidos, constatou-se que essa plataforma não possui os recursos de acessibilidade necessários para a interação de pessoas com necessidades específicas.
Repositório de Ideias Pedagogia Crítica na Educação em Computação
ABSTRACT Em uma sociedade cada vez mais informatizada, torna-se importante que o ensino de Computação não se restrinja apenas ao domínio técnico. Neste sentido, a Pedagogia Crítica e a Aprendizagem Baseada em Projetos são alternativas para uma educação reflexiva e problematizadora. Porém, desenvolver projetos que aliem estas duas abordagens é um desafio. Assim, este artigo apresenta os resultados de um projeto que teve como objetivo desenvolver um repositório digital de projetos para o ensino de Computação com foco na formação cidadã. Com finalidade de avaliar o funcionamento e a viabilidade de utilização do sistema foram categorizados e cadastrados dois projetos oriundos de publicações acadêmicas.
ABSTRACT Este artigo apresenta o jogo digital Mistério inFeliz, desenvolvido em um Clube de Desenvolvimento de Jogos formado por 12 estudantes do curso técnico em Informática de uma instituição pública federal, a partir da abordagem pedagógica de aprender criando jogos. A percepção das pessoas envolvidas no desenvolvimento foi avaliada a partir de respostas a um questionário. Os resultados obtidos indicaram que a experiência foi positiva para o desenvolvimento de habilidades técnicas, mas que precisa de ajustes quanto a distribuição e o acompanhamento das equipes. O jogo também foi avaliado em uma sessão de testes na qual participaram 75 pessoas. O resultado da avaliação apontou para uma boa aceitação do jogo e a necessidade de maiores informações sobre como interagir com os enigmas propostos.
Adog: Aplicativo de Auxílio na Adoção Responsável de Animais
RESUMO O crescente número de animais em condição de vulnerabilidade no Brasil é um problema de bem-estar animal e saúde pública, agravado pela falta de ferramentas eficazes para adoção. ONGs e protetores independentes enfrentam dificuldades na divulgação de animais para adoção, e adotantes potenciais encontram barreiras para localizar e adotar um pet de maneira segura. Adog é um aplicativo móvel projetado para otimizar o processo de adoção, facilitando a conexão entre adotantes e ONGs por meio de perfis detalhados dos animais, filtros de busca e um sistema inteligente de "match". O aplicativo foi desenvolvido com o uso de ferramentas de código aberto, adotando uma abordagem estruturada que incluiu entrevistas com proprietários de ONGs e ativistas da causa animal, além de uma análise comparativa de outras plataformas semelhantes. Atualmente, o Adog está em fase de implantação e testes com usuários. A expectativa é que a plataforma contribua para o aumento das adoções responsáveis, promovendo a conexão eficaz entre adotantes e ONGs, além de ajudar a reduzir os índices de abandono de animais.
Thaís Teixeira da Silva Thaís Teixeira da Silva
MED+ Aplicativo móvel de controle de medicamentos para pessoas em terapia polimedicamentosa do Asilo Santo Antônio (Leopoldina - MG)
RESUMO A utilização de medicamentos é algo comum na vida dos seres humanos, haja vista que o mesmo promove melhorias à saúde dos indivíduos, possibilitando longevidade e qualidade de vida. O objetivo deste trabalho é desenvolver um aplicativo para dispositivos móveis, permitindo o controle dos horários e dosagens de medicamentos por parte dos cuidadores responsáveis pelos idosos institucionalizados, além do índice glicêmico dos mesmos. A motivação inicial para a realização de um sistema voltado para cuidadores de pessoas em terapia polimedicamentosa residentes do Asilo Santo Antônio se deu pela percepção de que os mesmos encontram dificuldades para organizar os diversos tratamentos de maneira eficaz. A metodologia utilizada para consolidar o projeto MED+ se deu por meio de uma entrevista não estruturada com a assistente social e a enfermeira do asilo, localizado na cidade de Leopoldina, Minas Gerais. Dessa forma, foram debatidos problemas enfrentados na instituição para definir as funcionalidades do sistema.
Vintana: uma plataforma para publicação de acervos de rádios
Abstract Since its invention, radio has stood out as a means of communication with a wide reach, promoting interactivity and influencing opinions. In Brazil, its educational function has always been one of its central pillars, providing both entertainment and knowledge. However, many of the historical collections of Brazilian educational radios remain inaccessible to the public due to a lack of proper digitization and preservation. It is in this context that the Vintana project arises, with the aim of making these collections available and democratizing access to them by creating an intuitive and easy-to-navigate digital platform. Focused on researchers, students, and those interested in cultural collections, the platform aims to preserve and value the audiovisual heritage of educational radios, promoting broader and more organized access. Vintana stands out for its proposal to provide an accessible experience, gathering historical content that can be explored efficiently and practically. With a software engineering structure divided into six stages, the project ensures that the entire development process is carried out with a focus on preserving and disseminating content. In this way, it is hoped that the platform will contribute not only to the preservation of the cultural memory of educational radios but also to the dissemination of knowledge and culture, strengthening the importance of educational broadcasting in Brazil and ensuring its legacy for future generations.
Transistoras: A construção de uma plataforma para acolhimento de meninas na área de Informática
ABSTRACT A desigualdade de gênero na área da tecnologia é um problema persistente ainda no século XXI. Nos cursos da área é possível encontrar falta de representatividade feminina e a perpetuação de estereótipos de gênero, influenciando na autoconfiança e impactando na decisão de ingressar ou continuar na área. Muitas ações vêm sendo desenvolvidas no Brasil visando combater esses problemas. O projeto Meninas High-Tech soma-se a essas iniciativas e, a partir de ações de ensino, pesquisa e extensão, identificou a necessidade de espaços de acolhimento para meninas em cursos de Informática. Assim, foi planejada a Plataforma Transistoras, desenvolvida no ambiente Moodle, cujo objetivo é desenvolver um espaço virtual coletivo e seguro para as meninas de um Curso Técnico em Informática Integrado ao Ensino Médio, incentivando o compartilhamento de conhecimento, dúvidas e experiências relacionadas ao curso. Foi desenvolvida uma versão da Plataforma, no ambiente Moodle, oferecendo funcionalidades como: apresentações pessoais, informações sobre as disciplinas, compartilhamento de materiais, fóruns de discussão e um espaço privado. Para engajar as alunas foi implementado o uso da gamificação. A metodologia é baseada na pesquisa-ação, envolvendo a identificação de problemas, reflexão e implementação de medidas para solucioná-los. Uma primeira versão da Plataforma foi disponibilizada para uso e, posteriormente, foram coletados dados para embasar os ajustes.