Proceedings of the Regional School on Computing of Bahia, Alagoas, and Sergipe (ERBASE)
Sociedade Brasileira de Computação - SBC
Evaluation of Machine Learning Algorithms for Detecting DDoS Attacks in Container-Based WordPress Environments
The aim of this study is to compare the efficiency of machine learning algorithms in detecting DDoS attacks in containerized WordPress environments, with a focus on the REST API. To simulate realistic attack scenarios, the Locust tool was used to generate massive access to the WooCommerce REST API. The resulting traffic was captured and analyzed enabling the generation of real datasets. The tests showed that the Random Forest algorithm outperformed the others, achieving an accuracy and F1-score of 1.00 in balanced scenarios, while the other algorithms yielded significantly lower results.
Detection of Depressive and Suicidal Behaviors on Twitter Using Machine Learning and NLP Techniques
This study presents an automated approach for collecting and analyzing textual data from social networks to detect emotions related to depressive behaviors and suicidal ideation. Using text mining and natural language processing, Twitter posts were analyzed and classified into six emotions: joy, disgust, fear, anger, surprise, and sadness. The model achieved excellent results, with AUC values above 0.99 across all categories and high precision in detecting negative emotions such as anger and fear (95.6%). The confusion matrix indicated consistent classification, supporting the use of such technologies as complementary tools for mental health monitoring on digital platforms.
Forecasting Public Expenditures of the State of Sergipe
Forecasting public expenditures is a challenge in fiscal management, especially at the state level, where the literature is limited. This study aims to compare different time series models in projecting public expenditures for the State of Sergipe. Five methods were used: ARIMA, Holt-Winters, ETS, LSTM, and SVR, using monthly data from 2014 to 2024. To evaluate the performance of the models, the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) metrics were used. The ETS model demonstrated superiority in public expenditure forecasting with an accuracy of 8.45% (MAPE). These results indicate potential for optimizing budget preparation, contributing to fiscal sustainability.
Detection of Concept Drifts in the ICMS Revenue Time Series of the State of Sergipe
The collection of the Tax on the Circulation of Goods and Services (ICMS) is the main source of revenue for the State of Sergipe, essential for funding public policies. Understanding variations in the time series can support strategic government decisions. This study proposed the evaluation and selection of a concept drift detection algorithm in time series, aiming to identify significant changes in the monthly ICMS revenue. Different detectors with distinct mechanisms were tested. The Bottom-up algorithm showed the best performance among the explored options, achieving a score of 5.38 in the MTR metric, which combines a low false alarm rate and fast detection. In this context, the model proves to be a promising, efficient, and viable option for real-world applications.
Computational Intelligence and Image Processing Model for Human–Machine Interaction Through Gestures
This paper presents a visual target recognition system using the ESP32-CAM microcontroller in conjunction with remote processing via neural networks. The embedded device captures and transmits images to a server for processing, where they are pre-processed, segmented, and classified using a multilayer perceptron (MLP) implemented in PyTorch. The solution explores the integration between embedded systems and artificial intelligence, optimizing computational resources and reducing latency. Experiments demonstrate the feasibility of the approach for low-cost intelligent monitoring applications. The proposed architecture is modular and adaptable, favoring deployment in various IoT contexts.
Personal Development Atlas: A task management system
Time management is a daily challenge that affects personal, academic, and professional life. With increasing workloads and the need to balance study and well-being, the use of technological solutions for organization becomes essential. This study aimed to develop a task management system, providing resources and tools for creating daily schedules, facilitating time organization, and assisting in the
A Deep Learning–based Data Lake architecture for searching parasitic images of socially determined diseases
This work compares data pipelines using image files in PNG format and Deep Lake for medical images (SHdataset) through performance benchmarks and a Deep Metric Learning (DML) case study to analyze performance and effectiveness trade-offs. Results indicate that Deep Lake, while faster in data iteration, required 59.2% more storage; although it produced a model of comparable quality, the image file-based approach yielded a feature space with marginally superior quantitative separability. We conclude that modern formats present a trade-off between management benefits and storage/optimization costs while largely preserving model effectiveness.
Ontological Modeling for the Management of Nutritional Solutions in Hydroponic Crops
Hydroponic cultivation involves growing plants without soil, keeping their roots immersed in nutrient solutions made with water and fertilizers. Monitoring pH and Electrical Conductivity (EC) is essential in each solution production cycle to ensure adequate nutrient availability. This work proposes the development of an ontology to manage the production of nutrient solutions for hydroponic cultivation. The system managing the production consists of sensors and actuators, referred to as Industrial Assets, which are controlled by IoT devices. The ontology seeks to ensure system interoperability and provide a clear representation of the system's physical structure, facilitating replications.
Sentilytics: Automated Sentiment Analysis in Social Networks
Social media has become a crucial environment for the dissemination of opinions and sentiments, influencing areas such as politics, economics, and social behavior. However, automated sentiment analysis still presents challenges, including the need for intuitive tools capable of handling large volumes of data. This paper introduces Sentilytics, an application designed for automated sentiment analysis in social media. The application was developed using technologies such as Spring Boot, Angular, and Python, integrating post collection, text preprocessing, and sentiment classification into a seamless and customizable workflow. Sentilytics stands out for its flexibility, scalability, and accessibility, enabling users with little technical experience to conduct complex sentiment analyses. This paper discusses the application’s architecture, the technologies employed, and potential future developments.
A Model for Skin Tone Detection and Recognition Based on Artificial Intelligence and Image Processing
Computer Vision and Machine Learning have enabled the development of more accurate systems for detecting patterns in images, such as skin tone classification — a task relevant to product personalization and the analysis of racial diversity. However, low accuracy for darker skin tones and lack of diversity in datasets contribute to racial bias. This work proposes a model based on the Monk scale, combining facial detection, rule-based skin segmentation, and color extraction using K-means, comparing the results to a reference palette. Using an experimental and quantitative approach, the model achieved 99% accuracy in controlled environments and 80% in low-light conditions.
Assistive Fall Detection System with Embedded Architecture and Long-Range Communication
This work presents the development of a fall detection system aimed at supporting individuals with specific care needs, using embedded sensors and wireless connectivity. The system was designed as a low-cost assistive technology solution, focusing on accessibility and energy efficiency. To validate its functionality, a prototype was built and evaluated through a series of experiments conducted on the campus of the Federal University of Bahia (UFBA), assessing its detection capability and usability in simulated fall scenarios. The results demonstrated that the solution is promising in terms of applicability in real environments, standing out as a viable alternative for
Software-Defined Networks and Virtualized Network Functions: A Study Using Mininet
With the growing demand for more flexible networks, traditional solutions become limited and costly. Network Function Virtualization (NFV) and Software-Defined Networking (SDN) emerge as innovative alternatives. NFV executes virtualized network functions on common servers, while SDN centralizes control via software. This work explores these technologies using the Mininet emulator for simulations. Two topologies were tested with three distinct scenarios, focusing on connectivity and performance. The results showed low latency and high reliability. The study concludes that Mininet is effective for SDN experiments. The combination of SDN, NFV, and emulators allows for more efficient and economical network management.
Interface for Real-Time Monitoring of Anthropomorphic Gait Using Instrumented Piezoresistive Insoles
This work presents an interface for monitoring anthropomorphic gait, with estimation and analysis of movement kinematics based on data collected by sixteen prefabricated piezoresistive sensors arranged in insoles. The instrumented system also includes six inertial measurement units attached to the articular segments of the lower limbs, enabling the tracking of joint trajectories. For the estimation of these trajectories, four non-parametric machine learning computational models were evaluated: k-NN, ANN, decision tree, and random forest. The results demonstrate the feasibility of applying pattern classification-based models to estimate joint angles, with potential applications in driving active orthosis joints or rehabilitation devices.
Impact of Rainfall on Urban Traffic: A Case Study on BR-104 in Alagoas
This study investigates how rainfall events impact vehicle flow on highway BR-104, along the section that crosses the city of Maceió, the capital of the state of Alagoas. We analyzed traffic data from the National Traffic Counting Plan and precipitation data from the National Institute of Meteorology, collected between January and December 2022. Linear regression and mean percentage error (MPE) analysis were applied to assess traffic variation on rainy days. The results showed an increase in the number of passenger vehicles and a decrease in the volume of larger vehicles.
Development of a Line-Following Robot Using Arduino
Line-following robots are widely used in education, robotics competitions, and industrial applications as the basis for autonomous navigation systems. This paper presents the development of a line-following robot using the Arduino Uno platform, TCRT5000 sensors, and an L298N Mini H-bridge. The project aimed to create a robot capable of identifying and following predetermined trajectories, using a control algorithm based on discrete conditional logic. The implementation considers aspects such as sensor selection, motor control, and algorithm adjustments to improve navigation accuracy.
Development of an Autonomous Robot for the Mini Sumo Fighting Category Using Arduino Nano
Robotics, as a multidisciplinary field, has garnered increasing interest by encompassing the study and application of technologies in the conception and operation of robotic systems. In this context, the present project resulted in the creation of a robot dedicated to the mini-sumo combat modality, utilizing the Arduino platform. The control system developed for this prototype is based on a reactive conditional logic, granting it the ability to make autonomous decisions in the ring. To this end, the robot employs an infrared sensor for detecting the arena's edge (boundary) and an ultrasonic sensor to identify the opponent's presence. Its autonomy is based on a hierarchy of three fundamental behaviors: retreat, search, and attack, which ensure an adaptive and effective real-time operation, crucial for competitive performance.
PATRICIA: a real-time singing synthesizer for Brazilian Portuguese
This paper describes PATRICIA, a system that performs real-time singing voice synthesis (SVS) for the Brazilian Portuguese language. A technological mapping and a systematic review were conducted to study the latest developments in real-time SVS and to give directions for PATRICIA design and implementation. Sample-based concatenative synthesis with text files providing the song lyrics in advance was the approach chosen to perform the task. The most recent implemented functionalities are presented and future enhancements are pointed out to overcome the system’s current limitations.
This work explores the implementation of GraphBLAS, a linear algebra-based library for graph algorithms, using the Rust programming lan guage. GraphBLAS is a symbolic language that uses sparse matrix and vector operations to efficiently represent and manipulate graphs, allowing the deve lopment of algorithms related to them. Rust’s strong guarantees for memory safety, concurrency, and performance make it a candidate for building a library like GraphBLAS. This study applies the theoretical foundations of GraphBLAS, including its algebraic structures such as monoids and semirings, using Rust’s typing system and programming paradigms.
Development of an Autonomous and Modular Irrigation System Using Artificial Intelligence and ESP8266
The Brazilian Caatinga faces significant water challenges due to the scarcity and irregularity of rainfall, making efficient water management in agriculture crucial. To address this issue, a modular, scalable, and low-cost irrigation system was developed to reduce installation costs for both large farms and small properties, such as those in family farming. Its modular structure provides flexibility and adaptation to different needs, while its energy autonomy eliminates the need for wires and cables, significantly reducing costs, especially on large rural properties.
Malicious use of Deepfakes in IoT environments: a systematic mapping of threats, vectors, and mitigation strategies
Context: The growing integration of AI and IoT technologies fuels smart environments but also brings new cybersecurity threats like deepfakes. These synthetic media can deceive facial recognition, voice authentication, and surveillance in connected spaces. Objective: This work systematically maps the malicious use of deepfakes in IoT, pinpointing attack vectors, vulnerable sensors, and mitigation techniques. Method: We conducted a systematic mapping, searching IEEE Xplore and Scopus. From 85 initial studies, we selected 16, analyzing them based on three research questions concerning threats, vulnerabilities, and defense mechanisms. Results: Our analysis shows facial and voice authentication systems are most affected. Key attack vectors include face spoofing, voice cloning, and live video manipulation. Effective detection and mitigation techniques in IoT scenarios involve CNNs, blockchain, GNNs, and multimodal detection (Wi-Fi + video). Conclusion: Deepfakes are a concrete and evolving threat to IoT security. Robust, lightweight detection models, alongside biometric authentication frameworks and cryptographic protection, are crucial for real-time defense in resource-constrained IoT environments.
Big Data and Occupational Safety: A new paradigm for occupational risk management
This study proposes an intelligent tool to support occupational risk management, based on the integration of Big Data, IoT, Artificial Intelligence, and predictive analysis. A systematic review identified limitations in traditional reactive occupational health and safety (OHS) models, which often overlook contextual and dynamic variables. Therefore, the proposed solution aims to generate personalized checklists tailored to specific sectors, worker profiles, and operational conditions. This promotes a preventive, data-driven approach. Ultimately, the goal is to improve decision-making, reduce workplace accidents, and strengthen the culture of prevention within organizations.
Anatomy of viral games: design patterns in Lethal Company and its derivatives
Introduction: Viral indie games like Lethal Company blend tension, humor, and streamlined cooperation into simple, highly shareable loops. This paper investigates how mechanical, aesthetic, and emotional elements drive the popularity of so-called Lethal‑likes. Objective: To identify the design factors that contribute to their viral reach and relate them to engagement metrics. Methodology: A qualitative study supported by quantitative data from Steam and SteamDB; comparative analysis of five popular titles based on predefined thresholds. Results: A recurring pattern is observed, grounded in three pillars: cooperative high-risk loop, affordable pricing strategy, and recognizable aesthetic signs.
Parameters for annotating grammatical gender in brazilian portuguese nouns
This study investigates the attribution of grammatical gender in Brazilian Portuguese, with a focus on the Sergipan variety, using the Falares Sergipanos database and the spaCy tool. After automatic labeling and expert review, a significant discrepancy was observed between human and automatic classifications, highlighting error patterns related to gender stereotyping and technological limitations. Eight parameters were proposed to classify nouns in terms of grammatical gender according to morphological and syntactic features. The results contribute to the description of spoken Brazilian Portuguese, support the development of more inclusive language technologies and promote Open Science and replicability practices.
An Environment Integrated with Qiskit for Remote Execution of Quantum Algorithms on GPUs
This paper presents an environment integrated with Qiskit for the remote execution of quantum algorithms using GPUs, aiming to overcome the performance limitations of local hardware. The solution enables dynamic resource allocation and the efficient parallelization of quantum operations, thereby democratizing large-scale simulations. For validation, a system was developed combining a Qiskit-integrated API with GPUs for the computation of quantum matrices and vectors. Experimental results demonstrate significant performance gains over exclusively CPU-based approaches, which helps to advance research in quantum computing. Therefore, the environment facilitates the development, testing, and validation of algorithms for the scientific community.
Revisiting and adding gates to a processor dedicated to hardware-based quantum algorithm simulation
Current approaches to implementing quantum-algorithm simulations in hardware suffer from performance and complexity issues. In this article, we revisit an FPGA implementation of a dedicated 3-qubit processor that supports the S gate, T gate, Hadamard gate, Pauli-X gate, CNOT gate, and Toffoli gate. The proposed architecture combines high performance with design simplicity by enabling the efficient simulation of a quantum circuit’s behavior through a programmable, RISC-inspired model. This eliminates the need to re-synthesize the design, reduces potential errors, and overcomes some of the limitations of existing solutions. Such an approach could represent a viable alternative for the development and validation of new quantum-based systems.
FPGA Validation of a RISC-V Core Dedicated to Quantum Circuit Simulation
The cost of developing a quantum computer is currently high. In this context, this article aims to describe the processes and results of research focused on developing a RISC-V core dedicated to quantum circuit simulation. The adopted methodology involved simplifying the Tiny RISC-V processor and conducting tests and validations on a Field-Programmable Gate Array (FPGA). The results are promising, indicating the feasibility of instantiating multiple cores to explore parallelism to perform quantum circuits simulation, as an alternative to GPU simulation.
Interoperability of Keystroke Signatures for Use in Login-and Password-Based Security Systems: An Analysis of Temporal Features
Due to the increasing use of mobile phones at the expense of computers and notebooks, coupled with the absence of built-in sensors in these devices, the interoperability of passwords using keystroke dynamics on these two types of keyboards becomes relevant. For this analysis, a quantitative methodology with experimental elements was used, employing the extraction of mathematical and graphical data in addition to classification methods. Consequently, the results demonstrate that the interval behaviors of the touchscreen dataset are in line with previously proven scientific results. Therefore, it is suggested that interoperability between the two types of keyboards is possible, but with an interval equalization process.
Comparative analysis of techniques for forecasting u.s. dollar movements in the brazilian financial market: a Machine Learning approach
This paper compares machine learning algorithms for predicting the dollar’s exchange rate against the real, seeking the most accurate model. Using historical and time series data, supervised classification techniques were applied. The following algorithms were evaluated: Random Forest, Neural Network, SVR, SVC, Linear Regression, and KNN. Random Forest and Neural Network stood out, with accuracies of 93.68% and 89.47%. SVC, SVR, and KNN performed poorly. Linear Regression performed intermediately, serving as a benchmark. The conclusion is that models capable of capturing non-linear relationships are more effective in forecasting exchange rates in volatile scenarios.
This paper presents an idea that combines SOM (Self-Organizing Map) and convolutional autoencoders to create a hierarchical and interpretable model. The proposed method replaces each neuron in the SOM grid with an autoencoder. The SOM organizes the grid topologically, creating neighborhood relationships among the autoencoders. Each autoencoder serves as a feature extractor, as its encodings store the main features of an input in a latent representation. In a separate training phase, with the SOM training already completed, the latent layers generated by the autoencoders are used in Multilayer Perceptron (MLP) networks for image classification tasks.
Evaluation of a method for measuring the quality of SOM neural networks for the clustering task
This work evaluates a method for selecting Self-Organizing Maps based on the quality of topological preservation and representation of the neural network input data for clustering benchmark data based on the segmentation of the neural network weights. We evaluated five clustering algorithms: k-means, hierarchical agglomerative, and three methods based on graph partitioning. The results showed that the method for selecting the best neural network was effective for all four databases evaluated, although it did not generate optimal results. We observed that the performance of the clustering algorithms varies according to the type of data, with k-means presenting good performance for hyperspherical data and for the Iris database, the agglomerative hierarchical method being more effective for the MNIST database, and a method based on graph partitioning being more effective for data with arbitrary structure.
Generation of Synthetic Sonic Profiles for Drilled Wells in the Sergipe Basin: A Linearity Analysis Using Regression
The prediction of sonic logs is essential in the oil industry, as it allows the estimation of geological properties without direct measurements, which are often costly or unfeasible. This work applies linear regression to generate synthetic sonic logs based on other geophysical logs from the ANP database for the Sergipe Onshore Basin. The methodology uses Pearson correlation to select relevant variables for the prediction. The model achieved an r2 of 0.77, indicating its viability to reduce costs by replacing direct measurements with statistical predictions.
SPARC: A Prototype for Collecting Water and Electrical Resource Data in a Connected Campus
Facing the need for more efficient management of potable water and electrical energy, this work proposes the use of ”smart campus” technologies for academic environments. We investigated how the Internet of Things (IoT) can be employed to monitor the consumption and flow of these resources, aiming to reduce costs, control supply, and facilitate maintenance. For this purpose, we developed prototypes with diverse sensors, constructed using 3D printing. A crucial point of the project was the implementation of an authentication system for IoT devices, ensuring data integrity. The research demonstrates the effectiveness of this solution not only in educational institutions but in a variety of contexts.
Transparent Algorithmic Management in a Passenger Transportation Application in Urban Centers
This article investigates the impacts of digitalization on urban mobility, focusing on transportation apps that offer agility and personalization. However, these systems generate challenges related to the precariousness of work and algorithmic opacity in the remuneration and distribution of rides. The “Corridas” model is proposed, which integrates explainable artificial intelligence, transparent pricing, and modular architecture, prioritizing algorithmic fairness for drivers. The simulation of the model demonstrates its potential to mitigate costs for passengers and improve the prediction of drivers’ earnings. The results indicate the viability of fairer, more ethical urban mobility systems adapted to the Brazilian reality.
Machine Learning Approaches for Sign Recognition in Brazilian Sign Language (Libras)
The digital inclusion of deaf individuals still faces major challenges due to the lack of accessible technologies for automatic translation of Brazilian Sign Language (Libras). This paper investigates the use of machine learning to recognize static signs in Libras, addressing challenges such as regional variations, lighting conditions, and hand positioning. RNN, Random Forest, and XGBoost models were evaluated using landmarks extracted with MediaPipe from a dataset of 25,000 images. XGBoost achieved the best performance in both accuracy and F1-score. This study contributes by creating a public benchmark for static Libras signs and proposes future work involving dynamic signs and advanced deep learning techniques, aiming to enhance communication accessibility for the deaf community.
Methodologies and tools for data governance applied to data lake management: a systematic review
A data lake is a centralized repository designed to store diverse types of data, regardless of format or structure. While this flexibility offers significant advantages, it also poses the risk of turning the repository into a data swamp that is a scenario where disorganized, inconsistent, or low-value data accumulates. To mitigate this risk, the adoption of robust data governance practices is essential to ensure proper organization and efficient data management. Given the existing knowledge gaps concerning effective governance implementation in data lakes, this study presents a systematic literature review aimed at identifying the methodologies and tools currently employed in managing such repositories.
The Legend of the Fifth Sun: a digital game for the development of Computational Thinking
Computational Thinking (CT) is an essential skill that should be cultivated throughout Basic Education. In this context, game-based learning emerges as an innovative pedagogical strategy capable of promoting the acquisition of these competencies in an engaging manner. This article partially presents the development of The Legend of the Fifth Sun, an educational software designed to enhance CT core competencies among High School students. The project adopts Design Science Research as its methodology and is currently in its final development phase. The preliminary research results demonstrate the game's potential as a pedagogical tool for fostering computational skills.
Educational Technology Applied to Wind Energy: An Arduino-Assisted Horizontal-Axis Anemometer Prototype
The work aims to develop a teaching kit, a horizontal propeller anemometer, to assist in teaching wind energy to high school students. To this end, the propellers were modeled and designed using the Tinkercad tool and printed on a 3D printer. The project uses the Hall effect sensor module, through a magnetic field, to infer the rotations originating from the shells in relation to the wind speed and Arduino Uno R3 to interpret the calculations and represent them on the 16x2 LCD display in km/h and m/s. As a result, the objective is to also address, in an interdisciplinary way, contents of physics, basic electronics and programming, which are relevant to the prototype, in order to solidify the learning process in technological solutions.
Trancadura 2.0: A Low-Cost Modular Electronic Lock for Educational Environments
This paper presents the evolution of an open-source, low-cost modular electronic lock for access control in educational institutions. The new version adopts a modern web-based architecture, with a NestJS backend and a Next.js frontend, both in TypeScript. Key enhancements include OTA updates, secure HTTPS communication, JWT-based authentication, and a responsive web app for managing users, devices, and permissions. Powered by the ESP32 microcontroller, the system supports multiple authentication methods, including RFID, a physical button, and remote API commands. The project promotes accessible and scalable physical security solutions for resource-limited public institutions.
GeoTrack: A Platform and Digital Curation for Public-Interest Information with Integration of Social Networks and Geovisualization Tools
This study presents GeoTrack, a digital prototype focused on curating and geovisualizing public municipal data. Using accessible tools like Google My Maps and Instagram, the platform organizes georeferenced information and extends its reach through social media. Aligned with the Sustainable Development Goals (SDGs), the project demonstrates the potential of digital technologies to promote transparency, civic engagement, and territorial management. Results include a functional interactive map, dissemination strategies, and a detailed account of the development framework and technical challenges faced during prototyping.
Pedal Seguro: an application to promote modal shift through youth education
This article presents a proposal for the development of a mobile application as a tool to promote active modal shift – specifically the use of bicycles – among young students. The application aims to support the Pedal Seguro project, which is based on actions to address barriers such as safety, access to bicycles, maintenance and collective engagement, aiming to reduce dependence on motorized vehicles and, consequently, reduce the number of traffic victims and associated costs. The theoretical basis is anchored in studies on modal shift behavior, especially for the use of bicycles, mobile technologies for engagement and studies of similar programs. In addition, they reinforce the benefits of active mobility for health and the environment. A non-functional prototype was developed to illustrate the main functionalities of the proposed application.
This project aims to develop a low-cost CNC Router targeted at small entrepreneurs, technical students, and maker community enthusiasts. The motivation lies in the lack of accessible digital manufacturing solutions that combine precision, reproducibility, and affordability. The methodology involved research, reuse of components, electromechanical assembly, and Arduino Uno programming, using the GRBL firmware and Universal G-code Sender software. The tests demonstrated that the machine achieves adequate precision for applications in wood, plastic, and printed circuit boards (PCBs), confirming its functionality and replicability. The results highlight its educational and productive potential, representing a viable alternative to reduce technological exclusion in low-resource communities.
Smart device to improve the experience of people with hearing disabilities in Inclusive Smart Cities
This paper presents the development of a wearable device designed to support the mobility of deaf individuals in urban environments. The system is composed of two modules: a sensory belt equipped with directional microphones and vibration motors, and a wrist interface with an OLED display, both controlled by ESP32 microcontrollers. Communication between the modules is performed using the ESP-NOW protocol. The device detects urban sounds and converts them into visual and tactile alerts indicating direction and intensity. Initial tests demonstrated reliable detection, effective feedback, and intuitive user interaction. The project aligns with universal design principles and aims to contribute to more inclusive smart cities.
Development and Validation of an Educational Artifact via Design Science Research
In contemporary times, there is an increasing demand for the adoption of Active Methodologies that enable practical learning. In this context, the Open Digital Neolearning Paths were proposed in a Doctoral Thesis. However, it is necessary to provide university professors with an artifact that offers guidelines for implementing this new strategy. Thus, through the Design Science Research methodology, a Conceptual Framework was developed to support university professors in this process. The artifact was analyzed by 15 experts, who considered it valid for supporting faculty in the application of the Open Digital Neolearning Paths.
Learning Objects with Artificial Intelligence: Pathways to Personalization and Inclusion
This study explores the application of artificial intelligence (AI) in creating visual Learning Objects (LOs) and Open Educational Resources (OERs) in academic settings. The research analyzes the capability of generative AI image tools in producing comics, illustrations of biological processes, mathematical graphs, and reinterpretations of historical paintings. AI utilization can transform abstract concepts into concrete visual representations, facilitating the understanding of complex content, especially in Mathematics. OpenAI’s tools, such as DALL-E and Sora, stood out for their quality and interpretive capacity in generating scientific images and mathematical graphs. The study highlights AI’s potential to modernize education, personalize didactic materials, and democratize access to knowledge, while also emphasizing the need for ethical and methodological considerations.
JAEB: Evaluating an accessible JAVA editor for visually impaired students in an object-oriented programming course
Learning to program can be a challenging task due to the need for abstraction, logic, and knowledge of the programming language. Historically, programming courses have had high dropout and failure rates. This difficulty can be exacerbated to visually impaired students, given the challenges of using development environments and the limitations of screen readers. This paper presents an accessible development environment for programming in Java, designed based on the needs of blind students at an educational institution. The results indicate the student’s familiarity and motivation in using the tool, a positive evaluation of the developed features, and suggestions that emerged during the testing session.
EverydayTalks: A Serious Game to Support Adolescents with Autism in Social Interactions
The serious game EverydayTalks was created to help teenagers who are on the level 1 of support of the ASD (Autism Spectrum Disorder), with guidance that can make their social skills better, which can be challenging. For the development, a literary review about social skills and ASD was made to set the game’s content; as well as, about serious games to define the gameplay’s resources. The game was developed on the Godot Engine with GDScript and Dialogic, Scrum methodology, free assets, and royalty free music and sound effects. For the game evaluation it was used the Delphi methodology with a group of 18 psychology specialists. The results show that the objective can be achieved.
Analysis of actions carried out by the Digital Inclusion Center (CID) and its contribution to the professional training of university students
This article presents an analysis of the actions of the Digital Inclusion Center (CID) project at the Federal University of Alagoas (UFAL) over the last four years, focusing on its implementation, effectiveness, and contribution to the professional development of students at the Arapiraca Campus. It highlights its contribution to professional training in both bachelor's and licentiate degree programs. Among other findings, the data analysis reveals a significant and gradual increase in the offering of and student participation in short training courses during the post-pandemic period, which in itself indicates the growing relevance of digital technologies in the Brazilian educational landscape.
A Bibliographic Survey on Digital Technologies and Their Use in Teaching Practice
This article discusses the transformations in education brought about by the integration of Digital Information and Communication Technologies (DICTs). Based on bibliographic research, it addresses topics such as the evolution of educational technologies, changes in teaching methodologies, the digital competencies required by teachers, the challenges of ongoing training, and the strategic role of educators in the digital culture. The study shows that although DICTs are now a permanent part of schools, their effective integration depends on public policies, infrastructure, appropriate teacher training, and a shift in mindset. When properly implemented, technology can be a powerful ally in promoting more innovative, inclusive, and meaningful teaching practices.
Gamification in Programming Education for Beginners: A Systematic Literature Mapping
This article presents a systematic mapping on the use of gamification in introductory programming education, based on studies published between 2014 and 2024. The analysis of seven selected studies showed that techniques such as progressive challenges, symbolic rewards, rankings, immediate feedback, and group activities enhance student engagement. Results indicate improvements in motivation, understanding, and persistence, especially among beginners. When well implemented, gamification helps reduce dropout rates and fosters interest in programming logic.
Outreach in Data Science: Learnings and Challenges with Unplugged Computing in High School
Este artigo apresenta um relato de experiência durante a condução de um projeto de extensão curricular voltado ao ensino de ciência de dados com computação desplugada para estudantes do ensino médio público, no âmbito da disciplina Atividades Práticas Interdisciplinares de Extensão II do curso Tecnólogo em Analise e desenvolvimento de Sistemas do Centro Universitário Maurício de Nassau de Aracaju. Através do uso de diários reflexivos, construiu-se uma narrativa que evidenciou os desafios enfrentados e os aprendizados vivenciados ao longo do semestre. As atividades extensionistas, realizadas em uma escola da rede pública de Aracaju-SE, permitiram a aplicação prática de conteúdos técnicos e o desenvolvimento de habilidades essenciais, como comunicação, empatia e trabalho em equipe. O envolvimento com a comunidade e a mediação do conhecimento de forma acessível e lúdica revelaram-se fundamentais para consolidar uma formação acadêmica mais crítica, humana e socialmente comprometida. Conclui-se que a experiência da extensão curricular contribui significativamente para a formação integral de profissionais de Computação, ao articular teoria, prática e responsabilidade social em um processo transformador de aprendizagem.
Use of Games in Teaching Processor Architecture and Digital Systems: A Systematic Mapping
Context: The use of digital systems and processor architecture is essential but challenging in computing education due to its complexity. Objective: This study systematically maps research on the use of games in teaching these subjects, identifying methodologies, challenges, and educational impacts. Method: A systematic mapping study was conducted, using the PICOC method for research definition and selecting studies from academic databases based on inclusion and exclusion criteria. Results: The findings indicate that serious games, simulations, and gamification improve student engagement and comprehension, though challenges such as usability and methodological limitations remain. Conclusions: Games are a promising educational tool for digital systems and processor architecture, but further empirical research is needed to refine their implementation.
Exploring Computational Thinking with Minecraft and Arduino for Introduction to Programming and Microelectronics
Introducing children and teenagers to programming and electronics can be enhanced through playful and accessible strategies. This paper presents the development of three interactive prototypes that integrate the Minecraft game with the Arduino microcontroller, aiming to explore their potential in teaching computational thinking and basic electronics. The implemented systems include: (1) an automated crop triggered by a physical button; (2) a virtual intrusion-activated alarm system; and (3) a physical LED indicator for furnace activity. All prototypes successfully enabled digital signal exchange between physical and virtual environments, illustrating how such integration supports skills like logic, abstraction, and creativity. Although no empirical application with students was conducted, the results indicate that combining Minecraft and Arduino offers a feasible and engaging alternative for project-based learning in line with STEAM education principles.
Learning Objects in Computer Engineering for Basic Education: Awakening Scientific and Technological Interest
This work presents the development of two complementary Learning Objects (LOs) aimed at introducing Computer Engineering concepts in basic education. The first LO integrates a mobile application with an embedded system using an Arduino Nano, allowing the visualization of binary additions on seven-segment displays. The second LO involves the 3D modeling and printing of disassemblable transistors (MOSFET and BJT), facilitating the structural understanding of these components. The methodology is based on incremental prototyping and active learning principles. Expected outcomes include increased student engagement, improved comprehension of hardware concepts, and encouragement of scientific and technological interest.
Teaching Mathematical Logic to AI Models: Development of an Interactive Intelligent Agent
Artificial intelligence has shown itself to be a promising tool for supporting education by automating tasks and optimizing processes, making it possible to reduce school dropouts and include students with disabilities. The same applies to the teaching of mathematical logic, where AI makes it possible to create learning solutions. With this in mind, the aim of this work is to propose an interactive intelligent agent based on the GPT model to help elementary school students solve mathematical logic problems. To do this, we tried to integrate the GPT API with Whatsapp, allowing questions to be sent and detailed explanations to be received. The intelligent agent was therefore trained with standardized questions, following the guidelines of the National Common Core Curriculum (BNCC), from the stages of defining the scope of the agent’s action and improving the accuracy and quality of the AI model’s answers based on a bank of 900 questions on mathematical logic for elementary school. Tests were then carried out on a set of test questions. The tests validated the accuracy and suitability of the answers, demonstrating the feasibility of using intelligent agents to reinforce the teaching of mathematical logic in an accessible and interactive way. The results allow us to conclude that the intelligent agent is a potential tool for learning mathematical logic, although some challenges such as the difficulty of access by the population and the need for tests with larger audiences have been perceived.
Construction of an Educational Arcade with Reusable Materials: A Playful and Sustainable Approach to Technical Education in Informatics
This paper presents the development of an educational arcade using reusable parts, aiming to support the teaching of subjects in the technical Computer Science course, such as Basic Computing, Operating Systems, Computer Architecture, and Electronics. The project proposes a sustainable, low-cost, and hands-on approach, integrating theoretical knowledge with real-world application through gamification. In addition to promoting electronic waste reuse, the arcade encourages student engagement and interdisciplinary learning.
Presence of Partner Projects from Brazil’s Northeast in Meninas Digitais in WIT Articles: A Qualitative Analysis of Addressed Themes
This work aims to analyze reports from partner projects of Meninas Digitais from northeast of Brazil to encourage the creation of new groups that promote female participation in Information Technology (IT) in the region. The research follows a qualitative approach and began with the evaluation of 339 papers, which were filtered to 69 about northeastern presence. We selected only reports from female-led communities partnering with Meninas Digitais. The analysis of these experiences allowed for the identification of effective strategies, challenges faced, and impacts generated, providing guidance for new stakeholders interested in the cause. In conclusion, the study highlights the importance of expanding the network of initiatives aimed at the inclusion of women in STEM, strengthening gender equity actions, and promoting a more diverse environment in the IT field in the northeast of Brazil.
Female Presence in Various IT Areas in Brazil’s Northeast: An Exploratory Analysis of Challenges and Opportunities
This research analyzes gender inequality in the Information Technology (IT) field, focusing on the Brazilian Northeast, where female participation is critically low (14% in undergraduate courses and 20% in the workforce). Through a qualitative and exploratory approach, this study investigates the structural and sociocultural barriers that limit women’s career paths in the sector. A mapping of the academic literature and of female-led startups in the region was conducted to form an initial diagnosis of the current landscape. The results confirm the scarcity of female leadership and, notably, the lack of specific regional data, which hinders the creation of effective public policies to promote gender equity in the local innovation ecosystem.