Proceedings of the Integrated Software and Hardware Seminar (SEMISH)
O SEMISH, é um evento com periodicidade anual, sendo o principal e mais antigo fórum científico do Congresso da Sociedade Brasileira de Computação (CSBC). O hackathon tem como objetivo o desenvolvimento de soluções de computação nas quais a questão da sustentabilidade seja ou motivadora ou geradora de problemas. Os projetos serão avaliados por uma comissão julgadora, composta por membros da academia e do mercado.
Sociedade Brasileira de Computação - SBC
Message from the Program Committee Chairs of SEMISH 2025
This document presents the message from the Program Committee Chairs of SEMISH 2025.
Acoustic Identification of Aedes aegypti: Experimental Assessment of Different Machine Learning Techniques
Mosquito-borne diseases affect over 700 million people annually, demanding effective and scalable monitoring solutions. Traditional traps, while reliable, are costly and have limited coverage. Recent studies have shown that mosquito wingbeat audio can enable species identification using convolutional neural networks, but alternative machine learning (ML) models remain underexplored. This study compares five ML architectures – Residual Network, Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Conformer, and Audio Spectrogram Transformer (AST) – for classifying Aedes aegypti from audio data. Using public datasets and standardized preprocessing, we evaluate performance through cross-validation and multiple classification metrics. All architectures analyzed achieved accuracy and F1-scores above 88%, with particular emphasis on MLP and LSTM which, despite their simpler structures, showed competitive performance compared to other networks. These findings support the development of low-cost, audio-based mosquito monitoring systems.
Evaluating the Importance of the contributing.md File in Open-Source Projects
This article investigates whether the inclusion and updates on the contributing.md file in open source projects influence the participation of new contributors. Using a temporal association rules analysis methodology on pull request data, the study evaluates the impact of these creation and changes on the file, implemented by the project’s core team. The results indicate that the updates and active use of the contributing.md file facilitate the participation of new collaborators, reducing the chances of their contributions being rejected. Furthermore, the analysis reveals significant temporal variations of these chances, providing a more detailed understanding of the factors that affect the acceptance and lifetime of pull requests over time.
Deep Learning for Appearance Defect Inspection in Laptops: A Model Comparison
Daily, thousands of laptops pass through repair centers of electronic device manufacturers. During visual inspections, technicians manually identify potential cosmetic or structural damage, documenting findings with photos or videos. This process safeguards the repair center against claims related to damage incurred in custody. However, the manual approach is time-consuming and prone to human error. To address this, we propose a computer vision model to automatically detect appearance defects in laptops. Experiments were conducted using five deep neural networks: Mask R-CNN, SSD, Swin Transformer, YOLOv5, and YOLOv10. Our best-performing model achieved a mean Average Precision (mAP) of 70.2%, showcasing the viability of such application.
An Indoor Navigation Solution for Visually Impaired People
This article describes the development of a mobile application designed to assist the navigation of visually impaired individuals in indoor environments. The application interacts with Bluetooth devices called beacons, which help identify the trajectory. The beacons’ arrangement and the environment’s computational modeling were carried out using techniques such as trapezoidal decomposition and graph representation. The application uses threads to identify the shortest paths between source and destination beacons. It also provides audio instructions about the locations the user must pass through to reach the destination, correcting routes if the user is heading in the wrong direction or location. We validated the solution in a real-world environment and proved capable of successfully guiding the user within the studied environment.
Performance evaluation of neural networks in federated learning for classification of alzheimer’s disease stages
Alzheimer’s disease is a progressive neurodegenerative disorder where early detection is critical for improving treatment outcomes. This study investigates the performance of ResNet 50 and DenseNet 169 architectures integrated with Federated Learning (FL) techniques to classify the stages of Alzheimer’s disease into three main groups: AD (Alzheimer’s Disease), MCI (Mild Cognitive Impairment) and CN (Cognitively Normal). We evaluate three aggregation strategies (FedAvg, FedAvgM and FedProx), across varying client configurations (3-7), while introducing a hybrid FL-Split Learning (SL) approach to optimize computational efficiency and privacy preservation. As a results of the ResNet-FedProx combination achieved superior performance (91.2% accuracy) in 7-client federated scenarios. In contrast, FedAvgM showed unexpected limitations, with accuracy decreasing by 6.7 percentage points as the number of customers increased. Future work should explore hybrid neural networks that combine ResNet and DenseNet architectures and adapt them to other aggregation models available in the literature to optimize performance.
Enhanced Detection and Segmentation of Diabetic Retinopathy Lesions Using a Region of Interest Strategy
Diabetic retinopathy is a common microvascular complication of diabetes that can lead to vision loss. Experts diagnose it by identifying retinal lesions, including hard exudates, soft exudates, hemorrhages, and microaneurysms, the earliest indicators. These lesions often appear near the macula, making this region crucial for early diagnosis. This study proposes a YOLOv12based approach for detecting and segmenting retinal lesions, focusing on the macula. We trained two models: one for region extraction using the Indian Diabetic Retinopathy Dataset and another for lesion detection with the Dataset for Diabetic Retinopathy. The model achieved a mean Average Precision of 0.4530 on the validation set and 0.3020 on the testing set for lesion segmentation.
AVIN: A Case Study on a Supporting Tool for the Self-Assessment Process in Higher Education Institutions
Brazilian Higher Education Institutions (HEIs) periodically undergo self-assessment processes to identify weaknesses and opportunities for improvement. These evaluations help analyze issues such as dropout rates, inadequate infrastructure, and student disengagement, supporting the development of strategic actions. This paper presents AVIN, a tool designed to facilitate this process by leveraging Business Intelligence (BI) concepts. AVIN enables HEIs to collect, process, and visualize data efficiently, providing reliable insights for decision-making. The tool aligns with the Ministry of Education self-assessment framework, integrating institutional participant groups to ensure accurate data retrieval and visualization.
Psoriasis Detection Using Computer Vision: A Comparative Approach Between CNNs and Vision Transformers
This paper presents a comparison of the performance of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in the task of multiclassifying images containing lesions of psoriasis and diseases similar to it. Models pre-trained on ImageNet were adapted to a specific data set. Both achieved high predictive metrics, but the ViTs stood out for their superior performance with smaller models. Dual Attention Vision Transformer-Base (DaViT-B) obtained the best results, with an f1-score of 96.4%, and is recommended as the most efficient architecture for automated psoriasis detection. This article reinforces the potential of ViTs for medical image classification tasks.
Comparative Evaluation of Text Recognition Methods for Field Extraction in Brazilian National Driver’s Licenses
With the advancement of digitalization, the demand for document information extraction solutions has grown, but studies focusing on Brazilian Driver’s Licenses (CNH) remain scarce. This paper highlights this gap by evaluating OCR methods, including TrOCR (with varying settings), Tesseract, and GPT models, for extracting information from CNH documents. The study considers challenges such as lighting and variable image quality, using metrics such as error rate and recognition evaluation in specific fields. The results demonstrate promising performance using open source tools, offering insights into the advantages and limitations of each model.
Application and Analysis of Visual Tracking Algorithms for Ball Detection in Volleyball Matches
The use of Computer Vision has revolutionized sports analysis. In the context of volleyball, ball tracking presents challenges due to its size, speed, and occlusions. Given this, this study aims to evaluate tracking algorithms, including CSRT, MIL, TLD, MedianFlow, and Boosting, as well as train a YOLO model for ball detection. The analysis was conducted based on metrics such as IoU, FPS, and Mean Tracking Error. The methodology involved testing on videos of professional matches and controlled environments. The results indicate that controlled conditions favor superior performance, providing support for the development of systems to assist refereeing and sports analysis.
Detecting Deepfakes in Videos: A Simple and Efficient Approach Using Deep Residual Neural Networks
The proliferation of deepfakes presents a significant challenge with negative societal impacts. This paper proposes an efficient approaching for the automatic detection of deepfakes using deep learning and residual neural networks (ResNets). A ResNet18 model was trained in a supervised approaching using the Deepfake Detection Challenge (DFDC) dataset. The methodology included image preprocessing and training strategies such as transfer learning and class weight adjustment. Results evaluation showed an accuracy of over 90% for image classification and above 92% for video classification, highlighting the effectiveness of the approach for real-world applications.
Transcriber-AI: Using Artificial Intelligence for the Transcription of Historical Manuscripts
This paper presents Transcritor-IA, a tool for automated transcription of historical manuscripts using Handwritten Text Recognition (HTR) models trained with PyLaia. The platform performs hierarchical segmentation and iterative training based on user-verified transcriptions. We also compare Transcritor-IA with Transkribus©, analyzing performance similarity and factors affecting transcription quality.
Comparing TensorFlow and PyTorch for Image Recognition in NAO Robot Soccer
The study compares TensorFlow and PyTorch in image recognition tasks within NAO robot soccer. Image classes were created and trained using data augmentation to enhance robustness and generalization. The analysis considered training time, classification accuracy, and adaptability to different lighting conditions and angles. The results showed that TensorFlow outperformed PyTorch, achieving higher accuracy and better adaptation to challenging scenarios, making it more suitable for computer vision in dynamic environments. The novelty of this study lies in evaluating these frameworks on the NAO robot’s specific hardware, under realistic robotic conditions.
Evaluation of Tile-based Parallelism in VVC Encoders for 360-degree Videos
This work evaluates the parallelism potential and the coding efficiency of Tile-enabled encoding of 360-degree videos for the Versatile Video Coding (VVC) standard. Six 4K conventional videos and eight 360-degree videos were tested with various tile configurations. The results showed that increasing the number of tiles led to speedup gains, with a maximum speedup of 15.52. However, coding efficiency, measured by BD-Rate, generally increases as the number of tiles increases.The results suggest that while both video types benefit from tile configurations, optimal configurations provide better speed gains and less degradation in coding efficiency, particularly in 360-degree videos.
Printed Circuit Board Defect Detection with YOLOv10 Optimized by Ghost Convolution
The automated inspection of Printed Circuit Boards (PCBs) plays a fundamental role in ensuring quality and reliability in the electronics industry. This work proposes the application of the YOLOv10 model for PCB defect detection, with optimizations in the backbone architecture using the Ghost Convolution technique, aiming to reduce computational complexity without compromising precision. The experiments were conducted using the YOLOv10 Nano, Small and Medium models, trained on a public dataset containing 10,668 images. The results demonstrated that modifying the backbone reduced the number of parameters and GFLOPs, increasing the FPS of the Small model from 128 to 150 while maintaining a mAP@50 of 99.22%, surpassing previous approaches in the literature. These findings indicate that the Ghost Convolution technique can be an efficient solution for real-time industrial applications, enabling its adoption in other models and computationally constrained contexts.
Energy Performance in FreeRTOS with Parallelism Compared to a Sequential System in a LoRa Water Meter Prototype
This article analyzes the energy performance of an IoT system for water monitoring. The study compared two implementations: one based on the FreeRTOS real-time operating system and the other using the Arduino framework. The tests were conducted in a controlled environment, ensuring uniform conditions for both approaches and minimizing variations arising from the testing process. The results showed that FreeRTOS offers significant advantages in terms of energy efficiency due to its multitasking management, while Arduino stood out for its simplicity of implementation. The study highlights the impact of software choices on the autonomy of IoT-embedded systems, focusing on the importance of efficient energy management to ensure the sustainability of IoT systems in remote monitoring applications.
A Contribution to Home Peritoneal Dialysis Exploring IoT for Data Collection and Availability
The approach discussed in this paper, referred to as IoT PD-RPM, is applied to the remote monitoring of patients undergoing peritoneal dialysis, a treatment whose adherence can be enhanced through technological support by leveraging IoT for data acquisition and interoperability with the medical community. A solution is also proposed that integrates database auditing and rulebased alerts to ensure compliance with LGPD regulations. To achieve this, the proposed approach combines a platform for vital sign acquisition, a customizable rules environment for situational analysis and notifications to the medical team, and a remote visualization interface.
AIRCloud: A Segmented Dataset of 3D Point Clouds in Brazil
Semantic segmentation of 3D point clouds is pivotal to advancing autonomous vehicles because it provides detailed information about the surrounding environment. Nevertheless, major challenges stem from the overall scarcity of annotated datasets for low-resolution LiDAR sensors and the complete absence of collections acquired specifically within Brazil. This work introduces AIRCloud, a segmented 3D point-cloud dataset captured nationally with a 16-beam LiDAR sensor. For validation, we employ the Range-Image U-Net (RIU-Net) architecture previously trained on SemanticKITTI. Multiple preand post-processing techniques were assessed to ease the limitations imposed by the sensor’s low resolution. Results, expressed as mean Intersection over Union (mIoU), show that targeted strategies can boost RIU-Net performance—for example, raising mIoU from 40.9% (baseline) to 44.6% with nearest neighbors interpolation. These findings underscore the potential of lower-cost sensors in Brazilian contexts, broadening the outlook for autonomous-driving research in Brazil.
Gesture Detection System for Drone Control
This article explores the development and implementation of a drone control system using gesture detection, highlighting the use of computer vision techniques. The proposed system employs the use of tools for capturing and interpreting manual gestures, which are associated with action commands for the drone. The solution aims to facilitate human interaction with drones, offering intuitive control and without the need for traditional physical controls. Experiments carried out demonstrated the functionality of the system, which responds in real time to gesture commands. The study also discusses challenges such as calibration for gesture detection and the influence of environmental variables, such as lighting and visual background. The work concludes that gesture detection for drone control is a viable approach with the capacity for expansion in future applications.
Assessing the Performance of Deep Learning Networks for Real-Time Deforestation Segmentation in the Amazon and Atlantic Biomes
Deforestation poses significant ecological and societal challenges, while advances in satellite imagery and deep learning have enhanced monitoring precision and scalability. This study evaluates ten deep learning models for deforestation segmentation, including U-Net, ResNet, FCN, and YOLO variants, assessing accuracy and computational efficiency, focusing on the Amazon and Atlantic Forest biomes. The results highlight that U-Net and ResNet50 achieve the highest accuracy, while YOLOv8 and YOLOv11 offer an optimal balance between speed and performance. The findings contribute to the model selection for real-time deforestation detection, supporting conservation and environmental decision-making in underexplored areas like the Atlantic Forest.
Topic-Based Access Profiles for Distributed HLA Simulation
One of the main characteristics of distributed simulations that use the IEEE 1516-2010 communication standard High-Level Architecture (HLA) – is that all communicated information is accessible to all interested participants. This characteristic represents a security vulnerability, especially in simulation exercises involving multiple nations or distinct attack and defense teams, such as simulated military exercises. While HLA provides services that standardize and optimize data access, there is no standardized way to control access to sensitive data transmitted within the distributed simulation environment. This paper presents a new approach for implementing data access authorization in an HLA-based distributed simulation, where the use of topic-based access profiles in the HLA middleware message distribution is explored. Experiments conducted with the Portico open-source middleware demonstrate that the proposed approach ensures security and optimizes data access by limiting data delivery per profile through a pre-configured security policy.
An Intelligent Mobile System with Edge Computing for Public Safety Incident Response
This paper presents a mobile system that integrates functionalities for managing public safety incidents, from sending incident notifications to police vehicles to intelligent, real-time processing of videos captured by the mobile devices of police officers on duty. This processing is performed at the network’s edge and generates alerts to improve situational awareness of officers in the field. The paper also evaluates the performance of a face recognition algorithm under limited connectivity conditions, aiming to find a balance between video resolution, recognition accuracy, and energy and network bandwidth consumption on a mobile device.
Dependability Evaluation and Sensitivity Analysis of a Smart Care System for the Elderly
The aging population demands advanced technologies for remote monitoring of the elderly. This work uses IoT and wearable devices to collect and analyze physiological data in real time. Stochastic Petri Nets (SPN) were used to assess the reliability and availability of the system, identifying vulnerabilities before implementation. The analysis revealed that WiFi connectivity is the main critical factor for availability, impacting downtime. This approach aims to assist system designers in optimizing and adjusting system designs.
AutoTab: An Interactive System for Automatic Guitar Tablature Transcription
This work introduces AutoTab, an interactive system designed to simplify the process of transcribing music into guitar tablature. By combining sound event detection, Convolutional Neural Networks (CNNs), and graph-based optimization algorithms, AutoTab offers an intuitive and accessible solution for musicians of all skill levels. The system employs precise note and chord identification through CNNs, a WebSocket-based server for real-time audio analysis, and specialized algorithms such as the Audio Window Analyser for improved sound event detection and TabGen for optimized tablature generation. Initial results demonstrate robust performance, with the Notes Model achieving 87.8% accuracy in note identification and the Chords Model achieving a 78% Macro F1-score in chord recognition. AutoTab aims to democratize access to high-quality transcription technology, enhancing musical practice and learning for guitarists worldwide. Future work will focus on addressing the challenges in polyphonic transcription and expanding the system’s functionality.
Evaluating LLMs and Prompting Strategies for Automated Hardware Diagnosis from Textual User-Reports
Computer manufacturers offer platforms for users to describe device faults using textual reports such as “My screen is flickering”. Identifying the faulty component from the report is essential for automating tests and improving user experience. However, such reports are often ambiguous and lack detail, making this task challenging. Large Language Models (LLMs) have shown promise in addressing such issues. This study evaluates 27 open-source models (1B–72B parameters) and 2 proprietary LLMs using four prompting strategies: Zero-Shot, Few-Shot, Chain-of-Thought (CoT), and CoT+Few-Shot (CoT+FS). We conducted 98,948 inferences, processing over 51 million input tokens and generating 13 million output tokens. We achieve f1-score up to 0.76. Results show that three models offer the best balance between size and performance: mistral-small-24b-instruct and two smaller models, llama-3.2-1b-instruct and gemma-2-2b-it, that offer competitive performance with lower VRAM usage, enabling efficient inference on end-user devices as modern laptops or smartphones with NPUs.
A Proposal for Improving Landslide Prediction with Machine Learning
In Brazil, millions of people live in areas at risk of floods and landslides, especially in the Metropolitan Region of Recife. The increasing frequency of natural disasters, such as the heavy rains that caused deaths and damage in 2021 and 2022, highlights the need for effective alert systems. This study proposes the implementation of dynamic triggers in a hydrometeorological monitoring and landslide warning system, using machine learning to improve prediction accuracy and strengthen urban resilience. The results show that combining algorithms such as SVM with oversampling techniques like ADASYN achieved a recall of 0.91 for the landslide class, correctly identifying 10 out of 11 critical events, demonstrating the model’s potential to enhance event detection and mitigate risks through more precise monitoring. The main methodology used in this project is Design Science Research (DSR). In conclusion, the implementation of dynamic triggers, combined with the collection and standardization of relevant data, such as precipitation and landslide information, strengthens urban resilience and significantly contributes to the protection of lives and property. However, the study also emphasizes the need to expand the monitoring network, collect additional data, and improve the algorithms to ensure the system’s longterm accuracy and effectiveness.
Comparative Analysis of Detection Models for Sheep Identification in Natural Environments
Automated livestock monitoring is an essential strategy for modern livestock farming, as it allows optimizing resources, reducing human errors and improving decision-making in the field. This work aims to compare different convolutional neural network architectures applied to animal detection in natural environments, aiming to identify the most efficient solution. Four models widely used in the literature were evaluated: YOLOv8, SSD, RetinaNet and Faster R-CNN. Performance was measured using standardized metrics, such as precision, recall, mAP and inference time. The results demonstrated that YOLOv8 obtained the best overall performance, standing out for its high accuracy and processing speed. It is concluded that YOLOv8 is the most suitable approach for precision livestock applications focusing on real-time visual detection.
Inclusive Periodic Window: A Universal Design-Based Approach for Learning
School inclusion is the foundation for a more just and equitable society. The “Inclusive Periodic Window” proposes an accessible and interactive educational experience, integrating adaptive technologies into the Periodic Table, grounded in Universal Design for Learning. The proposal catered to students with hearing and visual impairments, autism, Attention Deficit Hyperactivity Disorder, and various educational challenges. Its prototype incorporates Libras (Brazilian Sign Language), Braille, audio narration, subtitles, and dynamic visual elements, ensuring an immersive and personalized experience. With an integrated hardware and software system, the initiative broadens the understanding of a fundamental concept in the field of chemistry, making it accessible to various students.
Patient Monitoring Systems with IoMT and Machine Learning in Healthcare
Internet of Things (IoT) facilitates remote surveillance and monitoring through integrated networks and protocols, and is widely used in the healthcare sector. Medical facilities are integrating connected health technologies to collect patient information, optimizing diagnosis, care, and operational efficiency. This research presents a monitoring framework for medical service facilities, combining the Internet of Medical Things (IoMT) with machine learning approaches. The solution performs facial recognition, detects falls through motion patterns, and triggers alerts for caregivers. Testing yielded promising results, demonstrating good performance in both facial recognition and fall detection.
Short-Term Demand Forecasting Using Machine Learning with Telemetry Data: A Case Study in Brazil
This study evaluates the performance of machine learning models in short-term demand forecasting for a consumer with distributed solar generation. Using a dataset with multi-seasonal patterns and high variability, we compare models such as Ridge, KNN, LGBM, XGBoost, CatBoost, Random Forest, and MLP. Hyperparameter optimization with time-series cross-validation was applied during the tuning phase. The results showed that Random Forest outperformed the others, achieving an MAE of 8.94 kW, surpassing the baseline model by more than 51%. XGBoost, in turn, performed 49% better than the baseline model and had a training time 16 times shorter than Random Forest.
Turning Android Smartphones into Dew Devices to Mitigate Offloading Limitations in Unstable Networks
Offloading helps overcome mobile devices’ computational and energy limits, but its performance depends on an efficient server language and a stable network, which are not always guaranteed. Dew Computing converts client devices into Dew devices, enabling local execution of server processes and prioritizing internal interactions. This work defines the Duplication Management Service (SGD), which transforms Android smartphones into Dew devices and evaluates its impact. Tests with real devices showed that Dew enables tasks previously infeasible, reduces network traffic by up to 2.5x, and computes tasks up to 2.9x faster in high competition scenarios despite consuming up to 19.4x more energy. It is a promising option for unstable networks or low bandwidth.
Forward-Looking Encryption Applied to Low-Latency File Systems
As storage devices evolve, the time spent by software to read or write data becomes increasingly relevant. This characteristic is accentuated in the context of cryptographic file systems, where the software layer is in charge of performing cryptographic operations. Seeking to address this problem, this work proposes a solution that hides the latency of cryptographic operations in low-latency environments through ahead-of-time encryption, taking advantage of low-level interfaces of the Operating System kernel.
Genetic Algorithm for Optimizing the Task Sequencing Problem with Multi-Stage Production, Parallel Machines, and Setup Time: A Case Study in Ornamental Stone Processing Logistics
The processing of ornamental stones involves a complex multi-stage system with parallel machines and sequence-dependent setup times influenced by material hardness. This paper proposes a Genetic Algorithm to optimize task sequencing and minimize makespan. The algorithm uses a hierarchical chromosome structure, customized genetic operators, and a local search strategy. Validation was performed on 26 realistic instances simulating production scenarios such as machine changes, productivity variations, and maintenance. Results show significant makespan reductions and confirm the method’s effectiveness in identifying bottlenecks and supporting logistics decision-making in stone processing plants.
Co-UserX Stories: An Approach for Usability / User Experience (UX) Evaluation
Digital transformation requires high-quality, effective, and efficient products, increasing the importance and need to have products that meet good recommendations regarding usability and UX. Integrating the perspectives of evaluators and designers in this process is a challenge. This work proposes Co-UserX Stories, an approach supported by a tool that helps evaluate the usability/UX of websites. Co-UserX Stories implements the Evaluative Co-Inspection technique, calculating relevant metrics such as success rate, task time, and lost clicks. The tool under development seeks to offer benefits such as ease of use and agility in the evaluation process; its preliminary results indicate feasibility.
Canguinha: an Intelligent Recommendation System for Optimizing Shopping Routes
This work introduces Canguinha Alagoas, an online platform designed to minimize the cost of the basic food basket, particularly for low-income consumers. Given a selected list of essential products and a search radius, the system identifies the most cost-effective stores within the specified area and generates an optimized route that reduces both travel time and overall purchase cost. The platform leverages real-time price data from the SEFAZ API (NFC-e), models the problem as a Traveling Purchaser Problem (TPP), and applies a metaheuristic algorithm to efficiently explore the solution space. Experimental results demonstrate that the platform achieves significant cost savings for consumers, contributing to economic efficiency and positively impacting community well-being.
Development of a Fire Detection and Prevention Prototype for Family Farming in Southeastern Pará
According to the NGO MapBiomas Fogo, 42.5% of the 200 million hectares burned in Brazil are concentrated in the Amazon biome, a loss that causes environmental, economic, and social impacts. With the goal of contributing to the reduction of these damages, this study proposes an IoT architecture focused on the detection and prevention of fires in small rural properties, combining environmental sensors, fuzzy logic, and LoRa communication. The solution classifies fire risk into three levels (low, medium, and high), automatically activating a water pump in case of danger. The system was validated in three scenarios (no fire, with flame, and at maximum range), and the results indicate an adequate system response in high-risk situations, as well as stable communication up to 400 meters, demonstrating the solution’s potential for fire detection and mitigation in rural areas.
Comparison of Embedding Models and LLMs for Retrieval-Augmented Generation in Portuguese
Large language models (LLMs) represent a breakthrough in natural language processing, boosting performance in tasks such as text generation and question answering. However, they face challenges such as hallucinations and lack of access to updated information. The Retrieval-Augmented Generation (RAG) technique seeks to mitigate these problems by integrating external information retrieval into text generation, improving the accuracy and timeliness of the answers. This work investigated several open-source and proprietary embedding models and LLMs applied to the RAG technique, considering three databases containing documents written in Brazilian Portuguese. The experimental results demonstrated that the Multilingual E5 large and Gemma 2 9B models obtained the best performance among the evaluated models based on different evaluation measures.
A Spectrogram Vision Transformer (ViT) Approach for Cross-Domain Bearing Fault Diagnosis on the UORED-VAFCLS Dataset
This paper addresses limited cross-domain generalization in bearing fault diagnosis from traditional time-series data. This study proposes a spectrogram-based approach using advanced Vision Transformer (ViT) models—ViT, DeiT, DINOv2, SwinV2, and MAE— validated on accelerometer-derived spectrogram images from the UORED-VAFCLS dataset. An existing domain-splitting strategy is iterated to evaluate the model performance across varying fault severities. Results demonstrate that the proposed ViT-driven spectrogram method substantially outperforms the state-of-the-art CNN-LSTM approach, setting a promising pathway for robust cross-domain bearing fault diagnostics.
Decentralized Synchronization Between Spectrum Access Systems via Blockchain
This paper proposes a hybrid architecture for Spectrum Access Systems (SAS) that integrates permissioned blockchain technology to enable secure, auditable, and interoperable communication between different SAS instances. The solution replaces traditional REST interfaces with a distributed infrastructure based on smart contracts, which automate processes such as data usage agreements, device registration, and spectrum coordination. The proposal is validated through a prototype implemented with Hyperledger Besu, demonstrating practical feasibility and compliance with regulatory requirements. The architecture provides advantages such as data immutability, shared governance, and operational resilience, representing a step forward in secure and dynamic spectrum management for emerging mobile networks.
Vulnerability Analysis of Government Computing Systems Based on OWASP Guidelines
Com a digitalização de diversos processos, sistemas governamentais enfrentam desafios cada vez maiores em cibersegurança. Uma abordagem eficaz para aumentar a confiabilidade do software é a identificação e mitigação de vulnerabilidades ainda na fase de desenvolvimento. Este trabalho propõe uma metodologia prática para a detecção e correção de falhas no código, conforme diretrizes da comunidade OWASP e utilizando ferramentas tais como SonarQube e Trivy. Para validar a abordagem, duas aplicações governamentais em uso foram analisadas de forma automatizada e contínua. Os resultados demonstram a eficácia da metodologia na mitigação de vulnerabilidades, reforçando a importância de uma estratégia formal de segurança no desenvolvimento de software governamental.
BellatorVR: A Virtual Reality Application to Support Firearms Training for Military Police
This paper presents BellatorVR, a virtual reality application designed to support firearms training, particularly by mitigating risks associated with live weapon use for novice users. The system offers a controlled and immersive environment for realistic weapon handling and tactical practice. Its development included optimized 3D modeling, interaction mechanics, and design patterns to ensure performance and usability. An exploratory pilot study with four participants was conducted to assess feasibility, user interaction, and initial perception. Preliminary feedback indicates that BellatorVR shows potential as a complementary tool for early-stage training, providing valuable insights to guide future development.
Fairness in Machine Learning: An Item Response Theory-Based Analysis
The popularization of Machine Learning tools has made a new problem evident: unfair models applied in sensitive contexts. In view of this, this article explores the use of Item Response Theory in the analysis of models trained on sensitive context data. As a case study, the Credit-G dataset was used to analyze the impact of an existing injustice in the dataset. The data presents a gender representation bias (male and female), therefore, the generation of synthetic data was used to mitigate this injustice. The evaluation of the item parameters for the two groups (biased and mitigated) by the IRT concepts indicate that biased data can be perceived by the occurrence of instances with negative discrimination. The results obtained show that the set of female instances presented average values of −1,675 of discrimination, while the male instances presented 3.2, for the mitigated set.
A GPU-Based Tabu Search Algorithm for Large Instances of the Generalized Max-Mean Dispersion Problem
The Generalized Max-Mean Dispersion Problem (GMaxMeanDP) is important in areas such as web page classification, community mining, and social network analysis. However, current methods for this problem face challenges with large-scale instances, primarily due to memory inefficiencies in their matrix-based representations and the escalating computational time required to execute these methods. This study proposes a parallel tabu search algorithm using GPUs to accelerate local search and optimized data structures to reduce memory overhead. Tests on instances with 2,000 to 20,000 vertices show that the algorithm outperforms existing methods in the literature in terms of solution quality and execution time, making it an effective and scalable solution for large-scale scenarios.
Sensitivity Analysis of the Nextcloud Service Hosted in a Private Cloud
Applications hosted on private cloud servers require high connectivity and on-demand access to resources. To prevent failures that could lead to service unavailability, it is essential for private cloud providers to have a deep understanding of the system components and to identify critical points that may cause failures. This study proposes a sensitivity analysis of the Nextcloud service, which provides a file system as a service. A case study was conducted to evaluate the availability and performance of the private cloud environment using a design of experiments (DoE) approach and Stochastic Petri Nets (SPN) models. The results indicate that component analysis contributes to identifying the system’s most critical points.
FedPEAC-Net: Feature Selection and Hybrid Neural Model Based on Autoencoders for Federated Intrusion Detection Systems
The accelerated growth of the Internet and smart devices has increased the volume of data generated at the network edge, also raising the incidence of attacks targeting the exploitation of sensitive information. Thus, the relevance and necessity of distributed, efficient Intrusion Detection Systems (IDS) that preserve user privacy become evident. This paper proposes FedPEAC-Net, an IDS based on federated learning that combines an autoencoder and a binary classifier trained in parallel, integrated with a feature selection technique guided by reconstruction error. The adopted methodology involves experimentation with the CICIDS2017 dataset in a federated scenario with 10 clients. The results show that FedPEAC-Net achieves an F1-score of 0.95 before and 0.93 after feature selection, showing only a 2.3% drop, while ResNet and LeNet-5 models showed reductions of 15.6% and 15%, respectively. These results confirm the effectiveness of the proposed approach in distributed environments, even with resource-constrained devices.
Real-Time Water Quality Monitoring and Mosquito Prevention System
Water quality is crucial for public health, especially in Brazil’s semi-arid regions, where cisterns are widely used. Lack of monitoring promotes mosquito proliferation, spreading diseases like dengue, Zika, and chikungunya. This work proposes an embedded, autonomous, and low-cost system for real-time water quality monitoring, measuring pH, turbidity, and TDS. Using ESP32, LoRa, and solar energy, it ensures autonomy and connectivity in remote areas. Data is sent to an application on the Vercel for remote monitoring, and aerators are automatically activated to prevent mosquito breeding.
VisionHead: Mouse Alternative for Tetraplegic People Using Computer Vision
This work develops a computer vision-based mouse control system using MediaPipe for real-time facial detection and PyAutoGUI to convert movements into cursor commands. The evaluation, based on Fitts’ Law, assessed efficiency and accuracy in target selection. Results showed a sensitivity of 75%, specificity of 67.78%, and precision of 60.81%, indicating improvements in click accuracy and error reduction, though further refinements are needed to minimize false negatives.
Personalized Study Guide with LLM and Multi-Agent Architecture
Teacher overload and the need for personalization are constant challenges in technical education. This study proposes to mitigate such issues through the use of a recommendation system based on large language models (LLMs) and intelligent agents. The tool collects data provided by students and automatically generates study plans adapted to each learner’s time availability and focus. An evaluation with 16 students, based on the Technology Acceptance Model (TAM), revealed high acceptance, with emphasis on interface clarity and ease of use. The system contributes to more effective pedagogical practices, reduces teacher workload, and preserves critical instructional mediation. It is a viable and scalable solution for contemporary educational contexts.
Improving Qiskit strategies for circuit synthesis using the Reed-Muller spectrum
Reversible computing and quantum computing have gained increasing attention due to their potential to overcome some of the fundamental limitations of classical computing, particularly regarding energy efficiency and computational power. In this paper, we compare the synthesis process using the well-known Qiskit framework developed by IBM and an approach based on Reed-Muller spectra. We show that some approaches are efficient for small values and others are better for the general use case. The synthesis results are presented using four strategies: unitary matrices, linear function synthesis, permutation synthesis, and Reed-Muller spectra. Our analysis shows that, on average, our proposal surpasses the Qiskit synthesis algorithms when considering the gate count metric. Additionally, a post-synthesis evaluation is performed using our proposed method, which employs a Reed-Muller–based representation to assess its effectiveness.
Investigating Cognitive-Driven Development: Insights on Code Readability and Maintainability
The complexity of source code directly impacts software readability and maintainability. Cognitive-driven development (CDD) is a method that helps developers manage cognitive complexity through Intrinsic Complexity Points (ICPs). This study investigated how CDD influences the perception of code readability and maintainability. A survey was conducted with 26 developers, who comparatively evaluated code snippets. The results indicate that code developed under the influence of CDD was perceived as more modular, organized, and easier to modify. However, factors such as experience and project context also influenced the choices.
Evaluation of Architecture and Performance of Contracts for Decentralized Digital Identity in Permissioned Blockchain Networks
This paper proposes an architectural and performance evaluation of smart contracts aimed at managing decentralized digital identities in permissioned blockchain networks. The analysis compares approaches based on Hyperledger Indy and Hyperledger Besu, considering fundamental operations such as identity creation, credential schemes, and revocation control. Metrics such as response time, throughput, resource usage, and scalability are analyzed to support decisions on secure and efficient infrastructure for digital identity in the context of digital public governance. The results show superior performance gains in the Besu-based approach, in addition to greater predictability in resource usage, reinforcing its potential for sustainable large-scale applications. Codes, contracts, and test plans are publicly available for reproducibility.
D-MTS: A Blockchain-Based System for Efficient and Transparent Sharing of Telecommunications Network Infrastructure
Blockchain technology has emerged as a decentralized alternative to ensure security, transparency, and trust in digital processes, directly impacting strategic sectors such as telecommunications. This work proposes the development of a decentralized marketplace for the sharing of infrastructure and telecom services, built on the Hyperledger Besu platform. The solution aims to optimize the use of physical assets—such as towers, links, and access networks—promoting operational efficiency and sustainability in the sector. Through smart contracts, decentralized digital identity, and asset tokenization, the platform automates negotiation and billing processes, contributing to the creation of a more interoperable, auditable, and secure ecosystem.
Uplink Resource Allocation Based on Reinforcement Learning Considering Power and Delay Optimization for 5G Networks with D2D Communications
In this paper, the problem of resource allocation for devices is investigated in a multi-sharing uplink scenario within cyclic prefix orthogonal frequency-division multiplexing (CP-OFDM) and millimeter-waves (mmWaves)-based wireless networks, incorporating device-to-device (D2D) communications. Specifically, a power and delay optimization uplink resource allocation algorithm with reinforcement learning (PDO-URA-RL) is proposed, divided into two stages. First, network resources are allocated to cellular user equipments (CUEs) in terms of power and transmission rate through an approach aimed at maximizing throughput. Subsequently, idle resources are allocated with a focus on minimizing delay. Computational simulations are conducted in a 5G-oriented communication scenario, leveraging mmWaves frequencies above 6 GHz, while comparing the performance with other literature algorithms in terms of quality of service (QoS) parameters, such as throughput and delay.
Evaluating Transformer-Based Architectures for Simultaneous Audio Speech Transcription and Background Audio Captioning
This study evaluates transformer-based models for simultaneous speech transcription and background audio captioning in mixed audio scenarios. Using Whisper for speech and Prompteus for environmental sounds, the models are tested on the Clotho Voice dataset, which combines Portuguese speech (Common Voice 5.1) and environmental audio (Clotho 2.1). Results using WER and FENSE metrics show that each model performs well in its domain but degrades with overlapping signals. Whisper is robust to moderate noise, while Prompteus struggles with dominant speech. The findings highlight the need for hybrid approaches to enable reliable, context-aware audio processing in complex environments.