The Brazilian Journal of Applied Computing (RBCA) is edited under the responsibility of the Graduate Program in Applied Computing (PPGCA) at the University of Passo Fundo and published by Editora UPF. It aims to disseminate national and international scientific productions in the areas of computing applied to agriculture, health, engineering, and other fields that require computing as a tool to solve their problems, also serving as an environment that fosters the exchange of experiences and ideas among researchers working in these application areas. We inform you that new submissions to RBCA can be made starting from March 2026, based on new submission guidelines. We emphasize that the submissions already made in 2025 are in the normal evaluation process for the next two editions of RBCA. Upcoming editions: v.18, n.1 (April/2026), v.18, n.2 (July/2026), and v.18, n.3 (November/2026)
Todos os artigos estão licenciados com a licença Creative Commons Atribuição-NãoComercial-SemDerivações 4.0 Internacional. Autores que publicam nesta revista concordam com os seguintes termos: a) Autores mantém os direitos autorais e concedem Ã revista o direito de primeira publicação, com o trabalho simultaneamente licenciado sob a Licença Creative Commons Attribution que permite o compartilhamento do trabalho com reconhecimento da autoria e publicação inicial nesta revista. b) Autores têm autorização para assumir contratos adicionais separadamente, para distribuição não-exclusiva da versão do trabalho publicada nesta revista (ex.: publicar em repositório institucional ou como capítulo de livro), com reconhecimento de autoria e publicação inicial nesta revista. c) Autores têm permissão e são estimulados a publicar e distribuir seu trabalho online (ex.: em repositórios institucionais ou na sua página pessoal) a qualquer ponto antes ou durante o processo editorial, já que isso pode gerar alterações produtivas, bem como aumentar o impacto e a citação do trabalho publicado (Veja O Efeito do Acesso Livre).
Performance Analysis of Ethernet Networks Through Quality of Service (QoS) Metrics Using Real and Virtual Machines
Quality of Service (QoS) encompasses technologies used in computer networks to ensure reliable application performance. Consequently, the main QoS performance metrics are throughput, delay, jitter, and packet loss rate. One problem regarding networks is finding an ideal environment that guarantees the minimum QoS requirements, especially in home office. Therefore, this work analyzed the performance of a computer network in two contexts, with and without virtual machines. Thus, Iperf3 was used to generate traffic in two ways: originating from an Iperf3 client on a real machine; and originating from an Iperf3 client installed on a virtualized OS. The tests used UDP connections to two Iperf3 servers: local (LAN) and remote (WAN). The goal of this was to differentiate services that require high throughput from those that require minimal delay. The QoS metrics were throughput, delay, jitter, and packet loss rate. The results highlighted the importance of minimizing packet loss to ensure efficient communication, especially in services such as e-mail delivery and file transfer.
Avaliação de Desempenho de Redes de Computadores
Copyright (c) 2025 Diones da Silva Rodrigues, Diógenes José http://creativecommons.org/licenses/by-nc-nd/4.0
Explaining the black-box or using the black-box to develop better interpretable solutions?
Understanding the decision-making processes behind Artificial Intelligence models became a crucial aspect of AI. This paper describes a study that compares the performance of models produced by both interpretable and black-box algorithms and evaluates if it is possible to use black-box models to assist in interpretable models' training. We verified a significant difference in performance between the two types of models. However, the interpretable model was able to mimic the behavior of the black-box models to a satisfactory degree. The promising initial results obtained from using black-box models to aid in interpretable models' training suggest the potential efficacy of this approach.
Copyright (c) 2025 Vinicius Alves Matias, Julia Machado Lechi, Norton Trevisan Roman, Luciano Antonio Digiampietri http://creativecommons.org/licenses/by-nc-nd/4.0
Comparative analysis of the Support Vector Machine and Convolutional Neural Network algorithms applied in classifying Acer Palmatum plant subspecies
Background: Plant identification is an essential task as it provides valuable information about plant characteristics and helps determine the population and distribution of species. This paper presents an artificial intelligence solution developed to automatically classify subspecies of the plant species Acer palmatum based on images. A database containing subspecies of Acer palmatum was created, and supervised learning algorithms such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN) were used to classify them. The experiments included different scenarios, such as original images, feature extraction, and data augmentation techniques. Results: The results showed that the CNN with transfer learning and data augmentation performed best, standing out as the best model tested regardless of the dataset evaluated. Conclusions: These findings suggest that advanced Machine Learning techniques can be highly effective in classifying subspecies of Acer palmatum, providing a valuable biodiversity monitoring and mapping tool.
Copyright (c) 2025 Patrick de Souza Sagioratto, Rúbia Eliza de Oliveira Schultz Ascari, Dalcimar Casanova http://creativecommons.org/licenses/by-nc-nd/4.0
Advances in scientific article recommendation systems: A systematic literature review
The abundance of available articles highlights the growing relevance of recommendation systems. This study conducted a systematic review of the literature with the aim of identifying the state of the art in scientific article recommendation systems. Approaches were identified that use embeddings to capture semantic similarity and probabilistic matrix factorization to integrate relationships between articles and authors. The results indicate a predominance of hybrid models, using LLMs and time-aware modeling. It is concluded that, although there have been significant advances, gaps remain in the standardization of metrics and reproducibility of experiments, as well as opportunities for the development of contextually customized models.
Copyright (c) 2025 Bruno de Santana Braga Contreras, Luciano Antonio Digiampietri http://creativecommons.org/licenses/by-nc-nd/4.0
Estimating Roadside Vegetation Height for Maintenance Decision-Making
The upkeep of roadside vegetation is essential for ensuring the safety of both motorists and pedestrians. However, identifying the necessity for such maintenance is frequently a time-consuming and costly process, with a high potential for errors in annotation. This work therefore proposes the development of a solution capable of estimating the height of vegetation along roadsides in an automated manner. A machine learning model was developed and evaluated using a dataset of manually annotated data. The model employs a convolutional neural network architecture, adapted for the task of classifying vegetation heights. The results demonstrate that the model is able to detect the different vegetation height classes with low error rates, indicating its potential for automating the decision-making process for mowing vegetation. This study contributes to the advancement of road monitoring techniques, providing greater operational efficiency and reducing costs for road maintenance.
Copyright (c) 2025 Mateus Reis Santos, Bruna Rocha Aguiar, Enzo Rigazzo Oliveira , Thiago Meirelles Ventura, Raoni Florentino da Silva Teixeira http://creativecommons.org/licenses/by-nc-nd/4.0
A methodology for assessing UX dimensions in standardized questionnaires
User Experience (UX) encompasses many and diverse variables, which contributes to making its evaluation a complex task. Self-evaluation through questionnaires is one of the most commonly used ways to evaluate UX, both in isolation and combined with other instruments or techniques, and there are currently many standardized and validated questionnaires that can be readily applied. Our work suggests a set of dimensions to describe different aspects of UX, and then proposes a methodology to quantify and compare how much different questionnaires emphasize each of these dimensions. We then select a set of ten questionnaires to exemplify the use of our methodology by comparing them in a particular case study. The results show that all selected questionnaires focus on only a few of the dimensions. Our research reinforces the importance of knowing different instruments and being able to compare them, choosing the one best suited to the context of use.
Copyright (c) 2025 Erico Souza Veriscimo, João Luiz Bernardes Júnior, Luciano Antonio Digiampietri http://creativecommons.org/licenses/by-nc-nd/4.0
A Systematic Review about Large Language Models (LLMs) applied to Code Generation
Large Language Models (LLMs) for code generation represent a significant advancement in software development by boosting productivity, simplifying repetitive tasks, enabling automated testing, and promoting best practices. This paper presents a systematic literature review (SLR) of studies focused on LLMs applied to code generation. The review enhances understanding of the capabilities and limitations of these models, outlining both their benefits and challenges. The review protocol included a search on the Google Scholar database using relevant keywords related to LLMs and code generation. A total of 112 articles were initially retrieved, from which 15 were selected based on relevance and quality criteria. Of these, 8 were analyzed in depth to evaluate various approaches and outcomes, while the remaining 7 provided the theoretical foundation for the study. This work contributes to the growing body of knowledge in the field and supports future research and applications of LLMs in software engineering.
Artificial Intelligence and Software Engineering
Copyright (c) 2025 Fabio Alecsandro Bacin, Braulio Adriano de Mello, Giancarlo Dondoni Salton, Samuel da Silva Feitosa http://creativecommons.org/licenses/by-nc-nd/4.0
Automatic Identification of Electrical Appliances in Domestic Settings
The Internet of Things (IoT) is pivotal for automation, monitoring, and control in modern environments. However, effectively managing numerous electrical devices within these settings presents significant challenges. A critical gap in understanding the operational status and power consumption of these devices often results in energy waste and can shorten their lifespan. This paper focuses on the automatic identification of electrical devices in IoT environments, leveraging features extracted from the frequency domain of electrical current signals. We collected power consumption measurements from household appliances and fed them to machine learning algorithms. Our results highlight the efficacy of the k-Nearest Neighbors classifier, which achieved an impressive F1-score of 99% in device identification.
Copyright (c) 2025 Antonio D. T. Amurim, Elvis M. G. Stancanelli http://creativecommons.org/licenses/by-nc-nd/4.0
Performance Analysis of Routing Algorithms based on V2V Topology using NS-3 Simulations
Vehicular Ad-Hoc Network (VANET) has a great potential to provide security and entertainment applications for ITS (Intelligent Transport System), and to improve road traffic safety. Vehicle-to-Vehicle (V2V) communication is essential for the vehicular automation process and is used in places where there is no Road Side Unit (RSU) structure implemented, and also to complement V2X communication, as it helps to reduce the load traffic in V2X with high density of vehicles. Thus, this work proposes the use of the Multi-Table Ad-Hoc On-Demand Distance Vector (MT-AODV) routing protocol on VANET/V2V networks to overcome the recurring obstacle of communication degradation in networks with high vehicle density and communication failures.
Copyright (c) 2025 Carlos P. Alves, Natália C. Fernandes, Tadeu N. Ferreira http://creativecommons.org/licenses/by-nc-nd/4.0
Efficient generation of high-quality initial solutions for the job sequencing and tool switching problem
As an optimization problem, the job sequencing and tool switching problem has been the subject of several studies in operations research on its different variations, emphasizing its academic and industrial relevance. Although current methods approaching this problem yield extremely high-quality solutions, the computational time required has proven prohibitive when considering the practical aspects of the problem. Thus, in this paper, a method is presented for generating valid, high-quality solutions in low computational time, which can be used as initial solutions by more robust methods, aiming to accelerate them and contribute to the final quality of the solutions. The proposed approach consists of a new implementation of the random variable-neighborhood descent method using traditional and tailored local searches. Five traveling salesman problem heuristics were considered to generate the initial exploration point for the proposed method. The results obtained were compared with a recent strategy in the literature for generating initial solutions, which demonstrated significant improvement. Additionally, the proposed method was compared to the current state-of-the-art method for the addressed problem, and an average gap of only 5.36% was reported, evidencing the high quality of the solutions achieved for the proposed objective.
Copyright (c) 2025 Clark Cerqueira Engelhardt Veronez, Leonardo Cabral da Rocha Soares http://creativecommons.org/licenses/by-nc-nd/4.0
Application of machine learning strategies in the detection of invasive plants in pastures
The presence of invasive plants on pastures is a challenge for livestock farmers. Weeds not only compete with forage for nutrients, but can also harm livestock. Chemical control is one of the options to tackle this problem. In this sense, automatic detection is a necessary alternative, as it increases agility and reduces process costs. In this sense, machine learning algorithms emerge as possible alternatives. In this work, different models for the classification of invasive plants were evaluated, using images of the genus Rumex as reference. The evaluated models were based on monolithic strategies and on systems with multiple classifiers. The experiments, performed on two datasets with different proportions of positive and negative classes, showed that KNN was the most competent single model in detecting the invasive plant, achieving an accuracy of more than 95% and a sensitivity of more than 0.90 in both datasets. Among the models based on ensembles, Random Forest stood out with a accuracy of over 0.95 and a sensitivity of about 90% in both scenarios.
Detecção de Plantas Invasoras em Pastagens
Copyright (c) 2025 Gabriel Tadioto Oliveira, André Luiz Brun http://creativecommons.org/licenses/by-nc-nd/4.0
CNOPsolution Tester: a web application for result validation in constrained optimization problems
In numerical optimization, solutions to optimization problems are developed using methods or techniques that generate approximate results for complex problems, such as metaheuristics. When these techniques generate results, it is essential to verify whether these values meet the optimization problem requirements, for example, values of the variables within the minimum and maximum range and the satisfaction of the problem constraints. Doing it by hand can be very difficult, especially considering optimization problems have different characteristics. This paper presents the development of a web application called CNOPsolution Tester, which allows researchers and end-users to validate the values of decision variables based on their ranges and constraints. The application was designed based on diagrams created with the Unified Modeling Language and developed in JavaScript. We tested the web application with multiple users and 24 benchmark optimization problems to evaluate its usability, measuring the number of clicks needed to solve each problem. We also validated the functionality using 20% of the problems. The results demonstrate that the CNOPsolution Tester is an efficient tool and easy to use by any end-user, as shown by the response time and the number of clicks used on the application. We applied the Pearson correlation to identify the relationship between the number of clicks, problem variables, and constraints.
Copyright (c) 2025 Adrian García-López, Oscar Chávez-Bosquez, Betania Hernández Ocaña http://creativecommons.org/licenses/by-nc-nd/4.0
AMIPDI: architecture model to identify pests in soybean defoliation images using convolutional neural networks
Pests that attack crops are one of the leading causes of low production and economic losses. Identifying and diagnosing forecasts is one way to reduce losses and maintain quality in production. Defoliation of crops harms production and production quality. Using computational technologies such as Artificial Intelligence andMachine Learning has enabled the identification of pests faster and earlier. In this paper, we propose a Deep Learning-based architecture aimed at identifying and diagnosing insects by analyzing defoliation in crop images. Different Convolutional Neural Network approacheswere considered to evaluate the proposed architecture. The training and testing of themodelswere performed using images collected by drone in a natural environment. The approach that presented the best performance in our scenario was the VGG16. The average accuracy in the validation phase was 0.95, while in the test set, we obtained 0.86.
Copyright (c) 2025 Maria Eloisa Mignoni, Aislan, Cesar, Gabriel de Oliveira Ramos, Rafael http://creativecommons.org/licenses/by-nc-nd/4.0
Benchmarking Machine Learning Algorithms in Fake Reviews Detection in Brazilian Portuguese
The proliferation of fake reviews has become a growing concern on e-commerce platforms, as these reviews can mislead consumers and harm the reputation of products and services offered. Automatic detection of fake reviews is a challenging task, as it requires analyzing textual data and identifying subtle patterns that indicate the veracity of reviews. Since fake review datasets in Portuguese are scarce, in this work, we generate and propose a dataset in Brazilian Portuguese for the detection of fake reviews. Then, four machine learning algorithms, combined with three text vectorization methods, are used in a transfer learning scheme for fake review classification. A comparative analysis is carried out using performance metrics such as accuracy, F1-score, and false positives. The results show that, for the proposed dataset, the combination of Logistic Regression and a pre-trained BERT model in Brazilian Portuguese, i.e., BERTimbau, reached the best metric values, reaching 96.61% of accuracy.
Copyright (c) 2025 Cristiano Mesquita Garcia, Eduardo C. R. Borges, Samuel da S. Feitosa, Carlos H. Radavelli http://creativecommons.org/licenses/by-nc-nd/4.0
Transfer Learning and Data Augmentation Analysis for Computer Vision in Robot Football using Deep Learning
Robot Soccer is a promising field for advances in computing and robotics, especially in computer vision with Convolutional Neural Networks (CNN). New models in this area can optimize the intelligence and autonomy of machine learning systems. This study developed databases to create Image Classification and Object Detection models in humanoid robot football, analyzing the impact of Transfer Learning (TL) and Data Augmentation (DA). Using the Edge Impulse platform, experiments with 5 image classes were carried out for Image Classification, while 4 databases with different image sizes were used for Object Detection. The results showed that TL increased the accuracy of the Classification models between 11.25% and 38%, and the combination with DA resulted in variations of up to 3.5% in accuracy in comparison to when applying only TL. In Object Detection, increasing the number of images improved accuracy by 33% and Recall by 37%. These findings highlight the importance of these techniques for the learning and interaction of robotic agents in complex environments. Future studies can focus on tuning hyperparameters and new architectures to optimize these models.
Copyright (c) 2025 Franklin Andrade de Brito, Rodrigo C. de Barros, André Luiz Carvalho Ottoni http://creativecommons.org/licenses/by-nc-nd/4.0
Semiotic Evaluation of the Communicability of a Collaborative Libras Dictionary
Communicability refers to the ability of a system to communicate effectively and understandably with the user. In this sense, this article investigates the communicability of a system developed to serve as a collaborative dictionary for the Brazilian Sign Language (Libras). The research uses semiotic inspection methodologies to evaluate how the developers' intentions are perceived and interpreted by users. The results point to the need for significant refinements in the tool, to improve the communicability and the user experience - essential aspects for the teaching and broader social inclusion of the deaf community. Such modifications aim to facilitate the use of the dictionary, promoting a clearer understanding of the content and more effective learning of Libras by its users.
Copyright (c) 2025 Carla Berkenbrock http://creativecommons.org/licenses/by-nc-nd/4.0
Multi-queue Round Robin Scheduling for Enhanced Performance in Integration Platforms
Contemporaneous environments involve a large amount of information received from many interconnected devices and integrated by many platforms. The Internet of Things and Cloud computing offer services to deal with such big data so that enterprises can play in their business core by outsourcing the development, configuration, and maintenance of those services and their computational resources. Many enterprises use integration Platforms as a Service to maintain their applications and data synchronously. However, current integration platform task scheduling algorithms face some difficulties when tackling large volumes of data. In this article, we propose a scheduling algorithm based on the round-robin heuristic through multiple task queues that presents better performance than the traditional First-in-first-out heuristic used by current platforms. We experimented with our algorithm in the execution of an integration process and validated the results using statistical techniques.
Copyright (c) 2025 Daniela Lopes Freire, Rafael Z. Frantz, Vitor Basto-Fernandes, Gerson Battisti, Sandro Sawicki, Fabricia Roos-Frantz http://creativecommons.org/licenses/by-nc-nd/4.0
On the Use of Fireworks Algorithm and Particle Swarm Optimization for Watermarking in Image Vector Quantization
Information hiding has been addressed in several studies. When the information is hidden in digital images, it can be carried out in three domains: spatial domain (original space of image pixels), frequency domain (or transformed domain, such as Discrete Cosine Transform domain) and compressed domain. In the last case, one can cite data embedding in images compressed by vector quantization (VQ). This paper addresses the problem of codebook partition in the scenario of invisible watermark embedding in digital images compressed by VQ. In this paper, two techniques are investigated for partitioning purposes: the PSO (Particle Swarm Optimization) algorithm and the EFA (Enhanced Fireworks Algorithm) as alternatives to the Genetic Algorithm. The performance of the techniques is evaluated with regard to the algorithms execution time. Robustness of the watermark against a variety of attacks is assessed for codebooks partitioned with the aforementioned algorithm.
Copyright (c) 2025 Marcos José Canêjo Estevão de Azevêdo http://creativecommons.org/licenses/by-nc-nd/4.0
Comparative study between neuromorphic implementations using Spiking Neural Network connected by Network-on-Chip
The efficient implementation of neural networks in hardware, especially using networks-on-chip for the interconnection of spiking neural networks in neuromorphic chips, is a significant advancement in computational systems that mimic the human brain. This study conducts a systematic review on the integration of these technologies in neuromorphic chips, using the PRISMA methodology to analyze various studies and compare different hardware approaches. The results highlight the importance of energy-efficient and high-performance solutions for artificial intelligence. Despite technical challenges, neuromorphic computing is rapidly evolving, with the potential to advance several emerging technologies. The challenges include exploring neuron models, learning techniques, and interconnection and routing strategies to improve efficiency and performance.
Copyright (c) 2025 Victor Benvenutti Cavalheiro, Beatriz Oliveira Câmara, Janaina Gonçalves Guimarães http://creativecommons.org/licenses/by-nc-nd/4.0
A Coloured Petri Nets Model of the 5G Authentication and Key Agreement Protocol
Background: The security in 5G networks must safeguard network functions and other components responsible for end-to-end communications, intending to provide confidentiality, integrity, availability, authenticity, and non-repudiation. Some security functions that protect 5G networks are part of relevant protocols such as the 5G Authentication and Key Agreement (5G-AKA). However, the 5G-AKA protocol has some limitations, becoming susceptible to attacks such as replay and man-in-the-middle. Results: Aiming to analyze the 5G-AKA protocol regarding security properties and help to identify possible vulnerabilities and threats, we used coloured Petri nets to model the protocol's components, their interactions, and an example of an attack scenario. We validated the model using simulations and the model-checking technique. Conclusions: This model may help improve the security of 5G networks by validating properties and behaviors using simulations and formal verification. The proposed 5G-AKA model can be a relevant tool for identifying vulnerabilities and proposing mitigation measures for different 5G topologies.
Copyright (c) 2025 José Sávio Gama Macêdo, Antônio Carlos de Oliveira Bezerra, Álvaro Sobrinho, Leandro Dias da Silva, Dalton C. G. Valadares, Danilo F. S. Santos, Angelo Perkusich http://creativecommons.org/licenses/by-nc-nd/4.0
Evaluation of Routing Protocols in Mobile Ad-Hoc Networks Applied to Military Scenarios: Challenges and ResearchGuidelines
Ad-hoc networks, widely studied by institutions such as the Internet Engineering Task Force, have gained prominence in military contexts due to their rapid deployment and the use of dynamic and adaptive routing protocols, which are essential for agile operations and training. Simulation-based studies have made it possible to identify the protocols best suited to different operational conditions, providing valuable guidance for their selection and use in military environments. Although the literature extensively addresses Ad-hoc networks, there is a noticeable lack of studies that jointly examine a set of protocols capable of supporting a complete service infrastructure tailored to military communication needs. The distinctive contribution of this work lies precisely in filling this gap: rather than analyzing isolated protocols, it proposes an integrated structure that takes into account the specificities of military networks—distinct from civilian and commercial networks—thus contributing to the development of a more robust and efficient technological framework to support military operations.
Copyright (c) 2025 Cláudia Rödel Bosaipo Sales da Silva, Ronaldo Ribeiro Goldschmidt, Ronaldo Moreira Salles http://creativecommons.org/licenses/by-nc-nd/4.0
A process for source code unstructured search
A consulta em código fonte representa um importante recurso para auxiliar desenvolvedores na compreensão de programas, bem como em atividades de refatoração, especialmente em grandes repositórios de código. Este trabalho apresenta um processo que busca facilitar a recuperação de informação, utilizando técnicas de consultas não estruturadas em código fonte. O processo apresenta arquivos classificados por ordem de importância com base nas características da linguagem de programação e nas preferências definidas pelos desenvolvedores. Foram realizadas duas instanciações do processo, nas linguagens de programação Java e AspectJ, demonstrando a aplicação do processo e como ele pode facilitar a identificação das informações recuperadas.
Copyright (c) 2025 Rodrigo de Castro Gil, Eduardo Kesser Piveta, Cristiano De Faveri, Deise de Brum Saccol, Lisandra Manzoni Fontoura http://creativecommons.org/licenses/by-nc-nd/4.0
Comparative analysis of PID control and MPC in CartPole and LunarLander environments
With technological advancement, increasingly complex systems emerge, driving the relentless pursuit of more effective control methods. In this study, the control of two Gymnasium environments will be explored: LunarLander and CartPole. LunarLander represents a simplification of vehicle autonomy, while CartPole is closely related to robotics. The goal is to apply Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) control to these environments, aiming to achieve their specific objectives. The techniques employed have shown success in controlling the environments, highlighting the effectiveness of these methods in complex challenges. Additionally, this study addresses various engineering concepts, from physical fundamentals to system identification, leveraging Python packages to offer an accessible approach for both academia and industry. This multidisciplinary analysis promises to significantly contribute to the advancement of knowledge and practical application in the field of complex system control.
Proportional-Integral-Derivative Control
Copyright (c) 2024 Samir Angelo Milani Martins http://creativecommons.org/licenses/by-nc-nd/4.0
Recognition of Brazilian Banknotes Using Artificial Intelligence to Assist Visually Impaired Individuals
Recognizing Brazilian Real banknotes remains a challenge for visually impaired individuals. This work presents a proposal that employs a convolutional neural network to assist visually impaired people in recognizing Real banknotes using a smartphone with a camera. The implementation provides an application called Banknotes Recognition, which performs the recognition process swiftly by capturing the banknote image with the smartphone's camera. Tests conducted with a set of 66 images of Real banknotes showed an accuracy of 100%, with all banknotes correctly classified even under unfavorable conditions, such as folded notes, complex backgrounds, various lighting conditions, and worn-out notes.
Copyright (c) 2025 Danillo Roberto Pereira, Karen de Almeida, Almir Olivette Artero, Danilo Medeiros Eler, Maurício Araújo Dias, Francisco Assis da Silva http://creativecommons.org/licenses/by-nc-nd/4.0
A systematic literature mapping on the security of 5G and vertical applications
Background: The deployment of 5G infrastructure is one of the vectors for new application scenarios since it enables enhanced data bandwidth, low latency, and comprehensive signal coverage. This communication system supports various vertical applications such as smart health, smart cities, smart grids, and transportation systems. However, these applications bring new challenges to 5G networks due to specific requirements for such scenarios. Furthermore, as software-based technologies, including network slicing, software-defined networks, network function virtualization, and multi-access edge computing, are a fundamental part of the 5G architecture, the network can expose these applications to new security and privacy concerns. Results: This study summarizes existing literature on 5G vertical applications security. We highlight vulnerabilities, threats, attacks, and solutions for 5G vertical applications. We conducted a systematic literature mapping to discuss security and privacy challenges regarding the 5G vertical applications. We reviewed 389 papers from 2,349 produced by searching with a curated search query and discussed vulnerabilities, threats, attacks, and solutions for 5G vertical applications. Conclusions: Smart cities, Industry 4.0, smart transportation, public services, smart grids, and smart health are vertical applications with relevant security concerns. We observed the need for more research since the 5G and vertical applications continuously evolve.
Copyright (c) 2024 Álvaro Sobrinho http://creativecommons.org/licenses/by-nc-nd/4.0
Pothole Detection Web App: an approach for detecting potholes in asphalt pavements using YOLO
The detection of potholes in asphalt pavements is a crucial task for road safety. This survey requires significant time and financial resources. An automatic method for this task can help in Pavement Management System, speeding up processes of road recovery and maintenance of materials. With this in mind, this work presents the Pothole Detection Web App, a smart web application for detecting potholes in asphalt pavements using the YOLOv7-tiny architecture. The solution allows performing the identification of potholes in asphalt pavements by photos, videos or live broadcasts. The images or videos are sent to a web server, where the detection model is applied and the results are returned to the user. Preliminary tests pointed out good results, with an accuracy of 74% (F1-Score = 66%). The solution proved to be capable of detecting potholes and estimating defect dimensions with a good approximation, in reports with organized information. In addition, it showed good performance for real-time analysis, considering different browsers. The approach can serve both urban roads, using internet, and open roads, using communication with a local area network.
Copyright (c) 2024 Jean Cássio Peres Barbosa, Francisco Dalla Rosa, Rafael Rieder http://creativecommons.org/licenses/by-nc-nd/4.0
MQTT Poller: an endogenous framework for large-scale MQTT protocol performance evaluation
MQTT is an application layer protocol that supports synchronous and asynchronous client communication. It employs an intermediary server/broker between clients and a topic system to send and receive messages. Thanks to its simplicity and low control cost, it is commonly used in the development of applications for the Internet of Things (IoT), and it is also of interest in several research works, such as those related to the scalability of the protocol. In this context, it is observed that there is a lack of performance evaluation and experiment orchestration instruments. This work presents a framework for evaluating the performance of the MQTT protocol in many scenarios. The development of the framework follows an endogenous communication model (i.e., based on the MQTT topic schema) for control and interaction between the architecture's elements. Starting from a reference performance evaluation tool (MQTTLoader), it is possible to configure an evaluation scenario that makes it feasible to run multiple instances of this tool, under the coordination of an orchestrator, on machines possibly hosted in different administrative domains. A case study demonstrates the functionalities and practicality of using the framework to conduct MQTT performance evaluation experiments.
Copyright (c) 2024 Diogo Maciel da Cunha, Marco Aurélio Spohn http://creativecommons.org/licenses/by-nc-nd/4.0
Preventing flow rules injection attacks in SDNs using Blockchains
Due to the programmability feature of Software-Defined Networks (SDNs), both the control software and network applications are susceptible to attacks, such as the injection of false flow rules. Thus, there is a need to investigate alternative solutions that enable the verification of integrity, authenticity, correctness, and agreement on the set of flow rules to be deployed on switches. In this context, the secure distribution of flow rules can be implemented through Distributed Ledgers (DLs), specifically utilizing Blockchain (BC) technology. This technology allows for the maintenance of a distributed and collaborative information registry among participants, who are responsible for the integrity and validation of the inserted data. With this in mind, this article presents an integrated model using BC to support security in the distribution and establishment of flow rules in SDNs. The proposed model was validated through experimental performance analysis and security assessment in an emulated environment. Results show that the proposed model is effective against attacks based on packet injection techniques.
Copyright (c) 2024 Ramon Amorim dos Santos Delgado Ama, Valério Rosset http://creativecommons.org/licenses/by-nc-nd/4.0
Due to the popularity of Blockchain technology, there have been many applications of it in many contexts. It is a technology that allows trusted communication between untrusted entities without a centralized third party. Also, Blockchain is a chronological sequence of blocks linked through the hash of each one. This characteristic ensures data integrity and immutability. Therefore, Blockchain is a powerful technology for recording data like access control rules. This work utilizes Blockchain to provide a framework applying the Smart Contract concept for a Role-Based Access Control (RBAC) system. It is the RBAC System Smart Contract-based (R2SC). Also, it uses the framework Truffle to test the Smart Contract functionalities. In counterpart to the related works, R2SC has scalability.
Copyright (c) 2024 lucas vargas dias, Tiago Rizzetti, Wagner Brignol, Luciane Canha http://creativecommons.org/licenses/by-nc-nd/4.0
Software Development Models in Game Development: a Literature Review
The present work is a systematic literature review (SLR) based on the PICOC framework (Population, Intervention, Comparison, Outcome, and Context), with the objective of mapping and analyzing studies on the application of software engineering methodologies to game development. We analyzed scientific papers up to 2024, retrievable from the ACM, IEEE, and SOL databases using the search string ("game development model"OR "game development models") OR (("software development model"OR "software development models") AND (games OR game)). As a result, 9 articles that met the inclusion criteria were found, and we present the main methodologies applied, their characteristics, application context, and performance. Overall, the analysis of the selected articles reveals that, although there are significant parallels with traditional software development, game development presents unique challenges and demands that require adapted and specific methodologies. This distinction becomes evident in how software engineering models are applied and modified to meet the specific needs of the gaming industry, which intrinsically combines technical and creative aspects.
Copyright (c) 2025 Gabriel Acassio Correia, Vinícius Carvalho Pereira, Cristiano Maciel http://creativecommons.org/licenses/by-nc-nd/4.0