Anais Estendidos do Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia)
O WebMedia, desde a sua primeira edição em 1995, transformou-se no mais importante fórum de debates para pesquisadores e profissionais das áreas de multimídia, hipermídia e Web no Brasil. O objetivo principal do WebMedia é integrar a comunidade científica nacional que desenvolve atividades em universidades, centros de pesquisa e desenvolvimento e empresas, relacionadas às áreas de multimídia e de tecnologias Web.
Sociedade Brasileira de Computação - SBC
Este documento traz o Prefácio e a Organização dos Anais Estendidos do WebMedia 2025.
Aprendizado de Máquina Aplicado à Redução do Custo Computacional do Test Zone Search e da Estimação de Movimento Affine do Codificador VVC
As the demand for video transmission surges in remote work, education, and streaming services, the need for continuous advancements in video encoding technologies becomes increasingly evident. Adapting to the evolving requirements of efficient video delivery and consumption, Versatile Video Coding (VVC) emerges as the state-of-the-art. This dissertation focuses on two key encoding tools within VVC Inter-Frame prediction: Test Zone Search (TZS) and Affine Motion Estimation (AME). A fast TZS and AME approach using Machine Learning is presented, specifically employing Decision Trees. The proposed approach achieved an average reduction of over 20% in total VVC encoding time while maintaining less than a 1% impact on BD-BR coding efficiency.
Planejamento automático de tratamento odontológico
Limited access, inequality in the distribution of dentists, and diagnostic failures compromise oral health in Brazil. This study developed a method and a clinical decision support system integrating deep neural networks and decision logic to assist in dental treatment planning. The automatic systematization of procedures such as dental treatment planning can provide support to professionals and their formal education.
Redução de Custo Computacional da Codificação de Nuvens de Pontos Dinâmicas
This dissertation presents a machine learning-based solution to reduce the computational cost of compressing dynamic point clouds under the V-PCC standard. The proposed method employs decision models to accelerate the video encoding stage, which accounts for most of the processing. Results show an average 60% reduction in encoding time in Random Access mode, with minimal impact on coding efficiency (approximately 1.3% increase in BD-Rate), making the solution suitable for real-time applications and resource-constrained devices.
Um Estudo sobre Espalhamento de Desinformação em Comunidades Antivacina no Telegram
Amid the biggest public health crisis of the century, the COVID-19 pandemic, social platforms amplified the spread of harmful narratives. In this context, this study explores how anti-vaccine communities operated on Telegram. By analyzing nearly 10 million messages from 779 channels and groups, and using machine learning to detect misinformation, harm potential, and toxicity, the results indicate recurring misinformation patterns, high engagement, and links between toxic content and virality. The study provides methodological contributions and insights for understanding and addressing informational disorder on messaging platforms.
Uma arquitetura de sensoriamento social para suporte à comunicação não planejada no contexto de violência urbana no projeto CidadeSocial
This research investigated how the concept of social sensing can be used as an approach to support unplanned communication among citizens in the context of urban violence. Also, it researched how to identify and classify severity levels in text messages published on social media about the daily life of a city. As results, two conference papers were published, a undergraduate student supervision was conducted, and a software that implements the proposed architecture was built. The research advanced the capability to democratize access to information in critical contexts, map violence patterns through automatic classification, and strengthen urban resilience by enabling unplanned communication about risk events.
A Comprehensive Exploitation of Instance Selection Methods for Automatic Text Classification
Progress in Natural Language Processing (NLP) has been dictated by the rule of more: more data, more computing power and more complexity, best exemplified by the Large Language Models. However, training (or fine-tuning) large dense models for specific applications usually requires significant amounts of computing resources. This Ph.D. dissertation focuses on an under-investigated NLP data engineering (DE) technique, whose potential is enormous in the current scenario known as Instance Selection (IS). The IS goal is to reduce the training set size by removing noisy or redundant instances while maintaining the effectiveness of the trained models and reducing the training process cost. We provide a comprehensive and scientifically sound comparison of IS methods applied to an essential NLP task – Automatic Text Classification (ATC), considering several classification solutions and many datasets. Our findings reveal a significant untapped potential for IS solutions.We also propose two novel IS solutions that are noise-oriented and redundancy-aware, specifically designed for large datasets and transformer architectures. Our final solution achieved an average reduction of 41% in training sets, while maintaining the same levels of effectiveness in all datasets. Importantly, our solutions demonstrated speedup improvements of 1.67x (up to 2.46x), making them scalable for datasets with hundreds of thousands of documents. This thesis strongly aligns with WebMedia’s objectives by addressing key challenges in processing vast web and social media data through innovative, scalable, and cost-effective strategies, falls under the (1) Document Engineering, Models and Languages; (2) AI, Machine Learning, and Deep Learning; and (3) NLP topics of the WebMedia call for papers.
A Computational Framework for Auditing Targeted Advertising
Political sponsored content has become a powerful yet potentially harmful campaigning tool on Online Social Networks (OSNs). Concerned about its misuse during the 2018 Brazilian elections, we developed a computational framework to audit targeted political advertising. Using a browser extension, we collected ads from over 2,000 Facebook volunteers and trained a Convolutional Neural Network with word embeddings, evaluated against classical machine learning methods on a labeled dataset of 10,000 ads. Our findings reveal gaps in Facebook’s Ad Library and show that coordinated posts on Facebook and Twitter can amplify political messaging, underscoring the need for independent auditing systems.
An exploration of recommender systems explanation paradigms: generating and evaluating syntactic, semantic, and generative models with knowledge graphs: An Extended Abstract
Recommender Systems (RS) generate suggestions for users based on their past interactions and the interactions of other users. However, because RS rely on identifying similarities between users, they may expose users to a limited range of content. In addition, as algorithms have become increasingly complex, users often cannot understand why a recommendation is generated. In this thesis, we explored three approaches—syntactic, semantic, and generative—to generate explanations for recommendations and investigate their potential impact on the diversity of recommendations, with the goal of broadening user interests. Our results show that explanations can play an important role in increasing item diversity, thereby helping mitigate filter bubble effects. We also observed an improvement in the explanation quality across the approaches, from syntactic to semantic and finally to generative models.
Enhancing Knowledge Graphs with Large Language Models: Contributions to E-commerce Question Answering Systems
E-commerce platforms demand structured and dynamic knowledge to deliver accurate, humanized, and scalable customer support. This Ph.D. Thesis introduces QART, a novel framework that integrates Large Language Models (LLMs) to automatically extract high-quality RDF triples from question-answer (Q&A) dialogues, enabling population of domain-specific Knowledge Graphs. The method combines semantic summarization, few-shot triple generation, and automated consistency validation to align outputs with existing ontologies. Experimental results across multiple LLMs demonstrate up to 20% gains in precision over baseline approaches and high accuracy in semantic validation tasks (F1 = 0.98). The solution has been validated in an industrial environment, showing potential to reduce human effort, enhance recommendation quality, and improve customer satisfaction through more informed and context-aware answers.
Autoria Semiautomática de Efeitos Sensoriais em Vídeos 360◦
Creating multisensory experiences (mulsemedia) in 360° videos faces the challenge of synchronizing effects, a process that is laborious when manual and inflexible when fully automatic. This thesis proposes a semi-automatic approach that integrates deep learning (DL) recommendations while maintaining author control. The core proposal is a scalable cloud architecture with multiple recognition modules and an object tracking algorithm for spatial continuity, implemented in the SensorySynch360 web tool. A study with 81 users demonstrated that the approach significantly reduces authoring effort and time, while preserving usability and quality of experience.
Socially Responsible and Explainable Automated Fact-Checking and Hate Speech Detection
Although Natural Language Processing (NLP) has traditionally relied on inherently interpretable “white-box” techniques, such as rule-based algorithms, decision trees, hidden Markov models, and logistic regression, the adoption of Large Language Models (LLMs) and language embeddings (often considered “black-box”) has significantly reduced interpretability. This lack of transparency introduces considerable risks, including biases, which have become a major concern in the field of Artificial Intelligence (AI). This Ph.D. thesis addresses these critical gaps by proposing new resources that ensure interpretability and fairness in NLP models for automated fact-checking and hate speech detection tasks. Specifically, we introduce five benchmark datasets (HateBR, HateBRXplain, HausaHate, MOL, and FactNews), four novel post-hoc and self-explaining methods (SELFAR, SSA, B+M and SRA), and one web platform (NoHateBrazil) designed to improve the interpretability and fairness of hate speech detection. The proposed models outperform the existing baselines for Portuguese and Hausa, both underrepresented languages. This research contributes to ongoing discussions on responsible and explainable AI, bridging the gap between model performance and interpretability to achieve positive real-world social impact. Finally, this thesis has had a significant impact both nationally and internationally, receiving citations from prestigious universities and research institutes abroad, inspiring new M.Sc. and Ph.D. in Brazil, and being recognized with multiple awards, including the Google LARA, the Maria Carolina Monard AI Award, and as a finalist at the Brazilian Computer Society Thesis and Dissertation Award.
A WhatsApp-Based Application for Automatic Data Collection and Crime Monitoring in Belo Horizonte
This work presents the development of a crime monitoring system for Belo Horizonte based on the automatic collection of messages from public WhatsApp groups. The system adopts a modular and scalable architecture, integrating services for filtering, structuring and storing data in a hierarchical database that supports queries by region, neighborhood and time period. Crimes are identified in two steps: (1) an initial filter detects potential crime-related messages using a predefined dictionary of keywords, and (2) a Machine Learning model classifies the messages with higher accuracy. This automated pipeline ensures continuous data processing without manual intervention and provides up-to-date geographic information on crimes. The proposed framework demonstrates the feasibility of using instant messaging platforms for real-time crime monitoring and offers scalability and adaptability for application in other cities and related data monitoring contexts.
Bula Fácil: aplicação de bulário inteligente
Introduction: This article aims to describe the development of an application called Bula Fácil, created to provide quick and easy access to information about medications. Objective: The project arose from the need to simplify access to drug package inserts, which are often lost or written in overly technical language, making them difficult for much of the population to understand. Methodology: The application uses tools such as image recognition to identify the medication and artificial intelligence to present summarized information such as indications, composition, dosage, contraindications, and side effects. Results: The tests showed high effectiveness of the Bula Fácil system, standing out with an excellent average score of 87.083 points on the System Usability Scale (SUS), which is considered excellent according to the criteria established by Brooke (1996) [2]. Additionally, the evaluation using the User Experience Questionnaire - Short version (UEQ-S) revealed positive ratings in all categories, especially in the efficiency of information presentation and the visual clarity of the interface. Most participants highlighted the system’s ease of use and usefulness, demonstrating its potential as an accessible and user-friendly tool for the safe use of medications by the general population.
ColabSomGeo: Ferramenta de monitoramento de poluição sonora subsidiada por modelo de aprendizagem
Sound pollution in urban centers is a public health issue, with excessive noise negatively impacting the physical and mental health of the population. Traditional monitoring methods are often costly, making widespread data collection difficult. This paper introduces ColabSomGeo, a low-cost, scalable web tool for visualizing and analyzing georeferenced sound pollution data. Data collection is performed collaboratively through a Telegram chatbot, where users submit audio recordings and their locations. A server application processes the data, classifies the noise type using a machine learning model, and stores the results. The tool aims to assist public managers in formulating policies, support scientific research, and raise public awareness about the impacts of noise on well-being. The code is available at: https://github.com/colabsomgeo
Desenvolvimento de um Dashboard para Monitoramento de Demandas da Ouvidoria do TCE-PE
Ombudsman offices promote public transparency and citizen participation by receiving citizen feedback. The analysis of this feedback reveals weaknesses in public services, guides oversight bodies, and improves management. At the Court of Accounts of Pernambuco (TCE-PE), the need arose for a tool to monitor these demands more dynamically and efficiently. This study proposes the development of a dashboard that enhances analytical capacity, provides accessible visualization, ensures usability and security, and integrates with data input channels. The results indicate improvements in efficiency, agility, and transparency, reinforcing the strategic role of ombudsman offices in building a data-driven public management.
EurecaChat: A Multi-Agent LLM-Based Chatbot for Supporting Students, Faculty, and University Administration
Academic data plays a crucial role in guiding students as they plan their curricular paths, supporting professors in refining their courses, and enabling administrators to make evidence-based decisions. Despite its importance, such data are often difficult to access. Even under open data policies, barriers to usability and accessibility frequently limit its impact. In this work, we present EurecaChat, a multi-agent chatbot system that allows users to retrieve academic data through natural language. Built on Large Language Models (LLMs), EurecaChat tailors responses to the needs of different user profiles: students, faculty, and staff, while ensuring compliance with data privacy constraints. Preliminary evaluation shows good performance in several use cases, underscoring the potential of the system to democratize access to academic data and promote more informed decision making in educational environments.
Florescer entre Sons e Silêncios: Ferramenta de Apoio à Classificação de Vocalizações Não Verbais com Inteligência Artificial
Communication, a fundamental human right, can be compromised in neurodevelopmental disorders, such as in nonverbal children with Autism Spectrum Disorder, whose vocalizations often lack intelligibility. This study explores how artificial intelligence can support phonological analysis in this context. A Brazilian Portuguese dataset was built, combining reference phonemes — processed with acoustic feature extraction and data augmentation — and vocalizations from a nonverbal child after preprocessing. Unsupervised methods revealed consistent phonological approximations, particularly in nasal categories. In the supervised analysis, samples were represented through Bag-of-Audio-Words (BoAW) combined with acoustic features, and class imbalance was addressed using SMOTE. The evaluated models included KNN, RF, MLP, SVM, and CNN. Results showed that SVM achieved the best performance in terms of phonetic/articulatory equivalences, RF demonstrated robustness in unbalanced scenarios, and CNN reached high accuracy on the validation set. Comparison with perceptual-auditory analyses by speech therapists confirmed relevant convergences. These findings highlight the feasibility of computational models as complementary resources to clinical listening, supporting therapeutic interventions and the development of child speech.
FoodConnect: um software de gestão logística para auxiliar no combate à fome e redução de desperdícios
Addressing the paradox between the high volume of discarded food and the number of people facing hunger, FoodConnect offers a technological solution to the logistical challenges faced by Non-Governmental Organizations (NGOs). The system connects donors, such as supermarkets and street market vendors, to recipients, such as NGOs, allowing the former to register products for donation and the latter to reserve them in an organized manner. The project was carried out following software engineering practices, including requirements gathering, modeling with use case and entity-relationship diagrams, and was implemented with modern technologies such as TypeScript, Node.js, and Prisma on the back-end, and React with Vite and Tailwind CSS on the front-end. The result is a functional web application that demonstrates the feasibility of utilizing technology to promote social responsibility and streamline the donation process.
HeroKeys: Um ambiente virtual gamificado para tocar e aprender piano
Musical education remains inaccessible to many due to the reliance on traditional methods, which demand sheet music, prior knowledge, or formal training. This paper presents HeroKeys, an open-source gamified web application designed to enhance piano learning through artificial intelligence-based audio transcription. Unlike traditional learning methods that rely on pre-existing transcriptions, HeroKeys allows users to practice any song of their choice by automatically, converting audio files into playable musical notation. The system features multiple visualization options including Piano Roll, traditional sheet music, and a display of the current chord being played, accommodating different learning preferences. The application incorporates gamification elements such as scoring systems, multipliers, and visual feedback to increase user engagement and motivation. HeroKeys supports external MIDI keyboard connections and provides customizable playback controls to facilitate progressive learning. Our approach combines audio processing technologies with interactive elements to create an accessible and motivating piano learning environment, making musical education more engaging and accessible to beginners.
Integrating Computer Vision into CACTO: A System for Fraud Prevention and Secure Transport Monitoring
The rapid growth of freight transport in Brazil has increased the demand for intelligent tax auditing tools capable of handling high-volume, real-time data. This paper presents the integration of three computer vision modules into CACTO, a large-scale monitoring platform used by the State Treasury Secretariat of Paraíba (SEFAZPB). The modules address key inspection tasks: vehicle re-identification and multi-camera tracking, direction recognition and route monitoring, and open-truck cargo detection. By combining distributed architectures with scalable machine learning pipelines, the system enhances proactive and evidence-based auditing, supporting fiscal enforcement and reducing vulnerabilities to tax evasion.
MicroMatch: A Content-Based Macroscopic Microbial Image Retrieval Module
Identifying microorganisms is essential for investigating the chemical substances they produce, which have potential applications in biotechnology and pharmaceuticals. Artificial intelligence, particularly machine learning and Content-Based Image Retrieval (CBIR), provides means to make this task more efficient by improving accuracy and reducing operational costs. While supervised learning models for classification are trained to predict taxonomic groups, such as genus and/or species, with high predictive performance from microbial image collections, CBIR supports domain experts by retrieving visually similar samples from these databases, contributing to more reliable decision-making with respect to recognition. This paper presents MicroMatch, a prototype computational module for CBIR of microorganisms cultivated in Petri dishes. We describe the tool herein, covering image acquisition with a customized low-cost device and extending to the design of a similarity search framework that integrates an indexing structure and a matching engine tailored to the specific characteristics of the aforementioned multimedia data type. Through an intuitive interface, MicroMatch promises to accelerate microbial biodiscovery.
Plataforma APS Digital: Ferramenta para gestão, monitoramento e atendimento na Atenção Primária à Saúde
Primary Health Care (PHC) plays a central role in healthcare delivery but faces significant access barriers, particularly in countries with large territorial extension and heterogeneous population distribution, such as Brazil. This paper presents a digital PHC platform that integrates multichannel interaction and generative artificial intelligence to support risk stratification, personalized recommendations, and patient engagement improvement. Its architecture is structured into four layers: communication and integration, patient– professional interface, data management, and generative AI, connected through secure and traceable data flows. The AI layer, supported by open-sourced models, incorporates automated conversation summarization and personalized message suggestions, while maintaining human-in-the-loop oversight as an ethical and clinical safeguard. Preliminary results indicate the platform’s potential to expand access, reduce territorial inequalities, and optimize multidisciplinary team efficiency, aligning with national goals for strengthening PHC and advancing digital health.
py360tool: Um Framework para Manipulação de Vídeo 360◦ com Ladrilhos
The streaming of 360◦ videos is one of the most bandwidth-demanding virtual reality (VR) applications, as the video must be encoded in ultra-high resolution to ensure an immersive experience. To optimize its transmission, current approaches partition the spherical video into tiles, which are encoded at different bitrates and selectively delivered, based on the viewing direction of the user (viewport). The complexity of this architecture, which involves viewport prediction, tile selection, bit rate adaptation, and handling of parallel streaming, requires new tools to evaluate quality of experience (QoE) and quality of service (QoS), especially due to its interactive nature and low reproducibility. This work introduces py360tools, a Python library to handle tile-based 360◦ video streaming. The library automates key client-side tasks, such as spherical projection reconstruction, viewport extraction, and tile selection, facilitating the playback and simulation of streaming sessions. Furthermore, py360tools offers a flexible architecture, enabling efficient analysis of different projections and tiling strategies.
Reapp: Uma Ferramenta Cocriada com Instituições Sociais para o Fortalecimento do Terceiro Setor
Sustainability and donor engagement represent constant challenges for Civil Society Organizations (CSOs) in Brazil, whose digital tools often lack participatory governance models. This work presents Reapp, a mobile and web platform that operates as a social network to connect donors and NGOs, aiming to enhance transparency and fundraising. The tool’s distinctive feature lies in its origin from a Co-designed methodology, in which the NGO community itself actively participated in the development with the goal of transforming the platform into a digital commons. As a result, the application has already supported several institutions in Maranhão, benefiting thousands of families, and has demonstrated a sustainable and scalable operational model. The article contributes by presenting not only a functional tool, but also a replicable model of civic technology development that integrates social innovation and community governance, offering new perspectives for strengthening the Third Sector.
TelePresenceVR: Ferramenta para Telemedicina Imersiva e Interativa
The scarcity of specialized medical assistance in remote areas poses significant challenges to the universalization of health services. Telemedicine has long been proposed as a solution to counter these challenges. Nevertheless, the user experience, especially for patients, is undermined by the lack of natural interaction and engagement when using flat screen devices. In this paper, we present a tool to augment the telemedicine experience with a focus on the patient side. The tool supports immersive visualization of the doctor’s practice via a VR headset worn by the patient during consultation, giving him/her some feeling of presence in the doctor’s practice. It also supports the interactive remote display of audio-visual content to the patient, allowing the doctor do control what the patient sees during the explanation of a diangosis or treatment. A prototype of the tool is currently available and its full implementation is the subject of ongoing work, along with its validation and evaluation in real-life scenarios.
Um agente conversacional para suporte emocional a pessoas com estados ansiosos
The article presents a conversational agent designed to provide emotional support for individuals with mild to moderate anxiety. Drawing on different understandings and definitions of anxiety in the psychological literature, a three-dimensional model of anxiety was developed, comprising fear, tension, and worry. This modeling aims to foster more responsive and contextualized interactions, aligned with the principles of active listening, by enabling the system to recognize variations in the experience of anxiety and adapt the conversational flow accordingly. Each dimension is operated by multiple agents that implement stages related to cognitive restructuring, enhancement of self-efficacy, anxiety management techniques, and responsible closure. The conversational agent proposes an adaptable approach to digital mental health interventions, upholding ethical and technical standards without diagnostic purposes and without equivalence to clinical or psychological treatment.
DengueMG: Um Dashboard Interativo para o Monitoramento de Dengue em Minas Gerais
Dengue remains a major public health challenge in Minas Gerais, demanding effective and accessible monitoring tools. This work presents DengueMG, an interactive dashboard developed using data from the InfoDengue API, initially focused on Minas Gerais. Informed by a review of epidemiological dashboards, the tool integrates a thematic map with case distribution, alert levels, and incidence rates, along with weekly, monthly, and yearly indicators, bar and line charts, and a quick search interface. Compared to the InfoDengue dashboard, it offers significantly faster response times, improved interactivity, and colorblind-friendly visualizations. Designed to be responsive and user-centered, the dashboard supports data-driven decision-making by policymakers, researchers, and citizens, with potential for replication in other regions and diseases.
Uma Ferramenta para Organização Semântica de Informações de Helpdesk por meio de Modelagem de Tópicos
In this work, we propose a semantic organization tool for textual data applied to enhance the automated analysis of helpdesk tickets (service requests) in large volumes of data. It leverages Topic Modeling (TM) techniques (i.e., CluWords) and Large Language Models (LLMs) to identify recurring patterns and themes (Llama3), aiming to improve categorization and service efficiency. Through a case study with more than 9,000 tickets, we demonstrate its application in real-world scenarios, supporting team sizing, monitoring of emerging demands, and time consumption analysis. The results indicate greater analytical efficiency and decision-making support with the use of the tool.
Uma Ferramenta Web Baseada em LLMs para Respostas a Perguntas Simples sobre Bases de Conhecimento Heterogêneas
We present KIF-QA, a Web tool for answering simple questions over heterogeneous knowledge graphs. The Web tool is built on top of a homonymous framework, KIF-QA, that uses in-context learning over off-the-shelf pre-trained large language models for semantic parsing. Because it relies on in-context (few-shot) learning to generate logical forms and disambiguate entities, and because it uses KIF (a knowledge integration framework based on Wikidata) to mediate all access to the target knowledge graphs, KIF-QA’s approach is general. It requires neither training nor fine-tuning and can be easily adapted to work with different graphs. The KIF-QA Web tool allows the user to change the target graphs (Wikidata, DBpedia, or PubChem) and the language model used. The user can also view and modify the details of each step taken by KIF-QA to generate the answers to the question. Both the Web tool and the KIF-QA framework are released as open-source.
VulnScan Suite: Uma Ferramenta Integrada para Análise Automatizada de Vulnerabilidades em Redes Locais
This paper presents VulnScan Suite, an integrated tool developed in Python for automated vulnerability analysis in local networks. The tool orchestrates multiple cybersecurity tools, including Nmap, Nikto, Dirb, TestSSL, Enum4linux, SearchSploit, and SNMP Scanner, providing a unified approach for security audits. The system implements automatic host discovery, parallel scan execution, and report generation in multiple formats. Experimental tests demonstrated the tool’s capability to identify 910 vulnerabilities in a single host, with optimized execution time through parallelization, resulting in 67% improvement compared to manual execution of the same tools. The tool aims to facilitate the work of security professionals and serve as an educational platform for cybersecurity students, being distributed under MIT license for maximum accessibility.
Yuma: Geração de Workflows Multiagentes a partir de Linguagem Natural
Large Language Models (LLMs) have enabled the translation of natural language into functional systems. However, creating multiagent systems based on these models still requires advanced technical knowledge. In this work, we present Yuma, a multi-agent tool that simulates software engineering roles — Requirements Engineer and Software Architect — to collaboratively generate multi-agent systems. This approach translates users’ informal requirements into deployable workflows, with human supervision at each step, ensuring transparency and validation. The tool coordinates requirements elicitation, architecture definition, and system generation, reducing the technical barrier to creating solutions. Finally, we present the tool’s architecture, the interaction flow, and a usage example, demonstrating how this approach democratizes access to multi-agent technologies, making them accessible and transparent to non-specialized users.
AppEduTV3.0: Aplicativo Educacional para a TV 3.0: Possibilidades e desafios para um aplicativo educacional considerando o estado da arte da TV 3.0
This paper presents the development of the AppEduTV3.0 project, which sought to explore the potential and limitations for the development and use of educational applications in the context of TV 3.0. AppEduTV3.0 proposes an ecosystem for the development of these applications by public television broadcasters, based on filling out templates that generate the application for a specific television program. The app can be run in three different scenarios: a single user with internet access; a single user without internet access; and a classroom with internet access. The prototype created as proof of concept allowed, among other things, to identify limitations due to the absence of standardization.
Integração de Dados JSON em Aplicações Ginga-NCL para Alerta de Pessoas Desaparecidas
Introduction: This paper explores the use of JSON for dynamic content management in interactive Digital TV applications developed with the Ginga-NCL middleware and NCLua scripting language Objective: The proposed application aims to simulate a missing persons alert system, inspired by the Amber Alert Brazil initiative. Methodology: The system’s architecture leverages Ginga-NCL’s modularity to facilitate maintenance and future scalability. Using JSON as the data format allows efficient, real-time updates of content, improving the responsiveness of the application. under the Brazilian standard. Results: The successful development of a functional prototype demonstrates the practicality of integrating JSONbased dynamic content in Digital TV applications, reinforcing its potential for public utility services
TV 3.0: Efeitos Sensoriais em Aplicações Ginga-HTML5
The TV 3.0 project aims to modernize broadcast television in Brazil, introducing, among other innovations, support for multisensory experiences (mulsemedia) that transcend traditional audiovisual content. However, the integration of sensory effects devices, such as lamps and aroma diffusers, into the TV receiver is hampered by the heterogeneity of their communication protocols. This work presents the design and implementation of a prototype for the Sensory Effect Presentation Engine (SEPE), a component designed in the TV 3.0 architecture to act as a bridge between the Ginga middleware and these devices. Through SEPE, Ginga-HTML5 applications, in addition to GINGA-NCL applications, can benefit from sensory effects to make content more engaging for viewers. Based on a modular architecture and the Adapter design pattern, SEPE was developed to be extensible. The prototype was validated by implementing adapters for devices with different communication models — one local (Xiaomi Yeelight smart bulb) and one cloud-based (Moodo aroma diffuser) — and through a quantitative performance evaluation. The results demonstrated the functionality and efficiency of the solution, with communication latencies within acceptable limits for good Quality of Experience, validating the proposed architecture as a viable solution for orchestrating sensory effects in the TV 3.0 ecosystem.
Impacto da Língua Brasileira de Sinais (Libras) na Programação da TV aberta brasileira
The inclusion of Brazilian Sign Language (Libras) in content produced by TV stations in Brazil has always been a major challenge. Recent solutions, such as the use of Libras avatars on broadcast television, have proven to be a viable solution for both broadcasters and digital TV receiver manufacturers. The use of avatars offers several benefits for the transmission and reception of this content. On the receiver side, the solution features low memory consumption, is easily synchronized with the video, can be disabled, and is also configurable on the screen. To evaluate the use of Libras avatars on TV, a usability survey was conducted with members of the deaf community, collecting feedback on their use compared to subtitles. During the survey, groups of signed, oral, and bilingual deaf individuals were evaluated regarding education levels and comprehension of the broadcast content. The results obtained demonstrated that the understanding of the content considering the 3D Avatar exceeds by 35% the understanding of the content considering the use of subtitles.
Estratégias para inserção das televisões universitárias no ecossistema da TV 3.0: da Web ao DTV+
This position paper presents preliminary findings from an ongoing master’s research project that investigates the challenges and opportunities for integrating Brazilian university television stations into the Digital TV 3.0 ecosystem. The analysis begins with a classification of the 190 university TV stations registered with the Brazilian Association of University Televisions (ABTU), based on their distribution models. It then proposes a typology of editorial profiles specifically for stations operating via over-the-air broadcast. As a case study, the research examines university television stations located in São Luís, Maranhão, to identify concrete technical, institutional, and regulatory barriers to their integration into the DTV+ environment. Building on these findings, the paper outlines a two-phase roadmap: first, to expand the presence of university TV stations on RNP’s Eduplay platform; and second, to advance toward a dedicated application for connected TVs and DTV+ receivers.
A plataforma “Rede Pública” e o futuro da TV interativa no Brasil: uma proposta para o Governo Federal na era da TV 3.0
This article presents the conception and development of the Rede Pública platform, a proposal based on TV 3.0 that seeks to integrate content from Brazilian public broadcasters with digital government services. The initiative is part of the evolution of the Brazilian Digital Television System (SBTVD), which incorporates the convergence between broadcast and broadband. The project was structured through requirements elicitationworkshops with government agencies, using the Design Thinking methodology to ensure adherence to public and social demands. Interactive prototypes were developed in Figma, including both the unified television platform and a second-screen application, which expands the possibilities of user engagement and mobility. The results highlight the potential of TV 3.0 as a strategic resource to strengthen digital citizenship, promoting inclusion, usability, and accessibility, as well as establishing a reference model for future national initiatives. As future work, the expansion of tests with real and diverse users is foreseen, in order to validate the user experience and consolidate interactive TV as an instrument of integration between the State and society.
Modelo Conceitual de Serviço de Recomendações para TV 3.0
The evolution toward TV 3.0 establishes a hybrid broadcast–broadband ecosystem, application-oriented and characterized by new requirements for personalization and interoperability. Although recommender systems are widely explored on OTT platforms, their use in the television context still faces specific challenges, such as limited interaction, shared viewing, and constraints on data collection. This work proposes a conceptual model for a recommendation service based on multiple specialized agents coordinated by a central orchestrator. The proposal aims to provide a minimum viable abstraction, modular, extensible, and aligned with the constraints of the television environment. In addition to detailing the architecture, we present a hypothetical use case that illustrates the end-to-end flow and discuss implications for transparency, explainability, and governance of recommendations. The results are intended to support the adoption of personalization mechanisms in TV 3.0, reconciling technological innovation with requirements for reliability and usability.
Artificial Intelligence Applications in TV Industry Workflow
The TV industry is going through a major change due consumers need for high-quality, diverse, and customized content. This leads to increase production costs while the production time shrink. To deal with these challenges, the industry is levering the use of Artificial Intelligence to improve and optimize their processes. This paper gives a summary of how it is being used in existing literature and publicly available use cases. But different than other works, we put them application in perspective of the TV Industry workflow of pre-production, production, post-production, and distribution.
Uma Arquitetura para Experimentação de Novos Serviços na Plataforma Orientada a Aplicações da TV 3.0
TV 3.0 brings several new functionalities to the Brazilian DTV system. Among these functionalities, it is possible to highlight the Application-Oriented Platform (AoP), execution of Ginga applications, viewer profile creation, and connection with smart devices using a local network. Given the wide spectrum of functionalities present in TV 3.0, this work proposes an architecture for experimenting with TV 3.0 AoP services. This architecture is designed to be extensible such that developers can easily create/extend its functionalities. To indicate its usefulness and versatility, this work presents two use cases implemented over this proposed architecture.
TV 3.0: Um Estudo sobre Core DTV+ na Nuvem
The digital broadcasting ecosystem is undergoing a significant transformation driven by the convergence of cloud computing, adaptive streaming protocols, and next-generation transmission standards such as ATSC 3.0 and DTV+. This study presents a review of recent advancements in the transition from traditional broadcasting infrastructures to cloud-based architectures. From a curated selection of recent academic and technical literature, the goal is to analyze how modern broadcast systems are evolving to support IP-based delivery, low-latency media workflows, and hybrid broadcast–broadband services. Particular attention is given to cloud-based media managing, DASH and ROUTE-based streaming solutions, and the virtualization of terrestrial broadcasting components. We also explore the implications of these developments on the scalability, interoperability, and flexibility of future digital TV systems, with a focus on both international standards and proposals relevant to the Brazilian DTV context. This work aims to map current trends, identify open challenges, and provide insights for future research and implementation strategies in the ongoing evolution of television broadcasting.
Toward a Certification Framework for TV 3.0: Building Trust in the Ecosystem
The transition from TV 2.x to TV 3.0 shifts the digital television ecosystem from middleware-mediated interactivity to an application-oriented platform, where broadcaster applications and cross-layer orchestration are central to new business models and user experiences. This evolution makes interoperability and reliability prerequisites, exposing the insufficiency of the self-certification model that characterized TV 2.x. We argue for the establishment of a network of accredited laboratories, coordinated by INMETRO and ANATEL, hosted in universities, and supported by industry contributions. Beyond technical assurance, it would foster broadcaster trust, enable new commercial opportunities, and serve the public interest, while simultaneously advancing research, innovation, and professional training in digital television.
Análise Aprimorada das Percepções dos Usuários por Meio de Abordagens de Processamento de Linguagem Natural
In this article, we present a framework for analyzing users’ textual comments, going beyond the evaluation carried out using the System Usability Scale, a traditional methodology for system assessment. The framework performs textual analysis through Topic Modeling, Text Summarization, and Sentiment Analysis. Topic Modeling identifies semantic topics in the comments, while Text Summarization generates summaries of each topic, enabling explainability. Sentiment Analysis, in turn, categorizes the sentiment of each topic, identifying its polarity. We propose an intuitive visual interface and apply our framework in a real-world scenario, where it enabled more accurate feedback.
Aprimorando a Eficiência e a Equidade de uma Abordagem Perspectivista Para Detecção de Ironia
Text classification in tasks like hate speech or irony detection is culturally influenced and personally interpretive. Perspectivism, unlike approaches that aggregate opinions by, for instance, majority voting, values specific annotator groups to create fairer models but often involves high computational costs due to fine-tuning of language models. This work explores traditional machine learning methods (SVM, Random Forest, XGBoost) to reduce costs and uses calibration to address inference biases. Results show up to 12 times faster processing without statistical effectiveness loss and improved fairness through reduced bias.
Assessing the Potential of Large Language Models (LLMs) for the Automatic Generation of Solutions for Programming Exercises
Since several AI chatbots have become available to the general public, the popularity of Large Language Models (LLMs) has risen, especially among students, who have used them as educational support tools. This directly impacts the reproduction of scientific knowledge and the learning process. In this context, it is relevant to evaluate the performance and limitations of these tools in providing correct answers. This article discusses the results of C++ code solutions requested from the Meta Llama 3 model for practical programming tasks of Olimpíada Brasileira de Informática (OBI). It was demonstrated that the method still needs some correctness checker, presenting only 30.4% of correct answers in general. In addition, it was evidenced that the model’s performance deteriorates in questions with images, since the model does not support them as input, presenting a 20.3% drop in correct answers. Finally, the model appears to work better the shorter the question text, with 31.1% more correct answers for questions with up to 200 words compared to questions with more than 400 words.
Building and Analyzing an Open Repository of Programming Exercises in Portuguese
Practical exercises are an important part of learning programming. For Brazilian Portuguese, however, there are few data sets with this availability. This work aims to collect and model a data repository for programming exercises, containing instructions, test cases, and exercise metadata. First, platforms are defined as data sources, and Web Scraping techniques are applied to extract the desired content. From the collection, the data is modeled and made available in a public repository called ProgramExBR. A characterization of the data is also presented, revealing a number of interesting discoveries about the text patterns it contains. We hope that this can be useful in various contexts, be it for supporting teachers and students, for training inference models and generating new exercises, or even for an application of personalized recommendation methods in education.
Modelos de Linguagem na Análise da Promoção de Jogos de Azar Online: Estudo de Caso do Jogo do Tigrinho no YouTube
This undergraduate thesis analyzes the dissemination of gambling-related content on YouTube, focusing on the case of Fortune Tiger, which has been widely promoted by influencers in Brazil. The study approaches the issue as a stance detection task in NLP, using RoBERTa and Llama 3.1 models combined through consensus classification. The analysis of more than 130,000 comments revealed linguistic patterns, polarized engagement, and evidence of automation and textual repetition, particularly in favorable messages originating from new or influential accounts. The results demonstrate the potential of language models in undergraduate research, contributing to the debate on content regulation and the social impacts of digital gambling promotion.
OACE: Multi-criteria Assessment of Assertiveness and Cost for Deep Learning Models
The rapid expansion of Deep Learning (DL) has intensified the need for more rigorous evaluation methodologies for implementing solutions in real-world scenarios. In view of this, this work presents Optimized Assertiveness-Cost Evaluation (OACE), a method based on Multi-Criteria Decision Making that integrates assertiveness and computational cost criteria into a single parameterizable function, aiming to systematize the evaluation of DL models. To demonstrate its effectiveness, an experiment was conducted with Random Walk and the CIFAR-10 database, evaluating five DL architectures in a balanced scenario. The findings identified MobileNetV2 as the best model for the defined scenario, with a score of 𝑆𝜙 (𝑚) = 0.9541, surpassing MobileNetB0 by 38%, due to its superior efficiency.
Operationalizing Knowledge Gain: Implementing and Testing the DKG Metric in Search Environments
Searching the Web is increasingly recognized as a process of knowledge construction rather than simple information retrieval, a perspective framed by the paradigm of Searching as Learning (SaL). A central challenge in this domain lies in evaluating the extent to which users actually acquire knowledge during search. Traditional approaches either rely on behavioral proxies, scalable but limited in capturing conceptual change, or structured assessments, which provide direct evidence but are often intrusive. The Degree of Knowledge Gain (DKG) metric addresses this gap by modeling reductions in uncertainty through Shannon’s entropy and integrating semantic similarity between queries and clicked documents. This paper reports on the operationalization of DKG within the CNPq project 3C-BPA: Comportamento de busca, Complexidade da informação e pensamento Crítico na Busca como um Processo de Aprendizagem. Two artifacts were developed: an initial search engine prototype embedding DKG computation, and a Chrome extension that estimates DKG in real time while users employ their preferred search engines. The latter artifact overcame earlier limitations by improving ecological validity, reducing costs, and enabling more natural experimentation. An experiment combined pre- and post-tests, the Concurrent Think-Aloud (CTA) protocol, and the plug-in’s automated logging. Preliminary findings show that DKG values are sensitive to differences in search strategies, with systematic reformulation and evaluation aligning with greater knowledge gains, while disorientation behaviors corresponded to more modest outcomes. A distinctive feature of this study was the active role of an undergraduate researcher, who contributed to artifact development, experiment setup, participant support, transcription, and ongoing content analysis.
Avaliação de Usabilidade do SofiaFala: Melhorando a Tecnologia Assistiva para Terapias de Fala
This paper presents a multifaceted usability evaluation protocol for SofiaFala-Fono, an assistive technology for speech therapists. Unlike traditional unimodal approaches, the protocol combines heuristic evaluation by HCI experts, usability testing with therapists, and affective response measurement (SAM questionnaire). This methodological triangulation offers a comprehensive assessment of effectiveness, efficiency, and user satisfaction, providing a replicable framework to support the development of more user-centered assistive technologies in speech therapy.
Desenvolvimento de Software para Videolaringoscópio Assistivo de Baixo Custo Aplicado ao Treinamento e Ensino de Laringoscopia
This work presents the development of a software system for an assistive technology video laryngoscope, integrating Internet of Things (IoT) principles and web technologies to enable real-time video transmission and remote interaction via mobile devices. The proposal aims to support healthcare professionals in their training and education for proper laryngoscope handling, addressing the shortage of practitioners adequately trained in endotracheal intubation. By combining hardware and software components in a client–server architecture—using an ESP32-CAM board, FSR (Force Sensing Resistor) sensors, and contemporary web technologies—the system offers a low-cost and accessible alternative, particularly suited for public educational institutions. With an estimated cost of R$195.00, compared to similar equipment exceeding R$10,000.00, the solution stands out as a viable, innovative, and scalable approach for healthcare education and clinical simulation. Initial system evaluations demonstrated the feasibility of building the video laryngoscope and confirmed its ability to connect, transmit, and receive data effectively.
Diagnóstico Hierárquico em Cardiologia Utilizando um Modelo de Mistura de Especialistas e Conhecimento Prévio de Cardiologistas
Although several predictive models exist for identifying cardiovascular disorders in electrocardiograms (ECGs), many disregard the well-established knowledge from medical literature, forcing the models to relearn known patterns. We propose a Mixture of Experts (MoE) model that incorporates cardiologists’ expertise to hierarchically organize the expert models.We classify six physiciandefined electrocardiographic abnormalities, covering both rhythm and conduction disorders. Trained on the CODE-15 dataset, our model outperformed a widely used baseline in the literature for this task, achieving an F1-score of 0.84 compared to 0.77 for the reference model. These results highlight the relevance of architectures guided by medical knowledge and demonstrate significant improvements over established approaches, underscoring the potential of our proposal to advance ECG classification performance.
From Voices to Data: Tools for Creating a Multitask Atypical Speech Corpus through Citizen Science
Speech data from people with atypical speech patterns remain underrepresented in research, limiting the development of effective assistive technologies for communication support. The SofiaFala Ecoa project addresses this gap by designing and deploying an accessible, citizen-science-driven platform for the collaborative collection of a multitask atypical speech corpus. This initiative combines a redesigned mobile application, a web-based recording portal, and interdisciplinary outreach activities to engage participants with speech disorders, their families, and healthcare professionals. The system enables the capture of speech in multiple tasks—such as reading, repetition, and spontaneous speech—while supporting multimodal data integration. Our objectives are threefold: (1) to establish a scalable and inclusive infrastructure for gathering speech data; (2) to expand the availability of publicly shareable corpora of atypical speech; and (3) to foster community participation in research on assistive speech technologies. The contributions of SofiaFala Ecoa include the creation of a pilot corpus covering multiple speech tasks, the implementation of tools and protocols to ensure ethical and secure data collection, and the demonstration of feasibility through initial engagement metrics. By bridging accessibility, inclusivity, and technological innovation, SofiaFala Ecoa paves the way for improved AI-based speech technologies that reflect the needs of people with speech disorders.
Reversão anaglífica baseada em método rápido
3D formats are still relevant due to their ability to convey depth through stereoscopic techniques. Among these, the anaglyph method stands out for its simplicity and low cost, encoding stereo pairs into a single image. Despite its advantages in compression and transmission, anaglyph encoding discards significant information, making its reversion complex. Reversing anaglyphs enables legacy content recovery and broader compatibility with various stereoscopic visualization methods. This work proposes an efficient approach to anaglyph reversion by exploring Block Matching algorithm, commonly used in video coding. Unlike traditional methods relying on pixel descriptors and global optimizations—often computationally expensive—our technique aims to handle radiometric differences between stereo views while maintaining high image quality and improved performance. Experimental results demonstrate the potential of this method as an alternative to existing solutions.
Soluções Tecnológicas para a Mobilidade de Pessoas com Deficiência Visual: Realidade Virtual e Navegação Interna com Wi-Fi
This study addresses navigation challenges for visually impaired individuals by developing two complementary solutions. First, a Virtual Reality (VR) training tool with multimodal feedback (audio and tactile) was created to help users develop spatial skills and mental mapping abilities safely, without real-world risks. The VR system supports Orientation and Mobility training techniques. Second, a cost-effective indoor localization system using Wi-Fi technology was developed to provide real-time positioning information. Experimental evaluation with 15 university participants showed excellent acceptance of the multimodal VR feedback, with users naturally adopting spatial reference techniques like wall-following. The localization system was tested across 135 reference points in CEFET buildings, achieving accurate area identification through a hybrid approach combining Euclidean distance methods. Both solutions address the high costs of existing assistive technologies while improving independent mobility for visually impaired people.
Como a tecnologia pode ajudar os idosos a se comunicarem sobre temas sensíveis?
The rapid growth of the elderly population raises challenges for designing technologies that support not only physical health but also emotional well-being. This study investigates how digital solutions can help older adults address socially sensitive issues such as grief, family conflicts, and fears of dependence. We conducted a workshop with elderly participants, combining activities like therapeutic writing, floral arrangements, and participatory design through drawing. Qualitative analysis of participant profiles, discussions, and visual artifacts revealed recurring themes including nostalgia, family, independence, respect, and vulnerability. Findings indicate that effective technological solutions for older adults should emphasize empathy, playfulness, and “lightness” in design, fostering autonomy, intergenerational connection, and safe spaces for dialogue without judgment. This work contributes preliminary guidelines for developing inclusive technologies that address the social and emotional complexities of aging, promoting active participation and dignity in later life.
Curso de Inteligência Artificial para Idosos - Inclusão Digital e Bem-Estar em Saúde
This extension activity presents a short course designed for older adults (60+), aimed at promoting digital literacy and understanding of artificial intelligence (AI). The course structure is based on a four-axis framework — educational, technological, ethical-legal, and socio-emotional — and was designed from the demands and interests of the participants, placing them at the center of the learning process. With a total of four hours divided into five interactive modules, the course combines brief lectures, guided practical activities, and collective reflection. Preliminary indicators, such as high demand and participant inquiries, demonstrate strong societal interest and the relevance of the initiative. This activity seeks to fill a significant gap in digital education for older adults, fostering autonomy, confidence, and critical awareness regarding the use of technology and AI in everyday life.
Dev.Learn - Relato de experiência de um curso de extensão para ensinar a desenvolver sites
This paper reports on a community outreach course focused on web development, conducted by the USPCodelab Sanca group. With a mission to foster technological innovation at USP, this initiative aimed to empower high school students from local schools by promoting their digital inclusion. The course featured weekly theoretical and practical classes with one-on-one mentorship, using essential technologies such as HTML, CSS, and introductory JavaScript. This experience demonstrated the potential of university outreach to create a significant social impact, aligning directly with the UN Sustainable Development Goal 4 (Quality Education) and Goal 8 (Promote sustained economic growth). The course not only increased participants’ interest in technology, but also effectively transitioned them from passive technology consumers to active creators, achieving meaningful digital inclusion.
Ganesh: um grupo de extensão para ensino de Segurança da Informação do ICMC/USP
This article describes the methodology of Ganesh, an extracurricular group at the University of São Paulo (USP) São Carlos, dedicated to promoting knowledge about Information Security among undergraduate students and the technology community. Ganesh began in 2010 as part of a university outreach project that, over the past few years, has developed teaching materials, specialized courses, and workshops, in addition to creating educational projects for public and private schools. Ganesh connects with the public through various outreach initiatives, including their YouTube channel, an annual selection process (PING), and participation in Capture the Flag (CTF) competitions. Using a methodology that combines hands-on learning, challenge solving, and weekly meetings has positioned them among the top teams in Brazil and among the country’s top university teams in the best Information Security competitions.
Informática para Terceira Idade - Socialização e Inclusão Digital do Público Idoso
This paper describes the structure and methodology of a digital literacy program for the elderly, conceived and implemented in the first semester of 2025, with plans for future editions. This university outreach project has yielded significant results for the surrounding community. Through weekly in-person classes, with the help of somewhat individualized monitoring, seniors are taught and encouraged to test and expand their digital skills, increasing their knowledge of social networks. Classes at our university demonstrated the relevance of digital literacy programs for seniors. This work presents accounts of the course’s structure and implementation, as well as accounts from some senior students and tutors.
Promoting Gender Inclusion in Computing: An Experience Report on a National Web Development School for Girls
Female representation remains low in undergraduate programs and research related to STEM fields, with women accounting for only an estimated 15% of the sector in Brazil as of 2022. Nevertheless, one factor contributing to the increased participation of women in STEM programs has been the implementation of strategies focused on strengthening girls’ mathematical and scientific foundations during childhood and adolescence. Therefore, aiming to overcome the economic and societal barriers that school-age girls face in accessing this kind of knowledge, the Web Development School for Girls course was developed to promote interest, skills development, and self-confidence among participants, encouraging them to pursue careers in STEM. The initiative was national and conducted fully online, featuring weekly lessons and assessment activities, daily mentoring sessions, and the application of project-based learning, which placed students at the center of the learning process. This program was explicitly aligned with the Sustainable Development Goals (SDGs 4, 5, and 10). The program demonstrated the effectiveness of the PBL methodology through the quality and complexity of the final web projects delivered by the students. While successful in its pedagogical goals, the initiative encountered key limitations, including a high dropout rate of 53.6%. It is concluded that strengthening engagement strategies, such as integrating more frequent live or face-to-face sessions, and refining administrative processes are essential for improving retention in future editions of the school.
Transformando a Presença Feminina em Computação a partir de Oficinas de Programação em Python
Female participation in Computing continues to face historical barriers of inequality and underrepresentation. Educational outreach initiatives play a strategic role in promoting equity and fostering the inclusion of girls and women in the field. This work reports on a project offering introductory Python programming workshops for high school students and graduates, using active learning methodologies, accessible materials, and multimedia resources. The aim was to spark interest in Computing, strengthen self-confidence, and deconstruct stereotypes. Results indicate greater engagement, improved understanding of programming concepts, and increased protagonism in learning processes. Aligned with the United Nations Sustainable Development Goals (SDGs), this initiative reinforces the social and academic relevance of outreach actions for cultural transformation and the expansion of female presence in Computing, addressing a gap in the literature by demonstrating the effectiveness of Python-focused workshops for this specific audience.
A Proposal for Automated Tax Collection on Swaps in Self-Custody Wallets in Brazil
The exponential growth of the cryptoasset market and the advancement of decentralized technologies have challenged traditional models of tax collection. In particular, self-custody wallets, which allow users to maintain direct control over their digital assets without the mediation of financial institutions, pose significant obstacles to oversight and tax compliance. This paper proposes an approach for the automatic collection of taxes on foreign exchange operations with stablecoins, using smart contracts on decentralized exchanges (DEXs). Through the implementation of a Proof of Concept (PoC), based on the Split Payment logic, we demonstrate the technical feasibility of applying a tax rate, analogous to the IOF, directly during transactions carried out by self-custody wallets, without the need for prior user identification. Experimental results, validated on the Polygon mainnet, reinforce the potential of the proposed model as a practical solution aligned with the principles of Web3, contributing to the debate on automated tax compliance mechanisms in decentralized environments.
AlimenTI: Um DApp de Suporte a Doação de Alimentos
This work presents AlimenTI, a blockchain-based system designed to manage food donations. The goal is to enhance transparency, security, and traceability throughout the donation process. By leveraging smart contracts on the Ethereum network, every stage, from the registration of donations by supermarkets to the delivery to communities, is immutably recorded. The solution has the potential to reduce food waste, strengthen trust among stakeholders, and provide a scalable alternative for the efficient redistribution of food resources.
Decifrando o Mercado de NFTs: A Influência das Conexões de Rede e Transações no Valor de Ativos
The market for Non-Fungible Tokens (NFTs) continues to evolve, yet it still lacks robust methodologies to estimate the future value of its assets. Unlike traditional financial markets, NFT pricing is challenged by intangible factors such as the artistic nature of the items and the influence of social and transactional networks among buyers and sellers. This study investigates whether the structural position of participants in the transaction network can serve as a relevant predictor of the future value of NFTs. To this end, we reconstructed the NFT trading network for the period 2020–2021, extracted both structural and transactional metrics of the participants, and applied supervised machine learning models, including deep neural networks. The results demonstrate the feasibility of the proposed approach, achieving 74% accuracy and a global F1-Score of 72%. Interpretability analysis using SHAP values revealed that, in addition to historical price averages, network metrics such as degree and neighborhood significantly contribute to prediction. These findings highlight the role of network dynamics in NFT valuation and point toward promising directions for more transparent and evidence-based pricing methodologies.
Evaluating Zero-shot Reasoning with Agentic LLMs for Smart Contract Vulnerability Detection
Smart contracts are fundamental to blockchain ecosystems, but remain susceptible to security vulnerabilities that can lead to severe financial losses. Recent advances in agentic AI systems, powered by large language models (LLMs), enable autonomous code analysis and decision-making without explicit task-specific supervision. These systems leverage prompt engineering and zero-shot reasoning to detect vulnerabilities in smart contracts without prior fine-tuning. In this work, we evaluate the effectiveness of agentic LLM-based approaches in identifying vulnerabilities using prompt engineering and zero-shot reasoning across a curated dataset of Solidity smart contracts. Our findings highlight the limitations of current LLMs in automated vulnerability detection, providing insights into their practical applicability for securing decentralized applications. Our best-performing configuration, which integrates zero-shot reasoning with the Tree of Thoughts framework, achieved an F1-score of 73.66%.
Repairing DeFi Vulnerabilities: Benchmarking LLMs with Executable Solidity Exploits
Decentralized finance protocols are frequently exploited, creating a demand for fast and reliable repair of vulnerable smart contracts and validation that reflects runtime security. Large language models are an emerging source of patches, yet many evaluations rely on manual checks or self-assessment, which cannot confirm whether attacker profit is actually prevented. We introduce an executable benchmark that replays verified real-world exploits against patched Solidity contracts under a resilient protocol that permits alternate attack paths and controlled state variation. Our framework compiles candidate patches, deploys them on a forked chain, and tests whether the exploit still yields profit. The benchmark covers six test cases drawn from reproducible incidents and is released as open-source. Among the nine evaluated models, GPT-5, GPT-4.1, and Claude Opus 4.1 performed the best, mitigating four of six test cases. Microsoft Phi-4 was the most reliable open-source model, mitigating two of six exploits and producing compilable patches for the remaining cases. No model mitigated the H2O case once resilient checks were enabled, while a simpler access control flaw, BTNFT, was often repaired with minimal edits. Grounding validation in executable exploit replay provides a precise and scalable method to measure whether proposed repairs harden contracts at runtime.
Mensageria Descentralizada como Infraestrutura Pública: Uma Perspectiva sobre a Web3 como Bem Comum Digital
Digital messaging plays a central role in modern society, but it is currently dominated by centralized platforms that compromise user privacy and control. This position paper argues that decentralized messaging systems, based on open protocols and free software, can serve as public communication infrastructure and exemplify Web3 as a digital common good. We discuss the limitations of current services (identity lock-in, user fragmentation, data exposure) and present decentralized approaches (peer-to-peer, federated, blockchain-based) according to recent literature. We relate these models to the concept of public digital infrastructure, essential communication systems managed in a non-exclusive manner, and the notions of digital commons. As illustrative cases, we cite open protocols like Matrix and Web3 initiatives that adopt blockchain-based identity (e.g., XMTP), showing that such systems exhibit the characteristics of common resources: public specification, collective governance, and open-source software.
Transformando a Sociedade com a Web 3.0: Um Relato de Projeto Inovador de P&D para Transferência do Cuidado do Paciente e Confiança Digital
Web 3.0 has emerged as a (r)evolution, transforming Web 2.0 into a more decentralized version of it. Its enabling technologies foster the development of innovative applications with high potential societal impact. In this context, a prospective project was established between the National Telecommunications Agency (ANATEL) and the Federal University of Goiás (UFG) to develop products leveraging Web 3.0 enabling technologies. The main contribution of this paper is to present an experience report on the project and to disseminate the products created, making them publicly available to society and to the Workshop audience. Two main products were developed: dHigeia, in the healthcare domain; and dAurora, in the field of combating disinformation. The results indicate that the decentralized applications developed have significant potential for social impact.
Uma infraestrutura de experimentação para pesquisa e desenvolvimento em Blockchains e Web3
Trust on the internet is an essential pillar for online interactions, and blockchain technologies offer a new paradigm of trust based on data integrity and decentralization, enabling innovative solutions such as theWeb3 applications. This paper presents the experimental infrastructure for blockchain research and development currently being built within the ILIADA project at RNP, and its use for the development of new Web3 applications.