Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia)
Sociedade Brasileira de Computação - SBC
This document brings the Foreword and the Organizing Committee of the Proceedings of WebMedia 2025.
A DCT-Based Contrastive Approach for Learning Visual Representations
Recent advances in self-supervised learning have significantly improved visual representation learning, creating better alternatives to supervised methods in image understanding tasks. Motivated by the demand for robust features under low-quality image scenarios, we propose adapting the SimCLR framework to operate in the frequency domain using quantized DCT coefficients from JPEG-compressed images. We aim to investigate the integration of the Discrete Cosine Transform (DCT) with self-supervised contrastive learning to enable visual representation learning. To evaluate the learned representations, we apply linear classification and JPEG artifact removal tasks, employing ResNet-50 and Vision Transformer (ViT) encoders. Results indicate that ViT-based embeddings, especially when combined with spatial or frequency-domain inputs, mitigate performance loss degraded by compression. Furthermore, artifact removal experiments with modified UNet architectures show that incorporating ViT embeddings and attention maps can slightly improve reconstruction quality. These findings suggest that frequency-domain self-supervised learning is a promising direction for building robust and transferable visual representations. However, the observed gains remain modest and limited by computational constraints.
The Evolution of Scientific Collaborations in WebMedia: A 15-Year Study (2010–2024)
This work presents a bibliometric and social network analysis applied to the study of scientific collaborations at the Brazilian Symposium on Multimedia and theWeb (WebMedia) over a fifteen-year period (2010–2024). Using data extracted from the Brazilian Computer Society platform, the study investigates the dynamics of interinstitutional partnerships, the temporal evolution of co-authorship networks, and the distribution of scientific production among different institutions. Through the construction of collaboration networks, analyzed using structural metrics and visualizations, it was possible to identify a pattern of strong concentration of production within a restricted group of institutions, while simultaneously observing increasing national integration and diversification of the actors involved. Furthermore, it was found that a few institutions are responsible for the majority of scientific production, highlighting the need to consider strategies and policies that encourage the participation and strengthening of other institutions, thereby expanding collaboration and fostering a more balanced and inclusive scientific environment.
A Gated Review Attention Framework for Topics in Graph-Based Recommenders
Recommender systems significantly reduce information overload by curating personalized content on digital platforms, thereby enhancing user experience. Traditional models often rely on sparse rating data, overlooking the rich semantic signals embedded in user reviews. To address this, we propose the Gated Review Attention Framework for Topics, a novel graph-based recommender system that integrates review-derived topic information with user-item interactions. Our approach leverages BERTopic to extract interpretable semantic topics from textual reviews and then embeds them into a graph attention network architecture. A gating mechanism dynamically regulates the influence of these topic representations relative to latent user and item embeddings, enabling adaptive feature fusion. We evaluate GRAFT on three benchmark datasets: Amazon Movies and TV, IMDb, and Rotten Tomatoes. Comparing it against classical and neural baselines, including SVD, DeepCoNN, and KANN. Experimental results demonstrate that GRAFT consistently achieves the lowest RMSE across all datasets, indicating superior rating prediction accuracy. Although traditional models perform better on ranking metrics, GRAFT achieves superior accuracy (lower RMSE), and our qualitative analysis demonstrates more substantial semantic alignment.
Misogyny on Brazilian YouTube: A Case Study on Content Produced by the Red Pill Community
YouTube, as one of the most influential video platforms in Brazil, has become a central space for the circulation of ideas and, consequently, a fertile ground for the spread of hate speech, particularly misogyny, within the Manosphere ecosystem. The growing visibility and influence of content creators highlight the urgency of understanding how their messages are constructed and received. While previous studies have examined online misogyny in other platforms and contexts, little is known about its large-scale manifestations in the Brazilian YouTube ecosystem, especially regarding the practices of particular communities. This study addresses this gap by examining the presence and activity of the Red Pill community on YouTube Brazil, aiming to characterize how misogynistic discourse is constructed, interpreted, and propagated. We analyzed 18,034 videos and over 2.2 million comments from 28 channels published between January 2024 and June 2025, applying state-of-the-art natural language processing techniques to investigate engagement patterns and discursive dynamics. Our findings reveal that this community reaches a wide audience through diverse content strategies shaped by creators themselves. While titles and descriptions predominantly convey neutral sentiment, possibly to maximize reach and avoid automated moderation, user comments display a pronounced prevalence of negative sentiment and elevated toxicity. These results underscore the role of content creators in exploring YouTube to amplify extremist narratives and contribute to the understanding of online misogyny in Brazil by shedding light on its mechanisms of construction, reception, and dissemination.
Analysis of Electroencephalogram Signals for Measuring Attention in an Immersive Multisensory Musical Environment
A compreensão dos ciclos de atenção em atividades criativas e dinâmicas tem ganhado destaque em pesquisas de interfaces cérebrocomputador (BCI), especialmente em ambientes imersivos que combinam estímulos auditivos, visuais e motores. Em tarefas musicais, o engajamento cognitivo pode flutuar de forma espontânea, não linear e sensível à estrutura da tarefa. Este estudo explora como diferentes tipos de estímulos sensoriais influenciam o foco atencional em uma bateria virtual imersiva. Este estudo investiga os ciclos de atenção de participantes através da captura e análise de sinais cerebrais durante a execução de tarefas musicais em um ambiente imersivo com e sem feedback tátil. Vinte e cinco indivíduos realizaram quatro exercícios rítmicos em uma bateria virtual, usando baquetas físicas com efeito vibratório (efeito háptico) em ambas. Os indivíduos tiveram três condições experimentais: livre (exploração livre), não háptica (exercícios definidos e baquetas sem efeito vibratório) e háptica (exercícios definidos e as baquetas com efeito vibratório). Os níveis de atenção foram monitorados continuamente e categorizados em três faixas (baixa, média e alta), com estimativas de tempo de ativação e tempo de permanência em cada faixa. Os dados mostraram que a atenção se concentrou no início da atividade, especialmente nos dois primeiros exercícios de ritmo mais lento e maior demanda motora. Nos exercícios finais, a ausência de alguns instrumentos pode ter sido causada pelo aumento da velocidade. Apesar de variações na faixa alta durante o uso de estímulo háptico, o teste de Wilcoxon indicou que não houve diferença significativa entre as experiências. Observou-se também que alcançar picos de atenção não implica maior tempo nesse estado. Conclui-se que os estímulos vibratórios não influenciaram de forma consistente a atenção sustentada, mas ritmo e complexidade motora podem afetar o engajamento.
Analysis of the Use of Large Language Models in Code Generation for Home Automation
This paper presents a comparative experimental evaluation of Large Language Models (LLMs) for generating YAML code in home automation using the Home Assistant (HA) platform. Four models were tested: GPT-4o, GPT-4.5, Gemini 2.5 Flash, and Gemini 2.5 Pro. Tests covered three complexity levels and two prompt types. Performance was analyzed with a 2𝑘 factorial design and validated with ANOVA. Task complexity was the main factor affecting success, while prompt specificity reduced errors. All models succeeded in basic tasks, but only Gemini 2.5 Pro maintained functionality in complex scenarios. Error analysis showed that logic errors were most frequent, followed by syntax and entity errors. A local fine-tuned model (DeepSeek-r1:14B) was also evaluated. It worked for simpler tasks but required manual adjustments. The results highlight the importance of model robustness, prompt quality, and deployment context in LLM-based code generation for home automation.
Automated Generation of End-to-End Web Test Cases via a Generic AI Agent: A Comparative Study of DeepSeek V3 and Claude Sonnet 5
Web applications are widespread and can be accessed from anywhere, in theory, using aweb browser on a computer or smartphone. Primarily due to the diversity of web browsers and frameworks available for developing web application interfaces, testing such applications is a challenging task. With the advent of large language models, several works are utilizing them to automate software engineering tasks, including test case generation. This use of LLMs for test case generation prioritizes unit testing. More recently, we have seen the advent of Generic Artificial Intelligence Agents, which are tools that utilize LLMs and also possess the ability to run additional tools, such as cloning repositories, navigating websites, and compiling programs. In this work, which is part of a research and development project, we evaluate a specific Generic AI Agent Assistant regarding its capability to navigate web applications and create fully automated end-to-end test cases, utilizing Selenium WebDriver and JUnit 5 framework. Results show that, considering a set of nine websites, in overall end-to-end test case generation, Suna configured with DeepSeek V3 produced 165 successful test cases out of 481 generated tests, a success rate of 34.3%. On the other hand, Suna configured with Claude Sonnet 4 produced 336 successful test cases out of 479 generated tests, a success rate of 70.1%, which is very impressive, mainly due to the complexity of creating end-to-end testing. In terms of cost, we used a free and a paid LLM model. The paid model generates successful test cases at an average price of $ 0.15 per test case.
Automatic 3D animation generation for Sign Language: A case study of Sign Language Machine Translation
Digital accessibility remains a critical challenge for the Deaf community, especially in Brazil, when it comes to universalizing Brazilian Sign Language (Libras) content through different media. Traditionally, animation pipelines for Sign Language videos, including those used in the VLibras suite, are frequently slow, costly, and limited in expressiveness. This work presents a novel approach that automates the transformation of human-generated Libras videos into expressive 3D animations using deep learning-based motion transfer techniques to be applied in VLibras workflow and extend its capability in order to improve the demanded time for implementation and cost dramatically. The system leverages recent progresses in motion capture, pose estimation, and animation retargeting to enable efficient and real-time animation of human based 3D models. The present study discusses the system architecture, automation pipeline, and the potential of value creation for the Deaf community through a survey and evaluation process. Results indicate that the approach used supported the improvement on realism, enhancement on a more immersive experience, acceleration of the development and potentially high quality compared to traditional methods. Future developments on fine-tuning the model and the generation of anthropomorphic enhanced 3D models with a better collision detection capability can enable a scalable path toward a more inclusive media content for the Deaf population in Brazil.
Automating Cloud Infrastructure Provisioning with Semantically-Enriched Large Language Models
The complexity of provisioning multi-cloud infrastructure has created a significant automation bottleneck, and while Large Language Models (LLMs) offer a promising solution, they consistently fail to generate reliable and deployable Infrastructure as Code (IaC) due to inherent ambiguity. To address this critical reliability gap, we propose a novel methodology that significantly improves IaC generation by augmenting LLM prompts with structured semantic context. Our approach utilizes OWL ontologies to formally model key infrastructure concepts, grounding the LLM in a machine-readable representation of the domain. This semantic enrichment provides the specific, structured context needed to resolve ambiguity and enhance the accuracy of the generated Terraform code. We evaluate our approach on the IAC-EVAL benchmark, comparing our semantically-enriched method against standard prompting strategies. Experimental results demonstrate a definitive improvement: our approach achieves a mean functional accuracy of 64.3%, a 126.4% increase over the baseline average of 28.4%. Syntactic validity also improved dramatically, with Terraform plan validation rates increasing by an average of 29.6%. These findings showcase that formal semantic grounding is a critical and highly effective technique for building reliable, LLM-driven automation for complex cloud environments.
BAH: Beyond Acoustic Handcrafted features for speech emotion recognition in Portuguese
It is through affective computing that we have the integration of human feelings and computing applications. One affective computing task is Speech Emotion Recognition (SER), which identifies emotions from spoken audio. Even though emotion is a universal aspect of human experience, each culture and language has different ways to express and understand emotions. So, when designing models for SER, it is common to focus on a single language. In this work, we explore VERBO, a Brazilian Portuguese dataset for categorical emotion recognition. Our main objective is to define the best way to extract acoustic features to train a classifier for SER.We compare 18 different methods to generate audio representations, grouped by handcrafted features and audio embeddings. The best representation for VERBO is TRILL embeddings, and with an SVM classifier, we achieved 92% accuracy in VERBO. As far as we know, this was the state of the art for this dataset.
Behind the Stars: Uncovering Hidden Adjustments in Letterboxd Film Ratings
Letterboxd’s movie ratings influence millions, yet its scoring algorithm is opaque. We investigate the discrepancy between the platform’s displayed score and the true user rating average, which we define as Δ. Analyzing a corpus of 1,737 Brazilian films and over 1.3 million ratings, we uncover the factors driving this distortion. Our analysis reveals a systematic algorithmic compression that pulls extreme scores toward the mean, with a strong negative correlation (−0.903) between a film’s true rating and its Δ. Using K-Means, we identify four distinct rating distribution profiles (e.g., Polarized, Highly-Rated) and demonstrate that these profiles, along with genre, are significant predictors of the score adjustment. Niche genres like documentaries and musicals, which often exhibit polarized or extremely high ratings, are penalized most heavily. Furthermore, we find that popularity acts as a stabilizer; as a film’s rating count increases, the magnitude of Δ decreases. Taken together, these results indicate that Letterboxd employs a normalization mechanism that mitigates the influence of outlier patterns, potentially fostering more representative aggregate scores and enhancing comparability across films. This study proposes greater transparency in these algorithms that shape cultural consumption.
Calibration of Recommender Systems with LLMs: Prompt Optimization to Balance Accuracy, Diversity, and Fairness
Recommender Systems (RSs) play a central role in digital platforms, aiming to deliver relevant and personalized content. However, issues like popularity bias persist, limiting diversity and fairness. Calibration techniques seek to align recommendations with user preferences, often through post-processing adjustments. With the advent of Large Language Models (LLMs) such as GPT and LLaMA, new opportunities have emerged to personalize recommendations using prompt engineering. While recent approaches like prompt optimization have shown improvements in ranking accuracy, they often neglect other key aspects such as diversity, coverage, and fairness. This study investigates the use of LLMs for recommendation calibration, comparing their performance against traditional methods. We also evaluate the impact of different prompt optimization strategies across multiple metrics, including MAP, NDCG@10, MRMC, LTC, and GAP. Additionally, we employ a multicriteria utility function (MAUT) to analyze trade-offs between accuracy and diversity. Our results highlight the potential of LLMs and prompt engineering to enhance both personalization and fairness in Rss.
Call2Go: A Cross-Platform Strategy Of Kids Online Social Media Influencers
The presence of children and teenagers (also known as kids) on social media platforms has been increasing steadily recently. Kids influencers, whether children or adults, attract millions of young followers, eager to consume their latest posts, often on multiple platforms such as Instagram and YouTube. While prior work has analyzed kids’ and their influencers’ use of social media platforms, the focus has been mainly on a single platform. Indeed, we are unaware of prior studies investigating how influencers explore their respective profiles on distinct platforms to extend the connection with kids and disseminate content to them. In this paper, we aim to fill this gap by presenting a methodology to build a cross-platform dataset with content produced by a selected set of kids influencers, gathered from two popular social media platforms: Instagram and YouTube.We then use this dataset to identify behavioral similarities and differences of kids influencers exploring their profiles on both platforms. Finally, we point out some evidence of a strategy adopted by these influencers to call their audience’s attention to content posted on another platform. This strategy, referred to as Call2Go, may increase kids’ online permanence, compromising the social development and the health of these young social media users.
Characterization of (Dis)Information in Vaccination Narratives in Public WhatsApp Groups in Brazil
WhatsApp has become one of the main channels for disseminating health information in Brazil, especially during the COVID-19 pandemic. In this work, we investigate the spread of (mis)information about vaccines in public groups on the platform, focusing on identifying dominant narratives, engagement patterns, and connections to other online information sources. Using a set of 84,640 messages collected between 2020 and 2023, we applied Natural Language Processing (NLP) techniques, including topic modeling (BERTopic) and textual similarity analysis. Overall, the results show that alarmist, misinformative, and politicized content is prevalent, with many messages repeated across multiple groups. We also observed a concentration of activity among a few highly active users, suggesting coordinated amplification strategies. This analysis helps to understand the mechanisms of misinformation spread in private messaging environments and provides insights for public policy and digital health countermeasures.
Characterization of Stream Recommendations on Twitch for Children and Teen Audiences in Brazil and the United States
Twitch is a livestream sharing and interaction platform that strongly attracts young audiences due to its strong gaming presence. When watching a livestream, users see a list of channels with live streams suggested by the platform. In this study, we used the concept of personas (aged 13, 15, and 18) to represent the child and adolescent audience on Twitch, to simulate their access in Brazil and the United States, and to characterize the recommendations to this audience in both countries. Our personas watched live streams of a set of popular games, and for each stream watched, we collected the suggested streams for more than a month for each persona. Our results highlight and fill a gap in the literature: the presence of inappropriate content in these platform suggestions for children and adolescents, including rewards, games with inappropriate age ratings, advertisements, tags, and titles with inappropriate words. Our results can contribute to fostering public policies and computational improvements that contribute to a safer user experience within social media for children and adolescents.
Comparison of Journalistic Coverage Between News Portals and YouTube: A Case Study of the Israel–Palestine Conflict
This study systematically investigates how the journalistic coverage of the same event can vary depending on the dissemination platform and the communication vehicle. The research focuses on the coverage of the Israel-Palestine conflict, analyzing content from official news portals and the YouTube channels of major mass-reach media organizations. To this end, we employed Natural Language Processing (NLP) techniques to comparatively characterize both the texts published on the portals and the video transcripts aired on the respective YouTube channels. For sentiment analysis, we utilized the TextBlob and VADER algorithms. Furthermore, we applied text similarity methods, such as the Longest Common Subsequence (LCS) technique and the SBERT (Sentence-BERT) model, to measure the degree of resemblance between headlines and correlated content. This helped to identify when seemingly similar texts actually converge or diverge in their focus. Using BERTopic, it was observed that, despite the existence of similar headlines between portals and videos, the associated texts frequently address distinct themes or emphasize varied aspects of the conflict. This methodological combination allowed us to demonstrate that the journalistic narrative is not uniform and can be significantly influenced by the dissemination platform, even when the base event is the same.
Connected Health in Action: An IoT-Driven Web Dashboard for Real-Time HRV and Stress Monitoring in a Private Cloud
Stress is one of the main factors associated with the worsening of cardiovascular diseases and psychological disorders, with Heart Rate Variability (HRV) being one of the most widely used physiological indicators for its detection and monitoring. This work proposes the development of a continuous stress monitoring architecture based on the Internet of Things (IoT), using the Polar H9 heart rate sensor connected via Bluetooth Low Energy (BLE) to an embedded processing unit based on the Raspberry Pi 4. The system performs real-time acquisition and transmission of heart signals through Python-based scripts. Communication with the private cloud is handled via the RabbitMQ, and data visualization is implemented through a web dashboard accessible via WebSocket, enabling continuous and multiplatform monitoring. The architecture was designed to extract classical HRV metrics, such as RMSSD, SDNN, and pNN50, which are collected from the R-R intervals transmitted by the system. The results obtained demonstrate that the architecture is capable of capturing, processing, and transmitting these metrics with high stability, providing a solid foundation for applications in HRV analysis and stress indicators. Future work will focus on integrating machine learning models for automatic stress classification and conducting clinical validation with multiple participants to enhance the system’s reliability and applicability.
Bio-Inspired Cryptography for IoT: A Comparative Study of DNA-Based Algorithms
Data security in Internet of Things (IoT) devices is a critical challenge due to their inherent limitations in memory, energy, and processing capacity. Traditional security mechanisms are often unfeasible, driving the search for lightweight cryptographic algorithms. In this context, DNA-based cryptography stands out as a promising bioinspired approach. This study presents an empirical and direct comparison of four DNA-based cryptographic algorithms, implemented on a real resource-constrained IoT device, the ESP8266. The analysis quantified performance and efficiency in both encryption and decryption modes, using instrumented measurements of energy consumption (mJ), execution time (𝜇s), electric current (mA), and RAM usage (B). The results reveal statistically significant differences among the algorithms, confirmed by Shapiro-Wilk and Wilcoxon tests. Alg_3 demonstrated the highest efficiency in time and energy, being the fastest and with the lowest total energy consumption, although it required the most RAM. The research highlights the potential of bioinspired cryptography and reinforces the importance of considering the constraints of the target environment when selecting a cryptographic scheme.
Data-Efficient Tabular Classification with Transformer-Based Small Language Models
The application of deep learning to tabular data remains an ongoing challenge, with tree-based models such as XGBoost consistently outperforming neural network methods in most real-world scenarios. Recent advances in Large Language Models (LLMs) highlight their ability to generalize to new tasks via few-shot or one-shot prompting, yet questions remain about their utility in structured, tabular domains—particularly for resource-efficient Small Language Models (SLMs). In this work, we systematically evaluate the effectiveness of both large and small transformer-based language models for tabular classification tasks using few-shot strategies. Our approach investigates input serialization schemes and prompt engineering to maximize performance in low-data regimes. Experiments on benchmark datasets—including Diabetes, Heart Failure, and German Credit Risk—demonstrate that well-designed SLMs can approach, and occasionally match, the performance of much larger models, substantially reducing the need for extensive labeled data and retraining. However, further advances are required for language models to consistently rival the strongest tree-ensemble baselines. Our findings support the idea that SLMs, when properly prompted, offer a promising, flexible, and label-efficient alternative for automating and dynamizing machine learning pipelines on tabular data.
Deep Learning Strategies for Visual Pattern Analysis in Metallic Surfaces
Advances in multimedia systems and computer vision have enabled each time more sophisticated methods that are able to analyze complex visual patterns in industrial contexts. The inspection of failures in metallic alloys is important for sectors such as aerospace and defense, yet it faces limitations due to reliance on manual methods and data scarcity. This research presents a systematic approach for selecting convolutional neural network architectures by integrating data augmentation, image segmentation, and self-supervised learning. In particular, it uses the DINO technique to generate interpretable attention maps, enhancing model transparency and performance in defect detection tasks. The method was validated using three architectures, ResNetInceptionV2, DenseNet, and Xception, with DenseNet combined with DINO achieving the best results. Key contributions include the release of a hybrid dataset (real and synthetic), effective strategies for handling class imbalance, and a comparative study of architectures, offering insights for future industrial applications and deep learning research applied to pattern detection on metallic surfaces.
Dermalyze: an application for assisting skin lesion screening based on deep learning
According to the Brazilian Institute of Cancer, skin cancer is the most common displasya in the country. To mitigate this situation and ensure quality of life for those affected by the disease, early diagnosis is fundamental. When treated early, 5-year survival rates can exceed 96%. In this context, we developed a skin lesion screening application to assist in the identification of skin cancer. This software, designed for use by healthcare professionals, leverages artificial intelligence to assess the cancerous potential of a lesion based on a clinical image captured by a mobile device. The developed model achieved a balanced accuracy of 80%, and the application in which it was embedded was tested in a clinical setting with a total of 82 users. Based on the predictions made during the case study, the model was able to correctly identify the severity of the lesions in approximately 70% of the cases. Additionally, about 92% of users reported the model to be useful during the screening process, describing their experience primarily with words such as comfort and safety.
Designing a Multimodal Feedback Component for VR Applications
Virtual Reality (VR) environments have shown great potential across a wide range of applications that benefit from interaction through touch and haptic feedback, such as entertainment, education, and rehabilitation. By simulating realistic tactile sensations, such as contact, texture, and pressure, VR systems can enhance user engagement, accessibility, and understanding of virtual worlds. In this context, haptic-enabled VR applications can play a fundamental role in diverse scenarios that demand precise object manipulation and spatial awareness. This paper presents a multimodal feedback system for VR applications to provide precision in object recognition using headset controllers and their collision with virtual objects. The proposed system generates haptic feedback regarding three types of collision: touch, stroke, and entering. It is intended to increase the user experience and sense of presence. In the evaluation, most users reported being satisfied with the application and stated that it provided a good sense of presence. They also found that, among the vibration patterns, the irregular one was the most successful in conveying the intended texture.
End-to-End Deep Learning Approach for Wind Turbine Bearing Fault Detection From Acoustic Data
Wind energy generation through wind turbines has become increasingly attractive as a clean and renewable energy source. Effective maintenance of wind turbines is crucial, as failure can result in significant economic losses and damage to equipment due to unplanned downtime. Nevertheless, ensuring effective maintenance remains challenging because these systems usually operate in severe and remote environments. Considering that rolling bearings are among the most necessary mechanical components in wind turbines, accurately detecting faults in these bearings is important for ensuring the regular and reliable operation of the equipment. This paper proposes a deep learning-based monitoring architecture that utilizes acoustic signals emitted from rolling bearings to detect faults in wind turbines. Using real-world data collected via microphones and a Raspberry Pi system, we constructed a structured and manually annotated dataset. A convolutional neural network (CNN) model was trained on mel-spectrogram representations to distinguish between healthy and faulty operational states. The system achieved promising performance, with 83.62% accuracy, 87.40% F1-score, 95% AUC-ROC, and 98% precision-recall AUC. The proposed end-to-end pipeline integrates data acquisition, pre-processing, classification, and confidence-based decision thresholds, making it suitable for deployment in operational monitoring scenarios. These results demonstrate the viability of audio-based fault detection as a scalable and non-invasive solution for predictive maintenance in wind energy systems.
Between Access and Protection: A Study on Age Verification and Child Safety on Digital Platforms
Given the ease of unsupervised access and growing risks for minors online, safeguarding children’s digital rights is increasingly urgent. As personal data becomes a major economic asset, governments are enforcing stricter age verification rules. This study examines age verification designs on the most-used social media platforms by Brazilian children and adolescents. In the first phase, we created test accounts and analyzed the terms of use on YouTube, TikTok, Instagram, and WhatsApp. In the second, we analyzed responses to a public consultation by the Brazilian Internet Steering Committee to identify concerns related to this topic. Findings show that platforms in Brazil mirror those in the EU, with easily bypassed age checks. The results call for a redesign of current mechanisms to ensure safer, more ethical digital experiences for children.
Interactive 3D Experience for Displaying Extinct Animals at the Emílio Goeldi Museum: An Educational and Technological Approach Based on WebGL
This work investigates the educational impact of an interactive web application based on WebGL for displaying three-dimensional models of extinct animals from Brazilian territory, applied to the Emílio Goeldi Museum context. The research addresses how interactive 3D visualization enhances visitor engagement and knowledge retention compared to traditional exhibitions. The methodology involved implementing advanced 3D model optimization techniques, responsive design, and intuitive interaction interfaces, ensuring accessibility across various devices. Five extinct Brazilian animals were selected as case studies: Megalonyx jeffersonii, Toxodon, Gomphotherium, Phorusrhacidae, and Glyptodon. The application was developed using React Three Fiber integrated with performance optimization libraries for mobile devices. A quasi-experimental study with 150 participants evaluated usability, engagement, and educational outcomes. Results demonstrated significantly higher engagement and knowledge retention. The research contributes to digital museology by demonstrating educational effectiveness, offering a replicable framework for museum institutions.
Frederico Guilherme Santana da Silva Filho
Exploring Models with Temporal Windows for Chord Prediction from Melodies
The task of predicting chords in music is highly important in the careers of composers and professional musicians. Composers create new melodies that require harmony to make them, among many other things, more marketable. Professional arranger musicians frequently need to learn new songs for which they lack supporting material. This work explores chord prediction in melodies using Machine Learning. Utilizing the POP909 dataset 1, composed of 909 songs in MIDI format, Random Forest (RF) and Long Short-Term Memory (LSTM) models were trained and compared, both with and without bidirectionality, employing features such as melody, note intensity, musical key, and the delta time of each melodic interval’s execution. The RF model achieved an overall average accuracy of approximately 77%, performing well on common chords. Conversely, the LSTM without bidirectionality achieved around 61% accuracy, and with the use of the technique (BLSTM), it obtained approximately 73% accuracy. Both models demonstrated challenges in predicting rare chords, such as diminished chords. The contributions aim to advance AI-assisted musical analysis, focusing on applications for composition and live accompaniment.
Extrapolation-Based Data Augmentation for Sequential Recommendation
With the advent of deep learning in the recommendation field, a lot ofwork has been and still is being done to bring deep learning-based models to their full potential. One line of work, Self-Supervised Learning (SSL), focuses on extracting the maximum potential of datasets to improve recommendation performance, and at the same time, attempts to diminish data-related problems, such as data sparsity, that are commonly seen in machine learning techniques. One branch of SSL for recommender systems uses predictive strategies to create new labels and examples for training by, for instance, adding new interactions to the user’s history of interactions. However, the existing models that explore this idea are somewhat limited. They focus on adding new interactions at the start of the sequences, ignoring the performance improvements that could be achieved by adding interactions in the middle and end of the sequences. We propose Extrapolation-based Sequence Augmentation for Sequential Recommendation (ESA4SRec), a model that uses the sequence reconstruction capabilities of BERT4Rec to generate new data at any position of a sequence by extrapolating the existing knowledge to unknown, novel interactions. The resulting augmented dataset is then used as input to a modelagnostic sequential recommender system. We compare our approach to related models and demonstrate the performance improvements when compared with the original datasets and the overall best performance of our method. ESA4SRec’s code available at https://github.com/viniciusgm000/ESA4SRec.
FaDeRS: Fairness and Depolarization in Recommender Systems
Recommender systems have become fundamental in modern society as they help navigate the vast amount of available data, enabling users to find information, products, or services more efficiently and personally. They directly impact how people consume data, goods, and resources. Recommender systems often lack fairness and diversity, resulting in unfair services and increased preference polarization. Solution: This work presents FaDeRS (Fairness and Depolarization in Recommender Systems), an approach aimed at increasing fairness and diversity in recommender systems. FaDeRS adjusts predictions through controlled perturbations and optimization to mitigate individual unfairness and polarization without modifying the input data. The research is related to socio-technical theory, addressing one of the socio-algorithmic problems, algorithmic discrimination. We consider a specific set of approaches to encode fair behaviors. The research applied a quantitative method with experimentation using two datasets in distinct contexts, implementing a post-processing algorithm based on the Simulated Annealing meta-heuristic. The proposed method demonstrated significant reductions in polarization (up to 78.64%) and individual unfairness (up to 33.97%), with only a small increase in the Root Mean Square Error (RMSE), indicating an improvement in the socially desirable qualities of the systems without unduly sacrificing accuracy. Notably, FaDeRS consistently outperformed a relevant benchmark methodology across both evaluated datasets. The main contribution is a mechanism that balances personalization and fairness, simultaneously addressing polarization and individual unfairness from the items’ perspective, promoting a fairer and more diverse approach to recommendation.
Fetal Heart Rate Estimation in Phonocardiograms Using Deep Learning and Digital Signal Processing
Fetal heart rate monitoring through phonocardiograms enables non-invasive assessment of fetal health, supporting the early detection of potential complications during pregnancy. However, accurate frequency estimation is hindered by various noise sources, such as maternal heart sounds and ambient interference. This study proposes a lightweight 1D-CNN regression model for fetal heart rate estimation, leveraging MFCC and Delta-MFCC coefficients extracted from audio segments. A simple but effective data augmentation technique was also employed to mitigate the scarcity of labeled data. Experiments conducted on the SUFHSDB dataset yielded a mean absolute error of 3.94±0.41 bpm. The results suggest that deep learning-based approaches, especially when combined with data augmentation, are promising alternatives for fetal heart rate estimation, potentially reducing reliance on traditional signal-processing pipelines.
FLEXA: A Modular and Flexible Framework for AI-Assisted Video Communication
Semantic communication offers a promising paradigm for efficient video transmission. However, existing implementations often rely on specialized, tightly coupled architectures that hinder the integration and comparative evaluation of new Artificial Intelligence (AI) models. In this paper, we present FLEXA, a modular framework for AI-assisted video communication that decouples AI processing from transport logic, enabling simplified integration of new models within a standard Web Real-Time Communication (WebRTC) environment. The architecture introduces policy-driven semantic orchestrators that dynamically manage and chain AI models during transmission. To demonstrate its capabilities, we integrated a Super-Resolution Generative Adversarial Network (SRGAN) and evaluated seven upscaling configurations under different operating policies. Results indicate that one configuration achieves the best balance between perceptual quality and efficiency, with LPIPS < 0.1 and SSIM > 0.7 under moderate processing cost. These findings demonstrate that FLEXA effectively manages different AI-driven policies, revealing an optimal operating point for resolution enhancement and quantifying the distinct performance trade-offs between different operating policies.
Procedural Framework for Generating Synthetic Data Based on Statistical Simulation: An Approach to Human Behavior Analysis
Data sets for training convolutional neural networks (CNNs) pose a challenge due to the difficulty of obtaining them and the sensitive nature of the information involved, leading to high costs, scarcity, and potential biases. To address these limitations, synthetic data generation provides a viable solution, enabling controlled and ethical research in human behavior. The main objective was to develop a framework capable of simulating environments and generating data procedurally for AI training, focusing on engagement monitoring and the identification of behavioral deviations in educational and corporate contexts. Among the specific objectives, the creation of an application persona, Visage Track, stands out, designed to calculate engagement and behavioral deviations, thereby validating the developed techniques. The proposed approach integrates procedural generation methods into a complex statistical simulation, allowing for customizable and controlled scenarios without the need for direct interaction with real subjects. The framework and Visage Track were constructed to facilitate experiments in simulated environments. In the evaluation, the prototype demonstrated high accuracy in presence detection (up to 98%), consistent behavioral simulations across multiple scenarios, and the ability to generate scalable, multimodal datasets. These results indicate the framework’s potential to support research in multimedia content generation, multimodal interaction, and affective computing, offering an adaptable and ethical infrastructure for studies involving human behavior in controlled environments.
Game Design and Development: A Multimedia Engineering Perspective on Cultural Preservation
Preserving cultural heritage is crucial for maintaining a sense of societal identity and fostering a deeper understanding of history. However, traditional methods often struggle to effectively engage younger audiences. Digital games offer unique potential to bridge this engagement gap by providing immersive and interactive experiences that can enhance how individuals connect with and understand cultural history. "Trilha do Cangaço", a mobile 3D game developed in collaboration with the Cangaço Museum — which preserves and exhibits the history of this banditry movement from early 20th-century Northeast Brazil—addresses this gap by integrating immersive gameplay with a multimedia engineering approach. Developed using Agile-based methodologies, the game features narrative-driven and management mechanics to foster interest in the historical topic among players. A questionnaire-based study with 28 participants evaluated its design, technical implementation, and engagement impact. Results show that a majority of players reported high enjoyment of the game, with a substantial portion expressing intention to explore further Cangaço resources. This research demonstrates the potential of digital games, grounded in multimedia engineering, as vital tools for cultural heritage education, offering a scalable model for museums and educational institutions to engage diverse audiences in historical narratives, enhancing cultural preservation through innovative multimedia approaches.
Identification and Classification of Pathologies in Flexible and Rigid Pavement Using YOLOv8
Road pavement infrastructure plays a fundamental role in mobility and economic development, with Brazil having over 213,500 km of paved roads. However, conventional inspection methods for pavement pathologies are predominantly manual, subjective, costly, and pose risks to evaluators. Recent advances in Deep Learning, particularly Convolutional Neural Networks (CNNs), have emerged as promising alternatives for automatic defect identification through computer vision. A systematic literature review identified 209 studies published between 2019-2024, revealing that most existing work focuses exclusively on flexible pavements, leaving gaps in rigid pavement pathology detection. Hence, this study proposes a hybrid model based on the YOLOv8 neural network for automatic identification and classification of pathologies in both flexible and rigid pavements. The proposed model incorporates a BACKbone + CSPNet architecture that reduces computational costs without compromising performance. The methodology involved collecting approximately 4,000 images using a customized approach with Go-Pro cameras equipped with GPS across different Brazilian regions under various climatic conditions, followed by manual annotation of 1,000 images for training. The model achieved promising results with mAP50 of approximately 90%, precision up to 96%, and mean Pixel Accuracy of 88%. An extra validation was conducted on 65 road segments of 1 km each across three Brazilian regions, where 51 segments with various pathologies were successfully identified and classified.
Image-to-UI Accessibility: Assessing ChatGPT’s Effectiveness in Generating Accessible Android Screens from Figma Templates
As accessibility remains a persistent challenge in mobile applications, recent advances in Large Language Models (LLMs) offer promising support for addressing these issues in the development process. This study investigates the potential of ChatGPT-4 to generate accessible Android user interfaces directly from screen mockups. Using a dataset of 60 screens in 8 Figma-based UI templates, we prompted ChatGPT to produce Jetpack Compose code solely from screen images. We then evaluated the generated code using Google’s Accessibility Scanner, identifying a total of 302 accessibility violations. Through iterative prompting guided by the scanner’s feedback, we achieved a reduction of more than 50% in the reported issues. However, the process also introduced new violations in some cases, highlighting important limitations in the consistency and reliability of the model. Our findings suggest that while LLMs like ChatGPT can support accessibility improvements in mobile User Interface (UI) development, human oversight remains essential. This study contributes to ongoing discussions on the role of AI in inclusive software design and the future of image-to-code generation pipelines.
Improving Soundscape Retrieval for Bioacoustic Monitoring: An Analysis of Fusion Techniques with Pre-trained Embeddings
The retrieval of similar soundscapes is essential for bioacoustic and ecoacoustic monitoring, yet it remains challenging due to the large volume of unlabeled data, environmental noise, and the complexity of acoustic scenes. To overcome the limitations of traditional, feature-based methods, this study proposes an efficient system that integrates embeddings extracted from a pretrained deep learning model, combined with a noise reduction technique and feature fusion strategies within a vector database to enable similarity-based retrieval. We evaluated the system using bird, amphibian, and mammal recordings across four experimental methodologies, including a use case focused on endangered species. Results show that embedding vectors consistently outperform traditional MFCC (Mel-frequency cepstral coefficients) features in capturing acoustic similarity, and that approximate search algorithms (HNSW) significantly improve both retrieval precision and query efficiency. Additionally, the system effectively retrieves recordings of the critically endangered species Crax alberti and maps their geographic distribution, highlighting its potential for conservation planning and early-warning monitoring.
Investigating the Dynamics of Hate Propagation in Toxicity Cascades on Twitch Live Chats
The popularization of live streaming on platforms such as Twitch has transformed how users interact in real time, creating dynamic environments that are also susceptible to the spread of toxic discourse. This poses a new challenge for the automated moderation of these online communities, especially in the face of coordinated hate actions. This study investigates how toxicity spreads on Twitch through the identification and analysis of cascades of toxic messages. To this end, we developed an automated data collection system that monitored, in real time, messages from streamings of popular Twitch channels. These messages were processed and classified using natural language processing tools, including sentiment analysis and hate speech detection via lexical dictionaries from Hatebase and WeaponizedWord, as well as deep learning models from PerspectiveAPI and pysentimiento. Based on this classification, offensive content in chats was identified, and 472 toxicity cascades were mapped, enabling the analysis of their frequency, intensity, and dissemination patterns. Our results show that although most hate speech is sporadic, some channels exhibit recurrent and intense episodes of collective toxicity. This study contributes to a better understanding of the dynamics of hate speech propagation in live environments and provides insights for future moderation and intervention strategies.
Investigating Contextual Word Embeddings in Semi-Supervised Learning for Toxic Comment Detection
The proliferation of toxic messages on the Web has led many social networks to limit or shut down user comments, as these messages are harmful to people and keep them away from online platforms. Most approaches deal with this unacceptable language form, focusing only on identifying if a comment is toxic or not, using a supervised learning strategy, leaving aside the various types of harmful messages, such as LGBT+phobia, racism, xenophobia, and others. In this paper, we investigate three contextual word embedding models: BERTimbau, BERTweet.BR, and LLaMA 3.1 within a semi-supervised approach to distinguish whether a comment is toxic. Also, we explore this strategy to detect six types of toxicity: LGBT+phobia, insult, racism, obscenity, xenophobia, and misogyny. This task is defined as a multi-label classification problem, as a comment may contain several types of abusive language. We evaluated our approach using the ToLD-BR corpus and achieved competitive results for the binary toxicity classification task. In the context of multi-label toxicity detection, our best result outperformed approaches based on supervised learning, using significantly fewer labeled data, and emphasized their efficiency and practicality.
LibrasDetec: A Movement Detection Component for Brazilian Sign Language Karaoke
The entertainment market has been expanding rapidly, driven in part by increased internet access, which has also contributed to the growth of the digital games industry. However, despite this expansion, investments in accessibility remain limited, compromising the quality of games for people with disabilities. In the context of entertainment or serious games, particularly regarding accessibility for deaf users, motion detection emerges as a promising strategy to enable interaction through gestures in their native language. In this scenario, we propose LibrasDetec, a component for detecting and evaluating Brazilian Sign Language (Libras) gestures, integrated into the educational game Libraskê. The solution captures user gestures via webcam and compares them with reference videos, providing real-time scoring. To validate the proposed component, we conducted tests with 12 participants from three different user profiles: deaf individuals, interpreters, and non-signers. The results showed a satisfactory alignment between automated and human evaluations, especially among deaf users, suggesting that LibrasDetec is a viable approach to enhance accessibility and engagement in serious games focused on Libras.
Multi-Agent LLM Approach for Moderating E-Commerce Customer Service Responses
Language model (LLM)-based solutions have been widely adopted in automated customer service systems, particularly on e-commerce platforms. However, such solutions still face challenges related to the accuracy, contextualization, and reliability of the generated responses. This study proposes an LLM-based multi-agent architecture for the automatic moderation of textual responses. The architecture is composed of specialized agents operating in an iterative review workflow, which includes semantic and contextual evaluation, improvement recommendations, textual rewriting, and final decision-making. The agents share a common context and operate in a coordinated manner to identify deficiencies, propose corrections, and validate the quality of the responses. The proposed approach was evaluated using real-world data from a multilingual e-commerce platform, leveraging two models from the Qwen3 family (32B and 30B-A3B). The results indicate that the approach is effective in enhancing response quality, achieving average gains of more than two points on an evaluation scale and enabling the correction of over 60% of initially inadequate responses. Additionally, the solution offers advantages in terms of auditability, modularity, and potential adaptability to different domains.
Fast Method for Dynamic Point Cloud Coding Based on Block Partition Prediction
Point clouds are a versatile 3D data representation format that captures spatial and attribute information about objects or environments. It has been widely used in applications like autonomous driving, augmented and virtual reality, and 3D scanning. However, the large volume of data involved in point cloud processing poses challenges in terms of storage, transmission, and real-time processing. The Video-based Point Cloud Compression (V-PCC) standard addresses these challenges by employing 2D video compression techniques to encode dynamic 3D point clouds. Despite its effectiveness, V-PCC demands a high computational cost, particularly in encoding geometry and attribute video sub-streams. This paper presents a machine learning-based approach to accelerate the V-PCC encoding, focusing on the Coding Unit partitioning process in the geometry and attribute sub-streams deployed in the reference software (TMC2). The proposed method significantly reduces the encoding complexity by using decision tree models with negligible impact on coding efficiency. Experimental results demonstrate an average encoding time reduction of 36.12% for geometry and attribute sub-streams, with a minimal impact on coding efficiency between -0.43% and 0.83% in terms of BD-Rate.
Gambling Narratives on Video Platforms: A Linguistic-Thematic Study of “Jogo do Tigrinho” on YouTube
The dissemination of online gambling, particularly when amplified by digital influencers, presents a growing social concern. In Brazil, the game Fortune Tiger has gained popularity through YouTube videos that often promote quick and effortless earnings. While there is existing research on gambling in social media, our study is among the first to delve into how influencers on video platforms construct and disseminate such narratives. We examine the structure and perception of gambling-related content on YouTube, using a large-scale, linguistically informed thematic analysis. Through transcription, topic modeling, and linguistic feature extraction, we identify two dominant narrative types: critical videos that address legal and social harms, and promotional videos that use simplified and repetitive language to encourage engagement. Promotional content overwhelmingly dominates the platform, while critical content exhibits greater linguistic richness and higher per-video engagement. These findings underscore the structural imbalance in online gambling discourse and offer significant implications for future research, platform moderation, and regulatory frameworks.
What makes a sentence toxic? A critical analysis of specialist models for toxicity detection
This study examines the performance of specialized Machine Learning models in the task of online toxicity detection, under the hypothesis that these systems disproportionately focus on isolated lexical items. We conduct a comparative analysis of the Detoxify and Perspective API models using the HateXplain (English) and ToLDE-Br (Portuguese) datasets. To assess model behavior, we employ the SHAP explainability framework, which enables the interpretation of feature importance in individual predictions. Our findings reveal a misalignment between model outputs and the nuanced, evolving nature of language on social media platforms. Furthermore, the results demonstrate an overreliance on negatively connoted keywords, which compromises the models’ classification accuracy and raises concerns regarding their robustness and fairness in real-world applications.
Optimizing and Evaluating a Retrieval-Augmented Generation System for Normative Document Retrieval in Hospital Settings
This paper presents the development and evaluation of a chatbot designed to consult documents written in Portuguese on regulatory procedures in a hospital environment, which uses a Retrieval-Augmented Generation (RAG) pipeline to increase the factual accuracy and relevance of its underlying Large Language Model (LLM). Using the RAG technique will allow for more efficient and accurate retrieval of information contained in hospital manuals and institutional documents, helping workers quickly access internal guidelines and procedures. The objective is to optimize each system component (retrieval, re-ranking, and generation) to analyze the impact of each step in developing a RAG system for a low-resource language such as Portuguese. The methodology can be divided into the following stages: (1) the Golden Set Preparation, formed by a set of questions and answer data; (2) comparison of three embedding models for initial retrieval and of three re-ranking methods, including Cross-Encoder, Reciprocal Rank Fusion (RRF), and an LLM-based re-ranker, using metrics such as MRR, NDCG@10; and (3) comparison of two generative models (Gemini 1.5 Flash and GPT-4o-mini), using the metric BERTScore. The results indicate that the intfloat/multilingual-e5-small embedding model minimizes retrieval failures. In the re-ranking stage, the LLM-based re-ranker achieved the highest ranking accuracy, yet the computationally lighter RRF method emerged as an excellent cost-effective alternative. We conclude that an optimized architecture for both efficiency and performance combines the intfloat embedding, the RRF reranker, and the Gemini generator.
Quantifying Polarization, Cohesion, and Permeability in Brazil’s Chamber of Deputies
We present a network analysis of voting behavior in the Brazilian Chamber of Deputies from 2003 to 2023, focusing on three structural dimensions of legislative dynamics: polarization, party cohesion, and ideological permeability. To measure polarization, we apply structural balance theory to signed networks, examining how the relative frequency of specific triadic configurations, particularly those with two negative and one positive edge, signals the consolidation of antagonistic political blocs. For party cohesion, we quantify how consistently members of each party cluster within ideological communities detected through modularity optimization. To evaluate ideological permeability, we simulate influence diffusion using a network-based adaptation of the classical SIR model, capturing how political positions can propagate through the legislative graph depending on node centrality and edge polarity. Our results reveal a marked rise in polarization beginning in the 55th legislature, reflected by a structural shift toward triads driven by shared opposition rather than shared agreement. While the Workers’ Party (PT) maintained high internal cohesion, its reach across the network declined sharply in the 56th legislature. Although focused on institutional dynamics, our findings also reflect broader patterns observed in digital political discourse, where processes of ideological clustering and antagonistic alignment are likewise prevalent. In this sense, the structural features identified in legislative networks may help inform the analysis of polarization and influence in both formal political contexts and digitally mediated environments.
Vocal Recovery of Tracheostomized Patients Using Audio Processing and Machine Learning in a Zero-Shot Scenario
This work proposes a method for voice reconstruction in individuals who have undergone laryngectomy, integrating advanced audio processing and machine learning techniques. The approach aims to restore features such as timbre, intonation, and prosody, which are often lost when using an electronic larynx, whose sound is constrained by a constant fundamental frequency (F0). To address the lack of public datasets containing voices of tracheostomized patients, a synthetic dataset was created to simulate the acoustic properties of these devices. The developed pipeline comprises three stages: (i) speech analysis, involving the extraction of linguistic content and style; (ii) mapping, combining this information with the mel-spectrogram through techniques such as conditional modulation and diffusion networks, with a particular focus on Flow Matching; and (iii) reconstruction and synthesis, using highfidelity vocoders. Experiments compared two preprocessing methods— timbre shifter and F0 fixation—evaluated in four training and testing combinations. Results show that the F0→F0 configuration outperformed the alternative in three out of the four analyzed metrics (MCD of 444.04, LSD of 0.47, and PSNR of 42.27), suggesting that F0 fixation favors voice reconstruction that more closely matches the original signal. These findings highlight the potential of the proposed approach to improve the naturalness and intelligibility of synthesized speech for laryngectomized patients.
SimpleHRTF-3D: From Head Mesh to Immersive Spatial Audio
Head-Related Transfer Functions (HRTFs) are essential for immersive spatial audio in multimedia applications like virtual reality and gaming, yet personalization remains challenging due to precise anthropometric measurement requirements. This paper introduces an open source pipeline for HRTF customization through three objectives. First, we validate a published Random Forest model on HUTUBS, achieving similar R2=89.8% and SD=4.45 dB with manual measures. Second, SimpleHRTF-3D automates extraction from 3D head meshes using two-step PSO, achieving 10.96% mean extraction error (a 2.31 percentage point absolute reduction from 13.27% single-step error), yielding R2=89.3% and SD=5.04 dB. As proof of concept, we extended the method to use photogrammetry, enabling photo-to-HRTF personalization. Validated on 58 HUTUBS subjects, our pipeline integrates manual, mesh, and image methods, providing reproducible tools for multimedia HRTF adaptation. The results demonstrate high-fidelity spatial audio capabilities for diverse immersive applications.
Small vs. Large Language Models: A Comparative Study on Multiple-Choice Question Answering in Portuguese
Generative models are widely used for Multiple-Choice Question Answering (MCQA). While performance often improves with model size, prior work reports inconsistencies depending on task, prompting strategy, and language. We evaluate eleven open models ranging from millions to billions of parameters, both monolingual and multilingual, on Portuguese MCQA built from college entrance exams, under six prompting strategies: zero-shot, one-shot, few-shot, shuffled-order (to probe positional effects), and two per-option label-only settings. We also quantify positional bias using a normalized positional-bias coefficient (BPC). Overall, performance increases with parameter count; however, the magnitude varies across strategies. LLaMA-3.1-Storm-8B achieves the best average accuracy, and Sabiá-7B, a model trained with a strong focus on Portuguese, performs competitively among models of similar size. Smaller models (e.g., Tucano-2B, Qwen2-0.5B) attain solid results in specific settings, particularly with per-option scoring. These findings suggest that, although larger models are generally more robust, carefully chosen prompting can make smaller models viable under resource constraints. In summary, performance scales with size but depends on prompting—per-option configurations reduce the SLM–LLM gap, and positional bias is measurable via BPC; future work includes multi-shuffle BPC estimation, calibration, and log-likelihood baselines for per-option scoring and extensions to additional domains and languages.
SpeechVis: Simplifying Speech Emotion Visualization
As the amount of online content increases, analyzing and following discussions becomes harder. Relevant information, such as the main discussion topics and the emotions expressed in audio, e.g., in a podcast, requires people to watch or listen to the entire content to understand the context. However, this can take a long time, and people’s interpretations of emotions can bias their understanding of them. A visual summarization of such information can help people quickly understand the audio context and analyze the content regarding speakers, their emotions, and the main topics covered. In this work, we introduce SpeechVis, a visual analytics tool that visually summarizes speech emotions from an audio source. SpeechVis extracts multiple information from the audio, such as the transcription, speakers, main topics, and emotions, to provide visualizations and statistics about the discussed topics and each speaker’s emotions. We used multiple off-the-shelf machine learning models to extract audio information and developed several visual representations that aim to facilitate audio analysis. To evaluate SpeechVis, we selected two use cases and performed an analysis to demonstrate how the SpeechVis visualizations can give valuable insights and facilitate audio interpretation.
Too Good to Be True? Generalization Challenges in LLM-Based Fake News Detection
Although recent studies report high accuracy in fake news classification using supervised models, such models often rely heavily on spurious correlations in training datasets and fail to generalize to unseen contexts. A smaller body of work has explored more realistic approaches based on linguistic cues, such as subjectivity, aiming to reduce the reliance on dataset-specific content learned during training. In this study, we assess the capabilities of four state-of-the-art large language models (LLMs) — Llama3.1-70B, Claude 3.5 Sonnet, GPT-4o, and Mistral Large 2 — in identifying fake news based solely on linguistic features, without access to external verification. Two experimentswere conducted: one with generic prompts and another with fine-grained instructions and examples, including classification of false excerpts into categories like untrue facts, exaggerations, and incorrect named entities. Despite the well-known linguistic capabilities of modern LLMs, our results show consistently low performance across both experiments. This outcome supports the hypothesis that detecting disinformation based solely on content or linguistic cues, without external factual grounding, is far more challenging than commonly reported, and that current evaluation protocols may overestimate the generalization capabilities of fake news classifiers.
Traditional vs. Neural Video Codecs: Compression Efficiency, Visual Artifacts, and Quality Analysis Beyond PSNR
Video compression technology is rapidly evolving, with end-to-end learned Neural Video Codecs (NVCs) emerging as powerful alternatives to traditional, block-based standards. This paper presents a comparative analysis of two traditional codecs, HEVC and VVC, against two leading NVCs, DCVC-FM and DCVC-RT. We conduct a thorough rate distortion analysis using multiple objective metrics (PSNR, SSIM, VMAF, and LPIPS) and introduce a formal method to identify rate-matched operating points for fair visual comparison. Our results demonstrate that NVCs offer substantial gains not only in signal fidelity but also in perceptual quality, achieving up to 37.82% bitrate savings over HEVC. Furthermore, our analysis reveals fundamental differences in distortion patterns: NVCs excel at preserving structural and color fidelity, producing visually pleasing results free of blocking artifacts, but can sometimes smooth over fine textures. Conversely, traditional codecs are prone to blockiness but can occasionally retain more high-frequency detail. These findings confirm the superior efficiency of NVCs and highlight the need for evaluation methodologies that look beyond PSNR.
A Hybrid Approach to Recommender Systems Based on Textual Reviews and Collaborative Filtering
Recommender systems are increasingly leveraging textual reviews to enhance user preference modeling. Although review-aware recommender systems (RARs) have shown advances in capturing semantic nuances, they often fall short in representing collaborative patterns among users and items. In contrast, traditional collaborative filtering methods, though limited in semantic modeling, are robust in exploiting structural relationships. In this study, we propose a hybrid approach that integrates semantic representations derived from RARs into traditional collaborative models. We evaluated this strategy across three Amazon datasets, comparing RARs, collaborative, and hybrid models in terms of preference modeling accuracy (MSE and MAE), and recommendation relevance (Hits, Precision, Recall). Our results showthat hybrid models outperform both baselines, achieving up to 2.8 times lower prediction error and 60% higher precision. These findings highlight the potential of hybrid models to deliver more robust and context-aware recommendations.
Using Temporal and Semantic Features to Detect Events in Urban Violence Videos
Videos published on platforms such as YouTube play a central role in covering urban violence events, but the high lexical similarity between different occurrences and the temporal proximity of postings hinder the automatic identification of which videos refer to the same event. Previous approaches predominantly explore semantic characteristics extracted from video, audio, and metadata, often relying on supervised techniques that require high computational costs and extensive annotations, making them impractical for continuous large-scale monitoring. Addressing this problem is of great importance for applications such as integrating multimedia records in investigations, fact-checking, assessing the impact of events, and building reliable historical archives. This work proposes two unsupervised and complementary heuristics: one based on temporal characteristics and anchor entities extracted via named entity recognition, and another based on multiple semantic attributes integrated into a similarity graph. We evaluate these approaches on a novel dataset of more than 1,400 manually annotated videos, collected between 2019 and 2024, covering both low- and high-impact events. Results show that the temporal heuristic consistently outperforms both the semantic heuristic and GPT-4 as a baseline, achieving accuracy up to 0.90 and NMI up to 0.98, while the semantic heuristic performs better in sparse-event scenarios. We also find that including full transcripts brings no substantial gains, indicating that titles and descriptions already contain the most relevant information for the task. These findings reinforce the potential of simple, low-cost, domain-adapted solutions to outperform generic approaches in challenging video clustering scenarios.
Visual Foundation Model-Based Classification of Characters in Narrative Media
The field of narratology has long worked on different ways to classify characters according to their narrative role and importance; meanwhile, studies on character design have observed the existence of unclear patterns that correlate specific visual features and those narrative classifications. Therefore, it becomes worth considering if modern image classification methods can be used to not only achieve satisfactory performance on this task, but also help us learn about the patterns that shape it. The task of classifying characters carries a vast array of practical uses, such as creating recommender systems based on specific visual-narrative correlations, automatic annotation for media preservation, adaptive interactive storytelling based on how a character is perceived, and the creation of education tools on how those patterns change depending on culture. In this work, we construct a dataset customized to assessing both performance and visual–narrative correlations within a specific medium, conduct an anonymous survey to evaluate human performance and tendencies, and compare the results with those of a traditional CNN classifier and a Foundation Model–augmented approach. Both models outperform the survey participants, with the latter achieving 76.93% accuracy, 18.11% higher than human performance, while also providing insights into the visual cues most closely tied to the given narrative roles.
Where Next? A Behavioral and Explainable Framework for City and Neighborhood Recommendation
Understanding why individuals choose to visit particular cities and specific neighborhoods within them is essential for advancing both urban mobility research and personalized tourism technologies. This paper proposes a novel multi-level (city and neighborhood levels), explainable recommendation framework that models user interest based on area similarities across geographic, demographic, cultural, and venue-category dimensions. Our approach predicts user interest through a behaviorally informed, interpretable machine learning model. Using large-scale review data from Google Places, enriched with U.S. Census, political, and cultural indicators, we analyze mobility through the lens of high-interest and lowinterest divisions and two behavioral archetypes: returners (who repeatedly visit familiar areas) and explorers (who seek out new destinations). Results show that explorers are more interested in geographically clustered cities, suggesting a search for new experiences in nearby locations. In contrast, returners attach to areas that align with their past experiences (e.g., venue categories). Beyond good predictive performance, our system provides natural-language explanations for each recommendation, offering actionable insights into user behavior. A demonstration system illustrates how our approach enables transparent, behavior-informed travel recommendations. This work bridges gaps in urban AI by integrating spatial granularity, behavioral segmentation, and explainability.
4D-STDF: Compressed Video Quality Enhancement with 3D Spatio-Temporal Fusion and Deformable Convolution
Compressed videos frequently present artifacts that compromise visual quality. Deep learning models have demonstrated significant effectiveness in mitigating such distortions. In this study, we introduce 3D-STDF, an architecture based on the well-known Spatio-Temporal Deformable Fusion (STDF) and augmented with 3D convolutions to more effectively model temporal dependencies across video frames. Furthermore, we refine the Quality Enhancement (QE) module by integrating residual blocks, thereby enabling the extraction and representation of more intricate spatial features. Experimental results indicate that models based on the 3D-STDF architecture achieved an overall average improvement of up to 0.607 dB in PSNR, clearly outperforming previous STDF-based solutions.
A Hardware Architecture for Smoothing Filters in H.266/VVC Intra-Prediction
This paper presents a hardware architecture for the preprocessing smoothing filters used in the intra prediction module of the H.266 Versatile Video Coding (H.266/VVC) standard, applicable to both the encoder and decoder sides. The design was fully described in VHDL and synthesized to an Intel Cyclone V FPGA, utilizing 6,961 adaptive logic modules and 4,158 registers, demonstrating feasibility for FPGA implementation with room for integration with a larger intra prediction module. Operating at a maximum frequency of 96.3 MHz without pipelining, the architecture achieves a decoding throughput of up to 2,972 frames per second (fps) for the 1080p resolution, 743 fps for UHD 4K, and 185 fps for UHD 8K, showing that a much lower clock operation can be used for real-time performance. For encoding, the throughput was estimated by targeting a 60 fps rate at 1080p resolution, resulting in an upper bound of approximately 3,171 CUs evaluated per CTU, compatible with fast decision algorithms used in practical encoders.
A Lightweight I3D-Based Approach for Real-Time Brazilian Sign Language Recognition
The demand for accessible technologies to support the deaf and hard-of-hearing community in Brazil is significant. However, many state-of-the-art deep learning models are too computationally intensive for practical, real-time applications. This study addresses this gap by proposing an efficient, lightweight pipeline approach for isolated Brazilian Sign Language (LIBRAS) recognition. We fine-tune a pre-trained Inflated 3D ConvNet (I3D) model on the MINDS-Libras dataset using an end-to-end methodology that operates directly on raw RGB videos, circumventing the need for heavy pre-processing steps like skeleton extraction. To ensure a realistic evaluation of the model’s generalization capabilities, we adopt a strict signer-independent protocol, where test subjects are completely unseen during training. Our proposed model achieves a competitive accuracy of 92.5% and is able to perform sign recognition in real-time, demonstrating strong performance comparable to more complex architectures. This work establishes a new, robust benchmark for signer-agnostic LIBRAS recognition, highlighting that an end-to-end approach can effectively balance high accuracy with the pipeline efficiency required for deployable, real-world accessibility tools.
Enhancing Recommender System Personalization Through Multicategory Calibration
Recommender systems (RSs) are widely used to personalize suggestions based on users’ historical data. Despite recent advances, many RSs still prioritize accuracy metrics, often generating nonpersonalized lists that fail to reflect users’ actual interests. Calibration strategies have been proposed to address this misalignment by tailoring recommendations to users’ profiles. However, most approaches consider only a single category (e.g., genre), limiting their ability to capture the multifaceted nature of user preferences. This paper proposes a multi-category calibration method that simultaneously incorporates multiple categories extracted from knowledge graphs. The approach aims to balance recommendation relevance with alignment across diverse user profile dimensions. We evaluate the method on two well-known datasets, MovieLens and LastFM, using two contrasting recommendation algorithms: NCF (neural) and BPR-MF (non-neural). Results show that multicategory calibration can improve alignment with multifaceted user preferences while maintaining or enhancing accuracy, fostering fairer, more personalized, and more relevant recommendations.
A Zero-Shot Prompting Approach for Automated Feedback Generation on ENEM Essays
Automated Essay Scoring (AES) has made significant progress in evaluating written texts, but the generation of constructive feedback for essays, particularly in Portuguese, remains underexplored. This paper proposes a zero-shot prompting approach to automatically generate feedback for ENEM essays, an essential component of formative assessments.We evaluate several Large Language Models (LLMs) – Gemini 2.0 Flash, Sabiá 3, Llama 3-8B, and Qwen 3-8B – to provide feedback on five competencies defined by the ENEM scoring rubric. Using the AES-ENEM dataset, we prompt the models to generate feedback for each competency, comparing their semantic similarity with human feedback using the BERTScore metric. Additionally, a linguist with expertise in ENEM essays evaluates the feedback for its constructiveness and informativeness. Our results demonstrate that while the models perform similarly in BERTScore evaluations, the Qwen model produces the most informative and constructive feedback. This work contributes to the development of automated systems that not only grade essays but also assist in improving student writing skills, potentially reducing teacher workload in large-scale assessments.
Analysis of the Compression Efficiency of the HEVC Encoder Embedded in the Samsung Galaxy S23 Plus Smartphone
The consumption of video content on mobile devices is becoming increasingly integrated into people’s daily routines. The intensive transmission of video between devices requires smartphones to include dedicated embedded hardware capable of performing real-time video encoding and decoding. Due to the high computational demands of the video encoding process, dedicated hardware designs must implement various resource-constraining strategies to reduce computational complexity. These constraints inevitably impact the rate-distortion performance, leading to a loss in coding efficiency. This study aims to analyze the constraints and their effects on the coding efficiency of the Qualcomm Snapdragon 8 Gen 2 chipset, embedded in the Samsung Galaxy S23 Plus smartphone. The results of this work can help hardware designers assess whether the coding efficiency losses introduced by their solutions align with those observed in commercial video encoders.
Linguistic Analysis of Human and Language Model Comments on Posts in Brazilian Reddit Communities
In this work, we analyze the linguistic differences between comments written by humans and those generated by large language models in social support communities on Reddit. Overall, the models tend to better align with the semantic content and linguistic style of the posts and are generally more informative, but their responses are often repetitive and harder to read. In contrast, human comments are typically more concise, diverse, and include personal experiences. Additionally, differences across communities suggest that the type of support being sought influences writing patterns.
Computational Approaches for Simplifying Educational Texts: A Proposal Using spaCy
This work presents an investigation into the application of Natural Language Processing (NLP) techniques for the automatic simplification of educational texts in Brazilian Portuguese. The study uses the spaCy library with the pt_core_news_sm model to perform syntactic analysis, named entity recognition, and textual readability assessment. The proposed methodology implements simplification rules based on syntactic dependency analysis, preserving essential elements such as the subject and main predicate while removing complex subordinate constructions. The results show that named entity analysis was effective in identifying people (PER), locations (LOC), organizations (ORG), and miscellaneous elements (MISC) in the analyzed texts. The original texts presented Flesch Reading Ease scores ranging from 25.23 to 54.57, indicating different levels of complexity. This research contributes to the advancement of automatic text simplification techniques in Portuguese and offers insights for the development of more accessible educational tools.
CoTaS@Home: A context-of-things provisioning solution for smart homes as a service
Internet of Things applications in smart environments rely on data from smart objects, particularly on the context of things. To provide the academic community with a service for context provisioning, this paper introduces CoTaS@Home. An architectural model for a Context-as-a-Service system is presented, along with its partial implementation through modeling and simulation. Furthermore, a smart home environment was established with five scenarios for metrics analysis. Preliminary results indicate that the absence of context management negatively impacts the complexity for applications to discover smart objects with the required profiles as well as the network’s scalability.
Detecting Evaluative Inconsistencies in E-Commerce with LLMs: A Case Study on Amazon Brazil
This study evaluates the ability of large language models (LLMs) to detect incoherence between the text of product reviews and their assigned rating (1 or 5 stars). Using ChatGPT-o3 in five independent runs, we observed high variability in labeling and low overall agreement (Fleiss’ 𝜅 = 0.177). A conservative approach selected 415 reviews unanimously labeled as incoherent, which were subsequently submitted for human evaluation. The agreement between human annotators was substantial (Cohen’s 𝜅 = 0.709), allowing the isolation of 231 cases with a clear sentiment judgment. The comparison showed that only 28.1% of the LLM classifications matched the human judgment. These results suggest that, while promising, LLMs still require rigorous validation and careful calibration for critical semantic interpretation tasks.
Discourse Dynamics on X and Bluesky Amid Brazil’s 2024 Environmental and Political-Digital Crises
This study presents a comparative analysis of discourses on the 2024 environmental crisis in Brazil across the platforms X and Bluesky, amid a parallel political-digital crisis triggered by X’s temporary suspension in the country. Over 170,000 posts collected between July and November 2024 were analyzed. Language models were employed for semantic filtering, topic modeling, and sentiment and toxicity analysis. Results indicate that Bluesky’s discourse was more technical and institutional, dominated by governmental and progressive actors, whereas X fostered popular and highly polarized narratives, with ideological disputes over climate responsibility.
Fast AV1 LocalWarped Motion Compensation Using Machine Learning
The growing demand for high-efficiency video compression has driven the development of advanced codecs like AV1, which achieve superior compression rates but face challenges related to computational complexity. This paper addresses these challenges by proposing a machine learning-based optimization for the AV1 Local Warped Motion Compensation (LWMC) tool. This solution uses a Decision Tree model to skip unnecessary LWMC executions, reducing its processing time by 52% while maintaining a low impact on coding efficiency of only 0.21% in BD-BR. Compared to complete LWMC deactivation, our method demonstrates significantly better performance, particularly for content with complex motion patterns. To the best of the author’s knowledge, this is the first work in the literature to explore machine learning-based solutions applied to the AV1 LWMC tool.
Fast VVC Angular Intra-Prediction for 360° Videos Based on Decision Trees
The rapid growth of immersive media has positioned 360° videos as a key component in applications such as virtual reality, remote education, tourism, and interactive entertainment. But the massive data volume required to represent 360° videos imposes significant computational challenges to deliver this type of content across the high diversity of current and future devices with support for multimedia. Then, highly efficient video encoding algorithms are required in this scenario. Versatile Video Coding (VVC) is the current state-of-the-art standard for video compression, offering specialized tools for efficient 360° video coding. This paper introduces a Machine Learning-based approach to reduce the computational effort of VVC Angular Intra-Prediction (AIP) tool when encoding 360° video content. The proposed method uses a Decision Tree model to adaptively skip vertical prediction modes in the AIP process. Experimental results show an average encoding time reduction of 10.11% with only a 0.66% impact on coding efficiency. To the best of our knowledge, this is the first work to explore the use of Machine Learning to reduce the computational effort of AIP for 360° videos.
Ubiquitous Vehicle Monitoring in Urban Environments: A Case Study in Salvador Using SDR and ADS-B
The growing demand for urban land mobility solutions has driven the development of vehicle tracking tools as a promising alternative for urban public transport in several parts of the world. A comprehensive literature review, using the Methodi Ordinatio, was carried out through a systematic search in electronic databases such as ScienceDirect, IEEE Xplore, Scopus, and Web of Science. Some articles were selected for a literature reviewthat addresses aspects of vehicle traceability applied in the context of public transport. We propose an approach based on SDR (Software Defined Radio) technology using ADS-B (Automatic Dependent Surveillance-Broadcast), the paper presents the results for a preliminary validation, considering a case study in the city of Salvador, Bahia.
Performance and Energy Consumption Evaluation of the VVC ALF Classification Step Across Different Programming Paradigms and Hardware Platforms
The Adaptive Loop Filter (ALF) in the Versatile Video Coding (VVC) standard enhances visual quality but introduces a significant computational burden, posing a challenge for its implementation on resource-constrained devices. This paper presents a comprehensive evaluation of the ALF classification step, analyzing its execution time and energy consumption across multiple programming paradigms (scalar, SIMD, CUDA) on both a high-performance desktop and an embedded platform. Results show that on the high-performance desktop, SIMD optimization provides a substantial speedup over the scalar baseline with lower energy consumption, while the discrete GPU’s performance is limited by data transfer overhead. Conversely, the embedded system demonstrates a different landscape: its ARM CPU offers superior energy efficiency when compared to the desktop, and its integrated GPU’s runtime is dominated by kernel execution rather than data transfers. These findings underscore that the optimal ALF implementation is highly platform-dependent, hinging on the specific design trade-offs between processing speed and energy efficiency.
Computer vision application for analyzing weight training exercises: use of Mediapipe library with Blazepose model and a cross-platform Web architecture
This research presents the development of a Proof of Concept for the use of the MediaPipe library and the BlazePose model in creating a computer vision application aimed at monitoring the execution of strength training exercises. The system is built on a web-based, cross-platform architecture, and all image analysis is done in the Front-end layer by using a Javascript shell. The results were satisfactory, and there are clear goals for future implementations to evolve the solution into a robust and viable product.
Similarity Detection in Android Screen: a comparative analysis using Information Retrieval, Embeddings and Vision Language Models
The growing mobile application development market brings an urgent need for tools capable of evaluating and comparing similarities between screens of different applications. This article proposes a comparative analysis of methods for similarity detection on Android screens, integrating Information Retrieval (IR), embedding models (BGE-M3, Snowflake-Arctic-Embed2, Nomic-Embed-Text) and Vision Language Models (VLM) such as qwen2.5vl:7b and gemma-3:27b. The study addresses challenges like design redundancies and UI test inconsistencies by using combined pipelines: IR, embeddings, and VLM for screen identification. The results demonstrate the strengths and limitations of each approach, providing insights into their applicability in mobile UI similarity detection.
Toxicity and Triggers: A Case Study in Reddit Communities in Brazil
In this work, we aim to understand linguistic features that may indicate potential triggers fostering toxic behavior among users in Portuguese-speaking Reddit communities. Our findings show that discussions involving such triggers tend to concentrate political terms and insults, shift topics more frequently, and make greater use of subordinating conjunctions.
A Study on the Use of LLM Prompts for Evaluating Web Accessibility of City Hall Portals in the Sertões dos Crateús Region
This study is an initial investigation into the use of Large Language Models (LLMs) with prompts written in Portuguese for web accessibility evaluation, using a set of 13 municipal government portals as a case study. ChatGPT 4o was employed to verify 37 success criteria from WCAG 2.2, and its coverage was compared with that of the automated inspection tools IBM, Achecker, andWAVE. Preliminary results indicate that the LLM provides positive contributions as a support tool in the web accessibility verification process.
AI-Based Approaches for Brazilian Sign Language Recognition: A Systematic Literature Review: Insights on Methods, Metrics, and Resources for LIBRAS Recognition
This Systematic Literature Review (SLR) investigates the application of Artificial Intelligence (AI), specifically with Computer Vision (CV) and Natural Language Processing (NLP) techniques for Brazilian Sign Language (LIBRAS) recognition, translation, and interpretation. Following the PRISMA protocol and using the Parsifal tool to support protocol definition and study management, 77 studies were initially identified. After removing duplicates and applying exclusion criteria, 23 studies were selected for in-depth analysis. The review addresses three research questions: (i) AI-based solutions currently employed for LIBRAS recognition, (ii) reported performance metrics and evaluation results, and (iii) datasets used for training and testing, along with their characteristics and limitations. The findings highlight advances in deep learning architectures and computer vision pipelines, while revealing persistent challenges such as limited integration of non-manual features, scarcity of large-scale and multimodal datasets, difficulties in continuous sign recognition, heterogeneous annotation practices, and inadequacy of common bilingual evaluation metrics for capturing syntactic and semantic differences between LIBRAS and Portuguese. Moreover, deployment-related factors, including inference time and computational cost, remain underexplored. These insights provide guidance for future research toward more accurate, efficient, and inclusive LIBRAS recognition systems.
Converting Natural Language to Query Languages Using Large Language Models: A Systematic Literature Review
The task of converting natural language commands into query languages (NL-to-QL) has gained attention due to its potential to improve data accessibility for non-technical users. Although several studies have explored rule-based and sequence-to-sequence models, there is still a lack of a literature review that presents the impact of using large language models (LLMs) on this task. As an output of a systematic literature review, this paper examines how recent studies have utilized LLMs by applying fine-tuning or prompt engineering techniques to address this task. Presenting a compilation of methods, architectures, and techniques, as well as evaluation metrics, datasets, and benchmarks applied, including the available competitions and educational platforms, it provides a comprehensive overview of NL-to-QL conversion, mapping current advancements, future research directions, and remaining challenges, including issues with schema generalization, query interpretability, and hallucination mitigation.
Cost Reduction Strategies in Cloud Computing from the User Perspective: A Systematic Mapping
Cloud-based systems operate under a pay-per-use pricing model, which brings challenges related to budget cost management. Cloud financial management, anchored in FinOps principles, balances performance with budget control. Existing cloud cost management solutions have explored different approaches, such as resource requirement estimation, historical data analysis, and automated scaling decisions, which are already widely adopted. However, existing studies still lack a comprehensive view of cost optimization strategies, which vary between user and provider perspectives, and there is no way to classify or choose the most appropriate approach for every scenario. In this context, this paper proposes a systematic mapping study covering cloud cost reduction strategies from the user’s perspective. In particular, a complete methodology approach was defined by formulating detailed research questions, using carefully crafted search strings in scientific databases, and establishing organized by and structured by (processing, storage, network), strategy, type strategy, service type, cloud type and resource. Finally, emerging trends, research gaps, and practical implications for industry and academia are discussed.
Authoring Tools for Digital TV Applications Aimed at Content Producers: A Systematic Literature Review
This systematic literature review aims to identify the main functional and non-functional requirements for the development of authoring tools for interactive digital TV content, targeting nonprogrammer content producers, in the context of the transition from channel-oriented TV to the application-oriented platform of TV 3.0. Based on a search across seven academic databases, 1,642 studies published between 2007 and 2025 were retrieved. After screening and applying inclusion criteria, 106 studies were selected, and 21 met the quality threshold for final review. Findings indicate that most proposed tools are designed for programmers or users familiar with programming languages such as NCL/NCLua, with only one solution explicitly targeting non-programmer content producers. Common functional requirements include visual editing interfaces, reusable templates, multimodal interaction configuration, application preview, automatic validation, and export to NCL/NCLua compatible with the Ginga middleware. Non-functional requirements emphasize usability for non-programmers, adherence to SBTVD standards, modularity, and cross-platform portability. However, significant gaps remain, particularly the lack of tools integrated into early editorial workflows and the limited support for collaborative authorship and content management systems.
Investigating LLM-based tools to support Usability, Accessibility, User Experience in HCI activities: A Systematic Literature Mapping
This study presents a Systematic Literature Mapping (SLM) of recent research on the application of Large Language Models (LLMs) within Human–Computer Interaction (HCI). Drawing from 104 peer-reviewed articles published between 2021 and 2025 in the ACM Digital Library and IEEE Xplore, the mapping investigates the role of LLMs in supporting usability, accessibility, and user experience (UX) activities. LLMs, rooted in Natural Language Processing (NLP), have demonstrated widespread utility across various domains, including healthcare, education, and software engineering. Within HCI, their applications include conversational agents, accessibility enhancements, user journey modeling, prototyping tools, and adaptive user interfaces. The findings highlight how UX professionals have integrated LLM-based solutions into design workflows, leveraging these technologies for improved engagement, personalization, and collaboration. This research addresses 4 guiding questions concerning the domains of application, methodologies, and practitioner adoption of LLMs. The study contributes by (i) offering a comprehensive mapping of HCI-related LLM literature, (ii) identifying areas of active research, and (iii) uncovering emerging trends and open challenges that may inform future investigations.
Francisco Carlos de Mattos Brito Oliveira
I’m Not Welcome Here: A Scoping Review on Toxicity and Hate Speech in Digital Games
Hate speech and toxic behaviors are increasingly common in digital games, affecting player wellbeing, diversity and community engagement. Although research on this topic has increased in recent years, studies remain fragmented and largely focused on the North American and European contexts. This study presents a scoping review of the scientific literature on hate speech in games, covering publications from 2010 to 2025, including international and Brazilian research. A search of four electronic library sources, complemented by manual searches, retrieved 8,040 records. After filtering and screening using predefined criteria, 49 studies were selected for full-text analysis. The review presents the investigated associations, detection approaches and intervention strategies, consolidates the scattered results and identifies areas for further investigation. Among these observations is the relative scarcity of research focused on Brazilian gaming communities, suggesting opportunities for more regionally contextualized studies in the future.
The Phenomenon of Disinformation on WhatsApp, Telegram, and Other Instant Messaging Platforms: A Systematic Literature Review
This literature review examines the current state of research on misinformation within Instant Messaging Platforms (IMPs) such as WhatsApp, Telegram and WeChat. By conducting comprehensive searches across multiple repositories, we identified 110 relevant studies published between 2018 and 2025, covering more than 20 countries and 7 distinct messaging platforms. These works were systematically analyzed to: (i) map internal and external factors of IMPs that facilitate the creation and spread of misinformation; (ii) identify its main impacts and negative consequences reported to our society; and (iii) examine strategies proposed or implemented to mitigate and to combat misinformation within this environment. Our findings reveal recurrent themes across studies employing diverse methodologies, indicating a growing convergence in this still-fragmented research field, while also exposing points of disagreement. The analysis highlights how specific architectural features make IMPs uniquely susceptible to misinformation. Unlike mainstream social networks, their information flow is governed directly by user interactions rather than feed algorithms. In this context, end-to-end encryption, high virality, and limited moderation or fact-checking emerge as internal components that sustain the misinformation ecosystem. Externally, factors such as low digital literacy, social polarization, and political bias further reinforce the problem. Notably, some elements such as distrust in institutions and political polarization are reported both as causes and as consequences of misinformation, suggesting a potential self-reinforcing cycle. Crucially, our review shed light on a paradoxical communication architecture in IMPs, where encrypted and seemingly personal messages coexist seamlessly with large-scale, viral dissemination. This duality blurs the distinction for users between intimate communication and mass circulation, making it harder to assess the origin and credibility of information.
Systematic review: gamification on smartphones for children in educational and therapeutic contexts
Caregiver concerns about children’s excessive mobile device use potential include impacts on mental health, physical well-being, social development, and academic performance. Statistics like 60% of under-12s spending most of their time playing on phones, and 71% of Brazilian children aged 0-12 are playing games on smartphones contextualized caregiver’s concerns. Parents perceive the use of smartphones are unproductive and detrimental to children development. However, mobile technology in serious games may offer a productive and stimulating way to engage children and adults, ranging from academic learning to therapeutic support. Thanks to their effectiveness and accessibility, research on the use of mobile devices and serious games has gained significant prominence. This work is dedicated to surveying scientific productions in the literature on the use of mobile devices and serious games in educational and therapeutic contexts. In this article, a systematic review of works focused on the use of gamification in mobile devices for children in educational and therapeutic contexts is presented. The objective is to evaluate the extent to which this area has been explored and to understand the potential of using technological resources to aid in the development of these activities. The initial search across seven digital libraries, using filters for open-access publications between 2019 and 2024, yielded a total of 104 articles; however, only 8 were selected after removing duplicates, applying exclusion and inclusion criteria, screening by title and abstract, and full-text review. This review finds that techniques like rewards, feedback, and visual narratives effectively boost engagement and adherence. The study emphasizes tailoring gamification to task nature and combining extrinsic and intrinsic motivators. However, limitations include small sample sizes, short intervention durations, and methodological heterogeneity, hindering generalizability and long-term efficacy conclusions. Future research should involve larger, longer-term randomized trials and focus on specific populations.
Systematic Mapping of Fault Monitoring Techniques Based on Data Mining
The increasing complexity and interconnectivity of modern software systems have made fault detection a significant challenge. Traditional testing techniques often fall short, especially in dynamic and large-scale environments. To address this issue, various real-time monitoring approaches based on data mining have been proposed. These approaches, through continuous system analysis and the use of anomaly detection techniques, are capable of identifying faults in software systems. This work presents a systematic mapping of the literature on these monitoring techniques, aiming to identify implementation challenges and provide a comprehensive overview of the current state of research. The mapping was conducted using the Scopus database and 84 studies were selected to help answer the research questions defined in this work. This analysis identified the main fault detection techniques, such as neural networks and Support Vector Machines (SVMs), the types of systems monitored, including Cloud and Big Data platforms, common fault categories like cybersecurity and performance, and the evaluation metrics used, such as the F1-score and prediction time. Despite these established approaches, the study also revealed significant challenges that hinder progress, including the difficulty of labeling data for supervised models, the complexity of interpreting the generated models, and the need for computationally efficient monitoring.
When Art meets Computer Science: a systematic review about technologies and user interaction in adaptive digital museums and archives
The digitization of artistic productions, or their creation in a natively digital format, has expanded their scope beyond original intentions, bringing them into close interaction with software-related aspects. In this context, the dissemination, preservation, and access to such works, especially within digital museums and digital archives, are inevitably influenced by the quality of the underlying software. Considering the inherent challenges at the intersection of Digital Humanities and Computing, particularly the need for user-centered and customizable experiences to effectively manage large, heterogeneous collections, this article presents a systematic literature review focused on adaptive, personalized, and customizable digital museums and archives. The review aims to identify the main purposes of these systems, the technologies employed in their construction and maintenance, and the interaction approaches proposed for users. Using a systematic protocol, searches were conducted across ACM Digital Library, IEEE Xplore, Science Direct, and Scopus, resulting in the analysis of 21 studies. Based on the analysis of studies, we discuss trends, challenges, and opportunities for the design and development of adaptive digital museums and archives, such as the limited incorporation of Human-Computer Interaction and Software Engineering principles and the need for greater standardization in the development of adaptive digital archives.