Anais da Brazilian Conference on Intelligent Systems (BRACIS)
Sociedade Brasileira de Computação - SBC
Este documento traz o Front Matter dos Anais do BRACIS 2025.
A Categorical Kalman Filter for Human Activity Recognition
Human Activity Recognition (HAR) has gained increasing attention in recent years due to its wide range of applications in health, sports, and surveillance. The ubiquity and unobtrusive nature of inertial measurement unit (IMU) sensors in consumer devices have contributed to the growing popularity of IMU-based HAR systems. However, many existing models lack transparency in their decision-making processes—an essential aspect for both users and health professionals. In this work, we propose a human activity classifier based on the Kalman filter, designed to be interpretable and efficient using IMU data. We evaluate its performance on standard HAR datasets and compare it against classical benchmark classifiers such as logistic regression and support vector machines. Experimental results demonstrate that the proposed model offers a viable and competitive alternative for classifying human activities.
A Comprehensive Study of Fitness Landscapes in AutoML: The Impact of Dataset Complexity and Search Space Neutrality
The search space in Automated Machine Learning (AutoML) consists of numerous combinations of machine learning pipelines. Understanding how optimal solutions are distributed within this space is key to improving search strategies and Fitness Landscape Analysis (FLA) provides useful tools for this purpose. However, traditional FLA metrics often fail to capture the complexity of AutoML tasks due to their high dimensionality and interdependencies. This study investigates the relationship between dataset complexity characteristics and FLA metrics in AutoML. While previous research has hinted at this connection, no comprehensive analysis has been presented. We focus on the concept of neutrality, which refers to regions where small pipeline configuration changes lead to minimal fitness changes. This property plays a critical role in the effectiveness of search algorithms. Our results reveal statistically significant correlations between neutrality and specific dataset complexity measures, particularly those related to class overlap and feature difficulty. These insights enhance the understanding of AutoML search space dynamics and support the development of more informed and adaptive optimization strategies.
A Generative Domain Adaptation Scheme for Swift Deployment of Parking Monitoring Systems
Deep learning models have demonstrated remarkable accuracy in distinguishing between empty and occupied parking spaces when large amounts of annotated training data are available from the target environment. However, in real-world deployments, the major bottleneck lies in the labor-intensive annotation process required whenever a new scenario arises, or retraining is needed due to changes in the camera setup, often driven by maintenance, repositioning, or environmental conditions. This paper addresses this challenge by proposing a generative domain adaptation scheme designed to reduce annotation requirements and accelerate deployment significantly. Instead of relying on extensive labeled datasets and computationally expensive model retraining, our method synthesizes new training samples based on a small subset of instances from the target domain. In particular, by combining generative augmentation with a lightweight convolutional network for inference, our approach achieves a favorable balance between annotation cost, computational efficiency, and accuracy. These results highlight the method’s potential as a cost-effective and rapidly deployable solution for real-world parking lot monitoring. Under a cross-dataset evaluation protocol, we highlight that our approach achieves competitive accuracy (close to 97%) using as few as 256 labeled samples, thus substantially reducing human annotation effort without sacrificing classification performance.
A Multidimensional Analysis of Swarm Dynamics in a Reinforcement Learning-Based Metaheuristic
Traditional assessments of metaheuristics typically focus on performance metrics when solving benchmark problems, with limited attention to the underlying mechanisms that drive collective intelligence. This performance-centric approach fails to indicate why certain metaheuristics are successful in specific scenarios and provides minimal guidance for improving their design. To better understand these inner dynamics, we conducted a multidimensional analysis of the swarm dynamics in a Reinforcement Learning-based metaheuristic that can combine search behaviors from different Swarm Intelligence metaheuristics. Using Interaction Networks, Search Trajectory Networks, Improvement Frequency, and Population Turnover metrics simultaneously, we reveal how the RL approach dynamically switches between different swarm behaviors depending on the problem. Our results demonstrate that the RL-based metaheuristic matches the performance of the most effective specialised algorithm for each benchmark function by adaptively mimicking its social interaction patterns, search trajectories, and fitness development. We show that for the F1 function, the RL approach adopts behaviors similar to GWO, while for the F2 function, it transitions to GPSO-like patterns, aligning with the expected best-performing characteristics reported in the literature. This multidimensional approach provides insights into how adaptive metaheuristics combine different behaviours at optimisation stages, suggesting a pathway toward self-adaptive metaheuristics that automatically select optimal search strategies according to the simulation scenario.
A NAS-Optimized Deep Learning Model for Customer Churn Prediction in Financial Services
Deep Learning (DL) methods offer competitive alternatives for Customer Churn Prediction (CPP) but are challenging to optimize due to their complexity. This study introduces STArS (Sequential Temporal Neural Architecture Search), a Genetic Algorithm-based Neural Architecture Search (NAS) method, to optimize Temporal Convolutional Neural Networks (TCNN) and Long Short-Term Memory (LSTM) models. The optimized STArS models outperform traditional models, such as Random Forest and XGBoost, as well as an empirically developed TCNN. The research also highlights the importance of feature analysis for retention strategies, using a real dataset from a financial services provider. This demonstrates the practical applicability of NAS techniques across multivariate time series datasets.
A New Approach for Obtain Reduced Sets in Least Squares Support Vector Machine: Lengthen via Levenberg-Marquardt
In this work, a new approach for obtaining reduced sets for training least squares support vector machine (LSSVM) in binary classification tasks is presented. The methodology consists of carrying out a vector quantization procedure, forming clusters in the original feature space, subsequently it is determined which of the clusters presents greater heterogeneity, that is, which one presents more patterns of distinct classes. Starting from the premise that the patterns with the greatest chance of being support vectors are those closest to the decision boundary, the patterns from this cluster are used as candidates for initial support vectors and then the feature space is divided into the most heterogeneous cluster and its complementary. Then, using only complementary set, the LSSVM problem is formulated and solved iteratively via the Levenberg-Marquardt algorithm. This algorithm, in each iteration, considers the lagrange multipliers with the largest absolute values and transfer them from the complementary set to the most heterogenous one. This process continues until the stopping criterion is met. This results in a reduced set formed by the final support vectors for the LSSVM containing both the closest and furthest patterns from the decision boundary which carry great significance for the model. Numerical simulations are conducted to demonstrate the predictive capacity of the proposal, as well as its efficiency in terms of processing time.
A Note on Learning Algorithms as Approximations of Solomonoff Induction
We present a theoretical result showing that a learning algorithm can be seen to be an approximation of Solomonoff induction. A constant multiplicative bound between the predictive probability of a learning algorithm and the Solomonoff semi-distribution characterises this approximation. Our main contribution is to state and prove this result concisely, in general, clarifying the theoretical role of algorithmic probability in practical learning. We also establish a positive correlation between the Vapnik Chervonenkis (VC) dimension of a model class and the Kolmogorov complexity of individual hypotheses within it, reinforcing the conceptual alignment between learnability and compressibility. We contextualise these findings by discussing implications on the MDL principle and the emergent behaviour of large language models (LLMs), which empirically approximate algorithmic prediction. An example and comparisons with PAC learning, Bayesian inference, and reinforcement learning are provided.
Adversarial Attack on Large Language Models for the Portuguese Language
Large language models (LLMs) have gained a lot of visibility recently and have been widely used. They revolutionized the field of artificial intelligence by allowing machines to process and reproduce human-like texts in an unprecedented way. In the same way that this technology promotes the creation of innovative applications, from chatbots and virtual assistants to content creation tools and personalized recommendation systems, it also faces major challenges and risks, such as adversarial attacks. Adversarial attacks aim to reveal the vulnerability of deep neural network models, the basis of LLMs, enabling the development of defense methods and thus making these models more robust. This research sought to investigate the robustness of LLMs fine-tuned for the Portuguese language against adversarial attacks, specifically the BERTimbau and Sabiá models. Experimental results demonstrate an attack success rate against the BERTimbau model of approximately 93% for the sentiment analysis task, while an attack success rate of approximately 83% was achieved for the textual entailment task, thus indicating that the model is susceptible to adversarial attacks. For the Sabiá model, a performance drop rate of 6.6% was obtained, which shows it is also vulnerable to adversarial attacks.
Agentic AI Meets Fundamentalist Financial Analysis: Limits and Possibilities
Large Language Models (LLMs) applications in the financial domain offer enhanced efficiency in generating insights and improving accessibility of information to the public. Common applications include sentiment analysis, financial report summarization, and financial database querying. However, despite their potential, LLMs still face limitations in this domain, such as limited up-to-date financial knowledge and insufficient mathematical skills. To address these gaps, recent research has explored agentic frameworks that integrate LLMs with specialized tools—employing Retrieval-Augmented Generation (RAG) for up-to-date knowledge and calculator modules for mathematical skills. Expanding the scope of financial tasks in which agents can be applied, this work investigates the Fundamental Stock Analysis task. Specifically, we examine whether an agentic LLM can extract balance sheet and income statement data from stock press releases and compute a number of key fundamental indicators. We evaluated two LLMs, one open and one proprietary, in three prompting strategies and three agentic frameworks in 19 listed companies in the Energy Sector of the Brazilian Stock Market. Our results highlight the feasibility of automating fundamental analysis with agents, while highlighting the strengths and limitations of current LLM-based agentic approaches. The code and resources are openly available at https://github.com/AIDA-BR/fundamentalist-agent.
AI Agents Powered by Open-Source Language Models for Causal Understanding in Portuguese
Causal reasoning is a key capability for developing more robust, fair, and explainable AI agents. However, the ability of open-source Large Language Models (LLMs) to perform causal reasoning, especially in low-resource languages like Portuguese, remains a significant challenge. This paper introduces two AI agents powered by open-source LLMs: the Causal Classifier Agent and the Causal Judge Agent. For the fine-tuning of the Causal Classifier Agent, we leveraged an expanded version of the Golden Collection with a corpus of 2,500 natural language questions in Portuguese designed to support multilevel causal classification, combined with an enhanced Few-Shot Learning prompting strategy. The results demonstrate that, with targeted training and carefully designed prompts, smaller open-source LLMs can match or even outperform larger models in causal reasoning tasks. In the Causal Judge Agent project, we adopted an enhanced Few-Shot Learning approach and employed an open-source LLM to identify gaps and errors in the fine-tuning process of the first agent. This study highlights the potential of corpus-based fine-tuning and LLM-as-a-judge techniques as cost-effective strategies for improving causal reasoning capabilities in open LLMs.
APVAT: Attention Perturbation in Virtual Adversarial Training for Semi-supervised Learning
A significant portion of the textual corpus lacks the annotated labels needed for training supervised models. Manual labeling of such data is costly and impractical. In response to this challenge, semi-supervised learning (SSL) has emerged as a pivotal approach to harnessing the potential of both labeled and unlabeled data. This paper explores the integration of adversarial perturbation with SSL, particularly focusing on text classification tasks. We propose an innovative approach called APVAT, which incorporates adversarial perturbation into the Attention Virtual Adversarial Training (VAT) model. We performed experiments in five benchmark datasets, exploring the impact of different embeddings such as fastText, GloVe BERT, and GPT-2. Our contributions are twofold: First, our approach improves classification accuracy in datasets with little training data compared to previous methods, even when labeled data is scarce (e.g., 10%). Perturbation augments the training data effectively, leading to efficient model learning and resource savings. Second, our method reduces the time of processing and required training epochs. The findings demonstrate that the fusion of adversarial perturbation in the attention mechanism with SSL, particularly when applied to text classification, offers a promising avenue for advancing the field.
Argumentation Schemes for Stress Inference
Stress arises from interactions between individuals and their environment, often exceeding coping resources and leading to severe consequences, especially in high-risk occupations like intensive care units (ICUs). This study reviews stress assessment models and proposes argumentation schemes for stress inference. Leveraging enthymematic reasoning, these schemes formalize patterns linking contextual, physiological, and psychological factors to stress attributions. Our approach enhances automated stress reasoning, supporting interventions in critical environments.
Boosting OOD Detection in Biomedical Data with Siamese Neural Networks
Out-of-Distribution (OOD) detection is critical for ensuring the reliability of machine learning models, particularly in sensitive domains such as biomedicine. In this work, we propose a novel OOD detection framework based on Siamese Neural Networks (SNNs) trained with triplet loss, aiming to generate discriminative feature spaces where known-class samples are tightly clustered and unknown samples are pushed apart. We introduce the “One-From-Each” triplet mining strategy, which selects hard negatives from each class individually, enhancing the diversity and representativeness of the training triplets. A committee of traditional OOD detectors—including k-Nearest Neighbors, Gaussian Mixture Models, Isolation Forests, and One-Class SVMs—is applied on the learned feature spaces for class-specific OOD detection. Extensive experiments are conducted on diverse biomedical datasets, including OrganSMNIST, TissueMNIST, OCTMNIST, Genomics OOD, and Voice Pathology. Results demonstrate that our method significantly improves AUROC compared to baseline and standard classifiers, with gains of up to 28.9% on OrganSMNIST. While the proposed approach consistently outperforms classical baselines, the results also highlight the difficulty of OOD detection in certain datasets, such as voice pathology. These findings emphasize the potential of feature-embedding strategies for robust OOD detection and point towards promising directions for future research.
BR-TaxQA-R: A Dataset for Question Answering with References for Brazilian Personal Income Tax Law, Including Case Law
This paper presents BR-TaxQA-R, a novel dataset designed to support question answering with references in the context of Brazilian personal income tax law. The dataset contains 715 questions from the 2024 official Questions and Answers document published by Brazil’s Internal Revenue Service, enriched with statutory norms and administrative rulings from the Conselho Administrativo de Recursos Fiscais (CARF). We implement a Retrieval-Augmented Generation (RAG) pipeline using OpenAI embeddings for searching and GPT-4o-mini for answer generation. We compare different text segmentation strategies and benchmark our system against commercial tools such as ChatGPT and Perplexity.ai using RAGAS-based metrics. Results show that our custom RAG pipeline outperforms commercial systems in Response Relevancy, indicating stronger alignment with user queries, while commercial models achieve higher scores in Factual Correctness and fluency. These findings highlight a trade-off between legally grounded generation and linguistic fluency. Crucially, we argue that human expert evaluation remains essential to ensure the legal validity of AI-generated answers in high-stakes domains such as taxation. BR-TaxQA-R is publicly available at https://huggingface.co/datasets/unicamp-dl/BR-TaxQA-R.
Classification with Reject Option: Building Meta-models to Predict Classification Error
Instance Hardness Measures (IHMs) have been adopted in literature to evaluate and understand the difficulty in classifying specific instances within a dataset. Based on this information, the quality of a dataset can be improved at training time (e.g., by instance filtering), thereby refining the learned Machine Learning (ML) models. In this work, we take a different direction, which is to leverage IHMs at deployment time to decide whether the predictions of an ML model are reliable enough to be accepted. In the proposed solution, initially, a set of IHMs adapted in such a way that the actual labels of the query instances are not needed is extracted. This is relevant to estimate the difficulty of instances at testing time, as they are not labeled. Then, we train meta-models to identify whether an instance will be misclassified by the ML model based on the IHM values. A prediction for a test instance is rejected if the meta-model indicates that the instance will be misclassified. To evaluate the viability of the proposal, we perform experiments with two synthetic and two real-world healthcare datasets. This rejection approach improved the reliability of the ML models by increasing the accuracy for the accepted instances.
CLS, Averaging, or Layer Combinations? Embedding Strategies for Text Classification Across BERT Variants
The literature provides inconsistent recommendations on the best method for extracting embeddings from BERT variants when designing a text classifier. Some studies suggest averaging the first and last layers, while others claim that only the CLS embedding is sufficient. Others indicate that averaging the last layer yields good results. To address these conflicting suggestions, we conducted a comparative empirical evaluation on text classification benchmarks: R8, SST2, Movie Review, TREC6 Overruling, R52, TREC6, Snippets, and Ohsumed, using well-known BERT variants, including RoBERTa, DistilBERT, DeBERTa-v3, MPNet, ModernBERT, and BERT. We tested all combinations of these datasets and algorithms with CLS embeddings, First+Last embeddings, and average embeddings from the last layer. We used these results to perform a significance test through ANOVA and a paired t-test specifically for text classification. Based on comparative empirical experiments, the main contributions include findings that clarify the best approach for obtaining embeddings to improve results in text classification, which can impact machine learning practitioners and research studies in this field.
Clustering Explainability: A Systematic Literature Review
The increasing application and complexity of machine learning models have intensified the need for their transparency and explainability. Although much of the research in Explainable Artificial Intelligence (XAI) has focused on supervised models, unsupervised learning contexts, such as clustering, also present an inherent need for explanation. Thus, this work presents a systematic literature review dedicated to the explainability and interpretability of clustering algorithms. The review identifies, categorizes, and analyzes existing strategies for the explainability of clustering algorithms. The systematic review identified 23 related studies published between 2000 and 2025. Based on the analysis of these selected studies, the work highlights the primary techniques employed, emphasizing the prevalence of decision tree-based methods and the growing interest in model-agnostic approaches. The study also examines the primary challenges in the field, including the absence of labels, the inherent limitations of tree-based explanations, and the difficulty in accurately describing clusters with complex structures. As a final contribution, the work discusses future research directions, highlighting the importance of developing new explainability techniques, improving validation methods, and creating more solutions that are independent of the clustering algorithms employed.
Combining Large Language Model Embeddings and Graph Neural Networks for Accurate Job Recommendations
Finding the right job is hard for candidates and platforms alike. With countless postings, candidates struggle to discover relevant roles, while platforms fail to deliver precise matches. This inefficiency calls for smarter AI solutions beyond basic filters. This paper presents a hybrid framework for job recommendation that combines Large Language Model (LLM) embeddings with Graph Neural Networks (GNNs) to improve accuracy in matching candidates to job postings. The proposed model leverages GTE-1.5 to generate semantic embeddings from job descriptions and user profiles, augmented with geographic data, and integrates these with structural information from a bipartite user-job graph using GraphSAGE. The architecture includes a two-layer GraphSAGE network with BatchNorm and ReLU activation, followed by a dot-product decoder for link prediction. Evaluated on the Job Recommendation Challenge dataset, the hybrid model achieves an AUC of 0.97, outperforming text-only (AUC 0.75), structure-only (AUC 0.85), and classical matrix factorization (AUC 0.78) baselines. It also demonstrates superior performance in Precision@K and Recall@K across multiple thresholds. The results highlight the synergy between LLM-derived contextual understanding and GNN-based structural reasoning, offering a robust solution for large-scale job recommendation systems. Limitations and future directions, such as handling cold-start nodes and incorporating explicit location data, are also discussed.
Cost-Efficient Visual Perception for Autonomous Vehicles: Leveraging Attention-Based Sensor Fusion to Maintain Performance
Autonomous vehicles rely on sophisticated perception systems to ensure safe navigation and decision-making. However, state-of-the-art sensor fusion models often demand extensive computational resources, hindering their deployment on cost-effective hardware. In this work, we address this challenge by modifying the BEVFusion framework to significantly reduce computational costs while maintaining high performance in 3D object detection and segmentation. Specifically, we replace the resource-intensive SwinTransformer backbone with the more efficient ResNet50 and integrate attention-based sensor fusion—leveraging channel and spatial attention mechanisms—to dynamically focus on the most relevant features. This approach reduces VRAM usage from 80 GB to approximately 20 GB, cuts training time from 20 d to 6 d, and boosts inference speed by up to 17.3% on lower-power GPUs. Experimental results on the nuScenes dataset demonstrate a 0.732% improvement in mean Average Precision (mAP) for 3D object detection, along with a 14.12% increase in mean Intersection over Union (mIoU) for semantic segmentation compared to the original BEVFusion model. These improvements underscore the feasibility of deploying advanced visual perception systems on more accessible hardware for real-world autonomous driving and mobile robotics applications.
Data Complexity and Instance Hardness Measures in Transferability Evaluation
Advancements in Machine Learning (ML) have driven the development of diverse pre-trained models. Through transfer learning, knowledge from these models can be transferred to other domains for solving challenging problems for which the amount of training data is still scarce. However, there are currently various pre-trained models with different architectures and possibly different source domains where learning occurs. This makes it challenging to choose a particular model for transferability. The hypothesis investigated in this paper is that the data complexity of the embedded target dataset, as represented by the feature representation provided by the pre-trained model, can be used to inform the choice of specific models for new target applications. Specifically, if the representation allows a good separation between the classes, it will be a better option for knowledge transfer than another representation where the classes overlap more. We experimentally demonstrate that specific data complexity and instance hardness measures can effectively evaluate the knowledge transferability of different pre-trained models for new target datasets, enabling the ranking of models for evaluation.
Data Stream Mining to Predict Recommended Actions for Firewall Network Traffic
The advance of the Internet has led to an increase in the continuous generation of data, exacerbating challenges related to cybersecurity. In this context, firewalls play a crucial role in protecting networks from threats, organizing log records based on specific rules that reflect organizational policies. However, updating these rules is complex, and improper configurations or decisions can compromise security. Related works that use traditional Machine Learning approaches often rely on static datasets, requiring new training cycles to incorporate changes, which is not feasible in dynamic network traffic scenarios. In this work, we develop methods to apply the known MINAS and CluStream data stream algorithms to predict recommended actions (Allow, Deny, Drop, and Reset-Both) in network traffic captured by firewalls. While CluStream is an incremental algorithm, MINAS combines incremental learning with novelty detection, essential features in data stream scenarios where patterns can change abruptly or gradually. Experiments conducted demonstrate that both algorithms showed distinct results in the data stream, with satisfactory performance across different metrics throughout the experiments.
Dead-End Discovery and Secure Exploration via Large Language Models
A common issue in stochastic shortest path problems (SSPs) is the presence of non-terminal dead-end states - states from which reaching the agent’s goal is impossible. After reaching a non-terminal dead-end state, the agent may continue interacting with the environment for a long time in a dead-end path. As a consequence, reinforcement learning (RL) techniques may require a prohibitively large number of steps before convergence. Therefore, it is crucial to develop efficient methods to discover and avoid dead-end states. Recently, it has been shown that large language models (LLMs) can enhance various aspects of RL, such as reward shaping, fail state prevention, and goal-directed action generation. In this work, we use LLMs knowledge to discover dead-end states to perform a secure exploration. The secure property of this exploration helps to avoid dead-end states. We propose LLM-based methods for dead-end discovery and exploration that can be used with different optimization criteria. Additionally, we apply them to improve the efficiency and safety of the Q-learning-eGUBS+Cmax algorithm - an RL algorithm based on the theory of expected utility that allows a balance with meaningful, between cost and probability to reach the goal. The experimental results show that our proposed methods can significantly reduce the likelihood of encountering dead-end states, leading to better performance than state-of-the-art approaches.
Deep Graph Clustering Using Graph Neural Networks and Seed Detection
Clustering plays a fundamental role in attributed graphs, which incorporate both topological structure and node attributes represented as feature vectors. Deep clustering methods based on Graph Neural Networks (GNNs) have proven effective in extracting patterns from such data. Most existing approaches use a traditional clustering algorithm to identify representative elements, which are later employed to train a GNN and, finally, the clustering task. However, when selecting representative elements, these clustering algorithms consider only the feature vector of each instance, neglecting topological information. This limitation negatively impacts the GNN learning process. To address this issue, we propose Deep Graph Clustering via Graph Neural Network and Seed Detection (DGCSD), a model consisting of three modules: (1) the seed detection module, which identifies representative nodes; (2) the embedding module, which employs a graph attentional network to capture global topological information; and (3) the self-supervised module, which leverages the representative nodes to guide the clustering task. An advantage of our algorithm is that it integrates both information, the topological structure and node attributes across all modules to identify representative elements. This is the first GNN-based clustering algorithm that incorporates seed detection, establishing a significant reference for future research. The empirical analysis of real-world graphs provides evidence that combining a seed detection algorithm with a GNN model is competitive compared to well-established algorithms.
Deep Learning for Helicobacter Pylori Classification: A Comparative Approach Using Transfer Learning and Data Augmentation
Gastric cancer ranks among the most common types of carcinoma worldwide, with gastritis and Helicobacter pylori (HP) infections being its main precursors. Consequently, early diagnosis plays a crucial role in combating this disease. The use of Deep Learning (DL), particularly Convolutional Neural Networks (CNNs), has become widespread in research aimed at supporting disease diagnosis across various medical fields. In this study, we apply transfer learning and data augmentation techniques to compare five CNN-based models for the classification of HP, using a dataset comprising 204 histopathological images. Among the evaluated models, ResNet-101 achieved the best performance, with an accuracy of 94.61%, F1-score of 94.38%, precision of 97.00%, and recall of 92.29%. Our results indicate that, despite the limitations of the dataset, the proposed approach was effective, outperforming previous studies and reaffirming the potential of the applied techniques.
Deep Neural Networks Techniques with Ensemble-Based Methods Applied to Data-Driven Proxy Reservoir Simulator
Determining oil and water production in petroleum reservoirs involves conducting several simulations, which can be very costly due to the complexity of flow physics in real-world conditions. The high computational expense of these simulations has led to the development of proxy models that utilize historical production data, offering a more efficient alternative to traditional reservoir simulators. Various machine learning models, including recurrent neural networks (RNN), convolutional neural networks (CNN) and support vector regression (SVR), have been used in creating these proxies. However, these individual models have limitations, particularly in situations characterized by high complexity and uncertainty. In this work, we propose a proxy model based on ensemble methods to enhance accuracy and address uncertainties related to predictive modeling. The approach involves constructing a heterogeneous ensemble (different types of machine learning models), specifically integrating RNN and CNN. Additionally, using diverse activation functions (AFs). The goal is to leverage each model type’s strengths with different AFs for better performance. Our methodology was applied to a reservoir model that is well-documented in the literature. The results indicated that the proposal outperformed both homogeneous ensembles and individual models, reducing the mean and variance of the root mean square error (RMSE). Over ten runs, it demonstrated a maximum improvement of 23.3% in the mean and 67.36% in the standard deviation of the RMSE, compared to the RNN model on the test set.
DiagHW: A Compact LLM for Hardware Failure Diagnosis via a Novel Knowledge Distillation Pipeline
Hardware failure diagnosis from textual user-reported issues presents significant challenges due to the ambiguous and non-technical nature of these reports. While Large Language Models show promise in this domain, state-of-the-art models with billions of parameters pose practical limitations for deployment on low-power consumer devices. This work introduces a novel knowledge distillation pipeline for hardware failure detection, leveraging a large teacher model to generate synthetic training data for a smaller student model. Then, we present the DiagHW model, a compact 1.2B parameter fine-tuned LLaMA-3.2-1b-instruct model, which achieves diagnostic accuracy comparable to much larger models (up to 72B parameters). Our extensive experimental validation involved 32,414 inferences across 28 baseline models and 10 fine-tuned variants, processing a total of 19,692,210 tokens for these evaluations, complemented by 105,925,876 tokens processed during the fine-tuning stages.
EffAttNet: Improving Early Glaucoma Detection with EfficientNet-B3 and Attention Modules
Glaucoma remains a leading cause of irreversible blindness worldwide and is particularly prevalent in Brazil, according to the Brazilian Society of Glaucoma (SBG). Early detection is critical to preventing vision loss. However, diagnosis in the initial stages is often challenging due to the asymptomatic nature of the disease and its visual similarities to other ophthalmic conditions. This work introduces EffAttNet (Efficient Attention Network), a novel deep learning architecture designed for automated glaucoma detection from retinal fundus images. EffAttNet leverages the representational power of EfficientNet-B3 while integrating residual attention modules to enhance feature focus and improve classification performance. Evaluated on the SMDG-19 dataset, EffAttNet achieved an accuracy of 88.27%, recall of 84.49%, and an AUC-ROC of 0.936, demonstrating its potential as a reliable tool for assisting in early glaucoma screening.
Enhanced Forecasting Model Using Transformers, Extended Long-Short-Term Memory, and Randomized Fuzzy Cognitive Maps
This paper presents TxL-RHFCM, a cutting-edge hybrid deep learning model designed for time series forecasting. The architecture seamlessly combines Transformers, extended long-short-term memory (xLSTM), and randomized high-order fuzzy cognitive maps (R-HFCM), with the latter serving as a reservoir computing mechanism. The model architecture is structured into four key components. Initially, the Transformer block processes the raw time series data, employing attention mechanisms to uncover intricate relationships between sequence elements and produce refined representations. These transformed sequences are then passed to the xLSTM unit to compute hidden states from the final tokens. Subsequently, the R-HFCM module leverages these hidden states to derive concept activation states. Finally, a feed-forward network extracts meaningful features from the R-HFCM output to generate the forecast. The proposed TxL-RHFCM model is rigorously evaluated on six benchmark case studies to predict electrical load consumption. Experimental results demonstrate its superior predictive accuracy compared to various state-of-the-art deep learning, machine learning, and fuzzy time series approaches.
Enhanced Quantum-Inspired Neural Architecture Search with Adaptive Pre-trained Backbone Integration
Neural Architecture Search (NAS) methods have become essential for automating the design of deep neural networks. However, their high computational cost, particularly when training architectures from scratch, remains a significant limitation. This paper proposes an enhancement to the Quantum-Inspired Neural Architecture Search (Q-NAS) algorithm by integrating a dynamically adjustable pre-trained backbone into the search process. The proportion of backbone depth used is jointly optimized with the design of additional building blocks selected from a flexible set of operators. Experiments conducted on four MedMNIST datasets show that the proposed method improves search efficiency, reducing GPU time by up to 66% compared to the baseline. Furthermore, it achieves competitive or superior classification accuracy. Notably, on the PathMNIST dataset, the enhanced QNAS achieved a 3.45% improvement in accuracy over the baseline. These findings demonstrate the effectiveness of combining partial backbone utilization with evolutionary NAS techniques for efficient medical image classification.
Enhancing Framingham Cardiovascular Risk Score Transparency Through Logic-Based XAI
Cardiovascular disease (CVD) remains one of the leading global health challenges, accounting for more than 19 million deaths worldwide. To address this, several tools that aim to predict CVD risk and support clinical decision making have been developed. In particular, the Framingham Risk Score (FRS) is one of the most widely used and recommended worldwide. However, it does not explain why a patient was assigned to a particular risk category nor how it can be reduced. Due to this lack of transparency, our approach introduces a logical explainer for the FRS. Based on first-order logic and explainable artificial intelligence (XAI) fundaments, our method is capable of identifying the minimal set of patient attributes that are sufficient to explain a given risk classification (abduction). The explainer produces actionable scenarios that illustrate which modifiable variables would reduce a patient’s risk category (counterfactual). We evaluated all possible input combinations of the FRS (over 22,000 samples) and tested them with our solution, successfully identifying important risk factors and suggesting focused interventions for each case. The results improves clinician trust and facilitates the wider implementation of CVD risk assessment by converting opaque scores into transparent, prescriptive insights, particularly in areas with restricted access to specialists.
Enhancing Graph Neural Networks for Multi-target Activity Prediction Through Multi-task Learning and Knowledge Distillation
The early phases of the drug discovery pipeline increasingly rely on machine learning to identify active compounds and prioritize candidates for experimental testing. Quantitative structure-activity relationship (QSAR) models are widely employed to predict molecular activity for biological targets. While traditional QSAR models are often trained independently for each target, recent studies have shown that multi-task learning can improve predictive performance by exploiting shared patterns across related targets. In this work, we investigate the application of graph-based neural networks, specifically message-passing neural networks (MPNNs), in multi-task molecular activity prediction. Our methodology incorporates (1) knowledge distillation from pre-trained target-specific models to enrich multi-target activity datasets and (2) hierarchical clustering to group related tasks, allowing the model to better exploit inter-target similarities. We assess multi-task MPNNs performance relative to single-task learning, classical machine learning baselines, and feed-forward neural networks trained on molecular fingerprints. Our evaluation includes standard random splits as well as realistic lead optimization and hit identification benchmark splits, which better reflect practical drug discovery scenarios. Our results highlight the strengths and limitations of graph-based architectures in multi-task settings and provide insights into their practical utility for improving early-phase drug discovery workflows.
Estimating Variable Importance and Interaction in Machine Learning via Genetic Algorithms
This work explores the use of the Genetic Algorithm with Linkage Learning (GAwLL) for the feature selection problem. A notable byproduct of GAwLL application for feature selection is the generation of a variable interaction graph, offering insights into feature dependencies. To extend the original C++ implementation—which was limited to the K-nearest neighbors (KNN) algorithm—we introduce PyGAwLLfs, a Python-based framework designed to support feature selection for a wide range of machine learning models. Using PyGAwLLfs, we assess GAwLL performance across various models, including decision trees, random forests, and artificial neural networks. Furthermore, based on the internal mechanisms of GAwLL, we present a novel method for estimating variable importance in machine learning. Experimental evaluation on multiple datasets, including one from particle physics, demonstrates PyGAwLLfs effectiveness in the estimation of variable importance and interaction. We compare the variable importance scores generated by PyGAwLLfs with those produced by traditional approaches.
Evaluating Question Answering Model Robustness Against Typographical Mistakes Using Adversarial Attacks
Question Answering is one of the prime applications of LLMs and other such transformer-based language models, which have been shown to achieve results as of yet unmatched in many NLP tasks. And yet, the automatic evaluation metrics used in benchmarking these systems are unsatisfactory, producing results that don’t correlate well to human judgement and offer no interpretation of the model’s failings. This paper introduces Q3AE, an evaluation method for QA systems that leverages adversarial attacks to measure the robustness of answerer models when tested against challenging input. We test the method by introducing spelling mistakes and other such keyboarding errors, common in day to day use, into the SQuAD dataset, translated to Portuguese, and use it as input against LLMs of the LLaMA and Gemma architectures, of different sizes. We find that these models are surprisingly sensitive to even the tiniest alterations, responding poorly to modified examples that a human being could still easily understand.
Evaluating the Power Measure with Choquet-Based Generalizations in Fuzzy Rule-Based Classification Systems
This paper presents an in-depth investigation of the Power Measure (PM) applied with varying values of the exponent q in Choquet-based generalizations for fuzzy rule-based classification systems (FRBCS). We evaluate ten fixed values of q in the interval [0.1, 1.0] across 33 datasets, assessing classification performance through non-parametric statistical analyses. Among the generalizations, the CF1F2-integral consistently achieves superior results, reaffirming its state-of-the-art status within this domain. Our findings reveal that optimal q values are inherently method-dependent: q = 0.4 is best suited for Choquet and CC-min, q = 0.6 for CT, q = 0.2 for CFAvg and CF1F2, q = 0.8 for dCF, and q = 0.5 for dXC. Statistical comparisons using the Aligned Friedman and Wilcoxon signed-rank tests show that while PM performs competitively across most configurations, it exhibits occasional underperformance when compared to specific generalizations such as Choquet and dCF. The results helps in the comprehension of method-specific q optimization and provide practical guidance for selecting aggregation strategies in FRBCS.
Evaluating the Use of Knowledge Graph Embeddings to Identify Influential Nodes in Heterogeneous Networks
The Influential Node Identification task aims to measure which nodes in a network could spread information more efficiently. However, most works focus on homogeneous networks, whereas there are still a few approaches regarding heterogeneous networks. Existing works for identifying influential nodes in heterogeneous networks use metapath latent representations. This approach is limited because it depends on prior knowledge about the network to establish random-walk strategies based on vertex types. In contrast, Knowledge Graph Embeddings (KGE) can model heterogeneous networks differently than those obtained using random-walks by transforming the heterogeneous networks into a knowledge graph, thus not needing prior knowledge about the network schema of types. Therefore, this work aims to study the effectiveness of KGE methods for the Influential Node Identification task in heterogeneous networks. We use two existing scoring methods based on node representations adapted to operate with KGE. Moreover, we simulate using a well known epidemic diffusion model (SIR) and calculate correlations to evaluate those modified measures and their obtained node ranking in three datasets widely used in heterogeneous network tasks. Results show potential outcomes using the TransE model to identify influential individuals, obtaining Kendall’s coefficient close to 0.9 under the SIR model.
Evaluation of Explainable Artificial Intelligence Methods for Deep Time Series Classification of Biosignals
Deep learning models for time series classification, such as 1D Convolutional Neural Networks, are increasingly being used to predict the outcome of time-dependent clinical data, such as electrocardiograms (ECGs) and electroencephalograms (EEGs). However, when dealing with such complex model architectures, which behave as opaque boxes, the interpretability of new predictions is lost. Explainable artificial intelligence (XAI) techniques aim to mitigate this issue by enabling some degree of explanation for the model output, given a new input. In the case of biosignals, represented by multivariate time series, it is desirable to indicate which dimensions and which segments led the model to the final outcome. In this context, this work aims to evaluate and compare the use of different XAI methods, gradient-based or perturbation-based, to better understand the predictions produced by deep learning models in biosignals classification tasks. The experimental results with a synthetic dataset and a real-world ECG dataset indicate that perturbation-based approaches are the best suited for faithful and robust explanations, while gradient-based methods achieve similar results, but are more cost-efficient.
Extending the Minimal Learning Machine for Classification and Regression on Interval-Valued Data
Solving machine learning problems involving interval-valued data is a challenging task that arises in various real-world applications, such as heart rate prediction and astronomical data analysis. Motivated by this, several nonlinear regression methods and classifiers have been proposed in recent years to address this specific data type. In this paper, we introduce two novel variants of the Minimal Learning Machine (MLM) adapted to interval-valued inputs, targeting both regression and classification tasks. The proposed variants explicitly model a direct dependency between input and output intervals: the lower (upper) bound of the output is predicted based on both bounds of the input. For regression tasks, we compare our models against seven state-of-the-art nonlinear approaches: three Extreme Learning Machine (ELM) variants for interval-valued data, two kernel regression extensions, without explicitly modeling interval dependencies. For classification, we evaluate three interval-based logistic regression models. Experimental results on synthetic datasets with varying configurations and on real-world data demonstrate that our MLM variants achieve comparable or superior performance. These findings highlight the effectiveness of the proposed methods and their potential as competitive alternatives for learning from interval-valued data.
Face-Obfuscated Deepfake Detection by Learning Spatial and High-Frequency Generative Artifacts
This paper presents a study of artifacts left in images by the generative process of facial deepfakes. We hypothesize that, even when facial regions are blurred or erased to protect privacy and biometric data, an image can still be classified as fake. To address such privacy-critical scenarios, our framework is specifically designed to reliably detect facial deepfakes, even when all facial regions are deliberately obfuscated to preserve individual privacy. Our approach is grounded in the observation that spatial artifacts, high-frequency components, and region-localized features in both the pixel and transformed domains serve as strong evidence of deepfake forgeries. To capture these cues, we extract three representations of each image - the raw pixel domain, wavelet based Multi-Resolution Analysis (MRA), and the Short-Time Fourier Transform (STFT) - and process them using a state-of-the-art pre-trained feature extractor, the as OpenAI’s CLIP embedding model. The generated embeddings are used to train two different classification methods (Logistic Regression and Multi-Layer Perceptron based) and we verify if the extra information presented to the feature extractor (STFT and MRA) improves deepfake identification metrics. Our contribution can be seen as a systematic approach to enhance deepfake classifier accuracies in information deprived scenarios, where the identity of a given subject is hidden by erasing or distorting its facial landmarks. We evaluate our framework on the DFFD deepfake dataset with some pre-processing of the database (to compute the transforms and hide facial identity) and we demonstrate promising detection performance under these conditions.
Generative SLMs Meet Brazilian Legal Documents: Efficient NER via LoRA Fine-Tuning
Despite the success of generative Large Language Models (LLMs) in tasks like question answering, their direct application to Named Entity Recognition (NER) still struggles to achieve better results than other approaches due to issues such as hallucinations, making them unreliable for direct use. Hallucinations are a consequence of the decoder-only architecture, and although LLMs can mitigate them through a fine-tuning process, this approach can be computationally expensive. To reduce the cost of the fine-tuning process, Parameter-Efficient Fine-tuning (PEFT) techniques such as Low-Rank Adaptation (LoRA) can be used to enable weight adjustments, improving training efficiency. Since LLMs have a massive size, Small Language Models (SLMs) have emerged as compact alternatives, reducing the number of parameters while preserving performance. However, this parameter cutoff diminishes the model’s learning capabilities, particularly in domain-specific tasks, such as legal document NER. In this work, we propose a decoder-only approach to Portuguese legal NER using generative SLMs fine-tuned with LoRA. We assess the impact of LoRA placement within the SLM architecture on performance. Our analysis reveals how LoRA’s arrangement and dimensionality affect training efficacy, yielding a fine-tuned SLM that outperforms prior generative SLMs on legal NER benchmarks.
Genetic Algorithm for Interpretability in Vision Language Models
The rapid advancement of deep learning has highlighted the need for methods capable of explaining model decisions to ensure transparency and trust in AI systems. In this context, Explainable Artificial Intelligence (XAI) has emerged as a key research area. This work introduces an optimization-based approach to interpretability, identifying the most and least semantically relevant regions of an image concerning textual descriptors. We formulate the task as an optimization problem and employ a genetic algorithm to explore the solution space, maximizing or minimizing the semantic alignment between image regions and text. We used a customized dataset derived from ImageNet, containing 1.000 classes, each with 5 images and 6–8 textual descriptors to evaluate our method. Several established interpretability methods were employed for comparison, including Saliency, Guided Backpropagation, Grad-CAM, Gradient Shap, Integrated Gradients, and InputXGradient. Experimental results show that our approach consistently outperforms baseline methods, achieving higher similarity scores between selected regions and their associated descriptors. Our findings demonstrate the effectiveness of genetic algorithms in solving interpretability problems, offering a flexible and scalable method for XAI.
GraphRAG on Electronic Health Record: A Knowledge Graph-Enhanced RAG Approach for Healthcare Information Access
The increasing importance of digital health and the need for better health literacy require effective methods to access and understand Electronic Health Records (EHRs). While Large Language Models (LLMs) show promise in this domain, traditional Retrieval-Augmented Generation (RAG) struggles to handle the complex, interconnected nature of clinical data. GraphRAG emerges as a powerful alternative, leveraging knowledge graphs (KGs) to capture semantic relationships within EHRs. This research evaluates the effectiveness of graph expansion in a GraphRAG to enhance information retrieval from FHIR-formatted medical data. We propose a 1-hop expansion approach built upon a lexical search baseline which, while inheriting some limitations of traditional keyword-based retrieval, significantly enhances LLMs’ access to comprehensive and diverse contextual information. Our evaluation, using synthetic patient data and a targeted set of questions across five models, reveals that the 1-hop expansion strategy consistently outperforms the baseline in subjective metrics like comprehensiveness and diversity, and frequently in quantitative metrics such as answer and contextual relevancy. These results highlight the potential of our proposal to enhance LLM response when querying intricate medical data.
Hardness Sampling: Exploring Instance Hardness in Pool-Based Active Learning
Predictive tasks usually require labeled data, which are often costly or impractical to acquire. Active Learning (AL) methods address this bottleneck, enabling the induction of predictive models with reduced label annotation cost. They optimize the labeling process by strategically sampling a subset of the unlabeled examples and sequentially selecting instances until a suitable labeled set is obtained. Several sampling strategies have been proposed in the literature. Although those based on Pool-based AL (PAL) scenario have presented some of the best results, they still have known limitations. This paper investigates how to address these limitations. PAL iteratively selects instances from a data pool to be labeled by an oracle and incorporated into the training set, increasing its representativeness for the current classification task. Its strategies, however, are limited by the active learners’ bias within the exploration-exploitation tradeoff. For instance, non-agnostic strategies are biased toward prospective sampling, as they depend on the learner’s assumptions. This has often resulted in suboptimal performance in the early stages of AL. To mitigate this issue, more exploratory biases have been introduced. Nevertheless, current strategies continue to exhibit shortcomings in data selection. On the other hand, recent studies have shown that identifying instances that are hard to classify significantly impacts the predictive performance of classifiers. This paper proposes a new non-agnostic PAL strategy, Hardness Sampling (HardS), which is based on Hardness Measures (HMs). HMs employ the Instance Hardness (IH) concept to identify instances with a higher probability of being misclassified. According to experiments carried out across diverse datasets, HardS is a competitive alternative to classical approaches, in particular Uncertainty Sampling, Expected Error Reduction, and Density-Weighted methods. The experimental results also suggest that some groups of HMs introduce a self-regulating balance between exploratory and prospective sampling biases, addressing such a key PAL challenge.
Hotel Intelligence: Maximizing Revenue with Nonlinear Dynamic Pricing and Predictive Demand Analysis
Dynamic pricing is fundamental for revenue maximization in the hotel industry, particularly given demand volatility. Machine Learning (ML) models offer a promising avenue for learning optimal pricing policies without necessitating direct market experimentation. However, evaluating the effectiveness of these models presents challenges, especially due to the scarcity of robust theoretical benchmarks. This study introduces a mathematical simulator employing constrained nonlinear programming to identify optimal daily rates across various pricing scenarios. The simulator is designed to serve as a reference for assessing the performance of ML-based pricing strategies. Simulations conducted demonstrate the model’s stability and its applicability for generating benchmark solutions in diverse operational contexts.
Identifying Asymptomatic Nodes in Network Epidemics Using Graph Neural Networks
Infected individuals in some epidemics can remain asymptomatic while still carrying and transmitting the infection. These individuals contribute to the spread of the epidemic and pose a significant challenge to public health policies. Identifying asymptomatic individuals is critical for measuring and controlling an epidemic, but periodic and widespread testing of healthy individuals is often too costly. This work tackles the problem of identifying asymptomatic individuals considering a classic SI (Susceptible-Infected) network epidemic model where a fraction of the infected nodes are not observed as infected (i.e., their observed state is identical to susceptible nodes). In order to classify healthy nodes as asymptomatic or susceptible, a Graph Neural Network (GNN) model with supervised learning is adopted where a set of node features are built from the network with observed infected nodes. The approach is evaluated across different network models, network sizes, and fraction of observed infections. Results indicate that the proposed methodology is robust across different scenarios, accurately identifying asymptomatic nodes while also generalizing to different network sizes and fraction of observed infections.
Improving Medical Image Segmentation with Semantic Mask-Guided Diffusion Models
Image segmentation plays a fundamental role in medical image analysis, especially in Computer-Aided Diagnosis (CAD) systems, by highlighting anatomical structures and assisting physicians in making accurate clinical decisions. Despite the remarkable success of deep learning-based segmentation models, their performance heavily relies on large and diverse annotated datasets, which are often limited in the medical domain due to the high cost and complexity of data acquisition and labeling. To address this limitation, this article proposes a data augmentation pipeline based on semantic mask-guided diffusion models, aiming to synthetically enhance medical image datasets with anatomically consistent samples. The proposed method was evaluated using annotated abdominal CT images from the MICCAI FLARE 2022 Challenge, employing the YOLO11 segmentation network to assess the impact of the augmented data. Experimental results show that the method improved segmentation performance, achieving an mAP@0.5 of 0.792 and an mAP@0.5:0.95 of 0.467, outperforming the baseline model trained solely on real data, which obtained 0.788 and 0.456, respectively. These findings highlight the effectiveness of diffusion-based augmentation in improving generalization and robustness of segmentation networks. Moreover, the results suggest that this strategy is particularly beneficial for segmenting larger and high-contrast anatomical structures, such as the liver and kidneys, which may guide future research toward structure-aware data synthesis techniques in medical imaging.
Increasing Time Series Forecasting Performance with Dynamic System Copula-Based Ensemble Models
Time series forecasting has significantly benefited from ensemble models, which consistently outperform individual approaches by combining predictions from diverse methodologies. This paper proposes a novel ensemble framework based on the copula formalism, enabling dynamic selection of copula functions tailored to the characteristics of each dataset. Our approach integrates three widely recognized forecasting models: ARIMA, Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) neural networks and applies them to five key financial time series: Petrobras, Google, Nasdaq, S&P500, and the GBT to USD exchange rate. The ensemble combines predictions using multiple copula functions and compares their performance against traditional ensemble methods, such as simple average, simple median, and MLP-based combination. Experimental results show that the proposed system achieves mean absolute percentage errors (MAPE) below 0.71, outperforming standard ensemble strategies. This improvement highlights the effectiveness of copula-based modeling as an adaptive and flexible tool for predictive fusion. We conclude that incorporating copula theory into ensemble learning brings substantial advancements in time series forecasting, as it allows dynamic model aggregation according to data behavior. The proposed method bridges statistical foundations and artificial intelligence, offering promising results in financial applications and potential for broader use across various forecasting domains.
Inference Time Debiasing Concepts in Diffusion Models
We propose DeCoDi, a debiasing procedure for text-to-image diffusion-based models that changes the inference procedure, does not significantly change image quality, has negligible compute overhead, and can be applied in any diffusion-based image generation model. DeCoDi changes the diffusion process to avoid latent dimension regions of biased concepts. While most deep learning debiasing methods require complex or compute-intensive interventions, our method is designed to change only the inference procedure. Therefore, it is more accessible to a wide range of practitioners. We show the effectiveness of the method by debiasing for gender, ethnicity, and age for the concepts of nurse, firefighter, and CEO. Two distinct human evaluators manually inspect 1200 generated images. Their evaluation results provide evidence that our method is effective in mitigating biases based on gender, ethnicity, and age. We also show that an automatic bias evaluation performed by the GPT4o is not significantly statistically distinct from a human evaluation. Our evaluation shows promising results, with reliable levels of agreement between evaluators and more coverage of protected attributes. Our method has the potential to significantly improve the diversity of images it generates by diffusion-based text-to-image generative models.
Interpolation of Multivariate Time Series with Nearly-Symmetric Time Translations
Modeling complex systems from irregular multivariate time series (MTS), common in engineering and scientific domains, presents significant challenges due to asynchronous measurements, varying sampling rates, and missing data. Building upon architectures that explicitly handle temporal irregularities using time-informed recurrent units, this work introduces a refined approach that fundamentally decomposes the latent state update process. We propose separating the function responsible for incorporating new measurements from the function governing the temporal evolution of the hidden state. Specifically, the temporal translation over a given time delta is modeled using a dedicated transformation parameterized by a matrix constrained to be near-orthogonal. A separate ingestion function updates this time-evolved state with new measurement information when available. This explicit decomposition allows for a more principled representation of continuous-time dynamics, acting as a general regression framework applicable to both imputation and forecasting tasks. We demonstrate through experiments that our model is competitive with state-of-the-art techniques, while providing the desirable properties of near-orthogonal time translations.
Is Machine-Translation Enough? Understanding Impacts in LLM Benchmarking
The Massive Multitask Language Understanding (MMLU) benchmark is widely employed to assess the general capabilities of Large Language Models (LLMs) across diverse domains. The predominance of English in training data and benchmark datasets introduces inherent limitations when extending assessments to a multilingual context. Portuguese results are often grouped within broad ’multilingual’ categories, preventing meaningful examination of model behavior and specific linguistic challenges. This study investigates the impact of English-to-Portuguese translation methods on MMLU performance, comparing: Google Translate, GPT-3.5, GPT-4o, and professional human translation. We evaluate three multilingual open-weight models and Sabiá 3, a state-of-the-art Portuguese-specialized model. Contrary to expectations, accuracy in machine-translated versions of MMLU is slightly higher than in the human-translated version. In a 95% confidence interval, the expected accuracy increase on GPT-4o’s translation is the highest, but still small, between 0.85% and 1.52%. We further evaluated these translation methods with standardized answer-order perturbations, while a slight decrease in accuracy was observed, the standard deviation among translation methods was 0.07%. The presented results support the trustworthiness of machine-translation for LLM benchmarking in Portuguese, providing a stepping stone for better performance evaluation through translated state-of-the-art benchmarks.
Juru: Legal Brazilian Large Language Model from Reputable Sources
The high compute cost associated with pretraining large language models limits their research. Two strategies have emerged to address this issue: domain specialization and pretraining with high-quality data. To explore these strategies, we specialized the Mistral-7B model with 1.9 billion unique tokens from reputable Brazilian legal sources and conducted few-shot evaluations on legal and general knowledge test suites. Our model, Juru, demonstrates the benefits of domain specialization by achieving improved performance on legal benchmarks, even with a reduced amount of pretraining data. However, this domain specialization through continued pretraining comes at the cost of increased forgetting in unrelated domains, as evidenced by performance degradation on general knowledge test suites in both Portuguese and English. This study contributes to the growing body of scientific evidence showing that pretraining data selection may enhance the performance of large language models, enabling the exploration of these models at a lower cost. Juru is publicly available at https://huggingface.co/roseval/Juru-7B.
Information Retrieval and In-Context Learning for Low-Resource Named Entity Recognition
Autoregressive language models, when combined with In-Context Learning, have demonstrated strong performance across various tasks using limited labeled data. However, the application of this approach to Named Entity Recognition remains underexplored, partly due to the unique structural challenges inherent to the task. We propose a new technique for named entity recognition using autoregressive language models based on In-Context Learning and Information Retrieval techniques. Given an input text, models using this technique can fetch similar examples from a training set. These examples are then used to improve the language model’s ability to recognize named entities within the provided text. This approach is modular and independent of the underlying language model and the Information Retrieval technique. Experimental results indicate that this technique can be effective for low-resource applications, as in the CrossNER collection we achieve state-of-the-art performance with our proposed technique, showing that Information Retrieval can increase the F-score by as much as 11% points.
Maximum Dispersion, Maximum Concentration: Enhancing the Quality of MOP Solutions
Multi-objective optimization problems (MOPs) often require a trade-off between conflicting objectives, maximizing diversity and convergence in the objective space. This study presents an approach to improve the quality of MOP solutions by optimizing the dispersion in the decision space and the convergence in a specific region of the objective space. Our approach defines a Region of Interest (ROI) based on a cone representing the decision maker’s preferences in the objective space, while enhancing the dispersion of solutions in the decision space using a uniformity measure. Combining solution concentration in the objective space with dispersion in the decision space intensifies the search for Pareto-optimal solutions while increasing solution diversity. When combined, these characteristics improve the quality of solutions and avoid the bias caused by clustering solutions in a specific region of the decision space. Preliminary experiments suggest that this method enhances multi-objective optimization by generating solutions that effectively balance dispersion and concentration, thereby mitigating bias in the decision space.
Mind the Gap: Investigating the Impact of Data Leakage on Machine Learning Predictive Models
Data leakage is one of the most critical and underestimated threats to the validity of machine learning (ML) model evaluation, often leading to substantially inflated performance estimates. While the concept has been acknowledged in the literature, this is the first study to provide large-scale empirical evidence of its effects across diverse modeling scenarios. We systematically investigated the impact of data leakage introduced at four common pipeline stages – normalization, imputation, feature selection, and hyperparameter tuning – using 30 datasets and six supervised learning algorithms. Our results show that leakage significantly inflated performance in nearly half of the datasets (p < 5), with feature selection causing the strongest distortions. Support Vector Classifiers were particularly affected, showing large average gains and highly significant differences. Normalization and imputation led to minimal or statistically insignificant changes. These findings offer robust empirical evidence of how different leakage scenarios can distort model evaluation, underscoring the importance of rigorous pipeline design and validation practices in ML research and applications.
Mitigating High Dimensionality and Few Training Data with a Quantum Kernel Learning Approach
In this work, we investigate the integration of quantum algorithms as kernel functions in Support Vector Machines (SVMs) to classify highly imbalanced datasets, using a case study on anomaly detection in wind turbine sensor data. A hybrid quantum-classical computational strategy was developed and benchmarked against four classical kernels, demonstrating superior or competitive performance in several scenarios. The evaluation focused on the model’s generalization ability to detect minority classes in predictive maintenance datasets. The main contributions of our research include: (i) the impact of kernel complexity on minority class detection, (ii) the comparative performance of quantum and classical models in identifying fault types, and (iii) the influence of different quantum entanglement strategies on SVM performance. Simulation results using the Qiskit (Quantum Information Software Kit) framework highlight how superposition and entanglement can effectively map data into higher-dimensional spaces, improving hyperplane separation and enhancing fault detection.
Multilevel Strategy for Community Detection in k-Partite Networks
Graph-based algorithms have aroused considerable interest in recent years, facilitating pattern recognition and learning by propagating information through the graph. However, with a large volume of data, it becomes computationally infeasible to execute specific algorithms. The multilevel strategy aims to recursively reduce the graph, performing successive reductions of the initial network based on edge contractions and vertex merges. The coarsening process makes the graph as small as desired, allowing the use of costly algorithms in this coarsened version. The multilevel strategy has three phases: the coarsening phase, consisting of matching and contraction; the solution-finding phase, consisting of applying the desired algorithm to the most compact network; and the uncoarsening phase, consisting of projection and refinement. In this work, a generalization of the unsupervised coarsening algorithm for bipartite networks is carried out based on the propagation of labels with weight restriction, making it possible to deal with k-partite networks. Furthermore, the coarsening process is used directly as a community detection algorithm, in which each super vertex of the final contraction represents a community. The results show that our coarsening strategy leads to significant gains regarding community detection quality.
Neural Networks with Adaptive and Trainable Activation Functions Applied to Solve Stochastic Differential Equations
This work investigates the use of artificial neural networks (ANNs) with adaptive and trainable activation functions in solving stochastic differential equations (SDEs). The study builds upon the growing application of ANNs in ordinary (ODEs) and partial differential equations (PDEs). Still, it explores the effectiveness of these networks when handling the stochastic factor present in SDEs. The activation function plays a crucial role in the performance and convergence of neural networks. In this context, we test the Universal Activation Function (UAF), comparing its results with two other adaptive and trainable functions: GLN-Mish and GNL-ReLU. Using the NeuroDiffEq library in Python, we solve equations such as the Langevin equation and the Cox-Ingersoll-Ross equation, adjusting various parameters of both the network and the equation itself. The accuracy of the solutions was evaluated using the Mean Squared Error (MSE). The Diebold-Mariano (DM) statistical test was then applied, at a confidence level 5%, confirming that differences in the predictive performance of activation functions were not statistically significant in certain cases. The results indicate that adaptive activation functions outperform in solving SDEs, offering flexibility and precision in adjusting stochastic models.
New Generalizations of the Choquet Integral by Restricted Dissimilarity Functions
This paper introduces new generalizations of the Choquet integral by Restricted Dissimilarity Functions (RDFs) to construct alternative fusion operators. The Choquer-RDF-Based generalization where applied in the Fuzzy Reasoning Method (FRM) of a Fuzzy Rule-Based Classification System (FRBCS) to cope with a benchmark composed by 33 different datasets. The results indicated that this approach is an alternative to generalizations that are based in the difference operator, considered in the original functions.
NFT Collection Classification: A Multimodal Approach Integrating Metadata and Textual Embeddings with Supervised Learning
The NFT market has experienced rapid growth, giving rise to collections with significant semantic diversity and structural variations that complicate their automatic classification. This paper proposes a supervised learning approach to classify NFT collections across three distinct scenarios: (i) statistical data on transactions, (ii) embeddings generated from the textual descriptions of collections, and (iii) the combination of both. The dataset was collected from the OpenSea platform, which is one of the largest NFT marketplaces. Eight textual preprocessing techniques were tested, combined with two embedding models, four balancing strategies, and five classifiers. The best performance was achieved in the combined scenario, with an accuracy of 75.7% and an F1-score of 75.4%. This outperformed the embeddings-only configuration (accuracy of 67.9%, F1-score of 68.5%) and the statistical-only approach (accuracy of 61.9%, F1-score of 60.3%). Although the class distribution was moderately balanced, the F1-score was adopted to better reflect model performance across categories. The results demonstrate that the integration of structured and semantic data significantly enhances classification effectiveness, reinforcing the potential of this multimodal approach for applications in curation, recommendation, and market analysis in the NFT ecosystem.
On the Combination of Machine Learning Techniques with Simulated Annealing to Identify Professional Accounts in Ethereum
The expansion of blockchain-based platforms has accelerated a paradigm shift in digital financial systems. In this scenario, Ethereum is at the forefront due to its programmability and broad adoption. However, the blockchain user’s pseudonymity is challenging for regulatory compliance, fraud detection, and user behavior analysis. In this sense, we investigate how to classify blockchain users, based solely on their transactions’ attributes. We jointly use a computational intelligence method and machine learning classifiers to perform the profile classification of Ethereum users as professional or common accounts. Furthermore, we combine machine learning classifiers with a simulated annealing optimization algorithm to enhance classification results. Our results show that it is a promising combination, improving recall and AUC-ROC curve by at least 6% and 7%, respectively, over the previous state of the art.
OnlineSIRUOS: An Inverse Random Under and Oversampling, Heterogeneous Ensemble, and Meta-learning Approach for Imbalanced Data Stream Classification
Data streams are potentially unbounded data sequences that are made available rapidly and over time. Due to their pervasiveness, mining data streams has become a major scientific and practical issue. Scenarios involving data streams present multiple challenges, including the requirement for single-pass processing due to constraints on computational resources and the necessity to respond to concept drift over time. Another common trait of several streaming scenarios is class imbalance, that is, a class, often of interest, is majorly outnumbered by others, thus hardening the learning process. This paper introduces Online Stacking Inverse Random Under and Over Sampling (OnlineSIRUOS). This ensemble-based approach combines meta-learning, sampling, and heterogeneous components to address class-imbalanced data stream classification. We evaluated our proposal against existing work tailored for class imbalance in data streams using synthetic and real-world datasets. Experimental results show that our proposal achieves competitive F1 scores in different imbalance ratios and is less computationally intensive than its competitors in processing time and memory consumption. The results also show that our proposal is particularly well-suited for highly imbalanced data streams.
Ontology-Enriched BERT for Financial Market State Prediction
Financial markets are heavily influenced by textual data, with financial news playing a pivotal role in predicting market states. Although BERT has proven effective in general text analysis, it often fails to capture domain-specific semantics and the complex structural relationships among financial entities. To address these limitations, this study proposes an ontology-enriched BERT model that integrates financial ontologies, graph-based embeddings (Node2Vec), and topological features such as degree centrality, betweenness centrality, and clustering coefficient. This integration enhances the model’s ability to capture semantic and structural interdependencies, improving market state classification. Experimental results demonstrate that the proposed framework achieves 91.2% classification accuracy and reduces prediction loss to 0.308, outperforming baseline models. These findings underscore the importance of combining semantic enrichment and topological insights for financial forecasting, offering valuable contributions to risk management, portfolio optimization, and decision-making processes in both academic and industry contexts.
Performance Analysis of Quantization in Federated Split Learning
Collaborative learning techniques address privacy concerns in deep learning when working with large datasets distributed across multiple entities or computing devices. Among these techniques, Federated Split Learning (FSL) brings Split Learning (SL) techniques to Federated Learning (SL) to minimize computation demands on the client side without compromising privacy requirements. However, even with FSL, client-side inference may remain computationally expensive, which motivates us to evaluate the impact of quantization on FSL in terms of model accuracy and resource usage on the client side.
Francisco Bruno Heron de Carvalho Junior
Pose-Based Fall Detection Across Views: Boosting Generalization with Synthetic Data and Data Augmentation
Fall detection models must generalize to diverse, uncontrolled environments for effective real-world deployment, yet achieving this remains a significant challenge in vision-based systems. This paper investigates techniques to enhance generalization, initially focusing on cross-view performance as a critical step toward broader robustness across datasets and settings. We leverage 2D pose data, synthetic training samples, and data augmentation to address this issue. A synthetic dataset was generated using Unity, simulating six 3D fall scenarios captured from ten virtual cameras with precise keypoint annotations. Experiments utilized a public, multi-view real-world dataset, training models on one camera and testing on others to simulate generalization to unseen perspectives. The baseline results showed notable drops in across test views, revealing overfitting to specific viewpoints. We found that lightweight data augmentations—such as flipping, rotation, and Gaussian noise—applied to pose sequences markedly improve cross-view generalization. Combining these with synthetic data further enhances performance, delivering consistent results across all cameras. The most effective strategy, integrating both approaches, achieved gains of up to 54.4% in over the baseline. These results emphasize the value of training diversity, via augmentation and synthetic data, in crafting robust, viewpoint-independent models. By advancing cross-view generalization, this work establishes a foundation for future efforts targeting broader generalization to diverse datasets and real-world environments, providing practical insights for assistive technology deployment.
Positive and Unlabeled Graph Learning with Large Language Models for Fake News Detection
Several automated approaches have been proposed to mitigate the spread of fake news, however, their performance heavily depends on the quality and representation of the data used for training, with the collection of fake news being particularly costly. Fact-checking websites emerge as viable alternatives, however, their texts often do not preserve the original format of fake news, incorporating journalistic commentary, irrelevant information and editorial biases, and generally presenting a much higher text volume than typical fake news. These inconsistencies hinder algorithm learning and compromise their generalization ability. In this work, we propose a framework to enhance the quality and representation of training data through the use of graphs. First, we propose the use of the Gemma3 language model, capable of automatically extracting the core content of fake news, removing irrelevant information and editorial biases. The generated fake content can be useful for composing new datasets and developing more robust content discrimination tools. Then, we propose the Yake-Graph algorithm, which constructs graphs by linking news pieces through keywords extracted from the fake news. We conduct an empirical analysis evaluating the performance of the curated dataset in positive and unlabeled learning (PUL) and positive and unlabeled graph learning (PUGL) scenarios, comparing different graph construction methods. The results show that the combination of graph-based learning models and the Yake-Graph outperforms established approaches, highlighting its potential to improve fake news detection.
Pre-trained Code Language Models for Just-in-Time Software Defect Prediction: An Empirical Study
Just-in-time Software Defect Prediction (JIT-SDP) aims to identify defects before they are introduced, enabling mitigation of risky software submission to the repository during the software development cycle. Recent approaches model JIT-SDP as a multimodal task that involves code changes, commit messages, and hand-crafted commit-level features, also known as expert features. The present study thoroughly compares the performance of pretrained code language models, including encoder-only, decoder-only, and encoder-decoder architectures across both open-source fine-tuned models (e.g., CodeT5+, UniXCoder) and closed API-based large language models (e.g., GPT models, Gemini) for the JIT-SDP task. Furthermore, to the best of our knowledge, this is the first study to compare open (trainable) and closed (prompt-based) decoder-only models in this context. Our experiments show that fine-tuned small and medium-sized open models significantly outperform zero-shot prompting with closed large language models. In particular, CodeT5+ and UniXCoder overcome the state-of-the-art performance in a cross-project setting. The results highlight the importance of architecture, fine-tuning strategies, and expert feature integration for accurate JIT-SDP.
Probabilities of Causation and Root Cause Analysis with Quasi-Markovian Models
Probabilities of causation provide principled ways to assess causal relationships but face computational challenges due to partial identifiability and latent confounding. This paper introduces both algorithmic simplifications, significantly reducing the computational complexity of calculating tighter bounds for these probabilities, and a novel methodological framework for Root Cause Analysis that systematically employs these causal metrics to rank entire causal paths.
Proposing an Efficient CNN-Based Architecture for Image Processing
This paper introduces the Defiber layer, a Convolutional Neural Network (CNN) architecture component that systematically reduces input size during the convolution phase. In order to assess the feasibility of the proposed model, an empirical analysis was conducted using two image datasets: the CIFAR-10 dataset and an additional in-house dataset. Aiming to provide a comprehensive comparison, several existing CNN architectures are also included in this empirical analysis. The models are assessed based on accuracy and hardware performance criteria, including inference time and throughput. Our model surpasses expectations, outperforming some of the other evaluated models, both deep and mobile. Notably, the Defiber layer demonstrates remarkable effectiveness and consistent results during performance tests.
Reducing Dependence on Labeled Data: A Self-Supervised Fine-Tuning Approach for Low-Resource Language Models
The development of domain-specific language models faces significant challenges due to the scarcity of labeled data, especially in low-resource languages such as Portuguese. Annotating data is expensive and time-consuming, limiting the ability to train effective models in specialized contexts. To address this, we investigate a self-supervised fine-tuning strategy based on the BERTimbau pre-training protocol. This approach allows the model to improve generalization using only unlabeled data, avoiding the need for manual annotation. We explore different combinations of unfrozen layers and learning rate configurations to identify training regimes that balance performance and computational cost. The method is evaluated on three sentiment analysis datasets in Portuguese, each from a distinct domain. Results show that unfreezing only the final layer, together with a properly tuned learning rate, achieves performance comparable to traditional fine-tuning approach. These findings confirm the method’s viability in low-resource settings and its potential to scale to large unlabeled datasets. The approach provides an efficient alternative for adapting language models when annotated data is limited.
Relational Expressivity Coverage: Comparing Risk-Sensitive Criteria for SSP Problems
Markov Decision Processes (MDPs) are commonly used to model sequential decision-making under uncertainty. A notable subclass is the Stochastic Shortest Path (SSP) problem, in which an agent aims to reach a goal state, modeled as absorbing, while minimizing accumulated costs. Policies in SSPs induce the random variable accumulated cost and its corresponding probability distribution function, and decision criteria summarize these distributions into scalar values to guide policy comparison and define optimality. In this work, we propose the Relational Expressivity Coverage method, which quantifies a criterion’s ability to represent preference relations through parameter variation. We apply this framework to the One-State Many-Actions (OSMA) problem and compare multiple risk-sensitive criteria from the literature in terms of their expressivity.
Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy
Sudden cardiac death (SCD) is unpredictable, and its prediction in Chagas cardiomyopathy (CC) remains a significant challenge, especially in patients not classified as high risk. While AI and machine learning models improve risk stratification, their adoption is hindered by a lack of transparency, as they are often perceived as black boxes with unclear decision-making processes. Some approaches apply heuristic explanations without correctness guarantees, leading to mistakes in the decision-making process. To address this, we apply a logic-based explainability method with correctness guarantees to the problem of SCD prediction in CC. This explainability method, applied to an AI classifier with over 95% accuracy and recall, demonstrated strong predictive performance and 100% explanation fidelity. When compared to state-of-the-art heuristic methods, it showed superior consistency and robustness. This approach enhances clinical trust, facilitates the integration of AI-driven tools into practice, and promotes large-scale deployment, particularly in endemic regions where it is most needed.
Retinal Lesion Detection and Segmentation Using a Region of Interest-Based Approach with YOLO11
Diabetic retinopathy is a common microvascular complication of diabetes that can lead to vision loss. It is typically identified through retinal lesions such as soft exudates, hard exudates, microaneurysms, and hemorrhages. Since diagnosis involves analyzing retinal images, computational models can support experts in identifying these lesions during clinical examinations. Among them, microaneurysms are the earliest detectable signs but present a particular challenge due to their small size and low contrast. These features often appear near the macula, making this region especially relevant for early diagnosis and treatment planning. In this work, we propose an approach based on the YOLO11 architecture to perform both detection and segmentation of retinal lesions, emphasizing the macular region as a region of interest. We train two deep learning models in a sequential pipeline: the first extracts macula-centered regions using the Indian Diabetic Retinopathy Image Dataset; the second detects and segments lesions within the extracted region of interest using the Dataset for Diabetic Retinopathy. Our results show that the lesion detection model achieved a mean average precision (mAP@50) of 0.4460 on the validation set and 0.2830 on the test set of the Dataset for Diabetic Retinopathy, demonstrating the effectiveness of macula-focused region of interest extraction in improving lesion detection performance.
RS4: Restricted Search Space for Snippet Selection
Time series snippet discovery aims to summarize complex sequences by extracting representative subsequences that capture predominant behaviors. While Matrix Profile-based methods like Snippet-Finder offer strong robustness, they face significant computational challenges with long-duration or high-resolution time series, often requiring O(M2) operations. We propose RS4 (Restricted Search Space for Snippet Selection), a hybrid method that integrates clustering techniques with a refined Matrix Profile Distance search strategy. Our approach first segments and normalizes subsequences, applies clustering to identify cohesive groups, selects medoids as candidate snippets, and computes a restricted MPdist profile for final selection. This approach reduces the search space complexity to O(Mn), where n is the number of clusters, without sacrificing pattern fidelity. Experimental evaluation on the MixedBag dataset and long-duration sleep recordings demonstrates that RS4 achieves a 78% reduction in computation time while maintaining 92% of the pattern coverage compared to exhaustive methods. The results highlight the potential of combining structural clustering with distance-based refinement for efficient time series summarization.
Semantic Analysis of Synthetic EULA Contracts, with Natural Language Processing Techniques for LGPD Compliance Assessment
The increasing digitization of online services has resulted in the proliferation of software license agreements (EULA), which are often accepted by users without reading. However, these documents may contain clauses that violate privacy legislation, such as the General Data Protection Law (LGPD) in Brazil. This paper proposes an automated semantic analysis approach using Natural Language Processing (NLP) techniques to assess the similarity between synthetic and real EULA contracts, with a focus on legal compliance with the LGPD. Models based on the BERT architecture, including Legal-BERT and BERTimbau, are used to extract clause embeddings, calculate semantic similarity, and classify legal compliance. The results demonstrate high performance, with the best model achieving 91.2% accuracy and F1-score of 0.89%, confirming the feasibility of using synthetic legal documents in the training and validation of classification systems. The proposed approach contributes to more scalable and privacy-secure compliance audits.
Slice and Explain: Logic-Based Explanations for Neural Networks Through Domain Slicing
Neural networks (NNs) are pervasive across various domains but often lack interpretability. To address the growing need for explanations, logic-based approaches have been proposed to explain predictions made by NNs, offering correctness guarantees. However, scalability remains a concern in these methods. This paper proposes an approach leveraging domain slicing to facilitate explanation generation for NNs. By reducing the complexity of logical constraints through slicing, we decrease explanation time by up to 40% less time, as indicated through comparative experiments. Our findings highlight the efficacy of domain slicing in enhancing explanation efficiency for Nns.
Style-Specific Diffusion for Architectural Classification: Enhancing Performance Through Class-Adapted LoRA Augmentation
This paper addresses the challenge of limited training data in architectural style classification by proposing a novel data augmentation framework based on latent diffusion models with class-specific Low-Rank Adapters (LoRA). We focus on Brazilian architectural styles as a case study of a broader class of visual classification problems that share key characteristics: subjective categorization with expert disagreement, ambiguous class boundaries, continuous rather than discrete feature spaces, and historically evolving categories. Our approach generates synthetic images preserving style-defining features while introducing meaningful variations to enhance classifier performance. Unlike traditional augmentation methods that operate at the pixel level, our diffusion-based approach captures underlying semantic distributions, making it particularly valuable for domains where class distinctions depend on subtle feature combinations rather than isolated elements. We evaluate multiple neural network architectures and augmentation strategies across five architectural styles (Baroque, Neoclassical, Eclectic, Neo-Gothic, and Modernist). Results demonstrate significant performance improvements, with combined generative and traditional augmentation yielding an average accuracy gain of 5.8%. Notably, single-layer architectures outperformed multi-layer counterparts when using augmented data, suggesting reduced overfitting. Style-specific improvements ranged from 0% for Modernist (already at 100% accuracy) to 10.5% for Baroque style. Our methodology provides a versatile framework applicable to numerous domains sharing these characteristics, including art style classification, historical artifact categorization, fashion trend analysis, and other fields where categories exist on continuums rather than as discrete entities.
Synthetic EEG Generation Using Diffusion Model for Motor Imagery Task
Electroencephalography (EEG) is a widely used, non-invasive method for capturing brain activity, and is particularly relevant for applications in Brain-Computer Interfaces (BCI). However, collecting high-quality EEG data remains a major challenge due to sensor costs, acquisition time, and inter-subject variability. To address these limitations, this study proposes a methodology for generating synthetic EEG signals associated with motor imagery brain tasks using Diffusion Probabilistic Models (DDPM). The approach involves preprocessing real EEG data, training a diffusion model to reconstruct EEG channels from noise, and evaluating the quality of the generated signals through both signal-level and task-level metrics. For validation, we employed classifiers such as K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), and U-Net to compare the performance of synthetic data against real data in classification tasks. The generated data achieved classification accuracies above 95%, with low mean squared error and high correlation with real signals. Our results demonstrate that synthetic EEG signals produced by diffusion models can effectively complement datasets, improving classification performance in EEG-based BCIs and addressing data scarcity.
Textual Prompt-Guided Segmentation of Hemorrhagic Stroke in Computed Tomography Images
A stroke occurs when the blood supply to a region of the brain is interrupted or reduced, depriving the tissue of oxygen and nutrients. In the case of a hemorrhagic stroke, this is caused by the rupture of a cerebral vessel. Early recognition of stroke signs is essential to ensure prompt medical intervention and minimize damage to the patient, and diagnosis is typically performed through computed tomography (CT). In this study, two state-of-the-art deep learning models were integrated to accurately segment regions affected by hemorrhagic stroke in CT images. First, the action area of the stroke is delimited by the detection method YOLOV8 (You Only Look Once Version 8), which follows the Single Shot Detector (SSD) architecture to enable high-speed and high-accuracy real-time detection. Next, the Florence-2 segmentation method refines the identified region by leveraging multimodal data (text and image), thereby enhancing the clarity and precision of the segmentation. The proposed framework employs a text prompt describing the task and utilizes the polygon coordinates provided by the detection model to define the area of cerebral hemorrhage. Our results demonstrate an accuracy of 99.76%, which is comparable to other state-of-the-art methods. The robust detection capabilities of YOLOV8, combined with Florence-2’s refined segmentation, yield an efficient and adaptable approach for hemorrhagic stroke segmentation in CT images.
The Impact of Prosodic Segmentation on Speech Synthesis of Spontaneous Speech
Spontaneous speech presents several challenges for speech synthesis, particularly in capturing the natural flow of conversation, including turn-taking, pauses, and disfluencies. Although speech synthesis systems have made significant progress in generating natural and intelligible speech, primarily through architectures that implicitly model prosodic features, such as pitch, intensity, and duration, the construction of datasets with explicit prosodic segmentation and their impact on spontaneous speech synthesis remains largely unexplored. This paper evaluates the effects of manual and automatic prosodic segmentation annotations in Brazilian Portuguese on the quality of speech synthesized by a non-autoregressive model, FastSpeech 2. Experimental results show that training with prosodic segmentation produced slightly more intelligible and acoustically natural speech. Although automatic segmentation tends to create more regular segments, manual prosodic segmentation introduces greater variability, which contributes to more natural prosody. Analysis of neutral declarative utterances showed that both training approaches reproduced the expected nuclear accent pattern, but the prosodic model aligned more closely with natural pre-nuclear contours. To support reproducibility and future research, all datasets, source codes, and trained models are publicly available under the CC BY-NC-ND 4.0 license.
Training and Test Machine Learning Models on Encrypted Data: Initial Results and Challenges
Privacy is critical when using Machine Learning (ML) models over sensitive data, like healthcare, finance, and legal systems. Many of these models are trained or executed on cloud services, meaning sensitive data is transmitted over the network, or third-party services operate directly on unprotected data during training and inference, increasing exposure to potential leaks. Data encryption is a promising solution that guarantees high privacy levels. An adequate cryptography solution for ML is Homomorphic Encryption, a cryptographic method that allows mathematical operations on ciphertexts, i.e., encrypted data, producing encrypted models and outputs that only authorized parties can decrypt. However, the protection offered by Homomorphic Encryption comes at a significant computational overhead. Additionally, only specific mathematical operations (typically additions and multiplications) are allowed, and encrypted computations accumulate noise that reduces the result’s precision. This paper discusses the challenges of using encrypted data in training and test steps of ML models. It experimentally analyzes the impact on error rates and processing times when traditional classifiers, such as Artificial Neural Network and Logistic Regression, are adapted to process encrypted data. We adopt the CKKS scheme, a Homomorphic Encryption method that supports approximate computations over real numbers and adapted the activation functions of the classifiers using three approximation methods in an experimental evaluation with five medical datasets.
TRIDENT: Tabular Representation Inference with Dedicated Embeddings for Null Tokens
Handling missing data remains a fundamental challenge in learning from tabular datasets. In this work, we introduce TRIDENT (Tabular Representation Inference with Dedicated Embeddings for Null Tokens), a Transformer-based architecture specifically designed for robust classification on tabular data with high missingness. TRIDENT employs tailored embedding strategies for numerical and categorical features, including learnable, column-specific embeddings for missing and masked values, allowing the model to natively interpret data absence without prior imputation. The architecture follows a two-stage training procedure: (i) a self-supervised pre-training phase, in which the model learns to reconstruct the original feature embeddings of artificially masked positions by leveraging the context from the partially corrupted input sequence, and (ii) supervised fine-tuning on classification tasks using the [CLS] token representation. To evaluate the model’s effectiveness under various levels of missingness, we constructed a benchmark composed of diverse real-world tabular datasets. Each dataset was artificially degraded by introducing missing values at 20%, 40%, 60%, and 80%, yielding five versions per dataset. We compared TRIDENT against six baselines, including three tree-based models and three Transformer-based models. Results indicate that TRIDENT consistently outperforms all baselines across most missingness levels, especially in high-missing regimes (60% and 80%), where it achieves up to 3.4% absolute improvement in average F1-score compared to the best-performing baseline. Notably, TRIDENT exhibits strong resilience to missing patterns without relying on explicit data imputation, and maintains stable performance across heterogeneous datasets. These findings suggest that leveraging learnable [NULL] embeddings and self-supervised contextual reconstruction provides a robust inductive bias for tabular learning with incomplete data.
U-Net-EL: A U-Net and Early Learning-Based Method for Histopathological Breast Image Segmentation
Breast cancer affected approximately 66 out of every 100,000 women between 2023 and 2025, making it the most common type of cancer among women worldwide. Early detection remains a crucial factor for successful treatment and improved patient outcomes. Among the various diagnostic techniques, histopathological imaging plays a fundamental role in the accurate identification and classification of breast cancer. In this context, the objective of this study was to develop a novel segmentation method tailored for histopathological tissue analysis, focusing on structures such as tumors, stroma, lymphocytes, and other relevant components. The proposed approach, named Early Learning, was designed to enhance the training process of deep neural networks by introducing segmentation feedback at early layers of the architecture. This method aims to increase training efficiency and segmentation precision. Experimental results demonstrated promising performance, achieving an Intersection over Union (IoU) of 0.6703 and a Dice Similarity Coefficient (DSC) of 0.7099. The integration of Early Learning into the U-Net architecture proved effective, reinforcing its potential to assist in breast cancer diagnosis and support histopathological decision-making.
Variable Importance and Interaction in Differential Evolution: Online Learning and a New Recombination Operator
Offline Linkage Learning (LL) techniques have been used for estimating variables interaction. An online LL technique for Differential Evolution (DE) is proposed. In addition, the proposed technique estimates variable importance. Variable importance and interaction can be relevant to understand properties of optimization instances. We estimate variable importance and interaction in problems of the IEEE CEC’17 Competition on Single Objective Real-Parameter Numerical Optimization using the proposed technique. The resulting importance and interaction graph provides valuable insights, revealing key properties of the problems. Variable importance and interaction can also be useful for designing efficient crossover. We propose the Real Variable Interaction Crossover (RVIntX), which explores the epistatic structure of the problem and the variables common to both parents to find good recombination masks. Experimental verification confirms that RVIntX can improve the quality of the results. Additionally, although some operators based on linkage information are not useful in solving problems with high epistasis, RVIntX does not suffer from this disadvantage.
A Fully Automatic Approach for COVID-19 Diagnosis in CT Imaging: Integrating Lung Segmentation, Fine-Tuning and Grad-CAM Visualization
The pandemic caused by the COVID-19 virus has highlighted the need for efficient and automated medical diagnostic tools to assist healthcare professionals in the fast and accurate identification of the disease. This study introduces a fully automatic approach for classifying COVID-19 in thoracic computed tomography (CT) images from the SARS-CoV-2 CT-scan dataset, employing deep learning, automatic lung segmentation, and fine-tuning models to the specific problem. Previous transfer learning experiments revealed that MobileNet emerged as the most effective architecture for feature extraction. Integrating Detectron2 for lung segmentation and subsequent fine-tuning of the selected MobileNet model significantly improved classification performance. Visual analysis of model interpretability using Grad-CAM demonstrated that with segmented images and the refined model, the network focused on lung regions, enhancing understanding of the model diagnosis. As a result, the proposed fully automatic method achieved superior evaluation metrics compared to other methods in the literature, reaching values of 99.60% for accuracy, precision, and F1-Score, along with 99.59% for recall and 99.20% for the Matthews Correlation Coefficient (MCC).
A Multi-Dimensional Comparative Study of Generative Adversarial Networks, Diffusion Models, and Statistical Methods for Synthetic Health Data Generation
Synthetic data is increasingly important in privacy-sensitive or data-scarce domains such as healthcare, where access to real data is constrained by confidentiality or data availability challenges. This study compares three approaches for the generation of synthetic tabular health data: a statistical method (Gaussian Copula), an adversarial deep learning model (CTGAN), and an implementation of a diffusion-based technique called SimpleTableDiffusion. These methods represent different modeling paradigms: statistical, adversarial, and stochastic, each offering trade-offs in interpretability, flexibility, training stability, and privacy. We assess their performance across three dimensions: (i) statistical fidelity, that is, how well they replicate real data distributions; (ii) utility, measured by the effectiveness of classifiers trained on synthetic data and evaluated on real data; and (iii) privacy preservation, using a disclosure risk metric that estimates the likelihood of sensitive attribute inference. Our results show that CTGAN achieves the best overall performance, leading in utility, privacy, and marginal distribution quality. The Gaussian Copula excels at modeling conditional dependencies but lags in predictive tasks. The diffusion-based model performs competitively across metrics but falls short of the other generative models. This work establishes a unified benchmark, pointing towards hybrid approaches that leverage complementary strengths.
A Novel Interpretable Approach to Deep Multimodal Data Fusion Applied to Cancer Diagnosis
The use of artificial intelligence (AI) in healthcare has seen significant growth, particularly in cancer diagnosis, which remains one of the leading causes of mortality globally. AI-based computer-aided diagnosis systems leveraging computer vision have explored multimodal data including images, text, and graphs, among others to improve diagnostic accuracy. However, many approaches, especially those involving middle fusion, are considered black-box models. Inspired by the clinical reasoning adopted by medical professionals, and aiming to develop a more interpretable approach based on deep multimodal fusion, this paper proposes a new hybrid approach. Firstly, the medical lesion image is classified using a convolutional neural network or transformer, generating a probability for one of the target classes. Next, this probability, combined with clinical and/or sociodemographic data, is used as input to an external classifier, which generates the final diagnostic. The proposed approach was evaluated: 1) on the skin cancer PAD-UFES-20 dataset consisting of clinical image and patient lesion information, and 2) on oral cancer NDB-UFES dataset consisting of histopathological images and sociodemographic data. On the one side, the obtained results indicate a slight inferior performance in terms of balanced accuracy compared to state-of-the-art using middle fusion, but on the other side our model provided interpretability using Shapley Additive Explanations (SHAP).
Anomaly Detection in Spatiotemporal Patterns: A Case Study of Brazilian Traffic Accidents During the COVID-19 Lockdown
This study proposes a methodology to assess pattern changes in spatio-temporal data as a descriptive analysis. As a study case, we focus on traffic accident profile changes in Brazil due to the lockdown of the COVID-19 pandemic, analyzing different categories of occurrences separately. The method uses mainly public data on highway incidents and applies spatial correlation and linear regression models. The combined indicator of dissimilarity and spatiotemporal autocorrelation identifies regional anomalies when regressed regarding data before and during the pandemic. The methodology revealed that the lockdown affected accident characteristics differently across states, leading to changes in state-level and regional rates that could be overlooked in exploratory analyses or when neglecting spatial relationships. This case study can inform public policies and guide future research on the impacts of lockdown traffic and on human behavior.
Assessing DeepSeek-R1’s Performance on Brazil’s Defining National Education Benchmark
This paper evaluates DeepSeek-R1’s performance on Brazil’s National High School Exam (ENEM), a culturally-specific educational benchmark that tests reasoning across multiple domains. Using the pass@k metric across three years (2022–2024) of ENEM questions, we found strong reasoning capabilities, particularly in Human Sciences, where 2023–2024 performance exceeded 0.98 pass@1 score. Mathematics showed the most variability, with diverse scores across the three years. The model demonstrated sophisticated self-translation capabilities when handling Brazilian Portuguese language questions without explicit translation instructions. Despite strong overall performance, inconsistencies across subject domains persist. Our findings suggest that recent advances in AI reasoning extend effectively beyond typical AI benchmarks to diverse cultural contexts, with implications for AI deployment in various global settings. This evaluation contributes to understanding how generative AI systems perform when facing multidisciplinary, culturally-situated reasoning challenges.
Assessing Demographic Bias and Fairness in Facial Recognition Systems: A Framework
This paper presents a structured framework for assessing bias in facial recognition (FR) models, with an application to a case study. Bias in FR technologies towards sensitive demographic groups remains a significant challenge and a shared responsibility among researchers and companies in the design and development of fair systems. Despite increasing attention to fairness in FR, most existing studies address isolated stages of bias evaluation, with few offering an end-to-end well-defined solution. The proposed framework was developed in a structured and objective manner: 1) the selection of appropriate datasets; 2) the application of bias evaluation metrics; and 3) the techniques and the comparative analysis of performance across demographic groups. That framework aims not only to assist researchers in assessing bias in FR models but also to support the comparison and establishment of benchmarking tests for bias across different models. As a second contribution, we present a case study that applies the proposed framework to widely used benchmark datasets and relevant state-of-the-art pre-trained FR models, providing a comprehensive evaluation of performance disparities across different demographic groups.
Better Call Lucas: A Conversational Assistant for Older Adult Support
This work presents the development of Lucas, a virtual assistant built entirely with open-source tools to support older adults in their daily routines. Built upon the Robot Operating System (ROS 2), the system is composed of modular nodes for wake word detection, speech processing, and natural language interaction. The assistant is triggered by the spoken expression “Opa Lucas” after which it records the user’s voice, transcribes the audio to text using Whisper, and processes the instruction using the LLaMA 3 Large Language Model. Depending on the user’s request, the system may access external tools, such as reminders and weather forecasts, via predefined APIs and SQL queries. The entire interaction is presented through a multimodal interface built with Pygame, combining speech synthesis and animated feedback. The assistant supports speaker identification through voice verification. To evaluate the assistant, a set of instructions was proposed to assess the accuracy, errors made, and the nature of the responses, including aspects such as harmfulness, helpfulness, and coherence. Additionally, the assistant was reviewed and tested by a geriatric care professional. The results demonstrate the assistant’s potential to deliver coherent, accurate, and timely instructions, suggesting its applicability in daily routines. The assistant’s source code is publicly available in a GitHub repository, along with documentation for setup and usage.
Comparative Analysis of Deep Convolutional Models for the Classification of Canine Dermatological Conditions
Dermatological diseases represent one of the leading causes of clinical consultations in dogs, affecting over 30% of sick animals. The application of artificial intelligence techniques to support automated diagnosis through image analysis emerges as a promising tool to facilitate and scale veterinary care, especially in regions with limited access to specialists. Although widely explored in human medicine, such approaches remain incipient in the field of veterinary medicine. Addressing this gap, the present study proposes an effective computational solution to assist veterinary diagnosis by developing an adapted deep learning model. The proposed optimized model achieved strong performance across all evaluation metrics, reaching 93.79% accuracy, 89.22% precision, 98.18% AUC, and 87.30% F1-score. Our approach was available in three public datasets of canine dermatological diseases, combined to enhance sample diversity and increase model robustness. This work contributes to advancing computational tools for supporting clinical decision-making in animal healthcare.
DWNNet-Therm: A Deep Wavelet Neural Network Architecture Dedicated to Multiclass Classification in Breast Thermography
Early diagnosis of breast cancer is essential to increase the chances of successful treatment and improve patient survival. Thermography is a non-invasive technique that records infrared radiation emitted by the skin, enabling the detection of thermal changes associated with pathological processes, such as breast lesions. This work proposes a method based on a Deep Wavelet Neural Network (DWNN) for the automatic classification of thermographic images into four clinical categories: cyst, benign lesion, malignant lesion, and no lesion. The approach combines the multiscale analysis provided by wavelet transform with the deep learning capabilities of convolutional neural networks, allowing efficient and robust extraction of relevant thermal features for diagnosis. The model was trained and validated using a specific thermographic database. The results obtained demonstrate an overall accuracy of 74.41% in the test set, with satisfactory performance in precision, recall, and F1-score metrics for the different classes. The high precision in identifying malignant lesions (0.84) stands out, although the recall for this class was moderate (0.67), indicating the need for improvements to reduce false negatives. In conclusion, the proposed methodology presents itself as a promising tool to assist healthcare professionals in the early diagnosis of breast cancer through thermography, contributing to the development of non-invasive, accessible, and effective methods.
DR-AIVis: A Hybrid Approach for Diabetic Retinopathy Detection Using U-Net Segmentation and CNN Classification with Grad-CAM Explainability
Diabetic retinopathy (DR) is a diabetes-related ocular complication that can lead to vision loss. Its detection is performed through fundus examinations, assisted by lesion segmentation techniques. The IDRiD and APTOS-2019 datasets are used for DR lesion segmentation and classification, respectively. Using the U-Net architecture, lesions such as microaneurysms and exudates were segmented, while CNNs classified disease stages. In this paper, we present DR-AIVis, an approach for segmentation, classification, and explainability of diabetic retinopathy (DR). Our results demonstrate an accuracy of 93.84% in segmentation and 98.30% in classification. Additionally, we employ Grad-CAM to highlight the most relevant regions of the image. As contributions, our work includes an automated system for DR segmentation and classification, as well as a mechanism to identify the most important image regions for decision-making, thereby enhancing confidence in the provided results using Grad-CAM.
Emotionally and Cognitively Aware Proactive Conversational LLM-Assistants for Healthcare
We propose MarIARel and MarIAToM, two proactive assistants based on Large Language Models (LLMs). They integrate principles from Relevance Theory and Theory of Mind (ToM) to analyze users’ interaction history and initiate personalized, contextually relevant conversations. MarIARel selects users and topics based on relevance, while MarIAToM also considers users’ estimated mental and emotional states. An eight-month experimental study was conducted with 385 patients, who first interacted with a non-proactive assistant that initiated dialogues only when guided by human intervention, and subsequently with MarIARel and MarIAToM. To evaluate the impact of proactive messages, we introduce the metric of response predictability, which quantifies the likelihood of user replies. Results show that MarIAToM, which leverages ToM features, achieved higher response predictability and engagement. We also developed a qualitative assessment method based on emotional impact, applied by human-computer interaction specialists. Experts unanimously found that MarIAToM’s messages were more emotionally engaging and better at sustaining user responses.
Evaluating Graph-Based Representations of ATR-FTIR Data Using Complex Networks
This study investigates different graph construction heuristics applied to attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) data, analyzing their topological properties using complex network metrics. In addition to this structural analysis, we also evaluate the data classification performance of each graph representation. We compare traditional approaches—such as co-occurrence (COOC), correlation (CORR), epsilon neighborhood (EPS), and k-nearest neighbors (kNN)—with visibility-based strategies (visibility graph - VG). These methods are applied to three biomedical datasets (COVID-19, Oral Cancer, and Diabetes), with their topological properties analyzed using complex network metrics. The topological analysis revealed that COOC and EPS generated structurally poor networks, due to fragmentation or overdensity. In contrast, kNN, CORR, and the two VG variants consistently produced sparse networks with high modularity, indicating a more informative representation. The classification results confirm that there is no universally superior method, with the best performance being dataset-dependent: kNN achieved the highest accuracy for COVID-19, CORR for Oral Cancer, and VG sim for Diabetes. These results collectively validate kNN, CORR, and visibility graphs approaches as effective and structurally expressive tools for spectral data, offering a robust alternative to traditional vector-based analysis in machine learning applications.
Evaluating Odor Selectivity in α-Synucleinopathy: ML-Based Odor Profiling
Cerebrospinal fluid (CSF) α-synuclein seed-amplification assay (SAA) is the most sensitive in vivo marker of misfolded α-synuclein. Still, its reliance on lumbar puncture—a costly, time-consuming, and invasive procedure—restricts broad clinical or population-based use. Low-cost, non-invasive smell tests such as the 40–item University of Pennsylvania Smell Identification Test (UPSIT) could serve as an efficient triage tool if they could reliably predict CSF-SAA positivity. This work aims to determine if item-level UPSIT responses reveal a selective olfactory pattern of SAA positivity after strict control for overall UPSIT score. Such a pattern could be used to enhance the diagnostic value of smell tests further. UPSIT and SAA data from the Parkinson’s Progression Markers Initiative were used to build two balanced SAA+ – SAA- subsets. Six classifiers were tuned and evaluated using leave-one-out cross-validation. Permutation SHAP values quantified the importance of each odor for the best models. Given the marginal gain over random chance that the models achieved, our findings parallel recent Parkinson’s disease studies, which suggest that SAA causes a general loss of smell without affecting specific smells more than others, or that such a difference is minor. Although odor selectivity was negligible, UPSIT and other olfactory tests remain valuable and scalable screens for potential CSF-SAA positivity, as overall olfactory capacity itself carries strong predictive power.
Exploring Content and Social Connections of Fake News with Explainable Text and Graph Learning
The global spread of misinformation and concerns about content trustworthiness have driven the development of automated fact-checking systems. Since false information often exploits social media dynamics such as “likes” and user networks to amplify its reach, effective solutions must go beyond content analysis to incorporate these factors. Moreover, simply labelling content as false can be ineffective or even reinforce biases such as automation and confirmation bias. This paper proposes an explainable framework that combines content, social media, and graph-based features to enhance fact-checking. It integrates a misinformation classifier with explainability techniques to deliver complete and interpretable insights supporting classification decisions. Experiments demonstrate that multimodal information improves performance over single modalities, with evaluations conducted on datasets in English, Spanish, and Portuguese. Additionally, the framework’s explanations were assessed for interpretability, trustworthiness, and robustness with a novel protocol, showing that it effectively generates human-understandable justifications for its predictions. The code and experiments are available at https://github.com/MeLLL-UFF/mu2X/.
Exploring Data-Oriented Strategies for Training ECG Self-supervised Models
The electrocardiogram (ECG) is a non-stationary signal used to assess heart health and to detect systemic conditions such as mental stress and drug toxicity. However, labeling ECG data is notably costly and can limit the applicability of supervised deep neural networks in tasks involving this physiological signal. Self-supervised learning (SSL) is a two-stage approach that has gained attention in this scenario due to its labeling efficiency and knowledge transfer capabilities in various data modalities. However, recent studies describe the difficulties in transferring knowledge across different time series datasets. This work investigates several data-oriented strategies for pretraining self-supervised learning models in a cross-dataset setting to address a challenging ECG classification task. We introduce a data-driven regularization approach that perturbs the pretrained model using signals from multiple domains. We evaluated three well-established SSL models as backbones with different classification architectures across over 100 experiments, achieving superior performance to traditional SSL methods and a state-of-the-art supervised deep learning classification model.
Highway to... Determining Fatal Outcomes in Traffic Accidents Based on Police Reports
Brazil faces significant traffic safety challenges with its vast territory and one of the world’s largest road networks. Road traffic accidents, particularly on federal highways, remain a leading cause of death in the country, with serious economic and social consequences. This work presents a case study of three machine learning methods—Random Forest (RF), k-Nearest Neighbors (kNN), and Multilayer Perceptron (MLP)—for classifying the severity of traffic accidents in the Brazilian southern region. Using an open dataset from the Brazilian Federal Highway Police (PRF) covering the years 2021 to 2024, extensive preprocessing was carried out, including categorical variable encoding, feature selection, and the application of the SMOTE technique to address class imbalance. Model performance was assessed through statistical metrics such as specificity, F1-score, and AUC-ROC. The results show that RF and kNN (with SMOTE) achieved the best performance in predicting fatal accidents, both with AUC-ROC of 0.99. In addition to an in-depth model evaluation, this study presents a post-hoc analysis of feature importance and contributions through Shapley Additive Explanations (SHAP) for the best performing model, in order to support knowledge discovery and highlight the most influential factors associated with fatalities.
Improving Legislative Accessibility with Retrieval-Augmented Generation
This study presents the design, development, and evaluation of a question-answering (Q&A) system based on the Retrieval-Augmented Generation (RAG) framework, aimed at improving public access to the legislative documents of Assembleia Legislativa do Estado do Rio Grande do Norte (Alern). The system integrates a vector-based document retrieval mechanism with large language models (LLMs) to enable users to submit natural language queries and receive relevant, accessible responses. It leverages embedding-based search to retrieve semantically relevant content and utilizes LLMs to generate clear answers. A comparative analysis of various embedding models – such as BGE-M3, ada-002, and GTE – was conducted to identify the most effective configuration for content retrieval. Results showed that BGE-M3 achieved the highest retrieval accuracy (96.86%) among the models tested. LLMs, including Llama 3.1 and DeepSeek-R1, were evaluated for response generation using BERTScore and human feedback. Llama 3.1 outperformed DeepSeek-R1 in coherence, grammar/spelling, and adequacy, achieving an average human evaluation score of 85.6%. Evaluation results, based on both synthetic datasets and human-curated questions, demonstrate the system’s potential to enhance legislative transparency and user engagement by facilitating accessible interaction with complex legal content.
Integrating Explainable AI with Species Distribution Models to Assess Anthropogenic Impacts on Amazonian Birds
The Amazon Rainforest, a critical hub of global biodiversity, faces increasing anthropogenic pressures, necessitating effective monitoring and conservation strategies. Species Distribution Models (SDMs) are vital tools for predicting habitat suitability, yet advanced machine learning-based SDMs, while accurate, often suffer from limited interpretability. This “black box” nature restricts ecological understanding and hinders the translation of model outputs into actionable conservation insights, a limitation evident in previous studies on Amazonian avifauna which primarily identified correlations without fully disentangling the influence of individual environmental drivers. This study overcomes these limitations by integrating Explainable Artificial Intelligence (XAI) techniques with SDMs to specifically assess the impacts of anthropogenic atmospheric pollutants on sensitive bird species from the Tyrannidae and Thraupidae families. We leverage unique, high-resolution atmospheric data (including aerosols and trace gases) from the GoAmazon 2014/15 campaign and apply SHapley Additive exPlanations (SHAP) to interpret a high-performing Support Vector Machine (SVM) model predicting bird occurrence. Our findings reveal that atmospheric pollutants strongly associated with human activity–notably methane, carbon monoxide, ozone, and acetonitrile–are among the most significant predictors of habitat suitability. SHAP analysis quantifies the complex, often non-linear contributions of these variables, demonstrating how specific pollutants drive habitat suitability predictions locally and globally. By enhancing SDM interpretability, this research provides transparent, ecologically meaningful insights into environmental stressors, offering a more robust tool to support evidence-based conservation planning for Amazonian biodiversity.
Isolated Sign Language Recognition in LIBRAS
Hearing loss affects a significant part of the world population. Sign Language Recognition is the task of recognizing isolated signs in videos and has the potential to ease communication between Deaf and non-Deaf people. Data are vital to creating better Sign Language Recognition models. LIBRAS, the Brazilian Sign Language, still lags behind in terms of data availability in comparison to other Sign Languages, such as American and Russian. Our study aims to bridge this gap by introducing a novel LIBRAS dataset for Isolated Sign Language Recognition, MALTA-LIBRAS. This dataset is assembled from open online sources and can be used as a more diverse and representative test set than the previous available LIBRAS dataset, MINDS. We perform several experiments with three transformer-based video classification models for this task: VideoMAE, TimeSformer, and ViViT. Our findings show that action recognition pretraining substantially improves generalization beyond fine-tuning data distribution. Additionally, we identify a set of data augmentation strategies that further increase the model’s generalization to new conditions. In contrast to what could be expected, transferring knowledge from other sign languages does not yield significant improvements for LIBRAS.
Mapping the Landscape of Robotics for Autism Spectrum Disorder: A Scoping Review of Technologies, Applications, and Future Directions
This scoping review explores the use of robotic technologies as intervention tools for children with Autism Spectrum Disorder (ASD), an area of growing interdisciplinary interest. Following the PRISMA-ScR guidelines, we conducted a comprehensive search in the Scopus, IEEE Xplore, and ACM Digital Library databases for studies published between 2018 and 2025. From an initial set of 1167 publications, 27 articles met the inclusion criteria and were analyzed in detail. The results demonstrate a diverse range of robotic applications, with the NAO robot featured in 13 out of the 27 selected studies. Robotics interventions were primarily implemented in assistive therapy, social skills training, educational settings, and joint attention initiatives, predominantly targeting children aged 5 to 10 years. The volume of relevant publications peaked in 2020 and declined in subsequent years, highlighting both increased early momentum and the current need for renewed research efforts in this domain. Overall, the review identifies critical research gaps, particularly in terms of personalized and interdisciplinary intervention approaches. While robotic technologies show considerable promise in supporting autistic children, further high-quality studies are required to assess their long-term effectiveness and to inform the development of individualized, evidence-based practices.
Optimizing Model Merging Configurations for Brazilian Portuguese Sentiment and Hate Speech Classification with TIES-Merging and SaDE
The detection of emotions and opinions from textual data plays a critical role in diverse social applications, including political analysis, content moderation, online safety assurance, and the monitoring of emotional well-being in healthcare contexts. Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing (NLP) tasks, such as sentiment and hate speech detection. However, their effectiveness in specialized domains remains a challenge, often requiring adaptation techniques such as fine-tuning to optimize performance. For Brazilian Portuguese, sentiment and hate speech classification are tasks less explored compared to English, emphasizing the need for efficient adaptation strategies. Model merging methods have emerged as promising alternatives to obtain new language models without incurring the high computational costs or dataset requirements associated with fine-tuning techniques. This study investigates the integration of Self-adaptive Differential Evolution (SaDE) with the TIES-Merging method to optimize merging configurations of BERTimbau and its fine-tuned versions into a single model for Brazilian Portuguese sentiment and hate speech classification. Experimental results show that applying TIES-Merging, supported by evolutionary methods, produces models that outperform the existing fine-tuned model in the Brazilian Portuguese sentiment classification, while maintaining competitive performance in hate speech detection. In the comparison of evolutionary strategies, SaDE achieved results comparable to CMA-ES, a method commonly used in the literature, highlighting opportunities for further investigation into the tuning of the learning period parameter.
Perception of Guardians on the Use of Artificial Intelligence in Educational Geometry Games for Children
The study aims to evaluate caregivers’ perceptions of the use of Artificial Intelligence (AI) in educational games for children, emphasizing the positive impact of this technology on the learning process. The research, conducted with 14 caregivers, utilized a comprehensive questionnaire to collect feedback on various aspects, such as the level of engagement fostered in children, the appropriateness and variety of the proposed activities, and the quality of AI-generated questions. The results revealed a positive response to the game, with a satisfaction rate of 93.4%, and indicated that most participants found the use of AI both engaging and enjoyable, highlighting the games’ ability to capture children’s attention in a meaningful way. Furthermore, the majority of caregivers rated the AI-generated questions as appropriate and diverse, considering the content well-suited to children’s educational needs. This assessment demonstrates the effectiveness of AI in providing an interactive and personalized geometry learning experience, allowing each child to progress at their own pace in an engaging manner aligned with their comprehension levels. This adaptive learning approach, which adjusts content according to each child’s progress, was widely recognized by caregivers as an important advancement for educational development, delivering a high-quality, engaging, and educational experience.
Predicting and Interpreting Clinical Deterioration in the Intensive Care Unit Using Machine Learning and Explainable AI
Intensive Care Units in hospitals have been the focus of significant research efforts, as patients require continuous monitoring of their physiological parameters due to their elevated risk of rapid clinical deterioration. In this study, we evaluated eighteen predictive models for clinical deterioration in ICU patients. Fifteen of these models were composed by combining three machine learning algorithms – LightGBM, Random Forest and XGBoost – with five data imputation techniques: linear interpolation, forward filling, carry forward, indicator imputation, and zero imputation. The use of data imputation techniques aims to address the challenge of missing clinical information. The remaining three were formed using each machine learning algorithm, with the inclusion of statistical metrics for each feature in the dataset. For all models, hyperparameter tuning was performed using the grid search method, and model efficiency was assessed using the G-mean metric. The results demonstrated that models based on the Random Forest algorithm achieved the best performance. Finally, we applied the SHAP technique to identify the most influential variables for predicting clinical deterioration in the best-performing models.
Predicting College Student Mental Health Levels: A Machine Learning Approach Using Sociodemographic and Quality of Life Data
Worldwide, mental health (MH) issues among students, including anxiety and depression, are rising. Predictive models are crucial for early intervention. This study proposes a novel MH prediction model for college students using data mining (DM) and machine learning (ML) techniques. The model integrates sociodemographic data and Quality of Life (QoL) assessments, employing various classification and regression methods, alongside feature selection. Using a database of 880 students and data from the Mental Health Inventory (MHI), WHOQOL-Bref, and a sociodemographic questionnaire, the model achieved a high accuracy of 82.62%, with an R2 of 0.7139. Furthermore, the study identifies key factors influencing MH prediction. This data-driven approach provides a valuable tool for identifying students needing support, potentially improving intervention strategies and campus well-being programs.
Pseudo-labeling for Multi-label Legal Text Classification
Data augmentation is a widely used strategy to improve classification performance, yet it is only applied to labeled training data. In many real-world scenarios, however, vast amounts of unlabeled data remain underutilized. Pseudo-labeling offers a semi-supervised approach to incorporate this unlabeled data into model training. In this paper, we propose a simple yet effective pseudo-labeling method that combines clustering and label propagation to enhance performance in multi-label text classification tasks. Our approach addresses common challenges such as biases arising from decision boundaries and class imbalance. As a case study, we apply this method to the classification of legal cases in accordance with the United Nations 2030 Agenda for Sustainable Development Goals. In this context, the proposed augmentation strategy led to notable improvements in both accuracy and sensitivity metrics when compared to models trained solely on the original labeled dataset. This approach provides a valuable means to expand the existing knowledge base without the need for labor-intensive manual classification efforts.
Swin Transformer for Classification of Whole-Body PET Cancer Images
Cancer remains a critical global health challenge, with the World Health Organization (WHO) projecting 35 million new cases by 2050, necessitating advanced diagnostic tools such as whole-body FDG-PET imaging, which detects metabolic activity in pathologies. While 3D PET scans are powerful, their computational demands become excessive when dealing with deep learning, motivating the use of efficient 2D representations, moreover, this representation would assist radiologists in analyzing exams, since there are too few specialized professionals to interpret all the scans. This work proposes a Swin Transformer-based method to classify 2D Maximum Intensity Projection (MIP) images from FDG-PET scans into binary categories (cancerous vs. healthy), addressing challenges of multi-cancer detection (melanoma, lymphoma, lung cancer) and variability in image coverage. The approach achieved results of 82.08%±4.3% accuracy, 82.43% ± 2.9% F1-Score, 84.31% ± 4.4% recall, and 81.35% ± 7.1% precision. These results demonstrate the viability of the proposed approach, which combines the computational efficiency of 2D representations with the discriminative capability of the Swin Transformer architecture for medical image analysis.
Synthetic Data for Mental Health: A Comparative Analysis of LLMs, BERT, and Copy-Based Augmentation
Depression screening through social media has emerged as a promising research avenue; however, the scarcity of annotated data remains a significant barrier to effective model training. In this work, we evaluate several textual data augmentation strategies for screening users with depression through Brazilian-Portuguese Instagram posts. We explore three techniques of increasing complexity: simple post duplication, contextual word substitution using BERT-based models, and synthetic posts generation via Large Language Models (LLMs), both with and without the modulation of psychometric data from the Beck’s Depression Inventory (BDI-II) to create new instances. Experiments were conducted under both Single-Instance Learning (SIL) and Multiple-Instance Learning (MIL) frameworks, using multilingual sentence embeddings and an XGBoost classifier. Results reveal statistically significant differences among augmentation strategies, with LLM-based generation without BDI-II modulation achieving the highest performance. Contextual substitution proved to be a competitive and computationally efficient alternative. In contrast, psychometric modulation reduced model effectiveness, suggesting that artificially aligning emotional tone may compromise data quality. These findings underscore the importance of semantically coherent augmentation for sensitive applications in mental health. Code and supplementary material: https://github.com/Matheusutino/depression-data-augmentation.
A Comparative Study of Machine Learning Models for Bee Flora Classification: Integrating Feature Extractors with Classifiers
The bee flora refers to the group of plants from which bees collect floral resources, such as pollen, nectar, or both. The potential for beekeeping production in a region is influenced by its floral coverage. Therefore, understanding the foraging resources available to bees is essential for effectively managing and optimizing apiaries. We propose developing a computational model based on machine learning to classify flower images into three categories: pollen-producing, nectar-producing, or both. The objective is to provide insights about the beekeeping production potential of a specific region. The experiments utilized a dataset of 1,145 flower images from the beekeeping flora of the Sertão Central region in Ceará, Brazil. For feature extraction, ten Convolutional Neural Networks (CNNs) were employed, while classification was performed using Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), and a CNN. The study aimed to identify the best combination of feature extractors and classifier methods. The results demonstrated the efficiency of the proposed approach, with the optimal combination being ResNet50 coupled with SVM, achieving a Matthews Correlation Coefficient (MCC) of 92.94%.
A Double Transfer Learning Approach for Improving Canine Breast Cancer Detection Using VGG-16 on Histopathological Images
Cancer is one of the most devastating health conditions worldwide, resulting from uncontrollable cell divisions that invade adjacent tissues and organs. Early detection is crucial to improving patients’ quality of life and, sometimes, enabling a cure. In this context, computer vision tools hold significant potential to expedite cancer diagnosis. This study proposes analyzing a double transfer learning approach using histopathological images, where the model is initially trained on ImageNet, followed by fine-tuning with BreaKHis pre-trained weights before applying it to veterinary images. The results indicate that double transfer learning improves model performance by approximately 5%, as observed in k-fold cross-validation with k = 5, highlighting the effectiveness of this approach in enhancing the accuracy and robustness of models for cancer diagnosis in veterinary imaging.
A Livestock Monitoring System and Machine Learning Sequence Classification Comparison for Behavior Classification
Timely disease diagnosis is essential for reducing livestock mortality, as delays caused by reliance on human visual inspection can lead to severe consequences. Early detection of illness hinges on access to detailed, individual animal data capable of capturing subtle behavioral and physiological changes before visible symptoms emerge. A robust classification system is therefore critical for automating behavior analysis, enabling prompt identification of deviations from normal patterns that may indicate illness and facilitating timely intervention. This work reports the development of an innovative livestock behavior classification and health monitoring device. The system incorporates a wireless neck collar equipped with motion and temperature sensors for continuous animal activity monitoring. A prototype collar was tested on two heifers in a controlled field setting under video surveillance. Collected data were manually annotated by human observers, classifying the heifers’ behaviors into five key categories: eating, drinking, walking, lying down, and standing. The study covers the entire process of creating the monitoring system, from the design and construction of the neck collar to data collection and annotation. It also details the complete data mining workflow, including data cleaning, pre-processing, and comparative analysis using six shallow Machine Learning (ML) algorithms. The results indicate that Gradient Tree Boosting is the best-performing model based on classification metrics appropriate for multiclass imbalanced data, such as the F1-score, Weighted F1-score, and Kappa coefficient. The designed device, integrated with a ML model, proved to be an effective and low-cost approach for classifying livestock behavior.
A Semantic Representation for the MIMIC-IV Database
The exponential growth of data in public databases, such as GenBank, underscores the need to consolidate information from multiple sources and formats. The diverse strategies adopted by each database create heterogeneities in schemas, file formats, and identifiers, making direct and efficient integration challenging. The absence of a common semantic model for querying various independent biological databases has driven the community to develop biomedical ontologies. In this context, Knowledge Graphs emerge as a viable strategy to represent information in a semantically rich and organized manner, playing an essential role in overcoming integration and semantic interoperability challenges, enabling applications ranging from protein function identification to clinical applications. This work proposes the conception of a semantic representation for the MIMIC-IV database, aimed at creating its Knowledge Graph. This initiative seeks to integrate MIMIC-IV with other existing biomedical databases, facilitating discoveries and significant insights in the biomedical field. The project also addresses an existing gap in the literature regarding MIMIC-IV by providing a detailed analysis of its semantic representation and its application in biomedical research.
A Stacked Deep Learning-Based Method for Multiclass Classification of Sandstones in Microtomographic Images
This study proposes a hybrid approach for the multiclass classification of micro-CT images of sandstones, aiming to support the automation of rock analysis in oil and gas applications, given that this process still heavily relies on manual analysis by specialists, which can be time-consuming, subjective, and error-prone. The methodology was developed using the 11 Sandstones: raw, filtered and segmented data dataset. Convolutional neural networks (DenseNet121, ResNet50, and VGG16) were trained individually, and their respective softmax outputs were weighted according to each model’s individual accuracy. The proposed method constitutes a stacked model by integrating the weighted averaging strategy with a multilayer perceptron (MLP). The results showed that the proposed approach outperformed both the individual models and simpler ensemble techniques, achieving up to 99% overall accuracy in the test evaluations. Furthermore, it proved effective in correcting errors observed in the other approaches, providing greater stability and robustness.
ADR Based on Fuzzy Logic with Dynamic Number of Uplink Transmissions Adjustment for LoRaWAN Networks in Mobility Scenarios
Adaptive Data Rate (ADR) mechanisms are essential for optimizing communication performance and energy efficiency in LoRaWAN networks. However, traditional ADR schemes often struggle to adapt reliably to dynamic channel conditions, especially when devices are mobile. To address these challenges, this work introduces the Fuzzy ADR sensitive to the Coefficient of Variation with a Dynamic Transmission Window (FADR-CVM), a fuzzy-logic-based mechanism that applies robust normalization of the Signal-to-Noise Ratio (SNR), the link’s Coefficient of Variation (CV), and the variation in the number of measured SNR values–also inferred by the fuzzy system. Based on these factors, the mechanism dynamically adjusts the number of future uplink transmissions, infers changes to the spreading factor (SF) and transmit power (TP), and thus aims to boost data-delivery reliability and improve energy efficiency. The proposed mechanism was evaluated through simulations in the NS-3 tool, considering a 100 km area with up to 1000 randomly distributed end devices (EDs) operating either statically or under mobility. Experimental results show that the proposed solution improves the packet delivery ratio (PDR) by up to 10% and increases energy efficiency by up to 60 bits/J. These results highlight the potential of integrating fuzzy logic with dynamic transmission adjustments as an effective strategy for enabling more robust and energy-efficient communications in LoRaWAN networks.
Explainable Artificial Intelligence for Behavioral Simulation: SHAP-Based Analysis of Decision-Making in Virtual Rats
The opacity of artificial neural networks (ANNs) has raised growing concerns about the interpretability of their decisions, particularly in behavioral simulations. To address this, the field of explainable artificial intelligence (XAI) has introduced tools that clarify the internal logic of machine learning models. This study applies SHAP (SHapley Additive exPlanations) to analyze the behavior of virtual rats controlled by ANNs in the Elevated Plus Maze, a widely used paradigm for assessing anxiety. The networks were evolved through genetic algorithms, and SHAP values were used to identify the contribution of each input (recurrent neurons and environmental sensors) to decision-making. The results show that long-range sensors, especially those detecting obstacles on the left, had the highest relevance, although most decisions were driven by a small subset of recurrent neurons. Network configurations with two or three hidden neurons were most frequent, indicating compact but effective internal processing. This approach enhances model transparency and provides an ethical alternative to live-animal testing, in alignment with the principles of the 3Rs (Reduction, Refinement, and Replacement).
Are Current Computer Vision Models for Handgun Detection Ready to Be Deployed in Camera Surveillance Systems?
Computer Vision (CV) has become a key component of modern camera surveillance systems, enhancing public and private security. One persistent challenge in this domain is the reliable detection of handguns in low-quality images, in which weapons are often small, partially occluded, and carried discreetly to avoid attention. To address this, recent advances have focused on specialized models trained on specific weapon types and human poses. However, two crucial aspects remain underexplored. First, while state-of-the-art models have improved handgun detection rates, they also exhibit a noticeable increase in false positives, often mistaking unrelated objects for firearms. Second, although combining gun detection with pose estimation is a common approach, it may offer limited value in practice, as criminals tend to maintain natural, non-suspicious postures to avoid drawing attention. In this work, we analyze the effectiveness of such models in scenarios that reflect real-world urban settings: low-resolution surveillance footage, small concealed weapons, and non-suspicious human behavior. To address these limitations, we propose two key contributions: an enhanced approach that extends a gun-and-pose model with a different method for computing the intersection between predictions and ground truth; and a new set of images designed to better train models for detecting handguns in the context of non-suspicious human behavior. In addition to improving performance in our experiments, our findings also highlight the need for more robust and context-aware handgun detection systems in CV-based surveillance.
Assessing an Evolutionary Search Engine for Small Language Models, Prompts, and Evaluation Metrics
The concurrent optimization of language models and instructional prompts presents a significant challenge for deploying efficient and effective AI systems, particularly when balancing performance against computational costs like token usage. This paper introduces and assesses a bi-objective evolutionary search engine designed to navigate this complex space, focusing specifically on Small Language Models (SLMs). We employ the NSGA-II algorithm and prompt grammar to simultaneously optimize for task accuracy and token efficiency across some reasoning tasks. Our results successfully identify diverse, high-performing model-prompt combinations, quantitatively revealing the critical trade-off between the two objectives. This research highlights task-specific affinities between particular SLMs and prompt structures (e.g., instructions, context, chain of thought). The generated practical Pareto fronts offer decision-makers a portfolio of optimized solutions adaptable to their specific constraints. This automated approach moves beyond traditional manual tuning, providing a foundational framework for discovering effective human-AI interaction patterns.
ASV-CM Score Fusion for Robust Speaker Verification in Spoofed Scenarios
Spoofing-Aware Speaker Verification (SASV) hybrid systems have become a necessity as traditional Automatic Speaker Verification (ASV), despite recent improvements, remain vulnerable to spoofing attacks. This work proposed a multi-model fusion that integrates ASV and Countermeasure (CM) systems by combining decision scores from both types. The individual and combined systems were evaluated on their ability to detect spoofing. Results show that spoofing attacks significantly degrade the performance of all standalone ASV systems. Among the CM models, Whisper and HuBERT outperformed the ASVspoof2019 baselines on both the Logical Access (LA) and Physical Access (PA) datasets. Among all hybrid configurations evaluated, the WeSpeaker Resnet34-HuBERT and WeSpeaker Resnet34-Whisper combinations demonstrated the best performance, achieving EER (Equal Error Rate) of 0.5% and 0.8% on the LA and PA datasets, respectively. These results achieved performance close to the state-of-the-art while offering improved and better generalization.
AutoFAR: An Intelligent Hybrid System for Boosting Autoformer Forecasts
Time series forecasting is essential for many real-world applications, spanning healthcare, climatology, finance, energy, manufacturing, etc.. Machine Learning (ML) models have garnered significant attention for their ability to capture complex, nonlinear patterns in temporal data, an area where traditional linear statistical methods often face limitations. The literature highlights that Autoformers have gained significant relevance due to their ability to effectively model autocorrelation structures in time series while performing trend and seasonality decomposition. However, recent studies have presented mixed results regarding its performance. This can occur because nonlinear models often struggle to simultaneously capture linear and nonlinear temporal patterns effectively. This paper proposes a novel intelligent hybrid system named AutoFAR to address this limitation. The proposal enhances Autoformer forecasting by modeling the residual series using an Autoregressive Integrated Moving Average (ARIMA) model. Initially, Autoformer is used to forecast the time series under study. Subsequently, its residuals are analyzed to identify any underlying linear patterns. If significant linear patterns exist, an ARIMA model is fitted to these residuals. The final forecast is obtained by adding the outputs from the Autoformer and ARIMA models. An experimental evaluation was conducted with five well-known datasets. The results show that AutoFAR consistently outperforms both Autoformer and well-established forecasting models, Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), Random Forest, eXtreme Gradient Boosting (XGBoost), and Transformers. For four out of five time series, AutoFAR achieves significant improvements, indicating that intelligently modeling linear components left by the Autoformer is a promising strategy.
Beyond Profit: An Analysis of Risk and Return Metrics in Automated Trading Systems
This study addresses the challenge of effectively evaluating the performance of automated trading systems based on Machine Learning (ML) techniques, particularly in light of the complexity of financial markets and their associated risks. The central issue identified is the lack of standardization in the selection of metrics to assess profitability and the robustness of strategies in the face of risk. To address this gap, a Systematic Literature Review (SLR) was conducted following Kitchenham’s guidelines to map the most commonly used ML techniques between 2019 and 2024, as well as the financial metrics employed in the evaluation of algorithmic trading strategies. As key findings, the study highlights the need for broader metrics that combine both profit and risk indicators to ensure more robust evaluations of automated trading strategies. Furthermore, it points to a research gap in studies applied to non-traditional markets. It proposes future investigations focused on analyzing the temporal evolution of Artificial Intelligence techniques and assessing the impact of regulation on the development of systems targeting the cryptocurrency market.
Coma Etiology Prediction Using a CNN-LSTM Model Guided by EEG Brain Regions
Accurate identification of coma etiology is essential for effective treatment in neurocritical care. However, significant challenges persist, especially in settings with limited resources. This study investigates the potential of electroencephalogram (EEG) signals from distinct brain regions for predicting coma etiology using an adapted Convolutional Neural Network combined with Long Short-Term Memory (CNN-LSTM) model. Building on prior hybrid CNN-LSTM architectures that processed all EEG channels collectively, we refined the model to integrate raw EEG data from specific regions (frontal, central, parietal, temporal, and occipital) with patient-specific clinical features (age, gender) and statistical EEG metrics. The model was evaluated on a real-world dataset of 50 patients with coma etiologies including traumatic brain injury, metabolic coma, and stroke, collected at the Clinical Hospital of the Federal University of Uberlândia (HC-UFU). Experimental results demonstrate that the frontal region, when combined with clinical features, yielded the highest predictive performance, achieving an accuracy of 70% and a F1 score (macro) of 68%. These findings highlight the frontal region’s critical role in capturing diagnostic patterns and underscore the value of integrating clinical data to enhance model robustness. By leveraging region-specific EEG analysis, this approach advances EEG-based predictive modeling. It offers a scalable and non-invasive tool to improve diagnostic precision in neurocritical care, particularly in underserved settings, and paves the way for future multimodal diagnostic strategies.
CRRF-Score - Cumulative Ranking Random Forest Scoring Function for Free Energy of Binding Prediction in Protein-Ligand Docking
The growing challenges posed by infectious diseases, antimicrobial resistance, increasing cancer prevalence, and untreatable rare genetic conditions highlight the necessity to accelerate drug discovery. This process remains expensive and time-consuming but computational approaches like molecular docking combined with machine learning (ML) can impact on this process reducing costs and time. In molecular docking, the interaction between drug candidates (ligands) and target receptors is evaluated with Scoring Fuctions (SFs). The accurate prediction of the binding free energy in protein-ligand complexes performed by SFs remains a critical challenge in computational drug discovery. This study proposes CRRF-Score (Cumulative Ranking Random Forest Scoring Function), a ML-Based SF that integrates feature selection and ensemble models to enhance binding affinity prediction. CRRF-Score combines descriptors from DeltaVinaRF20, AutoDock Vina, and RDKit 2D, employing a diverse set of algorithms - Random Forest, Decision Tree, Lasso, Principal Component Analysis and Automatic Relevance Determination Regression for robust feature ranking. Our approach is trained on PDBbind 2018 and evaluated using CASF-2016 benchmarks, demonstrating competitive performance in Scoring Power and Ranking Power. Results show that our cumulative ranking strategy for feature selection combined with random forest algorithm for training the SF matches state-of-the-art commercial SFs in key metrics, offering a promising solution for molecular docking.
Data-Driven Proxy Models Based on Recurrent Neural Network for Reservoir Simulators
Management of oil reservoirs involves conducting numerous simulations to understand the oil field, which results in high computational costs. These costs have an adverse effect on activities related to controlling production and injection wells for optimizing production. This study aims to develop a data-driven proxy model that uses only historical production data to replace the reservoir simulator. The proxy models must accurately predict responses and capture reservoir dynamics, i.e., a controlled time series that includes the time series of fluid phase flow rates and bottom hole pressure, based on the specified controls of injection and production wells. For this purpose, in this work we apply four neural network strategies to capture reservoir dynamics: Long Short-Term Memory (LSTM), LSTM with attention mechanism (LSTM-A), Gated Recurrent Unit (GRU), and GRU with attention mechanism (GRU-A). To assess the performance of these strategies, we used a synthetic reservoir model from the literature with 4 production wells and 8 injection wells. GRU-A demonstrated superior effectiveness and exhibited lower variability compared to other approaches, achieving up to 13% improvement in error reduction on the test set. Thus, even simpler neural network models prove useful for replacing simulators, saving time and computational resources. The source code is available here.
Deep Learning-Based Intrusion Detection for Automotive Ethernet: Evaluating and Optimizing Fast Inference Techniques for Deployment on Low-Cost Platform
Modern vehicles are increasingly connected, and in this context, automotive Ethernet is one of the technologies that promise to provide the necessary infrastructure for intra-vehicle communication. However, these systems are subject to attacks that can compromise safety, including flow injection attacks. Deep Learning-based Intrusion Detection Systems (IDS) are often designed to combat this problem, but they require expensive hardware to run in real time. In this work, we propose to evaluate and apply fast neural network inference techniques like Distilling and Pruning for deploying IDS models on low-cost platforms in real time. The results show that these techniques can achieve intrusion detection times of up to 727 μ s using a Raspberry Pi 4, with AUCROC values of 0.9890.
Enhancing Situational Awareness in Public Safety with Frame-Accumulated Face Recognition and Distance-Based Evaluation
Real-time face recognition systems are crucial in enhancing situational awareness, particularly in public safety scenarios where accurate individual identification can significantly influence critical decisions. Such environments are inherently unpredictable, with face images captured under non-ideal conditions involving varying angles, lighting, and motion. This paper presents a robust face recognition pipeline for public safety applications. The proposed pipeline combines YOLO-based face detection, FaceNet-based feature extraction, and high-performance vector retrieval using Qdrant. This work implements an embedding accumulation strategy that delays identity confirmation until recognition evidence is aggregated over several frames, reducing false positives while prioritizing precision over throughput. We evaluated the model’s performance across camera-to-subject distances, an important yet often overlooked factor in real-world deployments. We also evaluated the pipeline with and without the accumulation strategy using a hybrid dataset of CelebA, LFW, and custom-recorded video scenarios featuring controlled head movements and varying distances. The accumulation strategy extends the two-meter effective recognition range while preserving high precision. The results demonstrate the pipeline’s potential to significantly improve public safety situational awareness.
Evaluating the Accuracy and Stability of Frontier LLMs on ENADE Computer Science Questions
Large Language Models (LLMs) are increasingly evaluated on complex reasoning tasks, yet most benchmarks rely on single-shot prompting and fail to account for output variability. This study investigates both the accuracy and response stability of four multimodal frontier LLMs—GPT-4o, o1, Gemini 2.0 Flash, and DeepSeek-R1—on the multiple-choice Computer Science section of Brazil’s 2021 ENADE exam. Each model was prompted ten independent times per question, and their performance was evaluated using five complementary accuracy and stability metrics. Our results reveal substantial variation in both accuracy and stability across models and questions. Gemini 2.0 Flash achieved the highest accuracy, while also exhibiting strong response stability, followed by o1 and DeepSeek-R1. In contrast, GPT-4o showed lower accuracy and notably less stability. Interestingly, higher stability did not always correlate with higher accuracy, with some models being more stable than others, but less accurate. A per-question analysis shows that questions excluded from the ENADE score due to psychometric anomalies often correlate with high model disagreement or systematic errors, though some excluded questions were answered consistently by most models, providing insights into LLMs as complementary tools for exam validation. These findings reinforce the importance of multi-run evaluations and stability-focused metrics when benchmarking LLMs, especially in high-stakes educational contexts.
Fraud Detection in Dental Auditing: A Methodology Based on Similar Image Analysis
Health insurance plans provide access to medical and dental treatments at predictable costs, democratizing healthcare services that would otherwise be financially inaccessible to a significant portion of the population. To ensure the sustainability of this model, dental audits validate performed procedures by analyzing supporting images submitted by service providers. The identification of inconsistencies in these images, such as reuse, manipulation, or duplication, may result in claim denials and other contractual penalties. This study aims to evaluate computational methodologies for automatic detection of similar dental images to assist the audit process in identifying potential image duplication fraud. The methodology employed consisted of a systematic comparison of various visual feature extraction models, including pre-trained convolutional neural network architectures (VGG-16, ResNet-50, MobileNetV2, DenseNet-121, ShuffleNetV2) and the Bag of Visual Words (BoVW) method, combined with different similarity metrics (Euclidean distance, Chebyshev distance, and cosine similarity). The study was developed in two stages: initially, the models were evaluated on public dental datasets; subsequently, experiments were conducted with real data from Hapvida NotreDame Intermédica (HNDI), incorporating specific audit rules for qualifying suspicions. The experimental results demonstrated that the combination of the BoVW model with cosine similarity and audit rules presented superior performance, achieving a Top-1 precision of 95.65% in identifying duplicate images. The implementation of these techniques is expected to provide a significant increase in dental audit productivity and coverage, contributing to greater efficiency in resource allocation and economic sustainability of supplementary health systems.
Paloma Stephania Guilhermina Prado de Sá
From Code to Field: Evaluating the Robustness of Convolutional Neural Networks for Disease Diagnosis in Mango Leaves
The validation and verification of artificial intelligence (AI) models through robustness assessment are essential to guarantee the reliable performance of intelligent systems facing real-world challenges, such as image corruptions including noise, blurring, and weather variations. Despite the global importance of mango (Mangifera indica L.), there is a lack of studies on the robustness of models for the diagnosis of disease in its leaves. This paper proposes a methodology to evaluate convolutional neural networks (CNNs) under adverse conditions. We adapted the MangoLeafDB dataset, generating MangoLeafDB-C with 19 types of artificial corruptions at five severity levels. We conducted a benchmark comparing five architectures: ResNet-50, ResNet-101, VGG-16, Xception, and LCNN (the latter being a lightweight architecture designed specifically for mango leaf diagnosis). The metrics include the F1 score, the corruption error (CE) and the relative mean corruption error (relative mCE). The results show that LCNN outperformed complex models in corruptions that can be present in real-world scenarios such as Defocus Blur, Motion Blur, while also achieving the lowest mCE. Modern architectures (e.g., ResNet-101) exhibited significant performance degradation in corrupted scenarios, despite their high accuracy under ideal conditions. These findings suggest that lightweight and specialized models may be more suitable for real-world applications in edge devices, where robustness and efficiency are critical. The study highlights the need to incorporate robustness assessments in the development of intelligent systems for agriculture, particularly in regions with technological limitations.
Graph Condensation for Text Classification
TextGCN is a graph-based model that achieves strong text classification performance by effectively capturing corpus-level relationships between documents and words. However, its scalability is limited by the high computational cost of processing large graphs, particularly in environments with restricted resources. One way to handle this limitation is to apply Graph Condensation (GCond), which generates much smaller synthetic graphs while preserving key information. GCond has demonstrated the ability to achieve performance comparable to the original graphs when combined with other graph neural network architectures, such as GCN, SGC, and GraphSAGE. Its application with TextGCN remains unexplored in the literature. This paper addresses this gap and proposes integrating TextGCN and GCond for scalable text classification. We experimentally evaluated our proposal on three benchmark datasets by considering three metrics–accuracy, memory usage, and training time–and compared the performance on the full original graphs against various levels of graph reduction, with the smallest condensed graph retaining only 0.02% of the nodes. The experimental results indicate that the reduced graphs perform similarly to the originals. In the best-case scenario, the most condensed synthetic graph generated by GCond was up to 20 times faster to train and consumed approximately 315,000 times less memory than the original graph, with only a two-percentage-point drop in accuracy.
Hierarchical Modeling of Municipal Contextual Influences on Brazilian National High School Exam (ENEM) Performance: An AI Approach for Educational Policies
This paper applies Hierarchical Linear Modeling (HLM), a technique often employed in machine learning and statistical analysis, to investigate educational inequalities in Brazil, focusing on the understudied influence of contextual factors at the municipal level. Although HLM is established to analyze school-level effects, this research expands its application by integrating individual student performance data from the National High School Exam (ENEM) with municipal-level indicators from the Social Progress Index (IPS) for more than 5,000 Brazilian municipalities. We develop a multilevel model that quantifyes how the local social context impacts educational performance beyond individual and school characteristics. Our analysis reveals that a significant portion of the variance in ENEM scores is attributable to differences between municipalities (Intraclass Correlation Coefficient – ICC reported in the results). Beyond established individual predictors (e.g., parental education, family income proxy, school type), the model demonstrates that specific municipal social progress components significantly predict performance variations, even after controlling for individual factors. In particular, the analysis indicates that the performance advantage associated with attending private schools varies significantly between municipalities and is inversely related to the overall level of municipal performance, suggesting contextual moderation of educational inequalities. This research contributes interpretable models capable of informing targeted educational policies tailored to diverse municipal contexts, demonstrating how AI-related methodologies can address complex social challenges like educational equity.
Language Model Architectures for Behavioral Assessment: A Case Study on MAPER
Professional profile mapping tests (PPMT) are used to improve employees’ skills in organizations. With the increase in demand, professionals who perform these tests often become overloaded and saturated. Large language models (LLMs) have proven to be, when properly applied, excellent support tools for generating reports and diagnostics in several areas. In this work, we apply, for the first time in the literature, a LLM to perform personalized PPMT feedbacks. To achieve our results: we built a real database containing (inputs and outputs) PPMT feedback information; we built an optimal prompt for the task; and we fine-tuned a LLM with our database plus prompt. Our research focused on two forms of fine-tuning: i.e., Low-Rank Adaptation (LoRA) and Retrieval Augmented Generation (RAG). We evaluate our approach using popular metrics such as rouge 1 and 2, rougeL and BERTScore. We also compared the models in terms of generation time and resource consumption. Our experiments have shown that the RAG approach, was capable of generating semantically accurate feedback with a BERTScore precision of around 76%, but tends to be syntactically different from the expected pattern (i.e., that of the psychologist). The LoRA approach was closer both semantically and syntactically to the psychologist’s patter with a BERTScore precision of 89%.
Improving Automated Species Identification in Herbaria Using Contrastive Learning
Herbaria document plant specimens by preserving them as dried samples mounted on cardboard with metadata. These specimens are essential for botanical research but require accurate identification, a process that remains a major bottleneck due to its manual, error-prone nature and dependence on specialists. Recently, initiatives to mitigate this problem have leveraged images available in virtual repositories to create and provide datasets for training machine learning models that assist in species identification. However, the performance of these models has not been satisfactory for many botanical families due to factors such as high inter-species similarity, large intra-species variability, and particularly long-tailed distributions where only a few samples represent many species. Our work shows how Contrastive Learning (CL) methods improve the automated identification of species in herbaria. We evaluated three well-known CL frameworks on a dataset of Piperaceae specimens – a botanical family that is inherently complex to identify – and compared their results to classical learning approaches. Our experiments achieved an 18-percentage-point improvement over the baseline method. We also showed that CL methods can achieve strong performance in herbarium specimen classification, even under scenarios with limited labeled training data, making them a valuable tool to support experts in the identification.
MariNER: A Dataset for Historical Brazilian Portuguese Named Entity Recognition
Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task that aims to identify and classify entity mentions in texts across different categories. While languages such as English possess a large number of high-quality resources for this task, Brazilian Portuguese still lacks in quantity of gold-standard NER datasets, especially when considering specific domains. Particularly, this paper considers the importance of NER for analyzing historical texts in the context of digital humanities. To address this gap, this work outlines the construction of MariNER: Mapeamento e Anotações de Registros hIstóricos para NER (Mapping and Annotation of Historical Records for NER), the first gold-standard dataset for early 20th-century Brazilian Portuguese, with more than 9,000 manually annotated sentences. We also assess and compare the performance of state-of-the-art NER models for the dataset.
Metaheuristic-Based Optimization for Cascaded Hydropower Systems
This study presents a planning and optimization methodology for hydropower generation in run-of-river facilities. The research was applied to the Rio das Antas Energy Complex employing metaheuristic algorithms. This energy complex is composed of three hydropower plants with an installed capacity of 360 MW. The main objective is to improve the operation of these facilities, which is achieved through optimizing the use of available water resources. In order to attain this outcome, hydrological balance equations were applied, ensuring compliance with operational constraints while maximizing the plant’s generation efficiency. The study adopts three optimization algorithms: Particle Swarm Optimization (PSO), its variation Unified PSO (UPSO), and Differential Evolution (DE). The optimization process led to a significant improvement in the efficiency of the generating units of the power plants, surpassing the currently observed values by nearly 2%. This result demonstrates the effectiveness of the proposed approach in enhancing generation performance within the studied hydropower system. Additionally, the generating output recommended by the optimization algorithm was found to be consistent with both the scheduled dispatch and the historical generation patterns, which prioritize peak generation hours and result in fewer power unit shutdowns.
Modeling and Analysis of Fragility in Primary Electrical Distribution Networks
Electricity is essential for the functioning of modern society, underpinning both economic and social sectors. A reliable power system depends on efficient generation, transmission, and especially distribution networks, since failures in these systems can lead to serious socioeconomic consequences. The primary distribution network, operating at medium voltage, connects substations to consumers and plays a central role in ensuring continuity and quality of energy supply, which is monitored through indicators such as SAIDI and SAIFI. The vulnerability of these networks may result from aging infrastructure, increasing demand, adverse weather conditions, operational failures, or interactions with vegetation. This study aims to identify the main factors contributing to the fragility of primary distribution networks through data analysis. The research encompasses the collection and organization of historical failure data, the application of statistical analysis to identify principal agents of vulnerability, and the development of a statistical classification model utilizing logistic regression. The results highlight significant contributing factors, develop a predictive failure model, and offer both seasonal and spatial analyses, thereby facilitating the visualization of regions most vulnerable to disruptions. This approach supports more informed decision-making in grid management and maintenance, helping to prioritize investments and mitigate failures. Ultimately, this work contributes to the robustness, resilience, and continuity of electricity supply, ensuring better reliability and quality for end users.
On the Use of Pareto-Based Features in Meta-learning for Multi-objective Offshore Wind Farm Layout Optimization
The Multi-objective Wind Farm Layout Optimization Problem (MoWFLOP) seeks the optimal turbine placement in offshore wind farms, where construct cost minimization and power generation maximization are simultaneously optimized. As MoWFLOP belongs to NP-hard, previous work has focused on metaheuristic approaches to compute high-quality solutions. However, metaheuristics’ results can vary across wind farm instances, making manual algorithm selection impractical. This paper investigates the use of Pareto-based meta-features to guide a landscape-aware method based on meta-learning for automated metaheuristic selection applied to MoWFLOP. To our knowledge, no such investigation exists for this problem. We explore 30 different sampling configurations to extract Pareto-based meta-features from 300 wind farms, balancing computational cost with feature informativeness. We build a dataset for each configuration and train Random Forest models to predict the best-performing algorithm for unseen instances. The analyses comprised the metaheuristics’ results regarding Pareto-compliant indicators, the impact of sampling configurations on feature values, and the meta-learning performance for automated metaheuristic selection.
Optimal Water Distribution Networks Monitoring Using Observability Theory and State Estimation
This work concerns with the inference of the Water Distribution Networks (WDN) state by using the smallest possible subset of sensor elements in order to aid the design of WDN monitoring systems. These nodes, which serve as network sensors, can be used to build a distributed monitoring system. First, we present the model in which we can represent the network structure as a linear discrete time invariant dynamical system. Using this model, we define the concept of network structural observability and present an efficient algorithm to minimize the cardinality of the subset of monitoring sensors. As a case study, we use simulation data generated with the well established EPANET simulator. Using the proposed method, we assess the estimation of nodes and link states. Also, we present the minimum number of sensors for various network simulation models found in the literature. To the best of our knowledge, this is the first work to use the proposed model to design general optimal water distribution network monitoring systems.
Parallelization Strategies for the Feature Space Partition Algorithm Applied to Fault Detection and Stability Analysis in Smart Grids
This work presents the parallelization of the supervised learning algorithm Feature Space Partition (FSP), originally implemented sequentially in Python. To address computational challenges, multiple parallelization strategies were explored, including implementations based on CPU and GPU, while preserving the original algorithmic flow to ensure that classification performance remained unaffected. Among the approaches tested, CPU-based multiprocessing yielded the most effective results, achieving a reduction of 36.44% in execution time without compromising the predictive accuracy of the model. FSP has demonstrated high classification accuracy, ranging from 80% to 99% in identifying fault patterns in distribution networks and stability patterns in smart grid systems. This reinforces the viability of using existing Python-based parallel processing libraries to scale machine learning algorithms for critical electrical power grid applications.
Segmentation of Microtomography Images of Carbonate Rocks: A Comparison Between Ordinary Kriging and Universal Kriging
This study proposes a hybrid approach for segmenting micro-CT images of carbonate rocks, combining the UNet convolutional neural network with geostatistical kriging methods (ordinary and universal). The process is divided into two main stages: image preprocessing (using CLAHE and histogram specification) and segmentation, where UNet detects the edges of the regions of interest, and kriging is responsible for filling these regions. Using a dataset of 16 carbonate rock samples from Brazil’s pre-salt region, the model was trained and evaluated using metrics such as IoU, Precision, Recall, and F1-Score. The combined use of CLAHE and histogram specification significantly improved UNet’s performance, achieving over 96% across all metrics. In the final segmentation stage, ordinary kriging with a 60% threshold outperformed universal kriging, yielding superior results in IoU (81%), Precision (95%), Recall (84%), and F1-Score (89%). These results indicate that integrating deep learning with statistical interpolation is effective, with ordinary kriging standing out due to its higher accuracy and lower computational cost. Future work includes exploring different trend functions and variogram models, as well as applying alternative neural network architectures and geospatial interpolation techniques.
Segmentation-Augmented Flood Risk Classification for Nighttime River Monitoring
Computer vision-based monitoring of river flooding in urban environments is a cost-effective and low-maintenance method for flood risk management. Research in this area often employs image processing or deep learning techniques to determine the water level, frequently incorporating a water segmentation step. While these methods have been shown to perform reliably during daylight, assessments for nighttime images, in which reduced visibility complicates the use of computer vision, are limited as they do not consider enhancements provided by foundational segmentation models. This study presents a novel methodology that combines the Segment Anything Model (SAM) for water region segmentation and a deep learning-based classification architecture for assessing water depth based on images of an urban creek illuminated only by streetlights at night. The model relies on a hybrid representation in which the segmentation masks are merged with the original images, maintaining contextual information and enhancing feature extraction. Experiments demonstrate that this hybrid enhancement strategy improves classification accuracy by up to 2.56%. Among the classification architectures evaluated, Xception achieved the highest performance, with an accuracy of 94.87% and an F1-Score of 94.60%. These findings highlight the potential of the proposed approach to tackle environmental monitoring challenges using computer vision, even under varying lighting conditions.
Self-Supervised Few-Shot Pill Image Recognition
Pill recognition is a critical task in healthcare, with direct implications for patient safety. Yet, building accurate recognition systems remains challenging, especially when faced with limited labeled examples per pill category. Real-world pill images undergo high visual variability due to lighting, wear, and noise. These conditions give rise to hard negatives (distinct pills that look alike) and hard positives (instances of the same pill that appear different). To tackle these challenges, we propose SSL4PILL, a two-stage framework that leverages self-supervised learning for few-shot pill recognition. First, we train an Augmented Multiscale Deep InfoMax (AMDIM) model with self-supervision on the mini-ImageNet dataset, allowing the model to learn rich semantic representations from unlabeled images. Then, we fine-tune this pre-trained model on a pill dataset with a prototypical network (ProtoNet) under a 1-shot 5-way setting. This new approach promotes generalization to novel pill categories within a fine-grained domain, minimizing annotation costs. Our model consistently outperforms prior baselines, achieving 83.72% accuracy on the NLM PIR dataset and 60.78% on CURE, representing substantial improvements over previous few-shot models. These results demonstrate the effectiveness of self-supervised learning for generalizing to novel pill categories, highlighting its potential for real-world healthcare settings.
Towards Secure Retrieval-Augmented Generation: Preventing LLM Data Leaks with RBAC
Large language models (LLMs) have demonstrated remarkable skills in performing tasks that not so long ago were considered unfeasible. Despite their strong general performance, LLMs often lack in accuracy when dealing with domain-specific tasks, particularly due to susceptibility to hallucinations and reliance on outdated training data. A common solution is to use Retrieval Augmented Generation (RAG), which combines a retrieval step with the generative model to improve responses. However, this mechanism introduces additional risks of sensitive data leakage through both the retrieval and generation components. In this study, we implement a RAG framework and test common failure cases, especially attacks such as prompt injection. We explore different ways to reduce the risk of data leaks, such as choosing better models, prompt design, fine tuning, and filtering the retrieved documents. We show LLMs alone, even state-of-the-art ones, cannot handle security and privacy issues, even with fine-tuning or prompt engineering. The only reliable way to stop leaks is to control what the LLM sees. We propose a simple and effective retrieval filtering layer, based on role-based access control, to prevent sensitive data from ever reaching the model. This approach helps make RAG systems safer by design.
Towards Small Language Models for Text-to-SQL
The task of text-to-SQL, which involves translating natural language queries into structured SQL queries, is an industry demand that enables non-technical users to interact with relational databases. However, current state-of-the-art approaches primarily rely on Large Language Models (LLMs), which pose limitations such as high computational resource consumption and, when closed-source, reliability concerns. This research explores how Small Language Models (SLMs), specifically open-source models with up to 3 billion parameters, can be leveraged to enhance the text-to-SQL process through supervised fine-tuning, with a focus on incorporating query normalization and schema-linking to improve model performance. The findings highlight that SLMs can establish themselves as feasible alternatives to democratize the adoption of generative AI in business contexts, and schema-linking proves to be even more critical in the pipeline as the number of model parameters decreases, allowing improvements in execution accuracy (EX) ranging from 6.9% to 46.9%. This work was motivated by a business demand from a small company with limited computational resources. We have deployed an application powered by our fine-tuned SLM using a single NVIDIA A10 GPU with 24 GB of VRAM, validated through a case study using a real-world business database, achieving up to 97% EX for easy queries and 64.5% in general, without the need for domain adaptation, demonstrating the practical effectiveness and generalizability of our approach.
Use of Neural Networks in Dynamic Difficulty Adjustment (DDA) in a Roguelike Game
The video game industry is rapidly growing, increasing the focus on user experience (UX) and player retention. A common approach is Dynamic Difficulty Adjustment (DDA), which adapts game difficulty based on player performance to maintain the flow state. Rule-based DDA implementations are limited in scalability and require extensive manual effort. To address this, we propose using Neural Networks to infer the game state and suggest real-time difficulty adjustments. A roguelike game was developed as a test environment, and gameplay sessions with 38 players were conducted to build the training dataset. The trained model was able to generate difficulty adjustments consistent with the game’s intended design logic. It reached an R2 of 0.887 on the test set, demonstrating high accuracy and robustness in performance prediction.
Using a Visual-LSTM Enhanced U-Net for Natural Gas Segmentation in Clustered Seismic Data
The interpretation of seismic images is a complex and ambiguous task because it depends on the experience of the professional analyzing them. Seismic images have a fundamental use in the geosciences and are used in various tasks, including hydrocarbon exploration. Automated deep learning methods have recently been applied to seismic analysis tasks, such as seismic interpretation and event prediction. This work proposes a deep learning method for gas reservoir segmentation in 2D seismic data using a Vision-LSTM enhanced U-Net model. The method was applied to seismic data collected by Eneva, a Brazilian private energy company, from the Paleozoic Basin in Brazil. The data include seismic traces collected over time and two labels: confirmed gas reservoirs and the region of interest in each seismic image. The architecture was trained on clustered seismic datasets based on their features to create better generalization models, achieving an average F1-Score of 50% with variations between different clusters. The trained model showed excellent results in some clusters, with more than 80% in the evaluated metric. In contrast, other clusters had much lower results due to variations in seismic features, demonstrating that the proposed method has potential for automated gas reservoir identification.
YACSDB-NER: Yet Another Cybersecurity Database for Named Entity Recognition Task
The increasing number of cybersecurity reports poses a challenge to efficiently retrieving and sharing Cyber Threat Intelligence. However, publicly available cybersecurity datasets for Natural Language Processing (NLP) remain scarce, hindering progress in automated intelligence production. To tackle this challenge, this article presents Yet Another Cybersecurity Database (YACSDB), a dataset designed to enhance Named Entity Recognition (NER) using Structured Threat Information Expression (STIX) entities for interoperability. Our pipeline extracts STIX Domain Objects from unstructured reports, leveraging Google’s Gemini and Bidirectional Encoder Representations from Transformers (BERT) model to assist in labeling and reduce resource needs. The dataset uses Inside–Outside–Beginning (IOB) notation to facilitate fine-tuning in sequence tagging tasks. Reports were selected for representativeness across different years. To the best of our knowledge, it is among the largest cybersecurity NER dataset with temporal information annotated by a single machine-assisted annotator. To evaluate the dataset, we fine-tuned seven BERT models to demonstrate its effectiveness for NER. The results emphasize the importance of domain-specific datasets in cybersecurity NLP and highlight key challenges. YACSDB serves as a benchmark for model comparison, solution development and knowledge graph generation. It is publicly available to foster future research in cybersecurity NLP.