The vast amount of data required to model wildfires at a larger scale presents difficulties in the collection and curation of the data.

Kleber Mendonça Filho wants his films to reclaim lost history. For Kleber Mendonça Filho, filmmaking is an act of both provocation and preservation. Mendonça was born in 1968, in the early years of a ruthless military dictatorship--a time when cinema, like much else, was harshly constrained. His mother, Joselice Jucá, was a historian who studied Brazil's abolitionist movement, and she taught him that filling gaps in the cultural memory was a way to expose concealed truths. His relationship with film is inextricably linked with his home town, Recife--a port city where attractive beaches and high-rise developments coexist with sprawling favelas and rampant crime. In his youth, Mendonça was fascinated by the city's grand cinema palaces. He carried a Super 8 camera to the tops of marquees and shot dizzying images; he spent hours in projection booths, learning the mechanics of how films reached the screen. Over time, Mendonça watched those theatres fall into decline, an experience that he likened to being aboard a ship as it wrecked. But even as Recife lost its allure, he made the city a fixture of his films--a way of vindicating its place in history. His first narrative feature, "Neighboring Sounds," takes place on a street where he lived as a child, a setting that he spent years documenting. Later, he made "Pictures of Ghosts," a documentary about Recife told largely through its cinemas.

CodeT5+: CodeT5+ was initially chosen as one of the baselines because it was among the top-performing models in our experiments on defect prediction (Monteiro et al., 2025). Although CodeT5+ does not contain an explicit [CLS] token, as in BERT-based language models, we still use the first encoded token as the head of the classification layer. Therefore, we maintain the default practice of inspecting the weights of the first token attention heads. UniXCoder: In the same way as in CodeT5+, UniXCoder was also among the top performers in defect prediction experiments (Monteiro et al., 2025), so we keep the same default strategy of using the first encoded token attention weights. We also initially considered JIT-Block (Huang et al., 2024) and JIT-CF (Ju et al., 2025). Regarding JIT-Block, its authors reconstructed the dataset (JIT-Defects4J) into the changed block format, which preserves the relative positional information between added and deleted code lines -- information lost in traditional datasets -- thus facilitating the model's ability to learn the semantic meaning of code changes. So, as the dataset was changed, it would not be possible to conduct a fair comparison. Finally, according to its published results, JIT-CF does not achieve better results than JIT-Smart. A consolidated overview of the baseline classifiers is presented in Table 2. 3.4 Description of the Experiments RQ1 How do pre-trained language models perform in comparison to traditional machine learning approaches for continual within-project and cross-project Just-in-Time Software Defect Prediction (JIT-SDP)?

Software aging is a phenomenon that affects long-running systems, leading to progressive performance degradation and increasing the risk of failures. T o mitigate this problem, this work proposes an adaptive approach based on machine learning for software aging detection in environments subject to dynamic workload conditions. W e evaluate and compare a static model with adaptive models that incorporate adaptive detectors, specifically the Drift Detection Method (DDM) and Adaptive Windowing (ADWIN), originally developed for concept drift scenarios and applied in this work to handle workload shifts. Experiments with simulated sudden, gradual, and recurring workload transitions show that static models suffer a notable performance drop when applied to unseen workload profiles, whereas the adaptive model with ADWIN maintains high accuracy, achieving an F1-Score above 0.93 in all analyzed scenarios.

Large Language Models (LLMs) are increasingly being integrated into various components of Ontology Matching pipelines. This paper investigates the capability of LLMs to perform ontology matching directly on ontology modules and generate the corresponding alignments. Furthermore, it is explored how a dedicated fine-tuning strategy can enhance the model's matching performance in a zero-shot setting. The proposed method incorporates a search space reduction technique to select relevant subsets from both source and target ontologies, which are then used to automatically construct prompts. Recognizing the scarcity of reference alignments for training, a novel LLM-based approach is introduced for generating a synthetic dataset. This process creates a corpus of ontology submodule pairs and their corresponding reference alignments, specifically designed to fine-tune an LLM for the ontology matching task. The proposed approach was evaluated on the Conference, Geolink, Enslaved, Taxon, and Hydrography datasets from the OAEI complex track. The results demonstrate that the LLM fine-tuned on the synthetically generated data exhibits superior performance compared to the non-fine-tuned base model. The key contribution is a strategy that combines automatic dataset generation with fine-tuning to effectively adapt LLMs for ontology matching tasks.

Parkinson's disease (PD) is a progressive neurodegenerative disorder that, in addition to directly impairing functional mobility, is frequently associated with vocal impairments such as hypophonia and dysarthria, which typically manifest in the early stages. The use of vocal biomarkers to support the early diagnosis of PD presents a non-invasive, low-cost, and accessible alternative in clinical settings. Thus, the objective of this cross-sectional study was to consistently evaluate the effectiveness of a Deep Neural Network (DNN) in distinguishing individuals with Parkinson's disease from healthy controls, in comparison with traditional Machine Learning (ML) methods, using vocal biomarkers. Two publicly available voice datasets were used. Mel-frequency cepstral coefficients (MFCCs) were extracted from the samples, and model robustness was assessed using a validation strategy with 1000 independent random executions. Performance was evaluated using classification statistics. Since normality assumptions were not satisfied, non-parametric tests (Kruskal-Wallis and Bonferroni post-hoc tests) were applied to verify whether the tested classification models were similar or different in the classification of PD. With an average accuracy of $98.65\%$ and $92.11\%$ on the Italian Voice dataset and Parkinson's Telemonitoring dataset, respectively, the DNN demonstrated superior performance and efficiency compared to traditional ML models, while also achieving competitive results when benchmarked against relevant studies. Overall, this study confirms the efficiency of DNNs and emphasizes their potential to provide greater accuracy and reliability for the early detection of neurodegenerative diseases using voice-based biomarkers.

Current studies perceive LLMs' knowledge boundary

We introduce a methodology for the identification of notifiable events in the domain of healthcare. The methodology harnesses semantic frames to define fine-grained patterns and search them in unstructured data, namely, open-text fields in e-medical records. We apply the methodology to the problem of underreporting of gender-based violence (GBV) in e-medical records produced during patients' visits to primary care units. A total of eight patterns are defined and searched on a corpus of 21 million sentences in Brazilian Portuguese extracted from e-SUS APS. The results are manually evaluated by linguists and the precision of each pattern measured. Our findings reveal that the methodology effectively identifies reports of violence with a precision of 0.726, confirming its robustness. Designed as a transparent, efficient, low-carbon, and language-agnostic pipeline, the approach can be easily adapted to other health surveillance contexts, contributing to the broader, ethical, and explainable use of NLP in public health systems.

Exploring the Utilities of the Rationales from Large Language Models to Enhance Automated Essay Scoring Hong Jiao University of Maryland, College Park Hanna Choi University of Maryland, College Park Haowei Hua Princeton University Abstract This study explored the utilities of rationales generated by GPT-4.1 and GPT -5 in automated scoring using Prompt 6 essays from the 2012 Kaggle ASAP data . Essay-based scoring was compared with rationale-based scoring. The study found in general essay -based scoring performed better than rationale -based scoring with higher Quadratic Weighted Kappa (QWK). However, rationale-based scoring led to higher scoring accuracy in terms of F1 scores for score 0 which had less representation due to class imbalance issues . The ensemble modeling of essay-based scoring models increased the scoring accuracy at both specific score levels and across all score levels. The ensemble modeling of essay -based scoring and each of the rationale-based scoring performed about the same. Further ensemble of essay -based scoring and both rationale-based scoring yielded the best scoring accuracy with QWK of 0.870 compared with 0.848 reported in literature. Introduction Automated essay scoring methodology develops along with the advances in AI technology. Starting from the early supervised machine learning models based on engineered features ( e.g., Mahana et al., 2012) to recent use of large language models (LLMs), the methods for automated essay scoring as demonstrated in Appendix A evolved with the advances in machine learning, deep learning, language models, and LLMs. Using automated scoring of Prompt 6 in the Automated Student Assessment Prize (ASAP) dataset from Kaggle, this study intends to explore the utility of rationales generated by LLMs in enhancing automated essay scoring. For the ASAP Prompt 6, automated scoring models have been developed since 2012 after the Kaggle competition.

Burnout is a psychological syndrome marked by emotional exhaustion, depersonalization, and reduced personal accomplishment, with a significant impact on individual well-being and organizational performance. This study proposes a machine learning approach to predict burnout risk using the HackerEarth Employee Burnout Challenge dataset. Three supervised algorithms were evaluated: nearest neighbors (KNN), random forest, and support vector machine (SVM), with model performance evaluated through 30-fold cross-validation using the determination coefficient (R2). Among the models tested, SVM achieved the highest predictive performance (R2 = 0.84) and was statistically superior to KNN and Random Forest based on paired $t$-tests. To ensure practical applicability, an interactive interface was developed using Streamlit, allowing non-technical users to input data and receive burnout risk predictions. The results highlight the potential of machine learning to support early detection of burnout and promote data-driven mental health strategies in organizational settings.