Adverse event detection for non-pharmaceutical substances, such as dietary supplements, herbal remedies, and alternative therapies, remains a major blind spot in current pharmacovigilance systems, which are primarily designed for prescription drugs. This research addresses that gap by introducing a high-precision matching framework that links scientific literature from PubMed with real-world safety reports from the FDA Adverse Event Reporting System (FAERS). The proposed approach incorporates a robust pre-processing pipeline, followed by a threelayer matching engine based on rule-based substance overlap, metadata alignment, and temporal validation. The result is a curated set of 560 article-report pairs obtained by applying our multi-stage matching pipeline to two datasets: a filtered subset of 38,978 PubMed articles that mention non-pharmaceutical substances, drawn from the 2024 PubMed baseline distribution and 135,848 adverse event reports from the FAERS for Q3 2024 (JulySeptember). This represents the first structured dataset connecting peer-reviewed research with reported adverse events for nonpharmaceutical compounds. Experimental results show that while raw rule-based matching produced 670 links, our enhanced pipeline reduced noise and improved semantic accuracy, prioritizing reliability over quantity. This dataset lays the groundwork for safer, data-driven integration of alternative medicine into modern pharmacovigilance efforts. Moreover, results so far serve as an important first step toward making available a benchmark dataset for evaluating non-pharmaceutical adverse event extraction and article-report linking systems. This work provides a reproducible, high-precision framework for linking literature and adverse event reports, addressing challenges not covered in prior studies.
The absence of debugging symbols in stripped binaries, particularly descriptive function names, hinders reverse engineering and security analysis. This paper introduces a structured pipeline for automated function name inference, combining Transformer models, calibrated confidence estimation, and context-aware refinement using large language models pretrained on code. In the first stage, a specialized Transformer model is trained on normalized assembly instructions to predict initial function names, along with statistical indicators. These indicators are then mapped to reliable confidence scores using a logistic regression calibration module. In the final stage, lowconfidence functions are reprocessed using fine-tuned, pretrained code language models, which leverage the context derived from high-confidence caller and callee relationships. The method is validated on a dataset of approximately 21.3 million functions extracted from 6,824 Windows executables compiled with various MSVC versions and architectures. The results demonstrate that our approach supports the practical recovery of meaningful function identifiers and enhances malware analysis tasks.
In this work, we propose an innovative knowledge distillation framework tailored for promptable transformer-based segmentation models, focusing on Segment Anything Model 2 (SAM 2). Powerful models, such as transformer-based ones, which require large-scale datasets and extensive computation for good performance, are difficult to train, even with limited data, often yielding uncertain segmentation results across different use cases. We address this challenge by proposing a distillation strategy that allows a student model to inherit structured knowledge from a strong pre-trained teacher with the same architecture, even when trained on small datasets. The method combines hard supervision from ground-truth masks with soft guidance from the teacher’s logits, helping the student learn meaningful structures early in the training process. To address the challenge of noisy or overconfident predictions from the teacher, we introduce a confidence-aware agreement mask that modulates the soft loss and provides guidance for the student to focus on regions where the teacher is more confident. The work is extended with token-level distillation to enhance structural fidelity in student representations. Experiments demonstrate the effectiveness of our method in low-data regimes, with the student model achieving a notable reduction in the total loss (from 15.17 to 8.84) and up to $20 \%$ improvement in Intersection-over-Union (IoU) across various inference scenarios. Qualitative results further support these findings, showing visibly cleaner segmentation outputs with reduced noise, indicating improved generalization under data-scarce conditions.
Fine-grained change detection in structured remote sensing environments, such as agricultural fields, requires models that can reason over semantically coherent regions while preserving spatial precision. Existing methods often rely on convolutional or transformer-based architectures with uniform tokenization, limiting their ability to capture changes aligned with realworld semantic structures. In this paper, we propose SFCNet, a region-aware change detection framework that leverages explicit field-level segmentation masks to guide both token aggregation and spatial decoding. Our method introduces a Field-Aware Region Encoder (FARE) that aggregates features within each semantic region, producing interpretable region tokens. These are processed through a slot attention mechanism to model temporal differences across structured units. A re-projection decoder then restores field-wise predictions to pixel-space using the original geometry. We evaluate SFCNet on the CLCD dataset, where it outperforms prior state-of-the-art models by $\mathbf{+ 3. 3 1 \%}$.
Online platforms are complex systems that influence the commercial, social, and political environment, debating important real-life topics, e.g., health, emigration, elections, climate change, etc. These online environments offer users freedom of expression through anonymous posting. In addition to their obvious advantages, some users abuse this freedom to spread harmful content, e.g., misinformation, propaganda, harmful conspiracy theories, or abusive, aggressive, and offensive speech. Automated detection techniques can effectively reduce the negative influence of antisocial behavior used by these malicious actors. In this article, we propose HarmLLAMA, a fine-tuned LLAMA2 model using LORA. The experimental results on two real-world datasets show that our model, HarmLLaMA, outperforms current state-of-the-art models in terms of Accuracy, Precision, Recall, and F1-Score.
