User identification technologies are essential for ensuring security and privacy. Compared to conventional biometric identification methods, electroencephalogram (EEG)-based brainprint recognition provides unique advantages, including non-replicability, resistance to coercion, and inherent liveness detection. However, existing EEG-based brainprint recognition methods are typically tailored for specific tasks and evaluated under conditions that differ substantially from real-world use. To overcome these limitations, we propose BrainprintNet, a convolutional neural network architecture integrating fine-grained filter banks, grouped multiscale temporal convolutions, and cross-band spatial fusion to enhance EEG-based brainprint recognition. BrainprintNet surpasses previous architectures in challenging scenarios involving simultaneous cross-session and cross-task recognition, demonstrating its generalization ability under strict simulation for real-world applications. Comprehensive experiments were conducted using three publicly available datasets encompassing nine distinct tasks. Furthermore, visualization of the learned network weights revealed strong correlations between user identity and specific EEG frequency subbands and channels. The proposed BrainprintNet significantly advances the accuracy, flexibility, and practical applicability of EEG-based brainprint recognition systems.

As software complexity rises, research on vulnerability detection becomes increasingly important. Deep learning-based vulnerability detection, an emerging approach, can segment code and identify hidden vulnerability patterns. However, challenges remain: (1) accurately correlating code slices with scripts to minimize false positives; (2) enhancing the precision of root cause localization for vulnerable scripts. To alleviate these, this paper introduces a vulnerability detection and root cause localization approach leveraging large language models (LLMs). The approach preprocesses C/C++ source code, extracts graph structures, and combines them with the script to form prompts. A novel Hierarchical Regulation for Parameter-Efficient Language Model Tuning (HR-PELT) approach fine-tunes the LLM for vulnerability detection by maintaining parameters of shallow layers substantially preserved while enhancing the adaptability of deep layers. For root cause localization, we similarly create prompts and fine-tune another LLM. Experimental results on three datasets demonstrated improvements: in vulnerability detection, our approach boosted average Accuracy (ACC) by 4.82% and Macro-F1 (M-F1) by 5.29% over the state-of-the-art (SOTA); in root cause localization, it enhanced ACC10%by 4.80% and ACC20%by 3.93%.