Search Results for author:
Found 12 papers, 7 papers with code
Ignore Me But Don't Replace Me: Utilizing Non-Linguistic Elements for Pretraining on the Cybersecurity Domain
We use our domain-customized methodology to train CyBERTuned, a cybersecurity domain language model that outperforms other cybersecurity PLMs on most tasks.
GI-PIP: Do We Require Impractical Auxiliary Dataset for Gradient Inversion Attacks?
Deep gradient inversion attacks expose a serious threat to Federated Learning (FL) by accurately recovering private data from shared gradients.
Malla: Demystifying Real-world Large Language Model Integrated Malicious Services
The underground exploitation of large language models (LLMs) for malicious services (i. e., Malla) is witnessing an uptick, amplifying the cyber threat landscape and posing questions about the trustworthiness of LLM technologies.
DarkBERT: A Language Model for the Dark Side of the Internet
Recent research has suggested that there are clear differences in the language used in the Dark Web compared to that of the Surface Web.
OpenContrails: Benchmarking Contrail Detection on GOES-16 ABI
Contrails (condensation trails) are line-shaped ice clouds caused by aircraft and are likely the largest contributor of aviation-induced climate change.
Instance-wise Batch Label Restoration via Gradients in Federated Learning
Furthermore, we demonstrate that our method facilitates the existing gradient inversion attacks by exploiting the recovered labels, with an increase of 6-7 in PSNR on both MNIST and CIFAR100.
Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer Interfaces
In addition, we also find that the quality of the interpretation results is inconsistent for individual samples despite when a method with an overall good performance is used.
Subject-Independent Drowsiness Recognition from Single-Channel EEG with an Interpretable CNN-LSTM model
For EEG-based drowsiness recognition, it is desirable to use subject-independent recognition since conducting calibration on each subject is time-consuming.
Meta-Path-based Fake News Detection Leveraging Multi-level Social Context Information
We then propose Meta-Path instance encoding and aggregation methods to capture the temporal information of user engagement and produce news representation end-to-end.
EEG-based Cross-Subject Driver Drowsiness Recognition with an Interpretable Convolutional Neural Network
Results show that the model achieves an average accuracy of 78. 35% on 11 subjects for leave-one-out cross-subject drowsiness recognition, which is higher than the conventional baseline methods of 53. 40%-72. 68% and state-of-the-art deep learning methods of 71. 75%-75. 19%.
A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG
Driver drowsiness is one of main factors leading to road fatalities and hazards in the transportation industry.
Network Embedding with Completely-imbalanced Labels
Network embedding, aiming to project a network into a low-dimensional space, is increasingly becoming a focus of network research.
