no code implementations • 22 May 2024 • Pengzhou Cheng, Yidong Ding, Tianjie Ju, Zongru Wu, Wei Du, Ping Yi, Zhuosheng Zhang, Gongshen Liu
To improve the recall of the RAG for the target contexts, we introduce a knowledge graph to construct structured data to achieve hard matching at a fine-grained level.
no code implementations • 7 Mar 2024 • Pengzhou Cheng, Zongru Wu, Gongshen Liu
The STcAM with fine-pruning uses one-dimensional convolution (Conv1D) to extract spatial features and subsequently utilizes the Bidirectional Long Short Term Memory (Bi-LSTM) to extract the temporal features, where the attention mechanism will focus on the important time steps.
no code implementations • 29 Feb 2024 • Pengzhou Cheng, Wei Du, Zongru Wu, Fengwei Zhang, Libo Chen, Gongshen Liu
Specifically, $\mathtt{SynGhost}$ hostilely manipulates clean samples through different syntactic and then maps the backdoor to representation space without disturbing the primitive representation.
no code implementations • 19 Feb 2024 • Zongru Wu, Zhuosheng Zhang, Pengzhou Cheng, Gongshen Liu
In this paper, we investigate the learning mechanisms of backdoor LMs in the frequency space by Fourier analysis.
no code implementations • 23 Apr 2022 • Pengzhou Cheng, Mu Han, Aoxue Li, Fengwei Zhang
To address these limitations, we present a novel model for automotive intrusion detection by spatial-temporal correlation features of in-vehicle communication traffic (STC-IDS).