no code implementations • 26 Jan 2024 • Dan Lin, Philip Hann Yung Lee, Yiming Li, Ruoyu Wang, Kim-Hui Yap, Bingbing Li, You Shing Ngim
Driver Action Recognition (DAR) is crucial in vehicle cabin monitoring systems.
no code implementations • 17 May 2023 • Kai Wang, Siqiang Luo, Dan Lin
We study Graph Neural Networks (GNNs)-based embedding techniques for knowledge graph (KG) reasoning.
no code implementations • 26 Apr 2023 • Xinliang Zhou, Dan Lin, Ziyu Jia, Jiaping Xiao, Chenyu Liu, Liming Zhai, Yang Liu
However, the raw EEG data is inherently noisy and redundant, which is neglected by existing works that just use single-channel EEG data or full-head channel EEG data for model training, resulting in limited performance of driver drowsiness detection.
no code implementations • 9 Sep 2022 • Ke Li, Cameron Baird, Dan Lin
With the advances in deep learning, speaker verification has achieved very high accuracy and is gaining popularity as a type of biometric authentication option in many scenes of our daily life, especially the growing market of web services.
no code implementations • Findings (EMNLP) 2021 • Kai Wang, Yu Liu, Dan Lin, Quan Z. Sheng
Recent knowledge graph embedding (KGE) models based on hyperbolic geometry have shown great potential in a low-dimensional embedding space.
no code implementations • 5 Sep 2019 • Yuanfeng Ji, Hao Chen, Dan Lin, Xiaohua Wu, Di Lin
These kinds of information can be effectively captured by the relation of different anatomical parts of hand bone.
1 code implementation • 13 May 2019 • Jiajing Wu, Dan Lin, Zibin Zheng, Qi Yuan
By taking the realistic rules and features of transaction networks into consideration, we first model the Ethereum transaction network as a Temporal Weighted Multidigraph (TWMDG), where each node is a unique Ethereum account and each edge represents a transaction weighted by amount and assigned with timestamp.
Social and Information Networks Applications
no code implementations • 11 Aug 2018 • Kai Wang, Yu Liu, Xiujuan Xu, Dan Lin
Knowledge Graph Embedding (KGE) aims to represent entities and relations of knowledge graph in a low-dimensional continuous vector space.