Zobrazeno 1 - 10
of 270
pro vyhledávání: '"Tang, ZhiMin"'
Heterogeneous Graph Neural Networks (HGNNs) have expanded graph representation learning to heterogeneous graph fields. Recent studies have demonstrated their superior performance across various applications, including medical analysis and recommendat
Externí odkaz:
http://arxiv.org/abs/2408.15089
Autor:
Xue, Runzhen, Yan, Mingyu, Han, Dengke, Teng, Yihan, Tang, Zhimin, Ye, Xiaochun, Fan, Dongrui
Heterogeneous Graph Neural Networks (HGNNs) have broadened the applicability of graph representation learning to heterogeneous graphs. However, the irregular memory access pattern of HGNNs leads to the buffer thrashing issue in HGNN accelerators. In
Externí odkaz:
http://arxiv.org/abs/2404.04792
Autor:
Xue, Runzhen, Han, Dengke, Yan, Mingyu, Zou, Mo, Yang, Xiaocheng, Wang, Duo, Li, Wenming, Tang, Zhimin, Kim, John, Ye, Xiaochun, Fan, Dongrui
Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both structural and semantic information in HetGs, HGNNs first aggregate the
Externí odkaz:
http://arxiv.org/abs/2307.12765
Autor:
Zhou, Zhiqiang, Zhang, Chaoli, Ma, Lingna, Gu, Jing, Qian, Huajie, Wen, Qingsong, Sun, Liang, Li, Peng, Tang, Zhimin
The existing resource allocation policy for application instances in Kubernetes cannot dynamically adjust according to the requirement of business, which would cause an enormous waste of resources during fluctuations. Moreover, the emergence of new c
Externí odkaz:
http://arxiv.org/abs/2303.03640
Publikováno v:
Tongxin xuebao, Vol 45, Pp 148-158 (2024)
Considering the serious threat that ransomware poses to data security and the increasing intelligence and complexity of its attack methods, an anti-ransomware method based on active deception was proposed to address the limitations of traditional def
Externí odkaz:
https://doaj.org/article/a88ea552c8bf42658d2826fbfdfcd1c5
Publikováno v:
The 38th IEEE International Conference on Data Engineering (ICDE 2022)
Autoscaling is a critical component for efficient resource utilization with satisfactory quality of service (QoS) in cloud computing. This paper investigates proactive autoscaling for widely-used scaling-per-query applications where scaling is requir
Externí odkaz:
http://arxiv.org/abs/2204.07197
Autor:
Li, Shaofeng, Wuyun, Ta-na, Wang, Lin, Zhang, Jianhui, Tian, Hua, Zhang, Yaodan, Wang, Shaoli, Xia, Yongxiu, Liu, Xue, Wang, Ning, Lv, Fenni, Xu, Jihuang, Tang, Zhimin
Publikováno v:
In International Journal of Biological Macromolecules November 2024 279 Part 3
Publikováno v:
In Pattern Recognition November 2024 155
Publikováno v:
In International Journal of Biological Macromolecules October 2024 277 Part 2
Publikováno v:
In Computers & Security January 2025 148