104 citations · 118 across the 5 of their papers we have counts for
5 papers
CoG: a Two-View Co-training Framework for Defending Adversarial Attacks on Graph
Xugang Wu, Huijun Wu, Xu Zhou +1
Graph neural networks exhibit remarkable performance in graph data analysis. However, the robustness of GNN models remains a challenge. As a result, they are not reliable enough to…
Lightweight Container-based User Environment
Wenzhe Zhang, Kai Lu, Ruibo Wang +5
Modern operating systems all support multi-users that users could share a computer simultaneously and not affect each other. However, there are some limitations. For example, priva…
Adversarial Examples on Graph Data: Deep Insights into Attack and Defense
Huijun Wu, Chen Wang, Yuriy Tyshetskiy +3
Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models, graph deep lea…
Interpreting Shared Deep Learning Models via Explicable Boundary Trees
Huijun Wu, Chen Wang, Jie Yin +2
Despite outperforming the human in many tasks, deep neural network models are also criticized for the lack of transparency and interpretability in decision making. The opaqueness r…
HPDedup: A Hybrid Prioritized Data Deduplication Mechanism for Primary Storage in the Cloud
Huijun Wu, Chen Wang, Yinjin Fu +3
Eliminating duplicate data in primary storage of clouds increases the cost-efficiency of cloud service providers as well as reduces the cost of users for using cloud services. Exis…