456 citations · 706 across the 32 of their papers we have counts for
32 papers
GLA-GCN: Global-local Adaptive Graph Convolutional Network for 3D Human Pose Estimation from Monocular Video
Bruce X. B. Yu, Zhi Zhang, Yongxu Liu +3
3D human pose estimation has been researched for decades with promising fruits. 3D human pose lifting is one of the promising research directions toward the task where both estimat…
A Physics-Informed Low-Shot Learning For sEMG-Based Estimation of Muscle Force and Joint Kinematics
Yue Shi, Shuhao Ma, Yihui Zhao +1
Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle st…
CageCoach: Sharing-Oriented Redaction-Capable Distributed Cryptographic File System
Jason Carpenter, Zhi-Li Zhang
The modern data economy is built on sharing data. However, sharing data can be an expensive and risky endeavour. Existing sharing systems like Distributed File Systems provide full…
Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer
Jie Zhang, Yihui Zhao, Tianzhe Bao +5
Data-driven methods have become increasingly more prominent for musculoskeletal modelling due to their conceptually intuitive simple and fast implementation. However, the performan…
A Multiple-Entanglement Routing Framework for Quantum Networks
Tu N. Nguyen, Kashyab J. Ambarani, Linh Le +2
Quantum networks are gaining momentum in finding applications in a wide range of domains. However, little research has investigated the potential of a quantum network framework to…
Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG
Jie Zhang, Yihui Zhao, Fergus Shone +4
Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (…
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training
Likun Cai, Zhi Zhang, Yi Zhu +3
Multiple datasets and open challenges for object detection have been introduced in recent years. To build more general and powerful object detection systems, in this paper, we cons…
Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World
Hua Ma, Yinshan Li, Yansong Gao +7
Deep learning models have been shown to be vulnerable to recent backdoor attacks. A backdoored model behaves normally for inputs containing no attacker-secretly-chosen trigger and…
SoK: Rowhammer on Commodity Operating Systems
Zhi Zhang, Decheng Chen, Jiahao Qi +8
Rowhammer has drawn much attention from both academia and industry in the past years as rowhammer exploitation poses severe consequences to system security. Since the first compreh…
Improving Semantic Segmentation via Self-Training
Yi Zhu, Zhongyue Zhang, Chongruo Wu +6
Deep learning usually achieves the best results with complete supervision. In the case of semantic segmentation, this means that large amounts of pixelwise annotations are required…
ResNeSt: Split-Attention Networks
Hang Zhang, Chongruo Wu, Zhongyue Zhang +9
It is well known that featuremap attention and multi-path representation are important for visual recognition. In this paper, we present a modularized architecture, which applies t…
DRAMDig: A Knowledge-assisted Tool to Uncover DRAM Address Mapping
Minghua Wang, Zhi Zhang, Yueqiang Cheng +1
As recently emerged rowhammer exploits require undocumented DRAM address mapping, we propose a generic knowledge-assisted tool, DRAMDig, which takes domain knowledge into considera…
GhostKnight: Breaching Data Integrity via Speculative Execution
Zhi Zhang, Yueqiang Cheng, Surya Nepal
Existing speculative execution attacks are limited to breaching confidentiality of data beyond privilege boundary, the so-called spectre-type attacks. All of them utilize the chang…
TeleHammer: A Formal Model of Implicit Rowhammer
Zhi Zhang, Yueqiang Cheng, Dongxi Liu +2
The rowhammer bug allows an attacker to gain privilege escalation or steal private data. A key requirement of all existing rowhammer attacks is that an attacker must have access to…
Design and Evaluation of a Multi-Domain Trojan Detection Method on Deep Neural Networks
Yansong Gao, Yeonjae Kim, Bao Gia Doan +5
This work corroborates a run-time Trojan detection method exploiting STRong Intentional Perturbation of inputs, is a multi-domain Trojan detection defence across Vision, Text and A…
Stability and Generalization of Graph Convolutional Neural Networks
Saurabh Verma, Zhi-Li Zhang
Inspired by convolutional neural networks on 1D and 2D data, graph convolutional neural networks (GCNNs) have been developed for various learning tasks on graph data, and have show…
RetroFlow: Maintaining Control Resiliency and Flow Programmability for Software-Defined WANs
Zehua Guo, Wendi Feng, Sen Liu +3
Providing resilient network control is a critical concern for deploying Software-Defined Networking (SDN) into Wide-Area Networks (WANs). For performance reasons, a Software-Define…
Joint Switch Upgrade and Controller Deployment in Hybrid Software-Defined Networks
Zehua Guo, Weikun Chen, Ya-Feng Liu +2
To improve traffic management ability, Internet Service Providers (ISPs) are gradually upgrading legacy network devices to programmable devices that support Software-Defined Networ…
R-BBG: Recursive Bipartition of Bi-connected Graphs
Ngoc-Tu Nguyen, Zhi-Li Zhang
Given an undirected graph , it is well known that partitioning a graph into connected subgraphs of equal or specificed sizes is in general NP-hard problem. On the…
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Tianyi Chen, Sergio Barbarossa, Xin Wang +2
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT inclu…
Avoidance Markov Metrics and Node Pivotality Ranking
Golshan Golnari, Zhi-Li Zhang, Daniel Boley
We introduce the avoidance Markov metrics and theories which provide more flexibility in the design of random walk and impose new conditions on the walk to avoid (or transit) a spe…
Fast Distance Sensitivity Oracle for Multiple Failures
Golshan Golnari, Zhi-Li Zhang
When a network is prone to failures, it is very expensive to compute the shortest paths every time from the scratch. Distance sensitivity oracle provides this privilege to find the…
Graph Capsule Convolutional Neural Networks
Saurabh Verma, Zhi-Li Zhang
Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi-cross-domains incl…
Random Walk Fundamental Tensor and Its Applications to Network Analysis
Golshan Golnari, Zhi-Li Zhang, Daniel Boley
We first present a comprehensive review of various random walk metrics used in the literature and express them in a consistent framework. We then introduce fundamental tensor -- a…