most citedUnified, User and Task (UUT) Centered Artificial Intelligence for Metaverse Edge Computing

4 citations · 11 across the 13 of their papers we have counts for

collaborators

13 papers

cs.NI2023★ 3 cited

Mobile Edge Computing and AI Enabled Web3 Metaverse over 6G Wireless Communications: A Deep Reinforcement Learning Approach

Wenhan Yu, Terence Jie Chua, Jun Zhao

The Metaverse is gaining attention among academics as maturing technologies empower the promises and envisagements of a multi-purpose, integrated virtual environment. An interactiv…

cs.DC2023

Optimization for the Metaverse over Mobile Edge Computing with Play to Earn

Chang Liu, Terence Jie Chua, Jun Zhao

The concept of the Metaverse has garnered growing interest from both academic and industry circles. The decentralization of both the integrity and security of digital items has spu…

cs.AI2023

Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach

Wenhan Yu, Terence Jie Chua, Jun Zhao

The efficient deployment and fine-tuning of foundation models are pivotal in contemporary artificial intelligence. In this study, we present a groundbreaking paradigm integrating M…

cs.LG2023★ 1 cited

FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing

Terence Jie Chua, Wenhan Yu, Jun Zhao +1

The emergence of foundation models, including language and vision models, has reshaped AI's landscape, offering capabilities across various applications. Deploying and fine-tuning…

cs.CV2023

Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer

Terence Jie Chua, Wenhan Yu, Jun Zhao

Development of defenses against physical world attacks such as adversarial patches is gaining traction within the research community. We contribute to the field of adversarial patc…

cs.NI2023

Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning

Terence Jie Chua, Wenhan Yu, Jun Zhao

Real-time Digital Twinning of physical world scenes onto the Metaverse is necessary for a myriad of applications such as augmented-reality (AR) assisted driving. In AR assisted dri…

cs.NI2023★ 1 cited

Virtual Reality in Metaverse over Wireless Networks with User-centered Deep Reinforcement Learning

Wenhan Yu, Terence Jie Chua, Jun Zhao

The Metaverse and its promises are fast becoming reality as maturing technologies are empowering the different facets. One of the highlights of the Metaverse is that it offers the…

cs.NI2023

Play to Earn in the Metaverse with Mobile Edge Computing over Wireless Networks: A Deep Reinforcement Learning Approach

Terence Jie Chua, Wenhan Yu, Jun Zhao

The Metaverse play-to-earn games have been gaining popularity as they enable players to earn in-game tokens which can be translated to real-world profits. With the advancements in…

cs.NI2023★ 1 cited

User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks

Wenhan Yu, Terence Jie Chua, Jun Zhao

The Metaverse is emerging as maturing technologies are empowering the different facets. Virtual Reality (VR) technologies serve as the backbone of the virtual universe within the M…

cs.AI2022★ 4 cited

Unified, User and Task (UUT) Centered Artificial Intelligence for Metaverse Edge Computing

Terence Jie Chua, Wenhan Yu, Jun Zhao

The Metaverse can be considered the extension of the present-day web, which integrates the physical and virtual worlds, delivering hyper-realistic user experiences. The inception o…

cs.LG2022

Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications

Wenhan Yu, Terence Jie Chua, Jun Zhao

Technology advancements in wireless communications and high-performance Extended Reality (XR) have empowered the developments of the Metaverse. The demand for the Metaverse applica…

cs.LG2022

Time Minimization in Hierarchical Federated Learning

Chang Liu, Terence Jie Chua, Jun Zhao

Federated Learning is a modern decentralized machine learning technique where user equipments perform machine learning tasks locally and then upload the model parameters to a centr…

cs.NI2022★ 1 cited

Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approach

Terence Jie Chua, Wenhan Yu, Jun Zhao

Improving the interactivity and interconnectivity between people is one of the highlights of the Metaverse. The Metaverse relies on a core approach, digital twinning, which is a me…