Search Results for author: Shengli Xie

Found 22 papers, 1 papers with code

Service Reservation and Pricing for Green Metaverses: A Stackelberg Game Approach

no code implementations9 Aug 2023 Xumin Huang, Yuan Wu, Jiawen Kang, Jiangtian Nie, Weifeng Zhong, Dong In Kim, Shengli Xie

A single-leader multi-follower Stackelberg game is formulated between the MSP and users while each user optimizes an offloading probability to minimize the weighted sum of time, energy consumption and monetary cost.

Total Energy

Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness

no code implementations29 Jul 2023 Jiawen Kang, Jinbo Wen, Dongdong Ye, Bingkun Lai, Tianhao Wu, Zehui Xiong, Jiangtian Nie, Dusit Niyato, Yang Zhang, Shengli Xie

Given the revolutionary role of metaverses, healthcare metaverses are emerging as a transformative force, creating intelligent healthcare systems that offer immersive and personalized services.

Decision Making Federated Learning +1

Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction

no code implementations26 Jun 2023 Junlong Chen, Jiawen Kang, Minrui Xu, Zehui Xiong, Dusit Niyato, Chuan Chen, Abbas Jamalipour, Shengli Xie

Specifically, we propose a model to predict the future trajectories of intelligent vehicles based on their historical data, indicating the future workloads of RSUs. Based on the expected workloads of RSUs, we formulate the avatar task migration problem as a long-term mixed integer programming problem.

Trajectory Prediction

Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses

no code implementations6 Jun 2023 Jiawen Kang, Jiayi He, Hongyang Du, Zehui Xiong, Zhaohui Yang, Xumin Huang, Shengli Xie

In this article, we propose a hierarchical SemCom-enabled vehicular metaverses framework consisting of the global metaverse, local metaverses, SemCom module, and resource pool.

Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition

no code implementations28 May 2022 Kan Xie, Zhe Zhang, Bo Li, Jiawen Kang, Dusit Niyato, Shengli Xie, Yi Wu

However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information.

Federated Learning Privacy Preserving +1

Noisy Tensor Completion via Low-rank Tensor Ring

no code implementations14 Mar 2022 Yuning Qiu, Guoxu Zhou, Qibin Zhao, Shengli Xie

Experimental results on both synthetic and real-world data demonstrate the effectiveness and efficiency of the proposed model in recovering noisy incomplete tensor data compared with state-of-the-art tensor completion models.

Tensor Decomposition

Multi-view Data Classification with a Label-driven Auto-weighted Strategy

no code implementations3 Jan 2022 Yuyuan Yu, Guoxu Zhou, Haonan Huang, Shengli Xie, Qibin Zhao

However, existing strategies cannot take advantage of semi-supervised information, only distinguishing the importance of views from a data feature perspective, which is often influenced by low-quality views then leading to poor performance.


FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing

no code implementations19 Oct 2021 Xumin Huang, Peichun Li, Rong Yu, Yuan Wu, Kan Xie, Shengli Xie

In PVEC, different PLOs recruit PVs as edge computing nodes for offloading services through an incentive mechanism, which is designed according to the computation demand and parking capacity constraints derived from FedParking.

Edge-computing Federated Learning +2

Robust Output Regulation and Reinforcement Learning-based Output Tracking Design for Unknown Linear Discrete-Time Systems

no code implementations21 Jan 2021 Ci Chen, Lihua Xie, Yi Jiang, Kan Xie, Shengli Xie

To remove such a requirement, an off-policy reinforcement learning algorithm is proposed using only the measured output data along the trajectories of the system and the reference output.

Dynamical Systems

Graph Regularized Nonnegative Tensor Ring Decomposition for Multiway Representation Learning

no code implementations12 Oct 2020 Yuyuan Yu, Guoxu Zhou, Ning Zheng, Shengli Xie, Qibin Zhao

Tensor ring (TR) decomposition is a powerful tool for exploiting the low-rank nature of multiway data and has demonstrated great potential in a variety of important applications.

Clustering Representation Learning

An Efficient Tensor Completion Method via New Latent Nuclear Norm

no code implementations14 Oct 2019 Jinshi Yu, Weijun Sun, Yuning Qiu, Shengli Xie

In tensor completion, the latent nuclear norm is commonly used to induce low-rank structure, while substantially failing to capture the global information due to the utilization of unbalanced unfolding scheme.

Blockchain for Secure and Efficient Data Sharing in Vehicular Edge Computing and Networks

no code implementations IEEE INTERNET OF THINGS JOURNAL, VOL. 6, NO. 3 2018 Jiawen Kang, Rong Y u, Xumin Huang, Maoqiang Wu, Sabita Maharjan, Member, Shengli Xie, and Y an Zhang, Senior Member, IEEE

Due to limited resources with vehicles, vehicular edge computing and networks (VECONs) i. e., the integration of mobile edge computing and vehicular networks, can provide powerful computing and massive storage resources.


Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition under Reshuffling

no code implementations22 May 2018 Chao Li, Mohammad Emtiyaz Khan, Zhun Sun, Gang Niu, Bo Han, Shengli Xie, Qibin Zhao

Exact recovery of tensor decomposition (TD) methods is a desirable property in both unsupervised learning and scientific data analysis.

Image Steganography Tensor Decomposition

Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization

no code implementations20 Mar 2018 Jinshi Yu, Guoxu Zhou, Andrzej Cichocki, Shengli Xie

Nonsmooth Nonnegative Matrix Factorization (nsNMF) is capable of producing more localized, less overlapped feature representations than other variants of NMF while keeping satisfactory fit to data.


Low-rank Multi-view Clustering in Third-Order Tensor Space

no code implementations30 Aug 2016 Ming Yin, Junbin Gao, Shengli Xie, Yi Guo

Multi-view subspace clustering is based on the fact that the multi-view data are generated from a latent subspace.

Clustering Multi-view Subspace Clustering

Tensor Ring Decomposition

1 code implementation17 Jun 2016 Qibin Zhao, Guoxu Zhou, Shengli Xie, Liqing Zhang, Andrzej Cichocki

In this paper, we introduce a fundamental tensor decomposition model to represent a large dimensional tensor by a circular multilinear products over a sequence of low dimensional cores, which can be graphically interpreted as a cyclic interconnection of 3rd-order tensors, and thus termed as tensor ring (TR) decomposition.

Tensor Decomposition Tensor Networks

Neighborhood Preserved Sparse Representation for Robust Classification on Symmetric Positive Definite Matrices

no code implementations27 Jan 2016 Ming Yin, Shengli Xie, Yi Guo, Junbin Gao, Yun Zhang

Due to its promising classification performance, sparse representation based classification(SRC) algorithm has attracted great attention in the past few years.

Classification General Classification +2

Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds

no code implementations CVPR 2016 Ming Yin, Yi Guo, Junbin Gao, Zhaoshui He, Shengli Xie

Sparse subspace clustering (SSC), as one of the most successful subspace clustering methods, has achieved notable clustering accuracy in computer vision tasks.


Linked Component Analysis from Matrices to High Order Tensors: Applications to Biomedical Data

no code implementations29 Aug 2015 Guoxu Zhou, Qibin Zhao, Yu Zhang, Tülay Adalı, Shengli Xie, Andrzej Cichocki

With the increasing availability of various sensor technologies, we now have access to large amounts of multi-block (also called multi-set, multi-relational, or multi-view) data that need to be jointly analyzed to explore their latent connections.

Tensor Decomposition

Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness

no code implementations17 Apr 2014 Guoxu Zhou, Andrzej Cichocki, Qibin Zhao, Shengli Xie

Nonnegative Tucker decomposition (NTD) is a powerful tool for the extraction of nonnegative parts-based and physically meaningful latent components from high-dimensional tensor data while preserving the natural multilinear structure of data.

Accelerated Canonical Polyadic Decomposition by Using Mode Reduction

no code implementations15 Nov 2012 Guoxu Zhou, Andrzej Cichocki, Shengli Xie

Canonical Polyadic (or CANDECOMP/PARAFAC, CP) decompositions (CPD) are widely applied to analyze high order tensors.

Dimensionality Reduction

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