no code implementations • 9 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.
no code implementations • 29 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.
no code implementations • 26 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.
no code implementations • 6 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.
no code implementations • 28 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.
no code implementations • 14 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.
no code implementations • 3 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.
no code implementations • 19 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.
no code implementations • 21 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
no code implementations • 12 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.
no code implementations • 14 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.
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.
no code implementations • 10 Oct 2018 • Jiawei Wang, Zhaoshui He, Chengjian Feng, Zhouping Zhu, Qinzhuang Lin, Jun Lv, Shengli Xie
Data collection and annotation are time-consuming in machine learning, expecially for large scale problem.
no code implementations • 22 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.
no code implementations • 20 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.
no code implementations • 30 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.
1 code implementation • 17 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.
no code implementations • 27 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.
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.
no code implementations • 29 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.
no code implementations • 17 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.
no code implementations • 15 Nov 2012 • Guoxu Zhou, Andrzej Cichocki, Shengli Xie
Canonical Polyadic (or CANDECOMP/PARAFAC, CP) decompositions (CPD) are widely applied to analyze high order tensors.