262 citations · 837 across the 14 of their papers we have counts for
14 papers
Locality Sensitive Hash Aggregated Nonlinear Neighbourhood Matrix Factorization for Online Sparse Big Data Analysis
Zixuan Li, Hao Li, Kenli Li +3
Matrix factorization (MF) can extract the low-rank features and integrate the information of the data manifold distribution from high-dimensional data, which can consider the nonli…
Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning
Jianguo Chen, Kenli Li, Keqin Li +2
As a new generation of Public Bicycle-sharing Systems (PBS), the dockless PBS (DL-PBS) is an important application of cyber-physical systems and intelligent transportation. How to…
Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network
Jianguo Chen, Kenli Li, Keqin Li +2
Benefiting from convenient cycling and flexible parking locations, the Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. However, red…
SGD_Tucker: A Novel Stochastic Optimization Strategy for Parallel Sparse Tucker Decomposition
Hao Li, Zixuan Li, Kenli Li +3
Sparse Tucker Decomposition (STD) algorithms learn a core tensor and a group of factor matrices to obtain an optimal low-rank representation feature for the \underline{H}igh-\under…
Stochastic Client Selection for Federated Learning with Volatile Clients
Tiansheng Huang, Weiwei Lin, Li Shen +2
Federated Learning (FL), arising as a privacy-preserving machine learning paradigm, has received notable attention from the public. In each round of synchronous FL training, only a…
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee
Tiansheng Huang, Weiwei Lin, Wentai Wu +3
The issue of potential privacy leakage during centralized AI's model training has drawn intensive concern from the public. A Parallel and Distributed Computing (or PDC) scheme, ter…
A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19
Jianguo Chen, Kenli Li, Zhaolei Zhang +2
The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions…
Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing
Jianguo Chen, Kenli Li, Qingying Deng +2
In this paper, we propose a Distributed Intelligent Video Surveillance (DIVS) system using Deep Learning (DL) algorithms and deploy it in an edge computing environment. We establis…
Gradient Scheduling with Global Momentum for Non-IID Data Distributed Asynchronous Training
Chengjie Li, Ruixuan Li, Haozhao Wang +4
Distributed asynchronous offline training has received widespread attention in recent years because of its high performance on large-scale data and complex models. As data are dist…
A High-Performance CNN Method for Offline Handwritten Chinese Character Recognition and Visualization
Pavlo Melnyk, Zhiqiang You, Keqin Li
Recent researches introduced fast, compact and efficient convolutional neural networks (CNNs) for offline handwritten Chinese character recognition (HCCR). However, many of them di…
Parallel Protein Community Detection in Large-scale PPI Networks Based on Multi-source Learning
Jianguo Chen, Kenli Li, Kashif Bilal +3
Protein interactions constitute the fundamental building block of almost every life activity. Identifying protein communities from Protein-Protein Interaction (PPI) networks is ess…
A Periodicity-based Parallel Time Series Prediction Algorithm in Cloud Computing Environments
Jianguo Chen, Kenli Li, Huigui Rong +3
In the era of big data, practical applications in various domains continually generate large-scale time-series data. Among them, some data show significant or potential periodicity…
A Bi-layered Parallel Training Architecture for Large-scale Convolutional Neural Networks
Jianguo Chen, Kenli Li, Kashif Bilal +3
Benefitting from large-scale training datasets and the complex training network, Convolutional Neural Networks (CNNs) are widely applied in various fields with high accuracy. Howev…
Sorting with GPUs: A Survey
Dmitri I. Arkhipov, Di Wu, Keqin Li +1
Sorting is a fundamental operation in computer science and is a bottleneck in many important fields. Sorting is critical to database applications, online search and indexing,biomed…