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Phee Lep Yeoh - IEEE Xplore Author Profile

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Author details

Phee Lep Yeoh

Also published under: P. L. Yeoh

Publications

155

Citations

2,837

Publications by Year

20092025

Co-Authors:

Arman AhmadzadehYi AiNur Ilyana Anwar ApandiSaman AtapattuPaeiz Azmi

Show All Co-Authors (126)

Phee Lep Yeoh

Also published under: P. L. Yeoh

Affiliation

School of Science

Technology and Engineering

University of the Sunshine Coast, Australia

Publication Topics

Path Loss,
Time Slot,
Deep Reinforcement Learning,
Wireless Networks,
Base Station,
Federated Learning,
Internet Of Things,
Mobile Edge Computing,
Neural Network,
Optimization Problem,
Unmanned Aerial Vehicles,
Cloud Computing

Biography

Phee Lep Yeoh (Member, IEEE) received the B.E. degree (University Medal) and the Ph.D. degree from the University of Sydney (USYD), Australia, in 2004 and 2012, respectively.,From 2005 to 2008, he was a Wireless Technology Specialist with Telstra, Australia. From 2012 to 2016, he was a Lecturer and a Research Fellow of wireless communications with the University of Melbourne, Australia. Since 2016, he has been a Senior Lecturer with the School of Electrical and Information Engineering, USYD. His current research interests include secure wireless communications, ultrareliable and low-latency communications, and multiscale molecular communications. He is a recipient of the 2020 USYD Robinson Fellowship, the 2018 Alexander von Humboldt Researc...

Publications

155

Citations

2,837

Publications by Year

20092025

Co-Authors:

Arman Ahmadzadeh
Yi Ai
Nur Ilyana Anwar Apandi
Saman Atapattu
Paeiz Azmi

Show All Co-Authors (126)

Author's Published Works

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Showing 1-25 of 453 results

Journals (325)

Conferences (100)

Early Access Articles (27)

Magazines (1)

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Keqin Li(369)
Kenli Li(184)
Renfa Li(26)
Guoqi Xie(24)
Zhuo Tang(23)
Department of Computer Science, State University of New York, New Paltz, NY, USA(193)
College of Computer Science and Electronic Engineering, Hunan University, Changsha, China(73)
College of Information Science and Engineering, Hunan University, Changsha, China(32)
Department of Computer Science, State University of New York, New Paltz, NY(29)
Department of Computer Science, State University of New York, New Paltz, New York, USA(24)
IEEE Transactions on Parallel and Distributed Systems(50)
IEEE Transactions on Services Computing(28)
IEEE Transactions on Cloud Computing(26)
IEEE Access(25)
IEEE Transactions on Computers(23)
IEEE(452)
TUP(1)
Media(47)
Datasets(1)
Guangzhou, China(6)
Atlanta, GA, USA(5)
Ocean Flower Island, China(5)
Shanghai, China(5)
Anchorage, AK, USA(4)
Cloud Computing(72)
Energy Consumption(68)
Scheduling Algorithm(53)
Optimization Problem(47)
Parallelization(42)

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Optical Flow-Enhanced Mamba U-Net for Cardiac Phase Detection in Ultrasound Videos

Yuhuan Lu;Guanghua Tan;Bin Pu;Pak-Hei Yeung;Hang Wang;Shengli Li;Jagath C. Rajapakse;Kenli Li

IEEE Transactions on Medical Imaging

Year: 2025 | Early Access Article |

The detection of cardiac phase in ultrasound videos, identifying end-systolic (ES) and end-diastolic (ED) frames, is a critical step in assessing cardiac function, monitoring structural changes, and diagnosing congenital heart disease. Current popular methods use recurrent neu ral networks to track dependencies over long sequences for cardiac phase detection, but often overlook the short-term moti...Show More

Optical Flow-Enhanced Mamba U-Net for Cardiac Phase Detection in Ultrasound Videos

Yuhuan Lu;Guanghua Tan;Bin Pu;Pak-Hei Yeung;Hang Wang;Shengli Li;Jagath C. Rajapakse;Kenli Li

IEEE Transactions on Medical Imaging

Year: 2025 | Early Access Article |

TextHair3D: Text-driven 3D Hair Editing with Generative Priors

Xiaoxue Li;Huilong Pi;Yunchuan Qin;Ruihui Li;Kenli Li

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

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Text-driven hair editing on 3D heads is a challenging problem in computer vision and graphics. In this paper, we propose TextHair3D, a NeRF-based text-driven 3D hair editing method that uses 3D perception to generate priors, edit hair attributes from user-provided text, and preserve facial features. TextHair3D uses the Contrastive Language-Image Pre-training (CLIP) model to encode textual conditio...Show More

TextHair3D: Text-driven 3D Hair Editing with Generative Priors

Xiaoxue Li;Huilong Pi;Yunchuan Qin;Ruihui Li;Kenli Li

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

AP-Net: Semi-Supervised Ultrasound Cardiac Segmentation Using Enhanced Anatomical Prior

Yuhuan Lu;Jintang Li;Jianxin Lin;Ying Yuan;Jagath C. Rajapakse;Ningbo Zhu;Chunlian Wang;Kenli Li

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

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Semi-supervised segmentation is gaining popularity in medical image analysis due to challenges in data acquisition and annotation. However, most methods focus on generating additional training pairs from unlabeled data through augmentation or perturbation for contrastive learning, often overlooking the unique characteristics and inherent priors of medical images. We identified two key anatomical p...Show More

AP-Net: Semi-Supervised Ultrasound Cardiac Segmentation Using Enhanced Anatomical Prior

Yuhuan Lu;Jintang Li;Jianxin Lin;Ying Yuan;Jagath C. Rajapakse;Ningbo Zhu;Chunlian Wang;Kenli Li

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

Stackelberg Game-Based Pricing and Offloading for the DVFS-Enabled MEC Systems

Jing Mei;Cuibin Zeng;Zhao Tong;Zhibang Yang;Keqin Li

IEEE Transactions on Network and Service Management

Year: 2025 | Early Access Article |

Due to the limited computing resources of both mobile devices (MDs) and the mobile edge computing (MEC) server, devising reasonable strategies for MD task offloading, MEC server resource pricing, and resource allocation is crucial. In this paper, a scenario is considered, comprising multiple MDs and a single MEC server. Each MD has a divisible task in each time slot, allowing for partial offloadin...Show More

Stackelberg Game-Based Pricing and Offloading for the DVFS-Enabled MEC Systems

Jing Mei;Cuibin Zeng;Zhao Tong;Zhibang Yang;Keqin Li

IEEE Transactions on Network and Service Management

Year: 2025 | Early Access Article |

Robust Hashing with Bilinear Drift for Image-Text Retrieval

Huan Zhao;Zeyi Li;Song Wang;Zixing Zhang;Keqin Li

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2025 | Early Access Article |

Supervised hashing models for image-text retrieval are fundamental and versatile in social media analysis and cross-lingual web search. Among them, supervised bilinear drift hashing is one of the most popular approaches. However, it still faces several challenges. For instance, how to leverage the power of bilinear drift hashing to distinguish similar and dissimilar data samples effectively; how t...Show More

Robust Hashing with Bilinear Drift for Image-Text Retrieval

Huan Zhao;Zeyi Li;Song Wang;Zixing Zhang;Keqin Li

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2025 | Early Access Article |

Power Management Optimization for Data Centers: A Power Supply Perspective

Huikang Huang;Weiwei Lin;Jianpeng Lin;Keqin Li

IEEE Transactions on Sustainable Computing

Year: 2025 | Early Access Article |

With the escalating demand for cloud computing services, data centers (DCs) encounter formidable challenges extending beyond capital investment needs to accommodate increasing computational demands and routine infrastructure maintenance. These challenges include substantial electricity costs due to high energy consumption and the environmental issues caused by high carbon emissions. To reduce cost...Show More

Power Management Optimization for Data Centers: A Power Supply Perspective

Huikang Huang;Weiwei Lin;Jianpeng Lin;Keqin Li

IEEE Transactions on Sustainable Computing

Year: 2025 | Early Access Article |

A Low-rate DoS Attack Mitigation Scheme Based on Port and Traffic State in SDN

Dan Tang;Rui Dai;Chenguang Zuo;Jingwen Chen;Keqin Li;Zheng Qin

IEEE Transactions on Computers

Year: 2025 | Early Access Article |

Low-rate Denial of Service (DoS) attacks can significantly compromise network availability and are difficult to detect and mitigate due to their stealthy exploitation of flaws in congestion control mechanisms. Software-Defined Networking (SDN) is a revolutionary architecture that decouples network control from packet forwarding, emerging as a promising solution for defending against low-rate DoS a...Show More

A Low-rate DoS Attack Mitigation Scheme Based on Port and Traffic State in SDN

Dan Tang;Rui Dai;Chenguang Zuo;Jingwen Chen;Keqin Li;Zheng Qin

IEEE Transactions on Computers

Year: 2025 | Early Access Article |

FastCRL: A Fast Network With Adaptive Fourier Transform and Offset Prediction for Fetal Crown-Rump Length Measurement and Position Estimation in Ultrasound Images

Jiatao Liu;Ying Tan;Chunlian Wang;Kenli Li;Guanghua Tan;Chubo Liu

IEEE Journal of Biomedical and Health Informatics

Year: 2025 | Early Access Article |

Fetal crown-rump length (CRL) is one of the most accurate method for estimating gestational age in early pregnancy. Typically, the process of manual CRL measurement by physicians is cumbersome, prone to errors due to fetal position, and susceptible to inter-observer variability. To provide an accurate, real-time, and reliable fetal CRL measurement solution, we propose FastCRL that utilizes key lan...Show More

FastCRL: A Fast Network With Adaptive Fourier Transform and Offset Prediction for Fetal Crown-Rump Length Measurement and Position Estimation in Ultrasound Images

Jiatao Liu;Ying Tan;Chunlian Wang;Kenli Li;Guanghua Tan;Chubo Liu

IEEE Journal of Biomedical and Health Informatics

Year: 2025 | Early Access Article |

A Cost-Aware Operator Migration Approach for Distributed Stream Processing System

Jiawei Tan;Zhuo Tang;Wentong Cai;Wen Jun Tan;Xiong Xiao;Jiapeng Zhang;Yi Gao;Kenli Li

IEEE Transactions on Cloud Computing

Year: 2025 | Volume: 13, Issue: 1 | Journal Article |

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Stream processing is integral to edge computing due to its low-latency attributes. Nevertheless, variability in user group sizes and disparate computing capabilities of edge devices necessitate frequent operator migrations within the stream. Moreover, intricate dependencies among stream operators often obscure the detection of potential bottleneck operators until an identified bottleneck is migrat...Show More

A Cost-Aware Operator Migration Approach for Distributed Stream Processing System

Jiawei Tan;Zhuo Tang;Wentong Cai;Wen Jun Tan;Xiong Xiao;Jiapeng Zhang;Yi Gao;Kenli Li

IEEE Transactions on Cloud Computing

Year: 2025 | Volume: 13, Issue: 1 | Journal Article |

Joint Computation Offloading and Resource Allocation in Mobile-Edge Cloud Computing: A Two-Layer Game Approach

Zhenli He;Ying Guo;Xiaolong Zhai;Mingxiong Zhao;Wei Zhou;Keqin Li

IEEE Transactions on Cloud Computing

Year: 2025 | Volume: 13, Issue: 1 | Journal Article |

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Mobile-Edge Cloud Computing (MECC) plays a crucial role in balancing low-latency services at the edge with the computational capabilities of cloud data centers (DCs). However, many existing studies focus on single-provider settings or limit their analysis to interactions between mobile devices (MDs) and edge servers (ESs), often overlooking the competition that occurs among ESs from different prov...Show More

Joint Computation Offloading and Resource Allocation in Mobile-Edge Cloud Computing: A Two-Layer Game Approach

Zhenli He;Ying Guo;Xiaolong Zhai;Mingxiong Zhao;Wei Zhou;Keqin Li

IEEE Transactions on Cloud Computing

Year: 2025 | Volume: 13, Issue: 1 | Journal Article |

MSCNet: Multi-Scale Network with Convolutions for Long-term Cloud Workload Prediction

Feiyu Zhao;Weiwei Lin;Shengsheng Lin;Shaomin Tang;Keqin Li

IEEE Transactions on Services Computing

Year: 2025 | Early Access Article |

Accurate workload prediction is crucial for resource allocation and management in large-scale cloud data centers. While many approaches have been proposed, most existing methods are based on Recurrent Neural Networks (RNNs) or their variants, focusing on short-term cloud workload prediction without considering or identifying the long-term changes and different periodic patterns of cloud workloads....Show More

MSCNet: Multi-Scale Network with Convolutions for Long-term Cloud Workload Prediction

Feiyu Zhao;Weiwei Lin;Shengsheng Lin;Shaomin Tang;Keqin Li

IEEE Transactions on Services Computing

Year: 2025 | Early Access Article |

Towards Accurate Truth Discovery With Privacy-Preserving Over Crowdsourced Data Streams

Zhimao Gong;Zhibang Yang;Shenghong Yang;Siyang Yu;Kenli Li;Mingxing Duan

IEEE Transactions on Knowledge and Data Engineering

Year: 2025 | Volume: 37, Issue: 4 | Journal Article |

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Truth discovery endeavors to extract valuable information from multi-source data through weighted aggregation. Some studies have integrated differential privacy techniques into traditional truth discovery algorithms to protect data privacy. However, due to the neglect of outliers and limitations in budget allocation, these schemes still need improvement in the accuracy of discovery results. To sol...Show More

Towards Accurate Truth Discovery With Privacy-Preserving Over Crowdsourced Data Streams

Zhimao Gong;Zhibang Yang;Shenghong Yang;Siyang Yu;Kenli Li;Mingxing Duan

IEEE Transactions on Knowledge and Data Engineering

Year: 2025 | Volume: 37, Issue: 4 | Journal Article |

Zram Instance Pool Framework for Adaptive Memory Compression in Resource-Sensitive Embedded Operating Systems

Yin Deng;Guoqi Xie;Chenglai Xiong;Sirong Zhao;Wei Ren;Kenli Li

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Year: 2025 | Early Access Article |

Memory compression can reduce the size of the inactive data in the random access memory (RAM), thereby freeing up unused space and allowing more programs to run; however, current mainstream memory compression frameworks (e.g., Zram and Zswap) and algorithms (e.g., Zstd and Lz4) do not effectively solve the problem of increased CPU utilization, causing they cannot be directly applied to the resourc...Show More

Zram Instance Pool Framework for Adaptive Memory Compression in Resource-Sensitive Embedded Operating Systems

Yin Deng;Guoqi Xie;Chenglai Xiong;Sirong Zhao;Wei Ren;Kenli Li

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Year: 2025 | Early Access Article |

Reputation-Based Model Aggregation and Resource Optimization in Wireless Federated Learning Systems

Jie Feng;Yanyan Liao;Lei Liu;Qingqi Pei;Ning Zhang;Keqin Li

IEEE Transactions on Wireless Communications

Year: 2025 | Early Access Article |

Federated learning (FL) has received widespread attention from academia and industry because it overcomes traditional security limitations associated with model training data. However, the FL process is vulnerable to manipulation by locally malicious users, who can alter their local data, thus impacting the accuracy of the model’s training outcomes. Meanwhile, optimizing delay in FL needs to take ...Show More

Reputation-Based Model Aggregation and Resource Optimization in Wireless Federated Learning Systems

Jie Feng;Yanyan Liao;Lei Liu;Qingqi Pei;Ning Zhang;Keqin Li

IEEE Transactions on Wireless Communications

Year: 2025 | Early Access Article |

A Novel Completion Method for Sparse Traffic Data Imputation

Renqiu Ouyang;Yikun Hu;Haotian Wang;Rong Hu;Wangdong Yang;Kenli Li

IEEE Intelligent Transportation Systems Magazine

Year: 2025 | Early Access Article |

Traffic data imputation is essential in smart cities and the Internet of Things (IoT). Tensor completion is an efficient method for traffic data imputation. However, these methods overlook the integration of contextual and spatial information, which are important for traffic data imputation. Hence, this study proposes a novel tensor completion method leveraging contextual and spatial information f...Show More

A Novel Completion Method for Sparse Traffic Data Imputation

Renqiu Ouyang;Yikun Hu;Haotian Wang;Rong Hu;Wangdong Yang;Kenli Li

IEEE Intelligent Transportation Systems Magazine

Year: 2025 | Early Access Article |

Machine Unlearning Through Fine-Grained Model Parameters Perturbation

Zhiwei Zuo;Zhuo Tang;Kenli Li;Anwitaman Datta

IEEE Transactions on Knowledge and Data Engineering

Year: 2025 | Volume: 37, Issue: 4 | Journal Article |

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Machine unlearning involves retracting data records and reducing their influence on trained models, aiding user privacy protection, at a significant computational cost potentially. Weight perturbation-based unlearning is common but typically modifies parameters globally. We propose fine-grained Top-K and Random-k parameters perturbed inexact machine unlearning that address the privacy needs while ...Show More

Machine Unlearning Through Fine-Grained Model Parameters Perturbation

Zhiwei Zuo;Zhuo Tang;Kenli Li;Anwitaman Datta

IEEE Transactions on Knowledge and Data Engineering

Year: 2025 | Volume: 37, Issue: 4 | Journal Article |

A Novel Dynamic Hill Cipher and Its Applications on Medical IoT

Jie Jin;Mengfan Wu;Aijia Ouyang;Keqin Li;Chaoyang Chen

IEEE Internet of Things Journal

Year: 2025 | Early Access Article |

Cryptography is one of the most important areas in information security. Cryptography ensures secure communication and data privacy, and it is increasingly being applied in healthcare and related fields. As an important classical cryptographic method, the Hill cipher has always been closely studied by experts and scholars. In order to enhance the security of the conventional Hill cipher (CHC), a n...Show More

A Novel Dynamic Hill Cipher and Its Applications on Medical IoT

Jie Jin;Mengfan Wu;Aijia Ouyang;Keqin Li;Chaoyang Chen

IEEE Internet of Things Journal

Year: 2025 | Early Access Article |

AVL Function Table for LeafHooks Insertion With Obfuscated Control Flow Integrity

Sirong Zhao;Guoqi Xie;Chenglai Xiong;Kenli Li;Xuejun Yu;Bo Wan;Yiwen Jiang

IEEE Transactions on Computers

Year: 2025 | Volume: 74, Issue: 4 | Journal Article |

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Control flow is the execution order of individual statements, instructions, or function calls within an imperative program. Malicious operation of control flow (e.g., tampering with normal function addresses) leads to severe consequences such as data leakage and system crash. Control Flow Integrity (CFI) is a defense restricting the execution order of program within Control Flow Graph (CFG). Index...Show More

AVL Function Table for LeafHooks Insertion With Obfuscated Control Flow Integrity

Sirong Zhao;Guoqi Xie;Chenglai Xiong;Kenli Li;Xuejun Yu;Bo Wan;Yiwen Jiang

IEEE Transactions on Computers

Year: 2025 | Volume: 74, Issue: 4 | Journal Article |

Prediction of Heterogeneous Device Task Runtime Based on Edge Server-Oriented Deep Neuro-Fuzzy System

Haijie Wu;Weiwei Lin;Wangbo Shen;Xiumin Wang;C. L. Philip Chen;Keqin Li

IEEE Transactions on Services Computing

Year: 2025 | Volume: 18, Issue: 1 | Journal Article |

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Predicting the runtime of tasks is of great significance as it can help users better understand the future runtime consumption of the tasks and make decisions for their heterogeneous devices, or be applied to task scheduling. Learning features from user task history data for predicting task runtime is a mainstream method. However, this method faces many challenges when applied to edge intelligence...Show More

Prediction of Heterogeneous Device Task Runtime Based on Edge Server-Oriented Deep Neuro-Fuzzy System

Haijie Wu;Weiwei Lin;Wangbo Shen;Xiumin Wang;C. L. Philip Chen;Keqin Li

IEEE Transactions on Services Computing

Year: 2025 | Volume: 18, Issue: 1 | Journal Article |

GroupFace: Imbalanced Age Estimation Based on Multi-Hop Attention Graph Convolutional Network and Group-Aware Margin Optimization

Yiping Zhang;Yuntao Shou;Wei Ai;Tao Meng;Keqin Li

IEEE Transactions on Information Forensics and Security

Year: 2025 | Volume: 20 | Journal Article |

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With the recent advances in computer vision, age estimation has significantly improved in overall accuracy. However, owing to the most common methods do not take into account the class imbalance problem in age estimation datasets, they suffer from a large bias in recognizing long-tailed groups. To achieve high-quality imbalanced learning in long-tailed groups, the dominant solution lies in that th...Show More

GroupFace: Imbalanced Age Estimation Based on Multi-Hop Attention Graph Convolutional Network and Group-Aware Margin Optimization

Yiping Zhang;Yuntao Shou;Wei Ai;Tao Meng;Keqin Li

IEEE Transactions on Information Forensics and Security

Year: 2025 | Volume: 20 | Journal Article |

A Context-Awareness and Hardware-Friendly Sparse Matrix Multiplication Kernel for CNN Inference Acceleration

Haotian Wang;Yan Ding;Yumeng Liu;Weichen Liu;Chubo Liu;Wangdong Yang;Kenli Li

IEEE Transactions on Computers

Year: 2025 | Volume: 74, Issue: 4 | Journal Article |

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Sparsification technology is crucial for deploying convolutional neural networks in resource-constrained environments. However, the efficiency of sparse models is hampered by irregular memory access patterns in sparse matrix multiplication kernels. Hardware-level support for 2:4 granularity in sparse tensor cores presents an opportunity for designing efficient sparse matrix multiplication kernels....Show More

A Context-Awareness and Hardware-Friendly Sparse Matrix Multiplication Kernel for CNN Inference Acceleration

Haotian Wang;Yan Ding;Yumeng Liu;Weichen Liu;Chubo Liu;Wangdong Yang;Kenli Li

IEEE Transactions on Computers

Year: 2025 | Volume: 74, Issue: 4 | Journal Article |

Distributed Edge Intelligence for Rapid In-Vehicle Medical Emergency Response in Internet of Vehicles

Jianhui Lv;Keqin Li;Adam Slowik;Huamao Jiang

IEEE Internet of Things Journal

Year: 2025 | Volume: 12, Issue: 5 | Journal Article |

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The unparalleled possibilities of Internet of Vehicles (IoV) development prompt the enhancement of in-vehicle medical emergency response. Nevertheless, the IoV environment is still affected by data privacy, latency, and network instability, which hamper effective and reliable emergency medical systems. In this regard, this article suggests the emergency-aware distributed edge intelligence (DEI) fo...Show More

Distributed Edge Intelligence for Rapid In-Vehicle Medical Emergency Response in Internet of Vehicles

Jianhui Lv;Keqin Li;Adam Slowik;Huamao Jiang

IEEE Internet of Things Journal

Year: 2025 | Volume: 12, Issue: 5 | Journal Article |

TFEGRU: Time-Frequency Enhanced Gated Recurrent Unit With Attention for Cloud Workload Prediction

Feiyu Zhao;Weiwei Lin;Shengsheng Lin;Haocheng Zhong;Keqin Li

IEEE Transactions on Services Computing

Year: 2025 | Volume: 18, Issue: 1 | Journal Article |

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Accurate prediction of cloud workload is crucial for effective resource allocation in cloud computing. However, due to the complexity and high dimensionality of workloads in the cloud environment, achieving precise workload prediction is a complex and challenging problem. Current approaches to cloud workload prediction mainly rely on deep learning methods based on the Recurrent Neural Network (RNN...Show More

TFEGRU: Time-Frequency Enhanced Gated Recurrent Unit With Attention for Cloud Workload Prediction

Feiyu Zhao;Weiwei Lin;Shengsheng Lin;Haocheng Zhong;Keqin Li

IEEE Transactions on Services Computing

Year: 2025 | Volume: 18, Issue: 1 | Journal Article |

PetTC: Pairwise Joint Embedding Based Contrastive Tensor Completion for Network Traffic Monitoring Services

Haoxuan Wang;Kun Xie;Jigang Wen;Guangxing Zhang;Wei Liang;Gaogang Xie;Kenli Li

IEEE Transactions on Services Computing

Year: 2025 | Volume: 18, Issue: 1 | Journal Article |

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Network traffic matrices often suffer from incompleteness and sparsity due to various factors, including network device policies and system limitations. The incompleteness can undermine the reliability and accuracy of network traffic monitoring services, negatively impacting downstream tasks such as network planning and fault diagnosis. Our focus is on network traffic data recovery, intending to i...Show More

PetTC: Pairwise Joint Embedding Based Contrastive Tensor Completion for Network Traffic Monitoring Services

Haoxuan Wang;Kun Xie;Jigang Wen;Guangxing Zhang;Wei Liang;Gaogang Xie;Kenli Li

IEEE Transactions on Services Computing

Year: 2025 | Volume: 18, Issue: 1 | Journal Article |

Collaborative Recommendation of National Image Resources for Targeted International Communication via Multidimensional Features and E-CARGO Modeling

Hua Ma;Chao Xiong;Xiangru Fu;Haibin Zhu;Yuqi Tang;Keqin Li

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2025 | Volume: 55, Issue: 2 | Journal Article |

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With the acceleration of globalization, the targeted international communication of national images contributes to enhancing a nation’s soft power and international recognition. It is challenging to select appropriate resources from the mass candidates for creating promotional works of national image. Existing research only focuses on the methodologies and lacks the systematic modeling and solving...Show More

Collaborative Recommendation of National Image Resources for Targeted International Communication via Multidimensional Features and E-CARGO Modeling

Hua Ma;Chao Xiong;Xiangru Fu;Haibin Zhu;Yuqi Tang;Keqin Li

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2025 | Volume: 55, Issue: 2 | Journal Article |

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