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Feng Xia - IEEE Xplore Author Profile
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Feng Xia

Publications
204
Citations
6,422
Publications by Year
20082025
Co-Authors:
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Feng Xia

Affiliation

School of Computing Technologies
RMIT University
Melbourne, VIC, Australia

Publication Topics

Biography

Feng Xia (Senior Member, IEEE) received the BSc and PhD degrees from Zhejiang University, Hangzhou, China. He is a professor in School of Computing Technologies, RMIT University, Australia. He has published 2 books and more than 300 scientific papers in international journals and conferences. His research interests include data science, artificial intelligence, graph learning, and systems engineering. He is an ACM, and an ACM Distinguished Speaker.(Based on document published on 8 November 2023).
Publications
204
Citations
6,422
Publications by Year
20082025
Co-Authors:
Show All Co-Authors (378)
Author's Published Works

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Multiscale Graph Transformer for Brain Disorder Diagnosis

Ahsan Shehzad;Dongyu Zhang;Shuo Yu;Shagufta Abid;Dinesh Kant Kumar;Feng Xia

IEEE Transactions on Consumer Electronics
Year: 2025 | Early Access Article |
Multiscale brain networks are crucial for diagnosing brain disorders by revealing the hierarchical organization of brain function and connectivity. However, previous methods that explored multi-atlas approaches to model these networks often failed to represent this organization across multiple spatial and temporal scales, leading to limited representations and potentially inaccurate diagnoses. To ...Show More

Multiscale Graph Transformer for Brain Disorder Diagnosis

Ahsan Shehzad;Dongyu Zhang;Shuo Yu;Shagufta Abid;Dinesh Kant Kumar;Feng Xia

Year: 2025 | Early Access Article |

Path-aware Few-shot Knowledge Graph Completion

Shuo Yu;Yingbo Wang;Zhitao Wan;Yanming Shen;Qiang Zhang;Feng Xia

IEEE Transactions on Artificial Intelligence
Year: 2025 | Early Access Article |
Few-shot Knowledge Graph Completion (FKGC) has emerged as a significant area of interest for addressing the long-tail problem in knowledge graphs. Traditional approaches often focus on the sparse few-shot neighborhood to derive semantic representation, overlooking other critical information forms such as relation paths. In this paper, we introduce an innovative method, called PARE, which fully lev...Show More

Path-aware Few-shot Knowledge Graph Completion

Shuo Yu;Yingbo Wang;Zhitao Wan;Yanming Shen;Qiang Zhang;Feng Xia

Year: 2025 | Early Access Article |

Dynamic Graph Transformer for Brain Disorder Diagnosis

Ahsan Shehzad;Dongyu Zhang;Shuo Yu;Shagufta Abid;Feng Xia

IEEE Journal of Biomedical and Health Informatics
Year: 2025 | Early Access Article |
Dynamic brain networks play a pivotal role in diagnosing brain disorders by capturing temporal changes in brain activity and connectivity. Previous methods often rely on sliding-window approaches for constructing these networks using fMRI data. However, these methods face two key limitations: a fixed temporal length that inadequately captures brain activity dynamics and a global spatial scope that...Show More

Dynamic Graph Transformer for Brain Disorder Diagnosis

Ahsan Shehzad;Dongyu Zhang;Shuo Yu;Shagufta Abid;Feng Xia

Year: 2025 | Early Access Article |

Model-Heterogeneous Federated Graph Learning With Prototype Propagation

Zhi Liu;Hanlin Zhou;Xiaohua He;Haopeng Yuan;Jiaxin Du;Mengmeng Wang;Guojiang Shen;Xiangjie Kong;Feng Xia

IEEE Transactions on Artificial Intelligence
Year: 2025 | Volume: 6, Issue: 3 | Journal Article |
Federated graph learning (FGL) enables clients to collaboratively train a robust graph neural network (GNN) while ensuring their private graph data never leaves the local. However, existing FGL frameworks require all clients to train the identical GNN model, which limits their real-world applicability. Although many model-heterogenous frameworks have been proposed for traditional nongraph federate...Show More

Model-Heterogeneous Federated Graph Learning With Prototype Propagation

Zhi Liu;Hanlin Zhou;Xiaohua He;Haopeng Yuan;Jiaxin Du;Mengmeng Wang;Guojiang Shen;Xiangjie Kong;Feng Xia

Year: 2025 | Volume: 6, Issue: 3 | Journal Article |

Generative Adversarial Network Based Image-Scaling Attack and Defense Modeling

Junjian Li;Honglong Chen;Zhe Li;Anqing Zhang;Xiaomeng Wang;Xingang Wang;Feng Xia

IEEE Transactions on Emerging Topics in Computational Intelligence
Year: 2025 | Volume: 9, Issue: 1 | Journal Article |
Deep learning has achieved remarkable performance in computer vision applications, and gradually becomes one of the mainstream technologies. Image scaling, as an indispensable data pre-processing procedure for most of the computer vision applications, is implemented to resize the unmatched data to fit the input sizes of deep learning models. However, such kind of data pre-processing is vulnerable ...Show More

Generative Adversarial Network Based Image-Scaling Attack and Defense Modeling

Junjian Li;Honglong Chen;Zhe Li;Anqing Zhang;Xiaomeng Wang;Xingang Wang;Feng Xia

Year: 2025 | Volume: 9, Issue: 1 | Journal Article |

MAT: Medical AI-generated Text Detection Dataset from Multi-models and Multi-Methods

Bo Xu;Ruiyuan Wang;Lingfan Ping;Chaoyue Zhu;Xiaoxia Liu;Hongfei Lin;Linlin Tian;Feng Xia

2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2024 | Conference Paper |
Large language models (LLMs) have been widely used in society due to their amazing emergent capabilities, but they also bring security issues. A large amount of content on the Internet may be generated by AI. Whether it is social forums or more professional academic fields, the abuse of AI has become a problem. Especially in some professional fields, Blindly trusting what the Internet says is dang...Show More

MAT: Medical AI-generated Text Detection Dataset from Multi-models and Multi-Methods

Bo Xu;Ruiyuan Wang;Lingfan Ping;Chaoyue Zhu;Xiaoxia Liu;Hongfei Lin;Linlin Tian;Feng Xia

Year: 2024 | Conference Paper |

Stage-aware Brain Graph Learning for Alzheimer’s Disease

Ciyuan Peng;Mujie Liu;Chenxuan Meng;Sha Xue;Kathleen Keogh;Feng Xia

2024 IEEE Conference on Artificial Intelligence (CAI)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Current machine learning-based Alzheimer’s disease (AD) diagnosis methods fail to explore the distinctive brain patterns across different AD stages, lacking the ability to trace the trajectory of AD progression. This limitation can lead to an oversight of the pathological mechanisms of AD and suboptimal performance in AD diagnosis. To overcome this challenge, this paper proposes a novel stage-awar...Show More

Stage-aware Brain Graph Learning for Alzheimer’s Disease

Ciyuan Peng;Mujie Liu;Chenxuan Meng;Sha Xue;Kathleen Keogh;Feng Xia

Year: 2024 | Conference Paper |

mBGT: Encoding Brain Signals With Multimodal Brain Graph Transformer

Ciyuan Peng;Tianqi Guo;Chengyao Xie;Xiaomei Bai;Jingjing Zhou;Xiaohui Zhao;Estrid He;Feng Xia

IEEE Transactions on Consumer Electronics
Year: 2024 | Early Access Article |
Leveraging the multimodal brain signals collected from various electronic devices, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) data, has been regarded as a promising technique for automated brain disease diagnosis. Existing studies on multimodal brain signal analysis mainly focus on data alignment and basic feature integration, yet fail to capture the spa...Show More

mBGT: Encoding Brain Signals With Multimodal Brain Graph Transformer

Ciyuan Peng;Tianqi Guo;Chengyao Xie;Xiaomei Bai;Jingjing Zhou;Xiaohui Zhao;Estrid He;Feng Xia

Year: 2024 | Early Access Article |

Efficient Deployment and Scheduling of Shared VNF Instances in Mobile Edge Computing Networks

Guoxin Li;Honglong Chen;Liantao Wu;Xuejian Chi;Junmei Yao;Feng Xia;Jiguo Yu

IEEE Internet of Things Journal
Year: 2024 | Volume: 11, Issue: 19 | Journal Article |
Mobile edge computing (MEC) is considered a promising technology to provide low-latency services by keeping computing and other resources physically close to where they are needed. The functions implemented through network function virtualization (NFV) technology in MEC are called virtual network function (VNF) instances, and the deployment and scheduling of VNF instances have always been a hot to...Show More

Efficient Deployment and Scheduling of Shared VNF Instances in Mobile Edge Computing Networks

Guoxin Li;Honglong Chen;Liantao Wu;Xuejian Chi;Junmei Yao;Feng Xia;Jiguo Yu

Year: 2024 | Volume: 11, Issue: 19 | Journal Article |

Frequency Is Devious: Deep Stealthy Backdoor in Artificial Intelligence Based Consumer Internet of Things

Yudong Gao;Honglong Chen;Junjian Li;Xingang Wang;Abdullah Alharbi;Amr Tolba;Feng Xia

IEEE Transactions on Consumer Electronics
Year: 2024 | Early Access Article |
Artificial Intelligence (AI) based Consumer Internet of Things (CIoT) flourishes at a rapid speed due to its excellent data collection ability, which plays an important role in optimizing Deep Neural Networks (DNNs) of AI-based CIoT. However, since the CIoT devices can be accessed without the user’s permission, the DNNs of AI-based CIoT face various security threats, especially the backdoor attack...Show More

Frequency Is Devious: Deep Stealthy Backdoor in Artificial Intelligence Based Consumer Internet of Things

Yudong Gao;Honglong Chen;Junjian Li;Xingang Wang;Abdullah Alharbi;Amr Tolba;Feng Xia

Year: 2024 | Early Access Article |

HIM: Discovering Implicit Relationships in Heterogeneous Social Networks

Bo Xu;Jinpeng Wang;Zhehuan Zhao;Hongfei Lin;Feng Xia

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
To date, research on relation mining has typically focused on analyzing explicit relationships between entities, while ignoring the underlying connections between entities, known as implicit relationships. Exploring implicit relationships can reveal more about social dynamics and potential relationships in heterogeneous social networks to better explain complex social behaviors. The research prese...Show More

HIM: Discovering Implicit Relationships in Heterogeneous Social Networks

Bo Xu;Jinpeng Wang;Zhehuan Zhao;Hongfei Lin;Feng Xia

Year: 2024 | Conference Paper |

Few-Shot Semantic Segmentation for Consumer Electronics: An Inter-Class Relation Mining Approach

Huafei Huang;Xu Yuan;Shuo Yu;Wenhong Zhao;Osama Alfarraj;Amr Tolba;Feng Xia

IEEE Transactions on Consumer Electronics
Year: 2024 | Volume: 70, Issue: 1 | Journal Article |
Cited by: Papers (2)
Few-shot semantic segmentation (FSS), which can perform segmentation using only a limited number of annotated examples, is a promising technique that has been embedded in many electronic products. Existing approaches usually achieve segmentation for the query image by computing the similarity between the support and query images. However, when segmenting a new query image, the model prediction may...Show More

Few-Shot Semantic Segmentation for Consumer Electronics: An Inter-Class Relation Mining Approach

Huafei Huang;Xu Yuan;Shuo Yu;Wenhong Zhao;Osama Alfarraj;Amr Tolba;Feng Xia

Year: 2024 | Volume: 70, Issue: 1 | Journal Article |

Towards Maximizing Coverage of Targets for WRSNs by Multiple Chargers Scheduling

Huansheng Xue;Honglong Chen;Zhichen Ni;Xiaolong Liu;Feng Xia

IEEE Transactions on Mobile Computing
Year: 2024 | Volume: 23, Issue: 10 | Journal Article |
Cited by: Papers (3)
In recent years, wireless rechargeable sensor networks (WRSNs) have gained significant attention in the research community due to the current advancements in wireless power transfer technology. In mobile charger scheduling, previous works primarily emphasized the survival rate of sensor nodes. However, the primary task of a WRSN is to monitor targets in a given area. Therefore, the coverage of tar...Show More

Towards Maximizing Coverage of Targets for WRSNs by Multiple Chargers Scheduling

Huansheng Xue;Honglong Chen;Zhichen Ni;Xiaolong Liu;Feng Xia

Year: 2024 | Volume: 23, Issue: 10 | Journal Article |

Multitask Data Collection With Limited Budget in Edge-Assisted Mobile Crowdsensing

Xiaolong Liu;Honglong Chen;Yuping Liu;Wentao Wei;Huansheng Xue;Feng Xia

IEEE Internet of Things Journal
Year: 2024 | Volume: 11, Issue: 9 | Journal Article |
Cited by: Papers (3)
Due to the swift advancement of edge computing and mobile crowdsensing (MCS), edge-assisted MCS (EAMCS) has emerged as a promising paradigm, leveraging sensor-embedded mobile devices for the collection and sharing of environmental data. As the sensing scale increases in the modern urban, the application scenario becomes more and more complex, and the budget of users and platform is limited. Theref...Show More

Multitask Data Collection With Limited Budget in Edge-Assisted Mobile Crowdsensing

Xiaolong Liu;Honglong Chen;Yuping Liu;Wentao Wei;Huansheng Xue;Feng Xia

Year: 2024 | Volume: 11, Issue: 9 | Journal Article |

Unsupervised Anomaly Detection on Attributed Networks With Graph Contrastive Learning for Consumer Electronics Security

Bo Xu;Jinpeng Wang;Zhehuan Zhao;Hongfei Lin;Feng Xia

IEEE Transactions on Consumer Electronics
Year: 2024 | Volume: 70, Issue: 1 | Journal Article |
Cited by: Papers (3)
The proliferation of consumer electronic products has engendered a substantial surge in data generation and information exchange, concurrently escalating the potential for security threats. Detecting anomalies effectively on attributed networks has undeniable positive significance for consumer electronic security, such as fraudulent user detection, malicious consumption actions analysis, and netwo...Show More

Unsupervised Anomaly Detection on Attributed Networks With Graph Contrastive Learning for Consumer Electronics Security

Bo Xu;Jinpeng Wang;Zhehuan Zhao;Hongfei Lin;Feng Xia

Year: 2024 | Volume: 70, Issue: 1 | Journal Article |

From Electroencephalogram Data to Brain Networks: Graph-Learning-Based Brain Disease Diagnosis

Ke Sun;Ciyuan Peng;Shuo Yu;Zhuoyang Han;Feng Xia

IEEE Intelligent Systems
Year: 2024 | Volume: 39, Issue: 2 | Magazine Article |
Cited by: Papers (5)
Brain networks are built according to the structures or neural activities of different brain regions, which can be modeled as complex networks. Many studies exploit brains from the perspective of graph learning to diagnose the nerve diseases of brains. However, many of these algorithms are unable to automatically construct brain function topology based on electroencephalogram (EEG) and fail to cap...Show More

From Electroencephalogram Data to Brain Networks: Graph-Learning-Based Brain Disease Diagnosis

Ke Sun;Ciyuan Peng;Shuo Yu;Zhuoyang Han;Feng Xia

Year: 2024 | Volume: 39, Issue: 2 | Magazine Article |

Multiple Instance Learning for Cheating Detection and Localization in Online Examinations

Yemeng Liu;Jing Ren;Jianshuo Xu;Xiaomei Bai;Roopdeep Kaur;Feng Xia

IEEE Transactions on Cognitive and Developmental Systems
Year: 2024 | Volume: 16, Issue: 4 | Journal Article |
Cited by: Papers (3)
The spread of the Coronavirus disease-2019 epidemic has caused many courses and exams to be conducted online. The cheating behavior detection model in examination invigilation systems plays a pivotal role in guaranteeing the equality of long-distance examinations. However, cheating behavior is rare, and most researchers do not comprehensively take into account features such as head posture, gaze a...Show More

Multiple Instance Learning for Cheating Detection and Localization in Online Examinations

Yemeng Liu;Jing Ren;Jianshuo Xu;Xiaomei Bai;Roopdeep Kaur;Feng Xia

Year: 2024 | Volume: 16, Issue: 4 | Journal Article |

Z-Laplacian Matrix Factorization: Network Embedding With Interpretable Graph Signals

Liangtian Wan;Zhengqiang Fu;Yi Ling;Yuchen Sun;Xiaona Li;Lu Sun;Feng Xia;Xiaoran Yan;Charu C. Aggarwal

IEEE Transactions on Knowledge and Data Engineering
Year: 2024 | Volume: 36, Issue: 8 | Journal Article |
Network embedding aims to represent nodes with low dimensional vectors while preserving structural information. It has been recently shown that many popular network embedding methods can be transformed into matrix factorization problems. In this paper, we propose the unifying framework “Z-NetMF,” which generalizes random walk samplers to Z-Laplacian graph filters, leading to embedding algorithms w...Show More

Z-Laplacian Matrix Factorization: Network Embedding With Interpretable Graph Signals

Liangtian Wan;Zhengqiang Fu;Yi Ling;Yuchen Sun;Xiaona Li;Lu Sun;Feng Xia;Xiaoran Yan;Charu C. Aggarwal

Year: 2024 | Volume: 36, Issue: 8 | Journal Article |

Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems

Zhen Cai;Tao Tang;Shuo Yu;Yunpeng Xiao;Feng Xia

IEEE Internet of Things Journal
Year: 2024 | Volume: 11, Issue: 6 | Journal Article |
Cited by: Papers (1)
Federated recommender systems have been crucially enhanced through data sharing and continuous model updates, attributed to the pervasive connectivity and distributed computing capabilities of Internet of Things (IoT) devices. Given the sensitivity of IoT data, transparent data processing in data sharing and model updates is paramount. However, existing methods fall short in tracing the flow of sh...Show More

Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems

Zhen Cai;Tao Tang;Shuo Yu;Yunpeng Xiao;Feng Xia

Year: 2024 | Volume: 11, Issue: 6 | Journal Article |

EDSP-Edge: Efficient Dynamic Edge Service Entity Placement for Mobile Virtual Reality Systems

Xuejian Chi;Honglong Chen;Guoxin Li;Zhichen Ni;Nan Jiang;Feng Xia

IEEE Transactions on Wireless Communications
Year: 2024 | Volume: 23, Issue: 4 | Journal Article |
Cited by: Papers (12)
As one of the significant supporting technologies for mobile virtual reality (MVR), computer vision is latency-sensitive and always requires real-time response and accurate object analysis. However, the limited computational resources of mobile devices lead to high service delay and low analysis quality, resulting in poor quality of service (QoS). By placing the edge service entities (SEs) of the ...Show More

EDSP-Edge: Efficient Dynamic Edge Service Entity Placement for Mobile Virtual Reality Systems

Xuejian Chi;Honglong Chen;Guoxin Li;Zhichen Ni;Nan Jiang;Feng Xia

Year: 2024 | Volume: 23, Issue: 4 | Journal Article |

Efficiently Identifying Unknown COTS RFID Tags for Intelligent Transportation Systems

Kai Lin;Honglong Chen;Zhe Li;Na Yan;Huansheng Xue;Feng Xia

IEEE Transactions on Intelligent Transportation Systems
Year: 2024 | Volume: 25, Issue: 1 | Journal Article |
Cited by: Papers (4)
Over the last decade, the Internet of Things (IoT) technology has advanced significantly in a variety of fields. As a pivotal application of IoT, intelligent transportation systems (ITS) have harvested great attention from the research community. Radio frequency identification (RFID) which is an essential technology in IoT plays a key role in ITS to identify tagged vehicles. Unknown tag identifica...Show More

Efficiently Identifying Unknown COTS RFID Tags for Intelligent Transportation Systems

Kai Lin;Honglong Chen;Zhe Li;Na Yan;Huansheng Xue;Feng Xia

Year: 2024 | Volume: 25, Issue: 1 | Journal Article |

MissII: Missing Information Imputation for Traffic Data

Mingliang Hou;Tao Tang;Feng Xia;Ibrahim Sultan;Roopdeep Kaur;Xiangjie Kong

IEEE Transactions on Emerging Topics in Computing
Year: 2024 | Volume: 12, Issue: 3 | Journal Article |
Cited by: Papers (4)
Cyber-Physical-Social Systems (CPSS) offer a new perspective for applying advanced information technology to improve urban transportation. However, real-world traffic datasets collected from sensing devices like loop sensors often contain corrupted or missing values. The incompleteness of traffic data poses great challenges to downstream data analysis tasks and applications. Most existing data-dri...Show More

MissII: Missing Information Imputation for Traffic Data

Mingliang Hou;Tao Tang;Feng Xia;Ibrahim Sultan;Roopdeep Kaur;Xiangjie Kong

Year: 2024 | Volume: 12, Issue: 3 | Journal Article |

Coupled Attention Networks for Multivariate Time Series Anomaly Detection

Feng Xia;Xin Chen;Shuo Yu;Mingliang Hou;Mujie Liu;Linlin You

IEEE Transactions on Emerging Topics in Computing
Year: 2024 | Volume: 12, Issue: 1 | Journal Article |
Cited by: Papers (3)
Multivariate time series anomaly detection (MTAD) plays a vital role in a wide variety of real-world application domains. Over the past few years, MTAD has attracted rapidly increasing attention from both academia and industry. Many deep learning and graph learning models have been developed for effective anomaly detection in multivariate time series data, which enable advanced applications such a...Show More

Coupled Attention Networks for Multivariate Time Series Anomaly Detection

Feng Xia;Xin Chen;Shuo Yu;Mingliang Hou;Mujie Liu;Linlin You

Year: 2024 | Volume: 12, Issue: 1 | Journal Article |

Explainable Knowledge Distillation for On-Device Chest X-Ray Classification

Chakkrit Termritthikun;Ayaz Umer;Suwichaya Suwanwimolkul;Feng Xia;Ivan Lee

IEEE/ACM Transactions on Computational Biology and Bioinformatics
Year: 2024 | Volume: 21, Issue: 4 | Journal Article |
Cited by: Papers (5)
Automated multi-label chest X-rays (CXR) image classification has achieved substantial progress in clinical diagnosis via utilizing sophisticated deep learning approaches. However, most deep models have high computational demands, which makes them less feasible for compact devices with low computational requirements. To overcome this problem, we propose a knowledge distillation (KD) strategy to cr...Show More

Explainable Knowledge Distillation for On-Device Chest X-Ray Classification

Chakkrit Termritthikun;Ayaz Umer;Suwichaya Suwanwimolkul;Feng Xia;Ivan Lee

Year: 2024 | Volume: 21, Issue: 4 | Journal Article |

Efficient Throughput Maximization in Dynamic Rechargeable Networks

Jianhui Zhang;Yanhong Xu;Jiacheng Wang;Hanxiang Wang;Bei Zhao;Liming Liu;Feng Xia

IEEE Transactions on Mobile Computing
Year: 2024 | Volume: 23, Issue: 3 | Journal Article |
In the Dynamic Rechargeable Networks (DRNs), to maximize the throughput by efficient energy allocation, the existing studies usually consider the spatio-temporal dynamic factors of the harvested energy, and seldom the network dynamic factors simultaneously, such as the time variable network resources and wireless interference. To take the network dynamic factors together, this paper studies the qu...Show More

Efficient Throughput Maximization in Dynamic Rechargeable Networks

Jianhui Zhang;Yanhong Xu;Jiacheng Wang;Hanxiang Wang;Bei Zhao;Liming Liu;Feng Xia

Year: 2024 | Volume: 23, Issue: 3 | Journal Article |

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