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Oh-FedRec: One-Shot and Heterogeneous Vertical Federated Recommendation System
Xiangjie Kong;Xiaohua He;Xulin Ma;Xiaoran Yan;Lingyun Wang;Guojiang Shen;Zhi Liu
IEEE Transactions on Consumer Electronics
Year: 2025 | Early Access Article |
Federated learning has a wide range of applications in recommendation systems, but most federated recommendation systems can only achieve federated communication between users and servers. Only a few are vertical federated recommendation systems, achieving federated server communication. In addition, the current federated recommendation frameworks require that each participant have the same model....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
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
Unbiased Anomalous Trajectory Detection with Hierarchical Sequence Modeling
Xiangjie Kong;Yuwei He;Guojiang Shen;Jiaxin Du;Zhi Liu;Ivan Lee
IEEE Transactions on Consumer Electronics
Year: 2024 | Early Access Article |
Anomalous trajectory detection plays an important role in the field of trajectory big data mining, providing significant support for identifying drivers traveling at inappropriate speeds and detecting cab fraud. Current studies often use equal-sized grids to represent trajectory points, and they mainly focus on the general shape of trajectories while ignoring the spatial density distribution of tr...Show More
A Comprehensive Survey on Traffic Missing Data Imputation
Yimei Zhang;Xiangjie Kong;Wenfeng Zhou;Jin Liu;Yanjie Fu;Guojiang Shen
Intelligent Transportation Systems (ITS) are essential and play a key role in improving road safety, reducing congestion, optimizing traffic flow and facilitating the development of smart cities. The collection of data from ITS and its transformation into information is challenged by the presence of missing data in datasets. Timely and effective handling of missing data is crucial to facilitate in...Show More
Vehicular Social Dynamic Anomaly Detection With Recurrent Multi-Mask Aggregator Enabled VAE
Zehao Hu;Yuwei He;Yuqi Shen;Minho Jo;Mario Collotta;Guojiang Shen;Xiangjie Kong
Vehicle driving behavior analysis and detection tasks have become an indispensable part of intelligent transportation systems. Accurate pattern recognition of potential anomalies during the movement of entities is crucial for improving transportation efficiency. Current methods typically analyze vehicle trajectories independently without considering potential interactions among vehicles. To addres...Show More
Federated Adaptive Pseudo-Labeling Selection for Semi-Supervised Air Writing Recognition Systems
Wenyi Zhang;Xiangjie Kong;Guojiang Shen;Zhenpeng Wu;Youyang Qu
IEEE Transactions on Consumer Electronics
Year: 2024 | Early Access Article |
The rapid advancement of virtual and augmented reality technologies has catalyzed a new paradigm in consumer electronics: air writing, a form of touchless human-computer interaction with vast potential. Implementing air writing recognition systems, however, faces challenges such as label scarcity and privacy concerns. Addressing these, we propose the Federated Adaptive Pseudo-labeling Selection (F...Show More
A Diffusion Model for Inductive Knowledge Graph Completion
2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Knowledge graph completion (KGC) is dedicated to deducing absent facts within incomplete knowledge graphs (KGs). The majority of antecedent studies exclusively address the transductive scenario, wherein all entities are observed throughout the training process, which is impractical for the everyday reality with constantly emerging entities. Recent works that concern this issue mainly focus on repr...Show More
An Autonomous Underwater Vehicle-Assisted Adaptive Trust Prediction Model for UASNs
Jiaxin Du;Guangjie Han;Chuan Lin;Xiangjie Kong;Guojiang Shen
IEEE Transactions on Intelligent Vehicles
Year: 2024 | Early Access Article |
Underwater acoustic sensor networks (UASNs) provide significant support for marine intelligent transportation system. As an effective security mechanism, the trust prediction model has been gradually deployed in UASNs to detect anomalous attacks and guarantee the security of marine transportation. However, in existing research on trust models for UASNs, the impact of multidimensional trust feature...Show More
Point-Correlate Adversarial Transformer for Unsupervised Multivariate Time Series Anomaly Detection
Huan Li;Xiangjie Kong;Guojiang Shen;Xiaoran Yan;Yao Yang;Mario Collotta
2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
Year: 2024 | Conference Paper |
Multivariate time series anomaly detection plays a crucial role in industrial production. However, the inherent complexity and randomness of time series pose significant challenges. Furthermore, existing detection methods struggle to provide reliable explanations for outliers. To address these issues, this paper presents an unsupervised multivariate time series anomaly detection model named Point-...Show More
Deep Trajectory Recovery Approach of Offline Vehicles in the Internet of Vehicles
Xiao Han;Ding-Xuan Zhou;Guojiang Shen;Xiangjie Kong;Yulong Zhao
Multi-modal trajectory analysis and trajectory recovery are essential tasks in transportation research, especially for offline vehicles, which enable comprehensive understanding of complex transportation systems and address the issue of incomplete or missing trajectory data. In this paper, we propose a novel Deep Trajectory Recovery Framework, DTRF, which can effectively tackle both challenges by ...Show More
Asynchronous Federated Deep-Reinforcement-Learning-Based Dependency Task Offloading for UAV-Assisted Vehicular Networks
Si Shen;Guojiang Shen;Zhehao Dai;Kaiyu Zhang;Xiangjie Kong;Jianxin Li
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a valuable supplement to conventional MEC, offering unique advantages in temporary or emergency scenarios. However, the integration of UAVs into MEC introduces new challenges. First, the existing UAV-empowered MEC frameworks predominantly have not accounted for dependencies between the computing tasks. Second, the li...Show More
Federated Graph Anomaly Detection via Contrastive Self-Supervised Learning
Xiangjie Kong;Wenyi Zhang;Hui Wang;Mingliang Hou;Xin Chen;Xiaoran Yan;Sajal K. Das
IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Early Access Article |
Cited by: Papers (4)
Attribute graph anomaly detection aims to identify nodes that significantly deviate from the majority of normal nodes, and has received increasing attention due to the ubiquity and complexity of graph-structured data in various real-world scenarios. However, current mainstream anomaly detection methods are primarily designed for centralized settings, which may pose privacy leakage risks in certain...Show More
KGNext: Knowledge-Graph-Enhanced Transformer for Next POI Recommendation With Uncertain Check-Ins
The next point-of-interest (POI) recommendation aims to predict users’ future movements based on their historical trajectories. However, in reality, users may provide uncertain check-in records, resulting in uploaded data that lack precise location information and is instead ambiguous. Despite this challenge, only a limited number of studies have addressed this issue, often overlooking the intrica...Show More
Anomalous Sub-Trajectory Detection With Graph Contrastive Self-Supervised Learning
Some anomalous vehicle trajectories may contain fraudulent behavior or traffic accident information. Existing research mostly starts from a global view, treating the entire trajectory as a detection target to determine whether it is anomalous. However, they failed to fully capture the influence of sub-trajectories on the entire trajectory, thus ignoring some anomalous sub-trajectories. This study ...Show More
Mobile Trajectory Anomaly Detection: Taxonomy, Methodology, Challenges, and Directions
Xiangjie Kong;Juntao Wang;Zehao Hu;Yuwei He;Xiangyu Zhao;Guojiang Shen
IEEE Internet of Things Journal
Cited by: Papers (2)
The growing number of cars on city roads has led to an increase in traffic accidents, highlighting the need for traffic safety measures. Mobile trajectory anomaly detection is an important area of research that can identify unusual patterns or trajectories in urban environments and provide timely warnings to drivers to avoid accidents. However, there is a significant lack of research on the analys...Show More
Energy Consumption Optimization of UAV-Assisted Traffic Monitoring Scheme With Tiny Reinforcement Learning
Xiangjie Kong;Chenhao Ni;Gaohui Duan;Guojiang Shen;Yao Yang;Sajal K. Das
IEEE Internet of Things Journal
Cited by: Papers (7)
Unmanned aerial vehicles (UAVs) equipped with high-definition cameras have the capability to capture comprehensive and multiangled images of road conditions, facilitating more efficient collection of pertinent road data. However, drones encounter challenges in performing related tasks for an extended period due to their limited energy capacity. Therefore, a crucial concern is how to plan the path ...Show More
Exploring Bus Stop Mobility Pattern: A Multi-Pattern Deep Learning Prediction Framework
Xiangjie Kong;Zhehui Shen;Kailai Wang;Guojiang Shen;Yanjie Fu
The spatio-temporal prediction task in the transportation network is the core of the solutions for various traffic problems. On one hand, the mobility pattern in traffic can be reflected in the travel behavior of the crowd. In most traffic prediction tasks, the importance of the mobility pattern is often overlooked. On the other hand, traffic prediction also has a variety of predicting scenarios, ...Show More
A Real-Time 3-Dimensional Object Detection Based Human Action Recognition Model
Chhaya Gupta;Nasib Singh Gill;Preeti Gulia;Sangeeta Yadav;Giovanni Pau;Mohammad Alibakhshikenari;Xiangjie Kong
Computer vision technologies have greatly improved in the last few years. Many problems have been solved using deep learning merged with more computational power. Action recognition is one of society's problems that must be addressed. Human Action Recognition (HAR) may be adopted for intelligent video surveillance systems, and the government may use the same for monitoring crimes and security purp...Show More
FedAWR: An Interactive Federated Active Learning Framework for Air Writing Recognition
Xiangjie Kong;Wenyi Zhang;Youyang Qu;Xinwei Yao;Guojiang Shen
The rapid development of technology such as virtual reality and augmented reality, coupled with the reduced direct contact due to the COVID-19 pandemic, has led to the emergence of a more advanced mode of interaction: air handwriting. This new form of human-computer interaction allows users to input text by writing in the air freely. However, deploying and applying existing air handwriting recogni...Show More
UMIM: Utility-Maximization Incentive Mechanism for Mobile Crowd Sensing
Xin-Wei Yao;Xiao-Tian Yang;Qiang Li;Chu-Feng Qi;Xiang-Jie Kong;Xiang-Yang Li
Mobile Crowd Sensing (MCS) represents a novel paradigm which utilizes intelligent devices carried by mobile users to collect and transmit data. Appropriate incentives are essential to recruit enough participants for sensing tasks. Existing works have designed some incentive mechanisms for MCS, which are not suitable for scenarios when the participants increase significantly as a result of the boom...Show More
MissII: Missing Information Imputation for Traffic Data
Mingliang Hou;Tao Tang;Feng Xia;Ibrahim Sultan;Roopdeep Kaur;Xiangjie Kong
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
Urban Overtourism Detection Based on Graph Temporal Convolutional Networks
Xiangjie Kong;Zhiqiang Huang;Guojiang Shen;Hang Lin;Mingjie Lv
Urban overtourism results in heavy traffic, degraded tourist experiences, and overloaded infrastructure. Detecting urban overtourism at the early stage is important to minimize the adverse effects. However, urban overtourism detection (UOD) is a challenging task due to ambiguity, sparsity, and complex spatiotemporal relations of overtourism. In this article, we propose a novel UOD framework based ...Show More
Spatio-Temporal Digraph Convolutional Network-Based Taxi Pickup Location Recommendation
The recommendation of taxi pickup locations plays an important role for drivers in carrying passengers efficiently. In addition, the emergence of the Internet of Vehicles provides technical support for it. However, existing recommendation methods do not model dynamic global positioning system information well and in real-time. In this article, we propose a spatio-temporal digraph convolutional net...Show More
CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphs
Feng Xia;Lei Wang;Tao Tang;Xin Chen;Xiangjie Kong;Giles Oatley;Irwin King
Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step of GCNs is the information-passing framework that considers all information from neighbors to the central vertex to be equally important. Such equal importance, however, is inadequate for scale-free networks, where hub vertices propagate more dominan...Show More
Edge Computing for Internet of Everything: A Survey
IEEE Internet of Things Journal
Cited by: Papers (110)
In this era of the Internet of Everything (IoE), edge computing has emerged as the critical enabling technology to solve a series of issues caused by an increasing amount of interconnected devices and large-scale data transmission. However, the deficiencies of edge computing paradigm are gradually being magnified in the context of IoE, especially in terms of service migration, security and privacy...Show More
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