Author details
Author's Published Works
Scheduled Maintenance on Monday 1/13/2025
Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.
Author details
Showing 1-12 of 12 results
- Sort
Sort By
Results
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
Enhancing Human Action Recognition with Fine-grained Body Movement Attention
Rui Zhang;Junxiao Xue;Feng Lin;Qing Zhang;Pavel Smirnov;Xiao Ma;Xiaoran Yan
2024 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2024 | Conference Paper |
In the field of vision-language models (VLMs), human action recognition models, while effective, always rely on large pre-trained models or high-resolution inputs, leading to computational challenges. To address this, we propose a novel VLM approach with fine-grained attention to body movements. Unlike methods relying on coarse video-text matching, we guide the model to infer actions from fine-gra...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
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 (5)
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
FVFL: A Flexible and Verifiable Privacy-Preserving Federated Learning Scheme
Gang Wang;Li Zhou;Qingming Li;Xiaoran Yan;Ximeng Liu;Yuncheng Wu
IEEE Internet of Things Journal
Cited by: Papers (2)
With the development of deep learning, people are more and more concerned about the security of data. Federated learning can solve the problem of data island, but it also brings more serious data privacy problems. Furthermore, in the process of multisource data collaboration, the efficiency of the whole federated learning system is usually not high. In this article, we introduce a scheme named FVF...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
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
Investigations on Scientific Literature Meta Information Extraction Using Large Language Models
Menghao Guo;Fan Wu;Jinling Jiang;Xiaoran Yan;Guangyong Chen;Wenhui Li;Yunhong Zhao;Zeyi Sun
2023 IEEE International Conference on Knowledge Graph (ICKG)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
The meta information in scientific literature including article title, author, institutions, year, journal, etc., plays a critical role in providing useful information to research peers. Traditional meta information extraction methods usually rely on rules and templates. Recently, due to the booming of Large Language Models (LLMs), its application in scientific literature meta-information extracti...Show More
DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy
Jiehui Zhou;Xumeng Wang;Jason K. Wong;Huanliang Wang;Zhongwei Wang;Xiaoyu Yang;Xiaoran Yan;Haozhe Feng;Huamin Qu;Haochao Ying;Wei Chen
Data privacy is an essential issue in publishing data visualizations. However, it is challenging to represent multiple data patterns in privacy-preserving visualizations. The prior approaches target specific chart types or perform an anonymization model uniformly without considering the importance of data patterns in visualizations. In this paper, we propose a visual analytics approach that facili...Show More
Detecting Outlier Patterns With Query-Based Artificially Generated Searching Conditions
In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas, such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, and national security. However, subgraph matching remains a computationally challenging problem, let alone identifying special motifs among them. This i...Show More
Efficient network navigation with partial information
2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)
Year: 2020 | Conference Paper |
We propose a information theoretical framework to capture transition and information costs of network navigation models. Based on the minimum description length principle and the Markov decision process, we demonstrate that efficient global navigation can be achieved with only partial information. Additionally, we derived a scalable algorithm for optimal solutions under certain conditions. The pro...Show More
Graph Filters and the Z-Laplacian
Xiaoran Yan;Brian M. Sadler;Robert J. Drost;Paul L. Yu;Kristina Lerman
In network science, the interplay between dynamical processes and the underlying topologies of complex systems has led to a diverse family of models with different interpretations. In graph signal processing, this is manifested in the form of different graph shifts and their induced algebraic systems. In this paper, we propose the unifying Z-Laplacian framework, whose instances can act as graph sh...Show More
Bayesian model selection of stochastic block models
2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Year: 2016 | Conference Paper |
Cited by: Papers (22)
A central problem in analyzing networks is partitioning them into modules or communities. One of the best tools for this is the stochastic block model, which clusters vertices into blocks with statistically homogeneous pattern of links. Despite its flexibility and popularity, there has been a lack of principled statistical model selection criteria for the stochastic block model. Here we propose a ...Show More
IEEE Account
Purchase Details
Profile Information
Need Help?
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Contact Us
- Help
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Sitemap
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.