Yongheng Wang - IEEE Xplore Author Profile
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Yongheng Wang

Publications
9
Citations
56
Publications by Year
20232025
Co-Authors:
Show All Co-Authors (40)
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Yongheng Wang

Affiliation

Zhejiang Lab, Hangzhou, China

Publication Topics

Biography

Yongheng Wang received the PhD degree in computer science and technology from the National University of Defense Technology, Changsha, China, in 2006. Currently, he is a research specialist with the Research Center of Big Data Intelligence, Zhejiang Lab. His research interest covers Big data analysis, machine learning, computer simulation, and intelligent decision making.(Based on document published on 17 November 2022).
Publications
9
Citations
56
Publications by Year
20232025
Co-Authors:
Show All Co-Authors (40)
Author's Published Works

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ConceptThread: Visualizing Threaded Concepts in MOOC Videos

Zhiguang Zhou;Li Ye;Lihong Cai;Lei Wang;Yigang Wang;Yongheng Wang;Wei Chen;Yong Wang

IEEE Transactions on Visualization and Computer Graphics
Year: 2025 | Volume: 31, Issue: 2 | Journal Article |
Cited by: Papers (2)
Massive Open Online Courses (MOOCs) platforms are becoming increasingly popular in recent years. Online learners need to watch the whole course video on MOOC platforms to learn the underlying new knowledge, which is often tedious and time-consuming due to the lack of a quick overview of the covered knowledge and their structures. In this article, we propose ConceptThread, a visual analytics approa...Show More

ConceptThread: Visualizing Threaded Concepts in MOOC Videos

Zhiguang Zhou;Li Ye;Lihong Cai;Lei Wang;Yigang Wang;Yongheng Wang;Wei Chen;Yong Wang

Year: 2025 | Volume: 31, Issue: 2 | Journal Article |

ChartKG: A Knowledge-Graph-Based Representation for Chart Images

Zhiguang Zhou;Haoxuan Wang;Zhengqing Zhao;Fengling Zheng;Yongheng Wang;Wei Chen;Yong Wang

IEEE Transactions on Visualization and Computer Graphics
Year: 2024 | Early Access Article |
Chart images, such as bar charts, pie charts, and line charts, are explosively produced due to the wide usage of data visualizations. Accordingly, knowledge mining from chart images is becoming increasingly important, which can benefit downstream tasks like chart retrieval and knowledge graph completion. However, existing methods for chart knowledge mining mainly focus on converting chart images i...Show More

ChartKG: A Knowledge-Graph-Based Representation for Chart Images

Zhiguang Zhou;Haoxuan Wang;Zhengqing Zhao;Fengling Zheng;Yongheng Wang;Wei Chen;Yong Wang

Year: 2024 | Early Access Article |

JsonCurer: Data Quality Management for JSON Based on an Aggregated Schema

Kai Xiong;Xinyi Xu;Siwei Fu;Di Weng;Yongheng Wang;Yingcai Wu

IEEE Transactions on Visualization and Computer Graphics
Year: 2024 | Volume: 30, Issue: 6 | Journal Article |
Cited by: Papers (1)
High-quality data is critical to deriving useful and reliable information. However, real-world data often contains quality issues undermining the value of the derived information. Most existing research on data quality management focuses on tabular data, leaving semi-structured data under-exploited. Due to the schema-less and hierarchical features of semi-structured data, discovering and fixing qu...Show More

JsonCurer: Data Quality Management for JSON Based on an Aggregated Schema

Kai Xiong;Xinyi Xu;Siwei Fu;Di Weng;Yongheng Wang;Yingcai Wu

Year: 2024 | Volume: 30, Issue: 6 | Journal Article |

A Deep Spatiotemporal Trajectory Representation Learning Framework for Clustering

Chao Wang;Jiahui Huang;Yongheng Wang;Zhengxuan Lin;Xiongnan Jin;Xing Jin;Di Weng;Yingcai Wu

IEEE Transactions on Intelligent Transportation Systems
Year: 2024 | Volume: 25, Issue: 7 | Journal Article |
Cited by: Papers (1)
Learning trajectory representations is essential in many Location Based Services (LBS) applications. Most traditional methods extract trajectory representations based on manually defined features, while deep learning-based methods can reduce part of the human effort. We propose a Deep Spatiotemporal Trajectory Clustering (DSTC) framework to tackle the Spatiotemporal Trajectory Representation Learn...Show More

A Deep Spatiotemporal Trajectory Representation Learning Framework for Clustering

Chao Wang;Jiahui Huang;Yongheng Wang;Zhengxuan Lin;Xiongnan Jin;Xing Jin;Di Weng;Yingcai Wu

Year: 2024 | Volume: 25, Issue: 7 | Journal Article |

Deep Multiscale Fine-Grained Hashing for Remote Sensing Cross-Modal Retrieval

Jiaxiang Huang;Yong Feng;Mingliang Zhou;Xiancai Xiong;Yongheng Wang;Baohua Qiang

IEEE Geoscience and Remote Sensing Letters
Year: 2024 | Volume: 21 | Journal Article |
Hashing retrieval is a widely used technique in high spatial resolution remote sensing (RS) images due to its efficient retrieval speed and low memory overhead. However, existing hashing retrieval methods primarily focus on matching multilabel RS images, neglecting the extensive fine-grained semantic information in cross-modal RS data. Moreover, RS images exhibit notable object size differences an...Show More

Deep Multiscale Fine-Grained Hashing for Remote Sensing Cross-Modal Retrieval

Jiaxiang Huang;Yong Feng;Mingliang Zhou;Xiancai Xiong;Yongheng Wang;Baohua Qiang

Year: 2024 | Volume: 21 | Journal Article |

Ensemble Clustering via Co-Association Matrix Self-Enhancement

Yuheng Jia;Sirui Tao;Ran Wang;Yongheng Wang

IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Volume: 35, Issue: 8 | Journal Article |
Cited by: Papers (21)
Ensemble clustering integrates a set of base clustering results to generate a stronger one. Existing methods usually rely on a co-association (CA) matrix that measures how many times two samples are grouped into the same cluster according to the base clusterings to achieve ensemble clustering. However, when the constructed CA matrix is of low quality, the performance will degrade. In this article,...Show More

Ensemble Clustering via Co-Association Matrix Self-Enhancement

Yuheng Jia;Sirui Tao;Ran Wang;Yongheng Wang

Year: 2024 | Volume: 35, Issue: 8 | Journal Article |

Towards Effective Clustered Federated Learning: A Peer-to-Peer Framework With Adaptive Neighbor Matching

Zexi Li;Jiaxun Lu;Shuang Luo;Didi Zhu;Yunfeng Shao;Yinchuan Li;Zhimeng Zhang;Yongheng Wang;Chao Wu

IEEE Transactions on Big Data
Year: 2024 | Volume: 10, Issue: 6 | Journal Article |
Cited by: Papers (23)
In federated learning (FL), clients may have diverse objectives, and merging all clients’ knowledge into one global model will cause negative transfer to local performance. Thus, clustered FL is proposed to group similar clients into clusters and maintain several global models. In the literature, centralized clustered FL algorithms require the assumption of the number of clusters and hence are not...Show More

Towards Effective Clustered Federated Learning: A Peer-to-Peer Framework With Adaptive Neighbor Matching

Zexi Li;Jiaxun Lu;Shuang Luo;Didi Zhu;Yunfeng Shao;Yinchuan Li;Zhimeng Zhang;Yongheng Wang;Chao Wu

Year: 2024 | Volume: 10, Issue: 6 | Journal Article |

Multilevel Similarity-Aware Deep Metric Learning for Fine-Grained Image Retrieval

Congcong Duan;Yong Feng;Mingliang Zhou;Xiancai Xiong;Yongheng Wang;Baohua Qiang;Weijia Jia

IEEE Transactions on Industrial Informatics
Year: 2023 | Volume: 19, Issue: 8 | Journal Article |
Cited by: Papers (2)
Fast and accurate image retrieval is an important and challenging task in massive image data scenarios. As the core technology of image retrieval tasks, deep metric learning aims at learning effective embedding representations that possess two properties among data points: positive concentrated and negative separated. In this work, we propose a multilevel similarity-aware method based on deep loca...Show More

Multilevel Similarity-Aware Deep Metric Learning for Fine-Grained Image Retrieval

Congcong Duan;Yong Feng;Mingliang Zhou;Xiancai Xiong;Yongheng Wang;Baohua Qiang;Weijia Jia

Year: 2023 | Volume: 19, Issue: 8 | Journal Article |

Revealing the Semantics of Data Wrangling Scripts With Comantics

Kai Xiong;Zhongsu Luo;Siwei Fu;Yongheng Wang;Mingliang Xu;Yingcai Wu

IEEE Transactions on Visualization and Computer Graphics
Year: 2023 | Volume: 29, Issue: 1 | Journal Article |
Cited by: Papers (6)
Data workers usually seek to understand the semantics of data wrangling scripts in various scenarios, such as code debugging, reusing, and maintaining. However, the understanding is challenging for novice data workers due to the variety of programming languages, functions, and parameters. Based on the observation that differences between input and output tables highly relate to the type of data tr...Show More

Revealing the Semantics of Data Wrangling Scripts With Comantics

Kai Xiong;Zhongsu Luo;Siwei Fu;Yongheng Wang;Mingliang Xu;Yingcai Wu

Year: 2023 | Volume: 29, Issue: 1 | Journal Article |

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