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Yuanzhe Chen - IEEE Xplore Author Profile
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Yuanzhe Chen

Publications
9
Citations
446
Publications by Year
20162020
Co-Authors:
Show All Co-Authors (30)
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Yuanzhe Chen

Affiliation

Noah's Ark Lab, Huawei Technologies Co. Ltd.

Publication Topics

Biography

Yuanzhe Chen received the BE and ME degrees in electrical engineering from Shanghai Jiao Tong University, China, in 2014. He is currently working toward the PhD degree in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. His research interests include information visualization and human-computer interaction.(Based on document published on 30 September 2018).
Publications
9
Citations
446
Publications by Year
20162020
Co-Authors:
Show All Co-Authors (30)
Author's Published Works

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PlanningVis: A Visual Analytics Approach to Production Planning in Smart Factories

Dong Sun;Renfei Huang;Yuanzhe Chen;Yong Wang;Jia Zeng;Mingxuan Yuan;Ting-Chuen Pong;Huamin Qu

IEEE Transactions on Visualization and Computer Graphics
Year: 2020 | Volume: 26, Issue: 1 | Journal Article |
Cited by: Papers (12)
Production planning in the manufacturing industry is crucial for fully utilizing factory resources (e.g., machines, raw materials and workers) and reducing costs. With the advent of industry 4.0, plenty of data recording the status of factory resources have been collected and further involved in production planning, which brings an unprecedented opportunity to understand, evaluate and adjust compl...Show More

PlanningVis: A Visual Analytics Approach to Production Planning in Smart Factories

Dong Sun;Renfei Huang;Yuanzhe Chen;Yong Wang;Jia Zeng;Mingxuan Yuan;Ting-Chuen Pong;Huamin Qu

Year: 2020 | Volume: 26, Issue: 1 | Journal Article |

ViSeq: Visual Analytics of Learning Sequence in Massive Open Online Courses

Qing Chen;Xuanwu Yue;Xavier Plantaz;Yuanzhe Chen;Conglei Shi;Ting-Chuen Pong;Huamin Qu

IEEE Transactions on Visualization and Computer Graphics
Year: 2020 | Volume: 26, Issue: 3 | Journal Article |
Cited by: Papers (33)
The research on massive open online courses (MOOCs) data analytics has mushroomed recently because of the rapid development of MOOCs. The MOOC data not only contains learner profiles and learning outcomes, but also sequential information about when and which type of learning activities each learner performs, such as reviewing a lecture video before undertaking an assignment. Learning sequence anal...Show More

ViSeq: Visual Analytics of Learning Sequence in Massive Open Online Courses

Qing Chen;Xuanwu Yue;Xavier Plantaz;Yuanzhe Chen;Conglei Shi;Ting-Chuen Pong;Huamin Qu

Year: 2020 | Volume: 26, Issue: 3 | Journal Article |

SpeechLens: A Visual Analytics Approach for Exploring Speech Strategies with Textural and Acoustic Features

Linping Yuan;Yuanzhe Chen;Siwei Fu;Aoyu Wu;Huamin Qu

2019 IEEE International Conference on Big Data and Smart Computing (BigComp)
Year: 2019 | Conference Paper |
Cited by: Papers (5)
Public speaking is an effective way to move, persuade, and inspire. While many guidelines have been presented to teach public speaking skills, they are often based on anecdotal evidence and not customizable. Exploring high-quality speeches such as TED Talks could provide insights to eliminate limitations in existing guidelines. This study aims to explore and identify narration strategies by conduc...Show More

SpeechLens: A Visual Analytics Approach for Exploring Speech Strategies with Textural and Acoustic Features

Linping Yuan;Yuanzhe Chen;Siwei Fu;Aoyu Wu;Huamin Qu

Year: 2019 | Conference Paper |

StageMap: Extracting and Summarizing Progression Stages in Event Sequences

Yuanzhe Chen;Abishek Puri;Linping Yuan;Huamin Qu

2018 IEEE International Conference on Big Data (Big Data)
Year: 2018 | Conference Paper |
Cited by: Papers (6)
Temporal event sequences are becoming increasingly important in many application domains such as website click streams, user interaction logs, electronic health records and car service records. A real-world dataset with a large number of event sequences of varying lengths is complex and difficult to analyze. To support visual exploration of the data, it is desirable yet challenging to provide a co...Show More

StageMap: Extracting and Summarizing Progression Stages in Event Sequences

Yuanzhe Chen;Abishek Puri;Linping Yuan;Huamin Qu

Year: 2018 | Conference Paper |

Sequence Synopsis: Optimize Visual Summary of Temporal Event Data

Yuanzhe Chen;Panpan Xu;Liu Ren

IEEE Transactions on Visualization and Computer Graphics
Year: 2018 | Volume: 24, Issue: 1 | Journal Article |
Cited by: Papers (78)
Event sequences analysis plays an important role in many application domains such as customer behavior analysis, electronic health record analysis and vehicle fault diagnosis. Real-world event sequence data is often noisy and complex with high event cardinality, making it a challenging task to construct concise yet comprehensive overviews for such data. In this paper, we propose a novel visualizat...Show More

Sequence Synopsis: Optimize Visual Summary of Temporal Event Data

Yuanzhe Chen;Panpan Xu;Liu Ren

Year: 2018 | Volume: 24, Issue: 1 | Journal Article |

Understanding Hidden Memories of Recurrent Neural Networks

Yao Ming;Shaozu Cao;Ruixiang Zhang;Zhen Li;Yuanzhe Chen;Yangqiu Song;Huamin Qu

2017 IEEE Conference on Visual Analytics Science and Technology (VAST)
Year: 2017 | Conference Paper |
Cited by: Papers (124)
Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for...Show More

Understanding Hidden Memories of Recurrent Neural Networks

Yao Ming;Shaozu Cao;Ruixiang Zhang;Zhen Li;Yuanzhe Chen;Yangqiu Song;Huamin Qu

Year: 2017 | Conference Paper |

DropoutSeer: Visualizing learning patterns in Massive Open Online Courses for dropout reasoning and prediction

Yuanzhe Chen;Qing Chen;Mingqian Zhao;Sebastien Boyer;Kalyan Veeramachaneni;Huamin Qu

2016 IEEE Conference on Visual Analytics Science and Technology (VAST)
Year: 2016 | Conference Paper |
Aiming at massive participation and open access education, Massive Open Online Courses (MOOCs) have attracted millions of learners over the past few years. However, the high dropout rate of learners is considered to be one of the most crucial factors that may hinder the development of MOOCs. To tackle this problem, statistical models have been developed to predict dropout behavior based on learner...Show More

DropoutSeer: Visualizing learning patterns in Massive Open Online Courses for dropout reasoning and prediction

Yuanzhe Chen;Qing Chen;Mingqian Zhao;Sebastien Boyer;Kalyan Veeramachaneni;Huamin Qu

Year: 2016 | Conference Paper |

Visual Analytics in Urban Computing: An Overview

Yixian Zheng;Wenchao Wu;Yuanzhe Chen;Huamin Qu;Lionel M. Ni

IEEE Transactions on Big Data
Year: 2016 | Volume: 2, Issue: 3 | Journal Article |
Nowadays, various data collected in urban context provide unprecedented opportunities for building a smarter city through urban computing. However, due to heterogeneity, high complexity and large volumes of these urban data, analyzing them is not an easy task, which often requires integrating human perception in analytical process, triggering a broad use of visualization. In this survey, we first ...Show More

Visual Analytics in Urban Computing: An Overview

Yixian Zheng;Wenchao Wu;Yuanzhe Chen;Huamin Qu;Lionel M. Ni

Year: 2016 | Volume: 2, Issue: 3 | Journal Article |

PeakVizor: Visual Analytics of Peaks in Video Clickstreams from Massive Open Online Courses

Qing Chen;Yuanzhe Chen;Dongyu Liu;Conglei Shi;Yingcai Wu;Huamin Qu

IEEE Transactions on Visualization and Computer Graphics
Year: 2016 | Volume: 22, Issue: 10 | Journal Article |
Cited by: Papers (54)
Massive open online courses (MOOCs) aim to facilitate open-access and massive-participation education. These courses have attracted millions of learners recently. At present, most MOOC platforms record the web log data of learner interactions with course videos. Such large amounts of multivariate data pose a new challenge in terms of analyzing online learning behaviors. Previous studies have mainl...Show More

PeakVizor: Visual Analytics of Peaks in Video Clickstreams from Massive Open Online Courses

Qing Chen;Yuanzhe Chen;Dongyu Liu;Conglei Shi;Yingcai Wu;Huamin Qu

Year: 2016 | Volume: 22, Issue: 10 | Journal Article |

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