Mingyu Fan

Applied Filters

Mingyu Fan
AuthorRemove filter

Clear all

People

Colleagues

Mingyu Fan (25)
Xiaoqin Zhang (9)
Di Wang (7)
Dongchun Ren (6)
Bo Zhang (4)
Hong Qiao (4)
Liang Du (4)
Nannan Gu (4)
Xiuzi Ye (4)
Hai Huang (3)
Hao Zhou (3)
Huaxia Xia (3)
Deheng Qian (2)
Hujun Bao (2)
Xiaojun Chang* (2)
Xin Li (2)
Xu Yang (2)
Yanliang Zhu (2)
Yidong Shen (2)
Dacheng Tao (1)

Publication

Journal/Magazine Names

Pattern Recognition (5)
Neurocomputing (4)
Knowledge-Based Systems (2)
ACM Transactions on Intelligent Systems and Technology (1)
IEEE Transactions on Image Processing (1)
IEEE Transactions on Intelligent Transportation Systems (1)
Information Sciences: an International Journal (1)
Neural Computing and Applications (1)

Proceedings/Book Names

ICDM '12: Proceedings of the 2012 IEEE 12th International Conference on Data Mining (2)
2021 IEEE International Conference on Robotics and Automation (ICRA) (1)
AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (1)
AAAI'17: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (1)
IJCAI'15: Proceedings of the 24th International Conference on Artificial Intelligence (1)
IJCAI'15: Proceedings of the 24th International Conference on Artificial Intelligence (1)
IJCAI'15: Proceedings of the 24th International Conference on Artificial Intelligence (1)
IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence (1)

All Publications

Other periodicals (15)
Proceedings (9)
Journals (1)

Publisher

AAAI Press (6)
Elsevier Science Inc. (6)
Elsevier Science Publishers B. V. (6)
IEEE Press (3)
IEEE Computer Society (2)
Association for Computing Machinery (1)
Springer-Verlag (1)

Publication Date

Upon changing this filter the page will automatically refresh

Past 5 years
Past 2 years
Past year

Export Citations

Select Citation format

Please download or close your previous search result export first before starting a new bulk export.
Preview is not available.
By clicking download,a status dialog will open to start the export process. The process may takea few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress.
Download
- Download citation
- Copy citation

Publications

25 Results for: Author: Mingyu FanEdit SearchSave SearchFailed to save your search, try again laterSearch has been saved (My Saved Searches)

Save this search

Please login to be able to save your searches and receive alerts for new content matching your search criteria.

Searched The ACM Guide to Computing Literature (3,835,775 records)|Limit your search to The ACM Full-Text Collection (773,913 records)

Results

Showing 1 - 20of25 Results

Filters

Select All

Export Citations Save to Binder

Save to Binder

Create a New Binder

Name

per page:

Sort by: Latest

Recency
Earliest
Most Downloaded
Most Cited

research-article
Learning to match features with discriminative sparse graph neural network
- Yan Shi
  The Shenzhen Graduate School, Tsinghua University, Shenzhen 518071, China
  ,
- Jun-Xiong Cai
  The School of Computer Science and Technology, Tsinghua University, Beijing 100084, China
  ,
- Mingyu Fan
  The Institute of Artificial Intelligence, Donghua University, Shanghai 200051, China
  ,
- Wensen Feng
  The Shenzhen Graduate School, Tsinghua University, Shenzhen 518071, China
  ,
- Kai Zhang
  The Shenzhen Graduate School, Tsinghua University, Shenzhen 518071, China
Pattern Recognition, Volume 156, Issue C•Dec 2024 • https://doi.org/10.1016/j.patcog.2024.110784
Abstract
We propose a cluster-based sparse graph network to improve the efficiency of image feature matching. This architecture clusters keypoints with high correlations into the same subgraphs, where each keypoint interacts only with others within the ...
Highlights

A cluster-based sparse graph network architecture is introduced to enhance feature matching efficiency by clustering keypoints with strong correlations into the same subgraph, which reduces the propagation of redundant message, leading to ...
0Citation
Metrics
Total Citations0
Export Citations
Save to Binder
research-article
CSIR: Cascaded Sliding CVAEs With Iterative Socially-Aware Rethinking for Trajectory Prediction
- Hao Zhou
  National Key Laboratory of Science and Technology of Underwater Vehicle, Harbin Engineering University, Harbin, China
  ,
- Xu Yang
  State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Dongchun Ren
  Research Center for Autonomous Vehicles, Meituan, China
  ,
- Hai Huang
  National Key Laboratory of Science and Technology of Underwater Vehicle, Harbin Engineering University, Harbin, China
  ,
- Mingyu Fan
  Institute of Artificial Intelligence, Donghua University, Shanghai, China
IEEE Transactions on Intelligent Transportation Systems, Volume 24, Issue 12•Dec. 2023, pp 14957-14969 • https://doi.org/10.1109/TITS.2023.3300730
Pedestrian trajectory prediction is a hot research topic in many applications, such as video surveillance and autonomous driving. Although many efforts have been done on this topic, there are still many challenges, including accumulated prediction errors, ...
0Citation
Metrics
Total Citations0
Export Citations
Save to Binder
research-article
Static-dynamic global graph representation for pedestrian trajectory prediction
- Hao Zhou
  College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China
  Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  ,
- Xu Yang
  Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  ,
- Mingyu Fan
  Institute of Artificial Intelligence, Donghua University, Shanghai 200051, China
  ,
- Hai Huang
  College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China
  ,
- Dongchun Ren
  Intelligent Transportation Division, Meituan, Beijing 100102, China
  ,
- Huaxia Xia
  Intelligent Transportation Division, Meituan, Beijing 100102, China
Knowledge-Based Systems, Volume 277, Issue C•Oct 2023 • https://doi.org/10.1016/j.knosys.2023.110775
Abstract
Effectively understanding social interactions among pedestrians plays a significant role in accurate pedestrian trajectory prediction. Previous works used either distance-based or data-driven methods to model interactions. However, the ...
Highlights

We propose a novel global graph representation to model the social interactions among pedestrians by incorporating both the static and relative dynamic ...
1Citation
Metrics
Total Citations1
Export Citations
Save to Binder
research-article
CSR: Cascade Conditional Variational Auto Encoder with Socially-aware Regression for Pedestrian Trajectory Prediction
- Hao Zhou
  National Key Laboratory of Science and Technology of Underwater Vehicle, Harbin Engineering University, Harbin, China
  State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Dongchun Ren
  Research Center for Autonomous Vehicles, Meituan, Beijing, China
  ,
- Xu Yang
  State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Mingyu Fan
  Research Center for Autonomous Vehicles, Meituan, Beijing, China
  College of Computer Science, Wenzhou University, Wenzhou, China
  ,
- Hai Huang
  National Key Laboratory of Science and Technology of Underwater Vehicle, Harbin Engineering University, Harbin, China
Pattern Recognition, Volume 133, Issue C•Jan 2023 • https://doi.org/10.1016/j.patcog.2022.109030
Highlights

The proposed trajectory prediction method consists of a cascaded CVAE module and a socially aware regression module.
The cascaded CVAE module decouples and balances the loss function with respect to time steps and minimizes the losses ...
Abstract
Pedestrian trajectory prediction is a key technology in many real applications such as video surveillance, social robot navigation, and autonomous driving, and significant progress has been made in this research topic. However, there remain two ...
1Citation
Metrics
Total Citations1
Export Citations
Save to Binder
research-article
Published By ACM
Published By ACM
Simultaneous Past and Current Social Interaction-aware Trajectory Prediction for Multiple Intelligent Agents in Dynamic Scenes
- Yanliang Zhu
  Center for Autonomous Vehicles, Meituan, Beijing, China
  ,
- Dongchun Ren
  Center for Autonomous Vehicles, Meituan, Beijing, China
  ,
- Yi Xu
  Center for Autonomous Vehicles, Meituan, Beijing, China
  ,
- Deheng Qian
  Center for Autonomous Vehicles, Meituan, Beijing, China
  ,
- Mingyu Fan
  Computer Sciences and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, China
  ,
- Xin Li
  Center for Autonomous Vehicles, Meituan, Beijing, China
  ,
- Huaxia Xia
  Center for Autonomous Vehicles, Meituan, Beijing, China
ACM Transactions on Intelligent Systems and Technology, Volume 13, Issue 1•February 2022, Article No.: 10, pp 1-16 • https://doi.org/10.1145/3466182
Trajectory prediction of multiple agents in a crowded scene is an essential component in many applications, including intelligent monitoring, autonomous robotics, and self-driving cars. Accurate agent trajectory prediction remains a significant challenge ...
5Citation
581
Downloads
Metrics
Total Citations5
Total Downloads581
Last 12 Months78
Last 6 weeks8
Export Citations
Save to Binder
Get Access
research-article
Graph matching based point correspondence with alternating direction method of multipliers
- Jing Yang
  School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China
  State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  ,
- Xu Yang
  State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  ,
- Zhang-Bing Zhou
  School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China
  Computer Science Department, TELECOM SudParis, Evry 91011, France
  ,
- Zhi-Yong Liu
  State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  ,
- Ming-Yu Fan
  School of Maths & Info. Science, Wenzhou University, Wenzhou 325035, China
Neurocomputing, Volume 462, Issue C•Oct 2021, pp 344-352 • https://doi.org/10.1016/j.neucom.2021.08.002
Abstract
Graph matching is a fundamental problem in image processing and computer vision tasks, which can be used to solve the feature correspondence problem. Most graph matching tasks have been proved to be NP-Complete problems which are ...
0Citation
Metrics
Total Citations0
Export Citations
Save to Binder
research-article
Star Topology based Interaction for Robust Trajectory Forecasting in Dynamic Scene
- Yanliang Zhu
  Meituan,Center for Autonomous Vehicles,Beijing,China,100102
  ,
- Dongchun Ren
  Meituan,Center for Autonomous Vehicles,Beijing,China,100102
  ,
- Deheng Qian
  Meituan,Center for Autonomous Vehicles,Beijing,China,100102
  ,
- Mingyu Fan
  Wenzhou University,Department of Computer Sciences,Wenzhou,Zhejiang,China,325035
  ,
- Xin Li
  Meituan,Center for Autonomous Vehicles,Beijing,China,100102
  ,
- Huaxia Xia
  Meituan,Center for Autonomous Vehicles,Beijing,China,100102
2021 IEEE International Conference on Robotics and Automation (ICRA)•May 2021, pp 3255-3261• https://doi.org/10.1109/ICRA48506.2021.9561067
Motion prediction of multiple agents in a dynamic scene is a crucial component in many real applications, including intelligent monitoring and autonomous driving. Due to the complex interactions among the agents and their interactions with the surrounding ...
0Citation
Metrics
Total Citations0
Export Citations
Save to Binder
research-article
Unsupervised feature selection for balanced clustering
- Peng Zhou
  School of Computer Science and Technology, Anhui University, Hefei 230601, China
  The State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
  ,
- Jiangyong Chen
  School of Computer Science and Technology, Anhui University, Hefei 230601, China
  ,
- Mingyu Fan
  College of Maths and Information Science, Wenzhou University, Wenzhou 325035, China
  ,
- Liang Du
  School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
  ,
- Yi-Dong Shen
  The State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
  ,
- Xuejun Li
  School of Computer Science and Technology, Anhui University, Hefei 230601, China
Knowledge-Based Systems, Volume 193, Issue C•Apr 2020 • https://doi.org/10.1016/j.knosys.2019.105417
Abstract
In many real-world applications of data mining, such as energy load balance of wireless sensor networks, given data points with balanced distribution, i.e., each class contains approximately the same number of instances, we often need ...
8Citation
Metrics
Total Citations8
Export Citations
Save to Binder
research-article
Structure regularized self-paced learning for robust semi-supervised pattern classification
- Nannan Gu
  School of Statistics, Capital University of Economics and Business, Beijing, China 100070
  ,
- Pengying Fan
  School of Economics, Beijing Technology and Business University, Beijing, China 100048
  ,
- Mingyu Fan
  College of Mathematics and Information Science, Wenzhou University, Wenzhou, China 325035
  ,
- Di Wang
  College of Mathematics and Information Science, Wenzhou University, Wenzhou, China 325035
Neural Computing and Applications, Volume 31, Issue 10•Oct 2019, pp 6559-6574 • https://doi.org/10.1007/s00521-018-3478-1
Abstract
Semi-supervised classification is a hot topic in pattern recognition and machine learning. However, in presence of heavy noise and outliers, the unlabeled training data could be very challenging or even misleading for the semi-supervised ...
2Citation
Metrics
Total Citations2
Export Citations
Save to Binder
Article
Top-k supervise feature selection via ADMM for integer programming
- Mingyu Fan
  School of Maths & Info. Science, Wenzhou University, Wenzhou, China
  ,
- Xiaojun Chang
  School of Computer Science, Carnegie Mellon University, PA
  ,
- Xiaoqin Zhang
  School of Maths & Info. Science, Wenzhou University, Wenzhou, China
  ,
- Di Wang
  School of Computer Science, Carnegie Mellon University, PA
  ,
- Liang Du
  School of Computer & Information Technology, Shanxi University, Taiyuan, China
IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence•August 2017, pp 1646-1653

Recently, structured sparsity-inducing based feature selection has become a hot topic in machine learning and pattern recognition. Most of the sparsity-inducing feature selection methods are designed to rank all features by certain criterion and then ...
1Citation
Metrics
Total Citations1
Export Citations
Save to Binder
Article
Structure regularized unsupervised discriminant feature analysis
- Mingyu Fan
  College of Maths & Info. Science, Wenzhou University, Wenzhou, China
  ,
- Xiaojun Chang
  Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia
  ,
- Dacheng Tao
  Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia
AAAI'17: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence•February 2017, pp 1870-1876

Feature selection is an important technique in machine learning research. An effective and robust feature selection method is desired to simultaneously identify the informative features and eliminate the noisy ones of data. In this paper, we consider ...
0Citation
Metrics
Total Citations0
Export Citations
Save to Binder
research-article
Hierarchical mixing linear support vector machines for nonlinear classification
- Di Wang
  College of Mathematics & Information Science, Wenzhou University, Zhejiang, China
  ,
- Xiaoqin Zhang
  College of Mathematics & Information Science, Wenzhou University, Zhejiang, China
  ,
- Mingyu Fan
  College of Mathematics & Information Science, Wenzhou University, Zhejiang, China
  ,
- Xiuzi Ye
  College of Mathematics & Information Science, Wenzhou University, Zhejiang, China
Pattern Recognition, Volume 59, Issue C•November 2016, pp 255-267 • https://doi.org/10.1016/j.patcog.2016.02.018

Support vector machines (SVMs) play a very dominant role in data classification because of their good generalization performance. However, they suffer from the high computational complexity in the classification stage when there are a considerable ...
2Citation
Metrics
Total Citations2
Export Citations
Save to Binder
research-article
Efficient isometric multi-manifold learning based on the self-organizing method
- Mingyu Fan
  College of Mathematics & Information Science, Wenzhou University, Wenzhou 325035, China
  ,
- Xiaoqin Zhang
  College of Mathematics & Information Science, Wenzhou University, Wenzhou 325035, China
  ,
- Hong Qiao
  CAS Centre for Excellence in Brain Science and Intelligence Technology (CEBSIT), Shanghai 200031, China
  ,
- Bo Zhang
  LSEC and Institute of Applied Mathematics, AMSS, Chinese Academy of Sciences, Beijing 100190, China
Information Sciences: an International Journal, Volume 345, Issue C•June 2016, pp 325-339 • https://doi.org/10.1016/j.ins.2016.01.069

Geodesic distance, as an essential measurement for data similarity, has been successfully used in manifold learning. However, many geodesic based isometric manifold learning algorithms, such as the isometric feature mapping (Isomap) and GeoNLM, fail to ...
4Citation
Metrics
Total Citations4
Export Citations
Save to Binder
Article
Semi-supervised dictionary learning via structural sparse preserving
- Di Wang
  College of Mathematics & Information Science, Wenzhou University, Zhejiang, China
  ,
- Xiaoqin Zhang
  College of Mathematics & Information Science, Wenzhou University, Zhejiang, China
  ,
- Mingyu Fan
  College of Mathematics & Information Science, Wenzhou University, Zhejiang, China
  ,
- Xiuzi Ye
  College of Mathematics & Information Science, Wenzhou University, Zhejiang, China
AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence•February 2016, pp 2137-2144

While recent techniques for discriminative dictionary learning have attained promising results on the classification tasks, their performance is highly dependent on the number of labeled samples available for training. However, labeling samples is ...
2Citation
Metrics
Total Citations2
Export Citations
Save to Binder
research-article
Efficient sequential feature selection based on adaptive eigenspace model
- Nannan Gu
  School of Statistics, Capital University of Economics and Business, Beijing 100070, China
  ,
- Mingyu Fan
  Institute of Intelligent Systems and Decision, Wenzhou University, Wenzhou 325000, China
  ,
- Liang Du
  State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
  ,
- Dongchun Ren
  Beijing 7Invensun Technology Co., Ltd., Beijing 100080, China
Neurocomputing, Volume 161, Issue C•August 2015, pp 199-209 • https://doi.org/10.1016/j.neucom.2015.02.043

Though Fisher score is a representative and effective feature selection method, it has an unsolved drawback: it either evaluates the features individually and selects the top features, or selects features using the sequential search strategies. The ...
6Citation
Metrics
Total Citations6
Export Citations
Save to Binder
Article
An efficient classifier based on hierarchical mixing linear support vector machines
- Di Wang
  College of Mathematics & Information Science, Wenzhou University, Zhejiang, China
  ,
- Xiaoqin Zhang
  College of Mathematics & Information Science, Wenzhou University, Zhejiang, China
  ,
- Mingyu Fan
  College of Mathematics & Information Science, Wenzhou University, Zhejiang, China
  ,
- Xiuzi Ye
  College of Mathematics & Information Science, Wenzhou University, Zhejiang, China
IJCAI'15: Proceedings of the 24th International Conference on Artificial Intelligence•July 2015, pp 3897-3903

Support vector machines (SVMs) play a very dominant role in data classification due to their good generalization performance. However, they suffer from the high computational complexity in the classification phase when there are a considerable number of ...
1Citation
Metrics
Total Citations1
Export Citations
Save to Binder
Article
Robust multiple kernel K-means using ℓ_2;1-norm
- Liang Du
  State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences and School of Computer and Information Technology, Shanxi University
  ,
- Peng Zhou
  State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences and University of Chinese Academy of Sciences
  ,
- Lei Shi
  State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences and University of Chinese Academy of Sciences
  ,
- Hanmo Wang
  State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences and University of Chinese Academy of Sciences
  ,
- Mingyu Fan
  Institute of Intelligent System and Decision, Wenzhou University
  ,
- Wenjian Wang
  School of Computer and Information Technology, Shanxi University
  ,
- Yi-Dong Shen
  State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences
IJCAI'15: Proceedings of the 24th International Conference on Artificial Intelligence•July 2015, pp 3476-3482

The k-means algorithm is one of the most often used method for data clustering. However, the standard k-means can only be applied in the original feature space. The kernel k-means, which extends k-means into the kernel space, can be used to capture the ...
10Citation
Metrics
Total Citations10
Export Citations
Save to Binder
Article
Multi-modality tracker aggregation: from generative to discriminative
- Xiaoqin Zhang
  College of Mathematics & Information Science, Wenzhou University, Zhejiang, China
  ,
- Wei Li
  Taobao(China) Software Company Limited, Zhejiang, China
  ,
- Mingyu Fan
  College of Mathematics & Information Science, Wenzhou University, Zhejiang, China
  ,
- Di Wang
  College of Mathematics & Information Science, Wenzhou University, Zhejiang, China
  ,
- Xiuzi Ye
  College of Mathematics & Information Science, Wenzhou University, Zhejiang, China
IJCAI'15: Proceedings of the 24th International Conference on Artificial Intelligence•July 2015, pp 1937-1943

Visual tracking is an important research topic in computer vision community. Although there are numerous tracking algorithms in the literature, no one performs better than the others under all circumstances, and the best algorithm for a particular ...
1Citation
Metrics
Total Citations1
Export Citations
Save to Binder
article
A kernel-based sparsity preserving method for semi-supervised classification
Neurocomputing, Volume 139•September, 2014, pp 345-356 • https://doi.org/10.1016/j.neucom.2014.02.022

In this paper, we propose an effective approach to semi-supervised classification through kernel-based sparse representation. The new method computes the sparse representation of data in the feature space, and then the learner is subject to a cost ...
4Citation
Metrics
Total Citations4
Export Citations
Save to Binder
article
Dimension estimation of image manifolds by minimal cover approximation
- Mingyu Fan
  Institute of Intelligent System and Decision, Wenzhou University, China
  ,
- Xiaoqin Zhang
  Institute of Intelligent System and Decision, Wenzhou University, China
  ,
- Shengyong Chen
  College of Computer Science and Technology, Zhejiang University of Technology, China
  ,
- Hujun Bao
  College of Computer Science and Technology, Zhejiang University, China
  ,
- Steve Maybank
  Department of Computer Science and Information Systems, Birkbeck College, London, UK
Neurocomputing, Volume 105•April, 2013, pp 19-29 • https://doi.org/10.1016/j.neucom.2012.04.037

Estimating intrinsic dimension of data is an important problem in feature extraction and feature selection. It provides an estimation of the number of desired features. Principal Components Analysis (PCA) is a powerful tool in discovering the dimension ...
3Citation
Metrics
Total Citations3
Export Citations
Save to Binder

Author Profile Pages

Description: The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM bibliographic database, the Guide. Coverage of ACM publications is comprehensive from the 1950's. Coverage of other publishers generally starts in the mid 1980's. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community.
Please see the following 2007 Turing Award winners' profiles as examples:
History: Disambiguation of author names is of course required for precise identification of all the works, and only those works, by a unique individual. Of equal importance to ACM, author name normalization is also one critical prerequisite to building accurate citation and download statistics. For the past several years, ACM has worked to normalize author names, expand reference capture, and gather detailed usage statistics, all intended to provide the community with a robust set of publication metrics. The Author Profile Pages reveal the first result of these efforts.
Normalization: ACM uses normalization algorithms to weigh several types of evidence for merging and splitting names.
These include:
- co-authors: if we have two names and cannot disambiguate them based on name alone, then we see if they have a co-author in common. If so, this weighs towards the two names being the same person.
- affiliations: names in common with same affiliation weighs toward the two names being the same person.
- publication title: names in common whose works are published in same journal weighs toward the two names being the same person.
- keywords: names in common whose works address the same subject matter as determined from title and keywords, weigh toward being the same person.
The more conservative the merging algorithms, the more bits of evidence are required before a merge is made, resulting in greater precision but lower recall of works for a given Author Profile. Many bibliographic records have only author initials. Many names lack affiliations. With very common family names, typical in Asia, more liberal algorithms result in mistaken merges.

Automatic normalization of author names is not exact. Hence it is clear that manual intervention based on human knowledge is required to perfect algorithmic results. ACM is meeting this challenge, continuing to work to improve the automated merges by tweaking the weighting of the evidence in light of experience.
Bibliometrics: In 1926, Alfred Lotka formulated his power law (known as Lotka's Law) describing the frequency of publication by authors in a given field. According to this bibliometric law of scientific productivity, only a very small percentage (~6%) of authors in a field will produce more than 10 articles while the majority (perhaps 60%) will have but a single article published. With ACM's first cut at author name normalization in place, the distribution of our authors with 1, 2, 3..n publications does not match Lotka's Law precisely, but neither is the distribution curve far off. For a definition of ACM's first set of publication statistics, see Bibliometrics
Future Direction: The initial release of the Author Edit Screen is open to anyone in the community with an ACM account, but it is limited to personal information. An author's photograph, a Home Page URL, and an email may be added, deleted or edited. Changes are reviewed before they are made available on the live site.
ACM will expand this edit facility to accommodate more types of data and facilitate ease of community participation with appropriate safeguards. In particular, authors or members of the community will be able to indicate works in their profile that do not belong there and merge others that do belong but are currently missing.

A direct search interface for Author Profiles will be built.

An institutional view of works emerging from their faculty and researchers will be provided along with a relevant set of metrics.

It is possible, too, that the Author Profile page may evolve to allow interested authors to upload unpublished professional materials to an area available for search and free educational use, but distinct from the ACM Digital Library proper. It is hard to predict what shape such an area for user-generated content may take, but it carries interesting potential for input from the community.

Bibliometrics

The ACM DL is a comprehensive repository of publications from the entire field of computing.

It is ACM's intention to make the derivation of any publication statistics it generates clear to the user.

Average citations per article = The total Citation Count divided by the total Publication Count.
Citation Count = cumulative total number of times all authored works by this author were cited by other works within ACM's bibliographic database. Almost all reference lists in articles published by ACM have been captured. References lists from other publishers are less well-represented in the database. Unresolved references are not included in the Citation Count. The Citation Count is citations TO any type of work, but the references counted are only FROM journal and proceedings articles. Reference lists from books, dissertations, and technical reports have not generally been captured in the database. (Citation Counts for individual works are displayed with the individual record listed on the Author Page.)
Publication Count = all works of any genre within the universe of ACM's bibliographic database of computing literature of which this person was an author. Works where the person has role as editor, advisor, chair, etc. are listed on the page but are not part of the Publication Count.
Publication Years = the span from the earliest year of publication on a work by this author to the most recent year of publication of a work by this author captured within the ACM bibliographic database of computing literature (The ACM Guide to Computing Literature, also known as "the Guide".
Available for download = the total number of works by this author whose full texts may be downloaded from an ACM full-text article server. Downloads from external full-text sources linked to from within the ACM bibliographic space are not counted as 'available for download'.
Average downloads per article = The total number of cumulative downloads divided by the number of articles (including multimedia objects) available for download from ACM's servers.
Downloads (cumulative) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server since the downloads were first counted in May 2003. The counts displayed are updated monthly and are therefore 0-31 days behind the current date. Robotic activity is scrubbed from the download statistics.
Downloads (12 months) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 12-month period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (12-month download counts for individual works are displayed with the individual record.)
Downloads (6 weeks) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 6-week period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (6-week download counts for individual works are displayed with the individual record.)

ACM Author-Izer Service

Summary Description

ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.

Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.

ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.

To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.

How ACM Author-Izer Works

Authors may post ACM Author-Izer links in their own bibliographies maintained on their website and their own institution’s repository. The links take visitors to your page directly to the definitive version of individual articles inside the ACM Digital Library to download these articles for free.

The Service can be applied to all the articles you have ever published with ACM.

Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.

For authors who do not have a free ACM Web Account:

Go to the ACM DL http://dl.acm.org/ and click SIGN UP. Once your account is established, proceed to next step.

For authors who have an ACM web account, but have not edited their ACM Author Profile page:

Sign in to your ACM web account and go to your Author Profile page. Click "Add personal information" and add photograph, homepage address, etc. Click ADD AUTHOR INFORMATION to submit change. Once you receive email notification that your changes were accepted, you may utilize ACM Author-izer.

For authors who have an account and have already edited their Profile Page:

Sign in to your ACM web account, go to your Author Profile page in the Digital Library, look for the ACM Author-izer link below each ACM published article, and begin the authorization process. If you have published many ACM articles, you may find a batch Authorization process useful. It is labeled: "Export as: ACM Author-Izer Service"

ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.

Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.

FAQ

Q. What is ACM Author-Izer?
A. ACM Author-Izer is a unique, link-based, self-archiving service that enables ACM authors to generate and post links on either their home page or institutional repository for visitors to download the definitive version of their articles for free.
Q. What articles are eligible for ACM Author-Izer?
A. ACM Author-Izer can be applied to all the articles authors have ever published with ACM. It is also available to authors who will have articles published in ACM publications in the future.

Q. Are there any restrictions on authors to use this service?
A. No. An author does not need to subscribe to the ACM Digital Library nor even be a member of ACM.

Q. What are the requirements to use this service?
A. To access ACM Author-Izer, authors need to have a free ACM web account, must have an ACM Author Profile page in the Digital Library, and must take ownership of their Author Profile page.

Q. What is an ACM Author Profile Page?
A. The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM Digital Library. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community. Please visit the ACM Author Profile documentation page for more background information on these pages.

Q. How do I find my Author Profile page and take ownership?
A. You will need to take the following steps:
- Create a free ACM Web Account
- Sign-In to the ACM Digital Library
- Find your Author Profile Page by searching the ACM Digital Library for your name
- Find the result you authored (where your author name is a clickable link)
- Click on your name to go to the Author Profile Page
- Click the "Add Personal Information" link on the Author Profile Page
- Wait for ACM review and approval; generally less than 24 hours

Q. Why does my photo not appear?
A. Make sure that the image you submit is in .jpg or .gif format and that the file name does not contain special characters

Q. What if I cannot find the Add Personal Information function on my author page?
A. The ACM account linked to your profile page is different than the one you are logged into. Please logout and login to the account associated with your Author Profile Page.

Q. What happens if an author changes the location of his bibliography or moves to a new institution?
A. Should authors change institutions or sites, they can utilize ACM Author-Izer to disable old links and re-authorize new links for free downloads from a new location.

Q. What happens if an author provides a URL that redirects to the author’s personal bibliography page?
A. The service will not provide a free download from the ACM Digital Library. Instead the person who uses that link will simply go to the Citation Page for that article in the ACM Digital Library where the article may be accessed under the usual subscription rules.

However, if the author provides the target page URL, any link that redirects to that target page will enable a free download from the Service.

Q. What happens if the author’s bibliography lives on a page with several aliases?
A. Only one alias will work, whichever one is registered as the page containing the author’s bibliography. ACM has no technical solution to this problem at this time.

Q. Why should authors use ACM Author-Izer?
A. ACM Author-Izer lets visitors to authors’ personal home pages download articles for no charge from the ACM Digital Library. It allows authors to dynamically display real-time download and citation statistics for each “authorized” article on their personal site.

Q. Does ACM Author-Izer provide benefits for authors?
A. Downloads of definitive articles via Author-Izer links on the authors’ personal web page are captured in official ACM statistics to more accurately reflect usage and impact measurements.

Authors who do not use ACM Author-Izer links will not have downloads from their local, personal bibliographies counted. They do, however, retain the existing right to post author-prepared preprint versions on their home pages or institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library.

Q. How does ACM Author-Izer benefit the computing community?
A. ACM Author-Izer expands the visibility and dissemination of the definitive version of ACM articles. It is based on ACM’s strong belief that the computing community should have the widest possible access to the definitive versions of scholarly literature. By linking authors’ personal bibliography with the ACM Digital Library, user confusion over article versioning should be reduced over time.

In making ACM Author-Izer a free service to both authors and visitors to their websites, ACM is emphasizing its continuing commitment to the interests of its authors and to the computing community in ways that are consistent with its existing subscription-based access model.

Q. How do authors access ACM Author-Izer?
A. See How to Use

Q. Why can’t I find my most recent publication in my ACM Author Profile Page?
A. There is a time delay between publication and the process which associates that publication with an Author Profile Page. Right now, that process usually takes 4-8 weeks.

Q. How does ACM Author-Izer expand ACM’s “Green Path” Access Policies?
A. ACM Author-Izer extends the rights and permissions that authors retain even after copyright transfer to ACM, which has been among the “greenest” publishers. ACM enables its author community to retain a wide range of rights related to copyright and reuse of materials. They include:
- Posting rights that ensure free access to their work outside the ACM Digital Library and print publications
- Rights to reuse any portion of their work in new works that they may create
- Copyright to artistic images in ACM’s graphics-oriented publications that authors may want to exploit in commercial contexts
- All patent rights, which remain with the original owner

Mingyu Fan

Mingyu Fan

Applied Filters

People

Colleagues

Publication

Journal/Magazine Names

Proceedings/Book Names

All Publications

Content Type

Media Formats

Publisher

Publication Date

Learning to match features with discriminative sparse graph neural network

CSIR: Cascaded Sliding CVAEs With Iterative Socially-Aware Rethinking for Trajectory Prediction

Static-dynamic global graph representation for pedestrian trajectory prediction

CSR: Cascade Conditional Variational Auto Encoder with Socially-aware Regression for Pedestrian Trajectory Prediction

Simultaneous Past and Current Social Interaction-aware Trajectory Prediction for Multiple Intelligent Agents in Dynamic Scenes

Graph matching based point correspondence with alternating direction method of multipliers

Star Topology based Interaction for Robust Trajectory Forecasting in Dynamic Scene

Unsupervised feature selection for balanced clustering

Structure regularized self-paced learning for robust semi-supervised pattern classification

Top-k supervise feature selection via ADMM for integer programming

Structure regularized unsupervised discriminant feature analysis

Hierarchical mixing linear support vector machines for nonlinear classification

Efficient isometric multi-manifold learning based on the self-organizing method

Semi-supervised dictionary learning via structural sparse preserving

Efficient sequential feature selection based on adaptive eigenspace model

An efficient classifier based on hierarchical mixing linear support vector machines

Robust multiple kernel K-means using ℓ_2;1-norm

Multi-modality tracker aggregation: from generative to discriminative

A kernel-based sparsity preserving method for semi-supervised classification

Dimension estimation of image manifolds by minimal cover approximation