Meina Song
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- research-articlefreePublished By ACMPublished By ACM
CP-Prompt: Composition-Based Cross-modal Prompting for Domain-Incremental Continual Learning
- Yu Feng
Beijing University of Posts and Telecommunications, Beijing, China
, - Zhen Tian
Beijing University of Posts and Telecommunications, Beijing, China
, - Yifan Zhu
Beijing University of Posts and Telecommunications, Beijing, China
, - Zongfu Han
Beijing University of Posts and Telecommunications, Beijing, China
, - Haoran Luo
Beijing University of Posts and Telecommunications, Beijing, China
, - Guangwei Zhang
Beijing University of Posts and Telecommunications, Beijing, China
, - Meina Song
Beijing University of Posts and Telecommunications, Beijing, China
MM '24: Proceedings of the 32nd ACM International Conference on Multimedia•October 2024, pp 2729-2738• https://doi.org/10.1145/3664647.3681481The key challenge of cross-modal domain-incremental learning (DIL) is to enable the learning model to continuously learn from novel data with different feature distributions under the same task without forgetting old ones. However, existing top-...
- 0Citation
- 20
- Downloads
MetricsTotal Citations0Total Downloads20Last 12 Months20Last 6 weeks20
- Yu Feng
- research-article
Augmentation-free dense contrastive knowledge distillation for efficient semantic segmentation
- Jiawei Fan
Intel Labs China
, - Chao Li
Intel Labs China
, - Xiaolong Liu
HoloMatic Technology Co. Ltd.
, - Meina Song
BUPT
, - Anbang Yao
Intel Labs China
NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems•December 2023, Article No.: 2236, pp 51359-51370In recent years, knowledge distillation methods based on contrastive learning have achieved promising results on image classification and object detection tasks. However, in this line of research, we note that less attention is paid to semantic ...
- 0Citation
MetricsTotal Citations0- 1
Supplementary Material3666122.3668358_supp.pdf
- Jiawei Fan
- research-article
Free<inline-formula><tex-math notation="LaTeX">$\rm ^{3}$</tex-math></inline-formula>Net: Gliding Free, Orientation Free, and Anchor Free Network for Oriented Object Detection
- Zhonghong Ou
Beijing University of Posts and Telecommunications, Beijing, China
, - Zhongjie Chen
Beijing University of Posts and Telecommunications, Beijing, China
, - Shengyi Shen
Beijing University of Posts and Telecommunications, Beijing, China
, - Lina Fan
Beijing University of Posts and Telecommunications, Beijing, China
, - Siyuan Yao
Beijing University of Posts and Telecommunications, Beijing, China
, - Meina Song
Beijing University of Posts and Telecommunications, Beijing, China
, - Pan Hui
Computational Media and Arts Trust Area, Hong Kong University of Science and Technology, Hong Kong
IEEE Transactions on Multimedia, Volume 25•2023, pp 7089-7100 • https://doi.org/10.1109/TMM.2022.3217397Object detection for aerial images has achieved remarkable progress in recent years. Nevertheless, most exiting studies do not differentiate oriented object detection from horizontal detection. Certain schemes ignore the ambiguity of oriented object ...
- 0Citation
MetricsTotal Citations0
- Zhonghong Ou
- research-article
KFWC: A Knowledge-Driven Deep Learning Model for Fine-grained Classification of Wet-AMD
- Haihong E
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
Education Department Information Network Engineering Research Center, Beijing University of Posts and Telecommunications, Beijing, 100876, China
, - Jiawen He
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
Education Department Information Network Engineering Research Center, Beijing University of Posts and Telecommunications, Beijing, 100876, China
, - Tianyi Hu
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
Education Department Information Network Engineering Research Center, Beijing University of Posts and Telecommunications, Beijing, 100876, China
, - Lifei Yuan
Hebei Provincial Eye Hospital, Hebei, 054001, China
, - Ruru Zhang
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
Education Department Information Network Engineering Research Center, Beijing University of Posts and Telecommunications, Beijing, 100876, China
, - Shengjuan Zhang
Hebei Provincial Eye Hospital, Hebei, 054001, China
, - Yanhui Wang
Hebei Provincial Eye Hospital, Hebei, 054001, China
, - Meina Song
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
Education Department Information Network Engineering Research Center, Beijing University of Posts and Telecommunications, Beijing, 100876, China
, - Lifei Wang
Hebei Provincial Eye Hospital, Hebei, 054001, China
Computer Methods and Programs in Biomedicine, Volume 229, Issue C•Feb 2023 • https://doi.org/10.1016/j.cmpb.2022.107312Highlights- We construct a fine-grained classification dataset of wet Age-related macular degeneration (AMD).
Abstract Background and objectivesAutomated diagnosis using deep neural networks can help ophthalmologists detect the blinding eye disease wet Age-related Macular Degeneration (AMD). Wet-AMD has two similar subtypes, Neovascular AMD ...
- 0Citation
MetricsTotal Citations0
- Haihong E
- research-article
A knowledge distilled attention-based latent information extraction network for sequential user behavior
- Ruo Huang
School of Computer Science, Beijing University of Posts & Telecommunications, 100876, Beijing, China
Transport Planning and Research Institute, Ministry of Transport, 100028, Beijing, China
Laboratory for Traffic & Transport Planning Digitalization, 100028, Beijing, China
, - Shelby McIntyre
Leavey School of Business, Santa Clara University, 95053, Santa Clara, CA, USA
, - Meina Song
School of Computer Science, Beijing University of Posts & Telecommunications, 100876, Beijing, China
, - Haihong E
School of Computer Science, Beijing University of Posts & Telecommunications, 100876, Beijing, China
, - Zhonghong Ou
School of Computer Science, Beijing University of Posts & Telecommunications, 100876, Beijing, China
Multimedia Tools and Applications, Volume 82, Issue 1•Jan 2023, pp 1017-1043 • https://doi.org/10.1007/s11042-022-12513-yAbstractWhen modeling user-item interaction sequences to extract sequential patterns, current recommender systems face the dual issues of a) long-distance dependencies in conjunction with b) high levels of noise. In addition, with the complexity of ...
- 0Citation
MetricsTotal Citations0
- Ruo Huang
- research-article
MBNM: Multi-branch network based on memory features for long-tailed medical image recognition
- Ruru Zhang
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
Education Department Information Network Engineering Research Center, Beijing University of Posts and Telecommunications, Beijing, 100876, China
, - Haihong E
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
Education Department Information Network Engineering Research Center, Beijing University of Posts and Telecommunications, Beijing, 100876, China
, - Lifei Yuan
Hebei Eye Hospital, Hebei, 054001, China
, - Jiawen He
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
Education Department Information Network Engineering Research Center, Beijing University of Posts and Telecommunications, Beijing, 100876, China
, - Hongxing Zhang
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
Education Department Information Network Engineering Research Center, Beijing University of Posts and Telecommunications, Beijing, 100876, China
, - Shengjuan Zhang
Hebei Eye Hospital, Hebei, 054001, China
, - Yanhui Wang
Hebei Eye Hospital, Hebei, 054001, China
, - Meina Song
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
Education Department Information Network Engineering Research Center, Beijing University of Posts and Telecommunications, Beijing, 100876, China
, - Lifei Wang
Hebei Eye Hospital, Hebei, 054001, China
Computer Methods and Programs in Biomedicine, Volume 212, Issue C•Nov 2021 • https://doi.org/10.1016/j.cmpb.2021.106448Highlights- A long-tail medical image recognition algorithm based on memory features is proposed.
Abstract Background and objectivesDeep learning algorithms show revolutionary potential in computer-aided diagnosis. These computer-aided diagnosis techniques often rely on large-scale, balanced standard datasets. However, there are ...
- 4Citation
MetricsTotal Citations4
- Ruru Zhang
- research-articlePublished By ACMPublished By ACM
A Column-Level Data Lineage Processing System Based on Hive
- Zehua Tan
School of computer science Beijing University of Posts and Telecommunications Beijing, Beijing, China
, - Haihong E
School of computer science Beijing University of Posts and Telecommunications Beijing, Beijing, China
, - Meina Song
School of computer science Beijing University of Posts and Telecommunications Beijing, Beijing, China
ICBDT '20: Proceedings of the 3rd International Conference on Big Data Technologies•September 2020, pp 47-52• https://doi.org/10.1145/3422713.3422719For big data, the data warehouse stores all business data of the entire enterprise. The data collected in the data warehouse will generate new data collection through the operations of data union, splitting, and transformation. This data conversion ...
- 0Citation
- 170
- Downloads
MetricsTotal Citations0Total Downloads170Last 12 Months30Last 6 weeks5
- Zehua Tan
- research-articlePublished By ACMPublished By ACM
Transforming RDF to Property Graph in Hugegraph
- E. Haihong
Beijing University of Posts and Telecommunications, Beijing, China
, - Penghao Han
Beijing University of Posts and Telecommunications, Beijing, China
, - Meina Song
Beijing University of Posts and Telecommunications, Beijing, China
ICEMIS'20: Proceedings of the 6th International Conference on Engineering & MIS 2020•September 2020, Article No.: 97, pp 1-6• https://doi.org/10.1145/3410352.3410833The data form in the graph data is divided into RDF and property graph. RDF appeared earlier, but it is generally larger and the data is more redundant. In the property graph, a graph is defined by properties, nodes and edges. It is easier to set ...
- 2Citation
- 177
- Downloads
MetricsTotal Citations2Total Downloads177Last 12 Months23Last 6 weeks2
- E. Haihong
- Article
Turn-Level Recurrence Self-attention for Joint Dialogue Action Prediction and Response Generation
- Yanxin Tan
Beijing University of Posts and Telecommunications, Beijing, China
, - Zhonghong Ou
Beijing University of Posts and Telecommunications, Beijing, China
, - Kemeng Liu
Beijing University of Posts and Telecommunications, Beijing, China
, - Yanan Shi
Beijing University of Posts and Telecommunications, Beijing, China
, - Meina Song
Beijing University of Posts and Telecommunications, Beijing, China
AbstractIn task-oriented dialogue systems, semantically controlled natural language generation is the procedure to generate responses based on current context information. Seq2seq models are widely used to generate dialogue responses and achieve favorable ...
- 0Citation
MetricsTotal Citations0
- Yanxin Tan
- Article
KGWD: Knowledge Graph Based Wide & Deep Framework for Recommendation
- Kemeng Liu
Beijing University of Posts and Telecommunications, Beijing, China
, - Zhonghong Ou
Beijing University of Posts and Telecommunications, Beijing, China
, - Yanxin Tan
Beijing University of Posts and Telecommunications, Beijing, China
, - Kai Zhao
Beijing University of Posts and Telecommunications, Beijing, China
, - Meina Song
Beijing University of Posts and Telecommunications, Beijing, China
AbstractKnowledge Graph (KG) contains rich real-world auxiliary information, which can be leveraged to improve the performance of recommender systems. Nevertheless, existing recommender systems usually sample and aggregate neighbor entities and relations ...
- 0Citation
MetricsTotal Citations0
- Kemeng Liu
- research-articlePublished By ACMPublished By ACM
Distant Supervised Relation Extraction Model for Reinforcement Learning Combined with Noise Network
- Haihong E
College of Computer Science and Technology, Beijing University of Posts and Telecommunications
, - Xiaosong Zhou
College of Computer Science and Technology, Beijing University of Posts and Telecommunications
, - Meina Song
College of Computer Science and Technology, Beijing University of Posts and Telecommunications
ASSE '20: Proceedings of the 2020 Asia Service Sciences and Software Engineering Conference•May 2020, pp 16-20• https://doi.org/10.1145/3399871.3399893The distant supervised relation extraction has received wide attention from scholars in recent years. Existing methods for distant supervised relation extraction are based on bag-level for relation prediction, but they do not correspond to sentences and ...
- 0Citation
- 44
- Downloads
MetricsTotal Citations0Total Downloads44Last 12 Months3
- Haihong E
- research-articlePublished By ACMPublished By ACM
Algorithm for Automatic Layout of Logo Pictures for Visualization of Ecological Map in Strategic Consulting
- Meina Song
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing
, - Xiangyu Xu
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing
, - Haihong E
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing
, - Yucheng Hu
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing
ASSE '20: Proceedings of the 2020 Asia Service Sciences and Software Engineering Conference•May 2020, pp 120-126• https://doi.org/10.1145/3399871.3399876The mapping of the industrial ecological map is an important work in strategic consulting services, but excessive artificial dependence has caused it to become a pain point for the digital transformation of strategic consulting. This paper proposed a ...
- 0Citation
- 33
- Downloads
MetricsTotal Citations0Total Downloads33Last 12 Months1
- Meina Song
- research-articlePublished By ACMPublished By ACM
A Crowdsourcing Repeated Annotations System for Visual Object Detection
- Yucheng Hu
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
, - Zhonghong Ou
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
, - Xiangyu Xu
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
, - Meina Song
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
ICVISP 2019: Proceedings of the 3rd International Conference on Vision, Image and Signal Processing•August 2019, Article No.: 14, pp 1-6• https://doi.org/10.1145/3387168.3387242As a fundamental task in compute vision, object detection has been developed rapidly driven by the deep learning. The lack of a large number of images with ground truth annotations has become a chief obstacle to object detection applications in many ...
- 0Citation
- 121
- Downloads
MetricsTotal Citations0Total Downloads121Last 12 Months15
- Yucheng Hu
- research-articlePublished By ACMPublished By ACM
Crowd R-CNN: An Object Detection Model Utilizing Crowdsourced Labels
- Yucheng Hu
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
, - Meina Song
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
ICVISP 2019: Proceedings of the 3rd International Conference on Vision, Image and Signal Processing•August 2019, Article No.: 6, pp 1-7• https://doi.org/10.1145/3387168.3387180Accuracy of object detection has increased significantly in recent years because of the rapid development of deep learning techniques. Nevertheless, its applications in many fields are still limited, mainly due to the lack of large datasets, especially ...
- 0Citation
- 162
- Downloads
MetricsTotal Citations0Total Downloads162Last 12 Months23Last 6 weeks1
- Yucheng Hu
- research-articlePublished By ACMPublished By ACM
An Automatic Artificial Intelligence Training Platform Based on Kubernetes
- Chaoyu Wu
School of computer science, Beijing University of Posts and Telecommunications, Beijing, China
, - E. Haihong
School of computer science, Beijing University of Posts and Telecommunications, Beijing, China
, - Meina Song
School of computer science, Beijing University of Posts and Telecommunications, Beijing, China
BDET '20: Proceedings of the 2020 2nd International Conference on Big Data Engineering and Technology•January 2020, pp 58-62• https://doi.org/10.1145/3378904.3378921For large-scale AI training, the manual allocation of GPU resources is too inefficient, and it faces the problems of task allocation and fault restart. In this paper, a fully automatic machine learning platform is designed, which manages server resources ...
- 6Citation
- 255
- Downloads
MetricsTotal Citations6Total Downloads255Last 12 Months44Last 6 weeks4
- Chaoyu Wu
- research-articleOpen AccessPublished By ACMPublished By ACM
BMM-Net: automatic segmentation of edema in optical coherence tomography based on boundary detection and multi-scale network
- Ruru Zhang
Beijing University of Posts and Telecommunications
, - Jiawen He
Beijing University of Posts and Telecommunications
, - Shenda Shi
Beijing University of Posts and Telecommunications
, - Haihong E
Beijing University of Posts and Telecommunications
, - Zhonghong Ou
Beijing University of Posts and Telecommunications
, - Meina Song
Beijing University of Posts and Telecommunications
CHIL '20: Proceedings of the ACM Conference on Health, Inference, and Learning•April 2020, pp 51-59• https://doi.org/10.1145/3368555.3384447Retinal effusions and cysts caused by the leakage of damaged macular vessels and choroid neovascularization are symptoms of many ophthalmic diseases. Optical coherence tomography (OCT), which provides clear 10-layer cross-sectional images of the retina, ...
- 0Citation
- 367
- Downloads
MetricsTotal Citations0Total Downloads367Last 12 Months87Last 6 weeks11
- Ruru Zhang
- research-articlePublished By ACMPublished By ACM
Session-based Recommendation with Context-Aware Attention Network
- Jinsheng Wu
Beijing University of Posts and Telecommunications, Beijing, China
, - Zhonghong Ou
Beijing University of Posts and Telecommunications, Beijing, China
, - Meina Song
Beijing University of Posts and Telecommunications, Beijing, China
ICIT '19: Proceedings of the 2019 7th International Conference on Information Technology: IoT and Smart City•December 2019, pp 141-146• https://doi.org/10.1145/3377170.3377269Session-based recommendation aims to generate recommendation results based on user's anonymous session. Previous studies model the session as a sequence and use Recursive Neural Network (RNN) to represent user behavior for recommendations. Although ...
- 1Citation
- 209
- Downloads
MetricsTotal Citations1Total Downloads209Last 12 Months11
- Jinsheng Wu
- research-articlePublished By ACMPublished By ACM
Hybrid High-order in Graph Attention Layer
- Haihong E
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing
, - Di Zeng
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing
, - Meina Song
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing
CIIS '19: Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems•November 2019, pp 111-116• https://doi.org/10.1145/3372422.3372442As a result of approximating the Eigenbasis of the graph Laplacian proposed by GC-layer of Kipf & Welling [5], the convolution operation is efficiently applied from Euclidean domain to graph domain, and the end-to-end deep graph neural network is widely ...
- 0Citation
- 50
- Downloads
MetricsTotal Citations0Total Downloads50
- Haihong E
- research-articlePublished By ACMPublished By ACM
Distant Supervised Relation Extraction Based On Recurrent Convolutional Piecewise Neural Network
- E. Haihong
College of Computer Science and Technology, Beijing University of Posts and Telecommunications
, - Xiaosong Zhou
College of Computer Science and Technology, Beijing University of Posts and Telecommunications
, - Meina Song
College of Computer Science and Technology, Beijing University of Posts and Telecommunications
SSPS '19: Proceedings of the 2019 International Symposium on Signal Processing Systems•September 2019, pp 169-175• https://doi.org/10.1145/3364908.3365303Distant supervised relation extraction (RE) is currently an effective way to solve the problem of extracting relation from large amounts of unlabeled data.The purpose of distant supervised relation extraction is to identify the relation between the two ...
- 3Citation
- 68
- Downloads
MetricsTotal Citations3Total Downloads68Last 12 Months1Last 6 weeks1
- E. Haihong
- research-articlePublished By ACMPublished By ACM
Research and Top-level Framework Design on Unified Resource Management of Big Data in Strategic Consulting
- Meina Song
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing
, - Xiangyu Xu
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing
, - E. Haihong
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing
BDIOT '19: Proceedings of the 3rd International Conference on Big Data and Internet of Things•August 2019, pp 8-12• https://doi.org/10.1145/3361758.3361761This paper puts forward a set of top-level framework design methodology for unified data resource management aiming at the characteristics of big data, multi-source and heterogeneous, and the difficulty of unified organization and management, and ...
- 0Citation
- 99
- Downloads
MetricsTotal Citations0Total Downloads99Last 12 Months9
- Meina Song
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. 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