Junwei Bao
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- Junwei Bao (12)
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- Tiejun Zhao (6)
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- Zhao Yan (3)
- Bowen Zhou (2)
- Duyu Tang (2)
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Proceedings/Book Names
- AAAI'18/IAAI'18/EAAI'18: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence (1)
- CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (1)
- CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management (1)
- ICML'23: Proceedings of the 40th International Conference on Machine Learning (1)
- IJCAI'19: Proceedings of the 28th International Joint Conference on Artificial Intelligence (1)
- Natural Language Processing and Chinese Computing (1)
- Natural Language Processing and Chinese Computing (1)
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- research-article
An efficient confusing choices decoupling framework for multi-choice tasks over texts
- Yingyao Wang
https://ror.org/01yqg2h08Harbin Institute of Technology, Harbin, Heilongjjiang, China
, - Junwei Bao
JD AI Research, Beijing, China
, - Chaoqun Duan
JD AI Research, Beijing, China
, - Youzheng Wu
JD AI Research, Beijing, China
, - Xiaodong He
JD AI Research, Beijing, China
, - Conghui Zhu
https://ror.org/01yqg2h08Harbin Institute of Technology, Harbin, Heilongjjiang, China
, - Tiejun Zhao
https://ror.org/01yqg2h08Harbin Institute of Technology, Harbin, Heilongjjiang, China
Neural Computing and Applications, Volume 36, Issue 1•Jan 2024, pp 259-271 • https://doi.org/10.1007/s00521-023-08795-4AbstractThis paper focuses on the multi-choice tasks, which aim to select the correct choice for a given query by reasoning over texts, such as sentences and passages. Benefiting from the provided knowledge in these tasks, the reasoning of multi-choice ...
- 0Citation
MetricsTotal Citations0
- Yingyao Wang
- research-article
Operation-Augmented Numerical Reasoning for Question Answering
- Yongwei Zhou
Machine Intelligence and Translation Laboratory at the School of Computer Science of Technology, Harbin Institute of Technology, Harbin, China
, - Junwei Bao
JD AI Research, Beijing, China
, - Youzheng Wu
JD AI Research, Beijing, China
, - Xiaodong He
JD AI Research, Beijing, China
, - Tiejun Zhao
Machine Intelligence and Translation Laboratory at the School of Computer Science of Technology, Harbin Institute of Technology, Harbin, China
IEEE/ACM Transactions on Audio, Speech and Language Processing, Volume 32•2024, pp 15-28 • https://doi.org/10.1109/TASLP.2023.3316448Question answering requiring numerical reasoning, which generally involves symbolic operations such as sorting, counting, and addition, is a challenging task. To address such a problem, existing mixture-of-experts (MoE)-based methods design several ...
- 0Citation
- 86
- Downloads
MetricsTotal Citations0Total Downloads86Last 12 Months86Last 6 weeks20
- Yongwei Zhou
- research-article
SegCLIP: patch aggregation with learnable centers for open-vocabulary semantic segmentation
- Huaishao Luo
JD AI Research and Southwest Jiaotong University, Chengdu, China
, - Junwei Bao
JD AI Research
, - Youzheng Wu
JD AI Research
, - Xiaodong He
JD AI Research
, - Tianrui Li
Southwest Jiaotong University, Chengdu, China
ICML'23: Proceedings of the 40th International Conference on Machine Learning•July 2023, Article No.: 956, pp 23033-23044Recently, the contrastive language-image pre-training, e.g., CLIP, has demonstrated promising results on various downstream tasks. The pre-trained model can capture enriched visual concepts for images by learning from a large scale of text-image data. ...
- 0Citation
MetricsTotal Citations0
- Huaishao Luo
- research-articlePublished By ACMPublished By ACM
AutoQGS: Auto-Prompt for Low-Resource Knowledge-based Question Generation from SPARQL
- Guanming Xiong
Peking University, Beijing, China
, - Junwei Bao
JD AI Research, Beijing, China
, - Wen Zhao
Peking University, Beijing, China
, - Youzheng Wu
JD AI Research, Beijing, China
, - Xiaodong He
JD AI Research, Beijing, China
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management•October 2022, pp 2250-2259• https://doi.org/10.1145/3511808.3557246This study investigates the task of knowledge-based question generation (KBQG). Conventional KBQG works generated questions from fact triples in the knowledge graph, which could not express complex operations like aggregation and comparison in SPARQL. ...
- 2Citation
- 221
- Downloads
MetricsTotal Citations2Total Downloads221Last 12 Months73Last 6 weeks4- 1
Supplementary MaterialCIKM22-fp0088.mp4
- Guanming Xiong
- Article
CUSTOM: Aspect-Oriented Product Summarization for E-Commerce
- Jiahui Liang
JD AI Research, Beijing, China
, - Junwei Bao
JD AI Research, Beijing, China
, - Yifan Wang
JD AI Research, Beijing, China
, - Youzheng Wu
JD AI Research, Beijing, China
, - Xiaodong He
JD AI Research, Beijing, China
, - Bowen Zhou
JD AI Research, Beijing, China
Natural Language Processing and Chinese Computing•October 2021, pp 124-136• https://doi.org/10.1007/978-3-030-88483-3_10AbstractProduct summarization aims to automatically generate product descriptions, which is of great commercial potential. Considering the customer preferences on different product aspects, it would benefit from generating aspect-oriented customized ...
- 2Citation
MetricsTotal Citations2
- Jiahui Liang
- Article
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading Comprehension
- Yongwei Zhou
Harbin Institute of Technology, Harbin, China
, - Junwei Bao
JD AI Research, Beijing, China
, - Haipeng Sun
JD AI Research, Beijing, China
, - Jiahui Liang
JD AI Research, Beijing, China
, - Youzheng Wu
JD AI Research, Beijing, China
, - Xiaodong He
JD AI Research, Beijing, China
, - Bowen Zhou
JD AI Research, Beijing, China
, - Tiejun Zhao
Harbin Institute of Technology, Harbin, China
Natural Language Processing and Chinese Computing•October 2021, pp 439-452• https://doi.org/10.1007/978-3-030-88480-2_35AbstractReasoning machine reading comprehension (R-MRC) aims to answer complex questions that require discrete reasoning based on text. To support discrete reasoning, evidence, typically the concise textual fragments that describe question-related facts, ...
- 0Citation
MetricsTotal Citations0
- Yongwei Zhou
- Article
Weakly supervised multi-task learning for semantic parsing
- Bo Shao
School of Data and Computer Science, Sun Yat-sen University and Microsoft Research Asia
, - Yeyun Gong
Microsoft Research Asia
, - Junwei Bao
Microsoft Research Asia
, - Jianshu Ji
Microsoft AI and Research, Redmond, WA
, - Guihong Cao
Microsoft AI and Research, Redmond, WA
, - Xiaola Lin
School of Data and Computer Science, Sun Yat-sen University
, - Nan Duan
Microsoft Research Asia
IJCAI'19: Proceedings of the 28th International Joint Conference on Artificial Intelligence•August 2019, pp 3375-3381Semantic parsing is a challenging and important task which aims to convert a natural language sentence to a logical form. Existing neural semantic parsing methods mainly use 〈question, logical form〉 (Q-L) pairs to train a sequence-to-sequence model. ...
- 1Citation
MetricsTotal Citations1
- Bo Shao
- research-article
Text Generation From Tables
- Junwei Bao
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
, - Duyu Tang
Microsoft Research, Beijing, China
, - Nan Duan
Microsoft Research, Beijing, China
, - Zhao Yan
Beihang University, Beijing, China
, - Ming Zhou
Microsoft Research, Beijing, China
, - Tiejun Zhao
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
IEEE/ACM Transactions on Audio, Speech and Language Processing, Volume 27, Issue 2•February 2019, pp 311-320 • https://doi.org/10.1109/TASLP.2018.2878381This paper proposes a neural generative model, namely Table2Seq, to generate a natural language sentence based on a table. Specifically, the model maps a table to continuous vectors and then generates a natural language sentence by leveraging the ...
- 3Citation
- 210
- Downloads
MetricsTotal Citations3Total Downloads210Last 12 Months7Last 6 weeks2
- Junwei Bao
- research-article
Question Generation With Doubly Adversarial Nets
- Junwei Bao
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
, - Yeyun Gong
Microsoft Research Asia, Beijing, China
, - Nan Duan
Microsoft Research Asia, Beijing, China
, - Ming Zhou
Microsoft Research Asia, Beijing, China
, - Tiejun Zhao
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
IEEE/ACM Transactions on Audio, Speech and Language Processing, Volume 26, Issue 11•November 2018, pp 2230-2239 • https://doi.org/10.1109/TASLP.2018.2859777We study the problem of question generation on a specific domain, where there are no labeled data. To address this problem, we propose a novel neural question generation approach called DoubAN, or doubly adversarial nets, which fully utilizes labeled ...
- 3Citation
- 116
- Downloads
MetricsTotal Citations3Total Downloads116Last 12 Months3Last 6 weeks2
- Junwei Bao
- research-article
Response selection from unstructured documents for human-computer conversation systems
- Zhao Yan
Beihang University, Beijing, China
, - Nan Duan
Microsoft Research Asia, Beijing, China
, - Junwei Bao
Harbin Institute of Technology, Harbin, China
, - Peng Chen
Microsoft Xiaoice Team, Beijing, China
, - Ming Zhou
Microsoft Research Asia, Beijing, China
, - Zhoujun Li
Beihang University, Beijing, China
Knowledge-Based Systems, Volume 142, Issue C•Feb 2018, pp 149-159 • https://doi.org/10.1016/j.knosys.2017.11.033AbstractThis paper studies response selection for human-computer conversation systems. Existing retrieval-based human-computer conversation systems are intended to reply to user utterances based on existing utterance-response pairs. However, ...
- 2Citation
MetricsTotal Citations2
- Zhao Yan
- research-articlefree
Table-to-text: describing table region with natural language
- Junwei Bao
Harbin Institute of Technology, Harbin, China
, - Duyu Tang
Microsoft Research, Beijing, China
, - Nan Duan
Microsoft Research, Beijing, China
, - Zhao Yan
Beihang University, Beijing, China
, - Yuanhua Lv
Microsoft AI and Research, Sunnyvale CA
, - Ming Zhou
Microsoft Research, Beijing, China
, - Tiejun Zhao
Harbin Institute of Technology, Harbin, China
AAAI'18/IAAI'18/EAAI'18: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence•February 2018, Article No.: 615, pp 5020-5027In this paper, we present a generative model to generate a natural language sentence describing a table region, e.g., a row. The model maps a row from a table to a continuous vector and then generates a natural language sentence by leveraging the ...
- 0Citation
- 77
- Downloads
MetricsTotal Citations0Total Downloads77Last 12 Months57Last 6 weeks11
- Junwei Bao
- research-articlePublished By ACMPublished By ACM
Answering Questions with Complex Semantic Constraints on Open Knowledge Bases
- Pengcheng Yin
The University of Hong Kong, Hong Kong, Hong Kong
, - Nan Duan
Microsoft Research Asia, Beijing, China
, - Ben Kao
The University of Hong Kong, Hong Kong, Hong Kong
, - Junwei Bao
Harbin Institute of Technology, Harbin, China
, - Ming Zhou
Microsoft Research Asia, Beijing, China
CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management•October 2015, pp 1301-1310• https://doi.org/10.1145/2806416.2806542A knowledge-based question-answering system (KB-QA) is one that answers natural language questions with information stored in a large-scale knowledge base (KB). Existing KB-QA systems are either powered by curated KBs in which factual knowledge is ...
- 37Citation
- 711
- Downloads
MetricsTotal Citations37Total Downloads711Last 12 Months15Last 6 weeks1
- Pengcheng Yin
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