Dawn Xiaodong Song
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- research-articleOpen AccessPublished By ACMPublished By ACM
ThreatKG: An AI-Powered System for Automated Open-Source Cyber Threat Intelligence Gathering and Management
- Peng Gao
Virginia Tech, Blacksburg, VA, USA
, - Xiaoyuan Liu
University of California, Berkeley, Berkeley, CA, USA
, - Edward Choi
University of California, Berkeley, Berkeley, CA, USA
, - Sibo Ma
University of California, Berkeley, Berkeley, CA, USA
, - Xinyu Yang
Virginia Tech, Blacksburg, VA, USA
, - Dawn Song
University of California, Berkeley, Berkeley, CA, USA
LAMPS '24: Proceedings of the 1st ACM Workshop on Large AI Systems and Models with Privacy and Safety Analysis•November 2024, pp 1-12• https://doi.org/10.1145/3689217.3690613Open-source cyber threat intelligence (OSCTI) has become essential for keeping up with the rapidly changing threat landscape. However, current OSCTI gathering and management solutions mainly focus on structured Indicators of Compromise (IOC) feeds, which ...
- 0Citation
- 75
- Downloads
MetricsTotal Citations0Total Downloads75Last 12 Months75Last 6 weeks63
- Peng Gao
- research-article
LLM-PBE: Assessing Data Privacy in Large Language Models
- Qinbin Li
University of California, Berkeley
, - Junyuan Hong
University of Texas at Austin
, - Chulin Xie
University of Illinois Urbana-Champaign
, - Jeffrey Tan
University of California, Berkeley
, - Rachel Xin
University of California, Berkeley
, - Junyi Hou
National University of Singapore
, - Xavier Yin
University of California, Berkeley
, - Zhun Wang
University of California, Berkeley
, - Dan Hendrycks
Center for AI Safety
, - Zhangyang Wang
University of Texas at Austin
, - Bo Li
University of Chicago
, - Bingsheng He
National University of Singapore
, - Dawn Song
University of California, Berkeley
Proceedings of the VLDB Endowment, Volume 17, Issue 11•July 2024, pp 3201-3214 • https://doi.org/10.14778/3681954.3681994Large Language Models (LLMs) have become integral to numerous domains, significantly advancing applications in data management, mining, and analysis. Their profound capabilities in processing and interpreting complex language data, however, bring to ...
- 2Citation
- 148
- Downloads
MetricsTotal Citations2Total Downloads148Last 12 Months148Last 6 weeks39
- Qinbin Li
- research-articleOpen AccessPublished By ACMPublished By ACM
Ratel: MPC-extensions for Smart Contracts
- Yunqi Li
University of Illinois at Urbana-Champaign, Champaign, IL, USA
, - Kyle Soska
University of Illinois at Urbana-Champaign, Champaign, USA
, - Zhen Huang
Shanghai Jiao Tong University, Shanghai, China
, - Sylvain Bellemare
The Initiative for CryptoCurrencies and Contracts, New York City, USA
, - Mikerah Quintyne-Collins
HashCloack Inc., Toronto, Canada
, - Lun Wang
Google, Mountain View, United States of America
, - Xiaoyuan Liu
University of California, Berkeley, Berkeley, United States of America
, - Dawn Song
UC Berkeley, Berkeley, USA
, - Andrew Miller
University of Illinois at Urbana-Champaign, Champaign, United States of America
ASIA CCS '24: Proceedings of the 19th ACM Asia Conference on Computer and Communications Security•July 2024, pp 336-352• https://doi.org/10.1145/3634737.3661142Enhancing privacy on smart contract-enabled blockchains has garnered much attention in recent research. Zero-knowledge proofs (ZKPs) is one of the most popular approaches, however, they fail to provide full expressiveness and fine-grained privacy. To ...
- 0Citation
- 466
- Downloads
MetricsTotal Citations0Total Downloads466Last 12 Months466Last 6 weeks127- 1
Supplementary Materialp336-supp.pdf
- Yunqi Li
- research-article
Latent execution for neural program synthesis
- Xinyun Chen
UC Berkeley
, - Dawn Song
UC Berkeley
, - Yuandong Tian
Facebook AI Research
NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems•December 2021, Article No.: 1700, pp 22196-22208Program synthesis from input-output (IO) examples has been a long-standing challenge. While recent works demonstrated limited success on domain-specific languages (DSL), it remains highly challenging to apply them to real-world programming languages, ...
- 0Citation
MetricsTotal Citations0- 1
Supplementary Material3540261.3541961_supp.pdf
- Xinyun Chen
- research-article
Adversarial examples for k-nearest neighbor classifiers based on higher-order voronoi diagrams
- Chawin Sitawarin
UC Berkeley
, - Evgenios M. Kornaropoulos
George Mason University
, - Dawn Song
UC Berkeley
, - David Wagner
UC Berkeley
NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems•December 2021, Article No.: 1186, pp 15486-15497Adversarial examples are a widely studied phenomenon in machine learning models. While most of the attention has been focused on neural networks, other practical models also suffer from this issue. In this work, we propose an algorithm for evaluating the ...
- 0Citation
MetricsTotal Citations0- 1
Supplementary Material3540261.3541447_supp.pdf
- Chawin Sitawarin
- research-article
DiffAttack: evasion attacks against diffusion-based adversarial purification
- Mintong Kang
UIUC
, - Dawn Song
UC Berkeley
, - Bo Li
UIUC
NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems•December 2023, Article No.: 3234, pp 73919-73942Diffusion-based purification defenses leverage diffusion models to remove crafted perturbations of adversarial examples and achieve state-of-the-art robustness. Recent studies show that even advanced attacks cannot break such defenses effectively, since ...
- 0Citation
MetricsTotal Citations0
- Mintong Kang
- research-article
BIRD: generalizable backdoor detection and removal for deep reinforcement learning
- Xuan Chen
Purdue University
, - Wenbo Guo
Purdue University and UC Berkeley
, - Guanhong Tao
Purdue University
, - Xiangyu Zhang
Purdue University
, - Dawn Song
UC Berkeley
NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems•December 2023, Article No.: 1777, pp 40786-40798Backdoor attacks pose a severe threat to the supply chain management of deep reinforcement learning (DRL) policies. Despite initial defenses proposed in recent studies, these methods have very limited generalizability and scalability. To address this ...
- 0Citation
MetricsTotal Citations0
- Xuan Chen
- research-articleOpen AccessPublished By ACMPublished By ACM
“I Can’t Believe It’s Not Custodial!”: Usable Trustless Decentralized Key Management
- Tanusree Sharma
Informatics, University of Illinois at Urbana Champaign, United States
, - Vivek C Nair
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, United States
, - Henry Wang
University of Illinois Laboratory High School, United States
, - Yang Wang
University of Illinois at Urbana-Champaign, United States
, - Dawn Song
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, United States
CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems•May 2024, Article No.: 581, pp 1-16• https://doi.org/10.1145/3613904.3642464Key management has long remained a difficult unsolved problem in the field of usable security. While password-based key derivation functions (PBKDFs) are widely used to solve this problem in centralized applications, their low entropy and lack of a ...
- 0Citation
- 1,395
- Downloads
MetricsTotal Citations0Total Downloads1,395Last 12 Months1,395Last 6 weeks271- 2
- Tanusree Sharma
- research-article
Berkeley Open Extended Reality Recordings 2023 (BOXRR-23): 4.7 Million Motion Capture Recordings from 105,000 XR Users
- Vivek Nair
UC Berkeley, US
, - Wenbo Guo
Purdue University, US
, - Rui Wang
Carnegie Mellon, US
, - James F. O'Brien
UC Berkeley, US
, - Louis Rosenberg
Unanimous AI, US
, - Dawn Song
UC Berkeley, US
IEEE Transactions on Visualization and Computer Graphics, Volume 30, Issue 5•May 2024, pp 2239-2246 • https://doi.org/10.1109/TVCG.2024.3372087Extended reality (XR) devices such as the Meta Quest and Apple Vision Pro have seen a recent surge in attention, with motion tracking “telemetry” data lying at the core of nearly all XR and metaverse experiences. Researchers are just beginning to ...
- 0Citation
MetricsTotal Citations0
- Vivek Nair
- research-articleOpen Access
Truth in Motion: The Unprecedented Risks and Opportunities of Extended Reality Motion Data
- Vivek Nair
University of California, Berkeley, Berkeley, CA, USA
, - Louis Rosenberg
Unanimous AI, Pismo Beach, CA, USA
, - James F. O’Brien
Computer Science, University of California, Berkeley, Berkeley, CA, USA
, - Dawn Song
Computer Science, University of California, Berkeley, Berkeley, CA, USA
IEEE Security and Privacy, Volume 22, Issue 1•Jan.-Feb. 2024, pp 24-32 • https://doi.org/10.1109/MSEC.2023.3330392Motion-tracking telemetry data lie at the core of most modern extended reality (XR) and metaverse experiences. Recent studies have demonstrated that motion data have the potential to profile and deanonymize XR users, posing a threat to privacy in the ...
- 0Citation
MetricsTotal Citations0
- Vivek Nair
- research-articleOpen AccessPublished By ACMPublished By ACM
Going Incognito in the Metaverse: Achieving Theoretically Optimal Privacy-Usability Tradeoffs in VR
- Vivek C Nair
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, United States
, - Gonzalo Munilla-Garrido
Department of Computer Science, Technical University of Munich, Germany
, - Dawn Song
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, United States
UIST '23: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology•October 2023, Article No.: 61, pp 1-16• https://doi.org/10.1145/3586183.3606754Virtual reality (VR) telepresence applications and the so-called “metaverse” promise to be the next major medium of human-computer interaction. However, with recent studies demonstrating the ease at which VR users can be profiled and deanonymized, ...
- 18Citation
- 1,876
- Downloads
MetricsTotal Citations18Total Downloads1,876Last 12 Months1,484Last 6 weeks134- 1
Supplementary Material3606754.zip
- Vivek C Nair
- research-article
PATROL: provable defense against adversarial policy in two-player games
- Wenbo Guo
UC Berkeley
, - Xian Wu
Northwestern University
, - Lun Wang
UC Berkeley
, - Xinyu Xing
Northwestern University
, - Dawn Song
UC Berkeley
SEC '23: Proceedings of the 32nd USENIX Conference on Security Symposium•August 2023, Article No.: 221, pp 3943-3960Recent advances in deep reinforcement learning (DRL) takes artificial intelligence to the next level, from making individual decisions to accomplishing sophisticated tasks via sequential decision makings, such as defeating world-class human players in ...
- 0Citation
MetricsTotal Citations0
- Wenbo Guo
- abstractPublished By ACMPublished By ACM
ConsensusDay '22: ACM Workshop on Developments in Consensus
- Jorge M. Soares
Protocol Labs, San Francisco, CA, USA
, - Dawn Song
University of California, Berkeley, Berkeley, CA, USA
, - Marko Vukolic
Protocol Labs, San Francisco, CA, USA
CCS '22: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security•November 2022, pp 3543-3544• https://doi.org/10.1145/3548606.3563286Consensus - loosely defined as global agreement on the state of a decentralised network across its mutually untrusting participants - is an essential ingredient for decentralisation. At the same time, its scalability remains the Achilles' heel of ...
- 0Citation
- 115
- Downloads
MetricsTotal Citations0Total Downloads115Last 12 Months19Last 6 weeks5
- Jorge M. Soares
- research-articleOpen AccessPublished By ACMPublished By ACM
zkBridge: Trustless Cross-chain Bridges Made Practical
- Tiancheng Xie
UC Berkeley, Berkeley, CA, USA
, - Jiaheng Zhang
UC Berkeley, Berkeley, CA, USA
, - Zerui Cheng
Tsinghua University, Beijing, China
, - Fan Zhang
Yale University, New Haven, CT, USA
, - Yupeng Zhang
Texas A&M University, College Station, TX, USA
, - Yongzheng Jia
UC Berkeley, Berkeley, CA, USA
, - Dan Boneh
Stanford University, Stanford, CA, USA
, - Dawn Song
UC Berkeley, Berkeley, CA, USA
CCS '22: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security•November 2022, pp 3003-3017• https://doi.org/10.1145/3548606.3560652Blockchains have seen growing traction with cryptocurrencies reaching a market cap of over 1 trillion dollars, major institution investors taking interests, and global impacts on governments, businesses, and individuals.
Also growing significantly is ...
- 55Citation
- 7,741
- Downloads
MetricsTotal Citations55Total Downloads7,741Last 12 Months5,758Last 6 weeks1,441
- Tiancheng Xie
- research-articleOpen AccessPublished By ACMPublished By ACM
Cerberus: A Formal Approach to Secure and Efficient Enclave Memory Sharing
- Dayeol Lee
University of California, Berkeley, Berkeley, CA, USA
, - Kevin Cheang
University of California, Berkeley, Berkeley, CA, USA
, - Alexander Thomas
University of California, Berkeley, Berkeley, CA, USA
, - Catherine Lu
University of California, Berkeley, Berkeley, CA, USA
, - Pranav Gaddamadugu
University of California, Berkeley, Berkeley, CA, USA
, - Anjo Vahldiek-Oberwagner
Intel Labs, Hillsboro, OR, USA
, - Mona Vij
Intel Labs, Hillsboro, OR, USA
, - Dawn Song
University of California, Berkeley, Berkeley, CA, USA
, - Sanjit A. Seshia
University of California, Berkeley, Berkeley, CA, USA
, - Krste Asanovic
University of California, Berkeley, Berkeley, CA, USA
CCS '22: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security•November 2022, pp 1871-1885• https://doi.org/10.1145/3548606.3560595Hardware enclaves rely on a disjoint memory model, which maps each physical address to an enclave to achieve strong memory isolation. However, this severely limits the performance and programmability of enclave programs. While some prior work proposes ...
- 3Citation
- 1,369
- Downloads
MetricsTotal Citations3Total Downloads1,369Last 12 Months585Last 6 weeks88
- Dayeol Lee
- research-article
Characterizing Attacks on Deep Reinforcement Learning
- Xinlei Pan
University of California, Berkeley, Berkeley, CA, USA
, - Chaowei Xiao
NVIDIA & Arizona State University, Tempe, AZ, USA
, - Warren He
University of California, Berkeley, Berkeley, CA, USA
, - Shuang Yang
Alibaba, Hangzhou, China
, - Jian Peng
University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA
, - Mingjie Sun
Carnegie Mellon University, Pittsburgh, PA, USA
, - Mingyan Liu
University of Michigan, Ann Arbor, Ann Arbor, MI, USA
, - Bo Li
University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA
, - Dawn Song
University of California, Berkeley, Berkeley, CA, USA
AAMAS '22: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems•May 2022, pp 1010-1018Recent studies show that Deep Reinforcement Learning (DRL) models are vulnerable to adversarial attacks, which attack DRL models by adding small perturbations to the observations. However, some attacks assume full availability of the victim model, and ...
- 1Citation
- 75
- Downloads
MetricsTotal Citations1Total Downloads75Last 12 Months15- 1
Supplementary Materialfp350aux.zip
- Xinlei Pan
- research-article
Parallel and Asynchronous Smart Contract Execution
- Jian Liu
Zhejiang University, Hangzhou, China
, - Peilun Li
Tsinghua University, Beijing, China
, - Raymond Cheng
University of San Francisco, San Francisco, CA, USA
, - N. Asokan
University of Waterloo, Waterloo, ON, Canada
, - Dawn Song
University of California, Berkeley, Berkeley, CA, USA
IEEE Transactions on Parallel and Distributed Systems, Volume 33, Issue 5•May 2022, pp 1097-1108 • https://doi.org/10.1109/TPDS.2021.3095234Today's blockchains suffer from low throughput and high latency, which impedes their widespread adoption of more complex applications like smart contracts. In this article, we propose a novel paradigm for smart contract execution. It distinguishes ...
- 5Citation
MetricsTotal Citations5
- Jian Liu
- research-articleOpen AccessPublished By ACMPublished By ACM
Doubly Efficient Interactive Proofs for General Arithmetic Circuits with Linear Prover Time
- Jiaheng Zhang
University of California, Berkeley, Berkeley, CA, USA
, - Tianyi Liu
Texas A&M University, College Station, TX, USA
, - Weijie Wang
Shanghai Jiao Tong University, Shanghai, China
, - Yinuo Zhang
University of California, Berkeley, Berkeley, CA, USA
, - Dawn Song
University of California, Berkeley, Berkeley, CA, USA
, - Xiang Xie
Shanghai Key Laboratory of Privacy-Preserving Computation, Shangha, China
, - Yupeng Zhang
Texas A&M University, College Station, TX, USA
CCS '21: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security•November 2021, pp 159-177• https://doi.org/10.1145/3460120.3484767We propose a new doubly efficient interactive proof protocol for general arithmetic circuits. The protocol generalizes the interactive proof for layered circuits proposed by Goldwasser, Kalai and Rothblum to arbitrary circuits, while preserving the ...
- 30Citation
- 1,388
- Downloads
MetricsTotal Citations30Total Downloads1,388Last 12 Months444Last 6 weeks55- 1
Supplementary Materialccs21-fp265.mp4
- Jiaheng Zhang
- keynotePublished By ACMPublished By ACM
Towards Building a Responsible Data Economy
- Dawn Song
University of California, Berkeley, Berkeley, CA, USA
CCS '21: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security•November 2021, pp 3-3• https://doi.org/10.1145/3460120.3482789Data is a key driver of modern economy and AI/machine learning, however, a lot of this data is sensitive and handling the sensitive data has caused unprecedented challenges for both individuals and businesses. These challenges will only get more severe ...
- 0Citation
- 458
- Downloads
MetricsTotal Citations0Total Downloads458Last 12 Months45Last 6 weeks10
- Dawn Song
- abstractPublished By ACMPublished By ACM
ADVM'21: 1st International Workshop on Adversarial Learning for Multimedia
- Aishan Liu
Beihang University, Beijing, China
, - Xinyun Chen
University of California, Berkeley, Berkeley, CA, USA
, - Yingwei Li
Johns Hopkins University, Baltimore, MD, USA
, - Chaowei Xiao
NVIDIA Research, Phoenix, AZ, USA
, - Xun Yang
National University of Singapore, Singapore, Singapore
, - Xianglong Liu
Beihang University, Beijing, China
, - Dawn Song
University of California, Berkeley, Berkeley, CA, USA
, - Dacheng Tao
JD Explore Academy, Beijing, China
, - Alan Yuille
Johns Hopkins University, Baltimore, MD, USA
, - Anima Anandkumar
California Institute of Technology, Los Angeles, CA, USA
MM '21: Proceedings of the 29th ACM International Conference on Multimedia•October 2021, pp 5686-5687• https://doi.org/10.1145/3474085.3478572Deep learning has achieved significant success in multimedia fields involving computer vision, natural language processing, and acoustics. However research in adversarial learning also shows that they are highly vulnerable to adversarial examples. ...
- 0Citation
- 133
- Downloads
MetricsTotal Citations0Total Downloads133Last 12 Months16Last 6 weeks4
- Aishan Liu
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
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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.
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- 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.
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- 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