Recording Publication Dates for Articles
Updated July 2018
Official Version of Record Publication Policy
The official publication date of an ACM published article will be considered the date on which the article’s official Version of Record (VoR) is published online in the ACM Digital Library, and the official VoR of an ACM article shall be the final peer reviewed, edited, tagged, and identified (using a DOI or other standardized identifier) version that appears as the final published version in an ACM owned Publication (journal, magazine, conference proceedings, newsletter, book, etc.) inside the ACM Digital Library. For avoidance of doubt, only the official VoR shall be considered the “Published” version of the Work for purposes of attribution, rights & permissions, prior art, investigations into potential ethics & plagiarism violations, and relevant open access embargo periods.
Written by leading domain experts for software engineers, ACM Case Studies provide an in-depth look at how software teams overcome specific challenges by implementing new technologies, adopting new practices, or a combination of both. Often through first-hand accounts, these pieces explore what the challenges were, the tools and techniques that were used to combat them, and the solution that was achieved.
Why I Belong to ACM
Hear from Bryan Cantrill, vice president of engineering at Joyent, Ben Fried chief information officer at Google, and Theo Schlossnagle, OmniTI founder on why they are members of ACM.
ACM Queue’s “Research for Practice” is your number one resource for keeping up with emerging developments in the world of theory and applying them to the challenges you face on a daily basis. In this installment, Dan Crankshaw and Joey Gonzalez provide an overview of machine learning server systems. What happens when we wish to actually deploy a machine learning model to production, and how do we serve predictions with high accuracy and high computational efficiency? Dan and Joey’s curated research selection presents cutting-edge techniques spanning database-level integration, video processing, and prediction middleware. Given the explosion of interest in machine learning and its increasing impact on seemingly every application vertical, it's possible that systems such as these will become as commonplace as relational databases are today.