ACM SIGs may authorize the posting of Tables of Contents of their scheduled upcoming sponsored conference proceedings with Author-Ized links enabling free full-text downloads from the ACM Digital Library.
Effective as of 2014, these OpenTOCs may be kept permanently on the conference site or the SIG site. The SIG must decide which site(s) to use.
The sponsoring SIGs may choose OpenTOC for the upcoming volume (rolling off after 12 months), or a permanent OpenTOC that remains permanently on the chosen site(s) to build up a local series archive, or no OpenTOC at all.
For co-sponsored conferences, all co-sponsors must agree to the posting and each co-sponsor may choose its site(s).
ACM HQ will be informed of the site(s), prepare the OpenTOC, and deliver it to the designated contact person for each conference.
If the SIG authorizes posting the OpenTOC, the designated conference leader should carry out that decision for each given volume of proceedings.
If a SIG authorizes a permanent OpenTOC for a given volume, rather than a rolling annual OpenTOC, it is advisable to place it on the site where it is most likely to be maintained.
Full-text downloads from the ACM Digital Library via OpenTOCs, Author-Izer, OpenSurround, OA, SIG membership, and Free articles are all being tracked and will be regularly reported to the SGB separately from downloads via subscription.
Updated March 2016
Policy Adopted May 2015
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.
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