Updated January 22, 2018
The form of reviewing for most ACM journals and transactions, as well as for some magazines, is single-blind peer review. The "Roles and Responsibilities in ACM Publishing" policy assures that ACM will maintain the anonymity of reviewers. Editors and administrators of ACM publications must keep the identities of all reviewers of particular manuscripts hidden from authors, other reviewers, and the public. To facilitate this, reviewers access and perform their review of the text via a manuscript submission system, and their identities are not released. Identities of reviewers may be divulged to members of a publication's Editorial Board or to ACM staff as needed to solicit expert advice in special circumstances. In such cases, identities of a reviewer may also be made known to other reviewers of the same manuscript, provided that the consent of all affected reviewers is obtained. Reviewers must also maintain the confidentiality of reviewer identities, as well as the reviews themselves, that are communicated to them at any time.
An exception to the anonymity policy is made in the case of review of conference submissions by a program committee. It is permissible to make reviews and the identity of reviewers visible to the entire program committee, provided that all committee members and solicited reviewers are notified in advance of this practice. (A further exception occurs when a program committee member is also an author. Names of reviewers can never be disclosed to the author.) ACM is opposed to any attempt by authors to determine their reviewer's identities, and will not engage in any speculation regarding this.
This policy does not prevent the simple listing of all reviewers of a particular volume or proceedings without reference to particular manuscripts for the purpose of acknowledgement, or the appearance of reviewers names in a composite database for use by the editors.
Created November 19, 2003
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.
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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.