Submission System

The paper submission is through the CMT portal.

Use the following link to submit your papers: https://cmt3.research.microsoft.com/AIR2023/

A single PDF file must be submitted at the submission portal after filling up the required information.
Initial submission is in single column format without page limit, but the authors need to ensure/check that the same content will fit in a two column format within 6 pages for final camera ready submission.
AIR2023 follows a strict double-blind review process hence author names, affiliations, etc. should not be mentioned or the authors should not reveal their identity in the paper. Papers not adhering to double-blind review guidelines will be REJECTED without review.
Please submit your paper under the category most suitable for it. This will help in selecting suitable reviewers.
Formatting instruction for the submission of accepted papers will be communicated separately.
Accepted papers would be required to add the authors names etc. in the final camera ready copy.

Initial submission is in single column format without page limit, but the authors need to ensure/check that the same content will fit in a two column format within 6 pages for final camera ready submission.
Latex template (preferable) : Download (more details on formatting guidelines can be found here)
- For Initial submission in single column format the authors must use the “manuscript” call when using the ACM LaTeX Authoring template: “\documentclass[manuscript,screen,review]{acmart}” and to check that the same content will fit in a two column format within 6 pages for final camera ready submission authors must use the “sigconf” call when using the ACM LaTeX Authoring template: “\documentclass[sigconf, screen, review]{acmart}”
Microsoft Word template: Download (more details on formatting guidelines can be found here)
Overleaf users: Authors using the online collaborative platform overleaf can use the official ACM template on overleaf here.
It is necessary to include ACM CCS concepts in the paper. Accurate semantic tagging provides a reader with quick content reference; facilitates the ACM DL search for related literature; enables several ACM DL topic functions such as aggregated SIG and journal coverage areas; and helps ACM promote your work in other online resources.