CoNEXT welcomes the submission of both long and short papers. All the submissions should be double-anonymous (more instructions below) and will be peer-reviewed. Per the anonymity guidelines, you must remove any author names.
Long papers are the more traditional form to present technical work, and can have max 16 pages for main content, with unlimited pages for references and up to four pages for appendices.
Short papers are the preferred vehicle for contributions whose novelty and impact show the same technical excellence, and whose description does not need 16 pages. Short papers should be no more than 10 pages, with unlimited pages for references and up to two pages for appendices.
Upon acceptance, both short and long papers will be scheduled for publication in the closest issue of the Proceedings of the ACM on Networking (PACMNET).
Submit your contributions to https://conext25-june.hotcrp.com/.
Both long and short submissions use the same template!
Both types of submission needs to be double-anonymous.
Long papers are the more traditional form to present technical work.
Long papers need to adopt the single-column, 10pt, ACM acmsmall template.
ACM has partnered with Overleaf, a free cloud-based, collaborative authoring tool, to provide an ACM LaTeX authoring template. You can find it at this link.
When opening the template in overleaf, please checkout the sample-acmsmall.tex and set up the latext class as \documentclass[acmsmall,anonymous,review]{acmart} (by default the template is already with 10pt font size).
Complementary to the new template, the following rules apply
Short papers are the preferred vehicle for contributions whose novelty and impact show the same technical excellence, and whose description does not need 16 pages.
Short papers need to adopt the single-column, 10pt, ACM acmsmall template.
Short papers will be reviewed with a more open mind towards the scope of evaluation or breadth of topics compared to long papers. Note that position papers, critiques of networking research, and ideas that are not yet fully complete or evaluated are a better fit for the HotNets workshop. We welcome experience submissions that clearly articulate lessons learned, as well as submissions that refute prior published results.
ACM has partnered with Overleaf, a free cloud-based, collaborative authoring tool, to provide an ACM LaTeX authoring template. You can find it at this link.
When opening the template in overleaf, please checkout the sample-acmsmall.tex and set up the latext class as \documentclass[acmsmall,anonymous,review]{acmart} (by default the template is already with 10pt font size).
Complementary to the new template, the following rules apply
All submitted papers will be assessed through a double-anonymous review process. This means that the authors do not see who are the reviewers and the reviewers do not see who are the authors. As an author, you should do your best to ensure that your paper submission does not directly or indirectly reveal the authors' identities. The following steps are minimal requirements for a double-anonymous submission:
At the same time, PC members should not actively try to de-anonymize the authors' identities. Any violation of the double-anonymous reviewing process should be reported to the PC chairs.
While generative AI systems—such as large language models (LLMs)—are powerful and useful tools, there remain open questions as to how and which data they use to train, raising potential concerns over integrity and confidentiality. The ACM has established a set of guidelines pertaining to how authors and reviewers can use generative AI. We summarize some of the main points below, but we refer authors to the following links. Failure to adhere to these guidelines can result in paper rejection.
By authors: The ACM's author guidelines state that authors are allowed to use generative AI in limited ways, and that their use must be acknowledged in the paper. For small portions of text (phrases or sentences), papers must include a footnote stating that it was generated by AI; for larger portions of text, graphics, and other content, authors must disclose in the Appendix what content was generated by an AI, along with the specific tools and versions used. Please refer to the ACM guidelines for more specific instructions.
By reviewers: Reviewers are not allowed to upload any portion of the submitted papers to generative AI tools. However, according to the ACM's peer review guidelines, reviewers may upload the content of their own reviews to generative AI "with the sole purpose of improving the quality and readability of reviewer reports for the author, provided any and all parts of the review that would potentially identify the submission, author identities, reviewer identity, or other confidential content is removed prior to uploading into third party tools."