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Essential Guide to ACM Publishing

A straightforward guide to using the ACM article template effectively.

Harsh Rangwani

― 5 min read


ACM Publishing Made ACM Publishing Made Simple and format. Your quick guide to ACM article design
Table of Contents

Welcome to the world of ACM publishing! If you are dipping your toes into this pool for the first time, or if you’ve been here before and just need a quick refresher, this guide will help you make sense of the ACM article template. It's like a trusty map that shows you where to go without the confusing paths.

What Is the ACM Template?

The ACM template is a special set of rules and styles that helps everyone publish their research in a uniform way. Think of it as the outfit you wear when you go to an important event; everyone looks smart, and it makes a good impression. This template covers everything from how to write your title to how to format your References, ensuring that your work looks professional and is easy to read.

Getting Started with the Template

When you start writing, you will use the “acmart” Document Class. This is your main tool for creating documents meant for ACM publications. You can use this document class for various types of content-whether you’re submitting a full technical paper, writing an abstract, or prepping a journal article.

Choosing Your Template Style

The first thing you need to do is select a template style. This is like picking the right recipe based on the dish you want to serve. Different styles exist for different kinds of publications:

  • For Journals:

    • acmsmall: The standard style for most journals.
    • acmlarge: Used by specific journals.
    • acmtog: Another specialized journal style.
  • For Conferences:

    • acmconf: The go-to style for most conference papers.
    • sigchi: For SIGCHI conference papers.
    • sigplan: For SIGPLAN conferences.

Choose the correct style to ensure you are on the right track.

Template Parameters: Tweaking Your Document

Besides picking a style, there are parameters you can set to change a few things about how your document looks. But don’t get too carried away-ACM likes their formatting just so! Here are a few common parameters:

  • anonymous,review: Keeps your identity hidden during the review process.
  • authorversion: Great for sharing your own version online.
  • screen: Adds colorful hyperlinks to your text.

Remember, ACM has some strict rules against modifying certain things like margins and fonts. Stick to the guidelines, and your document will sail right through.

Titles and Authors

When it comes to your title, make sure to capitalize it correctly. This isn’t just about looking fancy; it helps your readers see what your work is about right away. If your title is too long, you’ll need to come up with a shorter version for headers.

When listing authors, write full names. This isn’t the place for nicknames or initials-everyone gets to shine! Also, don’t forget to include email addresses so that readers can reach out to you for more information.

Rights and Responsibilities

Whenever you publish with ACM, you’ll have to fill out a rights form. This isn’t just busywork; it’s to clarify what rights you keep and what rights you give to ACM. Your choices may include copyright transfer or open access options. After filling it out, you will get a copy, which includes important commands that you need to include in your document.

Classifying Your Work

To help people find your work easily, you need to categorize it using the ACM Computing Classification System. This is like adding tags to a social media post-it helps people who are interested in your topic to discover your article! You can also include your own keywords that describe your research.

Organizing Your Document

Your paper should follow a clear structure with sections and subsections. Make sure to number these sections. Don't be tempted to use bold or italics just to act like a section header; use the proper commands instead. Consistency is the name of the game!

Tables and Figures

If you have tables or figures, they should be clear and properly formatted. Place table captions above the table, and for figures, place captions below. Always add figure descriptions for accessibility. This ensures everyone knows what your visuals are about-especially those who can’t see them.

Writing Math Equations

If your work includes math, you can include equations in different styles. For example, inline equations fit naturally within your text, while display equations stand alone and are centered. Just make sure they are easy to read and neatly formatted.

Citations and References

When you reference others’ work, do so with care. Use BibTeX, which helps you manage and format your references neatly. Remember to include full names and detailed information about the sources so that readers can easily follow up if they wish.

Acknowledgments

Don't forget to express gratitude! Mention any individuals or groups that helped you with your research. This section should be placed before your references. Use the special “acks” environment to ensure your thanks are formatted correctly.

Adding an Appendix

If you have extra material that supports your work but doesn’t fit neatly into the main text, you can add an appendix. Just remember to label it, and use letters for section numbering instead of regular numbers.

Special Templates for Extended Abstracts

If you are submitting a SIGCHI Extended Abstract, there are specific templates that allow for unique formatting. You can include things in margins and add sidebars to make your work stand out even more.

Conclusion

Following the ACM article template helps ensure that your work is easily read and understood. Think of it as your trusty guide through the sometimes tricky process of academic publishing. Stick to the rules, keep it organized, and you’ll be well on your way to making a great impression with your research. Happy writing!

Original Source

Title: Learning from Limited and Imperfect Data

Abstract: The datasets used for Deep Neural Network training (e.g., ImageNet, MSCOCO, etc.) are often manually balanced across categories (classes) to facilitate learning of all the categories. This curation process is often expensive and requires throwing away precious annotated data to balance the frequency across classes. This is because the distribution of data in the world (e.g., internet, etc.) significantly differs from the well-curated datasets and is often over-populated with samples from common categories. The algorithms designed for well-curated datasets perform suboptimally when used to learn from imperfect datasets with long-tailed imbalances and distribution shifts. For deep models to be widely used, getting away with the costly curation process by developing robust algorithms that can learn from real-world data distribution is necessary. Toward this goal, we develop practical algorithms for Deep Neural Networks that can learn from limited and imperfect data present in the real world. These works are divided into four segments, each covering a scenario of learning from limited or imperfect data. The first part of the works focuses on Learning Generative Models for Long-Tail Data, where we mitigate the mode-collapse for tail (minority) classes and enable diverse aesthetic image generations as head (majority) classes. In the second part, we enable effective generalization on tail classes through Inductive Regularization schemes, which allow tail classes to generalize as the head classes without enforcing explicit generation of images. In the third part, we develop algorithms for Optimizing Relevant Metrics compared to the average accuracy for learning from long-tailed data with limited annotation (semi-supervised), followed by the fourth part, which focuses on the effective domain adaptation of the model to various domains with zero to very few labeled samples.

Authors: Harsh Rangwani

Last Update: 2024-11-11 00:00:00

Language: English

Source URL: https://arxiv.org/abs/2411.07229

Source PDF: https://arxiv.org/pdf/2411.07229

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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