Communications of the ACM

Journal Information
ISSN / EISSN : 0001-0782 / 1557-7317
Total articles ≅ 13,922
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Latest articles in this journal

Logan Kugler
Communications of the ACM, Volume 65, pp 21-23; https://doi.org/10.1145/3530687

Abstract:
Labor shortages have many companies turning to automation technology, but with mixed outcomes.
Peter G. Neumann
Communications of the ACM, Volume 65, pp 32-35; https://doi.org/10.1145/3532631

Abstract:
Considering how to achieve the long-term goal to systemically reduce risks.
Andrei Sukhov
Communications of the ACM, Volume 65, pp 14-15; https://doi.org/10.1145/3530685

Abstract:
The Communications website, http://cacm.acm.org, features more than a dozen bloggers in the [email protected] community. In each issue of Communications , we'll publish selected posts or excerpts. twitter Follow us on Twitter at http://twitter.com/blogCACM http://cacm.acm.org/blogs/blog-cacm Andrei Sukhov considers why and how the foundations of teaching mathematics for information technology specialties need to be revised.
Gabriele Kotsis, Vicki L. Hanson
Communications of the ACM, Volume 65, pp 7-7; https://doi.org/10.1145/3533677

Nihar B. Shah
Communications of the ACM, Volume 65, pp 76-87; https://doi.org/10.1145/3528086

Abstract:
Improving the peer review process in a scientific manner shows promise.
Julia Stoyanovich, Serge Abiteboul, Bill Howe, H. V. Jagadish, Sebastian Schelter
Communications of the ACM, Volume 65, pp 64-74; https://doi.org/10.1145/3488717

Abstract:
Perspectives on the role and responsibility of the data-management research community in designing, developing, using, and overseeing automated decision systems.
Dhruv Jain, Hung Ngo, Pratyush Patel, Steven Goodman, Khoa Nguyen, Rachel Grossman-Kahn, Leah Findlater, Jon Froehlich
Communications of the ACM, Volume 65, pp 100-108; https://doi.org/10.1145/3531447

Abstract:
Smartwatches have the potential to provide glanceable, always-available sound feedback to people who are deaf or hard of hearing (DHH). We present SoundWatch, a smartwatch-based deep learning application to sense, classify, and provide feedback about sounds occurring in the environment. To design SoundWatch, we first examined four low-resource sound classification models across four device architectures: watch-only, watch+phone, watch+phone+cloud, and watch+cloud. We found that the best model, VGG-lite, performed similar to the state of the art for nonportable devices although requiring substantially less memory (∼1/3 rd ) and that the watch+phone architecture provided the best balance among CPU, memory, network usage, and latency. Based on these results, we built and conducted a lab evaluation of our smartwatch app with eight DHH participants. We found support for our sound classification app but also uncovered concerns with misclassifications, latency, and privacy.
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