https://www.bibsonomy.org/tag/rulesBibSonomy RSS Feed for /tag/ruleshttps://journals.lib.washington.edu/index.php/acro/article/view/14236katarinamajdak2026-03-14T10:46:55+01:00Anglo-American Bibliographic Genre Records Requirements cataloging for rules <a itemprop="url" data-versiondate="2026-03-14T10:46:55+01:00" href="https://journals.lib.washington.edu/index.php/acro/article/view/14236" rel="nofollow" class="description-link">https://journals.lib.washington.edu/index.php/acro/article/view/14236</a>«Since the federated learning, which makes AI learning possible without moving local data around, was introduced by google in 2017 it has been actively studied particularly in the field of medicine. In fact, the idea of machine learning in AI without collecting data from local clients is very attractive because data remain in local sites. However, federated learning techniques still have various open issues due to its own characteristics such as non identical distribution, client participation management, and vulnerable environments. In this presentation, the current issues to make federated learning flawlessly useful in the real world will be briefly overviewed. They are related to data/system heterogeneity, client management, traceability, and security. Also, we introduce the modularized federated learning framework, we currently develop, to experiment various techniques and protocols to find solutions for aforementioned issues. The framework will be open to public after development completes».https://editorialia.com/2022/05/29/publications-r0identifier_507c2e7fe07ef9a317eb4c7a51869fdc-federated-learning-issues-in-medical-application/thebibleofai2022-05-29T10:24:02+02:00ai artificialintelligence datascience law rules <span itemprop="description">«Since the federated learning, which makes AI learning possible without moving local data around, was introduced by google in 2017 it has been actively studied particularly in the field of medicine. In fact, the idea of machine learning in AI without collecting data from local clients is very attractive because data remain in local sites. However, federated learning techniques still have various open issues due to its own characteristics such as non identical distribution, client participation management, and vulnerable environments. In this presentation, the current issues to make federated learning flawlessly useful in the real world will be briefly overviewed. They are related to data/system heterogeneity, client management, traceability, and security. Also, we introduce the modularized federated learning framework, we currently develop, to experiment various techniques and protocols to find solutions for aforementioned issues. The framework will be open to public after development completes».</span>https://github.com/rectorphp/rectorbrightbyte2021-02-25T12:27:27+01:00analysis code conversion manipulation php rules <a itemprop="url" data-versiondate="2021-02-25T12:27:27+01:00" href="https://github.com/rectorphp/rector" rel="nofollow" class="description-link">https://github.com/rectorphp/rector</a>https://github.com/github/codeqljil2020-10-01T10:36:20+02:00analysis code codeql github rules ruleset scanning security static <a itemprop="url" data-versiondate="2020-10-01T10:36:20+02:00" href="https://github.com/github/codeql" rel="nofollow" class="description-link">https://github.com/github/codeql</a>http://erikhollnagel.com/ideas/etto-principle/index.htmlschmidt22019-07-30T13:31:40+02:00efficiency erik_hollnagel etto principles productivity rules <a itemprop="url" data-versiondate="2019-07-30T13:31:40+02:00" href="http://erikhollnagel.com/ideas/etto-principle/index.html" rel="nofollow" class="description-link">http://erikhollnagel.com/ideas/etto-principle/index.html</a>Cucumber. Simple, Human Collaborationhttps://cucumber.io/posting-rulesschmidt22017-07-14T14:18:43+02:00best_practices code_of_conduct cucumber email mailinglist posting_rules rules <span itemprop="description">Cucumber. 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