Семинары

International KDD Workshop on Data Science for Social Good 2021

Технические науки
COVID-19, Анализ данных, Дистанционное обучение, Математическое моделирование, Наукометрия
Сингапур
14 августа 2021

Форма участия: Дистанционная

Срок подачи заявок: 20.05.2021

Организаторы: Virtual

emal: [email protected]

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Topics of interest include, but are not limited to:Applications of Learning and Optimization in Societally Beneficial Domains
ML Approaches for COVID-19 and Epidemics
Cybersecurity
Security applications of machine learning
Adversarial/robust learning
Data Science for environmental crime
Explainable Artificial Intelligence and Machine Learning
Data Science for Environmental Sustainability
Data Science for Urban Planning
Computational Sustainability
Data Science for Education
Data Science for Public Health
Data Science for International Relations
Data Science for Democracy in the Developing World

Submission Details
We solicit papers in two categories:
1. Research papers describing novel contributions in either the development of data science techniques (motivated by societal applications), or their deployment in practice. Both work in progress and recently published work will be considered. Submissions describing recently published work should clearly indicate the earlier venue and provide a link to the published paper. Papers in this category should be in standard KDD format, and at most 8 pages in length, with one additional page containing only references.
2. Position papers describing open problems or neglected perspectives on the field, proposing ideas for bringing data science methods into a new application area, or summarizing the focus areas of a group working on data science for social good. Papers in this category should be at most 4 pages (in KDD format), with one additional page containing only references.
All papers should be submitted in KDD format. Accepted papers will be selected for oral and poster presentation based on peer review. Submissions are not double-blind; the submitted paper should include author names and affiliations.

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