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Suggested Citation:"Appendix E: Acronyms." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
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E

Acronyms

AIDA Active Interpretation of Disparate Alternatives
CISE Directorate for Computer and Information Science and Engineering
DARPA Defense Advanced Research Projects Agency
DEFT Deep Exploration and Filtering of Text
DIVA Deep Intermodal Video Analytics
DOD Department of Defense
DOE Department of Energy
IARPA Intelligence Advanced Research Projects Activity
ICSB Intelligence Community Studies Board
LORELEI Low Resource Languages for Emergent Incidents
MediFor Media Forensics
NIST National Institute of Standards and Technology
NSF National Science Foundation
ODNI Office of the Director of National Intelligence
OUSD(I) Office of the Under Secretary of Defense for Intelligence
TREC Text REtrieval Conference
Suggested Citation:"Appendix E: Acronyms." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
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Suggested Citation:"Appendix E: Acronyms." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×
Page 59
Suggested Citation:"Appendix E: Acronyms." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
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Page 60
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The Intelligence Community Studies Board of the National Academies of Sciences, Engineering, and Medicine convened a workshop on August 9-10, 2017 to examine challenges in machine generation of analytic products from multi-source data. Workshop speakers and participants discussed research challenges related to machine-based methods for generating analytic products and for automating the evaluation of these products, with special attention to learning from small data, using multi-source data, adversarial learning, and understanding the human-machine relationship. This publication summarizes the presentations and discussions from the workshop.

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