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TSUNAMI: Topical Social Understanding via Natural-Language Analytics For Megacity Interventions

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911QX-17-P-0233
Agency Tracking Number: A171-036-0438
Amount: $99,995.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: A17-036
Solicitation Number: 2017.1
Timeline
Solicitation Year: 2017
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-08-30
Award End Date (Contract End Date): 2018-02-28
Small Business Information
1422 Sachem Pl., Unit #1
Charlottesville, VA 22901
United States
DUNS: 809180151
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Kolia Sadeghi
 Applied Mathematician
 (434) 284-9430
 kolia.sadeghi@ccri.com
Business Contact
 Heather Stamper
Phone: (434) 284-9444
Email: contracts@ccri.com
Research Institution
N/A
Abstract

Conducting operations in a Megacity will require new tactics involving cooperation with the local population. This necessitates gaining an understanding of the socio-cultural features of an area. The proposed TSUNAMI system will provide commanders with geographically referenced socio-cultural feature layers for publication on a Common Operational Picture display. These features will be derived from existing Civil Affairs data sources fused with open source data such as news feeds, Twitter, and Wikipedia. To achieve this, TSUNAMI will partition the city into a set of zones based on the existing spatial data. Textual data from the sources described earlier will then be processed by a novel, deep-learning based, Topic Modeling system. Each of the zones will be evaluated against the resulting topics. Each topics layer will be evaluated to determine how spatially relevant the topic is, and topics with high spatial relevance will be published to the Common Operational Picture. A spatial predictive model will also be run on these published layers to improve predictive accuracy and demonstrate the importance of these features.

* Information listed above is at the time of submission. *

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