Defense Advanced Research Projects AgencyTagged Content List

Analytics for Data at Massive Scales

Extracting information from large data sets

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The U.S. Government operates globally and frequently encounters so-called “low-resource” languages for which no automated human language technology capability exists. Historically, development of technology for automated exploitation of foreign language materials has required protracted effort and a large data investment. Current methods can require multiple years and tens of millions of dollars per language—mostly to construct translated or transcribed corpora.
Historically, the U.S. Government deployed and operated a variety of collection systems that provided imagery with assured integrity. In recent years however, consumer imaging technology (digital cameras, mobile phones, etc.) has become ubiquitous, allowing people the world over to take and share images and video instantaneously. Mirroring this rise in digital imagery is the associated ability for even relatively unskilled users to manipulate and distort the message of the visual media.
The goal of the Modeling Adversarial Activity (MAA) program is to develop mathematical and computational techniques for modeling adversarial activity for the purpose of producing high-confidence indications and warnings of efforts to acquire, fabricate, proliferate, and/or deploy weapons of mass terror (WMTs). MAA assumes that an adversary’s WMT activities will result in observable transactions.
Warfighters encounter foreign language images in many forms, including captured paper documents and computer files. Given the quantity of foreign-language material and the scarcity of linguists, military personnel and analysts can find it difficult to identify, translate and interpret important information in a timely fashion. What these personnel and analysts have lacked to date is the capability to automatically and rapidly convert foreign-language text images into English transcripts that provide relevant, distilled and actionable information.
DARPA's Oceans of Things program seeks to enable persistent maritime situational awareness over large ocean areas by deploying thousands of small, low-cost floats that could form a distributed sensor network. Each smart float would contain a suite of commercially available sensors to collect environmental data-such as ocean temperature, sea state, and location-as well as activity data about commercial vessels, aircraft, and even maritime mammals moving through the area. The floats would transmit data periodically via satellite to a cloud network for storage and real-time analysis.