Reginald Hobbs

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We develop data retrieval algorithms for crowd-sensing applications that reduce the underlying network bandwidth consumption or any additive cost metric by exploiting logical dependencies among data items, while maintaining the level of service to the client applications. Crowd sensing applications refer to those where local measurements are performed by(More)
—The paper describes a novel algorithm for timely sensor data retrieval in resource-poor environments under freshness constraints. Consider a civil unrest, national security, or disaster management scenario, where a dynamic situation evolves and a decision-maker must decide on a course of action in view of latest data. Since the situation changes, so is the(More)
Progress in the Machine Translation (MT) research community, particularly for statistical approaches, is intensely data-driven. Acquiring source language documents for testing, creating training datasets for customized MT lexicons, and building parallel corpora for MT evaluation require translators and non-native speaking analysts to handle large document(More)
Background • Who we are in the Multilingual Computing Branch • What we do – Basic and Applied Research in HLT – Research in MT for low density languages customized for military applications. – Engineering Lead for Sequoyah Program • What resources do we have available – Configurable, distributed MT testbed with COTS and GOTS systems – Linguistic data(More)
The paper considers the challenge of maximizing the quality of information collected to meet decision needs of real-time Internet-of-Things applications. A novel scheduling model is proposed, where applications need multiple data items to make decisions, and where individual data items can be captured at different levels of quality. We assume the existence(More)
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