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Learning to Understand Natural Language with Less Human Effort
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In: DTIC (2015)
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Enabling Efficient Intelligence Analysis in Degraded Environments
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In: DTIC (2013)
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Automated Extraction and Characterisation of Social Network Data from Unstructured Sources -- An Ontology-Based Approach
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In: DTIC (2013)
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Expanding the Toolkit and Resource Environment to Assist Translation (TREAT) and Its User Base
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In: DTIC (2011)
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Composite Artistry Meets Facial Recognition Technology: Exploring the Use of Facial Recognition Technology to Identify Composite Images
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In: DTIC (2011)
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Transfer Learning in Integrated Cognitive Systems
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In: DTIC (2010)
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Entity Profiling for Intelligence Using the Graphical Overview of Social and Semantic Interactions of People (GOSSIP) Software Tool
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In: DTIC (2010)
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Teaching Intelligence Analysis with TIACRITIS
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In: DTIC (2010)
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TIACRITIS System and Textbook: Learning Intelligence Analysis through Practice
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In: DTIC (2010)
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BASE
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13 |
Crosslingual Audio Information Retrieval Development
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In: DTIC (2009)
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Pictures from Words, Pictures from Text: Constructing Pictorial Representations of Meaning from Text
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In: DTIC (2009)
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BASE
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16 |
Laboratory for Computational Cultural Dynamics
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In: DTIC (2008)
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Detecting Deception in the Military Infosphere: Improving and Integrating Human Detection Capabilities with Automated Tools
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In: DTIC (2007)
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The Knowledge Matrix Approach to Intelligence Fusion
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In: DTIC (2007)
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Abstract:
As the U.S. military transforms to an information-based force, it will need processes and methods to collect, combine, and utilize the intelligence that is generated by its assets. The process known as fusion will play an important role in determining whether this intelligence is used in the most beneficial manner. The process of fusion, combining pieces of information to produce higher-quality information, knowledge, and understanding, is often poorly represented in constructive models and simulations that are used to analyze intelligence issues. This report describes one approach to capturing the fusion process in a constructive simulation, providing detailed examples to aid in further development and instantiation. The sequential fusion method in intended to determine whether separate intelligence observations are close enough geographically, have consistently identified the same battlefield entity, and contain high-quality information, all of which must be considered before fusion of intelligence can occur. The fusion process described in this report is, for the most part, an implicit representation of the generation of battlefield intelligence and can be used in a constructive simulation or fusion model to better understand the dynamics of intelligence-gathering systems and their effect on intelligence metrics. ; Online access|uhttp://www.rand.org/pubs/technical_reports/TR416/
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Keyword:
*COMPUTER PROGRAMS; *FUSIONS; *INFORMATION ASSURANCE; *KNOWLEDGE MANAGEMENT; *MILITARY INTELLIGENCE; *MILITARY'S VISION; *VISION; BATTLEFIELDS; CAISR(COMPUTER INTELLIGENCE SURVEILLANCE AND RECONNAISSANCE); Computer Programming and Software; COP(COMMON OPERATIONAL PICTURE); Information Science; Logistics; Military Facilities and Supplies; MILITARY PERSONNEL; SIMULATION
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URL: http://www.dtic.mil/docs/citations/ADA473459 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA473459
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19 |
Trust in Security-Policy Enforcement Mechanisms
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In: DTIC (2006)
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A Hybrid Approach for QA Track Definitional Questions
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In: DTIC (2006)
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