The Boeing Company

Hrl Laboratories, LLC

Other

Gm Global Technolgoy Operations, Inc.

Raytheon Company

David L Allen

Tsai-Ching Lu

Hankyu Moon

Steven J Yukawa

Timothy McNally Mitchell

Swarup S Medasani

Gavin D Holland

Bruce K Pollock

Yuri Owechko

Timothy William Anstey

Yang Chen

Michael D Howard

Qin Jiang

Suhas E Chelian

Krzysztof Wojtek Przytula

Ryan F Compton

Rajit Jain

Tim W Anstey

Steven R Ecola

Craig A Larson

Kent Loving

Deepak Khosla

Rajan Bhattacharyya

Yilu Zhang

Mutasim Abdurrahman Salman

John Lyle Vian

David J Huber

Jiejun Xu

Aleksey Nogin

Vincent De Sapio

Rashmi N Sundareswara

Matthew E Phillips

Matthias Ziegler

James Benvenuto

Ali Reza Mansouri

John Benjamin Sims, Iii

Alejandro Nijamkin

Eric P Tressler

Steven B Seida

Tuong Q Nguyen

Robert L Steed

Son T Nguyen

Wayne F Cavanaugh

Taji Shafaat

Kevin Leslie Gosling

Steven James Churchill

Linda A Hapgood

David E Savage

John M Cloutier

Denise M Smith

Mary Ann T Nakasone

Edward J Porisch

Bijan Honari

Mary A Nakasone

John R Cowin

Ronald C Doty

Steven J Churchill

Jain Rajit

Steven J Yukuwa

Steven J Churchil

Edward J Porish

Described is a system for geocoding social networks by minimizing total variation. A set of social media data from a social media platform having users is received. A social network is generated from the set of social media data based on communication between users. A ground truth set of locations is determined for users who make their locations public, and the ground truth set of locations is propagated across the social network to generate a location estimate for each user. A confidence measure for each location estimate is then generated.

Per-user accuracy measure for social network based geocoding algorithms

Described is a system for explaining how the human brain represents conceptual knowledge. A semantic model is developed, and a behavioral exam is performed to assess a calibration subject into a cohort and reveal semantic relationships to modify a personalized semantic space developed by the semantic model. Semantic features are extracted from the personalized semantic space. Neural features are extracted from neuroimaging of the human subject. A neuroceptual lattice is created having nodes representing attributes by aligning the semantic features and the neural features. Structures in the neuroceptual lattice are identified to quantify an extent to which the set of neural features represents a target concept. The identified structures are used to interpret conceptual knowledge in the brain of a test subject.

Method and system to predict and interpret conceptual knowledge in the brain

Described is a system for early detection of events via social media mining. The system receives, as input, social media blog posts comprising textual data. The system processes the social media blog posts through a cascade of filters. The cascade of filters comprises an event term detection filter, a location term detection filter following the event term detection filter, and a future date detection filter following the location term detection filter. A plurality of candidate social media blog posts describing an event of interest on a future date is output to a user for further analysis.

Social media mining system for early detection of civil unrest events

A method and apparatus for improving a performance of an antenna system. An influence network is generated for an array of elements in the antenna system using a reconfiguration algorithm. The influence network indicates an influence of an undesired event occurring at any element in the array of elements on a remaining portion of the array of elements given a current state of the array of elements. A relative ranking of vulnerability is created for elements in the array of elements based on the influence network. The reconfiguration algorithm is modified to take into account the relative ranking of vulnerability to form a modified reconfiguration algorithm. The antenna system using the modified reconfiguration algorithm to compensate for undesired events improves the performance of the antenna system.

Method and apparatus for modifying a reconfiguration algorithm for an antenna system

Described is a system and method for automated discovery of unknown patterns from multiple heterogeneous datasets in support of decision making. The system processes input data with a coarse-graining module to generate multi-scale symbolic representations of the input data having various levels of granularity. If the input data contains missing data, then values are computed for the missing data with an effective information transfer module. The multi-scale symbolic representations are sent to a local information dynamics module and a non-myopic value of information module. Previously unknown patterns in the multi-scale symbolic representations are discovered and extracted with the local information dynamics module. The previously unknown patterns and the multi -scale symbolic representations of the input data are analyzed, and the value of acquiring new input data is ranked with the non-myopic value of information module.

Multi-scale information dynamics for decision making

Failure in a self-healing array may be handled by: detecting a failing element of the self-healing array by monitoring characteristics of the failing element; auto-correcting a failing element of the self-healing array by adjusting characteristics of the failing element to compensate for a portion of the failing element which is failing; or correcting performance of the self-healing array when one or more elements of the self-healing array fail by detecting and modeling an impact of the one or more elements of the self-healing array which failed on the performance of the self-healing array.

Self-healing array system and method

A method for optimizing a phased array antenna. A predicted future health state for elements in the phased array antenna is identified. A configuration for the elements to use a radiation pattern based on the predicted future health state for the elements taking into account potential degradation of a group of the elements in the predicted future health state is also identified.

Antenna array optimization system

Described is a system for detecting group behaviors in dynamic relational data by monitoring individual events of interest. Data is collected from a domain of interest at predetermined time intervals. Examples of domains of interest include internet data, video behavior analysis, social networks, and diagnosis and prognosis. The data is then monitored for at least one local event of interest defined by a user. The system is configured to analyze a relationship between at least two monitored local events of interest. Finally, a visual representation of the relationship between the monitored local events of interest is generated and presented to the user for further analysis. Also described is a method and computer program product for detecting group behaviors in data.

Mining group patterns in dynamic relational data via individual event monitoring

A method and system for video-content based retrieval is described. A query video depicting an activity is processed using interest point selection to find locations in the video that are relevant to that activity. A set of spatio-temporal descriptors such as self-similarity and 3-D SIFT are calculated within a local neighborhood of the set of interest points. An indexed video database containing videos similar to the query video is searched using the set of descriptors to obtain a set of candidate videos. The videos in the video database are indexed hierarchically using a vocabulary tree or other hierarchical indexing mechanism.

Video content-based retrieval

Described is a system for supporting human intelligence analysis. The system detects changes in social relations among users within a dynamic information network and enables understanding of a current social situation in the dynamic information network through multiple integrated modules. An active network mining module identifies incomplete data that is related to at least one change in the social relations and resolves conflicting and missing data in the dynamic information network. A relevant network discovery module constructs a relevant network from hidden relations within the dynamic information network. An information-aware social network module constructs an information-aware social network using the relevant network, then classifies and prioritizes items of interest to provide an assessment of a current social situation to a user.

System and method of computational social network development environment for human intelligence

Described is a cyber security system for digital artifact genetic modeling and forensic analysis. The system identifies the provenance (origin) of a digital artifact by first receiving a plurality of digital artifacts, each digital artifact possessing features. Raw features are extracted from the digital artifacts. The raw features are classified into descriptive genotype-phonotype structures. Finally, lineage, heredity, and provenance of the digital artifacts are determined based on mapping of the genotype-phenotype structures.

System and methods for digital artifact genetic modeling and forensic analysis

The present invention is directed to a data processing apparatus and a computer implemented method for modeling and analyzing relational data represented in a network that includes a plurality of nodes and a plurality of connections between the nodes. The method includes assigning at least one weight to a connection between two nodes in the network. A set of possible dendrograms is then generated for the network, and a likelihood of each dendrogram in the set is determined. The determination of the likelihood is based on at least the one weight of the connection. One of the dendrograms from the set is selected as an optimal dendrogram based on the determined likelihood. The selected dendrogram is then output via an output device. The dendrogram may be used to predict missing links or identify any possible false-positive (noisy) links within a relational dataset.

System and method for modeling and analyzing data via hierarchical random graphs

Described is a system for detecting insider threats in a network. In detecting the insider threat, the system receives data from the network relevant to network activity and extracts observable actions from the data relevant to a mission. The observable actions are combined to provide contextual cues and reasoning results. Based on the observable actions and reasoning results, proposed security policy updates are proposed to force insiders into using more observable actions. Finally, the system detects potential insider threats through analyzing the observable actions and reasoning results.

System and method for insider threat detection

Described is a system for explosive network attack and mitigation analysis. A network structure is received as input. A network attack method that applies an Achlioptas process is selected. Then, an explosive mitigation strategy is selected. An attack-mitigation competing process is simulated for the network structure. A sequence of network structures under competing processes is generated. The effectiveness of the selected explosive mitigation strategy against the selected network attack method is quantified by analyzing the sequence of network structures under competing processes.

Explosive network attack and mitigation strategies

Described is a system for object and behavior recognition which utilizes a collection of modules which, when integrated, can automatically recognize, learn, and adapt to simple and complex visual behaviors. An object recognition module utilizes a cooperative swarm algorithm to classify an object in a domain. A graph-based object representation module is configured to use a graphical model to represent a spatial organization of the object within the domain. Additionally, a reasoning and recognition engine module consists of two sub-modules: a knowledge sub-module and a behavior recognition sub-module. The knowledge sub-module utilizes a Bayesian network, while the behavior recognition sub-module consists of layers of adaptive resonance theory clustering networks and a layer of a sustained temporal order recurrent temporal order network. The described invention has applications in video forensics, data mining, and intelligent video archiving.

Method for online learning and recognition of visual behaviors

Growing community of inventors

Thousand Oaks, CA, United States of America

David L Allen