Hrl Laboratories, LLC

The Boeing Company

Clarifai, Inc.

Ryan F Compton

Tsai-Ching Lu

David L Allen

Matthew S Keegan

Karim El Defrawy

Matthew Donald Zeiler

Hankyu Moon

Jiejun Xu

Christopher David Fox

Craig A Lee

Pape Maguette Sylla

David Joshua Eigen

Hyun Jin Kim

Patrick Park

Sofia S V Apreleva

Pejman Mahboubi

In certain embodiments, content items may be obtained, where each of the content items may include multiple data types. Machine learning models may be caused to be trained based on the content items to map data in a vector space by providing at least a first portion of each of the content items as input to at least one of the machine learning models and providing at least a second portion of each of the content items as input to at least another one of the machine learning models. A search request for results may be obtained, where the search request includes search parameters. One or more locations within the vector space may be predicted (e.g., by one or more of the machine learning models) based on the search parameters. Information (indicating content items mapped to or proximate the predicted locations) may be provided as a request response.

Prediction-model-based mapping and/or search using a multi-data-type vector space

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 discriminating between individual accounts and organization accounts in a social media network. The system processes data from a social media network comprising a set of user accounts. A directed mention network, a bi-directed mention network, and a set of computer-observable network metrics are generated. A classification model is trained using the set of computer-observable network metrics and a set of ground-truth user accounts. The trained classification model is used to classify each user account in the set of user accounts as an individual account or an organization account, resulting in a set of classified user accounts.

Network-based organization classification

Described is a system for event prediction on microblogs. In operation, the system receives a social media post stream from a social network to generate and maintain a social network database and an audit trail database. New social media posts are filtered to determine if the new social media posts are related to a possible future event. The new social media posts are compared against the social network database and audit trail database to provide a quality score, the quality score representing a likelihood that at least one of the new social media posts is a true prediction of a future event. If the quality score is determined to be true, the detection is published as a true prediction of a future event.

System and method for using network data to improve event predictions

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

Example methods and systems for mapping network service and/or application dependencies are provided. Some examples may visualize a large, complex network of network services and/or applications (e.g., Internet services and applications) and their dependencies over time. Each service (or application) may be represented as a node and the visualization may present information regarding the relationships among services and/or applications using directed edges (or lines) with varying thickness, colors, and/or line-styles depending on network data.

Mapping network service dependencies

Described is system for early detection of civil unrest events. A set of textual social media data having corresponding user identifications is received as input. The textual social media data is filtered with a keyword filter, resulting in a first set of data comprising keywords of interest. The first set of data is then filtered with a future date filter, resulting in a second set of data comprising future dates. The second set of data is filtered based on relevance to a civil unrest event with a text classifier module, resulting in a third set of data that is classified based on relevance to the civil unrest event. The third set of data is then filtered based on a location of interest, resulting in a fourth set of data having user identifications in the location of interest. A location is assigned to the civil unrest event, and a forecast of the civil unrest event is output.

Social media data mining for early detection of newsworthy civil unrest events

Described is a system for tracking and predicting social events. The system filters a time series of data obtained from a social media source. Enhanced filtered signals (EFS) are extracted from the filtered time series data based on an amplification signal obtained via a summation of signals relevant to a process of interest in the filtered time series data. A level of human social activity in the social media source is monitored by comparing the extracted EFS to an event database to detect an increase in a number of social activity events in the social media source compared to the event database.

Tracking and prediction of societal event trends using amplified signals extracted from social media

Described is a system for automatic date detection in digital texts written in Farsi. The system includes a unique date tagger that reviews the string of texts and detect dates either compatible with the Farsi grammar or some popular yet unofficial ways of writing dates in Farsi.

System and method for Farsi language temporal tagger

Described is a system for catastrophe prediction. The system generates a time series of observables at multiple time steps from data observed from a complex system. A surrogate time series based on the time series of observables is then generated. Inferred network structures for both the time series of observables and the surrogate time series are reconstructed. Next, spatial autocorrelation for each inferred network structure in both the time series of observables and the surrogate time series is computed. A statistical test of a detected trend between the time series of observables and the surrogate time series is computed to determine if the detected trend occurred by chance. Finally, an early warning signal of the detected trend occurring by chance is generated.

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Los Angeles, CA, United States of America

Ryan F Compton