Abstract: Systems and methods for scoring investment data using machine learning-based model training. The method includes receiving historical data over a time period. The method further includes determining positive investment data and negative investment data based on the historical data and investment preference data. The positive investment data including characteristics associated with positive assets that align with the investment preference data. The negative investment data including characteristics associated with negative assets that misalign with the investment data. The method further includes calculating machine learning model parameters based on the positive and negative investment data. The method also includes calculating a score corresponding to a new asset based on the machine learning model parameters and new investment data. The method further includes determining whether the new investment data aligns with the investment preference data based on the score and a threshold investment score.

Abstract: The present disclosure relates to methods and systems for ingesting content from data feeds and to generate an enhanced output of relevant content for presentation to a user to facilitate analysis of the relevant content. Content is received from data feeds, and filtered to identify relevant content with respect to a particular context. The relevant content is then processed, e.g., using natural language processes, to extract entities involved, and to also identify particular activities detailed in the relevant content. Activity-mining is applied to the identified relevant data to classify and assigned activity tags to the extracted entity. Based on the extracted and identified information, an enhanced output is generated for presentation to facilitate research operations. The enhanced output may include overlaid graphical annotations, indicators, and graphical controls over the relevant articles to provide a means for updating a database based on the relevant content.

Abstract: Described herein are systems and methods for an entity ranking system. In one embodiment, a computing system is configured to interface with one or more domains to identify a set of entities. The computing system can ascertain a first set of data components for each entity, normalize each of the first set of data components for each entity, and detect a failure to ascertain a second set of data components. The computing system can compute a statistical probability for a possible set of values for each data component of the second set based on the ascertained first set of data components. The computing system can rank each entity based on a derived quantile. The computing system can generate a graphical user interface including a visualization of the ranking of each entity, which can be rendered on a display of the at least one user computing devices.

Abstract: A system, method and non-transitory computer readable medium for detecting unsecured sensors in a network. A computing system can find an IP address associated with an unsecured sensor based on a port through which the unsecured sensor communicates with the network. The computing system can ascertain a prefix route associated with the IP address for the unsecured sensor based on a portion of the IP address. The computing system can determine geographic coordinate data associated with the prefix route. The computing system can identify location data of a set of facilities within a specified geographic radius of the geographic coordinate data. The computing system can associate the unsecured sensor with a first one of the facilities in the set of facilities based on a distance between geographic coordinate data and the location data associated with the first one of the facilities being less than a specified distance threshold.

Abstract: Systems and methods for scoring investment data using machine learning-based model training. The method includes receiving historical data over a time period. The method further includes determining positive investment data and negative investment data based on the historical data and investment preference data. The positive investment data including characteristics associated with positive assets that align with the investment preference data. The negative investment data including characteristics associated with negative assets that misalign with the investment data. The method further includes calculating machine learning model parameters based on the positive and negative investment data. The method also includes calculating a score corresponding to a new asset based on the machine learning model parameters and new investment data. The method further includes determining whether the new investment data aligns with the investment preference data based on the score and a threshold investment score.

Abstract: The present disclosure relates to methods and systems for ingesting content from data feeds and to generate an enhanced output of relevant content for presentation to a user to facilitate analysis of the relevant content. Content is received from data feeds, and filtered to identify relevant content with respect to a particular context. The relevant content is then processed, e.g., using natural language processes, to extract entities involved, and to also identify particular activities detailed in the relevant content. Activity-mining is applied to the identified relevant data to classify and assigned activity tags to the extracted entity. Based on the extracted and identified information, an enhanced output is generated for presentation to facilitate research operations. The enhanced output may include overlaid graphical annotations, indicators, and graphical controls over the relevant articles to provide a means for updating a database based on the relevant content.

Abstract: Described herein are systems and methods for an entity ranking system. In one embodiment, a computing system is configured to interface with one or more domains to identify a set of entities. The computing system can ascertain a first set of data components for each entity, normalize each of the first set of data components for each entity, and detect a failure to ascertain a second set of data components. The computing system can compute a statistical probability for a possible set of values for each data component of the second set based on the ascertained first set of data components. The computing system can rank each entity based on a derived quantile. The computing system can generate a graphical user interface including a visualization of the ranking of each entity, which can be rendered on a display of the at least one user computing devices.

Abstract: A system, method and non-transitory computer readable medium for detecting unsecured sensors in a network. A computing system can find an IP address associated with an unsecured sensor based on a port through which the unsecured sensor communicates with the network. The computing system can ascertain a prefix route associated with the IP address for the unsecured sensor based on a portion of the IP address. The computing system can determine geographic coordinate data associated with the prefix route. The computing system can identify location data of a set of facilities within a specified geographic radius of the geographic coordinate data. The computing system can associate the unsecured sensor with a first one of the facilities in the set of facilities based on a distance between geographic coordinate data and the location data associated with the first one of the facilities being less than a specified distance threshold.