Systems and techniques for end-to-end fuzzy entity matching are described herein. A first input and a second input may be received. The first input and the second input may be evaluated to identify common attribute types. A set of attribute entity matching models may be selected that correspond to the attribute types. The first input and the second input may be evaluated using the set of attribute entity matching models to determine a set of weighted scores for attribute pairs in the first input and the second input. The set of weighted scores may be evaluated using a table-level entity matching model to identify a common entity included in the first input and the second input. A linking dataset may be generated that includes a cross-linking facility indicating a relationship between a first entity descriptor in the first input and a second entity descriptor in the second input.

Systems and techniques for end-to-end fuzzy entity matching are described herein. A first input and a second input may be received. The first input and the second input may be evaluated to identify common attribute types. A set of attribute entity matching models may be selected that correspond to the attribute types. The first input and the second input may be evaluated using the set of attribute entity matching models to determine a set of weighted scores for attribute pairs in the first input and the second input. The set of weighted scores may be evaluated using a table-level entity matching model to identify a common entity included in the first input and the second input. A linking dataset may be generated that includes a cross-linking facility indicating a relationship between a first entity descriptor in the first input and a second entity descriptor in the second input.

A method for selecting an AI solution for an automated robotic process including receiving at least one functional media including information indicative of brain activity by a human engaged in a task of interest, analyzing the functional media, identifying an activity level in at least one brain region, identifying a brain region parameter and an activity parameter; identifying an action parameter based in part on the brain region parameter or the activity parameter; and selecting a component of the AI solution in part on the brain region parameter, the activity parameter, or the action parameter.

A search system performs a federated search across multiple databases and generates a ranked combined list of found genealogical records. The system receives a user query with one or more specified characteristics. The system may determine expanded characteristics derived from the specified characteristics. The system searches the various databases with the characteristics retrieving records according to the characteristics. The system combines the retrieved records and ranks them using a machine learning model. The machine learning model is configured to assign a weight to the records returned from each of the genealogical databases based on the characteristics specified in the user query. The machine learning model may be trained by any combination of one or more of: a Nelder-Mead method, a coordinate ascent method, and a simulated annealing method. The ranked combined results are provided in response to the user query.

A video augmentation apparatus is shown. The apparatus may comprise at least a processor and a memory. The processor may be configured to receive a plurality of videos, Additionally, the processor may generate a segment datum as a function of the plurality of videos. The segment datum may be classified to an augmentation datum. The classification may include training an augmentation classifier using a segment training data wherein the segment training data contains a plurality of data entries correlating required segment datum as an input to the augmentation datum as outputs. The classification may further include generating an augmentation classification datum, wherein augmentation classification datum is generated by classifying the segment datum to the augmentation datum using the augmentation classifier. The processor then may generate an augmented video as a function of the augmentation classification datum and display the augmented video using a user display device.

Aspects of the disclosure generally relate to computing devices and/or systems, and may be generally directed to devices, systems, methods, and/or applications for learning an avatar's or an application's operation in various circumstances, storing this knowledge in a knowledgebase (i.e. neural network, graph, sequences, etc.), and/or enabling autonomous operation of the avatar or the application.

Various aspects related to methods, systems, and computer readable media for automatic fuzz testing. An example method of automatic software fuzz testing can include, receiving a description of a target software application, determining, based on the description, a type of fuzzing, identifying one or more fuzzers based on the type of fuzzing, executing the one or more fuzzers on the target software application, extracting prioritized results of the executing of the one or more fuzzers, and, presenting the prioritized results.

Various aspects related to methods, systems, and computer readable media for automatic fuzz testing. An example method of automatic software fuzz testing can include, receiving a description of a target software application, determining, based on the description, a type of fuzzing, identifying one or more fuzzers based on the type of fuzzing, executing the one or more fuzzers on the target software application, extracting prioritized results of the executing of the one or more fuzzers, and, presenting the prioritized results.

The present disclosure relates to determining the shareability of values of node profiles. Record objects and electronic activities of a system of record corresponding to a data source provider may be accessed. Each record object may correspond to a record object type and have one or more object field-value pairs. Node profiles may be maintained. Values of fields corresponding to a predetermined type of field including fewer than a predetermined threshold number of data source providers may be identified. A restriction tag used to restrict populating other node profiles may be generated. Provision of the value with a second data source provider may be restricted.

The present disclosure relates to determining the shareability of values of node profiles. Record objects and electronic activities of a system of record corresponding to a data source provider may be accessed. Each record object may correspond to a record object type and have one or more object field-value pairs. Node profiles may be maintained. Values of fields corresponding to a predetermined type of field including fewer than a predetermined threshold number of data source providers may be identified. A restriction tag used to restrict populating other node profiles may be generated. Provision of the value with a second data source provider may be restricted.