Abstract: Methods, systems, and program products for streamlined auditing that receive an input audit request via the data interface; source entity type data (ETD) from one or more databases; prepare the ETD for input into an entity clustering module; match the ETD via the entity clustering module to locate linkages within the ETD and discover relationships amongst one or more entities identified within the ETD; cluster datapoints in the ETD that refer to the same real-world entities; analyze the ETD relationships via an entity intelligence module to identify and segment targeted entities, from the one or more entities, that are applicable to the audit request; generate inclusion lists of those targeted entities that are determined to fulfill the audit request; finalize the inclusion lists of targeted entities that fulfill the audit request to generate streamlined audit results; and output the streamlined audit results to an end user.

Abstract: Methods, systems, and program products for streamlined auditing that receive an input audit request via the data interface; source entity type data (ETD) from one or more databases; prepare the ETD for input into an entity clustering module; match the ETD via the entity clustering module to locate linkages within the ETD and discover relationships amongst one or more entities identified within the ETD; cluster datapoints in the ETD that refer to the same real-world entities; analyze the ETD relationships via an entity intelligence module to identify and segment targeted entities, from the one or more entities, that are applicable to the audit request; generate inclusion lists of those targeted entities that are determined to fulfill the audit request; finalize the inclusion lists of targeted entities that fulfill the audit request to generate streamlined audit results; and output the streamlined audit results to an end user.

Abstract: Tracking using sensor data is described, for example, where a plurality of machine learning predictors are used to predict a plurality of complementary, or diverse, parameter values of a process describing how the sensor data arises. In various examples a selector selects which of the predicted values are to be used, for example, to control a computing device. In some examples the tracked parameter values are pose of a moving camera or pose of an object moving in the field of view of a static camera; in some examples the tracked parameter values are of a 3D model of a hand or other articulated or deformable entity. The machine learning predictors have been trained in series, with training examples being reweighted after training an individual predictor, to favor training examples on which the set of predictors already trained performs poorly.

Abstract: Tracking using sensor data is described, for example, where a plurality of machine learning predictors are used to predict a plurality of complementary, or diverse, parameter values of a process describing how the sensor data arises. In various examples a selector selects which of the predicted values are to be used, for example, to control a computing device. In some examples the tracked parameter values are pose of a moving camera or pose of an object moving in the field of view of a static camera; in some examples the tracked parameter values are of a 3D model of a hand or other articulated or deformable entity. The machine learning predictors have been trained in series, with training examples being reweighted after training an individual predictor, to favour training examples on which the set of predictors already trained performs poorly.