Abstract: A reward learning system includes a user interface configured to receive modes of user information related to a state of the user. A cognitive computing system includes a reward system. The reward system includes a dynamically upgraded profile model of the user which is updated in accordance with the user information related to the state. The reward system is updated by machine learning employing feedback from user responses measured by the user interface and searched information by the cognitive computing system. The reward system includes an increasing reward protocol based on learned user preferences and responses and rewarded in accordance with user achievements.

Abstract: The system, method, and computer program product are disclosed that track the evolution of a network over time through the analysis of media corpora associated with nodes of the network at each time slice. The media corpora may be analyzed to generate word clusters for each time slice that are then compared across time slices to determine how the network has evolved. The evolution may be tracked by determining the similarity of each word cluster of a particular time slice to each word cluster of another time slice. The similarity may be measured by a similarity score for each comparison that may be combined to determine an overall similarity of the network between the two time slices.

Abstract: Methods and systems of detecting chip degradation are described. A processor may execute a test on a device at a first time, where the test includes executable instructions for the device to execute a task under specific conditions relating to a performance attribute. The processor may receive performance data indicating a set of outcomes from the task executed by the device during the test. The processor may determine a first value of a parameter of the performance attribute based on the identified subset. The processor may compare the first value with a second value of the parameter of the performance attribute. The second value is based on an execution of the test on the device at a second time. The processor may determine a degradation status of the device based on the comparison of the first value with the second value.

Abstract: A method of operating a configurable gaze tracking system includes initializing a plurality of sensors by determining positional information of the sensors, wherein the sensors establish a virtual framework, initializing a plurality of target objects by determining positional information of the target objects within the virtual framework, determining a current user using data output by the sensors, determining a gaze of the current user, matching the gaze to one of the target objects in the virtual framework, wherein a target object matched to the gaze is a current target object, and activating the current target object to receive input.

Abstract: A reward learning system includes a user interface configured to receive modes of user information related to a state of the user. A cognitive computing system includes a reward system. The reward system includes a dynamically upgraded profile model of the user which is updated in accordance with the user information related to the state. The reward system is updated by machine learning employing feedback from user responses measured by the user interface and searched information by the cognitive computing system. The reward system includes an increasing reward protocol based on learned user preferences and responses and rewarded in accordance with user achievements.

Abstract: Methods and systems for determining a time dependent relevancy score of an agent node among an evolving heterogeneous network are described. A processor may expand the heterogeneous network by generating temporal heterogeneous networks representing states of the heterogeneous network at different times. The processor may extract a set of agent nodes from each temporal heterogeneous network and may generate a relationship network based on the extracted agent nodes for each temporal heterogeneous network. The processor may remove the agent node from the temporal heterogeneous network to generate a conditional relationship network excluding the removed agent node. The processor may determine a relevancy score for the agent node based on the corresponding relationship network and the conditional relationship network. Each relevancy score for the agent node may correspond to a temporal heterogeneous network and may indicate an impact of removing the agent node from the corresponding temporal heterogeneous network.

Abstract: Methods and systems of detecting chip degradation are described. A processor may execute a test on a device at a first time, where the test includes executable instructions for the device to execute a task under specific conditions relating to a performance attribute. The processor may receive performance data indicating a set of outcomes from the task executed by the device during the test. The processor may determine a first value of a parameter of the performance attribute based on the identified subset. The processor may compare the first value with a second value of the parameter of the performance attribute. The second value is based on an execution of the test on the device at a second time. The processor may determine a degradation status of the device based on the comparison of the first value with the second value.

Abstract: A reward learning system includes a user interface configured to receive modes of user information related to a state of the user. A cognitive computing system includes a reward system. The reward system includes a dynamically upgraded profile model of the user which is updated in accordance with the user information related to the state. The reward system is updated by machine learning employing feedback from user responses measured by the user interface and searched information by the cognitive computing system. The reward system includes an increasing reward protocol based on learned user preferences and responses and rewarded in accordance with user achievements.

Abstract: A reward learning system includes a user interface configured to receive modes of user information related to a state of the user. A cognitive computing system includes a reward system. The reward system includes a dynamically upgraded profile model of the user which is updated in accordance with the user information related to the state. The reward system is updated by machine learning employing feedback from user responses measured by the user interface and searched information by the cognitive computing system. The reward system includes an increasing reward protocol based on learned user preferences and responses and rewarded in accordance with user achievements.

Abstract: A method and system are provided for chip testing. The method includes selectively deploying a chip for future use or discarding the chip to prevent the future use, responsive to a stress history of the chip determined using a non-destructive test procedure. The test procedure includes ordering each of a plurality of functional patterns by a respective minimum operating period corresponding thereto. The test procedure further includes ranking each of the plurality of patterns based on at least one preceding available pattern to provide a plurality of pattern ranks. The test procedure also includes calculating a sum by summing the plurality of pattern ranks. The sum calculated during an initial performance of the test procedure is designated as a baseline, and the sum calculated during a subsequent performance of the test procedure is compared to a threshold derived from the baseline to determine the stress history of the chip.

Abstract: The system, method, and computer program product are disclosed that track the evolution of a network over time through the analysis of media corpora associated with nodes of the network at each time slice. The media corpora may be analyzed to generate word clusters for each time slice that are then compared across time slices to determine how the network has evolved. The evolution may be tracked by determining the similarity of each word cluster of a particular time slice to each word cluster of another time slice. The similarity may be measured by a similarity score for each comparison that may be combined to determine an overall similarity of the network between the two time slices.

Abstract: Methods and systems for determining a time dependent relevancy score of an agent node among an evolving heterogeneous network are described. A processor may expand the heterogeneous network by generating temporal heterogeneous networks representing states of the heterogeneous network at different times. The processor may extract a set of agent nodes from each temporal heterogeneous network and may generate a relationship network based on the extracted agent nodes for each temporal heterogeneous network. The processor may remove the agent node from the temporal heterogeneous network to generate a conditional relationship network excluding the removed agent node. The processor may determine a relevancy score for the agent node based on the corresponding relationship network and the conditional relationship network. Each relevancy score for the agent node may correspond to a temporal heterogeneous network and may indicate an impact of removing the agent node from the corresponding temporal heterogeneous network.

Abstract: A method, computer system, and a computer program product for generating and reporting a plurality of health insurance cost predictions via private transfer learning is provided. The present invention may include retrieving a set of source data, and a set of target data. The present invention may then include creating and anonymizing a plurality of source data sets, and at least one target data set. The present invention may further include generating one or more source learner models, and a target learner model. The present invention may then include combining the one or more generated source learner models and the generated target learner model to generate a transfer learner. The present invention may further include generating a prediction based on the generated transfer learner.

Abstract: A system may predict costs for a set of members by building and using a predictive pipeline. The pipeline may be built using a set of historical data for training members. A set of member-level features can be identified by performing empirical testing on the set of historical data. The trained configurable predictive pipeline can generate a set of predictive data for each member, using historical test data for a set of testing members. The system can then generate a predictive report for each set of predictive data.

Abstract: A method and system are provided for chip testing. The method includes selectively deploying a chip for future use or discarding the chip to prevent the future use, responsive to a stress history of the chip determined using a non-destructive test procedure. The test procedure includes ordering each of a plurality of functional patterns by a respective minimum operating period corresponding thereto. The test procedure further includes ranking each of the plurality of patterns based on at least one preceding available pattern to provide a plurality of pattern ranks. The test procedure also includes calculating a sum by summing the plurality of pattern ranks. The sum calculated during an initial performance of the test procedure is designated as a baseline, and the sum calculated during a subsequent performance of the test procedure is compared to a threshold derived from the baseline to determine the stress history of the chip.

Abstract: A reward learning system includes a user interface configured to receive modes of user information related to a state of the user. A cognitive computing system includes a reward system. The reward system includes a dynamically upgraded profile model of the user which is updated in accordance with the user information related to the state. The reward system is updated by machine learning employing feedback from user responses measured by the user interface and searched information by the cognitive computing system. The reward system includes an increasing reward protocol based on learned user preferences and responses and rewarded in accordance with user achievements.

Abstract: A reward learning system includes a user interface configured to receive modes of user information related to a state of the user. A cognitive computing system includes a reward system. The reward system includes a dynamically upgraded profile model of the user which is updated in accordance with the user information related to the state. The reward system is updated by machine learning employing feedback from user responses measured by the user interface and searched information by the cognitive computing system. The reward system includes an increasing reward protocol based on learned user preferences and responses and rewarded in accordance with user achievements.

Abstract: A method and system are provided for chip testing. The method includes ascertaining a baseline for a functioning chip with no stress history by performing a non-destructive test procedure on the functioning chip. The method further includes repeating the test procedure on a chip under test using a threshold derived from the baseline as a reference point to determine a stress history of the chip under test. The test procedure includes ordering each of a plurality of functional patterns by a respective minimum operating period corresponding thereto, ranking each pattern based on at least one preceding available pattern to provide a plurality of pattern ranks, and calculating a sum by summing the pattern ranks. The sum calculated by the ascertaining step is designated as the baseline, and the sum calculated by the repeating step is compared to the threshold to determine the stress history of the chip under test.

Abstract: A method and system are provided for chip testing. The method includes ascertaining a baseline for a functioning chip with no stress history by performing a non-destructive test procedure on the functioning chip. The method further includes repeating the test procedure on a chip under test using a threshold derived from the baseline as a reference point to determine a stress history of the chip under test. The test procedure includes ordering each of a plurality of functional patterns by a respective minimum operating period corresponding thereto, ranking each pattern based on at least one preceding available pattern to provide a plurality of pattern ranks, and calculating a sum by summing the pattern ranks. The sum calculated by the ascertaining step is designated as the baseline, and the sum calculated by the repeating step is compared to the threshold to determine the stress history of the chip under test.