Abstract: Embodiments of the present invention may be directed toward a method, a system, and a computer program product of adaptive calibration of sensors through cognitive learning. In an exemplary embodiment, the method, the system, and the computer program product include (1) in response to receiving a data from at least one calibration sensor and data from an itinerant sensor, comparing the data from the at least one calibration sensor and the data from the itinerant sensor, (2) in response to the comparing, determining, by one or more processors, the accuracy of the itinerant sensor, (3) generating, by the one or more processors, one or more calibration parameters based on the determining and based on a machine learning associated with preexisting sensor information, and (4) executing, by the one or more processors, the one or more calibration parameters.

Abstract: Systems and methods for training a neural network model are disclosed. In the method, training data is obtained by a deep neural network (DNN) first, the deep neural network comprising at least one hidden layer. Then features of the training data are obtained from a specified hidden layer of the at least one hidden layer, the specified hidden layer being connected respectively to a supervised classification network for classification tasks and an autoencoder based reconstruction network for reconstruction tasks. And at last the DNN, the supervised classification network and the reconstruction network are trained as a whole based on the obtained features, the training being guided by the classification tasks and the reconstruction tasks.

Abstract: A method to train a machine learning model for in-vehicle air quality control in a knowledge-based system, executed by one or more computer processors, includes collecting data related to in-vehicle air quality from a plurality of probe cars where the data is collected by various on-board systems in each probe car. The method includes correlating the data related to in-vehicle air quality from each probe car with air quality measurements from each probe car, where the correlation is used to update the machine learning model. The method includes determining a situation when an in-vehicle air quality measurement of the air quality measurements is above a pre-determined in-vehicle air quality level and determining instructions for actions by one or more of the one or more on-board systems in each of the probe cars to maintain an in-vehicle air quality level at or below the pre-determined in-vehicle air quality level.

Abstract: An interconnection unit includes a first connector configured to be coupled to an electronic device. There is a second connector configured to be coupled to a power station and to provide a path to the electronic device via the first connector. There is a low pass filter coupled between the first connector and the second connector and configured to allow the electronic device to receive power from the power station while maintaining data security of the electronic device.

Abstract: A method, system, and computer program product, include receiving a plurality of requests for dynamic context information from a plurality of road segments, determining whether the plurality of road segments are included in a same cluster of road segments in a road network generated by clustering road segments in the road network based on connectivity of the road network; and consolidating the plurality of requests to generate a consolidated request in response to determining that the plurality of road segments are included in the same cluster.

Abstract: A method for object defect detection includes receiving digital data representing an image of an object with a repeated pattern. The method identifies a part of the image of the object as defined by a sample window of the digital data. The method generates one or more functions from at least the part of the image, wherein each of the one or more functions corresponds to one component of a pixel contained in the part of the image. Responsive to performing self-similarity analytics on the one or more functions, the method identifies a defect area of the object.

Abstract: A method of generating a predictor to classify data includes: training each of a plurality of first classifiers arranged in a first level on current training data; operating each classifier of the first level on the training data to generate a plurality of predictions; combining the current training data with the predictions to generated new training data; and training each of a plurality of second classifiers arranged in a second level on the new training data. The first classifiers are classifiers of different classifier types, respectively and the second classifiers are classifiers of the different classifier types, respectively.

Abstract: A system, a computer readable storage medium, and a method for detecting insurance fraud or for comparing references use visual analytics-based techniques. The method can include identifying a scratch and a scratch location on a vehicle in a 3-dimensional rendering in comparison to a base image or in comparison to a stored image in a database, comparing one or more features of the scratch such as color features of the scratch in the scratch location in comparison to the scratch location or comparing one or more texture features of the scratch in the scratch location in comparison to the scratch location in the base image or the stored image. The method can further generate a similarity calculation based on the one or more comparisons for the scratch, the scratch location, the one or more scratch features of the scratch and presents a result in response to the similarity calculation.

Abstract: An interconnection unit includes a first connector configured to be coupled to an electronic device. There is a second connector configured to be coupled to a power station and to provide a path to the electronic device via the first connector. There is a low pass filter coupled between the first connector and the second connector and configured to allow the electronic device to receive power from the power station while maintaining data security of the electronic device.

Abstract: Systems and methods training a model are disclosed. In the method, training data is obtained by a deep neural network (DNN) first, the deep neural network comprising at least one hidden layer. Then features of the training data are obtained from a specified hidden layer of the at least one hidden layer, the specified hidden layer being connected respectively to a supervised classification network for classification tasks and an autoencoder based reconstruction network for reconstruction tasks.

Abstract: Embodiments of the present invention may be directed toward a method, a system, and a computer program product of adaptive calibration of sensors through cognitive learning. In an exemplary embodiment, the method, the system, and the computer program product include (1) in response to receiving a data from at least one calibration sensor and data from an itinerant sensor, comparing the data from the at least one calibration sensor and the data from the itinerant sensor, (2) in response to the comparing, determining, by one or more processors, the accuracy of the itinerant sensor, (3) generating, by the one or more processors, one or more calibration parameters based on the determining and based on a machine learning associated with preexisting sensor information, and (4) executing, by the one or more processors, the one or more calibration parameters.

Abstract: A method for object defect detection includes receiving digital data representing an image of an object with a repeated pattern. The method identifies a part of the image of the object as defined by a sample window of the digital data. The method generates one or more functions from at least the part of the image, wherein each of the one or more functions corresponds to one component of a pixel contained in the part of the image. Responsive to performing self-similarity analytics on the one or more functions, the method identifies a defect area of the object.

Abstract: A method to train a machine learning model for in-vehicle air quality control in a knowledge-based system, executed by one or more computer processors, includes collecting data related to in-vehicle air quality from a plurality of probe cars where the data is collected by various on-board systems in each probe car. The method includes correlating the data related to in-vehicle air quality from each probe car with air quality measurements from each probe car, where the correlation is used to update the machine learning model. The method includes determining a situation when an in-vehicle air quality measurement of the air quality measurements is above a pre-determined in-vehicle air quality level and determining instructions for actions by one or more of the one or more on-board systems in each of the probe cars to maintain an in-vehicle air quality level at or below the pre-determined in-vehicle air quality level.

Abstract: A method, system, and computer program product, include receiving a plurality of requests for dynamic context information from a plurality of road segments, determining whether the plurality of road segments are included in a same cluster of road segments in a road network generated by clustering road segments in the road network based on connectivity of the road network, and consolidating the plurality of requests to generate a consolidated request in response to determining that the plurality of road segments are included in the same cluster.

Abstract: A method, system, and computer program product, include receiving a plurality of requests for dynamic context information from a plurality of road segments, determining whether the plurality of road segments are included in a same cluster of road segments in a road network generated by clustering road segments in the road network based on connectivity of the road network, and consolidating the plurality of requests to generate a consolidated request in response to determining that the plurality of road segments are included in the same cluster.

Abstract: A system, a computer readable storage medium, and a method for detecting insurance fraud or for comparing references use visual analytics-based techniques. The method can include identifying a scratch and a scratch location on a vehicle in a 3-dimensional rendering in comparison to a base image or in comparison to a stored image in a database, comparing one or more features of the scratch such as color features of the scratch in the scratch location in comparison to the scratch location or comparing one or more texture features of the scratch in the scratch location in comparison to the scratch location in the base image or the stored image. The method can further generate a similarity calculation based on the one or more comparisons for the scratch, the scratch location, the one or more scratch features of the scratch and presents a result in response to the similarity calculation.

Abstract: A method, system, and computer program product, include receiving a plurality of requests for dynamic context information from a plurality of road segments, determining whether the plurality of road segments are included in a same cluster of road segments in a road network generated by clustering road segments in the road network based on connectivity of the road network, and consolidating the plurality of requests to generate a consolidated request in response to determining that the plurality of road segments are included in the same cluster.

Abstract: A method of generating a predictor to classify data includes: training each of a plurality of first classifiers arranged in a first level on current training data; operating each classifier of the first level on the training data to generate a plurality of predictions; combining the current training data with the predictions to generated new training data; and training each of a plurality of second classifiers arranged in a second level on the new training data. The first classifiers are classifiers of different classifier types, respectively and the second classifiers are classifiers of the different classifier types, respectively.