Abstract: Methods, systems, and computer program products for auto-discovery of reasoning knowledge graphs in supply chains are provided herein. A computer-implemented method includes obtaining a spatiotemporal query related to a demand of at least one product in a supply chain; analyzing the spatiotemporal query to identify one or more parameters affecting the demand of the at least one product, wherein the one or more parameters comprise at least one of one or more climate parameters and one or more disruptive event parameters; generating a knowledge graph comprising information indicating an impact on the demand of the at least one product for at least a portion of the one or more parameters; and outputting, to a user interface, an explanation of a predicted demand forecast for the at least one product based at least in part on the knowledge graph.

Abstract: In an approach to jointly learning uncertainty-aware trend-informed neural network for a demand forecasting model, a machine learning model is trained to capture uncertainty in input forecasts. The uncertainty in a latent space is represented using an auto-encoder based neural architecture. The uncertainty-aware latent space is modeled and optimized to generate an embedding space. A time-series regressor model is learned from the embedding space. A machine learning model is trained for trend-aware demand forecasting based on said time-series regressor model.

Abstract: Methods, systems, and computer program products for auto-discovery of reasoning knowledge graphs in supply chains are provided herein. A computer-implemented method includes obtaining a spatiotemporal query related to a demand of at least one product in a supply chain; analyzing the spatiotemporal query to identify one or more parameters affecting the demand of the at least one product, wherein the one or more parameters comprise at least one of one or more climate parameters and one or more disruptive event parameters; generating a knowledge graph comprising information indicating an impact on the demand of the at least one product for at least a portion of the one or more parameters; and outputting, to a user interface, an explanation of a predicted demand forecast for the at least one product based at least in part on the knowledge graph.

Abstract: In an approach for guiding a visually impaired user to position a mobile device appropriately in relation to a screen so that dynamic information on the screen can be reliably extracted and conveyed to the visually impaired user, a processor receives an image captured by a camera of a mobile device. A processor performs object recognition on the image to identify a digital screen and a location of the digital screen in the image. A processor retrieves a template of the digital screen. A processor performs angle-sensitive optical character recognition (OCR) on the location of the digital screen in the image. Responsive to a processor determining text on the digital screen can be extracted, a processor conveys the text to a user. Responsive to a processor determining text on the digital screen cannot be extracted, a processor guides the user to re-orient the mobile device to capture a better image.

Abstract: In an approach for guiding a visually impaired user to position a mobile device appropriately in relation to a screen so that dynamic information on the screen can be reliably extracted and conveyed to the visually impaired user, a processor receives an image captured by a camera of a mobile device. A processor performs object recognition on the image to identify a digital screen and a location of the digital screen in the image. A processor retrieves a template of the digital screen. A processor performs angle-sensitive optical character recognition (OCR) on the location of the digital screen in the image. Responsive to a processor determining text on the digital screen can be extracted, a processor conveys the text to a user. Responsive to a processor determining text on the digital screen cannot be extracted, a processor guides the user to re-orient the mobile device to capture a better image.

Abstract: Concepts for classifying data are presented. Data to be classified is processed in accordance with a data decomposition algorithm so as to generate a plurality of data components, wherein each data component is associated with a respective different value or range of data transience. A subset of the data to be classified based on the plurality of data components. The selected subset of the obtained data is provided to a data classification process for classifying the data.

Abstract: Systems, computer-implemented methods and computer program products are provided facilitating explanation selection for machine learning system output. In one embodiment, the computer-implemented method includes: accessing, by a device operatively coupled to a processor, a plurality of explanation generation components to generate different types of explanations of a machine learning output; monitoring, by the device, indicators of an entity receiving the machine learning system output to obtain user emotional state descriptors; and providing, by the device, an explanation from a selected explanation generation component to a user interface for evaluation of the machine learning system output with the explanation.

Abstract: Concepts for classifying data are presented. Data to be classified is processed in accordance with a data decomposition algorithm so as to generate a plurality of data components, wherein each data component is associated with a respective different value or range of data transience. A subset of the data to be classified based on the plurality of data components. The selected subset of the obtained data is provided to a data classification process for classifying the data.