Abstract: A method for encoding dot-matrix product information method includes identifying, via a processor, a dot-matrix grid size. The method further includes evaluating, via the processor, one or more dot pattern variation levels. In some aspects, the method includes retrieving, via the processor, an encoding structure indicative of a plurality of product information attributes. The method also includes determining, via the processor, whether an alpha-numeric digit at a dot pattern variation level can include a plurality of product information. The method further includes outputting, via the processor, a dot pattern code map. In some aspects, the dot pattern code map is indicative of a relationship between each of the product information attributes and the plurality of values for each of the product information attributes.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification. One example method of operation may comprise one or more of identifying a production date associated with a product, creating a unique code to represent the production date and the product based on a hash of one or more blocks of a blockchain, and forwarding the unique code to a verification server to verify the product.

Abstract: An example operation may include one or more of identifying pending blockchain transactions in a transaction queue, determining states of the pending blockchain transactions, determining whether the pending blockchain transactions in the transaction queue are valid based on the determined states, retrieving a list of potential blockchain transaction conflicts associated with the pending blockchain transactions, and determining whether any conflicts exist for one or more of the pending blockchain transactions based on the list of blockchain transaction conflicts.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties. One example may comprise one or more of creating one or more control commands configured to control one or more smart devices, signing the one or more control commands via a key maintained by an entity creating the control commands, broadcasting the one or more control commands to the one or more smart devices, and storing the one or more control commands in a blockchain.

Abstract: Machine logic (for example, software) for protecting sensitive and confidential user data and sending the protected user data to an authorized third party for predictive analytics purposes. The machine logic rules perform linear transformation operations on the sensitive and confidential user data in order to obscure the underlying data before it is sent to an authorized third party for performing predictive analysis of the underlying data. After performing predictive analysis on the obscured data by the authorized third party, the owner of the sensitive and confidential data takes a consequential action (that is, the owner of the data makes a business-related decision) based on the prediction.

Abstract: A method for encoding dot-matrix product information method includes identifying, via a processor, a dot-matrix grid size. The method further includes evaluating, via the processor, one or more dot pattern variation levels. In some aspects, the method includes retrieving, via the processor, an encoding structure indicative of a plurality of product information attributes. The method also includes determining, via the processor, whether an alpha-numeric digit at a dot pattern variation level can include a plurality of product information. The method further includes outputting, via the processor, a dot pattern code map. In some aspects, the dot pattern code map is indicative of a relationship between each of the product information attributes and the plurality of values for each of the product information attributes.

Abstract: A method for encoding dot-matrix product information method includes identifying, via a processor, a dot-matrix grid size. The method further includes evaluating, via the processor, one or more dot pattern variation levels. In some aspects, the method includes retrieving, via the processor, an encoding structure indicative of a plurality of product information attributes. The method also includes determining, via the processor, whether an alpha-numeric digit at a dot pattern variation level can include a plurality of product information. The method further includes outputting, via the processor, a dot pattern code map. In some aspects, the dot pattern code map is indicative of a relationship between each of the product information attributes and the plurality of values for each of the product information attributes.

Abstract: An example operation may include one or more of identifying pending blockchain transactions in a transaction queue, determining states of the pending blockchain transactions, determining whether the pending blockchain transactions in the transaction queue are valid based on the determined states, retrieving a list of potential blockchain transaction conflicts associated with the pending blockchain transactions, and determining whether any conflicts exist for one or more of the pending blockchain transactions based on the list of blockchain transaction conflicts.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties. One example may comprise one or more of creating one or more control commands configured to control one or more smart devices, signing the one or more control commands via a key maintained by an entity creating the control commands, broadcasting the one or more control commands to the one or more smart devices, and storing the one or more control commands in a blockchain.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties. One example may comprise one or more of creating one or more control commands configured to control one or more smart devices, signing the one or more control commands via a key maintained by an entity creating the control commands, broadcasting the one or more control commands to the one or more smart devices, and storing the one or more control commands in a blockchain.

Abstract: Machine logic (for example, software) for protecting sensitive and confidential user data and sending the protected user data to an authorized third party for predictive analytics purposes. The machine logic rules perform linear transformation operations on the sensitive and confidential user data in order to obscure the underlying data before it is sent to an authorized third party for performing predictive analysis of the underlying data. After performing predictive analysis on the obscured data by the authorized third party, the owner of the sensitive and confidential data takes a consequential action (that is, the owner of the data makes a business-related decision) based on the prediction.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties. One example may comprise one or more of creating one or more control commands configured to control one or more smart devices, signing the one or more control commands via a key maintained by an entity creating the control commands, broadcasting the one or more control commands to the one or more smart devices, and storing the one or more control commands in a blockchain.

Abstract: A method for eliminating metal composites from a polycrystalline silicon cell piece, comprising the steps of: injecting current into the polycrystalline silicon cell piece under a certain temperature by means of an electric injection method, thereby eliminating the metal composites from the interior of the polycrystalline silicon cell piece; the present invention discloses a simple process, a short processing-time, a low manufacturing cost, and can easily be scaled for manufacture.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties. One example may comprise one or more of creating one or more control commands configured to control one or more smart devices, signing the one or more control commands via a key maintained by an entity creating the control commands, broadcasting the one or more control commands to the one or more smart devices, and storing the one or more control commands in a blockchain.

Abstract: Embodiments of the present invention may track a user's interaction trajectory associated with a problem occurred on a website. According to one embodiment of the present invention, an interaction history of interactions between a user and a website may be obtained. An action description associated with actions performed by a user on a website is received. Then, at least one interaction matching the action description is retrieved from an interaction history of interactions between the user and the website. Further, an interaction trajectory may be generated based on the retrieved at least one interaction.

Abstract: Technical solutions are described for improving a data-mining process for identifying a desired population of a dataset. An example computer-implemented method includes, receiving the dataset that includes a plurality of data dimensions. The method also includes determining a set of predictors for a target variable, where a predictor is a data dimension that is relevant to the target variable. The method also includes identifying from the set of predictors, a false predictor, where the false predictor is a data dimension that results in an empty set of the desired population. The method also includes removing the false predictor from the set of predictors used for the data-mining process for identifying the desired population of the dataset.

Abstract: A method for encoding dot-matrix product information method includes identifying, via a processor, a dot-matrix grid size. The method further includes evaluating, via the processor, one or more dot pattern variation levels. In some aspects, the method includes retrieving, via the processor, an encoding structure indicative of a plurality of product information attributes. The method also includes determining, via the processor, whether an alpha-numeric digit at a dot pattern variation level can include a plurality of product information. The method further includes outputting, via the processor, a dot pattern code map. In some aspects, the dot pattern code map is indicative of a relationship between each of the product information attributes and the plurality of values for each of the product information attributes.

Abstract: Embodiments describing an approach to receiving visual feedback. Generating a recommended visual pattern based on the visual feedback. Tracking a driver's real-time visual pattern. Extracting the driver's real-time visual patterns from the eye-tracking data. Determining the differences between the recommended visual pattern and the driver's real-time visual pattern. Generating enhanced headlamp control configurations based on the determined differences between the recommended visual pattern and the driver's real-time visual pattern; and adjusting the headlamp calibration controls based on the enhanced headlamp control configuration.

Abstract: Embodiments describing an approach to receiving visual feedback. Generating a recommended visual pattern based on the visual feedback. Tracking a driver's real-time visual pattern. Extracting the driver's real-time visual patterns from the eye-tracking data. Determining the differences between the recommended visual pattern and the driver's real-time visual pattern. Generating enhanced headlamp control configurations based on the determined differences between the recommended visual pattern and the driver's real-time visual pattern; and adjusting the headlamp calibration controls based on the enhanced headlamp control configuration.

Abstract: Embodiments describing an approach to receiving visual feedback. Generating a recommended visual pattern based on the visual feedback. Tracking a driver's real-time visual pattern. Extracting the driver's real-time visual patterns from the eye-tracking data. Determining the differences between the recommended visual pattern and the driver's real-time visual pattern. Generating enhanced headlamp control configurations based on the determined differences between the recommended visual pattern and the driver's real-time visual pattern; and adjusting the headlamp calibration controls based on the enhanced headlamp control configuration.