Abstract: Text blocks are semantically compared, and a semantic score is provided to a user. The semantic score is based on application of a machine learning model trained on a text corpus. One or both of the two text blocks may have one or more words that do not appear in the training text corpus (skip-words). Skip-words are used, rather than discarded, to adjust the semantic score via, for example, a penalization function. The user provides feedback about the accuracy of the adjusted semantic score, and the feedback is used to perform supervised learning model.

Abstract: Optimizing distribution of shipping items by receiving distribution data for a first item. The distribution data including a starting location and a destination. The distribution data also including a constraint for shipment with a second item. A time space network mode is created for tracking the first item relative to the constraint for shipment with the second item. An objection function is performed using the time space network model to optimize the distribution data by minimizing the sum of an objective function including a minimized fulfillment penalty and a minimized unmet order penalty. A delivery plan is executed with the distribution data that was optimized.

Abstract: A system identifies a first text fragment as being under evaluation, wherein the first text is in at least a first document being compared to a second document using a first semantic model. The system compares the first text fragment to one or more text fragments of the second document. The system identifies top-k text fragments in the second document that are most similar to the first text fragment based on processing using the first semantic model. The system presents a user, via a graphical user interface (GUI), the top-k text fragments in visual proximity to the first text fragment.

Abstract: User-specific augmentation of a real word field of view viewable through an augmented reality (AR) device is facilitated by a processor(s) receiving image data representative of a real world field of view viewable by a user through the AR device, and receiving sensor data indicative of the user's stress level, which is related, at least in part, to the user's real world field of view viewable through the AR device. The processor(s) processes the image data, based on the user's stress level, to identify one or more stress-inducing elements to be hidden in the real world field of view viewable through the AR device. Further, the processor(s) provides an augmented real world field of view for display to the user through the AR device, where the one or more stress-inducing elements are hidden from the user in the augmented real world field of view.

Abstract: User-specific augmentation of a real word field of view viewable through an augmented reality (AR) device is facilitated by a processor(s) receiving image data representative of a real world field of view viewable by a user through the AR device, and receiving sensor data indicative of the user's stress level, which is related, at least in part, to the user's real world field of view viewable through the AR device. The processor(s) processes the image data, based on the user's stress level, to identify one or more stress-inducing elements to be hidden in the real world field of view viewable through the AR device. Further, the processor(s) provides an augmented real world field of view for display to the user through the AR device, where the one or more stress-inducing elements are hidden from the user in the augmented real world field of view.

Abstract: A system identifies a first text fragment as being under evaluation, wherein the first text is in at least a first document being compared to a second document using a first semantic model. The system compares the first text fragment to one or more text fragments of the second document. The system identifies top-k text fragments in the second document that are most similar to the first text fragment based on processing using the first semantic model. The system presents a user, via a graphical user interface (GUI), the top-k text fragments in visual proximity to the first text fragment.

Abstract: Text blocks are semantically compared, and a semantic score is provided to a user. The semantic score is based on application of a machine learning model trained on a text corpus. One or both of the two text blocks may have one or more words that do not appear in the training text corpus (skip-words). Skip-words are used, rather than discarded, to adjust the semantic score via, for example, a penalization function. The user provides feedback about the accuracy of the adjusted semantic score, and the feedback is used to perform supervised learning model.

Abstract: Methods, systems, and non-transitory computer readable medium for predicting arrival time of components based on historical receipt data. A method includes receiving historical receipt data corresponding to features. The method further includes performing feature analysis to generate additional features for the historical receipt data. The method further includes selecting a first set of features including at least one of the additional features. The method further includes predicting, based on the first set of features, an arrival time for one or more components of a manufacturing facility. The method further includes causing, based on the predicted arrival time, modification of a schedule in a file associated with the one or more components of the manufacturing facility.

Abstract: Methods, systems, and non-transitory computer readable medium for predicting arrival time of components based on historical receipt data. A method includes receiving historical receipt data corresponding to features. The method further includes performing feature analysis to generate additional features for the historical receipt data. The method further includes selecting a first set of features including at least one of the additional features. The method further includes predicting, based on the first set of features, an arrival time for one or more components of a manufacturing facility. The method further includes causing, based on the predicted arrival time, modification of a schedule in a file associated with the one or more components of the manufacturing facility.