Abstract: Systems and methods for data analysis are described. Embodiments of the present disclosure data analysis include displaying, via a data analysis interface, a data visualization in a first region of the data analysis interface; and displaying, via the data analysis interface, an analysis thread visualization in a second region of the data analysis interface. The analysis thread visualization depicts an analysis thread graph including a first node corresponding to the data visualization and an edge corresponding to an analysis path between the first node and a second node.

Abstract: Techniques discussed herein generally relate to applying machine-learning techniques to design documents to determine relationships among the different style elements within the document. In one example, hypergraph model is trained on a corpus of hypertext markup language (HTML) documents. The trained model is utilized to identifying one or more candidate style elements for a candidate fragment and/or a candidate fragment. Each of the candidates are scored, and at least a portion of the scored candidates are presented as design options for generating a new document.

Abstract: A method, apparatus, and non-transitory computer readable medium for hypergraph processing are described. Embodiments of the present disclosure obtain, by a hypergraph component, a hypergraph that includes a plurality of nodes and a hyperedge, wherein the hyperedge connects the plurality of nodes; perform, by a hypergraph neural network, a node hypergraph convolution based on the hypergraph to obtain an updated node embedding for a node of the plurality of nodes; and generate, by the hypergraph component, an augmented hypergraph based on the updated node embedding.

Abstract: According to an aspect of an embodiment, a method may include obtaining a JavaScript Object Notation (JSON) schema that corresponds to legacy data. The legacy data may include a plurality of legacy data points corresponding to device features of a legacy device. The plurality of legacy data points may be delimited according to a legacy data format. The JSON schema may include a plurality of property definitions corresponding to the legacy data points. The JSON schema may additionally include a legacy object that describes the legacy data format in a manner that allows for processing of the legacy data using the JSON schema. The method may also include processing the legacy data using the plurality of property definitions and the legacy object included in the JSON schema in a manner that allows the legacy device to be used as a web-of-things Thing.

Abstract: According to an aspect of an embodiment, a method may include obtaining a JavaScript Object Notation (JSON) schema that corresponds to legacy data. The legacy data may include a plurality of legacy data points corresponding to device features of a legacy device. The plurality of legacy data points may be delimited according to a legacy data format. The JSON schema may include a plurality of property definitions corresponding to the legacy data points. The JSON schema may additionally include a legacy object that describes the legacy data format in a manner that allows for processing of the legacy data using the JSON schema. The method may also include processing the legacy data using the plurality of property definitions and the legacy object included in the JSON schema in a manner that allows the legacy device to be used as a web-of-things Thing.

Abstract: An intelligent medication dispenser provides automated dispensing of medication for personal use by a patient. The intelligent medication dispenser automatically detects and sorts refill medication and checks the accuracy of dispensed medication doses. The intelligent medication dispenser further enables direct communication between the patient and other stakeholders in the medical care process.

Abstract: A method may include receiving one or more of calendar data, user location data and third-party location data. The calendar data may include activity instructions that indicate that a mobile device be in a quiet mode during a particular activity included in the calendar data. The user location data may include user location instructions that indicate that the mobile device be in the quiet mode when the mobile device is at a particular location based on the particular location being of a particular location type. The third-party location data may include external request instructions that indicate that the mobile device be in the quiet mode when the mobile device is at a third-party location. The method may also include placing the mobile device in the quiet mode based on one or more of the activity instructions, the user location instructions and the external request instructions.

Abstract: A method may include receiving one or more of calendar data, user location data and third-party location data. The calendar data may include activity instructions that indicate that a mobile device be in a quiet mode during a particular activity included in the calendar data. The user location data may include user location instructions that indicate that the mobile device be in the quiet mode when the mobile device is at a particular location based on the particular location being of a particular location type. The third-party location data may include external request instructions that indicate that the mobile device be in the quiet mode when the mobile device is at a third-party location. The method may also include placing the mobile device in the quiet mode based on one or more of the activity instructions, the user location instructions and the external request instructions.