Abstract: An example computing system retrieves information from one or more applications, and receives an indication of a natural language input. The computing system applies a machine learning model to at least a portion of the information and at least a portion of the indication of the natural language input to generate instructions for generating at least one graphical component, e.g., a custom widget. The instructions may include a temporal parameter for the at least one graphical component, in which the computing system may update, based on the temporal parameter, the instructions for generating the at least one graphical component to update the at least one graphical component.

Abstract: An example computing system retrieves, using an application programming interface, information from one or more applications, and generates, based on at least a portion of the information, personal context information for a user. Responsive to receiving an indication of a natural language request to generate at least one graphical component, the computing system applies a machine learning model to the indication of the natural language request to determine at least one user intent. The computing system generates, using one or more of the information from the one or more applications and the personal context information for the user, a set of instructions including instructions for generating the at least one graphical component, e.g., a widget based on the at least one user intent.

Abstract: In an intelligent production method, external dependency relationships between different ME and internal dependency relationships within a same ME are determined. A composite DAG, a device model, a smart factory model are constructed. Then, the smart factory model is solved using a linear goal programming algorithm with an objective of minimizing a total cost, to obtain an optimal transmission power allocation ratio and an optimal offloading decision. An optimal production plan is determined based on the optimal transmission power allocation ratio and the optimal offloading decision. And an intelligent production is executed according to the optimal production plan.

Abstract: A method including receiving sound data captured by a wearable device of the user, the sound data indicative of contact between a first portion of the user wearing the wearable device and a second portion of the user wearing the wearable device; receiving motion data captured by the wearable device of the user, the motion data indicative of at least a movement of the first portion of the user wearing the wearable device; and determining, by a processor, based at least in part on the sound data and the motion data, a user input associated with the contact between a first portion of the user wearing the wearable device and a second portion of the user wearing the wearable device and the movement of the first portion of the user wearing the wearable device.

Abstract: A method including receiving sound data captured by a wearable device of the user, the sound data indicative of contact between a first portion of the user wearing the wearable device and a second portion of the user wearing the wearable device; receiving motion data captured by the wearable device of the user, the motion data indicative of at least a movement of the first portion of the user wearing the wearable device; and determining, by a processor, based at least in part on the sound data and the motion data, a user input associated with the contact between a first portion of the user wearing the wearable device and a second portion of the user wearing the wearable device and the movement of the first portion of the user wearing the wearable device.