Abstract: A data processing method includes: obtaining a first embedding vector for indicating a known data unit and a position of the known data unit and a second embedding vector for indicating a position of a to-be-predicted data unit; processing the first embedding vector by using a target encoder, to obtain an output vector; and processing the output vector and the second embedding vector by using a target prediction network, to obtain a to-be-predicted data unit. According to the method, M pieces of additional position information do not need to be separately set as input of the target encoder, and a quantity of latent variables of intermediate output of the target encoder is also consistent with a quantity of input embedding vectors, thereby reducing a computation amount and memory consumption of the target encoder.

Abstract: A system for scheduling mobile augmented reality tasks performed on at least one mobile device and a workspace includes: a mobile device, comprising a central processing unit (CPU) and a graphics processing unit (GPU); a Network Profiling Component, configured to gather network related context data; a Device Profiling Component, configured to gather hardware related context data; an Application Profiling Component, configured to gather application related context data; and a Scheduling Component, configured to receive the network related context data, the hardware related context data, and the application related context data, and to schedule tasks between the CPU, the GPU and the workspace.

Abstract: A system for scheduling mobile augmented reality tasks performed on at least one mobile device and a workspace includes: a mobile device, comprising a central processing unit (CPU) and a graphics processing unit (GPU); a Network Profiling Component, configured to gather network related context data; a Device Profiling Component, configured to gather hardware related context data; an Application Profiling Component, configured to gather application related context data; and a Scheduling Component, configured to receive the network related context data, the hardware related context data, and the application related context data, and to schedule tasks between the CPU, the GPU and the workspace.