Abstract: A reinforcement learning-based system for adaptively controlling computing performance is provided. The system includes an environment module and an agent module. The environment module is configured to collect environment information from the application environment. Based on the collected environment information, the environment module calculates a reward value using a reward function and then outputs the state data and the reward value to the agent module. The agent module receives the output from the environment module, including the reward value and the state data. Based on the received reward value and state data, the agent module determines a performance adjustment action to take, which is then fed back to the application environment. Upon receiving the performance adjustment action, the application environment executes the performance adjustment operation in response, causing the environment module to collect the updated environment information as a result of the performance adjustment operation.

Abstract: A reinforcement learning-based system for adaptively adjusting computing capacity is provided. The system includes an environment module and an agent module. The environment module is configured to collect environment information, including the actual power consumption and one or more power-related metrics, from an application environment. The environment module is further configured to determine a reward value based on the actual power consumption and the expected power consumption, and determine state data based on the one or more power-related metrics. The agent module is configured to receive the reward value and the state data from the environment module, and determine an adjustment action based on the reward value and the state data. The adjustment action involves adjusting the computing capacity and is dynamically executed by the application environment.

Abstract: An electronic device including a display panel and a CPU is provided. The display panel updates displayed images at a refresh rate. The CPU implements a latency monitor, a system resource controller, a display controller, and an application. The latency monitor collects time information related to touch latency. The touch latency is the duration between the time point at which the display panel detects a touch event and the time point at which the display panel displays an image generated by the application in response to said touch event. The display controller informs the system resource controller of the refresh rate. The system resource controller adjusts the resource allocation of the electronic device to cause the touch latency to be lower than a threshold, according to the time information and the refresh rate.

Abstract: The present invention relates to a learning and creation system based on social network, comprising: a central server installed with a social network based learning creation computer program product comprising a concept map collaborative module and an instant messaging module; and a plurality of user devices, each of which the plurality of user devices executes an internet browser, in communication with the central server through an internet, and accesses the social network based learning creation computer program product through the internet browser and provides the concept map collaborative module and the instant messaging module for a user to operate in response to an operation from the user, wherein the concept map collaborative module is configured to provide a plurality of graphical objects based on a graphical concept map for a plurality of the users to perform a co-construction for the same concept and provide for the plurality of the users to engage an instant messaging by enabling the instant messag