The present invention discloses an AVS scanning method, wherein the AVS scanning method includes the steps of: mounting a system on chip (SoC) on a printed circuit board (PCB), and connecting the SoC to a storage unit; enabling the SoC to read a boot code from the storage unit, and executing the boot code to perform an AVS scanning operation on the SoC to determine a plurality of target supply voltages respectively corresponding to a plurality of operating frequencies of the SoC to establish an AVS look-up table; and storing the AVS look-up table into the SoC or the storage unit.

A method for managing data includes obtaining, by a first data node, a notification, wherein the first data node is associated with a first power zone group (PZG), and in response to the notification: selecting a second data node, wherein the second data node is not associated with the first PZG, sending a data processing request to the second data node, obtaining a response based on the data processing request, wherein the response specifies a confirmation by the second data node to service the data processing request, storing a ledger entry in a ledger service that indicates the confirmation, and initiating a data transfer based on the data processing request, wherein the first data node is associated with the PZG based on a primary power source of the first data node.

Data corresponding to an image selected by a user is transmitted to a printing apparatus by a program being executed, and it is determined whether printing based on the data has been completed by the printing apparatus. Then, an input screen for the user to input a rating of the program is displayed on a display used by an information processing apparatus on a condition that the printing has been determined as having been completed.

In some embodiments, methods and systems for pushing audiovisual playlists based on a text-attentional convolutional neural network include a local voice interactive terminal, a dialog system server and a playlist recommendation engine, where the dialog system server and the playlist recommendation engine are respectively connected to the local voice interactive terminal. In some embodiments, the local voice interactive terminal includes a microphone array, a host computer connected to the microphone array, and a voice synthesis chip board connected to the microphone array. In some embodiments, the playlist recommendation engine obtains rating data based on a rating predictor constructed by the neural network; the host computer parses the data into recommended playlist information; and the voice terminal synthesizes the results and pushes them to a user in the form of voice.

Systems, apparatus, and methods that control power consumption in a processor are disclosed. One system apparatus, and method includes a processor that operates in at least a first power control mode including a first power amount and a second power control mode including a second power amount lower than the first power amount and a power control device. The power control device is configured to control power consumption in the processor, change a power control mode of the processor to the first power control mode in response to a first excess time period in which the power consumption of the processor exceeds a first reference power for a first period of time, and change the power control mode of the processor to the second power control mode in response to a second period of time in which the power consumption is less than or equal to a second reference power.

An information processor includes an operation history obtaining unit configured to obtain operation histories created user operations at a terminal device; a function identifying unit configured to, based on the obtained operation histories, identify a function performed by the user operations as an operation target function; an operation extracting unit configured to, based on information about the operation target function identified by the function identifying unit, extract predetermined operation histories from the obtained operation histories; an index calculating unit configured to calculate an index which indicates a level of efficiency of the operations for the operation histories extracted by the operation extracting unit; an operation selecting unit configured to, based on the index, select the operation histories having a predetermined efficiency; and an output controller configured to output a guide information based on the operation histories selected by the operation selecting unit.

Transaction-enabled systems and methods for royalty apportionment and stacking are disclosed. An example system may include a plurality of royalty generating elements (a royalty stack) each related to a corresponding one or more of a plurality of intellectual property (IP) assets (an aggregate stack of IP). The system may further include a royalty apportionment wrapper to interpret IP licensing terms and apportion royalties to a plurality of owning entities corresponding to the aggregate stack of IP in response to the IP licensing terms and a smart contract wrapper. The smart contract wrapper is configured to access a distributed ledger, interpret an IP description value and IP addition request, to add an IP asset to the aggregate stack of IP, and to adjust the royalty stack.

This application relates to the field of artificial intelligence technologies, and discloses a machine learning model search method, a related apparatus, and a device. In the method, before model search and quantization, a plurality of single bit models are generated based on a to-be-quantized model, and evaluation parameters of layer structures in the plurality of single bit models are obtained. Further, after a candidate model selected from a candidate set is trained and tested, to obtain a target model, a quantization weight of each layer structure in the target model may be determined based on a network structure of the target model and evaluation parameters of all layer structures in the target model, a layer structure with a maximum quantization weight in the target model is quantized, and a model obtained through quantization is added to the candidate set.

A technology trend prediction method and system are provided. The method comprises acquiring paper data, and further comprises following steps: processing the paper data to generate a candidate technology lexicon; screening the candidate technology lexicon based on mutual information; calculating an independent word forming probability of an OOV word; extracting missed words in a title using a bidirectional long short-term memory network and a conditional random field (BI-LSTM+CRF) model; predicting a technology trend. The technology trend prediction method and system provided analyzes relationship of technology changes in a high-dimensional space, and predicts a development of technology trend based on time by extracting technical features of papers through natural language processing and time sequence algorithms.

An intelligent adaptive recommendation method based on an MCM model. The method includes acquiring historical data of errors on knowledge points of all students, acquiring error-cause labels of current student, calculating error-cause priority value P(E) for each error-cause label of current student, and extracting, according to at least one of MCM labels corresponding to each error-cause label, MCM learning resources corresponding to at least one of MCM labels from a preset content management system, sorting error-cause labels according to descending order of error-cause priority value P(E), extracting part or all of MCM learning resources from MCM learning resources corresponding to at least one error-cause label according to sorting result and pushing part or all of MCM learning resources to current student, and when current student finishes learning MCM learning resources corresponding to each MCM label, pushing errors on knowledge points corresponding to each MCM label to current student.