Abstract: Provided is a method for authenticating identity.

Abstract: Provided is a recommendation method. The method includes: determining a target user identifier and a plurality of candidate item identifiers; acquiring the intermediate user feature vector corresponding to the target user identifier by searching for the target user identifier; acquiring the intermediate item feature vectors corresponding to the plurality of candidate item identifiers by searching for the plurality of candidate item identifiers; outputting degrees of matching of the plurality of candidate item identifiers and the target user identifier by predicting the intermediate user feature vector and the intermediate item feature vectors in the online prediction model; and determining a target item identifier to be recommended to the target user identifier from the plurality of candidate item identifiers.

Abstract: Provided is a method for training parameter estimation models. The method includes: estimating reconstruction parameters specified for three-dimensional face reconstruction by inputting training samples in a face image training set to a pre-constructed neural network model, and reconstructing three-dimensional faces corresponding to the training samples by inputting the reconstruction parameters to a pre-constructed three-dimensional morphable model; calculating a plurality of loss functions of a plurality of pieces of two-dimensional supervision information between the three-dimensional faces and the training samples, and adjusting weights corresponding to the plurality of loss functions; and generating fitting loss functions based on the plurality of loss functions and the weights corresponding to the plurality of loss functions, and acquiring a trained parameter estimation model by performing an inverse correction on the neural network model using the fitting loss functions.

Abstract: A method for training models is provided. The method includes: inputting an image training sample corresponding to a current iteration into a current posture detection network model, and acquiring a first loss function corresponding to the current iteration; re-projecting the current output result of the current posture detection network model, and acquiring a second loss function corresponding to the current iteration; and acquiring a posture detection network model for a next iteration by performing backpropagation on the current posture detection network model, and achieving training of the posture detection network model by performing the next iteration before an iteration end condition is met.

Abstract: Provided is a language labeling method, including: determining a language classifier; collecting a plurality of pieces of information correlated to video data, and determining the plurality of pieces of information as a plurality of pieces of video information; dividing the plurality of pieces of video information into target information and reference information; identifying languages to which the plurality of pieces of video information is attributed by inputting the plurality of pieces of video information into the language classifier; and checking a confidence level of a target language with an aid of reference languages, wherein the target language is a language to which the target information is attributed and the reference languages are a plurality of languages to which the reference information is attributed.

Abstract: Provided is a method and a device for segmenting words, and a storage medium. The method includes: acquiring a plurality of groups of high-resource language (HRL) data, and acquiring a plurality of groups of first word segmentation language data by processing the plurality of groups of HRL data; acquiring a plurality of groups of low-resource language (LRL) data, acquiring a plurality of candidate word segments, and selecting second word segmentation language data from the plurality of candidate word segments; acquiring a word segmentation model by training based on the second word segmentation language data, and outputting a plurality of candidate word segmentation results; and selecting the candidate word segmentation result with a highest matching degree as a word segmentation result based on a matching degree between each of the candidate word segmentation results and the first word segmentation corpus.

Abstract: Provided are an information management system and method for a communication application, and a display terminal. The system includes a personal information display module and a posted information display module; the personal information display module is configured to display personal information of a poster; and the posted information display module is configured to dynamically display a plurality of posted contents contained in a moment posted by the poster at current time.

Abstract: Provided is a method for video predictive coding. The method includes: acquiring decision information related to a current coding block in inter-frame predictive coding, wherein the decision information includes one of: pre-analysis information determined by a coder in a lightweight video coding pre-analysis, coding information of a plurality of sub-blocks acquired by recursively coding the current coding block, and information of an executed mode determined based on the executed mode; and determining, based on the decision information, whether to skip motion estimation (ME) coding of the current coding block.

Abstract: Provided is a method for allocating resource objects based on live streaming rooms. The method is applicable to a primary node on a server side, and includes: acquiring synchronization data and local data corresponding to a predetermined resource object in a target live streaming room, wherein the synchronization data is synchronously stored between the primary node and a secondary node, and includes a waiting queue of waiting users; and the local data is stored in the primary node, and includes second user data of an invited user; determining a target user based on a sequence of the waiting users in the waiting queue; acquiring a current state of the target user by comparing the synchronization data and the local data for the target user; and determining, based on the current state, whether to send an invitation for allocating the predetermined resource object to a target user side.

Abstract: Provided is a data encryption method. The data encryption method includes: determining source data requested to be encrypted by an application in an untrusted execution environment; generating reference data corresponding to the source data in the untrusted execution environment, wherein the reference data is data for one-time use in encrypting the source data; reading a primary key generated for the application in a trusted execution environment; acquiring security data by encrypting the reference data using the primary key in the trusted execution environment; and acquiring target data by encrypting the source data using the security data in the untrusted execution environment.

Abstract: Provided is a video denoising method, applicable to a mobile terminal. The method includes acquiring video data, acquiring environmental parameters related to denoising in an environment of the mobile terminal, calculating an extent of conflict between the environment and the denoising based on the environmental parameters, and determining, based on the extent of conflict, a state of denoising the video data.

Abstract: A method for detecting and tracking a target is provided. The method includes: acquiring a target tracking result of each video frame of a plurality of video frames received from a video stream by inputting the each video frame of the plurality of video frames into a tracking network; in response to a video frame of the plurality of video frames being a key frame, acquiring a target detection result output by a detection network upon receiving a last delay frame in a specified number of delay frames by inputting the key frame into the detection network and controlling the detection network to operate during a period of receiving the specified number of delay frames; and generating a final target tracking result based on the target detection result and a target tracking result of the last delay frame.

Abstract: Provided is a method for training a content moderation mode. The method includes extracting part of image data of a sample video file as sample image data; positioning a time point of the sample image data in the sample video file in the case that the sample image data contains offensive content; extracting salient image region data from the image data around the time point; and training the content moderation model based on the image region data and the sample image data.

Abstract: Provided is a method for training credit thresholds, including: acquiring a plurality of service features from history data of a service operation triggered based on a plurality of IP addresses; hierarchically calculating at least two correlation coefficients for each of the service features; generating, for each of the IP addresses based on the correlation coefficients corresponding to the service features, a credit value indicating the validity of each of the IP addresses; generating an evaluation indicator for the plurality of IP addresses; and determining a credit value corresponding to the evaluation indicator as a credit threshold in response to the evaluation indicator meeting a target condition.

Abstract: Provided is a method for training an image generation model, including: acquiring a first transformation model by training; acquiring a reconstruction model by training based on the first transformation model; acquiring a second transformation model by training; generating a grafted transformation model by grafting the first transformation model with the second transformation model; and generating the image generation model based on the reconstruction model and the grafted transformation model. The first transformation model is configured to generate a first training image according to a first noise sample. The first training image is an image of a first style. The reconstruction model is configured to map an original image sample to a latent variable corresponding to the original image sample. The second transformation model is configured to generate a second training image according to a second noise sample. The second training image is an image of a second style.

Abstract: Provided is a method for selecting a server. The method includes: receiving a first group call request from a calling party of a target group, wherein the first group call request carries information of target regions of session members in the target group; determining, according to first delay periods of user devices in each of the target regions in data communication with directly connected servers, a first server corresponding to the each of the target regions; and determining, according to an average delay period of the first server in data communication with the user devices in the each of the target regions, a central server for a group call of the target group.

Abstract: Provided is a task processing method based on a neural network, including: acquiring input data, wherein the input data is intended to trigger thread tasks, and is source input data or cache exchange data; generating processing result data by scheduling at least two corresponding module threads in parallel based on at least two triggered thread tasks to process the input data, wherein the at least two module threads respectively correspond to at least two network modules in the neural network; and outputting the processing result data to a cache as the cache exchange data of module threads except the at least two module threads, or outputting the processing result data as a processing result of the source input data.

Abstract: Provided is a method for video predictive coding. The method includes: determining, according to an executed mode, information of the executed mode in a decision making process of a best mode of a current prediction unit in inter-frame prediction, wherein the information of the executed mode includes a temporary best mode and a cost of the temporary best mode; and determining, based on the information of the executed mode, whether to skip an intra-frame prediction mode of the decision making process.

Abstract: A method for training isolation forests includes: acquiring a plurality of categories by classifying uniform resource identifiers; acquiring sample behavior data by monitoring a behavior of a client from each of Internet Protocol IP addresses in a plurality of IP addresses accessing the uniform resource identifiers under the plurality of categories; encoding the sample behavior data as a sample access vector; and training, based on the sample access vector, an isolation forest for recognizing a web crawler from the client.