Abstract: Provided is a graph data processing method, including: acquiring a degree of association of each node in a network graph; splitting the network graph to obtain a dense subgraph according to the degree of association of each node; determining stable nodes in the network graph and coreness of the stable nodes based on the dense subgraph, the coreness of the stable nodes being greater than a preset threshold; obtaining a sparse subgraph in the network graph according to the preset threshold, and remaining nodes in the network graph other than the stable nodes and connecting edges between the remaining nodes; and determining the coreness of each node in the sparse subgraph based on the sparse subgraph and the stable nodes.

Abstract: This application relates to a data processing method performed by a computer device. The method includes: for each block of a plurality of blocks in a first block file of a blockchain platform, detecting a to-be-maintained transaction record in the block; selecting, from a hash tree of the block, nodes used for proving that the transaction record belongs to the block; constructing a node maintenance tree according to the selected nodes; storing the transaction record at a corresponding leaf node of the node maintenance tree; migrating an association between a block head of the block and the node maintenance tree storing the transaction record from the first block file to a second block file, the block head of the block being adjacent to a block head of a previous block in the second block file; and deleting the first block file after the update processing for each block is completed.

Abstract: A method of porting a first application, corresponding to a third-party development tool, into a second application, corresponding to a sub-application that depends on a parent application to run, is performed by an electronic device. The device transforms the first application from a first project mechanism into a second project mechanism. The device converts a design resource in the transformed first application from a first resource format into a second resource format. The device exports the design resource to a development tool of the sub-application. The device converts a code resource in the transformed first application from a first programming language into a second programming language. The device exports the code resource to the development tool of the sub-application. The device also performs secondary development on the design resource and the code resource in the development tool of the sub-application.

Abstract: The present disclosure provides methods, apparatus, and storage medium for generating a training sample of a semantic segmentation model. The method includes obtaining an object identifier of a to-be-trained object and a model file corresponding to the object identifier, the model file comprising a three-dimensional model of the to-be-trained object and a skin map set, the skin map set comprising a plurality of solid-color skin maps and a plurality of colorful skin maps; determining transformation models corresponding to the three-dimensional model; generating a plurality of sample image groups corresponding to each transformation model according to the plurality of colorful skin maps and each transformation model; generating a labeled image of the each sample image group according to the object identifier, the plurality of solid-color skin maps, and each transformation model; and generating a training sample of the to-be-trained object according to the labeled image and the each sample image group.

Abstract: Embodiments of this disclosure include an encoding method and apparatus. The encoding may include obtaining a target paragraph and a context sentence of the target paragraph, and inputting the target paragraph and the context sentence into a memory encoding model. The encoding may further include obtaining an original vector set and a memory vector set in the input layer and obtaining a first target sentence matrix of the original vector set in the memory layer according to the original vector set and the memory vector set. The encoding may further include obtaining a paragraph vector of the target paragraph in the output layer according to the first target sentence matrix and performing processing based on the paragraph vector.

Abstract: Aspects of the disclosure provide methods and apparatuses for storing data. In some examples, an information processing apparatus that includes processing circuitry is provided. The processing circuitry is configured to receive data and determine a target blockchain based on an attribute associated with the data and blockchain correspondence information. The blockchain correspondence information indicates attributes associated with a plurality of blockchains. Further, the processing circuitry is configured to store a first block that is generated based on the received data in the target blockchain.

Abstract: A speech decoding method is performed by a computer device, the speech including a current audio frame and a previous audio frame. The method includes: obtaining a target token corresponding to a smallest decoding score from a first token list including first tokens obtained by decoding the previous audio frame, each first token including a state pair and a decoding score, the state pair being used for characterizing a correspondence between a first state of the first token in a first decoding network corresponding to a low-order language model and a second state of the first token in a second decoding network corresponding to a differential language model; determining pruning parameters according to the target token and an acoustic vector of the current audio frame when the current audio frame is decoded; and decoding the current audio frame according to the first token list, the pruning parameters, and the acoustic vector.

Abstract: Embodiments of this disclosure include a training method for an image processing model. In the method, parameters of an encoder in the image processing model are updated according to a to-be-replaced face in an original image, to configure the encoder to encode the to-be-replaced face to obtain a visual feature of the to-be-replaced face. Parameters of a decoder in the image processing model are updated according to the to-be-replaced face in the original image, to configure the decoder to perform decoding based on the visual feature of the to-be-replaced face. The parameters of the decoder are further updated according to a target face in a target image without changing the parameters of the encoder, to configure the decoder to perform decoding based on the visual feature of the to-be-replaced face and obtain a target face having the same visual feature as the to-be-replaced face.

Abstract: A spectator interaction method includes displaying an interactive control for a first spectator account watching an online battle, the interactive control configured to trigger an interaction between the first spectator account and one or more spectator accounts currently watching the online battle. The method further includes displaying first interactive content in response to receiving a trigger operation on the interactive control. The method further includes displaying a quantity of spectator accounts corresponding to the first interactive content, the quantity of spectator accounts including accounts that have interacted with the first interactive content, and accounts that have interacted with second interactive content triggered by a second spectator account. The second interactive content being a same interactive content type as the first interactive content.

Abstract: A conference data processing method includes acquiring a first meeting list of meetings created by a user, meetings in the first meeting list having been created by the user through an instant messaging (IM) client. The IM client includes an interface for calling a software development kit (SDK) of a separate conference application implementing at least one of audio or video conferencing. The method further includes requesting, through a fusion server, a server of the conference application to create a second meeting list according to the first meeting list, and receiving, through the fusion server, the second meeting list transmitted by the server of the conference application. The second meeting list includes meetings that correspond respectively to the meetings in the first meeting list. The method further includes establishing an association relationship between a meeting in the first meeting list and a meeting in the second meeting list.

Abstract: In a data processing method for a point cloud media, a media file of the point cloud media is obtained. The media file includes stream data of a plurality of point cloud frames and decoding indication information of each of the plurality of point cloud frames. The decoding indication information of each of the plurality of point cloud frames includes at least two attribute headers. Each of the at least two attribute headers includes attribute identifier information. The point cloud media is processed based on the stream data and the decoding indication information.

Abstract: A point cloud decoding method is provided. In the method, a target point cloud group of a plurality of point cloud groups is obtained. The target point cloud group includes at least one point. An attribute coding mode of the target point cloud group is determined. Prediction attribute information of each point in the target point cloud group is obtained based on attribute prediction of the respective point in the target point cloud group according to the attribute coding mode of the target point cloud group. Reconstruction residual information of each point in the target point cloud group is obtained based on attribute decoding of the respective point in the target point cloud group. Reconstruction attribute information of each point in the target point cloud group is determined according to the prediction attribute information and the reconstruction residual information of the respective point in the target point cloud group.

Abstract: A virtual interaction method including displaying a virtual scenario comprising a first virtual figure and a second virtual figure that participate in a battle game task, wherein the first virtual figure is controlled by a first device. The method further includes, in response to a trigger operation on the first device, displaying the first virtual figure and a target virtual figure performing an action directed to the second virtual figure together, wherein the target virtual figure is not controlled by the first device.

Abstract: The present disclosure provides an endoscopic image processing method and system, and a computer device. The method can include acquiring a current endoscopic image of a to-be-examined user, and predicting the current endoscopic image by using a deep convolutional network based on a training parameter. The training parameter can be determined according to at least one first endoscopic image and at least one second endoscopic image transformed from the at least one first endoscopic image, where the at least one endoscopic image corresponds to a human body part. The method can further include determining an organ category corresponding to the current endoscopic image. The method provided in the present disclosure can make a prediction process more intelligent and more robust, thereby improving resource utilization of a processing apparatus.

Abstract: A method for obtaining sensing data includes transmitting, by a network function element to a terminal device, sensing data request information comprising parameters for sensing and reporting data by the terminal device. The sensing data request information indicates, to the terminal device, to report sensing data to the network function element according to the parameters. The method further includes obtaining sensing data returned by the terminal device, the sensing data being transmitted by the terminal device according to the parameters indicated in the sensing data request information. The method further includes preprocessing, by the network function element, the sensing data and providing a result of the preprocessing to a server.

Abstract: A video encoding/decoding approach including generating a prediction vector candidate list according to displacement vectors of an encoded/decoded block whose reference frame is a current frame; selecting a prediction vector of a to-be-encoded/decoded current block from the prediction vector candidate list; and encoding/decoding the current block based on the prediction vector of the current block.

Abstract: A picture filtering method includes determining encoding information of a picture, the encoding information comprising classification information indicating one of an intra encoding mode or an inter encoding mode of the picture. The method further includes inputting the picture and the classification information indicating one of the intra encoding mode or the inter encoding mode into a general filtering model trained using deep learning, and obtaining a filtered picture based on the filtering model performing filtering on the picture based on the encoding information.

Abstract: A coding block processing method includes receiving a bit stream, and determining, according to the bit stream, that a current coding block is coded in intra block copy mode, and that the current coding block overlaps a reference coding block. The method further includes, in response to the determination that the current coding block overlaps the reference coding block, determining that the current coding block is a merged block and partitioning the merged block into plural sub-blocks. The method further includes determining motion information of the plural sub-blocks based on one set of motion information indicated in the bit stream, such that the plural subblocks have the same motion information.

Abstract: A point cloud including a plurality of points is acquired. Each of the plurality of points includes N pieces of attribute information. N is a positive integer greater than 1. A to-be-coded value is determined for each of the N pieces of attribute information of a current point of the plurality of points based on the N pieces of attribute information of a point prior to the current point being encoded. The to-be-coded values of the N pieces of attribute information of the current point are encoded based on at least one of a selected encoder or a selected coding mode to obtain a code stream of the point cloud. The to-be-coded value for each of the N pieces of attribute information of the current point is encoded based on a respective one of the one or more encoders and a respective one of the one or more coding modes.

Abstract: A translation method is provided, including: encoding to-be-processed text information to obtain a source vector representation sequence, the to-be-processed text information belonging to a first language; obtaining a source context vector corresponding to a first instance according to the source vector representation sequence, the source context vector indicating to-be-processed source content in the to-be-processed text information at the first instance; determining a translation vector according to the source vector representation sequence and the source context vector; and decoding the translation vector and the source context vector, to obtain target information of the first instance, the target information belonging to a second language.