Abstract: Provided are an interaction method and apparatus in a live streaming room, and a device and a storage medium. The method comprises: in response to a trigger operation for a preset duet entry on a live streaming room page, jumping from the live streaming room page to a camera capture page, wherein a host portrait cutout corresponding to the live streaming room page is displayed on the camera capture page and a capture control is provided on the camera capture page acquiring multimedia information captured by a camera, in response to a trigger operation for the capture control on the camera capture page, wherein the multimedia information comprises an image or a video; and synthesizing the multimedia information with the host portrait cutout to obtain a duet multimedia product.

Abstract: This specification provides a training method of a hybrid graph neural network model. The hybrid graph neural network model includes an encoding function and a decoding function. The method includes the following: using instances corresponding to all targets in training samples and several nearest neighbors of the instances as nodes in a graph, a graph representation vector of each instance is generated by using the encoding function based on graph data of all the instances. t rounds of training are performed on a decoding parameter; and in each round, bs targets are extracted from training samples, a predicted quantity of each target is generated by using the decoding function based on the graph representation vector of the instance corresponding to each target and non-graph data corresponding to each target, and the decoding parameter is optimized based on a loss quantity of the current round that is determined by the predicted quantities and label quantities of the bs targets in the current round.

Abstract: Provided are an interaction method and apparatus in a live streaming room, a device, and a storage medium. The method comprises: in response to a trigger operation for a preset drawing entry on a live streaming room page, jumping to a graphic drawing page from the live streaming room page, a drawing trajectory set for a preset object being displayed on the graphical drawing page; when a drawing stroke on the graphic drawing page is received, matching the drawing stroke with the drawing trajectory; and if it is determined that the drawing stroke is successfully matched with the drawing trajectory, displaying prompt information about successful participation in a preset activity, the preset activity and the preset drawing entry having a correspondence.

Abstract: A multi-machine cooperation method, a scheduling device, and a multi-machine cooperation system are described. The multi-machine cooperation method includes: determining, by a first autonomous robot when detecting an abnormal condition during operation, whether the abnormal condition can be independently processed; and when the abnormal condition cannot be independently processed, sending, by the first autonomous robot, an assistance request to another device in an Internet of Things in which the first autonomous robot is located. In the specification, a multi-machine cooperation operation between autonomous robots or between an autonomous robot and another device can be implemented.

Abstract: A cryptographic acceleration card generates, using an attribute unique to a blockchain integrated station that comprises the cryptographic acceleration card, an identity private key for the blockchain integrated station. The cryptographic acceleration card generates a private key ciphertext by encrypting the identity private key. The cryptographic acceleration card stores the private key ciphertext.

Abstract: A cryptographic acceleration card included in a blockchain integrated station sends negotiation information to a provider of a new disk image, where the negotiation information is used by the provider to determine a deployment key, and where the new disk image is used to update an old disk image included in the blockchain integrated station. The cryptographic acceleration card receives a new hash value encrypted by the provider using the deployment key, where the new hash value corresponds to the new disk image. The cryptographic acceleration card replaces an old hash value corresponding to the old disk image with the new hash value, where the new hash value is compared with a current hash value of a disk image included in the blockchain integrated station to determine whether the disk image matches the new disk image.

Abstract: Methods, systems, and apparatus for training a graph neural network. An example method includes obtaining a complete graph; dividing the complete graph into a plurality of subgraphs; obtaining a training graph to participate in graph neural network training based on selecting at least one subgraph from the plurality of subgraphs; obtaining, based on the training graph, a node feature vector of each node in the training graph; obtaining a node fusion vector of each current node in the training graph; determining a loss function based on node labels and the node fusion vectors in the training graph; and iteratively training the graph neural network to update parameter values of the graph neural network based on optimizing the loss function.

Abstract: Implementations of this specification provide a method, an apparatus, and an electronic device for improving performance of a central processing unit (CPU) comprising a plurality of CPU dies. The method includes the following: enabling threads in each CPU die of the CPU to compete for a mutex of a respective CPU die; identifying the plurality of threads that have obtained the mutexes; enabling the plurality of threads that have obtained the mutexes to compete for a spin lock of the CPU; identifying, from the plurality of threads, a target thread that has obtained the spin lock; executing a critical section corresponding to the target thread that has obtained the spin lock; and releasing the mutex and the spin lock that are obtained by the target thread.

Abstract: Methods, systems, and apparatus for training a graph neural network. An example method includes obtaining a complete graph; dividing the complete graph into a plurality of subgraphs; obtaining a training graph to participate in graph neural network training based on selecting at least one subgraph from the plurality of subgraphs; obtaining, based on the training graph, a node feature vector of each node in the training graph; obtaining a node fusion vector of each current node in the training graph; determining a loss function based on node labels and the node fusion vectors in the training graph; and iteratively training the graph neural network to update parameter values of the graph neural network based on optimizing the loss function.

Abstract: A computer-implemented method for multi-thread processing, the method including: compiling a first plurality of threads using a corresponding first register set for each thread in the first plurality of threads, to obtain a first plurality of corresponding machine instruction codes; and fusing the first plurality of machine instruction codes using first instructions in an instruction set supported by a processing core, to obtain machine instruction code of a fused thread, the machine instruction code of the fused thread including thread portions corresponding to each thread of the first plurality of threads, in which the first instructions include load effective address instructions and control transfer instructions, in which the load effective address instructions and the control transfer instructions are compiled using a second register set, and in which jump operations between thread portions are implemented by the control transfer instructions inserted into the machine instruction code of the fused thread

Abstract: A method for constructing a data processing model, includes: acquiring a model description parameter and sample data of a target data processing model; determining a base model according to the model description parameter and the sample data; and training the base model according to the sample data to obtain the target data processing model.

Abstract: A blockchain integrated station receives a startup instruction. The blockchain integrated station computes a current hash value of a disk image included in the blockchain integrated station. The blockchain integrated station sends the current hash value to a cryptographic acceleration card included in the blockchain integrated station. The blockchain integrated station receives, from the cryptographic acceleration card, a comparison result between a hash value stored in the cryptographic acceleration card and the current hash value. In response to determining that the comparison result indicates that the current hash value matches the hash value stored in the cryptographic acceleration card, the blockchain integrated station executes the disk image.

Abstract: A cryptographic acceleration card included in a blockchain integrated station sends negotiation information to a provider of a new disk image, where the negotiation information is used by the provider to determine a deployment key, and where the new disk image is used to update an old disk image included in the blockchain integrated station. The cryptographic acceleration card receives a new hash value encrypted by the provider using the deployment key, where the new hash value corresponds to the new disk image. The cryptographic acceleration card replaces an old hash value corresponding to the old disk image with the new hash value, where the new hash value is compared with a current hash value of a disk image included in the blockchain integrated station to determine whether the disk image matches the new disk image.

Abstract: A cryptographic acceleration card generates, using an attribute unique to a blockchain integrated station that comprises the cryptographic acceleration card, an identity private key for the blockchain integrated station. The cryptographic acceleration card generates a private key ciphertext by encrypting the identity private key. The cryptographic acceleration card stores the private key ciphertext.

Abstract: Embodiments of the specification provide a method and an apparatus for updating shared data in a multi-core processor environment. The multi-processor environment comprises a multi-core processor. The multi-core processor comprises a plurality of separate processing units (referred to as cores, or core processing units (CPUs) in the specification); the multi-core processor is configured to process a multi-threaded task; the multi-threaded task has shared data to update. The method is executed by any CPU. The method may comprise: requesting, by a first CPU, for a lock to execute a critical section function on the shared data, wherein the lock provides permission to update the shared data, and the critical section function updates the shared data; and setting, by the first CPU if the lock is occupied by a second CPU, a memory index corresponding to the critical section function in a memory of the lock for the second CPU to execute the critical section function based on the memory index.

Abstract: Implementations of this specification provide a method, an apparatus, and an electronic device for improving performance of a central processing unit (CPU) comprising a plurality of CPU dies. The method includes the following: enabling threads in each CPU die of the CPU to compete for a mutex of a respective CPU die; identifying the plurality of threads that have obtained the mutexes; enabling the plurality of threads that have obtained the mutexes to compete for a spin lock of the CPU; identifying, from the plurality of threads, a target thread that has obtained the spin lock; executing a critical section corresponding to the target thread that has obtained the spin lock; and releasing the mutex and the spin lock that are obtained by the target thread.

Abstract: Implementations of the present specification provide a method, an apparatus, and an electronic device for improving parallel performance of a CPU. The method includes: attempting to acquire data requests that are of a same type and that are allocated to the CPU core; determining a number of requests that are specified by the acquired one or more data requests; and in response to determining that the number of requests is greater than or equal to a maximum degree of parallelism: executing executable codes corresponding to the maximum degree of parallelism, wherein the maximum degree of parallelism is a maximum number of parallel threads executable by the CPU, and wherein the executable codes comprise code programs that are compiled and linked based on the maximum degree of parallelism at a time that is prior to a time of the executing.

Abstract: A Remote Direct Memory Access (RDMA) based data transmission method is disclosed. In this method, an RDMA network interface card is used to encrypt data to improve the security of data transmission. In addition, the data encryption and decryption operation is implemented inside the RDMA network interface card and is transparent to software, thereby ensuring security without adversely affecting performance of an application program.

Abstract: A Remote Direct Memory Access (RDMA) data sending method is disclosed. The method is applicable to a sending end, with a data-transmission RDMA device disposed thereon. The method includes: the data-transmission RDMA device acquiring raw data; the data-transmission RDMA device compressing the raw data by using a preset compression method to obtain compressed data; and the data-transmission RDMA device encapsulating the compressed data into a data packet, and transmitting the data packet to a receiving end. The data packet may include a method tag corresponding to the preset compression method. In this method, the compression and transmission of the raw data are conducted by the data-transmission RDMA device on the hardware level.