Abstract: A query request is received for querying graph data, including a filtering condition for an attribute value of a first attribute, and used to query a destination edge in a neighboring edge of a first node that satisfies the filtering condition. Graph data includes point data of the first node and edge data of a neighboring edge stored in a single-point data block corresponding to the first node and comprising index data of the edge data used to index a first attribute of the edge data, record the attribute value of the first attribute, and record a storage location of an edge corresponding to the attribute value of the first attribute in the single-point data block. Using the index data and filtering condition, a storage location of the destination edge in the single-point data block is determined and data of the destination edge obtained.

Abstract: Embodiments of this application provide methods and apparatuses for offline payment authorization. In an implementation, a method comprising: obtaining wallet account information, a current account balance, and current credit information of a target electronic wallet account in response to determining that a current state of the target electronic wallet account satisfies a predetermined certificate delivery condition, determining an offline payment limit based on the current account balance and the current credit information, signing summary information by using a private key of a server to obtain an electronic wallet signature, generating a user certificate based on the summary information and the electronic wallet signature, and sending the user certificate to a terminal device associated with the target electronic wallet account for an offline account corresponding to the target electronic wallet account to make offline payment within the offline payment limit based on the user certificate.

Abstract: Example methods, apparatuses, and computer-readable media for offline transaction processing are disclosed.

Abstract: This specification discloses data storage methods, apparatuses, devices, and storage media. One method comprises receiving data to be stored, wherein the data comprise data corresponding to a plurality of fields, determining whether a size of the data exceeds a predetermined threshold, and in response to determining that the size of the data exceeds the predetermined threshold: splitting a portion of the data to obtain data blocks, determining block identifiers of the data blocks based on a sequence of the data blocks, storing a data identifier of the data and the block identifiers of the data blocks that correspond to each other, and storing the block identifiers and the data blocks that correspond to each other.

Abstract: A resource transfer method includes obtaining, by using a target application, a resource transfer request triggered by a target user, where the resource transfer request includes verification information used to perform resource transfer processing and identity feature information of the target user. By using the target application, verifying the verification information, and invoking, if the verification succeeds by using the target application, a local device management rule, and determining, by using the local device management rule, whether the identity feature information of the target user matches identity feature information of a pre-registered user. If the identity feature information of the target user matches the identity feature information of the pre-registered user, sending the resource transfer request to a first server corresponding to the target application to trigger, based on the resource transfer request, the first server to perform resource transfer processing.

Abstract: A container kernel upgrade based on a programmable container kernel, includes, in response to receiving a container kernel upgrade request, freezing an application container in which a to-be-upgraded first container kernel is located. Current container status data of an application container including first container kernel status data and application status data is stored. The application container is restarted by using a second container kernel used for upgrading a container kernel, where restarted container status data of the restarted application container includes second container kernel status data corresponding to the second container kernel when the application container is restarted. Using the stored current container status data, a corresponding data field is updated in a data structure of the restarted container status data.

Abstract: Implementations of the present specification provide an inter-thread interrupt signal sending method and apparatus. In the inter-thread interrupt signal sending method, a processor in which a first thread is located sends a notification message to a PCI device via a PCI bus by using an MMIO write operation. The MMIO write operation is implemented based on a virtual space address of the first thread to which a memory address of an MMIO memory of the PCI device is mapped. The PCI device generates an interrupt signal for a second thread in response to receiving the notification message, and sends the interrupt signal to a processor in which the second thread is located based on an interrupt signal sending manner configured in interrupt configuration information of the PCI device.

Abstract: This specification describes a zero-trust security container data control. A computer-implemented method includes generating a zero-trust security policy based on a scenario. A data pool for a zero-trust security container is built. The zero-trust security policy is adopted for data to be in or out of the data pool, where admission or egress is allowed for the data to be in or out of the data pool only after the data is verified based on the zero-trust security policy. Data in the data pool is hierarchically isolated based on the zero-trust security policy, so that the data in the data pool can be read directionally.

Abstract: Voice data processing for voice intention understanding includes performing voice data feature extraction on voice data to obtain first feature data. Local frequency domain transform processing is performed on the first feature data to obtain at least one feature map including a plurality of elements and corresponding to one data block in the first feature data, each element corresponding to one frequency in a frequency domain. Constructing, by separately using an element corresponding to each frequency in the at least one feature map a frequency component channel feature map corresponding to each frequency. Selecting at least one target frequency component channel feature map from the constructed frequency component channel feature maps to obtain desensitized feature data of the voice data, where the selected target frequency component feature map is a critical channel feature for voice intention understanding and is a non-critical channel feature for voice signal recognition.

Abstract: Methods, computer-readable media and apparatuses are disclosed for graph data write. In an example, in response to receiving a first graph data write request, row lock indexes of corresponding row locks of target write objects are determined based on object identification information of the target write objects in the first graph data write request, where each target write object corresponds to a row lock. The target write objects are locked based on the row lock indexes of the target write objects; graph data write is performed for the first graph data write request after the target write objects are locked; the row locks held by the first graph data write request are unlocked after graph data of the target write objects is written; and a graph data write result is provided to a user after all the row locks held by the first graph data write request are unlocked.

Abstract: Connecting a product to a monitoring system is described. In a computer-implemented method, meta information of each entity that serves as a hardware basis of each functional part is determined based on each functional part comprised in a to-be-connected product. Based on the meta information of each entity, a model of each entity is constructed that is expressed in a form of a dimension table and that corresponds to each entity. Corresponding meta information content is obtained from the to-be-connected product based on meta information in the model of each entity. A monitoring task is configured based on the model of each entity and a functional part to which each entity belongs. The to-be-connected product is monitored based on the monitoring task and meta information content in the model of each entity.

Abstract: The present specification discloses video matching. In a computer-implemented method, a plurality of feature vectors of a target video is obtained. A candidate video similar to the target video is retrieved from a video database based on the plurality of feature vectors of the target video. A time domain similarity matrix feature map is constructed between the target video and the candidate video based on the target video and the candidate video. Using the time domain similarity matrix feature map as an input into a deep learning detection model, a video segment matching the target video in the candidate video and a corresponding similarity is output.

Abstract: Examples in this application disclose information sharing methods, media, and systems. One example computer-implemented method includes receiving, by a trusted execution environment (TEE), a first sharing request from a first institution and a second sharing request from a second institution, where the first sharing request comprises a user identity of a first user and first anti-money laundering (AML) risk information and the second sharing request comprises a user identity of a second user and second AML risk information, comparing the user identity of the first user with the user identity of the second user, in response to that the user identity of the first user is the same as the user identity of the second user, combining the first AML risk information and the second AML risk information, and sending the combined first AML risk information and second AML risk information to the first institution and the second institution.

Abstract: This specification disclose methods, apparatus, and systems for updating machine learning models. In one implementation, a method includes: determining, by a participant of a plurality of participants, a local gradient vector based on a local sample set and current model parameters, obtaining a perturbed gradient vector by performing random binarization processing on the local gradient vector based on a differential privacy algorithm, and sending the perturbed gradient vector to a server. The method further includes receiving a target gradient vector from the server. The target gradient vector is determined by performing binary representation on an aggregation result of aggregating a plurality of perturbed gradient vectors received from the plurality of participants. The method further includes updating, by the participant, the current model parameters based on the target gradient vector.

Abstract: Information processing performed by an information code server end includes receiving an information code processing request from a second client, where the information code processing request is sent after the second client scans a third-party information code embedded in a first client. To obtain identified risk information, risk identification is performed based on the information code processing request. Based on the identified risk information and a predetermined security rule, corresponding processing logic is executed. A processing result of the corresponding processing logic is sent to the first client.

Abstract: Implementations of the present specification provide a method and an apparatus for managing a TLB cache in a virtualization platform, where the virtualization platform runs a plurality of virtual machines, each virtual machine is allocated with a unique VPID, and all virtual logical processors in the virtual machine share the VPID; and a guest process running in the virtual machine is allocated with a PCID. An identifier field of a TLB entry in the TLB cache includes a VPID and a PCID.

Abstract: Automatically completing a query statement for a graph database, includes obtaining a current input character in a process that a user inputs a graph database query statement using a target query language. Matched target keywords from several syntax keywords are queried based on at least the current input character when the current input character is not a predetermined character, where the predetermined character is a reserved character of the target query language. The matched target keywords are determined as complementary content corresponding to the current input character.

Abstract: Some embodiments of this specification provide face liveness detection computer-implemented methods, devices, and media. In an example, multiple face images continuously captured for a first user are obtained. Two face images are selected from the multiple face images for a target attribute in one or more predetermined image attributes, where a degree of difference between two attribute values of the target attribute corresponding to the two face images satisfies a predetermined criterion. Both of the two face images are input into a pre-trained face liveness detection model corresponding to the target attribute to obtain a detection result corresponding to the target attribute.

Abstract: Implementations of this specification provide prediction methods and apparatuses for adjusting computing power. One method comprises receiving a prediction request, wherein the prediction request comprises a sample to be tested, determining a computing power coefficient allocated to the prediction request, wherein the computing power coefficient indicates a proportion of hardware computing power resources allocated to the prediction request to total hardware computing power resources needed for a neural network model to run on a computing platform, determining k sub-networks inn sub-networks of the neural network model to be used for a present time based on the computing power coefficient, where n>2, and inputting the sample to be tested to the k sub-networks to obtain a prediction result.

Abstract: In an implementation, an initial risk prevention and control model of a to-be-trained target service corresponding to a classification group that a terminal device belongs to is received from a server. To obtain a risk prevention and control sub-model corresponding to the terminal device and based on pre-stored training sample data including at least data related to a user of the terminal device and the to-be-trained target service, performing model training on the initial risk prevention and control model. To obtain a risk prevention and control model corresponding to the classification group that the terminal device belongs to, sending to the server the risk prevention and control sub-model. The risk prevention and control model is received from the server as acquired data of the to-be-trained target service. Risk prevention and control processing is performed on the acquired data of the to-be-trained target service.