Abstract: A method for identifying a user includes: controlling an electronic device to connect to a first communication network; obtaining target behavior data of a user to be identified from a data pool corresponding to the first communication network, in which, the data pool stores at least one type of candidate behavior data of a candidate user, the candidate behavior data is obtained from a data source corresponding to a second communication network, and a security level of the first communication network is higher than a security level of the second communication network; and obtaining a category of the user to be identified by analyzing the target behavior data based on the first communication network.

Abstract: Provided are a video generation method and apparatus, a device and a storage medium, relating to the field of artificial intelligence and, in particular, to the fields of computer vision and deep learning. The method includes changing a character emotion of an original character image according to a character emotion feature of a to-be-generated video to obtain a target character image; and driving the target character image by use of a character driving network and based on a speech segment to obtain the to-be-generated video.

Abstract: The present disclosure discloses a method and apparatus for recognizing a text. The method comprises: acquiring images of a text area of an input image, the acquired images including a text centerline graph, a text direction offset graph, a text boundary offset graph, and a text character classification graph; extracting coordinates of feature points of a character center from the text centerline graph; sorting the extracted coordinates of the feature points based on the text direction offset graph to obtain a coordinate sequence of the feature points; determining a polygonal bounding box of the text area based on the coordinate sequence of the feature points of the character center and the text boundary offset graph; and determining a classification result of the feature points of the character center, based on the coordinate sequence of the feature points of the character center and the text character classification graph.

Abstract: A method and apparatus for optimizing a recommendation system, a device and a computer storage medium are described, which relates to the technical field of deep learning and intelligent search in artificial intelligence. A specific implementation solution is: taking the recommendation system as an agent, a user as an environment, each recommended content of the recommendation system as an action of the agent, and a long-term behavioral revenue of the user as a reward of the environment; and optimizing to-be-optimized parameters in the recommendation system by reinforcement learning to maximize the reward of the environment. The present disclosure can effectively optimize long-term behavioral revenues of users.

Abstract: Methods and apparatuses for generating a model and generating a 3D animation, devices, and storage mediums are provided. The method for generating a model may include: acquiring a preset sample set; acquiring pre-established generative adversarial nets, the generative adversarial nets including a generator and a discriminator; and performing training steps as follows: selecting a sample from the sample set; extracting a sample audio feature from the sample audio of the sample; inputting the sample audio feature into the generator to obtain a pseudo 3D mesh vertex sequence of the sample; inputting the pseudo 3D mesh vertex sequence and the real 3D mesh vertex sequence of the sample into the discriminator to discriminate authenticity of 3D mesh vertices; and in response to determining that the generative adversarial nets meet a training completion condition, obtaining a trained generator as a model for generating a 3D animation.

Abstract: A method for retrieving an image is provided. The method includes: extracting a global feature and a local feature of an image to be retrieved, and a global feature and a local feature of an image to be recalled by employing a preset neural network model; determining a candidate image set by matching the global feature of the image to be retrieved with the global feature of the image to be recalled and matching the local feature of the image to be retrieved with the local feature of the image to be recalled; and determining a retrieval result from the candidate image set by performing local feature verification on the image to be retrieved and a candidate image in the candidate image set. An apparatus for retrieving an image, an electronic device, and a medium are further provided.

Abstract: The present disclosure discloses a method and apparatus for testing a stress based on a cloud service, and relates to the field of cloud computing technology, and further to the field of cloud operation and maintenance technology. A particular implementation comprises: acquiring, based on first stress test information of a business system in a cloud service, a number of expected stress test nodes corresponding to the business system; creating edge computing nodes, a number of the edge computing nodes being identical to the number of the expected stress test nodes; and performing a stress test on the business system by using the edge computing nodes, to acquire second stress test information of the business system.

Abstract: Provided are a classification method and apparatus, an electronic device and a storage medium, which relate to the field of artificial intelligence and in particular, to the fields of natural language processing and deep learning. The classification method comprises: performing coding processing on to-be-classified data to obtain a to-be-classified coding feature; determining reference coding features of reference classification data similar to the to-be-classified data according to the to-be-classified coding feature; and determining a target category of the to-be-classified data according to the reference coding features and reference categories of the reference classification data.

Abstract: A method for generating a license plate defacement classification model, a license plate defacement classification method an electronic device and a storage medium, and related to the technical field of artificial intelligence, and specifically, to the technical field of computer vision and the technical field of intelligent transportation are provided. The method for generating a license plate defacement classification model includes: acquiring training data, wherein the training data includes a plurality of annotated vehicle images, annotated content includes information indicating that a license plate is defaced or is not defaced, and the annotated content further includes location information of a license plate area; and training a first neural network by using the training data, to obtain the license plate defacement classification model for predicting whether the license plate in a target vehicle image is defaced.

Abstract: This application discloses a page displaying method and apparatus, an electronic device and a storage medium, relating to the technical field of computer vision, including: extracting a theme color from an image of a page to be displayed, and determining a coloration attribute of the theme color, where the coloration attribute is a chromatic attribute, or the coloration attribute is a non-chromatic attribute; and adjusting the page to be displayed according to the coloration attribute and displaying the page. Various pages are adjusted and displayed based on different coloration attributes of the theme color, thus avoiding a disadvantage related to lack of flexibility caused by filtering the theme color by a preset filtering range in related art, realizing diversity and flexibility in displaying for various pages, avoiding the disadvantage of distortion of images caused by simple filtering, and improving the reliability and accuracy of page display.

Abstract: The disclosure discloses a method and an apparatus for simulating a vehicle. The detailed implementation includes: determining, by a tire dynamics module, a tire translational force at a current moment based on a control instruction issued by an upper planning module and motion data of a vehicle body and a tire normal load outputted by a vehicle body dynamics module at a previous moment; and determining, by the vehicle body dynamics module, motion data of the vehicle body and a tire normal load at the current moment based on the control instruction and the tire translational force at the current moment, the motion data of the vehicle body at the current moment being used for vehicle simulation, and the tire normal load at the current moment being used to determine a tire translational force at a next moment.

Abstract: Provided are a method and a device for generating an avatar, an electronic equipment, a medium and a product. In the method, a to-be-detected face image of a current user is acquired. The to-be-detected face image is analyzed and at least one original component of the to-be-detected face image is obtained. Each original component of the at least one original component of the to-be-detected face image is matched with each candidate component in a component set corresponding to the each original component, and a target component corresponding to the each original component of the to-be-detected face image is obtained. The target component corresponding to the each original component of the to-be-detected face image is assembled into a personalized avatar of the current user.

Abstract: Embodiments of the present disclosure provide a method for training a face fusion model and an electronic device. The method includes: performing a first face changing process on a user image and a template image to generate a reference template image; adjusting poses of facial features of the template image based on the reference template image to generate a first input image; performing a second face changing process on the template image to generate a second input image; inputting the first input image and the second input image into a generator of an initial face fusion model to generate a fused face area image; and inputting the fused image and the template image into a discriminator of the initial face fusion model to obtain a result, and performing backpropagation correction on the initial face fusion model based on the result to generate a face fusion model.

Abstract: A method and apparatus for updating a point cloud. The method may include: determining an area point cloud matching newly added point clouds from historical point clouds to be updated based on a set formed by a pose of a point cloud acquisition device acquiring the newly added point clouds. The method may further include: merging the newly added point clouds into the matched area point cloud to obtain merged historical point clouds. The method may further include: performing global optimization on the merged historical point clouds based on a pose of each point cloud in the merged historical point clouds to obtain updated point clouds.

Abstract: Embodiments of the present application disclose a vehicle control method, an apparatus, a vehicle, an electronic device and a storage medium, which relate to automatic driving technology. The method includes: outputting a prompt message, where the prompt message is used for prompting an intention of lane changing of a host vehicle to a surrounding vehicle, and the surrounding vehicle is a vehicle having a potential influence on the lane changing of the host vehicle; determining first driving state prediction information of the surrounding vehicle, where the first driving state prediction information is related to a feedback on the prompt message; generating and executing a first driving strategy according to the first driving state prediction information, where the first driving strategy includes lane-changing driving or driving within the current lane.

Abstract: A method, an electronic device and a readable storage medium for point cloud data processing, which may be used for autonomous driving, are disclosed. The feature vectors of respective points in the first point cloud data and second point cloud data are pre-learned, and thus the feature vectors of the first key points may be determined directly based on the learnt second feature vectors of respective first neighboring points of the respective first key points in the first point cloud data, and the feature vectors of the candidate key points may be determined directly based on the learnt third feature vectors of the respective second neighboring points of respective candidate key points in the second point cloud data corresponding to the first key points.

Abstract: The present disclosure provides a method, apparatus and system for switching a virtual IP, relates to the field of artificial intelligence and particularly to cloud computing and cloud network technologies, and can be applied to intelligent cloud scenarios. A detailed implementation comprises: receiving a free ARP packet sent by a virtual machine to which a high-availability virtual IP is successfully switched; generating a first forwarding table of the high-availability virtual IP according to the free ARP packet; encapsulating the first forwarding table in a first UDP packet by VXLAN; and synchronizing the first UDP packet to another virtual router.

Abstract: This disclosure discloses a resident area prediction method, apparatus, device and storage medium, involving artificial intelligence technology, big data, deep learning and multi-task learning. The specific implementation plan is: acquiring a resident area data of a target user, and the resident area data including the resident area of the target user and the corresponding resident time; obtaining an association relationship between the resident areas of the target user by inputting the resident area data into an area relationship model, and the area relationship model is used to reflect a position relationship between the areas; determining a time-sequence relationship between the areas visited by the target user, according to the association relationship, the resident time and the visiting POI data; predicting a target resident area of the target user, according to the time-sequence relationship and the basic attribute information of the target user.

Abstract: The present invention relates to a method and an apparatus for controlling scraping pressure. The method includes: matching a website to be scraped to a pre-configured pressure unit based on a URL of the website to be scraped; determining a first scraping pressure limit and a second scraping pressure limit based on historical scraping data of the pressure unit; and controlling the scraping pressure of the pressure unit within a current scraping period based on the first scraping pressure limit and the second scraping pressure limit.

Abstract: A human behavior recognition method, a device, and a storage medium are provided, which are related to the field of artificial intelligence, specifically to computer vision and deep learning technologies, and applicable to smart city scenarios. The method includes: obtaining attribute information of a target object and N pieces of candidate behavior-related information of a target human from a target image, wherein N is an integer greater than or equal to 1; determining target behavior-related information based on comparison results between the N pieces of candidate behavior-related information and the attribute information of the target object; and determining a behavior recognition result of the target human based on the target behavior-related information.