Abstract: The present disclosure provides a neural network model training method and apparatus, a living body detecting method and apparatus, a device and a storage medium. It is feasible to, with respect to different scenarios, respectively obtain picture samples under the scenarios, each picture sample having a tag, the tag including a living body or a non-living body and a corresponding scenario; and according to obtained picture samples, train to obtain a neural network model whose output is a feature vector which is discriminative for different scenarios. As such, when the living body detection is performed, it is possible to determine the feature vector of the to-be-detected picture according to the neural network model, then determine a picture sample matched with the to-be-detected picture, and regard the tag of the matched picture sample as the tag of the to-be-detected picture, thereby obtaining the detection result of the to-be-detected picture.

Abstract: Provided are a voice packet recommendation method and apparatus, a device and a storage medium. The method includes selecting at least one target display video for a user from among candidate display videos associated with voice packets and using voice packets to which the at least one target display video belongs as candidate voice packets; selecting a target voice packet for the user from among the candidate voice packets according to attribute information of the candidate voice packets and attribute information of the at least one target display video; and recommending the target voice packet to the user.

Abstract: The disclosure provides human-tracking methods, systems, and storage media. The method includes: acquiring a plurality of human point clouds of a current frame from a plurality of cameras; generating a total point cloud of the current frame by integrating the plurality of human point clouds of the current frame; acquiring a plurality of human point clouds of a next frame from the plurality of cameras; acquiring a total point cloud of the next frame by integrating the plurality of human point clouds of the next frame; and performing human tracking based on the total point cloud of the current frame and the total point cloud of the next frame.

Abstract: A data processing method is applied to a computing device serving as a blockchain node. The method comprises: acquiring a data processing request; according to the data processing request, determining a current version identifier; according to the data processing request, determining a target key value pair used for processing data, and processing data in the value range of the target value pair; writing a newly produced target value pair into a storage space, wherein the key domain of a key value pair in the storage area stores a key identifier and a version identifier, and the version identifier in the key domain of the newly produced target key value pair is the current version identifier.

Abstract: The present disclosure provides a sentence recommendation method and apparatus based on associated points of interest. The method includes: obtaining an input sentence from a user; extracting a keyword in the input sentence, and searching for a current point of interest matching the keyword in a preset point of interest association network determining a plurality of associated points of interest matching the current point of interest according to the preset point of interest association network, and filtering out an extension point of interest from the plurality of associated points of interest according to a preset filter strategy; and retrieving a first reply sentence and a second reply sentence from a preset corpus according to the current point of interest and the extension point of interest, and providing them to the user.

Abstract: The technical solutions relate to the technical field of compilation of applications, and particularly to the technical field of mini programs. A developer tool constructs a first compilation result of the hosted APP before compilation optimization and a second compilation result of the hosted APP after the compilation optimization respectively based on a source code of the hosted APP, and uploads them to a management platform. The management platform sends the corresponding compilation results according to environment data of the hosted APP running environment provided by the host APP, so that the host APP uses the hosted APP running environment provided by the host APP to run the obtained compilation result.

Abstract: A method for exiting a voice skill, an apparatus, a device, and a storage medium are provided by embodiments of the present disclosure, wherein a user voice instruction is received; a target exit intention corresponding to the user voice instruction is identified according to the user voice instruction and a grammar rule of a preset exit intention; and a corresponding operation is executed on a current voice skill of a device according to the target exit intention. The embodiments of the present disclosure refine and expand the user's exit intention. After the target exit intention to which the user voice instruction belongs is identified, the corresponding operation is executed according to the target exit intention so as to meet the users' different exit requirements for the voice skills, enhance the fluency and convenience of user interaction with the device and improve the user's exit experience when using the voice skills.

Abstract: Sign language information processing method and apparatus, an electronic device and a readable storage medium provided by the present disclosure, achieve real-time collection of language data in a current communication of a user by obtaining voice information and video information collected by a user terminal in real time; and then match a speaking person with his or her speaking content by determining, in the video information, a speaking object corresponding to the voice information; and finally, make it possible for the user to clarify the corresponding speaking object when the user sees AR sign language animation in a sign language video by superimposing and displaying an augmented reality AR sign language animation corresponding to the voice information on a gesture area corresponding to the speaking object to obtain a sign language video. Therefore, it is possible to provide a higher user experience.

Abstract: The present disclosure provides an intelligent roadside unit. The intelligent roadside unit includes: a radar configured to detect an obstacle within a first preset range of the intelligent roadside unit; a camera configured to capture an image of a second preset range of the intelligent roadside unit; a master processor coupled to the radar and the camera, and configured to generate a point cloud image according to information on the obstacle detected by the radar and the image detected by the camera; and a slave processor coupled to the radar and the camera, and configured to generate a point cloud image according to the information on the obstacle detected by the radar and the image detected by the camera, in which the slave processor checks the master processor, and when the original master processor breaks down, it is switched from the master processor to the slave processor.

Abstract: An in-cloud wake-up method and system, a terminal and a non-volatile computer-readable storage medium are provided. The in-cloud wake-up method includes: receiving wake-up voice input by a user, and transmitting the wake-up voice to cloud; performing an decoding operation on the wake-up voice in cloud to generate a wake-up text; identifying wake-up characters in the wake-up text; and providing a wake-up instruction according to an identification result. With the in-cloud wake-up method, voice can be identified in cloud, and a smart device can be waken up in cloud according to the identified voice, so that resource consumption and power loss of the terminal device are reduced in the case of ensuring the quality of wake-up.

Abstract: A method and apparatus for predicting a mouth-shape feature, and an electronic device are provided. A specific implementation of the method comprises: recognizing a phonetic posterior gram (PPG) of a phonetic feature; and performing a prediction on the PPG by using a neural network model, to predict a mouth-shape feature of the phonetic feature, the neural network model being obtained by training with training samples and an input thereof including a PPG and an output thereof including a mouth-shape feature, and the training samples including a PPG training sample and a mouth-shape feature training sample.

Abstract: Embodiments of the present disclosure provide a method and apparatus for outputting information. A specific embodiment of the method includes: in response to receiving a query, detecting whether there is an entity slot in the query; in response to there being an entity slot in the query, adding the detected entity slot to a candidate slot; detecting, in the query, a relationship-determinative word of an entity; searching in a preset knowledge graph for a peripheral knowledge graph of the candidate slot; and inferring on the basis of the peripheral knowledge graph according to the relationship-determinative word, and outputting an entity word matching the relationship-determinative word.

Abstract: A method for sorting geographic location points, a method for training a sorting model and corresponding apparatuses are disclosed, which relates to the technical field of big data. A specific implementation solution is: receiving a query request for geographic location points of a vertical class from a user; inputting candidate geographic location point data of the vertical class into a preference model of the user, to obtain a preference score of the user for each candidate geographic location point; inputting the preference score of the user for each candidate geographic location point into a sorting model as one of sorting features of each candidate geographic location point, to obtain a sorting score of each candidate geographic location point; and determining, according to the sorting score of each candidate geographic location point, a query result returned to the user.

Abstract: Embodiments of the present disclosure relate to a dialogue interaction method and apparatus, a device and a storage medium, and relate to the field of artificial intelligence technology. The method may include: determining a first semantic encoding of received user information according to a sentence tree; determining a second semantic encoding for responding to the user information from a dialogue tree according to the first semantic encoding, the sentence tree and the dialogue tree being trained and obtained through sentence node information and/or word node information in a logical brain map sample; and determining a target response sentence of the second semantic encoding from the sentence tree, to be used for a dialogue with a user.

Abstract: A method and an apparatus for running an applet are provided. The method includes: reading, in response to issuing an applet to a host application, a configuration file to determine a dynamic library referenced by the applet; loading the dynamic library referenced by the applet from a sandbox directory and/or an applet platform; and running, in response to a running instruction of the applet, a script code of the applet to call the dynamic library referenced by the applet. When the applet is run, the dynamic library referenced by the applet is loaded from the sandbox directory and/or the applet platform, and the dynamic library is no longer integrated into the applet package.

Abstract: Embodiments of the present disclosure provide a method and apparatus for recommending a test question, and an intelligent device. The method includes: acquiring a plurality of skill entities of a post; calculating, according to the data of the post, a weight value of each of the plurality of skill entities; and acquiring, according to the weight value of each skill entity, a recommended test question of the post from a question bank.

Abstract: This disclosure discloses a method and apparatus for generating a machine learning model.

Abstract: The present disclosure provides a task migration method, apparatus, electronic device and storage medium, and relates to the technical field of data processing. The method may include: obtaining a task submitted by a user; in the case that the task is a Hadoop task and it is determined that task conversion is to be performed, converting Hadoop parameters in the task into parameters recognizable by a Spark; and injecting a conversion result into a predetermined kit and submitting the predetermined kit to a Spark cluster. The solution of the present disclosure may be applied to reduce the user's workload and enhance the processing efficiency etc.

Abstract: The present disclosure provides a method and apparatus for determining an extended reading content, a device, and a storage medium, relating to the field of data processing. The method may include: displaying a target page, in response to a viewing request to the target page; giving a reading prompt to an extended reading resource in the target page, based on a corresponding relationship between a requirement recognition result of at least one target reading content in the target page and the extended reading resource; and displaying the extended reading resource, in response to the viewing request matching the reading prompt.

Abstract: According to embodiments of the present disclosure, a method, an apparatus, a device, and a computer-readable storage medium for determining a category of an entity are provided. The method includes: based on a suffix of the entity, obtaining a suffix feature associated with the suffix; determining one or more candidate categories of the entity based on a name of the entity; and determining a set of categories of the entity based on the one or more candidate categories and the suffix feature.