Abstract: A dance segment recognition method includes: extracting an audio segment from a video segment in response to the audio segment including a music segment with a time length greater than or equal to a pre-specified time length, using a video sub-segment that is included in the video segment and that corresponds to the music segment as a candidate dance segment and in response to a quantity of image frames showing a dance move in a plurality of image frames of the candidate dance segment being greater than a first pre-specified threshold, determining the candidate dance segment to be a dance segment. The dance segment recognition method can increase the speed of recognizing a dance segment in a video segment.

Abstract: A dance segment recognition method, a dance segment recognition apparatus, and a storage medium. The dance segment recognition method comprises: extracting an audio segment in a video segment; in response to the audio segment comprising a music segment having a duration greater than or equal to a preset duration, taking a video sub-segment in the video segment that corresponds to the music segment to serve as a candidate dance segment; in response to the number of image frames having a dance action among a plurality of image frames of the candidate dance segment being greater than a first preset threshold, determining the candidate dance segment as a dance segment. The present dance segment recognition method can improve the speed of recognizing a dance segment in a video segment. The present dance segment recognition method can improve the speed of recognizing a dance segment in a video segment.

Abstract: Embodiments of the present disclosure provide a control method of an unmanned vehicle and an unmanned vehicle, which have excellent safety. The control method of the unmanned vehicle includes: detecting vibration information and running attitude information of the unmanned vehicle; according to the vibration information, the running attitude information and a running status of the unmanned vehicle, determining a condition of the unmanned vehicle, wherein the running status of the unmanned vehicle includes a stop status and a driving status; and when the condition of the unmanned vehicle is abnormal, controlling the unmanned vehicle according to an abnormal condition coping strategy.

Abstract: A method and apparatus for locating an express package. The method includes: receiving an express locating request, where the express locating request is used to locate an express waybill corresponding to a target express package from an express image presenting express packages, and the express locating request includes express information; searching for pre-stored location information of the express waybill corresponding to the express information; generating an express locating result image by means of the location information, where the express locating result image includes a locating identifier, and the locating identifier is used to indicate the express waybill corresponding to the target express package from the express image; and outputting the express locating result image.

Abstract: This invention discloses an electronic device, including a first housing; a second housing, where the second housing is slidably connected to the first housing; a first elastic piece, where a first end of the first elastic piece is connected to the second housing; a flexible display screen, where a first end of the flexible display screen is connected to a second end of the first elastic piece, a second end of the flexible display screen is connected to the first housing, and the first housing can drive the second end of the flexible display screen to move; a rotating shaft, where the rotating shaft is rotatably disposed in the second housing, the flexible display screen is in rolling contact with the rotating shaft, and the first housing can drive the flexible display screen to move around the rotating shaft.

Abstract: The disclosure herein describes creating and managing an editable rules-based model. A model is trained to generate output values for data sources based on input feature data of the data sources. Using the trained model, results are generated that include feature-output value pairs and associated confidence values for each data source. The confidence values are aggregated for each feature-output value pair of the generated results, and rules of a rules-based model are built using the feature-output value pairs and the respective aggregated confidence values of the generated results. A user is provided access to the rules of the rules-based model via a model management interface. The rules-based model is updated based on rule change instructions received from the user via the model management interface.

Abstract: The disclosure herein describes creating and managing an editable rules-based model. A model is trained to generate output values for data sources based on input feature data of the data sources. Using the trained model, results are generated that include feature-output value pairs and associated confidence values for each data source. The confidence values are aggregated for each feature-output value pair of the generated results, and rules of a rules-based model are built using the feature-output value pairs and the respective aggregated confidence values of the generated results. A user is provided access to the rules of the rules-based model via a model management interface. The rules-based model is updated based on rule change instructions received from the user via the model management interface.

Abstract: Examples provide a system and method for retraining a machine learning (ML) algorithm associated with a trained model using root cause pattern recognition. The system analyzes the results of parsing unstructured data and identifies a root cause pattern causing the trained model to underperform when parsing data including the identified pattern. Examples of data including the pattern are created for use in retraining the model to correctly detect and parse data following the identified pattern. Once retrained, the model is able to parse unstructured data, including data having the identified pattern, in accordance with expected performance metrics. The system automatically identifies parsing errors, identifies the root cause patterns for these errors and retrains the models to correctly handle those patterns for more accurate and efficient handing of unstructured data by trained models.

Abstract: A computer-implemented hypervisor system and method comprises utilizing the hypervisor system, which comprises a privileged domain (PD) and a guest domain (GD). The PD is an instance of a virtual machine that has direct access to hardware devices. The PD comprises a back-end interface for communicating with other GDs. The GD is an instance of a virtual machine that does not have direct access to hardware devices, and comprises a front-end interface for communicating with the PD and communicates only packets with the PD. On the GD, and in an initialization stage of the front-end interface, the system allocates a predefined number of packet data entries for storing packet data, allocates the predefined number of grant table entries, each comprising a grant table id. During the initialization stage, each grant table entry is bound with a packet data entry.

Abstract: In an approach to limiting container CPU usage based on network traffic, a packet CPU usage for each packet type of one or more packet types is determined. A network CPU usage is calculated for a specific container based on the network CPU usage for each packet type. A container process CPU usage and a total container CPU usage are calculated for the specific container, where the total container CPU usage is the sum of the network CPU usage and the container process CPU usage. Responsive to determining that the total container CPU usage exceeds a threshold, the container CPU consumption quota and the container network bandwidth setting for the specific container are adjusted to reduce the total container CPU usage, using a set of pre-configured parameters.

Abstract: Embodiments of the present disclosure provide a control method of an unmanned vehicle and an unmanned vehicle, which have excellent safety. The control method of the unmanned vehicle includes: detecting vibration information and running attitude information of the unmanned vehicle; according to the vibration information, the running attitude information and a running status of the unmanned vehicle, determining a condition of the unmanned vehicle, wherein the running status of the unmanned vehicle includes a stop status and a driving status; and when the condition of the unmanned vehicle is abnormal, controlling the unmanned vehicle according to an abnormal condition coping strategy.

Abstract: A data processing method includes obtaining N sampled data items from a sensor of a mobile platform, and separating the N sampled data items into a first data group, a second data group, and a third data group. Each of the first data group and the second data group includes M sampled data items, and the third data group includes one sampled data items. M is an integer greater than or equal to 2, and N=2×M+1. The method further includes obtaining a median data item of the N sampled data items according to a larger one of a minimum sampled data item in the first data group and a minimum sampled data item in the second data group, a smaller one of a maximum sampled data item in the first data group and a maximum sampled data item in the second data group, and the one sampled data item in the third data group.

Abstract: A computer-implemented hypervisor system and method comprises utilizing the hypervisor system, which comprises a privileged domain (PD) and a guest domain (GD). The PD is an instance of a virtual machine that has direct access to hardware devices. The PD comprises a back-end interface for communicating with other GDs. The GD is an instance of a virtual machine that does not have direct access to hardware devices, and comprises a front-end interface for communicating with the PD and communicates only packets with the PD. On the GD, and in an initialization stage of the front-end interface, the system allocates a predefined number of packet data entries for storing packet data, allocates the predefined number of grant table entries, each comprising a grant table id. During the initialization stage, each grant table entry is bound with a packet data entry.

Abstract: In some examples, feature and context based search result generation may include identifying, based on analysis of a query feature associated with a query context of a query, and an entity feature associated with an entity context of each entity of a plurality of entities, a reduced number of entities that match the query. Based on analysis of a further query feature and a further entity feature, further matching analysis of the query to the reduced number of entities may be performed. The query may be linked by a linking model to an entity of the reduced number of entities to generate a query and entity pair. Selection of an entity may be received, and a linked plurality of queries and entities may be searched. In this regard, search results may be generated and include a set of queries that is associated with the selected entity.

Abstract: An intelligent vehicle positioning method based on feature point calibration is provided. The intelligent vehicle positioning method includes: determining whether an intelligent vehicle is located in a blind area or a non-blind area; when the intelligent vehicle is located in a GNSS non-blind area, combining GNSS signals, odometer data and inertial measurement unit data, acquiring a current pose of the intelligent vehicle through a Kalman filtering, scanning a surrounding environment of the intelligent vehicle by using a laser radar, extracting corner points and arc features and performing processing to obtain the corner points and circle centers as the feature points, calculating global coordinates and weights of the feature points and storing the global coordinates and the weights of the feature points in a current feature point list.