Abstract: A method includes acquiring a root certificate of a first node; generating a target sub-certificate based on the root certificate, where the target sub-certificate includes a first sub-certificate of a first functional module of a first application associated with the first node; and sending the target sub-certificate to a terminal device. The target sub-certificate is used for the first functional module of the first application in the terminal device communicating through the target sub-certificate. In such a manner, a capability of managing the first sub-certificate of the first functional module in the terminal device can be improved.

Abstract: Embodiments of the disclosure disclose a method, apparatus, server, and storage medium for incorporating a structured entity, wherein the method for incorporating a structured entity can comprise: selecting a candidate entity associated with a to-be-incorporated structured entity from a knowledge graph, determining the to-be-incorporated structured entity being an associated entity based on prior attribute information of a category of the candidate entity and a preset model, merging the associated entity and the candidate entity, and incorporating the associated entity into the knowledge graph. The embodiments can select a candidate entity, and then integrate a preset model using prior knowledge, which can effectively improve the efficiency and accuracy in associating entities, and reduce the amount of calculation, to enable the structured entity to be simply and efficiently incorporated into the knowledge graph.

Abstract: A processor, processor implementation method, and a storage medium are disclosed, which relates to the field of artificial intelligence and deep learning. The processor includes: a system controller a data packing and unpacking module, a storage array module, and an operation module configured to perform operation processing on the acquired first packet, generate the second packet according to the operation result data, and return the second packet to the data packing and unpacking module. The storage array module comprises N1 storage units. The data packing and unpacking module comprises N2 data packing and unpacking units, each of the data packing and unpacking units is connected to the routing and switching module through a data channel. The universal operation module comprises M operation units. The activation operation module comprises P operation unit, each of the operation units is connected to the routing and switching module through a data channel.

Abstract: An image processing method, an electronic device and a readable storage medium, which relate to the technical field of computer vision, are disclosed. In an embodiment, a face recognition module and a service processing module maintains respectively an image buffer queue; the face recognition module maintains a face image buffer queue of face images, and the service processing module maintains a background image buffer queue of background images, i.e., first images; since the face recognition module only maintains the face image buffer queue, only a determined optimal face image, i.e., a face image to be matched, is transmitted to the service processing module, and then, the service processing module determines a background image matched with the optimal face image transmitted by the face recognition module from the maintained background image buffer queue, thus performing image recognition and image matching on a face appearing in a video source.

Abstract: A method for training a fusion ordering model, a search ordering method, an electronic device and a storage medium, and related to the technical field of artificial intelligence such as deep learning and the like are provided. The method includes: inputting prediction scores of a plurality of targets included in a candidate search result into a fusion ordering model, to obtain a fusion ordering result; collecting feedback information of the plurality of targets included in the fusion ordering result; and updating the fusion ordering model by utilizing the feedback information and a combined function, wherein the combined function is a function constructed by utilizing the plurality of targets. The solution of the embodiment is favorable for balancing the requirements of the plurality of targets, and the obtained updated fusion ordering model can provide a better search ordering result.

Abstract: A complex computing device, a complex computing method, an artificial intelligence chip and an electronic apparatus are provided. An input interface receives complex computing instructions and arbitrates each complex computing instruction to a corresponding computing component respectively, according to the computing types in the respective complex computing instructions Each computing component is connected to the input interface, acquires a source operand from a complex computing instruction to perform complex computing, and generates a computing result instruction to feed back to an output interface. The output interface arbitrates the computing result in each computing result instruction to the corresponding instruction source respectively, according to the instruction source identifier in each computing result instruction.

Abstract: The present disclosure provides a method and apparatus for processing a streaming media service, an electronic device, and a storage medium, and relates to the technical field of computers, particularly to technical fields such as industrial vision, deep learning, streaming media, and information flow. A specific implementation solution involves: acquiring registration information of an input source, the registration information including process information of a streaming media service process of the input source and streaming media address information of the input source; enabling the streaming media service process according to the process information; and controlling, by using the streaming media address information, the streaming media service process to process streaming media data of the input source.

Abstract: The present disclosure provides a method and an apparatus for training a voice wake-up model, a method and an apparatus for voice wake-up, a device and a storage medium, which relates to the field of artificial intelligence and particularly to the field of deep learning and voice technology. A specific implementation lies in: acquiring voice recognition training data and voice wake-up training data that are created, and firstly performing training on a base model according to the voice recognition training data to obtain a model parameter of the base model when a model loss function converges; then updating, based on a model configuration instruction, a configuration parameter of a decoding module in the base model to obtain a first model; and finally performing training on the first model according to the voice wake-up training data to obtain a trained voice wake-up model when the model loss function converges.

Abstract: A method for mining an entity focus in a text may include: performing word and phrase feature extraction on an input text; inputting an extracted word and phrase feature into a text coding network for coding, to obtain a coding sequence of the input text; processing the coding sequence of the input text using a core entity labeling network to predict a position of a core entity in the input text; extracting a subsequence corresponding to the core entity in the input text from the coding sequence of the input text, based on the position of the core entity in the input text; and predicting a position of a focus corresponding to the core entity in the input text using a focus labeling network, based on the coding sequence of the input text and the subsequence corresponding to the core entity in the input text.

Abstract: The present disclosure provides an exception stack handling method, system, electronic device and storage medium and relates to the field of mobile Internet. The method may include: at the level of any executor in a distributed stream-type processing system including at least two executors, performing the following processing of: obtaining at least one exception stack from a message middleware when the executor in an idle state each time, collected exception stacks generated by users being stored in the message middleware; as for any exception stack, obtaining an anti-obfuscation map file corresponding to the exception stack, and performing anti-obfuscation processing for the exception stack by using the anti-obfuscation map file. The solution of the present disclosure may be applied to improve the processing speed.

Abstract: A character recognition method, a character recognition apparatus, an electronic device and a computer readable storage medium are disclosed. The character recognition method includes: determining semantic information and first position information of each individual character recognized from an image; constructing a graph network according to the semantic information and the first position information of each individual character; and determining a character recognition result of the image according to a feature of each individual character calculated by the graph network.

Abstract: Embodiments of the present disclosure provide a method and apparatus for detecting a target object in an image. The method includes: performing following prediction operations using a pre-trained neural network: detecting a target object in a two-dimensional image to determine a two-dimensional bounding box of the target object; and determining a relative position constraint relationship between the two-dimensional bounding box of the target object and a three-dimensional projection bounding box obtained by projecting a three-dimensional bounding box of the target object into the two-dimensional image; and the method further including: determining the three-dimensional projection bounding box of the target object, based on the two-dimensional bounding box of the target object and the relative position constraint relationship between the two-dimensional bounding box of the target object and the three-dimensional projection bounding box.

Abstract: A method for visual question answering, a computer device implementing the method and a medium for storing instructions on performing the method are provided. The method includes: acquiring an input image and an input question; constructing a visual graph based on the input image, wherein the visual graph comprises a first node feature and a first edge feature; constructing a question graph based on the input question, wherein the question graph comprises a second node feature and a second edge feature; performing a multimodal fusion on the visual graph and the question graph to obtain an updated visual graph and an updated question graph; determining a question feature based on the input question; determining a fusion feature based on the updated visual graph, the updated question graph and the question feature; and generating a predicted answer for the input image and the input question.

Abstract: A method and an apparatus for processing information are provided. The method can include: acquiring a word sequence obtained by performing word segmentation on two paragraphs in a text; inputting the word sequence into a to-be-trained natural language processing model to generate a word vector corresponding to a word in the word sequence; inputting the word vector into a preset processing layer of the to-be-trained natural language processing model; predicting whether the two paragraphs are adjacent, and a replaced word in the two paragraphs; and acquiring reference information of the two paragraphs, and training the to-be-trained natural language processing model to obtain a trained natural language processing model, based on the prediction result and the reference information.

Abstract: Embodiments of a method and an apparatus for improving a model based on a pre-trained semantic model are provided. The method may include: based on the pre-trained semantic model, obtaining an initial improved model, where semantic result information of an input vector is determined in the initial improved model based on a hash search method; and based on a model distillation method, training the initial improved model to obtain an improved model. Some embodiments can obtain the semantic result information of the input vector by performing the hash search method on the input vector, replace the original complex iterative calculation process of a semantic model, and obtain the improved model with few model parameters and high compression ratio.

Abstract: A method for displaying a covering on a panoramic map image includes: acquiring covering information to be displayed, in which the covering information includes a plurality of coverings; acquiring a set of vertices of a maximum circumscribed polyhedron for each covering in a three-dimensional coordinate system of a panoramic scene; acquiring a mapped set of vertices of the maximum circumscribed polyhedron of each covering by mapping the set of vertices to a sphere; determining an overlapping result among the plurality of coverings based on the mapped set of vertices of each covering; and determining a target covering from the plurality of coverings based on the overlapping result, and displaying the target covering on the panoramic map image.

Abstract: A method is provided. The method comprises obtaining a first data stream; querying a query table to seek for storage information of a second data stream corresponding to the first data stream in a database, wherein a plurality of second data streams are stored in the database, one or more valid second data streams are included in the plurality of second data streams, and storage information of each of the one or more valid second data streams in the database is included in the query table; and in response to having found in the query table the storage information of the second data stream corresponding to the first data stream in the database, determining, in the database, the second data stream to be spliced with the first data stream based on the storage information of the second data stream.

Abstract: A method for training a point cloud processing model and a method for performing instance segmentation on a point cloud are provided. The method includes: labeling an unlabeled point cloud according to a labeled point cloud to obtain a sample point cloud; inputting the sample point cloud to a point cloud processing model to obtain first predicted semantic information of the sample point cloud and a first predicted offset of the sample point cloud; determining a training loss according to the first predicted semantic information, the first predicted offset, a sample label corresponding to the sample point cloud, and original coordinate information of the sample point cloud; and training the point cloud processing model with the training loss.

Abstract: A method is provided. The method includes: obtaining a maximally mixed state; repeatedly running a quantum measurement device to perform measurement for a first number of times on the maximally mixed state to obtain first measurement results; applying a phase gate to each quantum bit of the maximal superposition state; performing multiple times of sampling on the phase ?, for each value of ? obtained by sampling, repeatedly running the quantum measurement device to perform measurement for a second number of times on the maximal superposition state to obtain second measurement results; statistically calculating the first measurement result and the second measurement result corresponding to each ? value to obtain a first probability value and a second probability value; and determining the quantum noise intensity of the quantum measurement device based on a difference value between the first probability value and the second probability value.

Abstract: Multi-round dialogue processing solution is disclosed and relates to the field of artificial intelligence, such as intelligent voices, deep learning, natural language processing. The method may include: acquiring a first query input by a user in a current round dialogue, and identifying an intention of the first query; determining a scenario corresponding to the identified intention, determining a node matched with the first query in a node tree corresponding to the scenario, and determining a recommended script according to the matched node; acquiring a reply script corresponding to the first query and a guide script corresponding to the first query; and generating a reply corresponding to the first query according to the acquired reply script, guide script and recommendation script.