Abstract: Provided is a data labeling method based on artificial intelligence, an apparatus, and a storage medium relating to the field of artificial intelligence, particularly data labeling, image recognition, and natural language processing. The method includes: determining a plurality of samples involved in clustering; performing a plurality of following operations circularly to realize iterative processing, until a convergence condition is satisfied or a quantity of iterations reaches a number threshold, comprising: pre-clustering the plurality of samples according to a vector representation of the respective samples to obtain a plurality of class clusters, each class cluster containing at least one sample; receiving labeling information for the respective class clusters and re-determining the plurality of samples according to the labeling information; and determining a clustering result according to the labeling information for the respective class clusters.

Abstract: A method for sharing a storage device among multiple processors and an associated electronic device are provided. The method includes: controlling a first processor and a second processor to operate in an access mode and a detection mode, respectively; in response to the first processor operating in the access mode, utilizing the first processor to control a logic value of a busy signal, to indicate that the first processor has permission to access the storage device; in response to a first predetermined condition, controlling the first processor to enter the detection mode from the access mode; in response to a second predetermined condition, controlling the second processor to enter the access mode from the detection mode; and in response to the second processor operating in the access mode, utilizing the second processor to control the logic value of the busy signal, to indicate that the second processor has the permission.

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: Provided is a data labeling method based on artificial intelligence, an apparatus, and a storage medium relating to the field of artificial intelligence, particularly data labeling, image recognition, and natural language processing. The method includes: determining a plurality of samples involved in clustering; performing a plurality of following operations circularly to realize iterative processing, until a convergence condition is satisfied or a quantity of iterations reaches a number threshold, comprising: pre-clustering the plurality of samples according to a vector representation of the respective samples to obtain a plurality of class clusters, each class cluster containing at least one sample; receiving labeling information for the respective class clusters and re-determining the plurality of samples according to the labeling information; and determining a clustering result according to the labeling information for the respective class clusters.

Abstract: A method of recognizing an address, an electronic device, and a storage medium, which relate to fields of artificial intelligence and computer technologies, fields of knowledge graph, deep learning and cloud computing. The method includes: performing a location entity recognition on a content to be recognized, so as to obtain a target location entity, the target location entity including at least one of a standardized location entity, an alias location entity, or a landmark location entity; determining a standardized address corresponding to each type of the location entity in the target location entity according to an address graph to obtain at least one standardized address, the address graph including a standardized location entity, an alias location entity, a landmark location entity, and a corresponding relationship between location entities; and determining, from the at least one standardized address, a first target standardized address corresponding to the content to be recognized.

Abstract: A public opinion acquisition method and device, a word viscosity model training method and device, a server, and a medium are provided in the present disclosure. And the present disclosure relates to the technical field of artificial intelligence, specifically to image recognition and natural language processing, which can be used in a cloud platform. A video public opinion acquisition method includes: receiving a public opinion acquisition request, the public opinion acquisition request including a public opinion keyword to be acquired; matching the public opinion keyword to be acquired with video data including a recognition result, wherein the recognition result is obtained by performing predefined content recognition on the video data, the predefined content recognition including text recognition and image recognition; and determining video data that matches with the public opinion keyword to be acquired as result video data.

Abstract: A method of processing event data, a device, and a medium are provided, which relate to fields of deep learning, natural language processing, cloud services, etc. The method of processing event data includes: determining target feature data corresponding to target event data, in response to receiving a query request containing the target event data; selecting correlated feature data from candidate feature data based on the target feature data, wherein a similarity between the correlated feature data and the target feature data meets a preset similarity condition; determining operation data associated with the correlated feature data, wherein the operation data represents a level of attention to correlated event data corresponding to the correlated feature data; and determining a level of attention to the target event data based on the operation data.

Abstract: A method of determining a location information, an electronic device, and a storage medium, which relate to a field of an artificial intelligence technology, and in particular, to fields of NLP and knowledge graph. The method includes: determining at least one location chain corresponding to a location information in a text to be recognized, wherein each of the at least one location chain includes a plurality of chain nodes cascaded according to a subordination relationship, and each level of chain node represents a current level name corresponding to the location information; and determining, from the at least one location chain, a target location chain having a greatest degree of relevance to the text to be recognized, according to a feature word indicating a location attribute in the text to be recognized.

Abstract: A method and an apparatus for identifying an instruction, and a screen for voice interaction are provided. The method includes: acquiring a text vector and at least one word importance corresponding to a to-be-identified instruction; selecting a target number of quasi-matching instructions from a preset instruction library based on the text vector and the at least one word importance, where the instruction library includes a correspondence between an instruction and a text vector of the instruction, and the instruction in the instruction library includes an instruction type and an instruction-targeting keyword; and generating, based on the instruction type and the instruction-targeting keyword in the target number of quasi-matching instructions, an instruction type and an instruction-targeting keyword matching the to-be-identified instruction.

Abstract: A public opinion acquisition method and device, a word viscosity model training method and device, a server, and a medium are provided in the present disclosure. And the present disclosure relates to the technical field of artificial intelligence, specifically to image recognition and natural language processing, which can be used in a cloud platform. A video public opinion acquisition method includes: receiving a public opinion acquisition request, the public opinion acquisition request including a public opinion keyword to be acquired; matching the public opinion keyword to be acquired with video data including a recognition result, wherein the recognition result is obtained by performing predefined content recognition on the video data, the predefined content recognition including text recognition and image recognition; and determining video data that matches with the public opinion keyword to be acquired as result video data.

Abstract: The invention “simulation device and method for studying the influence of wetland plant litter decomposition on water quality”, belongs to the ecological engineering field. The simulation device comprises a distributing reservoir, a planting pool and a discharge bay, and the three are connected successively through a first-level pipeline; the distributing reservoir is used to supply water to the planting pool; the discharge bay is used to collect wastewater discharged from the planting pool; the planting pool comprises a plurality of planting units; the planting unit is a container-like structure and used to hold the planting substrate and plant wetland plants on the planting substrate. By using the simulation device and/or simulation method of the invention, the whole process of the influence of plant litters especially wetland plants litters on water quality under natural state can be highly simulated and restored.

Abstract: The invention “simulation device and method for studying the influence of wetland plant litter decomposition on water quality”, belongs to the ecological engineering field. The simulation device comprises a distributing reservoir, a planting pool and a discharge bay, and the three are connected successively through a first-level pipeline; the distributing reservoir is used to supply water to the planting pool; the discharge bay is used to collect wastewater discharged from the planting pool; the planting pool comprises a plurality of planting units; the planting unit is a container-like structure and used to hold the planting substrate and plant wetland plants on the planting substrate. By using the simulation device and/or simulation method of the invention, the whole process of the influence of plant litters especially wetland plants litters on water quality under natural state can be highly simulated and restored.

Abstract: The present invention discloses: when a first laser in N lasers is switched to a second idle laser in M lasers, a wavelength of a wavelength-selective optical element to which the first laser is coupled is adjusted from a first wavelength to a second wavelength, and the second wavelength is different from the N wavelengths. Similarly, when a first optical receiver in N optical receivers is switched to a second idle optical receiver in M optical receivers, a wavelength of a wavelength-selective optical element to which the first optical receiver is coupled is adjusted from a first wavelength to a second wavelength, and the second wavelength is different from the N wavelengths.

Abstract: The present invention discloses: when a first laser in N lasers is switched to a second idle laser in M lasers, a wavelength of a wavelength-selective optical element to which the first laser is coupled is adjusted from a first wavelength to a second wavelength, and the second wavelength is different from the N wavelengths. Similarly, when a first optical receiver in N optical receivers is switched to a second idle optical receiver in M optical receivers, a wavelength of a wavelength-selective optical element to which the first optical receiver is coupled is adjusted from a first wavelength to a second wavelength, and the second wavelength is different from the N wavelengths.

Abstract: An avalanche photodiode includes a P-type contact layer, a light absorption layer, a compositionally-graded symmetrical multiplication layer, and an N-type contact layer. The P-type contact layer is connected to the light absorption layer, the light absorption layer is connected to the compositionally-graded symmetrical multiplication layer, and the compositionally-graded symmetrical multiplication layer is connected to the N-type contact layer.