102 results about "Multiple category" patented technology

A fraud call recognition method and system based on causal graph and agent collaboration comprises the steps of obtaining a fraud ASR text, mapping extracted multi-category to-be-mapped objects into semantic concept variable vectors, then learning a causal structure based on a data matrix formed by all semantic concept variable vectors, constructing a fraud causal graph, and recognizing a fraud call in the fraud ASR text. Calculating a causal effect of the reason variable on the result variable, and storing the causal effect as a relation weight in a causal graph; the method comprises the steps of obtaining a to-be-recognized ASR text, mapping multiple categories of to-be-mapped objects extracted from the to-be-recognized ASR text into semantic concept variable vectors, then retrieving matched causal chains in a causal graph to obtain a plurality of candidate causal chains, and then generating fraud judgment and risk degree evaluation values based on a large language model inference engine. And determining whether the to-be-identified call is a fraud call. The invention relates to the field of telecommunication anti-fraud, can deeply fuse a causal knowledge base and a large language model, and effectively improves the effectiveness, adaptability and reliability of communication anti-fraud identification.

A database query processing method includes receiving a natural language request for information contained within a database, categorizing the natural language request in a particular category from a plurality of categories, and selecting a large language model and one or more database structures for which a SQL request is to be directed based in part on the particular category. The selected large language model is prompted to generate a SQL request directed to the selected set of one ore more database structures and the received SQL request is validated for the particular category. The validated particular SQL request is used to access data within the database and one or more visualizations is caused to be displayed based at least in part on a result of the validated particular SQL request.

This application discloses a display method, apparatus, electronic device, and computer-readable medium. The method includes: when a client is displaying a live video page and a first live video is displayed on the live video page, after the client receives a first operation triggered on the live video page, displaying an object aggregation interface on the live video page, and displaying a first candidate object corresponding to first category description information on the object aggregation interface, the first candidate object being used to describe the first candidate live video; then, after the client receives a trigger operation on a first control on the object aggregation interface, displaying at least one second category description information on the object aggregation interface, so that the user can view live videos under multiple categories through the object aggregation interface, thereby better meeting the user's live video viewing needs and effectively improving the user's live video viewing experience.

Systems and methods for providing information responsive to a user search are provided. Such a method includes receiving a search query from a client device and generating a plurality of categories associated with the search query using a first artificial intelligence (AI) model. The method also includes selecting a plurality of sponsored content options that are responsive to the search query and, for each category, generating respective freeform descriptive text for the category using a second AI model. The method also includes causing the client device to present a user interface that includes (i) the respective freeform descriptive text for each category and (ii) links to third party information resources associated with the selected plurality of sponsored content options. An arrangement of the links within the user interface indicates which of the plurality of sponsored content options correspond to which of the plurality of categories.

For at least a selected class attribute of the multiple class attributes, one or more bias metrics are determined that estimate a degree to which a particular workflow (having a set of processing stages) is biased in association with the class attribute. Each user of a set of users is associated with a set of user data to be processed by the particular workflow. At least one of the set of processing stages includes executing a machine-learning model. It can be detected that a bias-mitigation option corresponding to a specific class attribute has been selected. For each of at least two of the set of processing stages: a de-biasing technique is selected; and the processing stage is modified by applying the de-biasing technique. A modified version of the particular workflow (which includes the modified processing stages) is applied to each of a set of input data sets.

The present disclosure relates to systems, software, and computer-implemented methods for automatically identifying handwritten tips. An example method includes obtaining an image of a receipt, where the image includes a reference region in a pre-set format, a plurality of category identifiers, and at least one set of handwritten characters. The reference region and the plurality of category identifiers can be printed. The plurality of category identifiers can be located at pre-set positions relative to the reference region. The method further includes obtaining optical character recognition (OCR) information of the image and identifying the reference region from the OCR information based on the pre-set format of the reference region. The method further includes determining a plurality of amounts based on the OCR information and the reference region, where at least one of the plurality of amounts is associated with a tip category.

The application provides an attack detection method and device for an electric power private network and a public network interaction node, pre-processes each piece of traffic data obtained from a node to obtain target traffic data containing feature information, classifies all the target traffic data to obtain multiple category data sets, samples and optimizes the target traffic data in each category data set to obtain an optimized category data set, selects features for each feature information in the optimized category data set by using a mutual information feature selection algorithm to obtain a feature selection result, updates the optimized category data set according to the feature selection result, reduces the redundancy of the data in the optimized category data set, obtains a more refined to-be-detected traffic data set, shortens the time required for an attack detection classification model to detect the to-be-detected traffic data set, and improves the classification accuracy of the attack detection classification model.

The embodiment of the invention provides a data transmission method and a data transmission device, and relates to the technical field of communication. According to the method, the data of the first type can be received by sending the first information used for indicating to report the data of the first type. Wherein the data of the first category is one of a plurality of categories, and the data of each category in the plurality of categories is used for AI model training. For example, data of each of the plurality of categories is included in a training set. The training set is used for AI model training. Therefore, under the condition that the first category is the category with insufficient data volume in the training set, the data of the category with insufficient data volume can be collected in a targeted manner, so that the data volume of each category in the training set is sufficient, that is, the balance of the data features of the training set is relatively good, and the training efficiency is improved. And therefore, the generalization of the AI model trained by adopting the training set is relatively good.

Certain aspects of the disclosure provide techniques for categorization by a device. An example method includes receiving input information regarding a plurality of classification targets and a plurality of categories for classification of the plurality of classification targets; generating a plurality of embeddings for the input information using a first model, the plurality of embeddings including: a set of first embeddings associated with the plurality of classification targets, and a set of second embeddings associated with the plurality of categories; determining that a similarity score for the set of first embeddings and the set of second embeddings fails to satisfy a threshold; generating, based on the similarity score failing to satisfy the threshold and using a graph neural network (GNN), a classification of the plurality of classification targets in accordance with the plurality of categories; and outputting information regarding the classification.

The application discloses an Excel-based data processing and visualization method and system, which comprises the following steps: loading a visualization user interface in an Excel workbook, wherein the user interface comprises interactive controls for selecting independent variable fields, dependent variable fields and grouping fields; receiving non-continuous selection of multiple categories in the grouping field by the user through the interactive controls, and automatically classifying the unselected categories into other groups to form an extended grouping mapping; matching and reorganizing the original data based on the extended grouping mapping by using a hybrid indexing mechanism; automatically generating a dynamic chart according to the reorganized data, wherein the coordinate axis range, legend position, series color and auxiliary line style of the dynamic chart are automatically configured according to the number of groups and the dependent variable value range; performing statistical analysis and multivariate linear regression on the data of each group, and converting the regression results into an executable formula string containing cell references and outputting the executable formula string to a specified cell.

Similarity sensitive diversity is utilized to measure variation in a distribution of item listings along one or more categories. A similarity between category vectors of each category pair in a set of categories is determined and utilized to generate a pairwise similarity matrix. The pairwise similarity matrix may be pruned to remove category pairs below a threshold. Utilizing the pairwise similarity matrix, similarity sensitive diversity between one or more items of a plurality of items may be determined. In various aspects, the similarity sensitive diversity may be utilized to: generate a list of relevant items in an appropriate distribution, suggest refinements of a search query; generate navigation modules; categorize or recategorize the plurality of items; or generate autosuggestions.

Embodiments of the disclosure provide a method, an apparatus, a device and a storage medium for object processing. The method for object processing includes: in response to receiving a predetermined operation by a user on a first selection control for pre-trained at least one generic model presented in a user interface, selecting the at least one generic model; at least acquiring at least one generic feature that is generated by the at least one generic model and associated with a sample of an object of a target category among a plurality of categories; and training an individual model for processing the object of the target category at least based at least on the at least one generic feature and annotation information of the sample of the object of the target category. Therefore, the efficiency of model training can be improved.

The invention relates to an enterprise project information prediction method and system based on machine learning, and the method comprises the following steps: collecting enterprise data, and processing the enterprise data to obtain preprocessed data; extracting multi-dimensional enterprise features to construct enterprise feature vectors based on the preprocessed data, and dividing enterprises into a plurality of categories based on the enterprise feature vectors; aiming at each category obtained by division, respectively constructing and training an independent project information prediction model; performing intra-class verification and cross-class verification on the project information prediction model of each class, and dynamically adjusting classification of the classes or selection of the enterprise features according to a verification result; and inputting the data of the target enterprise into the corresponding project information prediction model, and outputting a project information prediction result. According to the method, a full-link closed loop from multi-source heterogeneous data integration to multi-modal accurate prediction to interpretable decision support is realized, and a high-accuracy, self-adaptive and trusted intelligent solution is provided for enterprise project information analysis.

The present disclosure provides a processing method and device of training samples and electronic equipment, and relates to the technical fields of data processing, artificial intelligence, classification model and the like. The method comprises: obtaining a training sample set of a classification model; wherein the training samples in the training sample set are labeled with real categories; performing cross-validation based on the training sample set to obtain probability information of any training sample under multiple categories; for any training sample, determining a classification result of the training sample according to the probability information of the training sample under multiple categories and the real category of the training sample; wherein the classification result is used to indicate the category recognition difficulty of the training sample; and performing screening on the training sample set according to the classification result of the training samples in the training sample set to obtain a screened training sample set; wherein the screened training sample set is used to train the classification model.

Techniques are described herein for anomaly detection and / or classification which may include obtaining a large language model (LLM) that has been trained to classify instances of input data based at least in part on a plurality of classes. Input data corresponding to at least one data instance that is associated with a user may be provided to the LLM to obtain output data that identifies one or more classes for the input data. In some embodiments, it may be determined whether the input data is anomalous based at least in part on the output data received from the LLM. One or more labels or the input data may be determined based at least in part on the output data received from the LLM. One or more operations may be executed based at least in part on the output data received from the LLM.

An apparatus and method for dynamic generation of visual elements in a user-interface. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a first entry and a second entry of a plurality of log data, categorize the first entry into at least a first category of a first plurality of categories and the second entry into at least a second category of a second plurality of categories, generate key entries, assign a first score to the first entry and a second score to the second entry as a function of key entries, dynamically generate a first dashboard, modify the first dashboard based on the score of a second entry to produce a second dashboard comprising a second plurality of categories associated with at least a second visual element, display the second dashboard.

A large language model (LLM) may be used to classify an input into one of a plurality of categories. However, given the machine-learning operation of the LLM, the output of the LLM does not represent a definitive statement, but is based on probability computations of the machine learning model. Therefore, the classification performed by the LLM might not be correct. Classification into the wrong category by the LLM results in downstream technical problems. In some implementations, when an LLM generates a response that classifies an input, one or more probability values associated with a token that forms the basis of the response may be used to determine a confidence value. The confidence value is indicative of confidence in the classification performed by the LLM. An action may be taken based on the confidence value.

Embodiments of the present invention relate to a method for classifying text, the method comprising: acquiring text to be classified; generating a correlation between the text and each of a plurality of categories based on the text via a first model constructed based on deep learning; determining entity words in the text based on the text; generating a correlation between the entity words and each category based on the entity words in the text via a second model constructed based on deep learning; and determining the category of the text based at least on the correlation between the text and each category, and the correlation between the entity words and each category. The present invention can improve the accuracy of text classification, and further improve the accuracy of hierarchical multi-label classification of text.

Data loss prevention may be applied to data that includes a plurality of records, with each record including a plurality of fields and with each field corresponding to a different one of a plurality of categories. Applying of data loss prevention may include selecting a subset of records, computing, for each category, a likelihood the category contains the sensitive information, selecting a subset of categories based on the computed likelihoods, searching the sensitive information in the selected subset of categories, and in response to detection of sensitive information in at least one of the subset of records, taking one or more data loss prevention related actions. The selected subset of records includes fewer records than the plurality of records, may include a number of non-consecutive records, and the subset of records may be selected such that the records are well distributed within the plurality of records of the data.

The application discloses a kind of processing methods of information related to article, comprising: obtaining target user determined target attention element;According to the evaluation information of the recommended article of multiple categories of article of target attention element and multiple categories, determine multiple categories of recommended article, and the evaluation information of each category of recommended article is associated with target attention element;Show aggregation page, aggregation page includes recommendation information unit, and different recommendation information unit corresponds to the recommended article of different categories.This method is convenient to show article category by the real evaluation of user, to improve the authenticity of article category recommendation process, improve user experience.Another kind of processing methods of information related to article of the application, comprising showing guide page including multiple candidate information unit, each candidate information unit corresponds to article category;In response to the selection of target information unit, show aggregation page;This method uses user evaluation information.The application also provides related devices, electronic equipment, storage medium.

In one embodiment, a method includes receiving information associated with a plurality of devices. The information includes a manufacturer, a model name, and a current version of software for each of the plurality of devices. The method also includes generating a plurality of classifications. Each of the plurality of classifications is associated with a particular manufacturer and a particular model name. The method further includes determining a number of the plurality of devices in a first classification of the plurality of classifications, categorizing the number of the plurality of devices in the first classification into a plurality of categories by the current version of software, and determining, independent of any type of device identifier, a latest available version of software for the first classification.

A computer-implemented method for determining a skill rating of a contractor comprises receiving experience data and training data; determining whether there is any training data; if there is no training data, then determining the skill rating as a maximum value of the skill rating times a constant; if there is some training data, then determining a plurality of evaluation scores, determining a plurality of category evaluation scores, determining a plurality of filtered category evaluation scores, determining an experience total score as a sum of the filtered category evaluation scores, determining an experience factor using the experience total score as an input, and determining the skill rating as a product of the maximum value of the skill rating and the experience factor.

The embodiment of the invention provides a radar target detection method, a radar system and a storage medium, and the method comprises the steps: carrying out the AD sampling, digital down conversion and pulse pressure processing of a received echo signal, and carrying out the discrimination and accumulation of a plurality of types of targets with different movement speeds in a slow time dimension, obtaining slow time dimension echo accumulation data corresponding to each type of target; constructing a clutter background data matrix corresponding to each type of target; and for different types of targets, performing constant false alarm target detection according to the corresponding slow time dimension echo accumulation data and the clutter background data matrix to obtain a target detection result. According to the method, differentiation processing of a plurality of categories of targets with different movement speeds is realized in a slow time dimension, so that the algorithm can adopt multi-core parallel, the signal processing operand is reduced, and the processing efficiency is improved; and different types of targets can complete constant false alarm target detection by adopting independent detection threshold parameters, so that the low, small and slow target detection probability can be improved and the false alarm rate can be reduced.

The invention provides a model training method and device, equipment, a storage medium and a program product, and relates to the technical field of data processing.The method comprises the steps that multiple pieces of first portrait data are obtained, and the multiple pieces of first portrait data are portrait data of multiple categories; calculating the complexity corresponding to each category based on the plurality of first portrait data, wherein the complexity is used for representing the distribution complexity and the feature complexity of the portrait data of the corresponding category and / or the balance degree among the plurality of categories; generating a training data set based on the multiple pieces of first portrait data and the complexity of each category; and training an initial model based on the training data set to obtain a target model, the initial model being a model for identifying portrait data, and the target model being used for identifying different types of portrait data. According to the invention, the generalization ability and robustness of the model can be improved.

The present disclosure provides a user classification method, a user classification device, a computer readable storage medium and an electronic device, and belongs to the technical field of data processing. The method comprises: obtaining user data of a plurality of users, wherein the user data comprises feature data of a plurality of feature variables and initial category labels of each user; dividing the user data into a plurality of data combinations comprising positive example data and negative example data according to the initial category labels; processing the positive example data and the negative example data in each data combination to determine feature scores of each feature variable; screening out target feature variables from the plurality of feature variables according to the feature scores of each feature variable, and dividing the plurality of users into a plurality of categories through the feature data corresponding to the target feature variables. The present disclosure can improve the accuracy and efficiency of user classification.

The application provides a knowledge graph construction method and device. The method comprises the following steps: determining the modal category of first data, wherein the first data refers to single-modal data extracted from original data used for constructing a knowledge graph; if the modal category comprises multiple categories, obtaining the representation vector of the first data corresponding to each modal category; constructing a first knowledge graph, wherein the first knowledge graph refers to a knowledge graph constructed by extracted knowledge after knowledge extraction based on all the representation vectors; and constructing a second knowledge graph, wherein the second knowledge graph refers to a knowledge graph obtained by knowledge fusion and knowledge completion on the knowledge contained in the first knowledge graph. The knowledge graph constructed by the method fully contains information associated with various modalities, does not split the association relationship between various modal information, has higher accuracy, and has better applicability.

To efficiently predict each of a plurality of classes for classifying target data to be classified among a plurality of classes forming a hierarchical structure.SOLUTION: An information processing apparatus according to the present application includes an acquisition unit configured to acquire learning data including data of a set of input data and a plurality of input label data indicating each of a plurality of classes for classifying the input data among a plurality of classes forming a hierarchical structure, and a generation unit configured to generate a machine learning model including a plurality of feature extraction mechanisms corresponding to each of a plurality of hierarchies in the hierarchical structure, each of the plurality of feature extraction mechanisms extracting a feature value indicating a feature of the input data from the input data, and a plurality of prediction mechanisms corresponding to each of the plurality of hierarchies, each of the plurality of prediction mechanisms being trained to predict the input label data corresponding to each of the plurality of hierarchies based on the feature value extracted by each of the plurality of feature extraction mechanisms.SELECTED DRAWING: Figure 6

The invention discloses an instance dependency part label learning method and device based on category enhancement, and the method comprises the steps: firstly, carrying out the data enhancement of an instance through a category setting enhancement method, so as to generate a plurality of category setting enhancement samples; secondly, utilizing a query network and a key network to generate a query representation and a key representation for the enhanced sample so as to carry out distance measurement; then, by utilizing comparative learning and enhancing alignment of samples, category representation is optimized, so that intra-class dislocation is reduced; thirdly, introducing a label disambiguation module, rewarding and punishing categories with high confidence coefficients through a punishment mechanism and weighted loss of confusion labels, and further optimizing the classification performance; and finally, by optimizing a loss function of the model, under the combined action of a label disambiguation module and a representation learning module, the classification precision of the model is improved, and the instance entanglement problem is relieved. According to the method, the problem of instance entanglement is solved by cooperatively adjusting the intra-class and inter-class distances.

A large language model (LLM) may be used to classify an input into one of a plurality of categories. However, given the machine-learning operation of the LLM, the output of the LLM does not represent a definitive statement, but is based on probability computations of the machine learning model. Therefore, the classification performed by the LLM might not be correct. Classification into the wrong category by the LLM results in downstream technical problems. In some implementations, when an LLM generates a response that classifies an input, one or more probability values associated with a token that forms the basis of the response may be used to determine a confidence value. The confidence value is indicative of confidence in the classification performed by the LLM. An action may be taken based on the confidence value.