39 results about "Ranking" patented technology

Disclosed is a chat group management method, including: detecting a message receiving mode corresponding to a chat group; obtaining a degree of interest of a user for chat group messages and an activity degree of the user in the chat group in accordance with a determination that the message receiving mode corresponding to the chat group is a mute-notification receiving mode; determining an importance ranking for the chat group according to the degree of interest and the activity degree; and updating the chat group's position among a plurality of chat groups in accordance with the importance ranking.

This invention discloses a method for ranking reserve projects for new power grid infrastructure, comprising: constructing a ranking indicator system from five dimensions: power grid development, security and supply, service quality, efficiency and effectiveness, and green and low-carbon development; collecting raw indicator data and distinguishing indicator types; obtaining objective weights for the indicators through a combination of two objective weighting methods; constructing a matrix by standardization transformation and weight combination; calculating relative proximity to form an objective ranking; subsequently integrating expert rankings; obtaining a subjective consensus ranking by constructing a pairwise preference matrix and an integer programming model; and finally, calculating standardized scores and weighted fusion of the subjective and objective ranking results using a weighted aggregation method to determine the ranking position of the reserve projects. This method balances data objectivity and expert experience, covers the core requirements of projects from multiple dimensions, has a rigorous ranking logic, and effectively improves the scientificity and accuracy of ranking reserve projects for new power grid infrastructure.

Various embodiments of the present disclosure provide computer forecasting techniques for initiating presentation of an interactive user interface. The techniques may include receiving one or more candidate prediction-based actions and generating a plurality of causal risk-based impact scores with respect to a candidate prediction-based action. The techniques include generating a plurality of causal quality-based impact scores and an action sequence for a plurality of evaluation entities and generating a causal net impact score based on (i) an aggregation of the plurality of causal risk-based impact scores and the plurality of causal quality-based impact scores and (ii) a sequence impact metric corresponding to the action sequence. The techniques include generating a sequence ranking for the action sequence and initiating a presentation of an interactive user interface reflective of the action sequence and the sequence ranking.

An example may train a cross encoder embedding model using a ranking instruction, a combined input, a pseudo label, and a combined loss. The combined loss includes a ranking loss and a first retrieval loss. A first entity embedding of an entity and a first item embedding of an item may be obtained from the trained cross encoder embedding model. A first input including the first entity embedding obtained from the trained cross encoder embedding model, a second input including the first item embedding obtained from the trained cross encoder embedding model, and a second retrieval loss, may be used to train a dual encoder retrieval model to produce a trained dual encoder retrieval model. A system may use output of the trained dual encoder retrieval model to include or exclude items from a presentation of digital content items to the entity via a device.

The invention relates to the technical field of information security, in particular to a sequential cipher security analysis method for a composite structure, which comprises the following steps of: acquiring round function parameters of a target cipher algorithm, nonlinear component definition and a key scheduling rule, and generating a standardized configuration data set; dividing the complete algorithm into a plurality of independent low wheel structures along the boundary of the nonlinear component according to the wheel function parameters, and constructing a directed graph data model containing state transition constraints to compress a state space; calling a password component vulnerability feature library to quantify a sub-structure active S box distribution abnormal value and a linear diffusion layer vulnerability path, and generating a risk weight priority ranking list; and inputting the state sequence data into the target application environment simulation platform, and generating a reproducible vulnerability report when the output deviation exceeds a security threshold. Through hierarchical compression, risk guidance and multi-environment verification, the technical defect of insufficient path coverage of a high-round-number lightweight algorithm in a resource-limited scene is solved.

A multi-label ranking method includes receiving, at a processor and from a first set of artificial neural networks (ANNs), multiple signals representing a first set of ANN output pairs for a first label. A signal representing a second set of ANN output pairs for a second label different from the first label is received at the processor from a second set of ANNs different from the first set of ANNs, substantially concurrently with the first set of ANN output pairs. A first activation function is solved based on the first set of ANN output pairs, and a second activation function is solved based on the second set of ANN output pairs. Loss values are calculated based on the solved activations, and a mask is generated based on at least one ground truth label. A signal, including a representation of the mask, is sent from the processor to each of the sets of ANNs.

This invention discloses a sequence recommendation method based on the fusion of positive and negative feedback and logical rules, comprising: dividing user behavior into positive and negative feedback; constructing a feedback sequence, including positive and negative feedback sequences, based on user behavior and time sequence; processing the features of the user behavior sequence and the feedback sequence to obtain corresponding embedded features; using the self-attention mechanism of a deep learning model to process the input embedded features, capturing the internal dependency features of the original user behavior sequence; and using a cross-attention mechanism to cross-associate the feedback sequence and the input user behavior sequence to capture the dynamic relationship between user behavior and feedback; and through an output layer, mapping the model's intermediate features to user preference ratings for target items, outputting a rating list for recommendation ranking. This invention significantly improves the performance, interpretability, and adaptability of recommendation systems by combining positive and negative feedback information from user behavior with logical rules.

The application provides a game ranking list updating method and device and electronic equipment, score change data in a current season, a first ranking list of the current season and a second ranking list of a last season of the current season are acquired; the first ranking list and the second ranking list save the same data; based on the score change data and the second ranking list, the first ranking list and a third ranking list of a next season of the current season are updated, to obtain an updated first ranking list and an updated third ranking list; when the current season ends, the updated first ranking list and the updated third ranking list are saved, and the updated first ranking list and the updated third ranking list save the same data. The method writes the current season and the next season at the same time when the ranking list is updated, so that there is a snapshot of the old season when the season is switched, and the ranking list of the new season directly inherits from the old ranking list, there is no off-season, and the original old season ranking list does not need to be blocked and copied.

Embodiments of the present disclosure provide a ranking optimization method and device, equipment and a storage medium. The method comprises: obtaining predicted scores and real scores of a recommendation object on each target; determining abnormal vertical categories and corresponding abnormal targets according to the predicted scores and real scores of the recommendation object on each target, to form abnormal combination items; generating weight optimization items matched with each abnormal combination item, correcting a multi-target fusion formula according to the weight optimization items; calculating final scores of each recommendation object using the corrected multi-target fusion formula, and ranking each recommendation object according to the final scores. Through the above technical solution, the accuracy of ranking is improved by adding weight optimization items to the multi-target fusion formula to optimize the abnormal vertical categories and corresponding targets of the prediction results.

The invention relates to the technical field of education and teaching management, in particular to a ranking increment-based teaching assessment method and analysis system. The total data comprises class ranking of the to-be-analyzed object, class average score of the to-be-analyzed object, class level of the to-be-analyzed object, class average score of all classes of the same level, end-of-period ranking of the to-be-analyzed object, end-of-period average score of the to-be-analyzed object, and end-of-period average score of all classes of the same level. According to the method, total data including class levels and test question difficulty coefficients are collected, and level feature factors are calculated according to historical score distribution of classes of all levels so as to distinguish ranking fluctuation sensitivity of classes of different levels, so that the problem of'one-cut 'of a unified regression equation of an existing method is avoided; and carrying out two-dimensional calibration of the level characteristic factor and the test question difficulty coefficient on the basic ranking difference, and correcting the interference of the level characteristic and the test difficulty on the ranking.

Certain examples of this disclosure provide a method (100) for determining points of interest (200) along a route (400), the method comprising: determining (101) a first set (300) of points of interest (POIs) that meet one or more first criteria (500) along the route; dividing (102) the route into multiple segments (700); associating each one or more points of interest in the first set of POIs with one of the multiple segments of the route (103); ranking each one or more POIs in the first set of POIs (104), wherein the ranking is at least partially based on one or more second criteria (900); for each segment, selecting (105) a threshold number (N) of the highest-ranked POIs associated with the corresponding segment; and generating a second set of POIs at least partially based on the selected POIs.

The application discloses a target attack priority ranking method and device based on intelligent evaluation, equipment and a medium, and relates to the technical field of data processing.The method comprises the following steps: a target attack priority ranking evaluation index system is constructed; the evaluation index system comprises first-layer indexes and second-layer indexes; the first-layer indexes at least comprise category-type indexes, and the second-layer indexes are used for predicting and evaluating the category-type indexes in the first-layer indexes; a target evaluation decision matrix is constructed based on the first-layer indexes, wherein the values of the category-type indexes in the first-layer indexes are calculated by using an intelligent evaluation method based on a neural network; the weights of the first-layer indexes are calculated by using an entropy weight method; the closeness degrees of the targets are calculated by using a TOPSIS method; the comprehensive correlation degrees of the targets are calculated by using a grey correlation analysis method; the comprehensive evaluation values of the targets are weighted and calculated based on the closeness degrees and the comprehensive correlation degrees; and the targets are ranked according to the comprehensive evaluation values. The application greatly improves the scientificity, objectivity and reliability of the target ranking result.

Aspects concern a method for processing a query for information for a control task, comprising receiving a query, retrieving a plurality of data elements from one or more data sources, wherein the data elements containing information related to the query, determining a ranking score for each of the plurality of data elements according to each of a plurality of ranking methods, determining a combined ranking score for each data element of the plurality of data elements by combining the ranking scores determined for the data element according to the plurality of ranking methods, selecting a subset of the plurality of data elements based on the combined ranking score, formulating a prompt with a request to respond to the query for a generative machine learning model using information from the selected subset, supplying the prompt to the generative machine learning model and generating a response to the query using an output provided by the generative machine learning model in response to the prompt.

An online system receives a search query from a client device associated with a user and queries a database including item data for a set of items matching the query, in which the set of items is at a retailer location associated with a retailer type and each item is associated with an item category. For each item of the set, a machine learning model is applied to predict a probability of conversion for the user and item and a score is computed based on an expected value, in which the expected value is based on a value associated with the item and the probability. The score for each item is boosted based on the item category, retailer type, or a user segment that is based on the user's historical order data. The items are ranked based on the boosted scores and the ranking is sent to the client device.

This application discloses a machine learning-based method for constructing a cognitive screening scale and a decision support system, relating to the fields of digital healthcare and medical information processing. The method includes: acquiring a sample dataset containing response data from an original candidate item set; performing feature selection on the original candidate item set to obtain the comprehensive contribution of each candidate item; determining the number and composition of items in the core item set based on the comprehensive contribution ranking; using the core item set as input features and cognitive state grading labels as output, selecting a model that meets preset performance indicators as the cognitive screening model; acquiring the response data of the core item set from new subjects and inputting it into the cognitive screening model to output cognitive state grading results. This application achieves significant simplification of the original joint scale items while maintaining high grading discrimination ability, and also reduces the administration burden and improves screening efficiency.

A search query is received. A first plurality of items is identified. A reference item is identified based on the search query and the first plurality of items. A plurality of contextual distances is determined using a distance calculation metric. A machine learning model is used to rank a second plurality of items based on the plurality of contextual distances. The second plurality of items is caused to be displayed. The second plurality of items is arranged in an order in accordance with the ranking.

Embodiments of the present application provide a pre-training model product and processing method, an electronic device and a computer storage medium, wherein a pre-training model processing method comprises: obtaining a rewritten text generated by a generator of a pre-training model based on a masked training text; predicting and generating a corresponding word quality ranking level for each word in the rewritten text through a hierarchical classifier of the pre-training model, wherein the word quality ranking level is used to indicate a semantic difference level of the rewritten text and the training text, and the word quality ranking level comprises at least three levels; and training the pre-training model comprising the generator and the hierarchical classifier according to the word quality ranking level. Through the embodiments of the present application, the training accuracy and efficiency of the pre-training model can be improved.

A method includes: generating a set of tasks; determining, by a machine learning model and based on multiple data types from multiple sources, that an overall risk score exceeds a first failure threshold due to a risk score of a task exceeding a second threshold; selecting a replacement task for the task, the selecting including: receiving, replacement candidates, each replacement candidate including a candidate offset potential and one or more candidate failure mechanisms; assigning, by the machine learning model and to each of the replacement candidates, a replacement score for the replacement candidate based on a failure correlation of the replacement candidate with respect to each other sets of the set of tasks; ranking the replacement candidates based on the replacement scores; and selecting, based on the ranking, the replacement task; and generating, an updated set of tasks including the replacement task.

Enhanced ranking and retrieval of documents and information, utilizing improved Retrieval- Augmented Generation (RAG). A method includes: (a) collecting historical query-document data from past interactions of users with a RAG system; (b) defining relevance criteria that determine whether a document that was retrieved by via RAG is relevant to a query posed to the RAG system; (c) labeling query-document pairs with a binary relevance indicator of Relevant or Irrelevant; (d) encoding queries and documents into numerical embedding vectors; (e) generating feature vectors; (f) organizing the feature vectors into a structured dataset; (g) training and validating a logistic regression Machine Learning (ML) model to predict the relevance of query-document pairs; (h) integrating the ML model into the RAG system for real- time relevance scoring of documents retrieved in response to new user queries.

An online system receives a search query from a client device associated with a user and queries a database including item data for a set of items matching the query, in which the set of items is at a retailer location associated with a retailer type and each item is associated with an item category. For each item of the set, a machine learning model is applied to predict a probability of conversion for the user and item and a score is computed based on an expected value, in which the expected value is based on a value associated with the item and the probability. The score for each item is boosted based on the item category, retailer type, or a user segment that is based on the user’s historical order data. The items are ranked based on the boosted scores and the ranking is sent to the client device.

This disclosure relates to a training method, sorting method, apparatus, device, and medium for a ranking model. The training method includes: acquiring sample input data, wherein the sample input data includes a first user description text and multiple first object description texts; wherein the first object description texts describe a first object to be recommended; inputting the sample input data into multiple ranking teacher models to obtain multiple reference ranking results output by the multiple ranking teacher models; wherein the reference ranking results represent the ranking results of the multiple first objects to be recommended; inputting the sample input data into an initial ranking model to obtain a predicted ranking result, and adjusting the parameters of the initial ranking model based on the multiple reference ranking results and the predicted ranking result until a preset convergence condition is reached, at which point the initial ranking model is used as a ranking student model. Thus, based on this ranking student model, high ranking accuracy and fast ranking speed are achieved.

The application discloses a recommendation method based on an interpretable generalized logistic transformation matrix decomposition, and comprises the following steps: converting an original score matrix into normally distributed data through a generalized logistic transformation function; constructing a similarity-based index and a ranking-based index; calculating a probability distribution of a similar user's score on a recommended item and an expected score, and combining the similarity index to generate an interpretability index; integrating the interpretability index into a matrix decomposition target function for optimization; solving a user feature matrix and an item feature matrix through an alternating least squares method or a stochastic gradient descent; calculating a predicted score and mapping back to an original score interval through a generalized logistic inverse transformation; and generating a recommendation list; the application has wider applicability and higher performance in practical application.

A method consisting of obtaining a definition of interestingness 233 and a plurality of test cases suitable for fuzz testing an autonomous system 234 and providing them to a large language model (LLM) 231 which then outputs 238 a score or ranking of the test cases based on the definition of interestingness. The method may further comprise providing the LLM with: information on the state of the system separately from the test scenario, the output format of the LLM, and a methodology for ranking or assigning scores. The LLM may output an explanation of its rankings / scores which may be done by chain-of-thought prompting. The methodology may involve carrying out fuzz testing using selected test cases 239. The test cases may be obtained by a test case generator 220 which may consist of modifying some seed case 222 to obtain a mutated seed case 223. A test case analyser 230 for doing the same is also disclosed. [Figure 2]

The application provides a context learning example adaptive ranking method based on performance supervision, comprising the following steps: for each query sample in a training query set, retrieving a fixed example set from an example library, defining a ranking space through arbitrary ranking, and sampling a subset of candidate rankings in the ranking space; for each sampled ranking, calling a target LLM to obtain a prediction result, and calculating the prediction result with a real answer to obtain a task index score; training a ranking predictor to learn a mapping relationship of "query-ranking-real performance" based on query embedding vectors and ordered context embedding vectors, and obtaining a trained ranking predictor; in an online inference stage, using the trained ranking predictor to perform adaptive ranking on the example set, and selecting an optimal ranking. The application solves the problems of too large data volume in ranking space with factorial explosion, unstable correspondence between heuristic / proxy indicators and real performance, and lack of learnable ranking selector.

To achieve a new performance technique in "a race game for determining ranking not by the order of arrival at a goal but by the height of an acquired score".SOLUTION: The information processing apparatus 10 according to the present disclosure includes the game executing unit 11 that moves the plurality of objects toward the first goal and updates the score acquired by each of the plurality of objects, and the race scene control unit 12 that displays the race scene in which the plurality of objects move toward the first goal. And a goal performance scene control part 14 for displaying a goal performance scene in which the plurality of objects move toward a second goal after a race scene in which at least one of the plurality of objects reaches the first goal, and the plurality of objects reach the second goal in the order of the score ranking.SELECTED DRAWING: Figure 1

According to one aspect, a method for elaborating a machine learning model for the classification of time series signals is proposed comprising obtaining features of training time series signals associated with different indicated classes, calculating a distinction coefficient between classes for each feature and calculating a distinction coefficient between classes for each combination of features from a distribution of the values of the features for each group of time series signals, ranking the features according to the distinction coefficient of each feature and the distinction coefficient of each combination of features, and training the machine learning model by taking as input for this model at least one feature chosen according to the ranking.

The application discloses a kind of generation method and device for explaining result, it is related to artificial intelligence technical field.The specific embodiment of the method includes: according to the score of each item of target user, the importance ranking of each item feature is obtained;According to the importance ranking of each item feature, the explanation feature is filtered from each item feature of recommended item;According to the evaluation data of the recommended item, an explanation library is constructed, so as to extract the theme corresponding to each explanation in the explanation library;The similarity of the theme corresponding to each explanation in the explanation library and each explanation feature of the recommended item is calculated, so as to determine the explanation result corresponding to the recommended item according to the similarity.The embodiment can solve the technical problem that it is difficult to explain the recommended result output by model.

Mechanisms are provided for automatic identification of a reconciliation computer tool for producing coherent reconciled data from base data generated by a computer model. A machine learning training operation is executed on one or more performance prediction computer model(s) (PPCMs) based on first input features of at least one hierarchical dataset, and second input features of a plurality of different reconciliation computer tools. The PPCM(s) generate a prediction of performance of a corresponding reconciliation computer tool based on the first and second input features. Features are extracted from a runtime hierarchical dataset and input into the trained PPCM(s) which generate predictions of performance of a plurality of reconciliation computer tools based on the extracted features of the runtime hierarchical dataset. The reconciliation computer tools are ranked relative to one another based on the predictions of performance. An output is generated based on the ranking of the reconciliation computer tools.

The invention relates to intelligent frame selection through activity-based ordering and optimization. Various examples, systems, and methods are disclosed herein, relating to frame selection through activity-based ordering and optimization. A first computing system may receive a plurality of frames and metadata from a capture device that captures a video stream. The first computing system may generate a plurality of rankings for the plurality of frames based on the plurality of video parameters of the plurality of frames and the metadata using a ranking model, where the plurality of rankings correspond to a summary of the video stream. The first computing system may determine, based on the plurality of rankings, at least one frame of the plurality of frames to provide to at least one buffer, where the at least one buffer stores a subset of the plurality of frames. The first computing system may provide the subset of frames from at least one buffer as input to a machine learning model.