42 results about "Pairwise similarity" patented technology

There are provided systems and methods for pairwise graph querying, merging, and computing for account linking. A service provider may provide an account graph system to identify pairwise similarities between different accounts based on shared data that may be identified through one or more linking characteristics. When providing pairwise graph similarities, a service provider may receive a query identifying two or more accounts and / or an account with a parameter for graph exploration and querying. The service provider may utilize connection, link, or relationship graphs, queried and generated using a graph database, to determine pairwise similarities between the designated seed account and one or more selected accounts. The graph may include vertices for different queried data points and edges connecting such queries, where directionality of the edges or other vectors may be used to identify links or hops between accounts for data querying and exploration.

The invention provides a course map fusion processing method and device, equipment and a medium, and the method comprises the steps: obtaining a plurality of knowledge points of a first course file and a plurality of knowledge points of a second course file, and enabling the knowledge points to be configured with index information; the index information comprises course names and chapter levels; dividing a plurality of knowledge point groups according to the pairwise similarity of each knowledge point; fusing the knowledge points in each knowledge point group to obtain a fusion result of each knowledge point group; and according to the fusion result of each knowledge point group and the index information, counting the fusion number of knowledge points of the first course file and the second course file under different chapter levels, and generating a thermodynamic diagram according to the fusion number. According to the method, high-level generation of fused new knowledge points with teaching value is realized, and the knowledge graph structure of the course is optimized, so that the high-level system structure of the course is better reflected, the system structure and association of the course are mined, and personalized teaching suggestions are given.

Embodiments herein describe techniques for optimizing prompts describing tasks for a large language model (LLM), to enable effective and efficient operations to optimize prompt generation and selection for various tasks in LLMs through human-AI collaboration. In an embodiment, a computing system applies a set of candidate prompts to the LLM, evaluates responses to the candidate prompts received from the LLM and calculates a pairwise similarity matrix of responses based on the received responses to the candidate prompts. The computing system evaluates the pairwise similarity matrix to determine whether or not to present a prompt to receive a user feedback input. The described techniques can enable enhanced processing speed, reducing an overall computer system time typically required for implementing optimized prompt generation, and enhancing performance of the computing system executing the LLM.

An example operation may include at least one of determining, by a transformer encoder trained on annotated image-text data, first latents for a first dataset stored in a memory, and second latents for a second dataset stored in the memory, generating a similarity matrix based on comparisons between the first latents and the second latents, constructing a graph comprising nodes corresponding to the first latents and edges based on pairwise similarity exceeding a threshold, identifying connected components in the graph and selecting, from each component, at least one latent having a highest score from a classifier trained to approximate divergence between the first dataset and the second dataset, forming a reduced dataset comprising the at least one latent, providing the reduced dataset to a model training module, and training an image classifier using the reduced dataset and the second dataset.

Techniques are provided for customer service ticket similarity determination using an updated encoding model based on similarity feedback from a user. One method comprises obtaining encodings of customer service tickets in a vector space using an encoding model; determining pairwise similarities for the encodings of the customer service tickets; obtaining feedback from a user regarding the pairwise similarities for a subset of the encodings; updating pairwise similarities for the subset of the encodings using the feedback from the user; generating an updated encoding model by processing the updated pairwise similarities for the subset of the encodings of the customer service tickets using a supervised learning algorithm; and processing at least one customer service ticket based at least in part on the updated encoding model. The feedback from the user may indicate a similarity of two or more of the plurality of customer service tickets.

The present application relates to apparatus, systems, and methods for grouping data records based on entities referenced by the data records. The disclosed grouping mechanism can include determining a pair-wise similarity between a large number of data records, and clustering a subset of the data records based on their pair-wise similarity.

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.

The invention discloses a system for carrying out snoRNA and disease association prediction based on a multi-graph SAGE network, and relates to the technical field of biological information, the system comprises the following components: a data acquisition and preprocessing module, which obtains data containing snoRNA and disease association information from MNDRv3.1, carries out feature extraction on a snoRNA sequence, calculates snoRNA pairwise similarity by using a Tanimoto coefficient, and sends the snoRNA pairwise similarity to the MNDRv3.1; meanwhile, the disease pairwise similarity is obtained by adopting a DAG-based semantic similarity method; according to the method, the multi-graph heterogeneous network is constructed by integrating the paired similarity data of the snoRNA and the disease and the associated network of the snoRNA and the disease, the feature information of the snoRNA and the disease is comprehensively captured by using the GraphSAGE architecture with an attention mechanism, the complex relationship between the snoRNA and the disease is effectively captured, the prediction precision of snoRNA-disease association is remarkably improved, and the prediction efficiency of the snoRNA-disease association is improved. According to the method, higher AUC, AUPRC and F1 scores are obtained on a test set, higher classification capability and prediction reliability are displayed, and a more accurate prediction result is provided for biomedical researchers.

A method for monitoring a work system including steps of receiving a plurality of scan data values acquired by a sensor of a sensor device and order indicators associated with the scan data values, determining pairwise similarities between the scan data values, determining a number of clusters for grouping the scan data values based on the determined pairwise similarities, grouping the scan data values into N clusters, wherein N is the number of clusters determined based on the determined pairwise similarities, assigning the scan data values to the clusters obtaining a grouped dataset of the scan data values, and outputting a signal based on the grouped dataset. Further, a system and a computer program are shown.

Methods and apparatus for media retrieval. In some examples a method of media retrieval includes receiving an input media dataset of a first modality and obtaining, with a first-modality encoder, a first embedding representing the input media dataset in a latent space. The method also includes selecting a subset of second embeddings based on a similarity metric configured to quantify pairwise similarity between different embeddings in the latent space. Each of the second embeddings represents, in the latent space, a respective candidate media dataset of a different second modality. The second embeddings are obtained using a second-modality encoder. The method also includes identifying one or more output media datasets of the different second modality among the respective candidate media datasets based on the selected subset of the second embeddings. The first- and second-modality encoders are jointly trained using semi-supervised contrastive learning.

Techniques for assessing multi-modal inputs to a machine learning model involve receiving a multimodal input containing an image, producing several transformed versions of that image, and generating embeddings for both the original and transformed images. A pairwise similarity analysis among all embeddings is conducted to determine distance values. Two dissimilarity metrics can then be calculated: one reflecting the differences among the transformed images, and another comparing the original image to its transformed versions. If the dissimilarity among the transformed images is greater than that between the original and transformed images plus a threshold, the system triggers a remediation action. This action either blocks the input from being processed by the machine learning model or prevents the model's output from being returned to the requester, thereby enhancing the reliability and security of the model.

The invention relates to a distance calculation-based old-town serving old-town task automatic matching method and a medium, and relates to the technical field of digital information processing. Performing pairwise similarity calculation on the basis of the acquired identity card address and residence address information of the inpatient and the identity card address and residence address of each medical worker in a hospital medical worker database to obtain a medical worker list with the highest similarity corresponding to a preset number threshold before each calculation; and calculating the distance between each medical worker in the medical worker list and the inpatient, calculating an integral based on the distance and a weight coefficient corresponding to the medical worker, and obtaining the old and rural medical worker list which has the highest matching degree and corresponds to a preset number threshold value, so as to realize the matching of the medical workers of the task of serving the old and rural areas by the old and rural areas. Compared with the prior art, the method can be used for quickly and accurately matching medical staff suitable for hospitalized patients.

There are described a method and a system for generating clusters of ballistic specimens. Using an image acquisition tool, topographic data for at least three ballistic specimens of at least one region of interest is acquired. From the topographic data, at least one parameter characterizing optimal toolmark alignment is determined for every distinct pair of the at least three ballistic specimens, each ballistic specimen having a plurality of toolmarks formed thereon. From the topographic data, at least one pairwise similarity score associated with optimal toolmark alignment is determined for every distinct pair of the at least three ballistic specimens. At least one triplet-wise consistency measure indicative of a consistency of optimal toolmark alignment is determined for every distinct triplet of the at least three ballistic specimens. A cluster analysis is conducted based on the at least one similarity score and the at least one consistency measure to generate the clusters.

The invention discloses a member leakage risk assessment method and system for a retrieval enhancement generation system based on a small amount of queries. Under the black box condition, according to the target member document, generating a plurality of queries with equivalent semantics and diverse syntaxes by the auxiliary large language model, submitting the queries to a target retrieval enhancement generation system, and collecting responses; the response is encoded as a sentence vector, an average pairwise similarity is calculated and a rejection response is identified to apply a penalty, and a final risk assessment score is synthesized and compared with a threshold to assess the likelihood of the target document as a knowledge base member. The method is implemented under the complete black box condition, does not depend on the internal architecture or parameter information of the system, is low in query overhead, is easy to calibrate, and is suitable for compliance auditing, red team evaluation and security detection before online, so as to quantify the member privacy leakage risk and guide a protection strategy.

The present application provides a method, system, medium and device for table picture matching, including: obtaining a first table picture and a second table picture to be compared; extracting features based on the first table picture to obtain first matching features; extracting features based on the second table picture to obtain second matching features; calculating the pairwise similarity between the first matching features and the second matching features; and determining the matching degree between the first table picture and the second table picture based on the pairwise similarity. By extracting visual features from table pictures and simultaneously extracting text features and title features, the present application can effectively improve the matching accuracy of different table pictures.

This invention discloses a method and apparatus for constructing a quantifiable indicator system based on semantic extraction and alignment. The method includes: extracting explicit and implicit indicators from the target text to form an initial indicator set; converting the explicit and implicit indicators into semantic vectors and determining the pairwise similarity between all vectors; clustering the explicit and implicit indicators based on vector similarity to select core indicators to form a core indicator set; matching each core indicator in the core indicator set with the target dataset to obtain a target dataset that meets preset matching conditions; and constructing the indicator system based on the target dataset and the corresponding core indicators. This invention relies on large language models and natural language processing technology to achieve fully automatic construction, redundancy removal, and localization adaptation of the indicator system, improving the efficiency of indicator construction, reducing subjective intervention, and enhancing the objectivity and practicality of the system.

The application discloses a multi-head attention mechanism-based super network representation learning method applied to breeding, relates to the technical field of crop breeding, and comprises the following steps: constructing a crop heterogeneous super network, and constructing an initial embedding matrix corresponding to all nodes; obtaining a normalized node representation vector corresponding to each tuple and a similarity score of each tuple; calculating the pairwise similarity between each two nodes; constructing a joint loss function, and optimizing the parameters of a target model; generating a final representation vector of a node in the crop heterogeneous super network by using the target model after parameter optimization; and predicting a crop variety according to the final representation vector. The application avoids complex graph conversion and explicit high-order combination calculation, improves the calculation efficiency and scalability, can more comprehensively and accurately encode semantic and structural information of phenotypic traits, and thus provides reliable and high-quality data basis for crop variety prediction based on the final representation vector.

Methods, systems and apparatuses for discovering novel artificial neural network architectures (ANN) architecture are disclosed. One method includes calculating ANN architecture fingerprints including an ANN architecture fingerprint of each of a plurality of existing ANN architectures, creating a plurality of next-generation candidate ANN architectures, calculating a plurality of next-generation candidate ANN architecture fingerprints including an ANN architecture fingerprint of each of the plurality of next-generation candidate ANN architectures, calculating ANN architecture pairwise similarities between each of the plurality of existing ANN architectures and each of the plurality of next-generation candidate ANN architectures using the plurality of existing ANN architecture fingerprints and the plurality of next-generation candidate ANN architecture fingerprints, retraining each of the plurality of next-generation candidate ANN architectures on the training dataset, obtaining a performance score of each of the next-generation candidate ANN architectures, and calculating a fitness score for each of the next-generation candidate ANN architectures.

Methods, systems and apparatuses for discovering novel artificial neural network architectures (ANN) architecture are disclosed. One method includes calculating ANN architecture fingerprints including an ANN architecture fingerprint of each of a plurality of existing ANN architectures, creating a plurality of next-generation candidate ANN architectures, calculating a plurality of next-generation candidate ANN architecture fingerprints including an ANN architecture fingerprint of each of the plurality of next-generation candidate ANN architectures, calculating ANN architecture pairwise similarities between each of the plurality of existing ANN architectures and each of the plurality of next-generation candidate ANN architectures using the plurality of existing ANN architecture fingerprints and the plurality of next-generation candidate ANN architecture fingerprints, retraining each of the plurality of next-generation candidate ANN architectures on the training dataset, obtaining a performance score of each of the next-generation candidate ANN architectures, and calculating a fitness score for each of the next-generation candidate ANN architectures.

This application discloses a method, apparatus, device, and medium for screening data features, relating to the field of data mining and analysis. The method includes: sequentially determining multiple information indicators for each candidate data feature based on a preset set of test threshold parameters; wherein the set of test threshold parameters is configured based on out-of-sample window data; screening the multiple candidate data features to obtain multiple screened data features based on the multiple information indicators and the set of test threshold parameters, combined with reference data features corresponding to each candidate data feature; wherein the reference data features are constructed based on the corresponding candidate data features; grouping the multiple screened data features according to the pairwise similarity between them to obtain multiple similar feature groups, and selecting a representative data feature for each similar feature group based on the multiple information indicators of the screened data features. By implementing this application, the accuracy of data feature screening can be improved.

A method is provided for data processing performed by a processing system. The method comprises determining a set of first tokens for first data and a set of second token for second data, each token comprising information associated with a segment of the respective data, determining pair-wise similarities between the set of first tokens and the set of second tokens, each pair comprising a first token in the set of first tokens and a second token in the set of second tokens, determining, for each first token in the set of first tokens, a maximum similarity based on the determined pair-wise similarities between the respective first token and the second tokens in the set of second tokens, and determining a first similarity between the first data and the second data by aggregating the maximum similarities corresponding to the first tokens in the set of first set of tokens.

Similarity sensitive diversity is utilized to measure variation in a distribution of item listings along one or more categories. A cosine 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.

This invention discloses a method, system, and rail vehicle for detecting axle box bearing faults, relating to the field of rail transport safety technology. It extracts frequency domain characteristic waveforms from the axle box bearing's state signals, performs pairwise similarity comparisons of the frequency domain characteristic waveforms of different components at the same axle position on the same bogie, and compares the similarity of the frequency domain characteristic waveforms of the same components at the same axle position on different bogies. By combining the comparison results of different components within the same axle position and the comparison results of the same components between different bogies, fault determination is made. This enables the detection of lateral anomalies in multiple components under the same operating conditions. Compared to traditional methods that use single-component, fixed-threshold determination, this method not only adapts to different operating conditions but also eliminates the need to pre-establish health baselines for different components. It effectively improves the accuracy of axle box bearing fault detection in rail vehicles while simplifying deployment and increasing detection sensitivity.

The invention discloses a semi-supervised open world radiation source individual identification method based on similarity self-adaption, and aims to solve the problems that unmarked data cannot be effectively utilized and unknown classes cannot be subdivided in an open world scene in existing open set and semi-supervised radiation source individual identification. The method comprises the following steps: acquiring a data set containing marked samples and unmarked samples, and extracting sample features by using a feature extractor; clustering is converted into sample pair similarity prediction through pairwise similarity loss, and a new category is identified; identifying a known class by using self-adaptive cross entropy loss, and balancing the learning speeds of the new class and the known class; introducing an entropy regularization item to prevent model overfitting; and through minimizing an overall objective function training model formed by pairwise similarity loss, adaptive cross entropy loss and entropy regularization, identification of known and new radiation sources is realized. According to the method, finite marked data and a large amount of unmarked data are effectively utilized, known classes and new classes can be accurately identified, and the adaptability to new class radiation sources is enhanced.

Similarity sensitive diversity is utilized to measure variation in a distribution of item listings along one or more categories. A cosine 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.

The present application relates to apparatuses, systems, and methods for grouping data records based on entities referenced by the data records. The disclosed grouping mechanism can include determining pairwise similarities among a large number of data records, and clustering subsets of data records based on their pairwise similarities.

This invention discloses a method for retrieving similar protein structures based on hash learning: First, a protein structure dataset is acquired, and the pairwise similarity information is calculated. For the query sample, positive and negative samples are sampled. The protein structures are modeled as a graph, and node and edge features are extracted, the feature representations of nodes and edges are updated, quantization loss and similarity loss are defined, and the final loss function is obtained. The model is then trained. Based on the trained model, each collected protein structure is represented as a binary vector, resulting in a binary vector database. During retrieval, the trained model represents the new protein structure to be retrieved as a binary vector. Similar structures are retrieved by directly calculating the Hamming distance between binary vectors or by constructing an inverted index. Alternatively, the top-ranked protein structures returned based on binary vector retrieval can be reordered using other real-valued vectors or more complex algorithms. This invention reduces storage overhead and improves retrieval speed.

The present application relates to apparatus, systems, and methods for grouping data records based on entities referenced by the data records. The disclosed grouping mechanism can include determining a pair-wise similarity between a large number of data records, and clustering a subset of the data records based on their pair-wise similarity.

In the disclosed systems and methods for categorizing medical data, a computer system obtains, in electronic form, a plurality of medical records. Each medical record includes corresponding medical data from a respective medical evaluation and corresponding metadata comprising a plurality of attributes about the respective medical evaluation. Each respective attribute comprises a corresponding string of text. The computer system determines, for each respective pair of medical records consisting of a first medical record and a second medical record, a corresponding pairwise similarity between, for each respective attribute in a set of attributes, the corresponding string of text for the first medical record and the corresponding string of text for the second medical record. The computer system identifies a first subset of the plurality of medical records. Each respective medical record in the first subset is connected to each other through pairwise similarities that each satisfies a similarity threshold.

The embodiments of this specification disclose an adaptive segmentation method, device and medium for migrating insect swarms, which relate to the field of data processing technology and are used to solve the problem of noise interference and low accuracy in traditional insect swarm segmentation methods. The method includes: determining the four-dimensional coordinates of each initial data point by high-resolution radar measurement; performing adaptive noise removal based on the average sparsity corresponding to the four-dimensional coordinates to obtain filtered data points; obtaining the nearest neighbor set of each data point to define an adjacency matrix based on the nearest neighbor sets of any two data points; determining the undirected weighted graph corresponding to the data point to determine the Laplace matrix of the current migrating insect swarm based on the pairwise similarity in the adjacency matrix; searching the Laplace matrix based on the preset subgraph optimal segmentation function to obtain the optimal segmentation of the subgraph, so as to segment the data points corresponding to the optimal segmentation of the subgraph as the segmentation result of the current migrating insect swarm.