9 results about "Functional similarity" patented technology

This invention relates to the field of Internet of Things (IoT) technology and is a cascading threat detection method for automated rules in IoT. It utilizes a large language model to parse rule text, extract semantic elements, and construct a semantic representation model. A semantic alignment model is built to measure the functional similarity of different rules in a rule set. A TAP rule heterogeneous graph is constructed, establishing explicit and implicit relationships respectively. A dual-attention context encoder is constructed to obtain rule embedding representations containing semantic dependency information. A global relation attention mechanism is introduced to measure the importance of explicit and implicit relation spaces under different node types. The invention determines whether cascading threat paths exist in the rule set and generates threat detection results. This invention can automatically parse rule semantics, construct explicit and implicit dual-relation graph models, and utilize multi-layer graph neural networks to achieve multi-hop inference, thereby comprehensively detecting potential rule-cascading threats.

The application relates to a cell infiltration inference method and system fusing GO function annotation and PPI network information, and the method comprises the following steps: collecting gene expression data, GO function annotation data and PPI network data; constructing a cell-cell function correlation network and a cell-cell physical interaction network respectively; performing weighted fusion processing on the two networks to obtain a comprehensive cell relationship network; calculating a final cell infiltration score through a restart walk algorithm, and inferring the infiltration degree in a tumor microenvironment according to the final cell infiltration score. The application innovatively fuses GO function annotation information and PPI network data, comprehensively considers the functional similarity and physical or signal interaction between cells, enables the model to understand cell synergy from the biological pathway level and analyze cell direct interaction from the protein interaction level, avoids one-sidedness of a single perspective, and provides a more stereoscopic cognitive framework for tumor microenvironment analysis.

The invention relates to a cell infiltration inference method and system fusing GO function annotation and PPI network information. The method comprises the following steps: collecting gene expression data, GO function annotation data and PPI network data; respectively constructing a cell * cell function association network and a cell * cell physical interaction network; carrying out weighted fusion processing on the two to obtain a comprehensive cell relation network; and calculating a final cell infiltration fraction through a restart migration algorithm, and deducing the infiltration degree in the tumor microenvironment according to the final cell infiltration fraction. According to the method, GO function annotation information and PPI network data are creatively fused, functional similarity and physical or signal interaction between cells are comprehensively considered, the model can understand cell synergy from the biological pathway level and can analyze cell direct interaction from the protein interaction level, one-sidedness of a single view angle is avoided, and the method has the advantages of being simple in structure and convenient to operate. And a more three-dimensional cognitive framework is provided for tumor microenvironment analysis.

The application belongs to the technical field of picture processing, and particularly relates to a picture generation method based on mutation data, a generation system and a cancer metastasis prediction method. Step one: constructing a pathway image by using the functional similarity of pathways on a gene interaction network; step two: constructing a patient characteristic image; the prediction method further comprises: training a prediction model by using the constructed patient characteristic image, and predicting the metastasis of mutation data by using the trained prediction model. The application aims to solve the problems of lacking high-performance generation of patient characteristic pictures based on single nucleotide variation and the technical problems of cancer metastasis prediction and determination.

Quantification and Detection of Immune Landscape Changes An immune response to an event is detected and quantified by accessing sets of immune cell sequences taken from a sample before and after an event, detecting the immune cell sequences showing a significant change between before and after the event, clustering the immune cell sequences so as to cluster together cell sequences having functional similarity, selecting the clusters containing at least one immune cell sequence showing a significant change and calculating an immune response score based on the selected clusters.

The invention discloses an AI-assisted test vector analysis and optimization method and system, and relates to the technical field of integrated circuit design automation. The method comprises the following steps: a feature extraction step: obtaining a test vector set and extracting multi-dimensional features including fault coverage fingerprints and a scan chain load mode, and constructing a feature matrix; an intelligent clustering step: clustering the test vector set by using an unsupervised clustering algorithm, and dividing vectors with similar functions into the same vector cluster; a representative vector selection step: selecting representative vectors in each vector cluster based on a fault coverage integrity principle to form an optimized vector set; and an iterative optimization step: verifying the optimized vector set, and if a preset condition is not met, dynamically adjusting the clustering parameter and repeating the previous steps. According to the method, deep compression of the test vector is realized through global function similarity analysis, the test time and the test cost can be remarkably reduced, and the method has the advantages of full-automatic post-processing, support of multi-objective optimization and the like.

The invention relates to a city charging prediction method and device based on a multi-semantic topological graph and a medium, and the method comprises the steps: dividing a target city region into a plurality of space nodes, obtaining the space information and semantic information of each space node, and constructing a geographic adjacency matrix and a plurality of single semantic similarity matrixes; constructing a long-range connection candidate set based on the single semantic similarity matrix; obtaining a scene demand, and performing sparsification on the long-range connection candidate set based on the scene demand to obtain a long-range shortcut matrix; constructing a multi-semantic topological graph of the target area based on the geographic adjacency matrix and the long-range shortcut matrix; historical city charging data is acquired, and the future city charging demand is predicted by using the space-time diagram neural network based on the multi-semantic topological graph and the historical city charging data. Compared with the prior art, the method has the advantages that a traditional topological graph is optimized by fusing multiple spatial semantics such as geographical adjacency, functional similarity, travel modes and behavior modes, and therefore more accurate charging prediction is achieved.

Disclosed is a cloud-native application programming interface (API) recommendation method fusing data augmentation and contrastive learning. Service information is included on the basis of a service information double-graph structure, and a mutual attention mechanism is designed to compute an importance degree of each layer of information. A data optimization method for sequence information based on functional similarity and a computation method for similarity between services based on two parts of information are provided; on this basis, data of a service invocation sequence is augmented with the idea of contrastive learning to form an augmented sequence pair; a computational contrastive loss function is combined with a pair-wise recommendation loss function to optimize an overall model, thereby improving the effect of a service recommendation model; and according to a feature embedding representation result of a service, pair-wise recommendation scores are computed to complete service recommendation.

The invention relates to the technical field of Internet of Things, in particular to an Internet of Things automation rule-oriented cascade threat detection method, which comprises the following steps of: analyzing a rule text by using a large language model, extracting semantic elements and constructing a semantic representation model; constructing a semantic alignment model, and measuring functional similarities of different rules in the rule set; constructing a TAP rule heterogeneous graph, and respectively establishing an explicit relationship and an implicit relationship; constructing a double-attention context encoder to obtain a rule embedding representation containing semantic dependency information; introducing a global relation attention mechanism, and measuring the importance of an explicit relation space and an implicit relation space under different node types; and judging whether a cascade threat path exists in the rule set, and generating a threat detection result. According to the method, rule semantics can be automatically analyzed, the explicit and implicit double-relation graph model is constructed, and multi-hop reasoning is realized by using the multi-layer graph neural network, so that potential rule cascade threats are comprehensively detected.