24 results about "Partial graph" patented technology

The invention discloses a redundant space robot inverse kinematics solving method based on a graph neural network, and relates to the technical field of robot motion control and artificial intelligence crossing. A distance geometric graph is constructed to represent the configuration of the mechanical arm, an inverse kinematics problem is converted into a completion problem of a partial graph, joint configuration is sampled, and data pairs of a complete graph and the partial graph are stored as a data set; training based on an isotropic graph neural network and a conditional variation auto-encoder, and training a model based on a loss function of an evidence lower bound; and in the reasoning process, constructing a partial graph, inputting the partial graph into the trained prior network, outputting hidden variables meeting Gaussian mixture distribution, extracting sampling points, and generating a reconstructed complete graph to obtain joint angle information. The mechanical arm structure is represented through the distance geometric diagram, the inverse kinematics problem is converted into the complementation problem of partial diagrams, probability distribution of a solution space is learned through a conditional variation auto-encoder frame, multi-solution generation of different mechanical arm configurations is supported, and the solving precision and efficiency are high.

Methods, apparatuses, or computer program products disclosed herein provide for receiving an API object via a communication channel, applying a machine learning model to (i) the API object and (ii) one or more graph embedding structures for a portion of graph data to generate one or more predictive inferences related to the API object, and initiating a resolution action related to the API object based at least in part on the one or more predictive inferences.

According to the method for training the target model based on reinforcement learning, the target model is used for executing tampering detection on an input image, and the method comprises the steps that a training sample is obtained, the training sample comprises a target image and boundary information of a plurality of truth value boxes, and the truth value boxes are borders of a part of images marked in the target image and comprising tampering content; and inputting the target image into the target model, and outputting boundary information of the plurality of prediction frames. And calculating the overall overlap ratio of the plurality of prediction frames and the plurality of true value frames. Determining a first reward score based on a preset piecewise function according to a numerical range of the overall overlap ratio; the function corresponding to each numerical range of the piecewise function is a constant function, and the value of the corresponding constant function is increased along with the improvement of the overall overlap ratio. And based on a reinforcement learning algorithm, according to the first reward score, updating the target model.

Disclosed herein are various approaches for visualizing a graph-organized file system (GOFS) using a graphical user interface. A GOFS may represent a graph comprising a plurality of nodes and one or more edges representing one or more relationships between the plurality of nodes. A user interface comprising a representation of at least a portion of the graph may be rendered in a display accessible to one or more computing devices. A file system command for the GOFS may be received via the user interface, and the user interface may be modified based at least in part on executing the file system command.

An information processing device includes at least one hardware processor. The hardware processor is configured to divide an entire graph indicating a relationship between a plurality of elements into a plurality of partial graphs. The hardware processor is configured to set an objective function for learning a representation vector of a partial graph for each of the plurality of partial graphs. The hardware processor is configured to calculate n representation vectors by learning to optimize the objective function using each of n (n is an integer of 1 or more) setting values for each of the plurality of partial graphs.

The application provides a longitudinal federated graph learning method and device satisfying differential privacy, which comprises the following steps: sending a learning request to a service party and other parties participating in longitudinal federated graph learning except a local party, obtaining graph structure coding information based on target conjecture graph structure information and an updated community set to generate disturbed graph structure coding information satisfying differential privacy; obtaining target shared graph structure information satisfying differential privacy based on the disturbed graph structure coding information and initial shared graph structure information to share with other parties; receiving target shared graph structure information from other parties, determining global graph structure information based on the target shared graph structure information and target conjecture graph structure information to reconstruct a global graph structure; and performing distributed training on a preset global graph model based on the global graph structure and local partial graph data by sending intermediate results to the service party to obtain a trained global graph model.

The compression device (101) is configured to include: a matrix acquisition unit (1) that sequentially acquires a partial feature matrix for each of a plurality of partial images into which an image is segmented; and a first global matrix calculation unit (3) that performs singular value decomposition on the first partial feature matrix acquired by the matrix acquisition unit (1), i.e., the first partial feature matrix, and calculates a first global singular value matrix containing the singular values ​​of the first partial feature matrix as the first global singular value matrix, and a first global right singular matrix containing the right singular vectors corresponding to the singular values ​​of the first partial feature matrix as the first global right singular matrix. In addition, the compression device (101) includes: an object matrix calculation unit (4) that calculates a matrix that is the object matrix of the bth singular value decomposition, which includes the bth partial feature matrix obtained by the matrix acquisition unit (1) (b is an integer of 2 or more), the (b-1)th global singular value matrix, and the (b-1)th global right singular matrix; and a second global matrix calculation unit (5) that performs singular value decomposition on the object matrix of the bth object matrix calculated by the object matrix calculation unit (4), calculates the singular value matrix of the object matrix of the bth object matrix that includes the singular values ​​of the object matrix of the bth object matrix as the global singular value matrix of the bth object matrix, and calculates the right singular matrix of the object matrix of the bth object matrix that includes the right singular vector corresponding to the singular value of the object matrix of the bth object matrix as the global right singular matrix of the bth object matrix.

This invention discloses a malware detection method based on adaptive node-edge joint masking. First, a function call graph of an Android application is constructed, and the structural and semantic features of nodes and edges are extracted. Then, a masking strategy is adaptively determined based on the relative importance of nodes and edges in the graph structure, and joint masking is applied to nodes and edges. On this basis, a graph neural network model is trained, enabling the model to learn stable and discriminative representations even when some graph structures or features are damaged. Finally, the trained model is used to detect malware in Android applications. This invention demonstrates effectiveness in improving the robustness and engineering practicality of Android malware detection models.

The present invention discloses a real-time anime avatar driving method, belonging to the field of image processing technology. The method comprises: obtaining a real-time user image; performing a 3DMM model solution on the real-time user image to obtain a target expression vector and a target head posture vector; semantically mapping the target expression vector to generate an expression change partial graph; and identically mapping the target head posture vector to the expression change partial graph to generate a three-dimensional reconstructed image, thereby driving the avatar. The present invention also discloses a real-time anime avatar driving system. The present invention can mass-produce high-quality datasets for anime avatar driving. Generating datasets using this method can implement a data-driven anime character avatar driving method. The present invention can also achieve lightweight mobile avatar driving.

This invention provides a method for putting a base station into a low-power consumption state. [Solution] The method includes the steps of receiving a measurement report transmitted wirelessly from a user device (UE), and creating a three-part graph in response to the measurement report, which includes a first set, a second set, and a third set of nodes. The first set of nodes represents a plurality of cells, and the third set of nodes represents a plurality of UEs that receive coverage from the plurality of cells. The method also includes the step of identifying cell coverage for the plurality of UEs based on the three-part graph using a set of active cells from the plurality of cells.

The application discloses a kind of based on multilayer perceptron's graph classification model construction method and graph classification method, the former includes: the classification task of required application scene is acquired to several graph data and constitutes training set;At least part of graph data of training set has class label;Using training set, selected graph neural network is trained, and the graph neural network that training is completed is used as teacher network and saves the classification result corresponding to graph neural network;Selected multilayer perceptron is used as student model, based on training set, teacher network and corresponding classification result, student model is trained using the method of knowledge distillation, and the student model that training is completed is used as graph classification model for the remaining graph data classification of required application scene.The graph classification model of the application combines the respective advantages of multilayer perceptron and graph neural network, without the dependency of graph in reasoning process, ensure higher accuracy while can greatly reduce model calculation complexity, improve reasoning speed, can be used for time limited engineering deployment.

The application discloses a kind of fusion fault and feature industrial pump knowledge graph construction method.The method comprises: first, after analyzing the characteristics of unstructured data, to the data after pre-processing, ontology construction and knowledge extraction are carried out, and industrial pump fault graph is created using neo4j graph database.Then on the basis of structured data analysis, through system mechanism, experience knowledge and feature extraction, the entity and attribute extraction of feature graph is completed;Then the related features are classified by machine learning and deep learning algorithm, to provide relationship extraction method for the creation of feature graph, and create industrial pump feature graph.Finally, the fault graph and feature graph are fused by graph fusion method, and a unified knowledge graph is formed.Two partial graphs are used as search engine, and then fault report is generated.This comprehensive diagnostic capability greatly improves the efficiency and accuracy of fault solving, and can automatically generate detailed fault diagnosis report.

An image processing device including a processor or circuit configured to: acquire an input image; divide the input image into a plurality of partial images to obtain a partial image sequence; convert the partial image sequence into a token sequence by respectively converting each of the partial images includes in the partial image sequence into a token having a fixed-dimensional vector; obtain an encoded representation sequence based on an attention map indicating a degree of association between tokens and the token sequence; obtain a feature vector from the encoded representation sequence; and adjust at least parameters of the feature vector to reduce an attention loss value. The attention loss value corresponds to an error between a target value of the degree of association between the partial images and a value indicated by the attention map.

This invention belongs to the field of tunnel defect detection technology and provides a method for identifying tunnel lining defects based on a lightweight semantic segmentation model. The method includes: acquiring multiple partial images of the tunnel lining; stitching the multiple partial images together to obtain an unfolded image of the tunnel lining; inputting the unfolded image of the tunnel lining into a defect segmentation model; wherein the defect segmentation model includes a lightweight encoder and a U-Net style decoder; and post-processing the probability map output by the defect segmentation model to obtain the defect identification result. The technical solution of this invention, while balancing recognition accuracy and computational complexity, particularly improves the ability to identify linear defects such as fine cracks, and features low computational complexity and ease of edge deployment.

A synthesis and distribution device 30 includes an encoding unit 32 for selecting a plurality of different partial images from a single image on the basis of a common identifier of the plurality of partial images and synthesizing the plurality of partial images, and transmits data of the synthesized plurality of partial images to another synthesis and distribution device including a decoding unit 36 for decoding an image using the data of the synthesized plurality of partial images.

The application discloses a kind of socialized recommendation method, device and medium based on user multi-intention, wherein method includes: obtaining data set, constructs graph structure data;Build the recommendation model containing two parts graph convolution network, for modeling user commodity interaction relationship and user social relationship in turn;Intention space mapping;Interaction intention decoupling learning;Self-adapting denoising;User social multi-influence learning;Unsupervised learning;Combination obtained final commodity feature vector, final user feature vector and unsupervised learning to train and learn recommendation model;The user and commodity feature vector that recommendation model finally learns are scored and predicted, and the recommended commodity order is obtained.The application fully mines the multi-intention influence in user commodity interaction, and the multi-influence of user social relationship, learns more fine-grained user representation and commodity representation, and obtains more accurate recommendation result.The application can be widely applied in the technical field of recommendation system.

1. Name of the product in this design: Progress control for live interactive dynamic graphical user interface of electronic device. 2. Purpose of this design: An electronic device. 3. The key design features of this product are found in the graphical user interface. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Electronic devices are designed in a conventional way, so other views are omitted. 6. Design 1 is designated as the basic design. 7. Purpose of the graphical user interface: The interface as a whole is used for live streaming interaction. The part for which protection is requested is the control used to display the progress. The crosses in the design interface represent text and / or numbers and / or letters and / or symbols. This design requests partial design protection. The part indicated by the solid line is the part for which protection is requested, and the part indicated by the dashed line is the part for which protection is not requested. 8. Human-computer interaction method of graphical user interface: Design 1 main view is the live broadcast information display page. After the data changes due to human-computer interaction, the corresponding progress change page is displayed. Design 2 main view displays the change state diagrams 1 and 2 of Design 2 in sequence when the human-computer interaction data changes. Design 3 main view displays the change state diagram of Design 3 when the human-computer interaction data changes.

A semiconductor integrated circuit design support method comprises the following steps: a conversion step of converting a hardware description text into a graph object; a reasoning step: inputting the graph object into a trained neural network through a GAT method, and reasoning a reasoning physical measurement index of the semiconductor integrated circuit; and an extraction step of extracting a significant portion of the hardware description text from the reasoning physical metric index, the graph object including nodes and edges, the nodes including a first node including description position information representing a position of a corresponding description in the hardware description text, and the first node including a second node representing a position of the corresponding description in the hardware description text. The neural network comprises an attention coefficient serving as a weight and representing the influence degree of an edge on the reasoning physical measurement index, and in the extraction step, a first edge is determined according to the attention coefficient, and a part in the hardware description text is extracted as a significant part according to description position information of a first node corresponding to the first edge.

1. The name of the design product: the main body of the robot-assisted surgery system operation graphical user interface of electronic equipment. 2. The use of the design product: an electronic device. 3. The design points of the design product: the graphical user interface drawn in solid lines. 4. The picture or photo that best shows the design points: front view. 5. The use of the graphical user interface: the overall interface is used to operate the robot-assisted surgery system, such as operating the robot-assisted surgery system to perform hip replacement surgery, etc.; the part to be protected is the main part. 6. The human-computer interaction mode of the graphical user interface: the user can expand or fold more icons or options arranged vertically by selecting the triangular arrow at the bottom of the left side of the menu bar through clicking, touching, etc., and the front view shown is in the expanded state. 7. Other circumstances that need to be explained: this design is a partial design, and the part shown by the dashed line in the view is not part of the application.

1. Name of the designed product: detailed information display area of biological information graphical user interface of electronic device. 2. Use of the designed product: an electronic device. 3. Design points of the designed product: in the local part of the graphical user interface. 4. Picture or photo best indicating the design points: front view. 5. Use of the graphical user interface: the overall use of the interface is to serve as the biological information graphical user interface of the electronic device. The local use of the interface is to display detailed information of biological information. 6. Other circumstances needing explanation: the present design relates to a partial design, and the part represented by solid lines is the part claimed by the partial design. The characters, numbers, symbols shown in the figure are only used to convey information to the user and do not belong to the part claimed by the partial design.

1. The name of this design product: desk drawer. 2. The purpose of this design product is to store items, support the desktop and keep it stable. 3. The key design point of this appearance design product lies in the connection between the table top and the table legs (the solid line part in the figure). 4. The picture or photo that best illustrates the design points: Stereoscopic drawing 1. 5. The top view has been expressed in the three-dimensional diagram, so the top view is omitted. 6. Other circumstances that require explanation Other explanations: The design for which protection is sought in this application is a partial design, and the partial part is the connecting portion between the table top and the table legs (the solid line portion in the figure).

1. Name of the product in this design: Viewing operation body of a graphical user interface for electronic devices. 2. Purpose of this design: An electronic device. 3. The key design features of this product are the graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Purpose of the graphical user interface: The graphical user interface for house viewing is used for human-computer interaction during the house viewing process, and the main body of the house viewing operation is used to display the human-computer interaction part. 6. Human-computer interaction methods of graphical user interface: Human-computer interaction can be carried out by clicking, pressing or swiping, or by capturing the user's eye movements, gesture recognition or voice. The main view displays the main viewing operation in the graphical user interface for viewing properties. Users can raise their left hand to display a property model. 7. Other matters requiring explanation: This design is a partial design, and the blue semi-transparent coated part is the part for which protection is not sought.

The invention discloses an LLM iterative subgraph reasoning method and system for a data consanguinity knowledge graph. The whole method comprises three parts of graph construction, iterative reasoning and response completion. The graph construction ensures that the knowledge graph can accurately reflect the data blood relationship; iterative reasoning further optimizes reasoning logic based on a result of graph construction, so that sub-graph reasoning is more accurate and efficient; and the response completion supplements and perfects the reasoning result so as to realize more comprehensive knowledge presentation and application. Through the staged design, the system aims at solving the problems that in the prior art, knowledge graph reasoning is not deep, response is not comprehensive, and knowledge graphs are missing, the overall efficiency of data blood relationship analysis is improved, meanwhile, the knowledge discovery capacity is enhanced, and more powerful technical support is provided for application scenes such as data analysis, data governance and data traceability.

The application provides a large model-based multi-modal multi-granularity feature fusion emoticon emotion recognition method, which comprises the following steps: inputting a picture-text data pair into a CLIP model; constructing a similarity graph representing fine-grained picture-text consistency based on cosine similarity; screening partial image block features with the highest similarity to text features; fusing image global features and image block features corresponding to sequence indexes to obtain multi-granularity image feature representation; adjusting and freezing the CLIP model; obtaining a to-be-recognized emoticon and repeatedly performing image fusion processing via the current frozen CLIP model image encoder to obtain to-be-recognized fusion image features, which are embedded into a preset prompt template and input into an LLM model for fine-tuning and emotion category prediction; adjusting the LLM model and performing iteration until the LLM model meets preset requirements in emotion recognition. Through two-stage fine-tuning, the application realizes accurate recognition and classification of emoticon emotions.