19 results about "Concept vector" patented technology

The invention relates to the technical field of artificial intelligence, in particular to a concept activation vector-based medical image lesion analysis and clinical decision interpretation system, which comprises a feature analysis module, a semantic mapping construction module, a concept activation extraction module, a boundary response fusion module and an interpretation path generation module. According to the method, the gradient direction and the gray abrupt change position of continuous elements in a focus area are detected, texture density and edge consistency are jointly judged to generate structured alignment mapping, microscopic image features are accurately captured, cross comparison is carried out on co-occurrence probability of lesion nouns and modifiers in phrase combinations and activation frequency of the same area, and therefore the accuracy of the lesion nouns and the modifiers in the phrase combinations is improved. Constructing a deep correlation map of pathological semantics and image features, screening an activation group according to continuous response of phrase pairs and image regions under case input change, generating a concept vector trigger track to dynamically track pathological feature evolution, and performing clinical consistency judgment; and an interpretable decision basis with strict logic support is output while subjective interference is eliminated.

Method and device for determining explanations associated with a semantic classification of input data by a classification engine. This method for determining explanations associated with a semantic classification of input data by a semantic classification engine, comprising a first module for transforming an input data vector into a latent vector, and a second module for predicting a classification result from the latent vector, comprises the following steps: for each input data vector, calculation (36) of a conceptual vector; determination (38) of the parameters of an operator enabling the transition from the conceptual vector to an intermediate vector, subject to a first constraint of minimizing the loss of fidelity between said intermediate vector (IV) and the latent vector (LV) and a second constraint of minimizing the loss of interpretability;determination (40) of a prediction function of a prediction result from an intermediate vector under a third constraint minimizing loss of fidelity between the prediction and classification results. Figure for the abstract: Figure 3;

The application discloses a dynamic cross-modal hash retrieval method and system based on concept vector learning, and the method comprises the following steps: constructing a concept vector matrix based on current label information and a Hadamard matrix; the concept vectors in the concept vector matrix are used for representing that samples with the same label have the same and invariable vectors; constructing a data similarity matrix based on current label information and historical label information; obtaining target hash codes based on the concept vector matrix and the data similarity matrix; obtaining a target hash function based on the target hash codes; analyzing the to-be-queried data according to the target hash function to obtain to-be-queried hash codes corresponding to the to-be-queried data; and performing similarity calculation on the to-be-queried hash codes and the target hash codes in a database to obtain target retrieval results. The application can overcome the defects of the concept drift problem in a dynamic data environment, improve the adaptability of a cross-modal hash model to the concept drift, improve the dynamic cross-modal hash retrieval performance, and can be widely applied to the technical field of information processing.

This application discloses a text label generation, model training, and text classification method and related equipment, which addresses the problem that category labels obtained in related technologies cannot accurately describe the category to which the sample corpus belongs, thus affecting the accuracy of the subsequently trained text classification model and the execution accuracy of text classification tasks based on the text classification model. The text label generation method includes: obtaining the meta-concept path corresponding to the sample text from a pre-constructed concept tree based on keywords in the sample text corresponding to the target classification task; the concept tree is used to represent the hierarchical relationship between multiple meta-concepts, and the meta-concept path is used to represent the hierarchical relationship between multiple target meta-concepts related to the sample text in the concept tree; searching for label words in a pre-set meta-concept table based on the concept vectors corresponding to the multiple target meta-concepts and the hierarchical relationship between the multiple target meta-concepts to determine the label words corresponding to the sample text and use them as the category labels corresponding to the sample text.

This invention discloses a knowledge tracking method and system decoupled from cognitive states, relating to the field of knowledge tracking technology. The decoupled cognitive state knowledge tracking system mainly includes: a problem and concept embedding representation module, used to construct a problem-concept heterogeneous relationship graph and its triple relationships, learn the embedding representations of problems and concepts based on the triple relationships to obtain problem vectors and concept vectors, and obtain basic interaction embeddings based on interactions; a cognitive state decoupling module, used to construct fluctuating cognitive states and stable cognitive states; and a cognitive state tracking module, used to fuse the fluctuating and stable cognitive states, perform decaying attention tracking, and obtain prediction probabilities. Implementing the decoupled cognitive state knowledge tracking method provided by this invention can improve the prediction performance, interpretability, robustness, and applicability of the knowledge tracking model.

The invention provides an interpretable image recognition system and a construction method thereof, the system comprises a feature encoder, a concept predictor, a concept locator and a category predictor, the feature encoder is used for encoding an image into a three-dimensional feature map; the concept predictor is used for encoding the three-dimensional feature map into a plurality of first concept activation maps and outputting a plurality of prediction concept tags of the image; the concept locator is used for covering other areas except a key feature area matched with a real concept label of each first concept activation graph to obtain a plurality of second concept activation graphs in one-to-one correspondence with the first concept activation graphs, performing spatial pooling on all the second concept activation graphs to obtain concept vectors of the image, and performing spatial pooling on all the second concept activation graphs to obtain the concept vectors of the image; outputting a second concept activation graph corresponding to each predicted concept label predicted by the concept predictor as a key feature region graph; and the category predictor is used for predicting and outputting a category label of the image based on the concept vector of the image.

The application provides a hypernym-hyponym relation extraction method based on concept definition and data enhancement, which comprises the following steps: extracting concept pairs from natural text by using a keyword extraction technology, constructing concept triples based on the concept pairs and the hypernym-hyponym relations corresponding to the concept pairs, and taking the set of concept triples as a training data set; obtaining concept vectors in each triple in the training data set, offset vectors between the concept vectors, and vectors of concept definitions; constructing a hypernym-hyponym relation prediction model with the training data set as the input and the fused vectors of the offset vectors between the concept vectors, the concept vectors, and the vectors of concept definitions as the output, training the hypernym-hyponym relation prediction model according to the training data set and the fused vectors; obtaining a to-be-predicted concept triple in a test text, inputting the to-be-predicted concept triple into the trained hypernym-hyponym relation prediction model, and predicting whether the to-be-predicted concept triple has a hypernym-hyponym relation according to the output components.

The invention discloses an interpretability method for a decision basis of a medical image classification model, and relates to an interpretability method for a classification model. A channel and a space attention mechanism are introduced in a prototype concept extraction stage, the model is guided to focus on a key semantic region related to a disease, background interference is reduced, and semantic discrimination and stability of a prototype concept vector are remarkably improved; a CARAFE up-sampling method based on content awareness is adopted to replace a traditional up-sampling mode, the problems of edge blur and structure distortion are effectively relieved in the prototype concept vector positioning and deconvolution visualization process, and important anatomical structures and focus contour information in medical images are better reserved; according to the method, semantic suppression of prototype concept vectors in a high-level semantic space is quantitatively analyzed in combination with an anti-fact intervention strategy, contribution of each prototype concept vector to model prediction can be objectively evaluated, and potential error correlation or redundant prototype concept vectors can be revealed, so that the fineness and credibility of model decision interpretation are improved.

The invention relates to an AI generated text recognition method, computer equipment and a readable storage medium. The method comprises the steps that to-be-detected text keywords are extracted, weight values of words in a text are calculated, and the keywords are screened; constructing a domain ontology concept vector space: taking a keyword as a center, obtaining an attribute word set of the keyword through a sliding window algorithm, and calculating a three-dimensional feature vector of each attribute word, including a sliding window TFIDF value taking the keyword as the center, mutual information of the attribute words and the keyword, and cross entropy of the attribute words in the sliding window; splicing the three-dimensional feature vectors through a splicing method to obtain a spliced matrix, mapping the spliced matrix by adopting the reconstruction coefficient matrix and carrying out Hash learning to generate a low-dimensional Hash code of the to-be-detected text; and calculating the similarity between the low-dimensional hash code of the to-be-detected text and the low-dimensional hash code of the domain reference text, and if the similarity is lower than a preset threshold value, determining that the to-be-detected text is an AI generation text. According to the method, the calculation complexity of similarity of different texts is reduced, and the text recognition precision is improved.

The invention provides a cooperative control system for knowledge transmission among multiple agents and an operation method thereof, and the system comprises at least two agent mechanisms which are used for carrying out the task analysis, knowledge extraction and storage of a shared task and an individual task, and obtaining a shared concept vector and an individual task concept vector; the concept sharing module is used for sending the individual task concept vector of the first intelligent agent mechanism to the second intelligent agent mechanism; and the task allocation module is used for allocating the to-be-executed individual task to a third agent structure, so that the third agent executes the to-be-executed individual task according to the shared concept vector and the individual task concept vector corresponding to the to-be-executed individual task. According to the system, lightweight and efficient knowledge transmission is realized, the communication overhead and the training cost are greatly reduced, each agent does not need to learn all tasks from the beginning through knowledge sharing, and the overall learning efficiency and the resource utilization rate are improved.

The invention belongs to the technical field of patent classification method conversion, and particularly relates to a method for converting an international patent classification method and a Chinese library classification method. The method comprises the following steps of: 1, constructing an international patent classification semantic vector set I: fusing various target word vector sets Iw, concept vector sets Ic and phrase vector sets Ip of an international patent classification (IPC); 2, constructing a Chinese library classification semantic vector set C: fusing various target word vector sets Cw, concept vector sets Cc and phrase vector sets Cp of a Chinese library classification method CLC; step 3, performing IPC and CLC category mapping; and 4, verifying the mapping result by using an IPC and CLC category mapping sample set established by an expert. According to the method, the patent and the scientific and technical literature can be retrieved and analyzed uniformly in the precise subdivision field limited by the same classification number, switching operation in different databases is not needed, information separation is avoided, retrieval and analysis efficiency is improved, comprehensive and accurate intelligence support is provided, and integrity and continuity of intelligence information are guaranteed.

The invention discloses a precise question and answer method based on a knowledge graph and a large language model. The method comprises the following steps of 1, obtaining a dual-carbon field natural language question input by a user; step 2, executing TransE model training to obtain a global embedded vector set; step 3, generating node representation by adopting CompGCN; 4, semantic enhanced embedding of improved DeBERTa is constructed, and gating fusion is carried out on lexical elements of the question text and corresponding concept vectors in ConceptNet; 5, sensing a position code by adopting a syntax in the improved DeBERTa, generating the position code according to the dependency information and using the position code for decoupling self-attention calculation, and outputting context representation of a problem; and 6, executing a bidirectional cross-modal fusion mechanism, and determining an answer text. The method is suitable for scenes with high requirements on accuracy and context understanding in the fields of complex questions and answers, intelligent customer service, education question answering, dual carbon and the like.

The application relates to the technical field of coal mine safety risk construction reasoning, and discloses a coal mine safety risk multi-modal knowledge graph construction and intelligent reasoning method, which comprises the following steps: constructing an ontology and coding a concept vector by using a pre-training model to establish a benchmark semantic space; extracting a time-space feature and projecting the time-space feature to the benchmark space to calculate a semantic similarity with the concept vector; synthesizing conflict evidence by using evidence theory to instantiate a node, generating a real-time dynamic graph, eliminating a redundant node to construct a Bayesian network, mapping a weight to execute propagation and output a posterior probability, quantifying an entropy value to measure uncertainty, and feeding back a control device to trigger reasoning iteration when the value exceeds a threshold. The application projects heterogeneous sensor time sequence data and monitoring video data into a unified dimension feature vector by constructing a benchmark semantic space aligned with a static ontology, introduces evidence theory to process semantic conflicts among multi-source evidence, and effectively solves the problem that different modal physical signals cannot be directly fused at a feature level.

The invention relates to a method for determining explanations associated with a semantic classification of input data by a semantic classification engine comprising a first module for transforming an input data vector into a latent vector, and a second module for predicting a classification result based on the latent vector, the method comprising the steps of: for each input data vector, calculating (36) a concept vector; determining (38) the parameters of an operator that makes it possible to change from the concept vector to an intermediate vector, under a first constraint of minimising loss of fidelity between the intermediate vector (VI) and the latent vector (VL) and a second constraint of minimising loss of interpretability; determining (40) a prediction function for a prediction result based on an intermediate vector under a third constraint of minimising loss of fidelity between the prediction and classification results.

The invention discloses a conversation emotion recognition method and device based on task self-adaption and multi-level situation collaborative understanding and a storage medium, and belongs to the field of natural language understanding and emotion calculation. The method comprises the steps that firstly, a pre-training model is used for obtaining dialogue initial representation, emotion concept vectors are embedded into a unified space through task adaptive prompt and comparative learning, and high-discrimination utterance representation is generated; then, a bidirectional GRU and double-mask LSTM architecture is adopted, and coupling characteristics of global emotion flow and local personal inertia-instant interaction are captured respectively; and finally, directly parameterizing the classifier by using the emotion concept vector so as to realize whole-course task self-adaption from representation learning to final decision making. The method is obviously superior to the prior art in low-resource, cross-session and multi-modal conflict scenes, and can be widely applied to low-delay and high-robustness demand scenes such as intelligent customer service and social robots.

The invention discloses a knowledge tracking method and system for decoupling a cognitive state, and relates to the technical field of knowledge tracking, the knowledge tracking system for decoupling the cognitive state mainly comprises: an exercise and concept embedding expression module, which is used for constructing an exercise-concept heterogeneous relation graph and a triple relation thereof, learning the embedding representation of exercises and concepts according to the triple relationship to obtain an exercise vector and a concept vector, and obtaining basic interaction embedding according to interaction; the cognitive state decoupling module is used for constructing a fluctuation cognitive state and a stable cognitive state; and the cognitive state tracking module is used for fusing the fluctuation cognitive state and the stable cognitive state and performing attenuation attention tracking to obtain a prediction probability. By implementing the knowledge tracking method for decoupling the cognitive state provided by the invention, the prediction performance, interpretability, robustness and applicability of the knowledge tracking model can be improved.

The invention relates to a method for processing a training sample set, a device for processing the training sample set, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a training sample from a first training sample set, and determining a representation vector of the training sample; based on the representation vector of the training sample, determining a linear combination of concept vectors corresponding to the training sample; determining a sampling probability of the training sample based on a linear combination of the concept vectors corresponding to the training sample; and determining a second training sample set based on the sampling probability of the training samples. According to the method and the device, the second training sample set without deviation or with little deviation can be generated from the first training sample set with potential deviation, and the quality of the training sample set for training various neural network models is remarkably improved.

The invention discloses an AI (artificial intelligence) auxiliary conception method integrating unexpectability and constraint balance, and relates to the technical field of artificial intelligence. The method comprises the following steps: initializing a constraint conformation field; generating a disturbance signal and corresponding disturbance signal intensity; applying a temporary field deformation to the constrained conformation field based on the disturbance signal and the disturbance signal intensity to generate a regulated constrained conformation field; synthesizing one or more candidate synthetic creative vectors based on an original concept vector under the guidance of the regulated constraint conformation field; and revoking the field deformation to enable the regulated constraint conformation field to relax back to the constraint conformation field, and performing selection verification on the one or more candidate synthetic creative vectors by adopting the constraint conformation field to obtain a final synthetic creative vector. According to the method, dynamic balance between novelty and practicability is realized in the creative generation process by dynamically and controllably regulating and controlling the constraint.

The application discloses a generation and evaluation method for obtaining long text description from a laparoscopic surgery image, comprising the following steps: constructing a prompt word containing a laparoscopic surgery image, a short text description, a target boundary box and surgery knowledge, and constructing long text description data by using a large language model based on the prompt word; constructing an image long text description generation model, which comprises a pre-trained visual encoder, a visual feature query transformer, a language decoder and an external surgery concept vector database; performing supervised training on the image long text description generation model by using the long text description data; constructing a long text description evaluation method to evaluate the trained model; obtaining an input laparoscopic image video stream through a laparoscopic image system, and screening key frames as to-be-processed images; inputting the to-be-processed images into the trained and evaluated model to obtain an image long text description result. The application can enhance the understanding ability of surgery details in the image and generate a more comprehensive surgery image description.