81 results about "Similitude" patented technology

The invention belongs to but is not limited to the technical field of artificial intelligence, and particularly relates to a controllable content large model generation method and system based on multi-modal thinking chain reasoning, and the method comprises the steps: S1, improving the capability of multi-agent planning task dependence and execution sequence through employing a thinking chain method, enabling a complex task to be subdivided into a plurality of subtasks through the method, and enabling the subtasks to be subdivided into a plurality of subtasks; a resource transmission process between the sub-tasks is precisely planned and is executed in a chain mode; s2, complex tools are combined into sub-agents with different modal characteristics, so that the number of selectable sub-tasks during task planning is reduced, and the planning capability is improved; and S3, storing scene information of a previous dialogue by means of an external database, and when a new request is encountered, improving correlation and accuracy of multi-agent response by quoting historical interaction information and historical multi-modal resources which have context similarity with the current request, and realizing a more intelligent reaction mechanism.

Method and system for analyzing at least one computing system for supply chain vulnerabilities of at least one machine learning model include configuring machine learning model and optionally additional data; decomposing operations of the machine learning model's computational graph into smaller decomposed components; associating properties of each decomposed component with properties of the original operations and associating additional data; detecting the semantic similarity of decomposed component and previously encountered decomposed components; converting decomposed components into a standardized representation; calculating signature of decomposed components; evaluating whether portions of the machine learning model are similar to previously calculated signature; testing for supply chain and model vulnerabilities that exist based on previous signatures; identifying vulnerabilities that persist and correlating defenses; storing the generated signatures, identified vulnerabilities, and identified defenses; and generating report detailing the machine learning model's supply chain, vulnerabilities, and defenses.

The invention discloses a test question management method based on multi-modal adaptive similarity learning, and relates to the technical field of data management, and the method comprises the steps: collecting an original test question set, generating an evolutionary test question set through a preset large language model, generating a variable test question set through a data enhancement technology, and carrying out the fusion processing of the test question set to form a test question ecological library, the diversity and coverage of test question resources are enriched; the test question ecological library is input into a preset semantic similarity model and a preset syntactic similarity model to obtain a semantic similarity score and a syntactic similarity score of each test question, so that the similarity of the test questions can be comprehensively measured, and the screening accuracy is improved; the multi-modal similarity score is obtained according to the semantic similarity score and the syntactic similarity score to be compared with the preset threshold value, then the test questions are imported into the auditing module or the question bank storage module according to the comparison result, intelligent distribution of the test questions is facilitated, and the timeliness and quality of the question bank are ensured.

Adaptive verifiable training enables the creation of machine learning models robust with respect to multiple robustness criteria. In general, such training exploits inherent inter-class similarities within input data and enforces multiple robustness criteria based on this information. In particular, the approach exploits pairwise class similarity and improves the performance of a robust model by relaxing robustness constraints for similar classes and increasing robustness constraints for dissimilar classes. Between similar classes, looser robustness criteria (i.e., smaller ∈) are enforced so as to minimize possible overlap when estimating the robustness region during verification. Between dissimilar classes, stricter robustness regions (i.e., larger ∈) are enforced. If pairwise class relationships are not available initially, preferably they are generated by receiving a pre-trained classifier and then applying a clustering algorithm (e.g., agglomerative clustering) to generate them. Once pre-defined or computed pairwise relationships are available, several grouping methods are provided to create classifiers for multiple robustness criteria.

The invention belongs to the technical field of artificial intelligence and natural language processing, and discloses an NL2SQL model training and storing method and device based on a GRPO reward function. According to the method, a high-quality training data set is constructed, and the training effect and the generation performance of the model are effectively improved in combination with grammar verification, execution verification and semantic consistency screening; according to the method, the GRPO is adopted as a basic framework, and a combined reward function with multiple dimensions of coverage execution accuracy, grammar legality, semantic similarity, mode link and the like is designed; according to the method, a staged course learning strategy is designed, the sub-reward functions are activated and adjusted in a staged mode, the model is guided to be gradually transited to semantic understanding and execution optimization from structure specifications, and the generalization ability is improved; meanwhile, a reward weight dynamic adjustment mechanism is introduced, the weight of a reward function is automatically adjusted when SQL query execution fails, and the stability and sensitivity of training feedback are enhanced.

The invention discloses a multi-feature fusion binary code similarity detection method based on a large language model, and belongs to the technical field of information security. Compared with a conventional detection method which only depends on a single feature, the method has the advantages that fine grammar and global structure information of a function can be bidirectionally captured by utilizing a large language model which is finely adjusted by prompt word engineering and a GraphCNN graph convolutional neural network, so that the comprehensiveness and accuracy of feature expression are greatly improved; meanwhile, a hierarchical Navigation Small World Graphs (HNSW) algorithm is applied to efficiently retrieve candidate similar functions in an offline vector library, so that the method is suitable for large-scale database application; and finally, through fine comparison of the Siamese network, similarity detection with higher accuracy is realized under the condition of low cost. According to the method, through multi-dimensional fusion of semantics and structure information, the accuracy and efficiency of similarity detection are remarkably improved, and a more accurate and robust solution on the basis of low cost is provided for binary code similarity detection.

The invention discloses a multi-dimensional example selection method and system applied to a numerical reasoning task, and relates to the technical field of natural language processing, and the method comprises the steps: employing a plurality of demonstration problems and corresponding demonstration code thinking chains to form a plurality of training demonstration examples; dividing an ordered positive example group and an ordered negative example group by adopting a large language model based on the prompt answer accuracy determined by each training demonstration example; performing model training based on the training query problem, the ordered positive example group and the ordered negative example group through a to-be-trained example scoring model according to similarity, uncertainty and complexity, and determining a trained example scoring model; when the target query problem is determined, candidate demonstration scores of candidate demonstration examples corresponding to the target query problem are determined through the trained example scoring model, and a target demonstration example is selected. On the basis of the scheme, the programmed thinking is introduced to generate examples to perform multi-dimensional collaborative evaluation on the examples, and the example scoring model is optimized through inter-group differences, so that the reliability of example selection is improved.

The invention discloses a similarity comparison-based large model supply chain automatic restoration method and device, and the method comprises the steps: firstly carrying out the semantic coding of an original vulnerability code through a pre-training model, and constructing a semantic and structure parallel dual-channel representation in combination with an abstract syntax tree and other program structures; cWE type intelligent classification is performed on vulnerabilities by using a locally deployed large language model subjected to LoRA fine tuning, and meanwhile, a zero sample semantic matching mechanism is introduced, so that the recognition capability of unknown vulnerability types is improved. And searching the closest historical case from the knowledge base through similarity vector comparison, and extracting a repair abstract to construct a model to generate a prompt. Patch codes and repair instructions are generated through a large model, automatic filing and knowledge base updating are supported, and the continuously-enhanced automatic repair capacity is achieved. The method can be widely applied to automatic vulnerability repair scenes of supply chain components such as large model plug-ins, code interfaces and dependent packages, and the safety, functionality and interpretability of code repair patches are greatly improved.

The invention discloses a code review comment generation method and system based on reinforcement learning, belongs to the technical field of crossing of software engineering and artificial intelligence, and aims to solve the technical problem of how to improve the accuracy, efficiency and expandability of code review and overcome the defects that an existing tool is low in comment quality and poor in practicability. According to the technical scheme, the method comprises the steps of collecting and preprocessing data of code differences, human comments and real correction codes, and constructing a data set; code review comments are generated, specifically, a pre-trained large language model LLM is finely adjusted through the data set, code difference data are collected, and the review comments are obtained; semantic similarity reward: calculating the semantic similarity between the generated comment and the real comment, and generating a reward signal Rsematic; the generated comment and code difference is input into a code optimization model, a corrected code patch is generated, the similarity between the generated patch and a real patch is evaluated, and a reward signal Rtask is generated; performing reinforcement learning and fine tuning on the large language model LLM; and deploying and generating comments.

The invention relates to the technical field of intelligent education systems, in particular to a subjective question automatic correction method based on multi-technology fusion, which comprises the following steps of: firstly, identifying whether a question is a strong proposition or not through a regular expression, and then performing knowledge point extraction, off-question judgment and double similarity evaluation on the strong proposition question; plagiarism detection, cosine similarity calculation and user-defined rounding are carried out on all the questions, and finally personalized comments are generated and correction results are output. According to the method, through double similarity evaluation, semantic relevance between answers and standard answers as well as questions is comprehensively considered, the misjudgment rate is remarkably reduced, meanwhile, through strong proposition recognition, knowledge point extraction and double similarity evaluation, the pertinence and accuracy of correction are remarkably improved, whether the answers of students meet the requirements of the questions or not can be deeply analyzed, and the correctness of the questions is improved. Misjudgment caused by insufficient semantic understanding in the prior art is avoided, and the method is particularly suitable for accurate scoring of strong proposition questions in subjective questions.

Methods and systems for generating output content using a generative artificial intelligence (AI) model based on an input. A similarity-assessment layer at the output of the generative AI model determines a similarity measure for the output content vis-à-vis pre-existing items in a repository. The similarity measure is compared to a threshold value and, responsive to the comparison indicating excessive similarity, one or both of the input and the generative AI model are adjusted, and the generative AI model is re-run to generate new output content.

The invention discloses a method and system for evaluating and improving paragraph similarity judgment precision of a large model, and belongs to the technical field of artificial intelligence and natural language process.The method comprises the steps that a scoring standard and an evaluation process are formulated, the scores account for 50% of the overall score; scoring by experts in the three fields of business, algorithm and test according to indexes; automatically scoring the paragraphs of the test set by adopting the trained large model, comparing with manual comprehensive scoring, considering that the model is accurate when the difference does not exceed 0.5, and calculating the accuracy rate of the model according to the result; model optimization and promotion: constructing a training set; carrying out model training and iteration; preparing a test set and accurately evaluating the test set; and performing cyclic feedback optimization. According to the method, the accuracy and practicability of the large model in paragraph similarity judgment can be improved, and the method is more reliable and efficient in practical application.

Methods and systems for generating output content using a generative artificial intelligence (AI) model based on an input. A similarity-assessment layer at the output of the generative AI model determines a similarity measure for the output content vis-à-vis pre-existing items in a repository. The similarity measure is compared to a threshold value and, responsive to the comparison indicating excessive similarity, one or both of the input and the generative AI model are adjusted, and the generative AI model is re-run to generate new output content.

THE WAY IN WHICH THE ESSENTIAL ASPECT OF THE STRATEGE "FROM NOTHING - SOMETHING" IS IMPLEMENTED IN INFORMATION TECHNOLOGIES. The initial essence "Nothing" (0, Zero) of the method is the abstract essence. The mathematical model of the Projected Object Entity (PEO) is embodied in material, software, and mixed entity objects from various disciplines—the "something" of the method. The methodology of comparative cause-and-effect analysis is applied in the creation of the PEO, and the methodology of essential synthesis is used in its design: subject-specific and essential interdisciplinary properties of similarity, universal mathematical models in the form of orgraphs and their matrices, graphs with algorithms of nodes and their matrices, as well as essential identities, equations, and differential equations that reflect the balance of actions and counteractions.And since the professional and interdisciplinary properties of similarity for individual objects of different disciplines continue to apply, the transfer of the parental properties, qualities and the collective image of "nothing" and its "something" takes place "according to the image and likeness" of the essence.

The invention provides an active man-machine cooperation method driven by process knowledge and interactive semantics, and the method comprises the steps: carrying out the step-by-step optimization and updating of a general task description text and a dynamic assembly task interaction description text in the manufacturing field, and obtaining an initial generation result; performing evaluation according to a semantic similarity classification standard and a real label to obtain a final generation result, performing observation sampling on a posture sequence when an operator implements an assembly behavior according to the generation result to obtain observation postures and dynamic scene semantic information, and extracting features to obtain a diffusion matrix; further processing to obtain an inherent matrix and a weight matrix to form point matrix features, mapping the point matrix features to a Gaussian distribution coding set feature map, sampling potential coding factors, mapping the sampling potential coding factors to a posture sequence to obtain a current posture sequence, predicting to obtain an operator assembly behavior prediction result, and combining an assembly environment state and an assembly task state to obtain an operator assembly behavior prediction result. Intelligent agent actions and operator assembly behaviors are evaluated, current state and reward function feedback are provided for the intelligent agents, and the robot is controlled to move.

The invention discloses a fine-grained class incremental learning method and system based on a decoupling Gaussian classifier, a storage medium and equipment, and belongs to the field of reinforcement learning of artificial intelligence. The method comprises the following steps: decoupling different types of features extracted by a backbone network into a plurality of independent Gaussian distributions, calculating the similarity among different Gaussian distributions through regularization penalty, encouraging to minimize the overlapping among similar features, and increasing the inter-class distance. According to the method, forgetting in fine-grained incremental learning is analyzed from the perspective of examples, and a universal Gaussian decoupling classifier is provided, so that the distinguishability of similar classes in the incremental process is enhanced, and the classification precision is improved.

The application discloses a small sample face recognition method based on prototype calibration and adaptive fine-tuning, comprising the following steps: 1) pre-training the encoder on a large-scale face database, capturing rich semantic information and class distribution, constructing an evaluation set, and ensuring that the training identity information does not overlap with the constructed evaluation set; 2) initializing the classifier weight through the weight imprint method, ensuring smooth transition, and inducing the discriminative structure of the encoder; 3) calculating the cosine similarity between the fine-tuning class prototype and the pre-training prototype, fusing the pre-training class information through weighted average, and calibrating the biased fine-tuning class prototype; 4) introducing an adaptive margin loss function, adjusting the sample penalty strength, and selectively fine-tuning the BatchNorm layer and the classifier weight; and 5) using the field-aware similarity NAC to calibrate the unknown class rejection, enhancing the similarity relationship with the field sample, and obtaining the final matching score. The method can improve the recognition accuracy and rejection ability of the model in the small sample learning scene.

The invention provides a code similarity review method based on artificial intelligence, and belongs to the field of artificial intelligence and software engineering.The code similarity review method comprises the steps that semantic features of codes are extracted through a deep learning model, code similarity classification is conducted through a machine learning model, the semantic features of the codes can be automatically learned, and the similarity of the codes is detected; the problems of potential code plagiarism, code reuse, code vulnerabilities and the like are identified, and the accuracy, efficiency and expandability of code similarity review are improved.

Comprising a method and apparatus including computer code configured to cause one or more processors to construct a similarity score between a plurality of words, initialize a similarity structure that depends on a task and is based on the similarity score, and initialize the similarity structure based on the similarity score by implementing a bilayer optimization. And performing machine learning on the task dependency of the similarity structure, the double-layer optimization comprising: a search stage comprising learning a model weight by estimating a model parameter corresponding to a first entry of the similarity structure, learning a parameter of a second entry of the similarity structure by applying the model parameter to the second entry; and a fine tuning stage: updating the model parameters under the condition of keeping the similarity structure unchanged.

The present application discloses a code clone detection method, apparatus, device, medium and product, relating to the technical field of deep learning. The method includes obtaining a code library to be detected, where the code library to be detected contains multiple code snippets to be detected; determining the hash value of each code snippet to be detected; based on the hash values of each code snippet to be detected, determining a set of high similarity code snippet pairs, where the set of high similarity code snippet pairs contains at least one set of high similarity code snippet pairs; inputting the set of high similarity code snippet pairs into a pre-trained code clone detection model based on a large language model to obtain the code clone detection result output by the code clone detection model. The present application can make full use of the capabilities of the large language model, avoid the model's over-reliance on the surface features of code syntax, and then accurately judge the similarity of code functions based on the semantic analysis of code snippets, thereby improving the accuracy of code clone detection.

Systems, methods, devices, and computer readable storage media described herein provide techniques for prioritizing software development using a trained model. In an aspect, model features are determined based on analysis of user behavior with respect to a software application. A software development prioritization (SDP) system determines data associated with the model features and utilizes a generative artificial intelligence (AI) model to summarize the model features based on the determined data. The SDP system determines, based on the summaries, a similarity between software development items and the model features and prioritizes one of the software development items over another based on the determined similarities. In a further embodiment, the SDP system causes a software development task corresponding to the prioritized software development item to be performed before another software development task corresponding to a different software development item. In an aspect, model features are determined utilizing a trained machine learning model.

A large language model-oriented multi-sample attack detection and defense method relates to the field of artificial intelligence security. A multi-level text screening and intervention mechanism is constructed to reduce the impact of multi-sample attacks through input content deduplication, format adjustment and context disturbance. Similarity analysis is performed on the text to identify highly repetitive examples, which are screened using an efficient matching strategy. The semantic features of the text are mapped using a pre-trained model, and historical data are compared to determine whether there is a potential inducement risk. The multi-round dialogue structure is analyzed, and the user and system interaction mode is extracted and analyzed to dynamically adjust the detection rules and improve the adaptability to different format inducement attacks. For high-risk text, deletion and truncation strategies are adopted, and for some medium-risk text, the order is adjusted or interference information is inserted to reduce its impact. The safety of LLM in complex interactive environments is improved, and the generation of non-compliant content is reduced, which is efficient, low in false positives and scalable.

A dynamic similarity design method for soil-structure super-scale reduced models. This method vectorizes the geometric dimensions. First, the geometric similarity constant is comprehensively determined based on the prototype mass of the soil and the structure and the equipment parameters of the centrifuge shaking table, so as to determine the centrifuge gravity field. Then, the physical quantities of the density and elastic modulus of the prototype structure and the soil are measured, the material of the reduced model structure is selected, and the physical quantities of the density and elastic modulus of the reduced model structure are measured. Using the dynamic equilibrium equation and based on the principle that the similarity of the structure frequency and the soil frequency is consistent, the geometric similarity constant in the direction of the seismic action is derived and determined. Finally, the similarity design of the test reduced model is completed according to the principle of dimensional coordination. The present invention is used for the centrifuge shaking table dynamic test of the soil-structure reduced model under unidirectional seismic action, can accurately restore the dynamic response characteristics of the prototype structure, and this method is simple and practical and easy to apply in the test.

The invention relates to a computer-implemented method for inspecting or operating a first product (TD1), the following steps being carried out: a) transmitting a provided global model (GM) of the first product (TD1) or the operation thereof from the server (S) to the first client (C1) and second client (C2), b) capturing a first data set (DS1) of the first product (TD1) or the operation thereof with first product parameters of the first product (TD1) as respective first subsets (C1.1-C1.3), and generating and training respective first local models (LM1.1-LM1.3) with the respective first subsets (C1.1-C1.3), c) capturing a respective second data set (DS2) of the respective second product (TD2) or the operation thereof with respective second product parameters of the respective second product (TD2) as respective second subsets (C2.1-C2.3), and generating and training respective second local models (LM2.1-LM2.3) with the respective second subsets (C2.1-C2.3), d) performing a similarity analysis between models of the first and the second local models (LM1.1-LM1.3, LM2.1-LM2.3), and assigning these similar models to a common model group (G1-G3), e) training a global detail model (M1) on the basis of the model of the common model group (G1-G3) and transmitting same to the first client (C1), f) applying the detail model (M1) to the first product (D1) for inspection or operation thereof, by the first client (C1).

Methods and systems for obtaining contextual information about a machine learning model are provided. The method includes: receiving raw data that is usable for training a model; training the by using the raw data; computing a set of common background data based on the raw data; computing a first explanation based on an output of the model and the set of common background data; computing, based on an output of the model, an agnostic model representation of the model; computing, based on the first explanation and the agnostic model representation, a deep, compact, and dense explanation-driven representation of the model; and determining, based on the explanation-driven representation, contextual information that relates to the model.

The application relates to the technical field of mechanical fault diagnosis and machine learning, and particularly relates to a bearing incremental fault diagnosis method and system based on causal distillation and a dynamic threshold. In the incremental learning process, firstly, a causal loss function is used to reserve potential causal features; secondly, the causal similarity between samples and category prototypes is quantified through a causal feature classification loss, and a dynamic threshold is calculated, so that the aggregation of samples of the same category and the separation of samples of different categories are adaptively constrained in the feature space; finally, a difference loss is used to estimate the relationship between feature distribution mapping drift and loss function change, so that the causal features are reserved and the change of confused features is allowed in the model updating process. The application realizes the balanced optimization of the stability and plasticity of the bearing fault diagnosis model, has high diagnosis accuracy, has strong ability to overcome catastrophic forgetting, and is suitable for the incremental fault diagnosis task of continuously emerging new fault categories.

The invention relates to the technical field of software engineering, in particular to an automatic code review method based on retrieval enhancement generation (RAG), and relates to the technical field of software engineering, in particular to an automatic code review method based on RAG. The method comprises the steps that a vector database containing historical code repair examples is constructed, after code change fragments are encoded, similar examples are retrieved, and a retrieval result is fused to guide a generation model to complete a review task; by introducing a contrast learning mechanism, the representation capability of an encoder is optimized, so that code snippets with similar semantics are more closely distributed in a vector space, and the retrieval accuracy is improved; furthermore, retrieval data is organized by adopting a graph structure, a graph is constructed according to meta-information such as defect types, project affiliation and semantic similarity, graph-guided retrieval is performed based on the graph, and a GraphRAG method is realized. According to the method, four tasks of code problem detection, problem positioning, problem type identification and optimized code generation are automatically completed, the accuracy, consistency and interpretability of a review result are remarkably improved, and the method is suitable for intelligent auxiliary development and quality guarantee scenes in a large-scale software project.

The invention discloses a product defect and test case correlation analysis method and device, and relates to the technical field of data processing, and the method comprises the steps: through matching a test case with a defect function module and a research and development responsible person, narrowing a similarity comparison case range, and improving correlation efficiency; the similarity between test case information and defect information is calculated and analyzed by using a Sension-BERT model, a mixed similarity calculation rule is adopted, the similarity is accurately calculated, feature weights can be dynamically adjusted according to different project characteristics, intelligent association of defects and cases is quickly and efficiently completed, and the problems that in the related technology, historical data is excessively depended, the updating cost is high, and the efficiency is high are solved. The technical problems of low query efficiency and the like in the prior art are solved, and the technical effects that correlation of the defects and the use cases can be quickly completed for the new project and the new field without depending on historical data, the maintenance cost is reduced, and long-term use is facilitated are achieved.

The invention discloses a code similarity detection method based on execution semantics, which adopts an end-to-end simulation execution mechanism to guide a model to model an instruction through a dynamic execution behavior of the instruction so as to deeply understand and analyze semantic connotation of codes and extract representation with execution semantic features; carrying out disassembling and control flow diagram construction on the binary program by utilizing a reverse analysis tool, and carrying out statistics and standardization processing on assembly instructions in the basic blocks so as to screen out key instructions with semantic representativeness; simulating an execution process of the instruction by adopting a Transform model, analyzing and capturing a semantic behavior of the instruction in a specific context, and further generating an instruction embedding representation with execution semantic information; and finally, modeling structure information of nodes in the control flow graph by means of a graph matching neural network, fusing execution semantics among basic blocks, constructing a function-level representation, and realizing accurate similarity measurement through a cosine distance.

The invention relates to a method for performing a simulation (12) for the generation of a geometric component (14), comprising the steps of: providing a first simulation file (16) of the geometric component (14); comparing the first simulation file (16) with a plurality of simulation files (18); determining a similarity between the first simulation file (16) and the plurality of simulation files (18); specifying a simulation parameter (26) for the simulation (12); selecting at least one simulation file (18) by evaluating the simulation files (18) depending on the specified simulation parameter (26) and depending on the determined similarity; identifying a change made in the selected simulation file (18) depending on the simulation parameter (26);and adapting the first simulation file (16) depending on the identified change and performing the simulation (12) with the adapted first simulation file (16) depending on the simulation parameter (26). Furthermore, the invention relates to a computer program product and an electronic computing device (10).