137 results about "Dependency tree" patented technology

The invention discloses a multi-layer graph multi-mode sentiment analysis method and device based on meta-information, and the method comprises the steps: firstly, extracting the high-level features of text, video and audio data through BERT, OpenFace 2.0 and Librosa, carrying out the time sequence coding through BiLSTM, and then mapping each mode feature to a unified space through MLP; then, respectively capturing modal invariant and specific features by using a shared multi-modal encoder and a private multi-modal encoder; for modal specific features, adopting GNN to construct a modal graph, and combining a grammar dependency tree and self-attention to calculate a graph edge weight so as to align meta-information in a modal; video and audio features are aligned with a text through Cross-Attention, a new multi-modal graph is constructed, and multi-level features are extracted; then, applying convolution, BiLSTM and GNN to extract local, time sequence and global features, and realizing multi-level feature fusion through a cross-modal attention mechanism; and after each modal feature graph and each modal invariant feature are input into a graph knowledge distillation module, dimension reduction processing is carried out, features are further fused through Transform and a multi-head attention mechanism, and finally an emotion prediction value is obtained.

The invention discloses a software test automation report generation optimization method which comprises the following steps: automatically registering and authenticating a multi-source test data access endpoint, uniformly collecting and normalizing test data with a multi-dimensional attribute tag, and forming a high-quality grouped data set through structured modeling and automatic clustering; further extracting event and result feature nodes, and establishing an interpretable tree rule dependency relationship; according to the method, the minimum redundancy and reusable customized rule path is generated by applying a dependency tree traversal and optimization algorithm and automatic reasoning, and the continuous adaptive evolution of the rule base among multiple projects is realized by combining conflict detection and visual interaction, so that the efficiency and expandability of data processing in a complex test scene are improved, and the test efficiency is improved. And the flexibility and automation level of report customization rule configuration are enhanced.

The invention relates to a large law model fine tuning data screening method and device and a medium, and belongs to the technical field of artificial intelligence. Generating and forming a preference data set, training a legal quality scoring model by using the preference data set, and dynamically adjusting weights of a simple compliance sample and a complex dispute sample by using a balance focus loss function; performing quality scoring on the candidate law fine tuning data set, calculating a complexity score of the candidate law fine tuning data set, and generating a quality-complexity scoring data set of the candidate law fine tuning data set; and constructing a dependency tree of a grammar dependency relationship, performing clustering analysis on vectorized dependency tree features, and performing cluster division and sorting on candidate law fine adjustment data sets so as to merge the candidate law fine adjustment data sets into final fine adjustment sub-data sets and complete screening. The problems of semantic distortion and training deviation caused by a traditional screening method are effectively solved, and a systematic solution is provided for efficient training of a large model in the legal vertical field.

The invention relates to the technical field of LINUX operating systems, and discloses a software customization method and system in an ISO mirror image of the LINUX operating system, and the method comprises the steps: recursively analyzing a construction dependency tree and an operation dependency tree of a target component through a pre-analysis module, recognizing a missing component and / or a conflicting component, creating an isolation task for the conflicting component, and generating a customized work task set; through a construction module, deploying a dependency environment of a to-be-constructed component, executing an isolation task, constructing an independent directory for a conflict component, and generating a temporary finished product warehouse; executing a test on the target component in the temporary finished product warehouse through a test module; and through a distribution module, constructing a customized ISO and distributing the customized ISO to a product warehouse. According to the embodiment of the invention, manual demand analysis and dependency relationship processing are replaced by an automatic mode in a software customization system, the dependence on high-skill research and development personnel is reduced, a complex customization process is standardized, and the research and development and implementation cost is reduced.

The invention belongs to the technical field of software management, and particularly relates to a cross-platform repair method and system based on a large language model.The method comprises the steps that a cross-platform package manager interface is used for conducting static analysis on a dependency list of a target software project, a cross-platform dependency tree is obtained, and the target software project is repaired based on the cross-platform dependency tree; respectively injecting lightweight monitoring probes into at least two operating system platforms, and collecting dynamic tracking data of each dependency component in the dependency tree; fusing the dependency tree and the dynamic tracking data, and establishing a cross-platform security map comprising a dependency calling edge, a version conflict edge and a platform vulnerability association edge; according to the method, the lightweight monitoring probe is injected into the multi-operating system platform, the loading sequence, the function call sequence and the memory layout difference of the dependent components are dynamically collected, and the cross-platform security map is constructed in combination with the static dependency tree, so that the specific potential safety hazard of the platform can be accurately identified, and the comprehensiveness and the accuracy of cross-platform risk perception are remarkably improved.

The invention discloses a threat intelligence triple combined extraction method based on data enhancement, comprising a data enhancement method which is responsible for performing multi-granularity high-quality expansion on a small amount of data in the field of threat intelligence through a data enhancement method based on a large model. And the syntactic information fusion method is responsible for analyzing an input sentence into a component tree and a dependency tree, extracting information in the component tree and the dependency tree by combining a Tree-GRU syntactic fusion algorithm to obtain a syntactic dependency matrix, and fusing the syntactic dependency matrix into an extraction model to enhance the understanding of the syntactic dependency matrix on an explicit text structure and semantics. And the form filling extraction method is responsible for simultaneously representing and extracting entities and relationships in sentences in the same form through a joint extraction algorithm based on a form filling method so as to enhance interaction between the entities and the relationships, and introducing an adversarial training mechanism based on an MDA method in training so as to enhance robustness and generalization ability of the model. The invention provides a new design thought for solving the problem of insufficient labels in the network threat intelligence triple extraction field and extracting complex, long and difficult sentences.

The invention belongs to the technical field of data processing, and particularly relates to a fingerprint generation method of a virtual evaluation model based on Hash operation, which comprises the following steps of: firstly, constructing a dependency tree by taking a main model file specified by a user as a root node, and initializing an access record table; establishing a static keyword protection area containing a core parameter rule base, scanning a dynamic file operated by a user, and comparing parameter definitions to generate an early warning; reading the binary data of the current node file, and generating a character string through SHA-256 after standardization; recursively processing the sub-nodes to obtain a node tree hash value, sorting the sub-nodes, splicing the sub-nodes according to a fixed format, and generating a character string through SHA-256 operation; then, a current node standardized path is spliced and generated through SHA-256 operation; and finally, by taking the root node as a starting point and the DFS as a sequential serialization tree structure, executing SHA-256 operation after adding a global collision salt value, and outputting a global unique fingerprint of the model. The problems of poor stability, low efficiency and high generation risk in a model fingerprint generation technology in the prior art can be solved.

The invention discloses a WebGL-based Unity asset cross-platform preview method and system, and a storage medium. The preview method comprises the following steps: 1) preprocessing an editor plug-in: scanning a target asset through the Unity editor plug-in, extracting a resource GUID,. Meta metadata and a dependency relationship, generating a lightweight AB packet and adding a dynamic thumbnail; step 2) cloud dependency analysis: the server side recursively constructs a dependency tree based on GUID, filters redundant resources, and generates a resource loading list which can be identified by the browser side; and step 3) a WebGL rendering engine: loading an AB packet in a browser, realizing particle effect regeneration, animation control and scene interaction by simulating a Unity Runtime environment, and adapting to a WebGL 1.0 / 2.0 rendering pipeline. The invention aims to provide a WebGL-based Unity asset cross-platform preview method and system and a storage medium, aims to solve the problem that a browser cannot directly preview Unity resource files, and provides a set of resource management workflow based on Unity-cloud storage-browser preview for the Unity resource files.

The invention relates to the technical field of computers, and provides a cross-platform installation package automatic adaptation method and system, and the method comprises the steps: obtaining a unified description file and an operating system fingerprint of each target platform; based on the operating system fingerprints of the target platforms and the installation package adaptation parameters, matching is carried out, and dependency trees of the target platforms are generated; and generating an adaptive installation package based on the dependency tree of each target platform and the installation package adaptation parameter. According to the cross-platform installation package automatic adaptation method, specific parameters and shared parameters of the platform are managed in a centralized mode through unified description files, environment intelligent perception is achieved and the dependency tree of the platform is generated in combination with dynamically obtained operating system fingerprints, and the adaptive installation package is generated through analysis of the installation package adaptation parameters of the platform and the dependency tree. The problems of low adaptation efficiency, high configuration error rate, high maintenance cost and the like are solved; in addition, a unified construction management mechanism realizes cross-platform centralized management and control, and the accuracy and efficiency of cross-platform installation package adaptation are improved.

System and method for aspect-level sentiment classification. The system includes a computing device, the computing device has a processer and a storage device storing computer executable code. The computer executable code is configured to: receive an aspect term-sentence pair; embed the aspect term-sentence pair; parse the sentence using multiple parsers to obtain dependency trees, and perform edge union to obtain a merged graph; combine the embedding and the merged graph to obtain a relation graph; perform a relation graph neural network on the relation graph; extract hidden representation of the aspect term from updated relation neural network; and classify the aspect term based on the extracted representation to obtain a predicted classification label of the aspect term. During training, the computer executable code is further configured to calculate a loss function based on the predicted label and the ground truth label, and adjust parameters of models.

The invention provides a language translation data processing method and system based on a large model, and the method comprises the steps: carrying out the coding segmentation of language translation data, and obtaining a plurality of text segments; performing dependency syntactic analysis on each text fragment, determining a dependency tree structure of each text fragment, and determining an explicit semantic dependency relationship between every two text fragments according to the semantic similarity between every two text fragments and the dependency tree structure of the corresponding text fragment; performing dependency analysis on an implicit semantic relationship between every two text segments according to the implicit semantic association degree between the text segments and the segmentation loss of the text segments to obtain an implicit semantic dependency relationship between every two text segments; and constructing a paragraph tag of the language translation data through a dependency relationship between explicit semantics and implicit semantics between every two text fragments, and translating the to-be-translated data based on the paragraph tags. By the adoption of the scheme, cross-segment semantic guidance translation of the long complex text can be achieved.

The invention provides an applet permission security detection method and system, and relates to software engineering.The method comprises the steps that API test code snippets of a super application are generated based on a large language model, the large language model is guided according to an API dependency tree to generate a plurality of API test code snippets, and executable test codes are constructed; dynamically executing the executable test code in the simulation environment, and obtaining the actual permission configuration of the applet according to the dynamic execution result; obtaining standard permission configuration of the super application, comparing the actual permission configuration with the standard permission configuration, and if the situation that the actual permission configuration does not accord with the standard permission configuration occurs, determining that the super application has an API permission exception problem; the method comprises the following steps: constructing an abstract syntax tree according to an applet source code, performing granularity optimization on the basis of the abstract syntax tree to construct an abstract method tree, searching an abnormal API (Application Program Interface) with a permission defect in the abstract method tree, determining a function body name where the abnormal API is located, performing front-end and rear-end dual confirmation according to the function body name, and determining a calling condition and a binding condition of the abnormal API. Accurate detection of the permission defect in the super application is realized.

The invention discloses an open source software supply chain security intelligent monitoring and risk assessment method, relates to the technical field of software supply chain security, and solves the problem that only existence of indirect dependence can be identified generally in the prior art, but whether a vulnerability code can be called on an actual execution path cannot be accurately judged. According to the method, through dependence locking and Hash fingerprint embedding during construction, cryptographic-level accurate traceability of a component version is realized, and version ambiguity is thoroughly eliminated; in combination with deep interception of a JVM probe for method calling and class loading events during operation, a loading source and a call stack context of each class in a real execution path can be captured, bidirectional association mapping is performed on dynamic behavior data and a static dependency tree, and whether a vulnerability component is located on an executable call chain or not is accurately identified. Compared with the defect that coarse-grained warning is carried out only through version matching in a traditional tool, the method has the advantage that by means of dynamic-static data fusion, it is avoided that a large number of unavailable indirect dependency vulnerabilities are misjudged to be high in risk.

The invention relates to the technical field of software migration, in particular to a Linux executable program migration method and device.The method comprises the steps that compatibility detection is conducted on system calling of a target ELF file, and gasket adaptation processing is conducted on incompatible system calling; performing dependency tree total analysis on the target ELF file, and identifying a dependency library required by the operation of the target ELF file; extracting an actual calling symbol list of each dependency library, comparing the actual calling symbol list with symbols of corresponding libraries on the target system, and collecting incompatible dependency libraries of the original system to an isolation directory; modifying a library search path when the target ELF file runs, so as to preferentially load an original system dependency library in the isolation directory; and finally, constructing a migration packet deployed to the target system, and carrying out adaptive migration verification on the migration packet. Compatibility and operation stability of the program in the target system are improved, only the software and the dependency library thereof are migrated, migration difficulty is reduced, and the migration process is accelerated.

The invention relates to a software project vulnerability detection method, device and equipment and a medium, a dependency tree of a software project is generated according to configuration information in a configuration file of the software project, and the dependency tree comprises direct dependency items and indirect dependency items in the software project. Furthermore, according to the attribute information of the direct dependency item and the attribute information of the indirect dependency item, querying vulnerability data obtained from a vulnerability database to determine whether vulnerability exists in the direct dependency item and the indirect dependency item, and according to the direct dependency item and / or the indirect dependency item with the vulnerability, generating a vulnerability detection report. According to the method, the vulnerability detection is performed on the direct dependency item and the indirect dependency item, so that comprehensive detection of the software project is realized, efficient matching with the vulnerability data is performed, a detailed vulnerability detection report is generated, the vulnerability detection accuracy of the software project is improved, and the security of a software project supply chain is improved.

The invention discloses a dynamic SQL (Structured Query Language) optimization method based on consanguinity dependency, and relates to the technical field of database query optimization. The method comprises the following steps: analyzing all indexes in a database, and dividing the indexes into atomic indexes and composite indexes; a blood relationship dependency tree is constructed according to the mathematical relationship between the indexes, each node represents one index, and each directed edge represents the dependency relationship; receiving a to-be-queried index set, and recursively searching all dependent target atomic indexes based on the graph structure; grouping the target atomic indexes according to data sources, and generating corresponding SQL query statements; and executing the SQL statement, and calculating a final query index result after obtaining an atomic index value. According to the invention, through unified management of the index dependence structure, repeated calculation across indexes is eliminated, the query performance is improved, the resource consumption is reduced, and the index development and maintenance efficiency is improved.

Systems and methods for extracting information from documents and constructing corresponding knowledge maps with respect to defined knowledge models. Deep-learning-based models for Natural Language Processing (NLP) are applied to tokenize words, tag, parse, and lemmatize sentences of input documents. Then an information extractor traverses the dependency tree of NLP object to recursively extract the entities of interest to the knowledge models. Finally, a knowledge map constructor traverses the dependency tree of NLP object to determine the relationships among the extracted entities and construct knowledge maps recursively following the defined knowledge models.

The invention discloses an artificial intelligence-based interactive question-answering method and system, and relates to the technical field of interactive question-answering, and the method comprises the steps: converting a user input text question into vector representation, constructing a relation graph by combining a dependency relation tree of a text entity, capturing a dependency relation in the question as an edge weight, forming an adjacent matrix, and constructing a node degree matrix; and determining a standardized graph Laplacian matrix, performing feature decomposition, determining an optimal clustering number, clustering language phrases of the text question, and determining task fragment sets of different clusters. According to the method, semantic association between language phrases is achieved by constructing the dependency relationship graph, meanwhile, syntactic relationships are fused, the integrity of text structure information is ensured, the degree of nodes serves as a quantitative index of local strength in the graph, the information directly supports subsequent Laplacian matrix normalization processing, a unified scale is provided, and the method is suitable for being applied to the field of text processing. And the imbalance problem of the data range is avoided.

Disclosed herein are systems and methods for automatic detection, fixing, and monitoring of vulnerabilities. In one aspect, an exemplary method comprises receiving as an input an artifact, when the received artifact is vulnerable, building the artifact for a new release, scanning a library containing the new release of the artifact to determine whether the library is safe for publishing, and publishing the library to an accessible artifact depository when the library is safe for publishing. In one aspect, the building of the artifact for the new release includes, scanning a dependency tree, replacing the vulnerable version by a safe version of the artifact when a safe version is available, fixing the vulnerable version of the artifact using known patches when a safe version of the artifact is not available, adding a postfix to a group ID of the artifact for distinguishing the fixed version from prior versions of the artifact.

The invention relates to the technical field of natural language processing and artificial intelligence, and discloses a large-model complex instruction-oriented fine-grained intention recognition method, which comprises the following steps of: constructing a candidate analysis forest of an input instruction, and executing entity alignment of each node and a domain knowledge graph; performing semantic compatibility verification on the candidate dependency trees by utilizing entity relationships in the atlas, and screening an optimal analytic tree in combination with syntactic probability and knowledge consistency scores; if the analytic tree meeting the threshold value does not exist, a semantic conflict edge is positioned, and candidate mounting points are searched by using a map neighborhood relation to reconstruct a dependency structure; and finally, generating standardized data containing a structured analytic path based on the optimal analytic tree, and performing fine adjustment on the large model. According to the method, logic constraint and dynamic repair are carried out on the syntactic structure by introducing the knowledge graph, analysis errors caused by multiple modification or structural ambiguity in a complex instruction are effectively solved, and the accuracy and robustness of intention recognition of a large model in the vertical field are improved.

The invention belongs to the technical field of information security, and particularly relates to a multi-security encapsulation method for a virtual evaluation simulation result, which comprises the following steps of: firstly, constructing a dependency tree by taking a main model file as a root node, and generating a unique digital fingerprint by hierarchical hash; writing the original script into the random naming new file, and starting simulation to output a binary file; then key indexes are extracted to generate a CSV file, and SHA-256 Hash is calculated; the digital fingerprints, the paths, the Hash and the like are integrated to generate a structured report; the sender signs the report Hash by using an ECDSA private key, packages and compresses the report Hash, and then encrypts and transmits the report Hash by using an RSA public key of the receiver; and finally, the receiver decrypts and decompresses, verifies and signs an ECDSA public key, and then checks report Hash in a cross manner to ensure that the result is safe and credible. According to the invention, the problems of low reliability and low security of a virtual evaluation simulation result security packaging technology in the prior art can be solved.

The embodiment of the invention provides a cross-translation-unit code vulnerability scanning method and device, equipment and a medium, and the method comprises the following steps: carrying out static inspection on a target translation unit by using an abstract syntax tree, and extracting an external dependency relationship of the target translation unit based on a source code; on the basis of the extracted external dependency relationship, generating a dependency chain of the translation unit, deleting a circular reference in the dependency chain, and generating a dependency tree; according to the dependency tree, context information related to the target translation unit is loaded, and the context information comprises declarations, method signatures, function signatures, class definitions, macro definitions and namespace information; and assembling the loaded context information into a target translation unit, generating an assembled target translation unit, and executing a vulnerability detection tool to perform static vulnerability analysis on the assembled target translation unit. The efficiency and accuracy of cross-translation unit vulnerability analysis are improved through an incremental mode of loading the dependent content as required.

The present application discloses a method for calculating sentence similarity, a related device, and a computer storage medium, wherein the method includes: obtaining two sentences to be compared and a decay factor; vectorizing the phrases and negations in the two sentences to be compared respectively to obtain word vectors for each phrase and negation in the two sentences to be compared; determining the word vector similarity between the two sentences to be compared based on the word vectors for each phrase and negation in the two sentences to be compared; generating a dependency tree for each sentence to be compared according to a preset dependency syntactic rule; and determining the sentence similarity between the two sentences to be compared based on the decay factor, the dependency tree for each sentence to be compared, and the word vector similarity between the two sentences to be compared. Compared with the prior art method of calculating the similarity between sentences based only on the dependency tree, the embodiments of the present application are more accurate.

The embodiment of the application provides a kind of based on syntax semantic fusion sentiment tuple extraction method and system, it is related to natural language processing technical field.The method can be after obtaining input text, using structure perception syntax encoder calculates the encoding embedding data of input text, again through bidirectional cross attention fusion, obtains the fusion representation of syntax representation and semantic representation depth interaction.Based on double similarity comparison learning framework, the unified representation of fusion representation is calculated, and candidate example is retrieved according to the unified representation.The method can be based on the structure perception graph converter of dependency tree coding to generate encoding embedding data, and through the syntax and semantic fusion realized by bidirectional cross attention mechanism, and carry out double similarity comparison learning, for the structured task of large language model Provide explicit syntax guidance, can simultaneously integrate syntax and semantic information, correctly identify complete sentiment tuple, improve the output precision of sentiment tuple extraction result.

The application provides a graphical programming code generation method, which comprises the following steps: generating a dependency tree based on a functional block instance set, performing depth-first traversal on the dependency tree, arranging the functional blocks in the order of traversal, and generating a code for calling the functional blocks according to the order of the traversal result. The application can extract necessary information from the user's programming result, determine the execution order of the functional blocks, and generate a suitable functional block calling code, which is convenient to use.

The invention belongs to the field of text labeling, and particularly relates to a Chinese electronic medical record relation extraction method based on a graph attention network, which comprises the following steps: labeling unlabeled data of an electronic medical record; preprocessing the Chinese electronic medical record data set to obtain part-of-speech tagging and syntactic analysis information; the characters are mapped into vector and label embedded representations, and splicing is carried out; a Bi-GRU network is used for reading the sequence from the forward direction and the backward direction, and a state vector is used for capturing context information to obtain coded representation of the whole sequence; the method comprises the following steps: constructing a dependency syntax dependency tree by using a GCN, and then constructing an adjacent matrix for an input dependency syntax analysis tree by adding a virtual edge by using a graph convolutional network; potential relation prediction and global correspondence are carried out, and finally relation triples are extracted. According to the method, syntactic structure information and global semantic information are fully fused, and the accuracy and robustness of relation extraction are improved.

The invention discloses a code editor cross-language function positioning method based on hierarchical context driving, which comprises the following steps: by taking an interpreted language as a source language and a compiled language as a target language, analyzing a dependency tree and an abstract syntax tree of a source language project to collect source variables in a module layer, a function layer and an instance layer to construct a source tracing table; constructing a layered mapping rule base and establishing a mapping relationship and confidence coefficient between a source variable type and a target variable type in combination with the traceability table, obtaining a file preloading context and an intermediate instruction sequence of a source language, capturing a multi-dimensional calling context during cross-language calling after incremental simulation execution, determining a polymorphic analysis priority, and performing multi-dimensional analysis on the multi-dimensional calling context; according to the method, the mapped target function is matched, then the source code path and the line number of the target function are selected in the target language source code, accurate jump of the code editor is achieved, meanwhile, the source variable type traceability chain is displayed at the target function, and the positioning accuracy and the positioning efficiency of the polymorphic function in the cross-language function are improved.

A data processing model builder precompiles a data processing model using a dependency tree and parameters provided by a provider of a product or service. New parameters can be provided in an intuitive way, without the need to understand computer program code, and a new data processing model can be compiled quickly and efficiently. The precompiled data processing model can then be deployed to provide personalized parameters for the product or service to end users almost instantaneously on request.

The embodiment of the invention discloses a dependency management method, device and equipment for an NPM (Network Platform Management). The scheme comprises the following steps: receiving a dependency statement execution request sent by a client; executing the dependency analysis task through an analysis engine, decomposing dependency statement data corresponding to the dependency statement execution request into a plurality of dependency collection tasks, and constructing a dependency relationship graph by executing the plurality of dependency collection tasks in parallel; a dependency tree is generated based on the dependency graph, and node attributes are updated through node read-write locks corresponding to nodes in the dependency tree in the generation process of the dependency tree; and the dependency tree is fed back to the client, and server caching is carried out.

This invention discloses an intensity-aware emotion classification method based on a hierarchical knowledge-enhanced graph neural network. Its key features include: preprocessing the Chinese text to be classified by word segmentation, syntactic parsing, and dependency tree construction; extracting word-level contextual semantic vectors and global sentence representations using a pre-trained language model; identifying sentiment words and degree words in the text, calculating the dynamic sentiment intensity of lemmas, and adaptively expanding the sentiment lexicon; extracting a sentiment knowledge subgraph and aggregating knowledge embeddings, followed by seed-driven pruning to obtain a sparse dependency subgraph; fusing semantic and knowledge features to generate initial node representations, and learning these representations using a hierarchical intensity-aware graph neural network; finally, generating graph-level features through dual-path pooling, fusing global semantics, and outputting the emotion classification result through a multi-task framework. This invention solves the problem of ambiguous emotion classification caused by insufficient modeling of the combined intensity of "degree words + sentiment words" in existing technologies.