22 results about "Base domain" patented technology

The invention provides a field NL2SQL data automatic synthesis method and system based on a large language model, and the method comprises the steps: building an SQL template library through collecting a public data set and real business query data, carrying out the grading according to the SQL complexity, and forming an SQL template set covering different difficulties; specific SQL queries are generated based on an SQL template library and a large language model, verification is carried out through multiple mechanisms such as grammar check, execution verification and result dimension consistency check, and correctness and performability of the SQL queries are ensured. Generating a plurality of candidate natural language questions according to the verified SQL query and the large language model, calculating a semantic consistency score of each candidate question and the SQL query through a cross consistency evaluation mechanism, screening out the question with the most consistent semantics as a final question-answer pair sample, and meanwhile, eliminating low-quality samples by setting a consistency threshold value, so as to obtain a final question-answer pair sample; and the data accuracy and consistency are further improved.

The invention discloses a field event element extraction method and system based on prompt learning and knowledge completion, and the method comprises the steps: converting an extraction task into a constrained generation problem through structured prompt, achieving the stable extraction under a small number of labeled samples through the existing knowledge prior of a large language model, reducing the dependence on large-scale labeled data, and improving the extraction efficiency. And the cross-domain adaptive capacity is improved. A vectorization knowledge retrieval mechanism is introduced, related evidences are obtained from an external knowledge source, elements which are not clearly expressed in a text are complemented, the integrity of information is enhanced, and the accuracy and credibility of a result are improved through knowledge verification. And through a self-adaptive fusion mechanism of prompt and knowledge, the model can flexibly balance text context and external knowledge, and the robustness of complex context and fuzzy expression is improved. And finally, standardized and structured element data are output, a convenient interface is provided for downstream event atlas construction and analysis tasks, and the automation degree of domain knowledge processing and the system integration efficiency are improved.

Systems and methods for favicon comparison-based similar domain detection include receiving a base domain, the base domain being associated with an enterprise; receiving a domain list comprising a plurality of domains; performing a favicon comparison between the base domain and each of the plurality of domains within the domain list; and classifying each of the plurality of domains within the domain list as one of being associated with the enterprise or not being associated with the enterprise based on the favicon comparison.

The invention relates to a domain adaptive target detection method and system based on adversarial double-teacher knowledge distillation, and the method comprises the steps: obtaining source domain data, carrying out the self-learning stage training of a Deformable DETR model through the source domain data, and obtaining a basic student model; generating a trunk teacher model and an auxiliary teacher model based on the basic student model, and setting different levels of domain discriminators in student branches in the basic student model for aligning resistance features; respectively inputting the target domain strong enhancement image into the trunk teacher model and the auxiliary teacher model, generating a pseudo label, training student branches by using the source domain strong enhancement image and the target domain strong enhancement image, and generating a target detection model; and in the process of training the student branches, supervising the student branch training by using the pseudo labels, and updating the weights of the auxiliary teacher model, the trunk teacher model and the student branches. According to the method, the detection precision is improved under the condition that target domain labeling is not needed.

The application discloses a specific field pipeline task type dialogue system based on an LLM, which comprises an inquiry subsystem and an answering subsystem, wherein: the inquiry subsystem parses and guides a user to supplement key information of a question according to user input task text, the answering subsystem summarizes key information of a dialogue task, and performs vector matching through a built-in vertical field local vector database to generate a final answer to the dialogue task. The application uses a large language model to realize natural language understanding, dialogue state tracking and natural language generation modules in a task type dialogue system pipeline, simultaneously realizes a rule matching-based strategy learning module, understands and replies to user input text, and combines the content of a local knowledge base, so that the whole process is more interpretable, the illusion problem of the large language model is reduced, only a small amount of sample prompt learning is needed, a large amount of data training or fine tuning is not needed, and the workload during field migration is reduced.

A method and system to provide annotation-based domain mapping are disclosed. The method includes receiving a plurality of source domain objects and destination domain objects from a user. Further, the method includes storing the plurality of source domain objects and destination domain objects. Further, the method includes analyzing the destination domain objects to extract predefined annotations. Further, the method includes parsing the predefined annotations to extract mapping details and conditional details. Further, the method includes traversing a metadata graph generated based on the predefined annotations to identify a matching path in a source domain that correlates with a class path present in the metadata graph, and evaluating at least one condition associated with the matching path using at least one source attribute value. Further, the method includes mapping the source attribute value to at least one destination attribute value, upon satisfying the at least one condition.

Malicious homoglyphic domain name (MHDN) generation and associated cyber security applications are described. MHDN generation may be performed by, for example, generating, based on training data, a set of operations for use in generating the one or more potential MHDNs, wherein each operation of the set of operations may be configured to modify a base domain name according to a respective homoglyphic characteristic. The set of operations may be used to generate one or more candidate MHDN mutators. The candidate MHDN mutators may be tested for fitness values corresponding to respective likelihoods of generating an MHDN and the candidate MHDN mutators may be applied to one or more base domain names to generate potential MHDNs.

This invention discloses a method and system for generating real-time content in a specific domain based on AI technology, belonging to the field of artificial intelligence technology. The method includes the following steps: 1. Constructing a local material knowledge base RAG_src in the form of word vector storage using AI technology, whose materials include multimodal materials such as text, voice, audio / video, or H5; 2. Constructing a local domain knowledge classification knowledge base RAG_policy in the form of file storage using AI technology, whose content includes domain specification documents and technical classification documents categorized by domain knowledge and information; 3. Constructing PageIndex by combining a Large Language Model (LLM) with RAG_policy; 4. Receiving content generation requests from real-time software, calling RAG_src and PageIndex through LLM to generate real-time content that meets the requirements of the corresponding scenario; 5. Checking the generated content and outputting compliant content. This invention solves the problems of non-standard content generation, disconnection from relevant standards, low reuse rate of proprietary materials, and low retrieval efficiency in existing technologies, achieving accurate, efficient, and real-time content generation. It is adaptable to various scenarios such as real-time content generation, content compliance checking, and real-time domain assistance in specific domains. Application scenarios include, but are not limited to, oral practice and oral examinations according to teaching syllabi.

The method and device for identifying DoH service, the electronic device and the storage medium are disclosed, wherein the method comprises: sending a first domain name resolution request to a domain name resolution server at a preset detection frequency, the first domain name resolution request being used to request a DoH-based domain name resolution service for a first detection domain name; updating a target network address corresponding to the first detection domain name in a database according to each first resolution result; comparing a network address in the database with a network address to be queried in a domain name resolution service type query request when the domain name resolution service type query request is received; and determining that the domain name resolution service in the domain name resolution service type query request is the DoH-based domain name resolution service when there is a network address same as the network address to be queried in the database.

A computer-implemented method for domain adaptation of an object detection model includes obtaining a domain vector for a domain from one or more images in the domain, the domain vector representing the property of the domain. The domain vector is input into a fully connected layers in the object detection model. A domain-specific result of the object detection model is provided as output. The method can further include computing a domain tensor and inputting the domain tensor into convolutional layers in the object detection model.

Systems and methods for providing a financial / clinical data interchange are provided. The financial / clinical data interchange provides a distributed implementation to a secure hash key (i.e., a token) and value pair data derived from a medical claim (e.g., patient identification, submitter identification, payer identification, encounter identification, and the like) and enriched with submitter-based domain data. The token may be used as a data attribute in an API that unlocks a pointer to the value (e.g., a fast healthcare interoperability resources (FHIR) uniform resource identifier (URI) to the patient encounter associated with the claim) to leverage a FHIR query for all documented medical records associated with the claim the payer is authorized by the submitter to view.

A method includes receiving a domain search query from a first user, generating a prompt based on the query to instruct a first model to provide a domain recommendation, where the first model is trained by utilizing a dataset including: first data reflecting interactions of users with domain portfolios, second data reflecting domain-related actions performed by the users, third data reflecting behaviors of a second user during a current domain search session, fourth data reflecting behaviors of the second user over a predetermined period covering multiple search sessions, and fifth data reflecting search scenarios of multiple users, determining the domain recommendation against a predetermined rule to form a sanitized domain recommendation, verifying an availability to register the sanitized domain recommendation to form an available domain recommendation, adding industry information to the available domain recommendation to form an enriched domain recommendation, and transmitting the enriched domain recommendation to the first user.

Various embodiments of the present disclosure provide model-based domain-aware autocomplete techniques for generating autocomplete suggestions in a complex search domain. Example embodiments are configured to generate, using a domain-aware autocomplete model, a label for an autocomplete suggestion based on a set of keywords within an autocomplete suggestion training dataset associated with a target domain source. Example embodiments are also configured to generate, using a weak-labeling model, an updated label for the autocomplete suggestion by decorrelating the set of keywords from the label. Example embodiments are also configured to generate, using a sentence classification model, a category for the autocomplete suggestion based on the updated label. Example embodiments are also configured to, using the domain-aware autocomplete model, generate a suggestion-category pair (SCP) based on the autocomplete suggestion and the category for the autocomplete suggestion. Example embodiments are also configured for initiating performance of a search query resolution based on the SCP.

This application provides a text processing method, apparatus, device, and medium based on ontology-based domain language, comprising: receiving text to be processed; constructing initial inference state data based on the text, which includes an inference plan and evidence information; determining the inference action to be executed according to the current inference state; executing the inference action, obtaining new evidence and updating it to the state data; if the updated evidence does not meet preset conditions, dynamically adjusting the inference plan and repeatedly executing the above inference steps; if the conditions are met, generating a response text based on the final evidence. This solution, by introducing ontology-based domain language, transforms the natural language processing process into an interpretable and controllable iterative inference task under a structured domain model. Furthermore, by maintaining and iteratively updating the inference state and plan, it ensures that each step of inference is based on evidence, thereby improving the reliability, accuracy, and traceability of answers in complex question-and-answer and decision-making tasks.

This invention relates to the field of cloud-native gateways and service meshes, and discloses a route-aware multi-tenant certificate management method and system, comprising: upon receiving a tenant domain name creation request, creating a custom resource for the tenant domain name and initializing it; when the tenant domain name controller listens for the creation event of the custom resource for the tenant domain name, reading the Layer 7 routing resource template from the routing template cache, reading the subdomain prefix, and concatenating the subdomain prefix with the tenant's base domain name to generate a derived subdomain; aggregating all derived subdomains into a multi-domain certificate and submitting it to the certificate issuance controller; when the gateway proxy initiates an incremental discovery request to the key discovery and distribution service during the TLS handshake phase, sequentially querying the local cache, certificate index, and routing template cache, and executing the handshake admission control steps. This invention solves the problems of write conflicts and consistency issues, low resource utilization, and long fault recovery time in scenarios with tens of thousands of domain names.

This application provides a decentralized domain name access method, apparatus, device, and storage medium, relating to the field of blockchain technology. The method includes: obtaining a target access request sent by a first access device, the target access request including a target decentralized identity identifier; whether the target decentralized identity identifier can be registered on a second blockchain is determined by the second blockchain based on a preset verification algorithm to verify the target registration request sent by a preset management system, and the target decentralized identity identifier has been successfully registered on the first blockchain; if it is determined that the target decentralized identity identifier has been successfully registered on the second blockchain, then accessing the target blockchain address corresponding to the target decentralized identity identifier according to the target access request, thereby enabling the registration of a target decentralized identity identifier representing the same user on multiple blockchains, thus ensuring domain name consistency across the entire blockchain.

Systems and methods for similar domain detection include receiving a base domain, the base domain being associated with an enterprise; receiving a domain list comprising a plurality of domains; performing a plurality of similarity checks between the base domain and each of the plurality of domains within the domain list; and generating a directory of domains comprising one or more domains determined to be associated with the enterprise based on the one or more similarity checks.

The invention provides a field large model fine tuning method and system based on low-rank self-adaption. The method comprises the steps that a problem data set is generated according to problems submitted by field users; based on the question data set, respectively obtaining expert answers provided by domain experts and reasoning answers generated by the domain large model, and forming expert answer and reasoning answer data sets; respectively adding knowledge anchor points to the question data set, the expert answer data set and the reasoning answer data set by utilizing expert feature vectors and reasoning feature vectors generated when answers are obtained, constructing an expert training data set and a reasoning training data set, and combining the two data sets to generate a training data set; and performing low-rank adaptive fine tuning on the field large model by using the training data set. The method can solve the problem that in the prior art, it is difficult to automatically generate a high-quality training data set and achieve continuous iteration upgrading by using runtime data after deployment of the field large model, and achieves standardized automatic upgrading of the field large model in the using process.

The application discloses a kind of based on field confrontation partial domain adaptation knowledge transfer method and system of outlying class inhibition, it is related to artificial intelligence and robot technical field.Knowledge transfer framework is constructed, including field discriminator and classifier;Feature is extracted from the data of source domain and target domain;The extracted feature is aligned by field discriminator through field confrontation, and the propagation of outlying class samples in source domain is inhibited using the outlying class inhibition function of classifier;After receiving observation input, the common features of source domain and target domain are calculated based on the feature output before action mapping layer, as the common knowledge of agent policy;According to the common features and the observation output of target agent, the transfer loss is calculated to update the policy network of target agent.The purpose is to solve the problem of poor adaptability of original strategy caused by change of task target point in mobile multi-agent same domain task scenario, and to improve the efficiency and accuracy of knowledge transfer through field confrontation alignment and outlying class inhibition dual mechanism.

The embodiments provide a system which receives user-provided natural-language input and processes the input to generate a structured workflow. The system analyzes the input using a Natural-Language Processing Engine to extract entities, intents, time references, and obligations, and classifies the extracted information using a Hierarchical Tag Graph. A Context Resolver identifies a governing context that includes jurisdictional or regulatory attributes. A Milestone Directed Acyclic Graph Compiler generates a milestone structure containing preconditions, action items, and verified events. A Cost Binder assigns structured cost data to the milestones, and an Engagement Generator produces an electronically signable agreement based on the milestone structure and associated cost data. A Runtime Workflow Tracker monitors the verified events and advances execution of the workflow. The system may operate across legal, administrative, commercial, or other service-based domains.

A method includes receiving a domain search query from a first user, generating a prompt based on the query to instruct a first model to provide a domain recommendation, where the first model is trained by utilizing a dataset including: first data reflecting interactions of users with domain portfolios, second data reflecting domain-related actions performed by the users, third data reflecting behaviors of a second user during a current domain search session, fourth data reflecting behaviors of the second user over a predetermined period covering multiple search sessions, and fifth data reflecting search scenarios of multiple users, determining the domain recommendation against a predetermined rule to form a sanitized domain recommendation, verifying an availability to register the sanitized domain recommendation to form an available domain recommendation, adding industry information to the available domain recommendation to form an enriched domain recommendation, and transmitting the enriched domain recommendation to the first user.

A test and measurement instrument comprising: a port to allow the test and measurement instrument to be connected to a device under test (DUT) to receive a signal from the DUT; one or more analog-to-digital converters (ADCs) to receive a signal from the DUT and convert the signal to one or more digital waveforms; a user interface to allow a user to enter a query; and one or more processors configured to execute code that causes the one or more processors to: construct one or more images of the one or more digital waveforms from the one or more ADCs; send the one or more images to a domain adapted multi-modal large language model (MLLM); receive parameters from the domain adapted MLLM; provide the parameters for the DUT to the user in response to the query; and apply the parameters to the DUT.