183 results about "Word list" patented technology

The invention relates to the technical field of passenger service, and discloses a construction method and device of a dynamic extension word bank, equipment and a medium. The method comprises the following steps: acquiring multi-source heterogeneous original knowledge data related to civil aviation passenger service, and converting the multi-source heterogeneous original knowledge data into a standard format text; processing the unstructured data in the standard format text based on the adaptive word segmentation model and the stop word list to obtain a plurality of candidate words; performing multi-dimensional corpus feature quantitative analysis on each candidate word to determine candidate keywords; and performing similarity calculation based on the entity word segmentation and the candidate keywords, determining effective candidate keywords, and adding the effective candidate keywords into the dynamic expansion word bank. By means of the method and device, the technical problems that in the prior art, a word bank used by a civil aviation service system is usually based on a static vocabulary or depends on manual compiling and updating, the static word bank cannot be updated in real time, the characteristics of the civil aviation field cannot be flexibly handled, and the maintenance cost is high due to manual updating and maintenance are solved.

The invention provides a dynamic scene 4D semantic map generation method, a dynamic scene 4D semantic map generation device and processing equipment, and aims to realize geometric perception and semantic alignment combined processing in a single framework by designing a first feedforward framework for 4D semantic map generation. The framework comprises two core components, namely a streaming visual geometric converter for capturing space-time geometric features of a dynamic scene and a semantic bridging decoder for mapping the space-time geometric features to language aligned semantic spaces, so that the structural integrity is kept, and the semantic interpretability is improved. Different from a traditional method depending on time-consuming scene-level optimization, the method can effectively support multi-dynamic scene merging training, can be directly applied during reasoning, and is high in calculation efficiency and generalization ability. According to the design, the practicability of large-scale deployment is remarkably improved, a new thought of open vocabulary 4D scene understanding is developed, good data support can be provided for scene understanding tasks of applications such as intelligence, meta universe and digital twinning, and the method has good application prospects.

The present invention facilitates communication between sign language users and machines by translating sign language and text using AI models, deep learning computer vision, and word embeddings. Users interact via sign language, captured and processed through deep learning and NLP modules. The system converts sign language videos into text, constructs coherent sentences, and generates contextually appropriate responses using a Retrieve and Generate (RAG) model. Responses are translated back into sign language videos, spelling out words not found in the dictionary. If requested, a human agent can respond. Key features include high-accuracy recognition, context-aware response generation, dynamic vocabulary updates, and optional human interaction. The method ensures efficient processing with LLM, embedding techniques, and deep learning, optimizing translation accuracy and user experience. The system adapts to multiple languages and dialects by training on specific sign languages, making it applicable globally.

Systems and methods are disclosed herein for receiving, based on user interaction with a user interface, a user input of a natural language search query for identifying a cybersecurity threat by way of a search interface, the natural language search query requesting a specialized search of a threat database. An application generates a search vocabulary based on the natural language search query. The application performs a query lookup using the threat database, the query lookup returning a plurality of files that at least partially match the search query. The application prompts a large language model to generate an answer to the natural language search query using the plurality of files that at least partially match the search query, and outputs for display the answer using the user interface.

A data processing system and method include receiving a set of documents from which to generate at least one topic, creating at least one information extraction model for the set of documents, executing the at least one information extraction model to extract a plurality of text segments from the set of documents by applying one or more rule sets defined for at least one entity, word list, or grammatical pattern and extracting a plurality of text portions from the set of documents based on the one or more rule sets as the plurality of text segments, inputting at least a subset of the plurality of text segments into an unsupervised machine learning model, and executing the unsupervised machine learning model to output the at least one topic for the set of the documents.

The invention relates to the technical field of large language model security, and particularly provides a large language model watermarking method based on a triple word list division strategy. The method comprises a watermark embedding step and a watermark detection step, wherein in the watermark embedding step, a vocabulary is dynamically divided into three mutually exclusive subsets of green, yellow and red based on context information at each time step; dynamically determining a gating value according to the context entropy value; applying a positive bias to the green subset vocabulary and a negative bias to the yellow subset vocabulary based on a gating value, and prohibiting selection of the red subset vocabulary, thereby adjusting an output probability distribution and embedding a watermark; in the watermark detection step, a subset corresponding to each word is reproduced, the hit rate of green and red in a sliding window is calculated, statistical significance is calculated based on Poisson-binomial distribution, and the existence of the watermark is judged through Fisher merge test. According to the method, the problems of low detection success rate, high false alarm rate, text quality reduction and insufficient robustness of the existing watermark technology are solved.

The invention discloses a proper noun speech recognition processing method based on instant messaging and a computer device. The method comprises the following steps: firstly, constructing annotation data sets of different scenes and a user-specific custom word list, and dynamically obtaining related data and a hot word list according to a to-be-recognized voice scene; afterwards, a training voice recognition model is subjected to fine tuning by using the annotation data set to obtain a first model, and after a recognition instruction is received, a hot word list is loaded for recognition to obtain a voice initial recognition text; and after the initial recognition text is obtained, dynamic optimization is carried out by utilizing a user-specific custom word list, and recognition errors are corrected. According to the method, matching data can be dynamically obtained, the model is combined with a scene to understand proper nouns, and the recognition difficulty caused by pronunciation and meaning complexity is reduced; in combination with targeted data and hot word information training, the proper noun recognition accuracy is improved, the defects of an existing model error correction mechanism are overcome, the accuracy of a final recognition result is remarkably improved, and powerful support is provided for speech recognition and subsequent application.

Various examples, systems, and methods are disclosed relating to domain-specific document retrieval that incorporates custom vocabulary integration and embedding model updates. A computing system can extract multiple segments from a collection of documents and generate queries that correspond to at least one segment. The computing system can identify terms that satisfy a uniqueness criterion and input the terms into a tokenizer to create a vocabulary dataset. The vocabulary dataset, the document segments, and the queries can be used to update an embedding model to support retrieval and semantic alignment within private documents.

The invention belongs to the technical field of financial science and technology, and provides an RAG financial credit decision-making method based on policy mask constraint decoding. The method comprises the steps of obtaining a structured financial credit granting policy codebook, screening credit granting parameter values which can be coded into single lexical units by a pre-training word segmentation device, and constructing an effective policy lexical unit ID set; adding an original logits vector and a mask vector during decoding through the mask vector matched with the vocabulary of the large language model, and forcibly constraining an output lexical element in an effective set to realize one-time decoding compliance; and meanwhile, a dynamic policy updating mechanism, a parameter type sub-mask mechanism, a semantic offset error recovery mechanism and a policy codebook structured verification mechanism are additionally arranged. The method guarantees the compliance of the credit decision text from the source, improves the decision efficiency, shortens the policy adjustment response time, and reduces the decision interruption rate.

The invention discloses a time series prediction method based on a multi-modal enhanced large language model, which comprises the following steps: acquiring historical time series data, constructing a standardized input matrix and setting core task parameters; dividing data blocks through a sliding window mechanism, and converting the data blocks into uniform dimension features through linear embedding; a semantic prototype is generated based on a large language model vocabulary, and time sequence features and semantic features are fused through multi-head cross-attention; alternately splicing the data blocks and the corresponding semantic information, and constructing a self-multi-modal input sequence; designing a three-level structured prompt including context, task target and modal guidance, and fusing the three-level structured prompt with a multi-modal sequence; and training the lightweight model by adopting a frozen training strategy, and outputting a prediction result of a specified time step in the future. According to the method, through single-source data enhancement and prompt guidance, the time sequence reasoning capability of a small-parameter large language model is activated, high-precision prediction is guaranteed, efficient deployment is achieved, and the method adapts to long-term and short-term prediction tasks in the fields of electric power, traffic, meteorology and the like.

The invention discloses a tongue diagnosis information intelligent collection method and device based on a cloud platform and a medium, and relates to the technical field of information collection, and the method comprises the steps: constructing a tongue diagnosis situation formula library, registering tongue diagnosis collection resources and business labels of all medical institutions, and forming a tongue diagnosis business knowledge base; based on the tongue diagnosis service knowledge base, receiving a tongue diagnosis service request and matching a tongue diagnosis situation formula, and generating a tongue diagnosis collection situation instance and a corresponding tongue diagnosis collection script; and based on the tongue diagnosis acquisition session packet, the corresponding tongue diagnosis situation formula and the tongue diagnosis acquisition situation instance, generating a tongue diagnosis acquisition business file. According to the method, the structured bidirectional association between the tongue diagnosis acquisition actions and the tongue diagnosis acquisition resources is realized by constructing the tongue diagnosis situation formula library, establishing the tongue diagnosis acquisition resource capability word list and the tongue diagnosis situation formula resource demand word list, calculating the scene adaptation level and forming the tongue diagnosis business knowledge base.

The invention discloses a privacy leakage detection method and device for an applet, electronic equipment and a storage medium, the method and device are applied to the electronic equipment and are used for carrying out privacy leakage detection on the applet, specifically, an API in the applet is recognized according to a developer document, the API involving user personal data reading serves as a taint source, and the taint source is used for detecting privacy leakage of the applet. An API related to user sensitive information transmission is used as a taint convergence position; performing static taint analysis on the program code of the to-be-analyzed applet to obtain a behavior set of the applet for processing the user sensitive information; constructing a cue word list of the applet based on the behavior set, wherein cue words comprise a data type and an operation type of data accessed by the applet; and based on a chain thinking prompt strategy and a multi-query mechanism, performing compliance judgment on the prompt words in the prompt word list by utilizing a large language model in combination with an applet privacy policy to obtain judgment results, and forming a detection report based on all the judgment results. Whether the privacy data use behavior of the applet is consistent with the privacy policy of the applet can be confirmed through the detection report, so that whether the applet has privacy leakage or not is confirmed.

A data processing system and method include receiving a set of documents from which to generate at least one topic, creating at least one information extraction model for the set of documents, executing the at least one information extraction model to extract a plurality of text segments from the set of documents by applying one or more rule sets defined for at least one entity, word list, or grammatical pattern and extracting a plurality of text portions from the set of documents based on the one or more rule sets as the plurality of text segments, inputting at least a subset of the plurality of text segments into an unsupervised machine learning model, and executing the unsupervised machine learning model to output the at least one topic for the set of the documents.

1. The name of the design product: electronic device's word book word list scene word graph user interface. 2. The use of the design product: for an electronic device. 3. The design points of the design product: the interface content of the graphical user interface in the screen. 4. The picture or photo that best indicates the design points: front view. 5. The product carrier is the conventional design, and the rear view, left view, right view, top view, and bottom view are omitted. 6. The use of the graphical user interface: for the user to switch 10 scene word books, and manage the words of each word book through the word list. 7. The human-computer interaction mode of the graphical user interface: the front view is the initial interface. Click the expand word list details button in the front view to enter the interface change state diagram.

The invention discloses a short entity recall model construction method and device, a short entity recall model comprising an encoder and a decoder is constructed based on a pre-training language model, the encoder is responsible for inputting semantic codes of a context, the decoder generates entity names in an autoregression mode, and the model parameter scale is related to the size of a vocabulary; after obtaining training data containing an input context and a corresponding target entity name, inputting the training data into the model, and training the model in an autoregression mode to learn a mapping relationship between the input context and the target entity name; configuring a limited decoding strategy to ensure that an entity name generated during model reasoning belongs to a preset candidate entity set, and obtaining a trained model; and inputting the context to be retrieved into the model, generating a corresponding entity name through the model, and completing short entity recall.

The invention provides a traffic information processing system for dynamic crawler and automatic labeling of news data. The traffic information processing system comprises a crawler module, an AI automatic labeling module, a short text feature extension module and a text classification module. The crawler module extracts and captures main body texts and pictures in batches from the content of each page of the traffic news data to obtain news; the AI automatic labeling module obtains a main body text, and various entity information in the main body text is recognized and labeled by calling a large pre-training language model through cue words; carrying out accident classification marking; a short text feature extension module extracts core words from the text through the TF-IDF score to obtain a core word list, and then performs text extension based on the core word list to obtain an extended text; and the text classification module is used for outputting accident classification according to the expanded text. According to the method, dynamic crawlers, intelligent annotation, feature expansion and deep classification are organically integrated, and the automation degree of data acquisition and analysis is improved.

The application provides a remote test restart method of a vehicle machine, an electronic device and a storage medium, relates to the technical field of remote test restart of a vehicle machine, and the method comprises the following steps: acquiring a time sequence waveform of each type of running data of a target machine in a preset historical time period to obtain a time sequence waveform list A; acquiring a historical time sequence waveform list corresponding to the i-th type of running data; obtaining a running data feature word list B corresponding to A; inputting B into a preset first N-Gram model for predicting a target machine restart instruction to generate a restart label corresponding to the target machine; if the restart label corresponds to a restart instruction, sending the restart instruction corresponding to the restart label to the target machine to control the target machine to perform a restart operation; otherwise, not sending the restart instruction to the target machine; the application avoids omission of key state testing and reduces repeated testing of non-key states, thereby comprehensively improving the completeness and pertinence of test coverage.

1. Name of the product in this design: Lyrics translation module for word translation display and interactive graphical user interface on player page of electronic device. 2. Purpose of this design: An electronic device. 3. The key design features of this product are: the parts in the graphical user interface, and the parts not marked with dotted lines are the parts for which protection is required. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: The overall appearance design of the interface is used to display the song playback page, display lyrics, control song playback, and display word translations based on the dictionary (words marked in Chinese lyrics will be switched to English words with Chinese translations, while words marked in foreign language lyrics will directly display the Chinese translations); the partial appearance design of the interface is used to display lyrics, control song playback, and display word translations based on the dictionary (words marked in Chinese lyrics will be switched to English words with Chinese translations, while words marked in foreign language lyrics will directly display the Chinese translations). 7. Human-computer interaction method of graphical user interface: In the main view of Design 1, the foreign language lyrics are displayed in the middle of the interface. The corresponding Chinese translation is displayed below the words marked by the dictionary in the lyrics. Users can click on the lyrics area to bring up the word list module. Design 2 uses the same human-computer interaction method as Design 1. In the main view of Design 3, the Chinese lyrics are displayed in the center of the interface. The words marked in the lyrics are switched to English words with Chinese translations. Users can click on the lyrics area to bring up the word list module. Design 4's main view is a landscape playback page, with foreign language lyrics displayed in the center of the interface. The corresponding Chinese translations are displayed below the words marked in the lyrics according to the dictionary. Users can click on the lyrics area to bring up the word list module. In the main view of Design 5, when the user clicks on the blank area in the middle of the interface, the lyrics are displayed in an immersive state, showing the interface changes from the main view of Design 5 to the interface change diagram of Design 5.

Embodiments of the present application disclose data processing methods, devices, equipment, media and products, which can be applied to the technical field of data processing. The method comprises: obtaining a target word list, and storing an inverted link of each word in the target word list in a first storage area; the target word list is at least one word determined by a data access node to meet a heat condition; finding the inverted link of the target word in a second storage area of a data retrieval node, and when the inverted link of the target word is not found, obtaining a document jump step length that meets a jump constraint condition; based on the storage address of the document identifier in the inverted link of the target word and the document jump step length, performing document jump addressing in a plurality of physical storage units contained in the first storage area, and loading the inverted link of the target word into the second storage area based on the addressed physical storage unit. The embodiments of the present application help to improve the efficiency of data preheating.

The invention relates to the technical field of minority image description generation, and discloses an image description generation method based on fine-grained attribute learning and a gated attention network, and the method comprises the steps: carrying out the salient region detection through a MaskR-CNN, extracting a region visual feature through a BLIP model, enabling the visual feature to be aligned with an attribute vocabulary through feature matching, and carrying out the recognition of the feature. An attribute-visual feature representation is generated. According to the method, the problem of scarcity of data in the field is solved by constructing a special ethnic costume data set; a fine-grained attribute learning module is provided, modeling is explicitly carried out through feature matching, local visual features and semantic attributes are associated, and the accuracy of detail recognition and attribute classification is remarkably improved; the designed Transform-LSTM-gated attention network can effectively fuse visual features based on attributes and context text prior, and dynamic utilization of cultural semantic information is realized, so that accurate description rich in cultural details is generated.

Ideographic contrastive autoencoder for large language model fine-tuning is disclosed, including: obtaining a set of user activities according to a specified task; obtaining respective sets of input features from the set of user activities; using an encoder network of an autoencoder to encode the respective sets of input features into a set of words; prompting a machine learning model to perform the specified task using the set of words, wherein the machine learning model has been fine-tuned using a custom lexicographical vocabulary associated with the autoencoder; and presenting, at a user interface, a message determined based at least in part on an output result from the machine learning model.

The invention discloses an input method first-choice frequency modulation method and a related device, and relates to the technical field of information input, the method comprises the steps that current input context information of a target user is acquired, and the current input context information comprises on-screen content, a current input character string and a corresponding initial candidate word list; determining a target preferred word from the candidate word list by utilizing a target large language model adaptive to an input method scene according to current input context information and combining a personalized offset vector corresponding to a target user; the personalized offset vector represents the difference between the input habit of the target user and the general input habit on the high-level semantic level of the target large language model; and reordering the initial candidate word list to enable the target preferred word to be located at the first place, and displaying the reordered candidate word list. According to the first-choice frequency modulation method for the input method, the accuracy of the first-choice words can be remarkably improved, and then the input efficiency and intelligent experience of a user can be greatly improved.

This invention discloses a sentence-level question generation method based on syntactic-aware prompt learning. First, a bidirectional syntactic dependency graph is constructed based on a given sentence. Its semantic representation is obtained through a relation-aware attention graph encoder. The encoded vectors are then input into a softmax layer, and the top k vectors are selected as continuous prompts based on probability. The prompts are concatenated to the given source text and the answer using prefix adjustment, and then input into a BERT model for encoding. The encoded result is then fed into a Transformer model for decoding. At each time step of decoding, the syntactic dependency information of the generated text sequence is modeled. This information, combined with the syntactic dependency information of the source sentence, determines the parts that the decoder needs to focus on, assisting in the generation of the current word. Simultaneously, a copying mechanism is introduced to handle situations where the generated word is not in the question vocabulary, allowing the model to directly copy words from the source text.

The invention relates to the technical field of computers, in particular to a text task cue word updating method based on a text processing task and related equipment. The text task cue word updating method comprises the steps of obtaining a first cue word sampling strategy parameter, a first output conversion mapping matrix and a text sample for a text processing task; sampling in a large model cue word library according to the first cue word sampling strategy parameter to obtain a first text processing task cue word; splicing the text sample and the cue word to obtain a first task query text, and inputting the query text into a target large language model to obtain a first output probability which is the output probability of each candidate lexical element in a large model vocabulary; performing vector space conversion on the first output probability through a first output conversion mapping matrix to obtain a first task processing output result; and updating the first cue word sampling strategy parameter and the first output conversion mapping matrix based on the result and the sample task output label.

Ideographic contrastive autoencoder for large language model fine-tuning is disclosed, including: obtaining a set of user activities according to a specified task; obtaining respective sets of input features from the set of user activities; using an encoder network of an autoencoder to encode the respective sets of input features into a set of words; prompting a machine learning model to perform the specified task using the set of words, wherein the machine learning model has been fine-tuned using a custom lexicographical vocabulary associated with the autoencoder; and presenting, at a user interface, a message determined based at least in part on an output result from the machine learning model.

The invention provides a knowledge graph ontology construction method and device based on a large model, and the method comprises the steps: generating a first cue word according to text information, employing the powerful knowledge background of the large language model, carrying out the sorting and induction of key words according to the first cue word, and obtaining a generated key word list, generating a second cue word according to the text information, the key vocabulary list and the hierarchical constraint, and calling a large model to generate a concept classification system according to the second cue word, the key vocabulary list and the text information; and finally, generating a third cue word according to the text information and the concept classification system, and calling the large model to generate a knowledge graph ontology according to the third cue word, the concept classification system and the text information. According to the method, the original text information is analyzed and applied to knowledge graph ontology construction, so that the problem of low ontology credibility caused by an illusion problem of a pre-training language model is reduced, and the efficiency, capability and quality of automatic construction of the knowledge graph ontology are effectively improved.

The application provides a privacy query method and device for a wireless body area network, and relates to the technical field of encrypted communication, and the method comprises the following steps: a trusted authorization center obtains and discloses public parameters by using elliptic curve cryptography; a server arranges a historical word list set according to corresponding key hash values of historical keywords, and completes user registration according to identity information and the trusted authorization center; a target hash value of a target keyword is obtained, and a blinding factor, an encryption parameter, a blinding point and an anonymous signature are generated according to a generated temporary false identity, identity information, a system registration public key and the public parameters, and a data query request is sent to the server; a second random number and the target keyword are used to decrypt a privacy data set fed back by the server, and a privacy query result is obtained, wherein, after the anonymous signature is verified successfully, the server queries the historical word list set according to the target hash value, and obtains a privacy data set according to a query result, thereby effectively improving the privacy query security.

The application provides a multilingual question and answer method and device, electronic equipment and storage medium. The question text input by a user is first acquired, then a paragraph selection model and a multilingual word list are used to determine a to-be-selected paragraph corresponding to the question text from a preset resource library, and finally an answer generation model and the multilingual word list are used to determine an answer of the question text from the to-be-selected paragraph. In the stages of searching for the to-be-selected paragraph and generating the answer, the multilingual word list is used to avoid multiple translations, solve the problem that multiple translations consume a long time and may cause semantic deviation and affect the quality of the answer, and thus affect the user experience. The technical effects of quickly generating a multilingual question and answer answer and providing a convenient and efficient solution for internationalization promotion or multilingual environment use of an intelligent question and answer system are achieved, and the user needs to set a language mode or select an intelligent question and answer system product corresponding to different languages.

The invention discloses a multilingual off-line simultaneous interpretation method based on a small bidirectional translation model. According to the method, voice front-end enhancement, automatic voice recognition, neural machine translation and text-to-voice conversion are sequentially executed locally on a terminal, and a unified sub-word list and a shared model are adopted to automatically judge translation between at least two languages and generate a target text / voice in a stream mode. In order to adapt to the mobile terminal, the translation model is quantized by an integer and deployed in a lightweight format, and the time delay is reduced in combination with beam width search, early stop and cache mechanisms. The voice front end adopts a causal U-shaped network to jointly suppress reverberation and noise, room impulse response and multi-noise are introduced in the training stage, a signal-to-noise ratio strategy is configured, and time sequence weighted filtering and mean square error gain are combined in the reasoning stage to improve intelligibility. The device is composed of a mobile terminal and a Bluetooth earphone, supports a collaborative chain of earphone side front enhancement and mobile phone side decoding, and realizes stable simultaneous transmission output in a weak network / no network scene.

The present disclosure provides a generative response method and related methods, devices, equipment and media. The method comprises: receiving a user input sentence; obtaining a semantic expression string of the user input sentence; obtaining an association degree between the user input sentence and a word in a candidate vocabulary according to the semantic expression string of the user input sentence; deleting the word whose association degree is less than a predetermined association degree threshold from the candidate vocabulary; and determining a response string to the user input sentence according to the semantic expression string of the user input sentence and the deleted candidate vocabulary. The present disclosure improves the speed of text response.