679 results about "Word embedding" patented technology

The invention discloses a retrieval generation method and system based on multi-agent collaboration, a terminal and a medium, and relates to the field of artificial intelligence. Performing semantic analysis on the input word embedding converted by the natural language query instruction through a query analysis agent, and determining a semantic intention vector; performing reinforcement learning and meta learning on the semantic intention vector through a strategy construction agent, and determining a retrieval strategy; performing semantic enhancement on the semantic intention vector according to knowledge graph node embedding to obtain a semantic enhancement vector; determining a data channel according to the semantic enhancement vector, a retrieval strategy and a real-time system load, and calling the data channel for retrieval to obtain candidate documents; and generating a target answer according to each candidate document based on an adaptive reflection feedback mechanism in combination with an auto-encoder and a generative adversarial network. The problems that the prior art depends on a fixed retrieval strategy, has limitation when facing complex query, multi-round interaction and cross-modal data fusion, is easily interfered by noise and is not accurate enough in semantic matching are effectively solved.

The invention provides a financial fraud detection method based on a large language model. The method comprises the steps of obtaining a to-be-recognized text; performing word segmentation on the to-be-recognized text through the target word segmentation tool and the financial fraud dictionary, and determining a fraud sensitive word list; calculating the weight of each sensitive word in the sensitive word list according to a target algorithm to obtain a sensitive word weight feature vector; inputting the sensitive word weight feature vector and a to-be-recognized text into a large language model, and determining a context semantic vector of the sensitive word in combination with a word embedding technology; determining the similarity between the context semantic vector of the sensitive word and a preset financial fraud type semantic vector; and determining a financial fraud type according to the similarity. Through the implementation of the method, the generalization ability of the pre-training model is utilized to capture text deep semantics, priori knowledge is injected in combination with a sensitive word weight mechanism, the model is guided to focus high-risk vocabularies, the defect of a traditional method in semantic comprehension is overcome, the financial fraud recognition rate is increased, and the omission ratio is reduced.

The invention discloses a word embedding vector extraction method and system based on a large language model, and belongs to the field of natural language processing, and the word embedding vector extraction method based on the large language model comprises the following steps: S1, embedding a global context attention module, generating a Q / K / V vector through linear transformation, and calculating a global attention weight in a cross-position manner; s2, splicing coding features of adjacent layers, and performing gating fusion to generate a global context dynamic semantic state; s3, multi-head attention joint coding is carried out on the dynamic semantic state of the current layer and the global state; s4, performing average pooling to generate a global semantic vector, and calculating a dynamic attention weight; s5, performing weighted fusion on the dynamic weight and the global vector to generate an enhanced code; s6, outputting a final word vector by a GELU nonlinear transformation full-connection layer; the method has the beneficial effects that the global context semantic capture capability is enhanced, and the expression effect of the word embedding vector is improved.

The invention provides a communication index prediction method based on a multi-modal large model and related equipment, relates to the technical field of data processing, and can obtain multi-dimensional time sequence data, geographic space data and corresponding text description data in a user communication process; performing time sequence feature extraction processing on the multi-dimensional time sequence data to obtain a time sequence feature vector, and performing spatial feature extraction processing on the geographic spatial data to obtain a spatial feature vector; encoding the text description data based on a large language model word embedding layer to obtain a semantic feature vector; performing cross-modal attention fusion on the three types of feature vectors based on a text prototype to generate joint feature representation; and the joint feature representation is input into the large language model to generate communication index prediction information, so that the coupling relationship among the multi-modal indexes can be effectively modeled, the prediction precision is improved, and the effectiveness of a network optimization decision is ensured.

The invention discloses a visual language alignment-based visual narrative generation method and device, electronic equipment and a storage medium, and belongs to the technical field of image understanding. The method comprises the steps of obtaining a to-be-processed image sequence and an instruction text; inputting the to-be-processed image sequence and the instruction text into a multi-modal large model to obtain a visual narrative text output by the multi-modal large model; the multi-modal large model comprises a visual encoder, a multi-modal mapper and a large language model which are connected in sequence, and further comprises a text encoder, and the output end of the text encoder is connected with the input end of the large language model; the step of inputting the to-be-processed image sequence and the instruction text into a multi-modal large model to obtain a visual narrative text output by the multi-modal large model comprises the following steps: sequentially encoding images in the to-be-processed image sequence through the visual encoder to generate visual features; sequentially projecting the visual features to a word embedding space of the large language model for semantic alignment through the multi-modal mapper to obtain a language embedding vector; through the text encoder, converting the instruction text into an instruction embedding vector; and through the large language model, performing logical reasoning according to the language embedded vector and the instruction embedded vector, and generating the visual narrative text. According to the method, visual information and language output can be effectively aligned, and the visual correlation of multi-graph narration is improved.

The invention relates to the technical field of power marketing, in particular to an AI large model-based power marketing data analysis method, which comprises the following steps of: acquiring structured data and unstructured data in a power marketing system, and generating unified coded data after space-time alignment and pre-training word embedding model processing; inputting the unified coding data into a pre-trained power field large model, and extracting static, dynamic and semantic features through multi-modal fusion, feature decoupling and semantic anchoring; constructing entity link feature pairs in combination with a power knowledge graph, and enhancing fusion feature expression through a graph attention mechanism; and finally, inputting a dynamic weight gating network, outputting an abnormal user identification tag, a demand response strategy and a customer loss early warning probability, and executing strategy optimization under specific conditions. The method can be widely applied to risk identification, strategy making and user behavior prediction tasks in power marketing.

Despite the usefulness of foundation models, they often return irrelevant or inaccurate responses to domain-specific queries, which often include technical jargon and patterns that are unique to the domain. Training data for specialized domains is not readily accessible or frequently discussed in online forums, so foundation models lack understanding in specialized domains. Moreover, while the embedding models underlying foundation models may be finetuned using annotated datasets, generating these datasets for niche and specialized domains is an extremely arduous, time-consuming, and expensive task. These issues are overcome by implementing a generative pseudo labeling (GPL) approach to creating labeled data for finetuning embedding models to recognize semantic similarities for specialized domains. In this way, the accuracy and relevance of foundation-model responses to domain-specific queries is improved.

The invention relates to a retrieval method and device based on static word embedding, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of performing word segmentation on an original text to obtain a first word segmentation result, training a static word embedding model by utilizing the first word segmentation result to obtain word vectors, and generating a synonym word library; the method comprises the following steps: establishing a full-text inverted index by utilizing an original text, and expanding query words by utilizing a synonym library in a retrieval stage; encoding the original text into a semantic vector by using a semantic generation model, and constructing a vector index based on the semantic vector; based on a to-be-queried text in a user query request, performing retrieval by using the full-text inverted index to obtain a first candidate document, and performing retrieval by using the vector index to obtain a second candidate document; performing fusion processing on the first candidate document and the second candidate document to obtain a target candidate document; and inputting the target candidate document into the text generation model to obtain a retrieval result. By adopting the method, the accuracy of text retrieval can be improved.

The invention provides a prediction method and device for operation parameters of a virtual power plant, a storage medium and a computer program product. Comprising the steps of performing data preprocessing on acquired influence factor data; using a time sequence decomposition algorithm to decompose the influence factor data into three-dimensional sequences including a trend component, a season component and a residual component, and segmenting each sequence into sequence blocks and mapping the sequence blocks into time sequence feature vectors; performing K-means clustering on word embedding used for pre-training the large language model, and selecting K clustering centers as semantic anchor points to be spliced with the time sequence feature vectors; finely adjusting the position embedding parameters of the large language model, the weight of the feedforward neural network in residual connection and the parameters of the normalization layer; and according to the semantic enhancement time sequence feature vector, predicting to obtain a trend component, a season component and a residual component of each dimension, and carrying out splicing and reverse normalization processing on the components to obtain a prediction result. According to the prediction method, the accuracy and real-time performance of virtual power plant load and electricity price prediction are improved.

The invention relates to an intelligent energy-saving method for a large building refrigerating system based on a large language model, and belongs to the field of intelligence of large building refrigerating systems. A reinforcement learning framework is constructed, a multi-dimensional state space formed by building thermal loads, equipment operation states and thermal environment parameters is defined, and a dynamic reward mechanism is designed by taking improvement of energy efficiency, guarantee of thermal safety constraints and maintenance of thermal comfort as optimization objectives; developing a word embedding model special for the refrigeration field and a cross-modal decision conversion model, and realizing bidirectional analysis of a natural language instruction and a physical control parameter through feature coding and embedding mapping; and performing low-rank adaptive fine tuning on the pre-trained large language model based on the reinforcement learning experience set, and constructing a closed-loop verification system. Generalized migration and multi-target collaborative decision-making of equipment control strategies are achieved, the self-adaptive regulation and control capacity of a refrigeration system under dynamic loads is improved, and a universal intelligent solution for energy efficiency optimization, thermal safety and comfort is provided for a large building.

The invention relates to the technical field of natural language processing and deep learning matching recommendation, and discloses a supply and demand matching method for digital science and technology personalized services, which comprises the following steps: carrying out semantic understanding and analysis on a submitted technical long text by adopting a natural language large model, effectively extracting a core text of technical contents, and carrying out semantic analysis on the core text; and based on the text classification model, accurately delimiting industry field labels, thereby solving matching obstacles caused by cross-industry term differences. Precise extraction of clear numerical parameters and performance indexes in technical texts is realized by using a natural language large model, a refined demand text set is established, and deep semantic vectorization representation is performed through a word embedding model; through cosine similarity calculation and a screening rule based on industry labels, the matching accuracy and reliability of cross-industry technical services are greatly improved; based on an association weight mechanism of performance indexes, semantic similarity and performance index association degree are comprehensively considered, and the accuracy of matching recommendation results is optimized.

A method, computer program product, and computing system for generating a plurality of chunks for a plurality of text portions of a document, wherein the document includes the plurality of text portions and a plurality of images. Each chunk is indexed using a word embedding. Each of the plurality of images is indexed based upon, at least in part, a position of a respective image relative to a corresponding chunk. An image placeholder is generated for each of the plurality of images. A plurality of image-enhanced embeddings is generated by inserting the image placeholder for each of the plurality of images into a respective word embedding for the corresponding chunk. The plurality of image-enhanced embeddings are provided for processing a query using a generative artificial intelligence (AI) model.

The invention is applied to the technical field of image subtitle generation, and particularly discloses an image subtitle method based on an attention and state space model, which comprises the following steps: S1, constructing an image subtitle dynamic hybrid network model based on an attention and state space; according to the image subtitle method based on the attention and the state space model, an encoder and a decoder serve as a framework, a hybrid encoder is constructed, multi-modal features of an image serve as input of the encoder, an attention mechanism is adopted to capture relevance in the modal features, serialization features are extracted in combination with the state space model, and the image subtitle method based on the attention and the state space model is obtained. Fusion is carried out through a self-adaptive gating mechanism, and rich feature information is provided for the decoder; according to the method, word embedding and multi-modal feature interaction are carried out in a decoder, a dependency relationship between word embedding and multi-modal features is obtained, a dynamic fusion module is designed to realize multi-modal dynamic fusion, multi-modal heterogeneous features of an image can be fully utilized to provide richer feature information, and sentence description better conforming to human cognition is generated.

The invention provides a full-text retrieval method and system fusing various types of documents, and relates to the technical field of information retrieval, and the method comprises the following steps: obtaining document representation through document content extraction and structure recognition, generating a cross-modal semantic vector by using word embedding and nonlinear transformation, constructing a hierarchical index and a cross-document association graph, and obtaining a full-text retrieval result; the basic correlation score is calculated after the query request is received, and the comprehensive score of the candidate content segments is calculated based on the association graph to determine the optimal retrieval result, so that unified representation and retrieval of heterogeneous documents are realized, the cross-document retrieval precision and relevance are improved, and the processing capability of a retrieval system on complex queries is enhanced.

The invention discloses an equipment operation and maintenance decision-making method based on large-scale knowledge graph multi-granularity learning reasoning, which comprises the following steps of: firstly, extracting a multi-granularity sub-graph of a subject entity in an equipment operation and maintenance knowledge graph, and integrating multilevel information such as an equipment state, a fault mode and an environmental factor to form a relation path set; secondly, performing joint coding on a question text and a relation path through an attention enhanced word embedding module based on a graph convolutional network, forming a word embedding vector, calculating a similarity score between a relation in the path and the question, pruning a sub-graph, and reducing the calculation amount; and finally, fusing a plurality of particle size sub-graphs after pruning processing, inputting the fused particle size sub-graphs into a reasoning decision module, extracting feature information from graph structure data by the reasoning decision module through graph convolution operation, dynamically adjusting a GCN learning process in combination with LSTM, finally realizing question reasoning and answer generation, and ensuring the accuracy of an equipment operation and maintenance decision.

The invention discloses a method and system for establishing a software code and demand tracking relation, and relates to the computer and software engineering technology, and the method comprises the steps: translating a Chinese demand and a code annotation into English, and separating a code logic from an annotation text based on a specified rule; word embedding is carried out on the preprocessed Chinese requirements, the code logic and the annotation text to obtain embedded semantic vector representation; performing hierarchical mixed matching based on the obtained semantic vector representation so as to associate the demand with the code; and fusing the association results through configurable decision logic to determine a final matching result. According to the method provided by the embodiment of the invention, based on a multi-modal semantic alignment technology of the pre-training model, high-precision and robust tracking relation identification is realized by combining deep semantic association of the modeling demand text and the code snippets.

The invention relates to the field of video description, in particular to a dense video description method based on multi-modal memory knowledge, which comprises the following steps: extracting visual features and audio features of an input video and carrying out cross-modal fusion to generate a final audio code and a final visual code; determining event visual features and event audio features of a plurality of candidate events from the input video based on the final audio code and the final visual code; for each candidate event, retrieving matched external knowledge from an external memory knowledge base based on the corresponding event visual feature and event audio feature, and generating corresponding multi-modal external memory knowledge; based on the multi-mode external memory knowledge, the event visual features and the event audio features of each candidate event, a word embedding sequence is constructed step by step through an autoregression mechanism, and description of the input video is generated. According to the method, the corresponding relation between the event and the description can be learned from more comprehensive information, and the accuracy and richness of generating the description are remarkably improved.

The invention provides a multi-dimensional feature fusion-based model training method and a trajectory prediction method, which can be applied to the technical field of artificial intelligence. The model training method comprises the following steps: clustering a plurality of track sequences to obtain track clusters respectively corresponding to a plurality of track behavior rules; performing bidirectional time feature extraction and spatial feature extraction on the plurality of track clusters; performing semantic coding on respective auxiliary semantic information of the plurality of track sequences by utilizing a word embedding model to obtain respective auxiliary feature sets of the plurality of track clusters; on the basis of the incidence relation among the bidirectional time feature sets, the spatial feature sets and the auxiliary feature sets of the multiple track clusters, feature fusion is conducted on the multiple track clusters, the obtained multi-dimensional fusion feature sets are used for training the initial model, and target prediction sub-models corresponding to the multiple track behavior rules are obtained; the misjudgment of prediction only depending on a bidirectional time feature set and a spatial feature set in the prior art on a complex scene is made up.

This disclosure describes example implementations for generating context-dependent embedding vectors of words in the multi-dimensional embedding space based on a generic and non-domain-specific pretrained word embeddings. Such an implementation requires no domain specific training corpus and is capable of generating context-dependent embedding vectors of multi-semantic words using a few contextual texts. Such an implementation thus provides an efficient way to generate a library of multiple domain specific embedding vectors for multi-semantic words without any domain-specific training process. Other example embodiments further apply the principles of the context-dependent word embedding generation to a text-to-image application.

The invention relates to a method for constructing a cross-modal time sequence diagram model of a complex common disease network, and belongs to the technical field of common disease networks, and the method comprises the following steps: S1, dividing the diagnosis and treatment data of a patient into text data, image data and time sequence data classification; s2, processing text information by adopting a word embedding model, processing image information by adopting a convolutional neural network, and processing time sequence data by adopting a time sequence modeling module; then, a cross-modal multi-head attention CM-MHA module is adopted to carry out cross-modal fusion on the three features, and a feature matrix Ffuse with a time sequence is formed; s3, according to the current diagnosis and treatment information of the patient, establishing a complex common disease network by adopting an advantage ratio RR method; and S4, embedding the feature matrix Ffuse with the time sequence into the complex common disease network by adopting a graph embedding technology, and according to a time sequence data updating rule, forming a cross-modal time sequence common disease network.

The invention relates to an enhanced question answering method based on agricultural large model training and RAG, and belongs to the field of agricultural large data, the method comprises the following steps: collecting agricultural data, and constructing an agricultural large model training data set; the method comprises the following steps of: constructing input embedding of a Transform and a Transform encoder to train a large model based on a Transform framework; after a user inputs a question text, the large model sequentially executes dynamic word embedding disambiguation, regional term replacement, entity perception position coding and generation of enhanced representation of an input sequence, then, a Transformer encoder extracts deep semantic features, a classification task directly outputs prediction labels, and a generation task generates answers word by word through a decoder; and performing RAG enhanced questioning and answering in combination with a retrieval enhanced generation mechanism. According to the dynamic word embedding mechanism, the ambiguity problem of agricultural terms in different contexts and regions is effectively solved, and the accuracy of agricultural semantic understanding is remarkably improved.

The invention provides a global and local feature fusion SMPL-X action-to-text generation method, and belongs to the field of artificial intelligence. The method comprises the following steps: preprocessing and coding an input SMPL-X action sequence into a double-flow action feature; through a cross-modal mapping module, the double-flow action features are mapped to a pre-trained large language model through independent projection branches, and global conditions and local action prefix embedding are obtained; through a text generation module, a decoder of a pre-trained large language model is used as a trunk network, text cue word embedding is extracted based on a text instruction given by a user, local action prefix embedding and text cue word embedding are spliced and then input into the decoder, and global conditions are injected into each layer of the decoder through a cross attention mechanism. And generating a description text in an autoregression mode. According to the method, the description text which is consistent with action semantics and has sufficient details can be stably and accurately generated, and the generation stability and the cross-scene applicability are improved when disturbance exists in the action sequence.

The invention discloses a high-precision geographical name translation method integrating artificial intelligence and multi-language syllable segmentation, and relates to the technical field of geographical name translations, the method comprises the following steps: firstly splitting an input geographical name into a sub-word Token sequence, and using a context coding model of a word embedding model to realize semantic context associated coding of a to-be-translated geographical name; and then modeling internal structured information and a dependency relationship in context semantics of the token to be translated in a local semantic relevance reconstruction enhancement mode, identifying recessive syllable relevance in compound words and adhesive words, and enhancing local morphological characteristics near syllable boundaries, so as to solve the problems of irregular spelling and cross-language interference, and improve the translation efficiency of the token to be translated. And further decoding and outputting the geographical name character string after syllable segmentation to carry out geographical name translation, generating a corresponding geographical name target language translation text, and realizing end-to-end effective conversion from an original geographical name to a target translation name.

The invention discloses a text generation method and device, equipment and a storage medium, and the method comprises the steps: obtaining a source text, and carrying out word segmentation sorting to obtain a text sequence; converting the text sequence into word embedding representation; performing semantic feature extraction based on a global encoder, and fusing output vectors of the last plurality of encoding layers based on an attention mechanism to obtain first semantic encoding information; performing semantic feature extraction based on a local encoder to obtain second semantic encoding information; performing feature fusion and filtering based on a global gating unit to obtain a context semantic vector; and decoding the context semantic vector based on a decoder to generate a target text. The context semantic vector is obtained through semantic feature fusion and filtering, redundant features are removed, and post-processing is not needed; the global encoder carries out multi-coding-layer output vector fusion based on an attention mechanism, and the local encoder fuses features through a gating unit, so that richer key information features can be obtained.

The invention relates to the technical field of artificial intelligence, in particular to a personalized soft skill training method and system based on artificial intelligence. The method comprises the following steps of performing evaluation by using a multi-dimensional questionnaire, and analyzing semantics of user answers in combination with a large language model to generate an initial user portrait; updating the initial user portrait; constructing a skill tree by using a word embedding technology and an improved Woltzz method; constructing and optimizing a global model based on the skill tree; and dynamically generating a personalized learning path according to the global model. By introducing an artificial intelligence large language model technology, a neural network word embedding algorithm and a graph theory algorithm and combining a'questionnaire-learning-practice 'closed-loop training mode, individuation, systematization and intelligentization of soft skill training are realized, the technical bottleneck of a traditional method in the aspect of subjective question type intelligent feedback is broken through, and the intelligent feedback of the subjective question type is realized. And the soft skill training effect and the user experience are obviously improved.

The invention provides a knowledge graph completion method based on a large language model and a graph neural network. The method comprises the steps of obtaining definitions and descriptions of a head entity, a tail entity and a relation in a triple, and obtaining a problem instruction for triple classification; a multi-hop sub-graph with an entity as the center is obtained, a word embedding model is used as a feature extractor, the multi-hop relation between triples is analyzed, and therefore high-quality semantic features existing in multiple hops are obtained. Secondly, complex graph structure information is captured by using a graph neural network, and an interaction relationship between nodes is learned, so that graph structure data can be better understood. And finally, fine-tuning the large language model in combination with the question and answer instruction to help the large language model focus on the key task. According to the method, the characteristics of strong context perception capability and complicated sentence structure and context understanding of a large language model are combined with aggregated node structure information to generate embedded representation with better expression capability, so that the performance of knowledge graph completion is improved.

The invention discloses a knowledge data de-duplication method and device, a storage medium and computer equipment, relates to the technical field of data processing, is suitable for businesses such as financial science and technology and smart medical treatment, and mainly aims to solve the problem of poor de-duplication effect when large-scale knowledge data is processed in existing knowledge data de-duplication. Comprising the steps of obtaining large-scale knowledge data of related businesses; performing clustering processing on the large-scale knowledge data by adopting a MinHash LSH model to obtain a repeated text clustering result; the repeated text clustering result comprises a plurality of groups of similar data sets; performing word embedding calculation on each group of similar data sets by adopting a bge-m3 model to obtain word embedding corresponding to each group of similar data sets; and respectively carrying out semantic similarity de-duplication processing on the word embedding in each group of similar data sets to obtain a de-duplication result of the knowledge data.

The invention relates to the technical field of natural language processing, in particular to a language structured review system based on a deep network, and the system comprises a semantic analysis module which is used for extracting entities and attributes in a document, and carrying out the modeling of the relation between the entities through a graph convolution network. According to the method and the device, after the entities and the attributes in the document are extracted, the entity relation matrix is established, so that text information can be expressed through a topological structure, and segmentation of language information is reduced. According to the deep feature extraction method based on the entity relation matrix, text semantic modeling is not limited to a lexical structure, but is fused into a context and a relation network, so that the meanings of the same term in different contexts can be accurately analyzed, and the ambiguity problem is reduced. The dynamic vocabulary monitoring technology can automatically collect new terms and changing vocabularies, and the dynamic word embedding technology is combined to realize adaptive updating of a term library, so that term recognition does not depend on a static dictionary any more, and the adaptability and accuracy of text analysis are improved.

The invention relates to the field of intelligent report generation, in particular to an intelligent insight report generation method and system based on an AI large model, and the method comprises the steps: inputting an insight demand, and generating an insight data package comprising insight contents, associated data and industry labels; extracting a basic keyword set of the insight data packet to form a mixed feature code; after mixed feature coding preprocessing, weight distribution is carried out; and after weight distribution of the AI large model, injecting a high-weight feature vector into a semantic understanding core layer, injecting a low-weight feature vector into a logical reasoning layer, and outputting analysis data to form an intelligent insight report. According to the method, word embedding parameters are optimized according to field semantic characteristics, a parameter verification mechanism is introduced, field text characteristics are adapted by adjusting vector dimensions, context windows and low-frequency vocabulary filtering threshold values, window parameter validity is verified through cosine similarity, word frequency threshold value reasonability is verified through standard deviation, and field text characteristic matching is achieved. And it is ensured that the feature vectors can accurately capture domain-specific semantics.

A multi-party joint fine-tuning language model method and device based on data protection, multiple parties comprise a first party holding a target language model and a second party providing computing power, the target language model comprises an embedding table and a plurality of serially arranged network layers, and the first party determines a plurality of confusion layers and a confusion embedding table; at least sending the multi-layer confusion layer to a second party; the confusion embedding table is obtained by performing second confusion on the embedding table, the confusion layer is obtained by performing first confusion on a parameter matrix in the corresponding network layer, and the first confusion mode corresponds to the second confusion mode; the second party obtains a training sample comprising a target confusion word embedding sequence and a label thereof, wherein the training sample is determined based on the text and the confusion embedding table; based on the target confusion word embedding sequence, obtaining a first output result through multiple confusion layers; and based on the difference between the first output result and the tag, adjusting a multi-layer confusion layer to realize a multi-party joint fine tuning model on the premise of protecting model parameters and data privacy.