29 results about "Mashup" patented technology

A system and method for creating complex, immersive, and interactive digital content is disclosed. The system integrates advanced artificial intelligence, multi-modal input processing, cloud-based shared environments, and immersive hardware to generate, optimize, and deliver rich interactive experiences. The platform supports content mashups, custom scenario generation, and adaptive AI behaviors, enabling the creation of unique and engaging digital environments across various media formats.

The invention provides a Web API recommendation method based on a multi-task deep neural network. The Web API recommendation method comprises the following steps: S1, cleaning a Mashup description document and a Web API description document in a data set; s2, mapping character information in the cleaned data set into digital codes; s3, generating sample data based on digital coding; s4, constructing a Web API recommendation model based on the multi-task deep neural network; s5, inputting the sample data into the Web API recommendation model for pre-training to obtain a pre-trained Web API recommendation model; s6, performing fine adjustment on the pre-trained Web API recommendation model by using a Mashup and API matching score prediction task; s7, obtaining a matching score list of the Mashup demand and each Web API (Application Program Interface); and S8, sorting the matching score lists of the Mashup requirements and the Web APIs in a descending order according to the matching scores, and selecting the first K Web APIs to form a final recommendation list. According to the method, the problem that no-interaction or low-interaction Web APIs in historical data are difficult to recommend accurately is solved, and indexes such as the recall rate and the accuracy rate of recommendation are improved.

The invention relates to the technical field of computers, and particularly discloses a Web API (Application Program Interface) recommendation method based on graph diffusion reconstruction and graph contrast learning, which comprises the following steps of: constructing a Mashup-API calling relation graph based on historical calling information; performing data expansion based on graph diffusion on the Mashup-API calling relation graph so as to generate a diffusion matrix of the Mashup-API calling relation; reconstructing the diffusion matrix by using adaptive graph reconstruction so as to generate a comparison view; a Mashup-API calling relation graph and a comparison view are obtained, the trained Light GCN is used as a basic graph encoder to process the Mashup-API calling relation graph and the comparison view, and a final feature representation vector of the node is generated; and sorting the APIs meeting the Mashup requirements based on an inner product between the feature representation vectors of the Mashup nodes and the feature representation vectors of the API nodes so as to generate a recommendation list. According to the method, the Mashup-API calling relation graph is expanded by adopting graph diffusion and an adaptive graph reconstruction model, so that the problem of sparse historical interaction data between the Mashup and the API is solved, and meanwhile, the recommendation accuracy is improved.

The invention relates to a multi-service combination recommendation method based on a large language model and a twin neural network, and the method comprises the steps: extracting entities and relationships from the text information of a service and a Mashup through an LLM, and constructing a service-entity graph or a Mashup-entity graph; thirdly, fusing and simplifying the service-entity graph and the Mashup-entity graph through a text vectorization means in combination with the calling relation and the vector similarity between entities; then, the vector representation of the relation is brought into an attention scoring function, a heterogeneous graph attention neural network is utilized to generate vector representations of services and Mashup requirements on a service-Mashup graph, and a group of services with the highest matching degree is returned; and finally, processing the service and a Mashup code through a twin neural network, calculating a demand feature which is not satisfied between the Mashup demand and the candidate service, and carrying out multi-service combination recommendation based on the feature. The result of comparison with the similar method shows that the recommendation accuracy of the multi-service combination recommendation task can be remarkably improved.

A system identifies a song structure by using beat markings and chord strings. The process includes steps of extracting of raw features using machine learning, creating beat markings and chord strings, and receiving mashup search details. The process iteratively analyses all songs in a catalog based on tempo, key, beat markings, chord strings, and creates a mashup using specific conditions. In case no matches are found, the process attempts to pitch-shift songs. This system facilitates automatic matching of songs enhancing rhythmic interplay and harmonic cohesion. It provides a systematic, granular examination of song structures, enabling accurate, efficient music matching and permitting the creation of high-quality mashups.

The application provides a Mashup service multi-label classification method based on double manifold regularization width learning, mainly comprising: using a hidden Dirichlet distribution topic model to extract features from preprocessed Mashup description documents; linearly mapping a Mashup description document topic feature matrix into n groups of feature nodes respectively; processing the feature nodes through an activation function to generate enhanced nodes; splicing the feature nodes and the enhanced nodes to generate enhanced feature nodes as the input of the model; constructing a target function of a Mashup service multi-label classification model based on double manifold regularization width learning; using a least square method to solve the target function to obtain a weight matrix of a double manifold regularization width learning network; obtaining a description document of a test Mashup service and sending it into a trained model to predict a multi-label classification result. The application improves a width learning model by using double manifold regularization, and realizes a Mashup service multi-label classification function by using an improved BLS model.

A Web long-tail service recommendation method based on multi-level views and comparative learning comprises the steps that firstly, a global interaction structure view and a local interaction cooperation view are constructed, the global interaction structure view focuses on the comprehensive interaction relation among users, API services and Mashup, and the local interaction cooperation view focuses on the local interaction cooperation view; a user-API service graph in the local interaction structure view mainly focuses on fine-grained interaction preferences of users, and a Mashup-API service graph focuses on service combination association preferences among API services; furthermore, in order to enable the long-tail service and the long-tail user to have better embedding representation quality, performing data enhancement operation of adding nodes on two sub-graphs of the local interactive cooperation view in the graph convolutional neural network; and finally, performing comparative learning among the multi-level views to encourage similar representations of the same user and service under different views and punish the similar representations of different users and services so as to improve the overall efficiency of service recommendation.

The application discloses a Web service representation method based on multi-view multi-level contrast learning, and comprises the following steps: acquiring a programmable Web dataset; acquiring Mashup text description and Web API text description based on the programmable Web dataset; inputting the Mashup text description and the Web API text description into a Sentence-BERT model to acquire Mashup text representation and Web API text representation; inputting the programmable Web dataset into a Web service interaction network model to acquire Mashup structure representation and Web API structure representation; respectively performing global contrast learning on corresponding text representation and structure representation to acquire corresponding global feature information; and adding the corresponding global feature information to acquire a Web representation vector. The method simultaneously considers feature information of Web services under multiple views, and extracts features in combination with multi-level contrast learning.

The invention discloses an association binding recommendation method based on a heterogeneous graph and an auto-encoder. The association binding recommendation method comprises the following steps: collecting description data in an existing Mashup application, a service binding package Bundle and a related service Service; selecting text description from the description data to perform text embedding operation; a calling relation and a service cooperation relation are selected from the description data, and a Mashup-Bundle-Service heterogeneous graph is constructed; constructing a graph propagation module, and extracting the potential implicit correlation of sparse interaction in the heterogeneous graph; a prediction module is constructed, a space attention layer is constructed, and sparse and discrete distribution of different subgraphs is fused; a self-encoding layer is introduced into the prediction module, service binding features are inscribed from the perspective of services, and semantic features of different services are adaptively aggregated; and fusing multi-model training, and integrating different types of feature descriptions. According to the method, the recall rate and the accuracy of the recommended service binding package in the Mashup development process can be greatly improved.

The application discloses a network API recommendation method and device based on hypergraph contrast learning, and the method comprises the following steps: acquiring Mashup information, API information and interaction information between the Mashup and the API; constructing an initial Mashup and API heterogeneous hypergraph according to the acquired information; converting the Mashup and API information into Mashup and API embedding vectors; obtaining initial Mashup and API feature vectors through hypergraph neural network aggregation and summation; performing dot product operation on the initial Mashup and API feature vectors to obtain initial prediction scores of the Mashup to the API; performing error calculation through a total loss function, finally obtaining target prediction scores, and then recommending the API to the Mashup. The application can aggregate higher-order neighbor information through the heterogeneous hypergraph, improves the accuracy of the recommendation, and can be widely applied to the computer technology application field.

The present invention belongs to the field of API recommendation, and specifically relates to a method for recommending diverse APIs based on an association graph, comprising: constructing a depopularized API association graph; obtaining candidate API combinations that meet development requirements by finding the minimum weighted subgraph; constructing a kernel matrix based on the feature vectors of each candidate API combination, solving the kernel matrix using a fast greedy MAP inference algorithm, and finding the API combination that maximizes the posterior probability from the candidate API combinations to obtain a recommendation result. The present invention constructs a depopularized API association graph based on the functional description keywords of the API and the historical interaction data between the mashup and the API. Based on the depopularized API association graph, the API recommendation problem is modeled as a problem of finding the minimum weighted subgraph to obtain a depopularized API combination that meets development requirements, has compatibility, and is depopularized, so that the recommendation results are depopularized while maintaining compatibility and meeting development requirements.

Device for controlling the execution of a mashup WoT service, the device comprising: a Web of Things mashup service function entity, WoT mashup service function entity, (52) configured to execute a mashup WoT service adapted for a WoT service user (10); a WoT service execution function entity (54) configured to handle the execution of simple WoT services for the execution of the mashup WoT service of the WoT mashup service function entity; and a WoT service repository (56) configured to store WoT service execution descriptions (56a, 56b, 56n), each describing execution logic for each of the simple WoT services; wherein the WoT mashup service functionality entity comprises: a WoT mashup engine (52b) configured to execute the mashup WoT service in accordance with the service instruction read from the relevant WoT service execution description;wherein each of the WoT service execution descriptions includes information about the mashup WoT service, a service instruction designed to execute the mashup WoT service created by combining the two or more simple WoT services, and service profiles representing parameters designed to identify the mashup WoT service customized for the WoT service user.

The application discloses a processing method and device of audio data, and relates to the technical field of multimedia. The method comprises the following steps: acquiring m (m is an integer greater than or equal to 2) audio segments; determining m-1 transition audio information according to the m audio segments; and generating target mashup audio according to the m audio segments and the m-1 transition audio information. The m-1 transition audio information is used to connect the m audio segments. For the first transition audio information in the m-1 transition audio information, the first transition audio information is used to connect a first audio segment and a second audio segment which are sequentially arranged in the m audio segments. The arrangement of the m audio segments refers to the mashup order of the m audio segments.

This invention provides a Web API recommendation method based on functional semantics and structural interaction, belonging to the field of Web API recommendation technology in the Web environment. It solves the problem of excessively short descriptions of existing Mashup service requirements. This Web API recommendation method based on functional semantics and structural interaction includes the following steps: Step S1: Collect description data from existing Mashup applications and their related API services, and extract textual description information of the Mashup applications and API services; Step S2: Preprocess the textual description information of the Mashup applications and API services; Step S3: Construct a Mashup-API bipartite graph based on the collaborative relationships between API services used in the Mashup application; Step S4: Construct functional semantic components and extract functional semantic features from the textual description information; Step S5: Construct structural interaction components and extract potential implicit correlations in sparse interactions; Step S6: Integrate multi-model training and integrate different types of feature descriptions. This invention has advantages such as reducing the impact of data sparsity on recommendation results.

The present invention belongs to the field of recommendation. Specifically, according to the requirements input by users, it recommends web API services that meet the users' needs, improving the accuracy and personalization of recommendations. It mainly includes four steps: A. Mashup clustering; B. User link prediction algorithm; C. Collaborative filtering algorithm based on link prediction; D. Popularity prediction of web API. E. Web API recommendation model. The present invention combines link prediction and collaborative filtering, improving the accuracy of recommendations while also enhancing the personalization of recommendations, making the results of recommendations more in line with the actual needs of users.

The present invention discloses an API recommendation method based on a heterogeneous information network meta-path, comprising: obtaining description information of an API and its Mashup application, building a heterogeneous information network around the API; generating an initial low-dimensional embedding of the API and the Mashup; generating a context meta-path embedding based on the interaction between the API and the Mashup; fusing the initial low-dimensional embedding of the API and the Mashup and the context meta-path embedding based on the interaction between the API and the Mashup, training a neural network model, and realizing API recommendation. The present invention can help users quickly find suitable APIs from a huge API library through accurate recommendations when developing Mashup applications, thereby improving the development efficiency of applications.

A method for providing, by a server, a creation and distribution service of lightweight immersive content according to an embodiment of the present invention may comprise the steps of: receiving, by the server, content data including at least one of 360-degree image data and 3D data for 3D content from a first user terminal; performing resource weight reduction on the received 360-degree image data or image data extracted from the 3D data; and when the server receives a transmission request for immersive content from a second user terminal, creating lightweight immersive content by performing a mashup of recombining the image data on which the resource weight reduction has been performed according to a predetermined criterion, and then distributing the created immersive content to the second user terminal.

The present invention provides a Web API recommendation method based on factorization machines, comprising the following steps: crawling Mashup and Web API metadata from the ProgrammableWeb website to construct a service library dataset; preprocessing the obtained Mashup and API functional text descriptions; inputting the preprocessed text into a Sentence-BERT model to obtain vector representations of sentences, and calculating the similarity between APIs and Mashups through the obtained sentence vectors and a multi-feature extraction component; calculating the popularity of APIs and the compatibility of API combinations according to the interaction records between Mashups and APIs; obtaining a feature matrix of Mashup-API by complete concatenation as the input of the AFMHN model; and outputting the top-k Web APIs with the highest probabilities by the AFMHN model. This method can make the recommended Web APIs meet the requirements of developers for developing Mashups by extracting different features of Web API metadata, using a deep neural network to capture the interaction between any low-order and high-order non-linear features, and using an attention mechanism to capture the different importance between features.

The application discloses a kind of based on the recommendation method of multi-modal service feature of meta-path fusion, belong to service recommendation and representation learning field.It includes: collection Mashup application and Service service, and constructs historical call matrix and the multi-modal feature of Service service;Meta-path and similarity calculation are used to Service service, respectively obtain meta-path isomorphism and modal isomorphism;Introduce multilayer perceptron and filter, further combined with all isomorphism, contrast loss and fusion modal feature are obtained by graph convolution network;Based on fusion modal feature and historical call matrix, respectively calculate to obtain second contrast loss and BRP loss;Regularization loss is introduced, to minimize all loss sum reverse training all network parameters, obtain the service retrieval table of training completion.The method of the application can accurately fuse the features of multiple modalities of traditional services, significantly enhance the generalization capability in cold start and sparse scenarios.

Techniques for obtaining mashup are described. Given a set of media content items for sequential playback, a subset of the media content items is selected for inclusion in the mashup based on selection criteria specified in a template associated with the feature story. The subset of the media content items is then arranged as a mashup, which is preset into the story. By automatically generating a mashup-a reduced version of the story-the mashup will increase user participation and encourage sharing since the mashup concentrates the content into a more understandable and more attractive format. By using optimized content selection criteria, the mashup will include only the best and most influential moments, highlights, or key elements of the story. The shorter versions catch the attention of viewers, maintain their interest, and prompt them to share concentrated experiences with others, thereby attracting them to find a complete story.

The present invention proposes a component service recommendation method for Mashup service requirements, including: constructing a candidate component Web API set for Mashup service requirements; generating potential application scenario feature words for the service requirements and Web APIs in the candidate component set; generating a scenario semantically enhanced Mashup service requirement vector and a service function vector; generating a heterogeneous association vector integrating multi-dimensional association features for the Web API; establishing a component service recommendation model for Mashup service requirements with grouped mashup optimization, and solving the recommended components. The present invention can better prioritize the extraction of feature words with high fit with the service application scenario. The proposed method for generating a service function vector with application scenario semantic enhancement can greatly improve the accuracy of matching Mashup service requirements with Web API functions. The Web API heterogeneous association compatibility determination method integrating four association features greatly improves the compatibility of recommended component services. The established grouped mashup optimization recommendation model makes the diversity of recommended component services better and the recommendation quality higher.

Aspects of the subject disclosure may include, for example, receiving first defining information for a first metaverse, receiving second defining information for a second metaverse, automatically identifying conflicts between the first defining information and the second defining information, automatically negotiating resolution of the conflicts between the first defining information and the second defining information to define rules for a merged metaverse, merging the first metaverse and the second metaverse according to the rules for the merged metaverse to define a destination metaverse, and interacting with a plurality of users to create for the plurality of users an immersive experience in the destination metaverse. Other embodiments are disclosed.

A system identifies a song structure by using beat markings and chord strings. The process includes steps of extracting of raw features using machine learning, creating beat markings and chord strings, and receiving mashup search details. The process iteratively analyses all songs in a catalog based on tempo, key, beat markings, chord strings, and creates a mashup using specific conditions. In case no matches are found, the process attempts to pitch-shift songs. This system facilitates automatic matching of songs enhancing rhythmic interplay and harmonic cohesion. It provides a systematic, granular examination of song structures, enabling accurate, efficient music matching and permitting the creation of high-quality mashups.

The invention relates to an interactive Web API recommendation method based on reinforcement learning, which belongs to the field of Web API recommendation, and comprises the following steps: abstracting multiple rounds of Web API recommendation problems into a Markov decision process; designing a state representation module fusing user positive and negative feedback, demand semantic features and a Mashup and Web API feature interaction relationship; designing and deploying an environment simulator based on an offline data set; a double-layer deep Q network is adopted as a strategy function, and recommended actions are output according to the current state. A priority experience playback mechanism based on a time sequence difference error is introduced, and sampling priorities are dynamically distributed for interaction samples in the training process; and performing multiple rounds of interaction through a simulator, storing interaction samples in a priority experience playback buffer pool, periodically updating DDQN network parameters, synchronizing a target network by adopting a soft update strategy, evaluating a trained learning strategy, and outputting a recommendation list. According to the method, the sample utilization efficiency and the sorting quality in a sparse feedback scene can be effectively improved.

A Web API recommendation method based on a multi-channel hypergraph convolution network belongs to the service recommendation scene field under Mashup development, first extracts a hypergraph structure from the service by using a model body, studies the complex relationship between service data; then, in order to reduce the noise, a convolution weight matrix guided by a hybrid random walk is used in the hypergraph convolution, finally, according to the obtained weight matrix, convolution is carried out on the constructed hypergraph, and finally the convolution results on different channels are combined to obtain the representation vector of the service, and the Web API is recommended. The precision of the recommended application is higher.

The invention discloses a live video mixing and cutting system based on an AI large model and a working method thereof, and belongs to the field of commercial application of an artificial intelligence information technology. The system comprises a video acquisition module, a voice processing module, a text processing module, a video clip interception module and a mixed video generation module. The working method comprises the following steps: S1, video acquisition and processing; s2, voice data processing; s3, text data processing; s4, intercepting and marking video clips; and S5, generating a mixed video. According to the live video mixing and cutting system based on the AI large model and the working method of the live video mixing and cutting system, the system can automatically extract key fragments and intelligently synthesize the mixing and cutting video according to the input theme, the workload of manual editing is reduced, and the efficiency and quality of content production are improved. According to the invention, a new way is provided for live video mixed clipping, and the intelligence of video clipping is promoted.

Aspects of the subject disclosure may include, for example, receiving first defining information for a first metaverse, receiving second defining information for a second metaverse, automatically identifying conflicts between the first defining information and the second defining information, automatically negotiating resolution of the conflicts between the first defining information and the second defining information to define rules for a merged metaverse, merging the first metaverse and the second metaverse according to the rules for the merged metaverse to define a destination metaverse, and interacting with a plurality of users to create for the plurality of users an immersive experience in the destination metaverse. Other embodiments are disclosed.

The application relates to the technical field of computers, and specifically discloses a Web API recommendation method based on graph diffusion reconstruction and graph contrast learning, which comprises the following steps: constructing a Mashup-API call relationship graph based on historical call information; performing graph diffusion-based data expansion on the Mashup-API call relationship graph, so as to generate a diffusion matrix of the Mashup-API call relationship; reconstructing the diffusion matrix by using adaptive graph reconstruction, so as to generate a contrast view; obtaining the Mashup-API call relationship graph and the contrast view, processing the Mashup-API call relationship graph and the contrast view by using a trained Light GCN as a basic graph encoder, and generating a final feature representation vector of a node; and sorting APIs that meet Mashup requirements based on the inner product between the feature representation vector of a Mashup node and the feature representation vector of an API node, so as to generate a recommendation list. The Mashup-API call relationship graph is expanded by using a graph diffusion and adaptive graph reconstruction model, the problem of sparse historical interaction data between Mashups and APIs is solved, and the accuracy of recommendation is improved.

The application discloses a Mashup service clustering method based on feature semantic enhancement and graph contrast learning. Mashup service data participating in clustering is preprocessed, then a service function vector is generated based on a singular value decomposition and a standardized space distribution correction method, and a feature semantic enhancement method of the service function vector is based on a non-functional entity word, a named entity recognition method is used to extract the non-functional entity word in the description text, and a feature semantic enhancement vector is constructed. The feature semantic enhancement vector and the service function vector are spliced; on this basis, two kinds of Mashup service association graphs facing sharing dependence are established, and a service function vector fusing a label association neighborhood feature and a fusion Web service association neighborhood feature is constructed. Finally, the two vectors are optimized through an infoNCE loss function, a service representation vector is constructed, and a spectral clustering algorithm is adopted to realize Mashup service clustering, so that the clustering quality can be effectively improved.