15 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 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.

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 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.

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.

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 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 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.

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.