Abstract: The present invention discloses a hierarchical routing method oriented to a cross-boundary service network. The hierarchical routing method includes the following steps: (1): proposing reference services for services with same or similar functions in a service network, with differences in format and usage mode, and establishing a unified and standardized usage view for a user; and (2): establishing a method for mapping between the reference services and ordinary services to map the multiple ordinary services with similar functions into the unified reference services, and automatically selecting an optimal ordinary service when called by the user. The hierarchical routing method provided by the present invention can implement high-speed routing of service call in the service network, thereby providing a basis for aggregation and interoperation of cross-boundary services.

Abstract: A cardinality estimation method for Skyline query based on deep learning comprises: parsing historical query log information of a database to obtain Skyline query on a given target dataset and its corresponding cardinality to construct a training set; constructing and training respective data distribution learning models according to distribution information of the target dataset and the training set; using model parameters of the trained data distribution learning models as initialization parameter of the cardinality estimation model, and training the cardinality estimation model according to the training set; inputting query points to obtain final cardinality estimates according to the trained cardinality estimation model. The present disclosure provides a solution for cardinality estimation for Skyline query variants, and ensures the monotonic nature of cardinality estimation for Skyline query variants, and proposes an efficient and accurate cardinality estimation method.

Abstract: The present invention discloses a hierarchical routing method oriented to a cross-boundary service network. The hierarchical routing method includes the following steps: (1): proposing reference services for services with same or similar functions in a service network, with differences in format and usage mode, and establishing a unified and standardized usage view for a user; and (2): establishing a method for mapping between the reference services and ordinary services to map the multiple ordinary services with similar functions into the unified reference services, and automatically selecting an optimal ordinary service when called by the user. The hierarchical routing method provided by the present invention can implement high-speed routing of service call in the service network, thereby providing a basis for aggregation and interoperation of cross-boundary services.

Abstract: Disclosed is a service quality prediction method in a service network environment. The method includes the steps of (1) constructing a service uncertainty quality model of a user through service quality log information generated when the user invokes the same or different services multiple times; (2) tapping a set of similar users of a target user based on the service uncertainty quality model of the user; and (3) improving a matrix factorization algorithm based on similarity information of the set of similar users of the target user to achieve accurate prediction of service quality. The service quality prediction method of the present invention can provide a basis for decision-making, perform targeted service optimization based on the service quality information, and provide more accurate service recommendation.

Abstract: The present invention discloses a network burst load evacuation method for edge servers, which takes a time and average penalty function of all tasks performed by the edge system as a minimum optimization goal. This method not only takes into account the fairness of all users in the system, but also ensures that the unloading tasks of all users in the system can be completed in a relatively shortest time, and a new quantitative measure is proposed for improving user QoS response. In the implementation process of the algorithm in the present invention, a particle swarm algorithm is used to solve an optimal target of the system, This algorithm has a fast execution speed and high efficiency, and is especially suitable for a scene of an edge computing network system, so that when a sudden load occurs, an edge computing network system can respond in a very short time and complete the evacuation of the load, which greatly improves the fault tolerance and stability of the edge network environment.

Abstract: The present invention discloses a REST interface specification encapsulation system based on network sniffing, comprising: an API traffic sniffing module, an API specification extraction module and an API sharing platform module. The API traffic sniffing module realizes the interception and analysis of the API call traffic in mobile applications based on the principle of Man-in-the-Middle Attack. The API specification extraction module is responsible for filtering and classifying the intercepted API call collection, and automatically generating the API specification in JSON HyperSchema format. The API sharing platform module implements sharing, openness and display of the API specification with JSON HYPERSCHEMA as the core, and provides sharing permission control, search, API call test, and API specification error correction function.

Abstract: The present invention discloses a service caching method for a cross-border service network, wherein the method includes: a cache space of a service switch node is divided into a resident area, a change area, a pre-reclaimed area and a maintenance index area; among them, a cache hit frequency is: a resident area>a change area>a pre-reclaimed area, and the maintenance index area is used for separate storage services call path. when a service call is generated, a cache content in the cache space is replaced according to a cache value of a missed cache or a hit cache; a service router and service switch nodes in the corresponding area jointly form a hierarchical cache mode. When the cache space of any node in the service switch node is insufficient, the service switch nodes in the same area perform collaborative cache and store them in other cache space of the service switch node through indexing.

Abstract: The present invention discloses a method and system for directed transfer of cross-domain data based on high-resolution remote sensing images. In the method of the present invention, first, an objective loss function which combines an image translation loss and a model adaptive loss of an image translation network model is established, thus overcoming the technical shortcoming that an existing data translation technique fails to take a specific task into full consideration and ignores a negative impact of data translation on the specific task. Further, a trained image translation network model is fine-tuned based on sample data, so that the image translation network model keeps translation towards the effect desired by the target model, thus avoiding over-interpretation or over-simplification during directed transfer of cross-domain data and improving accuracy of directed transfer of the cross-domain data based on the high-resolution remote sensing images.

Abstract: The present disclosure discloses an unsupervised domain adaptation method, a device, a system and a storage medium of semantic segmentation based on uniform clustering; first, a prototype-based source domain uniform clustering loss and an empirical prototype-based target domain uniform clustering loss are established, to reduce intra-class differences of pixels responding to the same category; meanwhile, the pixels with similar structures but different classes are driven away from each other, wherein they tend to be evenly distributed, increasing the inter-class distance and overcoming the problem that the category boundaries are unclear during the domain adaptation process; next, the prototype-based source domain uniform clustering loss and the empirical prototype-based target domain uniform clustering loss are integrated into an adversarial training framework, which reduces the domain difference between the source domain and the target domain, thus improving the accuracy of semantic segmentation.

Abstract: The present invention discloses a network burst load evacuation method for edge servers, which takes a time and average penalty function of all tasks performed by the edge system as a minimum optimization goal. This method not only takes into account the fairness of all users in the system, but also ensures that the unloading tasks of all users in the system can be completed in a relatively shortest time, and a new quantitative measure is proposed for improving user QoS response. In the implementation process of the algorithm in the present invention, a particle swarm algorithm is used to solve an optimal target of the system, This algorithm has a fast execution speed and high efficiency, and is especially suitable for a scene of an edge computing network system, so that when a sudden load occurs, an edge computing network system can respond in a very short time and complete the evacuation of the load, which greatly improves the fault tolerance and stability of the edge network environment.

Abstract: The present invention discloses a service caching method for a cross-border service network, wherein the method includes: a cache space of a service switch node is divided into a resident area, a change area, a pre-reclaimed area and a maintenance index area; among them, a cache hit frequency is: a resident area>a change area>a pre-reclaimed area, and the maintenance index area is used for separate storage services call path. when a service call is generated, a cache content in the cache space is replaced according to a cache value of a missed cache or a hit cache; a service router and service switch nodes in the corresponding area jointly form a hierarchical cache mode. When the cache space of any node in the service switch node is insufficient, the service switch nodes in the same area perform collaborative cache and store them in other cache space of the service switch node through indexing.

Abstract: A method and system of graph feature extraction and graph classification based on adjacency matrix is provided. The invention first concentrates the connection information elements in the adjacency matrix into a specific diagonal region of the adjacency matrix which reduces the non-connection information elements in advance. Then the subgraph structure of the graph is further extracted along the diagonal direction using the filter matrix. Further, it uses a stacked convolutional neural network to extract a larger subgraph structure. On one hand, it greatly reduces the amount of computation and complexity, getting rid of the limitations caused by computational complexity and window size. On the other hand, it can capture large subgraph structure through a small window, as well as deep features from the implicit correlation structures at both vertex and edge level, which improves speed and accuracy of graph classification.

Abstract: The present disclosure discloses an unsupervised domain adaptation method, a device, a system and a storage medium of semantic segmentation based on uniform clustering; first, a prototype-based source domain uniform clustering loss and an empirical prototype-based target domain uniform clustering loss are established, to reduce intra-class differences of pixels responding to the same category; meanwhile, the pixels with similar structures but different classes are driven away from each other, wherein they tend to be evenly distributed, increasing the inter-class distance and overcoming the problem that the category boundaries are unclear during the domain adaptation process; next, the prototype-based source domain uniform clustering loss and the empirical prototype-based target domain uniform clustering loss are integrated into an adversarial training framework, which reduces the domain difference between the source domain and the target domain, thus improving the accuracy of semantic segmentation.

Abstract: The present disclosure discloses a missing medical diagnosis data imputation method and apparatus, an electronic device and a medium. The method includes the following steps: acquiring a medical diagnosis data set with data missing; randomly dividing original data into initial sample point data and candidate sample point data, and constructing and training a generative adversarial network initial imputation model by utilizing the initial sample point data; estimating an influence of sample points on a parameter of the generative adversarial network initial imputation model and a prediction result of the generative adversarial network initial imputation model by utilizing an influence function; and sampling a sample point with highest influence among the candidate sample point data by utilizing a binary search algorithm, and further iteratively optimizing the generative adversarial network initial imputation model so as to impute missing data for the medical diagnosis data.

Abstract: The present invention discloses a service pattern quantitative evaluation method for crossover services: defining top elements of the service pattern, including defining participants, workflow, data flow, resource flow and cash flow; describing participants in the service pattern; describing the workflow among the participants based on existing participants; on the basis of the workflow among the participants, describing the data flow among the participants; on the basis of the workflow among the participants, describing the resource flow among the participants; on the basis of the workflow among the participants, describing the cash flow among the participants; based on the described workflow, data flow, resource flow and cash flow, calculating the evaluation indicators of the service pattern, including running time, consumption cost, transfer efficiency, value, and reliability; and calculating pattern entropy based on the evaluation indicators to conduct overall evaluation of the service pattern.

Abstract: Disclosed is system and method of connection information regularization, graph feature extraction and graph classification based on adjacency matrix. By concentrating the connection information elements in the adjacency matrix into a specific diagonal region of the adjacency matrix in order to reduce the non-connection information elements in advance. The subgraph structure of the graph is further extracted along the diagonal direction using the filter matrix. Then a stacked convolutional neural network is used to extract a larger subgraph structure. On the one hand, it greatly reduces the amount of computation and complexity, solving the limitations of the computational complexity and the limitations of window size. And on the other hand, it can capture large subgraph structure through a small window, as well as deep features from the implicit correlation structures at both vertex and edge level, which improves the accuracy and speed of the graph classification.

Abstract: The present invention provides a service packaging method based on web page segmentation and search algorithm, comprising the following steps: a service extraction stage, comprising dynamic packaging and/or static packaging; for dynamic packaging, parsing a dynamic web page, tagging forms that possibly exist in parsed dynamic form information, and tagging and defining, by a user, desired forms among the forms that possibly exist; for static packaging, parsing a static web page, blocking and tagging parsed static forms, and selecting and defining, by the user, desired blocks, and filling in a name, description information and an extraction rule of a service; and a service calling stage, comprising inputting, by the user, related information for calling a service, and generating, by a back end system, a corresponding service according to the received related information for calling the service and according to the extraction rule, and returning the corresponding service to a front end.

Abstract: Disclosed in the present invention is a spontaneous edge application deployment and pricing method based on an incentive mechanism. The method comprises the following steps: building an edge end application oriented spontaneous deployment system architecture; then proposing an incentive mechanism aiming at spontaneous edge application deployment and prizing; solving the spontaneous edge application deployment and prizing problem based on a backward induction method, thereby obtaining an optimal deployment solution of an edge server and an optimal prizing strategy of an application provider.

Abstract: The present invention discloses a method and system for directed transfer of cross-domain data based on high-resolution remote sensing images. In the method of the present invention, first, an objective loss function which combines an image translation loss and a model adaptive loss of an image translation network model is established, thus overcoming the technical shortcoming that an existing data translation technique fails to take a specific task into full consideration and ignores a negative impact of data translation on the specific task. Further, a trained image translation network model is fine-tuned based on sample data, so that the image translation network model keeps translation towards the effect desired by the target model, thus avoiding over-interpretation or over-simplification during directed transfer of cross-domain data and improving accuracy of directed transfer of the cross-domain data based on the high-resolution remote sensing images.

Abstract: A semantic segmentation method and system for a high-resolution remote sensing image based on random blocks. In the semantic segmentation method, the high-resolution remote sensing image is divided into random blocks, and semantic segmentation is performed for each individual random block separately, thus avoiding overflow of GPU memory during semantic segmentation of the high-resolution remote sensing image. In addition, feature data in random blocks neighboring each random block incorporated into the process of semantic segmentation, overcoming the technical shortcoming that the existing segmentation method for the remote sensing image weakens the correlation within the image. Moreover, in the semantic segmentation method, semantic segmentation is separately performed on mono-spectral feature data in each band of the high-resolution remote sensing image, thus enhancing the accuracy of sematic segmentation of the high-resolution remote sensing image.