Zhejiang University

Jianwei Yin

Shuiguang Deng

Ying Li

Zhaohui Wu

Jian Wu

Shengye Pang

Yongheng Shang

Bangpeng Zheng

Ge Su

Cheng Zhang

Yingchun Yang

Zhengwei Shen

Zitong Yang

Zhiling Luo

Yucheng Guo

Maolin Zhang

Tao Wang

Huan Zhang

Wenqiu Zeng

Meilin Liu

Chen Zhi

Xuhong Zhang

Xiaoye Miao

Liye Cheng

Yangyang Wu

Xiya Lv

Jintao Chen

Jiayin Luo

Naibo Wang

Xinkui Zhao

Yishan Chen

Jiazhen Peng

Yihua Mao

Meng Xi

Yuxiang Cai

Disclosed in the present disclosure is a low-cost and zero-shot online log parsing method based on a large language model, including: firstly, extracting content of a log in a log message using regular expressions, then, performing regular expression matching with a log template in a database; if the matching is successful, updating a log sample corresponding to the log template; if the matching fails, conducting a dialogue with the large language model to obtain a new log template; performing template correction to prevent the log template generated by the large language model from being incapable of performing regular expression matching with the log message; performing template merging when a new template is generated; performing template splitting when the log sample is updated; and for all log templates to be added to the database, firstly, normalizing the log templates by post-processing, and then storing the log templates to the database. The log template generated by the present disclosure outperforms conventional methods in terms of word parsing accuracy, with significant advantages in speed and cost compared with the direct use of the large language model to perform log parsing tasks.

Low-cost and zero-shot online log parsing method based on large language model

Disclosed in the present invention is a method for dynamic evolution of a composite service, which belongs to the field of service composition in software engineering, and comprises a composite service dynamic evolution process, a distributed service evolution mechanism and a multi-layer process evolution mechanism. The present invention can dynamically adjust the composite service according to a real-time state of an atomic service under a condition of dynamic change of the service quality of the atomic service, select different candidate services, and avoid falling into a local optimal solution. At the same time, the present invention can dynamically optimize a service process according to the selection of the atomic service under the condition of loose coupling and low constraints, while taking into account the evolution of a single service and the co-evolution of services. Further, in order to reduce the load of the atomic service, the present invention utilizes service performance data generated when the service is actually running, so as to avoid empty requests due to obtaining the service quality information of the atomic service.

Method for dynamic evolution of composite service

The present invention discloses a semantic segmentation method for cross-satellite remote sensing images based on unsupervised bidirectional domain adaptation and fusion. The method includes training of bidirectional source-target domain image translation models, selection of bidirectional generators in the image translation models, bidirectional translation of source-target domain images, training of source and target domain semantic segmentation models, and generation and fusion of source and target domain segmentation probabilities. According to the present invention, by utilizing source-target and target-source bidirectional domain adaptation, the source and target domain segmentation probabilities are fused, which improves the accuracy and robustness of a semantic segmentation model for the cross-satellite remote sensing images; and further, through the bidirectional semantic consistency loss and the selection of the parameters of the generators, the influence due to the instability problem of the generators in the bidirectional image translation models is avoided.

Semantic segmentation method for cross-satellite remote sensing images based on unsupervised bidirectional domain adaptation and fusion

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.

Cardinality estimation method and device for skyline query based on deep learning

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, the REST interface specification encapsulation system improves the efficiency and accuracy of API specification extraction, and the encapsulation system is versatility and practical.

Rest interface specification encapsulation system based on network sniffing

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.

Missing medical diagnosis data imputation method and apparatus, electronic device and medium

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.

Spontaneous edge application deployment and pricing method based on incentive mechanism

The present invention discloses a fair task offloading and migration method for edge service networks, taking the Pareto optimality of the utility function of all user tasks executed by the edge system as the optimization objective, this approach not only takes into account the constraints of edge network resources, but also ensures the maximization of the utility function of all user tasks in the system, it proposes a new quantitative measurement index for improving the task utility quality under multi-user competition. In addition, the present invention uses the graph neural network and reinforcement learning algorithm to solve the final optimization goal, this algorithm has high execution efficiency and returns accurate approximate results, which is particularly suitable for the scene of edge network system under multi-user complex tasks, so that when multi-user tasks compete for network resources, the edge computing network system can efficiently obtain the Pareto optimal result of multi-user utility function, greatly improving the service quality and user experience of edge network environments.

Fair task offloading and migration method for edge service networks

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. The present invention greatly increases the efficiency of acquiring data by a user.

Service packaging method based on web page segmentation and search algorithm

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.

Method for predicting Quality of Service in service network environment

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.

Hierarchical routing method oriented to cross-boundary service network

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.

Network burst load evacuation method for edge servers

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.

Method and system for directed transfer of cross-domain data based on high-resolution remote sensing images

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&#x3e;a change area&#x3e;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. The method provided by the present invention can improve the cache utilization efficiency in the cross-border service network, thereby accelerating service invocation.

Service caching method for a cross-border service network

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.

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Hangzhou, China

Jianwei Yin