该系统旨在帮助学生在线学习C语言程序,并解决开发环境免安装的问题。开发工具包括Visual Studio Code、IntelliJ IDEA 2022.2.3、MySQL数据库、Postman接口测试工具、Tomcat网络服务器用于后端开发,Node.js用于前端开发。后端使用Sprin...该系统旨在帮助学生在线学习C语言程序,并解决开发环境免安装的问题。开发工具包括Visual Studio Code、IntelliJ IDEA 2022.2.3、MySQL数据库、Postman接口测试工具、Tomcat网络服务器用于后端开发,Node.js用于前端开发。后端使用Spring Boot、Spring Data JPA、JWT、Redis框架来实现MVC模式。前端基于Vue框架,使用Element-Plus和Vant组件库进行界面搭建,并采用Axios进行数据交互,同时通过Node调用GCC库处理C语言代码的编译。总体来说,该系统设计为一个完全前后端分离的Web应用。系统的前端主要包含C语言编译、全部题目列表、个人题目发布、个人信息管理、账号安全、代码保存、日志记录等模块。展开更多
Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, t...Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.展开更多
文摘该系统旨在帮助学生在线学习C语言程序,并解决开发环境免安装的问题。开发工具包括Visual Studio Code、IntelliJ IDEA 2022.2.3、MySQL数据库、Postman接口测试工具、Tomcat网络服务器用于后端开发,Node.js用于前端开发。后端使用Spring Boot、Spring Data JPA、JWT、Redis框架来实现MVC模式。前端基于Vue框架,使用Element-Plus和Vant组件库进行界面搭建,并采用Axios进行数据交互,同时通过Node调用GCC库处理C语言代码的编译。总体来说,该系统设计为一个完全前后端分离的Web应用。系统的前端主要包含C语言编译、全部题目列表、个人题目发布、个人信息管理、账号安全、代码保存、日志记录等模块。
基金supported in part by NUS startup grantthe National Natural Science Foundation of China (52076037)。
文摘Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.
