在大数据时代,旅游市场竞争日益加剧,使用大数据分析来提高游客的满意度显得尤为必要。针对旅游业的需求,文中开发了一种国内热门旅游景点数据可视化系统,使用Selenium爬虫爬取携程网热门旅游景点数据,将爬取好的数据保存为CSV文件,再通...在大数据时代,旅游市场竞争日益加剧,使用大数据分析来提高游客的满意度显得尤为必要。针对旅游业的需求,文中开发了一种国内热门旅游景点数据可视化系统,使用Selenium爬虫爬取携程网热门旅游景点数据,将爬取好的数据保存为CSV文件,再通过Navicat for MySQL工具导入MySQL数据库中,然后利用Pandas进行数据清洗及数据分析,最后通过Flask+ECharts+Bootstrap搭建可视化界面,将可视化分析结果展示出来。系统具有较为强大的功能、良好的互动性,满足了旅游业和游客的实际需求。展开更多
Distributed secure quantum machine learning （DSQML） enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server with the privacy data preserved. More...Distributed secure quantum machine learning （DSQML） enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server with the privacy data preserved. Moreover, DSQML can be extended to a more general case that the client does not have enough data, and resorts both the remote quantum server and remote databases to perform the secure machi~ learning. Here we propose a DSQML protocol that the client can classify two-dimensional vectors to dif- ferent clusters, resorting to a remote small-scale photon quantum computation processor. The protocol is secure without leaking any relevant information to the Eve. Any eavesdropper who attempts to intercept and disturb the learning process can be noticed. In principle, this protocol can be used to classify high dimensional vectors and may provide a new viewpoint and application for future ＂big data＂.展开更多
文摘在大数据时代,旅游市场竞争日益加剧,使用大数据分析来提高游客的满意度显得尤为必要。针对旅游业的需求,文中开发了一种国内热门旅游景点数据可视化系统,使用Selenium爬虫爬取携程网热门旅游景点数据,将爬取好的数据保存为CSV文件,再通过Navicat for MySQL工具导入MySQL数据库中,然后利用Pandas进行数据清洗及数据分析,最后通过Flask+ECharts+Bootstrap搭建可视化界面,将可视化分析结果展示出来。系统具有较为强大的功能、良好的互动性,满足了旅游业和游客的实际需求。
基金supported by the National Natural Science Foundation of China(11474168 and 61401222)the Natural Science Foundation of Jiangsu Province(BK20151502)+1 种基金the Qing Lan Project in Jiangsu Provincea Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Distributed secure quantum machine learning （DSQML） enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server with the privacy data preserved. Moreover, DSQML can be extended to a more general case that the client does not have enough data, and resorts both the remote quantum server and remote databases to perform the secure machi~ learning. Here we propose a DSQML protocol that the client can classify two-dimensional vectors to dif- ferent clusters, resorting to a remote small-scale photon quantum computation processor. The protocol is secure without leaking any relevant information to the Eve. Any eavesdropper who attempts to intercept and disturb the learning process can be noticed. In principle, this protocol can be used to classify high dimensional vectors and may provide a new viewpoint and application for future ＂big data＂.
