从Web截图生成Web组件模板

Pattana Anunphop, P. Chongstitvatana
{"title":"从Web截图生成Web组件模板","authors":"Pattana Anunphop, P. Chongstitvatana","doi":"10.1145/3468784.3468787","DOIUrl":null,"url":null,"abstract":"AI-driven automation is the game-changer in this decade. The one concept that belongs to this domain is to simulate human working processes by using machine learning. An adaptation of this knowledge in web development is popularized topic in the web developer society. Moreover, Web Components, the new paradigm in software engineering practices in web development, becomes the new standard defined by World Wide Web Consortium (W3C). It is an essential building block for modularizing large and complex web applications into smaller pieces and then presenting them via the web browser on the user's computer or mobile. We combine knowledge between Computer Vision (CV) with deep learning and Web Components developer framework together to train the machine to recognize bounding boxes and category labels for each object of interest in an image. This paper introduces the methodology to automatically generate a website by neuron network model composite with many small web components. Our work's best result has a validation loss of 1.873, which can recognize the web object and transform it into the Web Components Template by React web framework.","PeriodicalId":341589,"journal":{"name":"The 12th International Conference on Advances in Information Technology","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Web Components Template Generation from Web Screenshot\",\"authors\":\"Pattana Anunphop, P. Chongstitvatana\",\"doi\":\"10.1145/3468784.3468787\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"AI-driven automation is the game-changer in this decade. The one concept that belongs to this domain is to simulate human working processes by using machine learning. An adaptation of this knowledge in web development is popularized topic in the web developer society. Moreover, Web Components, the new paradigm in software engineering practices in web development, becomes the new standard defined by World Wide Web Consortium (W3C). It is an essential building block for modularizing large and complex web applications into smaller pieces and then presenting them via the web browser on the user's computer or mobile. We combine knowledge between Computer Vision (CV) with deep learning and Web Components developer framework together to train the machine to recognize bounding boxes and category labels for each object of interest in an image. This paper introduces the methodology to automatically generate a website by neuron network model composite with many small web components. Our work's best result has a validation loss of 1.873, which can recognize the web object and transform it into the Web Components Template by React web framework.\",\"PeriodicalId\":341589,\"journal\":{\"name\":\"The 12th International Conference on Advances in Information Technology\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 12th International Conference on Advances in Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3468784.3468787\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 12th International Conference on Advances in Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3468784.3468787","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

人工智能驱动的自动化是这十年的游戏规则改变者。属于这个领域的一个概念是通过使用机器学习来模拟人类的工作过程。将这些知识应用到web开发中是web开发界的热门话题。此外,Web组件作为Web开发中软件工程实践的新范式,已成为W3C定义的新标准。它是将大型和复杂的web应用程序模块化成更小的部分,然后通过用户计算机或移动设备上的web浏览器呈现它们的基本构建块。我们将计算机视觉(CV)与深度学习和Web组件开发人员框架之间的知识结合在一起,训练机器识别图像中每个感兴趣对象的边界框和类别标签。本文介绍了一种由多个小网页组件组成的神经元网络模型自动生成网站的方法。我们的工作最好的结果是验证损失为1.873,它可以识别web对象并通过React web框架将其转换为web组件模板。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Web Components Template Generation from Web Screenshot
AI-driven automation is the game-changer in this decade. The one concept that belongs to this domain is to simulate human working processes by using machine learning. An adaptation of this knowledge in web development is popularized topic in the web developer society. Moreover, Web Components, the new paradigm in software engineering practices in web development, becomes the new standard defined by World Wide Web Consortium (W3C). It is an essential building block for modularizing large and complex web applications into smaller pieces and then presenting them via the web browser on the user's computer or mobile. We combine knowledge between Computer Vision (CV) with deep learning and Web Components developer framework together to train the machine to recognize bounding boxes and category labels for each object of interest in an image. This paper introduces the methodology to automatically generate a website by neuron network model composite with many small web components. Our work's best result has a validation loss of 1.873, which can recognize the web object and transform it into the Web Components Template by React web framework.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Privacy Preservation Techniques for Sequential Data Releasing OutViz: Visualizing the Outliers of Multivariate Time Series An Application of Evaluation of Human Sketches using Deep Learning Technique Investigation of SIFT and ORB descriptors for Indoor Maps Fusion for the Multi-agent mobile robots Computing Resource Estimation by using Machine Learning Techniques for ALICE O2 Logging System
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1