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Progressive Web Apps with React aims to give you everything you need to know about the future of web development. Progressive Web Apps (PWAs) are becoming more and more common for companies looking to leverage the best the web can offer, powered by cutting-edge technologies that bridge the gap between web apps and native apps. In this book, we'll leverage the power of the popular JavaScript library React.js to create a fast and functional UI. Then, we'll add Progressive Web App features such as push notifications and instant loading, using revolutionary new web technology. Finally, we'll streamline our app's performance and look at how to best measure its speed. By the end of this book, you will feel comfortable with both React and PWAs, and be ready for the future of the web.

Mastering Machine Learning with Python in Six Steps A Practical Implementation Guide to Predictive Data Analytics Using Python

H2O is software for machine learning and data analysis. Wanting to see what other magic deep learning could perform was what personally led me to H2O (though it does more than that: trees, linear models, unsupervised learning, etc.), and I was immediately impressed.

This book will cover the use of Vue.js 2. Vue can be used as both a frontend framework by including a JS file and also a backend framework with Node.js. The book was written using the frontend version of the framework—although it will be pointed out that it could easily be ported to using the node version if required, as the fundamentals of the framework remain the same in the two versions.

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Microsoft Captcha Decoder 验证码识别技术 Abstract: CAPTCHA is now almost a standard security technology. The most widely used CAPTCHAs rely on the sophisticated distortion of text images rendering them unrecognisable to the state of the art of pattern recognition techniques, and these text-based schemes have found widespread applications in commercial websites. The state of the art of CAPTCHA design suggests that such text-based schemes should rely on segmentation resistance to provide security guarantee, as individual character recognition after segmentation can be solved with a high success rate by standard methods such as neural networks. In this paper, we analyse the security of a text-based CAPTCHA designed by Microsoft and deployed for years at many of their online services including Hotmail, MSN and Windows Live. This scheme was designed to be segmentation-resistant, and it has been well studied and tuned by its designers over the years. However, our simple attack has achieved a segmentation success rate of higher than 90% against this scheme. It took on average ~80 ms for the attack to completely segment a challenge on a desktop computer with a 1.86 GHz Intel Core 2 CPU and 2 GB RAM. As a result, we estimate that this Microsoft scheme can be broken with an overall (segmentation and then recognition) success rate of more than 60%. On the contrary, its design goal was that "automatic scripts should not be more successful than 1 in 10,000" attempts (i.e. a success rate of 0.01%). For the first time, we show that a CAPTCHA that is carefully designed to be segmentation-resistant is vulnerable to novel but simple attacks. Our results show that it is not a trivial task to design a CAPTCHA scheme that is both usable and robust.

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