The WebSocket protocol enables two-way communication between a client
running untrusted code running in a controlled environment to a
remote host that has opted-in to communications from that code. The
security model used for this is the Origin-based security model
commonly used by Web browsers. The protocol consists of an opening
handshake followed by basic message framing, layered over TCP. The
goal of this technology is to provide a mechanism for browser-based
applications that need two-way communication with servers that does
not rely on opening multiple HTTP connections (e.g. using
The Base16, Base32, and Base64 Data Encodings.pdf【软件开发】
This document specifies an Internet standards track protocol for the
Internet community, and requests discussion and suggestions for
improvements. Please refer to the current edition of the "Internet
Official Protocol Standards" (STD 1) for the standardization state
and status of this protocol. Distribution of this memo is unlimited.
算法设计与应用(Algorithm Design and Applications).pdf【软件开发】
Introducing a NEW addition to our growing library of computer science titles, Algorithm Design and Applications, by Michael T. Goodrich & Roberto Tamassia! Algorithms is a course required for all computer science majors, with a strong focus on theoretical topics. Students enter the course after gaining hands-on experience with computers, and are expected to learn how algorithms can be applied to a variety of contexts. This new book integrates application with theory.
Goodrich & Tamassia believe that the best way to teach algorithmic topics is to present them in a context that is motivated from applications to uses in society, computer games, computing industry, science, engineering, and the internet. The text teaches students about designing and using algorithms, illustrating connections between topics being taught and their potential applications, increasing engagement.
Want to learn to code? Want to learn C++? Struggling to follow your lecturer or books and tutorials written for experts? You're not alone. As a professional C++ developer and former Harvard teaching fellow, I know what you need to know to be a great C++ programmer, and I know how to teach it, one step at a time. I know where people struggle, and why, and how to make it clear. I cover every step of the programming process, including:
Getting the tools you need to program and how to use them
Basic language feature like variables, loops and functions
Applied Spatial Data Analysis with R (利用R进行空间分).pdf【地理信息系统GIS】
Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature.
从零开始的数据科学(Data Science from Scratch).pdf【Python开发】
从零开始的数据科学.Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.
Get a crash course in Python
Learn the basics of linear algebra, statistics, and probability?and understand how and when they're used in data science
Collect, explore, clean, munge, and manipulate data
Dive into the fundamentals of machine learning
Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
An Introduction to Statistical Learning with Applications in R.pdf【R语言开发】
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
域名解析系统（DNS）是互联网安全的许多薄弱环节之一；可以将应用程序所访问的主机对应的 IP 地址误导到其它地方。也就是说，会连接到错误的位置，从而引发中间人（man-in-the-middle）攻击等等。而 DNSSEC 扩展协议则通过为 DNS 信息建立一条加密的可信通道来解决这个漏洞。在正确地配置好 DNSSEC 后，应用程序将可以得到可靠的主机查询信息。通过关于尝试将 DNSSEC 更好地集成到 GNU C 库里的讨论，我们知道，确保 DNS 查询信息安全这件事并不是那么简单。
这篇文章整合了许多编写高性能的 Swift 代码的提示与技巧。文章的受众是编译器和标准库的开发者。
这篇文章中的一些技巧可以帮助提高你的 Swift 程序质量，并且可以减少代码中的容易出现的错误，使代码更具可读性。显式地标记出最终类和类的协议是两个显而易见的例子。然而，文章中描述的一些技巧是不符合规定的，扭曲的，仅仅解决由于编译器或者语言暂时限制的问题。文章中的建议来自多方面的权衡，例如程序运行时，二进制大小，代码可读性等等。 8小时前