身份验证建立身份，是证明凭证与所有者关联正确性的过程。 现在，大多数应用程序都需要身份验证。 基于文本的密码通常用于身份验证。 然而，通过基于文本的密码提供安全性有些不受欢迎。 图形密码身份验证 (GPA) 是替代文本密码以增强安全性的替代方法。 GPA 的动机是图像比文本密码更容易记住。 本文利用图像转换的概念，提出了一种新的混合图形认证模型，该模型侧重于基于图像点击和文本密码选择。 对于每个图像，所提出的模型中考虑了三种类型的变换，例如正常图像、镜像和移位图像。 即使对于密码，也基于登录会话时显示的图像转换应用这三种类型的转换。 开发所提出的模型的目的是通过结合图像转换和基于转换后的图像更改密码的原理来提供安全性和可用性。 每个转换图像的密码的根本变化加强了所提出的混合图形认证系统的安全性，并且还提供了对肩冲浪攻击、猜测攻击等的抵抗力。

机器学期经典教材

介绍 Eclipse Graphical Editing Framework (GEF)插件开发.

学习labview的好东西，外国人写的，非常细致非常有意思

经典著作，不用多介绍了。 Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

概率图模型，全书1000多页，高清pdf版，注意，不是扫描版。 此书是一切有志研究机器学习或数据挖掘的同学必看的一本书，涵盖了所有概率的图模型，包括马尔科夫逻辑网，贝叶斯网络，等等很多很多。非常珍贵的资源

概率图模型是机器学习中的一种技术，它利用图论的概念来简明地表示和优化预测数据问题中的值。 图形模型为我们提供了在数据中寻找复杂模式的技术，并广泛应用于语音识别、信息提取、图像分割和基因调控网络建模等领域。 本书从概率论和图论的基础出发，接着讨论各种模型和推理算法。讨论了所有不同类型的模型以及创建和修改模型的代码示例，并对它们运行了不同的推理算法。有一整章将继续介绍朴素的贝叶斯模型和隐藏的马尔可夫模型。这些模型已经用实际例子进行了深入的讨论。

《Modeling Color Difference for Visualization Design》，该论文是2017年的一篇最佳会议论文，对可视化中的色差进行建模，比较有创新性，作为图形学课程报告，由于我专业不是图形方向，对这个不太了解，一开始入手很难理解，花了几天啃出来的重要内容都在报告中说明了~

BGGM：贝叶斯高斯图形模型 R包BGGM提供了用于在高斯图形模型（GGM）中进行贝叶斯推理的工具。 这些方法围绕用于贝叶斯推断的两种通用方法进行组织：（1）估计和（2）假设检验。 关键区别在于，前者着眼于后验或后验预测分布（Gelman，Meng和Stern，1996年；见Rubin 1984年的第5节），而后者着眼于与贝叶斯因子的模型比较（Jeffreys 1961年； Kass and Raftery（1995）。 什么是高斯图形模型？ 高斯图形模型捕获了一组变量之间的条件（非）依赖关系。 这些是成对关系（部分相关性），用于控制模型中所有其他变量的影响。 应用领域 高斯图形模型被用于各种科学领域，包括（但不限于）经济学（Millington和Niranjan 2020），气候科学（Zerenner等人，2014），遗传学（Chu等人，2009）和心理学（Rodriguez等人，

Probabilistic graphical models is a technique in machine learning that uses the concepts of graph theory to concisely represent and optimally predict values in our data problems. Graphical models gives us techniques to find complex patterns in the data and are widely used in the field of speech recognition, information extraction, image segmentation, and modeling gene regulatory networks. This book starts with the basics of probability theory and graph theory, then goes on to discuss various models and inference algorithms. All the different types of models are discussed along with code examples to create and modify them, and also run different inference algorithms on them. There is an entire chapter that goes on to cover Naive Bayes model and Hidden Markov models. These models have been thoroughly discussed using real-world examples.