Abstract—Although there has been substantial research in software analytics for effort estimation in traditional software projects, little work has been done for estimation in agile projects, especially estimating user stories or issues. Story points are the most common unit of measure used for estimating the effort involved in implementing a user story or resolving an issue. In this paper, we offer for the first time a comprehensive dataset for story points-based estimation that contains 23,313 issues from 16 open source projects. We also propose a prediction model for estimating story points based on a novel combination of two powerful deep learning architectures: long short-term memory and recurrent highway network. Our prediction system is endto- end trainable from raw input data to prediction outcomes without any manual feature engineering. An empirical evaluation demonstrates that our approach consistently outperforms three common effort estimation baselines and two alternatives in both Mean Absolute Error and the Standardized Accuracy.

包含Markéta Dubská的两篇paper：《Real Projective Plane Mapping for Detection of Orthogonal Vanishing Points》《PClines – Line Detection Using Parallel Coordinates》以及matlab测试代码

Distinctive Image Features from Scale-Invariant Keypoints译文

利用matlab，在可视化图像中，选择一个或多个点，通过查看、提取其（X, Y, Z）信息，以进行后续分析、检验等工作。 函数实现坐标信息提取，并生成一个包含该坐标信息的变量。

matlab程序，主要进行相机标定，基于灭点标定的简单实例，适用于初学者理解原理。 是一个很好的例程，首先需要给出灭点。

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天池FashionAI全球挑战赛—服饰属性标签识别 总决赛TOP5的答辩PPT

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