Lucene In Action 第二版 中文版 PDF扫描版，书中内容涉及Lucene3.0版本，非最新的Lucene版本，介意勿下

自定义Action继承ActionSupport实现简单登录，刚开始学习struts2记录一下。

压缩包包含Machine Learning in Action数据的中英文版本及书籍配套源代码，让我们一起走进ML的世界。

本书的作者Benjamin，是你希望能够从开源社区中闪现出来的一类专家。他是Gradle 的长期贡献者，也是多个Gradle 流行插件的作者。他拥有传播者和开发者两种角色。Benjamin 拥有一种很少见的能力，能够深入到一个特定开发挑战的核心细节中，我们并向用户解释这些细节。他在最近加入了Gradleware，并成为Gradle 开发团队的一员，我们感到非常的高兴。 我真心希望你能够喜欢这本书，也享受在工作中使用Gradle。祝你的软件交付过程变得有趣而且有效。

C++ Concurrency In Action -高清中文版C++ Concurrency In Action -高清中文版C++ Concurrency In Action -高清中文版C++ Concurrency In Action -高清中文版C++ Concurrency In Action -高清中文版C++ Concurrency In Action -高清中文版C++ Concurrency In Action -高清中文版C++ Concurrency In Action -高清中文版C++ Concurrency In Action -高清中文版

UCF Sport Action dataset 是一个不同的场景和角度人类运动视频数据集，从BBC和ESPN的电视新闻中采集，包含 150段 视频剪辑，视频分辨率为 720x480。运动种类及剪辑数为：跳水（14视频）、高尔夫秋千（18视频）、踢（20视频）、吊装（6视频）、骑马（12视频）、跑步（13视频）、滑板（12个视频）、秋千长椅（20视频）、摆动侧（13视频）、步行（22视频）。

C++ is the language of choice for thousands of applications and millions of lines of code. With C++/CLI, developers can integrate existing C++ code into the .NET platform without rewriting their applications. This book explores the C++/CLI syntax, teaches how to mix native C++ and managed .NET code, and shows how to integrate C++ with Windows Forms, WPF (Avalon), and WCF (Indigo). Imagine taking a C++-based program you’ve been using for a decade and giving it a snazzy new interface using Windows Presentation Foundation. How about making your old business applications talk to your new ones using Windows Communication Foundation. C++/CLI makes this–and more–possible. C++/CLI in Action shows you how to bridge the gap between your existing C++ code and the .NET platform. C++/CLI in Action will help you if: You’re hesitant to migrate to .NET because it means rewriting code in C# or VB. You have significant C++ expertise that you want to leverage in the .NET. You only need to use pieces of the .NET framework, such as Windows Forms or web services. There’s no fluff here. Designed for readers who already know C++, this book starts by teaching the unique aspects of the C++/CLI language. After a quick tour through the basics, readers work through examples of integrating standard C++ into the .NET-based applications and building programs that mix C++ and .NET code for maximum performance and efficiency.

3-D convolutional neural networks (3-D-convNets) have been very recently proposed for action recognition in videos, and promising results are achieved. However, existing 3- D-convNets has two “artificial” requirements that may reduce the quality of video analysis: 1) It requires a fixed-sized (e.g., 112×112) input video; and 2)most of the 3-D-convNets require a fixed-length input (i.e., video shots with fixed number of frames). To tackle these issues, we propose an end-to-end pipeline named Two-stream 3-D-convNet Fusion, which can recognize human actions in videos of arbitrary size and length using multiple features. Specifically, we decompose a video into spatial and temporal shots. By taking a sequence of shots as input, each stream is implemented using a spatial temporal pyramid pooling (STPP) convNet with a long short-term memory (LSTM) or CNN-E model, softmax scores of which are combined by a late fusion.We devise the STPP convNet to extract equal-dimensional descriptions for each variable-size shot, andwe adopt theLSTM/CNN-Emodel to learn a global description for the input video using these time-varying descriptions. With these advantages, our method should improve all 3-D CNN-based video analysis methods. We empirically evaluate our method for action recognition in videos and the experimental results show that our method outperforms the state-of-the-art methods (both 2-D and 3-D based) on three standard benchmark datasets (UCF101, HMDB51 and ACT datasets).

Keras中的两流CNN工具 在的基于骨架的动作识别中，提出了两流CNN，用于基于骨架的动作识别。 它将骨架序列映射到图像（坐标x，y，z到图像R，G，B）。 他们专门设计了骨架变压器模块，以自动重新排列和选择重要的骨架关节。 要求 Python3 凯拉斯 h5py matplotlib 麻木 网络架构 该网络主要由Skeleton Transformer ， ConvNet ， Feature Fusion和Classification四个模块组成。 两个流的输入分别是原始数据（x，y，z）和帧差。 如下图所示： 用法 function / data_generator.py ：生成两个流的输入numpy数组 layer / transformer ：Keras中的Skeleton Transformer工具层 网络/ ：褶皱有四只苍蝇，具有不同的特征融合方式 结果 模型 准确度（

R in Action, Second Edition presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze