本程序可以完成对以arpa语言模型文件格式保存的语言模型的建立，性能评估，包括如何计算交叉熵，如何计算困惑度perplexity.

作者：Microsoft Research AI首席科学家 - 邓力 俞栋 This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.

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).

Recog：识别框架 Recog是一个框架，用于通过将指纹与从各种网络探测器返回的数据进行匹配来识别产品，服务，操作系统和硬件。 Recog使从Web服务器标题，snmp系统描述字段等中提取有用的信息变得很容易。 Recog是开源的，请参阅文件以获取更多信息。 目录 安装 Recog由XML指纹文件和各种代码组成（大多数使用Ruby），这使得开发，测试和使用所包含的指纹变得容易。 为了使用随附的ruby代码，需要Ruby（2.31+）的最新版本，以及Rubygems和bundler gem。 这些依赖关系就绪后，使用以下命令获取最新的源代码并安装所有其他依赖关系。 $ git clone