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Image low-level information, such as color, texture, and shape, were generally dealt with separately and combined together gruffly. Their image-describing effect in image retrieval was weakened. This paper determines and extracts one group of texture elements from images to mainly express the image texture information and, during this procedure, the quantized HSV color information is added to develop the feature, Color Layer-Based Texture Elements Histogram (CLBTEH). Furthermore, Color Fuzzy Correlogram (CFC) is put forward and employed for further extraction of color features. The performance of the proposed approach is evaluated on different image databases, including Corel-1k, Corel-10k, and USPTex1.0, and it was found that the experimental results of the proposed approach are encouraging in comparison with similar algorithms. [ABSTRACT FROM AUTHOR]

The cyber security toolkit, CyberSecTK, is a simple Python library for preprocessing and feature extraction of cyber-security-related data. As the digital universe expands, more and more data need to be processed using automated approaches. In recent years, cyber security professionals have seen opportunities to use machine learning approaches to help process and analyze their data. The challenge is that cyber security experts do not have necessary trainings to apply machine learning to their problems. The goal of this library is to help bridge this gap. In particular, we propose the development of a toolkit in Python that can process the most common types of cyber security data. This will help cyber experts to implement a basic machine learning pipeline from beginning to end. This proposed research work is our first attempt to achieve this goal. The proposed toolkit is a suite of program modules, data sets, and tutorials supporting research and teaching in cyber security and defense. An example of use cases is presented and discussed. Survey results of students using some of the modules in the library are also presented. [ABSTRACT FROM AUTHOR]