No abstract

With the innovation and development of science and technology, the convenience and intelligence of home life has become a trend. This intelligent drying rack system based on the Internet of Things uses the OneNET cloud platform as the information port, and selects the STM32F103C8T6 single-chip microcomputer as the main control chip to read, judge and output the execution signal from the signal collected by the sensor. The smart drying rack can be operated manually and remotely through the touch screen, related hardware circuits and mobile phone APP, which can control the extension and recycling of the clothes drying rod, and the automatic drying and storage of clothes. It is convenient to use and improves the intelligent level of the drying rack. The problem that the drying rack cannot dry the wet clothes and needs to be stored manually is solved.

No abstract

Android has become the most popular mobile intelligent operating system with its open platform, diverse applications, and excellent user experience. However, at the same time, more and more attackers take Android as the primary target. The application store, which is the main download source for users, still does not have a complete security authentication mechanism. Given the above problems, we designed an Android application classification model based on multiple semantic features. Firstly, we use analysis tools to automatically extract the application’s dynamic and static features into the text document and use variance and chi-square tests to optimize the features. Combined with natural language processing (NLP), we transform the feature file into a two-dimensional matrix and use the convolution neural network (CNN) to learn features efficiently. Also, to make the model satisfy more application scenarios, we design a dynamic adjustment method according to user requirements, the number of features, and other indicators. The experimental results demonstrate that the detection accuracy of malware is 99.3921%. We also measure this model’s performance in detecting a malware family and benign application, with the classification accuracy of 99.5614% and 99.9046%, respectively.