Social e-commerce has been a hot topic in recent years, with the number of users increasing year by year and the transaction money exploding. Unlike traditional e-commerce, the main activities of social e-commerce are on social network apps. To classify sellers by the merchandise, this article designs and implements a social network seller classification scheme. We develop an app, which runs on the mobile phones of the sellers and provides the operating environment and automated assistance capabilities of social network applications. The app can collect social information published by the sellers during the assistance process, uploads to the server to perform model training on the data. We collect 38,970 sellers’ information, extract the text information in the picture with the help of OCR, and establish a deep learning model based on BERT to classify the merchandise of sellers. In the final experiment, we achieve an accuracy of more than 90%, which shows that the model can accurately classify sellers on a social network.
Abstract.As an open-source mobile platform, Android is facing with the severe problems of security and then the applications that running on this platform also confront with the same threats. This paper concludes the secure problems with which android applications are facing and gives a research on the current defense solutions. A security reinforcement system based on the Dex protection is proposed in order to defense the dynamic monitoring and modification. This system combines the static defense solution and dynamic defense solution, implements the purpose to tamper-proofing, anti-debugging for Android applications and improves the reliability and security of the software.
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