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A Novel Transfer Learning-Based Algorithm for Detecting Violence Images
Yuyan Meng,Deyu Yuan,Shaofan Su,Yang Ming 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.6
Violence in the Internet era poses a new challenge to the current counter-riot work, and according to research and analysis, most of the violent incidents occurring are related to the dissemination of violence images. The use of the popular deep learning neural network to automatically analyze the massive amount of images on the Internet has become one of the important tools in the current counter-violence work. This paper focuses on the use of transfer learning techniques and the introduction of an attention mechanism to the residual network (ResNet) model for the classification and identification of violence images. Firstly, the feature elements of the violence images are identified and a targeted dataset is constructed; secondly, due to the small number of positive samples of violence images, pre-training and attention mechanisms are introduced to suggest improvements to the traditional residual network; finally, the improved model is trained and tested on the constructed dedicated dataset. The research results show that the improved network model can quickly and accurately identify violence images with an average accuracy rate of 92.20%, thus effectively reducing the cost of manual identification and providing decision support for combating rebel organization activities.
A Testbed of Performance Evaluation for Fingerprint Based WLAN Positioning System
( Wanlong Zhao ),( Shuai Han ),( Weixiao Meng ),( Deyue Zou ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.6
Fingerprint positioning is a main stream and key technique for seamless positioning systems. In this paper, we develop a performance evaluation testbed for fingerprint based Wireless Local Area Network (WLAN) positioning system. The testbed consists of positioning server, positioning terminal, Access Point (AP) units, positioning accuracy analysis system and testing scenarios. Different from other testbeds tended to focus on testing in same situation, in the proposed testbed, a couple of scenarios are set to test the positioning system including indoor and outdoor environments. Handset-side positioning mode and network-side positioning mode are provided simultaneously. Variety of motion models, such as static model, low-speed model and high-speed model are considered as well as different positioning algorithms. Finally, some implementation cases are analyzed to verify the credibility of the testbed.