The decentralized cryptocurrency which was based on block chain has been thought the most successful one in history.In the system,public keys were used as the users’ accounts which guaranteed the anonymity in real transactions.However,all the transaction information was recorded in the block chain,it was a potential threat for users’ privacy which might leak the payment information.Moreover,to avoid double-spending,it was agreed that the transaction on the target block was valid only if another k blocks were generated after the target one.The long waiting time reduced the efficiency of the payment system.A model of payment system based on a proxy-cryptocurrency was proposed,and a solution based on blind signature techniques was proposed.The scheme introduced a proxy in the payment phase,by which transaction confirmation time could be reduced and the transaction efficiency could be improved.Meanwhile,the system implements better anonymity,namely as the privacy protection function. 相似文献
Sensor-based activity recognition (AR) depends on effective feature representation and classification. However, many recent studies focus on recognition methods, but largely ignore feature representation. Benefitting from the success of Convolutional Neural Networks (CNN) in feature extraction, we propose to improve the feature representation of activities. Specifically, we use a reversed CNN to generate the significant data based on the original features and combine the raw training data with significant data to obtain to enhanced training data. The proposed method can not only train better feature extractors but also help better understand the abstract features of sensor-based activity data. To demonstrate the effectiveness of our proposed method, we conduct comparative experiments with CNN Classifier and CNN-LSTM Classifier on five public datasets, namely the UCIHAR, UniMiB SHAR, OPPORTUNITY, WISDM, and PAMAP2. In addition, we evaluate our proposed method in comparison with traditional methods such as Decision Tree, Multi-layer Perceptron, Extremely randomized trees, Random Forest, and k-Nearest Neighbour on a specific dataset, WISDM. The results show our proposed method consistently outperforms the state-of-the-art methods.
