STSIIML: Study on token shuffling under incomplete information based on machine learning

Transfer learning based cascaded deep learning network and mask recognition for COVID-19

The COVID-19 is still spreading today, and it has caused great harm to human beings. The system at the entrance of public places such as shopping malls and stations should check whether pedestrians are wearing masks. However, pedestrians often pass the system inspection by wearing cotton masks, scarves, etc. Therefore, the detection system not only needs to check whether pedestrians are wearing masks, but also needs to detect the type of masks. Based on the lightweight network architecture MobilenetV3, this paper proposes a cascaded deep learning network based on transfer learning, and then designs a mask recognition system based on the cascaded deep learning network. By modifying the activation function of the MobilenetV3 output layer and the structure of the model, two MobilenetV3 networks suitable for cascading are obtained. By introducing transfer learning into the training process of two modified MobilenetV3 networks and a multi-task convolutional neural network, the ImagNet underlying parameters of the network models are obtained in advance, which reduces the computational load of the models. The cascaded deep learning network consists of a multi-task convolutional neural network cascaded with these two modified MobilenetV3 networks. A multi-task convolutional neural network is used to detect faces in images, and two modified MobilenetV3 networks are used as the backbone network to extract the features of masks. After comparing with the classification results of the modified MobilenetV3 neural network before cascading, the classification accuracy of the cascading learning network is improved by 7%, and the excellent performance of the cascading network can be seen.

Collaborative Energy Price Computing Based on Sarima-Ann and Asymmetric Stackelberg Games

The energy trading problem in smart grids has been of great interest. In this paper, we focus on two problems: 1. Energy sellers’ inaccurate grasp of users’ real needs causes information asymmetry in transactions, making it difficult for energy sellers to develop more satisfactory pricing strategies for users based on those real needs. 2. The uneven variation of user demand causes the grid costs to increase. In this paper, we design a collaborative pricing strategy based on the seasonal autoregressive integrated moving average-artificial neural network (Sarima-Ann) and an asymmetric Stackelberg game. Specifically, we propose a dissatisfaction function for users and an incentive function for grid companies to construct a utility function for both parties, which introduces an incentive amount to achieve better results in equilibrating user demand while optimizing the transaction utility. In addition, we constructed a demand fluctuation function based on user demand data and introduced it into the game model to predict the demand by Sarima-Ann, which achieves better prediction accuracy. Finally, through simulation experiments, we demonstrate the effectiveness of our scheme in balancing demand and improving utility, and the superiority of our Sarima-Ann model in terms of forecasting accuracy. Specifically, the peak reduction can reach 94.1% and the total transaction utility increase can reach 4.6 × 107, and better results can be achieved by adjusting the incentive rate. Our Sarima-Ann model improves accuracy by 64.95% over Arima and 64.47% over Sarima under MAE metric evaluation, and also shows superior accuracy under other metrics evaluation.

A designated verifier multi-signature scheme in multi-clouds

Multi-cloud computing provides services by used different clouds simultaneously multi-signature can be used as the interactive technology between multi-cloud and users. However, the limited resources of some terminal devices make multi-signature, which based on bilinear map, is not suitable for multi-cloud computing environment. In addition, the signers are disclosure in multi-signature so there is the risk of attack. To solve this issues, this paper proposes a certificateless designated verifier multi-signature scheme based on multivariable public key cryptography (MPKC). Firstly, the formalized definition and security model of the proposed scheme are given. Secondly, it is proved that the proposed scheme is against adaptive chosen-message attacks. Finally, the analysis shows that the proposed scheme is more efficiency and suitable for multi-cloud. Moreover, the proposed scheme can hidding signature source to achieve privacy protection.