Separable Reversible Data Hiding in Encrypted Images Based on Two-Dimensional Histogram Modification

An efficient method of completely separable reversible data hiding in encrypted images is proposed. The cover image is first partitioned into nonoverlapping blocks and specific encryption is applied to obtain the encrypted image. Then, image difference in the encrypted domain can be calculated based on the homomorphic property of the cryptosystem. The data hider, who does not know the original image content, may reversibly embed secret data into image difference based on two-dimensional difference histogram modification. Data extraction is completely separable from image decryption; that is, data extraction can be done either in the encrypted domain or in the decrypted domain, so that it can be applied to different application scenarios. In addition, data extraction and image recovery are free of any error. Experimental results demonstrate the feasibility and efficiency of the proposed scheme

The Optimal Confidence Intervals for Agricultural Products’ Price Forecasts Based on Hierarchical Historical Errors

With the levels of confidence and system complexity, interval forecasts and entropy analysis can deliver more information than point forecasts. In this paper, we take receivers’ demands as our starting point, use the trade-off model between accuracy and informativeness as the criterion to construct the optimal confidence interval, derive the theoretical formula of the optimal confidence interval and propose a practical and efficient algorithm based on entropy theory and complexity theory. In order to improve the estimation precision of the error distribution, the point prediction errors are STRATIFIED according to prices and the complexity of the system; the corresponding prediction error samples are obtained by the prices stratification; and the error distributions are estimated by the kernel function method and the stability of the system. In a stable and orderly environment for price forecasting, we obtain point prediction error samples by the weighted local region and RBF (Radial basis function) neural network methods, forecast the intervals of the soybean meal and non-GMO (Genetically Modified Organism) soybean continuous futures closing prices and implement unconditional coverage, independence and conditional coverage tests for the simulation results. The empirical results are compared from various interval evaluation indicators, different levels of noise, several target confidence levels and different point prediction methods. The analysis shows that the optimal interval construction method is better than the equal probability method and the shortest interval method and has good anti-noise ability with the reduction of system entropy; the hierarchical estimation error method can obtain higher accuracy and better interval estimation than the non-hierarchical method in a stable system

The Applications and Complexity Analysis Based on Network Embedding Behaviors under Evolutionary Game Framework

In this paper, we use the dynamic mechanism of biological evolution to simulate the enterprises’ bounded rational game. We construct game models of network embedding behaviors of horizontal and vertical enterprises in supply chain, explain the repeated games of random pairs of enterprises by replication dynamic differential equations, study the characteristics and evolution trend of this flow, conduct simulation experiments, clarify the evolution direction and law of network embedding strategy selection of supply chain enterprises, and discuss the stable state of evolutionary game and its dynamic convergence process. The results show that the probability of supply chain enterprises choosing a network embedding strategy is related to the enterprises’ special assets investment cost, cooperation cost, network income, and cooperation benefits. Supply chain enterprises should reduce the special assets investment cost and cooperation cost, maximize network income and cooperation income, narrow the gap between the extra-cooperation profit and the current cooperation profit, and restrain them from violating cooperation contracts or taking opportunistic actions

Evolutionary Games in the Agricultural Product Quality and Safety Information System: A Multiagent Simulation Approach

This paper aims at identifying the key factors to maintain the quality and safety of agricultural products in the agricultural product quality and safety information system (APQSIS). Based on the theoretical framework of information entropy and complexity, this paper uses the dynamic evolutionary game model and the multiagent modeling and simulation to discuss the APQSIS agents’ equilibrium strategies and the effects of their interactive behaviors on the APQSIS evolutionary stability with asymmetric information. The results show that the governmental supervision and intermediary organizations are significant to assuring agricultural product quality and safety (APQS) as well as the effective transmission of APQS information in stable environments with low complexity

Evolutionary Games in the Agricultural Product Quality and Safety Information System: A Multiagent Simulation Approach

This paper aims at identifying the key factors to maintain the quality and safety of agricultural products in the agricultural product quality and safety information system (APQSIS). Based on the theoretical framework of information entropy and complexity, this paper uses the dynamic evolutionary game model and the multiagent modeling and simulation to discuss the APQSIS agents’ equilibrium strategies and the effects of their interactive behaviors on the APQSIS evolutionary stability with asymmetric information. The results show that the governmental supervision and intermediary organizations are significant to assuring agricultural product quality and safety (APQS) as well as the effective transmission of APQS information in stable environments with low complexity