Search

The rapid development of technology has brought convenience for shooting and sharing images.However,with the ra-pid increase of image data,security problems such as leakage and tampering have frequently occurred.The application of image encryption technology is imminent.Especially the encryption of color images is in urgent need of improvement and development.The traditional encryption technology is mainly for data stream encryption,which is low in efficiency and large in calculation,ha-ving certain limitations.Based on the transformation domain encryption,the image is transformed from the spatial domain to the frequency domain for encryption,and then transformed to the spatial domain,which is a lossy encryption.Encryption based on chaos has a large key space,simple implementation,and fast encryption speed.However,multiple chaotic systems are generally required to enhance the security of encryption.For this reason,a simple and secure spatial encryption algorithm for the RGB three-channel color image is designed in this paper.The Logistic-Sine-Cosine mapping generates safer chaotic sequences.These chaotic sequences are used for four rounds of scrambling and spreading pixels.After a series of security analysis experiments,the security and effectiveness of the color image encryption algorithm based on Logistic-Sine-Cosine mapping have been verified.

This paper proposed a theoretical analysis model of the DCP cost and its assumption. Repatrolling the regular network topologies of Grid and Hexagonal, it reasoned the relationship of DCP cost, the DCP parameter (start TTL a, the fixed increment value b, and the patrol threshold L), network size, and the node density out. In order to validate the conclusion, this paper simulated it with a lot of AODV-DCP experiments in the JiST/SWANS simulation platform. It provides a necessary theoretical basis for setting the DCP parameter.

We are facing an era with annotated biological data rapidly and continuously generated. How to effectively incorporate new annotated data into the learning step is crucial for enhancing the performance of a bioinformatics prediction model. Although machine-learning-based methods have been extensively used for dealing with various biological problems, existing approaches usually train static prediction models based on fixed training datasets. The static approaches are found having several disadvantages such as low scalability and impractical when training dataset is huge. In view of this, we propose a dynamic learning framework for constructing query-driven prediction models. The key difference between the proposed framework and the existing approaches is that the training set for the machine learning algorithm of the proposed framework is dynamically generated according to the query input, as opposed to training a general model regardless of queries in traditional static methods. Accordingly, a query-driven predictor based on the smaller set of data specifically selected from the entire annotated base dataset will be applied on the query. The new way for constructing the dynamic model enables us capable of updating the annotated base dataset flexibly and using the most relevant core subset as the training set makes the constructed model having better generalization ability on the query, showing “part could be better than all” phenomenon. According to the new framework, we have implemented a dynamic protein-ligand binding sites predictor called OSML (On-site model for ligand binding sites prediction). Computer experiments on 10 different ligand types of three hierarchically organized levels show that OSML outperforms most existing predictors. The results indicate that the current dynamic framework is a promising future direction for bridging the gap between the rapidly accumulated annotated biological data and the effective machine-learning-based predictors. OSML web server and datasets are freely available at: http://www.csbio.sjtu.edu.cn/bioinf/OSML/ for academic use. [ABSTRACT FROM PUBLISHER]

In order to predict the likelihood of network risks accurately and in real-time, and help the administrators to manage network risks effectively, a Time-Varying Markov Model (TVMM) for real-time risk probability prediction was proposed. A real-time risk probability prediction method, which is able to predict the probability of network risk in future exactly with a real-time-updating-state probability transition matrix of TVMM, was presented. Combined with the theory of feature extraction and statistical learning, the model was used to calculate the risk probability of the network at different risk level in network attack environment. The result shows that TVMM has higher real-time, objectivity and accuracy than the traditional Markov model. [ABSTRACT FROM AUTHOR]