Xidian University

About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Computer science. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.

Proxy multi-signature scheme: a new type of proxy signature scheme

Abstract: Proxy signature schemes allow a proxy signer to generate a proxy signature on behalf of an original signer. However, since in previous proxy signature schemes a proxy signature is created on behalf of only one original signer, these schemes are referred to as proxy mono-signature schemes. A new type of proxy signature scheme is presented called the proxy multi-signature scheme in which a proxy signer can generate a proxy signature on behalf of two or more original signers.

Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

Abstract: Ternary change detection aims to detect changes and group the changes into positive change and negative change It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison Firstly, sparse autoencoder is used to transform log-ratio difference image into a suitable feature space for extracting key changes and suppressing outliers and noise And then the learned features are clustered into three classes, which are taken as the pseudo labels for training a CNN model as change feature classifier The reliable training samples for CNN are selected from the feature maps learned by sparse autoencoder with certain selection rules Having training samples and the corresponding pseudo labels, the CNN model can be trained by using back propagation with stochastic gradient descent During its training procedure, CNN is driven to learn the concept of change, and more powerful model is established to distinguish different types of changes Unlike the traditional methods, the proposed framework integrates the merits of sparse autoencoder and CNN to learn more robust difference representations and the concept of change for ternary change detection Experimental results on real datasets validate the effectiveness and superiority of the proposed framework

Multiobjective Cloud Workflow Scheduling: A Multiple Populations Ant Colony System Approach

Abstract: Cloud workflow scheduling is significantly challenging due to not only the large scale of workflow but also the elasticity and heterogeneity of cloud resources. Moreover, the pricing model of clouds makes the execution time and execution cost two critical issues in the scheduling. This paper models the cloud workflow scheduling as a multiobjective optimization problem that optimizes both execution time and execution cost. A novel multiobjective ant colony system based on a co-evolutionary multiple populations for multiple objectives framework is proposed, which adopts two colonies to deal with these two objectives, respectively. Moreover, the proposed approach incorporates with the following three novel designs to efficiently deal with the multiobjective challenges: 1) a new pheromone update rule based on a set of nondominated solutions from a global archive to guide each colony to search its optimization objective sufficiently; 2) a complementary heuristic strategy to avoid a colony only focusing on its corresponding single optimization objective, cooperating with the pheromone update rule to balance the search of both objectives; and 3) an elite study strategy to improve the solution quality of the global archive to help further approach the global Pareto front. Experimental simulations are conducted on five types of real-world scientific workflows and consider the properties of Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than both some state-of-the-art multiobjective optimization approaches and the constrained optimization approaches.

New Algorithms for Secure Outsourcing of Modular Exponentiations

Abstract: Modular exponentiations have been considered the most expensive operation in discrete-logarithm based cryptographic protocols. In this paper, we propose a new secure outsourcing algorithm for exponentiation modular a prime in the one-malicious model. Compared with the state-of-the-art algorithm [33], the proposed algorithm is superior in both efficiency and checkability. We then utilize this algorithm as a subroutine to achieve outsource-secure Cramer-Shoup encryptions and Schnorr signatures. Besides, we propose the first outsource-secure and efficient algorithm for simultaneous modular exponentiations. Moreover, we prove that both the algorithms can achieve the desired security notions.