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Zuo H.-J.,Beijing University of Posts and Telecommunications | Zuo H.-J.,Hebei Normal University | Zuo H.-J.,Hebei Key Laboratory of Computational Mathematics and Applications | Qin S.-J.,Beijing University of Posts and Telecommunications | Song T.-T.,Beijing University of Posts and Telecommunications
International Journal of Quantum Information | Year: 2013

Recently, Yin et al. (Int. J. Quantum Inform. 10 (2012) 1250041) proposed a quantum proxy group signature scheme with χ-type entangled states. The scheme combines the properties of group signature and proxy signature. The study points out that the semi-honest Trent can give the forged signature under the assumption of this scheme. And, we find that even if the three parties honestly perform the scheme, the signature still cannot be realized with high efficiency. © 2013 World Scientific Publishing Company. Source


Zuo H.,Beijing University of Posts and Telecommunications | Zuo H.,Hebei Normal University | Zuo H.,Hebei Key Laboratory of Computational Mathematics and Applications | Huang W.,Beijing University of Posts and Telecommunications | Qin S.,Beijing University of Posts and Telecommunications
Physica Scripta | Year: 2013

An arbitrated quantum signature scheme, which is mainly applied in electronic-payment systems, is proposed and investigated. The χ-type entangled states are used for quantum key distribution and quantum signature in this protocol. Compared with previous quantum signature schemes which also utilize χ-type entangled states, the proposed scheme provides higher efficiency. Finally, we also analyze its security under various kinds of attacks. © 2013 The Royal Swedish Academy of Sciences. Source


Dai J.,Zhejiang University | Dai J.,Hebei Key Laboratory of Computational Mathematics and Applications | Wang W.,Zhejiang University | Mi J.-S.,Hebei Key Laboratory of Computational Mathematics and Applications | Mi J.-S.,Hebei Normal University
Information Sciences | Year: 2013

Interval-valued information systems are generalized models of single-valued information systems. Accuracy and roughness are employed to depict the uncertainty of a set under an attribute subset in a Pawlak rough set model based on equivalence classes. Information-theoretic measures of uncertainty for rough sets have also been proposed. However, there are few studies on uncertainty measurements for interval-valued information systems. This paper addresses the uncertainty measurement problem in interval-valued information systems. The concept of the similarity degree, based on the possible degree, is introduced. Consequently, the similarity relation between two interval objects are constructed by a given similarity rate θ. Based on the similarity relation, θ-similarity classes are defined. Under this definition, θ-accuracy and θ-roughness are given for interval-valued information systems, which are generalizations of the concepts accuracy and roughness for the equivalence relation-based rough set model. Moreover, an alternative uncertainty measure, called the θ-rough degree, is proposed. Theoretical studies and numerical experiments show that the proposed measures are effective and suitable for interval-valued information systems. © 2013 Elsevier Inc. All rights reserved. Source


Zhang S.,Hebei Normal University | Zhang S.,Hengshui University | Hu Q.,Tianjin University | Xie Z.,Tianjin University | And 2 more authors.
Neurocomputing | Year: 2015

The classical ridge regression technique makes an assumption that the noise is Gaussian. However, it is reported that the noise models in some practical applications do not satisfy Gaussian distribution, such as wind speed prediction. In this case, the classical regression techniques are not optimal. So we derive an optimal loss function and construct a new framework of kernel ridge regression technique for general noise model (N-KRR). The Augmented Lagrangian Multiplier method is introduced to solve N-KRR. We test the proposed technique on artificial data and short-term wind speed prediction. Experimental results confirm the effectiveness of the proposed model. © 2014 Elsevier B.V. Source


Hu Q.,Hebei Normal University | Hu Q.,Tianjin University | Zhang S.,Hebei Normal University | Zhang S.,Hengshui University | And 4 more authors.
Neural Networks | Year: 2014

Support vector regression (SVR) techniques are aimed at discovering a linear or nonlinear structure hidden in sample data. Most existing regression techniques take the assumption that the error distribution is Gaussian. However, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy Gaussian distribution, but a beta distribution, Laplacian distribution, or other models. In these cases the current regression techniques are not optimal. According to the Bayesian approach, we derive a general loss function and develop a technique of the uniform model of ν-support vector regression for the general noise model (N-SVR). The Augmented Lagrange Multiplier method is introduced to solve N-SVR. Numerical experiments on artificial data sets, UCI data and short-term wind speed prediction are conducted. The results show the effectiveness of the proposed technique. © 2014 Elsevier Ltd. Source

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