It is necessary to construct an effective trust model to build trust relationship between peers in peer-to-peer (P2P) network and enhance the security and reliability of P2P systems. The current trust models only fo...It is necessary to construct an effective trust model to build trust relationship between peers in peer-to-peer (P2P) network and enhance the security and reliability of P2P systems. The current trust models only focus on the consumers' evaluation to a transaction, which may be abused by malicious peers to exaggerate or slander the provider deliberately. In this paper, we propose a novel trust model based on mutual evaluation, called METrust, to suppress the peers' malicious behavior, such as dishonest evaluation and strategic attack. METrust considers the factors including mutual evaluation, similarity risk, time window, incentive, and punishment mechanism. The trust value is composed of the direct trust value and the recommendation trust value. In order to inhibit dishonest evaluation, both participants should give evaluation information based on peers' own experiences about the transaction while computing the direct trust value. In view of this, the mutual evaluation consistency factor and its time decay function are proposed. Besides, to reduce the risk of computing the recommendation trust based on the recommendations of friend peers, the similarity risk is introduced to measure the uncertainty of the similarity computing, while similarity is used to measure credibility. The experimental results show that METrust is effective, and it has advantages in the inhibition of the various malicious behaviors.展开更多
互动智能通信是人工智能与互动通信相结合的产物,是智能信息推拉(Intelligent Information Push-Pull,IIPP)技术的延伸和发展。通过实现互动智能通信,可以提高网络及数据库的智能水平,从根本上解决推送和拉取技术应用过程中所遇到的难...互动智能通信是人工智能与互动通信相结合的产物,是智能信息推拉(Intelligent Information Push-Pull,IIPP)技术的延伸和发展。通过实现互动智能通信,可以提高网络及数据库的智能水平,从根本上解决推送和拉取技术应用过程中所遇到的难题及如何从海量信息中提取有用信息,如何为不同用户提供个性化信息服务等问题。应用模糊综合评判法对用户个人偏好模型进行建模,提出用灰色关联度分析确定权重的方法,并用Java编程语言实现通过手机短信和电子邮件两种方式与用户进行互动,取得了良好的应用效果。展开更多
In digital radiographic systems, a tradeoff exists between image resolution (or blur) and noise characteristics. An imaging system may only be superior in one image quality characteristic while being inferior to anoth...In digital radiographic systems, a tradeoff exists between image resolution (or blur) and noise characteristics. An imaging system may only be superior in one image quality characteristic while being inferior to another in the other characteristic. In this work, a computer simulation model is presented that is to use mutual-information (MI) metric to examine tradeoff behavior between resolution and noise. MI is used to express the amount of information that an output image contains about an input object. The basic idea is that when the amount of the uncertainty associated with an object before and after imaging is reduced, the difference of the uncertainty is equal to the value of MI. The more the MI value provides, the better the image quality is. The simulation model calculated MI as a function of signal-to-noise ratio and that of resolution for two image contrast levels. Our simulation results demonstrated that MI associated with overall image quality is much more sensitive to noise compared to blur, although tradeoff relationship between noise and blur exists. However, we found that overall image quality is primarily determined by image blur at very low noise levels.展开更多
A new procedure of learning in Gaussian graphical models is proposed under the assumption that samples are possibly dependent.This assumption,which is pragmatically applied in various areas of multivariate analysis ra...A new procedure of learning in Gaussian graphical models is proposed under the assumption that samples are possibly dependent.This assumption,which is pragmatically applied in various areas of multivariate analysis ranging from bioinformatics to finance,makes standard Gaussian graphical models(GGMs) unsuitable.We demonstrate that the advantage of modeling dependence among samples is that the true discovery rate and positive predictive value are improved substantially than if standard GGMs are applied and the dependence among samples is ignored.The new method,called matrix-variate Gaussian graphical models(MGGMs),involves simultaneously modeling variable and sample dependencies with the matrix-normal distribution.The computation is carried out using a Markov chain Monte Carlo(MCMC) sampling scheme for graphical model determination and parameter estimation.Simulation studies and two real-world examples in biology and finance further illustrate the benefits of the new models.展开更多
基金supported by National Natural Science Foundation of China (No.60873231)Research Fund for the Doctoral Program of Higher Education (No.20093223120001)+2 种基金Science and Technology Support Program of Jiangsu Province (No.BE2009158)Natural Science Fund of Higher Education of Jiangsu Province(No.09KJB520010)Special Fund for Fast Sharing of Science Paper in Net Era by CSTD (No.2009117)
文摘It is necessary to construct an effective trust model to build trust relationship between peers in peer-to-peer (P2P) network and enhance the security and reliability of P2P systems. The current trust models only focus on the consumers' evaluation to a transaction, which may be abused by malicious peers to exaggerate or slander the provider deliberately. In this paper, we propose a novel trust model based on mutual evaluation, called METrust, to suppress the peers' malicious behavior, such as dishonest evaluation and strategic attack. METrust considers the factors including mutual evaluation, similarity risk, time window, incentive, and punishment mechanism. The trust value is composed of the direct trust value and the recommendation trust value. In order to inhibit dishonest evaluation, both participants should give evaluation information based on peers' own experiences about the transaction while computing the direct trust value. In view of this, the mutual evaluation consistency factor and its time decay function are proposed. Besides, to reduce the risk of computing the recommendation trust based on the recommendations of friend peers, the similarity risk is introduced to measure the uncertainty of the similarity computing, while similarity is used to measure credibility. The experimental results show that METrust is effective, and it has advantages in the inhibition of the various malicious behaviors.
文摘互动智能通信是人工智能与互动通信相结合的产物,是智能信息推拉(Intelligent Information Push-Pull,IIPP)技术的延伸和发展。通过实现互动智能通信,可以提高网络及数据库的智能水平,从根本上解决推送和拉取技术应用过程中所遇到的难题及如何从海量信息中提取有用信息,如何为不同用户提供个性化信息服务等问题。应用模糊综合评判法对用户个人偏好模型进行建模,提出用灰色关联度分析确定权重的方法,并用Java编程语言实现通过手机短信和电子邮件两种方式与用户进行互动,取得了良好的应用效果。
文摘In digital radiographic systems, a tradeoff exists between image resolution (or blur) and noise characteristics. An imaging system may only be superior in one image quality characteristic while being inferior to another in the other characteristic. In this work, a computer simulation model is presented that is to use mutual-information (MI) metric to examine tradeoff behavior between resolution and noise. MI is used to express the amount of information that an output image contains about an input object. The basic idea is that when the amount of the uncertainty associated with an object before and after imaging is reduced, the difference of the uncertainty is equal to the value of MI. The more the MI value provides, the better the image quality is. The simulation model calculated MI as a function of signal-to-noise ratio and that of resolution for two image contrast levels. Our simulation results demonstrated that MI associated with overall image quality is much more sensitive to noise compared to blur, although tradeoff relationship between noise and blur exists. However, we found that overall image quality is primarily determined by image blur at very low noise levels.
文摘A new procedure of learning in Gaussian graphical models is proposed under the assumption that samples are possibly dependent.This assumption,which is pragmatically applied in various areas of multivariate analysis ranging from bioinformatics to finance,makes standard Gaussian graphical models(GGMs) unsuitable.We demonstrate that the advantage of modeling dependence among samples is that the true discovery rate and positive predictive value are improved substantially than if standard GGMs are applied and the dependence among samples is ignored.The new method,called matrix-variate Gaussian graphical models(MGGMs),involves simultaneously modeling variable and sample dependencies with the matrix-normal distribution.The computation is carried out using a Markov chain Monte Carlo(MCMC) sampling scheme for graphical model determination and parameter estimation.Simulation studies and two real-world examples in biology and finance further illustrate the benefits of the new models.