基于双曲线映射签名的高效可分电子现金系统

基于双曲线映射的签名设计了一种新型可分电子现金方案,方案中有效地加入了压缩支付和批处理支付,计算时间复杂度较小,从而使得系统的整体效率较高。另外,方案中无需可信第三方(TTP)的参与,因而系统的整体开销较小。方案的安全性基于q-SDK假设、计算离散对数困难性假设以及单向散列函数存在性假设。     

一种基于人工神经网络的快速超分辨率方法

图像的超分辨率是指利用一幅或者多幅的低分辨率图像,通过相应的算法来获得一幅对应的清晰的高分辨率图像.针对现有的基于学习的超分辨率方法低效率的问题,提出一种基于人工神经网络的快速超分辨率方法,该方法试图利用人工神经网络学习到高分辨率图像和低分辨率图像之间的函数关系,其理论基础来自于人工神经网络能够很好地求解流形学习中高维流形和低维流形之间的映射关系.     

基于模糊推理的多智能体强化学习

以电子市场智能定价问题为研究背景,提出基于模糊推理的多智能体强化学习算法(FI-MARL).在马尔科夫博弈学习框架下,将领域知识初始化为一个模糊规则集合,智能体基于模糊规则选择动作,并采用强化学习来强化模糊规则.该方法有效融合应用背景的领域知识,充分利用样本信息并降低学习空间维数,从而增强在线学习性能.在电子市场定价的...     

一种安全高效的Java操作系统IPC机制的设计与实现

安全高效的进程间通信机制是操作系统中实现进程间协同工作的关键.针对现有Java操作系统普遍使用的共享堆和Java远程过程调用(RMI)等进程间通信机制在安全性、高效性等方面的缺陷,面向Java操作系统的结构特点和进程间通信需求,设计和实现了一种新的安全、高效的进程间通信机制MSP.MSP机制通过区分小尺寸和大尺寸消息,...     

What affects information systems development team performance? An exploratory study from the perspective of combined socio-technical theory and coordination theory

Quality is an important factor in information systems development (ISD), and ISD team performance closely relates to quality. To better understand ISD teams, we empirically tested a model on ISD team performance by combining socio-technical theory and coordination theory. Using existing empirical studies and data collected from three well-known ISD companies in China, our research results identified influential characteristics of ISD team performance, and revealed similarities and differences between China’s ISD team performance and those in other countries. By the results, we find that knowledge sharing and major do not affect team performance. The compensation satisfaction to job performance is not significant in China, either. Our research provides suggestions for building and supporting ISD teams that could lead to performance improvements.     

Evaluating methods of investment project and optimizing models of portfolio selection in fuzzy uncertainty

This paper deals with the problems of both project valuation and portfolio selection under the assumption that the investment capitals and the net cash flows of the projects are fuzzy variables. Using the credibilistic expected value and the credibilistic lower semivariance of fuzzy variables, this paper proposes both the credibilistic return index and the credibilistic risk index, which are measures of investment return and investment risk with annuity form for evaluating single project. Moreover, a composite risk-return index for selecting the optimal investment strategy is also presented. Then, we set up a general project portfolio optimization model with fuzzy returns and two specific models: triangle and interval fuzzy returns. Furthermore, we provide two algorithms: the improved heuristic rules based on genetic algorithm and the traversal algorithm. Finally, two numerical examples are presented to illustrate the efficiency and the effectiveness of these proposed optimization methods.     

A hybrid trust‐enhanced collaborative filtering recommendation approach for personalized government‐to‐business e‐services

The information overload on the World Wide Web results in the underuse of some existing e‐government services within the business domain. Small‐to‐medium businesses (SMBs), in particular, are seeking “one‐to‐one'' e‐services from government in current highly competitive markets, and there is an imperative need to develop Web personalization techniques to provide business users with information and services specific to their needs, rather than an undifferentiated mass of information. This paper focuses on how e‐governments can support businesses on the problem of selecting a trustworthy business partner to perform reliable business transactions. In the business partner selection process, trust or reputation information is crucial and has significant influence on a business user's decision regarding whether or not to do business with other business entities. For this purpose, an intelligent trust‐enhanced recommendation approach to provide personalized government‐to‐business (G2B) e‐services, and in particular, business partner recommendation e‐services for SMBs is proposed. Accordingly, in this paper, we develop (1) an implicit trust filtering recommendation approach and (2) an enhanced user‐based collaborative filtering (CF) recommendation approach. To further exploit the advantages of the two proposed approaches, we develop (3) a hybrid trust‐enhanced CF recommendation approach (TeCF) that integrates both the proposed implicit trust filtering and the enhanced user‐based CF recommendation approaches. Empirical results demonstrate the effectiveness of the proposed approaches, especially the hybrid TeCF recommendation approach in terms of improving accuracy, as well as in dealing with very sparse data sets and cold‐start users. © 2011 Wiley Periodicals, Inc.     

The global solution for a class of systems of fractional nonlinear Schrödinger equations with periodic boundary condition

In this paper, we consider a class of systems of fractional nonlinear Schrödinger equations. We prove the existence and uniqueness of the global solution to the periodic boundary value problem by using the Faedo-Galërkin method.     

High sensitive and selective formaldehyde sensors based on nanoparticle-assembled ZnO micro-octahedrons synthesized by homogeneous precipitation method

Nanoparticle-assembled ZnO micro-octahedrons were synthesized by a facile homogeneous precipitation method. The ZnO micro-octahedrons are hexagonal wurtzite with high crystallinity. Abundant structure defects were confirmed on ZnO surface by photoluminescence. Gas sensors based on the ZnO micro-octahedrons exhibited high response, selectivity and stability to 1–1000 ppm formaldehyde at 400 °C. Especially, even 1 ppm formaldehyde could be detected with high response (S = 22.7). It is of interest to point out that formaldehyde could be easily distinguished from ethanol or acetaldehyde with a selectivity of about 3. The high formaldehyde response is mainly attributed to the synergistic effect of high contents of electron donor defects (Zn_i and V_O) and highly active oxygen species (O²⁻) on the ZnO surface.     

Stability analysis for discrete delayed Markovian jumping neural networks with partly unknown transition probabilities

This paper addresses the analysis problem of asymptotic stability for a class of uncertain neural networks with Markovian jumping parameters and time delays. The considered transition probabilities are assumed to be partially unknown. The parameter uncertainties are considered to be norm-bounded. A sufficient condition for the stability of the addressed neural networks is derived, which is expressed in terms of a set of linear matrix inequalities. A numerical example is given to verify the effectiveness of the developed results.