浅析电子商务中的安全问题

随着现今社会互联网、个人终端技术的飞越发展,电子商务越来越多的出现在人们的生活与工作中,这种全新的商务模式具有便捷、高效率、低成本的特点.同时,这种全新的商务模式对管理水平、信息传递技术都提出了更高的要求,其中安全的重要性尤为突出.     

发电机电气制动方法及其应用研究

利用Matlab对水轮发电机不同别动方法进行了仿真,计算出了不同水轮发电机组在不同的投入转速和不同的制动电流下的制动时间,为水轮发电机电气制动方案的设计以及选择最佳的制动参数提供了重要的依据.     

职业院校计算机网络教学的思考

为提高教学质量、保证学生后续课程的顺利学习,本文从计算机网络课程本身出发,并结合职业院校学生的特点,分别从讲授内容的整合、教学方法的改进、教学手段的更新和实验的精心设计四个方面进行了研究和探讨,强调培养学生解决问题的应用能力,提高教学质量,教学效果.     

The two-equal-disjoint path cover problem of Matching Composition Network

Embedding of paths have attracted much attention in the parallel processing. Many-to-many communication is one of the most central issues in various interconnection networks. A graph G is globally two-equal-disjoint path coverable if for any two distinct pairs of vertices (u,v) and (w,x) of G, there exist two disjoint paths P and Q satisfied that (1) P (Q, respectively) joins u and v (w and x, respectively), (2) |P|=|Q|, and (3) V(PQ)=V(G). The Matching Composition Network (MCN) is a family of networks which two components are connected by a perfect matching. In this paper, we consider the globally two-equal-disjoint path cover property of MCN. Applying our result, the Crossed cube CQ_n, the Twisted cube TQ_n, and the Möbius cube MQ_n can all be proven to be globally two-equal-disjoint path coverable for n5.     

Document recommendation for knowledge sharing in personal folder environments

Sharing sustainable and valuable knowledge among knowledge workers is a fundamental aspect of knowledge management. In organizations, knowledge workers usually have personal folders in which they organize and store needed codified knowledge (textual documents) in categories. In such personal folder environments, providing knowledge workers with needed knowledge from other workers’ folders is important because it increases the workers’ productivity and the possibility of reusing and sharing knowledge. Conventional recommendation methods can be used to recommend relevant documents to workers; however, those methods recommend knowledge items without considering whether the items are assigned to the appropriate category in the target user’s personal folders. In this paper, we propose novel document recommendation methods, including content-based filtering and categorization, collaborative filtering and categorization, and hybrid methods, which integrate text categorization techniques, to recommend documents to target worker’s personalized categories. Our experiment results show that the hybrid methods outperform the pure content-based and the collaborative filtering and categorization methods. The proposed methods not only proactively notify knowledge workers about relevant documents held by their peers, but also facilitate push-mode knowledge sharing.     

Social network,social trust and shared goals in organizational knowledge sharing

The aim of our study was to further develop an understanding of social capital in organizational-knowledge-sharing. We first developed a measurement tool and then a theoretical framework in which three social capital factors (social network, social trust, and shared goals) were combined with the theory of reasoned action; their relationships were then examined using confirmatory factoring analysis. We then surveyed of 190 managers from Hong Kong firms, we confirm that a social network and shared goals significantly contributed to a person's volition to share knowledge, and directly contributed to the perceived social pressure of the organization. The social trust has however showed no direct effect on the attitude and subjective norm of sharing knowledge.     

Hamiltonian-connected graphs

For a simple graph G, let . In this paper, we prove that if NCD(G)≥|V(G)|, then either G is Hamiltonian-connected, or G belongs to a well-characterized class of graphs. The former results by Dirac, Ore and Faudree et al. are extended.     

Application classification using packet size distribution and port association

Traffic classification is an essential part in common network management applications such as intrusion detection and network monitoring. Identifying traffic by looking at port numbers is only suitable to well-known applications, while signature-based classification is not applicable to encrypted messages. Our preliminary observation shows that each application has distinct packet size distribution (PSD) of the connections. Therefore, it is feasible to classify traffic by analyzing the variances of packet sizes of the connections without analyzing packet payload. In this work, each connection is first transformed into a point in a multi-dimensional space according to its PSD. Then it is compared with the representative points of pre-defined applications and recognized as the application having a minimum distance. Once a connection is identified as a specific application, port association is used to accelerate the classification by combining it with the other connections of the same session because applications usually use consecutive ports during a session. Using the proposed techniques, packet size distribution and port association, a high accuracy rate, 96% on average, and low false positive and false negative rates, 4–5%, are achieved. Our proposed method not only works well for encrypted traffic but also can be easily incorporated with a signature-based method to provide better accuracy.     

Evolving Least Squares Support Vector Machines for Stock Market Trend Mining

In this paper, an evolving least squares support vector machine (LSSVM) learning paradigm with a mixed kernel is proposed to explore stock market trends. In the proposed learning paradigm, a genetic algorithm (GA), one of the most popular evolutionary algorithms (EAs), is first used to select input features for LSSVM learning, i.e., evolution of input features. Then, another GA is used for parameters optimization of LSSVM, i.e., evolution of algorithmic parameters. Finally, the evolving LSSVM learning paradigm with best feature subset, optimal parameters, and a mixed kernel is used to predict stock market movement direction in terms of historical data series. For illustration and evaluation purposes, three important stock indices, S&P 500 Index, Dow Jones Industrial Average (DJIA) Index, and New York Stock Exchange (NYSE) Index, are used as testing targets. Experimental results obtained reveal that the proposed evolving LSSVM can produce some forecasting models that are easier to be interpreted by using a small number of predictive features and are more efficient than other parameter optimization methods. Furthermore, the produced forecasting model can significantly outperform other forecasting models listed in this paper in terms of the hit ratio. These findings imply that the proposed evolving LSSVM learning paradigm can be used as a promising approach to stock market tendency exploration.     

CoBTx-Net: A model for business collaboration reliability verification

Collaborative business process can become unreliable when business partners collaborate in a peer- based fashion without central control. Therefore, reliability checking becomes an important issue that needs to be dealt with for any generic solution in managing business collaboration. In this paper, we propose a novel Choreographical Business Transaction Net (CoBTx-Net) to model collaborative business process and to manage the collaboration by individual participants. Furthermore three reliability properties named Time-embedded dead marking freeness, Inter-organizational dead marking freeness, and Collaborative soundness are defined based on CoBTx-Net to verify (1) the violation of time constraint, (2) collaborative logic conflicts, and (3) the improper termination from individual organizations.