基于ASP技术的市场信息网站的实现

随着网络的发展,越来越多的人们倾向于在网上搜索信息。以建立一个提供市场信息的网站为例,介绍了如何使用ASP技术在Internet上建立一个信息系统,供用户通过WWW动态地访问Web数据库中的数据。     

会计信息化模式的架构——基于江苏省率先实现会计信息化的个案思考

会计信息化A-ASP模式包括内部A-ASP模式和外部A-ASP模式.内部A-ASP模式是在企业或组织总部所在地设立虚拟A-ASP,形成会计信息中心,所有异地授权用户均通过Internet访问会计信息中心,进行数据的录入、加工、处理和采集的模式;外部A-ASP模式是隶属于财政部门的A-ASP,以租用服务关系向用户提供服务应用系统的模式.     

高校网络管理与网络安全策略

随着校园网络规模的不断扩大 ,网络设备和网络用户的激增 ,有效的网络日常运行管理和网络安全防范显得尤其重要。本文论述了常规的高校网络所采用的路由信息和用户访问Web范围的IP控制 ,然后介绍了防止网络病毒和对非法电子邮件的阻断以及防火墙增强校园网络运行的安全性和控制管理。并针对这几个方面常用有效的网络管理手段进行探讨分析。     

兆维晓通与朗讯推出无线新感觉

近日,朗讯公司提出了全新的无线解决方案——RG1000,突破了传统有线连接电脑、拨号上网的方式,使用户可以在150米范围内支持多人同时访问Internet,在网络内部提供IIMBPS的高速网络互联。兆维晓通与朗讯结成战略性的合作伙伴,成为RG1000进驻中国的总供货商。     

浅谈电力系统自动化特点与应用

<正>电力系统自动化是电力系统一直以来力求的发展方向,它包括:发电控制的自动化,电力调度的自动化(输送电、配电)。对电能生产、传输和管理实现自动控制、自动调度和自动化管理。电力系统是一个地域分布辽阔,由发电厂、变电站、输配电网络和用户组成的统一调度和运行的复杂大系统。电力系统自动化的领域包括生产过程的自动检测、调节和控制,系统和元件的自动安全保护,网络信息的自动传输,系统生产的自动调度,以及企业的自动化经济管理等。电力系统自动化的主要目标是保证供电的电能质量(频率和     

对外经贸大学建构无线校园网案例分析

随着校园网络信息化的普及，校园内越来越要求尽可能方便、快速、移动式地使用网络，同时随着笔记本电脑的普及，越来越多的网络访问将走出有线网络的场合，加上如室外广场、大型教室、礼堂、会议室、图书馆、体育场馆等场所，也同样要求能够访问校园网络，这对于校园网的管理者和建设者来说，是亟需思考与解决的问题。     

利用网络加快商业企业发展

<正> 随着国际互联网(INTERNET)在全球范围内的迅速扩展,以为基础的企业网(INTRANET)技术也得到了广泛的应用。它能够为企业内部用户提供信息共享,实现管理、通信、协作、浏览、应用的一体化,并能提供目录、复制、安全与管理等网络服务。商业企业应该注重利用Internet和Intranet这两种先进的网络技术为自己的发展增添推动力。 一、先进的网络商业经营功能     

基于动态缓存的电子商务平台优化研究

本文主要就基于动态缓存的电子商务平台的优化设计和实现进行了研究。针对电子商务网站的特点和性能要求,在Web服务器前端设置动态缓存服务器,作为Web服务器的前置机,实现动态内容的缓存,加快了响应用户访问的速度;通过一定的替换和更新策略保证用户访问到最新的内容,对提高Web服务器峰值负载下的运行能力,减少访问动态内容的延迟时间起到优化作用。     

网络通信中路由器技术的分析研究

随着现代社会的Internet不断发展扩大，路由器应用范围越来越广。宽带路由器是为了给用户解决网络共享的问题，作为通信网络的枢纽，性能好坏直接影响着网络传输的效果及性能。本文介绍了路由器的分类和功能，着重对网络通信中路由器主要技术进行分析。     

网络管理经验点滴

一、通过访问路由器控制列表构造局域网防火墙为了对网络访问进行控制以及防止网络资源被非法入侵者破坏，首要因素就是如何禁止未授权的数据报文流进或流出内部网。目前大多数路由器都提供了一种基于报文过滤的访问控制列表（ACL），可以有效地控制数据报文的流进流出。CISCO公司路由器的数据包过滤特性，使局域网管理员能够控制数据包在局域网与广域网之间的流动，采取适当的控制手段可有效地防止外部用户对局域网的安全访问和限制网络流量；也可以限制局域网内的某些用户或设备使用网络资源。为了建立防火墙，实现对数据包通过网络接…     

Analyzing Risk Response Dynamics on the Web: The Case of Hurricane Katrina

Using a unique data set that documented the hourly web-surfing behavior of over 140,000 Internet users in five southeastern states in August 2005, we explore the dynamics of information gathering as Hurricane Katrina developed and then hit South Florida and the Northern Gulf Coast. Using both elementary statistical methods and advanced techniques from functional data analysis,^{( 1 )} we examine both how storm events (such as the posting of warnings) affected traffic to weather-related websites, and how this traffic varied across locations and by characteristics of the web user. A general finding is that spatial-temporal variation in weather-site web traffic generally tracked the timing and scale of the storm threat experienced by a given area. There was, however, considerable variation in this responsiveness. Residents in Florida counties that had been most directly affected by Hurricane Dennis just a month earlier, for example, displayed more active visitation rates than those who had been less affected. We also find evidence of a gender effect where male users displayed a disproportionately larger rate of visitation to weather sites given the onset of storm warnings than females. The implications of this work for the broader study of behavioral risk response dynamics during hazards are explored.     

Quality of Service Based Pricing Schemes for Content Sharing in Peer‐to‐Peer Networks

In this paper, we study quality‐of‐service (QoS) based pricing schemes that serve as incentive mechanisms to induce sharing behaviors in Peer‐to‐Peer (P2P) networks. We incorporate operational QoS metrics into users’ utility functions and demonstrate how they affect individual users’ content sharing decisions. Using a game‐theoretic model, our study reveals how organizations respond to the changes of operational QoS metrics in their design of pricing schemes for various business objectives at different stages of network evolution. Our results show that a higher upload capacity can foster rational sharing to start when the network is small; however, it also discourages sharing behaviors when the network becomes large. In order to induce a socially optimal behavior, a pricing scheme will not charge users for requesting content while compensating them for sharing content. Such compensation is found to increase faster with the network size when the network is large. In order to maximize the profit of a monopolistic provider, however, a pricing scheme will charge content requests with a positive price while providing less compensation to sharing users compared to the socially optimal scheme. When the network size is small, such compensation can be even negative, which implies that a monopolistic provider discourages content sharing when the network is small, but encourages it when the network becomes larger. In addition, we find that more information about peer upload capacity discourages peers to share.     

Finding symbolons for cyberspace: Addressing the issues of trust in electronic commerce

E-commerce is growing rapidly, but a majority of the Internet users is still hesitant to become involved in it. One big hurdle is lack of trust. This paper deals with trust in ecommerce and measures to increase it. It discusses one particular so-called web assurance service for business-to-consumer commerce in detail. It also analyses if such services remain viable in an era of more mature e-commerce, and how they should be adjusted to the business-to-business environment.     

在线负面口碑处理的专家识别方法研究

现有专家识别大多建立在专家知识能力的基础上，然而在负面口碑处理的专家识别中，仅考虑知识能力并不能满足各主体的价值需求。本文从资源角度理解专家识别，建立了专家识别资源映射框架，并依据此框架分析了专家识别的显性资源映射和隐性资源映射过程。除了考虑直接体现用户专业水平的知识能力，还考虑了专家参与的情感抚慰能力和互动程度能力，以此构建的人工神经网络模型在实验中表现出了良好的性能。     

网络舆情扰动下房地产投资者“追涨杀跌”行为扩散研究

一旦房地产投资者“追涨杀跌”行为发生大范围扩散,将诱发房地产市场的价格严重偏离甚至动荡。本文从传播学与行为金融学交叉视角,基于复杂网络理论构建了房地产投资者“追涨杀跌”行为的网络扩散模型,进而仿真分析了网络舆情与投资者行为偏好交互作用下房地产投资者“追涨杀跌”行为扩散的演化特征。研究表明：房地产投资者越偏向于随机建立关联,“追涨杀跌”行为越可能快速扩散;网络舆情因素在“追涨杀跌”行为扩散过程中发挥主导作用,同时对投资者行为偏好因素发挥较强的“强化效应”或“抑制效应”;当媒体权威性足够高或网民情绪足够乐观时,通过综合调节网络舆情和投资者行为偏好因素能够有效实现预防和限制“追涨杀跌”行为扩散的目的。     

User reward programs in online social media

Online social media (OSMs) have become a popular and growing Internet phenomenon, as exemplified by the millions of followers of websites like YouTube, Twitter, and Facebook. Given the Internet’s ease of access and the high degree of competition to attract users to these sites, a question arises as to whether OSMs should develop revenue-sharing programs as a way to reward their contributing users. We present an ex ante asymmetric duopoly OSM game, where heterogeneous users are either active or passive with respect to each OSM. The game includes two steps: First, the OSMs simultaneously announce their rewards for active users; and second, based on their preference, users choose their level of contribution with respect to each OSM. We show that this game has a unique Nash equilibrium in pure strategies, and we identify the conditions under which a symmetric equilibrium exists, despite the asymmetry between the OSMs. Moreover, at equilibrium, no user chooses to contribute content exclusively to the less favourable OSM, even when the more favourable firm shares a lower reward than the less favourable firm. Furthermore, in some circumstances, a higher asymmetry can diminish the net revenue of the more favourable firm and vice versa.     

在线社交网络虚假信息交互量最小化的边阻断策略研究

在线社交媒体的蓬勃发展改变了人们获取信息的模式,大量的信息通过社交平台传播,信息内容的真实性把关弱化,各类虚假信息依托社交媒体野蛮生长,网络空间治理,培育健康的网络生态意义重大。本文通过最小化用户之间的虚假信息交互量,研究社交网络中虚假信息传播路径的阻断策略。给定在线社交网络G=（V,E,P,H）,H表示用户之间信息交互量,已知虚假信息传播源集合S⊆V,虚假信息交互量最小化问题是从E中选取哪K条边,使得这些边被阻断之后,虚假信息在用户之间的交互总量最小。首先证明了该问题是NP-困难的,进而证明了问题的目标函数计算是#P-困难。其次,证明了该问题目标函数既不是次模函数也不是超模函数。再次,提出了两阶段贪婪算法（TSGA）来解决该问题,即先获取候选集合E^sa,然后选取阻断集合E'。最后,通过实际在线社交网络数据对模型和算法的有效性进行了分析,实验表明本文提出的算法比现有算法更加有效。     

Improved adaptive group testing algorithms with applications to multiple access channels and dead sensor diagnosis

We study group-testing algorithms for resolving broadcast conflicts on a multiple access channel (MAC) and for identifying the dead sensors in a mobile ad hoc wireless network. In group-testing algorithms, we are asked to identify all the defective items in a set of items when we can test arbitrary subsets of items. In the standard group-testing problem, the result of a test is binary—the tested subset either contains defective items or not. In the more generalized versions we study in this paper, the result of each test is non-binary. For example, it may indicate whether the number of defective items contained in the tested subset is zero, one, or at least two. We give adaptive algorithms that are provably more efficient than previous group testing algorithms. We also show how our algorithms can be applied to solve conflict resolution on a MAC and dead sensor diagnosis. Dead sensor diagnosis poses an interesting challenge compared to MAC resolution, because dead sensors are not locally detectable, nor are they themselves active participants. A preliminary version of this paper was presented at SPAA 2006.     

Fiber optical network design problems: A case for Turkey

In this paper, we consider problems originating from one of the largest Internet service providers operating in Turkey. The company mainly faces two different design problems: the green field design (area with no Internet access) and the copper field re-design (area with limited access over copper networks). In the green field design problem, the aim is to design a least cost fiber optical network that will provide high bandwidth Internet access from a given central station to a set of aggregated demand nodes. Such an access can be provided either directly by installing fibers or indirectly by utilizing passive splitters. Insertion loss, bandwidth level and distance limitations should simultaneously be considered in order to provide a least cost design to enable the required service level. In the re-design of the copper field application, the aim is to improve the current service level by augmenting the network with fiber optical wires, specifically by adding cabinets to copper rings in the existing infrastructure and by constructing direct fiber links from cabinets to distant demand nodes. Mathematical models are constructed for both problem specifications. Extensive computational results based on realistic data from Kartal (45 nodes) and Bakırköy (74 nodes) districts in Istanbul show that the proposed models are viable exact solution methodologies for moderate dimensions.     

Are crowds on the internet wiser than experts? The case of a stock prediction community

According to the “Wisdom of Crowds” phenomenon, a large crowd can perform better than smaller groups or few individuals. This article investigates the performance of share recommendations, which have been published by members of a stock prediction community on the Internet. Participants of these online communities publish buy and sell recommendations for shares and try to predict the stock market development. We collected unique field data on 10,146 recommendations that were made between May 2007 and August 2011 on one of the largest European stock prediction communities. Our results reveal that on an annual basis investments based on the recommendations of Internet, users achieve a return that is on average 0.59 % points higher than investments of professional analysts from banks, brokers and research companies. This means, that on average, investors are better off by trusting the crowd rather than analysts. We furthermore investigate how the postulated theoretical conditions of diversity and independence influence the performance of a large crowd on the Internet. While independent decisions can substantially improve the performance of the crowd, there is no evidence for the power of diversity in our data.