在线招投标Web系统安全结构及关键技术的研究

分析了网上在线招投标系统的安全性需求，提出了一种新的用于在线招投标Web应用系统的四层安全体系结构，并定义了其各层次的功能，描述了其安全认证过程，解决了传统三层体系结构在信息系统的安全性上存在的问题和不足。同时就系统中的数据安全性、系统的认证等关键安全技术，综合应用加密和Hash算法，给出了一种在传输和存储过程中保护数据的保密性和完整性的设计方法，以防止在数据库的应用中数据被非法窃取和篡改；给出了一种基于网上在线招投标系统的不可否认数字签名认证方案，使得可在不暴露用户安全信息的前提下实现其身份认证。     

A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments

We describe a novel life-long learning approach for intelligent agents that are embedded in intelligent environments. The agents aim to realize the vision of ambient intelligence in intelligent inhabited environments (IIE) by providing ubiquitous computing intelligence in the environment supporting the activities of the user. An unsupervised, data-driven, fuzzy technique is proposed for extracting fuzzy membership functions and rules that represent the user's particularized behaviors in the environment. The user's learned behaviors can then be adapted online in a life-long mode to satisfy the different user and system objectives. We have performed unique experiments in which the intelligent agent has learned and adapted to the user's behavior, during a stay of five consecutive days in the intelligent dormitory (iDorm), which is a real ubiquitous computing environment test bed. Both offline and online experimental results are presented comparing the performance of our technique with other approaches. The results show that our proposed system has outperformed the other approaches, while operating online in a life-long mode to realize the ambient intelligence vision.     

在线招投标系统中的加密和数字签名技术

分析了在线招投标系统的基本过程和研究意义，给出了一种基于网上在线招投标系统的不可否认数字签名认证方案，使得可在不暴露用户安全信息的前提下实现其身份认证。方案包括了基于Rijndael算法的投标信息加密方法、基于SHA算法的投标信息数字摘要方法、基于RSA算法的数字摘要加密方法等，并对Rijndael算法、RSA算法的应用问题及系统的安全性进行了分析。实验结果表明了该方法的有效性。     

AdROSA—Adaptive personalization of web advertising

One of the greatest and most recent challenges for online advertising is the use of adaptive personalization at the same time that the Internet continues to grow as a global market. Most existing solutions to online advertising placement are based on demographic targeting or on information gained directly from the user. The AdROSA system for automatic web banner personalization, which integrates web usage and content mining techniques to reduce user input and to respect users’ privacy, is presented in the paper. Furthermore, certain advertising policies, important factors for both publishers and advertisers, are taken into consideration. The integration of all the relevant information is accomplished in one vector space to enable online and fully personalized advertising.     

改进Web应用性能方法及性能测试分析

动态网页的实现机制中,对用户的响应需要内部多个服务器的协作,延长了响应时间.通过对动态网页实现中某些步骤顺序的调整,可以在用户请求之前得到响应数据,当有用户请求的时候,服务器只需把已经生成的静态页面返回给用户即可,响应速度可得到较大的提高.根据软件性能工程理论,对此方法作了性能评价,然后对使用此方法的系统做了对比性的性能测试,根据对测试所得数据的分析,进一步验证了此方法在实际系统中的可行性.     

Incremental mining of information interest for personalized web scanning

Businesses and people often organize their information of interest (IOI) into a hierarchy of folders (or categories). The personalized folder hierarchy provides a natural way for each of the users to manage and utilize his/her IOI (a folder corresponds to an interest type). Since the interest is relatively long-term, continuous web scanning is essential. It should be directed by precise and comprehensible specifications of the interest. A precise specification may direct the scanner to those spaces that deserve scanning, while a specification comprehensible to the user may facilitate manual refinement, and a specification comprehensible to information providers (e.g. Internet search engines) may facilitate the identification of proper seed sites to start scanning. However, expressing such specifications is quite difficult (and even implausible) for the user, since each interest type is often implicitly and collectively defined by the content (i.e. documents) of the corresponding folder, which may even evolve over time. In this paper, we present an incremental text mining technique to efficiently identify the user's current interest by mining the user's information folders. The specification mined for each interest type specifies the context of the interest type in conjunctive normal form, which is comprehensible to general users and information providers. The specification is also shown to be more precise in directing the scanner to those sites that are more likely to provide IOI. The user may thus maintain his/her folders and then constantly get IOI, without paying much attention to the difficult tasks of interest specification and seed identification.     

Online support for commerce processes by web retailers

This paper examines the extent to which companies in various industries are using the World Wide Web and its associated technologies to conduct retail business. Using the framework of an Electronic Commerce Architecture (ECA), a variety of commercial Web sites are analyzed to determine which commerce processes are being supported online in each industry and each type of industry. The results of this study provide useful insight not only for researchers on the state of Web technology adoption and electronic commerce practices in industry, but also for companies seeking to derive competitive advantage through electronic commerce.     

Customer-Centric Strategic Planning: Integrating CRM in Online Business Systems

Customer Relationship Management (CRM) is increasingly found at the top of corporate agendas. Online companies in particular are embracing CRM as a major element of corporate strategy, because online technological applications permit a precise segmentation, profiling and targeting of customers, and the competitive pressures of the digital markets require a customer-centric corporate culture. The implementation of CRM systems in online organisation determines a complex restructuring of all organisational elements and processes. The strategic planning process will have to adapt to new customer-centric procedures. The present paper analyses the implementation process of a CRM system in online retail businesses and develops a model of the strategic planning function in a customer-centric context.     

支持在线学习的增量式极端随机森林分类器

提出了一种增量式极端随机森林分类器(incremental extremely random forest,简称IERF),用于处理数据流,特别是小样本数据流的在线学习问题.IERF算法中新到达的样本将被存储到相应的叶节点,并通过Gini系数来确定是否对当前叶节点进行分裂扩展,在给定有限数量,甚至是少量样本的情况下,I...     

Digital content recommender on the Internet

On the Web, where information is vast and users are numerous, personalization that aims to offer suitable information to suitable users is essential. To sustain their competitive advantage, portal sites attract many users' attention by supplying personalized content. Most Web content providers offer all users the same content, failing to satisfy individual users' needs. Providers should be able to offer suitable users suitable content with suitable speed. To do so, they must be able to identify customers, predict their interests, determine appropriate content, and deliver it in a personalized format during customers' online sessions. In this paper, the author presents a digital-content recommender system that suggests Web content, in this case news articles, based on a user's preference when he or she visits an Internet news site and reads the published articles. This recommender system creates a one-to-one relationship between the content provider and the user, raises the user's satisfaction, and increases loyalty toward the content provider.     

基于用户实时反馈的点击率预估算法

当前主流的在线广告点击率（CTR）预估算法主要通过机器学习方法从大规模日志数据中挖掘用户与广告间的相关性从而提升点击率预估精度,其不足之处在于没有充分考虑用户实时行为对CTR的影响。对大规模真实在线广告日志进行分析后发现,在会话中,用户CTR的动态变化和用户先前的反馈行为高度相关,不同的用户行为对用户实时CTR的影响不尽相同。基于上述分析结果,提出一种基于用户实时反馈的点击率预估算法。首先,从大规模真实在线广告日志数据中定量分析用户反馈和点击率预估精度的相关关系;然后,根据分析结果将用户的反馈行为特征化;最后,使用机器学习方法对用户的行为进行建模,并根据用户的反馈实时动态调整广告投放,从而提升在线广告系统的点击率预估精度。实验结果表明,用户实时反馈特征和用户点击率高度相关;相比于传统没有用户实时反馈信息的预测模型,该算法在测试集上对AUC（Area Under the Curve）和RIG（Relative Information Gain）指标提升分别为0.83%和6.68%。实验结果表明,用户实时反馈特征显著提高点击率预估的精度。     

社交网络用户在在线媒体中点播行为预测

在线媒体快速发展,为用户带来丰富多彩信息的同时,用户的参与也给在线媒体本身带来巨大的经济利益。因此,如何通过精确预测用户的偏好以增加在线媒体点击,成为一个学术界和工业界均关注的问题。现有的预测方法主要是借助用户个人信息和历史行为来预测用户行为,然而此类方法没有考虑媒体本身缺乏用户信息造成无法预测的问题。随着社交网络的发展,在线媒体与服务运营商间的兼并或合作的增多,支持用户通过单一账户使用多个媒体网络服务的情况越来越常见,这就为基于用户在社交网络中的资料预测用户在其他媒体中的喜好提供海量可信的基础数据。该文基于社交网络Google+和视频媒体YouTube的数据,首先证明用户在YouTube偏好具有高度的集聚性,并提出用户在社交网络中偏好与其在线媒体点击行为具有关联性,基于这种关联性,该文使用社交网络用户信息预测用户在在线媒体中的点播行为。实验结果显示,使用社交网络用户信息可以有效预测用户偏好,预测准确率比仅使用媒体本身信息提高了17%,而且能满足用户个性化需求。     

Representing small business web presence content: the web presence pyramid model

This article investigates ‘stage’ models that have been developed for researchers, practitioners and policymakers to explain how small businesses move from a basic to a sophisticated online presence and/or provide advice as to how they could do this. The main problem of these models is that small businesses do not necessarily develop their online presence in this manner. In addition, they do not consider that the online presence of most small businesses extends beyond their own website to the use of third party web portals, business directories and more recently social media websites. A new model, the web presence pyramid model, is thus proposed. The model represents the adoption levels of different categories of website features and does this in a non-linear manner, overcoming a limitation of stage models. A further contribution of the article is the classification of the web presence of small business industry sectors into new categories: Basic Web Presence; Added Value Websites; Online Database Websites and Payment Web Presence. A survey approach was used to conduct an online content analysis of the web presence of Australian small businesses, with the different types of web presences across industry sectors presented using the web presence pyramid model.     

RSS与语义网研究

RSS正在把信息荻取的主动权还给用户,并在网络信息构建中得到越来越广泛的应用,这也标志着个性化信息聚合时代的来临.作为现有Web的扩展,语义网提供了一个通用的框架,允许跨越不同应用程序.企业和团体共享和重用数据.深入探讨了RSS及语义网的研究背景、特点与应用、相互间的关系、发展方向及可能面临的问题和挑战.     

基于元搜索引擎的专业式智能网络信息检索系统

随着网络信息资源的迅速增加和实时更新，如何及时准确地获取自己所需的有用信息是现代网络信息检索技术需要解决的主要问题。介绍了一种基于元搜索引擎的新型专业式智能网络信息检索系统。该系统建立专业词库和用户知识库，结合人工智能的Agent技术，能够很好地对用户的兴趣进行自适应学习，以实现个性化的信息检索，既保证了查全率，又提高了检索结果的查准率。     

基于中间件的Web应用组合工具的研究

为了利用Internet上现有的软件资源进行Web应用的开发,对当前已存在的构件技术、中间件技术和软件组合技术进行了研究.在充分考虑到Web应用特点的基础上,利用中间件联结异构构件方面的优势,提出一种基于中间件技术,利用构件,通过可视化编程模型构建用户工程的方法.最后通过实例说明用户构建的工程在中间件驱动下的运行原理,显示了该方法的可行性.     

主动联想型对话机器人的设计与实现

人机对话系统通常仅是用户命令的执行者,很少能主动发起对话,或依据对话以外的内容展开个性化的人机交互.本文提出了联想型对话机器人的概念和对应设计方案,即通过融合用户提问和用户个人特征的方式产生对话内容,这样的对话系统不仅可以主动发出对话邀请,也可以个性化地回答用户问题,提升人机对话的满意度.文章首先介绍了联想型对话机器人...     

基于用户特征分类的精准广告投放研究

互联网的普及和电子商务的飞速发展,网络广告成为一种新的经营方式。然而网络广告形式多样,目前现有的广告投放系统缺乏针对性,使得网络广告精确度不高,不能达到预期的目的。因此,分析用户行为进行精准广告投放成为一种必要。该文采用神经网络技术,对用户行为特征库提炼用户最重要的行为和最关注的内容点,从而对用户进行智能分类,以此作为向用户推荐广告的依据,达到精准投放的目的。     

Online web video topic detection and tracking with semi-supervised learning

With the pervasiveness of online social media and rapid growth of web data, a large amount of multi-media data is available online. However, how to organize them for facilitating users’ experience and government supervision remains a problem yet to be seriously investigated. Topic detection and tracking, which has been a hot research topic for decades, could cluster web videos into different topics according to their semantic content. However, how to online discover topic and track them from web videos and images has not been fully discussed. In this paper, we formulate topic detection and tracking as an online tracking, detection and learning problem. First, by learning from historical data including labeled data and plenty of unlabeled data using semi-supervised multi-class multi-feature method, we obtain a topic tracker which could also discover novel topics from the new stream data. Second, when new data arrives, an online updating method is developed to make topic tracker adaptable to the evolution of the stream data. We conduct experiments on public dataset to evaluate the performance of the proposed method and the results demonstrate its effectiveness for topic detection and tracking.