基于Mobile Agent技术的未来电子商务模式的研究

Ｉｎｔｅｒｎｅｔ上的信息量和商务量爆炸式的增长、网络环境的日益复杂以及产品供应的不确定性都迫切需要一种主动的、智能的、动态反映各类信息变化的新技术来满足人们对电子商务日趋完善的要求。Ｍｏｂｉｌｅ Ａｇｅｎｔ技术应用于电子商务可以为用户和商家提供从智能资源发现到在网上虚拟市场进行交易的一系列个性化服务。系统地研究了一种基于Ｍｏｂｉｌｅ Ａｇｅｎｔ技术的未来电子商务的实现模式，并详细探讨该模式所依赖的相关技术及方案。     

要闻集锦

·上海邮电Ｉｎｔｅｒｎｅｔ服务取得飞速发展,上海ＣＨＩＮＡＮＥＴ和“上海热线”网上用户日前突破１０万户。ＣＨＩＮＡＮＥＴ作为中国Ｉｎｔｅｒｎｅｔ骨干网自开通１６３特服号接入后,接通率大大提高,同时开通了首个４５ＭＩｎｔｅｒｎｅｔ出口通道,使用户访问速...     

Agent协作中的分布渐进协商冲突消除策略

多Ａｇｅｔ系统（ＭＡＳ）中,常常需要Ａｇｅｎｔ进行相互间的协作来完成动态环境中的复杂任务。然而同于各Ａｇｅｎｔ知识的不完备笥、不相容性与资源问题等,从而可能导致Ａｇｅｎｔ协作过程中冲突的出现。文章在Ａｇｅｎｔ交互概念的基础上,对冲突进行分类,并提出一种分布渐进协商冲突消除策略,最后还给出了这种策略的分布一致性。     

HP Internet专家系统

为了排除安装、维护或定位广域网的故障,需要测试物理故障、设备互操作故障和通信业务故障。ＨＰＩｎｔｅｒｎｅｔ专家系统对隔离故障和排除故障更有效,而且能够在网上任何地方捕捉全部所需的数据。ＨＰＩｎｔｅｒｎｅｔ专家系统提供了完整的广域网和局域网协议分析功能,提供比特误码率测试、激励和响应测试及统计分析功能。ＨＰＩｎｔｅｒｎｅｔ专家系统支持ＷＡＮ、ＬＡＮ和ＡＴＭ。而ＨＰＩｎｔｅｒｎｅｔ专家系统ＷＡＮ支持的主要ＬＡＮ协议有:ＴＣＰ/ＩＰ、ＡｐｐｌｅＴａｌｋ、ＯＳＩ、ＭｉｃｒｏｓｏｆｔＬＡＮ、Ｍａｎａｇｅｒ、Ｂ…     

INTERNET的服务功能

ＩＮＴＥＲＮＥＴ的服务功能吴龙生Ｉｎｔｅｒｎｅｔ的前身是ＡＲＰＡｎｅｔ．仅用于军事通信。１９８６年建立的ＮＳＰｎｅｔ使Ｉｎｔｅｒｎｅｔ开始为教育研究服务。次年５月，最早用于商业的ＵＵｎｅｔ开始提供电子邮件、电子新闻用的ＵＵＣＰ服务。它是在网上主机之间...     

对软件Agent技术的研究

从应用角度出发，阐述了作对Ａｇｅｎｔ的理解和定义，同时对软件Ａｇｅｎｔ领域内的相关技术进行了分析比较，指出了目前该研究领域内存在的问题。     

网上购物应用系统

本文介绍了Ｉｎｔｅｒｎｅｔ环境下利用ＡＳＰ技术,构建一网上购物Ｗｅｂ站点的实现技术,给出了实现过程中的系统结构,数据库设计以及网页设计。     

电信英语及其缩略语解释(四)

ＡＦＫ Ａｗａｙ Ｆｒｏｍ Ｋｅｙｂｏａｒｄ 暂不在位ＡＦＳ Ａｎｄｒｅｗ Ｆｉｌｅ Ｓｙｓｔｅｍ Ａｎｄｒｅｗ文件系统 这是在大型网络中易于访问远程文件的一种分布式文件系统。ＡＧ Ａｃｃｅｓｓ Ｇａｔｅｗａｙ 接入网关ＡＧＣ Ａｕｔｏｍａｔｉｃ Ｇａｉｎ Ｃｏｎｔｒｏｌ 自动增益控制 Ａｇｅｎｔ 代理 Ａｇｅｎｔ在计算机应用中经常使用，任务是提出关于被管理的网络元素状态的管理过程的报告，接收从主管进程来的有关被管对象执行的动作。Ａｇｅｎｔ一般设置在被管对象内。ＡＩ Ａｒｔｉｆｉｃｉａｌ Ｉｎｔｅｌｌｉｇｅｎｃｅ 人…     

基于MAS和HLA的DIS研究

文章论述了ＤＩＳ的提出,概念和特点,总结了ＤＩＳ的主要研究课题技术,叙述了ＨＬＡ的结构,ＲＴＩ的功能,概括了Ａｇｅｎｔ的特点及ＭＡＳ的研究内容,最后提出了基于Ａｇｅｎｔ的ＨＬＡ（ＡＯ－ＨＬＡ）的思想及ＡＯ－ＨＬＡ的研究目标。     

公开密钥加密方法

本文针对国外日益兴起通过Ｉｎｔｅｒｎｅｔ网上购物涉及到的金融信息安全问题，扼要介绍了著名的ＤＥＳ和ＲＳＡ加密算法的内容、发展过程及其安全可靠性能。     

Automatic Learning of User Profiles — Towards the Personalisation of Agent Services

Agent technology is able to provide increasingly more services for individuals, groups and organisations. Services such as information finding, filtering and presentation, service/contract negotiation, and electronic commerce are now possible. User profiling is fundamental to the personalisation of this technology. This paper describes experimental work conducted to investigate user profiling within a framework for personal agents. In particular investigations were aimed at discovering whether user interests could be automatically classified through the use of several heuristics. The results highlighted the need for minimal user feedback, and the need to consider the implications for the role of machine learning in user profiling.     

Hadoop cloud platform user dynamic access control model

In order to solve the problem that Hadoop cloud platform could not dynamically control user access request,a Hadoop cloud dynamic access control model based on user behavior assessment (DACUBA) was proposed.The model first collected the user instruction sequence in real time and the user behavior contour was obtained by parallel command sequence learning (PCSL).Then the global K model was established by using the forward profile,the subsequent sequence was classified and the classification results were evaluated.The evaluation results were combined with the improved Hadoop access control mechanism to make the cloud platform users’ access rights change dynamically with their own behaviors.Experimental results demonstrate that the model algorithm is effective and the dynamic access control mechanism is feasible.     

基于用户兴趣集的在线垃圾邮件快速识别新方法

为在不显著降低垃圾邮件识别精度的同时有效提高邮件识别速度,提出了一种在线垃圾邮件快速识别新方法.首先引入用户正、负兴趣集的概念,结合用户兴趣集及支持向量机对邮件进行分类;然后根据主动学习理论,结合训练集样本密度及改进角度差异方法寻找分类最不确定的样本并推荐给用户进行类别标注;最后将标注后样本及分类最确定性样本加入训练集,并使用样本价值评价新函数淘汰冗余样本以生成新的训练集.实验表明,本文方法的用户标注负担小,垃圾邮件识别精度高、速度快,具有较高的在线应用价值.     

Malware variants detection based on ensemble learning

Application programming interface (API) is a procedure call interface to operation system resource. API-based behavior features can capture the malicious behaviors of malware variants. However, existing malware detection approaches have a deal of complex operations on constructing and matching. Furthermore, graph matching is adopted in many approaches, which is a nondeterministic polynominal (NP)-complete problem because of computational complexity. To address these problems, a novel approach is proposed to detect malware variants. Firstly, the API of the malware are divided by their functions and parameters. Then, the classified behavior graph (CBG) is constructed from the API call sequences. Finally, the signature based on CBGs for each malware family is generated. Besides, the malware variants are classified by ensemble learning algorithm. Experiments on 1 220 malware samples show that the true positive rate (TPR) is up to 89.0% with the low false positive rate (FPR) 3.7% by ensemble learning.     

利用遗传编程排序图像

Web image retrieval is a challenging task. One central problem of web image retrieval is to rank a set of images according to how well they meet the user information need. The problem of learning to rank has inspired numerous approaches to resolve it in the text information retrieval, related work for web image retrieval, however, are still limited. We focus on the problem of learning to rank images for web image retrieval, and propose a novel ranking model, which employs a genetic programming architecture to automatically generate an effective ranking function, by combining various types of evidences in web image retrieval, including text information, image visual content features, link structure analysis and temporal information. The experimental results show that the proposed algorithms are capable of learning effective ranking functions for web image retrieval. Significant improvement in relevancy obtained, in comparison to some other well-known ranking techniques, in terms of MAP, NDCG@n and D@n.     

复杂人机交互场景下的指势用户对象识别

采用指势进行人机交互,可充分发挥人类日常技能,摆脱常规输入设备束缚.实现自然的指势人机交互的关键是,如何从复杂的人机交互场景中有效提取指势用户对象,提出了基于时/空运动特征的指势用户对象识别新方法.基于多尺度小波变换在时/空域所具有的优异局部化特性,从复杂场景中提取前景运动对象,克服环境条件约束以及动态环境变化及先验假设等不足;基于多尺度小波变换的梯度积分图方法,获取稳定可靠的指势手部HOG特征,采用机器学习方法,对上述特征向量分类,并基于指势手与指势用户对象的空间关联性识别指势用户对象.通过实验对比,结果表明本文方法有效、可行.     

Inexpensive fusion methods for enhancing feature detection

Recent successful approaches to high-level feature detection in image and video data have treated the problem as a pattern classification task. These typically leverage the techniques learned from statistical machine learning, coupled with ensemble architectures that create multiple feature detection models. Once created, co-occurrence between learned features can be captured to further boost performance. At multiple stages throughout these frameworks, various pieces of evidence can be fused together in order to boost performance. These approaches whilst very successful are computationally expensive, and depending on the task, require the use of significant computational resources. In this paper we propose two fusion methods that aim to combine the output of an initial basic statistical machine learning approach with a lower-quality information source, in order to gain diversity in the classified results whilst requiring only modest computing resources. Our approaches, validated experimentally on TRECVid data, are designed to be complementary to existing frameworks and can be regarded as possible replacements for the more computationally expensive combination strategies used elsewhere.     

Classifier Learning and Decision Making for a Connection Manager on a Heterogeneous Network

An attractive feature of the Connection Manager Intelligence Agent is its use of network traffic and multi-attribute behavior to locate the best network devices. This study has integrated this agent with a user interface; a network connection handoff; wired and wireless network device drivers; network management applications of the (plug-in) play interface; module-to-module communication authentication; and a DBus for added versatility. To reduce the time that developers of embedded systems spend on the software engineering of this module and to achieve rapid operational efficiency, an Open Source platform, such as MeeGo or Android, must be used. This study has implemented an interactive interface through the function (based on Fuzzy-AHP) of acquisition user behavior and machine designers, boosting iterations for User-Case. The algorithm maintains a set of weights as a distribution class table of cases, as in the parameter learning by user-case; it is quite possible that the expectation–maximization of maximum probability model can be classified by user behavior. In this study, user interaction showed that the agent satisfactorily matched user intent.     

Information modeling and handling for context-aware multimedia services

This article presents research challenges in wireless ad hoc networks supporting context-aware multimedia services from the point of view of different context-modeling approaches and a system architecture for context handling and delivery. Modeling techniques for context information are classified into six different categories and benchmarked according to their support with regard to 12 service requirements described in this work. One of these modeling techniques is used in the framework of a distributed context handling and delivery system that is also part of this article's contribution and a representative wireless ad hoc application scenario is presented. This end user scenario and many others are possible thanks to the flexibility and adaptability of the above mentioned context handling and delivery system