基于交易特征和反馈评价的P2P信任模型

提出了一种新的在P2P系统中基于交易特征和反馈评价的信任评估机制。为确定反馈评价的真实性,模型引入了时间衰减函数和事务影响参数,给出了反馈评价的可信度计算方法。实验仿真结果表明,该评估机制能够提高局部声誉和全局声誉的计算准确性,有效地抑制恶意节点。与信任模型PeerTrust和EigenTrust相比,该模型的性能优于其他两者,能够更好地应用于P2P电子商务系统中。     

P2P网络中声誉信息分发算法

基于声誉的信任模型中,节点如何获取所需要的声誉信息是个关键问题。提出了一种基于蚂蚁的声誉信息分发算法。该算法通过模拟蚁群觅食的行为,实现了声誉信息在无集中式控制的P2P环境下的分发,解决了节点为建立信任关系获取声誉信息问题。仿真实验表明,该算法能使节点选择最佳路径获取声誉信息,降低了系统的负载,保证了节点获取声誉信息的可靠性和安全性。     

一种基于信任的Web服务发现方法

在面向服务的环境下,由于Web服务本身的动态性和网络环境的不确定性,致使用户很难获得可信的高质量服务。当前基于反馈信息的信任评估方法没有考虑恶意评估对服务可信程度的影响,本文通过加入客观评价对此进行了修正,并提出一种从服务的能力、稳定性和声誉三方面来建立的Web服务信任模型,提升了信任评估的准确度。在此基础上提出一种可信的Web服务发现框架,通过服务交易历史信息获取比较客观的信任属性,同时结合非功能属性进行服务的综合评估,利用熵值法来确定其中各非功能属性的权重,从而发现满足用户需求的可信服务。仿真结果表明,该方法是可行和有效的。     

一种利用综合评分的相关反馈图像检索方法

提出一种利用综合评分的相关反馈检索方法.该方法将用户对检索的满意度信息连同反馈记录一起存于反馈日志数据库中,在进行新的检索时,将当前反馈样本集合与通过分析历史反馈信息得到的扩充样本集合一起作为软标签SVM的输入,训练出分类器,并用三种评分的加权和作为图像库图像最终的评分值.对1000幅图像数据库的实验表明,与现有相关反馈方法相比,该文方法在提高反馈效率,充分利用历史反馈信息,克服反馈噪声方面都有明显改善.     

基于TD学习的网格资源预测方法

提出了一种新的网格资源预测方法。该算法基于TD学习,不需建立模型,直接从预测环境获得反馈信息,再从反馈信息中学习相应的规则。给出了该算法Java语言的一种实现,并用该系统对电信的接入主机的性能进行了预测试验。结果表明,该预测算法是有效的。同时给出了学习速度参数和历史信息相关度参数对预测的影响。     

信任协商的双重反馈模型DFM研究

资源在被拒绝访问时,用户通常接收到的反馈信息是无具体原因的"拒绝访问",使得合法的资源请求者无来由地被系统拒绝,影响了人机交互的友好性以及信任协商过程中的效率和成功率。提出一种针对在资源被拒绝访问时应用的反馈机制,根据资源请求者的身份和当前的系统环境这两个重要因素,建立一个反馈模型。实例证明该模型能够在不泄露敏感信息和策略的前提下,根据请求者的身份以及当前的系统状态提供有用而又适当的反馈信息。     

一种基于文档相似度的检索结果重排序方法

对相关反馈问题的研究已有近30年的历史,相关反馈也被证明可以大程度稳定地提升检索系统的性能。当前网络环境下相关反馈的应用以及用户提供反馈信息的方式已经发生了明显的变化,因此相关反馈研究又一次引起了研究界的注意。该文提出了一种基于文档相似度的搜索结果重排序方法,该方法同时利用了反馈信息中的相关文档与不相关文档。在大规模网络信息检索标准实验数据上的实验结果表明:该方法不仅可以稳定地提高系统的检索性能,并且相较于经典的查询扩展方法有着明显的优势。     

美海军开放式体系架构计算环境发展综述及启示

文章简要分析美国海军开放式体系架构计算环境的发展思路、主要技术特点、主要设计方法等,总结了开放式体系架构计算环境在宙斯盾系统和DDG-1000等项目中的应用情况。开放式体系架构计算环境对构建我海军舰载电子信息系统有较好的参考价值。     

P2P网络中蚁群的声誉信息分发算法研究

声誉是一种新型的信任建立机制.在P2P网络中,由于P2P网络的匿名性和开放性,存在节点可信问题.利用基于声誉的信任机制,可以隔离和孤立恶意的peers,选择信任状况好的peers提供服务.文中提出了一种基于蚁群的声誉信息分发机制,该算法通过模拟蚁群寻找食物的行为,实现声誉信息在无集中控制的P2P环境下的分发,基于蚁群的声誉信息分发算法能根据节点选择最佳路径来获取声誉信息,这样既降低系统负载,又提高搜索的效率,实现了节点安全、高效获取声誉信息的问题.     

基于线性反馈移位寄存器和组合猫映射的伪随机序列生成方法

分析了线性反馈移位寄存器(LFSR)和猫映射的基本结构,给出了一种基于LFSR与猫映射的伪随机序列生成方法。该方法根据LFSR的计算结果产生相应的选择函数,通过选择函数确定当前迭代计算中猫映射的系数矩阵;应用选定的系数矩阵进行迭代计算产生相应的混沌序列,将其二值化后作为反馈值与LFSR的反馈值进行异或运算,运算结果作为LFSR的最终反馈值,实现对LFSR生成序列的随机扰动。通过实验对生成序列的性能进行了分析,结果表明,产生的混沌序列具有良好的随机性和安全性。     

Web 服务选择中信誉度评估方法

在开放的Web服务环境中,由于无法保证用户使用服务后给出的反馈等级是真实可靠的,导致服务的信誉度评估结果与实际值存在较大偏差,进而服务选择失败率较高.为了克服上述问题,提出了一种用于Web服务选择的信誉度评估方法.该方法的主要思想是,通过反馈核查、校正和检测这3个信誉度评估模块对服务的信誉度进行评估.仿真结果表明,所提出的方法不仅有效提高了信誉度评估的客观性,还显著减小了服务选择的偏离度.     

MAS环境中一种基于反馈可信度的多维信誉计算方法

在分布式体系结构的MAS中,Agent之间通过彼此的交互,协调完成共同的任务,但是由于没有中心化的管理权威可以依赖,导致对网络中Agent信誉信息进行判断存在一定困难.传统的基于评价反馈的信誉评估方法存在反馈评价属性信息利用不足以及缺少确保反馈评价信息可信的可行机制等问题.为此本文提出一种综合的信誉计算方法,该方法针对个别用户提交的恶意评价,采用CUSUM控制图理论对服务评价中的数据进行过滤;利用信息熵的方法对不同维度的评价数据进行整合;使用改进PageRank算法对评价影响力进行度量,最终得到融合反馈评价真值与评价影响力的综合信誉.仿真结果表明文中方法在提高信誉计算收敛性和准确性,抵抗恶意攻击行为等方面表现出较好的效果.     

Acquaintance-based trust model for the evolution of cooperation in business games

Reputation systems have been popular in several online market places involving anonymous players as it can provide crucial information on the trustworthiness of an object or individual player to combat selfish and deceptive behaviors from peers. Individual feedbacks on the quality of past association are the fundamental building blocks of reputation systems. Careful consideration in aggregating feedbacks from different sources is in fact very important in computing a reliable value for trust worthiness to facilitate decision making in a social dilemma situation like that of online market places. In this paper, we are considering a possible improvement to a reputation model like that of eBay, with our interest lying on investigating how the cooperativeness and population of cooperators would evolve if the weight of the feedback source was assigned on the basis of past association between players. We categorize the feedback sources into different types to define an aggregation method for trustworthiness assessment that considers applying a dynamically computed weight to each source of feedback. Our results show that breaking feedback sources on the basis of acquaintance and assigning weight accordingly favor the evolution of cooperativeness in the player society when compared to models which do not classify the feedback sources.     

Multifactor hierarchical fuzzy trust evaluation on peer-to-peer networks

Decentralized Peer-to-Peer (P2P) networks offer not only opportunities but also threats. Due to the autonomy, self-interest and heterogeneousness of peers, the interaction outcomes are uncertain. One way to minimize the threats in such an open environment is exploiting the reputation method to evaluate the trustworthiness and predict the future behaviors of peers. While most of the existing reputation-based trust models focus on preventing network from the malicious peers, peers’ capabilities to fulfill the tasks are mostly ignored. In this paper, we present a novel trust model MHFTrust which quantifies and compares the trustworthiness of peers based on hierarchical fuzzy system. Six capability factors are identified to describe the peers’ trust on the capability, and one security factor, named “Malicious behavior” is used to evaluate the peers’ trust on security. Our trust model consisted of local-trust computation based on fuzzy techniques and global reputation aggregation, which integrates feedback from other peers to produce a global reputation for each peer. Credibility and weight of feedback are introduced to facilitate the computation of global reputation. It is shown in simulation that our trust model greatly improves the efficiency of P2P system, while the number of inauthentic files on the network is significantly decreased.     

A Review-Based Reputation Evaluation Approach for Web Services

Web services are commonly perceived as an environment of both offering opportunities and threats. In this environment, one way to minimize threats is to use reputation evaluation, which can be computed, for example, through transaction feedback. However, the current feedback-based approach is inaccurate and ineffective because of its inner limitations (e.g., feedback quality problem). As the main source of feedback, the qualities of existing on-line reviews are often varied greatly from low to high, the main reasons include: (1) they have no standard expression formats, (2) dishonest comments may exist among these reviews due to malicious attacking. Up to present, the quality problem of review has not been well solved, which greatly degrades their importance on service reputation evaluation. Therefore, we firstly present a novel evaluation approach for review quality in terms of multiple metrics. Then, we make a further improvement in service reputation evaluation based on those filtered reviews. Experimental results show the effectiveness and efficiency of our proposed approach compared with the naive feedback-based approaches.     

Reputation rating modeling for open environment lack of communication by using online social cognition

This paper defines the available reputation rating models at present from the viewpoint of set theory. Furthermore, to solve the problem that present reputation models fail in the condition of open environment without previous exchanging information, i.e. lack of participant's communication and evidence, online social cognition reputation ratings model is offered. Information seeking online is used to achieve the online behaviors for rating and the reputation can be evaluated by social experience in virtual world based on individual and group social cognition according to the definition of online social cognition set in this model. As online social cognition experience is drawled from a sample set of the open system, when a newcomer enter the evaluation set default reasoning is used to guarantee the model's extension. Finally, the reputation rating evaluation in Health Care Products Web site demonstrates the validity of the model and the results also show that group online social cognition is better than individual's.     

An integrated trust and reputation model for open multi-agent systems

Trust and reputation are central to effective interactions in open multi-agent systems (MAS) in which agents, that are owned by a variety of stakeholders, continuously enter and leave the system. This openness means existing trust and reputation models cannot readily be used since their performance suffers when there are various (unforseen) changes in the environment. To this end, this paper presents FIRE, a trust and reputation model that integrates a number of information sources to produce a comprehensive assessment of an agent’s likely performance in open systems. Specifically, FIRE incorporates interaction trust, role-based trust, witness reputation, and certified reputation to provide trust metrics in most circumstances. FIRE is empirically evaluated and is shown to help agents gain better utility (by effectively selecting appropriate interaction partners) than our benchmarks in a variety of agent populations. It is also shown that FIRE is able to effectively respond to changes that occur in an agent’s environment.     

采用排名反馈的P2P名誉评价模型

P2P网络中的节点很可能从另外的节点那里收到质量很差的服务和信息,名誉评价是解决该问题的常见方法.基于评分反馈的P2P名誉计算机制存在下述缺点:无法区分恶意评价和诚实节点给出错误评价间的差别;需要对评分可信度进行二次评价,使名誉计算速度减慢;用数字来表示节点名誉的方式不够自然.实际上,名誉评价的用途是确定节点可信度的相...     

MAS中信任和信誉系统的研究进展

信任在人类社会的合作中起着非常重要的作用,在诸多领域也受到了广泛的关注。在开放多Agent系统(MAS)的研究中,引入了信任的方法,用于解决交互伙伴的选择问题。信誉与信任密切相关,可以视信誉为信任的信息来源之一,信誉系统是用于完成信任评价的机制。MAS中信任研究应担负起发现计算实体之间信任的一般规律的重任。讨论了信任和信誉模型研究的内容、要求以及应用。在技术层面,信任表示有认知和数值两种观点,形成了集中式、分布式和混合式的体系结构,用于信任的汇总包括统计、概率、信念理论及模糊推理等方法。群体信誉、信息不准确、信息贫乏、异构模型互操作等问题有待进一步深入研究。     

Cooperativeness prediction in P2P networks

Peer-to-peer systems are open communities, in which not only is there no overarching control, but neither is there any hierarchy of control among the system components. In such open communities where peers can join and leave freely and behave autonomously, selecting appropriate peers to cooperate with is a challenging problem, since the candidate peers may be unreliable or dishonest. Reputation systems have been proposed to boost trust and enhance collaboration among peers. However, conventional computational reputation systems tend to generate trust based on ad hoc aggregation techniques thus produce reputation values with ambiguous meanings. In this paper we propose a probabilistic computational approach to model and generate reputation. By explicitly separating the reputation between providing services and giving recommendations, our solution represents the estimate of service quality for a specific transaction as a probability conditioned upon each retrieved recommendation, thus taking the innate behaviours of reporters into account. A Kalman filter is applied to learn further the service reputation from the estimate. The proposed approach works well even when there is sparse feedback from the reporting peers giving output with well-defined semantics and useful meanings.