基于分布式信任管理的时延容忍网络数据转发方案

为解决时延容忍网络中恶意或自私节点严重影响网络性能的问题,提出一种基于分布式信任管理的时延容忍网络数据转发方案.通过计算节点信任值先滤除网络中的恶意节点,再执行数据转发行为.根据节点的转发行为以及它们的能量消耗信息计算出直接信任;邻居节点推荐的信息融合间接信任和推荐信誉计算出推荐信任;融合直接信任和推荐信任得到节点总体...     

PRIVACY LEAKAGE IN HEALTH SOCIAL NETWORKS

Members of health social networks may be susceptible to privacy leaks by the amount of information they leave behind. The threat to privacy increases when members of these networks reuse their pseudonyms in other social networks. The risk of re‐identifying users from such networks requires quantitative estimates to evaluate its magnitude. The estimates will enable managers and members of health social communities to take corrective measures. We introduce a new re‐identification attack, the social network attack, that takes advantage of the fact that users reuse their pseudonyms. To demonstrate the attack, we establish links between MedHelp and Twitter (two popular social networks) based on matching pseudonyms. We used Bayesian networks to model the re‐identification risk and used stylometric techniques to identify the strength of the links. On the basis of our model 7‐11. 8% of the MedHelp members in the sample population who reused their pseudonyms in Twitter were re‐identifiable compared with 1% who did not. The risk estimates were measured at the 5% risk threshold. Our model was able to re‐identify users with a sensitivity of 41% and specificity of 96%. The potential for re‐identification increases as more data is accumulated from these profiles, which makes the threat of re‐identification more serious.     

多维社会网络中链路预测方法研究

多维社会网络中链路预测是指通过已知的网络节点以及多维社会网络结构等信息预测多维社会网络中尚未产生连边的两个节点之间产生链接的可能性.在研究有关链路预测的相关技术后,基于节点之间的相似性,提出了一种多维社会网络中的链路预测算法.算法中考虑时间因素和权重因素对于链路预测的影响,同时将多维网络中的相关技术应用到链路预测的过程...     

DART: A DISTRIBUTED ANALYSIS OF REPUTATION AND TRUST FRAMEWORK

Artificial societies—distributed systems of autonomous agents—are becoming increasingly important in open distributed environments, especially in e‐commerce. Agents require trust and reputation concepts to identify communities of agents with which to interact reliably. We have noted in real environments that adversaries tend to focus on exploitation of the trust and reputation model. These vulnerabilities reinforce the need for new evaluation criteria for trust and reputation models called exploitation resistance which reflects the ability of a trust model to be unaffected by agents who try to manipulate the trust model. To examine whether a given trust and reputation model is exploitation‐resistant, the researchers require a flexible, easy‐to‐use, and general framework. This framework should provide the facility to specify heterogeneous agents with different trust models and behaviors. This paper introduces a Distributed Analysis of Reputation and Trust (DART) framework. The environment of DART is decentralized and game‐theoretic. Not only is the proposed environment model compatible with the characteristics of open distributed systems, but it also allows agents to have different types of interactions in this environment model. Besides direct, witness, and introduction interactions, agents in our environment model can have a type of interaction called a reporting interaction, which represents a decentralized reporting mechanism in distributed environments. The proposed environment model provides various metrics at both micro and macro levels for analyzing the implemented trust and reputation models. Using DART, researchers have empirically demonstrated the vulnerability of well‐known trust models against both individual and group attacks.     

COMBINING TRUST MODELING AND MECHANISM DESIGN FOR PROMOTING HONESTY IN E‐MARKETPLACES

In this paper, we propose a novel incentive mechanism for promoting honesty in electronic marketplaces that is based on trust modeling. In our mechanism, buyers model other buyers and select the most trustworthy ones as their neighbors to form a social network which can be used to ask advice about sellers. In addition, however, sellers model the reputation of buyers based on the social network. Reputable buyers provide truthful ratings for sellers, and are likely to be neighbors of many other buyers. Sellers will provide more attractive products to reputable buyer to build their own reputation. We theoretically prove that a marketplace operating with our mechanism leads to greater profit both for honest buyers and honest sellers. We emphasize the value of our approach through a series of illustrative examples and in direct contrast to other frameworks for addressing agent trustworthiness. In all, we offer an effective approach for the design of e‐marketplaces that is attractive to users, through its promotion of honesty.     

SELF-ORGANIZING PEER-TO-PEER SOCIAL NETWORKS

Peer-to-peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralization, dynamics, flexibility, autonomy, and cooperation, summarized as DDFAC in this paper. After a detailed analysis of the current P2P literature, this paper suggests to better exploit peer social relationships and peer autonomy to achieve efficient P2P structure design. Accordingly, this paper proposes Self-organizing peer-to-peer social networks (SoPPSoNs) to self-organize distributed peers in a decentralized way, in which neuron-like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self-organized networks capture social associations of peers in resource sharing, and hence are called P2P social networks. SoPPSoNs have improved search speed and success rate as peer social networks are correctly formed. This has been verified through tests on real data collected from the Gnutella system. Analysis on the Gnutella data has verified that social associations of peers in reality are directed, asymmetric and weighted, validating the design of SoPPSoN. The tests presented in this paper have also evaluated the scalability of SoPPSoN, its performance under varied initial network connectivity and the effects of different learning rules.     

基于h-水平截集的模糊神经网络在专家系统推理中的应用

本文给出了基于h-水平截集的模糊神经网络模型及其改进学习算法,并以减摇鳍故障诊断为例来实现专家系统的模糊推理,以克服传统专家系统推理机的不足.     

使用稳态系统和粒群优化算法进行基因调控网络推断

基因调控网络模型试图从海量的时序基因表达数据中研究基因的功能,推断基因之间的调控关系,从而揭示复杂的病理现象和生命现象.通过利用时序基因表达数据来推断一个基于稳态系统(S-system)模型的基因网络,提出使用粒群优化算法(PSO)来优化模型参数,从而捕捉基因表达数据中的动力学特性.实验结果表明,该方法能够使模型参数快速得到收敛,配置参数后模型仿真能力好,可以较好地识别基因调控关系.     

基于层次隐马尔可夫模型和神经网络的个性化推荐算法

传统推荐系统将推荐准确性作为主要目标,而推荐结果的多样性和个性化有所欠缺.对此,设计一种基于层次隐马尔可夫模型和神经网络的推荐算法.采用层次隐马尔可夫模型建模用户喜好和上下文环境的关系,并通过隐马尔可夫模型预测上下文.设计神经网络结构来解决协同过滤推荐的问题,同时神经网络满足贝叶斯个性化排序的条件,实现对推荐列表的个性...     

并行遗传算法与神经网络,模糊系统的结合

遗传算法是模拟自然界生物进化过程的计算模型。本文介绍了并行遗传算法的不同分类及不同并行策略，又将遗传算法分别与神经网络、模糊系统结合起来进行并行处理，并在曙光１０００系统上实现。算法分析表明，并行遗传算法可以有效地提高收敛速度。     

收割机智能CAD中的模糊推理与图形生成

本文介绍了联合收割机总体设计专家系统的总体思想，着重论述了模糊理论在设计型专家系统中的应用，以及用ＰＤＣｐｒｏｌｏｇ的ＢＧＩ系统构造联合收割机设计图的图形构件。     

TRUST BY ASSOCIATION: A META‐REPUTATION SYSTEM FOR PEER‐TO‐PEER NETWORKS

Trust mechanisms are used in peer‐to‐peer (P2P) networks to help well‐behaving peers find other well‐behaving peers with which to trade. Unfortunately, these trust mechanisms often do little to keep badly behaving peers from entering and taking advantage of the network, which makes the resulting network difficult or impossible to use for legitimate purposes such as e‐commerce. We propose trust by association, a way of tying peers together in invitation‐only P2P networks in such a way as to encourage the removal of badly behaving peers. We use invitations to create a structure within the otherwise ad hoc P2P network. Using this structure, we create a meta‐reputation system where we measure a peer’s trustworthiness not only by its own behavior, but also by the behavior of the peers it has invited to join. The connection created between the peers takes advantage of the external social relationship that must exist before a peer can be invited into the network. The result is a P2P network where, rather than just trying to marginalize badly behaving peers, there is incentive to kick them out of the network. We present results from a simple simulation showing that our approach works well in general when combined with and compared to an existing trust mechanism.     

基于最优模糊聚类的模糊推理系统及其在产品质量估计中的应用

研究以输入空间模糊聚类为基础的模糊推理模 型,通过模型结构奇异值分解与模糊统计信息准则相结合方法实现输入空间的最优模糊聚类 ,从而获得最优模糊推理模型．这一方法成功地应用于加氢裂化装置分馏塔航空煤油干点估 计器的建模．     

社交网络中虚拟团队的语义描述及本体构建

对存在于社交网络平台中的虚拟团队进行语义描述以及本体构建是研究社交网络下群体活动规律、演变规律的重要手段。运用形式概念分析的方法对虚拟团队的角色-行为及基本属性进行语义描述。首先,通过分析虚拟团队在社交网络中的静态特点,构造其基本形式概念背景;其次,根据虚拟团队中的各类角色与行为之间的关系,对虚拟团队的动态属性进行描述,再结合基本属性概念,生成更完善的概念格;最后,利用概念格转换语义本体的方法和工具,构建虚拟团队的语义本体。     

Max-Product型模糊Hopfield网络稳定性及其聚类方法研究

本文将模糊逻辑和神经网络相结合,提出了Ｍａｘ－Ｐｒｏｄｕｃｔ型Ｈｏｐｆｉｅｌｄ人工神经网络,给出了它的网络结构和形式化描述,证明了ＦｕｚｚｙＨＮ的稳定性,最后通过理论和数值实验对基于Ｍａｘ－Ｐｒｏｄｕｃｔ型Ｈｏｐｆｉｅｌｄ网络的动态聚类过程和有关性质进行了研究。     

COMPLEXITY AND ERROR IN SOCIAL DYNAMICS

A model of cultural dynamics based on the work of Colin Renfrew is briefly described. It is shown how the general concepts of complexity and error pertain to this kind of cultural model. In particular, it is suggested that conflict in cultural contexts arises through the generation of diverse simple models of the culture, created by different members of the culture. We indicate how general system-theoretic concepts can thus be brought to bear on problems of conflict in and between cultures.     

COMPLEXITY AND ERROR IN SOCIAL DYNAMICS

A model of cultural dynamics based on the work of Colin Renfrew is briefly described. It is shown how the general concepts of complexity and error pertain to this kind of cultural model. In particular, it is suggested that conflict in cultural contexts arises through the generation of diverse simple models of the culture, created by different members of the culture. We indicate how general system-theoretic concepts can thus be brought to bear on problems of conflict in and between cultures.     

单体模糊神经网络的学习规则及其收敛性研究

兴久祯教授在不久前研究了单体模糊神经网络（MFNNs)的函数逼近能力,在此基础上,提出了单体模糊神经网络（MFNNs)的学习规则并进一步研究了其收敛性,研究结果表明,所提出的学习规则百收敛的,这一结论为单体模糊神经网络的应用提供了坚实的理论基础。     

基于 Beta信誉系统的无线传感网络鲁棒安全自定位

针对现实应用中各种不利因素干扰而严重影响无线传感网络的定位性能的问题,提出一种鲁棒安全定位算法. 首先在Beta信誉系统的基础上建立信任评估框架;然后,传感器节点在多跳通信范围内将锚节点的最终信任值与所存储的阈值进行比较;最后,采用基于泰勒级数展开的加权最小二乘法估算普通传感器节点的坐标. 仿真结果验证了该算法的有效性和鲁棒性,相比其他几种定位算法,该算法具有更高的定位精度,不仅能够很好地识别出无线传感网络中的恶意锚节点,同时大大地提高了节点自定位技术的定位精度.     

基于簇内分层的无线传感器网络隐私保护数据融合算法

在无线传感器网络中,提供高效的数据融合的同时又保证数据的隐私性是一个具有挑战性的研究问题。提出一个基于簇内二叉分层的完整性和机密性保护的数据融合算法(TIPDA),算法将簇内节点按照二叉树的逻辑结构进行组织,并引入了数据切片的思想完成数据融合,同时将中国剩余定理的思想引入进行端到端的完整性验证。理论分析和实验结果都表明,TIPDA算法可以有效地保护无线传感器网络融合数据的机密性和完整性,且花费较少的通信开销和计算开销。