A Bayesian approach to fusing uncertain,imprecise and conflicting information

The Dezert–Smarandache theory (DSmT) and transferable belief model (TBM) both address concerns with the Bayesian methodology as applied to applications involving the fusion of uncertain, imprecise and conflicting information. In this paper, we revisit these concerns regarding the Bayesian methodology in the light of recent developments in the context of the DSmT and TBM. We show that, by exploiting recent advances in the Bayesian research arena, one can devise and analyse Bayesian models that have the same emergent properties as DSmT and TBM. Specifically, we define Bayesian models that articulate uncertainty over the value of probabilities (including multimodal distributions that result from conflicting information) and we use a minimum expected cost criterion to facilitate making decisions that involve hypotheses that are not mutually exclusive. We outline our motivation for using the Bayesian methodology and also show that the DSmT and TBM models are computationally expedient approaches to achieving the same endpoint. Our aim is to provide a conduit between these two communities such that an objective view can be shared by advocates of all the techniques.     

Behavior‐Based Propagation of Trust in Social Networks with Restricted and Anonymous Participation

Increasing interactions and engagements in social networks through monetary and material incentives is not always feasible. Some social networks, specifically those that are built on the basis of fairness, cannot incentivize members using tangible things and thus require an intangible way to do so. In such networks, a personalized recommender could provide an incentive for members to interact with other members in the community. Behavior‐based trust models that generally compute social trust values using the interactions of a member with other members in the community have proven to be good for this. These models, however, largely ignore the interactions of those members with whom a member has interacted, referred to as “friendship effects.” Results from social studies and behavioral science show that friends have a significant influence on the behavior of the members in the community. Following the famous Spanish proverb on friendship “Tell Me Your Friends and I Will Tell You Who You Are,” we extend our behavior‐based trust model by incorporating the “friendship effect” with the aim of improving the accuracy of the recommender system. In this article, we describe a trust propagation model based on associations that combines the behavior of both individual members and their friends. The propagation of trust in our model depends on three key factors: the density of interactions, the degree of separation, and the decay of friendship effect. We evaluate our model using a real data set and make observations on what happens in a social network with and without trust propagation to understand the expected impact of trust propagation on the ranking of the members in the recommended list. We present the model and the results of its evaluation. This work is in the context of moderated networks for which participation is by invitation only and in which members are anonymous and do not know each other outside the community. Copyright © 2014 John Wiley & Sons, Ltd.     

Uncertainty evaluation for a Dezert–Smarandache theory-based localization problem

Dezert–Smarandache theory (DSmT) is selected as the fusion method in a decision making system. To compare sensor fusion results, an uncertainty analysis is performed at each level of the decision making system. The supervision is based on the Generalized Aggregated Uncertainty (GAU) measure which is a generalization of Aggregated Uncertainty measure, whereas it is applicable for DSmT results. This measure helps to make decisions on better choice of combinations of sensors or methods of fusion. As an experimental study, localization of an object using cameras’ images is selected. Classic DSmT and hybrid DSmT by using extra knowledge, is applied and then uncertainty evaluation is carried out by the GAU uncertainty measure. The final decision in the presented framework has uncertainty less than each sensor’s measurement. By this method, more accurate and less uncertain results in localizing the object with high conflict sensory data are generated.     

广义证据推理融合结构

针对Dempster Shafer理论（DST）及Dezert Smarandache理论（DSmT）难以处理不确定信息的问题,定义了辨识框架中的不确定因子,提出了2种自适应通用分配法则（AUPR）.并提出了证据理论的广义融合框架,并在此基础上构建了广义证据推理机.以Pioneer 2 DXe机器人为实验平台,绘制了实验场景的信度分布图.实验结果验证了所提方法的有效性和实用性,为构建统一的信息融合框架提供了有力的依据.     

普适环境中基于模糊理论的信任评估模型

信任管理是当前普适环境中信息安全研究的热点。为了解决主观信任的模糊性和主体之间信任关系的动态性,在模糊数学中的模糊理论的基础上,提出了一个用户信任度的评估模型。该模型是将模糊理论应用到信任关系的不确定性和动态性上,对影响信任主体的主要因素进行综合评估,得出一个总的信任度来确定是否可以信任主体以便进行交互。该模型可以全面地评估用户的信任度,很大程度上保证了普适行为的安全可靠,为处理普适计算的信任管理的不确定研究提供了一个新的方法。     

一种考虑节点角色的P2P信任模型

在对等网络中,如何对节点行为进行准确的评估是信任模型研究的重点.经过学习和分析,发现现有的模型几乎都是基于节点作为被推荐者这一角色,而忽略了自身的推荐角色.对此本文提出两重推荐信任模型,从节点的两重角色考虑,为每个节点设计两重评估标准.若干次交易后,该模型能够使正常节点形成紧密的交易关系,有效地遏制了恶意节点的交易行为.实验表明该模型在面对夸大、合谋、伪装和单一行为威胁时较其他一些模型更具有效性.     

网格经济模型中基于信任机制的调度算法*

在Buyya设计的GRACE网格资源管理框架下,提出反映信任值动态变化规律的信任函数,在此基础上建立基于行为的网格信任机制。根据应用环境的不同需求,对网格经济模型DBC调度算法进行改进,分别提出了以时间优化、成本优化和时间—成本折中优化为目标的基于信任的DBC调度算法(Trust DBC)。理论分析及模拟实验结果表明,该算法性能明显优于相应的未考虑信任的调度算法。     

Pedestrian tracking using color, thermal and location cue measurements: a DSmT-based framework

In this paper, we address the problem of pedestrians tracking in cluttered scenes using location, color and thermal cues. The Dezert–Smarandache (DSm) theoretical framework is used to combine the measurements provided by the sensors into a single and unified frame. The use of DSm Theory allows modeling the conflicts that might arise between the sensors due to the presence of clutter and high levels of occlusion. The location cue is integrated as a prior knowledge, which increases the robustness of the tracking. A belief measure is derived and used as a step in a particle filtering algorithm. Finally, experimental results are given, where the developed approach is used to track walking persons in indoor scenes with high levels of occlusion and clutter.     

Contextual reliability discounting in welding process diagnostic based on DSmT

The monitoring and diagnosing of the welding process is an important task in assuring the quality of welded joints. Observation of such a dynamic process is often performed by measuring various signals of different modality. The main problem is how to use several of the possible signals simultaneously. Many techniques that can deal with this kind of task were elaborated and introduced in practical application. One of these is decision fusion, with a group of methods based on belief function theories: Dempster–Shafer and Dezert–Smarandache (DSmT). In this paper, extended fusion scheme for assessment of welding process condition, with contextual discounting of evidence sources reliability in DSmT framework, is presented. Discounting operation was used to distinguish the objective ability of each evidence source, to give a correct assessment of process condition. To validate this method, several series of welded joints representing different process conditions that result in various joint quality were made. On the basis of performed research, it was proven that contextual reliability discounting leads to increase of the condition recognition accuracy.     

Behavior-based reputation management in P2P file-sharing networks

Trust research has become a key issue in the last few years as a novel and valid solution to ensure the security and application in peer-to-peer (P2P) file-sharing networks. The accurate measure of trust and reputation is a hard problem, most of the existing trust mechanisms adopt the historical behavior feedback to compute trust and reputation. Thus exploring the appropriate transaction behavior becomes a fundamental challenge. In P2P system, each peer plays two roles: server and client with responsibility for providing resource service and trust recommending respectively. Considering the resource service behavior and trust recommending behavior of each peer, in this paper, we propose a new trust model adopting the technology to calculate eigenvectors of trust rating and recommending matrices. In our model, we define recommended reputation value to evaluate the resource service behavior, and recommending reputation value to evaluate the trust recommendation behavior. Our algorithm would make these two reputation values established an interrelated relation of reinforcing mutually. The normal peers provide authentic file uploading services, as well as give correct trust recommendation, so they can form a trusted and cooperative transaction community via the mutual reinforcement of recommended and recommending reputation values. In this way, the transaction behaviors of those malicious peers are isolated and confined effectively. Extensive experimental results also confirm the efficiency of our trust model against the threats of exaggeration, collusion, disguise, sybil and single-behavior.     

一种新的基于网格环境的行为信任模型

针对目前网格环境下资源管理和分配中所存在的安全问题,提出了一种新的基于实体行为的信任管理模型.着重描述了行为信任的定义和算法说明,实验和分析结果表明,与以往的信任模型相比,这种信任模型能更加精确地评估实体之间的信任关系,从而能更加有效地解决网格环境中存在的安全问题.     

网格环境下层次信任模型研究

信任管理作为网格研究的核心内容,受到研究人员的高度关注。目前设计的模型中,实体域的信任值由直接信任值和推荐信任值合成,但引入推荐信任就必须对网格域进行层次划分。现有的划分方法只是主观地根据网格域之间的交易情况来划分层次,这种划分方式得到的推荐值的可靠性难以保证,并且不能防止联合欺骗的行为。基于对网格实体域行为的深入研究,给出了一个基于实体域行为的直接信任值动态量化模型,在此基础上,引入匹配指标和粗糙度的概念,给出了一个层次划分模型,利用该模型很好地解决了推荐信任值的计算问题,给出了一个信任评估模型,理论分析和实验结果表明,该模型能够保证推荐信任值的可靠性,并且防止了推荐过程中的联合欺骗,有效地解决了网格环境中信任评估问题。     

A novel trust management scheme based on Dempster–Shafer evidence theory for malicious nodes detection in wireless sensor networks

With the development of Internet technology, social network has become an important application in the network life. However, due to the rapid increase in the number of users, the influx of a variety of bad information is brought up as well as the existence of malicious users. Therefore, it is emergent to design a valid management scheme for user’s authentication to ensure the normal operation of social networks. Node trust evaluation is an effective method to deal with typical network attacks in wireless sensor networks. In order to solve the problem of quantification and uncertainty of trust, a novel trust management scheme based on Dempster–Shafer evidence theory for malicious nodes detection is proposed in this paper. Firstly, by taking into account spatiotemporal correlation of the data collected by sensor nodes in adjacent area, the trust degree can be estimated. Secondly, according to the D–S theory, the trust model is established to count the number of interactive behavior of trust, distrust or uncertainty, further to evaluate the direct trust value and indirect trust value. Then, a flexible synthesis method is adopted to calculate the overall trust to identify the malicious nodes. The simulation results show that the proposed scheme has obvious advantages over the traditional methods in the identification of malicious node and data fusion accuracy, and can obtain good scalability.

     

An evidence clustering DSmT approximate reasoning method for more than two sources

Due to the huge computation complexity of Dezert–Smarandache Theory (DSmT), its applications especially for multi-source (more than two sources) complex fusion problems have been limited. To get high similar approximate reasoning results with Proportional Conflict Redistribution 6 (PCR6) rule in DSmT framework (DSmT + PCR6) and remain less computation complexity, an Evidence Clustering DSmT approximate reasoning method for more than two sources is proposed. Firstly, the focal elements of multi evidences are clustered to two sets by their mass assignments respectively. Secondly, the convex approximate fusion results are obtained by the new DSmT approximate formula for more than two sources. Thirdly, the final approximate fusion results by the method in this paper are obtained by the normalization step. Analysis of computation complexity show that the method in this paper cost much less computation complexity than DSmT + PCR6. The simulation experiments show that the method in this paper can get very similar approximate fusion results and need much less computing time than DSmT + PCR6, especially, when the numbers of sources and focal elements are large, the superiorities of the method are remarkable.     

网格经济模型中基于信任机制的调度算法

在网格环境中使用经济学原理进行资源管理和调度是网格从理论研究走向实际应用的有效途径之一。本文在Buyya设计的GRACE网格资源管理框架下,提出一种基于微分方程的信任值量化计算公式:信任=直接信任8声誉,在此基础上建立基于行为的网格信任机制。根据应用环境的不同需求,对网格经济模型调度算法(DBC)进行改进,分别提出了以时间优化、成本优化和时间成本折衷优化为目的的网格信任调度算法(TrustDBC)。理论分析及模拟实验结果表明,本文算法性能明显优于相应的未考虑信任的调度算法。     

A feature binding computational model for multi-class object categorization and recognition

We propose a feature binding computational model based on the cognitive research findings. Feature integration theory is widely approved on the principles of the binding problem, which supplies the roadmap for our computational model. We construct the learning procedure to acquire necessary pre-knowledge for the recognition network on reasonable hypothesis–maximum entropy. With the recognition network, we bind the low-level image features with the high-level knowledge. Fundamental concepts and principles of conditional random fields are employed to model the feature binding process. We apply our model to current challenging problems, multi-label image classification and object recognition, and evaluate it on the benchmark image databases to demonstrate that our model is competitive to the state-of-the-art method.     

Pseudo Trust: Zero-Knowledge Authentication in Anonymous P2Ps

Most of the current trust models in peer-to-peer (P2P) systems are identity based, which means that in order for one peer to trust another, it needs to know the other peer's identity. Hence, there exists an inherent tradeoff between trust and anonymity. To the best of our knowledge, there is currently no P2P protocol that provides complete mutual anonymity as well as authentication and trust management. We propose a zero-knowledge authentication scheme called Pseudo Trust (PT), where each peer, instead of using its real identity, generates an unforgeable and verifiable pseudonym using a one-way hash function. A novel authentication scheme based on Zero-Knowledge Proof is designed so that peers can be authenticated without leaking any sensitive information. With the help of PT, most existing identity-based trust management schemes become applicable in mutual anonymous P2P systems. We analyze the security and the anonymity in PT, and evaluate its performance using trace-driven simulations and a prototype PT-enabled P2P network. The strengths of our design include 1) no need for a centralized trusted party or CA, 2) high scalability and security, 3) low traffic and cryptography processing overheads, and 4) man-in-middle attack resistance.     

On the SAR change detection review and optimal decision

Change detection is the process of identifying differences in the state of an object or scene after the occurence of an event. In this paper, we will present several similarity measures for automatic synthetic aperture radar (SAR) change detection, which can be classified into two families: the first regroups the measures based on pixel radiometry and the second collects the detectors based on local statistics. This article details a judicious method for SAR image change detection using Dezert–Smarandache Theory (DSmT). On the one hand, a Rayleigh distribution function is used to characterize globally the radar texture data, which allows mass assignment through the Kullback–Leibler distance. On the other hand, local pixel measurements are introduced through the Rayleigh Distribution Ratio indicator to refine the mass attribution and take into account the context information. Finally, DSmT is carried out by comparing the modelling results between temporal images. This technique has been applied on both simulated and real data and allows very satisfactory change detection results.     

基于聚类分析的可信网络管理模型

针对可信网络中动态信任模型对终端用户行为信任评估有效性问题,提出一种新的基于聚类分析的可信网络管理模型。该模型在传统信任模型的基础上引入聚类分析方法,从行为预期的角度研究用户的行为信任。通过对用户的历史行为数据进行聚类分析以构建行为预期,并利用行为预期评估用户行为,最后以信任评估结果为依据实现对网络中的用户的管理。实验表明该模型可以对长期接入的正常用户产生稳定的信任评估结果,同时迅速发现并隔离恶意用户,对可信用户与不可信用户有较高的区分度,与传统的信任模型相比具有更高的准确度及效率,达到了提高网络可信性的目的。     

基于格式塔的建筑物图画抽象

格式塔(Gestalt)理论总结了人类认知规则、模式和语义的若干规律,已有大量的研究针对独立的格式塔规则建立计算模型,但建立多个格式塔规则相互作用的耦合模型仍然是一个挑战.基于格式塔理论的若干主要规则建立了一个针对多个格式塔规则耦合的计算模型,并将该模型用于建筑物线画图的抽象;通过减少图画中重复结构元素的数目,或者将复杂的结构用简单结构替换的方式对建筑物结构进行概括和抽象.实验结果表明,该方法可以应用于风格各异的建筑物线画图,并可以扩展到其他类型的图画以及三维模型.