SNS中结合声誉与主观逻辑的信任网络分析*

提出一种结合声誉与主观逻辑的信任网络分析方法,结合声誉与朋友间的信任关系来获得信任值。由于推荐来自信任的朋友也基于普遍的社会声誉,信任计算的结果更能反映真实情况。仿真实验及分析表明,该方法具有较高的效率和准确度。     

Legal reasoning with subjective logic

Judges and jurors must make decisions in an environment of ignoranceand uncertainty for example by hearing statements of possibly unreliable ordishonest witnesses, assessing possibly doubtful or irrelevantevidence, and enduring attempts by the opponents to manipulate thejudge's and the jurors' perceptions and feelings. Three importantaspects of decision making in this environment are the quantificationof sufficient proof, the weighing of pieces of evidence, and therelevancy of evidence. This paper proposes a mathematical frameworkfor dealing with the two first aspects, namely the quantification ofproof and weighing of evidence. Our approach is based on subjectivelogic, which is an extension of standard logic and probability theory,in which the notion of probability is extended by including degrees ofuncertainty. Subjective Logic is a framework for modelling humanreasoning and we show how it can be applied to legalreasoning.     

主观电子商务声誉双模糊综合评价模型

针对电子商务声誉具有模糊、主观、动态特性,提出一种对电子商务声誉进行综合评价的多层次模糊评判模型。对电子商务声誉进行模糊逻辑描述,建立论域以及论域上的模糊集,通过隶属度函数对各个阶段电子商务交易进行多级模糊评判。针对如何整合群体意见,提出一种模糊灰色关联层次分析法计算因素重要性程度的方法。通过算例示例电子商务声誉评判过程,并对评判结果进行分析和评价,结果表明有较好的一致性。     

Web服务中主观信任和客观声誉的研究

随着Web服务的出现,对软件服务和服务组合的安全可靠的运行使得服务提供方、服务请求方以及代理之间的相互信任变得尤为重要。首先给出信任和声誉的定义以及它们之间的区别和联系,由此提出一个用于度量软件服务间信任关系的信誉评估模型——WSTR。给出了更加灵活的计算直接信任度方法,并且将声誉引入推荐信任的合成中,利用现有的节点作为声誉存储实体,更加能够体现信任的主观性和客观性的结合。仿真结果表明声誉度能够客观地反映出真实的实际情况。     

基于主观逻辑的Web服务信任模型

在分析了传统主观信任评估模型存在问题的基础上提出了相应的改进方法,将改进后的信任评估模型方法应用到Web服务环境中,从而使Web服务环境中的实体信任度随着交易次数的增加能够自动更新.最后,通过模拟试验表明改进后的主观信任评估模型能更好的反应Web服务环境中实体信任度的变化.     

面向对等网络的主观逻辑信任模型

由于P2P系统的开放性、匿名性等特征,使得P2P系统对节点实体缺乏有效的管理机制,节点间的信任度无法进行有效的评定,就有可能存在欺诈节点,影响系统的可靠性,针对以上问题,在JΦsang主观逻辑理论的基础上,建立了一种基于主观逻辑理论的网络信任模型,该模型通过加权平均和方差来确定信任程度的取值方式,以及不确定度的可靠成分的确定,并在信任模型的基础上添加了风险机制,可以在一定程度上防止协同作弊和诋毁的安全隐患。通过仿真分析表明,这种信任模型能很好地解决P2P网络中存在的安全隐患。     

Multi-criteria assessment of user trust in Social Reviewing Systems with subjective logic fusion

By now people’s opinions and actions are more and more strongly influenced by what is posted and shared on the various social networks. Thus, malicious users can purposely manipulate other users posting fake news/reviews. In order to face this challenge, modern online social networks are beginning to adopt tool for user trustworthiness assessment. Current assessment solutions mainly adopt multi-criteria frameworks for user trustworthiness assessment but fail at properly dealing with uncertainty and vagueness in computed/collected scores and aggregating them in a robust manner. In this paper, we propose a larger set of criteria than existing related works, and the use of subjective logic to represent and combine subjective and objective scores. Specifically, several of assessment criteria are introduced for verifying user trust from different point of views (usefulness and quality of user reviews, users’ influence/importance in terms of activities and centrality within the social network, time dependent crown consensus investigating aspect-based sentiments and opinions of reviews w.r.t. the majority), aiming at improving accuracy and precision in trust estimation. The available fusion operators in the literature of subjective logic have been compared so as to find the best one fitting the needs of trust estimation. The proposed solution has been implemented and evaluated against public Yelp data-sets so as to prove its effectiveness and efficiency w.r.t. existing related works within the literature.     

Modeling uncertainty in clinical diagnosis using fuzzy logic.

This paper describes a fuzzy approach to computer-aided medical diagnosis in a clinical context. It introduces a formal view of diagnosis in clinical settings and shows the relevance and possible uses of fuzzy cognitive maps. A constraint satisfaction method is introduced that uses the temporal uncertainty in symptom durations that may occur with particular diseases. The method results in an estimate of the stage of the disease if the temporal constraints of the disease in relation to the occurrence of the symptoms are satisfied. A lightweight fuzzy process is described and evaluated in the context of diagnosis of two confusable diseases. The process is based on the idea of an incremental simple additive model for fuzzy sets supporting and negating particular diseases. These are combined to produce an index of support for a particular disease. The process is developed to allow fuzzy symptom information on the intensity and duration of symptoms. Results are presented showing the effectiveness of the method for supporting differential diagnosis.     

FB-NBAS:一种基于流的网络行为分析模型

传统的入侵检测系统通常需要对攻击预先了解,在流量分析和异常检测方面存在不足。该文提出一种新的基于流的统计分析模型,通过构建网络的行为特征库,实时监测和发现网络异常,基于该分析技术设计和实现了一个网络监控系统原型。该原型可以监测和发现网络中可疑代码,并进行实时跟踪。     

Output feedback tracking control of robot manipulators with model uncertainty via adaptive fuzzy logic

Many robot controllers require not only joint position measurements but also joint velocity measurements; however, most robotic systems are only equipped with joint position measurement devices. In this paper, a new output feedback tracking control approach is developed for the robot manipulators with model uncertainty. The approach suggested herein does not require velocity measurements and employs the adaptive fuzzy logic. The adaptive fuzzy logic allows us to approximate uncertain and nonlinear robot dynamics. Only one fuzzy system is used to implement the observer-controller structure of the output feedback robot system. It is shown in a rigorous manner that all the signals in a closed loop composed of a robot, an observer, and a controller are uniformly ultimately bounded. Finally, computer simulation results on three-link robot manipulators are presented to show the results which indicate good position tracking performance and robustness against payload uncertainty and external disturbances.     

A first-order logic for reasoning under uncertainty using rough sets

Reasoning with uncertain information is a problem of key importance when dealing with knowledge from real situations. Obtaining the precise numbers required by many uncertainty-handling formalisms can be a problem when building real systems. The theory of rough sets allows us to handle uncertainty without the need for precise numbers, and so has some advantages in such situations. The authors develop a set of symbolic truth values based upon rough sets which may be used to augment predicate logic, and provide methods for combining these truth values so that they may be propagated when augmented logic formulae are used in automated reasoning.     

Linear logic as a tool for planning under temporal uncertainty

The typical AI problem is that of making a plan of the actions to be performed by a controller so that it could get into a set of final situations, if it started with a certain initial situation.The plans, and related winning strategies, happen to be finite in the case of a finite number of states and a finite number of instant actions.The situation becomes much more complex when we deal with planning under temporal uncertainty caused by actions with delayed effects.Here we introduce a tree-based formalism to express plans, or winning strategies, in finite state systems in which actions may have quantitatively delayed effects. Since the delays are non-deterministic and continuous, we need an infinite branching to display all possible delays. Nevertheless, under reasonable assumptions, we show that infinite winning strategies which may arise in this context can be captured by finite plans.The above planning problem is specified in logical terms within a Horn fragment of affine logic. Among other things, the advantage of linear logic approach is that we can easily capture ‘preemptive/anticipative’ plans (in which a new action β may be taken at some moment within the running time of an action α being carried out, in order to be prepared before completion of action α).In this paper we propose a comprehensive and adequate logical model of strong planning under temporal uncertainty which addresses infinity concerns. In particular, we establish a direct correspondence between linear logic proofs and plans, or winning strategies, for the actions with quantitative delayed effects.     

Applications of type-2 fuzzy logic system: handling the uncertainty associated with candidate-well selection for hydraulic fracturing

The problem of selecting a target formation(s) in a reservoir among a vast number of zones/sub-layers within huge number of hydrocarbon producing wells for hydraulic fracturing (HF) by using interval type-2 fuzzy logic system (IT2-FLS) to maximize their net present value is studied in this paper. Classical fuzzy system which is called type-1 fuzzy logic system is not capable of accurately capturing the linguistic and numerical uncertainties in the terms used and the inconsistency of the expert’s decision-making. IT2-FLS is very useful in circumstances where it is difficult to determine an exact membership function for a fuzzy set; hence it is very effective for dealing with uncertainties. In highlighting this need, the question has been answered why IT2-FLS should be used in this study. The procedure of applying this study in the area of HF candidate-well selection is illustrated through a case study in an oil reservoir.     

P2P环境下的扩展主观逻辑信任模型

为了缓解P2P环境下信任缺失、抑制恶意节点攻击,降低不可靠服务风险,通过扩展J?sang主观逻辑思想,提出了一种新的P2P信任模型—ESLTrust。为更精确地描述信任关系的复杂性,采用肯定信任值和否定信任值取代J?sang主观逻辑中肯定事件数和否定事件数,并引入时间衰减因子和风险值,计算得到节点信任度。仿真实验与分析表明,该模型使节点的信任度受恶意行为的影响更灵敏,可有效增强对恶意节点的抵御能力,提高系统交互成功比例。     

A lightweight trust management algorithm based on subjective logic for interconnected cloud computing environments

The Journal of Supercomputing - The interconnected cloud computing paradigm is gaining considerable attention as a fundamental emerging model of cloud computing. It allows a wide range of...     

Handling imprecision and uncertainty in software development effort prediction: A type-2 fuzzy logic based framework

Traditional approaches for software projects effort prediction such as the use of mathematical formulae derived from historical data, or the use of experts judgments are plagued with issues pertaining to effectiveness and robustness in their results. These issues are more pronounced when these effort prediction approaches are used during the early phases of the software development lifecycle, for example requirements development, whose effort predictors along with their relationships to effort are characterized as being even more imprecise and uncertain than those of later development phases, for example design. Recent works have demonstrated promising results using approaches based on fuzzy logic. Effort prediction systems that use fuzzy logic can deal with imprecision; they, however, can not deal with uncertainty. This paper presents an effort prediction framework that is based on type-2 fuzzy logic to allow handling imprecision and uncertainty inherent in the information available for effort prediction. Evaluation experiments have shown the framework to be promising.     

DISLOG: programming in logic with discontinuities

In this paper, we present an extension to PROLOG we call DISLOG which is designed to deal with relations between non-contiguous elements in a structure. This extension turns out to be well suited for syntactic analysis of natural and artificial languages. It is also well adapted to express traversal constraints in applications such as planning and expert systems and deductive systems involving, for example, temporal reasoning, DISLOG belongs to the constrained logic programming paradigm and turns out to be more declarative, transparent, and simple than PROLOG to deal with longdistance relations.     

Reviews with revenue in reputation: Credibility management method for consumer-opinion platforms

Nowadays everyone can review everything. Online customer-opinion platforms often help potential buyers take a decision. Sometimes, however, the multitude of contradictory opinions may confuse customers. Submitting a review requires time and effort, yet it only benefits others. Therefore jokes and shill reviews represent quite a percentage of reviews, because an average reviewer has no motivation to submit reviews. Reviews with Revenue in Reputation (RRR) Method is designed to encourage reviewers by gamification. RRR allows customers to easily spot credible reviews and restricts the number of reviews an unreliable reviewer can submit.