A Study on Evidentiality in the Abstract Part of Learners’English Academic Papers

Evidentiality indicates the source of information and the speaker’s attitude towards the reliability of information.This paper analyzed evidentiality in 30 abstracts of English academic papers written by second-year science and engineering students and revealed that reporting evidentials and inferring evidentials have been used in the abstracts,while sensory evidentials and belief evidentials have not been used.The enlightenment from the analysis is that teachers can introduce to students the functions of different types of evidentials and the varied ways of expressions and help students raise the awareness of using evidentials consciously and properly to enhance their academic abstract writing.

Data-Driven Model for Risk Assessment of Cable Fire in Utility Tunnels Using Evidential Reasoning Approach

Cable fire is one of the most important events for operation and maintenance(O&M)safety in underground utility tunnels(UUTs).Since there are limited studies about cable fire risk assessment,a comprehensive assessment model is proposed to evaluate the cable fire risk in different UUT sections and improve O&M efficiency.Considering the uncertainties in the risk assessment,an evidential reasoning(ER)approach is used to combine quantitative sensor data and qualitative expert judgments.Meanwhile,a data transformation technique is contributed to transform continuous data into a five-grade distributed assessment.Then,a case study demonstrates how the model and the ER approach are established.The results show that in Shenzhen,China,the cable fire risk in District 8,B Road is the lowest,while more resources should be paid in District 3,C Road and District 25,C Road,which are selected as comparative roads.Based on the model,a data-driven O&M process is proposed to improve the O&M effectiveness,compared with traditional methods.This study contributes an effective ER-based cable fire evaluation model to improve the O&M efficiency of cable fire in UUTs.

Evidential method to identify influential nodes in complex networks

Identifying influential nodes in complex networks is still an open issue. In this paper, a new comprehensive centrality mea- sure is proposed based on the Dempster-Shafer evidence theory. The existing measures of degree centrality, betweenness centra- lity and closeness centrality are taken into consideration in the proposed method. Numerical examples are used to illustrate the effectiveness of the proposed method.

Integration-centric approach to system readiness assessment based on evidential reasoning

An integration-centric approach is proposed to handle inadequate information in the system readiness level （SRL） assessment using the evidential reasoning （ER） algorithm. Current SRL assessment approaches cannot be applied to handle inadequate information as the input. The ER-based approach is proposed to synthesize inadequate input information and an integration-centric perspective is applied to reduce the computational complexity. Two case studies are performed to validate the efficiency of the proposed approach. And these studies are also performed to study how the inadequate information will affect the assessment result. And the differences caused by the system＇s structure. The importance of the system＇s structure in the SRL assessment is demonstrated and the contributions made in this study are summarized as conclusions.

Fuzzy multi-criteria decision-making approach with incomplete information based on evidential reasoning

The weights of criteria are incompletely known and the criteria values are incomplete and uncertain or even default in some fuzzy multi-criteria decision-making problems.For those problems,an approach based on evidential reasoning is proposed,in which the criteria values are integrated on the basis of analytical algorithm of evidential reasoning,and then nonlinear programming models of each alternative are developed with the incomplete information on weights.The genetic algorithm is employed to solve the models,producing the weights and the utility interval of each alternative,and the ranking of the whole set of alternatives can be attained.Finally,an example shows the effectiveness of the method.

Reliability Analysis for Complex Systems Based on Dynamic Evidential Network Considering Epistemic Uncertainty

Fault tolerant technology has greatly improved the reliability of modern systems on one hand and makes their failure mechanisms more complex on the other.The characteristics of dynamics of failure,diversity of distribution and epistemic uncertainty always exist in these systems,which increase the challenges in the reliability assessment of these systems significantly.This paper presents a novel reliability analysis framework for complex systems within which the failure rates of components are expressed in interval numbers.Specifically,it uses a dynamic fault tree(DFT)to model the dynamic fault behaviors and copes with the epistemic uncertainty using Dempster-Shafer(D-S)theory and interval numbers.Furthermore,an approach is presented to convert a DFT into a dynamic evidential network(DEN)to calculate the reliability parameters.Additionally,a sorting method based on the possibility degree is proposed to rank the importance of components represented by interval numbers in order to obtain the most critical components,which can be used to provide the guidance for system design,maintenance planning and fault diagnosis.Finally,a numerical example is provided to illustrate the availability and efficiency of the proposed method.

Evidentiality in a Commencement Address—by J. K. Rowling in Harvard University

Evidentiality talks about the source of information and the speaker’s commitment to the statement.This paper aims at analyzing the visual,hearsay and inferential evidential markers in the Harvard University commencement address given by J.K.Rowing,and thus explore the interpersonal and evaluative functions of evidentiality based on the speech.If we say that a speech is an interaction between the speaker and the listener,the evidentiality just makes the interaction more fluent,by gaining the credibility from the listeners and influencing them.

A variable weight approach for evidential reasoning

一条可变重量途径被建议在证据的推理处理概率缺乏问题(嗯) 途径。概率缺乏问题显示在评价结果的不适当的信息应该是不到那在输入。然而，在某些情形下面， ER 途径不能解决概率缺乏问题，这被证明。可变重量途径基于二个假设：1 ) 更大的重量应该与更足够的信息被给规则；2 ) 更大的重量应该与不太迥异的信息被给规则。二概念的案例研究的评价结果显示出那 1 ) 概率缺乏问题用建议可变重量途径被解决，并且 2 ) 有更少的机能不全的信息和更多的不同被提供决定制造者帮助到达一致。

Review on Research of Evidentiality

This paper tries to comprehensively introduce different researches of evidentiality, and make some corresponding comments on these researches in terms of research features, methods or limitations and so on. In addition, after the review, conclusion and some questions will be put forward and further research will be recommended.

Land Cover Classification with Multi-source Data Using Evidential Reasoning Approach

Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.

Review on the Development of Evidentiality and Analysis of Non-Grammatical Evidential Systems

Evidentiality is the linguistic representation encoding the source of the said proposition and indicating its credibility,and also embodies the speaker’s assessment of information sources and the involvement in the said proposition.In the field of typology as rationale,the present study introduces the development of evidentiality in five stages,explores the definitions of evidentiality in the broad and narrow senses as well as grammatical and lexical evidential markers,and then illustrates non-grammatical evidential systems with English and Chinese examples,which provides a perspective for future empirical discourse studies in languages with only lexical evidential markers.

Tenses, aspects, and categories of evidentiality and egocentricity in spoken Lhasa Tibetan

This paper mainly discusses verbal aspects and their syntactic markers in Lhasa Tibetan. Since aspects in Tibetan always share markers with other important categories called evidentiality and egocentricity, the former of which assesses the evidence of speaker's statement and the latter denotes behavioral characteristics of a speaker'sego-centricity, this paper simultaneously describes all three types: aspect, evidentiality and egocentricity. There are nine types of verb aspects in Lhasa Tibetan, namely: Prospective, Imminent, Premediated, Realis, Durative, Resultative, Merely-past, Perfect, and Contextual aspects. There are four types of evidentialities, namely, Self-knowledge, Knowing-by-sight, Knowing-by-newly-found-information and Knowing-by-inference evidentialities. Categories of egocentricity demonstrate whether an action is towards the Self or the Other, whether the speaker gains or loses, and so on. Additionally, this essay also discusses the interactive relationship between syntactical verb aspects and personal pronouns, intentionality, modality, as well as temporal-spatial cognition.

Handling epistemic uncertainties in PRA using evidential networks

In order to overcome the limitations of traditional methods in uncertainty analysis, a modified Bayesian network(BN), which is called evidence network(EN), was proposed with evidence theory to handle epistemic uncertainty in probabilistic risk assessment(PRA). Fault trees(FTs) and event trees(ETs) were transformed into an EN which is used as a uniform framework to represent accident scenarios. Epistemic uncertainties of basic events in PRA were presented in evidence theory form and propagated through the network. A case study of a highway tunnel risk analysis was discussed to demonstrate the proposed approach. Frequencies of end states are obtained and expressed by belief and plausibility measures. The proposed approach addresses the uncertainties in experts' knowledge and can be easily applied to uncertainty analysis of FTs/ETs that have dependent events.

Evidential Reasoning in Air Battle Systems

Bayesian statistics assigns basic probabilities to singletons (single element sets). The Dempster-Shafer evidence theory generalizes Bayesian statistics by assigning basic probabilities to subsets to represent evidence and to develop evidential reasoning. This paper discusses what is the strength of evidence theory. As an application of evidence theory, evidential reasoning in air battle systems is discussed. In the air battle system, evidential reasoning is applied to fuse the multisensor information and identify the type of aircraft. The effectiveness of this fusion approach is evaluated by simulated data.

A Contrastive Study of Evidentiality in Presidential Debates—A Case Study of The US 2016 Presidential Candidate Debates

Evidentiality is a universal language phenomenon. Evidentiality not only refers to the source of knowledge that the speaker obtained but also can show and influence the speaker's attitude to the credibility of the information. Based on Hu Zhuanglin's theory of evidentiality, this paper was transcribed as corpus with a total of 50448 words by the videos of the 2016 Presidential Debates. Combined with quantitative and qualitative analysis, this paper uses software Ant Conc3.4.4 w and Excel as tools to complete data reduction, statistics and retrieval of the corpus which results are presented in the form of charts.This paper compares Hilary and Trump's evidentiality in terms of the types, frequency and distribution in American presidential debates aiming to explore how the American presidential candidates improve the reliability of language and convince the audience by using evidentiality. Some findings are as following: Four types of evidentials all appear in the corpus and each type has exerted different impact on presidential debate. Among the four types of evidentials, deduction evidential boasts the highest frequency of evidentials, followed by hearsay, belief and induction. The distribution of evidential is uneven. It is suggested that there are two main reasons for the difference: First, the difference between two candidates' personal language habits, their character, professional experience and background. Second, the different speaking time, strategies emphases and rules of the three presidential debates is inconsistent which has also influenced the difference.

半定量信息工业控制异构网络安全评估

工业控制系统的异构性及其与互联网深度融合给工控系统安全评估带来精度不高等诸多问题.本文提出一种基于半定量信息的工业异构网络安全态势评估模型,该模型可以提高评估精度,解决难以描述不确定性信息和难以契合工控异构网络复杂结构等现存问题.该方法将工业控制网络划分为不同异构子网,根据子网特点选取有针对性的评估属性,并使用性能优秀的ER规则对不同类型的评估属性进行融合,可以有效利用定性知识和定量数据,获得更加准确的评估结果.在多个实际工业控制网络数据集上进行评估实验得到的结果表明:本文提出的模型与网络安全评估实际参考值的均方根误差值比RF、BP、SVM模型分别降低了80.2%、40.8%和87.7%,验证了该模型的有效性和准确性.

刑事案件事实认定中经验法则的运用

经验法则是证据评价的依据、建立证据与事实之间联系的桥梁以及事实认定合理性的检验标准,在刑事案件事实认定中经验法则的运用不可或缺。经验法则的适当选取是其规范运用的前提和基础,对于指向一致的经验法则应以盖然性程度为选取的基准,而内容迥异的经验法则则以具体条件的符合性为选取标准。裁判文书应当展示经验法则的内容,并结合案件的事实与证据论证经验法则选取的准确性与运用的合理性;对于控辩双方援引的经验法则,裁判文书应区别情况选择回应的方式,以增强裁判结论的正当性。对于经验法则运用的错误,可以通过启动第二审程序、审判监督程序实现救济。经验法则的规范运用有赖于控辩双方运用经验法则的公开与论辩、人民陪审员制度功能的充分发挥、直接审理原则的彻底贯彻等程序支撑规则。

基于证据推理的隧道坍塌多源信息融合评估

由于影响因素众多,隧道坍塌风险评估是一个多属性决策问题.单源信息评估方法难以充分考虑所有的风险因素,导致预测结果存在偏差.为了评估隧道坍塌风险并提供更准确的风险控制策略,本研究提出了一种新的多源信息融合方法,该方法将云模型(CM)、支持向量机(SVM)和基于证据推理(ER)相结合.对多个信息源进行分析,得到不同的坍塌风险评估模型(目视检查数据通过SVM获取分类概率值,监测数据通过云模型获取概率值).每个模型的质量都由可信度和重要性权重来评价.然后运用ER规则融合各个评估模型的结果,给出总体的坍塌概率风险评估.与D-S理论相比,ER规则在处理高冲突信息方面具有更大的优势.当不同信息源的风险评估结果不一致时,D-S理论的融合结果往往会与常识相反,ER规则融合由于考虑了评估模型的重要性权重和可信度,更适用于高冲突信息的融合.该方法已成功应用于福建莆炎高速公路的鱼塘溪隧道.结果表明,所提出的多源信息融合方法的评价准确率为87.5%,而单源信息融合方法的评价准确率小于70%.此外,即使不同模型的风险结果存在较大的冲突,该融合模型也具有良好的性能.

考虑作战行动的单兵系统体系贡献率评估

针对单兵系统体系贡献率评估过程中存在的未考虑作战任务及评估信息不确定性等问题,提出了基于作战行动的两级证据推理评估方法,从基于能力需求的视角进行作战任务分解和能力分析,建立了作战能力需求指标体系;建立两级置信规则库,构建作战活动与能力需求的映射矩阵,采用证据推理进行能力满足度、作战任务完成率的计算,获得了单兵系统的体系贡献率,最后基于城市作战战场想定,给出了分析实例。基于作战行动的两级证据推理评估方法,对于单兵系统体系贡献率评估具有针对性和有效性,也为装备体系贡献率评估提供了一种新思路。

A GMDA clustering algorithm based on evidential reasoning architecture

The traditional clustering algorithm is difficult to deal with the identification and division of uncertain objects distributed in the overlapping region,and aimed at solving this problem,the Evidential Clustering based on General Mixture Decomposition Algorithm(GMDA-EC)is proposed.First,the belief classification of target cluster is carried out,and the sample category of target distribution overlapping region is extended.Then,on the basis of General Mixture Decomposition Algorithm(GMDA)clustering,the fusion model of evidence credibility and evidence relative entropy is constructed to generate the basic probability assignment of the target and achieve the belief division of the target.Finally,the performance of the algorithm is verified by the synthetic dataset and the measured dataset.The experimental results show that the algorithm can reflect the uncertainty of target clustering results more comprehensively than the traditional probabilistic partition clustering algorithm.