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.

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.

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.

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.

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.

A New Model for Network Security Situation Assessment of the Industrial Internet

To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment model.First,this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge.Second,the evaluation indicators are fused with expert knowledge and the ER algorithm.According to the fusion results,a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established,and the projection covariance matrix adaptive evolution strategy(P-CMA-ES)is used to optimize the model parameters.This method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial explosion.Moreover,it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient data.Finally,a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the method.The research results showthat this method has strong applicability to the network security situation assessment of complex Industrial Internet systems.It can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures,thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.

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.

国内言据性研究的可视化分析

文章根据中国知网计量可视化分析手段,对1994-2017年4月期间中国知网上有关言据性文献,从文献数量、高频关键词、学科分布、文献来源和研究内容五个方面进行分析和统计。言据性发展经历了萌芽阶段、起步阶段,目前正处于快速发展期;语言学科是言据性研究的主要学科,汉语言据性研究已经超过了英语;言据性文献有25%发表在核心期刊上说明其具有较高研究价值;言据性研究主要集中在语篇分析、语料库、功能研究以及多理论视角下的言据性解释。

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.

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.

A Subjective Risk Analysis Approach of Container Supply Chains

After the 9/11 terrorism attacks, the lock-out of the American West Ports in 2002 and the breakout of SARS disease in 2003 have further focused mind of both the public and industrialists to take effective and timely measures for assessing and controlling the risks related to container supply chains (CSCs). However, due to the complexity of the risks in the chains, conventional quantitative risk assessment (QRA) methods may not be capable of providing sufficient safety management information, as achieving such a functionality requires enabling the possibility of conducting risk analysis in view of the challenges and uncertainties posed by the unavailability and incompleteness of historical failure data. Combing the fuzzy set theory (FST) and an evidential reasoning (ER) approach, the paper presents a subjective method to deal with the vulnerability-based risks, which are more ubiquitous and uncertain than the traditional hazard-based ones in the chains.

Risk assessment of water security in Haihe River Basin during drought periods based on D-S evidence theory

The weights of the drought risk index （DRI）, which linearly combines the reliability, resiliency, and vulnerability, are difficult to obtain due to complexities in water security during drought periods. Therefore, drought entropy was used to determine the weights of the three critical indices. Conventional simulation results regarding the risk load of water security during drought periods were often regarded as precise. However, neither the simulation process nor the DRI gives any consideration to uncertainties in drought events. Therefore, the Dempster-Shafer （D-S） evidence theory and the evidential reasoning algorithm were introduced, and the DRI values were calculated with consideration of uncertainties of the three indices. The drought entropy and evidential reasoning algorithm were used in a case study of the Haihe River Basin to assess water security risks during drought periods. The results of the new DRI values in two scenarios were compared and analyzed. It is shown that the values of the DRI in the D-S evidence algorithm increase slightly from the original results of Zhang et al. （2005）, and the results of risk assessment of water security during drought periods are reasonable according to the situation in the study area. This study can serve as a reference for further practical application and planning in the Haihe River Basin, and other relevant or similar studies.

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.

Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs

Many properties of natural fractures are uncertain,such as their spatial distribution,petrophysical properties,and fluid flow performance.Bayesian theorem provides a framework to quantify the uncertainty in geological modeling and flow simulation,and hence to support reservoir performance predictions.The application of Bayesian methods to fractured reservoirs has mostly been limited to synthetic cases.In field applications,however,one of the main problems is that the Bayesian prior is falsified,because it fails to predict past reservoir production data.In this paper,we show how a global sensitivity analysis(GSA)can be used to identify why the prior is falsified.We then employ an approximate Bayesian computation(ABC)method combined with a tree-based surrogate model to match the production history.We apply these two approaches to a complex fractured oil and gas reservoir where all uncertainties are jointly considered,including the petrophysical properties,rock physics properties,fluid properties,discrete fracture parameters,and dynamics of pressure and transmissibility.We successfully identify several reasons for the falsification.The results show that the methods we propose are effective in quantifying uncertainty in the modeling and flow simulation of a fractured reservoir.The uncertainties of key parameters,such as fracture aperture and fault conductivity,are reduced.

Digital Mammogram Inferencing System Using Intuitionistic Fuzzy Theory

In the medical field,the detection of breast cancer may be a mysterious task.Physicians must deduce a conclusion from a significantly vague knowledge base.A mammogram can offer early diagnosis at a low cost if the breasts'satisfactory mammogram images are analyzed.A multi-decision Intuitionistic Fuzzy Evidential Reasoning(IFER)approach is introduced in this paper to deal with imprecise mammogram classification efficiently.The proposed IFER approach combines intuitionistic trapezoidal fuzzy numbers and inclusion measures to improve representation and reasoning accuracy.The results of the proposed technique are approved through simulation.The simulation is created utilizing MATLAB software.The screening results are classified and finally grouped into three categories:normal,malignant,and benign.Simulation results show that this IFER method performs classification with accuracy almost 95%compared to the already existing algorithms.The IFER mammography provides high accuracy in providing early diagnosis,and it is a convenient diagnostic tool for physicians.

An Approach for Designing and Implementing Evidence Management Systems

Generally speaking, the software of management systems in law field is based on the frames of events. However, we are going to study and develop the new software of management system in which the basic elements are evidence. This kind of software is often called evidence management system. Here we are supposed to present a designing plan and an implementing approach for the evidence management system in detail. Some functions such as global, dynamic and systematic managing of evidence can be implemented in this system. We attempt to provide a function of individual searching as well. Users may carry out multi-dimensional data analysis based on the information of our database of the management systems.

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.

On the evidential reasoning rule for dependent evidence combination

Evidential Reasoning(ER)rule,which can combine multiple pieces of independent evidence conjunctively,is widely applied in multiple attribute decision analysis.However,the assumption of independence among evidence is often not satisfied,resulting in ER rule inapplicable.In this paper,an Evidential Reasoning rule for Dependent Evidence combination(ERr-DE)is developed.Firstly,the aggregation sequence of multiple pieces of evidence is determined according to evidence reliability.On this basis,a calculation method of evidence Relative Total Dependence Coefficient(RTDC)is proposed using the distance correlation method.Secondly,as a discounting factor,RTDC is introduced into the ER rule framework,and the ERr-DE model is formulated.The aggregation process of two pieces of dependent evidence by ERr-DE is investigated,which is then generalized to aggregate multiple pieces of non-independent evidence.Thirdly,sensitivity analysis is carried out to investigate the relationship between the model output and the RTDC.The properties of sensitivity coefficient are explored and mathematically proofed.The conjunctive probabilistic reasoning process of ERr-DE and the properties of sensitivity coefficient are verified by two numerical examples respectively.Finally,the practical application of the ERr-DE is validated by a case study on the performance assessment of satellite turntable system.

A novel combination belief rule base model for mechanical equipment fault diagnosis

Due to the excellent performance in complex systems modeling under small samples and uncertainty,Belief Rule Base(BRB)expert system has been widely applied in fault diagnosis.However,the fault diagnosis process for complex mechanical equipment normally needs multiple attributes,which can lead to the rule number explosion problem in BRB,and limit the efficiency and accuracy.To solve this problem,a novel Combination Belief Rule Base(C-BRB)model based on Directed Acyclic Graph(DAG)structure is proposed in this paper.By dispersing numerous attributes into the parallel structure composed of different sub-BRBs,C-BRB can effectively reduce the amount of calculation with acceptable result.At the same time,a path selection strategy considering the accuracy of child nodes is designed in C-BRB to obtain the most suitable submodels.Finally,a fusion method based on Evidential Reasoning(ER)rule is used to combine the belief rules of C-BRB and generate the final results.To illustrate the effectiveness and reliability of the proposed method,a case study of fault diagnosis of rolling bearing is conducted,and the result is compared with other methods.