评估指标关联性处理方法研究

文章对评估指标关联性研究的现状和存在的问题进行了总结,探讨和分析了指标关联性处理的理论基础,并提出了关联性处理的一般步骤和方法,使得评估指标关联性处理的过程更加规范准确。     

基于异质信息的被执行人的评估决策问题研究

针对异质信息下被执行人财产隐匿行为甄别评估问题,考虑到被执行人评价指标关联性和执行人员的心理行为,提出一种基于复杂网络和前景理论的异质多属性决策方法.首先,对被执行人进行定量与定性分析,建立被执行人多维评价指标体系,进而利用复杂网络确定评价指标的权重;然后,考虑到被执行人评价指标的异质性,评价指标信息采用精确数、语言变量、概率语言等异质信息表示,在此基础上考虑决策者心理行为,利用前景理论计算各个被执行人的前景值,确定重点查控对象;最后,通过具体案例验证所提出方法的有效性和可行性,切实推动解决“执行难”问题,并为解决具有异质信息的实际管理决策问题提供新思路和新方法.     

基于ANP算法的高职院校评估方法的研究

为了对高职院校进行评估,针对高职院校评估指标的关联性和客观性不强的问题,提出一种基于Apriori-网络层次分析法的高职院校评估系统方法。首先在现有的研究和规范标准的基础上,通过阅读国家政策、文献、查阅现有的高职院校指标体系,以及对各省份高职院校近三年质量年度报告的梳理,明确了评估高职院校的指标,建立一套基于高职院校评估三级指标体系;然后通过Apriori算法来确定指标之间的关联程度,以此来构建ANP网络模型,提出一种基于改进Apriori算法+ANP算法的高职院校评估指标体系。     

机密稳健复杂系统安全性评估方法

机密稳健复杂系统具有高机密性要求、持续稳定运行需求、网络结构复杂等特点，但现有评估模型安全目标维度单一，缺乏指标间关联性分析。鉴于此，提出了基于指标关联性分析的综合安全评估模型，确立涵盖产品、系统和服务3个层面的机密性、可用性、可控性、可鉴别性、人员组织、风险管理、企业定级、可持续性8个安全目标。以智慧医疗物联网机密性为例，构建了基于指标关联性的排序确权算法，采用模糊综合评价法，对所选系统进行评估。     

漏洞威胁的关联评估方法

针对目前网络安全评估方法不能有效解决漏洞的关联性评价问题,提出一种基于漏洞间关联性的网络漏洞威胁评估方法。以攻击图为评估数据源,兼顾前序节点和后序节点的多样性,融合从前向后入(FI)方法和从后向前出(BO)方法,采用优化贝叶斯网络方法和加权平均法计算路径漏洞与主机漏洞的威胁值,得到关联环境下的漏洞量化评估结果。实验结果表明,该方法能有效弥补传统方法孤立评估漏洞的不足,能够更为有效地表达出系统的安全特性。     

基于泛在电力物联网的普适性智能电表状态实时评估方法

针对当前智能电表状态评估存在精确度低、泛化性差和实时困难等问题,本文采用泛在电力物联网构建状态实时评估方法解决该问题。首先,采用决策树算法实现智能电表的分类,整体增强方法的匹配度和适应性;随后针对不同类别的智能电表,采用Apriori算法对样本集数据的特征集进行识别和提取,从而降低特征维度并增强关联性;接着,基于决策引擎实现对智能电表状态实时评估,并以度量学习实现新增物联网采集数据的有效性评估,反馈优化传感设备部署,从而根据评估结果实现对新增部署传感器及其位置的调整,进而根据应用场景不断优化智能电表状态实时评估应用模式。实验结果表明,本方法可实现智能电表运行状况的实时、普适、精准运维评估,进一步解决泛在电力物联网设备现场部署经验不足、校验无目标等问题。     

基于分布式计算的电网运行可靠性评估算法研究

针对电网运行可靠性评估计算量大,但又要求计算时间短的问题,提出了一种基于分布式计算的电网运行可靠性评估算法。该算法采用改进的状态选择和分析方案,解决了计算终端间随机数序列的关联性问题,有效地降低了通信量,提高了计算速度。基于该算法编制的评估软件已通过RTS-24系统和某省级电网的实际运行测试,测试结果表明,该算法不仅能够有效评估电网运行可靠性,而且计算速度随计算终端数增加呈一定倍数提高。     

基于信息融合的网络安全评估方法研究

为了数字化衡量网络安全状况,该文提出了一个应用信息融合的网络安全评估模型,能够有效处理多源安全检测信息。信息融合中心提供三个层次的信息融合,实现安全事件的过滤、关联和融合,解决信息空间爆炸和信息关联性的问题。     

信息融合技术在网络安全评估中的应用

本文提出了一个应用信息融合的网络安全评估模型,能够有效处理多源安全检测信息。信息融合中心提供三个层次的信息融合,实现安全事件的过滤、关联和融合,解决信息空间爆炸和信息关联性的问题。并重点介绍了模糊数学信息融合的方法。     

基于关联性的漏洞评估方法

为了更加有效地评估漏洞威胁, 在原有的基于关联性漏洞评估方法的基础上, 从漏洞的可利用性出发, 分析了通用漏洞评估系统（CVSS）作为漏洞可利用性评价指标的缺点。在改进CVSS评价指标的基础上, 提出了更为合理的漏洞可利用性评估体系, 并改进了原有的漏洞评估方法。该方法能够有效地挖掘漏洞自身特点, 科学地进行漏洞评估, 并通过实验验证了该方法的合理性和有效性。     

On comparing two sequences of numbers and its applications to clustering analysis

A conceptual problem that appears in different contexts of clustering analysis is that of measuring the degree of compatibility between two sequences of numbers. This problem is usually addressed by means of numerical indexes referred to as sequence correlation indexes. This paper elaborates on why some specific sequence correlation indexes may not be good choices depending on the application scenario in hand. A variant of the Product-Moment correlation coefficient and a weighted formulation for the Goodman-Kruskal and Kendall’s indexes are derived that may be more appropriate for some particular application scenarios. The proposed and existing indexes are analyzed from different perspectives, such as their sensitivity to the ranks and magnitudes of the sequences under evaluation, among other relevant aspects of the problem. The results help suggesting scenarios within the context of clustering analysis that are possibly more appropriate for the application of each index.     

Answering contextual questions based on ontologies and question templates

Contextual question answering (CQA), in which user information needs are satisfied through an interactive question answering (QA) dialog, has recently attracted more research attention. One challenge is to fuse contextual information into the understanding process of relevant questions. In this paper, a discourse structure is proposed to maintain semantic information, and approaches for recognition of relevancy type and fusion of contextual information according to relevancy type are proposed. The system is evaluated on real contextual QA data. The results show that better performance is achieved than a baseline system and almost the same performance as when these contextual phenomena are resolved manually. A detailed evaluation analysis is presented.     

基于汉语复句的语义相关度计算及类别的标识

语义相关度计算作为中文信息处理领域中的一项关键技术,在信息检索、语义消岐、文本分类中起着重要的作用。利用汉语复句的句法理论和关系标记搭配理论,以汉语复句语料库以及搜索引擎获取的复句为语料,提出了一种基于汉语复句的语义相关度计算方法——SRCCS。本方法不仅能够计算词语的相关度,而且能够表明相关的性质与类别。与通过短文计算相关度的方法相比,本方法选取的计算对象范围更小,因而结果更准确,计算复杂度更低。在同一测试集上与搜索引擎方法的对比分析证明了基于汉语复句的语义相关度计算方法的有效性与优越性。     

基于灰色关联度的供应商评价体系研究*

将层次分析法与灰色关联度相结合,提出了一种新的供应商评价方法。针对供应商评价指标间关联性比较强的特点,采用层次分析法确定评价指标权值,以灰色关联度作为评价标准,对复杂多目标的供应商对象进行综合评价,其结果具有良好的性质且方法简单、利于实现。     

在线电影评论倾向性分类算法研究

研究网络在线评论的倾向性分类能够及时了解民众对当前事件、热点话题的态度和心理状态,从而为相关领域的决策提供依据。针对网络在线电影评论倾向性分类问题,提出了基于网络词语扩展及属性约简的解决算法,该算法利用相关度测量对垃圾评论进行剔除,针对网络语言自身特点对其属性进行扩展,使用词频和信息增益分两步进行特征选择,构建特征属性进行分类。实验结果表明,使用该算法后,分类准确率等各项指标得到了提高。     

Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set

A method to analyse links between binary attributes in a large sparse data set is proposed. Initially the variables are clustered to obtain homogeneous clusters of attributes. Association rules are then mined in each cluster. A graphical comparison of some rule relevancy indexes is presented. It is used to extract best rules depending on the application concerned. The proposed methodology is illustrated by an industrial application from the automotive industry with more than 80 000 vehicles each described by more than 3000 rare attributes.     

黏性土冻胀风险可拓评价方法研究

针对黏性土冻胀性分级指标单一、界限值大,无法反映冻胀影响因素间不确定性的问题,本文在基于可拓云模型在不确定性方面的优点,结合熵值客观赋权方法提出了以取样深度、含水率、干密度、孔隙比和超塑含水率为评价指标的可拓云综合评价模型.该模型可根据实测数据与冻胀风险等级的随机性处理离散化问题,真实反映指标间的模糊性、随机性以及不确定性关系,准确评价黏性土冻胀等级,通过工程案例对该模型的有效性进行了验证.研究结果表明,基于熵值法的可拓云综合评价模型在黏性土冻胀风险评价中的应用是有效的、可行的.     

Presenting a new search strategy to select synchronization values for classifying bipolar mood disorders from schizophrenic patients

There is a growing interest to employ synchronization methods to reveal natural connections among the brain lobes by measuring co-activations among EEG channels. Regarding high number of EEG channels, lots of synchronization indexes are determined between two by two channels leading to construct a high dimensional feature vector for each time frame. The objective of this paper is to propose an effective feature selection method to find discriminative synchronization indexes in order to classify patients with schizophrenia from those with bipolar mood disorder (BMD). The state-of-art synchronization methods from various domains such as phase-locking value (PLV), robust synchronization (RS), and synchronization likelihood (SL), were implemented to provide a rich feature set in order to classify the two groups. To increase the classification accuracy, a capable feature selection scheme is proposed entitled greedy overall relevancy (GOR) to select discriminative synchronization indexes. The elicited synchronization vectors of 53 subjects imposed to support vector machine (SVM) classifier and the classification result with and without applying GOR, provided 92.45% and 88.68% accuracy, respectively. Across-group variance (AGV) is chosen as a rival method to GOR; the selected features by AGV entered to the classifier resulting in 91.34% accuracy. Using pair T-test exhibits the significant superiority of GOR to AGV such that P-value determined less than 0.05. To the best of authors' knowledge, this is the first attempt to utilize the selected synchronization indexes as informative features applying to a classifier for diagnosing the psychiatric patients.     

面向词义消歧的词语相关度计算

为解决词义消歧问题,引入了语义相关度计算。研究并设计了词语相关度计算模型,即在充分考虑语义资源《知网》中概念间结构特点、概念信息量和概念释义的基础上,利用概念词与实例词间的搭配所表征的词语间强关联来进行词语相关度的计算。实验结果表明,该模型得到的语义相关度结果对于解决WSD问题提供了良好的支撑依据。