基于模糊AHP的大学课堂教学质量评价研究

为了对大学教师课堂教学质量进行有效的监督和管理,建立了现代信息技术环境下大学教师课堂教学质量评价指标体系,基于模糊综合评判法和层次分析法(AHP)建立了教师课堂教学质量评价模型,应用此模型实例分析某教师的课堂教学质量并提出相应的提升策略,提高了评价的科学性和实用性。     

基于AHP的樱园农家乐SWOT分析及发展对策

利用AHP和SWOT分析理论,提出了西安樱园观光旅游农家乐的发展策略.根据实地调研,探讨了樱园农家乐发展所面临的优势、劣势、机遇和威胁,建立了总体层次分析评价指标体系,确定了各层次的指标权重,分析了四种备选的优化方案.最后建议,樱园农家乐的发展应充分发挥“体验参与模式”的优势,把握政府引导和政策支持的机遇.     

DCKPDP：改进k-prototype聚类的差分隐私混合属性数据发布方法

当前混合属性数据发布中隐私保护方法大多存在隐私保护效果不佳或数据效用较差的问题,采用差分隐私与优化的k-prototype聚类方法相结合,提出改进k-prototype聚类的差分隐私混合属性数据发布方法(DCKPDP)。为解决传统k-prototype聚类算法没有考虑不同数值型属性对聚类结果有较大影响的问题,利用信息熵为每个数值型属性添加属性权重;为解决聚类初始中心点人为规定或者由随机算法随机确定,导致聚类结果精确度不高的问题,结合数据对象的局部密度和高密度对聚类过程中初始中心点进行自适应选择;为解决数据信息泄露风险较高的问题,对聚类中心值进行差分隐私保护。实验结果表明,DCKPDP算法满足差分隐私保护所需的噪声量更小,数据的可用性更好。     

结构正则化多视图非负矩阵分解

现存非负矩阵分解（non-negative matrix factorization,NMF）研究多考虑单一视图分解数据,忽略了数据信息的全面性。此外,NMF限制其获取数据的内在几何结构。针对以上问题,提出一个结构正则化多视图非负矩阵分解算法（structure regularized multi-view nonnegative matrix factorization,SRMNMF）。首先,通过主成分分析来对数据进行全局结构的判别式学习;其次,利用流形学习来捕获数据的局部结构;然后,通过利用多视图数据的多样性和差异性来学习表征。模型提升了算法聚类的整体性能,更加有效地挖掘数据的结构信息。此外,采用高效的交替迭代算法优化目标函数得到最优的因子矩阵。在六个数据集上与现存的代表性方法比较,所提出的SRMNMF的准确率、NMI和Purity分别最大提高4.4%、6.1%和4.05%。     

Spatial ecology of common carp (Cyprinus carpio) in Lake Winnipeg and its potential for management actions

Common carp feeding and spawning behaviours negatively impact the functioning of marsh ecosystems. In the Netley-Libau Marsh, situated on the southern end of Lake Winnipeg, water level regulations, nonpoint source nutrient pollution, and the non-native common carp are thought to be the main contributors to the degradation of the marsh habitat. Using acoustic telemetry, we analysed the movement rate, frequency and timing of suspected spawning migrations, spatial ecology, and aggregation of common carp in the Lake Winnipeg drainage over a three year time period. Common carp moved the farthest during the open water period when water temperature was >5 °C. Their annual migration into Netley-Libau Marsh was correlated to ordinal date. Common carp left the marsh in late spring/early summer, presumably feeding in Lake Winnipeg, before moving to overwintering sites situated in Traverse Bay and Lake Winnipeg, where they arrived typically by October and formed aggregations. These findings will inform habitat and fisheries managers in the effort to undertake evidence-based management actions. The predictability of the movements and the tendency for common carp to aggregate indicates that exclusion techniques and commercial fishing may represent viable management solutions.     

基于极限学习机的堤防工程多元风险指标评价方法

为提高堤防工程风险评价的准确性,提出了基于极限学习机的堤防工程多元风险指标评价方法。首先,综合考虑影响堤防风险的28个评价指标,利用层次分析法从预警系统、堤防工程系统、环境系统和社会经济系统这4个方面建立了堤防工程多元风险评价指标体系。接着,基于极限学习机算法对28个指标进行标准化处理及分级标准构建,以风险指标作为输入量,分级隶属度作为输出量,划分风险等级,量化评价指标,估计多元风险评价指标值和判断风险的严重程度。最后,依托鄱阳湖重点堤防——康山大堤,构建多元风险评价指标体系,运用极限学习机算法计算多元风险评价指标值。评价结果表明:康山大堤目前处于基本安全水平,符合康山大堤经过两次加固后的工程实际情况,并与其他方法进行对比,验证了提出方法的可靠性和有效性。该方法可拓展应用到其他重要水工结构工程风险评估中。     

基于级差最大化组合赋权法的地下水功能评价

针对地下水功能评价中单独采用主观赋权法或客观赋权法存在的问题,结合二者的优点,采用基于主观赋权的层次分析法与客观赋权的熵权法相结合的级差最大化组合赋权法评价地下水功能,并通过计算不同赋权方法权重结果与平均值的相对偏差来衡量级差最大化组合赋权法的准确性。将该方法应用于伊犁河谷西北部平原区浅层地下水功能评价,评价结果基本符合评价区实际情况,验证了该评价方法在地下水功能评价中的可行性。     

Potential applications of three-way multidimensional scaling and related techniques to integrate knowledge from multiple experts

Theknowledge transfer problem in artificial intelligence consists of finding effective ways to elicit information from a human expert and represent it in a form suitable for use by an expert system. One approach to formalizing and guiding this knowledge transfer process for certain types of expert systems is to use psychometric scaling methods to analyze data on how the human expert compares or groups solutions. For example, Butler and Corter [1] obtained judgments of thesubstitutability of solutions from an expert, then analyzed the resulting data via techniques for fitting trees and extended trees [2]. The expert's interpretation of certain aspects of the solutions were directly encoded as production rules, allowing rapid prototyping. In this paper we consider the problem of combining information from multiple experts. We propose the use of three-way or individual differences multidimensional scaling, tree-fitting, and unfolding models to analyze two types of data obtainable from the multiple experts: judgments of the substitutability of pairs of solutions, and judgments of the appropriateness of specific solutions to specific problems. An application is described in which substitutability data were obtained from three experts and analyzed using the SINDSCAL program [3] for three-way multidimensional scaling [4].     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

分布式信息系统风险评估研究-以安徽烟草为例

对分布式信息系统所面临的主要风险进行识别和评估是构建灾备系统的首要任务。以安徽烟草分布式信息系统为例,首先分析了该信息系统在环境、管理、技术、设备等方面所存在的风险,然后以层次分析法为核心构造了评估决策模型,结合专家打分,计算出各类风险对安徽烟草信息系统的威胁程度并进行排序,从而识别出主要的风险源,为构建灾备体系提供依据。