基于关键词权重的XML查询结果排序方法

XML关键字查询结果质量不高的一个很重要的原因是查询关键词难以反映用户真实的查询意图,而给关键词设置权重在一定程度上可以解决这一难题. 本文结合关键字之间的结构关系提出了一种新的结果排序方法,该方法给查询关键词设置权重,并参照查询关键词的权重给包含关键字的结点设定结点权重,然后根据关系树中的结点权重和关键词之间结构关系^[1]统计SLCA结点的重要程度,再以此依据对查询结果进行排序,最后返回给用户有序的查询结果. 实验结果和分析表明,提出的排序方法具有较高的准确率,能够较好地满足用户查询的需求和偏好.     

基于多维质量属性偏好的可信服务选择

现有的可信服务选择方法将信任值表示为单一的实数,并依据信任值对候选服务进行排序,不能充分体现服务可信性的特征和用户偏好.在传统服务质量模型的基础上引入可信属性构建多维服务质量模型,并阐述信任值的计算方法.在此基础上提出一种层次分析法和偏好排序组织法结合的服务选择方法,其中,层次分析法用于计算基于用户偏好的属性权重值,偏好排序组织法用于对候选服务进行排序.实验表明该方法可以有效地表达用户对于多维服务质量属性的偏好,提高服务排序和服务选择的可信性.     

基于模拟退火粒子群算法的AHP排序权值计算

层次分析法( AHP)中根据判断矩阵求解排序权重问题本质上为一个使一致性指标最小化的优化问题.针对现有解决方法中的不足,提出一种结合粒子群和模拟退火原理,并且根据AHP的特点引入特征粒子来求解判断矩阵排序权重的算法,同时,针对一致性不满足条件的矩阵或者残缺矩阵,在一致性指标中引入可信度参数,使算法能够动态修正不一致判断矩阵或者残缺矩阵,应用的范围更加广泛.文中对判断矩阵求解排序权重以及一致性检验、模拟退火粒子群算法解决AHP排序问题进行了介绍,并给出了实验数据以及分析.实例结果表明,算法可行且有效,计算结果精度高,稳定性好.     

浅谈层次分析法在变电站站址优选中的应用

本文首先对变电站选址中的影响因素展开分析,接着使用层次分析法中的单排序与总排序方式对选址因素进行了权重对比,并通过对比确定了最佳选址位置。旨在利用层次分析法使得变电站选址最优化,提高变电站运营的有效性。     

基于模糊层次分析法的Lucene网页排序算法研究

对改进后的Lucene网页排序算法中考虑的几个因素,用模糊层次分析法的方法对这些因素的权重进行确定,并运用算例对其过程进行说明,使权重向量的确定更有说服力。     

基于AHP的反病毒软件选优方法研究

针对四款专业反病毒软件产品的测评结果,提出一种基于层次分析的多方案选优方法。通过计算待选反病毒软件产品的总排序权重,得到反病毒软件的综合选优顺序,为选优提供量化的理论依据。结合实例说明了该选优方法的具体过程,结果验证了其有效性和实用性。     

基于核和灰度的区间灰数多属性决策方法

针对属性值为区间灰数且部分权重信息已知的多属性决策问题, 提出一种基于区间灰数的核和灰度的决策方法. 根据专家评价值的取值范围设置区间灰数的取值论域, 给出了区间灰数的基于核和灰度的简化形式, 建立了普通区间灰数到标准区间灰数的转化方法, 分别基于标准灰数的核和灰度分别求取属性的权重, 进而得到属性的综合权重, 并提出了一种基于标准区间灰数相对核的排序方法对方案进行排序. 最后通过一个算例验证了所提出方法的有效性和可行性.

     

层次分析法在择校问题中的应用

数学无处不在,比如小升初择校问题.公立学校升学率低、但校园环境好、私立学校升学率高、但其他条件却无法与公立学校相比,到底应该如何选择？使用层次分析法来指导家长择校,首先选取一些比较准则,如升学率、环境等,给出在家长心目中的权重,然后根据每一准则对学校进行排序,结合两层的排序得到总排序,排在第一位的学校就是最理想的学校.     

大型控制系统信息安全评估研究

针对目前大型流程工业控制系统信息安全的需求,结合工业实际应用情况,研究了大型控制系统信息安全评估模型。运用模糊层次分析法和综合评价,再结合专家经验,采用改进的评估指标权重系数排序算法,获得了大型控制系统信息安全各评估指标权重系数的排序。通过对改进前和改进后的评估指标权重系数排序公式的比较,结果表明所提出方法是合理有效的。该方法可挖掘出工业控制系统信息安全性能中一系列目前未被重视的安全盲区,可为大型流程工业控制系统选择与安全管理提供有效建议和改进方向。     

基于改进数据包络分析的高速列车开行方案的动态评价

针对数据包络分析(DEA)方法不能反映评价指标间权重的差异性以及不能对有效决策单元排序和调整的缺点,提出一种改进的DEA方法.首先, 运用层次分析法确定各指标的权重并建立偏好锥模型;然后, 运用交叉效率对所有决策单元进行排序并根据上座率和理想决策单元对部分决策单元进行调整; 最后,运用该方法对京沪高速列车开行方案进行评价.研究发现6条运行线中有4条是DEA有效的,需要对2条非有效和1条有效运行线进行调整.实验结果表明,改进的DEA方法能够为高速旅客列车开行方案的动态调整提供理论依据.     

A Meta-Learning Method to Select the Kernel Width in Support Vector Regression

The Support Vector Machine algorithm is sensitive to the choice of parameter settings. If these are not set correctly, the algorithm may have a substandard performance. Suggesting a good setting is thus an important problem. We propose a meta-learning methodology for this purpose and exploit information about the past performance of different settings. The methodology is applied to set the width of the Gaussian kernel. We carry out an extensive empirical evaluation, including comparisons with other methods (fixed default ranking; selection based on cross-validation and a heuristic method commonly used to set the width of the SVM kernel). We show that our methodology can select settings with low error while providing significant savings in time. Further work should be carried out to see how the methodology could be adapted to different parameter setting tasks.Supplementary material to this paper is available in electronic form at http://dx.doi.org/10.1023/B:MACH.0000015879.28004.9b     

Optimization of robust area traffic control with equilibrium flow under demand uncertainty

For area traffic control road network under realization of uncertain travel demand, a robust signal setting is investigated in this paper. Due to certain hierarchy in a decision-making order, a min–max bilevel program is proposed. A new solution method is presented to determine a Nash–Stackelberg solution where a proposed signal setting is found for area traffic control under demand uncertainty. In order to investigate the robustness of the proposed signal settings, numerical computations are performed for various initial data sets in a medium-sized example road network. Good computational results indicated that the proposed signal settings can successfully reduce a worst-case travel cost substantially while incurring a relatively slight loss of optimality with respect to the optimal deterministic solutions for nominal travel demands. Particularly, our computation results showed that the proposed signal settings become even attractive as demand growth increases under a worst-case realization taken by uncertain travel demands.     

A hybrid approach for learning concept hierarchy from Malay text using artificial immune network

A concept hierarchy is an integral part of an ontology but it is expensive and time consuming to build. Motivated by this, many unsupervised learning methods have been proposed to (semi-) automatically develop a concept hierarchy. A significant work is the Guided Agglomerative Hierarchical Clustering (GAHC) which relies on linguistic patterns (i.e., hypernyms) to guide the clustering process. However, GAHC still relies on contextual features to build the concept hierarchy, thus data sparsity still remains an issue in GAHC. Artificial Immune Systems are known for robustness, noise tolerance and adaptability. Thus, an extension to the GAHC is proposed by hybridizing it with Artificial Immune Network (aiNet) which we call Guided Clustering and aiNet for Learning Concept Hierarchy (GCAINY). In this paper, we have tested GCAINY using two parameter settings. The first parameter setting is obtained from the literature as a baseline parameter setting and second is by automatic parameter tuning using Particle Swarm Optimization (PSO). The effectiveness of the GCAINY is evaluated on three data sets. For further validations, a comparison between GCAINY and GAHC has been conducted and with statistical tests showing that GCAINY increases the quality of the induced concept hierarchy. The results reveal that the parameters value found by using PSO significantly produce better concept hierarchy than the vanilla parameter. Thus it can be concluded that the proposed approach has greater ability to be used in the field of ontology learning.     

Multimedia polysensory integration training system dedicated to children with educational difficulties

Ranking of search results and ads has traditionally been studied separately. The probability ranking principle is commonly used to rank the search results while the ranking based on expected profits is commonly used for paid placement of ads. These rankings try to maximize the expected utilities based on the user click models. Recent empirical analysis on search engine logs suggests unified click models for both ranked ads and search results (documents). These new models consider parameters of (i) probability of the user abandoning browsing results (ii) perceived relevance of result snippets. However, current document and ad ranking methods do not consider these parameters. In this paper we propose a generalized ranking function—namely Click Efficiency (CE)—for documents and ads based on empirically proven user click models. The ranking considers parameters (i) and (ii) above, optimal and has the same time complexity as sorting. Furthermore, the CE ranking exploits the commonality of click models, hence is applicable for both documents and ads. We examine the reduced forms of CE ranking based upon different underlying assumptions, enumerating a hierarchy of ranking functions. Interestingly, some of the rankings in the hierarchy are currently used ad and document ranking functions; while others suggest new rankings. Thus, this hierarchy illustrates the relationships between different rankings, and clarifies the underlying assumptions. While optimality of ranking is sufficient for document ranking, applying CE ranking to ad auctions requires an appropriate pricing mechanism. We incorporate a second price based mechanism with the proposed ranking. Our analysis proves several desirable properties including revenue dominance over Vickrey Clarke Groves (VCG) for the same bid vector and existence of a Nash equilibrium in pure strategies. The equilibrium is socially optimal, and revenue equivalent to the truthful VCG equilibrium. As a result of its generality, the auction mechanism and the equilibrium reduces to the current mechanisms including Generalized Second Price Auction (GSP) and corresponding equilibria. Furthermore, we relax the independence assumption in CE ranking and analyze the diversity ranking problem. We show that optimal diversity ranking is NP-Hard in general, and a constant time approximation algorithm is not likely. Finally our simulations to quantify the amount of increase in different utility functions conform to the results, and suggest potentially significant increase in utilities.     

A personalized search engine based on Web‐snippet hierarchical clustering

We propose a (meta‐)search engine, called SnakeT (SNippet Aggregation for Knowledge ExtracTion), which queries more than 18 commodity search engines and offers two complementary views on their returned results. One is the classical flat‐ranked list, the other consists of a hierarchical organization of these results into folders created on‐the‐fly at query time and labeled with intelligible sentences that capture the themes of the results contained in them. Users can browse this hierarchy with various goals: knowledge extraction, query refinement and personalization of search results. In this novel form of personalization, the user is requested to interact with the hierarchy by selecting the folders whose labels (themes) best fit her query needs. SnakeT then personalizes on‐the‐fly the original ranked list by filtering out those results that do not belong to the selected folders. Consequently, this form of personalization is carried out by the users themselves and thus results fully adaptive, privacy preserving, scalable and non‐intrusive for the underlying search engines. We have extensively tested SnakeT and compared it against the best available Web‐snippet clustering engines. SnakeT is efficient and effective, and shows that a mutual reinforcement relationship between ranking and Web‐snippet clustering does exist. In fact, the better the ranking of the underlying search engines, the more relevant the results from which SnakeT distills the hierarchy of labeled folders, and hence the more useful this hierarchy is to the user. Vice versa, the more intelligible the folder hierarchy, the more effective the personalization offered by SnakeT on the ranking of the query results. Copyright © 2007 John Wiley & Sons, Ltd.     

基于粒子群算法的模糊层次分析模型

针对模糊层次分析法中存在的模糊判断矩阵一致性检验和修正困难、元素权重计算繁琐的问题,从模糊判断矩阵的定义角度出发,构建了基于粒子群算法的模糊层次分析模型(PSO-FAHP),提出了包含模糊判断矩阵一致性修正及各元素排序过程的非线性带约束优化问题,引入粒子群算法实现了问题的求解,并分析了模型的合理性。最后通过数值算例对比了模型的计算结果,验证了模型的正确性。对模糊层次分析法的实践应用具有一定的指导意义。     

Continuous Improvement and Collaborative Improvement: Similarities and Differences

A substantial body of theoretical and practical knowledge has been developed on continuous improvement. However, there is still a considerable lack of empirically grounded contributions and theories on collaborative improvement, that is, continuous improvement in an inter‐organizational setting. The CO‐IMPROVE project investigated whether and how the concept of continuous improvement can be extended and transferred to such settings. The objective of this article is to evaluate the CO‐IMPROVE research findings in view of existing theories on continuous innovation. The article investigates the similarities and differences between key components of continuous and collaborative improvement by assessing what is specific for continuous improvement, what for collaborative improvement, and where the two areas of application meet and overlap. The main conclusions are that there are many more similarities between continuous and collaborative improvement. The main differences relate to the role of hierarchy/market, trust, power and commitment to collaboration, all of which are related to differences between the settings in which continuous and collaborative improvement unfold.     

RBAC中权限扩展的实现

针对RBAC以访问控制主体为中心,较少关注访问控制客体,造成了在权限设置和管理过程中工作量较大的问题,提出了对权限进行相应扩展的方法.该方法将权限分为操作和操作所针对的数据对象,提出了操作继承和数据对象继承概念,并在此基础上提出了权限继承概念.给出了具体的应用例子,表明该方法在权限设置和管理过程中将大大减少系统安全管理人员的工作量.     

Rank B2C e-commerce websites in e-alliance based on AHP and fuzzy TOPSIS

E-alliance is the union of e-commerce and its success and efficiency is related to comprehensive quality of e-commerce. Thus, ranking e-commerce websites in e-alliance is of importance, which is a multi-criteria decision-making (MCDM) problem. This paper proposes an evaluation model based on analytic hierarchy process (AHP), fuzzy sets and technique for order performance by similarity to ideal solution (TOPSIS), to tackle the issue in fuzzy environment. The AHP is applied to analyze the structure of ranking problem and to determine weights of the criteria, fuzzy sets is utilized to present ambiguity and subjectivity with linguistic values parameterized by triangular fuzzy numbers, and TOPSIS method is used to obtain final ranking. Case analysis is conducted to illustrate the utilization of the model for the problem. It demonstrates the effectiveness and feasibility of the proposed model.