Ranking alternatives in a preferential voting system using fuzzy concepts and data envelopment analysis

In this paper, a new method for aggregating the opinions of experts in a preferential voting system is proposed. The method, which uses fuzzy concept in handling crisp data, is computationally efficient and is able to completely rank the alternatives. Through this method, the number of votes for certain rank position that each alternative receives are first grouped together to form fuzzy numbers. The nearest point to a fuzzy number concept is then used to introduce an artificial ideal alternative. Data envelopment analysis is next used to find the efficiency scores of the alternatives in a pair-wise comparison with the artificial ideal alternative. Alternatives are rank based on these efficiency scores. If the alternatives are not completely ranked, a weight restriction method also based on fuzzy concept is used on the un-discriminated alternatives until they are completely ranked. Two examples are given for illustration of the method.     

多个大股东并存下的公司治理研究综述

在不同国家的企业中股权结构有很大差异.多个大股东并存下的股权制衡模式作为一种特殊的股权结构,产生了其特有的公司治理问题.文中主要从多个大股东并存的公司治理效应、对于公司决策和业绩的影响、与公司价值的关系三个方面对国外研究进行了综述,较为全面的总结了前人对于多个大股东并存下的股权制衡的公司治理问题的研究.     

New approaches for determining a common set of weights for a voting system

Data Envelopment Analysis (DEA) is one of the methods that have been proposed to determine the weights in a ranked voting system. DEA solves one model corresponding to each candidate and evaluates candidates with their own weights. However, sometimes solving only one model, and evaluating the candidates based on a common set of weights, is preferred. In this paper, some drawbacks of existing models are explained and new approaches for determining a common set of weights are proposed. Numerical examples are utilized to illustrate the content of the paper.     

Comparative analysis of different approaches to target differentiation and localization with sonar

This study compares the performances of different methods for the differentiation and localization of commonly encountered features in indoor environments. Differentiation of such features is of interest for intelligent systems in a variety of applications such as system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target tracking. Different representations of amplitude and time-of-flight measurement patterns experimentally acquired from a real sonar system are processed. The approaches compared in this study include the target differentiation algorithm, Dempster-Shafer evidential reasoning, different kinds of voting schemes, statistical pattern recognition techniques (k-nearest neighbor classifier, kernel estimator, parameterized density estimator, linear discriminant analysis, and fuzzy c-means clustering algorithm), and artificial neural networks. The neural networks are trained with different input signal representations obtained using pre-processing techniques such as discrete ordinary and fractional Fourier, Hartley and wavelet transforms, and Kohonen's self-organizing feature map. The use of neural networks trained with the back-propagation algorithm, usually with fractional Fourier transform or wavelet pre-processing results in near perfect differentiation, around 85% correct range estimation and around 95% correct azimuth estimation, which would be satisfactory in a wide range of applications.     

Analyzing User-Perceived Dependability and Performance Characteristics of Voting Algorithms for Managing Replicated Data

User-perceived dependability and performance metrics are very different from conventional ones in that the dependability and performance properties must be assessed from the perspective of users accessing the system. In this paper, we develop techniques based on stochastic Petri nets (SPN) to analyze user-perceived dependability and performance properties of quorum-based algorithms for managing replicated data. A feature of the techniques developed in the paper is that no assumption is made regarding the interconnection topology, the number of replicas, or the quorum definition used by the replicated system, thus making it applicable to a wide class of quorum-based algorithms. We illustrate this technique by comparing conventional and user-perceived metrics in majority voting algorithms. Our analysis shows that when the user-perceiveness is taken into consideration, the effect of increasing the network connectivity and number of replicas on the availability and dependability properties perceived by users is very different from that under conventional metrics. Thus, unlike conventional metrics, user-perceived metrics allow a tradeoff to be exploited between the hardware invested, i.e., higher network connectivity and number of replicas, and the performance and dependability properties perceived by users.     

Human Face Detection in Digital Video Using SVMEnsemble

This Letter proposes automatic human face detection in digital video using a support vector machine (SVM) ensemble to improve the detection performance. The SVM ensemble consists of several independently trained SVMs using randomly chosen training samples via a bootstrap technique. Next, they are aggregated in order to make a collective decision via a majority voting scheme. Experimental results show that the proposed face detection method using SVM ensemble outperforms conventional methods such as using only single SVM and Multi-Layer Perceptron in terms of classification accuracy, false alarms, and missing rates.     

Analogy-Based Practical Classification Rules for Software Quality Estimation

Software metrics-based quality estimation models can be effective tools for identifying which modules are likely to be fault-prone or not fault-prone. The use of such models prior to system deployment can considerably reduce the likelihood of faults discovered during operations, hence improving system reliability. A software quality classification model is calibrated using metrics from a past release or similar project, and is then applied to modules currently under development. Subsequently, a timely prediction of which modules are likely to have faults can be obtained. However, software quality classification models used in practice may not provide a useful balance between the two misclassification rates, especially when there are very few faulty modules in the system being modeled.This paper presents, in the context of case-based reasoning, two practical classification rules that allow appropriate emphasis on each type of misclassification as per the project requirements. The suggested techniques are especially useful for high-assurance systems where faulty modules are rare. The proposed generalized classification methods emphasize on the costs of misclassifications, and the unbalanced distribution of the faulty program modules. We illustrate the proposed techniques with a case study that consists of software measurements and fault data collected over multiple releases of a large-scale legacy telecommunication system. In addition to investigating the two classification methods, a brief relative comparison of the techniques is also presented. It is indicated that the level of classification accuracy and model-robustness observed for the case study would be beneficial in achieving high software reliability of its subsequent system releases. Similar observations are made from our empirical studies with other case studies.     

相关信息加权的自适应多标签分类算法

在文本分类中,传统单标签分类问题的解决方法无法简单地应用于多标签文本分类,现有的方法通常会通过单标签问题转化思想或者多标签自身算法改进实现对多标签的文本分类。提出一种相关信息加权的自适应多标签分类算法,该算法具有相关信息加权、自适应阈值调整、权重投票相结合的特点。实验结果表明,该算法的某些性能指标优于现有一些常用的多标签分类方法。     

CrackTree: Automatic crack detection from pavement images

Pavement cracks are important information for evaluating the road condition and conducting the necessary road maintenance. In this paper, we develop CrackTree, a fully-automatic method to detect cracks from pavement images. In practice, crack detection is a very challenging problem because of (1) low contrast between cracks and the surrounding pavement, (2) intensity inhomogeneity along the cracks, and (3) possible shadows with similar intensity to the cracks. To address these problems, the proposed method consists of three steps. First, we develop a geodesic shadow-removal algorithm to remove the pavement shadows while preserving the cracks. Second, we build a crack probability map using tensor voting, which enhances the connection of the crack fragments with good proximity and curve continuity. Finally, we sample a set of crack seeds from the crack probability map, represent these seeds by a graph model, derive minimum spanning trees from this graph, and conduct recursive tree-edge pruning to identify desirable cracks. We evaluate the proposed method on a collection of 206 real pavement images and the experimental results show that the proposed method achieves a better performance than several existing methods.     

一种基于数据聚类的鲁棒SIFT特征匹配方法

针对噪声敏感造成的SIFT特征匹配鲁棒性低问题,提出一种基于数据聚类的两阶段特征匹配方法.在满足特征匹配几何距离最邻近本质要求下扩展了k d数据结构,使其不但能够完成算术平均化匹配特征离线聚类,而且能够实现第1阶段聚类特征在线匹配.在此基础上,给出一种概率最优投票策略选择关键图像进行第2阶段匹配,最后合并两阶段属于关键图像的所有匹配特征对.实验结果表明,对于大量存在重叠关系的图像集合,该方法能够有效减少重复特征数量,降低噪声信息对特征匹配的干扰,极大地提高特征匹配的鲁棒性.