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A new similarity measurement method of Vague value is proposed which compare with the existing similarity measurement methods. The proposed method has good distinguishing degree and less computation steps and times which possess the basic properties and satisfying similarity measurement. By the data analysis of data membership degree investigate the pattern recognition to achieve the measuring. By the way, comparison with the results of both methods which has been proved that proposed method is a more reasonable to measure the similarity of Vague value.

The paper presents a contribution to minimization of fuzzy automata. Traditionally, the problem of minimization of fuzzy automata results directly from the problem of minimization of ordinary automata. That is, given a fuzzy automaton, describe an automaton with the minimal number of states which recognizes the same language as the given one. In this paper, we formulate a different problem. Namely, the minimal fuzzy automaton we are looking for is required to recognize a language which is similar to the language of the given fuzzy automaton to a certain degree a, such as a = 0.9, prescribed by a user. That is, we relax the condition of being equal to a weaker condition of being similar to degree a.

The global monsoon systems consist of regions with large seasonality (Zeng et al. (1994), Zeng and Zhang (1998), Xue and Zeng (1999)). It is discovered that in the lower troposphere there are one tropical monsoon region (coincident with the classical monsoon region), two subtropical monsoon regions (one each in Northern and Southern Hemisphere and occupied by the seasonal migration of subtropical high) and two middle-high latitudinal monsoon regions (associated with the storm tracks of westerlies). The division of seasons is determined by the normalized similarity (Zeng et al. (1994), Zeng and Zhang (1992), Zhang and Zeng (1998)). It is revealed that in the monsoon regions, the transitions from winter to summer and from summer to the next are abrupt.

This paper explores the relationship between object level intuitionistic fuzzy sets and predicate based intuitionistic fuzzy sets. Mass assignment uses a process called semantic unification to evaluate the degree to which one set supports another. Intuitionistic fuzzy sets are mapped onto a mass assignment framework and the mass assignment semantic unification operator is generalised to support both mass assignment and intuitionistic fuzzy sets. Transfer of inconsistent and contradictory evidence is also dealt with. As a consequence, by conjoining the mutual semantic unification of two sets a similarity measure emerges.

In this paper, we provide a clustering and indexing tool for the sitemap identification to search for services. We propose si-graph to represent the summary structure of sitemaps. We define the si-similarity metric measuring the structural similarity among si-graphs, and apply it to sitemap clustering. We also propose si-index, an index structure to support sitemap search that can quickly locate the structural similar websites. The searching algorithm avoids redundant processing of structural irrelevant candidates at early stage to attain a high lookup speed.

Texture image segmentation is the first essential and important step of low level vision. The extraction of texture features is a most fundamental problem to texture image segmentation. Many methods for extracting texture features of the image have been proposed, such as statistical features, co-occurrence features, two-dimensional AR features, and fractal based features etc. In this paper, a new method for extracting texture features of the image is proposed. In this method, the gray scale image is first decomposed into a series of binary images by variable thresholds, and then topological features of all of these binary images are computed. Using these topological features as texture features, we apply a pyramid linking with band-pass filter neural networks to segment the texture image into some homogeneous areas. Several experiments on synthetic texture images have been carried out to verify the efficacy of the new method.

Semantic relatedness (SR) is defined as a measurement that quantitatively identifies some form of lexical or functional association between two words or concepts based on the contextual or semantic similarity of those two words regardless of their syntactical differences. Section 1 of the entry outlines the working definition of SR and its applications and challenges. Section 2 identifies the knowledge resources that are popular among SR methods. Section 3 reviews the primary measurements used to calculate SR. Section 4 reviews the evaluation methodology which includes gold standard dataset and methods. Finally, Sec. 5 introduces further reading.

In order to develop appropriate SR methods, there are three key aspects that need to be examined: (1) the knowledge resources that are used as the source for extracting SR; (2) the methods that are used to quantify SR based on the adopted knowledge resource; and (3) the datasets and methods that are used for evaluating SR techniques. The first aspect involves the selection of knowledge bases such as WordNet or Wikipedia. Each knowledge base has its merits and downsides which can directly affect the accuracy and the coverage of the SR method. The second aspect relies on different methods for utilizing the beforehand selected knowledge resources, for example, methods that depend on the path between two words, or a vector representation of the word. As for the third aspect, the evaluation for SR methods consists of two aspects, namely (1) the datasets that are used and (2) the various performance measurement methods.

SR measures are increasingly applied in information retrieval to provide semantics between query and documents to reveal relatedness between non-syntactically-related content. Researchers have already applied many different information and knowledge sources in order to compute SR between two words. Empirical research has already shown that results of many of these SR techniques have reasonable correlation with human subjects interpretation of relatedness between two words.

An agent is asked to assess a real-valued variable Y_p based on certain characteristics $X_p=\left(X_p^1,\dots ,X_p^m\right)$, and on a database consisting of $\left(X_i^1,\dots ,X_i^m,Y_i\right)$ for i = 1, …, n. A possible approach to combine past observations of X and Y with the current values of X to generate an assessment of Y is similarity-weighted averaging. It suggests that the predicted value of Y, ${\overline{Y}}_p^s$, be the weighted average of all previously observed values Y_i, where the weight of Y_i, for every i = 1, …, n, is the similarity between the vector $X_p^1,\dots ,X_p^m$, associated with Y_p, and the previously observed vector, $X_i^1,\dots ,X_i^m$. We axiomatize this rule. We assume that, given every database, a predictor has a ranking over possible values, and we show that certain reasonable conditions on these rankings imply that they are determined by the proximity to a similarity-weighted average for a certain similarity function. The axiomatization does not suggest a particular similarity function, or even a particular form of this function. We therefore proceed to suggest that the similarity function be estimated from past observations. We develop tools of statistical inference for parametric estimation of the similarity function, for the case of a continuous as well as a discrete variable. Finally, we discuss the relationship of the proposed method to other methods of estimation and prediction.

Based on the theorem of hydrodynamics, the similarity conditions of Lifting Experiment System in Deep-sea Mining are considered first. The system is calculated numerically by the method of mathematic analysis. The relations among every parameter are explored. The conditions and criterions of geometric similarity, kinematic similarity and dynamic similarity for Lifting Experiment System are determined consequently. Results show that the principal similarity criterion is Reynolds (criterion of dynamic similarity). During the experiment, Velocity Change can be adjusted according to the range of Reynolds to achieve kinematic similarity. These results can be applied to setting up the laboratory and concrete experiment as theory foundation in future.

The similarity-attraction link has received largely consistent support across decades, with prior research on organizational leadership demonstrating how similarity positively influences employees' appraisals of their supervisors and facilitates leader-member exchanges (LMX). However, little research has applied this similarity-attraction link in understanding how similarity impacts perceptions of student leaders within a college context, where student body elections are common practice. Drawing on Construal Level Theory (CLT), this research explored how attitude similarity influenced undergraduates' likeability and mental representations of their student leaders. 124 undergraduates were presented with a hypothetical student leader who held either similar or dissimilar attitudes from them on a number of pertinent school issues. Additionally, information about the leader was framed either in terms of general character traits (high-level construal condition), or contextualized behaviours (low-level construal condition). Participants then completed a Leader Evaluation Scale (LES), which was an overall measure of likeability towards the leader. While analyses revealed significant effects of attitude similarity and construal level on likeability of student leaders, CLT was unsupported in this context. Results obtained support previous research conducted in this direction, and reinforce the integral role of attitude similarity in promoting positive first impressions towards student leaders in college.

This paper is dedicated to HAO Bailin on the occasion of his eighties birthday, the great scholar and very good friend who never tired to introduce us to the wonderful and complex intricacies of Chinese culture and history. In this paper, we present an analysis of oracle-bone characters for animals from a ‘cognitive’ point of view. After some general remarks on oraclebone characters presented in Section 1 and a short outline of the paper in Section 2, we collect various oracle-bone characters for animals from published resources in Section 3. In the next section, we begin analysing a group of 60 ancient animal characters from www.zdic.net, a highly acclaimed internet dictionary of Chinese characters that is strictly based on historical sources, and introduce five categories of specific features regarding their (graphical) structure that will be used in Section 5 to associate corresponding feature vectors to these characters. In Section 6, these feature vectors will be used to investigate their dissimilarity in terms of a family of parameterised distance measures. And in the last section, we apply the SplitsTree method as encoded in the NeighbourNet algorithms to construct a corresponding family of dissimilarity-based networks with the intention of elucidating how the ancient Chinese might have perceived the ‘animal world’ in the late bronze age and to demonstrate that these pictographs reflect an intuitive understanding of this world and its inherent structure that predates its classification in the oldest surviving Chinese encyclopedia from approximately the 3rd century BC, the ErYa, as well as similar classification systems in the West by one to two millennia. We also present an English dictionary of 70 oracle-bone characters for animals in Appendix 1. In Appendix 2, we list various variants of animal characters that were published in the Jia Gu Wen Bian (cf. , A Complete Collection of Oracle Bone Characters, edited by the Institute of Archaeology of the Chinese Academy of Social Sciences, published by the Zhonghua Book Company in 1965). We recall the frequencies of the 521 most frequent oracle-bone characters in Appendix 3 as reported in [7, 8]. And in Appendix 4, we list the animals registered in the last five chapters of the ErYa.