独体汉字的字形相似性研究

本文以速示呈现方式获得了一系列独体汉字和字符的混淆矩阵。用Luce（1963）的选择模型特混淆矩阵分解为对称的相似矩阵和反应倾向矢量。基于相似矩阵,以聚类分析和多维量表法探索了汉字和字符的视觉特征及其表征形式,为揭示汉字视觉特征的几何特性和潜在结构,在研究方法上作了有意义的偿试、此外,本研究的结果支持了作者（1995）关于在含“十”或“口”的独体汉字中,“十”与“口”是该类汉字的突出视觉特征的结论。     

Spatial,non-spatial and hybrid models for scaling

In this paper, hierarchical and non-hierarchical tree structures are proposed as models of similarity data. Trees are viewed as intermediate between multidimensional scaling and simple clustering. Procedures are discussed for fitting both types of trees to data. The concept of multiple tree structures shows great promise for analyzing more complex data. Hybrid models in which multiple trees and other discrete structures are combined with continuous dimensions are discussed. Examples of the use of multiple tree structures and hybrid models are given. Extensions to the analysis of individual differences are suggested.1976 Psychometric Society Presidential Address.While people too numerous to list here have contributed ideas, inspiration, and other help, I particularly wish to acknowledge the contributions of Sandra Pruzansky, without whom this paper could not have been written. I would also like to acknowledge the past contributions of my long-time colleague Jih-Jie Chang, without whose help I probably would not have beenasked to write it.     

A least squares algorithm for fitting additive trees to proximity data

A least squares algorithm for fitting additive trees to proximity data is described. The algorithm uses a penalty function to enforce the four point condition on the estimated path length distances. The algorithm is evaluated in a small Monte Carlo study. Finally, an illustrative application is presented.The author is Aspirant of the Belgian Nationaal Fonds voor Wetenschappelijk Onderzoek. The author is indebted to Professor J. Hoste for providing computer facilities at the Institute of Nuclear Sciences at Ghent.     

On universals: an extensionalist alternative to Quine’s resemblance theory

The notion of similarity plays a central role in Quine’s theory of Universals and it is with the help of this notion that Quine intends to define the concept of kind which also plays a central role in the theory. But as Quine has admitted, his attempts to define kinds in terms of similarities were unsuccessful and it is mainly because of this shortcoming that Quine’s theory has been ignored by several philosophers (see, e.g., Armstrong, D. M. (1978a). Nominalism and realism: Universals and Scientific realism (Vol. I). Cambridge: Cambridge University Press). In the present paper, I propose an alternative framework that accounts for the phenomena that Quine intends to explain with his resemblance theory. The framework agrees with Quine’s austere ontology; in particular, it does not assume the existence of properties and of possible worlds. (I will mention below Quine’s reason for rejecting properties and possible worlds. For a theory of Universals that assumes possible worlds, see, e.g., Rodriguez-Pereyra, G. (2002). Resemblance nominalism: A solution to the problem of Universals. Oxford: Clarendon Press.) Moreover, the framework is extensionalist since the abstract entities it assumes are classes and these can be individuated extensionally, for classes are identical if their members are identical. Finally, I will refute some of the objections to Quine’s approach that have been raised by Armstrong and Oliver [(1996). The metaphysics of properties. Mind, 105, 1–80.] and I will argue that, contrary to what has been claimed by Oliver in a comment on Lewis [(1986). On the plurality of worlds. Oxford: Blackwell.], Quine is able to specify an important set of sparse properties. Editor’s Note: Nathan Stemmer died on 6 April 2007. Due to this, the proofreading was only done by the editors. All colleagues being acquainted with Nathan Stemmers work are deeply concerned about the loss.     

Re-conceptualizing the past: Historical data in vocational interest research

Noteworthy progress has been made in the development of statistical models for evaluating the structure of vocational interests over the past three decades. It is proposed that historically significant interest datasets, when combined with modern structural methods of data analysis, provide an opportunity to re-examine the underlying assumptions of J.L. Holland’s [Holland, J. L. (1959). A theory of vocational choice. Journal of Counseling Psychology, 6, 35–45; Holland, J. L. (1997). Making vocational choices (3rd ed.). Odessa, FL: Psychological Assessment Resources] RIASEC model. To illustrate this potential, data obtained from J. P. Guilford’s study of interest structure were re-analyzed using modern circumplex and hierarchical clustering techniques to evaluate Holland’s and I. Gati’s [Gati, I. (1979). A hierarchical model for the structure of interests. Journal of Vocational Behavior, 15, 90–106; Gati, I. (1991). The structure of vocational interests. Psychological Bulletin, 109, 309–324] interest structures. Obtained results indicate that a circumplex model can be used to effectively represent the structure underlying Guilford’s interest measures. However, hierarchical clustering results suggest that Holland’s RIASEC types may not be the most effective categories for grouping specific interest measures into broader interest areas. The current findings provide support for the continued investigation of alternatives to Holland’s interest categories using modern measures of basic interests.     

Heterogeneous load balancing clustering protocol for Wireless Sensor Networks

Wireless Sensor Networks is one of the most significant area of research where large number of sensor nodes that are distributed in a geographical area operate on limited battery power. As these networks, depending on the application, are sometimes deployed in in-hostile environment, which makes energy conservation one of the major challenge faced in WSN. To reduce energy consumption, clustering is considered to be the most efficient technique. This work proposes a new clustering approach that decreases energy consumption and results in prolonged network lifetime which is an important requirement for networks operating in inaccessible areas. In the proposed approach, heterogeneity is also implemented to increase the stability and energy efficiency of random networks. We have evaluated the efficiency of the proposed protocol through simulations and comparison is performed with well-known existing distributed protocols. Proposed approach shows efficient results in terms of the stability period for different network configurations and Base Station positions. Also, the results are found better in terms of number of alive nodes and network lifetime.     

Testing a modified cognitive interview with category clustering recall in Iran

The cognitive interview (CI) has been an effective method for interviewing eyewitnesses often leading to changes in legislation and practice in many countries. This study was the first to employ the CI in Iran and test whether category clustering recall (CCR) was superior to a free recall when incorporated within an investigative interview. A between‐subjects design assigned 66 participants to one of three interview conditions after they watched a mock robbery. The participants were interviewed 48 hr later using either a structured interview (SI), the CI, or a modified cognitive interview (MCI) that replaced free recall with CCR at the first retrieval attempt. Analysis of variance suggests CCR was more effective than free recall and the CI group recalled more information than the SI group, replicating the CI superiority effect. This has implications for law enforcement in Iran and worldwide by suggesting these techniques can be used to enhance recall.     

Semantic clustering as a neuropsychological predictor for amnestic-MCI

ABSTRACT

Recent research has demonstrated that patients with Alzheimer's disease (AD) show deficits in semantic processing when compared to cognitively healthy individuals. This difference is thought to be attributed to losses in higher cortical systems that are predominantly associated with executive functioning. The first aim of the study will be to determine if differences in semantic clustering can accurately differentiate patients with amnestic mild cognitive impairment (aMCI) from cognitively normal (CN) individuals. The second aim will be to determine the extent to which semantic processing might be associated with executive functions. Data from 202 (134 CN, 68 aMCI) participants were analyzed to quantify differences in semantic clustering ratios on the HVLT-R. Study participants ages ranged from 51 to 87 with education ranging from 6 to 20 years. ANCOVA revealed statistically significant differences on semantic clustering ratios (p < .001). Moderate correlations between semantic clustering Category Fluency Test (r = .45) were also found. Statistically significant group differences were also present on Trails-B and WAIS-R Digit Symbol performance (p < .001). Overall, these data indicate that deficits in semantic clustering are present in aMCI patients.     

Emotion development system by interacting with human EEG and natural scene understanding

In this paper, we propose a mental development system for understanding the emotional status of humans, and sharing emotions with human subjects. According to the relationship between emotional factors and characteristics of an image, we incorporate the fuzzy concept to extract emotional features using L^*C^*H^* color and orientation information. On the other hand, we also consider the EEG signals which are stimulated by natural stimuli to form the semantic emotional features as well. Emotionally relevant features are firstly clustered into two categories with degrees of belongingness to each cluster to initialize the membership functions of a neuro-fuzzy system. The IF-THEN rules of a neuro-fuzzy system to understand the positive and negative human emotions will be constructed by interacting with human. Then the system attempts to extend the number of understandable emotion. Through the time, the system sub-clusters the emotional features so that the number of membership function of the neuro-fuzzy network will increase to incorporate more complicated human expertise considering more human emotions. Using such a developmental process, the proposed system can develop a mental ability to understand more complex human emotions by mining the characteristics of emotional features and interacting with its environment.     

Probability matrix decomposition models

In this paper, we consider a class of models for two-way matrices with binary entries of 0 and 1. First, we considerBoolean matrix decomposition, conceptualize it as alatent response model (LRM) and, by making use of this conceptualization, generalize it to a larger class of matrix decomposition models. Second,probability matrix decomposition (PMD) models are introduced as a probabilistic version of this larger class of deterministic matrix decomposition models. Third, an algorithm for the computation of the maximum likelihood (ML) and the maximum a posteriori (MAP) estimates of the parameters of PMD models is presented. This algorithm is an EM-algorithm, and is a special case of a more general algorithm that can be used for the whole class of LRMs. And fourth, as an example, a PMD model is applied to data on decision making in psychiatric diagnosis. This paper is based on a chapter of the first author's doctoral dissertation, written at the University of Leuven and supervised by Paul De Boeck.