A statistical information-based clustering approach in distance space

Clustering, as a powerful data mining technique for discovering interesting data distributions and patterns in the underlying database, is used in many fields, such as statistical data analysis, pattern recognition, image processing, and other business applications. Density-based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996) is a good performance clustering method for dealing with spatial data although it leaves many problems to be solved. For example,DBSCAN requires a necessary user-specified threshold while its computation is extremely time-consuming by current method such as OPTICS, etc. (Ankerst et al., 1999), and the performance of DBSCAN under different norms has yet to be examined. In this paper, we first developed a method based on statistical information of distance space in database to determine the necessary threshold. Then our examination of the DBSCAN performance under different norms showed that there was determinable relation between them. Finally, we used two artificial databases to verify the effectiveness and efficiency of the proposed methods.     

Using Greedy algorithm: DBSCAN revisited II

The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) (Esteret al., 1996), and has the following advantages: first, Greedy algorithm substitutes forR ^*-tree (Bechmannet al., 1990) in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second, the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally, authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency. Project (No. 2002AA2010) supported by the Hi-Tech Research and Development Program (863) of China     

Using Greedy algorithm:DBSCAN revisited Ⅱ

The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise(DBSCAN)(Ester et al.,1996),and has the following advantages: first,Greedy algorithm substitutes for R*-tree(Bechmann et al.,1990)in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second,the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally,authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.     

Using Greedy algorithm: DBSCAN revisited II

INTRODUCTION Clustering groups dataset data into meaning-ful subclasses in such a way that minimizes theintra-differences and maximizes the in-ter-differences of these subclasses; and is one ofthe most widely studied problems in data mining.There are many application areas for clusteringtechniques, such as statistical data analysis, patternrecognition, image processing, and other businessprocesses etc. Many clustering algorithms havebeen proposed, in part…     

基于密度的聚类算法研究

聚类算法是数据挖掘的核心技术,基于密度的聚类是一类已经被证明非常有效的聚类方法．与DBSCAN算法作比较,文章提出了一种基于密度的聚类算法（Clustering Using Centers and Density,CUCD）．该算法是基于中心点以及密度实现的,其核心对象是根据数据分布计算出来的虚拟的点,并且核心对象的代表性随程序的执行次数而提高;经实验验证,该算法具有较好的时间效率和聚类质量．     

Cryptanalysis of an image encryption scheme based on the Hill cipher

This paper studies the security of an image encryption scheme based on the Hill cipher （Ismail et al., 2006） and reports its following problems： （1） There is a simple necessary and sufficient condition that makes a number of secret keys invalid; （2） It is insensitive to the change of the secret key; （3） It is insensitive to the change of the plain-image; （4） It can be broken with only one known/chosen plaintext; （5） It has some other minor defects. The proposed cryptanalysis discourages any use of the scheme in practice.     

Elementary Pre-Service Teacher Perceptions of the Greenhouse Effect

Environmental issues are an important topic both in national news, and in science education. Previous studies revealed that students hold many misconceptions relating to such issues as global warming, ozone depletion, and acid rain. This article expands on earlier work by Boyes et al. to examine pre-service teachers' views about these issues, especially global warming and the related term, the greenhouse effect, and the effect of their views on elementary student performance. Results support the findings of Boyes et al. that elementary education majors hold many misconceptions about these environmental issues, and these can effect their teaching of these topics in elementary classes.     

Increasing school personnel's self-efficacy,knowledge, and response regarding nonsuicidal self-injury in youth

Rates of students engaging in nonsuicidal self-injury (NSSI) are rising and additional supports in the schools are needed (Nock, 2010, Ann Rev Clin Psychol, 6, 339–363; Stargell et al., 2017, Prof Sch Couns, 21, 37-46). School psychologists, school counselors, and school nurses are key personnel in responding to self-injurious behaviors within the school setting. The results of a practice-based research project are described, in which school psychologists, school counselors, and school nurses participated in training to increase their self-efficacy, knowledge, and response in regard to NSSI. The training provided information regarding best practice in responding to NSSI in youth (Hasking et al., 2016, Sch Psychol Int, 37(6), 644–663; Kanan et al., 2008, Sch Psychol Forum: Res Prac, 2, 67–79; Walsh & Muehlenkamp, 2013, Sch Psychol Forum: Res Prac, 7, 161–171). This exploratory study indicated that training positively impacted participants' perceived self-efficacy and knowledge with respect to responding to youth who engage in NSSI. Handouts and resources for school-based staff are included. Limitations and future directions are discussed.     

Restrictive Stochastic Item Selection Methods in Cognitive Diagnostic Computerized Adaptive Testing

This paper proposes two new item selection methods for cognitive diagnostic computerized adaptive testing: the restrictive progressive method and the restrictive threshold method. They are built upon the posterior weighted Kullback‐Leibler (KL) information index but include additional stochastic components either in the item selection index or in the item selection procedure. Simulation studies show that both methods are successful at simultaneously suppressing overexposed items and increasing the usage of underexposed items. Compared to item selection based upon (1) pure KL information and (2) the Sympson‐Hetter method, the two new methods strike a better balance between item exposure control and measurement accuracy. The two new methods are also compared with Barrada et al.'s (2008) progressive method and proportional method.     

基于改进DBSCAN算法的异常数据处理

随着智慧农业的发展,农业生产中海量数据不断涌现。在海量数据中难免存在噪声数据,这些数据不仅难以提供有效价值,还会影响信息挖掘。针对该问题,采用基于密度的DBSCAN聚类算法进行异常数据处理。鉴于DBSCAN算法对参数敏感,结合数据集本身特性与统计学思想以绘制各点之间的距离升序曲线,预估出DBSCAN的Eps参数。仿真实验结果表明,改进算法平均准确率达到99.6%,较传统算法提高了1.7个百分点,并且在10次检测中,改进算法只有3个数据判定错误,证明该参数设置方法对异常数据处理准确率更高,稳定性也更好。     

Measures for inclusion: coping with the challenge of visual impairment and blindness in university undergraduate level language learning

This paper develops from previous research ( Orsini‐Jones et al., 2005 ) and reports on the experience of three students, two visually impaired and one blind, reading languages at Coventry University, on the adjustments made to meet their needs and on the challenges encountered on their learning journey both from their point of view and from the point of view of the staff involved. It focuses on three students' experience of their languages course and considers the challenges they met in each of their years of study. It concludes by evaluating these students' language learning journey at Coventry University, reporting on their feedback on their experience and on whether or not they feel that staff have successfully catered for their individual needs. It is argued here, as in the previous related work ( Orsini‐Jones, et al., 2005 ) that ‘there still exists some tension between the reasonable anticipatory adjustments that lecturers can put in place and the necessary ad hoc ones that will be needed for a disabled student with very specific needs’.     

Document Clustering Based on Constructing Density Tree

This paper focuses on document clustering by clustering algorithm based on a DEnsityTree （CABDET） to improve the accuracy of clustering. The CABDET method constructs a density-based treestructure for every potential cluster by dynamically adjusting the radius of neighborhood according to local density. It avoids density-based spatial clustering of applications with noise （DBSCAN） ＇s global density parameters and reduces input parameters to one. The results of experiment on real document show that CABDET achieves better accuracy of clustering than DBSCAN method. The CABDET algorithm obtains the max F-measure value 0.347 with the root node＇s radius of neighborhood 0.80, which is higher than 0.332 of DBSCAN with the radius of neighborhood 0.65 and the minimum number of objects 6.     

Data-Generating Mechanisms Versus Constructively Defined Latent Variables in Multitrait–Multimethod Analysis: A Comment on Castro-Schilo,Widaman, and Grimm (2013)

In a recent article, Castro-Schilo, Widaman, and Grimm (2013) compared different approaches for relating multitrait–multimethod (MTMM) data to external variables. Castro-Schilo et al. reported that estimated associations with external variables were in part biased when either the correlated traits–correlated uniqueness (CT-CU) or correlated traits–correlated (methods–1) [CT-C(M–1)] models were fit to data generated from the correlated traits–correlated methods (CT-CM) model, whereas the data-generating CT-CM model accurately reproduced these associations. Castro-Schilo et al. argued that the CT-CM model adequately represents the data-generating mechanism in MTMM studies, whereas the CT-CU and CT-C(M–1) models do not fully represent the MTMM structure. In this comment, we question whether the CT-CM model is more plausible as a data-generating model for MTMM data than the CT-C(M–1) model. We show that the CT-C(M–1) model can be formulated as a reparameterization of a basic MTMM true score model that leads to a meaningful and parsimonious representation of MTMM data. We advocate the use confirmatory factor analysis MTMM models in which latent trait, method, and error variables are explicitly and constructively defined based on psychometric theory.     

Optimal velocity functions for car-following models

The integral part of the optimal velocity car-following models is the optimal velocity function （OVF）, which can be derived from measured velocity-spacing data. This paper discusses several characteristics of the OVF and presents regression analysis on two classical datasets, the Lincoln and Holland tunnels, with different possible OVFs. The numerical simulation of the formation of traffic congestion is conducted with three different heuristic OVFs, demonstrating that these functions give results similar to those of the famous Bando OVF （Bando et al., 1995）. Also an alternative method is present for determining the sensitivity and model parameters based on a single car driving to a fixed barrier.     

Threshold capabilities: threshold concepts and knowledge capability linked through variation theory

The Threshold Capability Integrated Theoretical Framework (TCITF) is presented as a framework for the design of university curricula, aimed at developing graduates’ capability to deal with previously unseen situations in their professional, social, and personal lives. The TCITF is a new theoretical framework derived from, and heavily dependent upon, the ideas of the Threshold Concepts Framework (Meyer and Land 2003a; Land et al. 2006) and Capability Theory (Bowden and Marton 1998; Bowden et al. 2000; Bowden 2004). Capability theory is firmly based in phenomenography and variation theory, is concerned with the development of knowledge capability, but has had limited application in practice. The threshold concepts framework has enjoyed greater acceptance by a large range of academics in many fields. This acceptance has initially focussed on analytic studies of what constitutes a threshold concept—and the location and distribution of such concepts—in a given domain. In many instances subsequent attention has focussed on issues of pedagogy and assessment, including the design of curricula. We propose a merging of capability theory and the threshold concepts framework and argue that capability and variation theories provide the ideal mechanism for developing a strong pedagogical approach based on newly emerging knowledge of the critical features of threshold concepts within different domains.     

Conceptions d'élèves sur la notion de probabilité conditionnelle révélées par une méthode d'analyse des donn ées: Implication - similarité - corrélation

Résumé L'introduction des notions de probabilité, a fortiori celle de probabilité conditionnelle, pose un problème didactique épineux en raison de préconceptions des élèves (Fischbein et al., 1991), nées des références concr ètes indispensables à cette introduction. Ces préconceptions peuvent se renforcer en conceptions qui se posent en obstacles épistémologiques et obstacles d'origine didactique. À l'aide d'une nouvelle méthode d'analyse de donn ées — l'implication statistique — et d'une méthodologie d'emploi post-corré1ative, nous mettons en évidence ces conceptions à partir des travaux d'élèves et en explicitant des procédures de résolution de problèmes, rév é1atrices de ces conceptions.

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Students' conceptions on conditional probability revealed by a data analysis method: Implication - similarity - correlation

The introduction of notions of probability, above all conditional probability, poses a thorny problem in didactics due to student's preconceptions (Fischbein et al., 1991), stemming from the concrete references indispensable for this introduction. These preconceptions may be reinforced by conceptions which become epistemological and especially didactical obstacles. Using a new method of data analysis — statistical implication - and a method of post-correlative treatment, we reveal these conceptions using student's work, and make explicite the procedures of problem solving used, that are a reflexion of these conceptions.

     

DVB-S2 inner receiver design for broadcasting mode

INTRODUCTION The second generation satellite digital video broadcasting standard (ETSI, 2004) has come out recently. Compared with the previous satellite stan-dard DVB-S (ETSI, 2003), DVB-S2 has more ad-vantages, such as the 30% increase in channel capac-ity, the more reliable performance, and the more ef-ficient usage of transponders. The DVB-S2 standard has been designed having in mind the peculiarities of the satellite channel, particularly the link fading im-pairments and the …     

Reliability analysis of DOOF for weibull distribution

Hierarchical Bayesian method for estimating the failure probabilityp _i under DOOF by taking the quasi-Beta distributionB(p _i−1, 1, 1,b) as the prior distribution is proposed in this paper. The weighted Least Squares Estimate method was used to obtain the formula for computing reliability distribution parameters and estimating the reliability characteristic values under DOOF. Taking one type of aerospace electrical connector as an example, the correctness of the above method through statistical analysis of electrical connector accelerated life test data was verified. Project (No. 59975081) supported by the National Natural Science Foundation of China     

Value-added measures of school effectiveness in the United Kingdom

Conclusion This paper has mostly focused on the performance of schools in terms of examination and test results. On their own, such results are insufficient for proper judgements about schools’ performance. Other pupil outcomes such as pupil attendance, capacity for independent learning, attitudes towards school and learning, behaviour and selfconcept are also important. School effectiveness research aims to investigate a broad range of educational outcomes. Examples of this approach are provided by the Junior School Project (Mortimore et al., 1988), the Lancashire project (Thomas & Mortimore, 1996) and the ISEP in Scotland (MacBeath & Mortimore, 1994) which involves collecting pupil, teacher and parent attitude data in addition to academic outcomes. Nevertheless, the main performance indicator for schools continues to be how successful they are at ensuring that as many pupils as possible achieve their full academic potential. Original language: English Sally Thomas (United Kingdom) Sally Thomas (United Kingdom) This research paper was completed via a grant awarded by the United Kingdom Economic and Social Science Research Council. I am very grateful for the helpful comments of my colleagues Louise Stoll, Pam Sammons, Harvey Goldstein and Hillary Street on a previous version of this paper.     

Parental Authoritarian Control and Cognitive Complexity

Studies on the conceptual system proposed by Harvey et aJ (1961) suggested that the concrete mode of cognitive functioning is associated with certain developmental factors such as restricted exploration of the environment, overcontrol by parents, avoidance of uncertainty, conformity to external superimposed norms, and discouragement of independence, etc. Using the framework of Harvey, Schroder et al (1967) specified two types of training methods, one of which he called the deductive method. Parents adopting this method provide the child with ready made and fixed rules. The child is taught to avoid uncertainty by looking externally for rules. The variety of perceptions and responses available to the child is highly restricted. The environment is so structured that it is unrealistically oversimplified. Schroder et al believe that this type of training method would bring out a cognitive structure which is characterized by compartmentalization and inability to think in terms of degrees or relativeness.