A latent growth examination of fear development in infancy: Contributions of maternal depression and the risk for toddler anxiety.

Growth modeling was used to examine the developmental trajectory of infant temperamental fear with maternal fear and depressive symptoms as predictors of infant fearfulness and change in infant fear predicting toddler anxiety symptoms. In Study 1, a sample of 158 mothers reported their own depressive symptoms and fear when their children were 4 months of age and infant fearfulness at 4, 6, 8, 10, and 12 months. Maternal symptoms of depression predicted steeper increases in infant fearfulness over time (z = 2.06, p z = 2.32, p z = 1.88, p z = 2.30, p z = 2.08, p     

Validating a model-driven software architecture evaluation and improvement method: A family of experiments

ContextSoftware architectures should be evaluated during the early stages of software development in order to verify whether the non-functional requirements (NFRs) of the product can be fulfilled. This activity is even more crucial in software product line (SPL) development, since it is also necessary to identify whether the NFRs of a particular product can be achieved by exercising the variation mechanisms provided by the product line architecture or whether additional transformations are required. These issues have motivated us to propose QuaDAI, a method for the derivation, evaluation and improvement of software architectures in model-driven SPL development.ObjectiveWe present in this paper the results of a family of four experiments carried out to empirically validate the evaluation and improvement strategy of QuaDAI.MethodThe family of experiments was carried out by 92 participants: Computer Science Master’s and undergraduate students from Spain and Italy. The goal was to compare the effectiveness, efficiency, perceived ease of use, perceived usefulness and intention to use with regard to participants using the evaluation and improvement strategy of QuaDAI as opposed to the Architecture Tradeoff Analysis Method (ATAM).ResultsThe main result was that the participants produced their best results when applying QuaDAI, signifying that the participants obtained architectures with better values for the NFRs faster, and that they found the method easier to use, more useful and more likely to be used. The results of the meta-analysis carried out to aggregate the results obtained in the individual experiments also confirmed these results.ConclusionsThe results support the hypothesis that QuaDAI would achieve better results than ATAM in the experiments and that QuaDAI can be considered as a promising approach with which to perform architectural evaluations that occur after the product architecture derivation in model-driven SPL development processes when carried out by novice software evaluators.     

Control Charts for Dispersed Count Data: An Overview

Control charts based on the Poisson distribution are commonly used to monitor count data in attributes. However, the Poisson distribution is based on the underlying equidispersion assumption that is limiting as discussed by different researchers in the literature. Therefore, a generalized control chart is required that can be used to monitor both overdispersed and underdispersed count data. This article reviews the methods to implement for dispersed count data and present ideas for future work in this area. A comprehensive literature review for researchers and practitioners is presented in this article. Copyright © 2014 John Wiley & Sons, Ltd.     

WAR: Weighted Association Rules for Item Intensities

In this paper, we extend the traditional association rule problem by allowing a weight to be associated with each item in a transaction to reflect the interest/intensity of each item within the transaction. In turn, this provides us with an opportunity to associate a weight parameter with each item in a resulting association rule; we call them weighted association rules (WAR). One example of such a rule might be 80% of people buying more than three bottles of soda will also be likely to buy more than four packages of snack food, while a conventional association rule might just be 60% of people buying soda will be also be likely to buy snack food. Thus WARs cannot only improve the confidence of the rules, but also provide a mechanism to do more effective target marketing by identifying or segmenting customers based on their potential degree of loyalty or volume of purchases. Our approach mines WARs by first ignoring the weight and finding the frequent itemsets (via a traditional frequent itemset discovery algorithm), followed by introducing the weight during the rule generation. Specifically, the rule generation is achieved by partitioning the weight domain space of each frequent itemset into fine grids, and the identifying the popular regions within the domain space to derive WARs. This approach does not only support the batch mode mining, i.e., finding WARs for the dataset, but also supports the interactive mode, i.e., finding and refining WARs for a given (set) of frequent itemset(s).     

Evaluation of ordinal attributes at value level

We propose a novel context sensitive algorithm for evaluation of ordinal attributes which exploits the information hidden in ordering of attributes’ and class’ values and provides a separate score for each value of the attribute. Similar to feature selection algorithm ReliefF, the proposed algorithm exploits the contextual information via selection of nearest instances. The ordEval algorithm outputs probabilistic factors corresponding to the effect an increase/decrease of attribute’s value has on the class value. While the ordEval algorithm is general and can be used for analysis of any survey with graded answers, we show its utility on an important marketing problem of customer (dis)satisfaction. We develop a visualization technique and show how we can use it to detect and confirm several findings from marketing theory. Responsible editor: Charu Aggarwal.     

声波测井资料与地震属性关系研究综述

声波测井资料和地震数据在井震联合勘探中发挥了重要的作用,由于采集机制的不同使得两者存在着一定的差异。本文就声波测井资料同地震属性的关系展开研究,详细地分析了影响两种资料差异的各种因素,并介绍了当前几种主要的匹配校正方法。另外,声波测井资料也常被用于合成地震记录以及测井约束地震反演,这也成为当前测井地震联合勘探开发的发展方向。     

An Investigation of a Method for Identifying a Software Architecture Candidate with Respect to Quality Attributes

To sustain the qualities of a software system during evolution, and to adapt the quality attributes as the requirements evolve, it is necessary to have a clear software architecture that is understood by all developers and to which all changes to the system adheres. This software architecture can be created beforehand, but must also be updated to reflect changes in the domain, and hence the requirements of the software. The choice of which software architecture to use is typically based on informal decisions. There exist, to the best of our knowledge, little factual knowledge of which quality attributes are supported or obstructed by different architecture approaches. In this paper we present an empirical study of a method that enables quantification of the perceived support different software architectures give for different quality attributes. This in turn enables an informed decision of which architecture candidate best fit the mixture of quality attributes required by a system being designed.     

钢铁行业企业资源计划的产品数据结构设计

在钢铁行业实施企业资源计（ERP）中,确定针对钢铁行业的、面向ERP产品数据管理应用的产品数据结构至关重要。本文结合某钢管制造厂在ERP实施中的实例,论述钢铁ERP系统的基于属性的产品数据结构设计及管理方案的原理。     

基于神经网络的股票预测系统研究

本文设计了一种基于粗集理论和神经网络的股票操作支持系统。系统根据对股票历史数据分析,预测股价未来一段时间内的走势,进而对投资者进行股票操作支持。指导投资者在投入资金一定的情况下,如何操作才会使总收益为最大。本系统首先利用粗集理论对预测数据进行属性约简等处理,然后把处理过的数据作为神经网络的输入。这样不仅减小了神经网络的规模,同时通过消除对象冗余减少了网络的训练和学习负担。与采用单技术的预测系统相比,本决策支持系统的可信度也有了较大的提高。     

基于最优属性约简的中文邮件过滤系统

对朴素贝叶斯理论作为中文邮件过滤技术进行了分析改进,邮件预处理后,对其进行分词处理,利用基于依赖性的粗糙集最优属性约简方法来对邮件集进行特征维数压缩。条件属性的数目大幅减少,提高了分类的效率。