类规模对面向对象度量与易维护性关联关系潜在混和效果元分析

用在设计阶段可以收集到的3个规模度量分析类规模对面向对象度量与易维护性之间关联关系的混和效果.为得到尽可能通用的结论,在127个C＋＋系统和113个Java系统上分析类规模的混和效果.首先,对每个面向对象度量,在单个系统上计算代表由类规模造成的关联扭曲的间接效果和它的方差.然后,利用统计的元分析方法计算这些系统上的平均间接效果并判断它是否显著地区别于零.实验结果表明,不论使用哪个类规模度量进行分析,类规模对面向对象度量与易维护性之间关联关系的混和效果通常是存在的.因此,验证面向对象度量与易维护性之间关系的实验研究应将类规模视为一个混和变量.     

论沈从文创作中的都市生命形式

在沈从文的创作中,湘西生命形式与都市生命形式具有对立与对照的性质,代表沈从文不同的生命理想与情感取向。沈从文对都市生命形式的审美观照包含着一种犀利的文化批判眼光,从中传达出沈从文独特的生命理想,并通过这种生命理想表现其独特的创作追求。因此,探讨沈从文创作中的都市生命形式,对于理解沈从文的全部创作,就成为一个关键性问题,由此可以发现其创作独特性的一些重要方面。     

推荐系统中的隐语义模型研究?

隐语义模型是一种有效的隐含语义分析技术,其核心思想是通过潜在特征联系用户和物品。本文从理论方法的角度,详细介绍了隐语义模型的工作原理、模型表示、学习方法和评价指标等,并通过具体实验分别讨论几种典型隐语义模型算法的推荐效果,以全面认识和理解该模型在推荐系统中的应用特点。     

MFV-Class: a multi-faceted visualization tool of object classes

Classes are key software components in an object-oriented software system. In many industrial OO software systems,there are some classes that have complicated structure and relationships. So in the processes of software maintenance,testing,software reengineering,software reuse and software restructure,it is a challenge for software engineers to understand these classes thoroughly. This paper proposes a class comprehension model based on constructivist learning theory,and implements a software visualization tool(MFV-Class)to help in the comprehension of a class. The tool provides multiple views of class to uncover manifold facets of class contents. It enables visualizing three object-oriented metrics of classes to help users focus on the understanding process. A case study was conducted to evaluate our approach and the toolkit.     

On knowledge-poor methods for person name matching and lemmatization for highly inflectional languages

Web person search is one of the most common activities of Internet users. Recently, a vast amount of work on applying various NLP techniques for person name disambiguation in large web document collections has been reported, where the main focus was on English and few other major languages. This article reports on knowledge-poor methods for tackling person name matching and lemmatization in Polish, a highly inflectional language with complex person name declension paradigm. These methods apply mainly well-established string distance metrics, some new variants thereof, automatically acquired simple suffix-based lemmatization patterns and some combinations of the aforementioned techniques. Furthermore, we also carried out some initial experiments on deploying techniques that utilize the context, in which person names appear. Results of numerous experiments are presented. The evaluation carried out on a data set extracted from a corpus of on-line news articles revealed that achieving lemmatization accuracy figures greater than 90% seems to be difficult, whereas combining string distance metrics with suffix-based patterns results in 97.6–99% accuracy for the name matching task. Interestingly, no significant additional gain could be achieved through integrating some basic techniques, which try to exploit the local context the names appear in. Although our explorations were focused on Polish, we believe that the work presented in this article constitutes practical guidelines for tackling the same problem for other highly inflectional languages with similar phenomena.

“五计学”在我国的发展演进分析

[目的/意义]随着科学技术的高速发展、知识交叉越来越多、科学与社会的关系越来越复杂,如何利用计量学的原理、方法与工具处理这些错综复杂的关系显得尤为重要,可见进一步厘清我国计量学发展轨迹具有重要意义。[方法/过程]本文基于CNKI、CSSCI数据库,利用可视化软件,对国内已有的文献进行分析,探析国内计量学发展的研究演进过程,通过文献的梳理分析,使用文献的共引分析、共词分析方法对我国计量学发展轨迹及研究趋势进行可视化分析。[结果/结论]本文通过文献共被引分析,发现我国计量学研究的两个核心领域;通过作者共被引分析,发现研究主题演进过程中代表人物及其更替;通过关键词共现分析,掌握目前国内"五计学"研究热点的演进。     

论巴渝格律诗人邹绛

邹绛是活跃于巴渝诗坛上的格律诗人,长期潜心于现代格律诗创作和理论研究,在体式建设、节奏建设和继承、借鉴与创新等方面丰富和发展了现代格律诗.其诗富于自然舒畅的节奏感,在自由的口语中体现严谨的格律,严谨的格律中透出自如活泼的气息.     

融合数据增广技术与机器学习算法的个人信用评分研究

为了提高个人信用评分模型算法预测精准率,受视觉领域数据增广思路启发,提出融合数据增广技术与机器学习算法的个人信用评分模型。该模型首先对原始个人信用数据进行数据增广处理,然后基于机器学习分类算法训练一个二分类个人信用评分模型,最后基于公开个人信用数据集,分别建立未经过数据增广和经过数据增广处理后的个人信用评分模型。对比准确率、精确率、召回率、F1 得分、AUC 值和 ROC 曲线等 6 个性能评价指标,结果显示,相较于仅基于机器学习算法的个人信用评分模型,融合了数据增广技术与机器学习算法的个人信用评分模型使得分类性能得到了一定提升,分类准确率平均高出 5%。     

Academic Digital Libraries of the Future: An Environment Scan

Libraries are attempting to face a future in which almost every fixed point has disappeared. Users are changing; content is changing; research is taking new forms. Indeed the very need for libraries is being questioned in some quarters. This paper explores the nature of the changes and challenges facing higher education libraries and suggests key areas of strength and core activities which should be exploited to secure their future.     

Impact maturity times and citation time windows: The 2-year maximum journal impact factor

Journal metrics are employed for the assessment of scientific scholar journals from a general bibliometric perspective. In this context, the Thomson Reuters journal impact factors (JIFs) are the citation-based indicators most used. The 2-year journal impact factor (2-JIF) counts citations to one and two year old articles, while the 5-year journal impact factor (5-JIF) counts citations from one to five year old articles. Nevertheless, these indicators are not comparable among fields of science for two reasons: (i) each field has a different impact maturity time, and (ii) because of systematic differences in publication and citation behavior across disciplines. In fact, the 5-JIF firstly appeared in the Journal Citation Reports (JCR) in 2007 with the purpose of making more comparable impacts in fields in which impact matures slowly. However, there is not an optimal fixed impact maturity time valid for all the fields. In some of them two years provides a good performance whereas in others three or more years are necessary. Therefore, there is a problem when comparing a journal from a field in which impact matures slowly with a journal from a field in which impact matures rapidly. In this work, we propose the 2-year maximum journal impact factor (2M-JIF), a new impact indicator that considers the 2-year rolling citation time window of maximum impact instead of the previous 2-year time window. Finally, an empirical application comparing 2-JIF, 5-JIF, and 2M-JIF shows that the maximum rolling target window reduces the between-group variance with respect to the within-group variance in a random sample of about six hundred journals from eight different fields.