Two-stage statistical language models for text database selection

As the number and diversity of distributed Web databases on the Internet exponentially increase, it is difficult for user to know which databases are appropriate to search. Given database language models that describe the content of each database, database selection services can provide assistance in locating databases relevant to the information needs of users. In this paper, we propose a database selection approach based on statistical language modeling. The basic idea behind the approach is that, for databases that are categorized into a topic hierarchy, individual language models are estimated at different search stages, and then the databases are ranked by the similarity to the query according to the estimated language model. Two-stage smoothed language models are presented to circumvent inaccuracy due to word sparseness. Experimental results demonstrate that such a language modeling approach is competitive with current state-of-the-art database selection approaches.     

Hierarchical clustering of a Finnish newspaper article collection with graded relevance assessments

Search facilitated with agglomerative hierarchical clustering methods was studied in a collection of Finnish newspaper articles (N = 53,893). To allow quick experiments, clustering was applied to a sample (N = 5,000) that was reduced with principal components analysis. The dendrograms were heuristically cut to find an optimal partition, whose clusters were compared with each of the 30 queries to retrieve the best-matching cluster. The four-level relevance assessment was collapsed into a binary one by (A) considering all the relevant and (B) only the highly relevant documents relevant, respectively. Single linkage (SL) was the worst method. It created many tiny clusters, and, consequently, searches enabled with it had high precision and low recall. The complete linkage (CL), average linkage (AL), and Ward's methods (WM) returned reasonably-sized clusters typically of 18–32 documents. Their recall (A: 27–52%, B: 50–82%) and precision (A: 83–90%, B: 18–21%) was higher than and comparable to those of the SL clusters, respectively. The AL and WM clustering had 1–8% better effectiveness than nearest neighbor searching (NN), and SL and CL were 1–9% less efficient that NN. However, the differences were statistically insignificant. When evaluated with the liberal assessment A, the results suggest that the AL and WM clustering offer better retrieval ability than NN. Assessment B renders the AL and WM clustering better than NN, when recall is considered more important than precision. The results imply that collections in the highly inflectional and agglutinative languages, such as Finnish, may be clustered as the collections in English, provided that documents are appropriately preprocessed.     

一种改进的二维曲线的strip树表示及其基本算法

本文给出了一种改进的二维曲线的ｓｔｒｉｐ树表示，并在此基础上，给出了二值图象中曲线的基本算法。     

OSI参考模型的教学方法

《计算机网络基础》课程是一门发展着的前沿学科,是我院本、专科各专业必修的一门专业基础课程,是通信技术与计算机技术相结合的产物,内容广泛,学习难度较大,尤其在学习计算机网络体系结构这部分知识时,学生的学和教师的教都认为是一重点,也是一难点。所以,本文将从一实例出发逐渐推导出计算机网络体系结构模型OSI/RM的教学内容,从而使学生进一步了解什么是计算机网络体系结构。     

基于统计-规则方法的网页层次分类技术研究

基于统计的自动分类是网页层次分类中常用的技术，但其有不足之处，主要表现为当子类之间出现严重的特征交叉现象时，分类精确率将大大下降。而网页层次分类的本质决定了同一大类下的子类存在许多相同的特征。针对这一局限性，结合基于规则的自动分类技术的优点，提出一种基于统计-规则方法的网页层次分类技术。实验表明，基于统计-规则方法的网页层次分类技术能够获得比较理想的分类效果。     

从心理学的角度论普通高校教师职业倦怠的成因及对策

职业倦怠已严重影响了教师、学生以及整个教育事业的健康发展,从马斯洛的需要层次理论、赫茨伯格的双因素理论和自我意识等心理学的角度分析了普通高校教师职业倦怠的成因,并在此基础上提出了一些切实可行的对策。     

地级市创新能力影响因素的分层线性模型研究

以2018年我国30个省份及其对应的280个地级市为研究对象,利用分层线性模型分析省域层面、地级市层面的因素以及二者的交互作用对地级市创新能力的影响。结果显示,我国地级市创新能力的差异中有57.6%可以由省域层面的影响因素解释;地级市自身的经济水平、产业结构、R&D经费支出可以提升其创新能力;省域层面的财政科技支出对其下辖地级市创新能力有正向影响,其他省域层面因素与地级市层面因素存在交互效应。     

Topic discovery based on text mining techniques

In this paper, we present a topic discovery system aimed to reveal the implicit knowledge present in news streams. This knowledge is expressed as a hierarchy of topic/subtopics, where each topic contains the set of documents that are related to it and a summary extracted from these documents. Summaries so built are useful to browse and select topics of interest from the generated hierarchies. Our proposal consists of a new incremental hierarchical clustering algorithm, which combines both partitional and agglomerative approaches, taking the main benefits from them. Finally, a new summarization method based on Testor Theory has been proposed to build the topic summaries. Experimental results in the TDT2 collection demonstrate its usefulness and effectiveness not only as a topic detection system, but also as a classification and summarization tool.     

Fabrication of hierarchical polycaprolactone/gel scaffolds via combined 3D bioprinting and electrospinning for tissue engineering

It is a severe challenge to construct 3D scaf- folds which hold controllable pore structure and similar morphology of the natural extracellular matrix （ECM）. In this study, a compound technology is proposed by com- bining the 3D bioprinting and electrospinning process to fabricate 3D scaffolds, which are composed by orthogonal array gel microfibers in a grid-like arrangement and inter- calated by a nonwoven structure with randomly distributed polycaprolactone （PCL） nanofibers. Human adipose- derived stem cells （hASCs） are seeded on the hierarchical scaffold and cultured 21 d for in vitro study. The results of cells culturing show that the microfibers structure with controlled pores can allow the easy entrance of cells and the efficient diffusion of nutrients, and the nanofiber webs layered in the scaffold can significantly improve initial cell attachment and proliferation. The present work demon- strates that the hierarchical PCL/gel scaffolds consisting of controllable 3D architecture with interconnected pores and biomimetic nanofiber structures resembling the ECM can be designed and fabricated by the combination of 3D bioprinting and electrospinning to improve biological per- formance in tissue engineering applications.     

高校定位影响因素分析——基于文献视角

通过运用SPSS16.0软件,主要采用层次聚类分析方法,对所检索文献中影响因素出现的频次来判断影响因素的重要程度,进而对我国高校定位影响因素进行分类和比较。分析发现:外部因素对高校定位的影响要比内部因素显著,政策制度因素和物质条件因素对高校定位的影响强于思想观念因素。因此,在高校定位过程中高等院校要在深刻认识自身特色的前提下,把握好高校、政府和社会三者的关系,同时还要高度重视办学理念的核心指导作用。