基于数据挖掘的学生投入模型与学习分析

学生投入是高等教育质量和学习成果的重要影响因素,对于高等教育评估与改革具有积极作用,受到了国内外研究者的广泛关注。文章以构建学生投入模型为基础,采用典型相关分析和数据挖掘方法相结合,识别学生投入的相关因素,并对学生学习行为进行分类研究。分析发现学生投入与学生家庭背景、学生入学前特征、学校特征及课程作业之间存在着显著相关关系,不同的学生投入及其学习行为表现有助于加深学校对学生学习行为的了解,更好地研究学习规律的新趋势,为审视高校以生为本、以学为中心的人才培养措施和多元性发展,提供了重要的参考与支持。     

岳阳方言子尾词的构成分类

普通话与湘语词法上最大的区别就在于普通话的儿化词丰富而湘语的子尾词较多,岳阳方言属于典型的湘语,因而具备湘语有着丰富子尾词的特点。岳阳方言的子尾词有单音节、双音节、三音节加子尾构成等3种类型,但它们在当下语言大融合的环境下正逐渐消失。     

创新驱动下的企业情报系统建设

本文对创新驱动下的企业情报系统建设进行了研究。首先,分析了企业情报系统建设过程中的情报资源体系和情报服务系统建设的现状;其次,提出了创新驱动下的企业情报系统建设思路,阐述了企业情报分类体系的构建、企业情报元数据规范制定、基于企业视角的第三方产品整合、面向主题情报产品的动态构建、面向用户的个性化订阅和推送五方面的内容;其后,以上海宝钢情报系统进行了案例分析;最后,给出了结论。     

User experience of government documents: A framework for informing design decisions

People still use documents in many everyday government processes. From tax payments to passport requests, citizens have to interact with low-interactivity information artifacts such as reports, maps and datasets, among many others. Despite efforts to improve information delivery in the public sector, effective information usage remains a critical topic of action and research. The user experience of government documents has rarely been assessed, despite them being regularly published and frequently used. Considering this, the two following research questions arise: 1) How can government documents be classified (or grouped) in terms of user experience? 2) How can the user experience of government documents be monitored over time in order to inform design decisions? Working with a public agency in Chile, we develop and test a classification and monitoring framework based on two online surveys (N?=?338 and N?=?298). We then propose a framework for understanding user experience of government documents in these three dimensions: interaction goal, volume of information and ease of understanding. Using a graphical representation to classify user experience provides greater visibility of the current status of information produced by a public organization. Furthermore, by monitoring the user experience of a government document at different times, organizations can understand the effect of their design decisions and improve their service quality by implementing user-centered processes.     

基于金泰尔动作技能分类法对乒乓球正手攻球技术教学的研究

运用金泰尔动作技能分类法的两个维度四个方面对乒乓球正手攻球技术的教学进行分析,研究认为乒乓球运动技能的特征符合金泰尔动作技能分类法的特点,其具体表现为:调节条件是运动的、调节条件存在尝试间变化、运动过程中身体是移动的、动作技术具有操纵性。在此基础上,提出金泰尔运动技能分类法对提高乒乓球正手攻球技术的教学策略。     

Automatic thematic classification of election manifestos

We digitized three years of Dutch election manifestos annotated by the Dutch political scientist Isaac Lipschits. We used these data to train a classifier that can automatically label new, unseen election manifestos with themes. Having the manifestos in a uniform XML format with all paragraphs annotated with their themes has advantages for both electronic publishing of the data and diachronic comparative data analysis. The data that we created will be disclosed to the public through a search interface. This means that it will be possible to query the data and filter them on themes and parties. We optimized the Lipschits classifier on the task of classifying election manifestos using models trained on earlier years. We built a classifier that is suited for classifying election manifestos from 2002 onwards using the data from the 1980s and 1990s. We evaluated the results by having a domain expert manually assess a sample of the classified data. We found that our automatic classifier obtains the same precision as a human classifier on unseen data. Its recall could be improved by extending the set of themes with newly emerged themes. Thus when using old political texts to classify new texts, work is needed to link and expand the set of themes to newer topics.     

非线性方程的智能化实现及应用

方程和方程组的解法一直是相关研究人员关注的焦点。针对非线性方程的解法做出智能化实现,以各种解方程的数学原理、方法为基础,以MATLAB为主要计算工具,编写程序实现智能化、最优化、简便化地解方程的根;进行各种分析,并应用于实际例子,验证其正确性、可行性以及智能化。     

中国古代荔枝分类方法的研究——以福建省为中心

本文主要依据历代《荔枝谱》,结合地方志对古代福建省荔枝分类方式进行探讨。宋代蔡襄首次将本草系统中的上中下品分类法应用到荔枝分类中,但此方法未得到延续。然而淳熙《三山志》按记录来源的归类方式却对弘治《八闽通志》产生了影响,并促使其产生了几种分类方法,其中成熟期分类法在明末已经完善。本文深入分析早、晚熟荔枝类群,发现现代生物学实验显示荔枝聚类成因与成熟期具有一定的相关性;清代《广东新语》采取的生态分类法也与成熟期分类法有相通之处;且成熟期分类法能够很好地凸显火山荔枝、实生荔枝和其它栽培荔枝类群之间的差异。由此,本文认为传统时代中国荔枝分类学在明末达到了最高峰,且福建省功不可没。     

浅谈我馆图书索书号取法

随着科学技术的发展,各高校图书馆都在探究适合自身的图书取号方法,并在实际的工作中不断摸索改进,基本上都确立了各自的取号方法。本文首先对索书号的构成进行了叙述,重点对华侨大学厦门工学院图书馆图书索书号的取法进行了阐述,针对一般图书、不同版本图书、多卷书、连续出版物、配套书以及套书种次号的取法进行论述,全面讲述了一个让图书馆的藏书排架更科学合理、便于利用的图书取号法。     

Key phrase aware transformer for abstractive summarization

Abstractive summarization aims to generate a concise summary covering salient content from single or multiple text documents. Many recent abstractive summarization methods are built on the transformer model to capture long-range dependencies in the input text and achieve parallelization. In the transformer encoder, calculating attention weights is a crucial step for encoding input documents. Input documents usually contain some key phrases conveying salient information, and it is important to encode these phrases completely. However, existing transformer-based summarization works did not consider key phrases in input when determining attention weights. Consequently, some of the tokens within key phrases only receive small attention weights, which is not conducive to encoding the semantic information of input documents. In this paper, we introduce some prior knowledge of key phrases into the transformer-based summarization model and guide the model to encode key phrases. For the contextual representation of each token in the key phrase, we assume the tokens within the same key phrase make larger contributions compared with other tokens in the input sequence. Based on this assumption, we propose the Key Phrase Aware Transformer (KPAT), a model with the highlighting mechanism in the encoder to assign greater attention weights for tokens within key phrases. Specifically, we first extract key phrases from the input document and score the phrases’ importance. Then we build the block diagonal highlighting matrix to indicate these phrases’ importance scores and positions. To combine self-attention weights with key phrases’ importance scores, we design two structures of highlighting attention for each head and the multi-head highlighting attention. Experimental results on two datasets (Multi-News and PubMed) from different summarization tasks and domains show that our KPAT model significantly outperforms advanced summarization baselines. We conduct more experiments to analyze the impact of each part of our model on the summarization performance and verify the effectiveness of our proposed highlighting mechanism.