数据库技术在专题学习网站中的应用

随着网络的发展和广泛应用，Internet中的信息已经由静态方式发展为动态的交互方式，网站与数据库的连接也显得越来越重要。先简要介绍了目前流行的几种Web数据库访问技术，进而详细探讨了数据库技术在专题学习网站中的应用。     

Mapping altruism

A great deal of work has been done to understand how science contributes to technological innovation and medicine. This is no surprise given the amount of money invested annually in R&D. However, what is not well known is that US science (R&D) investment is only one-sixth that of the annual revenue received by non-profit organizations (NPOs) in the US. The large majority of NPO revenues are devoted to the remaining landscape of altruistic causes – those not relying as heavily on scientific inquiry. Given this broader context, one might reasonably expect the non-profit world to have been as well characterized as that of scientific research. The unfortunate truth is that no map of altruistic missions and causes exists; the landscape of altruistic activity is virtually unknown. In this paper, we present the first maps of altruistic mission space. These maps were created using the text from websites of 125,000 non-profit organizations (NPOs) in the US. The maps consist of 357 topics covering areas such as religion, education, sports, culture, human services, public policy and medical care. The role of science in this altruistic landscape is examined. Possible applications are discussed.     

The use of query suggestions during information search

Query suggestion is a common feature of many information search systems. While much research has been conducted about how to generate suggestions, fewer studies have been conducted about how people interact with and use suggestions. The purpose of this paper is to investigate how and when people integrate query suggestions into their searches and the outcome of this usage. The paper further investigates the relationships between search expertise, topic difficulty, and temporal segment of the search and query suggestion usage. A secondary analysis of data was conducted using data collected in a previous controlled laboratory study. In this previous study, 23 undergraduate research participants used an experimental search system with query suggestions to conduct four topic searches. Results showed that participants integrated the suggestions into their searching fairly quickly and that participants with less search expertise used more suggestions and saved more documents. Participants also used more suggestions towards the end of their searches and when searching for more difficult topics. These results show that query suggestion can provide support in situations where people have less search expertise, greater difficulty searching and at specific times during the search.     

基于主题发现的专利发明人推荐方法

LDA 主题模型可用于识别大规模文档集中潜藏的主题信息,本文提出了一种基于LDA 建立发明人兴趣主题模型的方法,合并每位发明人的专利数据,专利信息基于发明人进行划分,将标准的文档- 主题-词的三层LDA 模型变为专利数据中的发明人- 主题- 词的发明人兴趣模型,实现发明人的主题发现,并利用该模型中主题分布之间的相似性进行发明人的个性化推荐。在采集真实专利数据集上的实验结果表明该方法相比传统的向量空间模型方法和隐马尔科夫模型方法具有更高的准确率,推荐效果更优。     

科学教育本科专业网站建设的设计与实践

专业网站建设是高等学校教学质量与教学改革工程的重要组成部分,其目的是实现优质教学资源的充分交流与共享,全面提高教学质量。本文就科学教育专业主题网站建设的研究背景、网站建设的内容、各平台功能模块的设计等进行了分析和探讨,并在了解各种支持技术,对其进行广泛的比较研究基础上,针对目前科学教育专业网站建设研究现状与科学教育专业网站建设的需求进行分析,努力为本专业发展营造良好的环境和条件。     

在线论坛中学习者兴趣与行为主题联合建模研究

在线论坛中的文本大数据,能够在一定程度体现学习者的个人情感与知识建构水平,对其进行深入挖掘能为个性化教学提供参考与依据。针对教育文本挖掘现有研究对兴趣和行为进行孤立分析的现状,将学习者潜在的兴趣主题和行为倾向纳入主题模型,构建了融合行为-情感-主题-时间的潜在语义分析模型,即BETTM(Behavior-Emotion-Time-Topic Model),以致力于挖掘四者之间的潜在关系。基于华中地区某高校开设课程的在线论坛数据,通过挖掘学习者在整个教学周期中的兴趣主题与行为倾向,探究二者与学习成绩的关系,以及二者随时间变化的规律。结果表明:(1)有关资源查找和组内成员之间交互的主题,对学习成绩有显著正向作用;(2)教学作品完成过程中学习者的信息发布行为,对学习成绩有正向影响作用;(3)兴趣主题和行为倾向联合后的12类行为分布,对学习成绩没有显著影响;(4)在时间上,前期学习者倾向于对资料进行查找与整合以及分配任务,中期则关注彼此的交互和个人观点的表达,后期更侧重于对作品修改和完善,以及对自己和他人在整个讨论过程中表现进行评价。研究所提出的模型,为在线论坛中学习者兴趣与行为主题挖掘、分析与预测,提供了参考和依据。     

Improving neural topic modeling via Sinkhorn divergence

Textual data have been a major form to convey internet users’ content. How to effectively and efficiently discover latent topics among them has essential theoretical and practical value. Recently, neural topic models(NTMs), especially Variational Auto-encoder-based NTMs, proved to be a successful approach for mining meaningful and interpretable topics. However, they usually suffer from two major issues:(1)Posterior collapse: KL divergence will rapidly reach zeros resulting in low-quality representation in latent distribution; (2)Unconstrained topic generative models: Topic generative models are always unconstrained, which potentially leads to discovering redundant topics. To address these issues, we propose Autoencoding Sinkhorn Topic Model based on Sinkhorn Auto-encoder(SAE) and Sinkhorn divergence. SAE utilizes Sinkhorn divergence rather than problematic KL divergence to optimize the difference between posterior and prior, which is free of posterior collapse. Then, to reduce topic redundancy, Sinkhorn Topic Diversity Regularization(STDR) is presented. STDR leverages the proposed Salient Topic Layer and Sinkhorn divergence for measuring distance between salient topic features and serves as a penalty term in loss function facilitating discovering diversified topics in training. Several experiments have been conducted on 2 popular datasets to verify our contribution. Experiment results demonstrate the effectiveness of the proposed model.     

TDTMF: A recommendation model based on user temporal interest drift and latent review topic evolution with regularization factor

This paper constructs a novel enhanced latent semantic model based on users’ comments, and employs regularization factors to capture the temporal evolution characteristics of users’ potential topics for each commodity, so as to improve the accuracy of recommendation. The adaptive temporal weighting of multiple preference features is also improved to calculate the preferences of different users at different time periods using human forgetting features, item interest overlap, and similarity at the semantic level of the review text to improve the accuracy of sparse evaluation data. The paper conducts comparison experiments with six temporal matrix-based decomposition baseline methods in nine datasets, and the results show that the accuracy is 31.64% better than TimeSVD++, 21.08% better than BTMF, 15.51% better than TMRevCo, 13.99% better than BPTF, 9.24% better than TCMF, and 3.19% better than MUTPD ,which indicates that the model is more effective in capturing users’ temporal interest drift and better reflects the evolutionary relationship between users’ latent topics and item ratings.     

教育学的坏理论研究之二：教育学的话题体系

任何理论都有其独立的命题网络。但教育学并不拥有自己的命题网络,倒是充满了各种争论性的话题。这些话题中,有些是其他学科＂观照＂教育的结果,本不属于教育学,也不是教育实践的核心关切,包括教育的起源、发展史、教育与其他事物的关系、教育者与受教育者的关系、所谓的教育规律和教育原理、各种教育的影响因素等等;有些话题无所谓结论,话题的争论本身似乎比结论更有吸引力,包括教育教学的本质、各种对象的构成要素和功能、教育是科学还是艺术、什么知识最有价值、教育是否具有相对独立性等等;有些话题只具有抽象的形而上学意义,比如教育的终极目的、各种教育主张以及相应的批判;有些话题只是职业行动规范及其解释,却被描述成了理论的样子。教育学拥有如此庞大的话题体系却让人体验到一种理论上的虚弱感。从性质上看,这些话题无法衍生出理论命题,这使得这个话题体系已经由曾经的学术成就转变为教育学难以摆脱的学术包袱。