Social‐Information‐Processing Skills of Kindergarten Children with Developmental Learning Disabilities

The study examined the social‐information‐processing skills of kindergarten children with developmental learning disabilities (LD) utilizing Crick and Dodge's (1994) model of children's social adjustment as a theoretical framework. Participants consisted of 20 kindergarten children with developmental LD who attended three integrated kindergartens and 20 children without developmental LD from the same kindergartens. Participants were assessed on social‐information‐processing skills, feelings of loneliness, sense of coherence, and teachers' ratings of behavioral problems and positive resources. The results indicated that girls with developmental LD performed significantly lower on two information‐processing steps—the response decision and the enactment steps—than did girls without LD. Such differences were not found for boys. The results also showed that the social‐information‐processing skills of children with developmental LD were correlated with teachers' ratings.     

The Technical Quality of Performance Assessments: Standard Errors of Percents of Pupils Reaching Standards

How accurate is the reported percent of students’reaching standards at the school level? How are standard errors for these statistics computed? How do estimates vary with choice of generalizability model?     

宪法权利与程序保障

面对国家权力的扩张和个人主义的危机,程序对公民宪法权利的保障作用已日益彰显。因此,从理论上科学地阐释宪法权利与程序保障内在要素之间的逻辑关系,对建构科学、规范、完善的程序保障体系,充分保障基本人权,具有十分重要的基础性意义。     

Review article

Ducellier, A., Kaplan, M., Martin, Bernadette, avec la collaboration de Françoise Micheau. Le Moyen Âge en Orient. Byzance et l'Islam. Des Barbares aux Ottomans. Paris: Hachette Histoire Université, 1990.     

基于无监督图神经网络的学术文献表示学习研究

学术文献特征表示,是学术文献搜索、分类组织、个性化推荐等学术大数据服务的关键步骤。研究表明,图神经网络能够有效学习文献的特征表示,然而当前研究主要集中在有监督学习方法上,不仅对数据集的大小和质量的要求较高,且学习到的文献特征表示与具体任务高度耦合。基于此,本文将四种无监督图神经网络方法引入学术文献表示学习,从Cora、CiteSeer和DBLP (database systems and logic programming)数据集的引文网络、共被引网络和文献耦合网络中学习文献的表示向量,并应用于文献分类和论文推荐两大下游任务。研究结果表明,(1)深度互信息图神经网络适合于文献分类任务,对抗正则化变分图自编码器则在论文推荐任务上性能更佳;(2)Cora数据集上的结果表明,相较于共被引和文献耦合网络,引文网络更适合于学习通用的文献表示向量。     

地方志引书挖掘系统的设计与实现

古籍文献普遍存在着引书现象,因而构建一套针对地方志引书的挖掘识别系统,对古籍的研究以及目录学史、藏书史、科技史,都具有重要意义.本文以地方志资料汇编<方志物产>为语料,设计并构建了一个古籍引书挖掘系统.重点讨论了引书的模式提取、N-gram分词识别等功能算法.