“死”类动词同义句式分析——以“死了一个人”和“一个人死了”为例

＂死了一个人＂和＂一个人死了＂是一价动词同义句式。国内对于同义句式的研究由来已久。本文准备在前人的基础上具体研究＂死了一个人＂和＂一个人死了＂这两个句式,对哪个是基本句式,哪个是派生句式进行确认,并分析由形式的差异所导致的意义的差异。     

实践意义与深度介入：“U—S”合作的京沪对话

由大学研究人员、中小学校长和教师组成的两支研究团队相会北京,就共同感兴趣的大中小学（即“U—S”）合作问题展开热烈对话,尽管双方对大中小学合作意义的认识“同中有异”,介入学校变革实践的过程与方式却是“异中有同”,对“U—S”合作的前景都充满信心,并且充分意识到自己在中国基础教育改革大业中不可旁贷的责任担当,对话留下的诸多问题也值得我们去继续更有深度的思索与行动。     

论海岩小说《深牢大狱》的人性光辉

人性是真理性在人身上的自由表达,是对真善美的追求。小说《深牢大狱》是一部全面反映监狱工作生活的作品,其中处处闪耀着人性的光辉,对国家和人民的忠诚、对事业的荣誉和使命感以及对爱情、亲情和友情的美好表达。     

IBM's Chess Players: On AI and Its Supplements

This article investigates the ways in which the reporting of technological developments in artificial intelligence (AI) can serve as occasions in which Occidental modernity's cultural antinomies are played out. It takes as its reference point the two chess tournaments (in 1996 and 1997) between the then world champion Gary Kasparov and the IBM dedicated chess computers Deep Blue and Deeper Blue and shows how these games of chess came to be seen as an arena where fundamental issues pertaining to human identity were contested. The article considers the dominant framing of these encounters in terms of a conflict between two opposed categories—“human” and “machine”—and argues the essential role of human agency, the human supplement, in the performances of machine intelligence.     

SNS平台在促进深度学习方面的比较研究

SNS技术及SNS网站作为一项迅速发展的基于Web 2.0网络平台的用户创造类媒体,正逐渐渗透到人们的生活和学习中,它们为学习者进行有效交流与互动协作提供了强大的支持和服务。因此,基于深度学习的相关理论,选择目前可用于深度学习的五类平台为研究对象,采用观察法和内容分析法,从知识维度、技术维度、交互维度、学习维度四个方面对SNS平台促进深度学习的实际情况进行了比较分析。SNS平台能为个人知识管理提供最大帮助,基础技术功能和交互方式完善,多采用内外链接结合的交互链接结构,基本支持各类学习工具和实现深度学习的认知方式。但部分SNS平台缺乏深层次知识和反映知识框架的形式,且技术支持和交互效能仍有待提高;认知效果也和设想不完全一致,部分网络学习者仍处于浅层学习的阶段。针对当前研究中存在的问题,提出相应的对策和建议。     

The Evolving Impact of the Invisible Web: Exploring Economic and Political Ramifications

The Invisible Web—defined as Web information that cannot be found by using general-purpose search engines—has been studied primarily as an access issue. In this article, the authors consider, for the first time, the economic and political implications of the Invisible Web. The article is divided into two parts; the first looks at the “open” Web, accessible through URLs, while the second delves into the Dark Web, hidden behind veils of anonymity. The two main questions asked are: How is the Invisible Web shaped by economic and political factors and how, in turn, does the Invisible Web shape those circumstances? With the answers to these questions, the authors hope to bring more awareness of the Invisible Web to the information literacy classroom.     

IBM's Chess Players: On AI and Its Supplements

This article investigates the ways in which the reporting of technological developments in artificial intelligence (AI) can serve as occasions in which Occidental modernity's cultural antinomies are played out. It takes as its reference point the two chess tournaments (in 1996 and 1997) between the then world champion Gary Kasparov and the IBM dedicated chess computers Deep Blue and Deeper Blue and shows how these games of chess came to be seen as an arena where fundamental issues pertaining to human identity were contested. The article considers the dominant framing of these encounters in terms of a conflict between two opposed categories—“human” and “machine”—and argues the essential role of human agency, the human supplement, in the performances of machine intelligence.     

基于本体的Deep Web自动标注方法研究

结合基于查询接口模式的Deep Web标注方法,提出一种基于网页视觉信息的Deep Web标注方法,用本体词组去替换原有标注信息,这种替换确保标注信息的一致性,可以很好地弥补原有方法的许多缺陷,并且有效提高原有方法的准确率和召回率.     

Temporal expert profiling: With an application to T-shaped expert finding

Cost optimization continues to be a critical concern for many human resources departments. The key is to balance between costs and business value. In particular, computer science organizations prefer to hire people who are expert in only one skill area and have a slight superficial knowledge in other areas that gives them the ability to collaborate across different aspects of project. Community Question Answering networks provide good platforms for people and organizations to share knowledge and find experts. An important issue in expert finding is that an expert has to constantly update his knowledge after being saturated in his field of expertise to still be identified as expert. A person who fails to preserve his expertise is likely to lose his expertise. This work justifies this question that does take the concept of time into account improve the quality of expertise retrieval. We propose a new method for T-shaped expert finding that is based on temporal expert profiling. The proposed method takes the temporal property of expertise into account to mine the shape of expertise for each candidate expert based on his profile. To this end, for each candidate expert, we take snapshots of his expertise trees at regular time intervals and learn the relation between temporal changes in different expertise trees and candidates’ profile. Finally, we use a filtering technique that is applied on top of the profiling method, to find shape of expertise for candidate experts. Experimental results on a large test collection show the superiority of the proposed method in terms of quality of results in comparison with state-of-the-art.     

The impact of deep learning on document classification using semantically rich representations

This paper presents a semantically rich document representation model for automatically classifying financial documents into predefined categories utilizing deep learning. The model architecture consists of two main modules including document representation and document classification. In the first module, a document is enriched with semantics using background knowledge provided by an ontology and through the acquisition of its relevant terminology. Acquisition of terminology integrated to the ontology extends the capabilities of semantically rich document representations with an in depth-coverage of concepts, thereby capturing the whole conceptualization involved in documents. Semantically rich representations obtained from the first module will serve as input to the document classification module which aims at finding the most appropriate category for that document through deep learning. Three different deep learning networks each belonging to a different category of machine learning techniques for ontological document classification using a real-life ontology are used.Multiple simulations are carried out with various deep neural networks configurations, and our findings reveal that a three hidden layer feedforward network with 1024 neurons obtain the highest document classification performance on the INFUSE dataset. The performance in terms of F1 score is further increased by almost five percentage points to 78.10% for the same network configuration when the relevant terminology integrated to the ontology is applied to enrich document representation. Furthermore, we conducted a comparative performance evaluation using various state-of-the-art document representation approaches and classification techniques including shallow and conventional machine learning classifiers.