税收优惠、财政补贴与中关村企业创新投入——基于倾向得分匹配法的实证研究

采用倾向得分匹配法评估税收优惠和财政补贴对中关村高新技术企业自主创新投入的激励效果。研究结果表明:经营状况越好、创新能力越强、知识资本越充足、规模越小的企业越易获得这两种政策扶持;成立时间越短的企业越易获得税收优惠,但这一因素对获得政府补贴无显著影响;税收优惠对企业创新投入有显著的激励作用;财政补贴对创新投入具有一定的正向影响,但与税收优惠相比效果不明显,在解决市场失灵的同时产生了一定程度的政策失灵。     

车牌识别系统中图像分割与识别技术研究

分析了汽车牌照的几何特征和成像特点,提出基于自适应性阈值的搜索策略,对图像中的车牌进行定位;利用Hough变换对图像进行倾斜矫正;然后设计一个三级分类器,对单个字符进行模式匹配,得到识别结果,最终实现对原始车牌图像的识别。     

数字图书馆中基于内容的视频拷贝检测关键技术研究

随着数字图书馆中数字视频资源的增长,数字视频版权保护问题逐渐暴露出来。基于内容的视频拷贝检测技术作为一种有效的版权保护技术,近年来逐渐受到研究人员的关注。本文对基于内容的视频拷贝检测的关键技术视频指纹提取和指纹匹配的研究成果进行了分析,总结了指纹提取和指纹匹配的研究现状,指出了该领域存在的问题,最后分析了该领域的发展方向。     

中文科技期刊专刊的策划与出版实践——以《生物工程学报》为例

【目的】通过梳理《生物工程学报》近7年来专刊的策划和出版实践,一方面总结出版专刊的成功经验,同时也为国内期刊同行提供借鉴与参考。【方法】详述《生物工程学报》在专刊选题策划及组织形式方面的做法,并通过中国知网(CNKI)的统计数据,追踪比较了该刊2009—2011年间出版的专刊与普通发文的被引频次。【结果】结果表明,组织专刊既可以扩大稿源,又可以提升论文质量、增强期刊学术影响力。【结论】专刊的策划与出版是现阶段提升科技期刊学术质量行之有效的方式之一。     

跨部门联盟的匹配因素与公司社会创业价值创造的关系研究

公司社会创业日益成为我国社会创业活动的重要内容，而跨部门联盟是其价值创造的必然工具。以我国10个具有代表性的公司社会创业项目为研究对象，采用扎根理论分析公司社会创业跨部门联盟匹配因素的内在构成以及其与价值创造的关联机理，归纳与提炼公司社会创业跨部门联盟的文化匹配、资源匹配和管理匹配3个关键匹配因素，并指出其对价值创造的作用存在互补或替代关系。本研究揭示了我国公司社会创业中跨部门联盟价值创造的路径，对于丰富和拓展公司社会创业和跨部门联盟理论具有一定意义。     

支持型组织责任对员工创新行为的影响——有调节的中介效应模型检验

引入积极情绪、供给需求匹配作为中间变量,建立支持型组织责任影响员工创新行为的有调节的中介效应模型。实证研究结果显示,支持型组织责任对员工创新行为具有积极的正向影响,其效用受到供给需求匹配的调节;人际支持完全通过个体积极情绪来影响员工创新行为,组织支持对员工创新行为的影响部分受到个体积极情绪的中介;积极情绪对员工创新行为的影响受到供给需求匹配的调节,研究结论有效地解释了以往研究的分歧。     

Dependency structure language model for topic detection and tracking

In this paper, we propose a new language model, namely, a dependency structure language model, for topic detection and tracking (TDT) to compensate for weakness of unigram and bigram language models. The dependency structure language model is based on the Chow expansion theory and the dependency parse tree generated by a linguistic parser. So, long-distance dependencies can be naturally captured by the dependency structure language model. We carried out extensive experiments to verify the proposed model on topic tracking and link detection in TDT. In both cases, the dependency structure language models perform better than strong baseline approaches.     

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.     

Interest in an expository text: How does it flow from reading to writing?

This study investigated the flow of interest in a reading-to-write activity. It was hypothesized that (a) different contents in a text would stimulate different types of interest and (b) different writing tasks would impact differently students’ use of interesting text segments. Participants were 247 11th- and 12th-grade students, who were divided into 6 groups according to a 2 (interesting vs. uninteresting topic) × 3 (type of writing task) design. While reading, students rated novelty, curiosity, impressiveness, importance, and willingness to reflect on each text segment. Participants were then assigned to one of three writing tasks, namely argumentation, text designing, and composition. Results showed that concepts were regarded as important, events/activities as impressive, and evaluations as most worthy of reflection. Topic interest was explicitly expressed mainly in the argumentation condition, where increased topic interest scores after reading and writing were also found.     

基于主题句相似度的标题党新闻鉴别技术研究

针对目前网络上的标题党新闻,提出一种标题党新闻自动识别的算法。通过分析新闻网页构成的特点,抽取出新闻标题和新闻正文;以句子关系矩阵为基础,提出一种以语句为单位的主题句抽取算法;根据句子相似度计算结果来进行判断。实验表明,本方法的识别精度可达到80%,是一种有效的方法。