跨境电商英语沟通中的语用移情策略研究

跨境电商的飞速发展离不开跨境电商英语沟通质量的提升,跨境电商英语沟通是在交流过程中,买卖双方传递、共享信息和感情的过程。运用语用学的移情理论,结合跨境电商英语沟通特点,从礼貌语、委婉语、模糊语和幽默语等方面,建构语用移情策略,有助于缩短买卖双方的心理距离,达到预期的语用效果,推动畅通有效地跨境电商沟通。     

日语自他动词与态相关的误用分析

日语自他动词是日语语法中的一个难点,尤其在和其他语态相关联时使用起来更加有难度.以汉语为母语的日语学习者,常常按照汉语思维习惯去套用日语,出现误用现象.本文从自他动词与被动表现、使役表现和可能表现相关的误用三方面入手,归纳总结中国的日语学习者常见的错误,并从社会文化心理方面探讨产生这些误用的原因,以期帮助中国学生解决有关母语干涉的问题.     

How Does Using a Mobile Device Change Investors’ Reactions to Firm Disclosures?

I examine how characteristics of investors’ information access tools change investors’ reactions to firm disclosures. I examine my research question in the context of information choice (i.e., allowing investors to choose the order of information and sections to read within a disclosure) and spatial layout (i.e., how information is displayed when viewing the disclosure). Results of an experiment are consistent with information choice improving investors’ judgments if the disclosure is viewed on a computer screen. Conversely, and consistent with cognitive overload, information choice harms investors’ judgments if the disclosure is viewed on a smaller screen, such as that of a mobile device. Follow-up experiments show that changing the disclosure presentation to reduce the need to scroll is one way to improve investors’ judgments on a smaller (or mobile) screen. My findings caution firms and regulators about expanding information choice within disclosures without considering the screen size used to access the disclosure.     

基于Android的自主学习软件的研究与实现

本文提出一种基于Android平台的用JAVA语言编写的自主学习软件,该软件安装在Android系统的手机上,用户可以进行在线学习,也能够在有WIFI的情况下提前更新题库后脱机学习,可以让用户利用碎片时间进行学习,真正的达到随时随地进行学习的目的。     

Comparing automated text classification methods

Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.     

基于XML语言的智能体语义通信研究

目前智能体之间的通信主要是基于KQML规范.但是KIF表达语义能力不强.如果将其应用于基于智能体Agent的分布式入侵防御系统中智能体之间的通信,会导致智能体之间由于语义通信能力差而难以沟通和协作等问题.为了解决这一问题,文中设计并实现了一种基于XML语言的入侵防御Agent通信机制.通过将 XML 语言作为智能体KQML通信语言的内容语言来提高入侵防御系统中智能体 Agent 之间语义通信的能力.     

合作创新网络结构特征对企业创新绩效的影响研究——以新能源汽车产业为例

本文在合作创新和社会网络理论基础上,以创新网络的结构特征为视角,将节点度、中介中心度和结构洞作为3个维度,提出了创新网络的结构特征对企业经济、社会和生态绩效影响的假设,并对新能源汽车企业的168份调查问卷结果运用多元回归分析进行验证。结果显示：合作创新网络的节点度对企业经济和社会创新绩效有一定的正向促进作用;中介中心度对企业经济创新绩效有正向影响;结构洞对企业经济和生态创新绩效有正向促进作用。实证结果对企业、中介机构和政府在发展新能源产业方面都有一定的启发意义。     

Features selection,data mining and finacial risk classification: a comparative study

The aim of this paper is to compare several predictive models that combine features selection techniques with data mining classifiers in the context of credit risk assessment in terms of accuracy, sensitivity and specificity statistics. The t‐statistic, Battacharrayia statistic, the area between the receiver operating characteristic, Wilcoxon statistic, relative entropy, and genetic algorithms were used for the features selection task. The selected features are used to train the support vector machine (SVM) classifier, backpropagation neural network, radial basis function neural network, linear discriminant analysis and naive Bayes classifier. Results from three datasets using a 10‐fold cross‐validation technique showed that the SVM provides the best accuracy under all features selections techniques adopted in the study for all three datasets. Therefore, the SVM is an attractive classifier to be used in real applications for bankruptcy prediction in corporate finance and financial risk management in financial institutions. In addition, we found that our best results are superior to earlier studies on the same datasets.     

女性高管、信任环境与企业社会责任信息披露——基于自愿披露社会责任报告A股上市公司的经验证据

以2008—2012年沪深两市自愿披露社会责任报告的A股上市公司为样本,实证检验了女性高管对企业社会责任信息披露的影响。研究结论表明,女性高管与企业社会责任信息披露水平显著正相关。进一步研究发现,信任环境对女性高管与企业社会责任信息披露之间的正相关关系产生了促进作用;相对于非国有企业,国有企业女性高管对企业社会责任信息披露水平的正面影响更为显著。     

三江平原湿地生态功能区划分区方案及分区特征

三江平原是我国最大的淡水湿地分布区,而湿地生态功能区划是进行湿地生态系统管理的重要途径。本文按照生态功能区划的原则,结合三江平原湿地生态系统服务功能特点和生态环境现状,将三江平原湿地生态系统服务功能区划分为3个一级区,14个二级区,并对其分区特征进行分析。为科学、合理地保护和利用湿地,创造可持续发展条件奠定基础。