In the process of Chinese culture going global,the overseas communication effect of Chinese culture leaves much to desire. With the arrival of new media and big data,the translation and transmission of Chinese culture...In the process of Chinese culture going global,the overseas communication effect of Chinese culture leaves much to desire. With the arrival of new media and big data,the translation and transmission of Chinese culture begin to embark on a new challenge. Based on big data and new media platform,this thesis mainly analyzes the material selection,transmission channels and inspirations from internet stars,for example,Li Ziqi,advocating reader analysis mechanism and diversified communication modes,so as to let foreigners understand and love Chinese culture through various channels,and promote Chinese culture going global.展开更多
This paper introduces the origin and development of tourism,the cultural connotation of tourism economy,the history of cultural tourism industry,and the strategic changes of business value and economic development in ...This paper introduces the origin and development of tourism,the cultural connotation of tourism economy,the history of cultural tourism industry,and the strategic changes of business value and economic development in the era of big data.The strategy of economic and cultural joint development under the environment of ecological tourism is explored by developing the way of ethnic culture tourism and strengthening the interactive development of culture and economy.The competitive advantage of big data is analyzed and the importance of basic content and data collection is emphasized.In addition,the present situation of tourism market is evaluated and improved by constructing reasonable economic benefit analysis model.展开更多
超大量的数据从诸如传感器、社交媒体、互联网应用等物联网产生,这些数据被统称为大数据。传统的工具和技术无法处理大数据。为了从大量数据中提取出对新技术有益的信息,大数据的挖掘尤为重要。非常受关注的关联规则挖掘和高频数据项挖...超大量的数据从诸如传感器、社交媒体、互联网应用等物联网产生,这些数据被统称为大数据。传统的工具和技术无法处理大数据。为了从大量数据中提取出对新技术有益的信息,大数据的挖掘尤为重要。非常受关注的关联规则挖掘和高频数据项挖掘都需要在内存中调入全部数据,但是大量的数据并不适合内存存储。为解决这一难题,业界提出了MapReduce进行并行的大数据处理。本文提出的改进Big FIM算法(Improved Big FIM,IBFIM)运行于MapReduce架构下用于大数据的挖掘.IBFIM相对于Big FIM增加了对更大规模数据的支持并提高了数据挖掘的速度。该研究为更快速、更高效地并行挖掘大数据内容提供参考。展开更多
The history of educational technology in the last 50 years contains few instances of dramatic improvements in learning based on the adoption of a particular technology.An example involving artificial intelligence occu...The history of educational technology in the last 50 years contains few instances of dramatic improvements in learning based on the adoption of a particular technology.An example involving artificial intelligence occurred in the 1990s with the development of intelligent tutoring systems( ITSs). What happened with ITSs was that their success was limited to well-defined and relatively simple declarative and procedural learning tasks(e. g.,learning how to write a recursive function in LISP; doing multi-column addition),and improvements that were observed tended to be more limited than promised(e. g.,one standard deviation improvement at best rather than the promised standard deviation improvement).Still,there was some progress in terms of how to conceptualize learning. A seldom documented limitation was the notion of only viewing learning from only content and cognitive perspectives( i. e.,in terms of memory limitations,prior knowledge,bug libraries,learning hierarchies and sequences etc.). Little attention was paid to education conceived more broadly than developing specific cognitive skills with highly constrained problems. New technologies offer the potential to create dynamic and multi-dimensional models of a particular learner,and to track large data sets of learning activities,resources,interventions,and outcomes over a great many learners. Using those data to personalize learning for a particular learner developing knowledge,competence and understanding in a specific domain of inquiry is finally a real possibility. While the potential to make significant progress is clearly possible,the reality is less not so promising. There are many as yet unmet challenging some of which will be mentioned in this paper. A persistent worry is that educational technologists and computer scientists will again promise too much,too soon at too little cost and with too little effort and attention to the realities in schools and universities.展开更多
基金one of the research achievements of the project"The translation of Children’s Literature and Chinese Culture Going Global"(NoY201840027)sponsored by the Education Department of Zhejiang Province。
文摘In the process of Chinese culture going global,the overseas communication effect of Chinese culture leaves much to desire. With the arrival of new media and big data,the translation and transmission of Chinese culture begin to embark on a new challenge. Based on big data and new media platform,this thesis mainly analyzes the material selection,transmission channels and inspirations from internet stars,for example,Li Ziqi,advocating reader analysis mechanism and diversified communication modes,so as to let foreigners understand and love Chinese culture through various channels,and promote Chinese culture going global.
基金the professional brand construction project in Jiangsu Colleges and Universities(PPZY2015A098)
文摘This paper introduces the origin and development of tourism,the cultural connotation of tourism economy,the history of cultural tourism industry,and the strategic changes of business value and economic development in the era of big data.The strategy of economic and cultural joint development under the environment of ecological tourism is explored by developing the way of ethnic culture tourism and strengthening the interactive development of culture and economy.The competitive advantage of big data is analyzed and the importance of basic content and data collection is emphasized.In addition,the present situation of tourism market is evaluated and improved by constructing reasonable economic benefit analysis model.
文摘超大量的数据从诸如传感器、社交媒体、互联网应用等物联网产生,这些数据被统称为大数据。传统的工具和技术无法处理大数据。为了从大量数据中提取出对新技术有益的信息,大数据的挖掘尤为重要。非常受关注的关联规则挖掘和高频数据项挖掘都需要在内存中调入全部数据,但是大量的数据并不适合内存存储。为解决这一难题,业界提出了MapReduce进行并行的大数据处理。本文提出的改进Big FIM算法(Improved Big FIM,IBFIM)运行于MapReduce架构下用于大数据的挖掘.IBFIM相对于Big FIM增加了对更大规模数据的支持并提高了数据挖掘的速度。该研究为更快速、更高效地并行挖掘大数据内容提供参考。
文摘The history of educational technology in the last 50 years contains few instances of dramatic improvements in learning based on the adoption of a particular technology.An example involving artificial intelligence occurred in the 1990s with the development of intelligent tutoring systems( ITSs). What happened with ITSs was that their success was limited to well-defined and relatively simple declarative and procedural learning tasks(e. g.,learning how to write a recursive function in LISP; doing multi-column addition),and improvements that were observed tended to be more limited than promised(e. g.,one standard deviation improvement at best rather than the promised standard deviation improvement).Still,there was some progress in terms of how to conceptualize learning. A seldom documented limitation was the notion of only viewing learning from only content and cognitive perspectives( i. e.,in terms of memory limitations,prior knowledge,bug libraries,learning hierarchies and sequences etc.). Little attention was paid to education conceived more broadly than developing specific cognitive skills with highly constrained problems. New technologies offer the potential to create dynamic and multi-dimensional models of a particular learner,and to track large data sets of learning activities,resources,interventions,and outcomes over a great many learners. Using those data to personalize learning for a particular learner developing knowledge,competence and understanding in a specific domain of inquiry is finally a real possibility. While the potential to make significant progress is clearly possible,the reality is less not so promising. There are many as yet unmet challenging some of which will be mentioned in this paper. A persistent worry is that educational technologists and computer scientists will again promise too much,too soon at too little cost and with too little effort and attention to the realities in schools and universities.
