This study investigates how universities are developing normativeframeworks to regulate the use of generative AI tools in higher education, with aparticular focus on balancing empowerment and discipline. Drawing on th...This study investigates how universities are developing normativeframeworks to regulate the use of generative AI tools in higher education, with aparticular focus on balancing empowerment and discipline. Drawing on theoreticallenses such as Foucault’s discipline theory and contemporary AI ethics, the paperanalyzes policy documents from institutions including Harvard, Oxford, and severaltop Chinese universities. Using Latent Dirichlet Allocation (LDA) topic modeling,the study reveals five dominant governance themes—ranging from academicintegrity enforcement to pedagogical empowerment. The findings highlight a globalshift from restrictive to balanced, ethics-informed AI governance, with significantdisciplinary variations. The paper concludes by proposing a “principledpermissiveness” model that combines transparency, accountability, andpedagogical innovation in future AI governance.展开更多
The progress of IT technology such as social network and mobile payment and the change of social economic environment promote the emergence of sharing economy.As a subversive business model,the sharing economy is grow...The progress of IT technology such as social network and mobile payment and the change of social economic environment promote the emergence of sharing economy.As a subversive business model,the sharing economy is growing at an alarming rate all over the world.However,the influencing factors of consumers'continuous participation in the sharing economy are not clear.The paper aims to clarify the relationship between consumer perceived value and repeat purchase intention in the sharing economy.Taking the sharing economy platform(Airbnb)as an example,it proposes a dimension framework of consumer perceived value in peer-to-peer(P2P)accommodation rental service,including functional value,hedonic value,epistemic value and social relationship value.This paper used big data technology to crawl online reviews of P2P accommodation platform.LDA(Latent Dirichlet Allocation)topic model and sentiment analytics method were applied to construct the measurement indicators of perceived value based on online reviews.And repeat purchase intention variables were extracted from online reviews.Then structural equation model was used to examine the effect of perceived value dimensions on it.The paper identified that perceived value has a positive impact on consumers'repurchase intention in P2P accommodation.Also,social relationship value was considered as the most important influencing factor.展开更多
In the modern economy,startups are not only significant drivers of innovation and technological progress but also key players in addressing employment issues and promoting economic diversification.However,startups oft...In the modern economy,startups are not only significant drivers of innovation and technological progress but also key players in addressing employment issues and promoting economic diversification.However,startups often face substantial operational risks and uncertainties in their early stages,especially regarding financing.To uncover the impact of different resource allocations and strategic choices on financing success,this study proposes a predictive method based on the latent Dirichlet allocation(LDA)topic model and deep neural networks through an in-depth analysis of startup financing cases.We systematically collected description text data from 2,000 startups and extracted text features from these descriptions using the LDA topic model.These features,combined with several traditional numerical indicators such as industry,product type,technology type,number of employees,and company size,were used to train a deep neural network to predict startup financing outcomes.The experimental results show that the prediction performance based on the LDA topic model is significantly better than that of traditional models relying solely on numerical data.This highlights the importance of text features in predicting the success of startup financing.展开更多
Architectural programming is of significant importance in improving urban planning efficiency,enhancing the sustainability of the construction industry,and supporting the development of social infrastructure.However,d...Architectural programming is of significant importance in improving urban planning efficiency,enhancing the sustainability of the construction industry,and supporting the development of social infrastructure.However,due to its broad scope and complex content,research in this area exhibits diverse and scattered characteristics.To comprehensively understand the current status,progress,and future directions of architectural programming research,and to provide recommendations for both China and other countries,this study conducted a comparative review of relevant literature using a mixed method approach of bibliometrics,latent Dirichlet allocation(LDA)topic modeling,and systematic review.The study summarized the differences between China and other countries in six topics within the field of architectural programming:(1)intelligent and digital methods,(2)sustainability and practices,(3)project management and decision support,(4)urban planning and existing development,(5)social infrastructure and public services,and(6)architectural education and talent cultivation.This study provides an overview and comparative analysis on architectural programming,offering important reference for future research and practice,and promoting the overall development of the construction industry.展开更多
Tracking and investigating tourist satisfaction and accurately identifying the key factors that affect tourist satisfaction have always been among the top priorities for academia and tourist attraction operators.With ...Tracking and investigating tourist satisfaction and accurately identifying the key factors that affect tourist satisfaction have always been among the top priorities for academia and tourist attraction operators.With the rise of online travel,analysis based on online comments has become an important method for tracking and surveying tourist satisfaction.This article examined the online comments of tourists for the Panjin Red Beach Scenic Corridor Scenic Area(hereinafter referred to as Red Beach)on Ctrip as an example.Using natural language processing to classify the tourist evaluations into topics,the main topics of concern were identified as tourism services,tourism attractions,scenic area management,and tourism experience.Through the 5-level rating of Ctrip’s online gaming customer satisfaction,an analysis was conducted on tourist satisfaction and the topics of greatest concern to the tourists were ranked.The results showed that the satisfaction levels from high to low are:tourism experience,tourism attractions,scenic area management,and tourism services.Therefore,satisfaction with related content under the service topic was the lowest so this aspect urgently needs to be improved and enhanced.展开更多
With the rapid growth of online shopping platforms, more and more customers intend to share theirshopping experience and product reviews on the Internet. Both large quantity and various forms ofonline reviews bring di...With the rapid growth of online shopping platforms, more and more customers intend to share theirshopping experience and product reviews on the Internet. Both large quantity and various forms ofonline reviews bring difficulties for potential consumers to summary all the heterogenous reviews forreference. This paper proposes a new ranking method through online reviews based on differentaspects of the alternative products, which combines both objective and subjective sentiment values.Firstly, weights of these aspects are determined with LDA topic model to calculate the objectivesentiment value of the product. During this process, the realistic meaning of each aspect is alsosummarized. Then, consumers＇ personalized preferences are taken into consideration while calculatingtotal scores of alternative products. Meanwhile, comparative superiority between every two productsalso contributes to their final scores. Therefore, a directed graph model is constructed and the finalscore of each product is computed by improved PageRank algorithm. Finally, a case study is given toillustrate the feasibility and effectiveness of the proposed method. The result demonstrates that whileconsidering only objective sentiment values of the product, the ranking result obtained by our proposedmethod has a strong correlation with the actual sales orders. On the other hand, if consumers expresssubjective preferences towards a certain aspect, the final ranking is also consistent with the actualperformance of alternative products. It provides a new research idea for online customer review miningand personalized recommendation.展开更多
文摘This study investigates how universities are developing normativeframeworks to regulate the use of generative AI tools in higher education, with aparticular focus on balancing empowerment and discipline. Drawing on theoreticallenses such as Foucault’s discipline theory and contemporary AI ethics, the paperanalyzes policy documents from institutions including Harvard, Oxford, and severaltop Chinese universities. Using Latent Dirichlet Allocation (LDA) topic modeling,the study reveals five dominant governance themes—ranging from academicintegrity enforcement to pedagogical empowerment. The findings highlight a globalshift from restrictive to balanced, ethics-informed AI governance, with significantdisciplinary variations. The paper concludes by proposing a “principledpermissiveness” model that combines transparency, accountability, andpedagogical innovation in future AI governance.
基金Evolution Trend and Coping Strategies of Online Public Opinions on Emergencies Based on Big Data Analysis(Grant No.:18CHLJ22)Social Science Planning Project in Shandong Province+1 种基金“Online Public Opinion Concerning Ethnic Factors Based on Big Data Analysis”(Grant No.:2018-GMB-022)Ethnic Research Project of State Ethnic Affairs Commission in China.
文摘The progress of IT technology such as social network and mobile payment and the change of social economic environment promote the emergence of sharing economy.As a subversive business model,the sharing economy is growing at an alarming rate all over the world.However,the influencing factors of consumers'continuous participation in the sharing economy are not clear.The paper aims to clarify the relationship between consumer perceived value and repeat purchase intention in the sharing economy.Taking the sharing economy platform(Airbnb)as an example,it proposes a dimension framework of consumer perceived value in peer-to-peer(P2P)accommodation rental service,including functional value,hedonic value,epistemic value and social relationship value.This paper used big data technology to crawl online reviews of P2P accommodation platform.LDA(Latent Dirichlet Allocation)topic model and sentiment analytics method were applied to construct the measurement indicators of perceived value based on online reviews.And repeat purchase intention variables were extracted from online reviews.Then structural equation model was used to examine the effect of perceived value dimensions on it.The paper identified that perceived value has a positive impact on consumers'repurchase intention in P2P accommodation.Also,social relationship value was considered as the most important influencing factor.
基金Supported by Suzhou Key Laboratory of Artificial Intelligence and Social Governance Technologies(SZS2023007)Smart Social Governance Technology and Innovative Application Platform(YZCXPT2023101)the Innovation System of the Integration between Industry and Education for Smart Governance(CJRH2024101)。
文摘In the modern economy,startups are not only significant drivers of innovation and technological progress but also key players in addressing employment issues and promoting economic diversification.However,startups often face substantial operational risks and uncertainties in their early stages,especially regarding financing.To uncover the impact of different resource allocations and strategic choices on financing success,this study proposes a predictive method based on the latent Dirichlet allocation(LDA)topic model and deep neural networks through an in-depth analysis of startup financing cases.We systematically collected description text data from 2,000 startups and extracted text features from these descriptions using the LDA topic model.These features,combined with several traditional numerical indicators such as industry,product type,technology type,number of employees,and company size,were used to train a deep neural network to predict startup financing outcomes.The experimental results show that the prediction performance based on the LDA topic model is significantly better than that of traditional models relying solely on numerical data.This highlights the importance of text features in predicting the success of startup financing.
基金funded by the National Natural Science Foundation of China(Grant No.52378034)。
文摘Architectural programming is of significant importance in improving urban planning efficiency,enhancing the sustainability of the construction industry,and supporting the development of social infrastructure.However,due to its broad scope and complex content,research in this area exhibits diverse and scattered characteristics.To comprehensively understand the current status,progress,and future directions of architectural programming research,and to provide recommendations for both China and other countries,this study conducted a comparative review of relevant literature using a mixed method approach of bibliometrics,latent Dirichlet allocation(LDA)topic modeling,and systematic review.The study summarized the differences between China and other countries in six topics within the field of architectural programming:(1)intelligent and digital methods,(2)sustainability and practices,(3)project management and decision support,(4)urban planning and existing development,(5)social infrastructure and public services,and(6)architectural education and talent cultivation.This study provides an overview and comparative analysis on architectural programming,offering important reference for future research and practice,and promoting the overall development of the construction industry.
基金The Basic Research Project of Liaoning Provincial Department of Education(LJKMZ20221469)The Economic and Social Development Research Project of Liaoning Province(2024Lsbkt-104)。
文摘Tracking and investigating tourist satisfaction and accurately identifying the key factors that affect tourist satisfaction have always been among the top priorities for academia and tourist attraction operators.With the rise of online travel,analysis based on online comments has become an important method for tracking and surveying tourist satisfaction.This article examined the online comments of tourists for the Panjin Red Beach Scenic Corridor Scenic Area(hereinafter referred to as Red Beach)on Ctrip as an example.Using natural language processing to classify the tourist evaluations into topics,the main topics of concern were identified as tourism services,tourism attractions,scenic area management,and tourism experience.Through the 5-level rating of Ctrip’s online gaming customer satisfaction,an analysis was conducted on tourist satisfaction and the topics of greatest concern to the tourists were ranked.The results showed that the satisfaction levels from high to low are:tourism experience,tourism attractions,scenic area management,and tourism services.Therefore,satisfaction with related content under the service topic was the lowest so this aspect urgently needs to be improved and enhanced.
文摘With the rapid growth of online shopping platforms, more and more customers intend to share theirshopping experience and product reviews on the Internet. Both large quantity and various forms ofonline reviews bring difficulties for potential consumers to summary all the heterogenous reviews forreference. This paper proposes a new ranking method through online reviews based on differentaspects of the alternative products, which combines both objective and subjective sentiment values.Firstly, weights of these aspects are determined with LDA topic model to calculate the objectivesentiment value of the product. During this process, the realistic meaning of each aspect is alsosummarized. Then, consumers＇ personalized preferences are taken into consideration while calculatingtotal scores of alternative products. Meanwhile, comparative superiority between every two productsalso contributes to their final scores. Therefore, a directed graph model is constructed and the finalscore of each product is computed by improved PageRank algorithm. Finally, a case study is given toillustrate the feasibility and effectiveness of the proposed method. The result demonstrates that whileconsidering only objective sentiment values of the product, the ranking result obtained by our proposedmethod has a strong correlation with the actual sales orders. On the other hand, if consumers expresssubjective preferences towards a certain aspect, the final ranking is also consistent with the actualperformance of alternative products. It provides a new research idea for online customer review miningand personalized recommendation.
