Urban sustainability assessment is an effective method for objectively presenting the current state of sustainable urban development and diagnosing sustainability-related issues.As the global community intensifies its...Urban sustainability assessment is an effective method for objectively presenting the current state of sustainable urban development and diagnosing sustainability-related issues.As the global community intensifies its efforts to implement the sustainable development goals(SDGs),the demand for assessing progress in urban sustainable development has increased.This has led to the emergence of numerous indicator systems with varying scales and themes published by different entities.Cities participating in these evaluations often encounter difficulties in matching indicators or the absence of certain indicators.In this context,urban decision makers and planners urgently need to identify substitute indicators that can express the semantic meaning of the original indicators and consider the availability of indicators for participating cities.Hence,this study explores the relationships of substitution between indicators and constructs a collection of substitute indicators to serve as a reference for sustainable urban development assessment.Specifically,building on a review of international and Chinese indicators related to urban sustainability assessment,this study employs natural semantic analysis methods based on the Word2Vec model and cosine similarity algorithm to calculate the similarity between indicators related to sustainable urban development.The results show that the Skip-gram algorithm with a word vector dimensionality of 600 has the best performance in terms of calculating the similarity between sustainable urban development assessment indicators.The findings provide valuable insights into selecting substitute indicators for future sustainable urban development assessment,particularly in China.展开更多
With the development of Internet technology,the explosive growth of Internet information presentation has led to difficulty in filtering effective information.Finding a model with high accuracy for text classification...With the development of Internet technology,the explosive growth of Internet information presentation has led to difficulty in filtering effective information.Finding a model with high accuracy for text classification has become a critical problem to be solved by text filtering,especially for Chinese texts.This paper selected the manually calibrated Douban movie website comment data for research.First,a text filtering model based on the BP neural network has been built;Second,based on the Term Frequency-Inverse Document Frequency(TF-IDF)vector space model and the doc2vec method,the text word frequency vector and the text semantic vector were obtained respectively,and the text word frequency vector was linearly reduced by the Principal Component Analysis(PCA)method.Third,the text word frequency vector after dimensionality reduction and the text semantic vector were combined,add the text value degree,and the text synthesis vector was constructed.Experiments show that the model combined with text word frequency vector degree after dimensionality reduction,text semantic vector,and text value has reached the highest accuracy of 84.67%.展开更多
The substantial competition among the news industries puts editors under the pressure of posting news articleswhich are likely to gain more user attention. Anticipating the popularity of news articles can help the edi...The substantial competition among the news industries puts editors under the pressure of posting news articleswhich are likely to gain more user attention. Anticipating the popularity of news articles can help the editorial teamsin making decisions about posting a news article. Article similarity extracted from the articles posted within a smallperiod of time is found to be a useful feature in existing popularity prediction approaches. This work proposesa new approach to estimate the popularity of news articles by adding semantics in the article similarity basedapproach of popularity estimation. A semantically enriched model is proposed which estimates news popularity bymeasuring cosine similarity between document embeddings of the news articles. Word2vec model has been used togenerate distributed representations of the news content. In this work, we define popularity as the number of timesa news article is posted on different websites. We collect data from different websites that post news concerning thedomain of cybersecurity and estimate the popularity of cybersecurity news. The proposed approach is comparedwith different models and it is shown that it outperforms the other models.展开更多
基金supported by the National Key Research and Development Program of China under the theme“Key technologies for urban sustainable development evaluation and decision-making support” [Grant No.2022YFC3802900]the Guangxi Key Research and Development Program [Grant No.Guike AB21220057].
文摘Urban sustainability assessment is an effective method for objectively presenting the current state of sustainable urban development and diagnosing sustainability-related issues.As the global community intensifies its efforts to implement the sustainable development goals(SDGs),the demand for assessing progress in urban sustainable development has increased.This has led to the emergence of numerous indicator systems with varying scales and themes published by different entities.Cities participating in these evaluations often encounter difficulties in matching indicators or the absence of certain indicators.In this context,urban decision makers and planners urgently need to identify substitute indicators that can express the semantic meaning of the original indicators and consider the availability of indicators for participating cities.Hence,this study explores the relationships of substitution between indicators and constructs a collection of substitute indicators to serve as a reference for sustainable urban development assessment.Specifically,building on a review of international and Chinese indicators related to urban sustainability assessment,this study employs natural semantic analysis methods based on the Word2Vec model and cosine similarity algorithm to calculate the similarity between indicators related to sustainable urban development.The results show that the Skip-gram algorithm with a word vector dimensionality of 600 has the best performance in terms of calculating the similarity between sustainable urban development assessment indicators.The findings provide valuable insights into selecting substitute indicators for future sustainable urban development assessment,particularly in China.
基金Supported by the Sichuan Science and Technology Program (2021YFQ0003).
文摘With the development of Internet technology,the explosive growth of Internet information presentation has led to difficulty in filtering effective information.Finding a model with high accuracy for text classification has become a critical problem to be solved by text filtering,especially for Chinese texts.This paper selected the manually calibrated Douban movie website comment data for research.First,a text filtering model based on the BP neural network has been built;Second,based on the Term Frequency-Inverse Document Frequency(TF-IDF)vector space model and the doc2vec method,the text word frequency vector and the text semantic vector were obtained respectively,and the text word frequency vector was linearly reduced by the Principal Component Analysis(PCA)method.Third,the text word frequency vector after dimensionality reduction and the text semantic vector were combined,add the text value degree,and the text synthesis vector was constructed.Experiments show that the model combined with text word frequency vector degree after dimensionality reduction,text semantic vector,and text value has reached the highest accuracy of 84.67%.
基金This research was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘The substantial competition among the news industries puts editors under the pressure of posting news articleswhich are likely to gain more user attention. Anticipating the popularity of news articles can help the editorial teamsin making decisions about posting a news article. Article similarity extracted from the articles posted within a smallperiod of time is found to be a useful feature in existing popularity prediction approaches. This work proposesa new approach to estimate the popularity of news articles by adding semantics in the article similarity basedapproach of popularity estimation. A semantically enriched model is proposed which estimates news popularity bymeasuring cosine similarity between document embeddings of the news articles. Word2vec model has been used togenerate distributed representations of the news content. In this work, we define popularity as the number of timesa news article is posted on different websites. We collect data from different websites that post news concerning thedomain of cybersecurity and estimate the popularity of cybersecurity news. The proposed approach is comparedwith different models and it is shown that it outperforms the other models.
