The standard form of address is ’madam’ and it’s commonly usedwhen serving the public, for example, in a shop: Assistant: Can I help you, madam? Customer: Yes, I’d like to buy some perfume. Assistant: Certainly, m...The standard form of address is ’madam’ and it’s commonly usedwhen serving the public, for example, in a shop: Assistant: Can I help you, madam? Customer: Yes, I’d like to buy some perfume. Assistant: Certainly, madam. Did you have anything particular in mind?It’s also used in addressing a woman formally in any situation whereyou don’t know her name, and don’t plan to discover her name.展开更多
The Voice of Art and Culture at the Two Sessions。At this year's two sessions,"new quality productive forces"has been frequently mentioned.At the same time,the development of new quality productive force...The Voice of Art and Culture at the Two Sessions。At this year's two sessions,"new quality productive forces"has been frequently mentioned.At the same time,the development of new quality productive forces has also been written into this year's Report on the Work of the Government。展开更多
In previous studies,scholars mainly focused on the 1950 edition and the 1958 one of"Khongor",and some studies mistakenly use the 1958 edition to introduce the 1950 one.Most of the studies have focused on 195...In previous studies,scholars mainly focused on the 1950 edition and the 1958 one of"Khongor",and some studies mistakenly use the 1958 edition to introduce the 1950 one.Most of the studies have focused on 1958"Khongor",Based on the above-mentioned problems,this paper looks to the 1950 and 1958 editions of"Khongor with comparative eyes,and explores the similarities and differences between the two editions in format,text,sentences,and lines of poetry.It has been found that the 1950 and 1958 editions of"Khongor"are very different,so the two editions should not be regarded as the same and cannot be substituted for each other.展开更多
Climate change has become one of the most serious challenges to the sustainable development of mankind,while green and low-carbon development provides a new important stimulus for the global economy.At the China Devel...Climate change has become one of the most serious challenges to the sustainable development of mankind,while green and low-carbon development provides a new important stimulus for the global economy.At the China Development Forum which was held recently,the term"green supply chain"was frequently mentioned.A green supply chain refers to a supply chain that adopts carbon reduction and decarbonization technologies thoroughly.The green supply chain system that China is currently committed to building will greatly contribute to the goal of carbon neutrality in the context of global climate change.展开更多
Shanghai sets an example for fostering new quality productive forces to accelerate growth New quality productive forces are key for China to continue building a modern industrial system and attain sustainable developm...Shanghai sets an example for fostering new quality productive forces to accelerate growth New quality productive forces are key for China to continue building a modern industrial system and attain sustainable development.The strategy,first mentioned by Chinese President Xi Jinping last September,is also crucial for achieving the objectives and tasks set out in the 14th Five-Year Plan(2021-2025)in its final year of implementation.展开更多
Inferring semantic types of the entity mentions in a sentence is a necessary yet challenging task. Most of existing methods employ a very coarse-grained type taxonomy, which is too general and not exact enough for man...Inferring semantic types of the entity mentions in a sentence is a necessary yet challenging task. Most of existing methods employ a very coarse-grained type taxonomy, which is too general and not exact enough for many tasks. However, the performances of the methods drop sharply when we extend the type taxonomy to a fine-grained one with several hundreds of types. In this paper, we introduce a hybrid neural network model for type classification of entity mentions with a fine-grained taxonomy. There are four components in our model, namely, the entity mention component, the context component, the relation component, the already known type component, which are used to extract features from the target entity mention, context, relations and already known types of the entity mentions in surrounding context respectively. The learned features by the four components are concatenated and fed into a softmax layer to predict the type distribution. We carried out extensive experiments to evaluate our proposed model. Experimental results demonstrate that our model achieves state-of-the-art performance on the FIGER dataset. Moreover, we extracted larger datasets from Wikipedia and DBpedia. On the larger datasets, our model achieves the comparable performance to the state-of-the-art methods with the coarse-grained type taxonomy, but performs much better than those methods with the fine-grained type taxonomy in terms of micro-F1, macro-F1 and weighted-F1.展开更多
[Purpose/Significance]The article investigated the automatic identification of the motivation of Facebook mention to scholarly outputs based on Light GBM algorithm,in order to achieve more in-depth usage of Facebook m...[Purpose/Significance]The article investigated the automatic identification of the motivation of Facebook mention to scholarly outputs based on Light GBM algorithm,in order to achieve more in-depth usage of Facebook mention on a large scale.[Methodology/Procedure]Based on three types of contextual data,including mentioned scholarly outputs,Facebook users who post scholarly outputs,and text of Facebook posts to scholarly outputs,promising relevant features were extracted,and machine learning algorithms were used to automatically identify the motivations.[Results/Conclusions](1)Features significantly correlated to the motivation of Facebook mention are identified in all three types of contextual data.In particular,relevant features are the altmetric attention score,the number of collaborative countries,the number of followers,the number of likes,the identities of Facebook users who post scholarly outputs and the number of comments on Facebook posts;(2)The prediction precision of the Light GBM classification model for motivation of Facebook mention was 0.31.In comparison,the classification precision without the text features of Facebook posts was 0.35,which was higher than the overall feature combination.The classification precision with only the post text features was 0.27.After combining the length and language of posts,the precision was improved to 0.30;(3)The classification precision of Facebook motivation has a positive correlation with users’activity.After combining all features,the classification precision of the first quartile users in terms of productivity reached 1,the classification precision of the second quartile was 0.36,and for the third quartile,the classification precision was 0.32.In conclusion,considering the high complexity of automatic classification of motivation of Facebook mentions,the study has achieved relatively high classification precision and could provide reference for future studies.展开更多
Algorithms play an increasingly important role in scientific work,especially in data-driven research.Investigating the mention of algorithms in full-text paper helps us understand the use and development of algorithms...Algorithms play an increasingly important role in scientific work,especially in data-driven research.Investigating the mention of algorithms in full-text paper helps us understand the use and development of algorithms in a specific domain.Current research on the mention of algorithms is limited to the academic papers in one language,which is hard to comprehensively investigate the use of algorithms.For example,in papers of Chinese conference,is the mention of algorithms consistent with it in English conference papers?In order to answer this question,this paper takes NLP as an example,and compares the mention frequency,mention location and mention time of the top10 data-mining algorithms between the papers of the famous international conference,Annual Meeting of the Association for Computational Linguistics(ACL),and the Chinese conference,China National Conference on Computational Linguistics(CCL).The results show that compared with ACL,the mention frequency of top10 data-mining algorithms in CCL is slightly lower and the mention time is slightly delayed,while the distribution of mention location is similar.This study can provide a reference for the research related to the mention,citation and evaluation of knowledge entities.展开更多
You’re welcome!Not at all!Think nothing about it!Don’t mention it!意思:没关系!别见外!当别人向你表示感谢或歉意时,你可以说Don’t mention it!相同表达:You’re welcome!Not at all!Think nothing about it!
Correction to:J.For.Res.https://doi.org/10.1007/s11676-022-01560-8 During production process,the below mentioned errors appeared in the original article and inadvertently published with error.
This is an erratum to our published paper entitled“Can physical activity eliminate the mortality risk associated with poor sleep?A 15-year follow-up of 341,248 MJ Cohort participants”.In the paper mentioned above,th...This is an erratum to our published paper entitled“Can physical activity eliminate the mortality risk associated with poor sleep?A 15-year follow-up of 341,248 MJ Cohort participants”.In the paper mentioned above,there are some errors that we want to clear in this erratum.On page 601,2 reference lines(i.e.,HR=1)in(A)and(B)of Fig.1 were not labeled.The details are as follows.展开更多
Dear Editor,This letter presents an inspection method for process monitoring of underwater oil transportation via multiple autonomous underwater vehicles(AUV).To improve the adaptability of our method in practice,we i...Dear Editor,This letter presents an inspection method for process monitoring of underwater oil transportation via multiple autonomous underwater vehicles(AUV).To improve the adaptability of our method in practice,we introduce the dynamic complex ocean current data to the previously mentioned case by using regional ocean modeling system(ROMS)for the first time.展开更多
文摘The standard form of address is ’madam’ and it’s commonly usedwhen serving the public, for example, in a shop: Assistant: Can I help you, madam? Customer: Yes, I’d like to buy some perfume. Assistant: Certainly, madam. Did you have anything particular in mind?It’s also used in addressing a woman formally in any situation whereyou don’t know her name, and don’t plan to discover her name.
文摘The Voice of Art and Culture at the Two Sessions。At this year's two sessions,"new quality productive forces"has been frequently mentioned.At the same time,the development of new quality productive forces has also been written into this year's Report on the Work of the Government。
文摘In previous studies,scholars mainly focused on the 1950 edition and the 1958 one of"Khongor",and some studies mistakenly use the 1958 edition to introduce the 1950 one.Most of the studies have focused on 1958"Khongor",Based on the above-mentioned problems,this paper looks to the 1950 and 1958 editions of"Khongor with comparative eyes,and explores the similarities and differences between the two editions in format,text,sentences,and lines of poetry.It has been found that the 1950 and 1958 editions of"Khongor"are very different,so the two editions should not be regarded as the same and cannot be substituted for each other.
文摘Climate change has become one of the most serious challenges to the sustainable development of mankind,while green and low-carbon development provides a new important stimulus for the global economy.At the China Development Forum which was held recently,the term"green supply chain"was frequently mentioned.A green supply chain refers to a supply chain that adopts carbon reduction and decarbonization technologies thoroughly.The green supply chain system that China is currently committed to building will greatly contribute to the goal of carbon neutrality in the context of global climate change.
文摘Shanghai sets an example for fostering new quality productive forces to accelerate growth New quality productive forces are key for China to continue building a modern industrial system and attain sustainable development.The strategy,first mentioned by Chinese President Xi Jinping last September,is also crucial for achieving the objectives and tasks set out in the 14th Five-Year Plan(2021-2025)in its final year of implementation.
文摘Inferring semantic types of the entity mentions in a sentence is a necessary yet challenging task. Most of existing methods employ a very coarse-grained type taxonomy, which is too general and not exact enough for many tasks. However, the performances of the methods drop sharply when we extend the type taxonomy to a fine-grained one with several hundreds of types. In this paper, we introduce a hybrid neural network model for type classification of entity mentions with a fine-grained taxonomy. There are four components in our model, namely, the entity mention component, the context component, the relation component, the already known type component, which are used to extract features from the target entity mention, context, relations and already known types of the entity mentions in surrounding context respectively. The learned features by the four components are concatenated and fed into a softmax layer to predict the type distribution. We carried out extensive experiments to evaluate our proposed model. Experimental results demonstrate that our model achieves state-of-the-art performance on the FIGER dataset. Moreover, we extracted larger datasets from Wikipedia and DBpedia. On the larger datasets, our model achieves the comparable performance to the state-of-the-art methods with the coarse-grained type taxonomy, but performs much better than those methods with the fine-grained type taxonomy in terms of micro-F1, macro-F1 and weighted-F1.
基金supported by Hum anity and Social Science Foundation of Ministry of Education of China(22YJA870016)National Natural Science Foundation of China(NO.72274227)
文摘[Purpose/Significance]The article investigated the automatic identification of the motivation of Facebook mention to scholarly outputs based on Light GBM algorithm,in order to achieve more in-depth usage of Facebook mention on a large scale.[Methodology/Procedure]Based on three types of contextual data,including mentioned scholarly outputs,Facebook users who post scholarly outputs,and text of Facebook posts to scholarly outputs,promising relevant features were extracted,and machine learning algorithms were used to automatically identify the motivations.[Results/Conclusions](1)Features significantly correlated to the motivation of Facebook mention are identified in all three types of contextual data.In particular,relevant features are the altmetric attention score,the number of collaborative countries,the number of followers,the number of likes,the identities of Facebook users who post scholarly outputs and the number of comments on Facebook posts;(2)The prediction precision of the Light GBM classification model for motivation of Facebook mention was 0.31.In comparison,the classification precision without the text features of Facebook posts was 0.35,which was higher than the overall feature combination.The classification precision with only the post text features was 0.27.After combining the length and language of posts,the precision was improved to 0.30;(3)The classification precision of Facebook motivation has a positive correlation with users’activity.After combining all features,the classification precision of the first quartile users in terms of productivity reached 1,the classification precision of the second quartile was 0.36,and for the third quartile,the classification precision was 0.32.In conclusion,considering the high complexity of automatic classification of motivation of Facebook mentions,the study has achieved relatively high classification precision and could provide reference for future studies.
基金supported by the National Natural Science Foundation of China(Grant No.72074113)
文摘Algorithms play an increasingly important role in scientific work,especially in data-driven research.Investigating the mention of algorithms in full-text paper helps us understand the use and development of algorithms in a specific domain.Current research on the mention of algorithms is limited to the academic papers in one language,which is hard to comprehensively investigate the use of algorithms.For example,in papers of Chinese conference,is the mention of algorithms consistent with it in English conference papers?In order to answer this question,this paper takes NLP as an example,and compares the mention frequency,mention location and mention time of the top10 data-mining algorithms between the papers of the famous international conference,Annual Meeting of the Association for Computational Linguistics(ACL),and the Chinese conference,China National Conference on Computational Linguistics(CCL).The results show that compared with ACL,the mention frequency of top10 data-mining algorithms in CCL is slightly lower and the mention time is slightly delayed,while the distribution of mention location is similar.This study can provide a reference for the research related to the mention,citation and evaluation of knowledge entities.
文摘You’re welcome!Not at all!Think nothing about it!Don’t mention it!意思:没关系!别见外!当别人向你表示感谢或歉意时,你可以说Don’t mention it!相同表达:You’re welcome!Not at all!Think nothing about it!
文摘Correction to:J.For.Res.https://doi.org/10.1007/s11676-022-01560-8 During production process,the below mentioned errors appeared in the original article and inadvertently published with error.
文摘This is an erratum to our published paper entitled“Can physical activity eliminate the mortality risk associated with poor sleep?A 15-year follow-up of 341,248 MJ Cohort participants”.In the paper mentioned above,there are some errors that we want to clear in this erratum.On page 601,2 reference lines(i.e.,HR=1)in(A)and(B)of Fig.1 were not labeled.The details are as follows.
基金supported by the National Natural Science Foundation of China(61871283)。
文摘Dear Editor,This letter presents an inspection method for process monitoring of underwater oil transportation via multiple autonomous underwater vehicles(AUV).To improve the adaptability of our method in practice,we introduce the dynamic complex ocean current data to the previously mentioned case by using regional ocean modeling system(ROMS)for the first time.
