On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rank...On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rankings:Chinese University Ranking,Chinese Graduate School Ranking,World University Ranking and Scholarly Journal Ranking.The annual reports of all these four rankings are published bythe Science Press,which have been recognized by the academia and China's government.展开更多
Purpose:Citation-based assessments of countries’research capabilities often misrepresent their ability to achieve breakthrough advancements.These assessments commonly classify Japan as a developing country,which cont...Purpose:Citation-based assessments of countries’research capabilities often misrepresent their ability to achieve breakthrough advancements.These assessments commonly classify Japan as a developing country,which contradicts its prominent scientific standing.The purpose of this study is to investigate the underlying causes of such inaccurate assessments and to propose methods for conducting more reliable evaluations.Design/methodology/approach:The study evaluates the effectiveness of top-percentile citation metrics as indicators of breakthrough research.Using case studies of selected countries and research topics,the study examines how deviations from lognormal citation distributions impact the accuracy of these percentile indicators.A similar analysis is conducted using university data from the Leiden Ranking to investigate citation distribution deviations at the institutional level.Findings:The study finds that inflated lower tails in citation distributions lead to undervaluation of research capabilities in advanced technological countries,as captured by some percentile indicators.Conversely,research-intensive universities exhibit the opposite trend:a reduced lower tail relative to the upper tail,which causes percentile indicators to overestimate their actual research capacity.Research limitations:The descriptions are mathematical facts that are self-evident.Practical implications:The ratios between the number of papers in the global top 10%and 1%by citation count to the total number of papers are commonly used to describe research performance.However,due to variations in citation patterns across countries and institutions with reference to the global pattern,these ratios can be misleading and lose their value as research indicators.Originality/value:Size-independent research performance indicators,obtained as the ratios between paper counts in top percentiles and the total numbers of publications,are widely used by public and private institutions.This study demonstrates that the use of these ratios for research evaluations and country rankings can be highly misleading.展开更多
On the basis of ESI data,all universities are ranked in 96 out of 109 world-class disciplines.There is no ESI data(either publications or citations)in the rest of 13 world-class disciplines.
Among natural disasters,flash floods are the most destructive events,causing signif-icant damage to the economy and posing a serious threat to human life and property.Comprehensive risk assessment of these sudden floo...Among natural disasters,flash floods are the most destructive events,causing signif-icant damage to the economy and posing a serious threat to human life and property.Comprehensive risk assessment of these sudden floods is a key strategy to mitigate their impact.Accurate analysis of flash flood hazards can greatly enhance prevention efforts and inform critical decision-making processes,ultimately improving our ability to protect communities from these fast-onset disasters.This study analyzed the driving forces of flash flood disaster-causing factors in Heilongjiang Province.Meanwhile,nine different categories of variables affecting the occurrence of flash floods were selected,and the degree of influence of each driving factor on flash floods was quantitatively analyzed,and the driving force analysis of the driving factors of flash floods in Hei-longjiang Province was carried out by using the geographic probe model.This paper employs an uncertainty approach,utilizing a statistical-based interval weight deter-mination technique for evaluation indices and a two-dimensional information-based interval number sorting method.These methodologies are combined to construct a comprehensive flash flood risk assessment model.On this basis,the model was implemented in six regions within China's Heilongjiang province to evaluate and prioritize flash flood risks.The resulting risk ranking for these areas was as follows:Bayan≻Shuangcheng≻Boli≻Suibin≻Hailun≻Yian.The findings demonstrate that the interval number-based evaluation method effectively handles uncertainty,providing a more reliable risk grading system.This approach,by leveraging modern scientific advances and risk quantification techniques,is crucial for improving disaster management and mitigating flash flood impacts.展开更多
Owing to the constraints of depth sensing technology,images acquired by depth cameras are inevitably mixed with various noises.For depth maps presented in gray values,this research proposes a novel denoising model,ter...Owing to the constraints of depth sensing technology,images acquired by depth cameras are inevitably mixed with various noises.For depth maps presented in gray values,this research proposes a novel denoising model,termed graph-based transform(GBT)and dual graph Laplacian regularization(DGLR)(DGLR-GBT).This model specifically aims to remove Gaussian white noise by capitalizing on the nonlocal self-similarity(NSS)and the piecewise smoothness properties intrinsic to depth maps.Within the group sparse coding(GSC)framework,a combination of GBT and DGLR is implemented.Firstly,within each group,the graph is constructed by using estimates of the true values of the averaged blocks instead of the observations.Secondly,the graph Laplacian regular terms are constructed based on rows and columns of similar block groups,respectively.Lastly,the solution is obtained effectively by combining the alternating direction multiplication method(ADMM)with the weighted thresholding method within the domain of GBT.展开更多
In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web page...In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web pages in accordance with user preferences is proposed.PWPR assigns the initial weights based on user interests and creates the virtual links and hubs according to user interests.By measuring user click streams,PWPR incrementally reflects users’ favors for the personalized ranking.To improve the accuracy of ranking, PWPR also takes collaborative filtering into consideration when the query with similar is submitted by users who have similar user interests. Detailed simulation results and comparison with other algorithms prove that the proposed PWPR can adaptively provide personalized ranking and truly relevant information to user preferences.展开更多
随着基于位置社交网络(location-based social network,LBSN)的发展,兴趣点推荐成为满足用户个性化需求、减轻信息过载问题的重要手段.然而,已有的兴趣点推荐算法存在如下的问题:1)多数已有的兴趣点推荐算法简化用户签到频率数据,仅使...随着基于位置社交网络(location-based social network,LBSN)的发展,兴趣点推荐成为满足用户个性化需求、减轻信息过载问题的重要手段.然而,已有的兴趣点推荐算法存在如下的问题:1)多数已有的兴趣点推荐算法简化用户签到频率数据,仅使用二进制值来表示用户是否访问一个兴趣点;2)基于矩阵分解的兴趣点推荐算法把签到频率数据和传统推荐系统中的评分数据等同看待,使用高斯分布模型建模用户的签到行为;3)忽视用户签到数据的隐式反馈属性.为解决以上问题,提出一个基于Ranking的泊松矩阵分解兴趣点推荐算法.首先,根据LBSN中用户的签到行为特点,利用泊松分布模型替代高斯分布模型建模用户在兴趣点上签到行为;然后采用BPR(Bayesian personalized ranking)标准优化泊松矩阵分解的损失函数,拟合用户在兴趣点对上的偏序关系;最后,利用包含地域影响力的正则化因子约束泊松矩阵分解的过程.在真实数据集上的实验结果表明:基于Ranking的泊松矩阵分解兴趣点推荐算法的性能优于传统的兴趣点推荐算法.展开更多
文摘On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rankings:Chinese University Ranking,Chinese Graduate School Ranking,World University Ranking and Scholarly Journal Ranking.The annual reports of all these four rankings are published bythe Science Press,which have been recognized by the academia and China's government.
文摘Purpose:Citation-based assessments of countries’research capabilities often misrepresent their ability to achieve breakthrough advancements.These assessments commonly classify Japan as a developing country,which contradicts its prominent scientific standing.The purpose of this study is to investigate the underlying causes of such inaccurate assessments and to propose methods for conducting more reliable evaluations.Design/methodology/approach:The study evaluates the effectiveness of top-percentile citation metrics as indicators of breakthrough research.Using case studies of selected countries and research topics,the study examines how deviations from lognormal citation distributions impact the accuracy of these percentile indicators.A similar analysis is conducted using university data from the Leiden Ranking to investigate citation distribution deviations at the institutional level.Findings:The study finds that inflated lower tails in citation distributions lead to undervaluation of research capabilities in advanced technological countries,as captured by some percentile indicators.Conversely,research-intensive universities exhibit the opposite trend:a reduced lower tail relative to the upper tail,which causes percentile indicators to overestimate their actual research capacity.Research limitations:The descriptions are mathematical facts that are self-evident.Practical implications:The ratios between the number of papers in the global top 10%and 1%by citation count to the total number of papers are commonly used to describe research performance.However,due to variations in citation patterns across countries and institutions with reference to the global pattern,these ratios can be misleading and lose their value as research indicators.Originality/value:Size-independent research performance indicators,obtained as the ratios between paper counts in top percentiles and the total numbers of publications,are widely used by public and private institutions.This study demonstrates that the use of these ratios for research evaluations and country rankings can be highly misleading.
文摘On the basis of ESI data,all universities are ranked in 96 out of 109 world-class disciplines.There is no ESI data(either publications or citations)in the rest of 13 world-class disciplines.
基金Basic Scientific Research Expense Project of IWHR-Extreme rainstorm development trends and prediction techniques,Grant/Award Number:JZ0145B142024National Natural Science Foundation of China,Grant/Award Number:42271095。
文摘Among natural disasters,flash floods are the most destructive events,causing signif-icant damage to the economy and posing a serious threat to human life and property.Comprehensive risk assessment of these sudden floods is a key strategy to mitigate their impact.Accurate analysis of flash flood hazards can greatly enhance prevention efforts and inform critical decision-making processes,ultimately improving our ability to protect communities from these fast-onset disasters.This study analyzed the driving forces of flash flood disaster-causing factors in Heilongjiang Province.Meanwhile,nine different categories of variables affecting the occurrence of flash floods were selected,and the degree of influence of each driving factor on flash floods was quantitatively analyzed,and the driving force analysis of the driving factors of flash floods in Hei-longjiang Province was carried out by using the geographic probe model.This paper employs an uncertainty approach,utilizing a statistical-based interval weight deter-mination technique for evaluation indices and a two-dimensional information-based interval number sorting method.These methodologies are combined to construct a comprehensive flash flood risk assessment model.On this basis,the model was implemented in six regions within China's Heilongjiang province to evaluate and prioritize flash flood risks.The resulting risk ranking for these areas was as follows:Bayan≻Shuangcheng≻Boli≻Suibin≻Hailun≻Yian.The findings demonstrate that the interval number-based evaluation method effectively handles uncertainty,providing a more reliable risk grading system.This approach,by leveraging modern scientific advances and risk quantification techniques,is crucial for improving disaster management and mitigating flash flood impacts.
基金National Natural Science Foundation of China(No.62372100)。
文摘Owing to the constraints of depth sensing technology,images acquired by depth cameras are inevitably mixed with various noises.For depth maps presented in gray values,this research proposes a novel denoising model,termed graph-based transform(GBT)and dual graph Laplacian regularization(DGLR)(DGLR-GBT).This model specifically aims to remove Gaussian white noise by capitalizing on the nonlocal self-similarity(NSS)and the piecewise smoothness properties intrinsic to depth maps.Within the group sparse coding(GSC)framework,a combination of GBT and DGLR is implemented.Firstly,within each group,the graph is constructed by using estimates of the true values of the averaged blocks instead of the observations.Secondly,the graph Laplacian regular terms are constructed based on rows and columns of similar block groups,respectively.Lastly,the solution is obtained effectively by combining the alternating direction multiplication method(ADMM)with the weighted thresholding method within the domain of GBT.
基金The Natural Science Foundation of South-Central University for Nationalities(No.YZZ07006)
文摘In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web pages in accordance with user preferences is proposed.PWPR assigns the initial weights based on user interests and creates the virtual links and hubs according to user interests.By measuring user click streams,PWPR incrementally reflects users’ favors for the personalized ranking.To improve the accuracy of ranking, PWPR also takes collaborative filtering into consideration when the query with similar is submitted by users who have similar user interests. Detailed simulation results and comparison with other algorithms prove that the proposed PWPR can adaptively provide personalized ranking and truly relevant information to user preferences.
文摘随着基于位置社交网络(location-based social network,LBSN)的发展,兴趣点推荐成为满足用户个性化需求、减轻信息过载问题的重要手段.然而,已有的兴趣点推荐算法存在如下的问题:1)多数已有的兴趣点推荐算法简化用户签到频率数据,仅使用二进制值来表示用户是否访问一个兴趣点;2)基于矩阵分解的兴趣点推荐算法把签到频率数据和传统推荐系统中的评分数据等同看待,使用高斯分布模型建模用户的签到行为;3)忽视用户签到数据的隐式反馈属性.为解决以上问题,提出一个基于Ranking的泊松矩阵分解兴趣点推荐算法.首先,根据LBSN中用户的签到行为特点,利用泊松分布模型替代高斯分布模型建模用户在兴趣点上签到行为;然后采用BPR(Bayesian personalized ranking)标准优化泊松矩阵分解的损失函数,拟合用户在兴趣点对上的偏序关系;最后,利用包含地域影响力的正则化因子约束泊松矩阵分解的过程.在真实数据集上的实验结果表明:基于Ranking的泊松矩阵分解兴趣点推荐算法的性能优于传统的兴趣点推荐算法.
