In this study,analyses are conducted on the information features of a construction site,a cornfield and subsidence seeper land in a coal mining area with a synthetic aperture radar (SAR) image of medium resolution. Ba...In this study,analyses are conducted on the information features of a construction site,a cornfield and subsidence seeper land in a coal mining area with a synthetic aperture radar (SAR) image of medium resolution. Based on features of land cover of the coal mining area,on texture feature extraction and a selection method of a gray-level co-occurrence matrix (GLCM) of the SAR image,we propose in this study that the optimum window size for computing the GLCM is an appropriate sized window that can effectively distinguish different types of land cover. Next,a band combination was carried out over the text feature images and the band-filtered SAR image to secure a new multi-band image. After the transformation of the new image with principal component analysis,a classification is conducted selectively on three principal component bands with the most information. Finally,through training and experimenting with the samples,a better three-layered BP neural network was established to classify the SAR image. The results show that,assisted by texture information,the neural network classification improved the accuracy of SAR image classification by 14.6%,compared with a classification by maximum likelihood estimation without texture information.展开更多
Mining operations can usually lead to environmental deteriorations. Underground mining activities could cause an extensive decrease in groundwater level and thus a dramatic variation in soil moisture content（SMC）. I...Mining operations can usually lead to environmental deteriorations. Underground mining activities could cause an extensive decrease in groundwater level and thus a dramatic variation in soil moisture content（SMC）. In this study, the spatial and temporal variations of SMC from 2001 to 2015 at two spatial scales（i.e., the Shendong coal mining area and the Daliuta Coal Mine） were analyzed using an improved thermal inertia model with a long-term series of Landsat TM/OLI（TM=Thematic Mapper and OLI=Operational Land Imager） data. Our results show that at large spatial scale（the Shendong coal mining area）, underground mining activities had insignificant negative impacts on SMC and that at small spatial scale（the Daliuta Coal Mine）, underground mining activities had significant negative impacts on SMC. Trend analysis of SMC demonstrated that areas with decreasing trend of SMC were mainly distributed in the mined area, indicating that underground mining is a primary cause for the drying trend in the mining region in this arid environment.展开更多
The massive web videos prompt an imperative demand on efficiently grasping the major events. However, the distinct characteristics of web videos, such as the limited number of features, the noisy text information, and...The massive web videos prompt an imperative demand on efficiently grasping the major events. However, the distinct characteristics of web videos, such as the limited number of features, the noisy text information, and the unavoidable error in near-duplicate keyframes （NDKs） detection, make web video event mining a challenging task. In this paper, we propose a novel four-stage framework to improve the performance of web video event mining. Data preprocessing is the first stage. Multiple Correspondence Analysis （MCA） is then applied to explore the correlation between terms and classes, targeting for bridging the gap between NDKs and high-level semantic concepts. Next, co-occurrence information is used to detect the similarity between NDKs and classes using the NDK-within-video information. Finally, both of them are integrated for web video event mining through negative NDK pruning and positive NDK enhancement. Moreover, both NDKs and terms with relatively low frequencies are treated as useful information in our experiments. Experimental results on large-scale web videos from YouTube demonstrate that the proposed framework outperforms several existing mining methods and obtains good results for web video event mining.展开更多
Abnormal crowd behaviors in high density situations can pose great danger to public safety. Despite the extensive installation of closed-circuit television(CCTV) cameras, it is still difficult to achieve real-time ale...Abnormal crowd behaviors in high density situations can pose great danger to public safety. Despite the extensive installation of closed-circuit television(CCTV) cameras, it is still difficult to achieve real-time alerts and automated responses from current systems. Two major breakthroughs have been reported in this research. Firstly, a spatial-temporal texture extraction algorithm is developed. This algorithm is able to effectively extract video textures with abundant crowd motion details. It is through adopting Gaborfiltered textures with the highest information entropy values. Secondly, a novel scheme for defining crowd motion patterns(signatures)is devised to identify abnormal behaviors in the crowd by employing an enhanced gray level co-occurrence matrix model. In the experiments, various classic classifiers are utilized to benchmark the performance of the proposed method. The results obtained exhibit detection and accuracy rates which are, overall, superior to other techniques.展开更多
The volume of trajectory data has become tremendously huge in recent years. How to effectively and efficiently maintain and compute such trajectory data has become a challenging task. In this paper, we propose a traje...The volume of trajectory data has become tremendously huge in recent years. How to effectively and efficiently maintain and compute such trajectory data has become a challenging task. In this paper, we propose a trajectory spatial and temporal compression framework, namely CLEAN. The key of spatial compression is to mine meaningful trajectory frequent patterns on road network. By treating the mined patterns as dictionary items, the long trajectories have the chance to be encoded by shorter paths, thus leading to smaller space cost. And an error-bounded temporal compression is carefully designed on top of the identified spatial patterns for much low space cost. Meanwhile, the patterns are also utilized to improve the performance of two trajectory applications, range query and clustering, without decompression overhead. Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of spatial-temporal compression and trajectory applications.展开更多
Over the past century,the Communist Party of China(CPC)has transformed China from extreme poverty to prosperity,leading the country into modernization.Utilizing descriptive statistics,the Latent Dirichlet Allocation(L...Over the past century,the Communist Party of China(CPC)has transformed China from extreme poverty to prosperity,leading the country into modernization.Utilizing descriptive statistics,the Latent Dirichlet Allocation(LDA)topic model,and word co-occurrence networks,this paper systematically explores the development and governing philosophy articulated in the CPC’s discourse,as presented in the official reports of 20 Party Congresses from 1921 to 2023,in order to comprehend the pathways of China’s rapid development.For better understanding through visualization,a thematic evolution chart is constructed to display the CPC’s ideological development,and aword co-occurrence network is established to illustrate the changes in terminology over time.The analysis reveals distinct characteristics of the CPC’s development and governance across different phases,specifically shifting from a focus on revolutionary ideals and class struggle in the early stages to an emphasis on economic reforms and modernization in recent stages.Such kind of works not only help to catch up the core concepts and working endeavors during the different period of development,but also highlight the significance of analyzing the political documents from a systemic perspective.展开更多
Predicting the future trajectories of multiple agents is essential for various applications in real life,such as surveillance systems,autonomous driving,and social robots.The trajectory prediction task is influenced b...Predicting the future trajectories of multiple agents is essential for various applications in real life,such as surveillance systems,autonomous driving,and social robots.The trajectory prediction task is influenced by many factors,including the individual historical trajectory,interactions between agents,and the fuzzy nature of the observed agents’motion.While existing methods have made great progress on the topic of trajectory prediction,they treat all the information uniformly,which limits the effectiveness of information utilization.To this end,in this paper,we propose and utilize a model-agnostic framework to regard all the information in a two-level hierarchical view.Particularly,the first-level view is the inter-trajectory view.In this level,we observe that the difficulty in predicting different trajectory samples varies.We define trajectory difficulty and train the proposed framework in an“easy-to-hard”schema.The second-level view is the intra-trajectory level.We find the influencing factors for a particular trajectory can be divided into two parts.The first part is global features,which keep stable within a trajectory,i.e.,the expected destination.The second part is local features,which change over time,i.e.,the current position.We believe that the two types of information should be handled in different ways.The hierarchical view is beneficial to take full advantage of the information in a fine-grained way.Experimental results validate the effectiveness of the proposed model-agnostic framework.展开更多
The construction method for chains of disasters or events is still one of the core scientific questions in studying the common rules of disaster’s evolution.Especially when dealing with the complexity and diversity o...The construction method for chains of disasters or events is still one of the core scientific questions in studying the common rules of disaster’s evolution.Especially when dealing with the complexity and diversity of disasters,it is critical to make a further investigation on reducing the dependency of prior knowledge and supporting the comprehensive chains of disasters.This paper tries to propose a novel approach,through collecting the big scholar and social news data with disasterrelated keywords,analysing the strength of their relationships with the co-word analysis method,and constructing a complex network of all defined disaster types,in order to finally intelligently extract the unique disaster chain of a specific disaster type.Google Scholar,Baidu Scholar and Sina News search engines are employed to acquire the needed data,and the respectively obtained disaster chains are compared with each other to show the robustness of our proposed approach.The achieved disaster chains are also compared with the ones concluded from existing research methods,and the very reasonable result is demonstrated.There is a great potential to apply this novel method in disaster management domain to find more secrets about disasters.展开更多
Purpose of the research:To identify the gaps between the rhetoric and reality of the role of citizen participation and its role in maintenance and monitoring of heritages and resources(including biodiversity monitorin...Purpose of the research:To identify the gaps between the rhetoric and reality of the role of citizen participation and its role in maintenance and monitoring of heritages and resources(including biodiversity monitoring),we analyzed the discourse of Globally Important Agricultural Heritage System(GIAHS)at municipality level.Methods:As an analytical framework,text mining is applied to interviews of officers at the municipal level of GIAHS in Noto which was amongst the first sites in Japan.The identification of such gap is critical for sustainability and to prevent conflicts from tourism,agriculture or educations.Results:The results reveal that(1)there is a gap between the official goals of that designation at the international level and local needs,(2)role of citizens is emphasized in the applications and action plans at rhetorical level but remain rather limited in practice and that(3)municipalities composing the GIAHS often have different priorities,even within the very same GIAHS sites,some municipalities even calling themselves“just a transition point to other destination municipalities.”Conclusions:It is critical for municipal officers to collaborate with various stakeholders,especially citizens.As such,citizen science is a bottom-up approach to promote biodiversity conservation and facilitate GIAHS managements.展开更多
基金Projects 40771143 supported by the National Natural Science Foundation of China2007AA12Z162 by the Hi-tech Research and Development Program of China
文摘In this study,analyses are conducted on the information features of a construction site,a cornfield and subsidence seeper land in a coal mining area with a synthetic aperture radar (SAR) image of medium resolution. Based on features of land cover of the coal mining area,on texture feature extraction and a selection method of a gray-level co-occurrence matrix (GLCM) of the SAR image,we propose in this study that the optimum window size for computing the GLCM is an appropriate sized window that can effectively distinguish different types of land cover. Next,a band combination was carried out over the text feature images and the band-filtered SAR image to secure a new multi-band image. After the transformation of the new image with principal component analysis,a classification is conducted selectively on three principal component bands with the most information. Finally,through training and experimenting with the samples,a better three-layered BP neural network was established to classify the SAR image. The results show that,assisted by texture information,the neural network classification improved the accuracy of SAR image classification by 14.6%,compared with a classification by maximum likelihood estimation without texture information.
基金supported by the National Natural Science Foundation of China (U1361214)the National Key Research and Development Program of China (2016YFC0501107)
文摘Mining operations can usually lead to environmental deteriorations. Underground mining activities could cause an extensive decrease in groundwater level and thus a dramatic variation in soil moisture content（SMC）. In this study, the spatial and temporal variations of SMC from 2001 to 2015 at two spatial scales（i.e., the Shendong coal mining area and the Daliuta Coal Mine） were analyzed using an improved thermal inertia model with a long-term series of Landsat TM/OLI（TM=Thematic Mapper and OLI=Operational Land Imager） data. Our results show that at large spatial scale（the Shendong coal mining area）, underground mining activities had insignificant negative impacts on SMC and that at small spatial scale（the Daliuta Coal Mine）, underground mining activities had significant negative impacts on SMC. Trend analysis of SMC demonstrated that areas with decreasing trend of SMC were mainly distributed in the mined area, indicating that underground mining is a primary cause for the drying trend in the mining region in this arid environment.
基金supported by the National Natural Science Foundation of China under Grant Nos. 61373121, 61071184, 60972111,61036008the Research Funds for the Doctoral Program of Higher Education of China under Grant No. 20100184120009+2 种基金the Program for Sichuan Provincial Science Fund for Distinguished Young Scholars under Grant Nos. 2012JQ0029, 13QNJJ0149the Fundamental Research Funds for the Central Universities of China under Grant Nos. SWJTU09CX032, SWJTU10CX08the Program of China Scholarships Council under Grant No. 201207000050
文摘The massive web videos prompt an imperative demand on efficiently grasping the major events. However, the distinct characteristics of web videos, such as the limited number of features, the noisy text information, and the unavoidable error in near-duplicate keyframes （NDKs） detection, make web video event mining a challenging task. In this paper, we propose a novel four-stage framework to improve the performance of web video event mining. Data preprocessing is the first stage. Multiple Correspondence Analysis （MCA） is then applied to explore the correlation between terms and classes, targeting for bridging the gap between NDKs and high-level semantic concepts. Next, co-occurrence information is used to detect the similarity between NDKs and classes using the NDK-within-video information. Finally, both of them are integrated for web video event mining through negative NDK pruning and positive NDK enhancement. Moreover, both NDKs and terms with relatively low frequencies are treated as useful information in our experiments. Experimental results on large-scale web videos from YouTube demonstrate that the proposed framework outperforms several existing mining methods and obtains good results for web video event mining.
基金funded by Chinese National Natural Science Foundation (No. 61671377)Shaanxi Smart City Technology Project of Xianyang (No. 2017k01-25-5)
文摘Abnormal crowd behaviors in high density situations can pose great danger to public safety. Despite the extensive installation of closed-circuit television(CCTV) cameras, it is still difficult to achieve real-time alerts and automated responses from current systems. Two major breakthroughs have been reported in this research. Firstly, a spatial-temporal texture extraction algorithm is developed. This algorithm is able to effectively extract video textures with abundant crowd motion details. It is through adopting Gaborfiltered textures with the highest information entropy values. Secondly, a novel scheme for defining crowd motion patterns(signatures)is devised to identify abnormal behaviors in the crowd by employing an enhanced gray level co-occurrence matrix model. In the experiments, various classic classifiers are utilized to benchmark the performance of the proposed method. The results obtained exhibit detection and accuracy rates which are, overall, superior to other techniques.
基金National Natural Science Foundation of China (Grant No. 61772371,No. 61972286)
文摘The volume of trajectory data has become tremendously huge in recent years. How to effectively and efficiently maintain and compute such trajectory data has become a challenging task. In this paper, we propose a trajectory spatial and temporal compression framework, namely CLEAN. The key of spatial compression is to mine meaningful trajectory frequent patterns on road network. By treating the mined patterns as dictionary items, the long trajectories have the chance to be encoded by shorter paths, thus leading to smaller space cost. And an error-bounded temporal compression is carefully designed on top of the identified spatial patterns for much low space cost. Meanwhile, the patterns are also utilized to improve the performance of two trajectory applications, range query and clustering, without decompression overhead. Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of spatial-temporal compression and trajectory applications.
基金supported by the National Social Science Fund of China(No.23&ZD331).
文摘Over the past century,the Communist Party of China(CPC)has transformed China from extreme poverty to prosperity,leading the country into modernization.Utilizing descriptive statistics,the Latent Dirichlet Allocation(LDA)topic model,and word co-occurrence networks,this paper systematically explores the development and governing philosophy articulated in the CPC’s discourse,as presented in the official reports of 20 Party Congresses from 1921 to 2023,in order to comprehend the pathways of China’s rapid development.For better understanding through visualization,a thematic evolution chart is constructed to display the CPC’s ideological development,and aword co-occurrence network is established to illustrate the changes in terminology over time.The analysis reveals distinct characteristics of the CPC’s development and governance across different phases,specifically shifting from a focus on revolutionary ideals and class struggle in the early stages to an emphasis on economic reforms and modernization in recent stages.Such kind of works not only help to catch up the core concepts and working endeavors during the different period of development,but also highlight the significance of analyzing the political documents from a systemic perspective.
基金supported by the Youth Innovation Promotion Association of Chinese Academy of Sciences under Grant No.2023112the National Natural Science Foundation of China under Grant No.62206266Zhao Zhang is supported by the China Postdoctoral Science Foundation under Grant No.2021M703273.
文摘Predicting the future trajectories of multiple agents is essential for various applications in real life,such as surveillance systems,autonomous driving,and social robots.The trajectory prediction task is influenced by many factors,including the individual historical trajectory,interactions between agents,and the fuzzy nature of the observed agents’motion.While existing methods have made great progress on the topic of trajectory prediction,they treat all the information uniformly,which limits the effectiveness of information utilization.To this end,in this paper,we propose and utilize a model-agnostic framework to regard all the information in a two-level hierarchical view.Particularly,the first-level view is the inter-trajectory view.In this level,we observe that the difficulty in predicting different trajectory samples varies.We define trajectory difficulty and train the proposed framework in an“easy-to-hard”schema.The second-level view is the intra-trajectory level.We find the influencing factors for a particular trajectory can be divided into two parts.The first part is global features,which keep stable within a trajectory,i.e.,the expected destination.The second part is local features,which change over time,i.e.,the current position.We believe that the two types of information should be handled in different ways.The hierarchical view is beneficial to take full advantage of the information in a fine-grained way.Experimental results validate the effectiveness of the proposed model-agnostic framework.
基金funded by National Key Research and Development Program of China(Grant No.2016YFC0803107,Grant No.2016YFB0502601)Shenzhen Science and Technology Innovation Commission(JCYJ20170307152553273).
文摘The construction method for chains of disasters or events is still one of the core scientific questions in studying the common rules of disaster’s evolution.Especially when dealing with the complexity and diversity of disasters,it is critical to make a further investigation on reducing the dependency of prior knowledge and supporting the comprehensive chains of disasters.This paper tries to propose a novel approach,through collecting the big scholar and social news data with disasterrelated keywords,analysing the strength of their relationships with the co-word analysis method,and constructing a complex network of all defined disaster types,in order to finally intelligently extract the unique disaster chain of a specific disaster type.Google Scholar,Baidu Scholar and Sina News search engines are employed to acquire the needed data,and the respectively obtained disaster chains are compared with each other to show the robustness of our proposed approach.The achieved disaster chains are also compared with the ones concluded from existing research methods,and the very reasonable result is demonstrated.There is a great potential to apply this novel method in disaster management domain to find more secrets about disasters.
基金This work was supported by the JSPS KAKENHI[JP16KK0053,JP17K02105]Research Institute for Humanity and Nature[No.14200126]+2 种基金Environment Research and Technology Development Fund[S-15-2(3)]the Toyota Foundation[D17-N-0107]Foundation for Environmental Conservation Measures,Keidanren.
文摘Purpose of the research:To identify the gaps between the rhetoric and reality of the role of citizen participation and its role in maintenance and monitoring of heritages and resources(including biodiversity monitoring),we analyzed the discourse of Globally Important Agricultural Heritage System(GIAHS)at municipality level.Methods:As an analytical framework,text mining is applied to interviews of officers at the municipal level of GIAHS in Noto which was amongst the first sites in Japan.The identification of such gap is critical for sustainability and to prevent conflicts from tourism,agriculture or educations.Results:The results reveal that(1)there is a gap between the official goals of that designation at the international level and local needs,(2)role of citizens is emphasized in the applications and action plans at rhetorical level but remain rather limited in practice and that(3)municipalities composing the GIAHS often have different priorities,even within the very same GIAHS sites,some municipalities even calling themselves“just a transition point to other destination municipalities.”Conclusions:It is critical for municipal officers to collaborate with various stakeholders,especially citizens.As such,citizen science is a bottom-up approach to promote biodiversity conservation and facilitate GIAHS managements.
