Evaluation measures play an important role in the design of new approaches, and often quality is measured by assessing the relevance of the obtained result set. While many evaluation measures based on precision/recall...Evaluation measures play an important role in the design of new approaches, and often quality is measured by assessing the relevance of the obtained result set. While many evaluation measures based on precision/recall are based on a binary relevance model, ranking correlation coefficients are better suited for multi-class problems. State-of-the-art rank- ing correlation coefficients like Kendall＇s T and Spearman＇s p do not allow the user to specify similarities between differ- ing object classes and thus treat the transposition of objects from similar classes the same way as that of objects from dissimilar classes. We propose ClasSi, a new ranking corre- lation coefficient which deals with class label rankings and employs a class distance function to model the similarities between the classes. We also introduce a graphical representation of ClasSi which describes how the correlation evolves throughout the ranking.展开更多
Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Prev...Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.展开更多
The goal of this paper is to enhance a practical nominal characteristic trajectory following(NCTF) controller that is specifically designed for two-mass point-to-point positioning systems. A nominal characteristics tr...The goal of this paper is to enhance a practical nominal characteristic trajectory following(NCTF) controller that is specifically designed for two-mass point-to-point positioning systems. A nominal characteristics trajectory contained in the NCTF controller acts as movement/motion reference and a compensator is utilized to force the object to detect and follow the reference/desired trajectory. The object must follow and track closely and should be as fast as possible. The NCTF controller is designed with two different intelligent based compensator approaches which are fuzzy logic and extended minimal resource allocation network. The proposed controller which is NCTF are compared with the conventional proportional integral compensator. Then the results of simulation are discussed for the positioning performances. The inertia variations due to the effect of the design parameters are also assessed to see the robustness of controllers. The results show that the NCTF control method designed from an intelligent based compensator has a better positioning performance in terms of percentage of overshoot, settling time, and steady state error than the classical based compensator.展开更多
Understanding the influence of environmental variables on the spatial distribution of ecological communities is essential to predict the response of vegetation to various environmental drivers.Ecological theory sugges...Understanding the influence of environmental variables on the spatial distribution of ecological communities is essential to predict the response of vegetation to various environmental drivers.Ecological theory suggests that multiple environmental factors shape local species assemblages and should influence the various components of community structure and composition in different ways.This study aimed to classify Pinus wallichiana dominated forests in the Swat Hindukush range mountains to understand the relative influence of multiple environmental filters on its composition and structure.These forests represent the most typical of the species distribution in northern Pakistan and were not subjected to any phytosociological study.For this purpose,thirty forest stands,spanning a wide range of physical habitats were sampled using 10 x 10 m plots and the importance value index was calculated.The floristic and environmental data were subjected to Ward’s agglomerative cluster analysis for objective classification and ordinated with NMS ordination for pattern description and testing the vegetation-environmental relationships.Three floristically and ecologically distinct communities were recognized along the topographic gradient(elevation,r=0.377;slope,r=0.5548) coupled with soil physical(clay,r=0.2782;silt,r=0.3225) and chemical properties(pH,r=0.4975;lime,r=3982).An elevation gradient of 100 m separated the low(Pinus wallichiana-Quercus dilatata community)and middle elevations forest stands(P.wallichiana pure population) from the highland population type(Pinus wallichiana-Cedrus deodara community).The floristics and structure of these forest types respond directly or indirectly to topographic and soil variables which were evidenced from the floristic composition,species richness,and community physiognomy.These characteristics of the communities changed from heterogenous,dense stands to sparsely dispersed conifers,broadleaved-evergreen,and deciduous vegetation types along the environmental gradients.We concluded that展开更多
2018年世界卫生组织(World Health Organization, WHO)在国际疾病分类第11版(11th revision of International Classi?cation of Diseases, ICD-11)目录中首次收录了慢性疼痛并做了分类标注,但由于仍以症状为主,分属不同母系目录,对大...2018年世界卫生组织(World Health Organization, WHO)在国际疾病分类第11版(11th revision of International Classi?cation of Diseases, ICD-11)目录中首次收录了慢性疼痛并做了分类标注,但由于仍以症状为主,分属不同母系目录,对大规模流调、临床疼痛诊治和预后评估等实践应用造成困难。为此,国际疼痛学会(International Association for the Study of Pain, IASP)有关专家组对ICD-11版慢性疼痛分类的标注内容进行了补充修订,并制订了一个系统的分级诊断分类目录。本文在总结《PAIN》杂志(2019年160卷1期)ICD-11叙述专栏文章的基础上,制作了一个中文版IASP修订的ICD-11版慢性疼痛4级诊断分类目录和评估标准。期待慢性疼痛4级诊断分类目录在今后大规模人口流行病学调查、临床疼痛专科和其它相关科室诊治与预后评估工作中有章可循并发挥重要作用。展开更多
基金Supported by A VIDI grant from the Netherlands Organiza-tion for Scientific Research(NWO,to Weersma RK),No.016.136.308an AGIKO grant from the Netherlands Organiza-tion for Scientific Research(NWO to Visschedijk MC),No.92.003.577MLDS grant of the Dutch Digestive Foundation,No.WO 11-72(to Alberts R)
文摘AIM: To validate the Montreal classification system for Crohn’s disease (CD) and ulcerative colitis (UC) within the Netherlands.
文摘Evaluation measures play an important role in the design of new approaches, and often quality is measured by assessing the relevance of the obtained result set. While many evaluation measures based on precision/recall are based on a binary relevance model, ranking correlation coefficients are better suited for multi-class problems. State-of-the-art rank- ing correlation coefficients like Kendall＇s T and Spearman＇s p do not allow the user to specify similarities between differ- ing object classes and thus treat the transposition of objects from similar classes the same way as that of objects from dissimilar classes. We propose ClasSi, a new ranking corre- lation coefficient which deals with class label rankings and employs a class distance function to model the similarities between the classes. We also introduce a graphical representation of ClasSi which describes how the correlation evolves throughout the ranking.
基金supported in part by the Beijing Natural Science Foundation under grants M21032 and 19L2029in part by the National Natural Science Foundation of China under grants U1836106 and 81961138010in part by the Scientific and Technological Innovation Foundation of Foshan under grants BK21BF001 and BK20BF010.
文摘Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.
文摘The goal of this paper is to enhance a practical nominal characteristic trajectory following(NCTF) controller that is specifically designed for two-mass point-to-point positioning systems. A nominal characteristics trajectory contained in the NCTF controller acts as movement/motion reference and a compensator is utilized to force the object to detect and follow the reference/desired trajectory. The object must follow and track closely and should be as fast as possible. The NCTF controller is designed with two different intelligent based compensator approaches which are fuzzy logic and extended minimal resource allocation network. The proposed controller which is NCTF are compared with the conventional proportional integral compensator. Then the results of simulation are discussed for the positioning performances. The inertia variations due to the effect of the design parameters are also assessed to see the robustness of controllers. The results show that the NCTF control method designed from an intelligent based compensator has a better positioning performance in terms of percentage of overshoot, settling time, and steady state error than the classical based compensator.
基金supported by Higher Education Commission of Pakistan
文摘Understanding the influence of environmental variables on the spatial distribution of ecological communities is essential to predict the response of vegetation to various environmental drivers.Ecological theory suggests that multiple environmental factors shape local species assemblages and should influence the various components of community structure and composition in different ways.This study aimed to classify Pinus wallichiana dominated forests in the Swat Hindukush range mountains to understand the relative influence of multiple environmental filters on its composition and structure.These forests represent the most typical of the species distribution in northern Pakistan and were not subjected to any phytosociological study.For this purpose,thirty forest stands,spanning a wide range of physical habitats were sampled using 10 x 10 m plots and the importance value index was calculated.The floristic and environmental data were subjected to Ward’s agglomerative cluster analysis for objective classification and ordinated with NMS ordination for pattern description and testing the vegetation-environmental relationships.Three floristically and ecologically distinct communities were recognized along the topographic gradient(elevation,r=0.377;slope,r=0.5548) coupled with soil physical(clay,r=0.2782;silt,r=0.3225) and chemical properties(pH,r=0.4975;lime,r=3982).An elevation gradient of 100 m separated the low(Pinus wallichiana-Quercus dilatata community)and middle elevations forest stands(P.wallichiana pure population) from the highland population type(Pinus wallichiana-Cedrus deodara community).The floristics and structure of these forest types respond directly or indirectly to topographic and soil variables which were evidenced from the floristic composition,species richness,and community physiognomy.These characteristics of the communities changed from heterogenous,dense stands to sparsely dispersed conifers,broadleaved-evergreen,and deciduous vegetation types along the environmental gradients.We concluded that
文摘2018年世界卫生组织(World Health Organization, WHO)在国际疾病分类第11版(11th revision of International Classi?cation of Diseases, ICD-11)目录中首次收录了慢性疼痛并做了分类标注,但由于仍以症状为主,分属不同母系目录,对大规模流调、临床疼痛诊治和预后评估等实践应用造成困难。为此,国际疼痛学会(International Association for the Study of Pain, IASP)有关专家组对ICD-11版慢性疼痛分类的标注内容进行了补充修订,并制订了一个系统的分级诊断分类目录。本文在总结《PAIN》杂志(2019年160卷1期)ICD-11叙述专栏文章的基础上,制作了一个中文版IASP修订的ICD-11版慢性疼痛4级诊断分类目录和评估标准。期待慢性疼痛4级诊断分类目录在今后大规模人口流行病学调查、临床疼痛专科和其它相关科室诊治与预后评估工作中有章可循并发挥重要作用。
