Web service recommendation is one of the most important fi elds of research in the area of service computing. The two core problems of Web service recommendation are the prediction of unknown Qo S property values and ...Web service recommendation is one of the most important fi elds of research in the area of service computing. The two core problems of Web service recommendation are the prediction of unknown Qo S property values and the evaluation of overall Qo S according to user preferences. Aiming to address these two problems and their current challenges, we propose two efficient approaches to solve these problems. First, unknown Qo S property values were predicted by modeling the high-dimensional Qo S data as tensors, by utilizing an important tensor operation, i.e., tensor composition, to predict these Qo S values. Our method, which considers all Qo S dimensions integrally and uniformly, allows us to predict multi-dimensional Qo S values accurately and easily. Second, the overall Qo S was evaluated by proposing an efficient user preference learning method, which learns user preferences based on users' ratings history data, allowing us to obtain user preferences quantifiably and accurately. By solving these two core problems, it became possible to compute a realistic value for the overall Qo S. The experimental results showed our proposed methods to be more efficient than existing methods.展开更多
Three global datasets, the History Database of the Global Environment （HYDE）, Kaplan and Krurnhardt （KK） and Pongratz of reconstructed anthropogenic land cover change （ALCC） were introduced and compared in this ...Three global datasets, the History Database of the Global Environment （HYDE）, Kaplan and Krurnhardt （KK） and Pongratz of reconstructed anthropogenic land cover change （ALCC） were introduced and compared in this paper. The HYDE dataset was recon- structed by Goldewijk and his colleagues at the National institute of Public ttealth and the Environment in Netherland, covering the past 12 000 years. The KK dataset was reconstructed by Kaplan and his colleagues, the Soil-Vegetation-Atmosphere Research Group at the Institute of Environmental Engineering in Switzerland, covering the past 8000 years. The Pongratz dataset was reconstructed by Pon- gratz and her colleagues at the Max Planck Institute for Meteorology in Germany, coveting AD 800-1992. The results show that the reconstructed datasets are quite different from each other due to the different methods used. The three datasets all allocated the historical ALCC according to human population density. The main reason causing the differences among the three datasets lies on the different relationships between population density and land use used in each reconstructed dataset. The KK dataset is better than the other two datasets for two important reasons. First, it used the nonlinear relationship between population density and land use, while the other two used the linear relationship. Second, Kaplan and his colleagues adopted the technological development and intensification parameters and considered the wood harvesting and the long-term fallow area resulted from shifting cultivation, which were neglected in the recon- structions of the other two datasets. Therefore, the KK dataset is more suitable as one of the anthropogenic forcing fields for climate simulation over the past two millennia that is recently concerned by two projects, the National Basic Research Program and the Strategic and Special Frontier Project of Science and Technology of the Chinese Academy of Sciences.展开更多
By using the existing historical earthquake investigation data in Xinjiang,this paper obtained the envelope curves of isoseismal maps of 103 destructive earthquakes occurring from 1716 to 2010 after digitization of th...By using the existing historical earthquake investigation data in Xinjiang,this paper obtained the envelope curves of isoseismal maps of 103 destructive earthquakes occurring from 1716 to 2010 after digitization of the data. The author summarized the seismic intensity attenuation laws in the Xinjiang region with the multiple regression fitting method. The intensity attenuation function of the elliptical model was provided and the fitting results in different periods and areas were compared. Finally, the intensity attenuation relationship in the Xinjiang region was obtained by the method of constraining the start and end of the attenuation curves.展开更多
基金supported by the Natural Science Foundation of Beijing under Grant No.4132048NSFC (61472047),and NSFC (61202435)
文摘Web service recommendation is one of the most important fi elds of research in the area of service computing. The two core problems of Web service recommendation are the prediction of unknown Qo S property values and the evaluation of overall Qo S according to user preferences. Aiming to address these two problems and their current challenges, we propose two efficient approaches to solve these problems. First, unknown Qo S property values were predicted by modeling the high-dimensional Qo S data as tensors, by utilizing an important tensor operation, i.e., tensor composition, to predict these Qo S values. Our method, which considers all Qo S dimensions integrally and uniformly, allows us to predict multi-dimensional Qo S values accurately and easily. Second, the overall Qo S was evaluated by proposing an efficient user preference learning method, which learns user preferences based on users' ratings history data, allowing us to obtain user preferences quantifiably and accurately. By solving these two core problems, it became possible to compute a realistic value for the overall Qo S. The experimental results showed our proposed methods to be more efficient than existing methods.
基金Under the auspices of Strategic and Special Frontier Project of Science and Technology of Chinese Academy of Sciences (No. XDA05080800)National Basic Research Program of China (No. 2010CB950102)National Natural Science Foundation of China (No. 40871007)
文摘Three global datasets, the History Database of the Global Environment （HYDE）, Kaplan and Krurnhardt （KK） and Pongratz of reconstructed anthropogenic land cover change （ALCC） were introduced and compared in this paper. The HYDE dataset was recon- structed by Goldewijk and his colleagues at the National institute of Public ttealth and the Environment in Netherland, covering the past 12 000 years. The KK dataset was reconstructed by Kaplan and his colleagues, the Soil-Vegetation-Atmosphere Research Group at the Institute of Environmental Engineering in Switzerland, covering the past 8000 years. The Pongratz dataset was reconstructed by Pon- gratz and her colleagues at the Max Planck Institute for Meteorology in Germany, coveting AD 800-1992. The results show that the reconstructed datasets are quite different from each other due to the different methods used. The three datasets all allocated the historical ALCC according to human population density. The main reason causing the differences among the three datasets lies on the different relationships between population density and land use used in each reconstructed dataset. The KK dataset is better than the other two datasets for two important reasons. First, it used the nonlinear relationship between population density and land use, while the other two used the linear relationship. Second, Kaplan and his colleagues adopted the technological development and intensification parameters and considered the wood harvesting and the long-term fallow area resulted from shifting cultivation, which were neglected in the recon- structions of the other two datasets. Therefore, the KK dataset is more suitable as one of the anthropogenic forcing fields for climate simulation over the past two millennia that is recently concerned by two projects, the National Basic Research Program and the Strategic and Special Frontier Project of Science and Technology of the Chinese Academy of Sciences.
基金funded by the project of Xinjiang Historical Earthquake Disaster Data Analysis ( CEA_EDEM-201016)
文摘By using the existing historical earthquake investigation data in Xinjiang,this paper obtained the envelope curves of isoseismal maps of 103 destructive earthquakes occurring from 1716 to 2010 after digitization of the data. The author summarized the seismic intensity attenuation laws in the Xinjiang region with the multiple regression fitting method. The intensity attenuation function of the elliptical model was provided and the fitting results in different periods and areas were compared. Finally, the intensity attenuation relationship in the Xinjiang region was obtained by the method of constraining the start and end of the attenuation curves.
