An active e-course is a self-representable and self-organizable document mechanism with a flexible structure. The kernel of the active e-course is to organize learning materials into a "concept space" rather...
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ISBN:
(纸本)1581139128
An active e-course is a self-representable and self-organizable document mechanism with a flexible structure. The kernel of the active e-course is to organize learning materials into a "concept space" rather than a "page space". Besides highly interactive service, it supports adaptive learning by dynamically selecting, organizing and presenting the learning materials for different students. During the learning progress, it also provides assessments on students' learning performances and gives suggestions to guide them in further learning. We have implemented an authoring tool and a course prototype to support the constructivist learning.
Networks and flows are everywhere in society, nature and virtual world organizing versatile resources and behaviors. Breaking boundaries, this keynote establishes a scenario of the future interconnection environment -...
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Machine vision is an active branch of Artificial Intelligence. An important problem in this area is the balance among efficiency, accuracy and huge computing. The visual system of human can keep watchfulness to the pe...
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Machine vision is an active branch of Artificial Intelligence. An important problem in this area is the balance among efficiency, accuracy and huge computing. The visual system of human can keep watchfulness to the perimeter of visual field while at same time their central attention is focused to the center of visual field for fine informationprocessing. This mechanism of computing resource assignment could ease the demand for huge and complex hardware structure. Therefore designing computer model based on biological visual
Knowledge Grid is a platform that enables uniform and effective knowledge sharing and management across the Internet. Based on this platform, this paper proposes a cooperative learning environment KGCL. It supports th...
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By introducing a discrete Frenet frame, this paper first proposes 3D discrete clothoid splines to extend the planar discrete clothoid splines of Schneider and Kobbelt. On the basis of 3D discrete clothoid spline curve...
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Accurate prediction of sea surface temperature (SST) is of high importance in marine science, benefiting applications ranging from ecosystem protection to extreme weather forecasting and climate analysis. Wide-area SS...
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Accurate prediction of sea surface temperature (SST) is of high importance in marine science, benefiting applications ranging from ecosystem protection to extreme weather forecasting and climate analysis. Wide-area SST usually shows diverse SST patterns in different sea areas due to the changes of temperature zones and the dynamics of ocean currents. However, existing studies on SST prediction often focus on small-area predictions and lack the consideration of diverse SST patterns. Furthermore, SST shows an annual periodicity, but the periodicity is not strictly adherent to an annual cycle. Existing SST prediction methods struggle to adapt to this non-strict periodicity. To address these two issues, we proposed the Cross-Region Graph Convolutional Network with Periodicity Shift Adaptation (RGCN-PSA) model which is equipped with the Cross-Region Graph Convolutional Network module and the Periodicity Shift Adaption module. The Cross-Region Graph Convolutional Network module enhances wide-area SST prediction by learning and incorporating diverse SST patterns. Meanwhile, the periodicity Shift Adaptation module accounts for the annual periodicity and enable the model to adapt to the possible temporal shift automatically. We conduct experiments on two real-world SST datasets, and the results demonstrate that our RGCN-PSA model obviously outperforms baseline models in terms of prediction accuracy. The code of RGCN-PSA model is available at https://***/ADMIS-TONGJI/RGCN-PSA/.
This volume presents the accepted papers for the 4th International Conference onGridandCooperativecomputing(GCC2005),heldinBeijing,China,during November 30 – December 3, *** conferenceseries of GCC aims to provide an...
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ISBN:
(数字)9783540322771
ISBN:
(纸本)9783540305101
This volume presents the accepted papers for the 4th International Conference onGridandCooperativecomputing(GCC2005),heldinBeijing,China,during November 30 – December 3, *** conferenceseries of GCC aims to provide an international forum for the presentation and discussion of research trends on the theory, method, and design of Grid and cooperative computing as well as their scienti?c, engineering and commercial applications. It has become a major annual event in this area. The First International Conference on Grid and Cooperative computing (GCC2002)***2003received550submissions,from which 176 regular papers and 173 short papers were accepted. The acceptance rate of regular papers was 32%, and the total acceptance rate was 64%. GCC 2004 received 427 main-conference submissions and 154 workshop submissions. The main conference accepted 96 regular papers and 62 short papers. The - ceptance rate of the regular papers was 23%. The total acceptance rate of the main conference was 37%. For this conference, we received 576 submissions. Each was reviewed by two independent members of the International Program Committee. After carefully evaluating their originality and quality, we accepted 57 regular papers and 84 short papers. The acceptance rate of regular papers was 10%. The total acc- tance rate was 25%.
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