This book constitutes the refereed proceedings of the Third International Workshop on Management of Information, Process and Cooperation, MiPAC 2016, held in Hangzhou, China, in September 2016. The 8 revised full pape...
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ISBN:
(数字)9789811039966
ISBN:
(纸本)9789811039959
This book constitutes the refereed proceedings of the Third International Workshop on Management of Information, Process and Cooperation, MiPAC 2016, held in Hangzhou, China, in September 2016. The 8 revised full papers were carefully reviewed and selected from 14 submissions. The papers are organized in topical sections on process modeling, process enactment, and data driven service computing.
Predicting future traffic conditions from urban sensor data is crucial for smart city applications. Recent traffic forecasting methods are derived from Spatio-Temporal Graph Convolution Networks (STGCNs). Despite thei...
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Predicting future traffic conditions from urban sensor data is crucial for smart city applications. Recent traffic forecasting methods are derived from Spatio-Temporal Graph Convolution Networks (STGCNs). Despite their remarkable achievements, these spatio-temporal models have mainly been evaluated on small-scale datasets. In light of the rapid growth of the Internet of Things and urbanization, cities are witnessing an increased deployment of sensors, resulting in the collection of extensive sensor data to provide more accurate insights into citywide traffic dynamics. Spatio-temporal graph modeling on large-scale traffic data is challenging due to the memory constraint of the computing device. For traffic forecasting, subgraph sampling from road networks onto multiple devices is feasible. Many GCN sampling methods have been proposed recently. However, combining these with STGCNs degrades performance. This is primarily due to prediction biases introduced by each sampled subgraph, which analyze traffic states from a regional *** these challenges, we introduce a parallel STGCN framework called PaSTG. PaSTG divides the road network into regions, each processed by an individual STGCN in a device. To mitigate regional biases, Aggregation Blocks in PaSTG merge spatial-temporal features from each STBlock. This collaboration enhances traffic forecasting. Furthermore, PaSTG implements pipeline parallelism and employs a graph partition algorithm for optimized pipeline efficiency. We evaluate PaSTG on various STGCNs using three traffic datasets on multiple GPUs. Results demonstrate that our parallel approach applies widely to diverse STGCN models, surpassing existing GCN samplers by up to 57.4% in prediction accuracy. Additionally, the parallel framework achieves speedups of up to 2.87x and 4.70x in training and inference compared to GCN samplers.
After further review and discussions among the authors, we want to do further experiment to improve the existing premature results. Specifically, authors want to add more complex analysis to support the results. Given...
After further review and discussions among the authors, we want to do further experiment to improve the existing premature results. Specifically, authors want to add more complex analysis to support the results. Given the importance of maintaining the highest standards of academic integrity, we believe that withdrawal is the most appropriate course of action. All authors are fully aware of this decision and have agreed to the withdrawal. We have had several rounds of correspondence regarding this matter, and all authors have been included in these communications to ensure transparency and prevent any potential disputes in the future.
This book constitutes the thoroughly refereed post-workshop proceedingsof the 9th International Conference on Web-Based Learning, ICWL 2010,held in Shanghai, China, in December 2010.The 36 revised full papers and 8 sh...
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ISBN:
(数字)9783642205392
ISBN:
(纸本)9783642205385
This book constitutes the thoroughly refereed post-workshop proceedings
of the 9th International Conference on Web-Based Learning, ICWL 2010,
held in Shanghai, China, in December 2010.
The 36 revised full papers and 8 short papers presented were carefully
reviewed and selected from 192 submissions. They deal with topics such
as e-learning platforms and tools, technology enhanced learning,
Web-based learning for oriental languages, mobile/situated e-learning,
learning resource deployment, organization and management, design, model
and framework of e-learning systems, e-learning metadata and standards,
collaborative learning and game-based learning, as well as practice and
experience sharing, and pedagogical issues.
In this paper, a uniform calculus-based approach for synthesizing monitors checking correctness properties specified by a large variety of logics at runtime is provided, including future and past time logics, interval...
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