A Deep Awareness Framework for Pervasive Video Cloud

IEEE Access

Volumn 3, Issue , 2015, Pages 2227-2237

  Zhang, Weishan ^a   Duan, Pengcheng ^a   Li, Zhongwei ^a   Lu, Qinghua ^a   Gong, Wenjuan ^a   Yang, Su ^b  

^a CHINA UNIVERSITY OF PETROLEUM   (China)

^b FUDAN UNIVERSITY   (China)

Author keywords

Cloud Computing; Context Awareness; Deep Learning; Framework; Pervasive Video Cloud

Indexed keywords

CLOUD COMPUTING; VIDEO RECORDING;

BIG DATA APPLICATIONS; CONTEXT DATA; CONTEXT- AWARENESS; DEEP LEARNING; EVALUATION RESULTS; FRAMEWORK; HISTORICAL VIEW; VIDEO DATA;

BIG DATA;

EID: 84961076767     PISSN: None     EISSN: 21693536     Source Type: Journal    
DOI: 10.1109/ACCESS.2015.2497278     Document Type: Article

References (27)

1. W. Zhang, K. M. Hansen, and P. Bellavista, "A research roadmap for context-awareness-based self-managed systems," in Proc. Int. Service-Oriented Comput. (ICSOC Workshops), 2013, pp. 275-283.
2. J. Zhao, H. Boley, and W. Du, "A fuzzy logic based approach to expressing and reasoning with uncertain knowledge on the semantic Web," in Computational Intelligence. New York, NY, USA: Springer-Verlag, 2012, pp. 167-181.
3. G. Nakamiti, V. E. da Silva, J. H. Ventura, and S. A. da Silva, "Urban traffic control and monitoring-An approach for the Brazilian intelligent cities project," in Practical Applications of Intelligent Systems. New York, NY, USA: Springer-Verlag, 2012, pp. 543-551.
4. W. Zhang, K. M. Hansen, and T. Kunz, "Enhancing intelligence and dependability of a product line enabled pervasive middleware," Pervasive Mobile Comput., vol. 6, no. 2, pp. 198-217, Apr. 2010.
5. G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504-507, Jul. 2006.
6. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Image Net Classification with deep convolutional neural networks," in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1097-1105.
7. K. Simonyan and A. Zisserman. (2014). "Very deep convolutional networks for large-scale image recognition." [Online]. Available: http://arxiv.org/abs/1409.1556
8. R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2014, pp. 580-587.
9. G. E. Hinton, S. Osindero, and Y.-W. Teh, "A fast learning algorithm for deep belief nets," Neural Comput., vol. 18, no. 7, pp. 1527-1554, 2006.
10. S. Ji, W. Xu, M. Yang, and K. Yu, "3D convolutional neural networks for human action recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 1, pp. 221-231, Jan. 2013.
11. C. Reiss, J. Wilkes, and J. L. Hellerstein, "Google cluster-usage traces: Format + schema," Google Inc., Mountain View, CA, USA, Tech. Rep., 2011. [Online]. Available: https://drive.google.com/file/d/0B5g07T-gRDg9Z0lsSTEtTWtpOW8/view
12. E. S. Gardner, Jr., and E. McKenzie, "Note-Seasonal exponential smoothing with damped trends," Manage. Sci., vol. 35, no. 3, pp. 372-376, 1989.
13. C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer-Verlag, 2006.
14. R. J. Hyndman and Y. Khandakar, "Automatic time series forecasting: The forecast package for R," J. Statist. Softw., vol. 27, no. 3, pp. 1-22, 2008.
15. J. Zurovac and R. Brown, "Orthogonal design: A powerful method for comparative effectiveness research with multiple interventions," Center Healthcare Effectiveness, Math. Policy Res., Washington, DC, USA, Tech. Rep., 2012, pp. 12-30. [Online]. Available: http://www.mathematica-mpr.com/~/media/publications/PDFs/health/orthogonaldesign-ib.pdf
16. G. P. Zhang, B. E. Patuwo, and M. Y. Hu, "A simulation study of artificial neural networks for nonlinear time-series forecasting," Comput. Oper. Res., vol. 28, no. 4, pp. 381-396, Apr. 2001.
17. P. A. Dinda and D. R. O'Hallaron, "Host load prediction using linear models," Cluster Comput., vol. 3, no. 4, pp. 265-280, Dec. 2000.
18. T. Kuremoto, S. Kimura, K. Kobayashi, and M. Obayashi, "Time series forecasting using a deep belief network with restricted Boltzmann machines," Neurocomputing, vol. 137, pp. 47-56, Aug. 2014.
19. S. Di, D. Kondo, and W. Cirne, "Host load prediction in a Google compute cloud with a Bayesian model," in Proc. IEEE/ACM 24th Int. Conf. High Perform. Comput., Netw., Storage Anal. (SC), Nov. 2012, pp. 1-11.
20. J. D. Hamilton, Time Series Analysis, vol. 2. Princeton, NJ, USA: Princeton Univ. Press, 1994.
21. J. W. Creswell and V. L. P. Clark, Designing and Conducting Mixed Methods Research, 2nd ed. New York, NY, USA: Sage Publications, 2010.
22. T. A. Matyas and K. M. Greenwood, "Visual analysis of single-case time series: Effects of variability, serial dependence, and magnitude of intervention effects," J. Appl. Behavior Anal., vol. 23, no. 3, pp. 341-351, 1990.
23. S. Zeng, X. Niu, and Y. Dou, "Vehicle recognition for surveillance video using sparse coding," in Pattern Recognition. New York, NY, USA: Springer-Verlag, 2014, pp. 228-234.
24. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. Le Cun. (2013). "OverFeat: Integrated recognition, localization and detection using convolutional networks." [Online]. Available: http://arxiv.org/abs/1312.6229
25. B. Huval et al. (2015). "An empirical evaluation of deep learning on highway driving." [Online]. Available: http://arxiv.org/abs/1504.01716
26. R. Hu, J. Jiang, G. Liu, and L. Wang, "efficient resources provisioning based on load forecasting in cloud," Sci. World J., vol. 2014, Feb. 2014, Art. ID 321231.
27. N. Roy, A. Dubey, and A. Gokhale, "efficient autoscaling in the cloud using predictive models forworkload forecasting," in Proc. IEEE Int. Conf. Cloud Comput. (CLOUD), Jul. 2011, pp. 500-507.