By Yu Wang, Ge Yu, Yanyong Zhang, Zhu Han, Guoren Wang
This ebook constitutes the court cases of the second one overseas convention on substantial information Computing and Communications, BigCom 2016, held in Shenyang, China, in July 2016. The 39 papers offered during this quantity have been rigorously reviewed and chosen from ninety submissions.
BigCom is a global symposium devoted to addressing the demanding situations rising from colossal info comparable computing and networking.
The convention is concentrated to draw researchers and practitioners who're attracted to gigantic info analytics, administration, defense and privateness, communique and excessive functionality computing in its broadest sense.
Read or Download Big Data Computing and Communications: Second International Conference, BigCom 2016, Shenyang, China, July 29-31, 2016. Proceedings PDF
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Additional resources for Big Data Computing and Communications: Second International Conference, BigCom 2016, Shenyang, China, July 29-31, 2016. Proceedings
2 Allocation Mechanism After receiving the encrypted bids and demands from the bidders, the auctioneer chooses a set of bidders as winners if the social eﬃciency is maximized. It has been proven in  that the social eﬃciency maximization problem in the combinatorial auction is NP hard,√and the upper bound of approximation ratios of polynomial time algorithms is h. Dong et al. propose an auction mechanism with a greedy allocation√mechanism in , which can approximate the optimal one within a factor of h.
In: IEEE INFOCOM 2011, pp. 3020–3028 (2011) 8. : Near-optimal truthful spectrum auction mechanisms with spatial and temporal reuse in wireless networks. In: ACM MobiHoc 2013, pp. 237–240 (2013) 9. : Spring: a strategy-proof and privacy preserving spectrum auction mechanism. In: IEEE INFOCOM 2013, pp. 827–835 (2013) 10. : (M+1)st-price auction protocol. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 85(3), 676–683 (2002) 11. : Auction Theory. Academic Press, San Diego (2009) 12. : The knapsack problem, fully polynomial time approximation schemes (FPTAS) (2006).
Besides, an unavoidable problem in Multi-label classiﬁcation algorithm is that most Multi-label datasets are imbalanced dataset , which aﬀects the classiﬁcation algorithm results. In this paper, we combine Binary Relevance (BR) problem transformation strategy with binary Approximate Extreme Points SVM (AESVM)  classiﬁcation algorithm to construct a new Multi-label classiﬁcation algorithm (AEMLSVM). It can be used to solve the problem of Multi-label SVM classiﬁcation algorithm used in Large-Scale datasets.
Big Data Computing and Communications: Second International Conference, BigCom 2016, Shenyang, China, July 29-31, 2016. Proceedings by Yu Wang, Ge Yu, Yanyong Zhang, Zhu Han, Guoren Wang