Active Media Technology: 8th International Conference, AMT by Yin-Fu Huang, Shih-Hao Wang (auth.), Runhe Huang, Ali A.

By Yin-Fu Huang, Shih-Hao Wang (auth.), Runhe Huang, Ali A. Ghorbani, Gabriella Pasi, Takahira Yamaguchi, Neil Y. Yen, Beijing Jin (eds.)

This e-book constitutes the refereed complaints of the eighth overseas convention on lively Media know-how, AMT 2012, held in Macau, China, in December 2012. The sixty five revised complete papers have been conscientiously reviewed and chosen from a various submissions. The papers are prepared in topical sections on understanding multi-agent platforms, information mining, ontology mining, net reasoning, social purposes of energetic media, human-centered computing, personalization and version, clever electronic artwork and e-learning.

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Extra resources for Active Media Technology: 8th International Conference, AMT 2012, Macau, China, December 4-7, 2012. Proceedings

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LNCS, vol. 6922, pp. 163–172. Springer, Heidelberg (2011) 20. : Linkage Learning Based on Differences in Local Optimums of Building Blocks with One Optima. International Journal of the Physical Sciences 6(14), 3419–3425 (2011) 21. : Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence. Expert Systems with Applications 38(1), 288–298 (2011) 22. : A New Clustering Algorithm with the Convergence Proof. C. ) KES 2011, Part I. LNCS, vol.

Tw/~cjlin/libsvm 2. : A trajectory-based ball tracking framework with visual enrichment for broadcast baseball videos. Journal of Information Science and Engineering 24(1), 143–157 (2008) 3. : Stance-based strike zone shaping and visualization in broadcast baseball video: providing reference for pitch location positioning. In: Proc. IEEE International Conference on Multimedia and Expo, pp. 302–305 (2009) 4. : Extraction of baseball trajectory and physics-based validation for single-view baseball video sequences.

The degree of user participation, position in the social network and influence on neighbors’ opinions are assessed to recognize the opinion leader. Besides, the user role during the topic propagation and evolution of network opinions need analyzing. The influence of a user is evaluated by the following equation: n Opindn (w, x) = i=1 nosi (w, x) nopi (w, x) bpi (w, x) ∗(ϕ∗ +ψ∗ceni (w)+δ∗ ) (6) T nopi (x) T nosi (x) T pi (x) where Opindn (w, x) is the influence of user w on topic x within the nth time unit, i denotes the ith time unit, nopi (w, x) is the number of opinions that user w publishes on topic x within the ith time unit, T nopi (x) is the total number of opinions that all users publish on topic x within the ith time unit, ceni (w) is the central degree of user w in the social network within the ith time unit, nosi (w, x) is the number of users who have the same opinion as user w within the ith time unit, T nosi (x) is the total number of users who publish opinions on topic x within the ith time unit, bpi (w, x) is the number of posts that user w publishes with respect to topic x and are recommended by the website within the ith time unit, T pi (x) is the total number of posts that all users publish with respect to topic x within the ith time unit, ϕ is the emotion coefficient, ψ is the position coefficient, and δ is the quotation coefficient.

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