By Dominik Slezak, Gerald Schaefer, Son T. Vuong, Yoo-Sung Kim

This booklet constitutes the lawsuits of the tenth foreign convention on energetic Media know-how, AMT 2014, held in Warsaw, Poland, in August 2014, held as a part of the 2014 internet Intelligence Congress, WIC 2014. The forty seven complete papers awarded have been conscientiously reviewed and chosen from quite a few submissions. the themes of those papers surround lively desktops; interactive platforms and purposes of AMT-based platforms; lively media laptop studying and knowledge mining ideas; AMT for the semantic net; social networks and cognitive foundations.

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Extra info for Active Media Technology: 10th International Conference, AMT 2014, Warsaw, Poland, August 11-14, 2014. Proceedings

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1. For a method qj , if fj (vl ) ≤ fj (vi ), then vl points to vi and weight of (vl , vi ) is added. Here, an example is used to illustrate how to construct G∗ and how FR strategy identifies the influential nodes from G∗ . Table 2 shows the ranking results of a small network GX with 5 nodes based on different methods. According to Eq. (8), we can 16 L. Zhong et al. assign a weight to each edge. For two nodes vl and vi , fj (v l ) and fj (v i ) denote their ranking results based on a certain method qj .

I-node] We refer to vertices or nodes in the preliminary phase as individual-nodes or i-node. For example, in figure 2, each independent node A, B, C, etc. corresponds to an i-node. All MKNN partners of an i-node lie at approximately the same edge similarity level from the i-node.

In this work, we propose to use a neighborhood local of each training instance to estimate the possibility instead. In this paper, we propose a regression transfer learning framework to predict users’ personality and two local regression transfer learning methods to address biased distribution. In the framework, we firstly check whether the training dataset and test dataset follow the same distribution, then introduce the regression transfer learning methods to reweight the importance of training instances and solve a weighted risk regression problem, and finally predict and test on the test dataset.

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