By Mikhail Yu. Khachay, Natalia Konstantinova, Alexander Panchenko, Dmitry Ignatov, Valeri G. Labunets
This e-book constitutes the complaints of the Fourth foreign convention on research of pictures, Social Networks and Texts, AIST 2015, held in Yekaterinburg, Russia, in April 2015.
The 24 complete and eight brief papers have been rigorously reviewed and chosen from a hundred and forty submissions. The papers are prepared in topical sections on research of pictures and video clips; development popularity and desktop studying; social community research; textual content mining and common language processing.
Read or Download Analysis of Images, Social Networks and Texts: 4th International Conference, AIST 2015, Yekaterinburg, Russia, April 9–11, 2015, Revised Selected Papers PDF
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Extra resources for Analysis of Images, Social Networks and Texts: 4th International Conference, AIST 2015, Yekaterinburg, Russia, April 9–11, 2015, Revised Selected Papers
The difference in error rates of the PHOG (4) and our approach (Fig. 1) is not statistically meaningful (Fig. 2). However, the average recognition time (Fig. 5 times lower in comparison with conventional aggregation (5) as in most cases (especially for large R) the reliable solution (6) was found at the ﬁrst level ðK ð1Þ ¼ 10Þ. Sequential Hierarchical Image Recognition 21 25 10x10 Error rate, % 20 20x20 15 PHOG (10x10+ 20x20) 10 Sequential PHOG (10x10+ 20x20) 5 PHOG (10x10+15x15+ 20x20) 0 65 75 150 225 Sequential PHOG (10x10+15x15+20x20) Number of models R Average recognition time, ms.
The second result is that the most valuable features that characterize the depression propensity are the (log) number of communities and the user’s transitivity within his or her personal ego network, which are suﬃcient for propensity detection in our setting. e. local clustering coeﬃcient). It is worth mentioning that not only age and sex but also the number of friends turned out to be statistically insigniﬁcant. According to our simulations, only (log) homophily can be considered signiﬁcant enough to be added to the model, but its improvement is negligible and thus redundant.
1 Dataset and Research Design User Sampling We identify depression propensity among social network users based on their demographic features and structural properties of their egocentric networks, introducing several important improvements over the methodology of  in the sampling design and methods. First, we chose people with depression propensity not only by the fact of their membership in the communities related to suicide and depression: obviously, plenty of members of such groups are not really depressed or have already overcome their depression.