Titlebook: Information Technologies in the Research of Biodiversity; Proceedings of the I Igor Bychkov,Victor Voronin Conference proceedings 2019 Spri

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E. V. Verkhozina,A. S. Safarov,V. A. Verkhozina,U. S. Bukinm. In a desirable scenario, technology should allow to automatically adapt the behavior of these devices to the needs and expectations of their users. To this extent, in previous work we proposed the Internet of People model to automatically develop virtual profiles of people stored in their smartph

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T. U. Biktashev,N. I. Fedorov,E. Z. Baishevablic polls). The pipeline follows a four-step methodology. First, social media posts and users metadata are crawled. Second, a filtering mechanism is applied to filter spammers and bot users. As a third step, demographics information is extracted out of the valid users, namely gender, age, ethnicity

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Mikhail Orlov,Alexander Sheludkovte a tag cloud so far is based on popularity of tags among users who annotate by those tags. This approach however has several limitations, such as suppressing number of tags which are not used often but could lead to interesting resources as well as tags which have been suppressed due to the defaul

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Anastasia K. Popova,Evgeny A. Cherkasin,Igor N. Vladimirovtellectual property rights to prevail the competitive advantage of technology and enhance technological competitiveness. As a result, the number of patents invented increases rapidly every year, and the ripple effects of the developed technologies are also increasing in terms of social and economic

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Marina Protopopova,Vasiliy Pavlichenko,Aleksander Gnutikov,Victor Chepinogaterior distribution by maximizing a lower bound of the log marginal likelihood of observations. We can implement VI as VAE by using a neural network called encoder to obtain parameters of approximate posterior. Our contribution is three-fold. First, we marginalize out per-document topic probabilitie

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Igor V. Bychkov,Gennady M. Ruzhnikov,Roman K. Fedorov,Yurii V. Avramenko,Alexander S. Shumilov,Alexeterior distribution by maximizing a lower bound of the log marginal likelihood of observations. We can implement VI as VAE by using a neural network called encoder to obtain parameters of approximate posterior. Our contribution is three-fold. First, we marginalize out per-document topic probabilitie