Advanced Behavioral Analyses Using Inferred Social Networks: A Vision
Authors
Holubová, I.; Svoboda, M.; Skopal, T.; Bernhauer, D.; Peška, L.
Year
2019
Published
Database and Expert Systems Applications. Springer, Cham, 2019. p. 210-219. ISSN 1865-0929. ISBN 978-3-030-27683-6.
Type
Proceedings paper
Departments
Annotation
The success of many businesses is based on a thorough knowledge of their clients. There exists a number of supervised as well as unsupervised data mining or other approaches that allow to analyze data about clients, their behavior or environment. In our ongoing project focusing primarily on bank clients, we propose an innovative strategy that will overcome shortcomings of the existing methods. From a given set of user activities, we infer their social network in order to analyze user relationships and behavior. For this purpose, not just the traditional direct facts are incorporated, but also relationships inferred using similarity measures and statistical approaches, with both possibly limited measures of reliability and validity in time. Such networks would enable analyses of client characteristics from a new perspective and could provide otherwise impossible insights. However, there are several research and technical challenges making the outlined pursuit novel, complex and challenging as we outline in this vision paper.
Approximate search in dissimilarity spaces using GA
Authors
Bernhauer, D.; Skopal, T.
Year
2019
Published
GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion. New York: Association for Computing Machinery, 2019. p. 279-280. ISBN 978-1-4503-6748-6.
Type
Proceedings paper
Departments
Annotation
Nowadays, the metric space properties limit the methods of indexing for content-based similarity search. The target of this paper is a data-driven transformation of a semimetric model to a metric one while keeping the data indexability high. We have proposed a genetic algorithm for evolutionary design of semimetric-to-metric modifiers. The precision of our algorithm is near the specified error threshold and indexability is still good. The paper contribution is a proof of concept showing that genetic algorithms can effectively design semimetric modifiers applicable in similarity search engines.
Recommender System as the Support for Binaural Audio
Authors
Bernhauer, D.; Skopal, T.
Year
2019
Published
Augmented Reality and Virtual Reality. Cham: Springer International Publishing AG, 2019. p. 233-246. ISSN 2196-8705. ISBN 978-3-030-06245-3.
Type
Book chapter
Departments
Annotation
Virtual reality devices nowadays can effectively utilise other senses besides vision, too. The most often used secondary sense is hearing with binaural audio as VR engine. Currently, practical usage of binaural audio as the source of VR is impossible because of the inaccuracy of a general model. On the contrary, measuring the personalised parameters can be time-consuming. Our task was to prove the possibility of reconstruction of the binaural audio parameters in domestic conditions. We have focused on the design of the user interface that can be used independently on the platform. Our proposed browser-based application uses collaborative filtering as a recommender system. We have proven that sound-based navigation in axial plane is possible with 6.6° inaccuracy. The gamification and browser-based implementation make it easier for all people to find the best possible parameters. The resulting profile can be used both with fully VR environment and with semi-VR games.