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2 Annals of Computer Science and Information Systems

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Integrated Human Tracking Based on Video and Smartphone Signal Processing within the Arahub System

Ludziejewski Jan, Grad Łukasz, Przebinda Łukasz, Tajmajer Tomasz

Annals of Computer Science and Information Systems

2020

Vol. 21

105--114

Embedded platforms with GPU acceleration, designed for performing machine learning on the edge, enabled the creation of inexpensive and pervasive computer vision systems. Smartphones are nowadays widely used for profiling and tracking in marketing, based on WiFi data or beacon-based positioning systems. We present the Arahub system, which aims at integrating world of computer vision systems with smartphone tracking for delivering data useful in interactive applications, such as interactive advertisements. In this paper we present the architecture of the Arahub system and provide insight about its particular elements. Our preliminary results, obtained from real-life test environments and scenarios, show that the Arahub system is able to accurately assign smartphones to their owners, based on visual and WiFi/Bluetooth positioning data. We show the commercial value of such system and its potential applications.

Clash Royale Challenge: how to select training decks for win-rate prediction

Janusz Andrzej, Grad Łukasz, Grzegorowski Marek

Annals of Computer Science and Information Systems

2019

Vol. 18

3--6

We summarize the sixth data mining competition organized at the Knowledge Pit platform in association with the Federated Conference on Computer Science and Information Systems series, titled Clash Royale Challenge: How to Select Training Decks for Win-rate Prediction. We outline the scope of this challenge and briefly present its results. We also discuss the problem of acquiring knowledge about new notions from video games through an active learning cycle. We explain how this task is related to the problem considered in the challenge and share results of experiments that we conducted to demonstrate usefulness of the active learning approach in practice.