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Retrieving Sound Samples of Subjective Interest With User Interaction

Jakubik Jan

Annals of Computer Science and Information Systems

2020

Vol. 21

387--390

This paper concerns the retrieval of audio samples with a high degree of user interaction, motivated by a practical use case. We consider an open set recognition scenario in which the goal is to find all occurrences of a subjectively interesting sound selected by a user within a particular audio file. We use only a single starting example and maintain interaction through yes-no answers from the user, indicating whether any new retrieved sound matches the target pattern. We present a small dataset for this task and evaluate a baseline solution based on Nonnegative Matrix Factorization and greedy feature selection.

Deconstructing the barriers of thought – the need for embeddedness of education and business

Calapez-Gomes Ana, Rodrigues Luiz

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

2019

z. 133

31--41

The need to match education and business goals is an endeavour and a challenge for any educational institution, especially for higher education institutions. This challenge has become an important focus over the last decades due to the claimed relationship between education and innovation as well as the potential for economic development. The biggest challenge seems to be to overcome the barriers of thought and objectives that exist between the two worlds of education and business. Indeed, although many attempts and programmes are being developed almost all over the world to bridge these two realities, many clashes and disillusions exist. For different reasons, this issue is viewed almost exclusively from the academic perspective, with the entrepreneurial perspective being completely absent. Based on the experience of both the academic and business worlds, the authors commence this contribution by analysing the causes of this mismatch of interests. They conclude by presenting some possible solutions for improving this misalignment.

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.