Abstract
With the constant development of neural networks, traditional algorithms relying on data structures lose their significance as more and more solutions are using AI rather than traditional algorithms. This in turn requires a lot of correctly annotated and informative data samples. In this paper, we propose a crowdsourcing based approach for data acquisition and tagging with support for Active Learning where the system acts as an oracle and repository of training samples. The paper presents the CenHive system implementing the proposed approach. Three different usage scenarios are presented that were used to verify the proposed approach.
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References
Boiński, T.: Game with a purpose for mappings verification. In: 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 405–409. IEEE (2016)
Curtis, V.: Motivation to participate in an online citizen science game: a study of foldit. Sci. Commun. 37(6), 723–746 (2015)
Fort, K., Adda, G., Cohen, K.B.: Amazon mechanical turk: gold mine or coal mine? Comput. Linguist. 37(2), 413–420 (2011)
Gal, Y., Islam, R., Ghahramani, Z.: Deep bayesian active learning with image data. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 1183–1192. JMLR. org (2017)
Garcia, I.: Learning a language for free while translating the web. Does Duolingo work? Int. J. Engl. Linguist. 3(1), 19 (2013)
Girshick, R., Radosavovic, I., Gkioxari, G., Dollár, P., He, K.: Detectron (2018). https://github.com/facebookresearch/detectron
Jagoda, J., Boiński, T.: Assessing word difficulty for quiz-like game. In: Szymański, J., Velegrakis, Y. (eds.) IKC 2017. LNCS, vol. 10546, pp. 70–79. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74497-1_7
Kellenberger, B., Marcos, D., Lobry, S., Tuia, D.: Half a percent of labels is enough: efficient animal detection in uav imagery using deep cnns and active learning. IEEE Trans. Geosci. Remote Sens. 57(12), 9524–9533 (2019)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Richardson, L., Ruby, S.: RESTful Web Services. O’Reilly Media, Inc., Newton (2008)
Settles, B.: Active learning literature survey. University of Wisconsin-Madison, Department of Computer Sciences, Technical report (2009)
Swanson, A., Kosmala, M., Lintott, C., Simpson, R., Smith, A., Packer, C.: Snapshot serengeti, high-frequency annotated camera trap images of 40 mammalian species in an african savanna. Sci. Data 2, 150026 (2015)
Szymański, J., Boiński, T.: Crowdsourcing-based evaluation of automatic references between wordnet and wikipedia. Int. J. Softw. Eng. Knowl. Eng. 29(03), 317–344 (2019). https://doi.org/10.1142/s0218194019500141
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, p. I. IEEE (2001)
Von Ahn, L.: Games with a purpose. Computer 39(6), 92–94 (2006)
Von Ahn, L., Maurer, B., McMillen, C., Abraham, D., Blum, M.: reCAPTCHA: Human-based character recognition via web security measures. Science 321(5895), 1465–1468 (2008)
Acknowledgements
We would like to thank Agata Krauzewicz and Łukasz Łepek who implemented part of the presented solution during their studies.
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Boiński, T., Szymański, J. (2020). Collaborative Data Acquisition and Learning Support. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2020. Lecture Notes in Computer Science(), vol 12133. Springer, Cham. https://doi.org/10.1007/978-3-030-47679-3_19
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