Abstract
The MOVING platform enables its users to improve their information literacy by training how to exploit data and text mining methods in their daily research tasks. In this paper, we show how it can support researchers in various tasks, and we introduce its main features, such as text and video retrieval and processing, advanced visualizations, and the technologies to assist the learning process.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Apaolaza, A., Vigo, M.: WevQuery: testing hypotheses about web interaction patterns. Proc. ACM Hum. Comput. Interact. 1(EICS), 4:1–4:17 (2017)
Apostolidis, E., Mezaris, V.: Fast shot segmentation combining global and local visual descriptors. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2014
Fessl, A., Wertner, A., Pammer-Schindler, V.: Digging for gold: motivating users to explore alternative search interfaces. In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds.) EC-TEL 2018. LNCS, vol. 11082, pp. 636–639. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98572-5_62
Galanopoulos, D., Markatopoulou, F., Mezaris, V., Patras, I.: Concept language models and event-based concept number selection for zero-example event detection. In: Proceedings of ACM ICMR (2017)
Galanopoulos, D., Mezaris, V.: Temporal lecture video fragmentation using word embeddings. In: Kompatsiaris, I., et al. (eds.) MMM 2019. LNCS, vol. 11296, pp. 254–265. Springer International Publishing (2019)
Galke, L., Mai, F., Vagliano, I., Scherp, A.: Multi-modal adversarial autoencoders for recommendations of citations and subject labels. In: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, UMAP 2018, pp. 197–205. ACM (2018)
Markatopoulou, F., Mezaris, V., Patras, I.: Implicit and explicit concept relations in deep neural networks for multi-label video/image annotation. IEEE Trans. Circ. Syst. Video Technol., 1 (2018, in press)
Nishioka, C., Scherp, A.: Profiling vs. time vs. content: what does matter for top-k publication recommendation based on twitter profiles? In: Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, JCDL 2016, pp. 171–180. ACM (2016)
Vagliano, I., Monti, D., Scherp, A., Morisio, M.: Content recommendation through semantic annotation of user reviews and linked data. In: Proceedings of the Knowledge Capture Conference, K-CAP 2017, pp. 32:1–32:4. ACM (2017)
Acknowledgments
This work was supported by the EU’s Horizon 2020 programme under grant agreement H2020-693092 MOVING. The Know-Center is funded within the Austrian COMET Program under the auspices of the Austrian Federal Ministry of Transport, Innovation and Technology, the Austrian Federal Ministry of Economy, Family and Youth and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency FFG.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Vagliano, I. et al. (2019). Training Researchers with the MOVING Platform. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11296. Springer, Cham. https://doi.org/10.1007/978-3-030-05716-9_46
Download citation
DOI: https://doi.org/10.1007/978-3-030-05716-9_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05715-2
Online ISBN: 978-3-030-05716-9
eBook Packages: Computer ScienceComputer Science (R0)