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Tulsi Patel, Mark W. Jones and Thomas Redfern
We present a novel approach to providing greater insight into the characteristics of an unlabelled dataset, increasing the efficiency with which labelled datasets can be created. We leverage dimension-reduction techniques in combination with autoencoders...
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Aleksei Triastcyn and Boi Faltings
We consider the problem of enhancing user privacy in common data analysis and machine learning development tasks, such as data annotation and inspection, by substituting the real data with samples from a generative adversarial network. We propose employi...
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Andrea M. Burfeid-Castellanos, Michael Kloster, Sára Beszteri, Ute Postel, Marzena Spyra, Martin Zurowietz, Tim W. Nattkemper and Bánk Beszteri
Diatom identification and counting by light microscopy of permanently embedded acid-cleaned silicate shells (frustules) is a fundamental method in ecological and water quality investigations. Here we present a new variant of this method based on ?digital...
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Tian Lan, Zhilin Li, Jicheng Wang, Chengyin Gong and Peng Ti
Schematic maps are popular for representing transport networks. In the last two decades, some researchers have been working toward automated generation of network layouts (i.e., the network geometry of schematic maps), while automated labelling of schema...
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Giulio Franzese, Nicola Linty and Fabio Dovis
This work focuses on a machine learning based detection of ionospheric scintillation events affecting Global Navigation Satellite System (GNSS) signals. We here extend the recent detection results based on Decision Trees, designing a semi-supervised dete...
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