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Auflistung nach Schlagwort "deep neural networks"

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  • Textdokument
    Calculation of the Photon Flux in a Photo-Multiplier Tube with Deep Learning
    (INFORMATIK 2022, 2022) Bhanderi,Jigar; Funk,Stefan; Malyshev,Dmitry; Vogel,Naomi; Zmija,Andreas
    Intensity interferometry is part of optical interferometry, which provides a sub-milliarcsecond resolution of astronomical objects. In intensity interferometry one correlates intensities of optical fluxes rather than amplitudes of waves. For a successful measurement one needs a large light collecting area for several telescopes separated by hundreds of meters and good time resolution of the intensity flux. Air Cherenkov telescopes, e.g., H.E.S.S. are a natural candidate for performing such a measurement. One of the important tasks is to determine the rate of photons hitting the PMTs to calculate expectations on the signal-to-noise ratio. For low rates, the individual pulses can be resolved and counted, but for high rates, relevant for the IACTs, the pulses from the photons overlap. We use different neural network algorithms in order to determine the rate of photons hitting the PMT, including the high rates.
  • Konferenzbeitrag
    KnuckleTouch: Enabling Knuckle Gestures on Capacitive Touchscreens using Deep Learning
    (Mensch und Computer 2019 - Tagungsband, 2019) Schweigert, Robin; Leusmann, Jan; Hagenmayer, Simon; Weiß, Maximilian; Le, Huy Viet; Mayer, Sven; Bulling, Andreas
    While mobile devices have become essential for social communication and have paved the way for work on the go, their interactive capabilities are still limited to simple touch input. A promising enhancement for touch interaction is knuckle input but recognizing knuckle gestures robustly and accurately remains challenging. We present a method to differentiate between 17 finger and knuckle gestures based on a long short-term memory (LSTM) machine learning model. Furthermore, we introduce an open source approach that is ready-to-deploy on commodity touch-based devices. The model was trained on a new dataset that we collected in a mobile interaction study with 18 participants. We show that our method can achieve an accuracy of 86.8% on recognizing one of the 17 gestures and an accuracy of 94.6% to differentiate between finger and knuckle. In our evaluation study, we validate our models and found that the LSTM gestures recognizing archived an accuracy of 88.6%. We show that KnuckleTouch can be used to improve the input expressiveness and to provide shortcuts to frequently used functions.
  • Zeitschriftenartikel
    Towards Human-Centered AI: Psychological concepts as foundation for empirical XAI research
    (it - Information Technology: Vol. 64, No. 1-2, 2022) Weitz, Katharina
    Human-Centered AI is a widely requested goal for AI applications. To reach this is explainable AI promises to help humans to understand the inner workings and decisions of AI systems. While different XAI techniques have been developed to shed light on AI systems, it is still unclear how end-users with no experience in machine learning perceive these. Psychological concepts like trust, mental models, and self-efficacy can serve as instruments to evaluate XAI approaches in empirical studies with end-users. First results in applications for education, healthcare, and industry suggest that one XAI does not fit all. Instead, the design of XAI has to consider user needs, personal background, and the specific task of the AI system.