@article{ReimannKlingbeilPasewaldtetal.2019,
  author    = {Reimann, Max and Klingbeil, Mandy and Pasewaldt, Sebastian and Semmo, Amir and Trapp, Matthias and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Locally controllable neural style transfer on mobile devices},
  series = {The Visual Computer},
  volume    = {35},
  journal   = {The Visual Computer},
  number    = {11},
  publisher = {Springer},
  address   = {New York},
  issn      = {0178-2789},
  doi       = {10.1007/s00371-019-01654-1},
  pages     = {1531 -- 1547},
  year      = {2019},
  abstract  = {Mobile expressive rendering gained increasing popularity among users seeking casual creativity by image stylization and supports the development of mobile artists as a new user group. In particular, neural style transfer has advanced as a core technology to emulate characteristics of manifold artistic styles. However, when it comes to creative expression, the technology still faces inherent limitations in providing low-level controls for localized image stylization. In this work, we first propose a problem characterization of interactive style transfer representing a trade-off between visual quality, run-time performance, and user control. We then present MaeSTrO, a mobile app for orchestration of neural style transfer techniques using iterative, multi-style generative and adaptive neural networks that can be locally controlled by on-screen painting metaphors. At this, we enhance state-of-the-art neural style transfer techniques by mask-based loss terms that can be interactively parameterized by a generalized user interface to facilitate a creative and localized editing process. We report on a usability study and an online survey that demonstrate the ability of our app to transfer styles at improved semantic plausibility.},
  language  = {en}
}
@article{PrasseKnaebelMachlicaetal.2019,
  author    = {Prasse, Paul and Knaebel, Rene and Machlica, Lukas and Pevny, Tomas and Scheffer, Tobias},
  title     = {Joint detection of malicious domains and infected clients},
  series = {Machine learning},
  volume    = {108},
  journal   = {Machine learning},
  number    = {8-9},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {0885-6125},
  doi       = {10.1007/s10994-019-05789-z},
  pages     = {1353 -- 1368},
  year      = {2019},
  abstract  = {Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traffic data can be collected at a large scale, and antivirus tools can be used to identify infected clients in retrospect. Domains, by contrast, have to be labeled individually after forensic analysis. We explore transfer learning based on sluice networks; this allows the detection models to bootstrap each other. In a large-scale experimental study, we find that the model outperforms known reference models and detects previously unknown malware, previously unknown malware families, and previously unknown malicious domains.},
  language  = {en}
}
@article{DerrasBardCotton2017,
  author    = {Derras, Boumediene and Bard, Pierre-Yves and Cotton, Fabrice Pierre},
  title     = {V-S30, slope, H-800 and f(0): performance of various site-condition proxies in reducing ground-motion aleatory variability and predicting nonlinear site response},
  series = {Earth, planets and space},
  volume    = {69},
  journal   = {Earth, planets and space},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {1880-5981},
  doi       = {10.1186/s40623-017-0718-z},
  pages     = {1623 -- 1629},
  year      = {2017},
  abstract  = {The aim of this paper is to investigate the ability of various site-condition proxies (SCPs) to reduce ground-motion aleatory variability and evaluate how SCPs capture nonlinearity site effects. The SCPs used here are time-averaged shear-wave velocity in the top 30 m (V-S30), the topographical slope (slope), the fundamental resonance frequency (f(0)) and the depth beyond which V-s exceeds 800 m/s (H800). We considered first the performance of each SCP taken alone and then the combined performance of the 6 SCP pairs [V-S30-f(0)], [V-S30-H-800], [f(0)-slope], [H-800-slope], [V-S30-slope] and [f(0)-H-800]. This analysis is performed using a neural network approach including a random effect applied on a KiK-net subset for derivation of ground-motion prediction equations setting the relationship between various ground-motion parameters such as peak ground acceleration, peak ground velocity and pseudo-spectral acceleration PSA (T), and Mw, RJB, focal depth and SCPs. While the choice of SCP is found to have almost no impact on the median groundmotion prediction, it does impact the level of aleatory uncertainty. VS30 is found to perform the best of single proxies at short periods (T < 0.6 s), while f(0) and H-800 perform better at longer periods; considering SCP pairs leads to significant improvements, with particular emphasis on [V-S30-H-800] and [f(0)-slope] pairs. The results also indicate significant nonlinearity on the site terms for soft sites and that the most relevant loading parameter for characterising nonlinear site response is the "stiff" spectral ordinate at the considered period.},
  language  = {en}
}
@article{BeyreutherHammerWassermannetal.2012,
  author    = {Beyreuther, Moritz and Hammer, Conny and Wassermann, Joachim and Ohrnberger, Matthias and Megies, Tobias},
  title     = {Constructing a hidden Markov Model based earthquake detector: application to induced seismicity},
  series = {Geophysical journal international},
  volume    = {189},
  journal   = {Geophysical journal international},
  number    = {1},
  publisher = {Wiley-Blackwell},
  address   = {Malden},
  issn      = {0956-540X},
  doi       = {10.1111/j.1365-246X.2012.05361.x},
  pages     = {602 -- 610},
  year      = {2012},
  abstract  = {The triggering or detection of seismic events out of a continuous seismic data stream is one of the key issues of an automatic or semi-automatic seismic monitoring system. In the case of dense networks, either local or global, most of the implemented trigger algorithms are based on a large number of active stations. However, in the case of only few available stations or small events, for example, like in monitoring volcanoes or hydrothermal power plants, common triggers often show high false alarms. In such cases detection algorithms are of interest, which show reasonable performance when operating even on a single station. In this context, we apply Hidden Markov Models (HMM) which are algorithms borrowed from speech recognition. However, many pitfalls need to be avoided to apply speech recognition technology directly to earthquake detection. We show the fit of the model parameters in an innovative way. State clustering is introduced to refine the intrinsically assumed time dependency of the HMMs and we explain the effect coda has on the recognition results. The methodology is then used for the detection of anthropogenicly induced earthquakes for which we demonstrate for a period of 3.9 months of continuous data that the single station HMM earthquake detector can achieve similar detection rates as a common trigger in combination with coincidence sums over two stations. To show the general applicability of state clustering we apply the proposed method also to earthquake classification at Mt. Merapi volcano, Indonesia.},
  language  = {en}
}