126 citations · 197 across the 10 of their papers we have counts for
8 papers · 1 filter
Digitizing Handwriting with a Sensor Pen: A Writer-Independent Recognizer
Mohamad Wehbi, Tim Hamann, Jens Barth +1
Online handwriting recognition has been studied for a long time with only few practicable results when writing on normal paper. Previous approaches using sensor-based devices encou…
Towards an IMU-based Pen Online Handwriting Recognizer
Mohamad Wehbi, Tim Hamann, Jens Barth +3
Most online handwriting recognition systems require the use of specific writing surfaces to extract positional data. In this paper we present a online handwriting recognition syste…
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks
Leo Schwinn, René Raab, An Nguyen +2
Progress in making neural networks more robust against adversarial attacks is mostly marginal, despite the great efforts of the research community. Moreover, the robustness evaluat…
Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis
Leo Schwinn, An Nguyen, René Raab +5
The susceptibility of deep neural networks to untrustworthy predictions, including out-of-distribution (OOD) data and adversarial examples, still prevent their widespread use in sa…
System Design for a Data-driven and Explainable Customer Sentiment Monitor
An Nguyen, Stefan Foerstel, Thomas Kittler +8
The most important goal of customer services is to keep the customer satisfied. However, service resources are always limited and must be prioritized. Therefore, it is important to…
Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring
An Nguyen, Srijeet Chatterjee, Sven Weinzierl +3
Predictive business process monitoring (PBPM) aims to predict future process behavior during ongoing process executions based on event log data. Especially, techniques for the next…
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks
Leo Schwinn, An Nguyen, René Raab +4
The vulnerability of deep neural networks to small and even imperceptible perturbations has become a central topic in deep learning research. Although several sophisticated defense…
Stride Length Estimation with Deep Learning
Julius Hannink, Thomas Kautz, Cristian F. Pasluosta +5
Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodologic…