Aktuelle Publikationen

Januar 2022

Explaining Machine Learning Models for Clinical Gait Analysis

Djordje Slijepcevic, Wojciech Samek, Sebastian Lapuschkin, Fabian Horst, Wolfgang I. Schöllhorn, Matthias Zeppelzauer, Anna-Maria Raberger, Christian Breiteneder, Brian Horsak, Andreas Kranzl

This article investigates the usefulness of Explainable Artificial Intelligence (XAI) methods to increase transparency in automated clinical gait classification based on time series. For this purpose, predictions of state-of-the-art...

Januar 2022

Finding and Removing Clever Hans: Using Explanation Methods to Debug and Improve Deep Models

Christopher J. Anders, Klaus-Robert Müller, Wojciech Samek, Sebastian Lapuschkin, David Neumann, Leander Weber

Contemporary learning models for computer vision are typically trained on very large (benchmark) datasets with millions of samples. These may, however, contain biases, artifacts, or errors that have gone unnoticed and are exploitable by the...

Dezember 2021

Terahertz Multilayer Thickness Measurements: Comparison of Optoelectronic Time and Frequency Domain Systems

Lars Liebermeister, Martin Schell, Simon Nellen, Björn Globisch, Robert Kohlhaas, Steffen Breuer, Milan Deumer, Sebastian Lauck

We compare a state-of-the-art terahertz (THz) time domain spectroscopy (TDS) system and a novel optoelectronic frequency domain spectroscopy (FDS) system with respect to their performance in layer thickness measurements on dielectric samples....

Dezember 2021

Inverse kinematics for full-body self representation in VR-based cognitive rehabilitation

Larissa Wagnerberger, Sebastian Bosse, Detlef Runde, David Przewozny, Paul Chojecki, Mustafa Tevfik Lafci

Being self-represented through an avatar increases embodiment and the feeling of presence in virtual reality. Nevertheless, currently users in VR are typically represented only by their hands, as not enough tracking data is available for full...

November 2021

Fiber-based Frequency Modulated LiDAR With MEMS Scanning Capability for Long-range Sensing in Automotive Applications

Sarah Cwalina, Volker Jungnickel, Patrick Runge, Ronald Freund, Christoph Kottke, Pascal Rustige, Thomas Knieling, Shansan Gu-Stoppel, Jörg Albers, Norman Laske, Frank Senger, Lianzhi Wen, Fabio Giovanneschi, Erdem Altuntac, Avinash Nittur Ramesh, Maria Antonia Gonzalez Huici, Andries Küter, Sangeeta Reddy

Safe operation of driver assistance systems remains a challenge, especially at higher speeds. It requires sensor technology that is capable of detecting surrounding conditions even at large distances. LiDAR technology is a cornerstone of this...

November 2021

Linearity Characteristics of Avalanche Photodiodes For InP Based PICs

Tobias Beckerwerth, Patrick Runge, Martin Schell, Felix Ganzer, Robert Behrends

We demonstrate InP based PICs with MMIs and waveguide integrated avalanche photodiodes (APD). We investigate these devices regarding their DC and RF linearity characteristics and find a high bandwidth beyond 20 GHz and a dynamic range of 30 dB...

November 2021

Vertical Federated Learning for Privacy-Preserving ML Model Development in Partially Disaggregated Networks

Nazila Hashemi, Johannes Fischer, Mohammad Behnam Shariati, Pooyan Safari

We present a novel framework that enables vendors and operators, with partial access to operational and monitoring features of a service, to collaboratively develop a ML-assisted solution without revealing any business-critical raw data to each...

November 2021

Demonstration of latency-aware 5G network slicing on optical metro networks

Mohammad Behnam Shariati, Ronald Freund, Johannes Fischer, R. Nejabati, Jörg-Peter Elbers, Dimitra Simeonidou, R. Casellas, O. González de Dios, A. Autenrieth, Luis Velasco, Ralf-Peter Braun, Annika Dochhan, Bodo Lent, Marc Ruiz, J.J. Pedreno-Manresa, A. S. Muqaddas, J. E. Lopez de Vergara, S. López-Buedo, F.J. Moreno, P. Pavón, S. Patri, A. Giorgetti, A. Sgambelluri, F. Cugini, L. Luque Canto

The H2020 METRO-HAUL European project has architected a latency-aware, cost-effective, agile, and programmable optical metro network. This includes the design of semidisaggregated metro nodes with compute and storage capabilities, which interface...

November 2021

Secure Multi-Party Computation and Statistics Sharing for ML Model Training in Multi-domain Multi-vendor Networks

Pooyan Safari, Johanna Fischer, Mohammad Behnam Shariati, Geronimo Bergk

We propose a secure aggregation algorithm that allows proprietary-owned domains, hosting statistically different datasets, train and operate ML models in a Horizontally Federated Learning fashion. The obtained results show a compelling test...

November 2021

On the Link between Subjective Score Prediction and Disagreement of Video Quality Metrics

Lohic Fotio Tiotsop, Sebastian Bosse, Florence Agboma, Glenn van Wallendael, Ahmed Aldahdooh, Lucjan Janowski, Marcus Barkowsky, Enrico Masala


It is common to observe signi?cant disagreements amongst the quality predictions of these VQMs for the same video sequence. Herein, a measure for quantifying the disagreement between VQMs is proposed. We propose a disagreement measure that...

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