Search Results for author: Paul Prasse

Found 7 papers, 5 papers with code

ScanDL: A Diffusion Model for Generating Synthetic Scanpaths on Texts

1 code implementation24 Oct 2023 Lena S. Bolliger, David R. Reich, Patrick Haller, Deborah N. Jakobi, Paul Prasse, Lena A. Jäger

However, scarcity of eye movement data and its unavailability at application time poses a major challenge for this line of research.

Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding

1 code implementation23 Oct 2023 Shuwen Deng, Paul Prasse, David R. Reich, Tobias Scheffer, Lena A. Jäger

We develop a model that integrates synthetic scanpath generation with a scanpath-augmented language model, eliminating the need for human gaze data.

Language Modelling Natural Language Understanding

Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading

1 code implementation21 Apr 2023 Shuwen Deng, David R. Reich, Paul Prasse, Patrick Haller, Tobias Scheffer, Lena A. Jäger

In this paper, we develop Eyettention, the first dual-sequence model that simultaneously processes the sequence of words and the chronological sequence of fixations.

Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models

1 code implementation12 Apr 2023 Daniel G. Krakowczyk, Paul Prasse, David R. Reich, Sebastian Lapuschkin, Tobias Scheffer, Lena A. Jäger

In this work, we employ established gaze event detection algorithms for fixations and saccades and quantitatively evaluate the impact of these events by determining their concept influence.

Event Detection Explainable Artificial Intelligence (XAI)

Detection of ADHD based on Eye Movements during Natural Viewing

1 code implementation4 Jul 2022 Shuwen Deng, Paul Prasse, David R. Reich, Sabine Dziemian, Maja Stegenwallner-Schütz, Daniel Krakowczyk, Silvia Makowski, Nicolas Langer, Tobias Scheffer, Lena A. Jäger

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that is highly prevalent and requires clinical specialists to diagnose.

Joint Detection of Malicious Domains and Infected Clients

no code implementations21 Jun 2019 Paul Prasse, Rene Knaebel, Lukas Machlica, Tomas Pevny, Tobias Scheffer

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.

Transfer Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.