Scholarship

Publications

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You can also find my articles on Google Scholar and DTU Research Database (Orbit).


2025 — ACM ETRA

CtxRead: Context Preservation Through Eye Tracking — Adaptive Reading Application Design for an Optimal Reading Experience

Jensen, H.E., Ilyas, C.M.A., Tashk, A., Cooreman, B., Beier, S. & Bækgaard, P.

ACM Symposium on Eye Tracking Research and Applications (ETRA 2025) · Tokyo, Japan · May 2025 · Article 78 · Open Access CC BY 4.0

Introduces context preservation — a gaze-based mechanism that helps readers resume reading after typographic adaptations. A 22-participant within-subjects experiment compared four intervention designs (Popup, Undo, Notification, Gradual). The Gradual intervention achieved the lowest Reading-Resume Time and highest participant satisfaction.

DOI · PDF · Dataset · DTU Orbit


RtR-AI: Reading the Reader’s Mind through Eye Tracking — Can AI Generated Texts Match Human Authors?

Ilyas, C.M.A., Noor, S.-E., Tashk, A., Cooreman, B., Beier, S. & Bækgaard, P.

ACM Symposium on Eye Tracking Research and Applications (ETRA 2025) · Tokyo, Japan · May 2025 · Article 113 · Open Access CC BY 4.0

First eye-tracking investigation comparing cognitive reading behaviour between LLM-generated and human-authored texts. Significant differences in fixation characteristics, pupil dilation, and reading speed identified between AI and human text (N=13, I2MC fixation detection).

DOI · PDF · DTU Orbit


2026 — IEEE ICCCMLA

NLIR-BERT: Native Language Identification from Gaze — Logistic Regression vs. BERT on MECO-L2

Tashk, A., Ilyas, C.M.A., Cooreman, B., Beier, S. & Bækgaard, P.

IEEE 7th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA 2025) · 1–2 November 2025 · In Press

First BERT-based classifier for Native Language Identification from Reading (NLIR) using gaze features from the MECO-L2 corpus. Monte Carlo simulation with 20 randomised splits for robust small-sample evaluation. BERT produces linguistically more meaningful language clusters than logistic regression.

PDF · DTU Orbit


2021 — WorkingAge / EU Horizon 2020

WorkAffect: Inferring User Facial Affect in Work-like Settings

Ilyas, C.M.A. et al.

arXiv preprint · WorkingAge / EU Horizon 2020 Project · November 2021 · arXiv:2111.11862

A system for continuous valence/arousal affect inference from facial cues in naturalistic work-like environments. Demonstrates that dimensional models outperform categorical classification for in-the-wild occupational affective computing.

arXiv · WorkingAge Project