Mayar Elfares

Institute for Visualisation and Interactive Systems, University of Stuttgart
Institute of Information Security, University of Stuttgart

  Pfaffenwaldring 5a, 70569 Stuttgart, Germany University of Stuttgart, SimTech Building, Room 01.025 Google Scholar

Biography

Mayar Elfares is a PhD student in the Collaborative Artificial Intelligence group since April 2022, jointly supervised with Prof. Ralf Küsters from the Institute of Information Security and funded by the University of Stuttgart AI Software Academy (AISA). She studied for her Master's and Bachelor's degrees at the German University in Cairo, Egypt, Department of Computer Science and Engineering. Her research interests are in Human-Computer Interaction, Computer Vision, and Privacy. She is conducting her Ph.D. in "Privacy-Preserving Eye Tracking" at the Institute for Visualization and Interactive Systems as well as the Institute of Information Security.

Teaching

2023

Artificial Intelligence and Software Academy (AISA) study projectsBachelor/Master/PhD

2022

Machine Perception and Learning (Tutor)Master

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Supervision

2024

Chaining in Large Language Models for Cryptographic Computation (InfoTech research project) Manpa Barman

 

Neurotypical vs Neurodevelopmental Scanpaths Modular Classification (TUM guided research project) Abdulkader Ghandoura

2023

Label Distribution Inference Attack on Gaze Estimation Models (HiWi project) Wenwu Tang

 

Efficient Federated Learning for Gaze Estimation (M.Sc.) Jonas Kettleburger

 

Private Mouse and Keyboard Behaviour Analysis (B.Sc.) Hossam Elfar

 

Gaze Poisoning Attacks (M.Sc. research project) Ayush Mittal

 

Large-scale Information Visualization Saliency Dataset Collection (B.Sc.) Abdullah Abdelhafez (German University of Cairo)**

**Co-supervised with Yao Wang

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Publications

(Elfares et al., 2024; Wang et al., 2024; Elfares et al., 2023; Ads et al., 2021)

1. 

   PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation

   Mayar Elfares, Pascal Reisert, Zhiming Hu, Wenwu Tang, Ralf Küsters, Andreas Bulling

   Proc. ACM on Human-Computer Interaction (PACM HCI), 8 (ETRA), pp. 1–23, 2024.

   Abstract Links BibTeX Project

   Latest gaze estimation methods require large-scale training data but their collection and exchange pose significant privacy risks. We propose PrivatEyes - the first privacy-enhancing training approach for appearance-based gaze estimation based on federated learning (FL) and secure multi-party computation (MPC). PrivatEyes enables training gaze estimators on multiple local datasets across different users and server-based secure aggregation of the individual estimators’ updates. PrivatEyes guarantees that individual gaze data remains private even if a majority of the aggregating servers is malicious. We also introduce a new data leakage attack DualView that shows that PrivatEyes limits the leakage of private training data more effectively than previous approaches. Evaluations on the MPIIGaze, MPIIFaceGaze, GazeCapture, and NVGaze datasets further show that the improved privacy does not lead to a lower gaze estimation accuracy or substantially higher computational costs - both of which are on par with its non-secure counterparts.

   doi: 10.1145/3655606

   Paper: elfares24_etra.pdf

   @article{elfares24_etra, title = {PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation}, author = {Elfares, Mayar and Reisert, Pascal and Hu, Zhiming and Tang, Wenwu and Küsters, Ralf and Bulling, Andreas}, year = {2024}, journal = {Proc. ACM on Human-Computer Interaction (PACM HCI)}, pages = {1--23}, volume = {8}, number = {ETRA}, doi = {10.1145/3655606} }

1. 

   SalChartQA: Question-driven Saliency on Information Visualisations

   Yao Wang, Weitian Wang, Abdullah Abdelhafez, Mayar Elfares, Zhiming Hu, Mihai Bâce, Andreas Bulling

   Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 1–14, 2024.

   Abstract Links BibTeX Project

   Understanding the link between visual attention and user’s needs when visually exploring information visualisations is under-explored due to a lack of large and diverse datasets to facilitate these analyses. To fill this gap, we introduce SalChartQA – a novel crowd-sourced dataset that uses the BubbleView interface as a proxy for human gaze and a question-answering (QA) paradigm to induce different information needs in users. SalChartQA contains 74,340 answers to 6,000 questions on 3,000 visualisations. Informed by our analyses demonstrating the tight correlation between the question and visual saliency, we propose the first computational method to predict question-driven saliency on information visualisations. Our method outperforms state-of-the-art saliency models, improving several metrics, such as the correlation coefficient and the Kullback-Leibler divergence. These results show the importance of information needs for shaping attention behaviour and paving the way for new applications, such as task-driven optimisation of visualisations or explainable AI in chart question-answering.

   doi: 10.1145/3613904.3642942

   Paper: wang24_chi.pdf

   Supplementary Material: wang24_chi_sup.pdf

   Dataset: https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-3884

   @inproceedings{wang24_chi, title = {SalChartQA: Question-driven Saliency on Information Visualisations}, author = {Wang, Yao and Wang, Weitian and Abdelhafez, Abdullah and Elfares, Mayar and Hu, Zhiming and B{\^a}ce, Mihai and Bulling, Andreas}, year = {2024}, pages = {1--14}, booktitle = {Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI)}, doi = {10.1145/3613904.3642942} }

1. 

   Federated Learning for Appearance-based Gaze Estimation in the Wild

   Mayar Elfares, Zhiming Hu, Pascal Reisert, Andreas Bulling, Ralf Küsters

   Proceedings of The 1st Gaze Meets ML workshop, PMLR, pp. 20–36, 2023.

   Abstract Links BibTeX Project

   Gaze estimation methods have significantly matured in recent years, but the large number of eye images required to train deep learning models poses significant privacy risks. In addition, the heterogeneous data distribution across different users can significantly hinder the training process. In this work, we propose the first federated learning approach for gaze estimation to preserve the privacy of gaze data. We further employ pseudo-gradient optimisation to adapt our federated learning approach to the divergent model updates to address the heterogeneous nature of in-the-wild gaze data in collaborative setups. We evaluate our approach on a real-world dataset (MPIIGaze) and show that our work enhances the privacy guarantees of conventional appearance-based gaze estimation methods, handles the convergence issues of gaze estimators, and significantly outperforms vanilla federated learning by 15.8% (from a mean error of 10.63 degrees to 8.95 degrees). As such, our work paves the way to develop privacy-aware collaborative learning setups for gaze estimation while maintaining the model’s performance.

   Paper: elfares23_gmml.pdf

   Paper Access: https://proceedings.mlr.press/v210/elfares23a.html

   @inproceedings{elfares23_gmml, title = {Federated Learning for Appearance-based Gaze Estimation in the Wild}, author = {Elfares, Mayar and Hu, Zhiming and Reisert, Pascal and Bulling, Andreas and K{\"u}sters, Ralf}, booktitle = {Proceedings of The 1st Gaze Meets ML workshop, PMLR}, pages = {20--36}, year = {2023}, editor = {Lourentzou, Ismini and Wu, Joy and Kashyap, Satyananda and Karargyris, Alexandros and Celi, Leo Anthony and Kawas, Ban and Talathi, Sachin}, volume = {210}, series = {Proceedings of Machine Learning Research}, publisher = {PMLR}, url = {https://proceedings.mlr.press/v210/elfares23a.html} }

1. Multi-limb Split Learning for Tumor Classification on Vertically Distributed Data

   Omar Ads, Mayar Elfares, Mohammed Salem

   Proceedings of the Tenth International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 88-92, 2021.

   Abstract BibTeX Project

   Brain tumors are one of the life-threatening forms of cancer. Previous studies have classified brain tumors using deep neural networks. In this paper, we perform the later task using a collaborative deep learning technique, more specifically split learning. Split learning allows collaborative learning via neural networks splitting into two (or more) parts, a client-side network and a server-side network. The client-side is trained to a specific layer named as the cut layer. Then, the rest of the training is resumed on the server-side network. Vertical distribution, a method for distributing data among organizations, was implemented where several hospitals hold different attributes of information for the same set of patients. To the best of our knowledge, this paper is the first paper to implement both split learning and vertical distribution for brain tumor classification. Using both techniques, we were able to achieve train and test accuracy greater than 90% and 70%, respectively.

   @inproceedings{ads21_icicis, author = {Ads, Omar and Elfares, Mayar and Salem, Mohammed}, title = {Multi-limb Split Learning for Tumor Classification on Vertically Distributed Data}, booktitle = {Proceedings of the Tenth International Conference on Intelligent Computing and Information Systems (ICICIS)}, year = {2021}, pages = {88-92} }