Below is an annotated bibliography containing publications in reverse chronological order for which I am listed as either the author or a co-author. A BibTeX entry is provided for each publication or you can also download a file with all entries. The BibTeX code is dedicated to the public domain under Creative Commons CC0 1.0 Universal.
| Archives | |
| Authors | Anne Ferger, Robin Grashof, André Frank Krause, Thomas Schmidt, Jordan Schneider, Sinan Yavuz |
| Venue | Zenodo |
| Notice | Creative Commons Attribution 4.0 International |
This whitepaper presents guidelines for handling research data when researching and developing pro-adaptive cognitive assistive technologies (pro-CAT), drawn from the experience gained during the project Center for Assistive Technologies Rhein-Ruhr (Zentrum Assistive Technologien Rhein-Ruhr, ZAT). Systems that fall under the term pro-CAT are those that intelligently adapt the assistance they provide to the needs of the user (i.e., participant in the context of collecting research data). Common bilingual (English and German) terminology and a classification for research data are introduced and methods and tools for validating data in CSV, XML, audio and video formats are proposed. This would be a useful starting point for interdisciplinary teams working with a mixture of qualitative and quantitative data in human-computer interaction scenarios to gain an overview of best practices for handling these types of research data.
@Misc{ ferger-and-others--2025--zat-research-data-guidelines-for-pro-cat,
author = {Ferger, Anne and Grashof, Robin and Krause, André Frank
and Schmidt, Thomas and Schneider, Jordan and Yavuz,
Sinan},
title = {{ZAT}: {Guidelines} for Research Data in Research and
Development Processes of Pro-adaptive Cognitive Assistive
Technologies (Pro-{CAT})},
year = 2025,
month = apr,
publisher = {Zenodo},
version = {1.0.0},
doi = {10.5281/zenodo.15187761}
}
| Archives | |
| Authors | Jordan Schneider, Swathy Satheesan Cheruvalath, Teena Hassan |
| Venue | UbiComp ’24: The 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing |
| Notice | This work is licensed under a Creative Commons Attribution-ShareAlike International 4.0 License. © 2024 Copyright held by the owner/author(s). |
In this mini-review, an overview of explainable artificial intelligence (XAI) is provided and papers using XAI techniques with physiobehavioural signals in practical applications are reviewed. The deficiencies of the current literature with respect to explanations for machine learning models are discussed and used to derive the DEEP Principles. The stand for define the audience of explanations, engage stakeholders to obtain feedback, embed domain knowledge into the model and prove the validity of explanations. The paper argues that future work should aim to involve the stakeholders more when designing explanations and rigorously evaluate these explanations against objective metrics. This paper might interest researchers who are concerned about the explainability of models trained on time-series data.
@InProceedings{ schneider-and-others--2024--time-for-an-explanation,
author = {Schneider, Jordan and Cheruvalath, Swathy Satheesan and
Hassan, Teena},
title = {Time for an Explanation: {A} Mini-Review of Explainable
Physio-Behavioural Time-Series Classification},
booktitle = {Companion of the 2024 on {ACM} International Joint
Conference on Pervasive and Ubiquitous Computing},
year = {2024},
series = {UbiComp '24},
location = {Melbourne VIC, Australia},
pages = {885--889},
address = {New York, NY, USA},
publisher = {ACM Press},
doi = {10.1145/3675094.3679001}
}