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xrai:start [2024/03/07 13:54] – created sbk | xrai:start [2024/05/29 07:45] (current) – [Submission details] sbk | ||
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====== Tutorial and workshop on Explainable and Robust AI for Industry 4.0 & 5.0 (X-RAI) ====== | ====== Tutorial and workshop on Explainable and Robust AI for Industry 4.0 & 5.0 (X-RAI) ====== | ||
- | The X-RAI tutorial & workshop convenes industrial AI professionals and explainability experts to explore XAI developments and applications in industrial settings. Participants engage with the latest research, best practices, and challenges, fostering collaboration between researchers and engineers. Integrating explainability into Industry 4.0 and 5.0 ensures AI system reliability, | + | {{ : |
+ | |||
+ | The 1st edition of X-RAI will be at the [[https:// | ||
- | The 1st edition of X-RAI will be at the [[https:// | ||
===== Organizers ===== | ===== Organizers ===== | ||
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* Szymon Bobek, Jagiellonian University, Krakow, Poland, szymon.bobek@uj.edu.pl | * Szymon Bobek, Jagiellonian University, Krakow, Poland, szymon.bobek@uj.edu.pl | ||
- | ===== Important Dates ===== | + | {{: |
- | TBA | + | |
- | ===== Call for papers ===== | ||
- | TBA | ||
- | ===== Aims and Scope ===== | + | ===== Tentative Tutorial Schedule ===== |
+ | - Introduction | ||
+ | - AI for industrial applications(45m) | ||
+ | - Predictive maintenance | ||
+ | - Optimizations of operations | ||
+ | - Decision support | ||
+ | - Explainable AI including types of Explanations and evaluations (50m) | ||
+ | - Robustness for ML (40m) | ||
+ | - Use Cases (60m) | ||
+ | - Metro Trains | ||
+ | - Commercial Vehicles | ||
+ | - Steel Plant | ||
+ | - Discussion and Open Questions (10m) | ||
+ | |||
+ | ===== Workshop ====== | ||
+ | ==== Important Dates ==== | ||
+ | * **Submission Deadline**: 2024-06-15 | ||
+ | * **Author Notification**: | ||
+ | * **Cemera Ready**: 2024-07-29 | ||
+ | * **Workshop Date**: 2024-09-13 | ||
+ | |||
+ | |||
+ | ==== Aims and Scope ==== | ||
The X-RAI tutorial & workshop aims to bring industrial AI professionals together with explainability experts to discuss the latest developments in XAI and their practical applications as well as theoretical works aiming at solving real-life problems in industrial settings. The tutorial & workshop will provide an opportunity for attendees to learn about the latest research, best practices, and challenges in this area. It is an opportunity to bridge researchers and engineers to discuss emerging topics and the newest trends. The integration of explainability in Industry 4.0 and 5.0 is crucial to ensure AI systems' | The X-RAI tutorial & workshop aims to bring industrial AI professionals together with explainability experts to discuss the latest developments in XAI and their practical applications as well as theoretical works aiming at solving real-life problems in industrial settings. The tutorial & workshop will provide an opportunity for attendees to learn about the latest research, best practices, and challenges in this area. It is an opportunity to bridge researchers and engineers to discuss emerging topics and the newest trends. The integration of explainability in Industry 4.0 and 5.0 is crucial to ensure AI systems' | ||
Although in X-RAI we focus mainly on Industrial applications, | Although in X-RAI we focus mainly on Industrial applications, | ||
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- | ===== Program Committee (tentative) | + | ==== Program Committee (tentative) ==== |
* Javier, del Ser, | * Javier, del Ser, | ||
* Ricardo, Aler, | * Ricardo, Aler, | ||
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- | ===== Submission details ===== | + | |
- | TBA | + | ==== Submission details ==== |
+ | Please use the following link to submit your paper: [[https:// | ||
+ | |||
+ | |||
+ | The Workshops and Tutorials will be included in a joint Post-Workshop proceeding published by Springer Communications in Computer and Information Science, in 1-2 volumes, organised by focused scope and possibly indexed by WOS. Papers authors will have the faculty to opt-in or opt-out. We suggest workshop papers are prepared and submitted in the format: [[https:// | ||
+ | |||
+ | Full papers should follow the Springer format of regular ECML submissions and be no longer than 16 pages (including references). | ||
+ | |||
+ | Following ECML review process, we will apply a double-blind review-process (author identities are not known by reviewers or area chairs; reviewers do see each other’s names). All papers need to be ‘best-effort’ anonymized. Papers must not include identifying information of the authors (names, affiliations, | ||