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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) ======
-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, trustworthiness, and transparency. While focusing on industrial applications, X-RAI encourages submissions applying various XAI paradigms across domains like healthcare and decision-making.
+{{ :xrai:ai-boost-f6s-logo.jpg?200|}} 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, trustworthiness, and transparency. While focusing on industrial applications, X-RAI encourages submissions applying various XAI paradigms across domains like healthcare and decision-making.
+The 1st edition of X-RAI will be at the [[https://2024.ecmlpkdd.org/|ECML-PKDD 2024]] conference.
-The 1st edition of X-RAI will be at the [[https://2024.ecmlpkdd.org/|ECML-PKCC 2024]] conference.
 ===== Organizers =====
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   * Szymon Bobek, Jagiellonian University, Krakow, Poland, szymon.bobek@uj.edu.pl
-===== Important Dates =====
+{{:xrai:uj-logo.jpg?200 |}}{{:xrai:halmstad-logo.svg?200|}}{{:xrai:porto-logo.jpg?200 |}}
-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**: 2024-07-15
+   * **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' reliability, trustworthiness, transparency and robustness.
 Although in X-RAI we focus mainly on Industrial applications, we are also encouraging to submit  works that apply different paradigms of XAI as a means of solving particular problems in many different  domains such as, healthcare, planning, decision making, etc. Each of these domains use different types of data, which require different techniques to display the model explanations properly. In this regard, it is common to find heatmaps on top of images highlighting the most important pixels for the model prediction, but the analogous for other types of data such as tabular data, time series or graphs is not so well studied. Thus, works that describe visual integrations of model explanations for other types of data rather than images and language will also be of interest in the session.
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-===== Program Committee (tentative) =====
+==== Program Committee (tentative) ====
   * Javier, del Ser,Tecnalia, Spain
   * Ricardo, Aler,Universidad Carlos III de Madrid, Spain
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-===== Submission details =====
-TBA
+==== Submission details ====
+Please use the following link to submit your paper: [[https://cmt3.research.microsoft.com/ECMLPKDDWorkshops2024/| ECMLPKDDWorkshops2024]]
+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://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines|LNCS format]].
+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, etc.), self-references, or links (e.g., GitHub, YouTube) that reveal the authors’ identities (e.g., references to own work should be given neutrally like other references, not mentioning ‘our previous work’ or similar). We strongly encourage making code and data available anonymously (e.g., in an anonymous Github repository, or Dropbox folder). The authors might have a (non-anonymous) pre-print published online, but it should not be cited in the submitted paper to preserve anonymity. Reviewers will be asked not to search for them. We recognize there are limits to what is feasible with respect to anonymization. For example, if you use data from your own organization and it is relevant to the paper to name this organization, you may do so.