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praxai:start [2022/10/13 19:37] – [Call for papers] sbkpraxai:start [2023/08/02 06:29] (current) – [Important Dates] sbk
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 **PRAXAI webpage address is [[http://praxai.geist.re]]** **PRAXAI webpage address is [[http://praxai.geist.re]]**
  
-PRAXAI  special session at [[http://dsaa2022.dsaa.co/|The 9th IEEE International+PRAXAI  special session at [[https://conferences.sigappfr.org/dsaa2023/|The 10th IEEE International
 Conference on Data Science and Advanced Analytics]] focuses on bringing the research on Explainable Conference on Data Science and Advanced Analytics]] focuses on bringing the research on Explainable
 Artificial Intelligence (XAI) to actual applications and tools that help Artificial Intelligence (XAI) to actual applications and tools that help
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 daily work. daily work.
  
-The PRAXAI 2022 session is related to the [[https://www.hh.se/english/research/our-research/research-at-the-school-of-information-technology/technology-area-aware-intelligent-systems/research-projects-within-aware-intelligent-systems/xpm-explainable-predictive-maintenance.html|CHIST-ERA XPM]] project.+The PRAXAI 2023 session is related to the [[https://www.hh.se/english/research/our-research/research-at-the-school-of-information-technology/technology-area-aware-intelligent-systems/research-projects-within-aware-intelligent-systems/xpm-explainable-predictive-maintenance.html|CHIST-ERA XPM]] project.
    
 ===== Important Dates ===== ===== Important Dates =====
-  * **Submission Deadline**: <del>June 1, 2022</del> June 152022 +  * **Submission Deadline**: <del>May 2</del> May 222023  
-  * **Notification**:  <del>July 31</del> August 10th2022 +  * **Notification**: <del>July 10</del> July 242023  
-  * **Camera Ready Due**: <del>August 15</del> <del>August 252022</del> September 6, 2022+  * **Camera Ready Due**: <del>August 7</del> August 212023  
 +  * **Conference date**: October 9-13
  
 ===== Call for papers ===== ===== Call for papers =====
-  * {{ :praxai:praxai-cfp-2022.pdf | Call For Papers}}+  * {{ :praxai:praxai-2023-cfp.pdf |Call for Papers}}
  
-===== Program ===== 
-Zoom Link: [[https://us02web.zoom.us/j/9257300129?pwd=RUxiZExKRHFadSszS01pZWk0WVNKQT09|Connect]] 
  
-PRAXAI Session A: 14:00-15:20 (CET) Session Chair: dr Szymon Bobek: 
-  -  Fast Hybrid Oracle-Explainer Approach to Explainability Using Optimized Search of Comprehensible Decision Trees 
-  - SurvSHAP: A Proxy-Based Algorithm for Explaining Survival Models with SHAP 
-  - Abstract Argumentation for Explainable Satellite Scheduling 
-  - Explaining Human Activities Instances Using Deep Learning Classifiers 
- 
-Break (10 minutes) 
- 
-PRAXAI Session B: 15:30-16:50 (CET) Session Chair: dr Victor Victor Rodriguez-Fernandez: 
-  - Explainable expected goal models for performance analysis in football analytics 
-  - Why is the prediction wrong? Towards underfitting case explanation via meta-classification 
-  - Streamlining models with explanations in the learning loop 
-  - Roll Wear Prediction in Strip Cold Rolling with Physics-Informed Autoencoder and Counterfactual Explanations 
 ===== Submission Instructions ===== ===== Submission Instructions =====
  
-The length of each paper submitted to the Research and Application tracks should be no more than 10 pages, whereas the maximum number of pages is 2 for each abstract submitted to the Poster and Journal track. Both types of papers should be formatted following the standard 2-column U.S. letter style of IEEE Conference template. See the IEEE Proceedings Author Guidelines: http://www.ieee.org/conferences_events/conferences/publishing/templates.html, for further information and instructions.+The length of each paper submitted to the Research and Application tracks should be no more than 10 pages, formatted following the standard 2-column U.S. letter style of IEEE Conference template. See the IEEE Proceedings Author Guidelines: http://www.ieee.org/conferences_events/conferences/publishing/templates.html, for further information and instructions.
  
 All submissions will be double-blind reviewed by the Program Committee on the basis of technical quality, relevance to the scope of the conference, originality, significance, and clarity. The names and affiliations of authors must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity. Submissions failing to comply with paper formatting and authors anonymity will be rejected without reviews. All submissions will be double-blind reviewed by the Program Committee on the basis of technical quality, relevance to the scope of the conference, originality, significance, and clarity. The names and affiliations of authors must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity. Submissions failing to comply with paper formatting and authors anonymity will be rejected without reviews.
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 Authors are also encouraged to submit supplementary materials, i.e., providing the source code and data through a GitHub-like public repository to support the reproducibility of their research results. Authors are also encouraged to submit supplementary materials, i.e., providing the source code and data through a GitHub-like public repository to support the reproducibility of their research results.
  
-Electronic submission site: https://cmt3.research.microsoft.com/DSAA2022+Electronic submission site: [[https://easychair.org/my/conference?conf=dsaa2023|EasyChair]]
  
  
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 ===== Topics of interest ===== ===== Topics of interest =====
- +  * Industry 4.0/5.0 and XAI
   * Model explanations verbalized in human-comprehensible natural language    * Model explanations verbalized in human-comprehensible natural language 
   * Explainable Reinforcement learning    * Explainable Reinforcement learning 
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   * Visualization of model explanations for different types of data apart from language and images (tabular data, time series, graphs, etc.)   * Visualization of model explanations for different types of data apart from language and images (tabular data, time series, graphs, etc.)
   * XAI software development and its integration into popular ML/DL libraries   * XAI software development and its integration into popular ML/DL libraries
 +  * Fairness and XAI
 +  * Ethics and XAI
 +  * Trust in XAI systems
 +  * XAI in real-world applications: case studies and success stories
 +
  
  
 ===== Program Committee (tentative) ===== ===== Program Committee (tentative) =====
   * Javier del Ser, Tecnalia   * Javier del Ser, Tecnalia
 +  * Eneko Osaba, Tecnalia
   * Ricardo Aler, Universidad Carlos III de Madrid   * Ricardo Aler, Universidad Carlos III de Madrid
-  * Felix José Fuentes Hurtado, Universidad Politécnica de Valencia+  * Felix José Fuentes Hurtado,Universidad Politécnica de Valencia 
 +  * Alejandro Martin, Universidad Politécnica de Madrid, Spain  
 +  * Angel Panizo, Universidad Politécnica de Madrid, Spain 
 +  * Javier Huertas, Universidad Politécnica de Madrid, Spain
   * Juan Pavón, Universidad Complutense de Madrid   * Juan Pavón, Universidad Complutense de Madrid
-  * Francesco Piccialli, University of Naples Federico II +  * Francesco Piccialli,University of Naples Federico II 
-  * Salvatore Cuomo, University of Naples Federico II +  * Salvatore Cuomo,University of Naples Federico II 
-  * Edoardo Prezioso, University of Naples Federico II +  * Edoardo Prezioso,University of Naples Federico II 
-  * Federico Gatta, University of Naples Federico II +  * Federico Gatta,University of Naples Federico II 
-  * Fabio Giampaolo, University of Naples Federico II +  * Fabio Giampaolo,University of Naples Federico II 
-  * Stefano Izzo, University of Naples Federico II+  * Stefano Izzo,University of Naples Federico II
   * Martin Atzmueller, Universitat Osnabruck   * Martin Atzmueller, Universitat Osnabruck
   * Kacper Sokół, University of Bristol   * Kacper Sokół, University of Bristol
   * Sławomir Nowaczyk, Halmstad University   * Sławomir Nowaczyk, Halmstad University
   * Michal Choras, UTP University of Science and Technology   * Michal Choras, UTP University of Science and Technology
-  * Boguslaw Cyganek, AGH University of Science and Technology in Krakow+  * Bogusław Cyganek, AGH University of Science and Technology in Krakow
   * Timos Kipouros, University of Cambridge   * Timos Kipouros, University of Cambridge
-  * Jerzy Stefanowski, Poznan University of Technology, Poland+  * Jerzy Stefanowski,  Poznan University of Technology, Poland 
 +  * Hubert Baniecki, Warsaw University, Poland 
 +  * Holzinger Andreas, Vienna University, Austria 
 +  * Bastian Pfeifer, Medical University of Graz, Austria 
 +  * Mustafa Cavuş, Eskisehir Technical University, Turkey 
 +  * Giuseppe Casalicchio, Ludwig-Maximilians-Universität in Munich, Germany 
 +  * Dawid Rymarczyk, Jagiellonian University, Poland 
 +  * Jacek Tabor, Jagiellonian University, Poland 
 +  * Bartosz Zieliński, Jagiellonian University, Poland 
 +  * Abraham Duarte, Universidad Rey Juan Carlos I de Madrid, Spain 
 +  * Sancho Salcedo Sanz, Universidad de Alcalá de Henares, Madrid, Spain 
 +  * Benslimane Djamal, Lyon 1 University, France 
 +  * Hujun Yin, University of Manchester, UK 
 +  * Boyan Xu, Guangdong University of Technology, China 
 +  * Cesar Analide, Universidad do Minho, Portugal 
 +  * Maria Alcina Alpoim Sousa Pereira, Universidad do Minho, Portugal 
 +  * Valery Naranjo, Universidad Politécnica de Valencia, Spain 
 +  * Adrián Colomer, Universidad Politécnica de Valencia, Spain 
  
  
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-===== Papers ===== 
-  * Fast Hybrid Oracle-Explainer Approach to Explainability Using Optimized Search of Comprehensible Decision Trees \\ Szczepanski, Mateusz; Pawlicki, Marek; Kozik, Rafal; Choras, Michal 
-  * SurvSHAP: A Proxy-Based Algorithm for Explaining Survival Models with SHAP \\ Alabdallah, Abdallah; Pashami, Sepideh; Rognvaldsson, Thorsteinn; Ohlsson, Mattias 
-  * Abstract Argumentation for Explainable Satellite Scheduling \\ Powell, Cheyenne; Riccardi, Annalisa 
-  * Explaining Human Activities Instances Using Deep Learning Classifiers \\ Arrotta, Luca; Civitarese, Gabriele; Fiori, Michele; Bettini, Claudio 
-  * Explainable expected goal models for performance analysis in football analytics \\ Cavus, Mustafa; Biecek, Przemyslaw 
-  * Why is the prediction wrong? Towards underfitting case explanation via meta-classification \\ ZHOU, Sheng; BLANCHART, Pierre; Crucianu, Michel; Ferecatu, Marin 
-  * Streamlining models with explanations in the learning loop \\ Lomuscio, Francesco; Bajardi, Paolo; Perotti, Alan; Amparore, Elvio 
-  * Roll Wear Prediction in Strip Cold Rolling with Physics-Informed Autoencoder and Counterfactual Explanations \\ Jakubowski, Jakub; Stanisz, Przemysław; Bobek, Szymon; Nalepa, Grzegorz 
  
 ===== Past events ===== ===== Past events =====
   * [[praxai:start2021|PRAXAI 2021 @ DSAA2021]]   * [[praxai:start2021|PRAXAI 2021 @ DSAA2021]]
 +  * [[praxai:start2022|PRAXAI 2022 @ DSAA2022]]
  
praxai/start.1665689853.txt.gz · Last modified: 2022/10/13 19:37 by sbk
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