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IAAA 2021 : IEEE ICDM 2021 Workshop on Intelligence-Augmented Anomaly Analytics

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Link: https://sites.google.com/view/iaaa2021/
 
When Dec 7, 2021 - Dec 7, 2021
Where Auckland, New Zealand
Submission Deadline Sep 3, 2021
Notification Due Sep 24, 2021
Final Version Due Oct 1, 2021
Categories    artificial intelligence   data mining   machine learning   data science
 

Call For Papers

CALL FOR PAPERS

IAAA 2021
International Workshop on Intelligence-Augmented Anomaly Analytics
December 7, 2021, Auckland, New Zealand
https://sites.google.com/view/iaaa2021/

A workshop of ICDM 2021 (The 21st IEEE International Conference on Data Mining): https://icdm2021.auckland.ac.nz/

Recent years have witnessed the rapid growth in the number of academics and practitioners interested in anomaly detection and closely related areas. In particular, various deep neural network models have been developed for anomaly detection. Without humans in the loop, however, deep models are hard to tune and hard to interpret.

The workshop aims at bringing together researchers and practitioners to discuss how to detect, predict, and describe anomalies effectively and efficiently. Several core challenges, such as human-in-the-loop, intelligence augmentation, tools and methods for detecting, predicting, and describing anomalies will be the main center of discussions at the workshop. In this workshop, our goal is to contribute to the next generation of anomaly analytics and exploring it using intelligence augmentation, artificial intelligence, data mining, deep learning, and other appropriate technologies.

This workshop would like to share exciting techniques to solve critical problems such as:
- What are the next-generation anomaly analytics models and techniques?
- Why and how to use human-in-the-loop machine learning in anomaly analytics?
- How to leverage intelligence augmentation (and artificial intelligence) in anomaly analytics?
- Can we develop automated anomaly detection systems to identify anomalous objects without any human interventions?

Topics of interest include but not limited to:
- Foundations and understanding of anomaly analytics
- Novel models and algorithms for anomaly analytics
- Intelligence-augmented techniques for anomaly analytics
- Human-in-the-loop deep learning and interactive intelligence
- Trustworthy anomaly analytics
- Fairness, transparency, explainability, and robustness
- Datasets and benchmarking
- Automated anomaly detection systems
- Anomaly analytics in various domains
- Innovative applications of anomaly analytics and/or intelligence augmentation.

IMPORTANT DATES:
September 3, 2021: Submission deadline
September 24, 2021: Notification to authors
October 1, 2021: Camera-ready deadline and copyright form
December 7, 2021: Workshop date

SUBMISSION INSTRUCTIONS:
Authors are invited to submit original papers that must not have been submitted to or published in any other workshop, conference, or journal. The workshop will accept full papers describing completed work, work-in-progress papers with preliminary results, as well as position papers reporting inspiring and intriguing new ideas.

Paper submissions should be limited to a maximum of eight (8) pages (plus 2 extra pages), in the IEEE 2-column format (https://www.ieee.org/conferences/publishing/templates.html), including the bibliography and any possible appendices. All submissions will be peer-reviewed by members of the Program Committee and be evaluated for originality, quality and appropriateness to the workshop.

By the unique ICDM tradition, all accepted workshop papers will be published in the dedicated ICDMW proceedings published by the IEEE Computer Society Press.

At least one author of each accepted paper must complete the ICDM 2021 conference registration and present the paper at the workshop. The exact format of the workshop (in person or online) will be decided later, though it is very likely that this workshop will be held virtually.

For access to the submission system, please visit the workshop website (https://sites.google.com/view/iaaa2021/).

Organizers:
Feng Xia, Federation University Australia
Leman Akoglu, Carnegie Mellon University
Charu Aggarwal, IBM
Huan Liu, Arizona State University

Contact Info:
Email: iaaachairs@googlegroups.com

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