ACM SIGMM Interest List


Options: Use Classic View

Use Monospaced Font
Show HTML Part by Default
Condense Mail Headers

Topic: [<< First] [< Prev] [Next >] [Last >>]

Print Reply
Sender: ACM SIGMM Interest List <[log in to unmask]>
Date: Thu, 23 Jun 2022 11:21:44 +1000
MIME-Version: 1.0
Message-ID: <[log in to unmask]>
Content-Type: multipart/alternative; boundary="00000000000080087605e21347eb"
From: Feng XIA <[log in to unmask]>
Parts/Attachments: text/plain (4085 bytes) , text/html (4 kB)
[Please accept our apologies if you received multiple copies of this call]


IEEE Intelligent Systems Special Issue on Deep Anomaly Analytics

Editor in Chief: Longbing Cao, University of Technology Sydney (Australia)

Submissions due: 1 August 2022
Publication: March/April 2023

Anomalies (a.k.a. outliers) commonly exist in various real-world scenarios,
such as fraud in finance and insurance, intrusion in cybersecurity, fault
in safety-critical systems, bushfire early warning, disease outbreak
control, fake news, images and videos in social media, and medical
diagnosis. Some anomalies could cause disasters that lead to immense
economic loss or even deaths unless discovered and dealt with on time.
These applications make anomaly analytics increasingly relevant in the
modern world. Due to its foremost importance, the study of anomaly
detection has a long history and has created a wealth of anomaly detection
methods. With the advent of big data, new challenges and questions are
introduced, which inspires novel ways of developing algorithms, methods,
and techniques to foster the analysis, modeling, interpretation, and
prediction as well as detection of anomalies.

Recent years have witnessed rapid growth in the number of academics and
practitioners interested in artificial intelligence (AI) for anomaly
detection. In particular, various deep learning models have been developed
for anomaly detection. In many cases, however, deep models are hard to tune
and hard to interpret. In addition, little attention has been paid to other
aspects/phases (rather than anomaly detection) of the whole lifecycle of
anomaly analytics. On the other hand, the increasing complexity of
real-world cyber-physical systems is giving rise to unprecedented
challenges facing anomaly analytics.

This special issue aims to promote innovative AI research and development
that address key challenges for detecting, describing, modelling,
predicting, understanding, suppressing, and eliminating anomalies in
various application domains. Topics of interest include, but are not
limited to:
- Foundations and principles of anomaly analytics
- Novel AI models and algorithms for anomaly analytics
- Graph learning for anomaly analytics
- Deep learning techniques for anomaly detection
- Anomaly modelling, analysis, and (deep) understanding
- Descriptive, predictive, and prescriptive analytics of anomalies
- Augmented intelligence for anomaly detection
- Human-in-the-loop machine learning for anomaly detection
- Trustworthy anomaly analytics
- Fairness, transparency, and explainability
- Privacy, safety, and security
- Tools, platforms, and systems for deep anomaly analytics
- Anomaly analytics in various domains

For author information and guidelines on submission criteria, please visit
the IS Author Information page (
Please submit papers through the ScholarOne system (, and be sure to select the
special-issue name. Manuscripts should not be published or currently
submitted for publication elsewhere. Please submit only full papers
intended for review, not abstracts, to the ScholarOne portal.

Contact the guest editors at [log in to unmask]

Guest Editors
- Feng Xia, Federation University Australia (Australia)
- Leman Akoglu, Carnegie Mellon University (USA)
- Charu Aggarwal, IBM T. J. Watson Research Center (USA)
- Huan Liu, Arizona State University (USA)



[log in to unmask]

If you don't already have a password for the LISTSERV.ACM.ORG server, we recommend
that you create one now. A LISTSERV password is linked to your email
address and can be used to access the web interface and all the lists to
which you are subscribed on the LISTSERV.ACM.ORG server.

To create a password, visit:


Once you have created a password, you can log in and view or change your
subscription settings at: