Connectionists: [IEEE TETC Special Section] Call for Papers: Emerging Techniques for Trusted and Reliable Machine Learning

[Shanshan Liu]

Dear Colleague,

   
IEEE Transactions on Emerging Topics in Computing (/TETC/) seeks
submissions for the upcoming special section on “TO BE SAFE AND
DEPENDABLE IN THE ERA OF ARTIFICIAL INTELLIGENCE: EMERGING TECHNIQUES FOR
TRUSTED AND RELIABLE MACHINE LEARNING”[1]. 
 
During the last decade, advances in areas such as convolutional neural
networks, deep learning, and hardware accelerators have enabled the
widespread and ubiquitous adoption of machine learning (ML) in real-world
systems. This trend is expected to continue and expand in the coming years,
leading to a world that depends heavily on ML-based systems.
 
To be safe and dependable in this new era of artificial intelligence, these
innovative systems have to be reliable and secure. This poses many research
challenges. For example, fault tolerance is commonly achieved by redundant
design, but the implementation of deep neural networks is already
challenging, so there is little room to add additional elements for fault
tolerance. Similarly, understanding the vulnerabilities of advanced ML
systems is a complex issue, as shown by recent attacks on image
classification implementations. Therefore, it is essential to learn how to
build ML systems that cannot be manipulated or corrupted by malicious
attackers and that can operate reliably when its underlying hardware or
software suffers from errors.
 
This special section is devoted to: 1) recent advances in techniques,
algorithms, and implementations for error-tolerant ML systems and 2)
trust/reliable aspects of ML systems and algorithms, including
vulnerabilities, management, protection, and mitigation schemes. Original
papers with substantial technical contribution are solicited on the
following topics:


   *   Design and analysis of trusted/reliable ML algorithms and systems
   *   Innovative computational paradigms for ML, such as
approximate/stochastic computing
   *   Fault/error-tolerant ML systems and techniques
   *   Trust, dependability, reliability, and security in ML implementations
   *   Adversarial and related techniques for ML systems and algorithms
   *   Techniques for trustworthy ML inclusive of detection, mitigation, and
defense
   *   Evaluation of ML for applications such as in safety-critical and
secure systems
 
SCHEDULE


   *   DEADLINE FOR SUBMISSIONS: October 15, 2021
   *   First decision (accept/reject/revise, tentative): January 15, 2022
   *   Submission of revised papers: March 15, 2022
   *   Notification of final decision (tentative): May 1, 2022
   *   Journal publication (tentative): second half of 2022

SUBMISSION GUIDELINES
Submitted papers must include new significant research-based technical
contributions in the scope of the journal. Purely theoretical,
technological, or lacking methodological-and-generality papers are not
suitable for this special section. The submissions must include clear
evaluations of the proposed solutions (based on simulation and/or
implementation results) and comparison to state-of-the-art solutions.
Papers under review elsewhere are not acceptable for submission. Extended
versions of published conference papers (to be included as part of the
submission together with a summary of differences) are welcome but there
must have at least 40% of new impacting technical/scientific material in
the submitted journal version, and there should be less than 50% verbatim
similarity level as reported by a tool (such as CrossRef). Guidelines
concerning the submission process, LaTeX, and Word templates can be found
on the Author Information page[2]. While submitting
through ScholarOne[3], please select this special-section option. As
per /TETC/ policies, only full-length papers (10-16 pages with technical
material, double column – papers beyond 12 pages will be subject to MOPC,
as per CS policies -) can be submitted to special sections. The
bibliography should not exceed 45 items and each author’s bio should not
exceed 150 words.

QUESTIONS?
Contact the guest editors at ftsmltetcss at gmail.com[4].
 
GUEST EDITORS:
Shanshan Liu, Northeastern University, USA (IEEE Member)
Pedro Reviriego, Universidad Carlos III de Madrid, Spain (IEEE Senior
Member)
Fabrizio Lombardi, Northeastern University, USA (IEEE Fellow)

CORRESPONDING /TETC/ EDITOR:
Patrick Girard, LIRMM, France (IEEE Fellow)

Further details
are available at https://www.computer.org/digital-library/journals/ec/call-for-papers-special-section-on-to-be-safe-and-dependable-in-the-era-of-artificial-intelligence-emerging-techniques-for-trusted-and-reliable-machine-learning

Links:
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[1]  
http://www.computer.org/digital-library/journals/ec/call-for-papers-special-section-on-to-be-safe-and-dependable-in-the-era-of-artificial-intelligence-emerging-techniques-for-trusted-and-reliable-machine-learning
[2]  
https://www.computer.org/csdl/journals/ec/write-for-us/15071?title=Author%20Information&periodical=IEEE%20Transactions%20on%20Emerging%20Topics%20in%20Computing
[3] https://mc.manuscriptcentral.com/tetc-cs
[4]
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