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
exceed 150 words.
QUESTIONS?
Contact the guest editors at ftsmltet...@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
