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

[Shanshan Liu]

  Dear Colleagues,

   

  This is just a friendly reminder.

The deadline of submission (October 15th) for TETC Special Issue on “TO
BE SAFE AND DEPENDABLE IN THE ERA OF ARTIFICIAL INTELLIGENCE: EMERGING
TECHNIQUES FOR TRUSTED AND RELIABLE MACHINE LEARNING”[1] is approaching
in one month.

   

  We sincerely appreciate your support and look forward to your
submission(s).


 

  Best regards,

  The Guest Editors


Quoting Shanshan Liu <ssliu at coe.neu.edu>:

> 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:
------
[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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