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Machine Learning and Software Intensive Systems: Theory, Methods and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (10 March 2022) | Viewed by 4749

Special Issue Editor


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Guest Editor
Department of Validation Intelligence for Autonomous Software Systems, Simula Research Laboratory, 0164 Oslo, Norway
Interests: artificial intelligence; machine learning; autonomous systems; autonomous shipping; software engineering/V&V
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Software-intensive systems (SIS) are increasingly adopting machine learning (ML) to enable adaptive and self-managing capabilities. Examples range from intelligent cyberphysical systems to robotics, smart homes, and autonomous vehicles. Machine learning enables these systems to learn and adapt to uncertain changes in their environment at run time and protect themselves from failures and adversaries. Development of novel methods and techniques for enabling adaptive and self-managing properties of software-intensive systems has been an active research field in recent years, with many promising advances. Deep learning, for example, has shown prominent performance in robotics and autonomous vehicle technologies, in tasks such as sensing and learning, and interaction with humans. Still, there remain many open challenges, including trustworthiness, that need to be addressed to realize the true potential of ML for SIS.

This Special Issue calls for innovative contributions in the theory, methods, and application of machine learning for software-intensive systems.

Topics of interest include but are not limited to the following:

  • Challenges of developing, testing, deploying, and maintaining ML-driven SIS;
  • Quality requirements for ML-driven SIS;
  • Methods and techniques for improving the development process of ML-driven SIS (including ML-based methods and techniques);
  • Verification and validation techniques for ML-driven SIS;
  • Data(sets) for training ML-based SIS;
  • Experiences from developing, testing, deploying, and maintaining ML-driven SIS;
  • Risks associated with ML-based SIS;
  • Trustworthy ML.

Dr. Dusica Marijan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning for software engineering
  • software development
  • software verification and validation
  • testing machine learning
  • adversarial testing
  • self-adaptive systems
  • autonomous systems
  • trustworthy machine learning

Published Papers (2 papers)

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Research

16 pages, 889 KiB  
Article
On the Safe Deployment of Matrix Multiplication in Massively Parallel Safety-Related Systems
by Javier Fernández, Jon Perez-Cerrolaza, Irune Agirre, Alejandro J. Calderon, Jaume Abella and Francisco J. Cazorla
Appl. Sci. 2022, 12(8), 3779; https://doi.org/10.3390/app12083779 - 8 Apr 2022
Cited by 2 | Viewed by 1657
Abstract
Deep learning technology has enabled the development of increasingly complex safety-related autonomous systems using high-performance computers, such as graphics processing units (GPUs), which provide the required high computing performance for the execution of parallel computing algorithms, such as matrix–matrix multiplications (a central computing [...] Read more.
Deep learning technology has enabled the development of increasingly complex safety-related autonomous systems using high-performance computers, such as graphics processing units (GPUs), which provide the required high computing performance for the execution of parallel computing algorithms, such as matrix–matrix multiplications (a central computing element of deep learning software libraries). However, the safety certification of parallel computing software algorithms and GPU-based safety-related systems is a challenge to be addressed. For example, achieving the required fault-tolerance and diagnostic coverage for random hardware errors. This paper contributes with a safe matrix–matrix multiplication software implementation for GPUs with random hardware error-detection capabilities (permanent, transient) that can be used with different architectural patterns for fault-tolerance, and which serves as a foundation for the implementation of safe deep learning libraries for GPUs. The proposed contribution is complementary and can be combined with other techniques, such as algorithm-based fault tolerance. In particular, (i) we provide the high-performance matrix multiplication CUTLASS library with a catalog of diagnostic mechanisms to detect random hardware errors down to the arithmetic operation level; and (ii) we measure the performance impact incurred by the adoption of these mechanisms and their achievable diagnostic coverage with a set of representative matrix dimensions. To that end, we implement these algebraic operations, targeting CUDA cores with single instructions and multiple-thread math instructions in an NVIDIA Xavier NX GPU. Full article
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16 pages, 2860 KiB  
Article
Semi-Supervised Training of Transformer and Causal Dilated Convolution Network with Applications to Speech Topic Classification
by Jinxiang Zeng, Du Zhang, Zhiyi Li and Xiaolin Li
Appl. Sci. 2021, 11(12), 5712; https://doi.org/10.3390/app11125712 - 20 Jun 2021
Cited by 3 | Viewed by 2310
Abstract
Aiming at the audio event recognition problem of speech recognition, a decision fusion method based on the Transformer and Causal Dilated Convolutional Network (TCDCN) framework is proposed. This method can adjust the model sound events for a long time and capture the time [...] Read more.
Aiming at the audio event recognition problem of speech recognition, a decision fusion method based on the Transformer and Causal Dilated Convolutional Network (TCDCN) framework is proposed. This method can adjust the model sound events for a long time and capture the time correlation, and can effectively deal with the sparsity of audio data. At the same time, our dataset comes from audio clips cropped by YouTube. In order to reliably and stably identify audio topics, we extract different features and different loss function calculation methods to find the best model solution. The experimental results from different test models show that the TCDCN model proposed in this paper achieves better recognition results than the classification using neural networks and other fusion methods. Full article
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