[REVIEW]: Explainable Artificial Intelligence with MicroPython: Lightweight Neural Networks for Students’ Deeper Learning

[editorialbot]

Submitting author: @statistical-thinking (Prof. Dr. habil. Dennis Klinkhammer)
Repository: https://github.com/statistical-thinking/KI.ENNA
Branch with paper.md (empty if default branch):
Version: 2.0
Editor: @osorensen
Reviewers: @samiralavi, @kalpan80
Archive: Pending

Status

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Markdown: [![status](https://joss.theoj.org/papers/67385d887eb9dacd15f01eb4693da74d/status.svg)](https://joss.theoj.org/papers/67385d887eb9dacd15f01eb4693da74d)

Reviewers and authors:

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Reviewer instructions & questions

@samiralavi & @kalpan80, your review will be checklist based. Each of you will have a separate checklist that you should update when carrying out your review.
First of all you need to run this command in a separate comment to create the checklist:

@editorialbot generate my checklist

The reviewer guidelines are available here: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html. Any questions/concerns please let @osorensen know.

✨ Please start on your review when you are able, and be sure to complete your review in the next six weeks, at the very latest ✨

Checklists

📝 Checklist for @kalpan80

📝 Checklist for @samiralavi

[editorialbot]

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[editorialbot]

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

✅ OK DOIs

- 10.3390/su12020492 is OK
- 10.62273/spyc4248 is OK
- 10.1016/j.iot.2023.100729 is OK
- 10.1016/j.techfore.2022.122120 is OK
- 10.1016/j.chbr.2022.100223 is OK
- 10.1038/nature14539 is OK
- 10.1016/j.jmsy.2021.07.007 is OK
- 10.1080/10580530.2020.1849465 is OK
- 10.1016/j.jksuci.2021.11.019 is OK
- 10.3390/en14206636 is OK
- 10.3389/fpsyg.2016.01390 is OK
- 10.1080/12460125.2020.1819094 is OK
- 10.1109/tpami.2024.3355495 is OK
- 10.1177/0950422221990990 is OK
- 10.3390/computation8010015 is OK

🟡 SKIP DOIs

- None

❌ MISSING DOIs

- None

❌ INVALID DOIs

- None

[editorialbot]

Software report:

github.com/AlDanial/cloc v 1.98  T=0.03 s (519.4 files/s, 278417.9 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
Python                           4            406            178           2697
CSV                              2              0              0            870
Jupyter Notebook                 5              0           4496            509
TeX                              1             15              0            339
Markdown                         2             22              0             78
Text                             4              0              0             38
-------------------------------------------------------------------------------
SUM:                            18            443           4674           4531
-------------------------------------------------------------------------------

Commit count by author:

   248	Dr. habil. Dennis Klinkhammer
     2	Dr. Dennis Klinkhammer

[editorialbot]

Paper file info:

📄 Wordcount for paper.md is 499

✅ The paper includes a Statement of need section

[editorialbot]

License info:

✅ License found: MIT License (Valid open source OSI approved license)

[editorialbot]

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

[kalpan80]

Review checklist for @kalpan80

Conflict of interest

• I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

• I confirm that I read and will adhere to the JOSS code of conduct.

General checks

• Repository: Is the source code for this software available at the https://github.com/statistical-thinking/KI.ENNA?
• License: Does the repository contain a plain-text LICENSE or COPYING file with the contents of an OSI approved software license?
• Contribution and authorship: Has the submitting author (@statistical-thinking) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
• Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
• Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
• Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
• Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

• Installation: Does installation proceed as outlined in the documentation?
• Functionality: Have the functional claims of the software been confirmed?
• Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
• Community guidelines: Are there clear guidelines for third parties wishing to 1. Contribute to the software 2. Report issues or problems with the software 3. Seek support

Software paper

• Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
• A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
• State of the field: Do the authors describe how this software compares to other commonly-used packages?
• Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
• References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

[samiralavi]

Review checklist for @samiralavi

Conflict of interest

• I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

• I confirm that I read and will adhere to the JOSS code of conduct.

General checks

• Repository: Is the source code for this software available at the https://github.com/statistical-thinking/KI.ENNA?
• License: Does the repository contain a plain-text LICENSE or COPYING file with the contents of an OSI approved software license?
• Contribution and authorship: Has the submitting author (@statistical-thinking) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
• Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
• Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
• Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
• Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

• Installation: Does installation proceed as outlined in the documentation?
• Functionality: Have the functional claims of the software been confirmed?
• Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
• Community guidelines: Are there clear guidelines for third parties wishing to 1. Contribute to the software 2. Report issues or problems with the software 3. Seek support

Software paper

• Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
• A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
• State of the field: Do the authors describe how this software compares to other commonly-used packages?
• Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
• References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

[kalpan80]

@osorensen - Noted a typo in the paper, Page 2, paragraph 2, Line 3. Lightweight is spelled incorrectly as leightweight.

[statistical-thinking]

@kalpan80 This made me sweat: Couldn't find the word “leightweight” anywhere in the JOSS paper... then I found it in the ArXiv PrePrint / Tutorial. THX :-)

[kalpan80]

@statistical-thinking and @osorensen - Have completed my review. Thank you

[statistical-thinking]

@kalpan80 Thanks for the quick review!

Since AI-ANNE can be put into operation simply by transferring main.py to the Raspberry Pi Pico and maps the exact results of a pre-trained neural network from Python to MicroPython via the transfer of weights and biases, I wouldn't know how to further demonstrate the functionality :-)

But I have added the transfer of main.py (with hardware) or ai-anne.py (without hardware) again on GitHub and added the above mentioned functionality in the section “How to Use and Support AI-ANNE”. I have also added the Community Guidelines there...

Thanks for your feedback!

[osorensen]

Thanks @kalpan80. Do you have any specific suggestions or comments related to how @statistical-thinking can address the unchecked items on your review checklist?

[kalpan80]

Thank you for your responses.

Agree with @statistical-thinking on the functionality aspect of the library. Have marked it as completed.

Community guidelines - Any enthusiastic contributor can be allowed to create a PR for the KI.ENNA project. If the PR is found helpful, author can decide to merge it with the master branch.

Automated unit testing - We can leverage some of the pointers mentioned in below article.
https://admantium.medium.com/raspberry-pico-unit-test-framework-for-your-projects-f92623524446

[statistical-thinking]

@kalpan80 @osorensen Thanks again for your input!

I have added a test_ai_anne() function in the basic code of AI-ANNE. This is based on a small neural network with just a few neurons and the easy-to-understand sigmoid function. The successful calculation of a Confusion Matrix and the Accuracy are defined as test criteria, as these are the goal of AI-ANNE.

I had considered implementing the Pi Pico's built-in LED as an additional signal in the test, but since not all users will have a Pi Pico and teaching with AI-ANNE can also take place only in Thonny as software environment, I decided against it :-)

[kalpan80]

Thank you @statistical-thinking and @osorensen

[statistical-thinking]

I have just added a GIF to the GitHub Readme, which shows AI-ANNE in use as a didactic tool, where you can select and compare different neural networks. In addition to the simple codes in Thonny, this is the most fun for learners...