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[PRE REVIEW]: EvoVis: Dashboard for visualizing Evolutionary Neural Architecture Search algorithms #7480

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editorialbot opened this issue Nov 14, 2024 · 11 comments
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CSS pre-review Python query-scope Submissions of uncertain scope for JOSS TeX Track: 5 (DSAIS) Data Science, Artificial Intelligence, and Machine Learning

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Submitting author: @leadang42 (Lea Dang)
Repository: https://github.com/ankilab/EvoVis.git
Branch with paper.md (empty if default branch):
Version: v1.0.0
Editor: Pending
Reviewers: Pending
Managing EiC: Chris Vernon

Status

status

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HTML: <a href="https://joss.theoj.org/papers/a03893dac91f2502844206fc084a2909"><img src="https://joss.theoj.org/papers/a03893dac91f2502844206fc084a2909/status.svg"></a>
Markdown: [![status](https://joss.theoj.org/papers/a03893dac91f2502844206fc084a2909/status.svg)](https://joss.theoj.org/papers/a03893dac91f2502844206fc084a2909)

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Thanks for submitting your paper to JOSS @leadang42. Currently, there isn't a JOSS editor assigned to your paper.

@leadang42 if you have any suggestions for potential reviewers then please mention them here in this thread (without tagging them with an @). You can search the list of people that have already agreed to review and may be suitable for this submission.

Editor instructions

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@editorialbot editorialbot added pre-review Track: 5 (DSAIS) Data Science, Artificial Intelligence, and Machine Learning labels Nov 14, 2024
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Hello human, I'm @editorialbot, a robot that can help you with some common editorial tasks.

For a list of things I can do to help you, just type:

@editorialbot commands

For example, to regenerate the paper pdf after making changes in the paper's md or bib files, type:

@editorialbot generate pdf

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Software report:

github.com/AlDanial/cloc v 1.90  T=0.25 s (3476.9 files/s, 36120.2 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
JSON                           814              1              0           3547
Python                          10            794            938           1932
CSV                             16              0              0            695
CSS                              8             64             13            349
TeX                              1             29              1            250
Markdown                         2             70              0            148
YAML                             1              1              0             19
-------------------------------------------------------------------------------
SUM:                           852            959            952           6940
-------------------------------------------------------------------------------

Commit count by author:

    12	Lea Dang
     1	Rene Groh
     1	leadang42

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Paper file info:

📄 Wordcount for paper.md is 1388

✅ The paper includes a Statement of need section

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License info:

✅ License found: Apache License 2.0 (Valid open source OSI approved license)

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

✅ OK DOIs

- None

🟡 SKIP DOIs

- No DOI given, and none found for title: Neural Architecture Search: Insights from 1000 Pap...
- No DOI given, and none found for title: A Survey on Evolutionary Neural Architecture Searc...
- No DOI given, and none found for title: Evolutionary computing
- No DOI given, and none found for title: A Survey of Advances in Evolutionary Neural Archit...
- No DOI given, and none found for title: Review of optimization techniques
- No DOI given, and none found for title: Evolutionary algorithms
- No DOI given, and none found for title: NSGA-Net: neural architecture search using multi-o...
- No DOI given, and none found for title: Automatically Designing CNN Architectures Using th...
- No DOI given, and none found for title: The Origin of Species by Means of Natural Selectio...
- No DOI given, and none found for title: Neural architecture search: A survey
- No DOI given, and none found for title: Large-Scale Evolution of Image Classifiers
- No DOI given, and none found for title: Efficient Multi-Objective Neural Architecture Sear...
- No DOI given, and none found for title: A comprehensive survey of neural architecture sear...
- No DOI given, and none found for title: Random Search and Reproducibility for Neural Archi...
- No DOI given, and none found for title: Evaluating the search phase of neural architecture...
- No DOI given, and none found for title: A Tour of TensorFlow
- No DOI given, and none found for title: Comparison of experiment tracking frameworks in ma...
- No DOI given, and none found for title: Visualization System for Evolutionary Neural Netwo...
- No DOI given, and none found for title: A Taxonomy of Tools for Reproducible Machine Learn...
- No DOI given, and none found for title: Python current trend applications-an overview
- No DOI given, and none found for title: Neural Architecture Search with Reinforcement Lear...
- No DOI given, and none found for title: Deep learning

❌ MISSING DOIs

- 10.1016/j.jai.2022.100002 may be a valid DOI for title: A survey on computationally efficient neural archi...
- 10.1109/coins57856.2023.10189194 may be a valid DOI for title: End-to-end evolutionary neural architecture search...
- 10.1609/aaai.v33i01.33014780 may be a valid DOI for title: Regularized Evolution for Image Classifier Archite...
- 10.1007/978-3-030-16670-0_13 may be a valid DOI for title: Fast DENSER: Efficient Deep NeuroEvolution
- 10.3390/computation10030037 may be a valid DOI for title: Clustering Analysis for the Pareto Optimal Front i...
- 10.1109/ifsa-scis.2017.8023302 may be a valid DOI for title: Deep convolutional networks for human sketches by ...
- 10.1093/bioinformatics/btv557 may be a valid DOI for title: Cytoscape.js: a graph theory library for visualisa...
- 10.1609/aaai.v35i12.17233 may be a valid DOI for title: Bananas: Bayesian optimization with neural archite...

❌ INVALID DOIs

- None

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

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Five most similar historical JOSS papers:

PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs
Submitting author: @diningphil
Handling editor: @arfon (Active)
Reviewers: @idoby, @sepandhaghighi
Similarity score: 0.6110

Autosubmit GUI: A Javascript-based Graphical User Interface to Monitor Experiments Workflow Execution
Submitting author: @wuruchi
Handling editor: @arfon (Active)
Reviewers: @djmitche, @ikayz
Similarity score: 0.6063

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Submitting author: @wathom
Handling editor: @danielskatz (Active)
Reviewers: @aturner-epcc, @phargogh
Similarity score: 0.5990

Generative DAGs as an Interface Into Probabilistic Programming with the R Package causact
Submitting author: @flyaflya
Handling editor: @arfon (Active)
Reviewers: @joethorley, @ChristopherLucas
Similarity score: 0.5966

GeneScape: A Python package for gene ontology visualization
Submitting author: @ialbert
Handling editor: @mahfuz05062 (Active)
Reviewers: @sridhar0605, @j-andrews7
Similarity score: 0.5951

⚠️ Note to editors: If these papers look like they might be a good match, click through to the review issue for that paper and invite one or more of the authors before considering asking the reviewers of these papers to review again for JOSS.

@crvernon
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@editorialbot query scope

👋 @leadang42 - I am going to run this one by our larger editorial board for review to ensure it meets our requirements for substantial scholarly effort. I'll get back in touch ASAP. Thank you!

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Submission flagged for editorial review.

@editorialbot editorialbot added the query-scope Submissions of uncertain scope for JOSS label Nov 14, 2024
@anki-xyz
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Background to this paper: This work has been led by @leadang42 and is the result of her BSc thesis. With this tool, we are able to not only monitor the progress of evolutionary neural architecture search but also perform meta analyses on the optimization procedure.

What do we need to do with the missing DOIs? Update the paper?

@crvernon
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@anki-xyz - concerning the DOI, you can ensure that the DOI are present in every reference in the bib file where possible. Thank you!

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