[
    {
        "Variant name": "main",
        "Reviewer name": "Julie Digne <julie.digne@liris.cnrs.fr>",
        "Is master variant (boolean)": true,
        "Is variant deprecated (boolean)": false,
        "Title": "Mode-adaptive neural networks for quadruped motion control",
        "DOI": "10.1145/3197517.3201366",
        "Year": 2018,
        "ACM Keywords": [
            "Neural networks",
            "Motion capture"
        ],
        "Topic {Rendering, Animation and Simulation, Geometry, Images, Virtual Reality, Fabrication}": "Animation and Simulation",
        "Co-authors from academia (boolean)": true,
        "Co-authors from industry (boolean)": true,
        "ACM Open Access (boolean)": false,
        "PDF on the authors' webpage / institution (boolean)": true,
        "PDF URL": "http://homepages.inf.ed.ac.uk/tkomura/dog.pdf",
        "PDF on Arxiv or any openarchive initiatives (boolean)": false,
        "Arxiv/OAI page URL": "",
        "Project URL": "",
        "Code available (boolean)": true,
        "If code not available, pseudo-code available (boolean)": false,
        "If pseudo-code, could the paper be trivially implemented? {0..4}": "",
        "Code URL": "https://github.com/ShikamaruZhang/MANN",
        "Code URL2": "",
        "MD5 sum (for archives)": "",
        "git/hg/svn commit hash or revision number": "914f5ddb06a3853f2f60a09d156702154b084cb3",
        "MD5 sum (for archives) URL2": "",
        "git/hg/svn commit hash or revision number URL2": "",
        "Software Heritage permalink": "https://archive.softwareheritage.org/swh:1:dir:a6e8e30292b31e92ecf2a6e99a6a105d97f0bbb5;origin=https://github.com/ShikamaruZhang/MANN/",
        "Software type {Code, Binary, Partial Code}": "Code",
        "Code License (if any)": "This code implementation is only for research or education purposes, and (especially the learned data) not freely available for commercial use or redistribution. The intellectual property and code implementation belongs to the University of Edinburgh.",
        "Are the code authors explicit? (boolean)": true,
        "Build/Configure mechanism": "Not applicable (python, Matlab..)",
        "Dependencies": "tensorflow",
        "Does the software require paywall/proprietary software/material (boolean)?": false,
        "Does the code need data (not examples) (boolean)": false,
        "Nature of the data (pretrained model, LUT...)": "",
        "License of the data": "",
        "Able to perform a replicability test (boolean)": true,
        "If not able to perform a test, was it due to missing hardware/software? (boolean)": false,
        "Documentation score {0=NA,1,2,3}": 0,
        "Dependencies score {0=NA, 1,2,3,4,5}": 5,
        "Build/configure score {0=NA, 1,2,3,4,5}": 5,
        "Fixing bugs score (if any) {0=NA, 1,2,3,4,5}": 0,
        "Replicate paper results score {0=NA, 1,2,3,4,5}": 3,
        "Adaptability score to other contexts {0=NA, 1,2,3,4,5}": 1,
        "Time spent for the test (code download to first successful run, [0,10], 10min slots, 100min max)": 10,
        "Operating system for the test": "Linux",
        "Build instructions/comments": "Was able to launch the training, which ran smoothly, but I was unable to visualize the result. No readme was provided.",
        "Misc. comments": "",
        "Software language": "Python"
    }
]