[
    {
        "Variant name": "main",
        "Reviewer name": "Julie Digne <julie.digne@liris.cnrs.fr>",
        "Is master variant (boolean)": true,
        "Is variant deprecated (boolean)": false,
        "Title": "Exploratory Font Selection Using Crowdsourced Attributes",
        "DOI": "10.1145/2601097.2601110",
        "Year": 2014,
        "ACM Keywords": [
            "Integrated and visual development environments"
        ],
        "Topic {Rendering, Animation and Simulation, Geometry, Images, Virtual Reality, Fabrication}": "Images",
        "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://www.dgp.toronto.edu/~donovan/font/fontSelection.pdf",
        "PDF on Arxiv or any openarchive initiatives (boolean)": false,
        "Arxiv/OAI page URL": "",
        "Project URL": "http://www.dgp.toronto.edu/~donovan/font/",
        "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": "http://www.dgp.toronto.edu/~donovan/font/similarity.zip",
        "Code URL2": "",
        "MD5 sum (for archives)": "7eedee95a1c075297b94c3f5da2e0d76",
        "git/hg/svn commit hash or revision number": "",
        "MD5 sum (for archives) URL2": "",
        "git/hg/svn commit hash or revision number URL2": "",
        "Software Heritage permalink": "",
        "Software type {Code, Binary, Partial Code}": "Code",
        "Code License (if any)": "Creative Commons license:  Attribution-NonCommercial (CC BY-NC)",
        "Are the code authors explicit? (boolean)": true,
        "Build/Configure mechanism": "Not applicable (python, Matlab..)",
        "Dependencies": "matlab",
        "Does the software require paywall/proprietary software/material (boolean)?": true,
        "Does the code need data (not examples) (boolean)": true,
        "Nature of the data (pretrained model, LUT...)": "Pre-trained models / Hardcoded data / lookup tables /...",
        "License of the data": "",
        "Able to perform a replicability test (boolean)": false,
        "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}": 3,
        "Time spent for the test (code download to first successful run, [0,10], 10min slots, 100min max)": 2,
        "Operating system for the test": "Linux",
        "Build instructions/comments": "The code works out of the box for learning the similarity. However only the metric learning part is provided and it is not trivial to test it on two new fonts since it would require computing features for which no code is provided.",
        "Misc. comments": "",
        "Software language": "Matlab / Mathematica / .."
    }
]