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9 results for “Versioning Benchmark”
The ICDAR 2003 Informal Competition for the Recognition of On-line Words: The Unipen-ICROW-03 benchmark set - Version 0.0
<p>Proposal for an informal benchmark on word recognition. See for the related ImUnipen collection<br> of word images from on-line vectorial handwriting data: https://zenodo.org/record/1195059</p> <p>At the time (ICDAR 2003) there was not a lot of interest so the project was not pursued.</p> <p>Lambert Schomaker - February 2023</p> <p>_______________________________________________________________________________</p> <p>The ICDAR 2003 Informal Competition for the Recognition of On-line Words:<br> The Unipen-ICROW-03 benchmark set <br> Version 0.0</p> <p>Lambert Schomaker / International Unipen Foundation</p> <p>The ICROW suite of test files for the recognition of isolated on-line<br> free-style (handprint, mixed and cursive) words has been<br> composed. Different tablets, nationalities and languages<br> are involved. Only the ASCII set is used within word labels.</p> <p>The set contains:</p> <p> 13119 written words<br> 884 unique lexical word entries<br> 72 writers </p> <p>Language: Dutch, English, Italian.<br> Nationalities: Dutch, Irish, Italian, + mixed</p> <p>The benchmark test is a good estimator for <br> "walk-up" recognition performance.</p> <p>[Note: some of the writers (NIC-Pc95*.dat set) are present in the<br> UNIPEN R01/V07 distribution, but the actual words are unseen <br> outside of the Int. Unipen Foundation.]</p> <p>Please note the Copyright notice in the <br> accompanying file 'Copyright'</p> <p>Wed Jul 16 21:20:10 CEST 2003</p> <p>Lambert Schomaker</p> <p>---------------------------------------------------------------------------</p> <p>Instructions for the ICDAR 2003 informal competition for<br> the recognition of on-line words.</p> <p>1 - unpack the .tgz file<br> 2 - use the UNIPEN files as input for your recognizer.<br> 3 - report, for each writer, a file <writer-id>.res</p> <p> Example: do-my-recognizer < NIC-Hi93b-marc.dat > NIC-Hi93b-marc.res</p> <p>Format of the .res file.</p> <p>No XML for this moment: simplicity does it.</p> <p>We assume that the recognizer is able to produce a top-10 list<br> of likely words, sorted from most likely to least likely.<br> The output for each word is on a single line. The correct<br> target word is in the first column.</p> <p><targetword 1> <best word hyp.> <2nd-best word hyp.> ... <10th-best word hyp><br> <targetword 2> <best word hyp.> <2nd-best word hyp.> ... <10th-best word hyp></p> <p>Example with two words:</p> <p>summertime slumbertime slipknot summertime somatome spumante simulative semitone schoolmate sermonette semimature<br> Aberdeen Adamson Aberdeen Addison Armageddon Abyssinian Araban Albanian Alabamian Abraham Adelaide</p> <p><br> 4 - pack the *.res files in a .tgz or .zip file and send them<br> to schomaker@ai.rug.nl<br> All *.dat files need to be processed.</p> <p>LS.<br> </p> <p> </p>
Virtual sensors for wind energy applications benchmark study data - preliminary version
<p>Test version of the time series data for the wind energy virtual sensing benchmark study data.</p>
Benchmark for Pairs of Papers in Semantic Scholar: 1 hop vs. 2-4 hops version 0.0
<p><strong>Benchmark for Pairs of Papers in Semantic Scholar: 1 hop vs. 2-4 hops (version 0.0)</strong></p> <p>There are two files: valid.txt and test.txt; both files use the same format.</p> <p>Columns 2 and 3 are corpus ids from Semantic Scholar.</p> <p>Column 1 is the distance between the two papers in the citation index.</p> <p>Columns 4 and 5 are the bins of the two paper, respectively. The bin is a number between 0 and 100. Papers are sorted by publication date. There are about 2M papers per bin, with the oldest papers in bin 0, and the newest papers in bin 99.</p> <p>Bin 100 is a catch-all for papers with unknown publication dates.</p> <p>head valid.txt</p> <p>1 248518397 1041744 97 51</p> <p>2 248518397 23848439 97 21</p> <p>3 248518397 4235810 97 12</p> <p>4 248518397 82079949 97 11</p> <p>1 3374228 140728989 79 0</p> <p>1 68334187 36144275 58 34</p> <p>2 68334187 7008060 58 4</p> <p>1 205881482 94036919 77 72</p> <p>2 205881482 95069173 77 53</p> <p>3 205881482 53480264 77 52</p> <p>Each row is assigned to a bin, B, where B = max(col4, col5).</p> <p> </p> <p><strong>Task</strong>: the task is to distinguish pairs of papers with distance == 1 from pairs of papers with distance > 1.</p> <p><strong>Test/Train splits</strong>: For all thresholds, 0 <= T_{train} <= 99, train a model on rows in bins between 0 and T_{train} (inclusively). Test these models on rows in all bins 0 <= T_{test} <= 99. Report average accuracy for all combinations of T_{train} and T_{test}.</p> <p> </p> <p>Average Accuracy is defined as: mean(Predict(row) == 1, Gold(row) == 1)</p> <p>The means are computed over rows in a test bin.</p> <p> </p>
Small version of other JSON benchmarking datasets
<p>10% size version of bestbuy, google, twitter, and walmart datasets used for benchmarking JSON engines.<br> <br> Full versions:<br> <br> https://zenodo.org/record/7607865</p> <p>https://zenodo.org/record/7607889</p> <p>https://zenodo.org/record/7607891</p> <p>https://zenodo.org/record/7607882</p>
Final Version of the Data associated to the publication "A Benchmark for Finite Prandtl Number Convection: Comparison of Boltzmann and Navier-Stokes Solutions"
<p>Final Version of V_rms and Nu for the three codes, GAIA, StreamV, TLBM, used for a benchmark manuscript titled "A Benchmark for Finite Prandtl Number Convection: Comparison of Boltzmann and Navier-Stokes Solutions", Authors: Gabriele Morra, Peter Mora, Christian Huttig, Nicola Tosi, Henri Samuel, David A. Yuen</p> <p>The Zip file contains both npy and out versions of V_rms and Nu. In the out files, time is embedded in each file. In the "npy" version, time is a separate npy file. In this final version, pdf figures for each case are also embedded.</p>
The benchmark test suites of the FJSP-MW (MATLAB version)
<p>Two benchmark test suites are provided in this file.</p> <p>LLG-A1--LLGA20 (the multitasking level is less than 3)<br>LLG-B1--LLGB20 (the multitasking level is less than 5)</p> <p><br>------------------------------------------------------</p> <p>The meaning of the main variables:<br>1. dt: The delivery time of each job<br>2. Jm: The eligible machine set of each operation.<br>3. JmNumber: The total number of the machines<br>4. Jw: The eligible worker set of each operation.<br>5. multitask: The multitasking level of each worker (The multitasking level of the worker is 3 indicates that this worker can process three operations at the same time).<br>6. pjob: The priority of each job.<br>7. rt: The release time of each job.<br>8. T: The processing time of each operation.</p>
The benchmark instances of the FJSP-F (MATLAB version)
<p>The flexible job shop scheduling problem considering the on-site machining fixtures (FJSP-F) benchmark test suite is provided in this file [1].</p> <p>The LLG-F includes 20 instances (LLG-F1 to LLG-F20) with various scales ranging from three jobs, four machines, and two fixtures to 20 jobs, ten machines, and 12 fixtures.</p> <p>------------------------------------------------------</p> <p>The meaning of the main variables:<br>1. dt: The delivery time of each job<br>2. Jm: The eligible machine set of each operation.<br>3. JmNumber: The total number of the machines<br>4. Jf: The eligible fixture set of each operation.<br>5. pjob: The priority of each job.<br>6. rt: The release time of each job.<br>7. T: The processing time of each operation.<br>8. fixnumbegin: The number of Type I fixtures of each fixture (The number of each fixture in inventory)<br>9. fixnumend: The sum of the number of Type I fixtures and Type II fixtures of each fixture (The number of each fixture after the production)<br>10. fixtype1: Categories of Type I fixtures<br>11. fixtype2: Categories of Type II fixtures<br>12. fixtype1seq: Sequences of Type I fixtures<br>13. fixtypewseq: Sequences of Type I fixtures</p> <p> </p> <p><span>[1] J. Li, X. Li, L. Gao, and Q. Liu, “A Flexible Job Shop Scheduling Problem Considering On-Site Machining Fixtures: A Case Study From Customized Manufacturing Enterprise,” </span><span>IEEE Transactions on Automation Science and Engineering</span><span>, pp. 1–12, 2024, doi: 10.1109/tase.2024.3485810.</span></p>
Extraction of Crossref datadump for benchmarking purpose (small version)
<p> Several aggregation of Crossref datadump to amplify the size for benchmarking purpose.</p>
Extraction of Crossref datadump for benchmarking purpose (minify version)
<p> A chunk of Crossref datadump minified (removing whitespace)</p>
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