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1,782 results for “algorithms”
Data used in the "H-FISTA: A hierarchical algorithm for phase retrieval with application to pulsar dynamic spectra" publication
<p>Dynamic spectra used in the "A new approach to phase retrieval for pulsar dynamic spectra" publication. These data can be used to reproduce the results in the paper</p>
Supplementary Materials for "Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms"
<p>This dataset was created as suplementary material for research article: <strong>Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms</strong></p> <p>This package contains packet capture files of 802.11 probe requests captured at Geotec office at University Jaume I, Spain by 5 ESP32 microcontrollers. The packet capture files are in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p> <p>The data are split between radio map data captured at all accessible reference positions in our office spread in 1m grid and evaluation data gathered alligned to 0.5m grid, as well as in hard to access locations. The location the data were collected are available in the office.</p> <p>The dataset has 4 parts, and all subsets of the dataset can be generated from the captured pcap files:</p> <p><strong>Data</strong></p> <p>This folder contains pcap files from all 5 ESP32 stations representing the whole radio environment map. The folder name stands for each of the 5 ESP32 sniffer stations and the name of the file points to a reference location the data were captured in. Example of the coordinates matching the reference location grid names are in following table:</p> <table> <caption>Data Point Coordinates</caption> <thead> <tr> <th scope="row"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col"><strong>...</strong></th> </tr> </thead> <tbody> <tr> <th scope="row">A1</th> <td>0.85</td> <td>0.1</td> <td><strong>B1</strong></td> <td>1.85</td> <td>0.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A2</th> <td>0.85</td> <td>1.1</td> <td><strong>B2</strong></td> <td>1.85</td> <td>1.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A3</th> <td>0.85</td> <td>2.1</td> <td><strong>B3</strong></td> <td>1.85</td> <td>2.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">...</th> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A11</th> <td>0.85</td> <td>10.1</td> <td><strong>B11</strong></td> <td>1.85</td> <td>10.1</td> <td><strong>...</strong></td> </tr> </tbody> </table> <p><strong>Data_Eval</strong></p> <p>This folder contains pcap files from all 5 ESP32 stations with data captured at 31 locations not found in the original reference location grid. The naming corresponds to the X and Y location in which the data were collected.</p> <p><strong>Processed_Data</strong></p> <p>Additionally, there are 3 folders with processed CSV files. One folder that combines all radio map values, second folder contains combined evaluation values and third is with linearly interpolated radio map values.</p> <p>The CSV files are in a format:</p> <blockquote> <p><code>X, Y, RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</code></p> </blockquote> <p><strong>Data_Scenarios</strong></p> <p>This folder for the ease of use, contains data for exact reproducibility of our results in the paper. There 14 scenarios described in the following table:</p> <table> <caption>Scenario Descriptions</caption> <thead> <tr> <th scope="col"> <p>Data Name</p> </th> <th scope="col"> <p>Scenario Description</p> </th> </tr> </thead> <tbody> <tr> <td>GPR00</td> <td>Only measured data, 50 samples per reference position</td> </tr> <tr> <td>GPR01</td> <td>Measured data with empty spots filled using Linear interpolation, 50 samples per reference position</td> </tr> <tr> <td>GPR02</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR03</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR04</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR05</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR06</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR07</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR08</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR09</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR10</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR11</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR12</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR13</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> </tbody> </table> <p>The folder contains 4 files for each scenario. The Beginning of the filename corresponds to the data name, with suffix describing what data are in the file. The descriptions of used suffixes are in the following table:</p> <table> <caption>File Suffix Descriptions</caption> <tbody> <tr> <td> <p><strong>Suffix</strong></p> </td> <td> <p><strong>Suffix Description</strong></p> </td> </tr> <tr> <td>_trncrd</td> <td>Training Labels</td> </tr> <tr> <td>_trnrss</td> <td>Training RSSI Values</td> </tr> <tr> <td>_tstcrd</td> <td>Evaluation Labels</td> </tr> <tr> <td>_tstrss</td> <td>Evaluation RSSI Values</td> </tr> </tbody> </table> <p>These data are in format compatible with systems that apart from X and Y coordinates also detect, building, floor etc.</p> <p>The RSSI data are in format:</p> <blockquote> <p>RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</p> </blockquote> <p>The Labels are in format: (Since we only use positioning in 1 office, apart X and Y coordinates are set to 0)</p> <blockquote> <p>X, Y, 0, 0, 0</p> </blockquote>
Algorithmic Fairness Datasets
<p>This dataset presents the description and the references for the datasets used in the fairness literature. We target this data documentation debt by surveying over two hundred datasets employed in algorithmic fairness research, and producing standardized and searchable documentation for each of them.</p> <p>For over 95% of the surveyed datasets, we identified at least one contact involved in the data curation process or familiar with the dataset, who received a preliminary version of the respective data brief and a request for corrections and additions. Data briefs are meant as short documentation providing essential information on datasets used in fairness research.</p>
Occupations on the map: Using a super learner algorithm to downscale labor statistics, data
<p>This repository contains all the input and output data (including maps) related to <a href="https://doi.org/10.1371/journal.pone.0278120">Van Dijk et al. (2022), Occupations on the map: Using a super learner algorithm to downscale labor statistics</a>. It does not contain several large (> 4GB) intermediate files, which summarize the results of the large number of machine learning models that were trained and tuned as part of the super learner algorithm. These files can be created by running the scripts in the supplementary GitHub repository: https://github.com/michielvandijk/occupations_on_the_map. All input and output maps produced as part of this study can also be accessed by means of an interactive web application: https://shiny.wur.nl/occupation-map-vnm.</p> <p>In this paper, we demonstrated an approach to create fine-scale gridded occupation maps by means of downscaling district-level labor statistics informed by remote sensing and other spatial information. We applied a super-learner algorithm that combined the results of different machine learning models to predict the shares of six major occupation categories and the labor force participation rate at a resolution of 30 arc seconds (~1x1 km) in Vietnam. The results were subsequently combined with gridded information on the working-age population to produce maps of the number of workers per occupation. The proposed approach can also be applied to produce maps of other (labor) statistics, which are only available at aggregated levels.</p>
Synthetic proton radiographs for testing direct inversion algorithms
<p>Proton radiographs generated by particle tracing in specified radial force profiles in cylinders and spheres saved in pradformat (github.com/phyzicist/pradformat) in a zipped folder intended as tests for direct inversion algorithms. For details see: J. R. Davies, and P. V. Heuer, https://arxiv.org/abs/2203.00495</p> <p>Version 2 includes 3 additional radiographs for a spherical Gaussian potential with a reduced bin width and more bins (0.02R and 200x200 bins)</p> <p>Version 3 corrects an error in the x values given for the original spherical Gaussian potentials with negative mu values sphGauss_mum0p25 and sphGauss_mum0p5. The bin widths were half that of the spherical Gaussian results with positive mu values. </p> <p>Version 4 corrects an error in the x values given for the mesh run and adds a smoothed version of the source intensity (mu0)</p>
Dijkstra's Algorithm using a Fibonacci Heap, Binary Heap and Self-balancing Binary Tree
<p>Efficient C++ implementation of Dijkstra's algorithm using Fibonacci Heaps, Binary Heaps and Self-balancing Binary Trees. Also contains two .csv data sets from expeiments using directed planar graphs and random graphs of varying densities.</p> <p>Paper is published at </p> <p>Lewis, R. (2023), "A Comparison of Dijkstra's Algorithm Using Fibonacci Heaps, Binary Heaps, and Self-Balancing Binary Trees", <a href="https://arxiv.org/abs/2303.10034">arXiv:2303.10034</a>, <a href="https://doi.org/10.48550/arXiv.2303.10034">https://doi.org/10.48550/arXiv.2303.10034</a></p>
Dataset for "Estimating truncation effects of quantum bosonic systems using sampling algorithms"
<p>Markov Chain Monte Carlo simulation data for the preprint.</p> <p>T010ad***S10000M*_1.txt: simulation history for a_{dig} = 0.3, 0.5, 0.7, m^2 = 1, -1, B_max = 5000, used for Table 1 and Figure 1.</p> <p>T010R100L401S10000M1_1.txt: simulation history for a_{dig} = 0.5, m^2 = 1, B_max = 1, used for Figure 2.</p> <p>Table2.zip: contains simulation history for Table 2 and Figure 3. File name "T1a0.2S1250M1L4s2101.txt" indicates that the temperature is 1, a_{dig} = 0.2, Delta = 1250, m^2 = 1, lattice size is 4 * 4, and the random seed is 2101. Lines contain the expectation values of the potential energy and the two correlation functions obtained for successive steps. The largest estimated auto-correlation length d_q, which is used for the analysis, is as follows:</p> <table align="center"> <tbody> <tr> <td><em>a</em><sub>dig</sub></td> <td><em>d</em><sub>(0,0)</sub></td> <td><em>d</em><sub>(π,π)</sub></td> </tr> <tr> <td>0.2</td> <td>37</td> <td>4</td> </tr> <tr> <td>0.25</td> <td>38</td> <td>4</td> </tr> <tr> <td>0.3</td> <td>37</td> <td>4</td> </tr> <tr> <td>0.4</td> <td>41</td> <td>4</td> </tr> <tr> <td>0.5</td> <td>59</td> <td>5</td> </tr> <tr> <td>0.6</td> <td>130</td> <td>7</td> </tr> <tr> <td>0.7</td> <td>369</td> <td>15</td> </tr> <tr> <td>0.8</td> <td>968</td> <td>55</td> </tr> <tr> <td>0.9</td> <td>2174</td> <td>148</td> </tr> <tr> <td>1.0</td> <td>4491</td> <td>319</td> </tr> </tbody> </table> <p>The initial 10 d_q steps are discarded as a burn-in period, regardless of whether we conducted a warm-up run prior to the steps contained in this dataset.</p>
Simulated data for "Small-angle scattering tensor tomography algorithm for robust reconstruction of complex textures"
<p>These are HDF5 files with the 15 simulated data sets which are used in the work "Small-angle scattering tensor tomography algorithm for robust reconstruction of complex textures", intended for use with the software MUMOTT.</p> <p> </p> <p>MUMOTT is <a href="https://pypi.org/project/mumott/">obtainable via PyPI</a>.</p>
Project's repository for: Co-immersion in Audio Augmented Virtuality: the Case Study of a Static and Approximated Late Reverberation Algorithm
<p>Repository of the VR scene and the audio data used for the experiment reported in the publication <a href="https://ieeexplore.ieee.org/document/10269056" target="_blank" rel="noopener">available in Open Access</a>:</p> <blockquote> <p>Davide Fantini, Giorgio Presti, Michele Geronazzo, Riccardo Bona, Alessandro Giuseppe Privitera and Federico Avanzini (2023) "Co-immersion in Audio Augmented Virtuality: the Case Study of a Static and Approximated Late Reverberation Algorithm" in <em>IEEE Transactions on Visualization and Computer Graphics (ISMAR special issue)</em></p> </blockquote> <p>The file <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/README.md">README.md</a> includes some instructions to use the data in this repository.</p> <p> </p> <p><strong>AUDIO</strong></p> <p>The file <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/audio.zip">audio.zip</a> includes the Reaper's projects and audio files used in the experiment to provide the auditory stimuli (simultaneous reverberated speeches) to the participants. Each subfolder corresponds to a different Virtual Acoustics Environment (VAE):</p> <ul> <li><<em>LivingRoom</em>|<em>MARCo</em>|<em>METU</em>> <ul> <li><<em>Living Room</em>|<em>MARCo</em>|<em>METU</em>><em>.rpp</em>: Reaper's project for the VAE</li> <li><em>Bin</em>: folder including the speech data convolved with the late reverberation part of the reverb condition \(B\) for each source position in the VAE</li> <li><em>Freeverb</em>: folder including the speech data convolved with the late reverberation part of the reverb condition \(F_\text{d}\) for each source position in the VAE</li> <li><em>HOA</em>: <ul> <li><em>ER</em>: folder including the speech data convolved with the early reflections part (HOA in A-format) of the reference reverb condition \(H\) for each source position in the VAE</li> <li><em>Ref</em>: folder including the speech data convolved with the entire reference reverb condition \(H\) (HOA in A-format) for each source position in the VAE</li> </ul> </li> </ul> </li> </ul> <p>The reverberated speech data in the <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/audio.zip">audio.zip</a> file are obtained using third-party datasets:</p> <ul> <li>The anechoic speech data are retrieved from four speakers (F2, F5, M3, M6) of the <a href="https://doi.org/10.5281/zenodo.6257551">ACE challenge corpus</a></li> <li>The Room Impulse Responses (RIR) in High-Order Ambisonics (HOA) format used to reverberate the speeches are retrieved from: <ul> <li><a href="https://doi.org/10.5281/zenodo.5747753">Living Room</a></li> <li><a href="https://doi.org/10.5281/zenodo.3477602">Concert hall (MARCo)</a></li> <li><a href="https://doi.org/10.5281/zenodo.2635758">Classroom (METU)</a></li> </ul> </li> </ul> <p> </p> <p><strong>VR SCENE</strong></p> <p>The file <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/VRscene.zip">VRscene.zip</a> includes the Virtual Reality (VR) scene provided to the participants during the experiment via an Oculus Quest 2. This file includes two subfolders:</p> <ul> <li><em>UDPServer</em>: C# code for the UDP server used for sending the OSC messages for head tracking <ul> <li><em>external/SharpOSC.dll</em>: external library (<a href="https://github.com/ValdemarOrn/SharpOSC">SharpOSC</a>) used to interact with the OSC protocol</li> </ul> </li> <li><em>VR_Headtracking</em>: folder including the Unity project with the VR scene</li> </ul> <p> </p>
A Linked Application of Discrete Differential Evolution Algorithm Coupled with Simulation- Optimization Model and Comparative Analysis by Genetic Algorithm for Discrete Groundwater Management Problems
<p>Complete dataset of publication name as "The complete publication dataset is "A Discrete Differential Evolution- Linear Programming Algorithm for Groundwater Management Problems." You can find all the written codes in the zip file.</p>
Results of the numerical experiments of the HyPaD algorithm
<p>This dataset is supplementary material for [1]. It provides the data obtained for the numerical experiments of the Hybrid Patch Decomposition (HyPaD) algorithm [2] that are presented in that paper. The following data is contained in the different zip-archives:</p> <ul> <li><strong>SNIA - full enumeration:</strong> A collection of .mat files with the output data for each of the 35 test instances using the full enumeration approach to realize the SNIA procedure.</li> <li><strong>SNIA - dynamic boxes:</strong> A collection of .mat files with the output data for each of the 35 test instances using the dynamic boxes approach to realize the SNIA procedure.</li> <li><strong>SNIA - fixed boxes [4]:</strong> A collection of .mat files with the output data for each of the 35 test instances using the fixed boxes approach with b=4 branching steps (i.e., 16 boxes in total) to realize the SNIA procedure.</li> <li><strong>MOMIX:</strong> A collection of folders that contain the .mat files with the results for the MOMIX (and MOMIX light) algorithm [3] which have been used as a reference point in the publication.</li> <li><strong>Figures:</strong> The .fig files used in [1].</li> </ul> <p> </p> <p><strong>References:</strong></p> <ol> <li>Gabriele Eichfelder and Leo Warnow, <strong>On implementation details and numerical experiments for the HyPaD algorithm to solve multi-objective mixed-integer convex optimization problems</strong>, <a href="http://www.optimization-online.org/DB_HTML/2021/08/8538.html">Optimization Online</a>, 2021.</li> <li>Gabriele Eichfelder and Leo Warnow, <strong>A hybrid patch decomposition approach to compute an enclosure for multi-objective mixed-integer convex optimization problems</strong>, <a href="https://link.springer.com/article/10.1007/s00186-023-00828-x">Mathematical Methods of Operations Research</a>, 2021.</li> <li>Marianna De Santis, Gabriele Eichfelder, Julia Niebling and Stefan Rocktäschel, <strong>Solving Multiobjective Mixed Integer Convex Optimization Problems</strong>, <a href="https://epubs.siam.org/doi/10.1137/19M1264709">SIAM Journal on Optimization</a>, 2020.</li> </ol>
Data for: "Unlocking the potential of LC-MS through an XIC-based algorithm for chromatographic optimisation"
<p>This dataset is for upload of supplementary info and data for my master research thesis at the University of Amsterdam.</p> <p>All the compounds in each pesticide mix of the RESTEK multiresidue kit can be found along with some descriptors.</p> <p>For easy use of the developed algorithm without having to generate any mzxml files, a few files are included on which SAFD and CompCreate have already been performed using three different LC methods, Their gradients are also provided. To run the code, a package has been developed and is ready for installation at: https://github.com/tobihul/LC_MS_Resolved_Peaks. </p> <p> </p>
Wintertime Arctic warm air intrusion detection algorithm for satellite sea ice concentration analysis
<p>This Dataset is related to the Article <em>Relevance of warm air intrusions for Arctic satellite sea ice concentration time </em>series in <em>The Cryosphere</em> (https://doi.org/10.5194/tc-2023-69).</p> <p>Provided are the core detection algorithm and a minimal working example as well as a list of all detected warm air intrusions between November 1979 and April 2020 (monthly data).</p>
Benne: A Modular Data Stream Clustering Algorithm with Flexible Design Choices
<p>All of the source dataset with preprocessed format [id features class] that have been used for evaluation in the paper.</p>
In-situ Heating-Stage EBSD Validation of Algorithms for Prior-Austenite Grain Reconstruction in Steel
<p>High temperature EBSD and dilatometry data from the manuscript "In-situ Heating-Stage EBSD Validation of Algorithms for Prior-Austenite Grain Reconstruction in Steel". This includes Gifs of the martensitic and bainitic phase transformations, individual frames as Tiff files and as CTF files. It also includes thermocouple read outs from the in-situ crucible and the raw data from the dilatometry experiments.</p>
SST_front_data: ocean thermal fronts detected by the Cayula and Cornillon SIED algorithm
<p>This dataset includes the post-processed data and a demo MATLAB script used for the paper titled "Global trends of fronts and chlorophyll in a warming ocean"</p> <p><strong>SST_FRONT_data.zip</strong> contains maps of sea surface temperature (SST) fronts detected by the Cayula and Cornillon single image edge detection algorithm over global ocean warming hotspot regions and covering the period 2003-2020. The original data was obtained from NASA OB.DAAC MODIS sea surface temperature (SST) product (MODIS Aqua Level 3 SST MID-IR 8 Day 4km Nighttime V2019.0: https://podaac.jpl.nasa.gov/dataset/MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0?ids=&values=&search=MODIS%20Aqua&provider=POCLOUD). </p> <p>SST_FRONT_data.zip also contains <strong>Fdens_Ffreq_Fstre_example.mlx</strong>, which<strong> </strong>is a MATLAB live script showing how to compute metrics of fronts based on frontal maps: frontal frequency (Ffreq), frontal density (Fdens), and frontal strength (Fstre). </p> <p><strong>Fdens_Ffreq_Fstre_example.pdf</strong> is intended for quick viewing of the script above.</p> <p> </p>
Data and Code for: Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals
<p>This repository contains the datasets and scripts used to obtain the figures of the paper "Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals".</p> <p>The repository is organized as follows:<br> - Part I) Monte Carlo simulation codes</p> <p>- Part II) Monte Carlo simulations using the parameter configuration "Param. 1" of Shi et al. (1999)</p> <p>- Part III) Monte Carlo simulation using the parameter configuration "Param. 1" of Shi et al. (1999) and a limited ion composition search space</p> <p>- Part IV) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012)</p> <p>- Part V) Monte Carlo simulation using the parameter configuration "Param. 2" of Wang et al. (2012) and a limited ion composition search space</p> <p>- Part VI) Codes to generate all figures of the manuscript</p> <p>All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.</p>
Performance results of LeanMD on the Joliot-Curie supercomputer using different load balancing algorithms
<p>This dataset contains the raw output files generated from the execution of LeanMD on the Joliot-Curie supercomputer (20 SKL Irene nodes), the scripts used to generate them, and the scripts used to parse these results for statistical analysis and plotting.</p> <p><strong>Software information</strong>:</p> <ul> <li>OS: Red Hat Enterprise Linux 7.6</li> <li>OpenMPI: version 2.0.4</li> <li>Compilers: C/C++ Intel 17.0.6.256</li> <li>Charm++ version: v6.9.0-rc3, build mpi-linux-x86_64 --with-production</li> <li>LeanMD source: <a href="https://charm.cs.illinois.edu/gerrit/gitweb?p=benchmarks/leanmd.git">https://charm.cs.illinois.edu/gerrit/gitweb?p=benchmarks/leanmd.git</a></li> <li>Additional load balancers source: <a href="https://github.com/viniciusmctf/packing-schemes/tree/packs_2019-v1">https://github.com/viniciusmctf/packing-schemes/tree/packs_2019-v1</a></li> <li>Charm++, LeanMD, and the load balancers were compiled with -O3</li> </ul> <p><strong>File information</strong>:</p> <p>The raw result files are organized in four directories (oct18, oct23, oct24, and oct24_2).<br> Each directory contains the results of one batched execution in the supercomputer.<br> Each batch is composed of 10 repetitions of a set of experiments.<br> Each set of experiments includes different load balancing algorithms and different problem sizes.<br> Each set is randomly ordered to avoid interference coming from a specific order of execution.<br> Each raw file contains the appended output of the application and its load balancer for all 10 repetitions.<br> The name of the files indicate the load balancer and size of the problem.<br> For instance, `PackStealLB.240` means that the application was run with PackStealLB and the problem size parameter is 240.</p> <p><strong>Problem sizes</strong>:</p> <ul> <li>80: 80×11×5 cells of dimensions 15×15×30</li> <li>120: 120×11×5 cells of dimensions 15×15×30</li> <li>160: 160×11×5 cells of dimensions 15×15×30</li> <li>240: 240×11×5 cells of dimensions 15×15×30</li> <li>320: 320×11×5 cells of dimensions 15×15×30</li> </ul> <p>Each execution of LeanMD ran for 301 iterations with load balancing calls at iterations 40, 140, and 240.</p> <p><strong>Raw output files</strong>:</p> <p>Each raw output file starts with a Charm++ header providing information on the execution.</p> <p>For each step of the application, its execution time in ms is provided.</p> <p>Load balancing calls usually provide information about their start time, end time, and duration. Depending on the load balancer, more information is provided.</p> <p>Each raw file contains ten executions of the application with a given load balancer and input size.</p> <p><strong>Generating plots</strong>:</p> <p>The analysis of the results can be done by running the Jupyter notebook named "Analysis of load balancing results.ipynb"</p>
The Experimental Data for the Study "Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value"
<p>The Experimental Data for the Study "Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value"</p> <p><strong>1. Introduction</strong></p> <p>Frequency Fitness Assignment (FFA) replaces the objective value in the selection step of an optimization method with its encounter frequency in any selection step so far. It turns static problems into dynamic ones. Here we experimentally investigated this approach in two important contexts: First, we integrated it into a basic (1+1)-EA, obtaining the (1+1)-FEA. We applied both algorithms to several well-known benchmark problems with bit-string based search spaces, including the OneMax, LeadingOnes, TwoMax, Jump, Plateau, and W-Model functions. We also applied them to the Max-3-Sat instances from SATLib. We then also integrated FFA into a Memetic Algorithm for the Job Shop Problem.</p> <p><strong>2. Paper</strong></p> <p>This data is used as the basis for the following article: Thomas Weise, Zhize Wu, Xinlu Li, and Yan Chen. Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value, originally submitted to <a href="https://arxiv.org/abs/2001.01416">arxiv</a> on 2020-01-06 (under the title Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function), updated with the new data in June 2020, and submitted to the IEEE Transactions on Evolutionary Computation.</p> <p><strong>3. Data</strong></p> <p>This data set contains all the results of these experiments, the source codes used in the experiments (i.e., the algorithm implementations), as well as the scripts used for evaluating the results.</p> <p><strong>4. Version History</strong></p> <p>This is the second version of the data set, including extended experiments and more evaluation results. Most importantly, data for larger scales of OneMax and LeadingOnes has been added. The original version is at <a href="http://dx.doi.org/10.5281/zenodo.3598172">10.5281/zenodo.3598172</a>.</p> <p><strong>5. Contact</strong></p> <p>If you have any questions or suggestions, please contact <a href="http://iao.hfuu.edu.cn/team/director">Prof. Dr. Thomas Weise</a> of the <a href="http://iao.hfuu.edu.cn/">Institute of Applied Optimization</a> at <a href="http://www.hfuu.edu.cn">Hefei University</a> in Hefei, Anhui, China via email to <a href="mailto:tweise@hfuu.edu.cn">tweise@hfuu.edu.cn</a> with CC to <a href="mailto:tweise@ustc.edu.cn">tweise@ustc.edu.cn</a>.</p>
Performance measurements for in-depth energy analysis of security algorithms and protocols for the Internet of Things
<p>Performance dataset of cryptographic algorithms running on the following embedded devices (results in ms):</p> <p><strong>nuc </strong>The NUCLEO-L073RZ is a STM32 Nucleo-64 Development Board of STMicroelectronics. It features the STM32L073RZT6 32~MHz ARM Cortex-M0+ microcontroller with 192~KB flash memory and 20~KB RAM.<br> <strong>msp </strong>The TI SimpleLink MSP-EXP432P401R development kit uses the MSP432P401R 48~MHz ARM Cortex-M4F microcontroller with 256~KB flash and 64~KB RAM.<br> <strong>max </strong>The MAXREFDES\#100 health sensor platform features the MAX32620 96~MHz ARM Cortex-M4F microcontroller with 2~MB flash and 256~KB RAM. It has a wide range of sensors, like a human body temperature sensor and a heart rate sensor.</p> <p>The measured cryptographic operations:</p> <ul> <li><strong>The basic arithmetic operations for elliptic curve cryptography </strong>(point addition~(PA), point doubling~(PD), point multiplication~(PM), and fixed-point multiplication~(PMG))</li> <li><strong>The AES symmetric-key cipher in five modes of operations</strong> (Electronic Codebook (ECB), Cipher Block Chaining (CBC), Counter (CTR), Counter with CBC-MAC (CCM), and Galois/Counter Mode (GCM))</li> <li><strong>Hash functions </strong>(SHA256 and SHA3-256)</li> </ul> <p>The performance of all identified basic operations is measured on the three platforms. 50 time measurements are done for each basic operation using the platforms' available timer. Moreover, the AES cipher operation is an encryption on 256 Bytes of data. We have chosen a multiple of the AES block size, because, longer time periods ensure less influence of potential timing inaccuracies like an early start and late end. For the hash function, the maximum input size of the respective algorithm for one round is chosen as follows: 55~B for SHA256 and 135~B for SHA3-256. The total available internal state size is not used for SHA256 and SHA3-256, as we take into account the minimal padding or suffix that is required for the last block of input data. Note that the most optimal scenario, i.e. the maximum amount of input data to fill up the internal state completely, is used for each of the operations.</p> <p>All basic operations are implemented using software libraries and cross-compiled with the GNU Tools for ARM Embedded Processors version 6-2017-q2-update. Furthermore, the compiler is configured to optimise for size (-Os). The RELIC-toolkit library is used to implement the EC arithmetic and the SHA256 hash function. We use the SECG K-256 prime elliptic curve, BASIC;COMBA;COMBA;MONTY;MONTY;SLIDE configuration for the prime field arithmetic, and PROJC;LWNAF;COMBS;INTER}} configuration for the prime elliptic curve arithmetic. For more information on how to configure RELIC and other examples that use it, we refer to the relic-toolkit wiki. The AES ciphers are implemented using Mbed TLS and SHA3 using wolfCrypt. We use the SHA3-256 hash function as specified in FIPS PUB 202.</p>
ScienceDex guides
Understand access before you commit
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International Brain Laboratory public data
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OpenNeuro
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