cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24785196.v4
title: Experimental Results (CBMC, CBMC-refine, and PBEAR)

description: The script files contain information for each target program (obj (P1), elev (P2), brobot (P3), and sv (SV-COMP)) along with the corresponding execution options:<i>origin</i>: Execute only CBMC without function summary replacement.<i>refine</i>: Execute only CBMC-refine without function summary replacement.<i>pbear</i>: Execute PBEAR.<i>no_alarm</i>: Execute PBEAR without symbolic alarm filtering (PBEAR<sup>base</sup>).<i>without_cov</i>: Execute PBEAR without considering output coverage (PBEAR<sup>-cov</sup>).In Results directory,filter/: the details of (concrete) symbolic alarm filtering process.synth/: the details of PBE-based FS refinement.target.c: the abstracted program.cbmc_result.txt: the result of CBMC with target.c.result.txt: the overall result of each property falsification. We have used different terminology than what was used in the paper. For example, we used 'FS' as 'stub' and 'symbolic alarm filtering' as 'true alarm filtering,' etc.Another important point is that, due to our mistake, the input/output examples of the "<i>check</i>" function in the "<i>seq-mthreaded"</i> category were missed. As a result, new input/output examples were created, leading to differences in the experimental results of PBEAR compared to what was initially documented in the paper. Nevertheless, PBEAR still successfully solved 2 out of 3 benchmarks in the category, while PBEAR<sup>-cov</sup> failed to solve any.
DOI: 10.6084/m9.figshare.24785196.v3
title: Experiment Results (CBMC, CBMC-refine, and PBEAR)

description: How to reproduce:Run "<i>mv scripts/compile.sh .</i>" and "<i>mv scripts/run.sh .</i>" commands. Next, run "<i>bash compile.sh</i>". To check if the compilation was successful, also execute "<i>bash run.sh</i>"Perform "<i>mv scripts/synthList.txt .</i>" and "<i>mv scripts/targetList.txt .</i>"Run "<i>bash scripts/test_*.sh</i>" filesThe remaining script files contain information for each target program (obj (P1), elev (P2), brobot (P3), and sv (SV-COMP)) along with the corresponding execution options:<i>origin</i>: Execute only CBMC without function summary replacement.<i>refine</i>: Execute only CBMC-refine without function summary replacement.<i>pbear</i>: Execute PBEAR.<i>no_alarm</i>: Execute PBEAR without symbolic alarm filtering (PBEAR<sup>base</sup>).<i>without_cov</i>: Execute PBEAR without considering output coverage (PBEAR<sup>-cov</sup>).Note that you can only run "<i>_no_alarm.sh" files after executing "</i><i>*</i><i>_pbear.sh</i>" files (i.e., after synthesizing function summaries). This is because the "<i>no_alarm</i>" option assumes the presence of function summaries sythesized by "<i>pbear.</i>"Another important point is that, due to our mistake, the input/output examples of the "<i>check</i>" function in the "<i>seq-mthreaded"</i> category were missed. As a result, new input/output examples were created, leading to differences in the experimental results of PBEAR compared to what was initially documented in the paper. Nevertheless, PBEAR still successfully solved 2 out of 3 benchmarks in the category, while PBEAR<sup>-cov</sup> failed to solve any.
DOI: 10.48597/sjfe-2whz
title: Measurement of Relative_humidity at K-puszta

description: Measurement of Relative_humidity at K-puszta
DOI: 10.48597/smp6-yd7q
title: Measurement of Aerosol light scattering at Ispra

description: Measurement of Aerosol light scattering at Ispra
DOI: 10.48597/spvn-wu4r
title: Measurement of Aerosol light scattering at Ispra

description: Measurement of Aerosol light scattering at Ispra
DOI: 10.48597/tqq2-huds
title: Measurement of Pressure at K-puszta

description: Measurement of Pressure at K-puszta
DOI: 10.48597/ufd9-p6rv
title: Measurement of Sensing_zero_signal at Ispra

description: Measurement of Sensing_zero_signal at Ispra
DOI: 10.48597/ufmg-a4fw
title: Measurement of Attenuation_coefficient, bypass_fraction, equivalent_black_carbon, flow_rate, reference_beam_signal, reference_zero_signal, sensing_beam_signal and sensing_zero_signal at Ispra

description: Measurement of Attenuation_coefficient, bypass_fraction, equivalent_black_carbon, flow_rate, reference_beam_signal, reference_zero_signal, sensing_beam_signal and sensing_zero_signal at Ispra
DOI: 10.48597/uw7y-gkm4
title: Measurement of Aerosol light scattering at K-puszta

description: Measurement of Aerosol light scattering at K-puszta
DOI: 10.48597/vy9p-hxgg
title: Measurement of Aerosol light scattering at Melpitz

description: Measurement of Aerosol light scattering at Melpitz
DOI: 10.48597/w3hv-833b
title: Measurement of Aerosol light scattering at K-puszta

description: Measurement of Aerosol light scattering at K-puszta
DOI: 10.48597/wr3z-cg4q
title: Measurement of Relative_humidity at K-puszta

description: Measurement of Relative_humidity at K-puszta
DOI: 10.48597/yam8-xtbe
title: Measurement of Aerosol_absorption_coefficient at Preila

description: Measurement of Aerosol_absorption_coefficient at Preila
DOI: 10.48597/253p-dwdm
title: Measurement of Magnesium at La Hague

description: Measurement of Magnesium at La Hague
DOI: 10.48597/yx3x-5k8c
title: Measurement of Aerosol_absorption_coefficient at Preila

description: Measurement of Aerosol_absorption_coefficient at Preila
DOI: 10.48597/2559-v2qm
title: Measurement of Conductivity at Bonnevaux

description: Measurement of Conductivity at Bonnevaux
DOI: 10.48597/2qw9-ap6x
title: Measurement of Sulphur_dioxide at La Crouzille

description: Measurement of Sulphur_dioxide at La Crouzille
DOI: 10.48597/3hpx-ts3w
title: Measurement of Chloride at La Hague

description: Measurement of Chloride at La Hague
DOI: 10.48597/4tzv-am4y
title: Measurement of Sodium at La Crouzille

description: Measurement of Sodium at La Crouzille
DOI: 10.48597/4ywb-93us
title: Measurement of Sulphur_dioxide at Bonnevaux

description: Measurement of Sulphur_dioxide at Bonnevaux
DOI: 10.48597/5bhx-vtr3
title: Measurement of Sodium at La Hague

description: Measurement of Sodium at La Hague
DOI: 10.48597/5ek7-xb57
title: Measurement of Sulphate_total at Revin

description: Measurement of Sulphate_total at Revin
DOI: 10.48597/5jvk-edxh
title: Measurement of Sulphate_total at Bonnevaux

description: Measurement of Sulphate_total at Bonnevaux
DOI: 10.48597/5msn-zq5j
title: Measurement of Chloride at Bonnevaux

description: Measurement of Chloride at Bonnevaux
DOI: 10.34740/kaggle/dsv/7760051
title: Mahanagar Yatayat Transport Dataset
DOI: 10.48597/8ww7-nb6t
title: Measurement of Sulphate_total at La Hague

description: Measurement of Sulphate_total at La Hague
DOI: 10.48597/9d3c-3q6h
title: Measurement of Potassium at Bonnevaux

description: Measurement of Potassium at Bonnevaux
DOI: 10.48597/b72a-jq69
title: Measurement of Sulphate_total at Morvan

description: Measurement of Sulphate_total at Morvan
DOI: 10.48597/c6uq-tatw
title: Measurement of pH at Bonnevaux

description: Measurement of pH at Bonnevaux
DOI: 10.48597/d37r-8djj
title: Measurement of Calcium at La Hague

description: Measurement of Calcium at La Hague
DOI: 10.48597/d44j-2cae
title: Measurement of Ammonium at La Hague

description: Measurement of Ammonium at La Hague
DOI: 10.48597/ev45-6u4w
title: Measurement of Sulphate_total at Bonnevaux

description: Measurement of Sulphate_total at Bonnevaux
DOI: 10.48597/hxt8-4d2q
title: Measurement of Calcium at Bonnevaux

description: Measurement of Calcium at Bonnevaux
DOI: 10.48597/jk6a-3kh9
title: Measurement of Sulphur_dioxide at Morvan

description: Measurement of Sulphur_dioxide at Morvan
DOI: 10.48597/jqmf-xuaz
title: Measurement of Sulphate_total at La Crouzille

description: Measurement of Sulphate_total at La Crouzille
DOI: 10.48597/jqt6-rygq
title: Measurement of Sulphur_dioxide at Donon

description: Measurement of Sulphur_dioxide at Donon
DOI: 10.48597/kqgu-p7dy
title: Measurement of Sulphate_total at La Hague

description: Measurement of Sulphate_total at La Hague
DOI: 10.48597/nbmv-f67k
title: Measurement of Sulphur_dioxide at La Hague

description: Measurement of Sulphur_dioxide at La Hague
DOI: 10.48597/q2e7-tcza
title: Measurement of Nitrate at La Hague

description: Measurement of Nitrate at La Hague
DOI: 10.48597/ry2b-gyk8
title: Measurement of Magnesium at Bonnevaux

description: Measurement of Magnesium at Bonnevaux
DOI: 10.48597/taem-9z93
title: Measurement of Sulphate_total at Iraty

description: Measurement of Sulphate_total at Iraty
DOI: 10.48597/te3b-ynyk
title: Measurement of Ammonium at Bonnevaux

description: Measurement of Ammonium at Bonnevaux
DOI: 10.48597/tz3f-x9sb
title: Measurement of Nitrate at Bonnevaux

description: Measurement of Nitrate at Bonnevaux
DOI: 10.48597/uj95-bdnv
title: Measurement of Benzo_bjk_fluoranthenes at Westerland

description: Measurement of Benzo_bjk_fluoranthenes at Westerland
DOI: 10.48597/urzu-hzsg
title: Measurement of Sulphate_total at Donon

description: Measurement of Sulphate_total at Donon
DOI: 10.48597/vvcj-4rn3
title: Measurement of Conductivity at La Hague

description: Measurement of Conductivity at La Hague
DOI: 10.6084/m9.figshare.24785706
title: Experimental Results (CPAChecker)

description: How to reproduce:download "CPAchecker-2.1-unix" from https://cpachecker.sosy-lab.org/download-oldversions.phpinstall "CPAchecker-2.1-unix" using the documentation (https://cpachecker.sosy-lab.org/doc.php)copy the "CPAchecker-2.1-unix" directory and paste it into both "cpa-results-svcomp" and "cpa-results-embedded" directoriesrun test_cpa*.sh files<br>predicateAnalysis.txt: the results of CPAchecker using the predicate abstraction option.svcomp22.txt: the results of CPAchecker using the svcomp22 option.We tried to use the same options used in SV-COMP 2022 when running with SV-COMP benchmarks.For three embedded software, we used -predicateAnalysis -stats -timelimit '259200s' -heap '80G' -preprocess.
DOI: 10.6084/m9.figshare.24785706.v4
title: Experiment Results (CPAChecker)

description: How to reproduce:download "CPAchecker-2.1-unix" from https://cpachecker.sosy-lab.org/download-oldversions.phpinstall "CPAchecker-2.1-unix" using the documentation (https://cpachecker.sosy-lab.org/doc.php)copy the "CPAchecker-2.1-unix" directory and paste it into both "cpa-results-svcomp" and "cpa-results-embedded" directoriesrun test_cpa*.sh files<br>
DOI: 10.6084/m9.figshare.24125037
title: Abundance table (input)

description: The table data used as the input described in the journal paper: "<b>Data mining techniques for endometriosis detection in a data-scarce medical dataset</b>".FormatCSV (comma-separated) with headers.To use it with the algorithms of the journal paper, it needs to be converted into a tab-separated file.
DOI: 10.6084/m9.figshare.24114681
title: SVC scores (rbf kernel and C=1)

description: The datasets presented in this repository are obtained by applying the support vector classifier (SVC) algorithm with the following specific hyperparameter setting and the different inputs that have been described in the journal paper: "<b>Data mining techniques for endometriosis detection in a data-scarce medical dataset</b>".HyperparametersC: 1.0kernel: rbfmax_iter: 1000000Files<b>result_eb.csv</b>: Results for EB sample type.<b>result_ef.csv</b>: Results for EF sample type.<b>result_vagina.csv</b>: Results for vagina sample type.<b>result_oral.csv</b>: Results for oral sample type.<b>result_feces.csv</b>: Results for feces sample type.<b>result_frt.csv</b>: Results for FRT (EB + EF + vagina) sample type.<b>result_frt2.csv</b>: Results for FRT2 (EB + vagina) sample type.<b>properties_eb.log</b>: Arguments and result information for EB sample type (C, n_split, kernel, gamma, max_iter, random_state, len_scores_before_filtering, len_scores_after_filtering, len_f1).<b>properties_ef.log</b>: Arguments and result information for EF sample type.<b>properties_vagina.log</b>: Arguments and result information for vagina sample type.<b>properties_oral.log</b>: Arguments and result information for oral sample type.<b>properties_feces.log</b>: Arguments and result information for feces sample type.<b>properties_frt.log</b>: Arguments and result information for FRT sample type.<b>properties_frt2.log</b>: Arguments and result information for FRT2 sample type.<br>
DOI: 10.6084/m9.figshare.24114681.v1
title: SVC scores (rbf kernel and C=1)

description: The datasets presented in this repository are obtained by applying the support vector classifier (SVC) algorithm with the following specific hyperparameter setting and the different inputs that have been described in the journal paper: "<b>Data mining techniques for endometriosis detection in a data-scarce medical dataset</b>".HyperparametersC: 1.0kernel: rbfmax_iter: 1000000Files<b>result_eb.csv</b>: Results for EB sample type.<b>result_ef.csv</b>: Results for EF sample type.<b>result_vagina.csv</b>: Results for vagina sample type.<b>result_oral.csv</b>: Results for oral sample type.<b>result_feces.csv</b>: Results for feces sample type.<b>result_frt.csv</b>: Results for FRT (EB + EF + vagina) sample type.<b>result_frt2.csv</b>: Results for FRT2 (EB + vagina) sample type.<b>properties_eb.log</b>: Arguments and result information for EB sample type (C, n_split, kernel, gamma, max_iter, random_state, len_scores_before_filtering, len_scores_after_filtering, len_f1).<b>properties_ef.log</b>: Arguments and result information for EF sample type.<b>properties_vagina.log</b>: Arguments and result information for vagina sample type.<b>properties_oral.log</b>: Arguments and result information for oral sample type.<b>properties_feces.log</b>: Arguments and result information for feces sample type.<b>properties_frt.log</b>: Arguments and result information for FRT sample type.<b>properties_frt2.log</b>: Arguments and result information for FRT2 sample type.<br>
DOI: 10.6084/m9.figshare.23904972
title: SVC scores (rbf kernel and C=100)

description: The datasets presented in this repository are obtained by applying the support vector classifier (SVC) algorithm with the following specific hyperparameter setting and the different inputs that have been described in the journal paper: "<b>Data mining techniques for endometriosis detection in a data-scarce medical dataset</b>".HyperparametersC: 100.0kernel: rbfmax_iter: 1000000Files<b>result_eb.csv</b>: Results for EB sample type.<b>result_ef.csv</b>: Results for EF sample type.<b>result_vagina.csv</b>: Results for vagina sample type.<b>result_oral.csv</b>: Results for oral sample type.<b>result_feces.csv</b>: Results for feces sample type.<b>result_frt.csv</b>: Results for FRT (EB + EF + vagina) sample type.<b>result_frt2.csv</b>: Results for FRT2 (EB + vagina) sample type.<b>properties_eb.log</b>: Arguments and result information for EB sample type (C, n_split, kernel, gamma, max_iter, random_state, len_scores_before_filtering, len_scores_after_filtering, len_f1).<b>properties_ef.log</b>: Arguments and result information for EF sample type.<b>properties_vagina.log</b>: Arguments and result information for vagina sample type.<b>properties_oral.log</b>: Arguments and result information for oral sample type.<b>properties_feces.log</b>: Arguments and result information for feces sample type.<b>properties_frt.log</b>: Arguments and result information for FRT sample type.<b>properties_frt2.log</b>: Arguments and result information for FRT2 sample type.<br>
DOI: 10.6084/m9.figshare.23904972.v1
title: SVC scores (rbf kernel and C=100)

description: The datasets presented in this repository are obtained by applying the support vector classifier (SVC) algorithm with the following specific hyperparameter setting and the different inputs that have been described in the journal paper: "<b>Data mining techniques for endometriosis detection in a data-scarce medical dataset</b>".HyperparametersC: 100.0kernel: rbfmax_iter: 1000000Files<b>result_eb.csv</b>: Results for EB sample type.<b>result_ef.csv</b>: Results for EF sample type.<b>result_vagina.csv</b>: Results for vagina sample type.<b>result_oral.csv</b>: Results for oral sample type.<b>result_feces.csv</b>: Results for feces sample type.<b>result_frt.csv</b>: Results for FRT (EB + EF + vagina) sample type.<b>result_frt2.csv</b>: Results for FRT2 (EB + vagina) sample type.<b>properties_eb.log</b>: Arguments and result information for EB sample type (C, n_split, kernel, gamma, max_iter, random_state, len_scores_before_filtering, len_scores_after_filtering, len_f1).<b>properties_ef.log</b>: Arguments and result information for EF sample type.<b>properties_vagina.log</b>: Arguments and result information for vagina sample type.<b>properties_oral.log</b>: Arguments and result information for oral sample type.<b>properties_feces.log</b>: Arguments and result information for feces sample type.<b>properties_frt.log</b>: Arguments and result information for FRT sample type.<b>properties_frt2.log</b>: Arguments and result information for FRT2 sample type.<br>
DOI: 10.6084/m9.figshare.23904954
title: SVC scores (rbf kernel and C=10)

description: The datasets presented in this repository are obtained by applying the support vector classifier (SVC) algorithm with the following specific hyperparameter setting and the different inputs that have been described in the journal paper: "<b>Data mining techniques for endometriosis detection in a data-scarce medical dataset</b>".HyperparametersC: 10.0kernel: rbfmax_iter: 1000000Files<b>result_eb.csv</b>: Results for EB sample type.<b>result_ef.csv</b>: Results for EF sample type.<b>result_vagina.csv</b>: Results for vagina sample type.<b>result_oral.csv</b>: Results for oral sample type.<b>result_feces.csv</b>: Results for feces sample type.<b>result_frt.csv</b>: Results for FRT (EB + EF + vagina) sample type.<b>result_frt2.csv</b>: Results for FRT2 (EB + vagina) sample type.<b>properties_eb.log</b>: Arguments and result information for EB sample type (C, n_split, kernel, gamma, max_iter, random_state, len_scores_before_filtering, len_scores_after_filtering, len_f1).<b>properties_ef.log</b>: Arguments and result information for EF sample type.<b>properties_vagina.log</b>: Arguments and result information for vagina sample type.<b>properties_oral.log</b>: Arguments and result information for oral sample type.<b>properties_feces.log</b>: Arguments and result information for feces sample type.<b>properties_frt.log</b>: Arguments and result information for FRT sample type.<b>properties_frt2.log</b>: Arguments and result information for FRT2 sample type.<br>
DOI: 10.6084/m9.figshare.23904954.v1
title: SVC scores (rbf kernel and C=10)

description: The datasets presented in this repository are obtained by applying the support vector classifier (SVC) algorithm with the following specific hyperparameter setting and the different inputs that have been described in the journal paper: "<b>Data mining techniques for endometriosis detection in a data-scarce medical dataset</b>".HyperparametersC: 10.0kernel: rbfmax_iter: 1000000Files<b>result_eb.csv</b>: Results for EB sample type.<b>result_ef.csv</b>: Results for EF sample type.<b>result_vagina.csv</b>: Results for vagina sample type.<b>result_oral.csv</b>: Results for oral sample type.<b>result_feces.csv</b>: Results for feces sample type.<b>result_frt.csv</b>: Results for FRT (EB + EF + vagina) sample type.<b>result_frt2.csv</b>: Results for FRT2 (EB + vagina) sample type.<b>properties_eb.log</b>: Arguments and result information for EB sample type (C, n_split, kernel, gamma, max_iter, random_state, len_scores_before_filtering, len_scores_after_filtering, len_f1).<b>properties_ef.log</b>: Arguments and result information for EF sample type.<b>properties_vagina.log</b>: Arguments and result information for vagina sample type.<b>properties_oral.log</b>: Arguments and result information for oral sample type.<b>properties_feces.log</b>: Arguments and result information for feces sample type.<b>properties_frt.log</b>: Arguments and result information for FRT sample type.<b>properties_frt2.log</b>: Arguments and result information for FRT2 sample type.<br>
DOI: 10.6073/pasta/df0b90f3d32d5782fd6ae3f4fdc8db2e
title: Microclimate at Harvard Forest HDW Tower since 2014

description: The HDW (Hardwood) microclimate and walkup tower at Harvard Forest became operational in April 2014. The site was chosen to represent the mixed hardwood forest around the EMS and NEON towers and to provide a platform for research projects without access to those towers. Microclimate data are archived annually and recent data are available upon request. Measured values include air temperature, relative humidity, PAR, net radiation, albedo, wind speed and direction, soil temperature and soil moisture.
DOI: 10.6084/m9.figshare.23904933
title: SVC scores (linear kernel and C=1)

description: The datasets presented in this repository are obtained by applying the support vector classifier (SVC) algorithm with the following specific hyperparameter setting and the different inputs that have been described in the journal paper: "<b>Data mining techniques for endometriosis detection in a data-scarce medical dataset</b>".HyperparametersC: 1.0kernel: linearmax_iter: 1000000Files<b>result_eb.csv</b>: Results for EB sample type.<b>result_ef.csv</b>: Results for EF sample type.<b>result_vagina.csv</b>: Results for vagina sample type.<b>result_oral.csv</b>: Results for oral sample type.<b>result_feces.csv</b>: Results for feces sample type.<b>result_frt.csv</b>: Results for FRT (EB + EF + vagina) sample type.<b>result_frt2.csv</b>: Results for FRT2 (EB + vagina) sample type.<b>properties_eb.log</b>: Arguments and result information for EB sample type (C, n_split, kernel, gamma, max_iter, random_state, len_scores_before_filtering, len_scores_after_filtering, len_f1).<b>properties_ef.log</b>: Arguments and result information for EF sample type.<b>properties_vagina.log</b>: Arguments and result information for vagina sample type.<b>properties_oral.log</b>: Arguments and result information for oral sample type.<b>properties_feces.log</b>: Arguments and result information for feces sample type.<b>properties_frt.log</b>: Arguments and result information for FRT sample type.<b>properties_frt2.log</b>: Arguments and result information for FRT2 sample type.<br>
DOI: 10.6084/m9.figshare.23904903
title: LogisticRegression scores

description: The datasets presented in this repository are obtained by applying the logistic regression (<i>LogisticRegression</i>) algorithm with the following specific hyperparameter setting and the different inputs that ,have been described in the journal paper: "<b>Data mining techniques for endometriosis detection in a data-scarce medical dataset</b>".HyperparametersC: 1Solver: liblinearmax_iter: 1000000Files<b>result_eb.csv</b>: Results for EB sample type.<b>result_ef.csv</b>: Results for EF sample type.<b>result_vagina.csv</b>: Results for vagina sample type.<b>result_oral.csv</b>: Results for oral sample type.<b>result_feces.csv</b>: Results for feces sample type.<b>result_frt.csv</b>: Results for FRT (EB + EF + vagina) sample type.<b>result_frt2.csv</b>: Results for FRT2 (EB + vagina) sample type.<b>properties_eb.log</b>: Arguments and result information for EB sample type (C, n_split, solver, max_iter, random_state, len_scores_before_filtering, len_scores_after_filtering, len_f1).<b>properties_ef.log</b>: Arguments and result information for EF sample type.<b>properties_vagina.log</b>: Arguments and result information for vagina sample type.<b>properties_oral.log</b>: Arguments and result information for oral sample type.<b>properties_feces.log</b>: Arguments and result information for feces sample type.<b>properties_frt.log</b>: Arguments and result information for FRT sample type.<b>properties_frt2.log</b>: Arguments and result information for FRT2 sample type.<br>