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The following results are related to Digital Humanities and Cultural Heritage. Are you interested to view more results? Visit OpenAIRE - Explore.
39 Research products

  • Digital Humanities and Cultural Heritage
  • 2019-2023
  • Research data
  • Research software
  • 4TU.ResearchData | science.engineering.design

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Yu, Cehao;

    We have compiled a dataset of 194 images for studying time of day perception in paintings. The images were sourced from the Materials in Painting (materialsinpaintings.tudelft.nl) and National Gallery (nationalgallery.org.uk/paintings) datasets and depict outdoor scenes under varying illumination conditions, ranging from dawn to night. The paintings included in the dataset span the 17th to 20th centuries. The dataset is divided into two groups, each used as stimuli for two online rating experiments. Group 1 comprises 104 images, while Group 2 contains 90 images with metadata indicating the depicted time of day. The metadata includes information about the artist, time period of creation, and other relevant information. All images are licensed under Creative Commons Zero (CC0) except for those from the National Gallery dataset, which are subject to Creative Commons Attribution-NonCommercial (CC BY-NC) 4.0. However, users can still access these images directly from the National Gallery dataset using the metadata provided. Our dataset is designed to facilitate research on time of day perception in paintings and improve our understanding of the factors that may influence this perception.File FormatExcel (.xlsx) and ZIP (.zip) filesContent OverviewExcel Sheets(A) List_of_paintings including metadata(A-1) List_of_paintings_for_Group 1.xlsxThis file contains the list of paintings in Group 1 with their corresponding file names and metadata. The metadata includes information about the artist, the time period in which the painting was created, the location of the collection where the painting is held, and any relevant content of the painting that might be related to the depiction of time of day.(A-2) List_of_paintings_for_Group 2.xlsxThis file contains the list of paintings in Group 2 with their corresponding file names and metadata. The metadata includes information about the artist, the time period in which the painting was created, the location of the collection where the painting is held, and any relevant content of the painting that might be related to the depiction of time of day.Images(B) Images of paintings(B-1) Images of paintings for Group 1.zipThe images in this file belong to Group 1, as denoted by the file name, and comprise a set of paintings available for download in JPG digital format. Please note that images originally sourced from the National Gallery dataset are not included here, and users should download them directly from the National Gallery website at nationalgallery.org.uk/paintings.(B-2) Images of paintings for Group 2.zipThis file includes a set of images of paintings that are part of Group 2, as indicated by its name. These images can be downloaded in a JPG digital format.Usage NotesThis dataset is intended for academic research purposes.Please cite the dataset as follows: Yu, C. (2023). An image dataset for studying time of day perception in paintings. 4TU. ResearchData, https://doi.org/10.4121/22154798. FundingH2020 Marie Skłodowska-Curie Actions (H2020-MSCA-ITN-2017) “DyViTo: Dynamics in Vision and Touch,” project number 765121.The Netherlands Organization for Scientific Research, VIDI project “Visual communication of material properties,” project number 276.54.001. Contact InformationCehao Yu, C.Yu-2@tudelft.nl ReferencesYu, C., Van Zuijlen, M. J. P., Spoiala, C., Pont, S. C., Wijntjes, M. W. A., & Hurlbert, A. (2023). Time-of-day perception in paintings. Journal of Vision, 0(0):08633, 1–27, https://doi.org/10.1167/jov.0.0.08633.Yu, C., Van Zuijlen, M. J., Spoiala, C., Pont, S. C., Wijntjes, M., & Hurlbert, A. (2023, August 24). Time of day perception in paintings. PsyArXiv Preprints. https://doi.org/10.31234/osf.io/7xyrn.

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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • Authors: Liscio, Enrico; Cavalcante Siebert, Luciano; C.M. (Catholijn) Jonker; Murukannaiah, Pradeep K.;

    Code for the paper "Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems", under review at JAIR. This code contains five value preferences estimation methods based on participants' choices and textual justifications for a survey. Further, it implements an Active Learning pipeline with three sampling strategies---random, uncertainty, and driven by the estimation of value preferences. The pre-trained Dutch mode RobBERT is used to perform value classification.

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  • Authors: Liscio, Enrico; Dondera, Alin E.; Geadau, Andrei; C.M. (Catholijn) Jonker; +1 Authors

    Code for the paper "Cross-Domain Classification of Moral Values", published at NAACL '22. This code implements a cross-domain evaluation of BERT, fastText, and LSTM across the seven datasets of MFTC. After being trained, the models are evaluated for generalizability, transferability, and catastrophic forgetting.

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    Authors: Liscio, Enrico; Dondera, Alin E.; Geadau, Andrei; C.M. (Catholijn) Jonker; +1 Authors

    Raw results for the paper "Cross-Domain Classification of Moral Values", published at NAACL '22. This dataset contains the raw results of the experiments described in the paper, including the evaluations of generalizability, transferability, and catastrophic forgetting with BERT, fastText, and LSTM. Further, it contains the Jupyter Notebooks needed to generate the analysis and the tables present in the paper. The code for generating this results is available at this DOI: 10.4121/8360d02b-b32f-4f07-bbf4-168915392bd9

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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Liscio, Enrico; Araque, Oscar; Gatti, Lorenzo; Constantinescu, Ionut; +3 Authors

    Trained models for the paper "What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric", published at ACL '23. The models were trained on the MFTC datasets with the sequential paradigm. Each of the seven models was trained on six MFTC datasets and continued training on a portion of the seventh. The code that contains instructions on how to use the models is available at this DOI: 10.4121/1e71138c-be26-4652-971a-48a84837df8e

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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Liscio, Enrico; Araque, Oscar; Gatti, Lorenzo; Constantinescu, Ionut; +3 Authors

    Raw results and crowd task material for the paper "What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric", published at ACL '23. This repository contains two main folders. First, the instructions and informed consent used to perform the crowd study described in the paper, where crowd workers are asked to compare word bubbles describing moral values in a domain. Second, the results of the pairwise comparisons performed with the seven models trained on the MFTC datasets and the comparison of the results with the crowd annotations.The code to generate the results is available at this DOI: 10.4121/1e71138c-be26-4652-971a-48a84837df8eThe seven models are available at this DOI: 10.4121/646b20e3-e24f-452d-938a-bcb6ce30913c

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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
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  • Authors: Liscio, Enrico; Araque, Oscar; Gatti, Lorenzo; Constantinescu, Ionut; +3 Authors

    Code for the paper "What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric", published at ACL '23. This code implements Tomea, an Explainable AI method for investigating the difference in how language models represent morality across domains. Given a pair of datasets and models trained on the datasets, Tomea generates 10 m-distances and one d-distance to measure the difference between the datasets, based on the SHAP method. We make pairwise comparisons of seven models trained on the MFTC datasets (available at this DOI: 10.4121/646b20e3-e24f-452d-938a-bcb6ce30913c).

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    Authors: Aleo, Alessandro; Jerardino, Antonieta; Chasan, Rivka; Despotopoulou, Myrto; +3 Authors

    The dataset contains data collected as part of the Ancient Adhesives project under the European Union’s Horizon 2020 research and innovation programme Grant Agreement No. 678 804151 (Grant holder G.H.J.L.). It is being made public to act as supplementary data for a publication and for other researchers to use this data in their own work.The dataset includes eight .zip with the raw GC-MS data and one .Xlsx files containing the processed information used in the manuscript "A multi-analytical approach reveals flexible compound adhesive technology at Steenbokfontein Cave, Western Cape ". Each .zip file contains the files necessary to open and manipulate the data using the original software Agilent OpenLab 2.5.

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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: de Vos, Wiebe M.; Regenspurg, Jurjen; Wurm, Frederik; Peil, Stefan; +2 Authors

    The data includes the following datasets:dn_dc_for_Q_factor_determination: refractive indexes of lignin solutions measured at different concentrations (g/L) of lignin at different wavelengths (nm) of incoming light. The linear fit function in excel was used to determine the dn/dc from the presented data. The fit data can be found in the SI of the article.Filtration: Contains experimental results of crossflow filtration measurements for pure water permeability, 90% molecular weight cut off (MWCO) data based on polyethylene glycol (PEG) retentions (100-10000 g/mol) as measured by Gell permeation chromatography (GPC), and MgSO4 retention data. Measured quantities are: active fibre length (m), average [inlet and outlet] pressure (bar), permeate mass (g) [filled beaker-empty beaker], permeation time (s), feed and permeate conductivity (microS/cm).Fourier transform infrared spectroscopy data (FTIR): From the 3 different cationic lignin batches used in the article, the lignosulfonates and the unmodified Kraft lignin. batch number 1 was used for figure 2 in the article; batch number 2 was used for the salt stability, pH stability and biodegradability experiments (Figure 4, 5 and 6 of the main article and figure S4, S6, S7 and S9 of the SI); batch number 3 was used for the reflectometry and layer growth experiments at different NaCl concentrations (figure 3 in the article). the wavelength (cm-1) and signal intensity (a.u.) are presented.H-NMR: 1H-NMR data of the different cationic lignin batches, lignosulfonate and unmodified Kraft lignin. The chemical shift (ppm) and signal intensity (a.u.) are presentedP-NMR: 31P-NMR data of the unmodified Kraft lignin and lignosulfonate. To calculate the OH content of the lignin from this data the MestreNova software was used. The chemical shift (ppm) and signal intensity (a.u.) are presentedSEM: FESEM images of the bare support and the support coated with 9.5 or 10.0 bilayers of ligninReflectometry: Reflectometry data for the lignins coated from 5, 50 and 500 mM NaCl solutions onto a silicon waver. The Q factors as determined from the dn/dc data and the professor Huygens software are presented together with the signal (S) base signal (So) and calculated adsorption (Ads) in mg/m2.

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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2023
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • Authors: van Ede, Thijs; Binyamini, Hodaya; Engelberg, Gal; Klein, Dan; +5 Authors

    This repository contains the code for EAGLE by the authors of the Threat Intelligence Processing paper. Please cite EAGLE when using it in academic publications. This repository provides a stable artefact version of EAGLE. For the most up-to-date version that receives updates, please see the Github repository.

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39 Research products
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Yu, Cehao;

    We have compiled a dataset of 194 images for studying time of day perception in paintings. The images were sourced from the Materials in Painting (materialsinpaintings.tudelft.nl) and National Gallery (nationalgallery.org.uk/paintings) datasets and depict outdoor scenes under varying illumination conditions, ranging from dawn to night. The paintings included in the dataset span the 17th to 20th centuries. The dataset is divided into two groups, each used as stimuli for two online rating experiments. Group 1 comprises 104 images, while Group 2 contains 90 images with metadata indicating the depicted time of day. The metadata includes information about the artist, time period of creation, and other relevant information. All images are licensed under Creative Commons Zero (CC0) except for those from the National Gallery dataset, which are subject to Creative Commons Attribution-NonCommercial (CC BY-NC) 4.0. However, users can still access these images directly from the National Gallery dataset using the metadata provided. Our dataset is designed to facilitate research on time of day perception in paintings and improve our understanding of the factors that may influence this perception.File FormatExcel (.xlsx) and ZIP (.zip) filesContent OverviewExcel Sheets(A) List_of_paintings including metadata(A-1) List_of_paintings_for_Group 1.xlsxThis file contains the list of paintings in Group 1 with their corresponding file names and metadata. The metadata includes information about the artist, the time period in which the painting was created, the location of the collection where the painting is held, and any relevant content of the painting that might be related to the depiction of time of day.(A-2) List_of_paintings_for_Group 2.xlsxThis file contains the list of paintings in Group 2 with their corresponding file names and metadata. The metadata includes information about the artist, the time period in which the painting was created, the location of the collection where the painting is held, and any relevant content of the painting that might be related to the depiction of time of day.Images(B) Images of paintings(B-1) Images of paintings for Group 1.zipThe images in this file belong to Group 1, as denoted by the file name, and comprise a set of paintings available for download in JPG digital format. Please note that images originally sourced from the National Gallery dataset are not included here, and users should download them directly from the National Gallery website at nationalgallery.org.uk/paintings.(B-2) Images of paintings for Group 2.zipThis file includes a set of images of paintings that are part of Group 2, as indicated by its name. These images can be downloaded in a JPG digital format.Usage NotesThis dataset is intended for academic research purposes.Please cite the dataset as follows: Yu, C. (2023). An image dataset for studying time of day perception in paintings. 4TU. ResearchData, https://doi.org/10.4121/22154798. FundingH2020 Marie Skłodowska-Curie Actions (H2020-MSCA-ITN-2017) “DyViTo: Dynamics in Vision and Touch,” project number 765121.The Netherlands Organization for Scientific Research, VIDI project “Visual communication of material properties,” project number 276.54.001. Contact InformationCehao Yu, C.Yu-2@tudelft.nl ReferencesYu, C., Van Zuijlen, M. J. P., Spoiala, C., Pont, S. C., Wijntjes, M. W. A., & Hurlbert, A. (2023). Time-of-day perception in paintings. Journal of Vision, 0(0):08633, 1–27, https://doi.org/10.1167/jov.0.0.08633.Yu, C., Van Zuijlen, M. J., Spoiala, C., Pont, S. C., Wijntjes, M., & Hurlbert, A. (2023, August 24). Time of day perception in paintings. PsyArXiv Preprints. https://doi.org/10.31234/osf.io/7xyrn.

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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2023
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      4TU.ResearchData | science.engineering.design
      Dataset . 2023
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  • Authors: Liscio, Enrico; Cavalcante Siebert, Luciano; C.M. (Catholijn) Jonker; Murukannaiah, Pradeep K.;

    Code for the paper "Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems", under review at JAIR. This code contains five value preferences estimation methods based on participants' choices and textual justifications for a survey. Further, it implements an Active Learning pipeline with three sampling strategies---random, uncertainty, and driven by the estimation of value preferences. The pre-trained Dutch mode RobBERT is used to perform value classification.

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  • Authors: Liscio, Enrico; Dondera, Alin E.; Geadau, Andrei; C.M. (Catholijn) Jonker; +1 Authors

    Code for the paper "Cross-Domain Classification of Moral Values", published at NAACL '22. This code implements a cross-domain evaluation of BERT, fastText, and LSTM across the seven datasets of MFTC. After being trained, the models are evaluated for generalizability, transferability, and catastrophic forgetting.

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