arabic-digital-humanities/adhtools: v0.1.0

First release of adhtools</p

nlppln/nlppln: 0.3.0

CWL files for NLP functionality (so they don't have to be downloaded separately) Dockerfile to run nlppln on Windows First tests Python 3 support By default, save workflows using working directory Documentation on Read the Docs Command to copy and rename files (copy-and-rename-files.cwl) Command to generate data for TextDNA visualization (textDNA-generate.cwl) Command to normalize whitespace and punctuation in text files (normalize-whitespace-punctuation.cwl) Command to run LIWC on tokenized text (liwc_tokenized.cwl) Command to save a directory to a subdirectory (save-dir-to-subdir.cwl) Command to save a list of files to a directory (save-files-to-dir.cwl) Command to merge csv files (merge-csv.cwl) Command to filter Named Entities extracted with frog (frog-filter-nes.cwl) Command to list al files in a directory (ls.cwl) Command to lowercase a text (lowercase.cwl) Command to clear xml elements (clear-xml-elements.cwl) Command saf_to_text.py (saf-to-text.cwl) outputs space separated sentences Give Python commands a meaningful name (#5) Use InitialWorkDirRequirement instead of list of input files (#1 #16) GUI (users are recommended to use Juyter notebooks for inspection and analysis tasks) Functionality to generate boilerplate Python commands and CWL files (moved to https://github.com/nlppln/nlppln-gen) Command to create a file list (use cwltool option --make-template instead) (#14

Evaluation & automated correction of historical newspaper OCR using Deep Learning

<p>This poster was presented at the National eScience Symposium and describes the research project the KB is undertaking with the eScience Center on the evaluation and correction of historical newspaper OCR using deep learning.</p

Emotion ModelingTowards Pragmatic Computational Models of Affective Processes /

VII, 167 p. 34 illus.online resource

The E↵ect of Variations in Emotional Expressiveness on Social Support

Abstract. There is a growing interest in employing embodied agents to achieve beneficial outcomes for users, such as improving health, or increasing motivation for learning. The goal of our research is to explore how and to what extent embodied agents can provide social support to victims of cyberbullying. To this end, we implemented a proof of concept virtual buddy that uses verbal and nonverbal behavior to comfort users. This paper presents the results of a study into the e↵ect of variations in the virtual buddy’s emotional expressiveness (no emotion, verbal emotion only, nonverbal emotion only, or verbal &amp; nonverbal emotion) on user experience, the e↵ectiveness of the support, and perceived social support. The results show that the virtual buddy is successful at conveying support. However, we found no statistically significant di↵erences between conditions.