Artifacts of the paper entitled:

Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning

Authors: Hemanth Gudaparthi, Nan Niu, Boyang Wang, Tanmay Bhowmik, Hui Liu, Jianzhang Zhang, Juha Savolainen, Glen Horton, Sean Crowe, Thomas Scherz and Lisa Haitz

The artifacts location is https://scholar.uc.edu/ that is stored and managed by the University of Cincinnati IT (Information Technology) services.

The artifacts license is Open Data Commons Open Database License (ODbL) [please see more details at https://opendatacommons.org/licenses/dbcl/1-0/]

File A: Datasets (.txt); these files serve as INPUTS to the proposed traceable and adversarial deep learning approach

    A1: Webex

    A2: Zoom

    A3: Teams

    A4: Word

    A5: PowerPoint

    A6: Excel

File B: Python Code (both our approach and the baseline); the code implements our proposed traceable and adversarial deep learning approach as well as the baseline approach by Do. et al. JSS 2020

    B1: pert_class.ipynb

    B2: Baseline.ipynb

File C: Trend of Adversarial Shifts (graphs); the graphs provide more complete adversarial shifts RESULTS, which due to space constraints was not provided in our paper

    C1: Webex Adversarial Shifts

    C2: Zoom Adversarial Shifts

    C3: Teams Adversarial Shifts

    C4: Word Adversarial Shifts

    C5: Powerpoint Adversarial Shifts

    C6: Excel Adversarial Shifts

File D: Trend of Non-Adversarial Shifts (graphs); the graphs provide more complete non-adversarial shifts RESULTS, which due to space constraints was not provided in our paper

    D1: Webex Non-Adversarial Shifts

    D2: Zoom Non-Adversarial Shifts

    D3: Teams Non-Adversarial Shifts

    D4: Word Non-Adversarial Shifts

    D5: Powerpoint Non-Adversarial Shifts

    D6: Excel Non-Adversarial Shifts