Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning

File A: Datasets (.txt) A1: Webex A2: Zoom A3: Teams A4: Word A5: PowerPoint A6: Excel File B: Python Code (Both ours and baseline) B1: pert_class.ipynb B2: Baseline.ipynb FIle C: Result Tables (.xlsx) C1: Table of perturbed outputs in Webex C2: Table of perturbed outputs in Zoom C3: Table of perturbed outputs in Teams C4: Table of perturbed outputs in Word C5: Table of perturbed outputs in PowerPoint C6: Table of perturbed outputs in Excel File D: Trend of Adversarial Shifts (Graphs) D1: Webex Adversarial Shifts D2: Zoom Adversarial Shifts D3: Teams Adversarial Shifts D4: Word Adversarial Shifts D5: Powerpoint Adversarial Shifts D6: Excel Adversarial Shifts File E: Trend of Non-Adversarial Shifts (Graphs) E1: Webex Non-Adversarial Shifts E2: Zoom Non-Adversarial Shifts E3: Teams Non-Adversarial Shifts E4: Word Non-Adversarial Shifts E5: Powerpoint Non-Adversarial Shifts E6: Excel Non-Adversarial Shifts File F: Adversarial Examples (.pdf) F1: Adversarial vs original in Webex F2: Adversarial vs original in Zoom F3: Adversarial vs original in Teams F4: Adversarial vs original in Word F5: Adversarial vs original in PowerPoint F6: Adversarial vs original in Excel File G: Questionnaire<br> <br> <br> <br> <br>