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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

ShareScore

8/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship: 4
Harmonization: 4
Access: 0
Reuse readiness: 0
Engagement: 0

Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning

Score breakdown

Your academic CV and NIH biosketch