Con-Detect: Detecting Adversarially Perturbed Natural Language Inputs to Deep Classifiers Through Holistic Analysis by Hassan Ali (3348749)

“…Even in the worst case, we note a 100% increase in the required number of queries and a 50% increase in the number of words perturbed, suggesting that Con-Detect is hard to evade. …”

Con-Detect: Detecting adversarially perturbed natural language inputs to deep classifiers through holistic analysis by Hassan, Ali

“…Even in the worst case, we note a 100% increase in the required number of queries and a 50% increase in the number of words perturbed, suggesting that Con-Detect is hard to evade.…”
Get full text
Get full text
Get full text

Relationship between Asymmetry Indices, Anthropometric Parameters, and Physical Fitness in Obese and Non-Obese High School Students by Monoem Haddad (5345357)

“…In the second regression analysis, controlling for age, body mass, and BMI, the regression coefficients associated with height (B = −1.692, 95% CI [−3.115, −0.269], p < 0.05] and body fat percentage (B = 0.529, 95% CI [0.075, 0.983], p < 0.05) indicated that with each additional unit of height or body fat percentage, the CMJ AI decreased by 1.692 units and increased by 0.529 units. …”