PPT-Usability of Security Critical Protocols Under Adversarial

Author : jane-oiler | Published Date : 2016-10-21

Tyler Kaczmarek PhD Advancement to Candidacy Presentation Computer Science Department University of California Irvine Outline Previous Work On Usability of Pairing

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Usability of Security Critical Protocols Under Adversarial: Transcript


Tyler Kaczmarek PhD Advancement to Candidacy Presentation Computer Science Department University of California Irvine Outline Previous Work On Usability of Pairing techniques when exposed to adversarial stimuli. Lecture /slide deck produced by Saul Greenberg, University of Calgary, Canada. . Notice: some material in this deck is used from other sources without permission. Credit to the original source is given if it is known,. Alexei Oulanov, PhD, MBA, MSLIS. Medgar. Evers College/ City University of New York (USA). aoulanov@mec.cuny.edu. Abstract. The focus of the present research is on the Affect component of the Usability evaluation of the . (or not) . of Health Informatics Standards. Stephen Kay . MSc PhD FACMI FBCS CITP. … records had different structures & semantics?. … clinical systems were functionally incompatible?. … different interchange capabilities existed?. User interfaces. Jaana Holvikivi. Metropolia. 2. Methods for usability evaluation. Wide variety of methods, often ad-hoc testing (not too systematic). More than one approach may be needed. one test cannot find all problems. and. Communicating Value. Eka. Grguric . @. egrguric. NCSU Libraries Fellow. Code4Lib conference, 8 March 2016. 1. . What. is usability and usability testing. 2. . Where . it fits in a project. 3. . etc. Convnets. (optimize weights to predict bus). bus. Convnets. (optimize input to predict ostrich). ostrich. Work on Adversarial examples by . Goodfellow. et al. , . Szegedy. et. al., etc.. Generative Adversarial Networks (GAN) [. Adversarial examples. Ostrich!. Adversarial examples. Ostrich!. Intriguing properties of neural networks. . Christian . Szegedy. , . Wojciech. . Zaremba. , Ilya . Sutskever. , Joan Bruna, . Dumitru. Presenters: Pooja Harekoppa, Daniel Friedman. Explaining and Harnessing Adversarial Examples. Ian J. . Goodfellow. , Jonathon . Shlens. and Christian . Szegedy. Google Inc., Mountain View, CA. Highlights . ML Reading . Group. Xiao Lin. Jul. 22 2015. I. . Goodfellow. , J. . Pouget-Abadie. , M. Mirza, B. Xu, D. . Warde. -Farley, S. . Ozair. , A. . Courville. and Y. . Bengio. . . "Generative adversarial nets." . Use . adversarial learning . to suppress the effects of . domain variability. (e.g., environment, speaker, language, dialect variability) in acoustic modeling (AM).. Deficiency: domain classifier treats deep features uniformly without discrimination.. JUNIOR TIDAL. ASSOCIATE PROFESSOR, WEB SERVICES & MULTIMEDIA LIBRARIAN. NEW YORK CITY COLLEGE OF TECHNOLOGY, CUNY. @JUNIORTIDAL. SLA STUDENT CHAPTER SKILL SHARE. PRATT INSTITUTE . APRIL 23, 2016. JUNIOR TIDAL. WEB SERVICES & MULTIMEDIA LIBRARIAN. NEW YORK CITY COLLEGE OF TECHNOLOGY, CUNY. April 6. th. , 2016. INTRODUCTIONS. . WHAT’S . THIS WORKSHOP ABOUT, ANYWAY?. WHAT IS USABILITY?. WHY USABILITY IMPORTANT. Want to know the advantages and disadvantages of critical illness cover? Explore here or connect with Mountview FS for insurance advice. Dr. Alex Vakanski. Lecture 6. GANs for Adversarial Machine Learning. Lecture Outline. Mohamed Hassan presentation. Introduction to Generative Adversarial Networks (GANs). Jeffrey Wyrick presentation.

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