PDF-A formally verified OS kernel .now what?

showthattherewillbeanumberofchallengesThemainonesaredierenttheoremproversdierentmemorymodelsanddierentClanguagesubsetsFirstlyourproofisinIsabelleHOLtheCompCertproofisinCoqWhileitshouldinpr. Osck. Owen Hofmann, Alan Dunn, . Sangman. Kim, . Indrajit Roy*, Emmett Witchel. UT Austin. *HP Labs. Rootkits are dangerous. Adversary exploits insecure system. Leave backdoor . to facilitate long-term access. Jyotirmoy V. Deshmukh. (with Jim Kapinski, Xiaoqing Jin, Isaac Ito & Ken Butts). What is a Powertrain?. © http://www.greencarcongress.com/2014/11/20141118-mirai.html. © Google Image search. © Motor Trend. Tundra powertrain. Steven C.H. Hoi, . Rong. Jin, . Peilin. Zhao, . Tianbao. Yang. Machine Learning (2013). Presented by Audrey Cheong. Electrical & Computer Engineering. MATH 6397: Data Mining. Background - Online. with Multiple Labels. Lei Tang. , . Jianhui. Chen and . Jieping. Ye. Kernel-based Methods. Kernel-based methods . Support Vector Machine (SVM). Kernel Linear Discriminate Analysis (KLDA). Demonstrate success in various domains. 1-Caryophyllanes. Approximately 30 . naturally abundant . caryophyllanes. . . are derived from . humulanes. in . which C-2 and C-10 close a . cyclobutane. . ring.. (-)-. b. -. Caryophyllene. . occurs as a mixture with its . . Dr. M. . Asaduzzaman. . Professor. Department  of  Mathematics . University . of . Rajshahi. Rajshahi. -6205, Bangladesh. E-mail: md_asaduzzaman@hotmail.com. Definition. Let . H. be a Hilbert space comprising of complex valued . 0.2 0.4 0.6 0.8 1.0 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 kernel(b) kernel(c) kernel(d) (a)blurredimage(b)no-blurredimage0.900.981.001.021.10 (5.35,3.37)(4.80,3.19)(4.71,3.22)(4.93,3.23)(5.03,3.22 Debugging as Engineering. Much of your time in this course will be spent debugging. In industry, 50% of software dev is debugging. Even more for kernel development. How do you reduce time spent debugging?. Rootkits. with lightweight Hook Protection. Authors: . Zhi. Wang, . Xuxian. Jiang, . Weidong. Cui, . Peng. . Ning. Presented by: . Purva. . Gawde. Outline. Introduction. Prior research. Problem overview. A B M Shawkat Ali. 1. 2. Data Mining. ¤. . DM or KDD (Knowledge Discovery in Databases). Extracting previously unknown, valid, and actionable information . . . crucial decisions. ¤. . Approach. Presented by:. Nacer Khalil. Table of content. Introduction. Definition of robustness. Robust Kernel Density Estimation. Nonparametric . Contamination . Models. Scaled project Kernel Density Estimator. Machine Learning. March 25, 2010. Last Time. Recap of . the Support Vector Machines. Kernel Methods. Points that are . not. linearly separable in 2 dimension, might be linearly separable in 3. . Kernel Methods. Object Recognition. Murad Megjhani. MATH : 6397. 1. Agenda. Sparse Coding. Dictionary Learning. Problem Formulation (Kernel). Results and Discussions. 2. Motivation. Given a 16x16(or . nxn. ) image . Francesco Logozzo. , Thomas Ball. RiSE - Microsoft Research Redmond. The problem. Programs . have bugs. Bug finders, static analyzers and verifiers, etc. help spot them. However, they provide little or no help for .