PPT-How & When The Kernel Runs

David Ferry Chris Gill Department of Computer Science and Engineering Washington University St Louis MO davidferrywustledu 1 Traditional View of Process Execution However the kernel is not a traditional process. A jogger runs 145m in a direction 20.0 degrees east of north (vector A), and then runs 105m in a direction 35.0 degrees south of east (vector B). Using components, determine the magnitude and direction of the resultant vector C for these two displacements.. 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. NO NAME RUNS NO NAME RUNS NO NAME RUNS 1 1550 653 AARON BYRNES 25 1 KEVIN ANDREWS 1550 2 BOB ISHERWOOD 1270 416 ADAM CARTMEL 77 8 STEVE HUGHES 1329 3 RON SCHULTZ 1017 862 ADAM COUCHMAN 138 17 MARGARE 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?. Attributed . Graphs . Yu Su. University of California at Santa Barbara. with . Fangqiu. Han, Richard E. . Harang. , and . Xifeng. Yan . Introduction. A Fast Kernel for Attributed Graphs. Graph Kernel. KAIST . CySec. Lab. 1. Contents. About Rootkit. Concept and Methods. Examples. Ubuntu Linux (Network Hiding. ). Windows 7 (File Hiding). Android Rootkit Demonstration (DNS Spoofing). Exercise (Rootkit Detection). 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. John Erickson, . Madanlal. . Musuvathi. , Sebastian Burckhardt, Kirk . Olynyk. Microsoft . Research. Motivations. Need for race detection in Kernel modules. Also must detect race conditions between hardware and Kernel. Machine Learning. March 25, 2010. Last Time. Basics of the Support Vector Machines. Review: Max . Margin. How can we pick which is best?. Maximize the size of the margin.. 3. Are these really . “equally valid”?. 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. 23. rd. USENIX Security Symposium. August 20. th. . 2014. Rob . Jansen. US Naval Research Laboratory. John Geddes University of Minnesota. Chris . Wacek. Georgetown University. Micah . Sherr. Georgetown University. David Ferry, Chris Gill. Department of Computer Science and Engineering. Washington University, St. Louis MO. davidferry@wustl.edu. 1. Traditional View of Process Execution. However, the kernel is not a traditional process!. 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 .