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α Absorption in Exoplanetary Exospheres Seth Redfield Wesleyan University Adam Jensen Wes Mike Endl UT Bill Cochran UT Lars Koesterke UT Travis. we have evolved the process and methodology of leak detection and location into a science and can quickly and accurately locate leaks in homes, office buildings, swimming pools and space, as well as under streets and sidewalks, driveways, asphalt parking lots and even golf courses. CSE 576. Face detection. State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Face detection. Where are the faces? . Introduction and Use Cases. Derick . Winkworth. , Ed Henry and David Meyer. Agenda. Introduction and a Bit of History. So What Are Anomalies?. Anomaly Detection Schemes. Use Cases. Current Events. Q&A. Shabana. . Kazi. Mark Stamp. HMMs for Piracy Detection. 1. Intro. Here, we apply metamorphic analysis to software piracy detection. Very similar to techniques used in malware detection. But, problem is completely different . Main Advantages. H . 2. 1. Fiber Optics Technology. . -Covert design. Caused no physical alteration to present building outlook. -Full Fiber Structure thus immune to lightning strike and EMI. Sarah Riahi and Oliver Schulte. School . of Computing Science. Simon Fraser University. Vancouver, Canada. With tools that you probably have around the . house. lab.. A simple method for multi-relational outlier detection. applications. The 10th IEEE Conference on Industrial Electronics and Applications (ICIEA 2015. ), Auckland , 15-17 June 2015. Kai Ki Lee. 1. ,Ying Kin Yu. 2. and Kin Hong . Wong. 2+. 1. Dept. of Information Engineering, The Chinese University of Hong Kong (CUHK). 2011/12/08. Robot Detection. Robot Detection. Better Localization and Tracking. No Collisions with others. Goal. Robust . Robot . Detection. Long . Range. Short. . Range. Long Range. C. urrent . M. ethod. Abstract. Link error and malicious packet dropping are two sources for packet losses in multi-hop wireless ad hoc network. In this paper, while observing a sequence of packet losses in the network, we are interested in determining whether the losses are caused by link errors only, or by the combined effect of link errors and malicious drop. . Ke Wang, Gabriela Cretu, Salvatore Stolfo. Computer Science, Columbia University. Mike Kopps. CS591. Agenda. The Problem. Existing Solutions. Solution. Methodology. Collaboration. Evaluation. Even . More Problems. Problem motivation. Machine Learning. Anomaly detection example. Aircraft engine features:. . = heat generated. = vibration intensity. …. (vibration). (heat). Dataset:. New engine:. Density estimation. Stephen Huang. Sept. 20, 2013. News. 2. http://arstechnica.com/security/2013/09/meet-hidden-lynx-the-most-elite-hacker-crew-youve-never-heard-of/. 3. Jobs. http://www.homelandsecuritynewswire.com/dr20130809-cybersecurity-jobs-average-over-100-000-a-year. 1. Content. What is . OpenCV. ?. What is face detection and . haar. cascade classifiers?. How to make face detection in Java using . OpenCV. Live Demo. Problems in face detection process. How to improve face detection. State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Face detection. Where are the faces? . Face Detection. What kind of features?.