PPT-Global Detection of

Complex Copying Relationships Between Sources Xin Luna Dong ATampT LabsResearch Joint work w Laure BertiEquille Yifan Hu Divesh Srivastava VLDB2010 Information Propagation Becomes Much Easier with the Web Technologies. Kallol Dey. Rahul. . Mitra. Shubham. . Gautam. What is Spam ?. According to . wikipedia. … . Email spam, also known as junk email or unsolicited bulk email (UBE),is a subset of electronic spam involving nearly identical messages sent to numerous recipients by email. Clicking on links in spam email may send users to phishing web sites or sites that are hosting malware. . 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. 10/30/11. Ordering and . C. onsistent . C. uts. 1. Introduction. D. istributed systems. Loosely coupled processes cooperating to solve a bigger problem. N. ovelty of distributed systems. Lamport published his paper in . and Its Applications. . 基於電腦視覺之手指偵測及其應用. Yi-Fan Chuang. Advisor: Prof. Yi-Ping Hung. Prof. Ming-Sui Lee. Outline. Introduction. Related Work. Fingertip Detection and Tracking. 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. Ross . Girshick. , Jeff Donahue, Trevor Darrell, . Jitandra. Malik (UC Berkeley). Presenter: . Hossein. . Azizpour. Abstract. Can CNN improve . s.o.a. . object detection results?. Yes, it helps by learning rich representations which can then be combined with computer vision techniques.. DASFAA 2011. By. Hoang Vu Nguyen, . Vivekanand. . Gopalkrishnan. and Ira . Assent. Presented By. Salman. Ahmed . Shaikh. (D1). Contents. Introduction. Subspace Outlier Detection Challenges. Objectives of Research. 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). 2. /86. Contents. Statistical . methods. parametric. non-parametric (clustering). Systems with learning. 3. /86. Anomaly detection. Establishes . profiles of normal . user/network behaviour . Compares . inWireless. Sensor Networks. Abstract. Wireless sensor networks are vulnerable to the node clone, and several distributed protocols have been proposed to . de¬tect. this attack. . However, they require too strong assumptions to be practical for large-scale, randomly deployed sensor networks. . Spectrogram. Sponsor: Tom Dickey, SENSCO Solutions. Technical . Advisor: Dr. Harold P.E. Stern. Project # 1.7. Team Members. Project # 1.7. Today’s Presentation Covers. Motivation. Description of Project. 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.