PPT-Large-Scale Copy Detection

Xin Luna Dong Divesh Srivastava 1 Outline Motivation Why does copy detection matter Examples of copying not copying Copy detection In documents In software In databases Summary 2. 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. Feature detection with . s. cale selection. We want to extract features with characteristic scale that is . covariant. with the image transformation. Blob detection: Basic idea. To detect blobs, convolve the image with a “blob filter” at multiple scales and look for . Large-scale Single-pass k-Means . Clustering. Large-scale . k. -Means Clustering. Goals. Cluster very large data sets. Facilitate large nearest neighbor search. Allow very large number of clusters. Achieve good quality. Achieving scale covariance. Goal: independently detect corresponding regions in scaled versions of the same image. Need . scale selection. mechanism for finding characteristic region size that is . covariant. Raw Scale Raw Scale Raw Scale Raw Scale Score Score Score Score Score Score Score Score 86 100 64 80 42 66 20 42 85 98 63 79 41 66 19 41 84 97 62 79 40 65 18 39 83 95 61 78 39 64 17 38 82 94 60 77 38 (b)(Ex10.7)1 8x:(Cube(x)^Large(x))$:9x(Cube(x)^Large(x))3 Tet(c)!:Cube(c)4 Tet(c)5 8x:(Cube(x)^Large(x))Theargumenthasthetruth-functionalform:1 A!(B!C)2 D$:A3 E!:B4 E5 DThisisnottautologicallyvalid:by Abstract. Cloud computing economically enables customers with limited computational resources to outsource large-scale computations to the cloud. . However, how to protect customers’ confidential data involved in the computations then becomes a major security concern. In this paper, we present a secure outsourcing mechanism for solving large-scale systems of linear equations (LE) in cloud.. Pre-SUSY Summer School. Melbourne, June 29-July 1, 2016 . An . Introduction. to. Particle. Dark . Matter. Santa Cruz Institute for Particle Physics. University of California, Santa Cruz. Quick . summary. The following link is for the . prezi. presentation... What if money grows on trees? . ( PMI ) . Plus . Minus . Interesting . Money is accessible. . Everyone can be rich.. People. will fight more.. Scale. A comparison between the . actual size . of an object and the . size of its diagram/image. ; can be expressed as a ratio, fraction, percent, in words, or in a diagram. .. Ratio 1:2. Fraction ½ . 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?. Seng Chan You. What should OHDSI studies look like?. 2. A study should be like a pipeline. A fully automated process from database to paper. ‘Performing a study’ = building the pipeline. Database. Institute of High Energy Physics, CAS. Wang Lu (Lu.Wang@ihep.ac.cn). Agenda. Introduction. Challenges and requirements of anomaly detection in large scale storage systems . Definition and category of anomaly. Xindian. Long. 2018.09. Outline. Introduction. Object Detection Concept and the YOLO Algorithm. Object Detection Example (CAS Action). Facial Keypoint Detection Example (. DLPy. ). Why SAS Deep Learning .