PPT-Modeling and Detecting Anomalous Topic Access

Siddharth Gupta 1 Casey Hanson 2 Carl A Gunter 3 Mario Frank 4 David Liebovitz 4 Bradley Malin 6 1234 Department of Computer Science 35 Department of Medicine 6 Department of Biomedical Informatics. Ahmad Bashir MPISWS Mark Crovella Boston University Saikat Guha MSR India Krishna P Gummadi MPISWS Balachander Krishnamurthy ATT LabsResearch Alan Mislove Northeastern University Abstract Users increasingly rely on crowdsourced information such as r Sekar M Bendre D Dhurjati P Bollineni State University of New York Iowa State Univeristy Stony Brook NY 11794 Ames IA 50014 sekarmbendredinakar cssunysbyedu pradeepcsiastateedu Abstract Forrest et al introduced a new intrusion detection ap proach th : A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades. Xinran He. 1. , . Theodoros. Rekatsinas. 2. , . James . Foulds. 3. , . Lise. Getoor. 3. and Yan Liu. 1. 07/08/2015. Using Inventor. 3D Modeling in Inventor. Getting Started. This is what you will see when you first open Inventor.. You can begin a new file or open an existing file with either the New or Open commands on the Launch panel, the Quick Access Toolbar, or the Application Menu.. EMR . Audit . Automation. Carl A. Gunter. University of Illinois. Accountable Systems . Workshop. Situation. Access to hospital Electronic Medical Record (EMR) data suffers risk of high loss in the event of false negatives (incorrect refusal of access).. Liangjie Hong. and Brian D. Davison. Computer Science and Engineering. Lehigh University. Bethlehem, PA USA. SOMA 2010 . Why. . we care about text modeling in Twitter ?. SOMA 2010 . Why. . we care about text modeling in Twitter ?. 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. part 1. Andrea Tagarelli. Univ. of Calabria, Italy. Statistical topic modeling. . (1/3). Key assumption: . text . data represented as a mixture . of . topics. , i.e., probability distributions . over . Name: Samer Al-Khateeb. I. nstructor. : Dr. . Xiaowei. . Xu. Class. : Information Science Principal/ Theory (IFSC 7321). Topic Modeling For Associated Press Articles Using Latent . Dirichlet. Allocation [LDA] . Jie Tang. *. , Limin Yao. #. , and Dewei Chen. *. *. Dept. of Computer Science and Technology. Tsinghua University. #. Dept. of Computer Science, University of Massachusetts Amherst. April, 2009. ?. What are the major topics in the returned docs?. Padhraic Smyth. Department of Computer Science. University of California, Irvine . . Progress Report. New deadline. In class, Thursday February 18. th. (not Tuesday). Outline. 3 to 5 pages maximum. Detecting Variation. In populations or when comparing closely related species, one major objective is to identify variation among the samples. AKA, one of the main goals in genomics is to identify what genomic features make individuals/populations/species different. Presented By: . Bob . Grumbine. (. NWS/NCEP). Contributors: . Hendrik Tolman . 2. Operational System Attribute(s). System Name. Acronym. Areal. . Coverage. Horz. Res. Cycle . Freq. Fcst. Length (. By. Harshith Reddy . Sarabudla. Anomaly detection approaches. Command-centric – focus on attack syntax. Mostly capture attack queries that have similar columns but process or display different row contents from those of normal queries.