Exploiting Automatically Inferred Constraint-Models for Building Identification in Satellite Imagery
|
|
|
- Job Terry
- 5 years ago
- Views:
Transcription
1 Exploiting Automatically Inferred Constraint-Models for Building Identification in Satellite Imagery Martin Michalowski, Craig A. Knoblock University of Southern California Information Sciences Institute Research funded by the AFSOR, grant numbers FA and FA Kenneth M. Bayer, Berthe Y. Choueiry University of Nebraska-Lincoln Constraint Systems Lab Research funded by NSF CAREER Award No. IIS
2 Problem Statement!Goal: Annotating satellite imagery with addresses!addresses can be assigned by exploiting sets of addressing rules!many traditional and non-traditional data sources available online!how can we combine our knowledge of addressing with the available data? 2
3 Building Identification Process Traditional Sources Non-traditional Sources 3
4 Challenges!Integrating heterogeneous data!modeling data and addressing characteristics!supporting various addressing schemes "One model tailored & stored per area! BAD "Non-homogenous addressing within one area!efficiently solving the constructed problem 4
5 Initial Approach [Michalowski & Knoblock, 2005] 5
6 Building Identification as a CSP [Michalowski+, 2005]! Constraint Satisfaction Problem "Variables: Buildings "Variable Domains: Potential street addresses "Constraints: Global addressing characteristics (parity, ascending direction, etc.)! Demonstrated the feasibility of modeling data integration for building identification as a CSP! Limitations "Relied on a single-model approach "Limited to small homogeneous areas "Did not scale 6
7 Why a Single Model Doesn t Work Block Numbering Constraints apply in different contexts YES 1 NO Addresses increase West 2 Addresses increase East 7
8 Our Solution 8
9 Constraint Inference Problem Instance Constraint Library Input information F = {F 1,F 2,,F n } Generic model C B = { C 1,C 2,,C i } User-defined (& learned) constraints C L = {C l1,c l2,,c lz } Inference Engine Inference rules R t = {R 1,R 2,,R z } R k : F i # F $ C I # C L Refined model: C new = C B " C I 9
10 Example! Data points " Landmark points that describes a particular instance! Obtained from any online point repository (e.g. gazetteers) " Features: Address Number, Street Name, Lat, Lon 852 Hillcrest St 859 Loma Vista St. 834 Hillcrest St! Constraints! Context (El Segundo) 1 2
11 Inferring Constraints! Inference rules are evaluated using data points "Supports (+,-) provided for the constraints! Constraints are partitioned based on support level "Status: Applicable, Unknown, Non-applicable! Applicable constraints added to generic model Constraint Library Status: Applicable Unknown Non-applicable Support: Positive Null Negative 11
12 Model Generation!Generates constraint model from variables and inferred constraints!model improvements over previous work "Reduces total number of variables and constraints arity "Reflects topology: Constraints can be declared locally & in restricted contexts B 1 B 2 B 3 B 4 B 5 12
13 Constraint Solver!Backtrack-search with nfc3 and conflictdirected back-jumping!exploits structure of problem (backdoor variables)!implements domains as (possibly infinite) intervals!incorporates new reformulations that increase the scalability by large factors "Details available in [Bayer+, 2007] 13
14 Case Studies! All cases are beyond what our initial work could solve 14
15 Experimental Results! 26 points used to infer correct model (inference time < 2 secs)! Inferred model greatly reduces runtime! Domain reduction leads to higher precision by a significant factor! Additional results show an even greater improvement (see paper) 15
16 Observations!Constraint inference provides framework for data integration!inferred models lead to more precise results!ability to solve more complex instances!dynamic modeling makes global coverage possible and easier 16
17 Related Work!Geospatial "Geocoding "Computer Vision!Modeling [Bakshi+, 2004] [Agouris+, 1996; Doucette+, 1999] "Learning constraint networks from data [Coletta+, 2003; Bessière+, 2005]
18 Current Work! Eliminating incorrect constraint inference "Support levels associate confidence with inferences! Dealing with a lack of expressiveness in data points "Iterative algorithm with constraint propagation! Generalizing context-inference mechanism "Classification in the variable space using SVMs! Learning constraints to populate library "Agglomerative clustering combined with set covering 18
19 Thank you!!! 19
A Generalized Query Framework for Geospatial Reasoning
A Generalized Query Framework for Geospatial Reasoning Martin Michalowski University of Southern California/ Information Sciences Institute July 17 th 2006 Introduction What is geospatial reasoning? The
Exploiting Problem Data to Enrich Models of Constraint Problems
Exploiting Problem Data to Enrich Models of Constraint Problems Martin Michalowski 1, Craig A. Knoblock 1, and Berthe Y. Choueiry 1,2 1 University of Southern California, Information Sciences Institute
A Constraint Satisfaction Approach to Geospatial Reasoning
A Constraint Satisfaction Approach to Geospatial Reasoning Martin Michalowski and Craig A. Knoblock Information Sciences Institute, Department of Computer Science, University of Southern California Outline
Automatically Identifying and Georeferencing Street Maps on the Web
Automatically Identifying and Georeferencing Street Maps on the Web Sneha Desai, Craig A. Knoblock, Yao-Yi Yi Chiang, Ching-Chien Chien Chen and Kandarp Desai University of Southern California Department
Automatic Extraction of Road Intersection Position, Connectivity and Orientations from Raster Maps
Automatic Extraction of Road Intersection Position, Connectivity and Orientations from Raster Maps Yao-Yi Chiang and Craig Knoblock University of Southern California epartment of Computer Science and Information
Automatic Extraction of Road Intersections from Raster Maps
Automatic Extraction of Road Intersections from Raster Maps Yao-Yi Chiang, Craig A. Knoblock and Ching-Chien Chen University of Southern California Department of Computer Science and Information Sciences
Snehal Thakkar, Craig A. Knoblock, Jose Luis Ambite, Cyrus Shahabi
From: AAAI Technical Report WS-02-07. Compilation copyright 2002, AAAI (www.aaai.org). All rights reserved. Dynamically Composing Web Services from On-line Sources Snehal Thakkar, Craig A. Knoblock, Jose
Automatic Record Linkage using Seeded Nearest Neighbour and SVM Classification
Automatic Record Linkage using Seeded Nearest Neighbour and SVM Classification Peter Christen Department of Computer Science, ANU College of Engineering and Computer Science, The Australian National University,
Constraint Solving by Composition
Constraint Solving by Composition Student: Zhijun Zhang Supervisor: Susan L. Epstein The Graduate Center of the City University of New York, Computer Science Department 365 Fifth Avenue, New York, NY 10016-4309,
A New Efficient Algorithm for Solving the Simple Temporal Problem
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln CSE Conference and Workshop Papers Computer Science and Engineering, Department of 2003 A New Efficient Algorithm for Solving
Identifying Maps on the World Wide Web
Identifying Maps on the World Wide Web Matthew Michelson, Aman Goel and Craig A. Knoblock Information Sciences Institute University of Southern California 2008 Motiv Earthquake map Population density Alignment
Overview Citation. ML Introduction. Overview Schedule. ML Intro Dataset. Introduction to Semi-Supervised Learning Review 10/4/2010
INFORMATICS SEMINAR SEPT. 27 & OCT. 4, 2010 Introduction to Semi-Supervised Learning Review 2 Overview Citation X. Zhu and A.B. Goldberg, Introduction to Semi- Supervised Learning, Morgan & Claypool Publishers,
Cluster Evaluation and Expectation Maximization! adapted from: Doug Downey and Bryan Pardo, Northwestern University
Cluster Evaluation and Expectation Maximization! adapted from: Doug Downey and Bryan Pardo, Northwestern University Kinds of Clustering Sequential Fast Cost Optimization Fixed number of clusters Hierarchical
Implementation of Trust Issues in Ecommerce
Implementation of Trust Issues in Ecommerce Mrs.Pallavi Chandratre ME computer Vidyalankar Institute of Technology, Mumbai. E-mail pallavi.chandratre@vit.edu.in Prof. Umesh Kulkarni DepartementOf Computer
PULP: Fast and Simple Complex Network Partitioning
PULP: Fast and Simple Complex Network Partitioning George Slota #,* Kamesh Madduri # Siva Rajamanickam * # The Pennsylvania State University *Sandia National Laboratories Dagstuhl Seminar 14461 November
Bus Detection and recognition for visually impaired people
Bus Detection and recognition for visually impaired people Hangrong Pan, Chucai Yi, and Yingli Tian The City College of New York The Graduate Center The City University of New York MAP4VIP Outline Motivation
IMPLEMENTATION OF TRUST RANK ALGORITHM ON WEB PAGES
IMPLEMENTATION OF TRUST RANK ALGORITHM ON WEB PAGES 1 Mrs.Pallavi Chandratre, 2 Prof. UmeshKulkarni 1 ME Computer, 2 DepartementOf Computer Engg. Vidyalankar Institute of Technology, Mumbai (India) ABSTRACT
Web Data Extraction. Craig Knoblock University of Southern California. This presentation is based on slides prepared by Ion Muslea and Kristina Lerman
Web Data Extraction Craig Knoblock University of Southern California This presentation is based on slides prepared by Ion Muslea and Kristina Lerman Extracting Data from Semistructured Sources NAME Casablanca
Data Provenance in Distributed Propagator Networks
Data Provenance in Distributed Propagator Networks Ian Jacobi Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 jacobi@csail.mit.edu Abstract.
Propagate the Right Thing: How Preferences Can Speed-Up Constraint Solving
Propagate the Right Thing: How Preferences Can Speed-Up Constraint Solving Christian Bessiere Anais Fabre* LIRMM-CNRS (UMR 5506) 161, rue Ada F-34392 Montpellier Cedex 5 (bessiere,fabre}@lirmm.fr Ulrich
Toward Architecture-based Reliability Estimation
Toward Architecture-based Estimation Roshanak Roshandel, Nenad Medvidovic Computer Science Department University of Southern California roshande@usc.edu ICSE Workshop on Architecting Dependable System
Bolt: I Know What You Did Last Summer In the Cloud
Bolt: I Know What You Did Last Summer In the Cloud Christina Delimitrou1 and Christos Kozyrakis2 1Cornell University, 2Stanford University Platform Lab Review February 2018 Executive Summary Problem: cloud
Data Mining in the Context of Entity Resolution
Data Mining in the Context of Entity Resolution Sofus A. Macskassy Fetch Technologies, Inc 841 Apollo Street El Segundo, CA 90245 Evan S. Gamble Fetch Technologies, Inc 841 Apollo Street El Segundo, CA
CSE 158. Web Mining and Recommender Systems. Midterm recap
CSE 158 Web Mining and Recommender Systems Midterm recap Midterm on Wednesday! 5:10 pm 6:10 pm Closed book but I ll provide a similar level of basic info as in the last page of previous midterms CSE 158
Dynamic Load Partitioning Strategies for Managing Data of Space and Time Heterogeneity in Parallel SAMR Applications
Dynamic Load Partitioning Strategies for Managing Data of Space and Time Heterogeneity in Parallel SAMR Applications Xiaolin Li and Manish Parashar The Applied Software Systems Laboratory Department of
WiSHF L. Stathes Hadjiefthymiades National and Kapodistrian University of Athens
CONTEXTUAL INFERENCE OVER IOT NODES - UNITE - UFRJ Stathes Hadjiefthymiades National and Kapodistrian University of Athens The research leading to these results has received funding from the European Horizon
An efficient consistency algorithm for the Temporal Constraint Satisfaction Problem
AI Communications 17 (2004) 213 221 213 IOS Press An efficient consistency algorithm for the Temporal Constraint Satisfaction Problem Berthe Y. Choueiry and Lin Xu Constraint Systems Laboratory, Department
Automatically and Accurately Conflating Raster Maps with Orthoimagery
Geoinformatica (2008) 12:377 410 DOI 10.1007/s10707-007-0033-0 Automatically and Accurately Conflating Raster Maps with Orthoimagery Ching-Chien Chen Craig A. Knoblock Cyrus Shahabi Received: 20 April
Exploring Parameterized Relational Consistency
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln CSE Technical reports Computer Science and Engineering, Department of 2009 Exploring Parameterized Relational Consistency
Limitations of XPath & XQuery in an Environment with Diverse Schemes
Exploiting Structure, Annotation, and Ontological Knowledge for Automatic Classification of XML-Data Martin Theobald, Ralf Schenkel, and Gerhard Weikum Saarland University Saarbrücken, Germany 23.06.2003
automatic digitization. In the context of ever increasing population worldwide and thereby
Chapter 1 Introduction In the recent time, many researchers had thrust upon developing various improvised methods of automatic digitization. In the context of ever increasing population worldwide and thereby
Dynamic Load Balancing of Unstructured Computations in Decision Tree Classifiers
Dynamic Load Balancing of Unstructured Computations in Decision Tree Classifiers A. Srivastava E. Han V. Kumar V. Singh Information Technology Lab Dept. of Computer Science Information Technology Lab Hitachi
Learning Similarity Metrics for Event Identification in Social Media. Hila Becker, Luis Gravano
Learning Similarity Metrics for Event Identification in Social Media Hila Becker, Luis Gravano Columbia University Mor Naaman Rutgers University Event Content in Social Media Sites Event Content in Social
Evaluation of Seed Selection Strategies for Vehicle to Vehicle Epidemic Information Dissemination
Evaluation of Seed Selection Strategies for Vehicle to Vehicle Epidemic Information Dissemination Richard Kershaw and Bhaskar Krishnamachari Ming Hsieh Department of Electrical Engineering, Viterbi School
Inferring Maps from GPS Data
Inferring Maps from GPS Data 1. Why infer maps from GPS traces? 2. Biagioni/Eriksson algorithm 3. Evaluation metrics 4. Similar approaches: satellite images, map update 5. Lab 4 1 2 3 OpenStreetMap Licensed
Contents. Preface to the Second Edition
Preface to the Second Edition v 1 Introduction 1 1.1 What Is Data Mining?....................... 4 1.2 Motivating Challenges....................... 5 1.3 The Origins of Data Mining....................
Generic Topology Mapping Strategies for Large-scale Parallel Architectures
Generic Topology Mapping Strategies for Large-scale Parallel Architectures Torsten Hoefler and Marc Snir Scientific talk at ICS 11, Tucson, AZ, USA, June 1 st 2011, Hierarchical Sparse Networks are Ubiquitous
Color Image Segmentation
Color Image Segmentation Yining Deng, B. S. Manjunath and Hyundoo Shin* Department of Electrical and Computer Engineering University of California, Santa Barbara, CA 93106-9560 *Samsung Electronics Inc.
Data Mining: Concepts and Techniques. (3 rd ed.) Chapter 3
Data Mining: Concepts and Techniques (3 rd ed.) Chapter 3 Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University 2011 Han, Kamber & Pei. All rights
Data Mining: Concepts and Techniques. (3 rd ed.) Chapter 3. Chapter 3: Data Preprocessing. Major Tasks in Data Preprocessing
Data Mining: Concepts and Techniques (3 rd ed.) Chapter 3 1 Chapter 3: Data Preprocessing Data Preprocessing: An Overview Data Quality Major Tasks in Data Preprocessing Data Cleaning Data Integration Data
Craig A. Knoblock University of Southern California
Web-Based Learning Craig A. Knoblock University of Southern California Joint work with J. L. Ambite, K. Lerman, A. Plangprasopchok, and T. Russ, USC C. Gazen and S. Minton, Fetch Technologies M. Carman,
Using CP When You Don't Know CP. Christian Bessiere LIRMM (CNRS/U. Montpellier)
Using CP When You Don't Know CP Christian Bessiere LIRMM (CNRS/U. Montpellier) An illustrative example 5-rooms flat (bedroom, bath, kitchen, sitting, dining) on a 6-room pattern The pattern: Constraints
CSE 7/5337: Information Retrieval and Web Search Document clustering I (IIR 16)
CSE 7/5337: Information Retrieval and Web Search Document clustering I (IIR 16) Michael Hahsler Southern Methodist University These slides are largely based on the slides by Hinrich Schütze Institute for
A Protection Mechanism Against Pure Worms
A Protection Mechanism Against Pure Worms by Dr. Frederick B. Cohen Abstract In this paper, we propose a viable protection mechanism against pure worms based on limited transitivity. We show that this
Predicting poverty from satellite imagery
Predicting poverty from satellite imagery Neal Jean, Michael Xie, Stefano Ermon Department of Computer Science Stanford University Matt Davis, Marshall Burke, David Lobell Department of Earth Systems Science
Lab 9. Julia Janicki. Introduction
Lab 9 Julia Janicki Introduction My goal for this project is to map a general land cover in the area of Alexandria in Egypt using supervised classification, specifically the Maximum Likelihood and Support
THREE-DIMENSIONA L ELECTRON MICROSCOP Y OF MACROMOLECULAR ASSEMBLIE S. Visualization of Biological Molecules in Their Native Stat e.
THREE-DIMENSIONA L ELECTRON MICROSCOP Y OF MACROMOLECULAR ASSEMBLIE S Visualization of Biological Molecules in Their Native Stat e Joachim Frank CHAPTER 1 Introduction 1 1 The Electron Microscope and
AutoODC: Automated Generation of Orthogonal Defect Classifications
AutoODC: Automated Generation of Orthogonal Defect Classifications LiGuo Huang 1 Vincent Ng 2 Isaac Persing 2 Ruili Geng 1 Xu Bai 1 Jeff Tian 1 Dept. of Computer Science and Engineering, Southern Methodist
Extreme-scale Graph Analysis on Blue Waters
Extreme-scale Graph Analysis on Blue Waters 2016 Blue Waters Symposium George M. Slota 1,2, Siva Rajamanickam 1, Kamesh Madduri 2, Karen Devine 1 1 Sandia National Laboratories a 2 The Pennsylvania State
Contract Programming For C++0x
Contract Programming For C++0x WG21/N1800 and J16/05-0060 Lawrence Crowl and Thorsten Ottosen lawrence.crowl@sun.com and nesotto@cs.aau.dk 2005-04-27 Overview This is an annotated version of the presentation
OOPLs - call graph construction Compile-time analysis of reference variables and fields. Example
OOPLs - call graph construction Compile-time analysis of reference variables and fields Determines to which objects (or types of objects) a reference variable may refer during execution Primarily hierarchy-based
INTEGRATION OF TREE DATABASE DERIVED FROM SATELLITE IMAGERY AND LIDAR POINT CLOUD DATA
INTEGRATION OF TREE DATABASE DERIVED FROM SATELLITE IMAGERY AND LIDAR POINT CLOUD DATA S. C. Liew 1, X. Huang 1, E. S. Lin 2, C. Shi 1, A. T. K. Yee 2, A. Tandon 2 1 Centre for Remote Imaging, Sensing
Picasso Panel Thinking Beyond 5 G David Corman
Picasso Panel Thinking Beyond 5 G David Corman Program Director Directorate for Computer and Information Science and Engineering National Science Foundation June 19, 2018 Some Motivation: Toward Smart
TensorFlow: A System for Learning-Scale Machine Learning. Google Brain
TensorFlow: A System for Learning-Scale Machine Learning Google Brain The Problem Machine learning is everywhere This is in large part due to: 1. Invention of more sophisticated machine learning models
Lessons Learned in Static Analysis Tool Evaluation. Providing World-Class Services for World-Class Competitiveness
Lessons Learned in Static Analysis Tool Evaluation 1 Overview Lessons learned in the evaluation of five (5) commercially available static analysis tools Topics Licensing Performance Measurement Limitations
Thwarting Traceback Attack on Freenet
Thwarting Traceback Attack on Freenet Guanyu Tian, Zhenhai Duan Florida State University {tian, duan}@cs.fsu.edu Todd Baumeister, Yingfei Dong University of Hawaii {baumeist, yingfei}@hawaii.edu Abstract
Network-on-Chip Architecture
Multiple Processor Systems(CMPE-655) Network-on-Chip Architecture Performance aspect and Firefly network architecture By Siva Shankar Chandrasekaran and SreeGowri Shankar Agenda (Enhancing performance)
Global Optimality in Neural Network Training
Global Optimality in Neural Network Training Benjamin D. Haeffele and René Vidal Johns Hopkins University, Center for Imaging Science. Baltimore, USA Questions in Deep Learning Architecture Design Optimization
2014 Google Earth Engine Research Award Report
2014 Google Earth Engine Research Award Report Title: Mapping Invasive Vegetation using Hyperspectral Data, Spectral Angle Mapping, and Mixture Tuned Matched Filtering Section I: Section II: Section III:
A Software LDPC Decoder Implemented on a Many-Core Array of Programmable Processors
A Software LDPC Decoder Implemented on a Many-Core Array of Programmable Processors Brent Bohnenstiehl and Bevan Baas Department of Electrical and Computer Engineering University of California, Davis {bvbohnen,
Flat Clustering. Slides are mostly from Hinrich Schütze. March 27, 2017
Flat Clustering Slides are mostly from Hinrich Schütze March 7, 07 / 79 Overview Recap Clustering: Introduction 3 Clustering in IR 4 K-means 5 Evaluation 6 How many clusters? / 79 Outline Recap Clustering:
Architecture Conscious Data Mining. Srinivasan Parthasarathy Data Mining Research Lab Ohio State University
Architecture Conscious Data Mining Srinivasan Parthasarathy Data Mining Research Lab Ohio State University KDD & Next Generation Challenges KDD is an iterative and interactive process the goal of which
Information Integration for the Masses
Information Integration for the Masses Jim Blythe Dipsy Kapoor Craig A. Knoblock Kristina Lerman USC Information Sciences Institute 4676 Admiralty Way, Marina del Rey, CA 90292 Steven Minton Fetch Technologies
Confidence Intervals. Dennis Sun Data 301
Dennis Sun Data 301 Statistical Inference probability Population / Box Sample / Data statistics The goal of statistics is to infer the unknown population from the sample. We ve already seen one mode of
Aligning and Integrating Data in Karma. Craig Knoblock University of Southern California
Aligning and Integrating Data in Karma Craig Knoblock University of Southern California Data Integration Approaches 3 Data Integration Approaches Data Warehousing 4 Data Integration Approaches Data Warehousing
A Scalable Architecture for Extracting, Aligning, Linking, and Visualizing Multi-Int Data
A Scalable Architecture for Extracting, Aligning, Linking, and Visualizing Multi-Int Data Craig Knoblock & Pedro Szekely University of Southern California Introduction Massive quantities of data available
Database and Knowledge-Base Systems: Data Mining. Martin Ester
Database and Knowledge-Base Systems: Data Mining Martin Ester Simon Fraser University School of Computing Science Graduate Course Spring 2006 CMPT 843, SFU, Martin Ester, 1-06 1 Introduction [Fayyad, Piatetsky-Shapiro
Software testing. Ian Sommerville 2006 Software Engineering, 8th edition. Chapter 23 Slide 1
Software testing Ian Sommerville 2006 Software Engineering, 8th edition. Chapter 23 Slide 1 Objectives To discuss the distinctions between validation testing and defect testing To describe the principles
Automatically and Accurately Conflating Orthoimagery and Street Maps
Automatically and Accurately Conflating Orthoimagery and Street Maps Ching-Chien Chen, Craig A. Knoblock, Cyrus Shahabi, Yao-Yi Chiang, and Snehal Thakkar University of Southern California Department of
Neighborhood Interchangeability and Dynamic Bundling for Non-Binary Finite CSPs
Neighborhood Interchangeability and Dynamic Bundling for Non-Binary Finite CSPs Anagh Lal and Berthe Y. Choueiry Constraint Systems Laboratory University of Nebraska-Lincoln {alal choueiry}@cse.unl.edu
PuLP: Scalable Multi-Objective Multi-Constraint Partitioning for Small-World Networks
PuLP: Scalable Multi-Objective Multi-Constraint Partitioning for Small-World Networks George M. Slota 1,2 Kamesh Madduri 2 Sivasankaran Rajamanickam 1 1 Sandia National Laboratories, 2 The Pennsylvania
Building a Bridge: from Pre-Silicon Verification to Post-Silicon Validation
Building a Bridge: from Pre-Silicon Verification to Post-Silicon Validation FMCAD, 2008 Moshe Levinger 26/11/2008 Talk Outline Simulation-Based Functional Verification Pre-Silicon Technologies Random Test
Algorithms for the Minimal Network of a CSP and a Classifier for Choosing Between Them
Algorithms for the Minimal Network of a CSP and a Classifier for Choosing Between Them Shant Karakashian 1 Robert J. Woodward 1 Berthe Y. Choueiry 1 Stephen D. Scott 1 Constraint Systems Laboratory University
ESC/Java2 extended static checking for Java Erik Poll Radboud University Nijmegen
ESC/Java2 extended static checking for Java Erik Poll Radboud University Nijmegen Erik Poll - JML p.1/19 Extended static checker for Java ESC/Java by Rustan Leino et.al. Extension ESC/Java2 by David Cok
EntityBases: Compiling, Organizing and Querying Massive Entity Repositories
EntityBases: Compiling, Organizing and Querying Massive Entity Repositories Craig A. Knoblock José Luis Ambite Kavita Ganesan, Maria Muslea University of Southern California Information Sciences Institute
Bolt: I Know What You Did Last Summer In the Cloud
Bolt: I Know What You Did Last Summer In the Cloud Christina Delimitrou 1 and Christos Kozyrakis 2 1 Cornell University, 2 Stanford University ASPLOS April 12 th 2017 Executive Summary Problem: cloud resource
Variable Selection 6.783, Biomedical Decision Support
6.783, Biomedical Decision Support (lrosasco@mit.edu) Department of Brain and Cognitive Science- MIT November 2, 2009 About this class Why selecting variables Approaches to variable selection Sparsity-based
Exploring Geotagged Images for Land-Use Classification
Exploring Geotagged Images for Land-Use Classification Daniel Leung Electrical Engineering & Computer Science University of California, Merced Merced, CA 95343 cleung3@ucmerced.edu Shawn Newsam Electrical
The testing process. Component testing. System testing
Software testing Objectives To discuss the distinctions between validation testing and defect testing To describe the principles of system and component testing To describe strategies for generating system
Mobile Wireless Sensor Network enables convergence of ubiquitous sensor services
1 2005 Nokia V1-Filename.ppt / yyyy-mm-dd / Initials Mobile Wireless Sensor Network enables convergence of ubiquitous sensor services Dr. Jian Ma, Principal Scientist Nokia Research Center, Beijing 2 2005
Active Query Sensing for Mobile Location Search
(by Mac Funamizu) Active Query Sensing for Mobile Location Search Felix. X. Yu, Rongrong Ji, Shih-Fu Chang Digital Video and Multimedia Lab Columbia University November 29, 2011 Outline Motivation Problem
Successfully Filing Broadband Deployment Data with USAC
Successfully Filing Broadband Deployment Data with USAC Meet our Team Alex Wolff Manager of Project and Product Management alex.wolff@usac.org Bill Johnson Director of Geographic Information Systems (GIS)
Information Retrieval and Web Search Engines
Information Retrieval and Web Search Engines Lecture 7: Document Clustering December 4th, 2014 Wolf-Tilo Balke and José Pinto Institut für Informationssysteme Technische Universität Braunschweig The Cluster
CS 4100 // artificial intelligence
CS 4100 // artificial intelligence instructor: byron wallace Constraint Satisfaction Problems Attribution: many of these slides are modified versions of those distributed with the UC Berkeley CS188 materials
Automatic training example selection for scalable unsupervised record linkage
Automatic training example selection for scalable unsupervised record linkage Peter Christen Department of Computer Science, The Australian National University, Canberra, Australia Contact: peter.christen@anu.edu.au
Variety-Aware OLAP of Document-Oriented Databases
Variety-Aware OLAP of Document-Oriented Databases Enrico Gallinucci, Matteo Golfarelli, Stefano Rizzi DISI University of Bologna, Italy Introduction In recent years, schemalessdbmss have progressively
Location Prediction With Sparse GPS Data
Location Prediction With Sparse GPS Data Ayush Jaiswal1, Yao-Yi Chiang2, Craig A. Knoblock3, Liang Lan4 1 2 3 National Institute of Technology Calicut, India Email: ayush_bcs10@nitc.ac.in Spatial Sciences
Visualizing Metaclusters
Visualizing Metaclusters Casey Smith May, 007 Introduction The metaclustering process results in many clusterings of a data set, a handful of which may be most useful to a user. Examining all the clusterings
Data Reorganization Interface
Data Reorganization Interface Kenneth Cain Mercury Computer Systems, Inc. Phone: (978)-967-1645 Email Address: kcain@mc.com Abstract: This presentation will update the HPEC community on the latest status
Security Issues Formalization
Security Issues Formalization V. T. Dimitrov University of Sofia, Faculty of Mathematics and Informatics, 5 James Bourchier Blvd, 1164, Sofia, Bulgaria E-mail: cht@fmi.uni-sofia.bg Software bugs are primary
IJREAT International Journal of Research in Engineering & Advanced Technology, Volume 1, Issue 5, Oct-Nov, ISSN:
IJREAT International Journal of Research in Engineering & Advanced Technology, Volume 1, Issue 5, Oct-Nov, 20131 Improve Search Engine Relevance with Filter session Addlin Shinney R 1, Saravana Kumar T
Profile-Guided Program Simplification for Effective Testing and Analysis
Profile-Guided Program Simplification for Effective Testing and Analysis Lingxiao Jiang Zhendong Su Program Execution Profiles A profile is a set of information about an execution, either succeeded or
WebSphere Application Server, Version 5. What s New?
WebSphere Application Server, Version 5 What s New? 1 WebSphere Application Server, V5 represents a continuation of the evolution to a single, integrated, cost effective, Web services-enabled, J2EE server
Locally Linear Landmarks for large-scale manifold learning
Locally Linear Landmarks for large-scale manifold learning Max Vladymyrov and Miguel Á. Carreira-Perpiñán Electrical Engineering and Computer Science University of California, Merced http://eecs.ucmerced.edu
Information Integration of Partially Labeled Data
Information Integration of Partially Labeled Data Steffen Rendle and Lars Schmidt-Thieme Information Systems and Machine Learning Lab, University of Hildesheim srendle@ismll.uni-hildesheim.de, schmidt-thieme@ismll.uni-hildesheim.de
Lab 2. Decision (Classification) Tree
Lab 2. Decision (Classification) Tree Represented as a set of hierarchically-arranged decision rules (i.e., tree-branch-leaf) Could be generated by knowledge engineering, neural network, or statistic methods.
A Distinctive View across the Continuum of Care with Oracle Healthcare Master Person Index ORACLE WHITE PAPER NOVEMBER 2015
A Distinctive View across the Continuum of Care with Oracle Healthcare Master Person Index ORACLE WHITE PAPER NOVEMBER 2015 Disclaimer The following is intended to outline our general product direction.
Motivation. Overview. Scalable Dynamic Analysis for Automated Fault Location and Avoidance. Rajiv Gupta. Program Execution
Motivation Scalable Dynamic Analysis for Automated Fault Location and Avoidance Rajiv Gupta Funded by NSF grants from CPA, CSR, & CRI programs and grants from Microsoft Research Software bugs cost the
Knowledge Discovery in Data Bases
Knowledge Discovery in Data Bases Chien-Chung Chan Department of CS University of Akron Akron, OH 44325-4003 2/24/99 1 Why KDD? We are drowning in information, but starving for knowledge John Naisbett
Milind Kulkarni Research Statement
Milind Kulkarni Research Statement With the increasing ubiquity of multicore processors, interest in parallel programming is again on the upswing. Over the past three decades, languages and compilers researchers