Big Geospatial Data + Deep Learning + High Performance Computing = Geospatial Intelligence Bingcai Zhang

Size: px
Start display at page:

Download "Big Geospatial Data + Deep Learning + High Performance Computing = Geospatial Intelligence Bingcai Zhang"

Transcription

1 GEOSPATIAL EXPLOITATION PRODUCTS Big Geospatial Data + Deep Learning + High Performance Computing = Geospatial Intelligence Bingcai Zhang Tech Fellow GXP Xplorer and SOCET GXP are registered trademarks of BAE Systems. All other brands, product names, and trademarks are property of their respective owners. This document gives only a general description of the product(s) or service(s) offered by BAE Systems and, except where expressly provided otherwise, shall not form part of any contract. From time to time, changes may be made in the products or conditions of supply. Approved for public release on 02/25/

2 Do We Have Enough Parking? Demo 300 drone images GSD = 3.5cm 1600 cars detected 99% detection accuracy 6 pixel positional accuracy 10 degree orientation accuracy 0.001% false positive error rate Data from Prof. Dunn Model trained with 7.5cm GSD 2

3 What Is Deep Learning? It works just like the brain (least favorite definition according to LeCun) car 0.5 car 1.0 not car 0.5 not car 0.0 IEEE SPECTRUM 3

4 Simplicity Learning Object detection very complex Breakup objects Learn one type of object at a time Detect one type of object at a time Inspired by 4 year old pre-school best learning practice Learn one alphabet per week Learn letter A five days in a row (reinforcement learning) Inspired by drug discovery One specific drug for one specific disease No panacea Based on two decades of research experience Transform a complex problem into its simplest components Solve each component one at a time 4

5 Data Normalization vs. Data Augmentation Scale normalization vs. scale augmentation Color normalization vs. color augmentation Rotation normalization (geospatial images) 5

6 Simplicity Learning vs. Non-Simplicity Learning vehicle and anything else vehicle, stop sign, and anything else 6

7 Handcrafted Automatic Feature Extraction 7

8 3D Features (3D Glasses) 8

9 Rotation Variant Object Detection avg min max std

10 Rotation Variant Object Detection probability probability

11 Rotation Variant Object Detection 3 11

12 Rotation Variant Object Detection 4 12

13 Singular Classification soft max : c e j 1 zi e z j Open-ended negative training examples problem Not work just like the brain Three new algorithms reducing false positive by 10 times 13

14 Human vs. Machine Human: Radoslav Gaidadjiev Two master s degree Twenty years of experience with imagery Machine: DeepObject with one K40 GPU Human achieved accuracy of 99.9% Understand parking lot Machine achieved accuracy of 99.3% Demo Everyone participates human vs. machine 14

15 Quality Training Samples = $ Quality training samples are the new currency in deep learning Non-deep learning: AOD failed to recognize a car and an image analyst found this missing car A DR is generated and the cost to fix this DR is $1000 Deep learning: This missing car could be automatically collected as a positive training example and added to the training sample database Train deep learning network again with the new training example database Cost could be as low as $10 Potential cost saving is very significant 15

16 Mistakes = Quality Training Samples We learn from our mistakes (so does deep learning) Not all training samples are created equal Mistakes are more likely to have greater gradient Have stronger influence on decision boundary Quality of training samples is as important as quantity of training samples Data augmentation increases quantity Mistakes increases quality Future geospatial intelligence software should collect users intelligence Every mistake could translate into an enhancement to geospatial intelligence software 16

17 Future Intelligent Geospatial Intelligence System An intelligent system that could become smarter and smarter by learning from its mistakes An intelligent system that could detect and monitor defense relevant objects at 99% accuracy With 99% accuracy, this may be the game changer in geospatial intelligence domain Significantly reduce software engineering and enhancement costs 17

18 Questions? Dr. Bingcai Zhang

Deep search. Enhancing a search bar using machine learning. Ilgün Ilgün & Cedric Reichenbach

Deep search. Enhancing a search bar using machine learning. Ilgün Ilgün & Cedric Reichenbach #BaselOne7 Deep search Enhancing a search bar using machine learning Ilgün Ilgün & Cedric Reichenbach We are not researchers Outline I. Periscope: A search tool II. Goals III. Deep learning IV. Applying

More information

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should

More information

CS Machine Learning

CS Machine Learning CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing

More information

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler Machine Learning and Data Mining Ensembles of Learners Prof. Alexander Ihler Ensemble methods Why learn one classifier when you can learn many? Ensemble: combine many predictors (Weighted) combina

More information

Australian Journal of Basic and Applied Sciences

Australian Journal of Basic and Applied Sciences AENSI Journals Australian Journal of Basic and Applied Sciences ISSN:1991-8178 Journal home page: www.ajbasweb.com Feature Selection Technique Using Principal Component Analysis For Improving Fuzzy C-Mean

More information

MYCIN. The MYCIN Task

MYCIN. The MYCIN Task MYCIN Developed at Stanford University in 1972 Regarded as the first true expert system Assists physicians in the treatment of blood infections Many revisions and extensions over the years The MYCIN Task

More information

Generative models and adversarial training

Generative models and adversarial training Day 4 Lecture 1 Generative models and adversarial training Kevin McGuinness kevin.mcguinness@dcu.ie Research Fellow Insight Centre for Data Analytics Dublin City University What is a generative model?

More information

Knowledge Transfer in Deep Convolutional Neural Nets

Knowledge Transfer in Deep Convolutional Neural Nets Knowledge Transfer in Deep Convolutional Neural Nets Steven Gutstein, Olac Fuentes and Eric Freudenthal Computer Science Department University of Texas at El Paso El Paso, Texas, 79968, U.S.A. Abstract

More information

Laboratorio di Intelligenza Artificiale e Robotica

Laboratorio di Intelligenza Artificiale e Robotica Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning

More information

Human Emotion Recognition From Speech

Human Emotion Recognition From Speech RESEARCH ARTICLE OPEN ACCESS Human Emotion Recognition From Speech Miss. Aparna P. Wanare*, Prof. Shankar N. Dandare *(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati

More information

Lecture 1: Machine Learning Basics

Lecture 1: Machine Learning Basics 1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3

More information

MINUTE TO WIN IT: NAMING THE PRESIDENTS OF THE UNITED STATES

MINUTE TO WIN IT: NAMING THE PRESIDENTS OF THE UNITED STATES MINUTE TO WIN IT: NAMING THE PRESIDENTS OF THE UNITED STATES THE PRESIDENTS OF THE UNITED STATES Project: Focus on the Presidents of the United States Objective: See how many Presidents of the United States

More information

Managerial Decision Making

Managerial Decision Making Course Business Managerial Decision Making Session 4 Conditional Probability & Bayesian Updating Surveys in the future... attempt to participate is the important thing Work-load goals Average 6-7 hours,

More information

Rule Learning With Negation: Issues Regarding Effectiveness

Rule Learning With Negation: Issues Regarding Effectiveness Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United

More information

SIMPLY THE BEST! AND MINDSETS. (Growth or fixed?)

SIMPLY THE BEST! AND MINDSETS. (Growth or fixed?) SIMPLY THE BEST! AND MINDSETS (Growth or fixed?) SIMPLY THE BEST Why American Schools are the Best in the World! Kindergarten through High School EVERYONE! No exceptions. No disclaimers. So why all the

More information

Linking the Ohio State Assessments to NWEA MAP Growth Tests *

Linking the Ohio State Assessments to NWEA MAP Growth Tests * Linking the Ohio State Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. August 2016 Introduction Northwest Evaluation Association (NWEA

More information

Data Modeling and Databases II Entity-Relationship (ER) Model. Gustavo Alonso, Ce Zhang Systems Group Department of Computer Science ETH Zürich

Data Modeling and Databases II Entity-Relationship (ER) Model. Gustavo Alonso, Ce Zhang Systems Group Department of Computer Science ETH Zürich Data Modeling and Databases II Entity-Relationship (ER) Model Gustavo Alonso, Ce Zhang Systems Group Department of Computer Science ETH Zürich Database design Information Requirements Requirements Engineering

More information

ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF

ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF Read Online and Download Ebook ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF Click link bellow and free register to download

More information

Geospatial Visual Analytics Tutorial. Gennady Andrienko & Natalia Andrienko

Geospatial Visual Analytics Tutorial. Gennady Andrienko & Natalia Andrienko Geospatial Visual Analytics Tutorial Gennady Andrienko & Natalia Andrienko http://geoanalytics.net Outline Visual Analytics Introduction - Definition of Visual Analytics - Roots - What is new? Where are

More information

Enduring Understandings: Students will understand that

Enduring Understandings: Students will understand that ART Pop Art and Technology: Stage 1 Desired Results Established Goals TRANSFER GOAL Students will: - create a value scale using at least 4 values of grey -explain characteristics of the Pop art movement

More information

Guide to Teaching Computer Science

Guide to Teaching Computer Science Guide to Teaching Computer Science Orit Hazzan Tami Lapidot Noa Ragonis Guide to Teaching Computer Science An Activity-Based Approach Dr. Orit Hazzan Associate Professor Technion - Israel Institute of

More information

Contents. Foreword... 5

Contents. Foreword... 5 Contents Foreword... 5 Chapter 1: Addition Within 0-10 Introduction... 6 Two Groups and a Total... 10 Learn Symbols + and =... 13 Addition Practice... 15 Which is More?... 17 Missing Items... 19 Sums with

More information

Laboratorio di Intelligenza Artificiale e Robotica

Laboratorio di Intelligenza Artificiale e Robotica Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning

More information

An OO Framework for building Intelligence and Learning properties in Software Agents

An OO Framework for building Intelligence and Learning properties in Software Agents An OO Framework for building Intelligence and Learning properties in Software Agents José A. R. P. Sardinha, Ruy L. Milidiú, Carlos J. P. Lucena, Patrick Paranhos Abstract Software agents are defined as

More information

Speech Emotion Recognition Using Support Vector Machine

Speech Emotion Recognition Using Support Vector Machine Speech Emotion Recognition Using Support Vector Machine Yixiong Pan, Peipei Shen and Liping Shen Department of Computer Technology Shanghai JiaoTong University, Shanghai, China panyixiong@sjtu.edu.cn,

More information

The University of Amsterdam s Concept Detection System at ImageCLEF 2011

The University of Amsterdam s Concept Detection System at ImageCLEF 2011 The University of Amsterdam s Concept Detection System at ImageCLEF 2011 Koen E. A. van de Sande and Cees G. M. Snoek Intelligent Systems Lab Amsterdam, University of Amsterdam Software available from:

More information

Disambiguation of Thai Personal Name from Online News Articles

Disambiguation of Thai Personal Name from Online News Articles Disambiguation of Thai Personal Name from Online News Articles Phaisarn Sutheebanjard Graduate School of Information Technology Siam University Bangkok, Thailand mr.phaisarn@gmail.com Abstract Since online

More information

OCR for Arabic using SIFT Descriptors With Online Failure Prediction

OCR for Arabic using SIFT Descriptors With Online Failure Prediction OCR for Arabic using SIFT Descriptors With Online Failure Prediction Andrey Stolyarenko, Nachum Dershowitz The Blavatnik School of Computer Science Tel Aviv University Tel Aviv, Israel Email: stloyare@tau.ac.il,

More information

Ansys Tutorial Random Vibration

Ansys Tutorial Random Vibration Ansys Tutorial Random Free PDF ebook Download: Ansys Tutorial Download or Read Online ebook ansys tutorial random vibration in PDF Format From The Best User Guide Database Random vibration analysis gives

More information

Citrine Informatics. The Latest from Citrine. Citrine Informatics. The data analytics platform for the physical world

Citrine Informatics. The Latest from Citrine. Citrine Informatics. The data analytics platform for the physical world Citrine Informatics The data analytics platform for the physical world The Latest from Citrine Summit on Data and Analytics for Materials Research 31 October 2016 Our Mission is Simple Add as much value

More information

Lecture 1: Basic Concepts of Machine Learning

Lecture 1: Basic Concepts of Machine Learning Lecture 1: Basic Concepts of Machine Learning Cognitive Systems - Machine Learning Ute Schmid (lecture) Johannes Rabold (practice) Based on slides prepared March 2005 by Maximilian Röglinger, updated 2010

More information

Exploration. CS : Deep Reinforcement Learning Sergey Levine

Exploration. CS : Deep Reinforcement Learning Sergey Levine Exploration CS 294-112: Deep Reinforcement Learning Sergey Levine Class Notes 1. Homework 4 due on Wednesday 2. Project proposal feedback sent Today s Lecture 1. What is exploration? Why is it a problem?

More information

Intel-powered Classmate PC. SMART Response* Training Foils. Version 2.0

Intel-powered Classmate PC. SMART Response* Training Foils. Version 2.0 Intel-powered Classmate PC Training Foils Version 2.0 1 Legal Information INFORMATION IN THIS DOCUMENT IS PROVIDED IN CONNECTION WITH INTEL PRODUCTS. NO LICENSE, EXPRESS OR IMPLIED, BY ESTOPPEL OR OTHERWISE,

More information

Session 2B From understanding perspectives to informing public policy the potential and challenges for Q findings to inform survey design

Session 2B From understanding perspectives to informing public policy the potential and challenges for Q findings to inform survey design Session 2B From understanding perspectives to informing public policy the potential and challenges for Q findings to inform survey design Paper #3 Five Q-to-survey approaches: did they work? Job van Exel

More information

Disciplinary Literacy in Science

Disciplinary Literacy in Science Disciplinary Literacy in Science 18 th UCF Literacy Symposium 4/1/2016 Vicky Zygouris-Coe, Ph.D. UCF, CEDHP vzygouri@ucf.edu April 1, 2016 Objectives Examine the benefits of disciplinary literacy for science

More information

Humboldt-Universität zu Berlin

Humboldt-Universität zu Berlin Humboldt-Universität zu Berlin Department of Informatics Computer Science Education / Computer Science and Society Seminar Educational Data Mining Organisation Place: RUD 25, 3.101 Date: Wednesdays, 15:15

More information

DOUBLE DEGREE PROGRAM AT EURECOM. June 2017 Caroline HANRAS International Relations Manager

DOUBLE DEGREE PROGRAM AT EURECOM. June 2017 Caroline HANRAS International Relations Manager DOUBLE DEGREE PROGRAM AT EURECOM June 2017 Caroline HANRAS International Relations Manager KEY FACTS 1991 Creation by EPFL and Telecom ParisTech 3 Main Fields of Expertise 300 23 Master Students Professors

More information

Word Segmentation of Off-line Handwritten Documents

Word Segmentation of Off-line Handwritten Documents Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department

More information

Unit 7 Data analysis and design

Unit 7 Data analysis and design 2016 Suite Cambridge TECHNICALS LEVEL 3 IT Unit 7 Data analysis and design A/507/5007 Guided learning hours: 60 Version 2 - revised May 2016 *changes indicated by black vertical line ocr.org.uk/it LEVEL

More information

Natural Language Processing. George Konidaris

Natural Language Processing. George Konidaris Natural Language Processing George Konidaris gdk@cs.brown.edu Fall 2017 Natural Language Processing Understanding spoken/written sentences in a natural language. Major area of research in AI. Why? Humans

More information

Stopping rules for sequential trials in high-dimensional data

Stopping rules for sequential trials in high-dimensional data Stopping rules for sequential trials in high-dimensional data Sonja Zehetmayer, Alexandra Graf, and Martin Posch Center for Medical Statistics, Informatics and Intelligent Systems Medical University of

More information

Notes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1

Notes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1 Notes on The Sciences of the Artificial Adapted from a shorter document written for course 17-652 (Deciding What to Design) 1 Ali Almossawi December 29, 2005 1 Introduction The Sciences of the Artificial

More information

HIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10K OBJECTS CLASSIFICATION

HIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10K OBJECTS CLASSIFICATION HIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10K OBJECTS CLASSIFICATION Atul Laxman Katole 1, Krishna Prasad Yellapragada 1, Amish Kumar Bedi 1, Sehaj Singh Kalra 1 and Mynepalli Siva Chaitanya 1 1 Samsung

More information

Individual Differences & Item Effects: How to test them, & how to test them well

Individual Differences & Item Effects: How to test them, & how to test them well Individual Differences & Item Effects: How to test them, & how to test them well Individual Differences & Item Effects Properties of subjects Cognitive abilities (WM task scores, inhibition) Gender Age

More information

CLASSIFICATION OF TEXT DOCUMENTS USING INTEGER REPRESENTATION AND REGRESSION: AN INTEGRATED APPROACH

CLASSIFICATION OF TEXT DOCUMENTS USING INTEGER REPRESENTATION AND REGRESSION: AN INTEGRATED APPROACH ISSN: 0976-3104 Danti and Bhushan. ARTICLE OPEN ACCESS CLASSIFICATION OF TEXT DOCUMENTS USING INTEGER REPRESENTATION AND REGRESSION: AN INTEGRATED APPROACH Ajit Danti 1 and SN Bharath Bhushan 2* 1 Department

More information

Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling

Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling Notebook for PAN at CLEF 2013 Andrés Alfonso Caurcel Díaz 1 and José María Gómez Hidalgo 2 1 Universidad

More information

Controlled vocabulary

Controlled vocabulary Indexing languages 6.2.2. Controlled vocabulary Overview Anyone who has struggled to find the exact search term to retrieve information about a certain subject can benefit from controlled vocabulary. Controlled

More information

Activities, Exercises, Assignments Copyright 2009 Cem Kaner 1

Activities, Exercises, Assignments Copyright 2009 Cem Kaner 1 Patterns of activities, iti exercises and assignments Workshop on Teaching Software Testing January 31, 2009 Cem Kaner, J.D., Ph.D. kaner@kaner.com Professor of Software Engineering Florida Institute of

More information

PL Preceptor News June 2012

PL Preceptor News June 2012 PL Preceptor News June 2012 In This Issue: Save your spot in the summer Preceptor Live CE webinars Get the new PL Journal Club materials 18 hours of home-study Preceptor Training CE available How to update

More information

Course Content Concepts

Course Content Concepts CS 1371 SYLLABUS, Fall, 2017 Revised 8/6/17 Computing for Engineers Course Content Concepts The students will be expected to be familiar with the following concepts, either by writing code to solve problems,

More information

CSL465/603 - Machine Learning

CSL465/603 - Machine Learning CSL465/603 - Machine Learning Fall 2016 Narayanan C Krishnan ckn@iitrpr.ac.in Introduction CSL465/603 - Machine Learning 1 Administrative Trivia Course Structure 3-0-2 Lecture Timings Monday 9.55-10.45am

More information

Millersville University Degree Works Training User Guide

Millersville University Degree Works Training User Guide Millersville University Degree Works Training User Guide Page 1 Table of Contents Introduction... 5 What is Degree Works?... 5 Degree Works Functionality Summary... 6 Access to Degree Works... 8 Login

More information

VISTA GOVERNANCE DOCUMENT

VISTA GOVERNANCE DOCUMENT VISTA GOVERNANCE DOCUMENT Volvo Trucks and Buses Performance is everything 1 Content 1 Definitions VISTA 2017-2018 4 1.1 Main Objective 5 1.2 Scope/Description 5 1.3 Authorized Volvo dealers/workshop 5

More information

System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks

System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 Tzu-Hsuan Yang, 2 Tzu-Hsuan Tseng, and 3 Chia-Ping Chen Department of Computer Science and Engineering

More information

The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, / X

The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, / X The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, 2013 10.12753/2066-026X-13-154 DATA MINING SOLUTIONS FOR DETERMINING STUDENT'S PROFILE Adela BÂRA,

More information

1.1 Background. 1 Introduction

1.1 Background. 1 Introduction Information Fusion for Situational Awareness Dr. John Salerno, Mr. Mike Hinman, Mr. Doug Boulware, Mr. Paul Bello AFRL/IFEA, Air Force Research Laboratory, Rome Research SiteRome, NY, USA John.Salerno@rl.af.mil,

More information

(Sub)Gradient Descent

(Sub)Gradient Descent (Sub)Gradient Descent CMSC 422 MARINE CARPUAT marine@cs.umd.edu Figures credit: Piyush Rai Logistics Midterm is on Thursday 3/24 during class time closed book/internet/etc, one page of notes. will include

More information

Chapter 10 APPLYING TOPIC MODELING TO FORENSIC DATA. 1. Introduction. Alta de Waal, Jacobus Venter and Etienne Barnard

Chapter 10 APPLYING TOPIC MODELING TO FORENSIC DATA. 1. Introduction. Alta de Waal, Jacobus Venter and Etienne Barnard Chapter 10 APPLYING TOPIC MODELING TO FORENSIC DATA Alta de Waal, Jacobus Venter and Etienne Barnard Abstract Most actionable evidence is identified during the analysis phase of digital forensic investigations.

More information

LEGO MINDSTORMS Education EV3 Coding Activities

LEGO MINDSTORMS Education EV3 Coding Activities LEGO MINDSTORMS Education EV3 Coding Activities s t e e h s k r o W t n e d Stu LEGOeducation.com/MINDSTORMS Contents ACTIVITY 1 Performing a Three Point Turn 3-6 ACTIVITY 2 Written Instructions for a

More information

Rule Learning with Negation: Issues Regarding Effectiveness

Rule Learning with Negation: Issues Regarding Effectiveness Rule Learning with Negation: Issues Regarding Effectiveness Stephanie Chua, Frans Coenen, and Grant Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX

More information

STAT 220 Midterm Exam, Friday, Feb. 24

STAT 220 Midterm Exam, Friday, Feb. 24 STAT 220 Midterm Exam, Friday, Feb. 24 Name Please show all of your work on the exam itself. If you need more space, use the back of the page. Remember that partial credit will be awarded when appropriate.

More information

Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming

Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming Data Mining VI 205 Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming C. Romero, S. Ventura, C. Hervás & P. González Universidad de Córdoba, Campus Universitario de

More information

Linking Task: Identifying authors and book titles in verbose queries

Linking Task: Identifying authors and book titles in verbose queries Linking Task: Identifying authors and book titles in verbose queries Anaïs Ollagnier, Sébastien Fournier, and Patrice Bellot Aix-Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296,

More information

CHEMISTRY 400 Senior Seminar in Chemistry Spring 2013

CHEMISTRY 400 Senior Seminar in Chemistry Spring 2013 CHEMISTRY 400 Senior Seminar in Chemistry Spring 2013 Instructor: Prof. C. J. Nichols PHSC 308 898-5541 cjnichols@csuchico.edu http://www.csuchico.edu/~cjnichols Office Hours: W 9-10:30; Th 10-12; F 9-10:30

More information

content First Introductory book to cover CAPM First to differentiate expected and required returns First to discuss the intrinsic value of stocks

content First Introductory book to cover CAPM First to differentiate expected and required returns First to discuss the intrinsic value of stocks content First Introductory book to cover CAPM First to differentiate expected and required returns First to discuss the intrinsic value of stocks presentation First timelines to explain TVM First financial

More information

A Case Study: News Classification Based on Term Frequency

A Case Study: News Classification Based on Term Frequency A Case Study: News Classification Based on Term Frequency Petr Kroha Faculty of Computer Science University of Technology 09107 Chemnitz Germany kroha@informatik.tu-chemnitz.de Ricardo Baeza-Yates Center

More information

Data Fusion Models in WSNs: Comparison and Analysis

Data Fusion Models in WSNs: Comparison and Analysis Proceedings of 2014 Zone 1 Conference of the American Society for Engineering Education (ASEE Zone 1) Data Fusion s in WSNs: Comparison and Analysis Marwah M Almasri, and Khaled M Elleithy, Senior Member,

More information

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System QuickStroke: An Incremental On-line Chinese Handwriting Recognition System Nada P. Matić John C. Platt Λ Tony Wang y Synaptics, Inc. 2381 Bering Drive San Jose, CA 95131, USA Abstract This paper presents

More information

TD(λ) and Q-Learning Based Ludo Players

TD(λ) and Q-Learning Based Ludo Players TD(λ) and Q-Learning Based Ludo Players Majed Alhajry, Faisal Alvi, Member, IEEE and Moataz Ahmed Abstract Reinforcement learning is a popular machine learning technique whose inherent self-learning ability

More information

USER ADAPTATION IN E-LEARNING ENVIRONMENTS

USER ADAPTATION IN E-LEARNING ENVIRONMENTS USER ADAPTATION IN E-LEARNING ENVIRONMENTS Paraskevi Tzouveli Image, Video and Multimedia Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens tpar@image.

More information

Semi-Supervised Face Detection

Semi-Supervised Face Detection Semi-Supervised Face Detection Nicu Sebe, Ira Cohen 2, Thomas S. Huang 3, Theo Gevers Faculty of Science, University of Amsterdam, The Netherlands 2 HP Research Labs, USA 3 Beckman Institute, University

More information

K-12 Blueprint Logo Placement

K-12 Blueprint Logo Placement K-12 Blueprint Logo Placement The K-12 Blueprint logo is a sturdy symbol of the combined elements that encompass and support what K-12 Blueprint is all about. To represent the beauty of this Brand please

More information

Reducing Features to Improve Bug Prediction

Reducing Features to Improve Bug Prediction Reducing Features to Improve Bug Prediction Shivkumar Shivaji, E. James Whitehead, Jr., Ram Akella University of California Santa Cruz {shiv,ejw,ram}@soe.ucsc.edu Sunghun Kim Hong Kong University of Science

More information

Modeling user preferences and norms in context-aware systems

Modeling user preferences and norms in context-aware systems Modeling user preferences and norms in context-aware systems Jonas Nilsson, Cecilia Lindmark Jonas Nilsson, Cecilia Lindmark VT 2016 Bachelor's thesis for Computer Science, 15 hp Supervisor: Juan Carlos

More information

Crestron BB-9L Pre-Construction Wall Mount Back Box Installation Guide

Crestron BB-9L Pre-Construction Wall Mount Back Box Installation Guide Crestron BB-9L Pre-Construction Wall Mount Back Box Installation Guide This document was prepared and written by the Technical Documentation department at: Crestron Electronics, Inc. 15 Volvo Drive Rockleigh,

More information

11:00 am Robotics and the Law: An American Perspective Prof. Ryan Calo, University of Washington School of Law

11:00 am Robotics and the Law: An American Perspective Prof. Ryan Calo, University of Washington School of Law Workshop Robotics and Autonomous Systems International Law and Social Neuroscience Insights 20 June, 2016 Pressezentrum Ost, AUTOMATICA, Messe München, 81823 Munich Agenda 10:00 am Welcome Dr. Alexander

More information

Classify: by elimination Road signs

Classify: by elimination Road signs WORK IT Road signs 9-11 Level 1 Exercise 1 Aims Practise observing a series to determine the points in common and the differences: the observation criteria are: - the shape; - what the message represents.

More information

Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems

Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems Ajith Abraham School of Business Systems, Monash University, Clayton, Victoria 3800, Australia. Email: ajith.abraham@ieee.org

More information

Forget catastrophic forgetting: AI that learns after deployment

Forget catastrophic forgetting: AI that learns after deployment Forget catastrophic forgetting: AI that learns after deployment Anatoly Gorshechnikov CTO, Neurala 1 Neurala at a glance Programming neural networks on GPUs since circa 2 B.C. Founded in 2006 expecting

More information

Standards-Based Bulletin Boards. Tuesday, January 17, 2012 Principals Meeting

Standards-Based Bulletin Boards. Tuesday, January 17, 2012 Principals Meeting Standards-Based Bulletin Boards Tuesday, January 17, 2012 Principals Meeting Questions: How do your teachers demonstrate the rigor of the standards-based assignments? How do your teachers demonstrate that

More information

Characteristics of the Text Genre Realistic fi ction Text Structure

Characteristics of the Text Genre Realistic fi ction Text Structure LESSON 14 TEACHER S GUIDE by Oscar Hagen Fountas-Pinnell Level A Realistic Fiction Selection Summary A boy and his mom visit a pond and see and count a bird, fish, turtles, and frogs. Number of Words:

More information

Relating Math to the Real World: A Study of Platonic Solids and Tessellations

Relating Math to the Real World: A Study of Platonic Solids and Tessellations Sheila Green Professor Dyrness ED200: Analyzing Schools Curriculum Project December 15, 2010 Relating Math to the Real World: A Study of Platonic Solids and Tessellations Introduction The study of Platonic

More information

Welcome to. ECML/PKDD 2004 Community meeting

Welcome to. ECML/PKDD 2004 Community meeting Welcome to ECML/PKDD 2004 Community meeting A brief report from the program chairs Jean-Francois Boulicaut, INSA-Lyon, France Floriana Esposito, University of Bari, Italy Fosca Giannotti, ISTI-CNR, Pisa,

More information

STUDENTS' RATINGS ON TEACHER

STUDENTS' RATINGS ON TEACHER STUDENTS' RATINGS ON TEACHER Faculty Member: CHEW TECK MENG IVAN Module: Activity Type: DATA STRUCTURES AND ALGORITHMS I CS1020 LABORATORY Class Size/Response Size/Response Rate : 21 / 14 / 66.67% Contact

More information

November 17, 2017 ARIZONA STATE UNIVERSITY. ADDENDUM 3 RFP Digital Integrated Enrollment Support for Students

November 17, 2017 ARIZONA STATE UNIVERSITY. ADDENDUM 3 RFP Digital Integrated Enrollment Support for Students November 17, 2017 ARIZONA STATE UNIVERSITY ADDENDUM 3 RFP 331801 Digital Integrated Enrollment Support for Students Please note the following answers to questions that were asked prior to the deadline

More information

Mobile Technology Selection Apps for Communication and Cognition

Mobile Technology Selection Apps for Communication and Cognition Mobile Technology Selection Apps for Communication and Cognition Joan L. Green, M.A. CCC-SLP 6/7/13 Innovative Speech Therapy www.innovativespeech.com Joan@innovativespeech.com Whirlwind Tour of Top App

More information

Corporate learning: Blurring boundaries and breaking barriers

Corporate learning: Blurring boundaries and breaking barriers IBM Global Services Corporate learning: Blurring boundaries and breaking barriers A learning culture Introduction With the American Society for Training and Development (ASTD) reporting that the average

More information

AGENDA. Truths, misconceptions and comparisons. Strategies and sample problems. How The Princeton Review can help

AGENDA. Truths, misconceptions and comparisons. Strategies and sample problems. How The Princeton Review can help ACT, SAT OR BOTH? AGENDA 1 Truths, misconceptions and comparisons 2 Strategies and sample problems 3 How The Princeton Review can help TEXT YOUCAN TO 877877 to get a discount code and keep up-to-date on

More information

Challenges in Deep Reinforcement Learning. Sergey Levine UC Berkeley

Challenges in Deep Reinforcement Learning. Sergey Levine UC Berkeley Challenges in Deep Reinforcement Learning Sergey Levine UC Berkeley Discuss some recent work in deep reinforcement learning Present a few major challenges Show some of our recent work toward tackling

More information

Tulsa Community College Staff Salary Schedule (Effective July 1, 2015)

Tulsa Community College Staff Salary Schedule (Effective July 1, 2015) Grade 4 Minimum $16,377 Midpoint $20,062 Maximum $23,747 Grade 5 Minimum $17,761 Midpoint $21,868 Maximum $25,975 Grade 6 Minimum $19,309 Midpoint $23,895 Maximum $28,481 Grade 7 Minimum $21,044 Midpoint

More information

GRADUATE PROGRAM Department of Materials Science and Engineering, Drexel University Graduate Advisor: Prof. Caroline Schauer, Ph.D.

GRADUATE PROGRAM Department of Materials Science and Engineering, Drexel University Graduate Advisor: Prof. Caroline Schauer, Ph.D. GRADUATE PROGRAM Department of Materials Science and Engineering, Drexel University Graduate Advisor: Prof. Caroline Schauer, Ph.D. 05/15/2012 The policies listed herein are applicable to all students

More information

Software Maintenance

Software Maintenance 1 What is Software Maintenance? Software Maintenance is a very broad activity that includes error corrections, enhancements of capabilities, deletion of obsolete capabilities, and optimization. 2 Categories

More information

Pragmatic Use Case Writing

Pragmatic Use Case Writing Pragmatic Use Case Writing Presented by: reducing risk. eliminating uncertainty. 13 Stonebriar Road Columbia, SC 29212 (803) 781-7628 www.evanetics.com Copyright 2006-2008 2000-2009 Evanetics, Inc. All

More information

Strategies for Solving Fraction Tasks and Their Link to Algebraic Thinking

Strategies for Solving Fraction Tasks and Their Link to Algebraic Thinking Strategies for Solving Fraction Tasks and Their Link to Algebraic Thinking Catherine Pearn The University of Melbourne Max Stephens The University of Melbourne

More information

Introduction to CS 100 Overview of UK. CS September 2015

Introduction to CS 100 Overview of UK. CS September 2015 Introduction to CS 100 Overview of CS @ UK CS 100 1 September 2015 Outline CS100: Structure and Expectations Context: Organization, mission, etc. BS in CS Degree Program Department Locations Our Faculty

More information

Learning Microsoft Office Excel

Learning Microsoft Office Excel A Correlation and Narrative Brief of Learning Microsoft Office Excel 2010 2012 To the Tennessee for Tennessee for TEXTBOOK NARRATIVE FOR THE STATE OF TENNESEE Student Edition with CD-ROM (ISBN: 9780135112106)

More information

Python Machine Learning

Python Machine Learning Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled

More information

Information Event Master Thesis

Information Event Master Thesis Information Event Master Thesis Dr. Michael J. Kendzia Deputy Program Director MSc IB Building Competence. Crossing Borders. Overview Introduction Prior to the master thesis assignment procedure During

More information

WHY GRADUATE SCHOOL? Turning Today s Technical Talent Into Tomorrow s Technology Leaders

WHY GRADUATE SCHOOL? Turning Today s Technical Talent Into Tomorrow s Technology Leaders WHY GRADUATE SCHOOL? Turning Today s Technical Talent Into Tomorrow s Technology Leaders (This presentation has been ripped-off from a number of on-line sources) Outline Why Should I Go to Graduate School?

More information

Curriculum Vitae Bharat K. Soni

Curriculum Vitae Bharat K. Soni Curriculum Vitae Bharat K. Soni Business Address One William Jones Drive, Derryberry Hall 305 Tennessee Tech University P. O. Box 5036 Cookeville, TN 38505 Phone: (931) 372-6074 E-mail: bsoni@tntech.edu

More information