Uncovering Plagiarism, Authorship, and Social Software Misuse
|
|
|
- Dina Armstrong
- 10 years ago
- Views:
Transcription
1 Uncovering Plagiarism, Authorship, and Social Software Misuse Bauhaus-Universität Weimar Universitat Politècnica de València University of Lugano Duquesne University Universitat Politècnica de Catalunya University of the Aegean Bar-Ilan University Illinois Institute of Technology Martin Potthast, Tim Gollub, Maik Anderka, Matthias Hagen, Jan Graßegger, Johannes Kiesel, Maximilian Michel, Arnd Oberländer, Martin Tippmann, and Benno Stein Parth Gupta and Paolo Rosso Giacomo Inches and Fabio Crestani Patrick Juola Alberto Barrón-Cedeño Efstathios Stamatatos Moshe Koppel Shlomo Argamon
2 Uncovering Plagiarism, Authorship, and Social Software Misuse Outline Plagiarism Detection Author Identification and Sexual Predator Identification Wikipedia Quality Flaw Prediction Summary
3 Plagiarism Detection To plagiarize means to reuse someone else s work, pretending it to be one s own. Suspicious document Plagiarism Detection Candidate retrieval Candidate documents Detailed comparison Knowledge-based post-processing Document collection Suspicious passages Contributions: q Manually written plagiarism from the ClueWeb q ChatNoir search engine for candidate retrieval q Software submissions for detailed comparison 3 [^] c pan.webis.de 2012
4 Plagiarism Detection Candidate retrieval (search for source documents): Total Time to Reported Downloaded Team Workload 1st Detection Sources Sources Queries Dwnlds Queries Dwnlds Precision Recall Precision Recall Jayapal Suchomel more... Detailed comparison (alignment of plagiarized passages): Team PlagDet Precision Recall Granularity Avg. Runtime (s) Kong Suchomel Grozea more... 4 [^] c pan.webis.de 2012
5 Lessons Learned Plagiarism detection: q Software submissions are manageable, provide repeatability. q Task-wise evaluation allows for more tailored evaluation. q Fully automatic plagiarism detection evaluation within reach. 5 [^] c pan.webis.de 2012
6 Author Identification and Sexual Predator Identification An author s personal traits are encoded in her writing. Task: A 11 A 12 A 1... q Given (part of) a document, who wrote it? A 10 A2 q The task covers 8 variants of this problem (closed vs. open class, author clustering, intrinsic plagiarism detection) A9 A8 A? A4 A3... A7 A5 A 6 6 [^] c pan.webis.de 2012
7 Author Identification and Sexual Predator Identification An author s personal traits are encoded in her writing. Task: A 11 A 12 A 1... q Given (part of) a document, who wrote it? A 10 A2 q The task covers 8 variants of this problem (closed vs. open class, author clustering, intrinsic plagiarism detection) A9 A8 A? A4 A3... A7 A5 A 6 Evaluation Results: Team Avg. Correct Decisions Team Overall Correct Decisions Grozea 86.37% Ryan 88.38% Akiva 83.40% Akiva 81.74% Ryan 82.41% Grozea 81.33% Tanguy 70.81% Tanguy 77.59% Castillo 62.13% Vartapetiance 75.93% more... 7 [^] c pan.webis.de 2012
8 Author Identification and Sexual Predator Identification 8 [^] c pan.webis.de 2012
9 Author Identification and Sexual Predator Identification Task: q Given a chat log, identify a sexual predator, if there is one. q Given chat logs, identify all lines coming from sexual predators. Corpus: 152k adult chats (8k of which predator/victim chats), 70k other chats. 9 [^] c pan.webis.de 2012
10 Author Identification and Sexual Predator Identification Task: q Given a chat log, identify a sexual predator, if there is one. q Given chat logs, identify all lines coming from sexual predators. Corpus: 152k adult chats (8k of which predator/victim chats), 70k other chats. Evaluation Results: Predator Identification Predator Line Flagging Team Precision Recall F 0.5 Team Precision Recall F 3 Villatoro-Tello Grozea Snider Kontostathis Parapar Peersman Morris Sitarz more [^] c pan.webis.de 2012
11 Lessons Learned Plagiarism detection: q Software submissions are manageable, provide repeatability. q Task-wise evaluation allows for more tailored evaluation. q Fully automatic plagiarism detection evaluation within reach. Author identification: q Lack of corpora is still a major obstacle to evaluation. q Performance measures are rudimentary; their weighting is not clear. q Large variety of problem classes adds to the difficulties. 11 [^] c pan.webis.de 2012
12 [citation needed] The PAN Competition Wikipedia Quality Flaw Prediction This presentation does not cite any references or sources. Please help improve this presentation by adding citations to reliable sources. Unsourced material may be challenged and removed. (September 2012) 12 [^] c pan.webis.de 2012
13 [citation needed] The PAN Competition Wikipedia Quality Flaw Prediction Task: This presentation does not cite any references or sources. Please help improve this presentation by adding citations to reliable sources. Unsourced material may be challenged and removed. (September 2012) q Given a sample of Wikipedia articles containing a specific quality flaw, decide whether or not a previously unseen article contains the same flaw. Corpus: q 170k Wikipedia articles, each tagged with one of 10 quality flaws. q 50k random untagged articles. 13 [^] c pan.webis.de 2012
14 [citation needed] The PAN Competition Wikipedia Quality Flaw Prediction Task: This presentation does not cite any references or sources. Please help improve this presentation by adding citations to reliable sources. Unsourced material may be challenged and removed. (September 2012) q Given a sample of Wikipedia articles containing a specific quality flaw, decide whether or not a previously unseen article contains the same flaw. Corpus: q 170k Wikipedia articles, each tagged with one of 10 quality flaws. q 50k random untagged articles. Evaluation Results: Team Precision Recall F 1 Ferretti Ferschke Pistol [^] c pan.webis.de 2012
15 [citation needed] The PAN Competition Wikipedia Quality Flaw Prediction Task: This presentation does not cite any references or sources. Please help improve this presentation by adding citations to reliable sources. Unsourced material may be challenged and removed. (September 2012) q Given a sample of Wikipedia articles containing a specific quality flaw, decide whether or not a previously unseen article contains the same flaw. Corpus: q 170k Wikipedia articles, each tagged with one of 10 quality flaws. q 50k random untagged articles. Evaluation Results: Team Precision Recall F 1 Ferretti Ferschke Pistol [^] c pan.webis.de 2012
16 Lessons Learned Plagiarism detection: q Software submissions are manageable, provide repeatability. q Task-wise evaluation allows for more tailored evaluation. q Fully automatic plagiarism detection evaluation within reach. Author identification: q Lack of corpora is still a major obstacle to evaluation. q Performance measures are rudimentary; their weighting is not clear. q Large variety of problem classes adds to the difficulties. Wikipedia quality flaw prediction: q This task subsumes the vandalism detection task of previous years. q Doznes of more flaw types need to be investigated. q Promising performance for some flaws; automatic tagging possible. 16 [^] c pan.webis.de 2012
17 Lessons Learned Plagiarism detection: q Software submissions are manageable, provide repeatability. q Task-wise evaluation allows for more tailored evaluation. q Fully automatic plagiarism detection evaluation within reach. Author identification: q Lack of corpora is still a major obstacle to evaluation. q Performance measures are rudimentary; their weighting is not clear. q Large variety of problem classes adds to the difficulties. Wikipedia quality flaw prediction: q This task subsumes the vandalism detection task of previous years. q Doznes of more flaw types need to be investigated. q Promising performance for some flaws; automatic tagging possible. A lot to accomplish for PAN 2013 and beyond! 17 [^] c pan.webis.de 2012
Plagiarism Detection Using Stopword n-grams
Plagiarism Detection Using Stopword n-grams Efstathios Stamatatos Dept. of Information and Communication Systems Eng. University of the Aegean 83200 Karlovassi, Greece [email protected] Abstract In
Improved implementation for finding text similarities in large collections of data
Improved implementation for finding text similarities in large collections of data Notebook for PAN at CLEF 2011 Ján Grman and udolf avas SVOP Ltd., Bratislava, Slovak epublic {grman,ravas}@svop.sk Abstract.
YouTube Comment Classifier. mandaku2, rethina2, vraghvn2
YouTube Comment Classifier mandaku2, rethina2, vraghvn2 Abstract Youtube is the most visited and popular video sharing site, where interesting videos are uploaded by people and corporations. People also
Blogs and Twitter Feeds: A Stylometric Environmental Impact Study
Blogs and Twitter Feeds: A Stylometric Environmental Impact Study Rebekah Overdorf, Travis Dutko, and Rachel Greenstadt Drexel University Philadelphia, PA {rjo43,tad82,greenie}@drexel.edu http://www.cs.drexel.edu/
Report on the Evaluation-as-a-Service (EaaS) Expert Workshop
WORKSHOP REPORT Report on the Evaluation-as-a-Service () Expert Workshop Frank Hopfgartner, Allan Hanbury, Henning Müller, Noriko Kando, Simon Mercer, Jayashree Kalpathy-Cramer, Martin Potthast, Tim Gollub,
Instructor: See information provided in the Syllabus link in the classroom
Fire Science FSMT299 Fire Science Sophomore Seminar 3 Credit Hours 8 Week Course Prerequisite(s) This is the last course in the AS Fire Science degree path Instructor Information Course Description Course
Identifying Online Sexual Predators by SVM Classification with Lexical and Behavioral Features 1
Identifying Online Sexual Predators by SVM Classification with Lexical and Behavioral Features 1 Master of Science paper, Department of Computer Science, University of Toronto Colin Morris January 30,
WHITE PAPER Plagiarism and the Web: Myths and Realities
WHITE PAPER Plagiarism and the Web: Myths and Realities An Analytical Study on Where Students Find Unoriginal Content on the Internet Prevent Plagiarism. Engage Students. www.turnitin.com Table of Contents
STOP. THINK. CONNECT. Online Safety Quiz
STOP. THINK. CONNECT. Online Safety Quiz Round 1: Safety and Security Kristina is on Facebook and receives a friend request from a boy she doesn t know. What should she do? A. Accept the friend request.
Determining if Two Documents are by the Same Author
DETERMINING IF TWO DOCUMENTS ARE BY THE SAME AUTHOR 1 Determining if Two Documents are by the Same Author Moshe Koppel (Corresponding Author) Dept. of Computer Science Bar-Ilan University Ramat-Gan, Israel
RICH TOWNSHIP HIGH SCHOOL Adopted: 7/10/00 DISTRICT 227 Olympia Fields, Illinois
6.55 Page 1 of 1 INSTRUCTION Acceptable Use Policy Computer equipment, including access to the Internet, is to be used in a responsible, efficient, ethical and legal manner in accordance with the mission
Summer Term 2010. Prof. Dr. Roland Kirstein Economics of Business and Law Faculty of Economics and Management
Master/Diplom Seminar: Intellectual Property Rights Summer Term 2010 Prof. Dr. Roland Kirstein Economics of Business and Law Faculty of Economics and Management Otto-von-Guericke University Magdeburg Today
Project 2: Web Security Pitfalls
EECS 388 September 19, 2014 Intro to Computer Security Project 2: Web Security Pitfalls Project 2: Web Security Pitfalls This project is due on Thursday, October 9 at 6 p.m. and counts for 8% of your course
Plagiarism and Anti-Plagiarism Software
EUROPEAN ACADEMIC RESEARCH Vol. I, Issue 10/ January 2014 ISSN 2286-4822 www.euacademic.org Impact Factor: 0.485 (GIF) DRJI Value: 5.9 (B+) Plagiarism and Anti-Plagiarism Software MUKTA GARG Moradabad
Distributed Tuning of Machine Learning Algorithms using MapReduce Clusters
Distributed Tuning of Machine Learning Algorithms using MapReduce Clusters Yasser Ganjisaffar University of California, Irvine Irvine, CA, USA [email protected] Rich Caruana Microsoft Research Redmond,
Http://Troy.bncollege.com
TROY UNIVERSITY Criminology SYLLABUS T1 2015 August 10, 2015 to October 9, 2015 For course syllabus posted prior to the beginning of the term, the instructor reserves the right to make minor changes prior
Technology Acceptable Use Policy. Introduction
Technology Acceptable Use Policy Introduction Shiloh Christian School recognizes that access to technology in school gives students greater opportunities to learn, engage, communicate, and develop skills
9. Text & Documents. Visualizing and Searching Documents. Dr. Thorsten Büring, 20. Dezember 2007, Vorlesung Wintersemester 2007/08
9. Text & Documents Visualizing and Searching Documents Dr. Thorsten Büring, 20. Dezember 2007, Vorlesung Wintersemester 2007/08 Slide 1 / 37 Outline Characteristics of text data Detecting patterns SeeSoft
Helpdesk Ticketing User Guide
Helpdesk Ticketing User Guide University College of the North 1/5/2012 Table of Contents UCN Helpdesk Ticketing System Requirements... 1 Logging into Helpdesk Ticketing System... 1 Support Center Options...
COMPSCI 760 S2 C 2014 Machine Learning and Data Mining Computer Science Department
COMPSCI 760 S2 C 2014 Machine Learning and Data Mining Computer Science Department Research Projects 2014 This year students will work in groups of 3 on projects. Each project will be a small research
Understand the purpose of a writing sample 1. Understand the writing sample requirements for this job. Provide exactly what the posting requests.
Writing Sample Tips While cover letters and resumes are samples of your writing, many employers require the submission of a formal legal writing sample. A formal legal writing sample alone will not get
TS3: an Improved Version of the Bilingual Concordancer TransSearch
TS3: an Improved Version of the Bilingual Concordancer TransSearch Stéphane HUET, Julien BOURDAILLET and Philippe LANGLAIS EAMT 2009 - Barcelona June 14, 2009 Computer assisted translation Preferred by
DEPARTMENTAL POLICIES
DEPARTMENTAL POLICIES Adherence to Department s Technical Standards Any student registered for a Salisbury University social work course will be expected to meet and abide by the Social Work Department
PSYC 1000A General Psychology 2014-15, 1 st Term Department of Psychology The Chinese University of Hong Kong
PSYC 1000A General Psychology 2014-15, 1 st Term Department of Psychology The Chinese University of Hong Kong 1. Course Description: What is the course about? This is a course introducing psychology, the
CRIM 200: Introduction to Criminal Justice
CRIM 200: Introduction to Criminal Justice Catalog Description: This course will provide an overview of the criminal justice system, its history, its philosophical development, and its contemporary configurations.
Real-Time Identification of MWE Candidates in Databases from the BNC and the Web
Real-Time Identification of MWE Candidates in Databases from the BNC and the Web Identifying and Researching Multi-Word Units British Association for Applied Linguistics Corpus Linguistics SIG Oxford Text
Search and Information Retrieval
Search and Information Retrieval Search on the Web 1 is a daily activity for many people throughout the world Search and communication are most popular uses of the computer Applications involving search
Online Social Culture: Does it Foster Original Work or Encourage Plagiarism?
Online Social Culture: Does it Foster Original Work or Encourage Plagiarism? Janet Salmons, Ph.D. Vision2Lead, Inc. [email protected] For more on this model, see the chapter titled, "Expect Originality!
DGT-TM: A freely Available Translation Memory in 22 Languages
DGT-TM: A freely Available Translation Memory in 22 Languages Ralf Steinberger, Andreas Eisele, Szymon Klocek, Spyridon Pilos & Patrick Schlüter ( ) European Commission Joint Research Centre (JRC) Ispra
Running head: SAMPLE APA 1
Running head: SAMPLE APA 1 The upper heading should be created using the Headers & Footers function in Word, be ½ from the top of the page, and show up on every page of the paper. The content of the upper
International Baccalaureate Diploma and DP Course Academic Honesty Policy 2014-2015
International Baccalaureate Diploma and DP Course Academic Honesty Policy 2014-2015 Principles of Academic Honesty Students enrolled in the IB Diploma and IB Certificate programs at International School
University of Glasgow Terrier Team / Project Abacá at RepLab 2014: Reputation Dimensions Task
University of Glasgow Terrier Team / Project Abacá at RepLab 2014: Reputation Dimensions Task Graham McDonald, Romain Deveaud, Richard McCreadie, Timothy Gollins, Craig Macdonald and Iadh Ounis School
The effectiveness of plagiarism detection software as a learning tool in academic writing education
The effectiveness of plagiarism detection software as a learning tool in academic writing education Brad Stappenbelt University of Wollongong, Australia Chris Rowles University of Western Australia Abstract
Getting Started with SandStorm NoSQL Benchmark
Getting Started with SandStorm NoSQL Benchmark SandStorm is an enterprise performance testing tool for web, mobile, cloud and big data applications. It provides a framework for benchmarking NoSQL, Hadoop,
THE CHINESE UNIVERSITY OF HONG KONG DEPARTMENT OF TRANSLATION COURSE OUTLINE
THE CHINESE UNIVERSITY OF HONG KONG DEPARTMENT OF TRANSLATION 2015 16 COURSE OUTLINE COURSE CODE TRAN2610 ENGLISH TITLE Introduction to Computer aided Translation CHINESE TITLE 電 腦 輔 助 翻 譯 導 論 NUMBER OF
Knowledge Discovery using Text Mining: A Programmable Implementation on Information Extraction and Categorization
Knowledge Discovery using Text Mining: A Programmable Implementation on Information Extraction and Categorization Atika Mustafa, Ali Akbar, and Ahmer Sultan National University of Computer and Emerging
User (Student) Instruction Manual Local Document Archive Maintenance System (SOLAD)
User (Student) Instruction Manual Local Document Archive Maintenance System (SOLAD) Warsaw School of Economics 1/15 The Local Document Archive Maintenance System (SOLAD) is a programme that serves to submit,
CYBER SCIENCE 2015 AN ANALYSIS OF NETWORK TRAFFIC CLASSIFICATION FOR BOTNET DETECTION
CYBER SCIENCE 2015 AN ANALYSIS OF NETWORK TRAFFIC CLASSIFICATION FOR BOTNET DETECTION MATIJA STEVANOVIC PhD Student JENS MYRUP PEDERSEN Associate Professor Department of Electronic Systems Aalborg University,
Running head: WRITING RESEARCH PAPERS 1. A Guide for Writing APA Style Research Papers. Susan B. Smith. Capital Community College
PROVIDE A RUNNING HEAD OR SHORT TITLE OF YOUR PAPER IN ALL CAPS FOR EACH PAGE OF THE PAPER Based on the Sixth Ed. of the Publication Manual of the American Psychological Association. Updated September
MGMT S-5033 Course Syllabus Supply Chain Management Online. Spring 2014. Harvard University Cambridge, MA. Zal Phiroz, MBA Instructor
MGMT S-5033 Course Syllabus Supply Chain Management Online Spring 2014 Harvard University Cambridge, MA Zal Phiroz, MBA Instructor Abstract This course introduces the concept of Supply Chain management
Web-Scale Extraction of Structured Data Michael J. Cafarella, Jayant Madhavan & Alon Halevy
The Deep Web: Surfacing Hidden Value Michael K. Bergman Web-Scale Extraction of Structured Data Michael J. Cafarella, Jayant Madhavan & Alon Halevy Presented by Mat Kelly CS895 Web-based Information Retrieval
Personality Psychology (PSYC 330) Summer 2015
Class Time and Location: July 6-28, 2015 Monday to Friday 4:30pm to 6:20pm David Turpin Building A102 Sessional Instructor: Eric Huang, PhD Candidate E-mail: [email protected] Office: Cornett B310 Office
entigral whitepaper 10 Success Factors for RFID Asset Tracking Deployments www.entigral.com 877.822.0200
entigral whitepaper 10 Success Factors for RFID Asset Tracking Deployments www.entigral.com 877.822.0200 10 Success Factors for RFID Deployments How to maximize ROI on RFID asset tracking deployments RFID
CHAT: A System for Stylistic Classification of Hebrew-Aramaic Texts
CHAT: A System for Stylistic Classification of Hebrew-Aramaic Texts Moshe Koppel Dror Mughaz Navot Akiva Dept. of Computer Science Bar-Ilan University Ramat Gan, Israel 1. Objectives CHAT, is a fully self-sufficient
HIT 240 HEALTHCARE QUALITY PERFORMANCE IMPROVEMENT AND MANAGEMENT SYLLABUS
HIT 240 HEALTHCARE QUALITY PERFORMANCE IMPROVEMENT AND MANAGEMENT SYLLABUS Instructor Information and Availability Donna Thompson, RHIT has been an instructor for the SBCC HIT program and the Workforce
Minnesota Virtual Academy Online Syllabus for Eng403 British and World Literature Course Instructor and Communications Name: Mrs.
Minnesota Virtual Academy Online Syllabus for Eng403 British and World Literature Course Instructor and Communications Name: Mrs. Sara Horne Phone #: 651-964-2370 or 866-215-292, option 4, ext. 2113 Fax
A Performance Analysis of Distributed Indexing using Terrier
A Performance Analysis of Distributed Indexing using Terrier Amaury Couste Jakub Kozłowski William Martin Indexing Indexing Used by search
Terminology Extraction from Log Files
Terminology Extraction from Log Files Hassan Saneifar 1,2, Stéphane Bonniol 2, Anne Laurent 1, Pascal Poncelet 1, and Mathieu Roche 1 1 LIRMM - Université Montpellier 2 - CNRS 161 rue Ada, 34392 Montpellier
STUDENT HANDBOOK. Policies and Procedures. 1 of 11
STUDENT HANDBOOK Policies and Procedures 2012 2013 1 of 11 GENERAL INFORMATION K-12 schools arrange for Founders Education to deliver comprehensive virtual courses to the school s students. The virtual
Students will know and be able to: 1.1. Basic Operations
Information Technology Grades 9-10 Basic Operations, Concepts, and Productivity Tools Basic Operations Word Processing Database Spreadsheet Identify the platform, version, properties, function, and interoperability
Ventura Charter School of Arts & Global Education Board Policy for Acceptable Use and Internet Safety
Ventura Charter School of Arts & Global Education Board Policy for Acceptable Use and Internet Safety Student Policy--SP004 Ventura Charter School of Arts and Global Education ("School") recognizes the
National Initiative for Cybersecurity Careers and Studies (NICCS) Cybersecurity Training and Education Catalog Training Provider Instruction Guide
National Initiative for Cybersecurity Careers and Studies (NICCS) Cybersecurity Training and Education Catalog Training Provider Instruction Guide Overview During this presentation, you will: Learn about
INTRODUCTION TO CRIMINAL JUSTICE 101- Hybrid
INTRODUCTION TO CRIMINAL JUSTICE 101- Hybrid Instructor: Shawn Curtis, MSCJ, PsyD Candidate Quarter Dates: 04/06/15-06/19/15 Classroom: D-103 Days and Times: Tu and Th 12:30-2:20 (Friday on line) Office:
Desire2Learn Learning Environment
Desire2Learn Learning Environment Empower your organization to deliver engaging content, collaborate effectively, and achieve your elearning vision through a comprehensive and flexible suite of online
Comparing Support Vector Machines, Recurrent Networks and Finite State Transducers for Classifying Spoken Utterances
Comparing Support Vector Machines, Recurrent Networks and Finite State Transducers for Classifying Spoken Utterances Sheila Garfield and Stefan Wermter University of Sunderland, School of Computing and
MIRACLE at VideoCLEF 2008: Classification of Multilingual Speech Transcripts
MIRACLE at VideoCLEF 2008: Classification of Multilingual Speech Transcripts Julio Villena-Román 1,3, Sara Lana-Serrano 2,3 1 Universidad Carlos III de Madrid 2 Universidad Politécnica de Madrid 3 DAEDALUS
HIT 284 Coding Exam Prep Course Syllabus
HIT 284 Coding Exam Prep Course Syllabus Instructor Email address Instructor Office Hours Office hours location Course Introduction Lynette M. Williamson, MBA, RHIA, CCS, CPC Use Course Email (Internal
How To Cluster Of Complex Systems
Entropy based Graph Clustering: Application to Biological and Social Networks Edward C Kenley Young-Rae Cho Department of Computer Science Baylor University Complex Systems Definition Dynamically evolving
Collecting Polish German Parallel Corpora in the Internet
Proceedings of the International Multiconference on ISSN 1896 7094 Computer Science and Information Technology, pp. 285 292 2007 PIPS Collecting Polish German Parallel Corpora in the Internet Monika Rosińska
Identifying free text plagiarism based on semantic similarity
Identifying free text plagiarism based on semantic similarity George Tsatsaronis Norwegian University of Science and Technology Department of Computer and Information Science Trondheim, Norway [email protected]
Guide to Knowledge Elicitation Interviews
Guide to Knowledge Elicitation Interviews Purpose Gather knowledge from individuals in a manner that others will find useful. Description Knowledge interviews are conversations between people who have
Comprehensive i-safe Curriculum International Scope of Lessons and Language Availability
Comprehensive i-safe Curriculum International Scope of Lessons and Language Availability The i-safe comprehensive curriculum provides a unique approach to Internet safety education, by meaningfully integrating
Kayako 4.0 Helpdesk Upgrade Questionnaire Form 1: Users. https://kayako-test.cv.nrao.edu/
Kayako 4.0 Helpdesk Upgrade Questionnaire Form 1: Users NAME ROLE: USER To submit helpdesk tickets you can access the helpdesk directly at: https://kayako-test.cv.nrao.edu/ NOTE: Due diligence needs to
Report on the embedding and evaluation of the second MT pilot
Report on the embedding and evaluation of the second MT pilot quality translation by deep language engineering approaches DELIVERABLE D3.10 VERSION 1.6 2015-11-02 P2 QTLeap Machine translation is a computational
Realist A Quick Start Guide for New Users
Realist A Quick Start Guide for New Users Realist is a service from: First American Real Estate Solutions 4 First American Way Santa Ana, CA 92707 Contents 1. Welcome to Realist... 2 2. How to Access Realist...
Course Materials Required Text:
Lamar University, a member of The Texas State University System, is accredited by the Commission on Colleges of the Southern Association of Colleges and Schools to award Associate, Baccalaureate, Masters,
Minnesota Virtual Academy Online Syllabus for Web Design
Minnesota Virtual Academy Online Syllabus for Web Design Course Instructor and Communications Name: Jami Kruschke Phone #: Fax #: Skype name: 866-215-2292, option 3, x2181 NA jami.kruschke Kmail address:
Data and Analysis. Informatics 1 School of Informatics, University of Edinburgh. Part III Unstructured Data. Ian Stark. Staff-Student Liaison Meeting
Inf1-DA 2010 2011 III: 1 / 89 Informatics 1 School of Informatics, University of Edinburgh Data and Analysis Part III Unstructured Data Ian Stark February 2011 Inf1-DA 2010 2011 III: 2 / 89 Part III Unstructured
Email Security 8.0 User Guide
Email Security 8.0 User Guide 1 Notes, Cautions, and Warnings NOTE: A NOTE indicates important information that helps you make better use of your system. CAUTION: A CAUTION indicates potential damage to
The Slingshot Project
The Slingshot Project Measuring, analyzing, and defending against targeted attacks Stevens Le Blond Software security model Obscurity Reactivity Correctness Isolation 2 # users Skype enables large-scale
CIRGIRDISCO at RepLab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet
CIRGIRDISCO at RepLab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet Muhammad Atif Qureshi 1,2, Arjumand Younus 1,2, Colm O Riordan 1,
Course Delivery Educational Program Development Onsite Blackboard Training
Overview Workshop Content The workshop for Blackboard Learn Course Delivery equips institutions with the knowledge, skills, and planning tools to effectively administer and teach with Blackboard Learn
A Statistical Text Mining Method for Patent Analysis
A Statistical Text Mining Method for Patent Analysis Department of Statistics Cheongju University, [email protected] Abstract Most text data from diverse document databases are unsuitable for analytical
Security Testing with Selenium
with Selenium Vidar Kongsli Montréal, October 25th, 2007 Versjon 1.0 Page 1 whois 127.0.0.1? Vidar Kongsli System architect & developer Head of security group Bekk Consulting Technology and Management
Accelerating Change & Transformation (ACT) TM Training
Accelerating Change & Transformation (ACT) TM Training 80% of CEOs cite the ability to adapt to change as a source of competitive advantage. Emergent can help you develop your internal change capability
MICROSOFT OUTLOOK TIPS AND TRICKS
Materials Provided by Tim McCurdy MICROSOFT OUTLOOK TIPS AND TRICKS Tips for Outlook Mail 1. Set a reminder to reply to a message 2. Add your own words to a follow-up flag for a new message 3. Send a message
Machine Learning Log File Analysis
Machine Learning Log File Analysis Research Proposal Kieran Matherson ID: 1154908 Supervisor: Richard Nelson 13 March, 2015 Abstract The need for analysis of systems log files is increasing as systems
Haddon Township School District Acceptable Use of Information and Communication Technology for Students
Haddon Township School District Acceptable Use of Information and Communication Technology for Students Please read the following carefully before signing the Acceptable Use Policy for Students Agreement.
B2B opportunity predictiona Big Data and Advanced. Analytics Approach. Insert
B2B opportunity predictiona Big Data and Advanced Analytics Approach Vodafone Global Enterprise Manu Kumar, Head of Targeting, Optimization & Data Science Insert Agenda Why B2B opportunities are hard to