Daniel Oblinger received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. He has over 20 years of experience in machine learning, data mining, and artificial intelligence research at IBM T.J. Watson Research Center. His research interests include programming by demonstration, statistical pattern recognition, and data mining of email, speech, and protein sequences. He has authored over a dozen patents and publications in major conferences and journals.
Human Being Character Analysis from Their Social Networking ProfilesBiswaranjan Samal
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In this paper, characteristics of human beings obtained from profile statement present in their social
networking profile status are analyzed in terms of introvert, extrovert or ambivert. Recently, Machine learning
plays a vital role in classifying the human characteristics. The user profile status is collected from LinkedIn, a
popular professional social networking application. Oauth2.0 protocol is used for login into the LinkedIn and
web scrapping using JavaScript is used for information extraction of the registered users. Then, Word Net: a
lexical database is used for forming the word clusters such as: extrovert and introvert using semi-supervised
learning techniques. K-nearest neighbor classification algorithm is finally considered for classifying the profiles
into various available categories. The results obtained in the proposed method are encouraging with good
accuracy
This document provides a summary of Dragomir R. Radev's research interests, employment history, education, funding, patents, and publications. Specifically:
- Radev's research interests include information retrieval, natural language processing, text summarization, and artificial intelligence.
- He is currently an Associate Professor at the University of Michigan across several departments and centers. He has also held positions at IBM and Columbia University.
- Radev received his Ph.D. in Computer Science from Columbia University and has received funding from several agencies including NSF and NIH.
- He holds one U.S. patent and has one additional patent pending. Radev has authored or co-authored over 15
The document provides information on leaders in various disciplines related to artificial intelligence, including:
1) Natural language processing leaders John McCarthy, Paul Jacobs, and Roger Schank. Joseph Weizenbaum created the ELIZA program.
2) Knowledge representation leaders Douglas Lenat, George Luger, Marvin Minsky, and Simon Parsons.
3) Expert system pioneers Donald Michie, Edward Shortliffe, and John McDermott.
4) Genetic programming innovators John Holland, John Koza, and Ingo Rechenberg.
5) Game playing innovators Alan Turing, Feng-Hsiung Hsu, Jonathan Schaeffer,
Identifying Evaluation Standards for Online Information Literacy Tutorials (O...Hang Dong
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This document summarizes existing research on evaluating online information literacy tutorials (OILTs). It finds that the most common evaluation methods are usability testing and pre- and post-tests. It also finds that each study evaluates a specific OILT rather than using standardized evaluation criteria. The document then proposes categorizing OILTs in different ways, such as modular vs. non-modular and use of text vs. video, before developing comprehensive evaluation standards. It concludes that OILTs should be evaluated according to these categories to ensure elements like appropriate length, interactive components, and accuracy of resources.
Peter Bock is a professor of machine intelligence and cognition at The George Washington University. He received a BA from Ripon College in 1962 and an MS from Purdue University in 1964. His areas of expertise include research and development methods, statistical methods, machine intelligence, and cognitive science. He is the principal investigator on several grants involving adaptive learning and image processing. He has published two books and over 50 papers on topics related to collective learning systems and applying machine learning to problems in various domains.
Robert C. Miller is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT. His areas of research include human-computer interaction, user interfaces, software engineering, end-user programming, and web browsing automation. He received his PhD from Carnegie Mellon University in 2002. He has received several awards for his research and teaching, including an NSF CAREER Award and the Louis Smullins Award for Teaching Excellence.
Karen Hovsepian is seeking a position in computer science research and development, particularly in data mining applications related to finance and bioinformatics. She has a Ph.D. in computer science from New Mexico Tech and has developed several machine learning and data mining tools, including algorithms for classification, clustering, and volatility prediction. She has also taught various computer science courses as an instructor at New Mexico Tech.
This document provides a summary of Christopher J. Pal's professional background and qualifications. It lists his education, including a Ph.D. in Computer Science from the University of Waterloo in 2005. It details his appointments as a research scientist at UMass Amherst since 2005 and previous research internships at Microsoft and the University of Toronto. It also lists his publications, awards, and professional service activities in the field of computer science.
Ting-Chu Lin is a software engineer at Facebook with experience in machine learning, computer vision, and natural language processing. He received his M.S. in computer science from Columbia University and National Taiwan University, and conducted research on instance search in videos and multiple kernel learning. His work includes developing report generation services at Facebook and classifying key types at KeyMe.
Lei Zheng has over 15 years of experience in areas such as machine learning, data mining, and software development. He currently works as a Senior Software Engineer at Yahoo, where he develops algorithms for spam filtering and detection of abusive behavior. Previously he held research positions at the University of Pittsburgh and JustSystems Evans Research, where he implemented algorithms and systems for information retrieval, natural language processing, and data mining.
The document discusses cognitive information agents that can effectively learn from interactions with information to support complex human tasks. It describes an architecture that can automatically build and execute analytic solutions by specifying input/output types, information sources, and example datasets. It then trains, measures performance, analyzes errors, and proposes new learning tasks to iteratively improve. Example applications discussed are question answering systems for bioinformatics and decision support that can be automatically optimized for new datasets.
Dmitri G. Roussinov has over 15 years of experience in research and development in information technology, focusing on search engines, text mining, natural language processing, and business information systems. He has taught IT topics for over 12 years at highly ranked universities. He has 20+ years of hands-on experience in software design, programming languages, and computer graphics. He currently holds a senior lecturer position at the University of Strathclyde in Glasgow, UK.
Rohana Rajapakse is a senior developer at GOSS Interactive Ltd in Plymouth, UK. He received his PhD in Computer Science from the University of Plymouth in 2004. His research interests include digital information management, text processing, and neural networks. He has published several journal articles and conference papers on topics such as adaptive information retrieval, document categorization, and computational linguistics.
The document provides guidelines for designing effective e-learning objects and asynchronous instruction. It discusses best practices from sources like the Association of College and Research Libraries (ACRL) and Project Information Literacy. These include establishing learning outcomes, developing content that limits cognitive load, and ensuring accessibility for all students regardless of location. The document then outlines steps for instructional design using the ADDIE model of analysis, design, development, implementation and evaluation. Examples are provided for each step, with a focus on incorporating principles of multimedia learning and usability testing.
Mahesh Joshi has studied computer science and engineering, obtaining degrees from universities in India and the US. He is currently pursuing a Masters in Language Technologies at Carnegie Mellon University. His research focuses on natural language processing techniques such as word sense disambiguation and abbreviation expansion, especially in medical texts. He has published papers in conferences and released several related software tools.
Shihao Jin has a Master's degree in Computer Science from USC and a Bachelor's degree in Electrical Information Engineering from UESTC in China. He has skills in languages like Java, Python, HTML/CSS, and databases like PostgreSQL and MySQL. For projects, he developed a stock search Android app and webpage using various technologies. He also worked on a Go-Moku game player and a Weenix kernel implementation. His experience includes technology roles supporting an international school and assisting with aerial robotics research.
The document discusses domain modeling for personalized learning. It defines a domain model as representing domain knowledge through concepts and their relationships. Domain models serve as the basis for individual student models and for indexing and classifying learning content. They can be used to model student knowledge and decide on appropriate next steps for learning. The document describes different types of domain models, including vector, network, conceptual, and procedural models. It also discusses using ontologies and different aspects in domain modeling and applying domain models to student modeling, content indexing, and personalized guidance.
Deep learning is finding applications in science such as predicting material properties. DLHub is being developed to facilitate sharing of deep learning models, data, and code for science. It will collect, publish, serve, and enable retraining of models on new data. This will help address challenges of applying deep learning to science like accessing relevant resources and integrating models into workflows. The goal is to deliver deep learning capabilities to thousands of scientists through software for managing data, models and workflows.
Minita Jalan Shah is pursuing a Master of Science in Computer Science (Computational Biology) at Columbia University. She has relevant experience as a summer intern at the Itsik Pe'er Lab of Computational Genetics at Columbia, where she analyzed human genome data. She received her Bachelor of Science in Computer Engineering from Fr. Conceicao Rodrigues College of Engineering in Mumbai, India, where she developed several projects including a tool for identity-by-descent analysis and modeling of microRNA evolution.
Student Achievement Review (initially presented during Inauguration Function of the Ohio Center of Excellence in Knowledge-Enabled Computing at Wright State (Kno.e.sis)) - updated since
Center overview: http://bit.ly/coe-k
Invitation: http://bit.ly/COE-invite
H2O World - Intro to Data Science with Erin LedellSri Ambati
?
This document provides an introduction to data science. It defines data science as using data to solve problems through the scientific method. The roles of data scientists, data analysts, and data engineers on a data science team are discussed. Popular tools for data science include Python, R, and APIs that connect data processing engines. Machine learning algorithms are used to perform tasks like classification, regression, and clustering by learning from data rather than being explicitly programmed. Deep learning and ensemble methods are also introduced. Resources for learning more about data science and machine learning are provided.
Imran Khan is a PhD candidate at Shenzhen Institutes of Advanced Technology in China. His research focuses on ensemble clustering methods and algorithms for analyzing smart grid streaming data. He has a strong publication record, including papers in Neurocomputing and the International Journal of Data Science. Prior to his PhD studies, Khan worked as a lecturer and database architect. He is seeking opportunities to apply his skills and expertise in a prestigious organization.
The document summarizes a research paper on DBLP Search Support Engine (SSE), a system that aims to provide intelligent and personalized search beyond traditional search engines. It extracts users' research interests based on publication frequency and recency using interest retention models. The system represents users and their interests using RDF and provides additional functionalities like query refinement, domain analysis and tracking based on users' interests. Future work includes improving the interest prediction model and providing a unified architecture for different system functions.
Ratinov Lev is a PhD candidate at the University of Illinois at Urbana-Champaign studying machine learning and natural language processing under Professor Dan Roth. He has published several papers in these areas and held internships at Google, Honda Research Institute, and Microsoft. Ratinov holds an MSc from Ben-Gurion University in Israel and is fluent in Russian, Hebrew, and English.
EarthCube Monthly Community Webinar- Nov. 22, 2013EarthCube
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This webinar features project overviews of all EarthCube Awards (Building Blocks, Research Coordination Networks, Conceptual Designs, and Test Governance), followed by a call for involvement, and a Q&A session.
Agenda:
EarthCube Awards ¨C Project Overviews
1.. EarthCube Web Services (Building Block)
2. EC3: Earth-Centered Community for Cyberinfrastructure (RCN)
3. GeoSoft (Building Block)
4. Specifying and Implementing ODSIP (Building Block)
5. A Broker Framework for Next Generation Geoscience (BCube) (Building Block)
6. Integrating Discrete and Continuous Data (Building Block)
7. EAGER: Collaborative Research (Building Block)
8. A Cognitive Computer Infrastructure for Geoscience (Building Block)
9. Earth System Bridge (Building Block)
10. CINERGI ¨C Community Inventory of EC Resources for Geoscience Interoperability (BB)
11. Building a Sediment Experimentalist Network (RCN)
12. C4P: Collaboration and Cyberinfrastructure for Paleogeosciences (RCN)
13. Developing a Data-Oriented Human-centric Enterprise for Architecture (CD)
14. Enterprise Architecture for Transformative Research and Collaboration (CD)
15. EC Test Enterprise Governance: An Agile Approach (Test Governance)
A Call for Involvement!
This document summarizes the education and experience of James Edmondson. He is currently a PhD student studying computer science at Vanderbilt University, with a focus on distributed real-time and embedded systems. He has a bachelor's degree in computer science from Middle Tennessee State University and work experience in software development. His research interests include machine learning, parallel systems, and software engineering.
Este documento analiza el modelo de negocio de YouTube. Explica que YouTube y otros sitios de video online representan un nuevo modelo de negocio para contenidos audiovisuales debido al cambio en los h¨¢bitos de consumo causado por las nuevas tecnolog¨ªas. Describe c¨®mo YouTube aprovecha la participaci¨®n de los usuarios para mejorar continuamente y atraer una audiencia diferente a la de los medios tradicionales.
Ting-Chu Lin is a software engineer at Facebook with experience in machine learning, computer vision, and natural language processing. He received his M.S. in computer science from Columbia University and National Taiwan University, and conducted research on instance search in videos and multiple kernel learning. His work includes developing report generation services at Facebook and classifying key types at KeyMe.
Lei Zheng has over 15 years of experience in areas such as machine learning, data mining, and software development. He currently works as a Senior Software Engineer at Yahoo, where he develops algorithms for spam filtering and detection of abusive behavior. Previously he held research positions at the University of Pittsburgh and JustSystems Evans Research, where he implemented algorithms and systems for information retrieval, natural language processing, and data mining.
The document discusses cognitive information agents that can effectively learn from interactions with information to support complex human tasks. It describes an architecture that can automatically build and execute analytic solutions by specifying input/output types, information sources, and example datasets. It then trains, measures performance, analyzes errors, and proposes new learning tasks to iteratively improve. Example applications discussed are question answering systems for bioinformatics and decision support that can be automatically optimized for new datasets.
Dmitri G. Roussinov has over 15 years of experience in research and development in information technology, focusing on search engines, text mining, natural language processing, and business information systems. He has taught IT topics for over 12 years at highly ranked universities. He has 20+ years of hands-on experience in software design, programming languages, and computer graphics. He currently holds a senior lecturer position at the University of Strathclyde in Glasgow, UK.
Rohana Rajapakse is a senior developer at GOSS Interactive Ltd in Plymouth, UK. He received his PhD in Computer Science from the University of Plymouth in 2004. His research interests include digital information management, text processing, and neural networks. He has published several journal articles and conference papers on topics such as adaptive information retrieval, document categorization, and computational linguistics.
The document provides guidelines for designing effective e-learning objects and asynchronous instruction. It discusses best practices from sources like the Association of College and Research Libraries (ACRL) and Project Information Literacy. These include establishing learning outcomes, developing content that limits cognitive load, and ensuring accessibility for all students regardless of location. The document then outlines steps for instructional design using the ADDIE model of analysis, design, development, implementation and evaluation. Examples are provided for each step, with a focus on incorporating principles of multimedia learning and usability testing.
Mahesh Joshi has studied computer science and engineering, obtaining degrees from universities in India and the US. He is currently pursuing a Masters in Language Technologies at Carnegie Mellon University. His research focuses on natural language processing techniques such as word sense disambiguation and abbreviation expansion, especially in medical texts. He has published papers in conferences and released several related software tools.
Shihao Jin has a Master's degree in Computer Science from USC and a Bachelor's degree in Electrical Information Engineering from UESTC in China. He has skills in languages like Java, Python, HTML/CSS, and databases like PostgreSQL and MySQL. For projects, he developed a stock search Android app and webpage using various technologies. He also worked on a Go-Moku game player and a Weenix kernel implementation. His experience includes technology roles supporting an international school and assisting with aerial robotics research.
The document discusses domain modeling for personalized learning. It defines a domain model as representing domain knowledge through concepts and their relationships. Domain models serve as the basis for individual student models and for indexing and classifying learning content. They can be used to model student knowledge and decide on appropriate next steps for learning. The document describes different types of domain models, including vector, network, conceptual, and procedural models. It also discusses using ontologies and different aspects in domain modeling and applying domain models to student modeling, content indexing, and personalized guidance.
Deep learning is finding applications in science such as predicting material properties. DLHub is being developed to facilitate sharing of deep learning models, data, and code for science. It will collect, publish, serve, and enable retraining of models on new data. This will help address challenges of applying deep learning to science like accessing relevant resources and integrating models into workflows. The goal is to deliver deep learning capabilities to thousands of scientists through software for managing data, models and workflows.
Minita Jalan Shah is pursuing a Master of Science in Computer Science (Computational Biology) at Columbia University. She has relevant experience as a summer intern at the Itsik Pe'er Lab of Computational Genetics at Columbia, where she analyzed human genome data. She received her Bachelor of Science in Computer Engineering from Fr. Conceicao Rodrigues College of Engineering in Mumbai, India, where she developed several projects including a tool for identity-by-descent analysis and modeling of microRNA evolution.
Student Achievement Review (initially presented during Inauguration Function of the Ohio Center of Excellence in Knowledge-Enabled Computing at Wright State (Kno.e.sis)) - updated since
Center overview: http://bit.ly/coe-k
Invitation: http://bit.ly/COE-invite
H2O World - Intro to Data Science with Erin LedellSri Ambati
?
This document provides an introduction to data science. It defines data science as using data to solve problems through the scientific method. The roles of data scientists, data analysts, and data engineers on a data science team are discussed. Popular tools for data science include Python, R, and APIs that connect data processing engines. Machine learning algorithms are used to perform tasks like classification, regression, and clustering by learning from data rather than being explicitly programmed. Deep learning and ensemble methods are also introduced. Resources for learning more about data science and machine learning are provided.
Imran Khan is a PhD candidate at Shenzhen Institutes of Advanced Technology in China. His research focuses on ensemble clustering methods and algorithms for analyzing smart grid streaming data. He has a strong publication record, including papers in Neurocomputing and the International Journal of Data Science. Prior to his PhD studies, Khan worked as a lecturer and database architect. He is seeking opportunities to apply his skills and expertise in a prestigious organization.
The document summarizes a research paper on DBLP Search Support Engine (SSE), a system that aims to provide intelligent and personalized search beyond traditional search engines. It extracts users' research interests based on publication frequency and recency using interest retention models. The system represents users and their interests using RDF and provides additional functionalities like query refinement, domain analysis and tracking based on users' interests. Future work includes improving the interest prediction model and providing a unified architecture for different system functions.
Ratinov Lev is a PhD candidate at the University of Illinois at Urbana-Champaign studying machine learning and natural language processing under Professor Dan Roth. He has published several papers in these areas and held internships at Google, Honda Research Institute, and Microsoft. Ratinov holds an MSc from Ben-Gurion University in Israel and is fluent in Russian, Hebrew, and English.
EarthCube Monthly Community Webinar- Nov. 22, 2013EarthCube
?
This webinar features project overviews of all EarthCube Awards (Building Blocks, Research Coordination Networks, Conceptual Designs, and Test Governance), followed by a call for involvement, and a Q&A session.
Agenda:
EarthCube Awards ¨C Project Overviews
1.. EarthCube Web Services (Building Block)
2. EC3: Earth-Centered Community for Cyberinfrastructure (RCN)
3. GeoSoft (Building Block)
4. Specifying and Implementing ODSIP (Building Block)
5. A Broker Framework for Next Generation Geoscience (BCube) (Building Block)
6. Integrating Discrete and Continuous Data (Building Block)
7. EAGER: Collaborative Research (Building Block)
8. A Cognitive Computer Infrastructure for Geoscience (Building Block)
9. Earth System Bridge (Building Block)
10. CINERGI ¨C Community Inventory of EC Resources for Geoscience Interoperability (BB)
11. Building a Sediment Experimentalist Network (RCN)
12. C4P: Collaboration and Cyberinfrastructure for Paleogeosciences (RCN)
13. Developing a Data-Oriented Human-centric Enterprise for Architecture (CD)
14. Enterprise Architecture for Transformative Research and Collaboration (CD)
15. EC Test Enterprise Governance: An Agile Approach (Test Governance)
A Call for Involvement!
This document summarizes the education and experience of James Edmondson. He is currently a PhD student studying computer science at Vanderbilt University, with a focus on distributed real-time and embedded systems. He has a bachelor's degree in computer science from Middle Tennessee State University and work experience in software development. His research interests include machine learning, parallel systems, and software engineering.
Este documento analiza el modelo de negocio de YouTube. Explica que YouTube y otros sitios de video online representan un nuevo modelo de negocio para contenidos audiovisuales debido al cambio en los h¨¢bitos de consumo causado por las nuevas tecnolog¨ªas. Describe c¨®mo YouTube aprovecha la participaci¨®n de los usuarios para mejorar continuamente y atraer una audiencia diferente a la de los medios tradicionales.
The defense was successful in portraying Michael Jackson favorably to the jury in several ways:
1) They dressed Jackson in ornate costumes that conveyed images of purity, innocence, and humility.
2) Jackson was shown entering the courtroom as if on a red carpet, emphasizing his celebrity status.
3) Jackson appeared vulnerable, childlike, and in declining health during the trial, eliciting sympathy from jurors.
4) Defense attorney Tom Mesereau effectively presented a coherent narrative of Jackson as a victim and portrayed Neverland as a place of refuge, undermining the prosecution's arguments.
Michael Jackson was born in 1958 in Gary, Indiana and rose to fame in the 1960s as the lead singer of The Jackson 5, topping music charts in the 1970s. As a solo artist in the 1980s, his album Thriller broke music records. In the 1990s and 2000s, Jackson faced several legal issues related to child abuse allegations while continuing to release music. He married Lisa Marie Presley and Debbie Rowe and had two children before his death in 2009.
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...butest
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This document appears to be a list of popular books from various authors. It includes over 150 book titles across many genres such as fiction, non-fiction, memoirs, and novels. The books cover a wide range of topics from politics to cooking to autobiographies.
The prosecution lost the Michael Jackson trial due to several key mistakes and weaknesses in their case:
1) The lead prosecutor, Thomas Sneddon, was too personally invested in the case against Jackson, having pursued him for over a decade without success.
2) Sneddon's opening statement was disorganized and weak, failing to effectively outline the prosecution's case.
3) The accuser's mother was not credible and damaged the prosecution's case through her erratic testimony, history of lies and con artist behavior.
4) Many prosecution witnesses were not credible due to prior lawsuits against Jackson, debts owed to him, or having been fired by him. Several witnesses even took the Fifth Amendment.
Here are three examples of public relations from around the world:
1. The UK government's "Be Clear on Cancer" campaign which aims to raise awareness of cancer symptoms and encourage early diagnosis.
2. Samsung's global brand marketing and sponsorship activities which aim to increase brand awareness and favorability of Samsung products worldwide.
3. The Brazilian government's efforts to improve its international image and relations with other countries through strategic communication and diplomacy.
The three most important functions of public relations are:
1. Media relations because the media is how most organizations reach their key audiences. Strong media relationships are crucial.
2. Writing, because written communication is at the core of public relations and how most information is
Michael Jackson Please Wait... provides biographical information about Michael Jackson including his birthdate, birthplace, parents, height, interests, idols, favorite foods, films, and more. It discusses his background, career highlights including influential albums like Thriller, and films he appeared in such as The Wiz and Moonwalker. The document contains photos and details about Jackson's life and illustrious music career.
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazzbutest
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The document discusses the process of manufacturing celebrity and its negative byproducts. It argues that celebrities are rarely the best in their individual pursuits like singing, dancing, etc. but become famous due to being products of a system controlled by wealthy elites. This system stifles opportunities for worthy artists and creates feudalism. The document also asserts that manufactured celebrities should not be viewed as role models due to behaviors like drug abuse and narcissism that result from the celebrity-making process.
Michael Jackson was a child star who rose to fame with the Jackson 5 in the late 1960s and early 1970s. As a solo artist in the 1970s and 1980s, he had immense commercial success with albums like Off the Wall, Thriller, and Bad, which featured hit singles and groundbreaking music videos. However, his career and public image were plagued by controversies related to allegations of child sexual abuse in the 1990s and 2000s. He continued recording and performing but faced ongoing media scrutiny into his private life until his death in 2009.
Social Networks: Twitter Facebook SL - ºÝºÝߣ 1butest
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The document discusses using social networking tools like Twitter and Facebook in K-12 education. Twitter allows students and teachers to share short updates and can be used to give parents a window into classroom activities. Facebook allows targeted advertising that could be used to promote educational activities. Both tools could help facilitate communication between schools and communities if used properly while managing privacy and security concerns.
Facebook has over 300 million active users who log on daily, and allows brands to create public profile pages to interact with users. Pages are for brands and organizations only, while groups can be made by any user about any topic. Pages do not show admin names and have no limits on fans, while groups display admin names and are limited to 5,000 members. Content on pages should aim to provoke action from subscribers and establish a regular posting schedule using a conversational tone.
Executive Summary Hare Chevrolet is a General Motors dealership ...butest
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Hare Chevrolet is a car dealership located in Noblesville, Indiana that has successfully used social media platforms like Twitter, Facebook, and YouTube to create a positive brand image. They invest significant time interacting directly with customers online to foster a sense of community rather than overtly advertising. As a result, Hare Chevrolet has built a large, engaged audience on social media and serves as a model for how brands can use online presences strategically.
Welcome to the Dougherty County Public Library's Facebook and ...butest
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This document provides instructions for signing up for Facebook and Twitter accounts. It outlines the sign up process for both platforms, including filling out forms with name, email, password and other details. It describes how the platforms will then search for friends and suggest people to connect with. It also explains how to search for and follow the Dougherty County Public Library page on both Facebook and Twitter once signed up. The document concludes by thanking participants and providing a contact for any additional questions.
Paragon Software announces the release of Paragon NTFS for Mac OS X 8.0, which provides full read and write access to NTFS partitions on Macs. It is the fastest NTFS driver on the market, achieving speeds comparable to native Mac file systems. Paragon NTFS for Mac 8.0 fully supports the latest Mac OS X Snow Leopard operating system in 64-bit mode and allows easy transfer of files between Windows and Mac partitions without additional hardware or software.
This document provides compatibility information for Olympus digital products used with Macintosh OS X. It lists various digital cameras, photo printers, voice recorders, and accessories along with their connection type and any notes on compatibility. Some products require booting into OS 9.1 for software compatibility or do not support devices that need a serial port. Drivers and software are available for download from Olympus and other websites for many products to enable use with OS X.
To use printers managed by the university's Information Technology Services (ITS), students and faculty must install the ITS Remote Printing software on their Mac OS X computer. This allows them to add network printers, log in with their ITS account credentials, and print documents while being charged per page to funds in their pre-paid ITS account. The document provides step-by-step instructions for installing the software, adding a network printer, and printing to that printer from any internet connection on or off campus. It also explains the pay-in-advance printing payment system and how to check printing charges.
The document provides an overview of the Mac OS X user interface for beginners, including descriptions of the desktop, login screen, desktop elements like the dock and hard disk, and how to perform common tasks like opening files and folders. It also addresses frequently asked questions for Windows users switching to Mac OS X, such as where documents are stored, how to save or find documents, and what the equivalent of the C: drive is in Mac OS X. The document concludes with sections on file management tasks like creating and deleting folders, organizing files within applications, using Spotlight search, and an overview of the Dashboard feature.
This document provides a checklist for securing Mac OS X version 10.5, focusing on hardening the operating system, securing user accounts and administrator accounts, enabling file encryption and permissions, implementing intrusion detection, and maintaining password security. It describes the Unix infrastructure and security framework that Mac OS X is built on, leveraging open source software and following the Common Data Security Architecture model. The checklist can be used to audit a system or harden it against security threats.
This document summarizes a course on web design that was piloted in the summer of 2003. The course was a 3 credit course that met 4 times a week for lectures and labs. It covered topics such as XHTML, CSS, JavaScript, Photoshop, and building a basic website. 18 students from various majors enrolled. Student and instructor evaluations found the course to be very successful overall, though some improvements were suggested like ensuring proper software and pairing programming/non-programming students. The document also discusses implications of incorporating web design material into existing computer science curriculums.
1. Daniel Anthony Oblinger
IBM T.J. Watson Research Citizenship: United States
19 Skyline Drive, Hawthorne, NY 10532 Phone: (917) 494-1272
Web: http://pobox.com/~oblinger E-mail: oblinger@pobox.com
EDUCATION
Ph.D. in Computer Science at the University of Illinois at Urbana-Champaign
M.S. in Computer Science from Ohio State University, GPA 4.0
B.S. in Mathematics and Computer Science from Northern Kentucky University
Summa Cum Laude ? GPA 3.92 overall ? GPA 4.0 in both majors
Thesis: ¡°Plausible Inference: A knowledge-intensive, inductive approach to domain modeling.¡±
Doctoral Committee: Gerald DeJong (chair), Shankar Subramaniam, Larry Rendell, and Caroline Hayes
RESEARCH INTERESTS
Areas: Machine Learning, Data Mining, Artificial Intelligence.
PROFESSIONAL EXPERIENCE
Programming By Demonstration. Research Staff Member. IBM TJ Watson Research. (2002 - present)
? Won 12 person-years of "Adventurous Research" IBM funding
(sought-after funding is the most independent long-range funding offered within IBM)
? Approach combines my bioinformatics background in sequence alignment with structure-based
learning algorithms to learn structured scripts with loops and conditionals.
Scripts are automatically learned by passively observing human operators in action.
? On going efforts have yielded a dozen patent filings, publications, and two spin off projects.
? One spin off is aimed at IBM product sales driven by automatically generated product walk throughs.
? Second spin off integrates personal wizards into the IBM-owned Lotus Rich Client platform as an
end-user customization tool.
Statistical Pattern Recognition. Adjunct Faculty. Columbia University. (2002-3)
? Taught graduate-level courses in Machine Learning.
? Covered underlying theory and practical application of fourteen state of the art learning techniques.
? Class project involved open-ended research based on IBM dataset.
Email Mining. Research Staff Member. IBM TJ Watson Research. (2001-2)
? Project lead for two email mining systems: Mail Assistant facilitated personal contacts visualization
and retrieval based on email content and from/to graph linkages.
? Skills Miner utilized skill-related documents to learn a skill-word dictionary. This in turn drove
multi-instance learning approach for assessing employee skills based on email traffic patterns and
content.
? Side Activity: Co-created an IBM Research worldwide AI special interest group.
Speech Data Mining. Research Staff Member. IBM TJ Watson Research. (1998-2000)
? Developed speech analysis tool enabling teachers to identify reading progress and reading problems
in beginning readers. Tool relied on data from a companion system that read for and with children.
? In daily use in bi-lingual education programs at over 100 schools across the United States.
Protein Sequence Modeling. Research Assistant with S. Subramaniam. University of Illinois. (1995-7)
? Data mining consultant for a group of computational biologists.
? Learned protein similarity metrics based on atom-level descriptions of protein fold classes.
? Encoded approximate physical & chemical models of atomic interaction in order to constrain learning.
? Implemented an inductive learning algorithm that used these models to mine rules from 4 Gigs. of data
on protein structures. Implemented parallel version of algorithm for a 512-node CM5 supercomputer.
Learning/Inferencing Algorithms. Research Assistant with Gerald DeJong. Univ. of Illinois. (1989-95)
2. ? Developed formal and computational models of plausible inference. This technique combines
a general but inaccurate expert model with specific and accurate entries from a database to learn
accurate and general rules describing the data.
? System Administrator for the AI group's file/mail/web/printer/xdm services.
Designed, scripted, and maintained a three-level backup scheme for this 50-seat network.
Software Engineer. IBM T.J. Watson Research Center. (1989)
? Collaborated on the initial design of an expert system that automatically generates configurations for
IBM mainframe computers.
Teaching Associate. Ohio State University. (1987-9)
? Designed and taught a course new to OSU¡¯s curriculum: LISP for Engineers (CIS 459).
? Instructor: Pascal for Engineers (CIS 221; four courses).
? Invited to teach an OSU-accredited, off-campus version of Pascal (CIS 221) at Bell Core Corp.
Selected by department chair from over 60 candidates based on my previous student evaluation scores.
Research Associate. Ohio State University. (1988)
? Developed the semantics, designed, and implemented a data-directed control flow mechanism used to
drive part of the AI Tool Set, upon which many AI applications have been written.
Runtime Library Developer. IBM Santa Theresa Lab. (1988)
? Designed and implemented task synchronization primitives used in a run-time library that supports
more than five languages on the IBM 370.
Physical Attendant, Tutor, Engineer. Doug Ragland. Lexington, KY. (1984-6)
? Attendant for a mute quadriplegic. Assisted in all functions, including dressing, eating, swimming,
transportation, taking class notes, etc. One on one tutored for four computer science courses.
? Designed and implemented a text processing and programming environment for quadriplegics.
Environment included an extensible programmable editor, hierarchical note retrieval facility, and an
auto-word-completion dictionary; system designed to minimize keystrokes.
PROGRAMMING SKILLS
Java, C/C++, LISP/CLOS, Perl, XML, programming interfaces (Windows, X-windows, UNIX), a number
of more specific languages and many assemblers.
COLLEGIATE HONORS, ACTIVITIES, AND AWARDS
? Cognitive Science / Artificial Intelligence Fellowship (UIUC 1994)
? ACM Regional Programming Contest in Kalamazoo (1986) and Pittsburgh (1988)
Participated on OSU¡¯s team (6th place out of over 50 schools) and NKU¡¯s team (7th place)
? Student of the year in both mathematics and computer science at NKU (1987)
? NKU Dean¡¯s Scholarship junior and senior years (1985¨C87)
? Placed in the top third in the annual Putnam Mathematics Competition (NKU 1985)
? AHP Mathematics Competition: second place as freshman (1984) and first place as sophomore (1984)
? NKU Mathematics Departmental Scholarship (1984¨C87)
? Kentucky State Science Fair: second place in physics and fourth overall (1982)
? Volunteer at the McKinley Foundation¡¯s Emergency Men¡¯s Shelter of Champaign (1995¨C96)
? President of the NKU Computer Science Club (1987)
? Captain for a team in the NKU intramural volleyball league (1986)
3. Daniel A. Oblinger
PROFESSIONAL ACTIVITIES
WORKSHOP/CONFERENCE CHAIR
¡°Workshop on human-understandable machine learning.¡± Twentieth National Conference on Artificial
Intelligence. (will be held July 9, 2005.)
¡°What works well where workshop¡± The Seventeenth International Conference on Machine Learning.
Stanford, CA. July 2000.
¡°Inductive Learning track¡± International Conference on Artificial Intelligence 2000.
Las Vegas, NV. June 2000.
¡°The Joint Beckman Institute / Hitachi Advanced Research Laboratory¡¯s Symposium on Artificial
Intelligence¡± workshop at The Beckman Institute. Champaign, IL. May 1997.
ASSOCIATE EDITOR
International Conference on Artificial Intelligence. 2000.
ADJUNCT FACULTY
Co Professor for ¡°ELEN E 6880 Statistical Pattern Recognition¡± at Columbia University, 2002-3.
REVIEWER
Machine Learning Journal special issue on Meta Learning. 2004.
The Fourth IEEE International Conference on Data Mining. 2004.
International Conference on Machine Learning. 2003.
The Third IEEE International Conference on Data Mining. 2003.
The Second IEEE International Conference on Data Mining. 2002.
The International Conference on Artificial Intelligence. 2000.
International Conference on Artificial Intelligence in Education. 2001.
International Joint Conference on Artificial Intelligence. 2001.
European Conference on Artificial Intelligence. 1994.
National Conference on Artificial Intelligence Student Program. 1993.
AFFILIATIONS
Advisory Board Member. Electrical and Computer Science Department, Northwestern University.
AAAI. American Association for Artificial Intelligence. 1991¨C
Phi Beta Kappa Honor Society. 1995¨C
ACM. Association for Computing Machinery. 1986¨C89
4. Daniel A. Oblinger
ISSUED PATENTS
6,873,990 Customer self service subsystem for context cluster discovery and validation.
6,853,998 Subsystem for classifying user contexts.
6,785,676 Subsystem for response set ordering and annotation.
6,778,193 Iconic interface for portal entry and search specification.
6,701,311 System for resource search and selection.
6,693,651 Iconic interface for resource search results display and selection.
6,643,639 Subsystem for adaptive indexing of resource solutions and resource lookup.
PUBLICATIONS
JOURNAL ARTICLES
R. Vilalta, D. Oblinger, "Evaluation metrics in classification: A quantification of distance-bias"
Computational Intelligence, Vol. 54, No. 3, pp. 187-193. 2003.
D. Oblinger, M. Reid, M. Brodie, R. Braz, "Cross Training and its application to skill mining"
IBM System Journal, Vol. 41, No. 3 pp. 449-460. 2002.
FIRST TIER CONFERENCES
T. Lau, L. Bergman, V. Castelli, D. Oblinger, ¡°Sheepdog: Learning Procedures for Technical Support,¡±
Proceedings of the 2004 International Conference on Intelligent User Interfaces (IUI 2004). Madeira,
Portugal. 2004. pp. 109-116.
N. Mishra, D. Oblinger, and L. Pitt, ¡°Sublinear time approximate clustering¡±
Proceedings of the Twelfth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2001).
San Francisco, CA. 2001.
R. Vilalta, D. Oblinger, ¡°A Quantification of Distance-Bias Between Evaluation Metrics In Classification.¡±
Proceedings of the Seventeenth International Conference on Machine Learning, Stanford, CA. 2000. pp.
1087-1094.
D. Oblinger, G. DeJong, ¡°An Alternative to Deduction.¡± Proceedings of the Thirteenth Annual Conference
of the Cognitive Science Society. Chicago, IL. 1991. pp. 837¨C841.
K. Forbus, D. Oblinger, ¡°Making SME Greedy and Pragmatic.¡± Proceedings of the Twelfth Annual
Conference of the Cognitive Science Society. Cambridge, MA. 1990. pp. 61¨C68.
BOOK CHAPTER
5. Daniel A. Oblinger
G. DeJong, D. Oblinger, ¡°A First Theory of Plausible Inference and Its Use in Continuous Domain
Planning.¡± Machine Learning Methods for Planning, Steven Minton (ed.). San Mateo, CA: Morgan
Kaufmann. 1993. pp. 93¨C124. (Selected from the Symposium on Learning Methods for Planning and
Scheduling for publication in extended book form.)
OTHER CONFERENCES, WORKSHOPS, AND SYMPOSIA
T. Lau, L. Bergman, V. Castelli, D. Oblinger, ¡° Programming Shell Scripts By Demonstration,''
The Nineteenth National Conference on Artificial Intelligence (AAAI 2004): Supervisory Control of
Learning and Adaptive Systems workshop. San Jose, CA. 2004.
L. Bergman, T. Lau, V. Castelli, D. Oblinger, ¡°Programming-by-demonstration for Behavior-based User
Interface Customization,¡± Proceedings of the Workshop on Behavior-Based User Interface
Customization, (IUI 2004). Madeira, Portugal. 2004.
T. Lau, D. Oblinger, L. Bergman, V. Castelli, C. Anderson, ¡°Learning Procedures for Autonomic
Computing,¡± Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence
(JCAI 2003): Developing a Research Agenda for Self-Managing Computer Systems workshop,.
Acapulco, Mexico. 2003.
L. Bergman, T. Lau, V. Castelli, D. Oblinger, ¡°Personal Wizards: Collaborative End-User
Programming,¡± Proceedings of the CHI 2003 Conference on Human Factors: Workshop on Perspectives
in End User Development, Fort Lauderdale, Florida. 2003.
R. Vilalta, M. Brodie, D. Oblinger, and I. Rish, "A Unified Framework For Evaluation Metrics In
Classification Using Decision Trees." Proceedings of the 12th European Conference on Machine
Learning (ECML 2001), Freiburg, Germany. 2001.
D. Gordin, R. Farrell, D. Oblinger, Mapping knowledge production in network organizations.
IBM Academy of Technology Conference on Knowledge Management. Zurich, Switzerland. April 2001
D. Oblinger, G. DeJong, ¡°Dynamic-Bias Induction.¡± American Association for Artificial Intelligence
Fall Symposium Series on Relevance (AAAI-94). New Orleans, LA. 1994. pp. 164¨C67
G. DeJong, D. Oblinger, ¡°A First Theory of Plausible Inference and Its Use in Continuous Domain
Planning.¡¯¡¯ Symposium on Learning Methods for Planning and Scheduling. 1991
INVITED TALKS AND SEMINARS
¡°Learning System Management Procedure By Demonstration.¡± Computer Science Colloquium, New
York University. November 2003.
¡°Personal Wizards: A Programming by Demonstration Approach¡± Northwestern University, April 2003.
¡°Knowledge-Based Induction of Protein Tertiary Structure.¡± The Joint Beckman Institute / Hitachi
Advanced Research Laboratory¡¯s Symposium on Artificial Intelligence. Beckman Institute. August
1995
¡°Minimum Description Length Principle.¡± The Machine Learning Seminar Series. Beckman Institute.
April 1994
TECHNICAL REPORTS AND WORKING PAPERS
6. Daniel A. Oblinger
D. Oblinger, V. Castelli, T. Lau, L. Bergman. "Similarity-Based Alignment and Generalization: A New
Paradigm for Programming by Demonstration."
IBM T.J. Watson Research Center: Technical Report: RC23140. 2004.
V. Castelli, D. Oblinger; L. Bergman; T. Lau. "Dynamic Model Selection in IOHMMs"
IBM T.J. Watson Research Center: Technical Report: RC23395. 2004.
T. Lau, L. Bergman, V. Castelli, D. Oblinger, ¡°Programming Shell Scripts by Demonstration¡±
IBM T.J. Watson Research Center: Technical Report: RC23218. 2004.
L. Bergman, T. Lau, V. Castelli, D. Oblinger. "Programming-by-Demonstration for Behavior-based User
Interface Customization" IBM T.J. Watson Research Center: Technical Report: RC23116. 2004.
T. Lau, D. Oblinger, L. Bergman, V. Castelli, C. Anderson. "Learning Procedures for Autonomic
Computing"
IBM T.J. Watson Research Center: Technical Report: RC23115. 2004.
D. Oblinger, ¡°Plausible Inference: A Knowledge-Intensive, Inductive Approach to Domain Modeling.¡±
University of Illinois: Technical Report UIUC-DCS-R-97-2004. Urbana, IL. 1997.
D. Oblinger, G. DeJong, ¡°Towards an Inductive Model of Defeasible Inference.¡± Beckman Institute,
University of Illinois: Technical Report UIUC-AI-BI-95-01. Urbana, IL. 1995.
D. Oblinger, G. DeJong, ¡°Dynamic Bias Induction.¡± Beckman Institute, University of Illinois: Technical
Report UIUC-AI-BI-9403. Urbana, IL. 1994. (Extended version)
D. Oblinger, G. DeJong, ¡°An Alternative to Deduction.¡± Department of Computer Science, University of
Illinois: Technical Report UIUC-DCS-R-91-1688. Urbana, IL. 1991. (Extended version)
J. Josephson, D. Smetters, R. Fox, D. Oblinger, A. Welch, G. Northrup, ¡°The Integrated Generic Task
Toolset¡ªFafter release 1.0¡ªIntroduction and User¡¯s Guide.¡± The Ohio State University Laboratory for
Artificial Intelligence Research: Technical Report 89-JJ-FAFNER. Columbus, OH. 1992.
REFERENCES AVAILABLE UPON REQUEST