Corpora: Google Ngram Viewer and the Corpus of Historical American English
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Copyright Reform in the EU: Grappling with the Google Effect
Copyright Reform in the EU: Grappling with the Google Effect Annemarie Bridy Sweeping changes are coming to copyright law in the European Union. Following four years of negotiations, the European Parliament in April 2019 approved the final text of the Digital Single Market Directive (DSMD).1 EU member states now have two years to transpose its provisions intodomestic law. The new directive, which is the most substantial change to EU copyright law in a generation, contains provisions for enhancing cross-border access to content available through digital subscription services, enabling new uses of copyrighted works for education and research, and, most controversially, ‘clarifying’ the role of online services in the distribution of copyrighted works. The provisions associated with the last of these goals—Article 15 (the ‘link tax’) and Article 17 (‘upload filters’) take aim directly at two services operated by Google: Google News and YouTube. Article 15 is intended to provide remuneration for press publishers when snippets of their articles are displayed by search engines and news aggregators.2 Article 17, which this article takes for its subject, is intended to address the so-called ‘value gap’—the music industry’s longstanding complaint that YouTube undercompensates music rightholders for streams of user videos containing claimed copyrighted content.3 The text of the DSMD nowhere mentions YouTube, but anyone versed in the political economy of digital copyright knows that Article 17 was purpose-built to make YouTube pay. The important questions to ask in the wake of Article 17 are who else will pay—and in what ways. This article offers a focused examination of Article 17 as public law created to settle a private score between the music industry and YouTube. -
Contents Libraries and Google
Waller http://www.uic.edu/htbin/cgiwrap/bin/ojs/index.php/fm/article/viewArt... First Monday, Volume 14, Number 9 - 7 September 2009 HOME ABOUT LOG IN REGISTER SEARCH CURRENT ARCHIVES SUBMISSIONS Home > Volume 14, Number 9 - 7 September 2009 > Waller This article explores the implications of a shift from public to private provision of information through focusing on the relationship between Google and public libraries. This relationship has sparked controversy, with concerns expressed about the integrity of search results, the Google Book project, and Google the company. In this paper, these concerns are treated as symptoms of a deeper divide, the fundamentally different conceptions of information that underpin the stated aim of Google and libraries to provide access to information. The paper concludes with some principles necessary for the survival of public libraries and their contribution to a robust democracy in a rapidly expanding Googleverse. Contents Libraries and Google The romance phase: ‘We have everything in common’ Cracks appear in the relationship Reality check: ‘We want different things’ The word ‘information’ Conclusion: Negotiating a healthy relationship Libraries and Google ‘To google’ has become a household verb, meaning “to search for information on the Internet.” [1] In the month of April 2008, Google handled 5.1 billion queries in the U.S. alone [2]. Its market share is almost 90 percent in the U.K. and Australia [3], 80 percent in Europe [4] and 70 percent in the United States [5]. By 2004 Google had indexed eight billion pages; in 2006 Google claimed to have worked out how to index billions more [6]. -
BEYOND JEWISH IDENTITY Rethinking Concepts and Imagining Alternatives
This book is subject to a CC-BY-NC license. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc/4.0/ BEYOND JEWISH IDENTITY Rethinking Concepts and Imagining Alternatives This book is subject to a CC-BY-NC license. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc/4.0/ This book is subject to a CC-BY-NC license. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc/4.0/ BEYOND JEWISH IDENTITY rethinking concepts and imagining alternatives Edited by JON A. LEVISOHN and ARI Y. KELMAN BOSTON 2019 This book is subject to a CC-BY-NC license. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc/4.0/ Library of Congress Control Number:2019943604 The research for this book and its publication were made possible by the generous support of the Jack, Joseph and Morton Mandel Center for Studies in Jewish Education, a partnership between Brandeis University and the Jack, Joseph and Morton Mandel Foundation of Cleveland, Ohio. © Academic Studies Press, 2019 ISBN 978-1-644691-16-8 (Hardcover) ISBN 978-1-644691-29-8 (Paperback) ISBN 978-1-644691-17-5 (Open Access PDF) Book design by Kryon Publishing Services (P) Ltd. www.kryonpublishing.com Cover design by Ivan Grave Published by Academic Studies Press 1577 Beacon Street Brookline, MA 02446, USA [email protected] www.academicstudiespress.com Effective May 26th 2020, this book is subject to a CC-BY-NC license. To view a copy of this license, visit https://creativecommons.org/licenses/ by-nc/4.0/. -
CS15-319 / 15-619 Cloud Computing
CS15-319 / 15-619 Cloud Computing Recitation 13 th th November 18 and 20 , 2014 Announcements • Encounter a general bug: – Post on Piazza • Encounter a grading bug: – Post Privately on Piazza • Don’t ask if my answer is correct • Don’t post code on Piazza • Search before posting • Post feedback on OLI Last Week’s Project Reflection • Provision your own Hadoop cluster • Write a MapReduce program to construct inverted lists for the Project Gutenberg data • Run your code from the master instance • Piazza Highlights – Different versions of Hadoop API: Both old and new should be fine as long as your program is consistent Module to Read • UNIT 5: Distributed Programming and Analytics Engines for the Cloud – Module 16: Introduction to Distributed Programming for the Cloud – Module 17: Distributed Analytics Engines for the Cloud: MapReduce – Module 18: Distributed Analytics Engines for the Cloud: Pregel – Module 19: Distributed Analytics Engines for the Cloud: GraphLab Project 4 • MapReduce – Hadoop MapReduce • Input Text Predictor: NGram Generation – NGram Generation • Input Text Predictor: Language Model and User Interface – Language Model Generation Input Text Predictor • Suggest words based on letters already typed n-gram • An n-gram is a phrase with n contiguous words Google-Ngram Viewer • The result seems logical: the singular “is” becomes the dominant verb after the American Civil War. Google-Ngram Viewer • “one nation under God” and “one nation indivisible.” • “under God” was signed into law by President Eisenhower in 1954. How to Construct an Input Text Predictor? 1. Given a language corpus – Project Gutenberg (2.5 GB) – English Language Wikipedia Articles (30 GB) 2. -
The Google Effect: Transactive Memory
GENERATION AND THE GOOGLE EFFECT: TRANSACTIVE MEMORY SYSTEM PREFERENCE ACROSS AGE by JESSICA SILER A thesis submitted in partial fulfillment of the requirements for the Honors in the Major Program in Psychology in the College of Sciences and in the Burnett Honors College at the University of Central Florida Orlando, Florida Summer Term 2013 Thesis Chair: Dr. Peter A. Hancock ABSTRACT A transactive memory system (TMS) is a means by which people may store information externally; in such a system the task of remembering is offloaded by remembering where information is located, rather than remembering the information itself. As Sparrow et al. (2011) suggest in the article Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips, people are beginning to use the internet and computers as a TMS, and this use is changing the way people encounter and treat information. The purpose of this thesis is to investigate whether preference for TMS type (either with books or with computers) varies across age groups. An interaction between TMS preference and age was hypothesized. Before the onset of the internet age, information was primarily found in books and other print materials whereas now the internet is more frequently used, thus this shift in thinking and habit across generations was expected to emerge in the data. The study yielded a total of 51 participants, 32 from the young age group (ages 18-24) and 19 from the old (ages 61-81). A modified Stroop task and question blocks (for priming purposes) were employed to examine whether people are prone to think of book- or computer-related sources when in search of information. -
Internet and Data
Internet and Data Internet and Data Resources and Risks and Power Kenneth W. Regan CSE199, Fall 2017 Internet and Data Outline Week 1 of 2: Data and the Internet What is data exactly? How much is there? How is it growing? Where data resides|in reality and virtuality. The Cloud. The Farm. How data may be accessed. Importance of structure and markup. Structures that help algorithms \crunch" data. Formats and protocols for enabling access to data. Protocols for controlling access and changes to data. SQL: Select. Insert. Update. Delete. Create. Drop. Dangers to privacy. Dangers of crime. (Dis-)Advantages of online data. [Week 1 Activity: Trying some SQL queries.] Internet and Data What Exactly Is \Data"? Several different aspects and definitions: 1 The entire track record of (your) online activity. Note that any \real data" put online was part of online usage. Exception could be burning CD/DVDs and other hard media onto a server, but nowadays dwarfed by uploads. So this is the most inclusive and expansive definition. Certainly what your carrier means by \data"|if you re-upload a file, it counts twice. 2 Structured information for a particular context or purpose. What most people mean by \data." Data repositories often specify the context and form. Structure embodied in formats and access protocols. 3 In-between is what's commonly called \Unstructured Information" Puts the M in Data Mining. Hottest focus of consent, rights, and privacy issues. Internet and Data How Much Data Is There? That is, How Big Is the Internet? Searchable Web Deep Web (I maintain several gigabytes of deep-web textual data. -
Cloud Computing Bible Is a Wide-Ranging and Complete Reference
A thorough, down-to-earth look Barrie Sosinsky Cloud Computing Barrie Sosinsky is a veteran computer book writer at cloud computing specializing in network systems, databases, design, development, The chance to lower IT costs makes cloud computing a and testing. Among his 35 technical books have been Wiley’s Networking hot topic, and it’s getting hotter all the time. If you want Bible and many others on operating a terra firma take on everything you should know about systems, Web topics, storage, and the cloud, this book is it. Starting with a clear definition of application software. He has written nearly 500 articles for computer what cloud computing is, why it is, and its pros and cons, magazines and Web sites. Cloud Cloud Computing Bible is a wide-ranging and complete reference. You’ll get thoroughly up to speed on cloud platforms, infrastructure, services and applications, security, and much more. Computing • Learn what cloud computing is and what it is not • Assess the value of cloud computing, including licensing models, ROI, and more • Understand abstraction, partitioning, virtualization, capacity planning, and various programming solutions • See how to use Google®, Amazon®, and Microsoft® Web services effectively ® ™ • Explore cloud communication methods — IM, Twitter , Google Buzz , Explore the cloud with Facebook®, and others • Discover how cloud services are changing mobile phones — and vice versa this complete guide Understand all platforms and technologies www.wiley.com/compbooks Shelving Category: Use Google, Amazon, or -
Introduction to Text Analysis: a Coursebook
Table of Contents 1. Preface 1.1 2. Acknowledgements 1.2 3. Introduction 1.3 1. For Instructors 1.3.1 2. For Students 1.3.2 3. Schedule 1.3.3 4. Issues in Digital Text Analysis 1.4 1. Why Read with a Computer? 1.4.1 2. Google NGram Viewer 1.4.2 3. Exercises 1.4.3 5. Close Reading 1.5 1. Close Reading and Sources 1.5.1 2. Prism Part One 1.5.2 3. Exercises 1.5.3 6. Crowdsourcing 1.6 1. Crowdsourcing 1.6.1 2. Prism Part Two 1.6.2 3. Exercises 1.6.3 7. Digital Archives 1.7 1. Text Encoding Initiative 1.7.1 2. NINES and Digital Archives 1.7.2 3. Exercises 1.7.3 8. Data Cleaning 1.8 1. Problems with Data 1.8.1 2. Zotero 1.8.2 3. Exercises 1.8.3 9. Cyborg Readers 1.9 1. How Computers Read Texts 1.9.1 2. Voyant Part One 1.9.2 3. Exercises 1.9.3 10. Reading at Scale 1.10 1. Distant Reading 1.10.1 2. Voyant Part Two 1.10.2 3. Exercises 1.10.3 11. Topic Modeling 1.11 1. Bags of Words 1.11.1 2. Topic Modeling Case Study 1.11.2 3. Exercises 1.11.3 12. Classifiers 1.12 1. Supervised Classifiers 1.12.1 2. Classifying Texts 1.12.2 3. Exercises 1.12.3 13. Sentiment Analysis 1.13 1. Sentiment Analysis 1.13.1 2. -
Google Ngram Viewer Turns Snippets Into Insight Computers Have
PART I Shredding the Great Books - or - Google Ngram Viewer Turns Snippets into Insight Computers have the ability to store enormous amounts of information. But while it may seem good to have as big a pile of information as possible, as the pile gets bigger, it becomes increasingly difficult to find any particular item. In the old days, helping people find information was the job of phone books, indexes in books, catalogs, card catalogs, and especially librarians. Goals for this Lecture 1. Investigate the history of digitizing text; 2. Understand the goal of Google Books; 3. Describe logistical, legal, and software problems of Google Books; 4. To see some of the problems that arise when old texts are used. Computers Are Reshaping Our World This class looks at ways in which computers have begun to change the way we live. Many recent discoveries and inventions can be attributed to the power of computer hardware (the machines), the development of computer programs (the instructions we give the machines), and more importantly to the ability to think about problems in new ways that make it possible to solve them with computers, something we call computational thinking. Issues in Computational Thinking What problems can computers help us with? What is different about how a computer seeks a solution? Why are computer solutions often not quite the same as what a human would have found? How do computer programmers write out instructions that tell a computer how to solve a problem, what problem to solve, and how to report the answer? How does a computer \think"? How does it receive instructions, store infor- mation in a memory, represent and manipulate the numbers and words and pictures and ideas that we refer to as we think about a problem? Computers are Part of a Complicated World We will see that making a computer carry out a task in a few seconds actually can take years of thinking, research, and experiment. -
1 Indian Council of Medical Research New Delhi
Volume 7 , No. 4 October–December 2010 INDIAN COUNCIL OF MEDICAL RESEARCH NEW DELHI 1 Google: impact on Libraries EDITORIAL BOARD MEMBERS EDITOR Dr. K Satyanarayana Dr. K V Ratnakar Dr. Rashmi Arora Dr. Chandrashekhar ASST. EDITOR Dr. D.K. Shukla Dr. Vijay Kumar Shri R K Pandey TECHNICAL SUPPORT Smt. Suresh Arora Shri Praveen Kumar Shri Laxman Singh Shri Satish Chandra Shri Avinash Kumar Rajesh Shri Ayekpam surbanta Meitei Shri Maneesh Patel Shri Yogesh Kumar Published by Indian Council of Medical Research V.Ramalingaswami Bhawan, Ansari Nagar, New Delhi-110029 2 3 Contents 1. Google: impact on Libraries…………. 2. New Arrivals………………………………. 3. News………………………………………… 4 Google: impact on Libraries Abstract The many ethical questions relating to Google and library values cannot all be addressed in a single article. The value of Google is Universal evident. The increased popularity of Google search engine in the daily routine in one's workplace and in the academic information seeking process is undeniable. 'Googling' challenges the traditional skills of librarians as information providers and the role of library and information service provision in the digital era. This paper seeks to show that elements essential to Google‟s success can be mapped directly to certain traditional library values. Because of this focus, most of the examples given here will indicate a positive correlation. Introduction Only now in the bright light of the Google Era do ways to use Google. It is now routine for the we see how dim and gloomy our pregooglian world romantically savvy to Google a prospective date. In was. In the distant future, historians will have a the dot-com world, nothing stays the same for long, common term for the period prior to the appearance of and it's not clear that Google will forever maintain its Google: the Dark Ages. -
Arxiv:2007.16007V2 [Cs.CL] 17 Apr 2021 Beddings in Deep Networks Like the Transformer Can Improve Performance
Exploring Swedish & English fastText Embeddings for NER with the Transformer Tosin P. Adewumi, Foteini Liwicki & Marcus Liwicki [email protected] EISLAB SRT Department Lule˚aUniversity of Technology Sweden Abstract In this paper, our main contributions are that embeddings from relatively smaller cor- pora can outperform ones from larger corpora and we make the new Swedish analogy test set publicly available. To achieve a good network performance in natural language pro- cessing (NLP) downstream tasks, several factors play important roles: dataset size, the right hyper-parameters, and well-trained embeddings. We show that, with the right set of hyper-parameters, good network performance can be reached even on smaller datasets. We evaluate the embeddings at both the intrinsic and extrinsic levels. The embeddings are deployed with the Transformer in named entity recognition (NER) task and significance tests conducted. This is done for both Swedish and English. We obtain better performance in both languages on the downstream task with smaller training data, compared to re- cently released, Common Crawl versions; and character n-grams appear useful for Swedish, a morphologically rich language. Keywords: Embeddings, Transformer, Analogy, Dataset, NER, Swedish 1. Introduction The embedding layer of neural networks may be initialized randomly or replaced with pre- trained vectors, which act as lookup tables. One of such pre-trained vector tools include fastText, introduced by Joulin et al. Joulin et al. (2016). The main advantages of fastText are speed and competitive performance to state-of-the-art (SotA). Using pre-trained em- arXiv:2007.16007v2 [cs.CL] 17 Apr 2021 beddings in deep networks like the Transformer can improve performance. -
DIGITAL AMNESIA Why We Need to Protect What We No Longer Remember
THE RISE AND IMPACT OF DIGITAL AMNESIA Why we need to protect what we no longer remember Key findings from the study include: • Across the United States, the study shows that an overwhelming EXECUTIVE number of consumers can easily admit their dependency on the Internet and devices as a tool for remembering. Almost all (91.2%) of those surveyed agreed that they use the Internet as The results suggest a direct an online extension of their brain. Almost half (44.0%) also admit SUMMARY link between data available that their smartphone serves as their memory–everything they at the click of a button and need to recall and want to have easy access to is all on it. a failure to commit that data to memory. Kaspersky Lab • In addition, many consumers are happy to forget, or risk has termed this phenomenon forgetting information they can easily find–or find again- Digital Amnesia: the online. When faced with a question, half of U.S. consumers experience of forgetting would turn to the Internet before trying to remember and 28.9% information that you trust would forget an online fact as soon as they had used it. a digital device to store and remember for you. • Although dependence on devices appears high, when asked, most participants could phone the house they lived in at 15 (67.4%) as well as their partners (69.7%), children (34.5%), and place of work (45.4%). They could not however call their siblings (44.2%), friends (51.4%), or neighbors (70.0%) without first looking up the number.