Academic Libraries Have the Most Trusted Resources, but Their Tools Are Hard to Use
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Duckduckgo Search Engines Android
Duckduckgo search engines android Continue 1 5.65.0 10.8MB DuckduckGo Privacy Browser 1 5.64.0 10.8MB DuckduckGo Privacy Browser 1 5.63.1 10.78MB DuckduckGo Privacy Browser 1 5.62.0 10.36MB DuckduckGo Privacy Browser 1 5.61.2 10.36MB DuckduckGo Privacy Browser 1 5.60.0 10.35MB DuckduckGo Privacy Browser 1 5.59.1 10.35MB DuckduckGo Privacy Browser 1 5.58.1 10.33MB DuckduckGo Privacy Browser 1 5.57.1 10.31MB DuckduckGo Privacy browser © DuckduckGo. Privacy, simplified. This article is about the search engine. For children's play, see duck, duck, goose. Internet search engine DuckDuckGoScreenshot home page DuckDuckGo on 2018Type search engine siteWeb Unavailable inMultilingualHeadquarters20 Paoli PikePaoli, Pennsylvania, USA Area servedWorldwideOwnerDuck Duck Go, Inc., createdGabriel WeinbergURLduckduckgo.comAlexa rank 158 (October 2020 update) CommercialRegregedSeptember 25, 2008; 12 years ago (2008-09-25) was an Internet search engine that emphasized the privacy of search engines and avoided the filter bubble of personalized search results. DuckDuckGo differs from other search engines by not profiling its users and showing all users the same search results for this search term. The company is based in Paoli, Pennsylvania, in Greater Philadelphia and has 111 employees since October 2020. The name of the company is a reference to the children's game duck, duck, goose. The results of the DuckDuckGo Survey are a compilation of more than 400 sources, including Yahoo! Search BOSS, Wolfram Alpha, Bing, Yandex, own web scanner (DuckDuckBot) and others. It also uses data from crowdsourcing sites, including Wikipedia, to fill in the knowledge panel boxes to the right of the results. -
EFF, Duckduckgo, and Internet Archive Amicus Brief
Case: 17-16783, 11/27/2017, ID: 10667942, DktEntry: 42, Page 1 of 39 NO. 17-16783 IN THE UNITED STATES COURT OF APPEALS FOR THE NINTH CIRCUIT HIQ LABS, INC., PLAINTIFF-APPELLEE, V. LINKEDIN CORPORATION, DEFENDANT-APPELLANT. On Appeal from the United States District Court for the Northern District of California Case No. 3:17-cv-03301-EMC The Honorable Edward M. Chen, District Court Judge BRIEF OF AMICI CURIAE ELECTRONIC FRONTIER FOUNDATION, DUCKDUCKGO, AND INTERNET ARCHIVE IN SUPPORT OF PLAINTIFF-APPELLEE Jamie Williams Corynne McSherry Cindy Cohn Nathan Cardozo ELECTRONIC FRONTIER FOUNDATION 815 Eddy Street San Francisco, CA 94109 Email: [email protected] Telephone: (415) 436-9333 Counsel for Amici Curiae Case: 17-16783, 11/27/2017, ID: 10667942, DktEntry: 42, Page 2 of 39 DISCLOSURE OF CORPORATE AFFILIATIONS AND OTHER ENTITIES WITH A DIRECT FINANCIAL INTEREST IN LITIGATION Pursuant to Rule 26.1 of the Federal Rules of Appellate Procedure, Amici Curiae Electronic Frontier Foundation, DuckDuckGo, and Internet Archive each individually state that they do not have a parent corporation and that no publicly held corporation owns 10 percent or more of their stock. i Case: 17-16783, 11/27/2017, ID: 10667942, DktEntry: 42, Page 3 of 39 TABLE OF CONTENTS CORPORATE DISCLOSURE STATEMENTS ..................................................... i TABLE OF CONTENTS ...................................................................................... ii TABLE OF AUTHORITIES ............................................................................... -
The Autonomous Surfer
Renée Ridgway The Autonomous Surfer CAIS Report Fellowship Mai bis Oktober 2018 GEFÖRDERT DURCH RIDGWAY The Autonomous Surfer Research Questions The Autonomous Surfer endeavoured to discover the unknown unknowns of alternative search through the following research questions: What are the alternatives to Google search? What are their hidden revenue models, even if they do not collect user data? How do they deliver divergent (and qualitative) results or knowledge? What are the criteria that determine ranking and relevance? How do p2p search engines such as YaCy work? Does it deliver alternative results compared to other search engines? Is there still a movement for a larger, public index? Can there be serendipitous search, which is the ability to come across books, articles, images, information, objects, and so forth, by chance? Aims and Projected Results My PhD research investigates Google search – its early development, its technological innovation, its business model of the past 20 years and how it works now. Furthermore, I have experimented with Tor (The Onion Router) in order to find out if I could be anonymous online, and if so, would I receive diver- gent results from Google with the same keywords. For my fellowship at CAIS I decided to first research search engines that were incorporated into the Tor browser as default (Startpage, Disconnect) or are the default browser now (DuckDuckGo). I then researched search engines in my original CAIS proposal that I had come across in my PhD but hadn’t had the time to research; some are from the Society of the Query Reader (2014) and others I found en route or on colleagues’ suggestions. -
GOGGLES: Democracy Dies in Darkness, and So Does the Web
GOGGLES: Democracy dies in darkness, and so does the Web Brave Search Team Brave Munich, Germany [email protected] Abstract of information has led to a significant transfer of power from cre- This paper proposes an open and collaborative system by which ators to aggregators. Access to information has been monopolized a community, or a single user, can create sets of rules and filters, by companies like Google and Facebook [27]. While everything is called Goggles, to define the space which a search engine can pull theoretically still retrievable, in practice we are looking at the world results from. Instead of a single ranking algorithm, we could have through the biases of a few providers, who act, unintentionally or as many as needed, overcoming the biases that a single actor (the not, as gatekeepers. Akin to the thought experiment about the tree search engine) embeds into the results. Transparency and openness, falling in the forest [3], if a page is not listed on Google’s results all desirable qualities, will become accessible through the deep re- page or in the Facebook feed, does it really exist? ranking capabilities Goggles would enable. Such system would be The biases of Google and Facebook, whether algorithmic, data made possible by the availability of a host search engine, providing induced, commercial or political dictate what version of the world the index and infrastructure, which are unlikely to be replicated we get to see. Reality becomes what the models we are fed depict without major development and infrastructure costs. Besides the it to be [24]. -
Mass Surveillance
Mass Surveillance Mass Surveillance What are the risks for the citizens and the opportunities for the European Information Society? What are the possible mitigation strategies? Part 1 - Risks and opportunities raised by the current generation of network services and applications Study IP/G/STOA/FWC-2013-1/LOT 9/C5/SC1 January 2015 PE 527.409 STOA - Science and Technology Options Assessment The STOA project “Mass Surveillance Part 1 – Risks, Opportunities and Mitigation Strategies” was carried out by TECNALIA Research and Investigation in Spain. AUTHORS Arkaitz Gamino Garcia Concepción Cortes Velasco Eider Iturbe Zamalloa Erkuden Rios Velasco Iñaki Eguía Elejabarrieta Javier Herrera Lotero Jason Mansell (Linguistic Review) José Javier Larrañeta Ibañez Stefan Schuster (Editor) The authors acknowledge and would like to thank the following experts for their contributions to this report: Prof. Nigel Smart, University of Bristol; Matteo E. Bonfanti PhD, Research Fellow in International Law and Security, Scuola Superiore Sant’Anna Pisa; Prof. Fred Piper, University of London; Caspar Bowden, independent privacy researcher; Maria Pilar Torres Bruna, Head of Cybersecurity, Everis Aerospace, Defense and Security; Prof. Kenny Paterson, University of London; Agustín Martin and Luis Hernández Encinas, Tenured Scientists, Department of Information Processing and Cryptography (Cryptology and Information Security Group), CSIC; Alessandro Zanasi, Zanasi & Partners; Fernando Acero, Expert on Open Source Software; Luigi Coppolino,Università degli Studi di Napoli; Marcello Antonucci, EZNESS srl; Rachel Oldroyd, Managing Editor of The Bureau of Investigative Journalism; Peter Kruse, Founder of CSIS Security Group A/S; Ryan Gallagher, investigative Reporter of The Intercept; Capitán Alberto Redondo, Guardia Civil; Prof. Bart Preneel, KU Leuven; Raoul Chiesa, Security Brokers SCpA, CyberDefcon Ltd.; Prof. -
Applying Library Values to Emerging Technology Decision-Making in the Age of Open Access, Maker Spaces, and the Ever-Changing Library
ACRL Publications in Librarianship No. 72 Applying Library Values to Emerging Technology Decision-Making in the Age of Open Access, Maker Spaces, and the Ever-Changing Library Editors Peter D. Fernandez and Kelly Tilton Association of College and Research Libraries A division of the American Library Association Chicago, Illinois 2018 The paper used in this publication meets the minimum requirements of Ameri- can National Standard for Information Sciences–Permanence of Paper for Print- ed Library Materials, ANSI Z39.48-1992. ∞ Cataloging-in-Publication data is on file with the Library of Congress. Copyright ©2018 by the Association of College and Research Libraries. All rights reserved except those which may be granted by Sections 107 and 108 of the Copyright Revision Act of 1976. Printed in the United States of America. 22 21 20 19 18 5 4 3 2 1 Contents Contents Introduction .......................................................................................................ix Peter Fernandez, Head, LRE Liaison Programs, University of Tennessee Libraries Kelly Tilton, Information Literacy Instruction Librarian, University of Tennessee Libraries Part I Contemplating Library Values Chapter 1. ..........................................................................................................1 The New Technocracy: Positioning Librarianship’s Core Values in Relationship to Technology Is a Much Taller Order Than We Think John Buschman, Dean of University Libraries, Seton Hall University Chapter 2. ........................................................................................................27 -
Online-Quellen Nutzen: Recherche Im Internet
ONLINE-QUELLEN NUTZEN: RECHERCHE IM INTERNET Katharina Gilarski, Verena Müller, Martin Nissen (Stand: 08/2020) Inhaltsverzeichnis 1 Recherche mit Universalsuchmaschinen ............................................................. 2 1.1 Clever googlen ......................................................................................................... 2 1.2 Suchoperatoren für die einfache Suche ................................................................... 2 1.3 Die erweiterte Suche mit Google .............................................................................. 3 1.4 Die umgekehrte Bildersuche mit Google .................................................................. 4 1.5 Das Geheimnis der Treffersortierung in Google ....................................................... 5 1.6 Alternative universelle Suchmaschinen .................................................................... 6 1.7 Surface vs. Deep Web ............................................................................................. 9 1.8 Vor- und Nachteile von universellen Suchmaschinen ............................................. 10 1.9 Bewertung von Internetquellen ............................................................................... 10 2 Recherche mit wissenschaftlichen Suchmaschinen ......................................... 11 2.1 Google Scholar: Googles Suchmaschine für Wissenschaftler ................................ 11 2.2 Suchmöglichkeiten von Google Scholar ................................................................ -
Tipping Points: Cancelling Journals When Arxiv Access Is Good Enough
Tipping points: cancelling journals when arXiv access is good enough Tony Aponte Sciences Collection Coordinator UCLA Library ASEE ELD Lightning Talk June 17, 2019 Preprint explosion! Brian Resnick and Julia Belluz. (2019). The war to free science. Vox https://www.vox.com/the-highlight/2019/6/3/18271538/open- access-elsevier-california-sci-hub-academic-paywalls Preprint explosion! arXiv. (2019). arXiv submission rate statistics https://arxiv.org/help/stats/2018_by_area/index 2018 Case Study: two physics journals and arXiv ● UCLA: heavy users of arXiv. Not so heavy users of version of record ● Decent UC authorship ● No UC editorial board members 2017 Usage Annual cost Cost per use 2017 Impact Factor Journal A 103 $8,315 ~$80 1.291 Journal B 72 $6,344 ~$88 0.769 Just how many of these articles are OA? OAISSN.py - Enter a Journal ISSN and a year and this python program will tell you how many DOIs from that year have an open access version2 Ryan Regier. (2018). OAISSN.py https://github.com/ryregier/OAcounts. Just how many of these articles are OA? Ryan Regier. (2018). OAISSN.py https://github.com/ryregier/OAcounts. Just how many of these articles are OA? % OA articles from 2017 % OA articles from 2018 Journal A 68% 64% Journal B 11% 8% Ryan Regier. (2018). OAISSN.py https://github.com/ryregier/OAcounts. arXiv e-prints becoming closer to publisher versions of record according to UCLA similarity study of arXiv articles vs versions of record Martin Klein, Peter Broadwell, Sharon E. Farb, Todd Grappone. 2018. Comparing Published Scientific Journal Articles to Their Pre-Print Versions -- Extended Version. -
Microsoft Academic Search and Google Scholar Citations
Microsoft Academic Search and Google Scholar Citations Microsoft Academic Search and Google Scholar Citations: A comparative analysis of author profiles José Luis Ortega1 VICYT-CSIC, Serrano, 113 28006 Madrid, Spain, [email protected] Isidro F. Aguillo Cybermetrics Lab, CCHS-CSIC, Albasanz, 26-28 28037 Madrid, Spain, [email protected] Abstract This paper aims to make a comparative analysis between the personal profiling capabilities of the two most important free citation-based academic search engines, namely Microsoft Academic Search (MAS) and Google Scholar Citations (GSC). Author profiles can be very useful for evaluation purposes once the advantages and the shortcomings of these services are described and taken into consideration. A total of 771 personal profiles appearing in both the MAS and the GSC databases are analysed. Results show that the GSC profiles include more documents and citations than those in MAS, but with a strong bias towards the Information and Computing sciences, while the MAS profiles are disciplinarily better balanced. MAS shows technical problems such as a higher number of duplicated profiles and a lower updating rate than GSC. It is concluded that both services could be used for evaluation proposes only if they are applied along with other citation indexes as a way to supplement that information. Keywords: Microsoft Academic Search; Google Scholar Citations; Web bibliometrics; Academic profiles; Research evaluation; Search engines Introduction In November 2009, Microsoft Research Asia started a new web search service specialized in scientific information. Even though Google (Google Scholar) already introduced an academic search engine in 2004, the proposal of Microsoft Academic Search (MAS) went beyond a mere document retrieval service that counts citations. -
Fingerprinting Detection
ACTIONS SPEAK LOUDER THAN WORDS: SEMI-SUPERVISED LEARNING FOR BROWSER FINGERPRINTING DETECTION Sarah Bird Vikas Mishra Steven Englehardt Mozilla INRIA Mozilla [email protected] [email protected] [email protected] Rob Willoughby David Zeber Walter Rudametkin University of British Colombia Mozilla INRIA [email protected] [email protected] [email protected] Martin Lopatka Mozilla [email protected] November 1, 2019 ABSTRACT As online tracking continues to grow, existing anti-tracking and fingerprinting detection techniques that require significant manual input must be augmented. Heuristic approaches to fingerprinting de- tection are precise but must be carefully curated. Supervised machine learning techniques proposed for detecting tracking require manually generated label-sets. Seeking to overcome these challenges, we present a semi-supervised machine learning approach for detecting fingerprinting scripts. Our approach is based on the core insight that fingerprinting scripts have similar patterns of API access when generating their fingerprints, even though their access patterns may not match exactly. Using this insight, we group scripts by their JavaScript (JS) execution traces and apply a semi-supervised approach to detect new fingerprinting scripts. We detail our methodology and demonstrate its abil- ity to identify the majority of scripts (>94.9%) identified by existing heuristic techniques. We also show that the approach expands beyond detecting known scripts by surfacing candidate scripts that arXiv:2003.04463v1 [cs.CR] 9 Mar 2020 are likely to include fingerprinting. Through an analysis of these candidate scripts we discovered fin- gerprinting scripts that were missed by heuristics and for which there are no heuristics. In particular, we identified over one hundred device-class fingerprinting scripts present on hundreds of domains. -
Online Legal Research Handout
The New On-Line Search Fundamentals 1. Search Process Natural language searches – Natural language searching is essentially the keyword searching we all know and love; the search results you get reflect algorithmic decision- making by the people who designed the search engine. Often the results you get will be based on the number of times your search terms appear in a document or on a web page. Boolean searches – Boolean searches are also referred to as “terms and connectors” searches. These are the searches using quotation marks, minus signs, slashes, and other symbols to “tell” the search engine exactly what you are searching for. Boolean searching gives you a way to limit your searches by dates, run searches in specific fields, and overall return more precise results. Natural Language v. Boolean searching compared - Most search engines (including Google, Bing, Westlaw Next, and Lexis Advance) are designed for natural language searching. - You can override natural language searching 1. If you know the terms and connectors used by that search engine. (I often click on “help” or something similar to see what terms and connectors the search engine uses.); or 2. If you can find the “advanced search” page for the search engine. - Natural language searching provides the “best” search results when You are not sure where to start and you need to figure out what search terms to use (i.e., you don’t know the words used on the webpages or in the documents you are looking for); You want a large number of search results you can sift through to figure out what kinds of results to expect; or You are researching a conceptual issue rather than a specific topic. -
Scholarly Metrics Recommendations for Research Libraries: Deciphering the Trees in the Forest Table of Contents
EUROPE’S RESEARCH LIBRARY NETWORK Scholarly Metrics Recommendations for Research Libraries: Deciphering the Trees in the Forest Table of Contents 01 3D. Learn About Platforms Before Implementing Them In Services & 15 INTRODUCTION 03 Homogenize Different Sources ABOUT LIBER 3E. Work With Researchers To Build Awareness Of Benefits But Educate 16 1. DISCOVERY & DISCOVERABILITY 05 About Weaknesses of Scholarly Metrics 1A. Provide Contextual Information To Allow the Discovery of Related 05 3F. Expand Your Perspective When Developing Services; Avoid Single- 17 Work & Users Purpose Approaches 1B. Exploit Rich Network Structures & Implement Bibliometric Methods To 07 3G. Teach Colleagues New Skills & Library Goals & Services 17 Enable Discovery 1C. Encourage Sharing Of Library Collections Under Open Licenses 08 4. RESEARCH ASSESSMENT 19 4A. Establish Appropriate Goals for Assessment Exercises Before Selecting 19 2. SHOWCASING ACHIEVEMENTS 09 Databases & Metrics; Be Transparent About Use & Interpretation 2A. Incentivize Researchers To Share Scholarly Works, Promote Achievements 09 4B. Use Different Data Sources to Include Various Scholarly Works & 20 Online & Engage With Audiences Disciplinary Communication & Publication Cultures 2B. Encourage Researchers To Showcase Scientific Contributions & Monitor 11 4C. Rely on Objective, Independent & Commonly-Accepted Data Sources 21 Impact To Provide Sound & Transparent Scholarly Metrics 4D. Avoid Using Composite Indicators & Conflating Different Aspects 21 3. SERVICE DEVELOPMENT 13 Of Scholarly Works & Impact; Don’t Lose The Multifaceted Nature of 3A. Join Forces With Stakeholders To Provide Services Reusing Existing 13 Metrics & Distort Interpretation Resources, Tools, Methods & Data 3B. Value Various Levels of Engagement; Favour Standardized, Well-Established 15 23 CONCLUSION & CALL FOR ACTION Practices & Easy-To-Use Tools 25 REFERENCES 3C.