Business Intelligence Resources 2018
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KYC) Platform
Design and Prototypical Implementation of an Open Source and Smart Contract-based Know Your Customer (KYC) Platform Sebastian Emmanuel Allemann Zurich, Switzerland Student ID: 14-721-807 – Communication Systems Group, Prof. Dr. Burkhard Stiller HESIS T Supervisor: Sina Rafati, Eder John Scheid ACHELOR Date of Submission: January 31, 2019 B University of Zurich Department of Informatics (IFI) Binzmühlestrasse 14, CH-8050 Zurich, Switzerland ifi Bachelor Thesis Communication Systems Group (CSG) Department of Informatics (IFI) University of Zurich Binzmühlestrasse 14, CH-8050 Zurich, Switzerland URL: http://www.csg.uzh.ch/ Zusammenfassung Fur¨ Finanzinstitutionen ist es eine Notwendigkeit, ihre Kunden zu kennen. Know Your Customer (KYC) Prozesse wurden zu diesem Zweck entwickelt. Mit der Zunahme der Blockchain Technologie, wird es immer wichtiger die Identit¨aten von Personen best¨atigen zu k¨onnen, da man nur der Technologie, ohne kontrollierende Drittpartei, vertraut. Perso- nen mussen¨ bei jeder Bank, mit welcher sie eine Gesch¨aftsbeziehung aufnehmen m¨ochten, aufs Neue einen KYC Prozess durchlaufen. Dies ist wenig effizient und kostet viel Zeit und Geld. Es sollte daher m¨oglich sein, eine Plattform aufzubauen, welche KYC Prozesse als Dienstleistung anbietet. Diese k¨onnte durch Smart Contracts identifizierte Personen best¨atigen und dadurch die Effizienz steigern. Das Ziel dieser Arbeit ist es, ein solches System in Form eines Prototypen zu entwickeln. Der Video Identifikationsprozess ist von der Eidgen¨ossischen Finanzmarktaufsicht FINMA reguliert und definiert die Anforderungen, die zu berucksichtigen¨ sind. Das Resultat des Identifikationsprozesses liefert einen Beweis fur¨ die Identit¨at in Form eines einzigartigen Schlussels.¨ Zus¨atzlich verarbeitet ein erfolgreich entwickelter Smart Contract alle Anfra- gen von externen Plattformen, die den soeben genannten Schlussel¨ verifizieren m¨ochten, wenn ein Kunde sich mit dem Schlussel¨ registriert. -
Technological Advances in Corpus Sampling Methodology
Technological Advances in Corpus Sampling Methodology Stephen Michael Wattam, BSc (Hons), MRes Mathematics and Statistics School of Computing and Communications Lancaster University Submitted for the degree of Doctor of Philosophy August 2015 Abstract Current efforts in corpus linguistics and natural language processing make heavy use of corpora— large language samples that are intended to describe a wide population of language users. The first modern corpora were manually constructed, transcribed from published texts and other non-digital sources into a machine-readable format. In part due to their hard-won nature, larger corpora have become widely shared and re-used: this has significant benefits for the scientific community, yet also leads to a stagnation of sampling methods and designs. The rise of Web as Corpus (WaC), and the use of computers to author documents, has provided us with the tools needed to build corpora automatically, or with little supervision. This offers an avenue to re-examine and, in places, exceed the limitations of conventional corpus sampling methods. Even so, many corpora are compared against conventional ones due to their status as a de-facto gold standard of representativeness. Such practices place undue trust in aging sample designs and the expert opinion therein. In this thesis I argue for the development of new sampling procedures guided less by concepts of linguistic balance and more by statistical sampling theory. This is done by presenting three different areas of potential study, along with exploratory results and publicly-available tools and methods that allow for further investigation. The first of these is an examination of temporal bias in sampling online. -
STEFANN: Scene Text Editor Using Font Adaptive Neural Network
STEFANN: Scene Text Editor using Font Adaptive Neural Network Prasun Roy1∗, Saumik Bhattacharya2∗, Subhankar Ghosh1∗, and Umapada Pal1 1Indian Statistical Institute, Kolkata, India 2Indian Institute of Technology, Kharagpur, India https://prasunroy.github.io/stefann Abstract Textual information in a captured scene plays an im- portant role in scene interpretation and decision making. Though there exist methods that can successfully detect and interpret complex text regions present in a scene, to the best of our knowledge, there is no significant prior work that aims to modify the textual information in an image. The ability to edit text directly on images has several advan- tages including error correction, text restoration and image reusability. In this paper, we propose a method to modify text in an image at character-level. We approach the prob- lem in two stages. At first, the unobserved character (tar- get) is generated from an observed character (source) be- ing modified. We propose two different neural network ar- chitectures – (a) FANnet to achieve structural consistency (a) (b) with source font and (b) Colornet to preserve source color. Figure 1. Examples of text editing using STEFANN: (a) Original Next, we replace the source character with the generated images from ICDAR dataset; (b) Edited images. It can be ob- character maintaining both geometric and visual consis- served that STEFANN can edit multiple characters in a word (top tency with neighboring characters. Our method works as row) as well as an entire word (bottom row) in a text region. a unified platform for modifying text in images. We present the effectiveness of our method on COCO-Text and ICDAR the conventional text editors. -
OSINT Handbook September 2020
OPEN SOURCE INTELLIGENCE TOOLS AND RESOURCES HANDBOOK 2020 OPEN SOURCE INTELLIGENCE TOOLS AND RESOURCES HANDBOOK 2020 Aleksandra Bielska Noa Rebecca Kurz, Yves Baumgartner, Vytenis Benetis 2 Foreword I am delighted to share with you the 2020 edition of the OSINT Tools and Resources Handbook. Once again, the Handbook has been revised and updated to reflect the evolution of this discipline, and the many strategic, operational and technical challenges OSINT practitioners have to grapple with. Given the speed of change on the web, some might question the wisdom of pulling together such a resource. What’s wrong with the Top 10 tools, or the Top 100? There are only so many resources one can bookmark after all. Such arguments are not without merit. My fear, however, is that they are also shortsighted. I offer four reasons why. To begin, a shortlist betrays the widening spectrum of OSINT practice. Whereas OSINT was once the preserve of analysts working in national security, it now embraces a growing class of professionals in fields as diverse as journalism, cybersecurity, investment research, crisis management and human rights. A limited toolkit can never satisfy all of these constituencies. Second, a good OSINT practitioner is someone who is comfortable working with different tools, sources and collection strategies. The temptation toward narrow specialisation in OSINT is one that has to be resisted. Why? Because no research task is ever as tidy as the customer’s requirements are likely to suggest. Third, is the inevitable realisation that good tool awareness is equivalent to good source awareness. Indeed, the right tool can determine whether you harvest the right information. -
The Web Is for Reading? the Rise and Fall of Text on the Web Anthony Cocciolo ABSTRACT Introduction. This Study Looks to Addres
The Web is for Reading? The Rise and Fall of Text on the Web Anthony Cocciolo ABSTRACT Introduction. This study looks to address the research questions: Is the use of text on the World Wide Web declining? If so, when did it start declining, and by how much has it declined? Method. Web pages are downloaded from the Internet Archive for the years 1999, 2002, 2005, 2008, 2011 and 2014, producing 600 captures for 100 prominent and popular webpages in the United States from a variety of sectors. Analysis. Captured webpages were analysed to uncover if the amount of text they present to users has declined over the past fifteen years using a computer vision algorithm, which deciphers text from non-text. Percentage of text per webpage is computed as well mean percentage of text per year, and a one-way ANOVA is used to uncover if the percentage of text on webpages is reliant on the year the website was produced. Results. Results reveal that the percentage of text on webpages climbed from the late 1990s to 2005 where it peaked (with 32.4% of the webpage), and has been on the decline ever since. Websites in 2014 have 5.5% less text than 2005 on average, or 26.9% text. This is more text than in the late 1990s, with webpages having only 22.4% text. Conclusion. Because of reduction in text lengths, the World Wide Web is becoming a less likely place where extensive reading will occur. The value of this study is that it confirms using a systematic approach what many have observed anecdotally: that the amount of text on webpages is decreasing. -
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100 Tech Hacks [PDF]
100 Tech Hacks Full details with images at http://carlcheo.com/tech-life-hacks. 1. Need to jot down something quickly? Open a temporary notepad in your browser by typing data:text/html, <html contenteditable> into the address bar. 2. Ambient music to boost your productivity. http://www.rainymood.com/ http://endlessvideo.com/watch?v=HMnrl0t md3k http://endlessvideo.com/watch?v=DIx3aM RDUL4 https://coffitivity.com/ (optional) 3. At website login pages, you can view masked passwords by changing the password input type as text. 4. Test battery easily by dropping it few inches from the ground. A fully charged battery bounces very https://www.youtube.com/watch?v=nwfFB little, whereas a dead battery bounces around. UVxpac 5. Broken keyboard feet? Replace them with binder clips. 6. Find expensive stuff for cheap on Craigslist using the following search terms: divorce, wife, husband, new baby, pcs, Kia, health, surgery, moving 7. In Windows, create a new folder and name it Control Panel Shortcuts.{ed7ba470-8e54-465e-825c- 99712043e01c}. Access any control settings in the folder. 8. In Chrome, go to Settings -> Extensions -> Allow Adblock Plus in incognito 9. Goo gle [food] vs [food] to compare nutritional values. 10. Use wall hooks to attach iPad to the wall. http://cdn4.carlcheo.com/wp- content/uploads/2015/05/Wall-mount- iPad.jpg 11. Amplify iPad speaker with tennis ball. http://cdn4.carlcheo.com/wp- content/uploads/2015/05/Amplify-iPad- speaker-with-tennis-ball.jpg 12. On calculator, the C (clear) button is to clear all entry, whereas the CE (clear entry) button is to clear the most recent entry only. -
The Rise and Fall of Text on the Web: a Quantitative Study of Web Archives
Forthcoming in Information Research 1 The Rise and Fall of Text on the Web: A Quantitative Study of Web Archives Anthony Cocciolo Pratt Institute School of Information and Library Science ABSTRACT Introduction. This study looks to address the research questions: Is the use of text on the World Wide Web declining? If so, when did it start declining, and by how much has it declined? Method. Web pages are downloaded from the Internet Archive for the years 1999, 2002, 2005, 2008, 2011 and 2014, producing 600 captures for 100 prominent and popular webpages in the United States from a variety of sectors. Analysis. Captured webpages were analysed to uncover if the percentage of text they present to users has declined over the past fifteen years using a computer vision algorithm, which deciphers text from non-text. Percentage of text per webpage is computed as well mean percentage of text per year, and a one-way ANOVA is used to uncover if the percentage of text on webpages is reliant on the year the website was produced. Results. Results reveal that the percentage of text on webpages climbed from the late 1990s to 2005 where it peaked (with 32.4% of the webpage), and has been on the decline ever since. Websites in 2014 have 5.5% less text than 2005 on average, or 26.9% text. This is more text than in the late 1990s, with webpages having only 22.4% text. Conclusion. The value of this study is that it confirms using a systematic approach what many have observed anecdotally: that the percentage of text on webpages is decreasing. -
Tandem 2.0: Image and Text Data Generation Application
City University of New York (CUNY) CUNY Academic Works All Dissertations, Theses, and Capstone Projects Dissertations, Theses, and Capstone Projects 2-2017 Tandem 2.0: Image and Text Data Generation Application Christopher J. Vitale The Graduate Center, City University of New York How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/gc_etds/1870 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] TANDEM 2.0: IMAGE AND TEXT DATA GENERATION APPLICATION by CHRISTOPHER VITALE A capstone project submitted to the Graduate Faculty in Liberal Studies in partial fulfillment of the requirements for the degree of Master of Arts, The City University of New York. 2017 © 2017 CHRISTOPHER VITALE All Rights Reserved TANDEM 2.0: Image and Text Data Generation Application: A Case Study by Christopher Vitale ii This manuscript has been read and accepted for the Graduate Faculty in Liberal Studies in satisfaction of the thesis requirement for the degree of Master of Arts. ____________________ ______________________________ Date Dr. Matthew K. Gold Thesis Advisor ____________________ ______________________________ Date Dr. Elizabeth Macaulay-Lewis Acting Executive Officer THE CITY UNIVERSITY OF NEW YORK iii ABSTRACT TANDEM 2.0: Image and Text Data Generation Application: A Case Study by Christopher Vitale Advisor: Dr. Matthew Gold First created as part of the Digital Humanities Praxis course in the spring of 2012 at the CUNY Graduate Center, Tandem explores the generation of datasets comprised of text and image data by leveraging Optical Character Recognition (OCR), Natural Language Processing (NLP) and Computer Vision (CV). -
Megabyteact-GSA-2016.Pdf
This document is made available through the declassification efforts and research of John Greenewald, Jr., creator of: The Black Vault The Black Vault is the largest online Freedom of Information Act (FOIA) document clearinghouse in the world. The research efforts here are responsible for the declassification of hundreds of thousands of pages released by the U.S. Government & Military. Discover the Truth at: http://www.theblackvault.com Office of Administrative Services Freedom of Information Act Office December 8, 2016 Mr. John Greenewald The Black Vault Dear Mr. Greenewald: This letter is in response to your U.S. General Services Administration Freedom of Information Act (FOIA) request, (GSA-2017-000141), submitted on November 7, 2016, in which you requested: “Records pertaining to a copy of records, electronic or otherwise, of the most recent inventory of software licenses at your agency.” Enclosed please find the records responsive to your request. You should find the following file named 20161101-Summary Report.csv. This completes our action on this request. Should you have any press-related questions, please contact Ashley Nash-Hahn, GSA Press Secretary, by email at [email protected]. You may also contact the GSA FOIA Public Liaison, Audrey Brooks, at (202) 205-5912 or by email at [email protected] for any additional assistance and to discuss any aspect of your FOIA request. Sincerely, Travis Lewis Program Manager Enclosure U.S General Services Administration 1800 F. Street, Northwest Washington, DC 20405 Telephone: (202) 501-0800 -
Open Source Intelligence Tools and Resources Handbook 2020
OPEN SOURCE INTELLIGENCE TOOLS AND RESOURCES HANDBOOK 2020 OPEN SOURCE INTELLIGENCE TOOLS AND RESOURCES HANDBOOK 2020 Aleksandra Bielska Noa Rebecca Kurz, Yves Baumgartner, Vytenis Benetis 2 Foreword I am delighted to share with you the 2020 edition of the OSINT Tools and Resources Handbook. Once again, the Handbook has been revised and updated to reflect the evolution of this discipline, and the many strategic, operational and technical challenges OSINT practitioners have to grapple with. Given the speed of change on the web, some might question the wisdom of pulling together such a resource. What’s wrong with the Top 10 tools, or the Top 100? There are only so many resources one can bookmark after all. Such arguments are not without merit. My fear, however, is that they are also shortsighted. I offer four reasons why. To begin, a shortlist betrays the widening spectrum of OSINT practice. Whereas OSINT was once the preserve of analysts working in national security, it now embraces a growing class of professionals in fields as diverse as journalism, cybersecurity, investment research, crisis management and human rights. A limited toolkit can never satisfy all of these constituencies. Second, a good OSINT practitioner is someone who is comfortable working with different tools, sources and collection strategies. The temptation toward narrow specialisation in OSINT is one that has to be resisted. Why? Because no research task is ever as tidy as the customer’s requirements are likely to suggest. Third, is the inevitable realisation that good tool awareness is equivalent to good source awareness. Indeed, the right tool can determine whether you harvest the right information. -
STEFANN: Scene Text Editor Using Font Adaptive Neural Network
STEFANN: Scene Text Editor using Font Adaptive Neural Network Prasun Roy1∗, Saumik Bhattacharya2∗, Subhankar Ghosh1∗, and Umapada Pal1 1Indian Statistical Institute, Kolkata, India 2Indian Institute of Technology, Kharagpur, India https://prasunroy.github.io/stefann Abstract Textual information in a captured scene plays an im- portant role in scene interpretation and decision making. Though there exist methods that can successfully detect and interpret complex text regions present in a scene, to the best of our knowledge, there is no significant prior work that aims to modify the textual information in an image. The ability to edit text directly on images has several advan- tages including error correction, text restoration and image reusability. In this paper, we propose a method to modify text in an image at character-level. We approach the prob- lem in two stages. At first, the unobserved character (tar- get) is generated from an observed character (source) be- ing modified. We propose two different neural network ar- chitectures – (a) FANnet to achieve structural consistency (a) (b) with source font and (b) Colornet to preserve source color. Figure 1. Examples of text editing using STEFANN: (a) Original Next, we replace the source character with the generated images from ICDAR dataset; (b) Edited images. It can be ob- character maintaining both geometric and visual consis- served that STEFANN can edit multiple characters in a word (top tency with neighboring characters. Our method works as row) as well as an entire word (bottom row) in a text region. a unified platform for modifying text in images. We present the effectiveness of our method on COCO-Text and ICDAR the conventional text editors. -
A Quantitative Study of Web Archives
The rise and fall of text on the Web: a quantitative study of Web archives VOL. 20 NO. 3, SEPTEMBER, 2015 Contents | Author index | Subject index | Search | Home The rise and fall of text on the Web: a quantitative study of Web archives Anthony Cocciolo Pratt Institute, School of Information & Library Science, 144 W. 14th St. 6th Floor, New York, NY 10011 USA Abstract Introduction. This study addresses the following research question: is the use of text on the World Wide Web declining? If so, when did it start declining, and by how much has it declined? Method. Web pages are downloaded from the Internet Archive for the years 1999, 2002, 2005, 2008, 2011 and 2014, producing 600 captures of 100 prominent and popular Webpages in the United States from a variety of sectors. Analysis. Captured Webpages were analysed to uncover if the percentage of text they present to users has declined over the past fifteen years using a computer vision algorithm, which deciphers text from non-text. The percentage of text per Webpage is computed as well as the mean percentage of text per year. A one-way ANOVA is used to uncover if the percentage of text on Webpages is reliant on the year the Website was produced. Results. Results reveal that the percentage of text on Webpages climbed from the late 1990s to 2005 where it peaked (with 32.4% of the Webpage), and has been in decline ever since. Websites in 2014 have 5.5% less text than 2005 on average, or 26.9% text. This is more text than in the late 1990s, with Webpages having only 22.4% text.