Understanding Card Data Formats
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An Overview of the 50 Most Common Web Scraping Tools
AN OVERVIEW OF THE 50 MOST COMMON WEB SCRAPING TOOLS WEB SCRAPING IS THE PROCESS OF USING BOTS TO EXTRACT CONTENT AND DATA FROM A WEBSITE. UNLIKE SCREEN SCRAPING, WHICH ONLY COPIES PIXELS DISPLAYED ON SCREEN, WEB SCRAPING EXTRACTS UNDERLYING CODE — AND WITH IT, STORED DATA — AND OUTPUTS THAT INFORMATION INTO A DESIGNATED FILE FORMAT. While legitimate uses cases exist for data harvesting, illegal purposes exist as well, including undercutting prices and theft of copyrighted content. Understanding web scraping bots starts with understanding the diverse and assorted array of web scraping tools and existing platforms. Following is a high-level overview of the 50 most common web scraping tools and platforms currently available. PAGE 1 50 OF THE MOST COMMON WEB SCRAPING TOOLS NAME DESCRIPTION 1 Apache Nutch Apache Nutch is an extensible and scalable open-source web crawler software project. A-Parser is a multithreaded parser of search engines, site assessment services, keywords 2 A-Parser and content. 3 Apify Apify is a Node.js library similar to Scrapy and can be used for scraping libraries in JavaScript. Artoo.js provides script that can be run from your browser’s bookmark bar to scrape a website 4 Artoo.js and return the data in JSON format. Blockspring lets users build visualizations from the most innovative blocks developed 5 Blockspring by engineers within your organization. BotScraper is a tool for advanced web scraping and data extraction services that helps 6 BotScraper organizations from small and medium-sized businesses. Cheerio is a library that parses HTML and XML documents and allows use of jQuery syntax while 7 Cheerio working with the downloaded data. -
Data and Computer Communications (Eighth Edition)
DATA AND COMPUTER COMMUNICATIONS Eighth Edition William Stallings Upper Saddle River, New Jersey 07458 Library of Congress Cataloging-in-Publication Data on File Vice President and Editorial Director, ECS: Art Editor: Gregory Dulles Marcia J. Horton Director, Image Resource Center: Melinda Reo Executive Editor: Tracy Dunkelberger Manager, Rights and Permissions: Zina Arabia Assistant Editor: Carole Snyder Manager,Visual Research: Beth Brenzel Editorial Assistant: Christianna Lee Manager, Cover Visual Research and Permissions: Executive Managing Editor: Vince O’Brien Karen Sanatar Managing Editor: Camille Trentacoste Manufacturing Manager, ESM: Alexis Heydt-Long Production Editor: Rose Kernan Manufacturing Buyer: Lisa McDowell Director of Creative Services: Paul Belfanti Executive Marketing Manager: Robin O’Brien Creative Director: Juan Lopez Marketing Assistant: Mack Patterson Cover Designer: Bruce Kenselaar Managing Editor,AV Management and Production: Patricia Burns ©2007 Pearson Education, Inc. Pearson Prentice Hall Pearson Education, Inc. Upper Saddle River, NJ 07458 All rights reserved. No part of this book may be reproduced in any form or by any means, without permission in writing from the publisher. Pearson Prentice Hall™ is a trademark of Pearson Education, Inc. All other tradmarks or product names are the property of their respective owners. The author and publisher of this book have used their best efforts in preparing this book.These efforts include the development, research, and testing of the theories and programs to determine their effectiveness.The author and publisher make no warranty of any kind, expressed or implied, with regard to these programs or the documentation contained in this book.The author and publisher shall not be liable in any event for incidental or consequential damages in connection with, or arising out of, the furnishing, performance, or use of these programs. -
Research Data Management Best Practices
Research Data Management Best Practices Introduction ............................................................................................................................................................................ 2 Planning & Data Management Plans ...................................................................................................................................... 3 Naming and Organizing Your Files .......................................................................................................................................... 6 Choosing File Formats ............................................................................................................................................................. 9 Working with Tabular Data ................................................................................................................................................... 10 Describing Your Data: Data Dictionaries ............................................................................................................................... 12 Describing Your Project: Citation Metadata ......................................................................................................................... 15 Preparing for Storage and Preservation ............................................................................................................................... 17 Choosing a Repository ......................................................................................................................................................... -
Data Management, Analysis Tools, and Analysis Mechanics
Chapter 2 Data Management, Analysis Tools, and Analysis Mechanics This chapter explores different tools and techniques for handling data for research purposes. This chapter assumes that a research problem statement has been formulated, research hypotheses have been stated, data collection planning has been conducted, and data have been collected from various sources (see Volume I for information and details on these phases of research). This chapter discusses how to combine and manage data streams, and how to use data management tools to produce analytical results that are error free and reproducible, once useful data have been obtained to accomplish the overall research goals and objectives. Purpose of Data Management Proper data handling and management is crucial to the success and reproducibility of a statistical analysis. Selection of the appropriate tools and efficient use of these tools can save the researcher numerous hours, and allow other researchers to leverage the products of their work. In addition, as the size of databases in transportation continue to grow, it is becoming increasingly important to invest resources into the management of these data. There are a number of ancillary steps that need to be performed both before and after statistical analysis of data. For example, a database composed of different data streams needs to be matched and integrated into a single database for analysis. In addition, in some cases data must be transformed into the preferred electronic format for a variety of statistical packages. Sometimes, data obtained from “the field” must be cleaned and debugged for input and measurement errors, and reformatted. The following sections discuss considerations for developing an overall data collection, handling, and management plan, and tools necessary for successful implementation of that plan. -
Parity Alternating Permutations Starting with an Odd Integer
Parity alternating permutations starting with an odd integer Frether Getachew Kebede1a, Fanja Rakotondrajaob aDepartment of Mathematics, College of Natural and Computational Sciences, Addis Ababa University, P.O.Box 1176, Addis Ababa, Ethiopia; e-mail: [email protected] bD´epartement de Math´ematiques et Informatique, BP 907 Universit´ed’Antananarivo, 101 Antananarivo, Madagascar; e-mail: [email protected] Abstract A Parity Alternating Permutation of the set [n] = 1, 2,...,n is a permutation with { } even and odd entries alternatively. We deal with parity alternating permutations having an odd entry in the first position, PAPs. We study the numbers that count the PAPs with even as well as odd parity. We also study a subclass of PAPs being derangements as well, Parity Alternating Derangements (PADs). Moreover, by considering the parity of these PADs we look into their statistical property of excedance. Keywords: parity, parity alternating permutation, parity alternating derangement, excedance 2020 MSC: 05A05, 05A15, 05A19 arXiv:2101.09125v1 [math.CO] 22 Jan 2021 1. Introduction and preliminaries A permutation π is a bijection from the set [n]= 1, 2,...,n to itself and we will write it { } in standard representation as π = π(1) π(2) π(n), or as the product of disjoint cycles. ··· The parity of a permutation π is defined as the parity of the number of transpositions (cycles of length two) in any representation of π as a product of transpositions. One 1Corresponding author. n c way of determining the parity of π is by obtaining the sign of ( 1) − , where c is the − number of cycles in the cycle representation of π. -
Computer Files & Data Storage
STORAGE & FILE CONCEPTS, UTILITIES (Pages 6, 150-158 - Discovering Computers & Microsoft Office 2010) I. Computer files – data, information or instructions residing on secondary storage are stored in the form of a file. A. Software files are also called program files. Program files (instructions) are created by a computer programmer and generally cannot be modified by a user. It’s important that we not move or delete program files because your computer requires them to perform operations. Program files are also referred to as “executables”. 1. You can identify a program file by its extension:“.EXE”, “.COM”, “.BAT”, “.DLL”, “.SYS”, or “.INI” (there are others) or a distinct program icon. B. Data files - when you select a “save” option while using an application program, you are in essence creating a data file. Users create data files. 1. File naming conventions refer to the guidelines followed while assigning file names and will vary with the operating system and application in use (see figure 4-1). File names in Windows 7 may be up to 255 characters, you're not allowed to use reserved characters or certain reserved words. File extensions are used to identify the application that was used to create the file and format data in a manner recognized by the source application used to create it. FALL 2012 1 II. Selecting secondary storage media A. There are three type of technologies for storage devices: magnetic, optical, & solid state, there are advantages & disadvantages between them. When selecting a secondary storage device, certain factors should be considered: 1. Capacity - the capacity of computer storage is expressed in bytes. -
File Format Guidelines for Management and Long-Term Retention of Electronic Records
FILE FORMAT GUIDELINES FOR MANAGEMENT AND LONG-TERM RETENTION OF ELECTRONIC RECORDS 9/10/2012 State Archives of North Carolina File Format Guidelines for Management and Long-Term Retention of Electronic records Table of Contents 1. GUIDELINES AND RECOMMENDATIONS .................................................................................. 3 2. DESCRIPTION OF FORMATS RECOMMENDED FOR LONG-TERM RETENTION ......................... 7 2.1 Word Processing Documents ...................................................................................................................... 7 2.1.1 PDF/A-1a (.pdf) (ISO 19005-1 compliant PDF/A) ........................................................................ 7 2.1.2 OpenDocument Text (.odt) ................................................................................................................... 3 2.1.3 Special Note on Google Docs™ .......................................................................................................... 4 2.2 Plain Text Documents ................................................................................................................................... 5 2.2.1 Plain Text (.txt) US-ASCII or UTF-8 encoding ................................................................................... 6 2.2.2 Comma-separated file (.csv) US-ASCII or UTF-8 encoding ........................................................... 7 2.2.3 Tab-delimited file (.txt) US-ASCII or UTF-8 encoding .................................................................... 8 2.3 -
Overview of Data Format, Data Model and Procedural Metadata to Support Iot
Overview of data format, data model and procedural metadata to support IoT Nakyoung Kim Scattered IoT ecosystem • Service and application–dedicated data formats & models • Data formats and models have been developed to suit the specific requirements of each industry, service, and application. • The current scattered-data ecosystem has been established, and it requires high costs to process and manage data for service and application convergence. • Needs of data integration and aggregation • The boundaries between industries are gradually crumbling down due to domain convergence. • For the future convergence markets of IoT and smart city, data integration and aggregation are important. Application-dedicated data management 2 Interoperability through the Web • Bridging the data • Data formats and models have been settled in the current forms over a period to resolve issues at each moment and fit case-by-case requirements. • Therefore, it is hardly possible to reformulate or replace the existing data formats and models. • Bridging the data formats and models is feasible. • Connecting via Web • A semantic information space for heterogeneous data, platforms, and application domains, Web can provide technologies that support the interoperability of IoT. • Enhanced interoperability in IoT ecosystems can be fulfilled with the concepts of Web of Things. Web Data-driven data management 3 Annotation and Microdata format • Structured data for annotation • To exploit what the Web offers, IoT data first needs to be structured and annotated just like how the other data is handled on the Web. • Microdata format • Microdata formats refer structured data markups describing and embedding the meanings of resources on the Web along with their properties and relationships. -
On the Parity of the Number of Multiplicative Partitions and Related Problems
PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 140, Number 11, November 2012, Pages 3793–3803 S 0002-9939(2012)11254-7 Article electronically published on March 15, 2012 ON THE PARITY OF THE NUMBER OF MULTIPLICATIVE PARTITIONS AND RELATED PROBLEMS PAUL POLLACK (Communicated by Ken Ono) Abstract. Let f(N) be the number of unordered factorizations of N,where a factorization is a way of writing N as a product of integers all larger than 1. For example, the factorizations of 30 are 2 · 3 · 5, 5 · 6, 3 · 10, 2 · 15, 30, so that f(30) = 5. The function f(N), as a multiplicative analogue of the (additive) partition function p(N), was first proposed by MacMahon, and its study was pursued by Oppenheim, Szekeres and Tur´an, and others. Recently, Zaharescu and Zaki showed that f(N) is even a positive propor- tion of the time and odd a positive proportion of the time. Here we show that for any arithmetic progression a mod m,thesetofN for which f(N) ≡ a(mod m) possesses an asymptotic density. Moreover, the density is positive as long as there is at least one such N. For the case investigated by Zaharescu and Zaki, we show that f is odd more than 50 percent of the time (in fact, about 57 percent). 1. Introduction Let f(N) be the number of unordered factorizations of N,whereafactorization of N is a way of writing N as a product of integers larger than 1. For example, f(12) = 4, corresponding to 2 · 6, 2 · 2 · 3, 3 · 4, 12. -
HP Proximity Card Readers, It’S Easy Technologies and Support the Wide Range of to Secure Networked Printers Mobile Devices
Solution brief Enhance security and improve productivity with unified printer authentication across your organization HP proximity, smart card, and Bluetooth® Low Energy (BLE)/ NFC-enabled card readers What if you could… • Help protect confidential documents by releasing print jobs only to the right user? • Enhance the security of networked printers by easily and accurately authenticating users? • Support a wide variety of proximity and smart cards, and digital credentials on mobile devices with one reader? • Leverage the flexibility and accessibility of mobile credentials for secure print release on HP MFPs and printers? • Meet Health Insurance Portability and Accountability Act (HIPAA) privacy requirements for access to patient records? With HP's proximity card readers with mobile credential support—you can. Multicard authentication for Network security for the world’s HP printers most secure MFPs and printers1 Unified, enterprise-wide access control no HP printers and MFPs are the most secure longer has to be cumbersome or expensive. printing devices in the world.1 But it’s easy Now you can protect confidential information for customers to overlook the fact that these HP card readers support and manage access to printing and imaging network-connected devices serve as access devices by enabling user authentication through points to the corporate network. As with any proximity cards, smart cards, access cards, badges, and digital credentials network endpoint, organizations need to on mobile devices. HP dual frequency card actively control who has physical access and and digital credentials on readers incorporate multiple communication how. With HP proximity card readers, it’s easy technologies and support the wide range of to secure networked printers mobile devices. -
Unlocking the Smart Card
Episode Four: Unlocking the Smart Card This is an excerpt from Unlocked — an ASSA ABLOY podcast series on campus security. Unlocked explores the security issues and challenges that colleges and universities face as they strive to create a safe and secure learning environment. Visit intelligentopenings.com/unlocked to hear more. How We Got Smart Before diving into the current broken cards and physical wear on the credential technologies, it helps to readers. Prox solved these problems. understand where we came from. Lower maintenance costs, increased In 1960, a young engineer from IBM user convenience, and new options named Forrest Parry invented the for form factors like fobs made the magnetic stripe card. Once prox card a winner. But the low- ubiquitous on campus doors, more frequency proximity technology is reliable and secure technologies not without its limitations. are quickly eclipsing the mag stripe. Mag stripe cards are simple. A card gets swiped in a reader. That reader then reads a sequence of numbers Outside of higher stored on the stripe of that card. education and If the number matches what’s stored in the access system’s older hotels, hardly Whether installing a new door access database, the door unlocks. system for your campus or upgrading from a legacy system you have a lot of Many campuses still use the mag anyone still uses decisions to make. stripe card for their door access. This is mainly because the cards are mag stripe cards You first must choose the right access inexpensive, the cost to replace the software and locking hardware. -
The ℓ-Parity Conjecture Over the Constant Quadratic Extension
THE `-PARITY CONJECTURE OVER THE CONSTANT QUADRATIC EXTENSION KĘSTUTIS ČESNAVIČIUS Abstract. For a prime ` and an abelian variety A over a global field K, the `-parity conjecture predicts that, in accordance with the ideas of Birch and Swinnerton-Dyer, the Z`-corank of the `8-Selmer group and the analytic rank agree modulo 2. Assuming that char K ¡ 0, we prove that the `-parity conjecture holds for the base change of A to the constant quadratic extension if ` is odd, coprime to char K, and does not divide the degree of every polarization of A. The techniques involved in the proof include the étale cohomological interpretation of Selmer groups, the Grothendieck–Ogg–Shafarevich formula, and the study of the behavior of local root numbers in unramified extensions. 1. Introduction 1.1. The `-parity conjecture. The Birch and Swinnerton-Dyer conjecture (BSD) predicts that the completed L-function LpA; sq of an abelian variety A over a global field K extends meromorphically to the whole complex plane, vanishes to the order rk A at s “ 1, and satisfies the functional equation LpA; 2 ´ sq “ wpAqLpA; sq; (1.1.1) where rk A and wpAq are the Mordell–Weil rank and the global root number of A. The vanishing assertion combines with (1.1.1) to give “BSD modulo 2,” namely, the parity conjecture: ? p´1qrk A “ wpAq: Selmer groups tend to be easier to study than Mordell–Weil groups, so, fixing a prime `, one sets 8 rk` A :“ dimQ` HompSel` A; Q`{Z`q bZ` Q`; where Sel 8 A : lim Sel n A ` “ ÝÑ ` is the `8-Selmer group, notes that the conjectured finiteness of the Shafarevich–Tate group XpAq implies rk` A “ rk A, and instead of the parity conjecture considers the `-parity conjecture: ? p´1qrk` A “ wpAq: (1.1.2) 1.2.