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Merchandising System Installation Guide Release 13.2.4 E28082-03
Oracle® Retail Merchandising System Installation Guide Release 13.2.4 E28082-03 August 2012 Oracle® Retail Merchandising System Installation Guide, Release 13.2.4 Copyright © 2012, Oracle. All rights reserved. Primary Author: Wade Schwarz Contributors: Nathan Young This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this software or related documentation is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs, including any operating system, integrated software, any programs installed on the hardware, and/or documentation, delivered to U.S. Government end users are "commercial computer software" pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, use, duplication, disclosure, modification, and adaptation of the programs, including any operating system, integrated software, any programs installed on the hardware, and/or documentation, shall be subject to license terms and license restrictions applicable to the programs. No other rights are granted to the U.S. -
Managing Many Files with Disk Archiver (DAR)
Intro Manual demos Function demos Summary Managing many files with Disk ARchiver (DAR) ALEX RAZOUMOV [email protected] WestGrid webinar - ZIP file with slides and functions http://bit.ly/2GGR9hX 2019-May-01 1 / 16 Intro Manual demos Function demos Summary To ask questions Websteam: email [email protected] Vidyo: use the GROUP CHAT to ask questions Please mute your microphone unless you have a question Feel free to ask questions via audio at any time WestGrid webinar - ZIP file with slides and functions http://bit.ly/2GGR9hX 2019-May-01 2 / 16 Intro Manual demos Function demos Summary Parallel file system limitations Lustre object storage servers (OSS) can get overloaded with lots of small requests I /home, /scratch, /project I Lustre is very different from your laptop’s HDD/SSD I a bad workflow will affect all other users on the system Avoid too many files, lots of small reads/writes I use quota command to see your current usage I maximum 5 × 105 files in /home, 106 in /scratch, 5 × 106 in /project (1) organize your code’s output (2) use TAR or another tool to pack files into archives and then delete the originals WestGrid webinar - ZIP file with slides and functions http://bit.ly/2GGR9hX 2019-May-01 3 / 16 Intro Manual demos Function demos Summary TAR limitations TAR is the most widely used archiving format on UNIX-like systems I first released in 1979 Each TAR archive is a sequence of files I each file inside contains: a header + some padding to reach a multiple of 512 bytes + file content I EOF padding (some zeroed blocks) at the end of the -
Hydraspace: Computational Data Storage for Autonomous Vehicles
HydraSpace: Computational Data Storage for Autonomous Vehicles Ruijun Wang, Liangkai Liu and Weisong Shi Wayne State University fruijun, liangkai, [email protected] Abstract— To ensure the safety and reliability of an The current vehicle data logging system is designed autonomous driving system, multiple sensors have been for capturing a wide range of signals of traditional CAN installed in various positions around the vehicle to elim- bus data, including temperature, brakes, throttle settings, inate any blind point which could bring potential risks. Although the sensor data is quite useful for localization engine, speed, etc. [4]. However, it cannot handle sensor and perception, the high volume of these data becomes a data because both the data type and the amount far burden for on-board computing systems. More importantly, exceed its processing capability. Thus, it is urgent to the situation will worsen with the demand for increased propose efficient data computing and storage methods for precision and reduced response time of self-driving appli- both CAN bus and sensed data to assist the development cations. Therefore, how to manage this massive amount of sensed data has become a big challenge. The existing of self-driving techniques. As it refers to the installed vehicle data logging system cannot handle sensor data sensors, camera and LiDAR are the most commonly because both the data type and the amount far exceed its used because the camera can show a realistic view processing capability. In this paper, we propose a computa- of the surrounding environment while LiDAR is able tional storage system called HydraSpace with multi-layered to measure distances with laser lights quickly [5], [6]. -
Intro to HCUP KID Database
HEALTHCARE COST AND UTILIZATION PROJECT — HCUP A FEDERAL-STATE-INDUSTRY PARTNERSHIP IN HEALTH DATA Sponsored by the Agency for Healthcare Research and Quality INTRODUCTION TO THE HCUP KIDS’ INPATIENT DATABASE (KID) 2016 Please read all documentation carefully. The 2016 KID CONTAINS ICD-10 CM/PCS CODES. The 2016 KID includes a full calendar year of ICD-10-CM/PCS data. Data elements derived from AHRQ software tools are not included because the ICD-10-CM/PCS versions are still under development. THE KID ENHANCED CONFIDENTIALITY BEGINNING WITH 2012. Starting in data year 2012, the KID eliminated State identifiers, unencrypted hospital identifiers, and other data elements that are not uniformly available across States. These pages provide only an introduction to the KID 2016 package. For full documentation and notification of changes, visit the HCUP User Support (HCUP-US) Web site at www.hcup-us.ahrq.gov. Issued September 2018 Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project (HCUP) Phone: (866) 290-HCUP (4287) Email: [email protected] Web site: www.hcup-us.ahrq.gov KID Data and Documentation Distributed by: HCUP Central Distributor Phone: (866) 556-4287 (toll-free) Fax: (866) 792-5313 Email: [email protected] Table of Contents SUMMARY OF DATA USE RESTRICTIONS ............................................................................. 1 HCUP CONTACT INFORMATION ............................................................................................. 2 WHAT’S NEW IN THE 2016 KIDS’ INPATIENT DATABASE -
Algorithms for Compression on Gpus
Algorithms for Compression on GPUs Anders L. V. Nicolaisen, s112346 Tecnical University of Denmark DTU Compute Supervisors: Inge Li Gørtz & Philip Bille Kongens Lyngby , August 2013 CSE-M.Sc.-2013-93 Technical University of Denmark DTU Compute Building 321, DK-2800 Kongens Lyngby, Denmark Phone +45 45253351, Fax +45 45882673 [email protected] www.compute.dtu.dk CSE-M.Sc.-2013 Abstract This project seeks to produce an algorithm for fast lossless compression of data. This is attempted by utilisation of the highly parallel graphic processor units (GPU), which has been made easier to use in the last decade through simpler access. Especially nVidia has accomplished to provide simpler programming of GPUs with their CUDA architecture. I present 4 techniques, each of which can be used to improve on existing algorithms for compression. I select the best of these through testing, and combine them into one final solution, that utilises CUDA to highly reduce the time needed to compress a file or stream of data. Lastly I compare the final solution to a simpler sequential version of the same algorithm for CPU along with another solution for the GPU. Results show an 60 time increase of throughput for some files in comparison with the sequential algorithm, and as much as a 7 time increase compared to the other GPU solution. i ii Resumé Dette projekt søger en algoritme for hurtig komprimering af data uden tab af information. Dette forsøges gjort ved hjælp af de kraftigt parallelisérbare grafikkort (GPU), som inden for det sidste årti har åbnet op for deres udnyt- telse gennem simplere adgang. -
Backing up Linux - Ii
BACKING UP LINUX - II 1 Software recommendations, disrecommendations and examples This chapter describes some details and examples of a number or programs. Please note that there are far more backup programs in existence than the ones I mention here. The reason I mention these, is because these are the ones I have a lot experience with, and it shows how to apply or consider the concepts described above, in practice. Sometimes as specifically refer to the GNU version of an application, the standard version on Linux installations, which can be fundamentally different than the classic version, so keep that in mind. 1.1. Dar First a word of caution. It's highly recommended that you use version 2.3.3 (most recent stable release at the time of writing) or newer because it contains a major bugfix. Read the announcement on Dar's news list for more info. Dar is very well thought through and has solutions for classic pitfalls. For example, it comes with a statically compiled binary which you can copy on (the first disk of) your backup. It supports automatic archive slicing, and has an option to set the size of the first slice separately, so you have some space on the first disk left for making a boot CD, for example. It also lets you run a command between each slice, so you can burn it on CD, or calculate parity information on it, etc. And, very importantly, its default options are well chosen. Well, that is, except for the preservation of atimes (see atime-preserveration above). -
TO: Charles F. Bolden, Jr. Administrator December 17,2012 FROM: Paul K. Martin (79- Inspector General SUBJECT: NASA's Efforts To
National Aeronautics and Space Administration Office of Inspector General N~~, J. Washington, DC 20546-0001 December 17,2012 TO: Charles F. Bolden, Jr. Administrator FROM: Paul K. Martin (79- Inspector General SUBJECT: NASA's Efforts to Encrypt its Laptop Computers On October 31, 2012, a NASA-issued laptop was stolen from the vehicle ofa NASA Headquarters employee. The laptop contained hundreds of files and e-mails with the Social Security numbers and other forms of personally identifiable information (PH) for more than 10,000 current and former NASA employees and contractors. Although the laptop was password protected, neither the laptop itself nor the individual files were encrypted. l As a result of this loss, NASA contracted with a company to provide credit monitoring services to the affected individuals. NASA estimates that these services will cost between $500,000 and $700,000. This was not the first time NASA experienced a significant loss of PH or other sensitive but unclassified (SBU) data as a result of the theft of an unencrypted Agency laptop. For example, in March 2012 a bag containing a government-issued laptop, NASA access badge, and a token used to enable remote-access to a NASA network was stolen from a car parked in the driveway of a Kennedy Space Center employee. A review by information technology (IT) security officials revealed that the stolen computer contained the names, Social Security numbers, and other PH information for 2,400 NASA civil servants, as well as two files containing sensitive information related to a NASA program. As a result of the theft, NASA incurred credit monitoring expenses of approximately $200,000. -
Proceedings of the 17 Large Installation Systems Administration
USENIX Association Proceedings of the 17th Large Installation Systems Administration Conference San Diego, CA, USA October 26–31, 2003 THE ADVANCED COMPUTING SYSTEMS ASSOCIATION © 2003 by The USENIX Association All Rights Reserved For more information about the USENIX Association: Phone: 1 510 528 8649 FAX: 1 510 548 5738 Email: [email protected] WWW: http://www.usenix.org Rights to individual papers remain with the author or the author's employer. Permission is granted for noncommercial reproduction of the work for educational or research purposes. This copyright notice must be included in the reproduced paper. USENIX acknowledges all trademarks herein. Further Torture: More Testing of Backup and Archive Programs Elizabeth D. Zwicky ABSTRACT Every system administrator depends on some set of programs for making secondary copies of data, as backups for disaster recovery or as archives for long term storage. These programs, like all other programs, have bugs. The important thing for system administrators to know is where those bugs are. Some years ago I did testing on backup and archive programs, and found that failures were extremely common and that the documented limitations of programs did not match their actual limitations. Curious to see how the situation had changed, I put together a new set of tests, and found that programs had improved significantly, but that undocumented failures were still common. In the previous tests, almost every program crashed outright at some point, and dump significantly out-performed all other contenders. In addition, many programs other than the backup and archive programs failed catastrophically (most notably fsck on many platforms, and the kernel on one platform). -
Compressed Linear Algebra for Large-Scale Machine Learning
The VLDB Journal manuscript No. (will be inserted by the editor) Compressed Linear Algebra for Large-Scale Machine Learning Ahmed Elgohary · Matthias Boehm · Peter J. Haas · Frederick R. Reiss · Berthold Reinwald Received: date / Accepted: date Abstract Large-scale machine learning (ML) algo- tions in order to find interesting patterns and build ro- rithms are often iterative, using repeated read-only data bust predictive models [24,28]. Applications range from access and I/O-bound matrix-vector multiplications to traditional regression analysis and customer classifica- converge to an optimal model. It is crucial for perfor- tion to recommendations. In this context, data-parallel mance to fit the data into single-node or distributed frameworks such as MapReduce [29], Spark [95], or main memory and enable fast matrix-vector opera- Flink [4] often are used for cost-effective parallelized tions on in-memory data. General-purpose, heavy- and model training on commodity hardware. lightweight compression techniques struggle to achieve Declarative ML: State-of-the-art, large-scale ML both good compression ratios and fast decompression systems support declarative ML algorithms [17], ex- speed to enable block-wise uncompressed operations. pressed in high-level languages, that comprise lin- Therefore, we initiate work|inspired by database com- ear algebra operations such as matrix multiplications, pression and sparse matrix formats|on value-based aggregations, element-wise and statistical operations. compressed linear algebra (CLA), in which heteroge- Examples|at different levels of abstraction|are Sys- neous, lightweight database compression techniques are temML [16], Mahout Samsara [78], Cumulon [41], applied to matrices, and then linear algebra opera- DMac [93], Gilbert [75], SciDB [82], SimSQL [60], and tions such as matrix-vector multiplication are executed TensorFlow [2]. -
New Media and Arts Education Using Email to Conduct a Survey
Higher Learning - Technology Serving Education New Media and Arts Education Using Email to Conduct a Survey The Challenge of Distance Education Sept/Oct. 2002 contentsCONTENTS FEATURES Book Review . 18 Courses and Programs . 8 Steal These Ideas Please! Great Marketing • MIT’s Java Revolution Reaches Eight New Media and Arts Education . 19 Ideas for Continuing Education Sub-Saharan Countries What can new media tell us about Edited by Susan Goewey Carey • UBC’s New Master of Educational Technology how we teach the arts? • UCCB Joins With Intel Canada By Ana Serrano DEPARTMENTS • UMass Announces New Information Technology Minor The Challenge of Distance Education . 23 Higher Notes . 3 • UMUC Designs Courses in Network Security Problems that distance educators and their students •A letter from the editor • UNC-CH Will Offer North Carolina’s First face, and possible solutions for both. Information Science Degree By Cable Starlings News . 5 •A Digital Repository for Faculty Research Technology Supplement . 24 SPOTLIGHT • Blackboard’s Multi-Language Edition Caters • Books to Staff and Students Around the World • Software Adobe Looks to the Library . 10 • Cornell Plans to Digitize Entire Library •Web Adobe gives libraries new options for managing Card Catalog System their digital collections with Content Server 3. • Department of Computer Science Projects . 27 HIGHER LEARNING -Contents By Jennifer Kavur Becomes a School • Electronic Publishing and Online Databases • Free Recruiting System for Co-ops Across Canada •McGraw-Hill-Microsoft Press Using Email to Conduct a Survey . 11 contents How UC Davis and Simon Fraser University •New Documentary Explores How Digital use email to conduct their research. Media Affects Learning By Jennifer Kavur • University of San Francisco Launches Digital Campus REVIEWS Summer Software Review (Part 2) . -
Ten Thousand Security Pitfalls: the ZIP File Format
Ten thousand security pitfalls: The ZIP file format. Gynvael Coldwind @ Technische Hochschule Ingolstadt, 2018 About your presenter (among other things) All opinions expressed during this presentation are mine and mine alone, and not those of my barber, my accountant or my employer. What's on the menu Also featuring: Steganograph 1. What's ZIP used for again? 2. What can be stored in a ZIP? y a. Also, file names 3. ZIP format 101 and format repair 4. Legacy ZIP encryption 5. ZIP format and multiple personalities 6. ZIP encryption and CRC32 7. Miscellaneous, i.e. all the things not mentioned so far. Or actually, hacking a "secure cloud disk" website. EDITORIAL NOTE Everything in this color is a quote from the official ZIP specification by PKWARE Inc. The specification is commonly known as APPNOTE.TXT https://pkware.cachefly.net/webdocs/casestudies/APPNOTE.TXT Cyber Secure CloudDisk Where is ZIP used? .zip files, obviously Default ZIP file icon from Microsoft Windows 10's Explorer And also... Open Packaging Conventions: .3mf, .dwfx, .cddx, .familyx, .fdix, .appv, .semblio, .vsix, .vsdx, .appx, .appxbundle, .cspkg, .xps, .nupkg, .oxps, .jtx, .slx, .smpk, .odt, .odp, .ods, ... .scdoc, (OpenDocument) and Offixe Open XML formats: .docx, .pptx, .xlsx https://en.wikipedia.org/wiki/Open_Packaging_Conventions And also... .war (Web application archive) .rar (not THAT .rar) .jar (resource adapter archive) (Java Archive) .ear (enterprise archive) .sar (service archive) .par (Plan Archive) .kar (Karaf ARchive) https://en.wikipedia.org/wiki/JAR_(file_format) -
Sistema De Entradas BFA Vía Servicio
Características técnicas para llamada a Servicio de entradas (v. 1.0.0) Versiones: Fecha Autor Versión Comentario respecto a cambio producido 05/05/20 Lantik S.A.M.P. 1.0.0 Versión inicial Servicio_Entradas_descripcion_tecnica_LLAMADA_V_1_0.DOCX 07/05/2020 2/13 ÍNDICE 1 INTRODUCCIÓN ....................................................................................................... 4 1.1 Objeto del documento ................................................................................................. 4 1.2 Referencias ..................................................................................................................... 4 1.3 Actores involucrados .................................................................................................... 5 1.4 Periodo de validez ......................................................................................................... 5 2 DEFINICIÓN DE LA LLAMADA AL SERVICIO DE ENTRADAS ...................... 6 2.1 Introducción ................................................................................................................... 6 2.2 URL del servicio de entradas ....................................................................................... 6 2.3 Tipo de método de llamada admitida ........................................................................ 6 2.4 Llamada con certificado digital ................................................................................... 6 2.5 Definición cabecera HTTP de llamada al servicio entradas .................................