Neural Network FAQ, Part 1 of 7: Introduction
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Evaluation of the CSF Firewall
Bachelor Thesis in Software Engineering, 15 credits May 2013 Evaluation of the CSF Firewall Ahmad Mudhar Contact Information: Ahmad Mudhar E-mail: [email protected] University advisor: Kari Rönkkö Nina Fogelström School of Computing Internet : www.bth.se/com Blekinge Institute of Technology Phone : +46 455 38 50 00 SE-371 79 Karlskrona Fax : +46 455 38 50 57 Sweden 1 Abstract The subject of web server security is vast, and it is becoming bigger as time passes by. Every year, researches, both private and public, are adding to the number of possible threats to the security of web servers, and coming up with possible solutions to them. A number of these solutions are considered to be expensive, complex, and incredibly time-consuming, while not able to create the perfect web to challenge any breach to the server security. In the study that follows, an attempt will be made to check whether a particular firewall can ensure a strong security measure and deal with some security breaches or severe threat to an existing web server. The research conducted has been done with the CSF Firewall, which provides a suit of scripts that ensure a portal’s security through a number of channels. The experiments conducted under the research provided extremely valuable insights about the application in hand, and the number of ways the CSF Firewall can help in safety of a portal against Secured Shell (SSH) attacks, dedicated to break the security of it, in its initial stages. It further goes to show how simple it is to actually detect the prospective attacks, and subsequently stop the Denial of Service (DoS) attacks, as well as the port scans made to the server, with the intent of breaching the security, by finding out an open port. -
Kosten Senken Dank Open Source?
View metadata, citation and similar papers at core.ac.uk brought to you by CORE Kosten senken dank Open Source? provided by Bern Open Repository andStrategien Information System (BORIS) | downloaded: 13.3.2017 https://doi.org/10.7892/boris.47380 source: 36 Nr. 01/02 | Februar 2014 Swiss IT Magazine Strategien Kosten senken dank Open Source? Mehr Geld im Portemonnaie und weniger Sorgen im Gepäck Der Einsatz von Open Source Software kann das IT-Budget schonen, wenn man richtig vorgeht. Viel wichtiger sind aber strategische Vorteile wie die digitale Nachhaltigkeit oder die Unabhängigkeit von Herstellern, die sich durch den konsequenten Einsatz von Open Source ergeben. V ON D R . M ATTHIAS S TÜR M ER ines vorweg: Open Source ist nicht gra- tis. Oder korrekt ausgedrückt: Der Download von Open Source Software (OSS) von den vielen Internet-Portalen wieE Github, Google Code, Sourceforge oder Freecode ist selbstverständlich kostenlos. Aber wenn Open-Source-Lösungen professionell eingeführt und betrieben werden, verursacht dies interne und/oder externe Kosten. Geschäftskritische Lösungen benötigen stets zuverlässige Wartung und Support, ansonsten steigt das Risiko erheblich, dass zentrale Infor- matiksysteme ausfallen oder wichtige Daten verloren gehen oder gestohlen werden. Für den sicheren Einsatz von Open Source Soft- ware braucht es deshalb entweder interne INHALT Ressourcen und Know-how, wie die entspre- chenden Systeme betrieben werden. Oder es MEHR GELD im PORTemoNNaie UND weNIGER SorgeN im GEPÄck 36 wird ein Service Level Agreement (SLA) bei- MarkTÜbersicHT: 152 SCHweiZer OPEN-SOUrce-SPEZialisTEN 40 spielsweise in Form einer Subscription mit einem kommerziellen Anbieter von Open- SpareN ODER NICHT spareN 48 Source-Lösungen abgeschlossen. -
Reverse Image Search for Scientific Data Within and Beyond the Visible
Journal, Vol. XXI, No. 1, 1-5, 2013 Additional note Reverse Image Search for Scientific Data within and beyond the Visible Spectrum Flavio H. D. Araujo1,2,3,4,a, Romuere R. V. Silva1,2,3,4,a, Fatima N. S. Medeiros3,Dilworth D. Parkinson2, Alexander Hexemer2, Claudia M. Carneiro5, Daniela M. Ushizima1,2,* Abstract The explosion in the rate, quality and diversity of image acquisition instruments has propelled the development of expert systems to organize and query image collections more efficiently. Recommendation systems that handle scientific images are rare, particularly if records lack metadata. This paper introduces new strategies to enable fast searches and image ranking from large pictorial datasets with or without labels. The main contribution is the development of pyCBIR, a deep neural network software to search scientific images by content. This tool exploits convolutional layers with locality sensitivity hashing for querying images across domains through a user-friendly interface. Our results report image searches over databases ranging from thousands to millions of samples. We test pyCBIR search capabilities using three convNets against four scientific datasets, including samples from cell microscopy, microtomography, atomic diffraction patterns, and materials photographs to demonstrate 95% accurate recommendations in most cases. Furthermore, all scientific data collections are released. Keywords Reverse image search — Content-based image retrieval — Scientific image recommendation — Convolutional neural network 1University of California, Berkeley, CA, USA 2Lawrence Berkeley National Laboratory, Berkeley, CA, USA 3Federal University of Ceara,´ Fortaleza, CE, Brazil 4Federal University of Piau´ı, Picos, PI, Brazil 5Federal University of Ouro Preto, Ouro Preto, MG, Brazil *Corresponding author: [email protected] aContributed equally to this work. -
Databits: an Electronic Newsletter for Information Managers
DataBits: An electronic newsletter for Information Managers Wade Sheldon (GCE) and Margaret O’Brien (SBC), co-editors Spring 2008 Issue Table of Contents Feature Articles The FCE LTER Website has a NEW look! - Linda Powell ......................................................1 Developing a Searchable Document and Imagery Archive for the GCE-LTER Web Site - Wade Sheldon....................................................................................................................5 Improving the Basic Keyword Search for Datasets by Employing Text Mining Techniques and Indexing - Hung V. Nguyen, Corinna Gries, Hasan Davulcu ..................9 Developing and Using APIs in System Design - James Connors, Mason Kortz ....................12 FinLTSER Network: New addition to the global LTER network - Helena Karasti ..............14 Commentary Using Xen and VMWare to Manage Virtual Machines: A New User’s Saga - John H. Porter.................................................................................................................18 Making Sense of Collaboration Software - James W Brunt....................................................20 Big Science and Local Meetings - Karen Baker, Sabine Grabner..........................................23 Preservation Metadata: Another Chapter in the Metadata Story - Lynn Yarmey ....................25 Data Quality: Yet Another Chapter in the Metadata Story - Lynn Yarmey.............................27 Good Tools and Programs Specifik, a Taxonomic Reference Tool to Support Synthesis of Vegetation -
Neural Network FAQ, Part 1 of 7
Neural Network FAQ, part 1 of 7: Introduction Archive-name: ai-faq/neural-nets/part1 Last-modified: 2002-05-17 URL: ftp://ftp.sas.com/pub/neural/FAQ.html Maintainer: [email protected] (Warren S. Sarle) Copyright 1997, 1998, 1999, 2000, 2001, 2002 by Warren S. Sarle, Cary, NC, USA. --------------------------------------------------------------- Additions, corrections, or improvements are always welcome. Anybody who is willing to contribute any information, please email me; if it is relevant, I will incorporate it. The monthly posting departs around the 28th of every month. --------------------------------------------------------------- This is the first of seven parts of a monthly posting to the Usenet newsgroup comp.ai.neural-nets (as well as comp.answers and news.answers, where it should be findable at any time). Its purpose is to provide basic information for individuals who are new to the field of neural networks or who are just beginning to read this group. It will help to avoid lengthy discussion of questions that often arise for beginners. SO, PLEASE, SEARCH THIS POSTING FIRST IF YOU HAVE A QUESTION and DON'T POST ANSWERS TO FAQs: POINT THE ASKER TO THIS POSTING The latest version of the FAQ is available as a hypertext document, readable by any WWW (World Wide Web) browser such as Netscape, under the URL: ftp://ftp.sas.com/pub/neural/FAQ.html. If you are reading the version of the FAQ posted in comp.ai.neural-nets, be sure to view it with a monospace font such as Courier. If you view it with a proportional font, tables and formulas will be mangled. -
From Digital Computing to Brain-Like Neuromorphic Accelerator
Mahyar SHAHSAVARI Lille University of Science and Technology Doctoral Thesis Unconventional Computing Using Memristive Nanodevices: From Digital Computing to Brain-like Neuromorphic Accelerator Supervisor: Pierre BOULET Thèse en vue de l’obtention du titre de Docteur en INFORMATIQUE DE L’UNIVERSITÉ DE LILLE École Doctorale des Sciences Pour l’Ingénieur de Lille - Nord De France 14-Dec-2016 Jury: Hélène Paugam-Moisy Professor of University of the French West Indies (Université des Antilles), France (Rapporteur) Michel Paindavoine Professor of University of Burgundy (Université de Bourgogne), France (Rapporteur) Virginie Hoel Professor of Lille University of Science and Technology, France(Président) Said Hamdioui Professor of Delft University of Technology, The Netherlands Pierre Boulet Professor of Lille University of Science and Technology, France Acknowledgment Firstly, I would like to express my sincere gratitude to my advisor Prof. Pierre Boulet for all kind of supports during my PhD. I never forget his supports specially during the last step of my PhD that I was very busy due to teaching duties. Pierre was really punctual and during three years of different meetings and discussions, he was always available on time and as a record he never canceled any meeting. I learned a lot form you Pierre, specially the way of writing, communicating and presenting. you were an ideal supervisor for me. Secondly, I am grateful of my co-supervisor Dr Philippe Devienne. As I did not knew any French at the beginning of my study, it was not that easy to settle down and with Philippe supports the life was more comfortable and enjoyable for me and my family here in Lille. -
Unconventional Computing Using Memristive Nanodevices: from Digital Computing to Brain-Like Neuromorphic Accelerator
Mahyar SHAHSAVARI Lille University of Science and Technology Doctoral Thesis Unconventional Computing Using Memristive Nanodevices: From Digital Computing to Brain-like Neuromorphic Accelerator Supervisor: Pierre BOULET Thèse en vue de l’obtention du titre de Docteur en INFORMATIQUE DE L’UNIVERSITÉ DE LILLE École Doctorale des Sciences Pour l’Ingénieur de Lille - Nord De France 14-Dec-2016 Jury: Hélène Paugam-Moisy Professor of University of the French West Indies (Université des Antilles), France (Rapporteur) Michel Paindavoine Professor of University of Burgundy (Université de Bourgogne), France (Rapporteur) Virginie Hoel Professor of Lille University of Science and Technology, France(Président) Said Hamdioui Professor of Delft University of Technology, The Netherlands Pierre Boulet Professor of Lille University of Science and Technology, France Acknowledgment Firstly, I would like to express my sincere gratitude to my advisor Prof. Pierre Boulet for all kind of supports during my PhD. I never forget his supports specially during the last step of my PhD that I was very busy due to teaching duties. Pierre was really punctual and during three years of different meetings and discussions, he was always available on time and as a record he never canceled any meeting. I learned a lot form you Pierre, specially the way of writing, communicating and presenting. you were an ideal supervisor for me. Secondly, I am grateful of my co-supervisor Dr Philippe Devienne. As I did not knew any French at the beginning of my study, it was not that easy to settle down and with Philippe supports the life was more comfortable and enjoyable for me and my family here in Lille. -
Virtual Organization Clusters: Self-Provisioned Clouds on the Grid Michael Murphy Clemson University, [email protected]
Clemson University TigerPrints All Dissertations Dissertations 5-2010 Virtual Organization Clusters: Self-Provisioned Clouds on the Grid Michael Murphy Clemson University, [email protected] Follow this and additional works at: https://tigerprints.clemson.edu/all_dissertations Part of the Computer Sciences Commons Recommended Citation Murphy, Michael, "Virtual Organization Clusters: Self-Provisioned Clouds on the Grid" (2010). All Dissertations. 511. https://tigerprints.clemson.edu/all_dissertations/511 This Dissertation is brought to you for free and open access by the Dissertations at TigerPrints. It has been accepted for inclusion in All Dissertations by an authorized administrator of TigerPrints. For more information, please contact [email protected]. Virtual Organization Clusters: Self-Provisioned Clouds on the Grid A Dissertation Presented to the Graduate School of Clemson University In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Computer Science by Michael A. Murphy May 2010 Accepted by: Dr. Sebastien Goasguen, Committee Chair Dr. Jim Martin Dr. Walt Ligon Dr. Robert Geist Abstract Virtual Organization Clusters (VOCs) provide a novel architecture for overlaying dedicated cluster systems on existing grid infrastructures. VOCs provide customized, homogeneous execution environments on a per-Virtual Organization basis, without the cost of physical cluster construction or the overhead of per-job containers. Administrative access and overlay network capabilities are granted to Virtual Organizations (VOs) that choose to implement VOC technology, while the system remains completely transparent to end users and non-participating VOs. Unlike alternative systems that require explicit leases, VOCs are autonomically self-provisioned according to configurable usage policies. As a grid computing architecture, VOCs are designed to be technology agnostic and are implementable by any combination of software and services that follows the Virtual Organization Cluster Model. -
Comparing User Experiences in Using Twiki & Mediawiki To
Liang, M., Chu, S., Siu, F., & Zhou, A. (2009). Comparing User Experiences in Using Twiki & Mediawiki to Facilitate Collaborative Learning. Proceedings of the 2009 International Conference on Knowledge Management [CD-ROM]. Hong Kong, Dec 3-4, 2009. COMPARING USER EXPERIENCES IN USING TWIKI & MEDIAWIKI TO FACILITATE COLLABORATIVE LEARNING MICHAEL LIANG* IT and Management Consulting Division, WiderWorld Company Ltd. [email protected] † SAM CHU , FELIX SIU and AUDREY ZHOU Faculty of Education, the University of Hong Kong † [email protected] This research seeks to determine the perceived effectiveness of using TWiki and MediaWiki in collaborative work and knowledge management; and to compare the use of TWiki and MediaWiki in terms of user experiences in the master’s level of study at the University of Hong Kong. Through a multiple case study approach, the study adopted a mixed methods research design which used both quantitative and qualitative methods to analyze findings from specific user groups in two study programmes. In the study, both wiki platforms were regarded as suitable tools for group work co-construction, which were found to be effective in improving group collaboration and work quality. Wikis were also viewed as enabling tools for knowledge management. MediaWiki was rated more favorably than TWiki, especially in the ease of use and enjoyment experienced. The paper should be of interest to educators who may want to explore wiki as a platform to enhance students’ collaborative group work. 1. Introduction Rote learning, which focuses on the direct transmission of knowledge from teachers to students using a traditional didactic approach, is the prevalent norm in education in many countries (Jang, 2007). -
Tutorial: Wikis in the Workplace" for Wiki Symposium, Montreal, Canada, 22 Oct 2007
This is the presentation material for the 3 hour session on "Tutorial: Wikis in the Workplace" for Wiki Symposium, Montreal, Canada, 22 Oct 2007. Slide 1: Tutorial: Wikis in the Workplace "Wikis have changed the way we run meetings, plan releases, document our product and generally communicate with each other" - Eric Baldeschwieler, Director of Software Development of Yahoo! • Wiki, a writable web: Communities can share content and organize it in a way most meaningful and useful to them • If extended with the right set of functionality, a wiki can be applied to the workplace to schedule, manage, document, and support daily activities • A structured wiki combines the benefits of a wiki and a database • This tutorial explains publishing wikis and structured wiki, covers its deployment, and shows some sample applications using TWiki, an open source enterprise wiki platform Presentation for Wiki Symposium, Montreal, Canada, 22 Oct 2007 -- Peter Thoeny - [email protected] - TWIKI.NET Slide 2: About Peter • Peter Thoeny - [email protected] • Founder of TWiki, the leading wiki for corporate collaboration, managing the open-sourced project for the last 9 years • Co-founder of TWIKI.NET , a company offering services and support for TWiki • Co-author of Wikis for Dummies book • Invented the concept of Structured Wikis - where free form wiki content can be structured with tailored wiki applications • Recognized thought-leader in Wikis and social software, featured in numerous articles and technology conferences including -
Neural Networks
SLAC-TN-03-028 Neural Networks Patrick Smith Office of Science, Student UndergraduateLaboratory Internship (SULI) Stanford University Stanford Linear Accelerator Center Menlo Park, California August 14,2003 Preparedin partial fulfillment of the requirementsof the Office of Science,Department of Energy’s ScienceUndergraduate Laboratory Internship under the direction of Tony Johnson. Participant: Signature ResearchAdvisor: Signature Work supported in part by the Department of Energy contract DE-AC03-76SF00515. INTRODUCTION Physicistsuse large detectorsto measureparticles createdin high-energy collisions at particle accelerators.These detectors typically producesignals indicating either where ionization occurs along the path of the particle, or where energyis depositedby the.particle.The data producedby thesesignals is fed into pattern recognition programsto try to identify what particles were produced,and to measurethe energy and direction of theseparticles. Ideally, there are many techniquesused in this pattern recognition software. One technique,neural networks, is particularly suitable for identifying what type of particle causedby a set of energy deposits. Neural networks can derive meaningfrom complicatedor imprecisedata, extract patterns,and detect trendsthat are too complex to be noticed by either humansor other computer related processes. To assistin the advancementof this technology,Physicists use a tool kit to experimentwith severalneural network techniques.The goal of this researchis interface a neural network tool kit into JavaAnalysis Studio (JAS3), an applicationthat allows data to be analyzedfrom any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desiredoutput while using JAS3 to analyzethe results or output. Before an implementationof a neural network can take place, a firm understandingof what a neural network is and how it works is beneficial. -
Regular Expressions Search String Counts
"regular expressions" (image) "regular expressions" blog"regular expressions" blog"regular (image) expressions" cheat"regular sheet expressions" cheat"regular sheet (image) expressions" examples "regular expressions" examples"regular (image) expressions" in "regular excel expressions" in (image) "regular excel expressions" in "regular vba expressions" in "regular vba (image) expressions" python"regular expressions" python"regular (image) expressions" tester"regular expressions" tester "regular (image) expressions" tutorial"regular expressions" tutorial"regular (image) Counts URL Per Search_Strings expressions""regular Search_String_Counts 99 91 101 89 99 85 ## 85 ## 80 96 77 99 78 ## 87 ## 86 Primary_URLs www.stackoverflow.com 1 1 0 0 0 0 1 1 1 6 1 5 1 2 1 3 1 0 25 www.youtube.com 0 3 0 0 0 1 0 3 0 3 0 4 0 5 0 2 0 4 25 www.medium.com 1 1 1 0 1 1 1 2 1 1 1 1 1 2 1 1 1 3 21 www.pinterest.com 0 0 0 0 1 9 0 1 0 0 0 1 0 4 0 1 0 2 19 www.regular-expressions.info 1 3 1 0 1 0 2 0 1 0 1 1 1 0 2 2 1 1 18 www.slideshare.net 0 1 0 0 1 2 0 5 0 1 0 0 0 3 0 2 0 2 17 www.scribd.com 0 0 0 2 1 8 0 0 0 0 0 1 0 2 0 0 0 1 15 www.slideplayer.com 0 1 0 1 0 1 0 4 0 1 0 0 0 3 0 2 0 2 15 www.oreilly.com 1 1 0 0 0 0 1 1 0 1 0 0 1 1 1 1 1 1 11 www.regexbuddy.com 1 3 0 0 0 0 0 1 0 0 0 0 1 1 1 2 1 0 11 www.amazon.com 1 1 0 0 1 0 0 0 1 0 1 1 1 0 0 0 1 2 10 www.cheatography.com 0 1 0 0 1 6 0 0 0 0 0 1 0 1 0 0 0 0 10 www.chegg.com 0 0 0 0 0 0 0 1 0 0 0 0 1 6 0 0 0 2 10 www.dataquest.io 0 1 1 1 1 1 0 0 1 0 0 0 1 1 1 0 1 0 10 www.regexr.com 1 1 0 0 1 1 0 0 1 0 1 0 0 0