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Mcafee Foundstone Fsl Update
2018-JUL-25 FSL version 7.6.38 MCAFEE FOUNDSTONE FSL UPDATE To better protect your environment McAfee has created this FSL check update for the Foundstone Product Suite. The following is a detailed summary of the new and updated checks included with this release. NEW CHECKS 23889 - (HT208932) Apple iCloud Vulnerabilities Prior To 7.6 Category: Windows Host Assessment -> Miscellaneous (CATEGORY REQUIRES CREDENTIALS) Risk Level: High CVE: CVE-2018-4261, CVE-2018-4262, CVE-2018-4263, CVE-2018-4264, CVE-2018-4265, CVE-2018-4266, CVE-2018-4267, CVE- 2018-4270, CVE-2018-4271, CVE-2018-4272, CVE-2018-4273, CVE-2018-4278, CVE-2018-4284, CVE-2018-4293 Description Multiple vulnerabilities are present in some versions of Apple iCloud. Observation Apple iCloud is a manager for the Apple's cloud-based storage service. Multiple vulnerabilities are present in some versions of Apple iCloud. The flaws lie in multiple components. Successful exploitation could allow an attacker to obtain sensitive information, execute arbitrary code or cause a denial of service. 23893 - (HT208938) Apple iOS Multiple Vulnerabilities Prior To 11.4.1 Category: Wireless Assessment -> NonIntrusive -> iOS Risk Level: High CVE: CVE-2018-4248, CVE-2018-4260, CVE-2018-4261, CVE-2018-4262, CVE-2018-4263, CVE-2018-4264, CVE-2018-4265, CVE- 2018-4266, CVE-2018-4267, CVE-2018-4270, CVE-2018-4271, CVE-2018-4272, CVE-2018-4273, CVE-2018-4274, CVE-2018-4275, CVE-2018-4277, CVE-2018-4278, CVE-2018-4280, CVE-2018-4282, CVE-2018-4284, CVE-2018-4290, CVE-2018-4293 Description Multiple vulnerabilities are present in some versions of Apple iOS. -
How to Access Python for Doing Scientific Computing
How to access Python for doing scientific computing1 Hans Petter Langtangen1,2 1Center for Biomedical Computing, Simula Research Laboratory 2Department of Informatics, University of Oslo Mar 23, 2015 A comprehensive eco system for scientific computing with Python used to be quite a challenge to install on a computer, especially for newcomers. This problem is more or less solved today. There are several options for getting easy access to Python and the most important packages for scientific computations, so the biggest issue for a newcomer is to make a proper choice. An overview of the possibilities together with my own recommendations appears next. Contents 1 Required software2 2 Installing software on your laptop: Mac OS X and Windows3 3 Anaconda and Spyder4 3.1 Spyder on Mac............................4 3.2 Installation of additional packages.................5 3.3 Installing SciTools on Mac......................5 3.4 Installing SciTools on Windows...................5 4 VMWare Fusion virtual machine5 4.1 Installing Ubuntu...........................6 4.2 Installing software on Ubuntu....................7 4.3 File sharing..............................7 5 Dual boot on Windows8 6 Vagrant virtual machine9 1The material in this document is taken from a chapter in the book A Primer on Scientific Programming with Python, 4th edition, by the same author, published by Springer, 2014. 7 How to write and run a Python program9 7.1 The need for a text editor......................9 7.2 Spyder................................. 10 7.3 Text editors.............................. 10 7.4 Terminal windows.......................... 11 7.5 Using a plain text editor and a terminal window......... 12 8 The SageMathCloud and Wakari web services 12 8.1 Basic intro to SageMathCloud................... -
Goless Documentation Release 0.6.0
goless Documentation Release 0.6.0 Rob Galanakis July 11, 2014 Contents 1 Intro 3 2 Goroutines 5 3 Channels 7 4 The select function 9 5 Exception Handling 11 6 Examples 13 7 Benchmarks 15 8 Backends 17 9 Compatibility Details 19 9.1 PyPy................................................... 19 9.2 Python 2 (CPython)........................................... 19 9.3 Python 3 (CPython)........................................... 19 9.4 Stackless Python............................................. 20 10 goless and the GIL 21 11 References 23 12 Contributing 25 13 Miscellany 27 14 Indices and tables 29 i ii goless Documentation, Release 0.6.0 • Intro • Goroutines • Channels • The select function • Exception Handling • Examples • Benchmarks • Backends • Compatibility Details • goless and the GIL • References • Contributing • Miscellany • Indices and tables Contents 1 goless Documentation, Release 0.6.0 2 Contents CHAPTER 1 Intro The goless library provides Go programming language semantics built on top of gevent, PyPy, or Stackless Python. For an example of what goless can do, here is the Go program at https://gobyexample.com/select reimplemented with goless: c1= goless.chan() c2= goless.chan() def func1(): time.sleep(1) c1.send(’one’) goless.go(func1) def func2(): time.sleep(2) c2.send(’two’) goless.go(func2) for i in range(2): case, val= goless.select([goless.rcase(c1), goless.rcase(c2)]) print(val) It is surely a testament to Go’s style that it isn’t much less Python code than Go code, but I quite like this. Don’t you? 3 goless Documentation, Release 0.6.0 4 Chapter 1. Intro CHAPTER 2 Goroutines The goless.go() function mimics Go’s goroutines by, unsurprisingly, running the routine in a tasklet/greenlet. -
Organized by the International Astronautical Federation (IAF) and Supported by the United Nations Office for Outer Space Affairs (UNOOSA)
Organized by the International Astronautical Federation (IAF) and supported by the United Nations Office for Outer Space Affairs (UNOOSA) Hosted by the American Institute of Aeronautics and Astronautics (AIAA) VENUE: Walter E. Washington Convention Center, 801 Mt Vernon PI NW, Washington, DC 20001, United States of America DATES: 18-20 October 2019, in conjunction with the 70th International Astronautical Congress Programme at a glance 18 October 2019 19 October 2019 20 October 2019 Morning 08:00-09:00 Registration 09:00-10:35 Session 3: 09:00-09:20 Keynote speech Opportunities for space emerging 09:00-10:15 Opening ceremony 09:20-10:30 High Level Panel: countries and industries to join Efforts of the space community 10:15-10:30 Coffee break efforts on space science and to ensure no one is left behind 10:30-10:50 Keynote speech technology 10:30-11:00 Coffee break 10:50-12:30 Session 1: Space for 10:35-11:00 Coffee break 11:00-11:20 Keynote speech Inclusiveness: Leaving no one 11:00-11:30 High Level Keynote behind speech 11:20-11:45 Closing ceremony 11:30-13:00 Session 4: Space exploration for everyone Lunch 12:30-13:30 Lunch break 13:00-14:00 Lunch break Afternoon 13:30-15:00 Session 1 14:00-15:30 Interactive session (continued): Space for 15:30-16:00 Coffee break and Inclusiveness: Leaving no one poster session behind 16:00-17:30 Session 5: 15:00-15:30 Coffee break and Developing collaborations for poster session space applications 15:30-15:50 Keynote speech 15:50-17:30 Session 2: Mobilizing everyone: Innovative space applications for socio-economic development 18:00-21:00 Reception* *The reception will be held in the South Prefunction area of the Walter E. -
IMPLEMENTING OPTION PRICING MODELS USING PYTHON and CYTHON Sanjiv Dasa and Brian Grangerb
JOURNAL OF INVESTMENT MANAGEMENT, Vol. 8, No. 4, (2010), pp. 1–12 © JOIM 2010 JOIM www.joim.com IMPLEMENTING OPTION PRICING MODELS USING PYTHON AND CYTHON Sanjiv Dasa and Brian Grangerb In this article we propose a new approach for implementing option pricing models in finance. Financial engineers typically prototype such models in an interactive language (such as Matlab) and then use a compiled language such as C/C++ for production systems. Code is therefore written twice. In this article we show that the Python programming language and the Cython compiler allows prototyping in a Matlab-like manner, followed by direct generation of optimized C code with very minor code modifications. The approach is able to call upon powerful scientific libraries, uses only open source tools, and is free of any licensing costs. We provide examples where Cython speeds up a prototype version by over 500 times. These performance gains in conjunction with vast savings in programmer time make the approach very promising. 1 Introduction production version in a compiled language like C, C++, or Fortran. This duplication of effort slows Computing in financial engineering needs to be fast the deployment of new algorithms and creates ongo- in two critical ways. First, the software development ing software maintenance problems, not to mention process must be fast for human developers; pro- added development costs. grams must be easy and time efficient to write and maintain. Second, the programs themselves must In this paper we describe an approach to tech- be fast when they are run. Traditionally, to meet nical software development that enables a single both of these requirements, at least two versions of version of a program to be written that is both easy a program have to be developed and maintained; a to develop and maintain and achieves high levels prototype in a high-level interactive language like of performance. -
Visualizing the Universe with NASA Data: the Art of Science Visualizations
Science Briefing April 12, 2018 Visualizing the Universe Kimberly Kowal Arcand (SAO) Dr. Robert Hurt (Caltech/IPAC) with NASA Data Dr. Frank Summers (STScI) Facilitator: Dr. Emma Marcucci (STScI) Additional Resources http://nasawavelength.org/list/2130 Hands-on Activities: ReColoring the Universe How to talk to a spacecraft 3D Printing the X-ray Universe Tinkercad: Universe in 3D Visualization Products and Process: Walking Among the Stars Flight through the Orion Nebula Art of Astrophysics Eyes on Exoplanets Science Visualization Databases: HubbleSite Science Videos AstroPix NASA Scientific Visualization Studio 2 Outline of this Science Briefing 1. Kimberly Arcand (SAO) Exploring Hidden and Exotic Worlds: How Astronomical Data Transports Us 2. Robert Hurt (Caltech/IPAC) Visualizing the Universe with NASA Data: The Art of Science Visualizations 3. Frank Summers (STScI) Cinematic Scientific Visualizations 4. Discussion / Questions 3 Kimberly Arcand, Visualization Lead NASA’s Chandra X-ray Observatory, Smithsonian Astrophysical Observatory Twitter/Instagram: @kimberlykowal 4 5 @kimberlykowal @kimberlykowal Delicate filamentary structure at 10,000° 7 @kimberlykowal 8 @kimberlykowal 9 @kimberlykowal Hwang & Laming, 2012, ApJ@,kimberlykowal 746, 130 10 @kimberlykowal 11 @kimberlykowal 1100011010101001010101101001100001001110001 1010110101010100001010101011010101010011010 0011010101101001100001010010101010101001010 1100011000001001010101101001100001001010001 1011110101010101001010101011011001010001011 0011000101101001111001010010101010101001010 -
High-Performance Computation with Python | Python Academy | Training IT
Training: Python Academy High-Performance Computation with Python Training: Python Academy High-Performance Computation with Python TRAINING GOALS: The requirement for extensive and challenging computations is far older than the computing industry. Today, software is a mere means to a specific end, it needs to be newly developed or adapted in countless places in order to achieve the special goals of the users and to run their specific calculations efficiently. Nothing is of more avail to this need than a programming language that is easy for users to learn and that enables them to actively follow and form the realisation of their requirements. Python is this programming language. It is easy to learn, to use and to read. The language makes it easy to write maintainable code and to use it as a basis for communication with other people. Moreover, it makes it easy to optimise and specialise this code in order to use it for challenging and time critical computations. This is the main subject of this course. CONSPECT: Optimizing Python programs Guidelines for optimization. Optimization strategies - Pystone benchmarking concept, CPU usage profiling with cProfile, memory measuring with Guppy_PE Framework. Participants are encouraged to bring their own programs for profiling to the course. Algorithms and anti-patterns - examples of algorithms that are especially slow or fast in Python. The right data structure - comparation of built-in data structures: lists, sets, deque and defaulddict - big-O notation will be exemplified. Caching - deterministic and non-deterministic look on caching and developing decorates for these purposes. The example - we will use a computionally demanding example and implement it first in pure Python. -
On the Journey from Earth to the Universe Best Practices
On the Journey From Earth to the Universe Best Practices Kimberly Kowal Arcand Megan Watzke Chandra X-ray Center/SAO Chandra X-ray Center/SAO E-mail: [email protected] E-mail: [email protected] Summary Key Words The From Earth to the Universe (FETTU) project is a worldwide effort to International Year of Astronomy 2009 bring the striking beauty and intriguing science of astronomy to the public. IYA2009 Cornerstone project By showcasing some of the best images from the fleet of space-based Image exhibition observatories and wide array of telescopes (and astrophotographers) on the From Earth to the Universe ground, FETTU strives to engage as many people as possible in the wonders of the Universe. As one of the 12 global Cornerstone projects being supported by International Year of Astronomy 2009, FETTU is, in fact, reaching its goals halfway through IYA2009. Over 60 countries in more than 250 separate exhibitions are participating in FETTU. From tiny villages to the largest cities — with budgets large and small — FETTU has been featured on every continent except Antarctica. Since we have framed this project — largely by way of the title — as a journey, we decided to take a tour of the destinations that we have already visited, take stock of our experiences and look at where FETTU might go in the future. Getting started is recognised as a successful tool for the spirit of IYA2009. We encouraged those learners of all ages and increases interest preparing the exhibits — who we dubbed Like most trips into the unknown, this in science, technology, engineering, and “local organisers” — to use whatever mon- one required a fair amount of research, a mathematics in both children and adults1. -
WORLD FINTECH REPORT 2018 Contents 4 Preface
in collaboration with WORLD FINTECH REPORT 2018 Contents 4 Preface 8 Executive Summary FinTechs Are Redefining the Financial Services Customer Journey 13 Competition and Rising Expectations Spur 14 Customer-Centricity Push – Identify gaps left by traditional Financial Services providers and explore changing customer expectations. Emerging Technologies Enable Customer 19 Journey Transformation – Data and insights are reshaping personalized experiences through automation and distributed ledger technology. Alignment with Customer Goals, Creation of 27 Trust, and Delivery of Digital, Agile, and Efficient Processes Are Catalysts for Success – Firms are driving innovation and operational excellence through agile and digital teams. World FinTech Report 2018 The Symbiotic Relationship between FinTechs and Traditional Financial Institutions 35 FinTech and Incumbent Firms’ Respective 36 Competitive Advantages and Shortcomings Make Collaboration a Logical Fit – A partnership ecosystem of FinTechs, incumbents, and other vendors fosters a win-win for stakeholders. Finding the Right Partners for Collaboration 44 Is Essential – Maintaining and accelerating scale is a common FinTech firm struggle, so the right collaboration partner is critical. Successful Collaboration Requires Commitment 49 and Agility from FinTechs and Incumbents – Selection of the appropriate engagement model boosts FinTech scale-up efforts. The Path Forward: An Impending Role for BigTechs? 60 – BigTechs could have massive impact on the Financial Services industry. Preface Once rather homogenous and somewhat staid, the financial services marketplace has transformed into a dynamic milieu of bar-raising specialists. These new-age professionals are devoted to meeting and exceeding the expectations of consumers who have become accustomed to personalized services from industries such as retail, travel, and electronics. Financial services customers no longer rely on one or two firms. -
Circuitpython Documentation Release 0.0.0
CircuitPython Documentation Release 0.0.0 Damien P. George, Paul Sokolovsky, and contributors Jun 26, 2020 API and Usage 1 Adafruit CircuitPython 3 1.1 Status...................................................3 1.2 Supported Boards............................................3 1.2.1 Designed for CircuitPython...................................3 1.2.2 Other..............................................4 1.3 Download.................................................4 1.4 Documentation..............................................4 1.5 Contributing...............................................4 1.6 Differences from MicroPython......................................4 1.6.1 Behavior.............................................5 1.6.2 API...............................................5 1.6.3 Modules.............................................5 1.6.4 atmel-samd21 features.....................................5 1.7 Project Structure.............................................5 1.7.1 Core...............................................6 1.7.2 Ports...............................................6 1.8 Full Table of Contents..........................................7 1.8.1 Core Modules..........................................7 1.8.2 Supported Ports......................................... 47 1.8.3 Troubleshooting......................................... 56 1.8.4 Additional Adafruit Libraries and Drivers on GitHub..................... 57 1.8.5 Design Guide.......................................... 59 1.8.6 Architecture.......................................... -
Giant List of Web Browsers
Giant List of Web Browsers The majority of the world uses a default or big tech browsers but there are many alternatives out there which may be a better choice. Take a look through our list & see if there is something you like the look of. All links open in new windows. Caveat emptor old friend & happy surfing. 1. 32bit https://www.electrasoft.com/32bw.htm 2. 360 Security https://browser.360.cn/se/en.html 3. Avant http://www.avantbrowser.com 4. Avast/SafeZone https://www.avast.com/en-us/secure-browser 5. Basilisk https://www.basilisk-browser.org 6. Bento https://bentobrowser.com 7. Bitty http://www.bitty.com 8. Blisk https://blisk.io 9. Brave https://brave.com 10. BriskBard https://www.briskbard.com 11. Chrome https://www.google.com/chrome 12. Chromium https://www.chromium.org/Home 13. Citrio http://citrio.com 14. Cliqz https://cliqz.com 15. C?c C?c https://coccoc.com 16. Comodo IceDragon https://www.comodo.com/home/browsers-toolbars/icedragon-browser.php 17. Comodo Dragon https://www.comodo.com/home/browsers-toolbars/browser.php 18. Coowon http://coowon.com 19. Crusta https://sourceforge.net/projects/crustabrowser 20. Dillo https://www.dillo.org 21. Dolphin http://dolphin.com 22. Dooble https://textbrowser.github.io/dooble 23. Edge https://www.microsoft.com/en-us/windows/microsoft-edge 24. ELinks http://elinks.or.cz 25. Epic https://www.epicbrowser.com 26. Epiphany https://projects-old.gnome.org/epiphany 27. Falkon https://www.falkon.org 28. Firefox https://www.mozilla.org/en-US/firefox/new 29. -
Bfree: Enabling Battery-Free Sensor Prototyping with Python
BFree: Enabling Battery-free Sensor Prototyping with Python VITO KORTBEEK, Delft University of Technology, The Netherlands ABU BAKAR, Northwestern University, USA STEFANY CRUZ, Northwestern University, USA KASIM SINAN YILDIRIM, University of Trento, Italy PRZEMYSŁAW PAWEŁCZAK, Delft University of Technology, The Netherlands JOSIAH HESTER, Northwestern University, USA Building and programming tiny battery-free energy harvesting embedded computer systems is hard for the average maker because of the lack of tools, hard to comprehend programming models, and frequent power failures. With the high ecologic cost of equipping the next trillion embedded devices with batteries, it is critical to equip the makers, hobbyists, and novice embedded systems programmers with easy-to-use tools supporting battery-free energy harvesting application development. This way, makers can create untethered embedded systems that are not plugged into the wall, the desktop, or even a battery, providing numerous new applications and allowing for a more sustainable vision of ubiquitous computing. In this paper, we present BFree, a system that makes it possible for makers, hobbyists, and novice embedded programmers to develop battery-free applications using Python programming language and widely available hobbyist maker platforms. BFree provides energy harvesting hardware and a power failure resilient version of Python, with durable libraries that enable common coding practice and off the shelf sensors. We develop demonstration applications, benchmark BFree against battery-powered