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Revolution R Enterprise 6.0 launched!

Just got the email-more software is good news!

Revolution R Enterprise 6.0 for 32-bit and 64-bit Windows and 64-bit Red Hat Enterprise Linux (RHEL 5.x and RHEL 6.x) features an updated release of the RevoScaleR package that provides fast, scalable data management and data analysis: the same code scales from data frames to local, high-performance .xdf files to data distributed across a Windows HPC Server cluster or IBM Platform Computing LSF cluster.  RevoScaleR also allows distribution of the execution of essentially any R function across cores and nodes, delivering the results back to the user.

Detailed information on what’s new in 6.0 and known issues:
http://www.revolutionanalytics.com/doc/README_RevoEnt_Windows_6.0.0.pdf

and from the manual-lots of function goodies for Big Data

 

  • IBM Platform LSF Cluster support [Linux only]. The new RevoScaleR function, RxLsfCluster, allows you to create a distributed compute context for the Platform LSF workload manager.
  •  Azure Burst support added for Microsoft HPC Server [Windows only]. The new RevoScaleR function, RxAzureBurst, allows you to create a distributed compute context to have computations performed in the cloud using Azure Burst
  • The rxExec function allows distributed execution of essentially any R function across cores and nodes, delivering the results back to the user.
  • functions RxLocalParallel and RxLocalSeq allow you to create compute context objects for local parallel and local sequential computation, respectively.
  • RxForeachDoPar allows you to create a compute context using the currently registered foreach parallel backend (doParallel, doSNOW, doMC, etc.). To execute rxExec calls, simply register the parallel backend as usual, then set your compute context as follows: rxSetComputeContext(RxForeachDoPar())
  • rxSetComputeContext and rxGetComputeContext simplify management of compute contexts.
  • rxGlm, provides a fast, scalable, distributable implementation of generalized linear models. This expands the list of full-featured high performance analytics functions already available: summary statistics (rxSummary), cubes and cross tabs (rxCube,rxCrossTabs), linear models (rxLinMod), covariance and correlation matrices (rxCovCor),
    binomial logistic regression (rxLogit), and k-means clustering (rxKmeans)example: a Tweedie family with 1 million observations and 78 estimated coefficients (categorical data)
    took 17 seconds with rxGlm compared with 377 seconds for glm on a quadcore laptop

     

    and easier working with R’s big brother SAS language

     

    RevoScaleR high-performance analysis functions will now conveniently work directly with a variety of external data sources (delimited and fixed format text files, SAS files, SPSS files, and ODBC data connections). New functions are provided to create data source objects to represent these data sources (RxTextData, RxOdbcData, RxSasData, and RxSpssData), which in turn can be specified for the ‘data’ argument for these RevoScaleR analysis functions: rxHistogramrxSummary, rxCube, rxCrossTabs, rxLinMod, rxCovCor, rxLogit, and rxGlm.


    example, 

    you can analyze a SAS file directly as follows:


    # Create a SAS data source with information about variables and # rows to read in each chunk

    sasDataFile <- file.path(rxGetOption(“sampleDataDir”),”claims.sas7bdat”)
    sasDS <- RxSasData(sasDataFile, stringsAsFactors = TRUE,colClasses = c(RowNum = “integer”),rowsPerRead = 50)

    # Compute and draw a histogram directly from the SAS file
    rxHistogram( ~cost|type, data = sasDS)
    # Compute summary statistics
    rxSummary(~., data = sasDS)
    # Estimate a linear model
    linModObj <- rxLinMod(cost~age + car_age + type, data = sasDS)
    summary(linModObj)
    # Import a subset into a data frame for further inspection
    subData <- rxImport(inData = sasDS, rowSelection = cost > 400,
    varsToKeep = c(“cost”, “age”, “type”))
    subData

 

The installation instructions and instructions for getting started with Revolution R Enterprise & RevoDeployR for Windows: http://www.revolutionanalytics.com/downloads/instructions/windows.php

Visualizing Bigger Data in R using Tabplot

The amazing tabplot package creates the tableplot feature for visualizing huge chunks of data. This is a great example of creative data visualization that is resource lite and extremely fast in a first look at the data. (note- The tabplot package is being used and table plot function is being used . The TABLEPLOT package is different and is NOT being used here).

library(ggplot2)
data(diamonds)
library(tabplot)
tableplot(diamonds)
system.time(tableplot(diamonds))

visualizing a 50000 row by 10 variable dataset in 0.7 s is fast !!

click on screenshot to see it

and some say R is slow ;)

 

Note I used a free Windows Amazon EC2 Instance for it-

See screenshot for hardware configuration

 

the best thing is there is a handy GTK GUI for this package. You can check it out at

 

 

Software Review- Google Drive versus Dropbox

Here are some notes from reviewing Google Drive  https://drive.google.com/ vs Dropbox https://www.dropbox.com/.

1) Google Drive gives more free space upfront  than Dropbox.5GB versus 2GB

2) Dropbox has a referral system 500 mb per referral while there is no referral system for Google Drive

3) The sync facility with Google Docs makes Google Drive especially useful for prior users of Google Docs.

4) API access to Google Drive is only for Chrome apps which is intriguing!

https://developers.google.com/drive/apps_overview

Apps will not have any API access to files unless users have first installed the app in Chrome Web Store.

You can use the Dropbox API much more easily -

See the platforms at

https://www.dropbox.com/developers/start/core

Choose your platform:

iOS Android Python Ruby

But-

(though I wonder if you set the R working directory to the local shared drive for Google Drive it should sync up as well but of course be slower -http://scrogster.wordpress.com/2011/01/29/using-dropbox-with-r-2/)

5) Google Drive icon is ugly (seriously, dude!) , but the features in the Windows app is just the same as the Dropbox App. Too similar ;)

 

6) Upgrade space is much more cheaper to Google Drive than Dropbox ( by Google Drive prices being exactly  a quarter of prices on Dropbox and max storage being 16 times as much). This will affect power storage users. I expect to see some slowdown in Dropbox new business unless G Drive has outage (like Gmail) . Existing users at Dropbox probably wont shift for the small dollar amount- though it is quite easy to do so.

 

Install Google Drive on your local workstation and cut and paste your Dropbox local folder to the Google Drive local folder!!

7) Dropbox deserves credit for being first (like Hotmail and AOL) but Google Drive is almost better in all respects!

Google Drive

Free
5 GB of Drive (0% used)
10 GB of Gmail (48% used)
1 GB of Picasa (0% used)

Upgrade:

25 GB
2,49 $ / Month
+25 GB for Drive and Picasa
Bonus: Your Gmail storage will be upgraded to 25 GB.
Choose this plan

100 GB
4,99 $ / Month
+100 GB for Drive and Picasa
Bonus: Your Gmail storage will be upgraded to 25 GB.
Choose this plan

 Need more storage?

Up to 16 TB available

Dropbox–

Current account type

Large DropboxDropbox Badge greenFree
Free
Up to 18 GB (2 GB + 500 MB per referral)
Account info 

Other account types

Large DropboxDropbox Badge orange50 GB +
Pro 50
+1 GB per referral, up to +32 GB
$9.99/month or $99.00/year Upgrade to Pro 50
Large DropboxDropbox Badge purple100 GB +
Pro 100
+1 GB per referral, up to +32 GB
$19.99/month or $199.00/year Upgrade to Pro 100
Triple DropboxDropbox For Teams Badge1 TB +
Teams
Plans starting at 1 TB
Large shared quota, centralized admin and billing, and more!

 

 

 

Oracle R Updated!

Interesting message from https://blogs.oracle.com/R/ the latest R blog

 

_——–_

Oracle just released the latest update to Oracle R Enterprise, version 1.1. This release includes the Oracle R Distribution (based on open source R, version 2.13.2), an improved server installation, and much more.  The key new features include:

  • Extended Server Support: New support for Windows 32 and 64-bit server components, as well as continuing support for Linux 64-bit server components
  • Improved Installation: Linux 64-bit server installation now provides robust status updates and prerequisite checks
  • Performance Improvements: Improved performance for embedded R script execution calculations

In addition, the updated ROracle package, which is used with Oracle R Enterprise, now reads date data by conversion to character strings.

We encourage you download Oracle software for evaluation from the Oracle Technology Network. See these links for R-related software: Oracle R DistributionOracle R EnterpriseROracleOracle R Connector for Hadoop.  As always, we welcome comments and questions on the Oracle R Forum.

 

 

Oracle R Distribution 2-13.2 Update Available

Oracle has released an update to the Oracle R Distribution, an Oracle-supported distribution of open source R. Oracle R Distribution 2-13.2 now contains the ability to dynamically link the following libraries on both Windows and Linux:

  • The Intel Math Kernel Library (MKL) on Intel chips
  • The AMD Core Math Library (ACML) on AMD chips

 

To take advantage of the performance enhancements provided by Intel MKL or AMD ACML in Oracle R Distribution, simply add the MKL or ACML shared library directory to the LD_LIBRARY_PATH system environment variable. This automatically enables MKL or ACML to make use of all available processors, vastly speeding up linear algebra computations and eliminating the need to recompile R.  Even on a single core, the optimized algorithms in the Intel MKL libraries are faster than using R’s standard BLAS library.

Open-source R is linked to NetLib’s BLAS libraries, but they are not multi-threaded and only use one core. While R’s internal BLAS are efficient for most computations, it’s possible to recompile R to link to a different, multi-threaded BLAS library to improve performance on eligible calculations. Compiling and linking to R yourself can be involved, but for many, the significantly improved calculation speed justifies the effort. Oracle R Distribution notably simplifies the process of using external math libraries by enabling R to auto-load MKL orACML. For R commands that don’t link to BLAS code, taking advantage of database parallelism usingembedded R execution in Oracle R Enterprise is the route to improved performance.

For more information about rebuilding R with different BLAS libraries, see the linear algebra section in the R Installation and Administration manual. As always, the Oracle R Distribution is available as a free download to anyone. Questions and comments are welcome on the Oracle R Forum.

How to learn Hacking Part 2

Now that you have read the basics here at http://www.decisionstats.com/how-to-learn-to-be-a-hacker-easily/ (please do read this before reading the below)

 

Here is a list of tutorials that you should study (in order of ease)

1) LEARN BASICS – enough to get you a job maybe if that’s all you wanted.

http://www.offensive-security.com/metasploit-unleashed/Main_Page

2) READ SOME MORE-

Lena’s Reverse Engineering Tutorial-“Use Google.com  for finding the Tutorial

Lena’s Reverse Engineering tutorial. It includes 36 parts of individual cracking techniques and will teach you the basics of protection bypassing

01. Olly + assembler + patching a basic reverseme
02. Keyfiling the reverseme + assembler
03. Basic nag removal + header problems
04. Basic + aesthetic patching
05. Comparing on changes in cond jumps, animate over/in, breakpoints
06. “The plain stupid patching method”, searching for textstrings
07. Intermediate level patching, Kanal in PEiD
08. Debugging with W32Dasm, RVA, VA and offset, using LordPE as a hexeditor
09. Explaining the Visual Basic concept, introduction to SmartCheck and configuration
10. Continued reversing techniques in VB, use of decompilers and a basic anti-anti-trick
11. Intermediate patching using Olly’s “pane window”
12. Guiding a program by multiple patching.
13. The use of API’s in software, avoiding doublechecking tricks
14. More difficult schemes and an introduction to inline patching
15. How to study behaviour in the code, continued inlining using a pointer
16. Reversing using resources
17. Insights and practice in basic (self)keygenning
18. Diversion code, encryption/decryption, selfmodifying code and polymorphism
19. Debugger detected and anti-anti-techniques
20. Packers and protectors : an introduction
21. Imports rebuilding
22. API Redirection
23. Stolen bytes
24. Patching at runtime using loaders from lena151 original
25. Continued patching at runtime & unpacking armadillo standard protection
26. Machine specific loaders, unpacking & debugging armadillo
27. tElock + advanced patching
28. Bypassing & killing server checks
29. Killing & inlining a more difficult server check
30. SFX, Run Trace & more advanced string searching
31. Delphi in Olly & DeDe
32. Author tricks, HIEW & approaches in inline patching
33. The FPU, integrity checks & loader versus patcher
34. Reversing techniques in packed software & a S&R loader for ASProtect
35. Inlining inside polymorphic code
36. Keygenning

If you want more free training – hang around this website

http://www.owasp.org/index.php/Cheat_Sheets

OWASP Cheat Sheet Series

Draft OWASP Cheat Sheets

3) SPEND SOME MONEY on TRAINING

http://www.corelan-training.com/index.php/training/corelan-live/

Course overview

Module 1 – The x86 environment

  • System Architecture
  • Windows Memory Management
  • Registers
  • Introduction to Assembly
  • The stack

Module 2 – The exploit developer environment

  • Setting up the exploit developer lab
  • Using debuggers and debugger plugins to gather primitives

Module 3 – Saved Return Pointer Overwrite

  • Functions
  • Saved return pointer overwrites
  • Stack cookies

Module 4 – Abusing Structured Exception Handlers

  • Abusing exception handler overwrites
  • Bypassing Safeseh

Module 5 – Pointer smashing

  • Function pointers
  • Data/object pointers
  • vtable/virtual functions

Module 6 – Off-by-one and integer overflows

  • Off-by-one
  • Integer overflows

Module 7 – Limited buffers

  • Limited buffers, shellcode splitting

Module 8 – Reliability++ & reusability++

  • Finding and avoiding bad characters
  • Creative ways to deal with character set limitations

Module 9 – Fun with Unicode

  • Exploiting Unicode based overflows
  • Writing venetian alignment code
  • Creating and Using venetian shellcode

Module 10 – Heap Spraying Fundamentals

  • Heap Management and behaviour
  • Heap Spraying for Internet Explorer 6 and 7

Module 11 – Egg Hunters

  • Using and tweaking Egg hunters
  • Custom egghunters
  • Using Omelet egghunters
  • Egghunters in a WoW64 environment

Module 12 – Shellcoding

  • Building custom shellcode from scratch
  • Understanding existing shellcode
  • Writing portable shellcode
  • Bypassing Antivirus

Module 13 – Metasploit Exploit Modules

  • Writing exploits for the Metasploit Framework
  • Porting exploits to the Metasploit Framework

Module 14 – ASLR

  • Bypassing ASLR

Module 15 – W^X

  • Bypassing NX/DEP
  • Return Oriented Programming / Code Reuse (ROP) )

Module 16 – Advanced Heap Spraying

  • Heap Feng Shui & heaplib
  • Precise heap spraying in modern browsers (IE8 & IE9, Firefox 13)

Module 17 – Use After Free

  • Exploiting Use-After-Free conditions

Module 18 – Windows 8

  • Windows 8 Memory Protections and Bypass
TRAINING SCHEDULES AT

ALSO GET CERTIFIED http://www.offensive-security.com/information-security-training/penetration-testing-with-backtrack/ ($950 cost)

the syllabus is here at

http://www.offensive-security.com/documentation/penetration-testing-with-backtrack.pdf

4) HANG AROUND OTHER HACKERS

At http://attrition.org/attrition/

or The Noir  Hat Conferences-

http://blackhat.com/html/bh-us-12/training/bh-us-12-training_complete.html

or read this website

http://software-security.sans.org/developer-how-to/

5) GET A DEGREE

Yes it is possible

 

See http://web.jhu.edu/jhuisi/

The Johns Hopkins University Information Security Institute (JHUISI) is the University’s focal point for research and education in information security, assurance and privacy.

Scholarship Information

 

The Information Security Institute is now accepting applications for the Department of Defense’s Information Assurance Scholarship Program (IASP).  This scholarship includes full tuition, a living stipend, books and health insurance. In return each student recipient must work for a DoD agency at a competitive salary for six months for every semester funded. The scholarship is open to American citizens only.

http://web.jhu.edu/jhuisi/mssi/index.html

MASTER OF SCIENCE IN SECURITY INFORMATICS PROGRAM

The flagship educational experience offered by Johns Hopkins University in the area of information security and assurance is represented by the Master of Science in Security Informatics degree.  Over thirty courses are available in support of this unique and innovative graduate program.

———————————————————–

Disclaimer- I havent done any of these things- This is just a curated list from Quora  so I am open to feedback.

You use this at your own risk of conscience ,local legal jurisdictions and your own legal liability.

 

 

 

 

 

 

send email by R

For automated report delivery I have often used send email options in BASE SAS. For R, for scheduling tasks and sending me automated mails on completion of tasks I have two R options and 1 Windows OS scheduling option. Note red font denotes the parameters that should be changed. Anything else should NOT be changed.

Option 1-

Use the mail package at

http://cran.r-project.org/web/packages/mail/mail.pdf

> library(mail)

Attaching package: ‘mail’

The following object(s) are masked from ‘package:sendmailR’:

sendmail

>
> sendmail(“ohri2007@gmail.com“, subject=”Notification from R“,message=“Calculation finished!”, password=”rmail”)
[1] “Message was sent to ohri2007@gmail.com! You have 19 messages left.”

Disadvantage- Only 20 email messages by IP address per day. (but thats ok!)

Option 2-

use sendmailR package at http://cran.r-project.org/web/packages/sendmailR/sendmailR.pdf

install.packages()
library(sendmailR)
from <- sprintf(“<sendmailR@%s>”, Sys.info()[4])
to <- “<ohri2007@gmail.com>”
subject <- “Hello from R
body <- list(“It works!”, mime_part(iris))
sendmail(from, to, subject, body,control=list(smtpServer=”ASPMX.L.GOOGLE.COM”))

 

 

BiocInstaller version 1.2.1, ?biocLite for help
> install.packages(“sendmailR”)
Installing package(s) into ‘/home/ubuntu/R/library’
(as ‘lib’ is unspecified)
also installing the dependency ‘base64’

trying URL ‘http://cran.at.r-project.org/src/contrib/base64_1.1.tar.gz&#8217;
Content type ‘application/x-gzip’ length 61109 bytes (59 Kb)
opened URL
==================================================
downloaded 59 Kb

trying URL ‘http://cran.at.r-project.org/src/contrib/sendmailR_1.1-1.tar.gz&#8217;
Content type ‘application/x-gzip’ length 6399 bytes
opened URL
==================================================
downloaded 6399 bytes

BiocInstaller version 1.2.1, ?biocLite for help
* installing *source* package ‘base64’ …
** package ‘base64’ successfully unpacked and MD5 sums checked
** libs
gcc -std=gnu99 -I/usr/local/lib64/R/include -I/usr/local/include -fpic -g -O2 -c base64.c -o base64.o
gcc -std=gnu99 -shared -L/usr/local/lib64 -o base64.so base64.o -L/usr/local/lib64/R/lib -lR
installing to /home/ubuntu/R/library/base64/libs
** R
** preparing package for lazy loading
** help
*** installing help indices
** building package indices …
** testing if installed package can be loaded
BiocInstaller version 1.2.1, ?biocLite for help

* DONE (base64)
BiocInstaller version 1.2.1, ?biocLite for help
* installing *source* package ‘sendmailR’ …
** package ‘sendmailR’ successfully unpacked and MD5 sums checked
** R
** preparing package for lazy loading
** help
*** installing help indices
** building package indices …
** testing if installed package can be loaded
BiocInstaller version 1.2.1, ?biocLite for help

* DONE (sendmailR)

The downloaded packages are in
‘/tmp/RtmpsM222s/downloaded_packages’
> library(sendmailR)
Loading required package: base64
> from <- sprintf(“<sendmailR@%s>”, Sys.info()[4])
> to <- “<ohri2007@gmail.com>”
> subject <- “Hello from R”
> body <- list(“It works!”, mime_part(iris))
> sendmail(from, to, subject, body,
+ control=list(smtpServer=”ASPMX.L.GOOGLE.COM”))
$code
[1] “221”

$msg
[1] “2.0.0 closing connection ff2si17226764qab.40″

Disadvantage-This worked when I used the Amazon Cloud using the BioConductor AMI (for free 2 hours) at http://www.bioconductor.org/help/cloud/

It did NOT work when I tried it use it from my Windows 7 Home Premium PC from my Indian ISP (!!) .

It gave me this error

or in wait_for(250) :
SMTP Error: 5.7.1 [180.215.172.252] The IP you’re using to send mail is not authorized

 

PAUSE–

ps Why do this (send email by R)?

Note you can add either of the two programs of the end of the code that you want to be notified automatically. (like daily tasks)

This is mostly done for repeated business analytics tasks (like reports and analysis that need to be run at specific periods of time)

pps- What else can I do with this?

Can be modified to include sms or tweets  or even blog by email by modifying the   “to”  location appropriately.

3) Using Windows Task Scheduler to run R codes automatically (either the above)

or just sending an email

got to Start>  All Programs > Accessories >System Tools > Task Scheduler ( or by default C:Windowssystem32taskschd.msc)

Create a basic task

Now you can use this to run your daily/or scheduled R code  or you can send yourself email as well.

and modify the parameters- note the SMTP server (you can use the ones for google in example 2 at ASPMX.L.GOOGLE.COM)

and check if it works!

 

Related

 Geeky Things , Bro

-

Configuring IIS on your Windows 7 Home Edition-

note path to do this is-

Control Panel>All Control Panel Items> Program and Features>Turn Windows features on or off> Internet Information Services

and

http://stackoverflow.com/questions/709635/sending-mail-from-batch-file

 

R for Predictive Modeling- PAW Toronto

A nice workshop on using R for Predictive Modeling by Max Kuhn Director, Nonclinical Statistics, Pfizer is on at PAW Toronto.

Workshop

Monday, April 23, 2012 in Toronto
Full-day: 9:00am – 4:30pm

R for Predictive Modeling:
A Hands-On Introduction

Intended Audience: Practitioners who wish to learn how to execute on predictive analytics by way of the R language; anyone who wants “to turn ideas into software, quickly and faithfully.”

Knowledge Level: Either hands-on experience with predictive modeling (without R) or hands-on familiarity with any programming language (other than R) is sufficient background and preparation to participate in this workshop.


What prior attendees have exclaimed


Workshop Description

This one-day session provides a hands-on introduction to R, the well-known open-source platform for data analysis. Real examples are employed in order to methodically expose attendees to best practices driving R and its rich set of predictive modeling packages, providing hands-on experience and know-how. R is compared to other data analysis platforms, and common pitfalls in using R are addressed.

The instructor, a leading R developer and the creator of CARET, a core R package that streamlines the process for creating predictive models, will guide attendees on hands-on execution with R, covering:

  • A working knowledge of the R system
  • The strengths and limitations of the R language
  • Preparing data with R, including splitting, resampling and variable creation
  • Developing predictive models with R, including decision trees, support vector machines and ensemble methods
  • Visualization: Exploratory Data Analysis (EDA), and tools that persuade
  • Evaluating predictive models, including viewing lift curves, variable importance and avoiding overfitting

Hardware: Bring Your Own Laptop
Each workshop participant is required to bring their own laptop running Windows or OS X. The software used during this training program, R, is free and readily available for download.

Attendees receive an electronic copy of the course materials and related R code at the conclusion of the workshop.


Schedule

  • Workshop starts at 9:00am
  • Morning Coffee Break at 10:30am – 11:00am
  • Lunch provided at 12:30 – 1:15pm
  • Afternoon Coffee Break at 2:30pm – 3:00pm
  • End of the Workshop: 4:30pm

Instructor

Max Kuhn, Director, Nonclinical Statistics, Pfizer

Max Kuhn is a Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He has been applying models in the pharmaceutical industries for over 15 years.

He is a leading R developer and the author of several R packages including the CARET package that provides a simple and consistent interface to over 100 predictive models available in R.

Mr. Kuhn has taught courses on modeling within Pfizer and externally, including a class for the India Ministry of Information Technology.

Source-

http://www.predictiveanalyticsworld.com/toronto/2012/r_for_predictive_modeling.php

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