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Personally I think a browser with inbuilt backdoors to Tor Relays and data transfer by Bit Torrrents could be worthy a project too.
Quit the bullshit, Google- you are as evil as The Russian Communist Empire
I was just reading up on my weekly to-read list and came across this interesting method. It is called Play Color Cipher-
Each Character ( Capital, Small letters, Numbers (0-9), Symbols on the keyboard ) in the plain text is substituted with a color block from the available 18 Decillions of colors in the world  and at the receiving end the cipher text block (in color) is decrypted in to plain text block. It overcomes the problems like “Meet in the middle attack, Birthday attack and Brute force attacks ”.
It also reduces the size of the plain text when it is encrypted in to cipher text by 4 times, with out any loss of content. Cipher text occupies very less buffer space; hence transmitting through channel is very fast. With this the transportation cost through channel comes down.
Visual Cryptography is indeed an interesting topic-
Visual cryptography, an emerging cryptography technology, uses the characteristics of human vision to decrypt encrypted
images. It needs neither cryptography knowledge nor complex computation. For security concerns, it also ensures that hackers
cannot perceive any clues about a secret image from individual cover images. Since Naor and Shamir proposed the basic
model of visual cryptography, researchers have published many related studies.
Visual cryptography (VC) schemes hide the secret image into two or more images which are called
shares. The secret image can be recovered simply by stacking the shares together without any complex
computation involved. The shares are very safe because separately they reveal nothing about the secret image.
Visual Cryptography provides one of the secure ways to transfer images on the Internet. The advantage
of visual cryptography is that it exploits human eyes to decrypt secret images .
ESPECIALLY SEE |THIS AND THIS
Even more fun—– visual cryptography using a series of bar codes – leaving the man in middle guessing how many sub images are there and which if at all is the real message
Color Visual Cryptography Scheme Using Meaningful Shares
Visual cryptography for color images
- Visual Crypto – One-time Image Create two secure images from one by Robert Hansen
- Visual Crypto Java Applet at the University of Regensburg
- Visual Cryptography Kit Software to create image layers
- On-line Visual Crypto Applet by Leemon Baird
- Extended Visual Cryptography (pdf) by Mizuho Nakajima and Yasushi Yamaguchi
- Visual Cryptography Paper by Moni Noar and Adi Shamir
- Visual Crypto Talk (pdf) by Frederik Vercauteren ESAT Leuven
- t the University of Salerno web page on visual cryptogrpahy.
- Visual Crypto Page by Doug Stinson
Constructions and Bounds for Visual Cryptography
Lecture Notes in Computer Science 1099 (1996), 416-428 (23rd International Colloquium on Automata, Languages and Programming).
- Visual Cryptography for General Access Structures
Information and Computation 129 (1996), 86-106 (this paper is an expanded and revised version of the conference paper).
- On the Contrast in Visual Cryptography Schemes
Journal of Cryptology 12 (1999), 261-289.
- Extended Schemes for Visual Cryptography
Theoretical Computer Science 250 (2001), 143-161.
- Threshold Visual Cryptography Schemes With Specified Whiteness Levels of Reconstructed Pixels
Designs, Codes and Cryptography 25 (2002), 15-61.
- Contrast Optimal Threshold Visual Cryptography Schemes
SIAM J. on Discrete Math. 16 (2003), 224-261.
- “Visual Cryptography: Seeing is Believing” availablehere,
- example- face http://cacr.uwaterloo.ca/~dstinson/VCS-happyface.html
- flag http://cacr.uwaterloo.ca/~dstinson/VCS-flag.html
- pi http://cacr.uwaterloo.ca/~dstinson/VCS-pi.html
- Simple implementation of the visual cryptography scheme based on Moni Naor and Adi Shamir, Visual Cryptography, EUROCRYPT 1994, pp1–12. This technique allows visual information like pictures to be encrypted so that decryption can be done visually.The code outputs two files. Try printing them on two separate transparencies and putting them one on top of the other to see the hidden message. http://algorito.com/algorithm/visual-cryptography
- Moni Naor and Adi Shamir, Visual Cryptography , Eurocrypt 94. Postscript , gzipped Postscript
- Moni Naor and Adi Shamir, Visual Cryptography II , Cambridge Workshop on Protocols, 1996. Postscript, gzipped Postscript
- Moni Naor and Benny Pinkas, Visual Authentication , Crypto 97. Postscript, gzipped Postscript
Ajay- I think a combination of sharing and color ciphers would prove more helpful to secure Internet Communication than existing algorithms. It also levels the playing field from computationally rich players to creative coders.
My favorite GUI (or one of them) R Commander has a relatively new plugin called KMGGplot2. Until now Deducer was the only GUI with ggplot features , but the much lighter and more popular R Commander has been a long champion in people wanting to pick up R quickly.
RcmdrPlugin.KMggplot2: Rcmdr Plug-In for Kaplan-Meier Plot and Other Plots by Using the ggplot2 Package
As you can see by the screenshot- it makes ggplot even easier for people (like R newbies and experienced folks alike)
This package is an R Commander plug-in for Kaplan-Meier plot and other plots by using the ggplot2 package.
|Depends:||R (≥ 2.15.0), stats, methods, grid, Rcmdr (≥ 1.8-4), ggplot2 (≥ 0.9.1)|
|Imports:||tcltk2 (≥ 1.2-3), RColorBrewer (≥ 1.0-5), scales (≥ 0.2.1), survival (≥ 2.36-14)|
|Author:||Triad sou. and Kengo NAGASHIMA|
|Maintainer:||Triad sou. <triadsou at gmail.com>|
|CRAN checks:||RcmdrPlugin.KMggplot2 results|
---------------------------------------------------------------- NEWS file for the RcmdrPlugin.KMggplot2 package ---------------------------------------------------------------- ---------------------------------------------------------------- Changes in version 0.1-0 (2012-05-18) o Restructuring implementation approach for efficient maintenance. o Added options() for storing package specific options (e.g., font size, font family, ...). o Added a theme: theme_simple(). o Added a theme element: theme_rect2(). o Added a list box for facet_xx() functions in some menus (Thanks to Professor Murtaza Haider). o Kaplan-Meier plot: added confidence intervals. o Box plot: added violin plots. o Bar chart for discrete variables: deleted dynamite plots. o Bar chart for discrete variables: added stacked bar charts. o Scatter plot matrix: added univariate plots at diagonal positions (ggplot2::plotmatrix). o Deleted the dummy data for histograms, which is large in size. ---------------------------------------------------------------- Changes in version 0.0-4 (2011-07-28) o Fixed "scale_y_continuous(formatter = "percent")" to "scale_y_continuous(labels = percent)" for ggplot2 (>= 0.9.0). o Fixed "legend = FALSE" to "show_guide = FALSE" for ggplot2 (>= 0.9.0). o Fixed the DESCRIPTION file for ggplot2 (>= 0.9.0) dependency. ---------------------------------------------------------------- Changes in version 0.0-3 (2011-07-28; FIRST RELEASE VERSION) o Kaplan-Meier plot: Show no. at risk table on outside. o Histogram: Color coding. o Histogram: Density estimation. o Q-Q plot: Create plots based on a maximum likelihood estimate for the parameters of the selected theoretical distribution. o Q-Q plot: Create plots based on a user-specified theoretical distribution. o Box plot / Errorbar plot: Box plot. o Box plot / Errorbar plot: Mean plus/minus S.D. o Box plot / Errorbar plot: Mean plus/minus S.D. (Bar plot). o Box plot / Errorbar plot: 95 percent Confidence interval (t distribution). o Box plot / Errorbar plot: 95 percent Confidence interval (bootstrap). o Scatter plot: Fitting a linear regression. o Scatter plot: Smoothing with LOESS for small datasets or GAM with a cubic regression basis for large data. o Scatter plot matrix: Fitting a linear regression. o Scatter plot matrix: Smoothing with LOESS for small datasets or GAM with a cubic regression basis for large data. o Line chart: Normal line chart. o Line chart: Line char with a step function. o Line chart: Area plot. o Pie chart: Pie chart. o Bar chart for discrete variables: Bar chart for discrete variables. o Contour plot: Color coding. o Contour plot: Heat map. o Distribution plot: Normal distribution. o Distribution plot: t distribution. o Distribution plot: Chi-square distribution. o Distribution plot: F distribution. o Distribution plot: Exponential distribution. o Distribution plot: Uniform distribution. o Distribution plot: Beta distribution. o Distribution plot: Cauchy distribution. o Distribution plot: Logistic distribution. o Distribution plot: Log-normal distribution. o Distribution plot: Gamma distribution. o Distribution plot: Weibull distribution. o Distribution plot: Binomial distribution. o Distribution plot: Poisson distribution. o Distribution plot: Geometric distribution. o Distribution plot: Hypergeometric distribution. o Distribution plot: Negative binomial distribution.
The lovely colors at http://ColorBrewer.org can be used for much better color palettes in R.
and we use the function
brewer.pal(N,”Name”) as the col parameter for the new color palettes
where we can see name of palettes from the list above
data(VADeaths) par(mfrow=c(2,3)) hist(VADeaths,col=brewer.pal(3,"Set3"),main="Set3 3 colors") hist(VADeaths,col=brewer.pal(3,"Set2"),main="Set2 3 colors") hist(VADeaths,col=brewer.pal(3,"Set1"),main="Set1 3 colors") hist(VADeaths,col=brewer.pal(8,"Set3"),main="Set3 8 colors") hist(VADeaths,col=brewer.pal(8,"Greys"),main="Greys 8 colors") hist(VADeaths,col=brewer.pal(8,"Greens"),main="Greens 8 colors")
• Colors from [http://www.ColorBrewer.org] by Cynthia A. Brewer, Geography, Pennsylvania State University
• Erich Neuwirth (2011). RColorBrewer: ColorBrewer palettes. R package version 1.0-5. [http://CRAN.R-project.org/package=RColorBrewer]
Note-ColorBrewer is Copyright (c) 2002 Cynthia Brewer, Mark Harrower, and The Pennsylvania State University. All rights reserved. The ColorBrewer palettes have been included in the R package with permission of the copyright holder.
There are still some graphs that cannot be yet made in R using a straightforward function or package.
One is sunburst (which is radial kind of treemap-that can be made in R). See diagrams below to see the difference. Note sunburst is visually similar to coxcomb (Nightangle) graphs. Coxcombs can also be manipulated and made- but I am yet to find a straight package to make coxcomb using a single function _histdata package in R comes close in terms on historical datasets.
The Treemap uses a rectangular, space-filling slice-and-dice technique to visualize objects in the different levels of a hierarchy. The area and color of each item corresponds to an attribute of the item as well.
The Sunburst technique is an alternative, space-filling visualization that uses a radial rather than a rectangular layout. An example Sunburst display is shown below. citation- http://www.cc.gatech.edu/gvu/ii/sunburst/
Other is cartogram -whose packages are MIA -RCartogram is very basic package http://www.omegahat.org/Rcartogram/ - It is better to use Toad Scraper software than R for this kind of map.
Cartograms are used to produce spatial plots where the boundaries of regions can be transformed to be proportional to density/counts/populations. This is illustrated in plots such as
Mark Newman’s plot of People living with HIV/AIDS
Citation: Friendly, Michael (2001), Gallery of Data Visualization, Electronic document, http://www.datavis.ca/gallery/,Accessed: 03/23/2012 18:23:33