I like to play with VirtualBox. So in this post I will show you how to install Kali Linux on a USB disk with VirtualBox which can be run both on VirtualBox and directly bootable.

kali-linux-sticker

Every change you made with VirtualBox (or direct access) will be stored on USB and can be accessed with Direct Access (or VirtualBox). It is awesome to use VirtualBox when you want to work beside another OS. And also awesome when you want to boot directly from Physical Computer.

Continue reading Installing Kali Linux on USB with VirtualBox

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You may be like to collect and watch movies, TV series, or music archives. Then you have to store them in your storage ([External] Hard drives, NAS, …) and use some software to manage them.

However the scenario sounds good but there are other great ways to turn your home network to a full capable media center. Read more to learn how to configure and change your home network to media center.

osmc-rapberrypi

Continue reading How to turn your TV to Media Center

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Recently I’ve interested in byte code structure of Java and Dalvik. I’ve found some useful tools for playing with them.

Destination Byte Code

Java byte codes are simple to reverse engineering because they compile in run time. i.e. JVM will execute the byte codes in run time, thus Java code is cross platform but executes with more delay than direct compiled machine codes (for example using C++ and gcc).

Continue reading Reversing Java: Part I

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In the previous post, I’ve described the LDA process and how it can be applied on documents.

In this post I will explain how the probabilities can be estimated using collapsed Gibbs sampling.

Lets start with the LDA Probabilistic Graph Model.

Latent Dirichlet Allocation

Where W is the sampled word from document, Z is the topic assigned by Document (d), θ is the Dirichlet distribution of d, α and β are the input of Dirichlets. More info about hyperparameters can be found here.

So the only known variables are α, β, and w. All others (z, θ, and φ) are unknown. So based on the LDA graph we have:

p(w, z, θ, φ | α, β) = p(φ|β) p(θ|α) p(z|θ) p(w|φz)

The right side of the above conditional probability can be reached by the probabilistic graph model where each variable only depends on its parent nodes.

Continue reading Mathematical LDA

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