DOWNLOAD AT http://www.4shared.com/file/kLYjQ2GI/email_bobmer.html
Jumaat, 13 Januari 2012
How to Hide Secret Messages in Audio Files
Mp3stegz is an application that apply steganographic (steganography) algorithm in mp3 files. Mp3stegz will maintain original mp3 files size and sound quality. The hidden message is compressed and encrypted.
DOWNLOAD AT http://sourceforge.net/projects/mp3stegz/
Chapter 1 – theory
What can I use artificial neural networks (ANN) for?
ANN are used for pattern recognition. E.g. letter identification (handwritten or printed), voice recognition (to identify the talker or the content), prognoses (stock, weather), analysis of ECG, EEG, X-rays ...
For whom this tutorial is written?
This is for beginners. The last exercise in the second chapter needs Java knowledge, but isn't required to be done.
How complicated will it be?
To completely understand the functionality of ANN, you actually need advanced mathematical knowledge (if you can operate with gradients, you may have the right state of knowledge). But I don't want this take as a given. I want to provide a minimized mathematical introduction instead, giving you an incitement for self-study.
The first chapter gives you an understanding of the essential functionality.
Exercises with a freeware program and training an artificial neural network for letter recognition by using a java framework follow in chapter two.
ANN are amazing, because you can experiment a lot with them.
What are ANN and what are they used for?
Artificial neural networks are based on the natural neural networks like our brain. The brain is a net of nerve cells (= neurons). Neurons in an artificial network are little calculating devices which can be made of hard- or software.
Now you can chip in that a computer works much faster than the brain. So why the heck should we use the brain as a model? Comparing the frequency of a modern computer with the frequency of our brain (100 – 1000 Hz is the neural frequency, which means a neuron can fire again after 10^-3 seconds) this is an absolutely correct statement. No one of us is able to keep up in calculation with a computer. But there are a lot of tasks a brain can accomplish much better. How would an algorithm look like that is able to identify the face of your neighbour? Can you tell from your experience how you recognize someone? And how does it happen with your aunt who recently came from the hairdresser and also has new glasses? Why do you recognize her anyway and how can this be so fast? Why does it work with different light incidences, different perspectives and different facial expressions?
We are not aware of the process. We don't know how we do it, we are just able to. That's why it is so hard to invent an algorithm for it. In addition computers are normally very accurate but not fault-tolerant, whereas it is vice versa with the human being. The secret lies in the parallelism the neurons are working with. Each one of our 10^11 nerve cells has 100 000 to 200 000 connections to other neurons. A huge amount of data can be processed simultaneously. Our computers work mostly sequential. Although there is a delevelopement to more parallelism (multi core processors, ...) it can not keep up to our brains.
Whereas chemical substances play an important role in our brain, those are ignored in artificial neural networks. For simplicity they only use the electrical model.
Just like brains ANN are able to learn.
How does a neuron look like?
The most simple model for artificial neurons is the McCulloch-Pitts neuron, named after their inventors Warren McCulloch and Walter Pitts (1943). It is also called Threshold Logic Unit (TLU).
The TLU has the following buildup: There are several inputs and one output. The input values may be 1 or 0. Our neuron only sums up all input values. For instance: if three inputs have the value 1 and two inputs have the value 0, the neuron will calculate the total of 3. If this sum overruns a special threshold, the neuron will fire which means the output will show 1. Otherwise the output is 0.
e.g.: A neuron has five inputs and the threshold is 3. If only two inputs have the value 1, the neuron won't fire. It needs at least three running inputs to fire.
Now this neuron shall be able to learn. Therefor weights are put to the inputs. The weight predicates how powerful this input is. Weights may be real numbers. They are multiplied with their input. Afterwards the sum will be computed. The higher the weight, the more the input effects the sum.
This is related to the functionality of natural nerve cells. The nerve fibers are of variable thickness. The thicker they are the more powerful are their signals to other nerve cells and the more they have an impact on them. While learning the thickness is adjusted. Nerve fibers which are seldom used may degenerate whereas the often used ones became even stronger. This means for our artificial cell, that a higher weight indicates a stronger connection and impact from another cell.
You will be pleased if you have knowledge in digital technique: Our neuron model allows us to copy gates.
NOT-gate:
For this case we just need an input whose weight is -1.
The treshhold may be 0. The effect: Our neuron will fire if and only if the input is 0, because this reaches the threshold. If the input value is a 1, the sum will be -1 and the neuron won't fire, because the sum is below our limit.
AND-gate:
The neuron has two inputs. It shall only fire if both inputs are running. One possibility: We define our treshold to 2 and all weights to 1. As soon as both inputs fire, the sum equals 1*1+1*1 = 2
OR-gate:
This is for your practice. The neuron has two inputs. Define weights and threshold (there is more than one solution).
What did this show us?
With ANN we are able to copy every circuit. But this is not its purpose. It only demonstrates their abilities.
In fact the research came to a stagnancy in 1969, because it was verified that one neuron can not realise an XOR-function (but it is possible with three neurons). It was said that ANN research is a dead end since neurons can only learn simple logical functions.
Today we know that they are able to do more if we use them the right way.
How does a neural network look like?
Therefor I want to introduce a frequently used net model: the feedforward neural network. Such a net has clearly defined layers the neurons are arranged.
There is an input and an output layer. There also may be hidden layers between. They are called hidden, because the user is not able to see their values. Each neuron in one layer has connections to each neuron in the next layer. This may look like this:
Task: Create a net that realizes XOR. As a reminder:
0 xor 0 = 0
0 xor 1 = 1
1 xor 0 = 1
1 xor 1 = 0
For assistance this may be a beginning:
How does an ANN learn? (two examples of learning techniques)
1. Supervised Learning
You can teach an ANN by showing it examples of patterns. As already mentioned learning means the modulation of the weights. How the weights are changed is determined by formulas.
If you show a net a pattern, e.g. a picture of one letter (look little net, this is an „A“), the net will compute the discrepancy from the designated output to the real output.
Afterwards the net modulates its weights, so it approaches the designated output. This has to be repeated several times (like we had to repeat learning matter for school).
In this way you can train your net to recognize an „A“ in three different fonts. After this you will discover the fascinating: The net will be able to recognize an „A“ in fonts it never has learned. It knows from your examples, how an „A“ approximately looks like and that is pretty good for character recognition. Of course a net may be mistaken, it isn't accurate. But its fault-tolerance is needed for some tasks (humans are also fault-tolerant and make a lot of mistakes).
2. Unsupervised learning
The net is given some patterns which it shall categorize by itself. You only use this way of learning if you don't know how the result has to be. E.g. you can encode characteristics of animals (Has it a fur? Yes would be 1, no would be 0. There has to be an input for each question that takes the encoded answer) and give them to the net.
After working through all the given animals, the net will arrange those animals closely that have similar characteristics. Closely means that the outputs will look similar, e.g. for the encoded inputs of an horse and a zebra. Instead of this the outputs for a lion and a midge look probably different.
Chapter 2 - practical experience
What do I need?
We work with neuroph now. You can download this here:
http://neuroph.sourceforge.net/
The zip-file contains a Java program called easyNeurons.jar which is for creating, training and testing ANN.
In addition it contains a Java framework that enables you to code your own ANN. I will explain both ways.
How can I use that?
You can start easyNeurons.jar with a simple doubleclick.
To use the framework you need to embedd neuroph.jar into your Java project.
E.g. Eclipse:
Create a new project. Rightclick on your project and choose „Properties“. In Java-Build-Path - Libraries click the button „Add External JARs ...“. Choose the file neuroph.jar. That's all.
There are a lot of english tutorials for Neuroph which I recommend to use for further studies. You find them on the neuroph website in Documentation – Tutorials.
1. XOR with easyNeurons
Your task: Create and train a simple ANN, so it can realize the XOR-function.
The menu item "networks" allows you to choose a networktype. At this point we pick the „Multi Layer Perceptron“. The term perceptron has different meanings according to the references and should be used with caution. It can stand for a single neuron whose output is 0 or 1, a layer of parallel neurons or a whole feedforward network with several layers.
This program uses perceptron in the manner of an feedforward network that has two layers (in- and output-layer). Since we want to add hidden layers, we choose the option „Multi Layer Perceptron“.
For the input-layer we declare two neurons, for the output-layer we declare one. The hidden-layer gets four neurons. We apply this in the dialog. The other options are leaved at the presetting (they for instance indicate which mathematical functions are used for learning).
Now we should create a training set – that are pattern examples after which the net will be trained. Click on „Training“ - „New Training Set“. The type is „supervised“. We name the set „XOR“ and give it two inputs and one output. A click on „next“ guides us to a dialog for examples. We just have to put in the XOR-table. Via „Add Row“ we add three more rows:
Input1, Input2, Output1
0, 0, 0
0, 1, 1
1, 0, 1
1, 1, 0
If you are done, click „OK“ and set our XOR trainig set at the top. Clicking the button „Train“ will lead you to the settings for our training program.
The option „Stopping Criteria“ declares when the trainig shall be aborted. This can be done after the error/discrepancy has been reduced to a special limit (max error). It can also happen after a given number of training episodes (how often the examples are shown to the net?). The quality after the training may be varying.
We leave „Max error“ at the presetting. For „Limit max iterations“ we try „2000“.
The second option determines the learning rate. The lower the rate, the less the weights will be changed per iteration and the more slowly our net will learn. Be careful: A higher learning rate is not necessarily better. If it is too high, the net will modify the weights so strong that good solutions are jumped over. Here you need to experiment with it. We take the presettings and click on „Train“.
Now an error graph is shown. It should decrease, otherwise it is not working well. After the training you can test the net with „set input“. Single inputs are seperated by the space character. The outputs won't be perfect, but the rounded ones should be the right solution. Try other settings, experiment with it to get the best results possible.
2. Letter recognition with the neuroph framework
You will find several examples from the framework's authors in the zip-file. Search for XorMulitLayerPerceptronSample.java. Try it out. You find this file in the neuroph-folder and this path: sources/neuroph/src/org/neuroph/sample/XorMulitLayerPerceptronSample.java
Your task: Write a program that trains an ANN to recognize letters.
Therefor you need pictures of letters. You can make them with paint. Don't create them too big. 30X30 pixels should be enough. Make sure that all letters are placed in the same area of the picture and have the same size (otherwise your ANN will have problems).
For the beginning you can train a net to determine, wether it sees an „A“ or not. Teach it several fonts and test it with an unknown font.
Afterwards you can expand it to more letters. For instance you may create three letters and a net with three output-neurons. Each neuron would stand for one letter. If neuron 1 fires, the answer may be „it is an 'A'“, if neuron 2 fires, the answer would be „B“ and so on. You determine what the outputs stand for by creating the training set.
Some help to read the images with java:
The following program reads an bmp-file into a vector. Black pixels will give the vector a 1 and white pixels a 0.
File file = new File("img/smile.bmp");
BufferedImage img = null;
try {
img = ImageIO.read(file);
} catch (IOException e) {
e.printStackTrace();
}
int[] vector = new int[img.getWidth() * img.getHeight()];
for (int y = 0; y < img.getHeight(); y++) {
for (int x = 0; x < img.getWidth(); x++) {
int rgb = img.getRGB(x, y);
if (rgb == 0xffffffff) { // white pixel
vector[x * y] = 0;
} else {
vector[x * y] = 1;
}
System.out.print(vector[x * y]);
}
System.out.println();
}
Displaying the vector as a matrix (as it is given in the program above for testing), you can see the output as a picture. A smiley-picture would look like this:
You may also teach a net to recognize the mood of a smiley, if you want to.
These basics, the sample-neuroph-program and the image-reading, should suffice to accomplish the task. If you have questions anyway you may ask here.
Remark: Neuroph already provides methods to use images as an ANN-input. But I recommend doing this like given above, because this will be better for your understanding how ANN work.
I wish you fun and success with it.
Java frameworks:
http://neuroph.sourceforge.net/
http://www.heatonresearch.com/encog (also for .Net & Silverlight)
Sources
I study computer science and had this as an optional subject.
What can I use artificial neural networks (ANN) for?
ANN are used for pattern recognition. E.g. letter identification (handwritten or printed), voice recognition (to identify the talker or the content), prognoses (stock, weather), analysis of ECG, EEG, X-rays ...
For whom this tutorial is written?
This is for beginners. The last exercise in the second chapter needs Java knowledge, but isn't required to be done.
How complicated will it be?
To completely understand the functionality of ANN, you actually need advanced mathematical knowledge (if you can operate with gradients, you may have the right state of knowledge). But I don't want this take as a given. I want to provide a minimized mathematical introduction instead, giving you an incitement for self-study.
The first chapter gives you an understanding of the essential functionality.
Exercises with a freeware program and training an artificial neural network for letter recognition by using a java framework follow in chapter two.
ANN are amazing, because you can experiment a lot with them.
What are ANN and what are they used for?
Artificial neural networks are based on the natural neural networks like our brain. The brain is a net of nerve cells (= neurons). Neurons in an artificial network are little calculating devices which can be made of hard- or software.
Now you can chip in that a computer works much faster than the brain. So why the heck should we use the brain as a model? Comparing the frequency of a modern computer with the frequency of our brain (100 – 1000 Hz is the neural frequency, which means a neuron can fire again after 10^-3 seconds) this is an absolutely correct statement. No one of us is able to keep up in calculation with a computer. But there are a lot of tasks a brain can accomplish much better. How would an algorithm look like that is able to identify the face of your neighbour? Can you tell from your experience how you recognize someone? And how does it happen with your aunt who recently came from the hairdresser and also has new glasses? Why do you recognize her anyway and how can this be so fast? Why does it work with different light incidences, different perspectives and different facial expressions?
We are not aware of the process. We don't know how we do it, we are just able to. That's why it is so hard to invent an algorithm for it. In addition computers are normally very accurate but not fault-tolerant, whereas it is vice versa with the human being. The secret lies in the parallelism the neurons are working with. Each one of our 10^11 nerve cells has 100 000 to 200 000 connections to other neurons. A huge amount of data can be processed simultaneously. Our computers work mostly sequential. Although there is a delevelopement to more parallelism (multi core processors, ...) it can not keep up to our brains.
Whereas chemical substances play an important role in our brain, those are ignored in artificial neural networks. For simplicity they only use the electrical model.
Just like brains ANN are able to learn.
How does a neuron look like?
The most simple model for artificial neurons is the McCulloch-Pitts neuron, named after their inventors Warren McCulloch and Walter Pitts (1943). It is also called Threshold Logic Unit (TLU).
The TLU has the following buildup: There are several inputs and one output. The input values may be 1 or 0. Our neuron only sums up all input values. For instance: if three inputs have the value 1 and two inputs have the value 0, the neuron will calculate the total of 3. If this sum overruns a special threshold, the neuron will fire which means the output will show 1. Otherwise the output is 0.
e.g.: A neuron has five inputs and the threshold is 3. If only two inputs have the value 1, the neuron won't fire. It needs at least three running inputs to fire.
Now this neuron shall be able to learn. Therefor weights are put to the inputs. The weight predicates how powerful this input is. Weights may be real numbers. They are multiplied with their input. Afterwards the sum will be computed. The higher the weight, the more the input effects the sum.
This is related to the functionality of natural nerve cells. The nerve fibers are of variable thickness. The thicker they are the more powerful are their signals to other nerve cells and the more they have an impact on them. While learning the thickness is adjusted. Nerve fibers which are seldom used may degenerate whereas the often used ones became even stronger. This means for our artificial cell, that a higher weight indicates a stronger connection and impact from another cell.
You will be pleased if you have knowledge in digital technique: Our neuron model allows us to copy gates.
NOT-gate:
For this case we just need an input whose weight is -1.
The treshhold may be 0. The effect: Our neuron will fire if and only if the input is 0, because this reaches the threshold. If the input value is a 1, the sum will be -1 and the neuron won't fire, because the sum is below our limit.
AND-gate:
The neuron has two inputs. It shall only fire if both inputs are running. One possibility: We define our treshold to 2 and all weights to 1. As soon as both inputs fire, the sum equals 1*1+1*1 = 2
OR-gate:
This is for your practice. The neuron has two inputs. Define weights and threshold (there is more than one solution).
What did this show us?
With ANN we are able to copy every circuit. But this is not its purpose. It only demonstrates their abilities.
In fact the research came to a stagnancy in 1969, because it was verified that one neuron can not realise an XOR-function (but it is possible with three neurons). It was said that ANN research is a dead end since neurons can only learn simple logical functions.
Today we know that they are able to do more if we use them the right way.
How does a neural network look like?
Therefor I want to introduce a frequently used net model: the feedforward neural network. Such a net has clearly defined layers the neurons are arranged.
There is an input and an output layer. There also may be hidden layers between. They are called hidden, because the user is not able to see their values. Each neuron in one layer has connections to each neuron in the next layer. This may look like this:
Task: Create a net that realizes XOR. As a reminder:
0 xor 0 = 0
0 xor 1 = 1
1 xor 0 = 1
1 xor 1 = 0
For assistance this may be a beginning:
How does an ANN learn? (two examples of learning techniques)
1. Supervised Learning
You can teach an ANN by showing it examples of patterns. As already mentioned learning means the modulation of the weights. How the weights are changed is determined by formulas.
If you show a net a pattern, e.g. a picture of one letter (look little net, this is an „A“), the net will compute the discrepancy from the designated output to the real output.
Afterwards the net modulates its weights, so it approaches the designated output. This has to be repeated several times (like we had to repeat learning matter for school).
In this way you can train your net to recognize an „A“ in three different fonts. After this you will discover the fascinating: The net will be able to recognize an „A“ in fonts it never has learned. It knows from your examples, how an „A“ approximately looks like and that is pretty good for character recognition. Of course a net may be mistaken, it isn't accurate. But its fault-tolerance is needed for some tasks (humans are also fault-tolerant and make a lot of mistakes).
2. Unsupervised learning
The net is given some patterns which it shall categorize by itself. You only use this way of learning if you don't know how the result has to be. E.g. you can encode characteristics of animals (Has it a fur? Yes would be 1, no would be 0. There has to be an input for each question that takes the encoded answer) and give them to the net.
After working through all the given animals, the net will arrange those animals closely that have similar characteristics. Closely means that the outputs will look similar, e.g. for the encoded inputs of an horse and a zebra. Instead of this the outputs for a lion and a midge look probably different.
Chapter 2 - practical experience
What do I need?
We work with neuroph now. You can download this here:
http://neuroph.sourceforge.net/
The zip-file contains a Java program called easyNeurons.jar which is for creating, training and testing ANN.
In addition it contains a Java framework that enables you to code your own ANN. I will explain both ways.
How can I use that?
You can start easyNeurons.jar with a simple doubleclick.
To use the framework you need to embedd neuroph.jar into your Java project.
E.g. Eclipse:
Create a new project. Rightclick on your project and choose „Properties“. In Java-Build-Path - Libraries click the button „Add External JARs ...“. Choose the file neuroph.jar. That's all.
There are a lot of english tutorials for Neuroph which I recommend to use for further studies. You find them on the neuroph website in Documentation – Tutorials.
1. XOR with easyNeurons
Your task: Create and train a simple ANN, so it can realize the XOR-function.
The menu item "networks" allows you to choose a networktype. At this point we pick the „Multi Layer Perceptron“. The term perceptron has different meanings according to the references and should be used with caution. It can stand for a single neuron whose output is 0 or 1, a layer of parallel neurons or a whole feedforward network with several layers.
This program uses perceptron in the manner of an feedforward network that has two layers (in- and output-layer). Since we want to add hidden layers, we choose the option „Multi Layer Perceptron“.
For the input-layer we declare two neurons, for the output-layer we declare one. The hidden-layer gets four neurons. We apply this in the dialog. The other options are leaved at the presetting (they for instance indicate which mathematical functions are used for learning).
Now we should create a training set – that are pattern examples after which the net will be trained. Click on „Training“ - „New Training Set“. The type is „supervised“. We name the set „XOR“ and give it two inputs and one output. A click on „next“ guides us to a dialog for examples. We just have to put in the XOR-table. Via „Add Row“ we add three more rows:
Input1, Input2, Output1
0, 0, 0
0, 1, 1
1, 0, 1
1, 1, 0
If you are done, click „OK“ and set our XOR trainig set at the top. Clicking the button „Train“ will lead you to the settings for our training program.
The option „Stopping Criteria“ declares when the trainig shall be aborted. This can be done after the error/discrepancy has been reduced to a special limit (max error). It can also happen after a given number of training episodes (how often the examples are shown to the net?). The quality after the training may be varying.
We leave „Max error“ at the presetting. For „Limit max iterations“ we try „2000“.
The second option determines the learning rate. The lower the rate, the less the weights will be changed per iteration and the more slowly our net will learn. Be careful: A higher learning rate is not necessarily better. If it is too high, the net will modify the weights so strong that good solutions are jumped over. Here you need to experiment with it. We take the presettings and click on „Train“.
Now an error graph is shown. It should decrease, otherwise it is not working well. After the training you can test the net with „set input“. Single inputs are seperated by the space character. The outputs won't be perfect, but the rounded ones should be the right solution. Try other settings, experiment with it to get the best results possible.
2. Letter recognition with the neuroph framework
You will find several examples from the framework's authors in the zip-file. Search for XorMulitLayerPerceptronSample.java. Try it out. You find this file in the neuroph-folder and this path: sources/neuroph/src/org/neuroph/sample/XorMulitLayerPerceptronSample.java
Your task: Write a program that trains an ANN to recognize letters.
Therefor you need pictures of letters. You can make them with paint. Don't create them too big. 30X30 pixels should be enough. Make sure that all letters are placed in the same area of the picture and have the same size (otherwise your ANN will have problems).
For the beginning you can train a net to determine, wether it sees an „A“ or not. Teach it several fonts and test it with an unknown font.
Afterwards you can expand it to more letters. For instance you may create three letters and a net with three output-neurons. Each neuron would stand for one letter. If neuron 1 fires, the answer may be „it is an 'A'“, if neuron 2 fires, the answer would be „B“ and so on. You determine what the outputs stand for by creating the training set.
Some help to read the images with java:
The following program reads an bmp-file into a vector. Black pixels will give the vector a 1 and white pixels a 0.
File file = new File("img/smile.bmp");
BufferedImage img = null;
try {
img = ImageIO.read(file);
} catch (IOException e) {
e.printStackTrace();
}
int[] vector = new int[img.getWidth() * img.getHeight()];
for (int y = 0; y < img.getHeight(); y++) {
for (int x = 0; x < img.getWidth(); x++) {
int rgb = img.getRGB(x, y);
if (rgb == 0xffffffff) { // white pixel
vector[x * y] = 0;
} else {
vector[x * y] = 1;
}
System.out.print(vector[x * y]);
}
System.out.println();
}
Displaying the vector as a matrix (as it is given in the program above for testing), you can see the output as a picture. A smiley-picture would look like this:
You may also teach a net to recognize the mood of a smiley, if you want to.
These basics, the sample-neuroph-program and the image-reading, should suffice to accomplish the task. If you have questions anyway you may ask here.
Remark: Neuroph already provides methods to use images as an ANN-input. But I recommend doing this like given above, because this will be better for your understanding how ANN work.
I wish you fun and success with it.
Java frameworks:
http://neuroph.sourceforge.net/
http://www.heatonresearch.com/encog (also for .Net & Silverlight)
Sources
I study computer science and had this as an optional subject.
Jumaat, 30 Disember 2011
How People Do Hack Facebook Poker
The Facebook poker game has become very popular and is played by a number of people across the world. This popularity has also invited a lot of hackers who are there to hack your account and make a fool of you and steal all your Facebook poker chips. One should be aware of such people. These days you come across many ways, which will promise you to earn some poker chips. But are these ways legal or to be trusted? Do they give you free Facebook poker chips? The answer is no. There are many ways to fool people by these attracting offers. These are some ways, which one should be aware of so that risk of losing information or chips can be avoided.
Many threads are there on Facebook, which say that they can lead you to earn chips. One should stay away from these threads, as they are nothing but ways to fool people and hack their accounts by getting their passwords and steal their chips, they will lead you to phishing sites that will promise bonus in chips, double your stack, all of them will end up doing a Facebook poker hack against you.
You may also receive many friend requests from beautiful girls but you should avoid accepting unknown friend requests as accepting these requests can also lead you to lose your account or chips exposing personal data which they can use for reverse engineering to obtain your password. There will be many weird links coming up enticing you to click on that. Just resist this as these are fake ones and on doing so your account will be hacked. Here are a lot of people ready to do Facebook poker hack to your account.
Many duplicate websites are there which look same as Facebook in appearance but they aren’t Facebook. These websites will take your passwords and use it in wrong ways. They can treat your account as they want after getting your Facebook password. Many people on Facebook will offer you their chips at lower rates than the application but they can also cheat you and you may end up losing your chips for Facebook.
You may find posts on Facebook, which provide link so that poker chips can be earned freely. This is one of the easiest ways to fool people and there are no such ways to earn poker chips. Some other methods may ask you to give your login id and password and any fool can also understand that these is some scam and not a real thing. One should be very careful while playing online poker and should avoid your account from felling into a Facebook poker hack.
While playing Facebook poker is generally a harmless act, its important to watch out for the ways that people have found that allows them to hack Facebook poker. Certainly players familiar to online poker games will have the patience and know-how to create a Facebook poker bot to automatically bet and win chips for them with an intelligent logic. The bot works by responding to a set number of variables involving the cards they have, the bets that other players make, and how much of their chip stack is left. It is thus extremely hard for a person of even great programming ability to find a bot to Facebook poker hack that works reliably well enough to leave up and running at the end of the day (to gain automatic chips while they sleep).
Looking To Jailbreak Your iPhone?
How to jailbreak iPhone 4? Let me tell you how and why you want to know how to jailbreak iPhone 4.The iPhone is one of the best phones in the world. Everybody loves the iPhone for its sleek look, its technology, and the different features that it is. Sure, the iPhone is great by itself, but you can get so much more out of your iPhone by jailbreaking your iPhone. You may be wondering how to jailbreak iPhone 4 and what they benefits are to learning how to jailbreak iPhone 4. Apple has some of the best technology and the iPhone 4 is the best yet. A lot of people love Apple and the iPhone because of iTunes and for the apps. There are many reasons why the iPhone 4 is a very desirable phone. When I think of jailbreaking and iPhone 4, these things pop into my mind: how to jailbreak iPhone 4, jailbreak apps, apple, 3gs, firmware, jailbreakme, iTunes, unlock, mac, device, and iPhone 3gs.
In our family, Apple is the only way to go. My family has had all of the different iPhone, the iPod, and the iPad, over the years. Once we found out that we could learn how to jailbreak iPhone 4, we were sold. Lets talk about some of the different reasons you want to learn how to jailbreak iPhone 4.The first reason why I would want to learn how to jailbreak iPhone 4 is so that I could use my iPhone 4 on a different network besides AT&T. Getting out of your service contract is pricey if you are with a wireless carrier that does not have the iPhone 4. If you learn how to jailbreak iPhone 4, you can use your iPhone on any network, which will save you a lot of money. The ability to choose which service provider with, makes jailbreaking an iPhone 4 very coveted.
Not only will you be saving a lot of money when you learn how to jailbreak iPhone 4, but you can download apps that are not available on the apps store. With your jailbreak iPhone, you can also turn your iPhone into a wireless hotspot, allowing you to use the internet from a different computer, mac, or device.
Now, you are probably wondering how to jailbreak iPhone 4? There are lots of ways to learn how to jailbreak iPhone 4. The internet offers lots of materials and information on how to jailbreak iPhone 4. There is a lot of software that is available for your device that will unlock your iPhone. Jailbreaking software is very popular, and you can find lots of it. It will give you step by step information on how to jailbreak iPhone 4. Jailbreaking is very popular, and there are a lot of great version of jailbreaking firmware and software that offer different benefits on how to jailbreak iPhone 4.
Once you learn how to jailbreak iPhone 4, you will be amazed at all of the things that your iPhone can do, that it could not do before. Jailbreaking your iPhone leads to a lot of different possibilities. You can customize your iPhone 4 to your personal liking and use. Once you have an unlock iPhone, you will never want to go back. With a jailbroken iPhone, you will have endless possibilities of what your iPhone can do and where it will take you next. You will want to tell everyone how to jailbreak iPhone 4 so they can have the same experience as you.
Hack Password On Any Account
When you first meet someone, the sparks fly and everything is great. Whether you marry them or not, over time, you might find that you cant help but wonder exactly what it is your partner is doing online. Even worse, you might find that your partner is beginning to act differently. Maybe they try very hard to make sure you never see what is on their screen or perhaps they suddenly have changed their passwords when previously you were more than welcome to use their email, instant messenger or computer. If they do that, they probably wont give you an explanation. Sure, maybe they just want more privacy, or maybe their friends told them that they were being silly letting you check their email or something whenever you wanted to. However, if there are other secretive behaviors, you may be thinking something is wrong. But without the passwords, how do you check to make sure there isn’t something going on? Your children are always a concern online. When they are younger, the concern mainly is whom they are talking to and who they might give personal information to. Younger children usually don’t mind if you’re looking over their shoulder, but they do sometimes forget what they shouldn’t tell someone. Older children can be the targets of predators, and they usually do mind if you are looking over their shoulder. If you have teenagers, you know that you will probably never find out who exactly they are talking to or about what. You could try to check up on them when they leave, especially if they seem to be behaving differently or spending an incredible amount of time online, but again, if you don’t have the passwords how can you check on them?
The answer is a simple little program (Ardamax 2.8) that will let you hack their passwords. You can download a program that will allow you to hack. Passwords are easy enough to figure out, but if you have a limited amount of time or have no idea what the password could be you need this program. Its extremely easy to use; all you have to do is follow the step-by-step interface. You can hack the passwords to many different emails accounts, social networking accounts, and even instant messengers. This will allow you to see who your mate or your children are talking to online, where they might be going, and who they know. In the case of your children, it could help you to protect them while in the case of your partner, it could help you know if you are being cheated on or not.
This is not a program that you should use just to spy on someone. Its only to be used if you really have a worry that something is going on that shouldn’t be. However, if you have lost your own passwords to your email or profile, you could use this program to recover your passwords and be able to keep doing what you always do. As long as you aren’t trying to spy or steal, this program is perfectly legal.
How To Build A Hackintosh from Start To Finish
If you wish to install Snow Leopard on a Windows pc with VMWare Workstation 7 Here’s a quick guide.
Pre-requisites:
- VMWare Workstation 7.
- Custom Package (VMWare image .vmdk, darwin_snow.iso)
- (Vmware sound card drivers)
- Snow Leopard Retail DVD.
Step-by-step guide:
Step 1. Open the .vmdk file and Edit virtual machine settings. Adjust RAM, Graphics, CPU settings to your preferred values. I recommend 1.5 Gb and above of RAM if you have physical RAM > 2.5GB. For the Graphics keep it somewhere above 50 mb to get QE/CI on Snow Leopard. And for the processor, use as many cores as possible.
Step 2. Set your CD/DVD location to ISO image Darwin_Snow.iso
Step 3. Now Run the virtual machine and hit F8 quickly. You`ll get boot options as shown below:
Boot Menu c: Boot DVD h: Boot First Hard Drive e: Eject DVD Your choice:
Step 4. Before continuing, you need to swap your CD image with Snow Leopard ISO/DVD. To do this, right click on the CD icon in toolbar in the bottom right and clicksettings. Now located your DVD drive or set the ISO image.
Step 5. Return to the boot menu that appeared in Step 3 and hit c for “boot DVD”. Press F8 again for advanced options on Snow Leopard installer. On the boot loader enter “-v” and Enter to start installation in verbose mode.
Step 6. It would be couple of minutes before you see the main installer User Interface. Install Snow Leopard the usual way by creating Mac OS Journaled partition and de-selecting few un-needed drivers, other stuff under the customization tab. You should finish up booting into Snow Leopard without any issues.
Post Install:
Fixing Sound:
Take a VMWare snapshot, then Mount the darwin_snow.iso as the virtual drive. You now attempt to make your graphics and sound to work properly. “Install VMware Tools” and Reboot. You should boot up all fine with better graphics and resolution.
Now you will attempt installing your sound card drivers. Take a snapshot of the VM. Run the pkg EnsoniqAudioPCI.mpkg, choose both options, reboot.
Fixing Video Resolution:
- Edit the boot.plist file to anywhere and edit it. /Library/Preferences/SystemConfiguration/com.apple.Boot.plist
- Edit boot.plist and Reboot
<key>Graphics Mode</key> <string>1280×1024x32</string>
You can specify any resolution above.
Note: if you have problems editing the boot.plist, edit via command line with root permissions.
After the Reboot, you should be able to see the new resoltion in Place. If you wish to enable full Graphics acceleration (QE/CI), Install EFI and then change the following parameter in boot.plist:
<key>GraphicsEnabler</key> <string>y</string>
IMPORTANT: You will always need to use the darwin_snow.iso to boot your VM. So keep it configured under CD/DVD
Once everything is done, you can use the VMWare image to run under VMWare player, free of cost 
.
.Enjoy Your Hackintosh
Next lesson will be installing apple natively on a windows pc.
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