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Full Discussion: C Brain Teaser
Top Forums Programming C Brain Teaser Post 302221139 by redoubtable on Sunday 3rd of August 2008 02:17:22 PM
Old 08-03-2008
There's a more complex way of doing it.

We could open (argv[0], O_RDONLY), then unlink(argv[0]), and then re-open open(argv[0], O_RDWR).

Like that, you could read the content of your program before it was unlinked ('cause the kernel still holds the reference upon unlink() if the file is open by any process) and write a new content after it was unlinked.

Now, from here you could go to very funny stuff like changing the ELF format on-the-fly (something like changing the .comment section) and then have something on your file that says "if the .comment section has a certain sha1 hash that matches whatever, the program should run, otherwise it won't run". Be aware that someone could strip the comment section very easily lol.

Another way would also be very funny like creating an assembly program that just makes your program exit, then, extracting the hex opcodes (machine-language) of that program using something like objdump and injecting those opcodes on your other program's main function. Of course you would have to know the right offset to mmap() to, etc. With this, you could actually alter the way that your program feeds back after the first execution, because you could also inject write()'s, etc. (a lot more work would be needed)
 

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FANN_INIT_WEIGHTS(3)							 1						      FANN_INIT_WEIGHTS(3)

fann_init_weights - Initialize the weights using Widrow + Nguyen's algorithm

SYNOPSIS
bool fann_init_weights (resource $ann, resource $train_data) DESCRIPTION
Initialize the weights using Widrow + Nguyen's algorithm. This function behaves similarly to fann_randomize_weights(3). It will use the algorithm developed by Derrick Nguyen and Bernard Widrow to set the weights in such a way as to speed up training. This technique is not always successful, and in some cases can be less efficient than a purely random initialization. The algorithm requires access to the range of the input data (for example largest and smallest input), and therefore accepts a second argument, data, which is the training data that will be used to train the network. PARAMETERS
o $ann -Neural network resource. o $train_data -Neural network training data resource. RETURN VALUES
Returns TRUE on success, or FALSE otherwise. SEE ALSO
fann_randomize_weights(3), fann_read_train_from_file(3). PHP Documentation Group FANN_INIT_WEIGHTS(3)
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