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T1T4N / gist:a735f4f5239fb9d9192a7839d5445774
Created October 17, 2016 00:04 — forked from Cr4sh/gist:fe910f0d1b0559efd43d
Dynamically finding sys_call_table on Linux x86_64 systems
void **find_sys_call_table(void *kernel_addr, int kernel_size)
{
/*
Check for the system_call_fastpath() signature, hand-written piece of
assembly code from arch/x86/kernel/entry_64.S:
ja badsys
mov rcx, r10
call sys_call_table[rax * 8]
mov [rsp + 20h], rax
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T1T4N / uac_bypass.c
Created October 17, 2016 00:04 — forked from Cr4sh/uac_bypass.c
void TestCopy()
{
BOOL cond = FALSE;
IFileOperation *FileOperation1 = NULL;
IShellItem *isrc = NULL, *idst = NULL;
BIND_OPTS3 bop;
SHELLEXECUTEINFOW shexec;
HRESULT r;
do {
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T1T4N / WoW64_call.cpp
Created October 17, 2016 00:03 — forked from Cr4sh/WoW64_call.cpp
WoW64 Heaven's Gate
#include "stdafx.h"
#define DB(_val_) __asm __emit (_val_)
#define INVALID_SYSCALL (DWORD)(-1)
// code selectors
#define CS_32 0x23
#define CS_64 0x33
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T1T4N / gist:6bfc5055f9c7838d50845351b6980875
Created April 28, 2016 00:59 — forked from liamcurry/gist:2597326
Vanilla JS vs jQuery

Moving from jQuery

Events

// jQuery
$(document).ready(function() {
  // code
})
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T1T4N / installation.md
Created April 18, 2016 19:12 — forked from guillaumevincent/installation.md
A simple guide to install PyQt5 on Mac OS X 10.9 (Maverick) and use python 3.4 on a virtualenv.

Guide to install PyQt5 on Mac OS X with python 3.4 virtualenv

Description

A simple guide to install PyQt5 on Mac OS X 10.9 (Maverick) and use python 3.4 on a virtualenv.

Requirements

  • xcode 5.1.1
  • python 3.4.0
  • Qt libraries 5.2.1
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T1T4N / README.md
Created March 24, 2016 19:54 — forked from dannguyen/README.md
Using Google Cloud Vision API to OCR scanned documents to extract structured data

Using Google Cloud Vision API's OCR to extract text from photos and scanned documents

Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.

The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.

On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:

####### 1. A low-resolution photo of road signs