bdqnghi

import types
import tensorflow as tf 
import numpy as np

# Expressions are represented as lists of lists,
# in lisp style -- the symbol name is the head (first element)
# of the list, and the arguments follow.

# add an expression to an expression list, recursively if necessary.
def add_expr_to_list(exprlist, expr):

bdqnghi

# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
# where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
# Similarly, for h * W_2 + b_2
import tensorflow as tf
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

bdqnghi

""" Deep Auto-Encoder implementation
	
	An auto-encoder works as follows:

	Data of dimension k is reduced to a lower dimension j using a matrix multiplication:
	softmax(W*x + b)  = x'
	
	where W is matrix from R^k --> R^j

	A reconstruction matrix W' maps back from R^j --> R^k

bdqnghi

1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

bdqnghi

The easiest way to start using the LLVM C++ API by example is to have LLVM generate the API usage for a given code sample. In this example it will emit the code required to rebuild the test.c sample by using LLVM:

$ clang -c -emit-llvm test.c -o test.ll
$ llc -march=cpp test.ll -o test.cpp

bdqnghi

#!/bin/bash
# create .bc file
clang -emit-llvm -c testcfg.c
# create .dot file
opt -dot-cfg testcfg.bc
# cteate png,pdf 
dot -Tpng -o main.png cfg.main.dot 

bdqnghi

package com.smu.software;

import java.util.Arrays;

/**
 * Hello world!
 */
public class App {
    public static void main(String[] args) {

bdqnghi

package com.software.mining;

import polyglot.visit.DataFlow;
import soot.*;
import soot.jimple.toolkits.callgraph.CallGraph;
import soot.jimple.toolkits.callgraph.Edge;
import soot.jimple.toolkits.callgraph.Targets;
import soot.options.Options;
import soot.toolkits.graph.ExceptionalUnitGraph;
import soot.toolkits.graph.pdg.HashMutablePDG;

bdqnghi

#!/usr/bin/python

import socket,sys,time,datetime,argparse,os
flag = 0  # we're gonna use this flag later. Just keep it in mind
os.system('clear') # Clear the console window
line = "+" * 80 # Just a fancy line consisting '+'
desc = line+'''\nA Simple port scanner that works!! (c) digitz.org
	Example usage: python port_scanner.py example.com 1 1000
	The above example will scan the host \'example.com\' from port 1 to 1000
	To scan most common ports, use: python port_scanner.py example.com\n'''+line+"\n"