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#!/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"

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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;

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package com.smu.software;

import java.util.Arrays;

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

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#!/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

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

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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

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""" 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

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# 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

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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

NLP and source code papers, very scattered and partial listing
(collected by Nathan Schneider and Brendan O'Connor)


ICML 2014
Maddison and Tarlow
Structured Generative Models of Natural Source Code
http://jmlr.org/proceedings/papers/v32/maddison14.pdf

ACL 2013