romanking98

Table of Contents

1. Disclamair
2. House Of Roman
   ------> 2.1 Assumptions
   ------> 2.2 Protections
   ------> 2.3 Quick Walkthrough
   ------> 2.4 Setting the FD to malloc_hook
   ------> 2.5 Fixing the 0x71 freelist
   ------> 2.6 Unsorted Bin attack on malloc_hook

maxim5

#!/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import print_function

__author__ = 'maxim'

import numpy as np
import gensim
import string

mizchi

/* @flow */
import React from 'react'
import ReactDOM from 'react-dom'

type State = {
  editing: boolean,
  editingValue: ?string
}

type Props = {

laoar

#include <linux/module.h>
#include <linux/kernel.h>
#include <linux/device.h>
#include <linux/init.h> 
#include <linux/fs.h> 
#include <linux/mm.h> 
#include <asm/uaccess.h>

#define MAX_SIZE (PAGE_SIZE * 2)   /* max size mmaped to userspace */
#define DEVICE_NAME "mchar"

spitis

"""adapted from https://github.com/OlavHN/bnlstm to store separate population statistics per state"""
import tensorflow as tf, numpy as np
RNNCell = tf.nn.rnn_cell.RNNCell

class BNLSTMCell(RNNCell):
    '''Batch normalized LSTM as described in arxiv.org/abs/1603.09025'''
    def __init__(self, num_units, is_training_tensor, max_bn_steps, initial_scale=0.1, activation=tf.tanh, decay=0.95):
        """
        * max bn steps is the maximum number of steps for which to store separate population stats
        """

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

bellbind

# How to build and to run
#   mkdir build ; cd build/ ; cmake .. ; make ; ./test
#   (cleanup: rm -r build/)
set(CMAKE_C_STANDARD 11)
list(APPEND CMAKE_C_FLAGS "-Wall -Wextra -pedantic")
add_library(mymalloc SHARED mymalloc.c)
add_executable(test test.c)
link_directories(.)
target_link_libraries(test mymalloc)

voluntas

WebRTC コトハジメ

更新:	2024-05-20
作者:	@voluntas
バージョン:	2024.1
URL:	https://voluntas.github.io/

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

domenic

This article has been given a more permanent home on my blog. Also, since it was first written, the development of the Promises/A+ specification has made the original emphasis on Promises/A seem somewhat outdated.

You're Missing the Point of Promises

Promises are a software abstraction that makes working with asynchronous operations much more pleasant. In the most basic definition, your code will move from continuation-passing style:

getTweetsFor("domenic", function (err, results) {
 // the rest of your code goes here.