top button
Flag Notify
    Connect to us
      Facebook Login
      Site Registration

Facebook Login
Site Registration

Introduction about CAPTCHA.

0 votes
871 views

What is CAPTCHA?

A CAPTCHA acronym - Completely Automated Public Turing test to tell Computers and Humans Apart

CAPTCHA is a type of challenge-response test used in computing to determine whether or not the user is human.

A CAPTCHA differentiates between human and bot by setting some task that is easy for most humans to perform but is more difficult and time-consuming for current bots to complete.

CAPTCHAs are often used to stop bots and other automated programs.

Sample Example for CAPTCHA
image

Advantages:

1)Distinguishes between a human and a machine
2)Makes online polls more legitimate
3)Reduces spam and viruses
4)Makes online shopping safer
5)Diminishes abuse of free email account services

Disadvantages:

1)Sometimes very difficult to read
2)Are not compatible with users with disablilities
3)Time-consuming to decipher
4)Technical difficulties with certain internet browsers
5)May greatly enhance Artificial Intelligence

Video for What is Captcha

posted Nov 27, 2014 by Ujjwal Mehra

  Promote This Article
Facebook Share Button Twitter Share Button LinkedIn Share Button
CAPTCHA is like artificial intelligence service provided for the online security purpose.
Captcha code is available in both numerical and alphabetic way. If any user accesses some site and edits some information in the websites, captcha code is used for the security  purpose so that customer can easily access the websites. While the captcha code is not matched the required code than the customer can't able to access the website.


Related Articles

What is aiohttp?
Asynchronous HTTP client/server framework for asyncio and Python 

Features:

  • Supports both client and server side of HTTP protocol.
  • Supports both client and server Web-Sockets out-of-the-box and avoids Callback Hell.
  • Provides Web-server with middlewares and pluggable routing.

Commands

pip install aiohttp

You may want to install optional cchardet library as faster replacement for chardet:

pip install cchardet

For speeding up DNS resolving by client API you may install aiodns as well. This option is highly recommended:

pip install aiodns

Example

import aiohttp
import asyncio

async def fetch(session, url):
    async with session.get(url) as response:
        return await response.text()

async def main():
    async with aiohttp.ClientSession() as session:
        html = await fetch(session, 'http://python.org')
        print(html)

if __name__ == '__main__':
    loop = asyncio.get_event_loop()
    loop.run_until_complete(main())

Video for aiohttp

https://www.youtube.com/watch?v=Z784Mwm4VBg

 

 

READ MORE

What is Seaborn?
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Features

  • A dataset-oriented API for examining relationships between multiple variables
  • Specialized support for using categorical variables to show observations or aggregate statistics
  • Options for visualizing univariate or bivariate distributions and for comparing them between subsets of data
  • Automatic estimation and plotting of linear regression models for different kinds dependent variables
  • Convenient views onto the overall structure of complex datasets
  • High-level abstractions for structuring multi-plot grids that let you easily build complex visualizations
  • Concise control over matplotlib figure styling with several built-in themes
  • Tools for choosing color palettes that faithfully reveal patterns in your data

Seaborn aims to make visualization a central part of exploring and understanding data. Its dataset-oriented plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots.

Example Code

import seaborn as sns
sns.set()
tips = sns.load_dataset("tips")
sns.relplot(x="total_bill", y="tip", col="time",
            hue="smoker", style="smoker", size="size",
            data=tips);

Video for Seaborn
https://www.youtube.com/watch?v=eMkEL7gdVV0

READ MORE

What is Mlpack Library?

mlpack is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. 

This is done by providing a set of command-line executables which can be used as black boxes, and a modular C++ API for expert users and researchers to easily make changes to the internals of the algorithms.

As a result of this approach, mlpack outperforms competing machine learning libraries by large margins; see the BigLearning workshop paper and the benchmarks for details.

mlpack is developed by contributors from around the world. It is released free of charge, under the 3-clause BSD License (more information). (Versions older than 1.0.12 were released under the GNU Lesser General Public License: LGPL, version 3.)

mlpack was originally presented at the BigLearning workshop of NIPS 2011 [pdf] and later published in the Journal of Machine Learning Research [pdf], with version 3 being published in the Journal of Open Source Software [pdf]. Please cite mlpack in your work using this citation.

mlpack bindings for R are provided by the RcppMLPACK project.

Currently mlpack supports the following algorithms:

  • Collaborative Filtering
  • Decision stumps (one-level decision trees)
  • Density Estimation Trees
  • Euclidean Minimum Spanning Trees
  • Gaussian Mixture Models (GMMs)
  • Hidden Markov Models (HMMs)
  • Kernel Principal Component Analysis (KPCA)
  • K-Means Clustering
  • Least-Angle Regression (LARS/LASSO)
  • Linear Regression
  • Local Coordinate Coding
  • Locality-Sensitive Hashing (LSH)
  • Logistic regression
  • Max-Kernel Search
  • Naive Bayes Classifier
  • Nearest neighbor search with dual-tree algorithms
  • Neighbourhood Components Analysis (NCA)
  • Non-negative Matrix Factorization (NMF)
  • Principal Components Analysis (PCA)
  • Independent component analysis (ICA)
  • Rank-Approximate Nearest Neighbor (RANN)
  • Simple Least-Squares Linear Regression (and Ridge Regression)
  • Sparse Coding, Sparse dictionary learning

For more detail visit here - http://mlpack.org/docs.html

Video for Mlpack

https://www.youtube.com/watch?v=yQtp3gf5wtY

READ MORE

What is PyShark?

PyShark is a wrapper for the Wireshark CLI interface, tshark, so all of the Wireshark decoders are available to PyShark!

Python wrapper for tshark, allowing python packet parsing using wireshark dissectors.

There are quite a few python packet parsing modules, this one is different because it doesn't actually parse any packets, it simply uses tshark's (wireshark command-line utility) ability to export XMLs to use its parsing.

This package allows parsing from a capture file or a live capture, using all wireshark dissectors you have installed. Tested on windows/linux.

Example Code for Reading a File

import pyshark
cap = pyshark.FileCapture('/tmp/mycapture.cap')
cap
>>> <FileCapture /tmp/mycapture.cap>
print cap[0]
Packet (Length: 698)
Layer ETH:
        Destination: aa:bb:cc:dd:ee:ff
        Source: 00:de:ad:be:ef:00
        Type: IP (0x0800)
Layer IP:
        Version: 4
        Header Length: 20 bytes
        Differentiated Services Field: 0x00 (DSCP 0x00: Default; ECN: 0x00: Not-ECT (Not ECN-Capable Transport))
        Total Length: 684
        Identification: 0x254f (9551)
        Flags: 0x00
        Fragment offset: 0
        Time to live: 1
        Protocol: UDP (17)
        Header checksum: 0xe148 [correct]
        Source: 192.168.0.1
        Destination: 192.168.0.2​

Video for PyShark

https://www.youtube.com/watch?v=gstHeldo61w

READ MORE

What is FastText?

FastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices.
FastText builds on modern Mac OS and Linux distributions. Since it uses C++11 features, it requires a compiler with good C++11 support.

Steps for Installing

- git clone https://github.com/facebookresearch/fastText.git
- cd fastText
- make

Text classification is a core problem to many applications, like spam detection, sentiment analysis or smart replies. In this tutorial, we describe how to build a text classifier with the fastText tool.

What is text classification?
The goal of text classification is to assign documents (such as emails, posts, text messages, product reviews, etc...) to one or multiple categories. Such categories can be review scores, spam v.s. non-spam, or the language in which the document was typed. 

Nowadays, the dominant approach to build such classifiers is machine learning, that is learning classification rules from examples. In order to build such classifiers, we need labeled data, which consists of documents and their corresponding categories (or tags, or labels).

Video for FastText

https://www.youtube.com/watch?v=tQvghqdefTM

READ MORE
Contact Us
+91 9880187415
sales@queryhome.net
support@queryhome.net
#280, 3rd floor, 5th Main
6th Sector, HSR Layout
Bangalore-560102
Karnataka INDIA.
QUERY HOME
...