After purchasing audiobooks on Audible you may want to store the files on your computer in case Amazon decides to pull the books later on. Audible allows you to download encrypted copies of your books from your account library.

Clicking on the “Download” link for any audiobook will download a .aax file to your computer. This file contains audio data that has been encrypted using a 4-byte key unique to your Audible account. Because the key is so short it is trivial to break it using brute force and there is plenty of software available specifically for that purpose. In this blog post, I’ll be covering two ways to decrypt the file.

OpenAudible

OpenAudible a free open-source graphical program available for Linux, Windows, and macOS. It’s specifically designed to remove DRM from your Audible files and hides a lot of the complexity.

EDIT: OpenAudible appears to have become closed source and paid software. You can buy it if you want or try to find an old version, but see below for a free method.

Once you install OpenAudible from its website you can drag and drop the .aax files you downloaded from Audible into it. They will show up in a list at the bottom of the window.

With your audiobooks loaded select them (Ctrl + A) and right-click to select “Convert to MP3”.

OpenAudible will convert each of your audiobooks to a DRM-free mp3 file and save them in the ~/OpenAudible folder on your computer. If you can’t find the mp3 files then right-click one of the books and select “Show MP3”.

One nice thing about OpenAudible over the FFMPEG method is that the book’s metadata (author, reader, publisher, etc.) will be preserved in the resulting mp3 file.

FFMPEG

ffmpeg is a popular free and open-source command line utility for processing video and audio. It can decrypt the Audible DRM but requires you to input the specific 4-byte encryption key unique to your Audible account. You can brute force your downloaded .aax files (you only need to get the key from one, and it will work for the others) using this website.

Once you’ve gotten your key you can use it to convert your .aax files to mp3s using ffmpeg like so (replace XXXX with your key):

ffmpeg -activation_bytes XXXX -i audiobook.aax audiobook.mp3

Unfortunately, this does not appear to migrate the metadata to the new mp3 files created like the OpenAudible approach does.

I was recently tasked with solving a fun epidemiology puzzle for one of my university classes. Below is an excerpt from the assignment describing the scenario.

11 people get sick enough to go to a local hospital with severe diarrhea and vomiting that lasts four days or so in each patient. All the patients turn out to all have the same strain of norovirus.

It turned out that they all knew each other and over the summer had been sharing produce from their gardens. The nurse’s hypothesis was that one person had norovirus, and had transmitted the virus to others on the food. She made a list, numbered the patients, starting with the patient that had first shared, and who they had shared with. It turned out a total of 16 people had shared produce, so she contacted the additional people who had not gotten sick, and asked them who they had shared produce with and when. In the end, she came up with the list below. So, patient 1 first shared vegetables with patient 12, then with patient 14. Patient 2 first shared vegetables with patient 5, then with patient 15, and so on. And patient 1 never got ill, while patient 2 did. Any time that two people come in contact with each other, the virus can move either way. For example, it would be possible for patient 2 to have infected patient 5, or patient 5 to infect patient 2.

After studying the list, she said, “I know who started this!” She asked that patient where they had been recently and it turned out they’d been on a cruise ship that had had a severe outbreak of norovirus! Based on her data, which patient was the one who went on the cruise and started the epidemic?

Data

Below is the dataset of patients and who they met with.

Patient	Meetings	Sick
1	12,14	FALSE
2	5,15	TRUE
3	6,16	TRUE
4	1,7,11	TRUE
5	10,3,16	FALSE
6	13,2	FALSE
7	2,8	TRUE
8	3,10	TRUE
9	15,5	TRUE
10	9	TRUE
11	14	TRUE
12	13,15	FALSE
13	16,3	TRUE
14	9	TRUE
15	16,5	FALSE
16	9	TRUE

Rules

So based on the above passage we can derive some simplified rules to use in solving the puzzle

1. Meetings happen sequentially going left to right
2. Rows are in chronological order going top to bottom
3. We don’t move onto the next row until all the meetings of the current row are complete
4. Each meeting has only two people
5. When two people meet the disease can go either direction

Solution

Theory

To solve this we need to find an algorithm that can identify patient zero based on their interactions with others. At first, I thought about using a graph-based approach to model each meeting, but the temporal nature of the data makes that untenable. Instead, I opted for a much simpler and intuitive approach that takes advantage of the ordering.

Since we know the precise sequence in which meetings occurred and we know that each meeting contains only two people we can generate a list of interaction tuples from the dataset. For example, 1 meets with 12, then 14, and then 2 meets with 5. So we could have a list like so:

[(1, 12), (1, 14), (2, 5)]

Once we have our sequential list of interactions we can iterate through them to simulate the effect of any given individual being patient zero. Then it’s just a game of trial and error trying out different possible patients. If we find a contradiction in our simulation based on the data we were given (ie. someone gets sick in the simulation but was healthy in the table) then we know that our guess for patient zero was wrong and can move on to the next one. But if we get all the way to the end of the simulation and everyone who was supposed to get sick is sick and everyone who was supposed to be healthy is healthy then we found our culprit.

Code

So now that we have a game-plan, we just need to code it up and find out who got everyone sick.

We’ll be using the Pandas python library for working with our table.

import pandas as pd

I’ve placed the table into a CSV file called data.csv which we’ll open as a Pandas DataFrame.

# Load the dataset into a DataFrame
df = pd.read_csv('data.csv')

We need to get the list of interactions in chronological order from the table. To do this I’ll use a Python generator to iterate over the rows and for each row I’ll split the meetings up and yield them.

def get_interactions():
    # Iterate through the rows of the DataFrame
    for index, row in df.iterrows():
        # Get each meeting in order
        for meeting in row['Meetings'].split(','):
            # Yield the interaction
            yield {row['Patient'], int(meeting)}

Great, so far so good. All we have left is the actual simulation to write. For this, what we’ll do is keep a list of patients who are sick in the simulation. It will start with just our guess for patient zero and grow as they interact with others.

When we iterate through the interactions there are three possible situations that can happen:

1. The interaction has no sick people in it
2. The interaction has one sick person and one healthy person
3. The interaction has two sick people

If the interaction has no sick people or two sick people then we just move along to the next one. But if it has one sick person and one healthy person then we need to make the healthy person “sick” by adding them to the sick_people set. However, before doing that we check with our real data to see if the healthy person was recorded as being sick. If they were then they get added and we keep going, but if they are supposed to be healthy then we know that our hypothesis was wrong and can return False.

Finally, if we make it through all of the iterations without invalidating our hypothesis then it must be true and we will return.

# Test if our patient zero hypothesis is correct
def test_hypothesis(id, interactions):
    # A set of sick people in the simulation
    # starts with just patient zero
    sick_people = {id}
    # Iterate over the interactions in
    # chronological order
    for interaction in interactions:
        # Check if the interaction has at least
        # one sick person in it
        if sick_people.intersection(interaction):
            # If there is a sick person then
            # check if everyone in this interaction
            # was supposed to get sick.
            for person in interaction:
                # If they were then add them to the set
                if df[df['Patient'] == person]['Sick'].bool():
                    sick_people.add(person)
                # If they weren't then we are done and
                # can return False
                else:
                    return False

    return True

Alright, well that’s it! Just add a few more lines of code to run our functions and let’s see who it was.

# Get list of interactions
interactions = list(get_interactions())
# Iterate through the 16 candidates
for candidate in range(1, 17):
    # Check if our guess is correct
    if test_hypothesis(candidate, interactions):
        # Yay! We found them.
        print('It was {}!'.format(candidate))
        break

Run it!

(env) kyle@IntelNuc:~/Code/Python/Virus Spread$ python who_did_it.py 
It was 7!

Well, it looks like it was patient #7 who got everyone sick. Mystery solved.

I decided to write another script using similar code to produce a tree diagram of the infection which is pictured below. As you can see the norovirus does indeed start with patient #7 and moves to all of the other sick people from there. I encourage you to follow the path of the tree through the table to confirm the results for yourself.

Basic Exploration

In-depth Exploration

1. Can you find a pattern that returns to its starting point after more than two time steps?
2. What’s the longest you can see a pattern go without repeating a configuration?

To answer these questions I decided to build my own implementation of Conway’s Game of Life in Python to brute force all possible starting positions.

"""
Kyle's Game of Life Implementation
1) live cells die if they have 0, 1, or 4+ neighbors
2) empty cells have a birth if they have exactly three neighbors
"""

import numpy as np

# Create a blank board
board = np.zeros((5, 5))

def iterate(board):
	"""
	This function takes the current board state and returns the next state.
	"""
	conv_board = np.zeros((7, 7))
	conv_board[1:6, 1:6] = board
	conv = np.lib.stride_tricks.as_strided(
		conv_board, 
		(5, 5, 3, 3), # view shape
		(56, 8, 56, 8) # strides
	)
	
	# The new board
	b = np.zeros((5, 5))
	for i in range(5):
		for j in range(5):
			# Count the number of neighbor live cells
			if conv[i, j, 1, 1] == 1:
				# Subtract itself from total count
				b[i, j] = conv[i, j].sum() - 1
			else:
				b[i, j] = conv[i, j].sum()

	# Cells with 0, 1, or 4+ die
	b[np.any([b <= 1, b >= 4], axis=0)] = 0
	# Living cells with 2 neighbors get to keep living
	b[np.all([b == 2, board == 1], axis=0)] = 1
	# Dead cells with 2 neighbors stay dead
	b[np.all([b == 2, board == 0], axis=0)] = 0
	# All cells with 3 neighbors live
	b[b == 3] = 1
	# Return the new board state
	return b

if __name__ == '__main__':
	while input('Continue? [y/n] ') == 'y':
		print(board)
board = iterate(board)

Results

It took approximately two hours to play all 2²⁵ possible starting positions. There were 300,477,379 total steps taken with an average of 8.95 steps per game. The game with the longest period was 39 steps.

The Longest Game

This article was originally published on Steemit.com

Over the past month, I’ve been building a new search engine that indexes the steem blockchain. It’s currently live at asksteem.com. The goal of AskSteem is to provide a reliable, powerful, and fast search engine that is optimized for steem. In this post, I’d like to cover some of the features that are available.

Query Syntax

There are many different ways that you can query the AskSteem index. I’ve created a video demonstrating each of them, but you may also read their descriptions and examples below.

Keyword/Phrase Search

Like many other search engines, you can search for general phrases and terms. AskSteem will try its best to find the document that is most relevant to your query based on our ranking algorithm. Example Queries: Tip: Click the example to go to that query on AskSteem How to buy bitcoin What is steem Markdown tutorial

Exact Search

Putting a query into quotes requests that AskSteem only returns documents that have exactly that phrase in that order. Example Queries: "How to buy bitcoin" "What is steem" "Markdown tutorial"

Tag Search

AskSteem allows you to filter posts by tag. Example Queries: tags:life tags:steemit

Author Search

You can filter posts by the author too. Example Queries: author:thekyle author:abit author:steemit

Creation Date Search

AskSteem provides a highly flexible and powerful date search tool for posts. You can search by exact date or by date range. Dates must be in the form of YYYY-MM-DD. Example Queries: Search for all posts posted on June 2, 2017 created:2017-06-02 Search for all posts posted between May 1, 2017, and May 31, 2017 created:[2017-05-01 TO 2017-05-31]

Search by Number of Votes/Comments

Similar to dates AskSteem has another set of robust tools that allow searches based on the number of upvotes or comments a post receives. Example Queries: Posts with 150 votes: net_votes:150 Posts with between 100 and 150 votes net_votes:[100 TO 150] Posts with 50 comments: children:50 Posts with between 40 and 50 comments: children:[40 TO 50] Posts with more than 50 comments: children:>50 or less than 50: children:<50 this also works with votes, less than or equal to 10 votes: net_votes:<=10

Searches with Boosts

You can prioritize certain parts of your query with boosts. These are indicated by placing a ^n at the end of a term, where n is the power you want to boost that part of the query to. Example Queries: Give the term bitcoin a boost of two: I really want posts to have the term bitcoin^2 in them. Give the term mine a boost of two, and term steem a boost of three: How to mine^2 steem^3

Inclusive/Exclusive Search

You can indicate whether you want documents to contain certain terms by placing a + or a - in front of the term. Example Queries: Find documents about mining but not bitcoin: cryptocurrency +mining -bitcoin

Wildcard Search

You can use the wildcard expressions of ? for a single character, or * to match any number of characters. Example Queries: How to mine any cryptocurrency: How to mine *

Boolean Search

AskSteem supports any combination of the previously mentioned search types in a single powerful query. This uses boolean values of AND, OR, and NOT, along with parenthesis to separate statements.
Example Queries: Posts tagged with asksteem by @thekyle: tags:asksteem AND author:thekyle Posts with between 50 and 100 comments that have more than 500 upvotes and that are tagged with ‘bitcoin’ or have the term bitcoin in the document: (bitcoin OR tags:bitcoin) AND (net_votes:>500 AND children:(>50 AND <100)) Posts created on June 2, 2017, with 100 or more upvotes but less than 10 comments: created:2017-06-02 AND net_votes:>=100 AND children:<10

Developers

https://www.youtube.com/watch?v=aiqasuhUPXU Because AskSteem integrates directly into the steem blockchain it can read metadata directly from posts and use that data when performing queries and displaying results. We encourage developers to add AskSteem compatible metadata to their posts so that we can show links to your application in our search results. The full documentation can be found at asksteem.com/developers, however, in this post I will summarize the most important tags.

Tag	Description	Example
domain	The domain name or web address that your application is hosted on.	example.com
locator	The path to reach the post on the domain relative to the root.	/CATEGORY/@AUTHOR/PERMLINK
protocol	Either ‘http’ or ‘https’ if not provided then http will be used by default	https

If none of the above metadata is provided then AskSteem will link to steemit.com for all posts by default, however, it is assumed that the platform creating the content will have the best interface for viewing it, so we would rather link there.

The domain and locator tags are required for custom linking to work, however, the protocol tag is optional and will default to http.

• The domain tag should be the domain name that your web-based steem application is hosted on and is subdomain sensitive (so if your hosting on www subdomain then put that).
• The locator should be the permalink to that particular post in your applications URL structure, also notice the leading forward slash, this is required.

The final URL that we point to will be generated by concatenating the domain and locator together with the protocol at the beginning which will be http unless otherwise specified.

Additionally, if you are building an application on the steem blockchain and need a search API please email us at contact@asksteem.com, we are able to query custom metadata and make various other customizations to the ranking algorithm to support your use case.

Funding

The harsh reality is that search engines are expensive to run and that adding new features and improving performance are difficult if the basic funding needs of the project are not covered. AskSteem currently costs me about $100/month to run, and that number will, of course, continue to increase as steem grows and the index size increases.

Ideally, my goal is the have those costs covered through upvotes from the steem community and to use any extra money for adding new features and scaling the search infrastructure to meet demand. If this works then AskSteem will be the first search engine in the world to use a cryptocurrency based revenue model, instead of selling advertising.

Thank you for your time, and happy searching!

The New iPod Touch from Apple is a great way of getting an iOS 8/9 experience without having to pay the hefty price of the iPhone. However while cheaper than the iPhone it does come with a few drawbacks, one of which is that it cannot be used to make Phone calls or send text messages (by default). However, with today’s technologies, it’s pretty easy for even the most non-technical people to start making phone calls from their new iPod.

Step #1 – Create a Google Account

If you’ve already got a Google account this can be skipped however the service will be using is owned by Google and requires a free Google account to start using it. You can register here.

Step #2 – Get Google Voice

Next in your browser while signed into your Google account visit voice.google.com and run through the basic setup process.

 Google Voice Setup:

1. Choose I want a New Number from the Initial Setup or if you prefer you can use your current number.
2. Add any of your current phones as a Forwarding phone. (What Phone You Choose doesn’t matter for this tutorial)
3. Enter Your Zipcode, or a Keyword to search for available numbers to choose from, then a list should appear and you can pick a number that you like.

Step #3 – Edit Basic Settings (Optional)

Once you set up your Google Voice account and select your number you can change specific settings such as your voicemail and pins by clicking on the gear icon in the upper right corner of the user interface and selecting settings.

Step #4 – Install Google Voice App

Next, you just have to install the Google Voice App on your iPod and connect it to your Google Account. Once you complete the Application setup your ready to send and receive text messages, and phone calls from your iPod for free. The number that you give to people to call you is the same number you choose when you set up Google Voice. If you forgot your number then you can find it easily by logging into Google Voice and finding the listed phone number in the left column.

1. Gigapros Web Hosting

While Gigapros do have Shared and VPS hosting, where they really shine is in their Dedicated Servers. They have a very different way of operating that differentiates them for many other web-hosts and puts them as number one on this list. The main one being how they allow you to pick exactly the specs you need for your server, including RAM, CPU, and any Operating System from CentOS to Windows. On top of that, you can add, some extremely competitive pricing such as a 64 GB of RAM, and 3.8 GHz Processor server coming in at just 119 USD a month.

2. BlueHost

I like to think of Bluehost as the Apple of the web hosting world. While they do shine in some areas the main that I find really unique are support and simplicity. To address the first one, in all the times I’ve ever had an Issue with any of my Bluehost servers I never had to wait for more than 5 minutes before getting into their real-time chat with someone who can actually help me, instead of some low-level salesperson. The simplicity part is not necessarily as big of a deal with the Pro web-designers and Entrepreneurs as with the newbies, but its always nice to have everything you need available in one place (such as Domains, Hosting, etc.). Another quick side note is that for some reason I’ve never had to wait more than 5 minutes for a domain I purchased through Bluehost to propagate for their servers.

3. DreamHost

I’ve always had mixed feelings about Dreamhost based on my first experiences with them, however as of the time I’m writing this review I’m glad to say that they’ve really become one of the more competitive web-hosts. While they really don’t offer any of the simplicity of Bluehost or the low prices of Gigapros if you’re looking for a good mix between Price, Support, and Reliability then Dreamhost is defiantly a good candidate to look into.

4. HostGator

HostGator is another company similar to BlueHost as in they put customer satisfaction, and support before anything else. While neither their VPS or Dedicated servers give you as much bang for your buck as Gigapros, they still offer very powerful Dedicated servers. While you shouldn’t expect to go starting your own social network on HostGator, you can certainly get some really large WordPress or other basic CMS sites going. HostGator also similar to Gigapros offers both Linux and Windows server hosting.

5. GoDaddy

I’d be amiss not to at least talk about GoDaddy in this list considering they are probably the most popular web hosting company. Although to be Frank nearly every experience I’ve had with actually hosting with GoDaddy has been just terrible, and I can’t recommend you buy anything but Domain Names from them.

So you’re doing good for yourself and making decent earnings off AdSense. But then one day you get that email in your inbox with the subject “AdSense Account Disabled” and everything changes. You suddenly realize that any other respectable Ad Networks either have low CPM or are just pure scams. So here I’ve compiled a list of all the other Advertising Networks I tried before I got my AdSense Account back.

8. Amazon Associates

While I am aware that Amazon Associates is technically an affiliate network I wanted to include it on this list simply because it’s a great source of extra revenue if you’ve got a site with a heavy and loyal following or a high conversion rate. It pays out similar to any other Affiliate program (based on sales) however since Amazon has such a large variety of products its easier to find relevant products than with other affiliate sites.

7. Chitika

Chitika pays via PayPal and allows you to customize your ads to better suit your websites like AdSense. The Downside to Chitika is its search-driven meaning the ads shown, are based on what Search Terms were used to get to your site not the keywords on your site.

6. InfoLinks

This is ad network creates blue underlined words like hyperlinks in your content automatically, and only shows ads when the links are hovered over by the user. Currently, InfoLinks doesn’t support any type of banner ads however they do have lower requirements so if your site is just starting up it might be worth a try.

5. ProjectWonderful

Despite having a rather strange name ProjectWonderful has a very unique model based on advertisers renting time on your site. The best part being that the system works on a bidding system so you usually end up making more money than you would with CPM or PPC Ads.

4. BuySellAds

Similar to ProjectWonderful, BuySellAds allows advertisers to pay for Ad space on your websites based on how much they are willing to pay or how much your minimum advertising cost is.

3. Advertising.com

Advertising.com is AOL’s solution to advertising (yes AOL) and has become a very large and respectable ad network online, although they do have rather high requirements compared to some of the others listed above.

2. Tribal Fusion

This Ad Network is by far the king of the CPM Networks and requires about 500,000 Impressions per month. Although it has a high need it pays off well with some of the highest CPM Rates on the internet, even when compared to AdSense.

1. Media.net

Media.net is part of the Yahoo | Bing Ad Network, and so is basically Microsoft’s equivalent of AdSense. While the exact requirements are unknown its very difficult to get invited into this ad network is so far we’ve only seen sites with millions of monthly users using.

Bad Eggs

During my time looking for Google Adsense alternatives, I also found a few spammy/scam Advertising networks that look legit but will end up not paying you or showing fewer clicks/impressions then your ads actually get.

RevenueHits

While this site would convince anyone that it’s a legit ad-network I found that all of there ads were spammy download buttons, and sometimes opened up popups or tried to download malicious software to the visitor’s computer. I have also heard stories of people getting their entire sites blacklisted from Google as being malicious because of RevenueHits Ads.

AdHexa

While Adhexa will indeed pay you and has better quality ads then RevenueHits they often seem to only count a fraction of the impressions your actually getting which may simply be a software glitch on their servers but still stay away until they become more reputable.

If you haven’t been following it then you might be surprised that a young start-up by the name of ‘DuckDuckGo’ is challenging the mighty search giant Google. But on February 19, 2008, the new privacy-focused search engine emerged and was incorporated. The main difference between DuckDuckGo and Google is that DuckDuckGo claims not to track your searches, and not to customize search results based on your previous searches. DuckDuckGo also offers many features that Google does not such as a Clean modern interface, Automatically Searching a site, and many more. However, it should be noted that most of the features that DuckDuckGo has that Google doesn’t aren’t really all that useful in your everyday searching experience. DuckDuckGo also does not rely heavily on a Web crawler, but instead API’s from other search engines like Bing, Yahoo, and smaller specialty search engines such as Sound Cloud.

While all this sounds great, Google still has the advantage over DuckDuckGo when it comes to actually finding you what you want. Because DuckDuckGo won’t track you, and won’t give you personalized results, you also won’t be getting as relevant results as you would by using Google. Another thing is that even with its many API’s and Search Sources DuckDuckGo has a much smaller Index than Google so when you search you will find far less relevant results simply because there is less diversity. Overall it comes down to Privacy VS Better Results, as DuckDuckGo offers Privacy, but Google offers better results.

Almost every site on the web has a robots.txt file located at Example.com/robots.txt which tells web crawlers or bots what they are allowed to visit and what they are not allowed to visit. This can be helpful for blocking off admin areas or backend files that you do not need or want crawled or giving bots information like the URL(s) to your sitemap(s).

So what actually is a robots.txt? Well, it’s just a simple text file made in notepad or any other word processor that has several tags that web crawlers understand. The Following are some examples of common elements that you might see in a Robots.txt file.

# Basic Robots.txt allowing bots to visit your whole site
User-agent: *
Disallow:

# Robots file telling bots not to crawl any part of the site
User-agent: *
Disallow:/

# Robots file telling bots not to crawl any files in the admin directory (backend)
User-agent: *
Disallow: /admin/

You can also use tags such as the Sitemap tag to tell bots where to find your XML sitemap or RSS feeds to increase crawl rates.

There are a few drawbacks to using a robots.txt file though, mainly being that it is public to anyone who wants it so if you are trying to hide an exposed backend directory or an admin area with the Disallow tag anybody can view your robots, and it does not stop regular users from visiting those pages. Another thing is that not all bots obey the Robots.txt file, these are mainly malicious bots that are trying to spam post comments or upload malware to your web servers. These bots may use your Robots file against you by visiting you disallow links in an attempt to crack your admin accounts.

In most cases the benefits of having an up to day robots.txt file out weight the drawbacks because of the increased search engine crawling from spiders like GoogleBot or BingBot. It also helps to give information about your site to smaller search engines that might not have a “webmaster tools” section like DuckDuckGo or AskClash about where your sitemaps are located.

Overall it’s up to you how much time you want to spend designing your robots.txt file and how you want to deal with the possible threats too using one.

I feel that there is a lot of confusion as too exactly what DoFollow and NoFollow links do and don’t do. A common misconception is that if something is marked NoFollow then search engines won’t follow the link at all. This, however, is not true at all, because web crawlers simply aren’t going to pass up a potentially content rich page because its NoFollow. However, this doesn’t mean that search engines don’t treat the NoFollow links different than Dofollow.

The real thing that the two types of links determine is the transfer of Page Rank or commonly referred to as “PR”. Page rank is how search engines determine how important a website is by counting the number of backlinks to the site. The idea behind this is that if your website has lots of other website linking too it then your website must have good and quality content, so it should be ranked higher in the search results. The higher number of backlinks you have to your site the higher Page Rank you will have.

NoFollow links are basically links that search engines don’t count as a backlink. This is a common practice on High PageRank sites because the higher the PageRank of the site linking to your the more it will affect your PageRank.

DoFollow tells the search engines to count the link as a backlink to the site and thus gives the site being linked to a higher PageRank based on the PR of the site linking to it.

Many sites on the web now use NoFollow links because of automated bots designed to browse the web and post links as an attempt to build backlinks to a certain domain, usually of questionable content.