What is AutoMatic Network (AUMI) | What is AUMI token

In this article, we'll discuss information about the AutoMatic Network project and AUMI token.

What is AutoMatic?

AutoMatic is a yield aggregator on the Polygon Chain. It uses an auto-compounding function to increase returns on yield farms. Once set, the protocol runs itself, without the need for further attention from the investor.

How does it work?

Investors purchase tokens or create liquidity and place it into an AutoMatic pool. The pool then collects the rewards, reinvests them, and grows the value placed in the pool. This compounding creates an optimal growth curve that is next to impossible to achieve manually.

What are the risks?

Any cryptocurrency investment carries risk. Yield farming does not guarantee profitable returns. Only invest what you can afford to lose, and do your own research before making any financial decisions.

Why Use Automatic?

Less Time, More Compounding

Maximizing yield on farming pools can be a time-consuming task. Frequent compounding leads to the best yields, as farmers start earning on rewards immediately. The problem in this approach comes in two parts: time and gas. Optimized yields take frequent attention to your account. Frequent compounding incurs a significant amount of gas fees. By using the protocol to handle compounding, AutoMatic saves you time. By combining the entire pool into a single transaction, AutoMatic uses far less gas than would be needed to handle every account individually.

Leverage Compound Interest

AutoMatic Pools reward investors for their commitment to an investment. By autocompounding rewards back into the liquidity pair or single stake initially invested, rewards grow exponentially. This type of growth is extremely powerful as time passes, allowing for returns far in excess of what can be gained by simply using the simple APY of a standard yield farming pool.

The AUMI Token

The AUMI token is the core of the AutoMatic ecosystem. The token can either be purchased and staked, or earned as a reward from various pools. For every 10 Matic earned through performance fees, 50 AUMI will be minted. AUMI buys and sells are subject to an adjustable tax of 0-10% on trading that will be converted to WMATIC, which will fund the rewards for The Vault.

Total Supply: 100,000 AUMI

Circulating Supply: 75,000 AUMI

Governance

AUMI token holders will control the decision making and will receive most of the farm performance fee profits. Governance weight is determined by the amount of AUMI you have in the The Vault. The more AUMI you have in the Vault, the more influence you will have in the ecosystem and the future direction of the project.

Pool Types

Pools are investment instruments that employ specific sets of strategies for yield farming. They make use of automation to continually invest and reinvest deposited funds to achieve high levels of compounded interest. In pools, you earn more of the asset you stake in it regardless if this is a liquidity pool (LP) token or a single asset. For example, pools where one can stake QUICK-MATIC LP will result in more QUICK-MATIC LP or QUICK over time, effectively growing your share in the pool and thus allowing for more and more rewards over time. All pools have a 30% performance fee and a 0.5% withdrawal fee for withdrawals in less than 72 hours from the time of staking. These fees will be less than the opportunity and gas cost of attempting to replicate the growth curve as an individual. Fees will be used as rewards for AUMI holders.

The Vault

AUMI rewards from single-asset pools and LP pools are deposited into The Vault. The Vault has a three month penalty period for deposits, grouped by week. After the three month period has elapsed, funds for that week are available for withdrawal at no penalty. Funds can be withdrawn at any time before the three months have elapsed with a 50% penalty. Penalty fees are burned, creating a deflationary aspect to the protocol. Funds in The Vault continue to earn rewards while they are left unclaimed, as well as providing weight for governance purposes.

Single-asset Pools

Single-asset pools are the simplest form of yield farming. Users stake their tokens in the pool, and the protocol handles everything else. AutoMatic will compound the value investors put in at an optimal rate to ensure the best rewards.

Liquidity Pools

Liquidity pools (LP) provide further options for farmers. These are a slightly more complex form of investing, and provide both more opportunities for growth and higher risk.

To create LP and get LP tokens, users will take two assets and add them to the larger pool of liquidity that underlies every Automated Market Maker (AMM). AMMs such as Quickswap on the Polygon Chain, Pangolin on the Avalanche Chain, or PancakeSwap on the Binance Smart Chain provide anyone the chance to exchange their tokens by using the liquidity pools provided by teams and investors.

Liquidity pools accept and maintain fixed value ratios of assets, typically 50-50 but other ratios such as 30-70, 20-80, or more complex multiasset pools at any ratio the designer wishes. Investors do not get to choose the ratio at which they invest; this is fixed by the pool at the time of its creation.

AutoMatic LP pools come in a variety of options and all autocompound rewards. Rewards come in two types: the specific reward from the pool (such as more LP, Quick, or AUMI) and AUMI. LP rewards and specific token rewards are available immediately, but AUMI rewards are placed into The Vault. The Vault is described in the previous section.

Impermanent Loss

Impermanent loss is a risk carried in LP pools. The short version is that if one part of a pair drastically changes value relative to the other parts, an investor may experience higher loss or lower gains on the assets held. This loss is not locked in unless an investor breaks the liquidity pair in the midst of the volatile period. To balance the larger risk in these kinds of farms, investors are rewarded in both higher APR and a portion of the transaction fees on the assets in the pair. The goal of any investor is a return that exceeds any impermanent loss as well as exceeding the returns available on single asset farms or simply holding the underlying assets. AutoMatic can help with this through continuous reinvestment of rewards and optimal yields based on this approach.

How and Where to Buy AUMI token?

AUMI has been listed on a number of crypto exchanges, unlike other main cryptocurrencies, it cannot be directly purchased with fiats money. However, You can still easily buy this coin by first buying Bitcoin, ETH, USDT, BNB from any large exchanges and then transfer to the exchange that offers to trade this coin, in this guide article we will walk you through in detail the steps to buy AUMI token.

You will have to first buy one of the major cryptocurrencies, usually either Bitcoin (BTC), Ethereum (ETH), Tether (USDT), Binance (BNB)…

We will use Binance Exchange here as it is one of the largest crypto exchanges that accept fiat deposits.

Binance is a popular cryptocurrency exchange which was started in China but then moved their headquarters to the crypto-friendly Island of Malta in the EU. Binance is popular for its crypto to crypto exchange services. Binance exploded onto the scene in the mania of 2017 and has since gone on to become the top crypto exchange in the world.

Once you finished the KYC process. You will be asked to add a payment method. Here you can either choose to provide a credit/debit card or use a bank transfer, and buy one of the major cryptocurrencies, usually either Bitcoin (BTC), Ethereum (ETH), Tether (USDT), Binance (BNB)…

☞ SIGN UP ON BINANCE

Step by Step Guide : What is Binance | How to Create an account on Binance (Updated 2021)

You need a wallet address to Connect to Pancakeswap Decentralized Exchange, we use Metamask wallet

If you don’t have a Metamask wallet, read this article and follow the steps ☞ What is Metamask wallet | How to Create a wallet and Use

Transfer $BNB to your new Metamask wallet from Binance wallet

Next step - Transfer your cryptos to an Altcoin Exchange

Since AUMI is an altcoin we need to transfer our coins to an exchange that AUMI can be traded. Below is a list of exchanges that offers to trade AUMI in various market pairs, head to their websites and register for an account.

Once finished you will then need to make a BTC/ETH/USDT/BNB deposit to the exchange from Binance depending on the available market pairs. After the deposit is confirmed you may then purchase AUMI from the exchange.

The top exchange for trading in AUMI token is currently: QuickSwap

Find more information AUMI token

☞ Website ☞ Explorer ☞ Social Channel ☞ Social Channel 2 ☞ Social Channel 3 ☞ Coinmarketcap

Top exchanges for token-coin trading. Follow instructions and make unlimited money

☞ Binance ☞ Bittrex ☞ Poloniex ☞ Bitfinex ☞ Huobi ☞ MXC ☞ ProBIT ☞ Gate.io ☞ Coinbase

🔺DISCLAIMER: The Information in the post isn’t financial advice, is intended FOR GENERAL INFORMATION PURPOSES ONLY. Trading Cryptocurrency is VERY risky. Make sure you understand these risks and that you are responsible for what you do with your money.

🔥 If you’re a beginner. I believe the article below will be useful to you ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner

⭐ ⭐ ⭐The project is of interest to the community. Join to Get free ‘GEEK coin’ (GEEKCASH coin)!

☞ **-----https://geekcash.org-----**⭐ ⭐ ⭐

I hope this post will help you. Don't forget to leave a like, comment and sharing it with others. Thank you!

#bitcoin #cryptocurrency

What is GEEK

Buddha Community

What is AutoMatic Network (AUMI) | What is AUMI token

Royce Reinger

1658068560

WordsCounted: A Ruby Natural Language Processor

WordsCounted

We are all in the gutter, but some of us are looking at the stars.
-- Oscar Wilde

WordsCounted is a Ruby NLP (natural language processor). WordsCounted lets you implement powerful tokensation strategies with a very flexible tokeniser class.

Features

Out of the box, get the following data from any string or readable file, or URL:
- Token count and unique token count
- Token densities, frequencies, and lengths
- Char count and average chars per token
- The longest tokens and their lengths
- The most frequent tokens and their frequencies.
A flexible way to exclude tokens from the tokeniser. You can pass a string, regexp, symbol, lambda, or an array of any combination of those types for powerful tokenisation strategies.
Pass your own regexp rules to the tokeniser if you prefer. The default regexp filters special characters but keeps hyphens and apostrophes. It also plays nicely with diacritics (UTF and unicode characters): Bayrūt is treated as ["Bayrūt"] and not ["Bayr", "ū", "t"], for example.
Opens and reads files. Pass in a file path or a url instead of a string.

Installation

Add this line to your application's Gemfile:

gem 'words_counted'

And then execute:

$ bundle

Or install it yourself as:

$ gem install words_counted

Usage

Pass in a string or a file path, and an optional filter and/or regexp.

counter = WordsCounted.count(
  "We are all in the gutter, but some of us are looking at the stars."
)

# Using a file
counter = WordsCounted.from_file("path/or/url/to/my/file.txt")

.count and .from_file are convenience methods that take an input, tokenise it, and return an instance of WordsCounted::Counter initialized with the tokens. The WordsCounted::Tokeniser and WordsCounted::Counter classes can be used alone, however.

API

WordsCounted

WordsCounted.count(input, options = {})

Tokenises input and initializes a WordsCounted::Counter object with the resulting tokens.

counter = WordsCounted.count("Hello Beirut!")

Accepts two options: exclude and regexp. See Excluding tokens from the analyser and Passing in a custom regexp respectively.

WordsCounted.from_file(path, options = {})

Reads and tokenises a file, and initializes a WordsCounted::Counter object with the resulting tokens.

counter = WordsCounted.from_file("hello_beirut.txt")

Accepts the same options as .count.

Tokeniser

The tokeniser allows you to tokenise text in a variety of ways. You can pass in your own rules for tokenisation, and apply a powerful filter with any combination of rules as long as they can boil down into a lambda.

Out of the box the tokeniser includes only alpha chars. Hyphenated tokens and tokens with apostrophes are considered a single token.

#tokenise([pattern: TOKEN_REGEXP, exclude: nil])

tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise

# With `exclude`
tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise(exclude: "hello")

# With `pattern`
tokeniser = WordsCounted::Tokeniser.new("I <3 Beirut!").tokenise(pattern: /[a-z]/i)

See Excluding tokens from the analyser and Passing in a custom regexp for more information.

Counter

The WordsCounted::Counter class allows you to collect various statistics from an array of tokens.

#token_count

Returns the token count of a given string.

counter.token_count #=> 15

#token_frequency

Returns a sorted (unstable) two-dimensional array where each element is a token and its frequency. The array is sorted by frequency in descending order.

counter.token_frequency

[
  ["the", 2],
  ["are", 2],
  ["we",  1],
  # ...
  ["all", 1]
]

#most_frequent_tokens

Returns a hash where each key-value pair is a token and its frequency.

counter.most_frequent_tokens

{ "are" => 2, "the" => 2 }

#token_lengths

Returns a sorted (unstable) two-dimentional array where each element contains a token and its length. The array is sorted by length in descending order.

counter.token_lengths

[
  ["looking", 7],
  ["gutter",  6],
  ["stars",   5],
  # ...
  ["in",      2]
]

#longest_tokens

Returns a hash where each key-value pair is a token and its length.

counter.longest_tokens

{ "looking" => 7 }

#token_density([ precision: 2 ])

Returns a sorted (unstable) two-dimentional array where each element contains a token and its density as a float, rounded to a precision of two. The array is sorted by density in descending order. It accepts a precision argument, which must be a float.

counter.token_density

[
  ["are",     0.13],
  ["the",     0.13],
  ["but",     0.07 ],
  # ...
  ["we",      0.07 ]
]

#char_count

Returns the char count of tokens.

counter.char_count #=> 76

#average_chars_per_token([ precision: 2 ])

Returns the average char count per token rounded to two decimal places. Accepts a precision argument which defaults to two. Precision must be a float.

counter.average_chars_per_token #=> 4

#uniq_token_count

Returns the number of unique tokens.

counter.uniq_token_count #=> 13

Excluding tokens from the tokeniser

You can exclude anything you want from the input by passing the exclude option. The exclude option accepts a variety of filters and is extremely flexible.

A space-delimited string. The filter will normalise the string.
A regular expression.
A lambda.
A symbol that names a predicate method. For example :odd?.
An array of any combination of the above.

tokeniser =
  WordsCounted::Tokeniser.new(
    "Magnificent! That was magnificent, Trevor."
  )

# Using a string
tokeniser.tokenise(exclude: "was magnificent")
# => ["that", "trevor"]

# Using a regular expression
tokeniser.tokenise(exclude: /trevor/)
# => ["magnificent", "that", "was", "magnificent"]

# Using a lambda
tokeniser.tokenise(exclude: ->(t) { t.length < 4 })
# => ["magnificent", "that", "magnificent", "trevor"]

# Using symbol
tokeniser = WordsCounted::Tokeniser.new("Hello! محمد")
tokeniser.tokenise(exclude: :ascii_only?)
# => ["محمد"]

# Using an array
tokeniser = WordsCounted::Tokeniser.new(
  "Hello! اسماءنا هي محمد، كارولينا، سامي، وداني"
)
tokeniser.tokenise(
  exclude: [:ascii_only?, /محمد/, ->(t) { t.length > 6}, "و"]
)
# => ["هي", "سامي", "وداني"]

Passing in a custom regexp

The default regexp accounts for letters, hyphenated tokens, and apostrophes. This means twenty-one is treated as one token. So is Mohamad's.

/[\p{Alpha}\-']+/

You can pass your own criteria as a Ruby regular expression to split your string as desired.

For example, if you wanted to include numbers, you can override the regular expression:

counter = WordsCounted.count("Numbers 1, 2, and 3", pattern: /[\p{Alnum}\-']+/)
counter.tokens
#=> ["numbers", "1", "2", "and", "3"]

Opening and reading files

Use the from_file method to open files. from_file accepts the same options as .count. The file path can be a URL.

counter = WordsCounted.from_file("url/or/path/to/file.text")

Gotchas

A hyphen used in leu of an em or en dash will form part of the token. This affects the tokeniser algorithm.

counter = WordsCounted.count("How do you do?-you are well, I see.")
counter.token_frequency

[
  ["do",   2],
  ["how",  1],
  ["you",  1],
  ["-you", 1], # WTF, mate!
  ["are",  1],
  # ...
]

In this example -you and you are separate tokens. Also, the tokeniser does not include numbers by default. Remember that you can pass your own regular expression if the default behaviour does not fit your needs.

A note on case sensitivity

The program will normalise (downcase) all incoming strings for consistency and filters.

Roadmap

Ability to open URLs

def self.from_url
  # open url and send string here after removing html
end

Are you using WordsCounted to do something interesting? Please tell me about it.

RubyDoc documentation.

Demo

Visit this website for one example of what you can do with WordsCounted.

Contributors

See contributors.

Contributing

Fork it
Create your feature branch (git checkout -b my-new-feature)
Commit your changes (git commit -am 'Add some feature')
Push to the branch (git push origin my-new-feature)
Create new Pull Request

Author: Abitdodgy
Source Code: https://github.com/abitdodgy/words_counted
License: MIT license

#ruby #nlp

Mohammed Richards

1659601560

Words Counted: A Ruby Natural Language Processor.

WordsCounted

We are all in the gutter, but some of us are looking at the stars.
-- Oscar Wilde

WordsCounted is a Ruby NLP (natural language processor). WordsCounted lets you implement powerful tokensation strategies with a very flexible tokeniser class.

Are you using WordsCounted to do something interesting? Please tell me about it.

Demo

Visit this website for one example of what you can do with WordsCounted.

Features

Out of the box, get the following data from any string or readable file, or URL:
- Token count and unique token count
- Token densities, frequencies, and lengths
- Char count and average chars per token
- The longest tokens and their lengths
- The most frequent tokens and their frequencies.
A flexible way to exclude tokens from the tokeniser. You can pass a string, regexp, symbol, lambda, or an array of any combination of those types for powerful tokenisation strategies.
Pass your own regexp rules to the tokeniser if you prefer. The default regexp filters special characters but keeps hyphens and apostrophes. It also plays nicely with diacritics (UTF and unicode characters): Bayrūt is treated as ["Bayrūt"] and not ["Bayr", "ū", "t"], for example.
Opens and reads files. Pass in a file path or a url instead of a string.

Installation

Add this line to your application's Gemfile:

gem 'words_counted'

And then execute:

$ bundle

Or install it yourself as:

$ gem install words_counted

Usage

Pass in a string or a file path, and an optional filter and/or regexp.

counter = WordsCounted.count(
  "We are all in the gutter, but some of us are looking at the stars."
)

# Using a file
counter = WordsCounted.from_file("path/or/url/to/my/file.txt")

API

WordsCounted

WordsCounted.count(input, options = {})

Tokenises input and initializes a WordsCounted::Counter object with the resulting tokens.

counter = WordsCounted.count("Hello Beirut!")

Accepts two options: exclude and regexp. See Excluding tokens from the analyser and Passing in a custom regexp respectively.

WordsCounted.from_file(path, options = {})

Reads and tokenises a file, and initializes a WordsCounted::Counter object with the resulting tokens.

counter = WordsCounted.from_file("hello_beirut.txt")

Accepts the same options as .count.

Tokeniser

Out of the box the tokeniser includes only alpha chars. Hyphenated tokens and tokens with apostrophes are considered a single token.

#tokenise([pattern: TOKEN_REGEXP, exclude: nil])

tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise

# With `exclude`
tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise(exclude: "hello")

# With `pattern`
tokeniser = WordsCounted::Tokeniser.new("I <3 Beirut!").tokenise(pattern: /[a-z]/i)

See Excluding tokens from the analyser and Passing in a custom regexp for more information.

Counter

The WordsCounted::Counter class allows you to collect various statistics from an array of tokens.

#token_count

Returns the token count of a given string.

counter.token_count #=> 15

#token_frequency

Returns a sorted (unstable) two-dimensional array where each element is a token and its frequency. The array is sorted by frequency in descending order.

counter.token_frequency

[
  ["the", 2],
  ["are", 2],
  ["we",  1],
  # ...
  ["all", 1]
]

#most_frequent_tokens

Returns a hash where each key-value pair is a token and its frequency.

counter.most_frequent_tokens

{ "are" => 2, "the" => 2 }

#token_lengths

Returns a sorted (unstable) two-dimentional array where each element contains a token and its length. The array is sorted by length in descending order.

counter.token_lengths

[
  ["looking", 7],
  ["gutter",  6],
  ["stars",   5],
  # ...
  ["in",      2]
]

#longest_tokens

Returns a hash where each key-value pair is a token and its length.

counter.longest_tokens

{ "looking" => 7 }

#token_density([ precision: 2 ])

counter.token_density

[
  ["are",     0.13],
  ["the",     0.13],
  ["but",     0.07 ],
  # ...
  ["we",      0.07 ]
]

#char_count

Returns the char count of tokens.

counter.char_count #=> 76

#average_chars_per_token([ precision: 2 ])

Returns the average char count per token rounded to two decimal places. Accepts a precision argument which defaults to two. Precision must be a float.

counter.average_chars_per_token #=> 4

#uniq_token_count

Returns the number of unique tokens.

counter.uniq_token_count #=> 13

Excluding tokens from the tokeniser

You can exclude anything you want from the input by passing the exclude option. The exclude option accepts a variety of filters and is extremely flexible.

A space-delimited string. The filter will normalise the string.
A regular expression.
A lambda.
A symbol that names a predicate method. For example :odd?.
An array of any combination of the above.

tokeniser =
  WordsCounted::Tokeniser.new(
    "Magnificent! That was magnificent, Trevor."
  )

# Using a string
tokeniser.tokenise(exclude: "was magnificent")
# => ["that", "trevor"]

# Using a regular expression
tokeniser.tokenise(exclude: /trevor/)
# => ["magnificent", "that", "was", "magnificent"]

# Using a lambda
tokeniser.tokenise(exclude: ->(t) { t.length < 4 })
# => ["magnificent", "that", "magnificent", "trevor"]

# Using symbol
tokeniser = WordsCounted::Tokeniser.new("Hello! محمد")
tokeniser.tokenise(exclude: :ascii_only?)
# => ["محمد"]

# Using an array
tokeniser = WordsCounted::Tokeniser.new(
  "Hello! اسماءنا هي محمد، كارولينا، سامي، وداني"
)
tokeniser.tokenise(
  exclude: [:ascii_only?, /محمد/, ->(t) { t.length > 6}, "و"]
)
# => ["هي", "سامي", "وداني"]

Passing in a custom regexp

The default regexp accounts for letters, hyphenated tokens, and apostrophes. This means twenty-one is treated as one token. So is Mohamad's.

/[\p{Alpha}\-']+/

You can pass your own criteria as a Ruby regular expression to split your string as desired.

For example, if you wanted to include numbers, you can override the regular expression:

counter = WordsCounted.count("Numbers 1, 2, and 3", pattern: /[\p{Alnum}\-']+/)
counter.tokens
#=> ["numbers", "1", "2", "and", "3"]

Opening and reading files

Use the from_file method to open files. from_file accepts the same options as .count. The file path can be a URL.

counter = WordsCounted.from_file("url/or/path/to/file.text")

Gotchas

A hyphen used in leu of an em or en dash will form part of the token. This affects the tokeniser algorithm.

counter = WordsCounted.count("How do you do?-you are well, I see.")
counter.token_frequency

[
  ["do",   2],
  ["how",  1],
  ["you",  1],
  ["-you", 1], # WTF, mate!
  ["are",  1],
  # ...
]

A note on case sensitivity

The program will normalise (downcase) all incoming strings for consistency and filters.

Roadmap

Ability to open URLs

def self.from_url
  # open url and send string here after removing html
end

Contributors

See contributors.

Contributing

Fork it
Create your feature branch (git checkout -b my-new-feature)
Commit your changes (git commit -am 'Add some feature')
Push to the branch (git push origin my-new-feature)
Create new Pull Request

Author: abitdodgy
Source code: https://github.com/abitdodgy/words_counted
License: MIT license

#ruby #ruby-on-rails

Lisa joly

1624658400

PAID NETWORK Review, Is it worth Investing in? Token Sale Coming Soon !!

Hey guys, in this video I review PAID NETWORK. This is a DeFi project that aims to solve complex legal process using decentralised protocols and DeFi products for 2021.

PAID Network is an ecosystem DAPP that leverages blockchain technology to deliver DeFi powered SMART Agreements to make business exponentially more efficient. We allow users to create their own policy, to ensure they Get PAID.

📺 The video in this post was made by Crypto expat
The origin of the article: https://www.youtube.com/watch?v=ZIU5javfL90
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
Cryptocurrency trading is VERY risky. Make sure you understand these risks and that you are responsible for what you do with your money
🔥 If you’re a beginner. I believe the article below will be useful to you ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner
⭐ ⭐ ⭐The project is of interest to the community. Join to Get free ‘GEEK coin’ (GEEKCASH coin)!
☞ **-----CLICK HERE-----**⭐ ⭐ ⭐
Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#bitcoin #blockchain #paid network #paid network review #token sale #paid network review, is it worth investing in? token sale coming soon !!

aaron silva

1622197808

SafeMoon Clone | Create A DeFi Token Like SafeMoon | DeFi token like SafeMoon

SafeMoon is a decentralized finance (DeFi) token. This token consists of RFI tokenomics and auto-liquidity generating protocol. A DeFi token like SafeMoon has reached the mainstream standards under the Binance Smart Chain. Its success and popularity have been immense, thus, making the majority of the business firms adopt this style of cryptocurrency as an alternative.

A DeFi token like SafeMoon is almost similar to the other crypto-token, but the only difference being that it charges a 10% transaction fee from the users who sell their tokens, in which 5% of the fee is distributed to the remaining SafeMoon owners. This feature rewards the owners for holding onto their tokens.

Read More @ https://bit.ly/3oFbJoJ

#create a defi token like safemoon #defi token like safemoon #safemoon token #safemoon token clone #defi token

Marlon Boyle

1594312560

Autonomous Driving Network (ADN) On Its Way

Talking about inspiration in the networking industry, nothing more than Autonomous Driving Network (ADN). You may hear about this and wondering what this is about, and does it have anything to do with autonomous driving vehicles? Your guess is right; the ADN concept is derived from or inspired by the rapid development of the autonomous driving car in recent years.

Image for post

Driverless Car of the Future, the advertisement for “America’s Electric Light and Power Companies,” Saturday Evening Post, the 1950s.

The vision of autonomous driving has been around for more than 70 years. But engineers continuously make attempts to achieve the idea without too much success. The concept stayed as a fiction for a long time. In 2004, the US Defense Advanced Research Projects Administration (DARPA) organized the Grand Challenge for autonomous vehicles for teams to compete for the grand prize of $1 million. I remembered watching TV and saw those competing vehicles, behaved like driven by drunk man, had a really tough time to drive by itself. I thought that autonomous driving vision would still have a long way to go. To my surprise, the next year, 2005, Stanford University’s vehicles autonomously drove 131 miles in California’s Mojave desert without a scratch and took the $1 million Grand Challenge prize. How was that possible? Later I learned that the secret ingredient to make this possible was using the latest ML (Machine Learning) enabled AI (Artificial Intelligent ) technology.

Since then, AI technologies advanced rapidly and been implemented in all verticals. Around the 2016 time frame, the concept of Autonomous Driving Network started to emerge by combining AI and network to achieve network operational autonomy. The automation concept is nothing new in the networking industry; network operations are continually being automated here and there. But this time, ADN is beyond automating mundane tasks; it reaches a whole new level. With the help of AI technologies and other critical ingredients advancement like SDN (Software Defined Network), autonomous networking has a great chance from a vision to future reality.

In this article, we will examine some critical components of the ADN, current landscape, and factors that are important for ADN to be a success.

The Vision

At the current stage, there are different terminologies to describe ADN vision by various organizations.
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Even though slightly different terminologies, the industry is moving towards some common terms and consensus called autonomous networks, e.g. TMF, ETSI, ITU-T, GSMA. The core vision includes business and network aspects. The autonomous network delivers the “hyper-loop” from business requirements all the way to network and device layers.

On the network layer, it contains the below critical aspects:

Intent-Driven: Understand the operator’s business intent and automatically translate it into necessary network operations. The operation can be a one-time operation like disconnect a connection service or continuous operations like maintaining a specified SLA (Service Level Agreement) at the all-time.
**Self-Discover: **Automatically discover hardware/software changes in the network and populate the changes to the necessary subsystems to maintain always-sync state.
**Self-Config/Self-Organize: **Whenever network changes happen, automatically configure corresponding hardware/software parameters such that the network is at the pre-defined target states.
**Self-Monitor: **Constantly monitor networks/services operation states and health conditions automatically.
Auto-Detect: Detect network faults, abnormalities, and intrusions automatically.
**Self-Diagnose: **Automatically conduct an inference process to figure out the root causes of issues.
**Self-Healing: **Automatically take necessary actions to address issues and bring the networks/services back to the desired state.
**Self-Report: **Automatically communicate with its environment and exchange necessary information.
Automated common operational scenarios: Automatically perform operations like network planning, customer and service onboarding, network change management.

On top of those, these capabilities need to be across multiple services, multiple domains, and the entire lifecycle(TMF, 2019).

No doubt, this is the most ambitious goal that the networking industry has ever aimed at. It has been described as the “end-state” and“ultimate goal” of networking evolution. This is not just a vision on PPT, the networking industry already on the move toward the goal.

David Wang, Huawei’s Executive Director of the Board and President of Products & Solutions, said in his 2018 Ultra-Broadband Forum(UBBF) keynote speech. (David W. 2018):

“In a fully connected and intelligent era, autonomous driving is becoming a reality. Industries like automotive, aerospace, and manufacturing are modernizing and renewing themselves by introducing autonomous technologies. However, the telecom sector is facing a major structural problem: Networks are growing year by year, but OPEX is growing faster than revenue. What’s more, it takes 100 times more effort for telecom operators to maintain their networks than OTT players. Therefore, it’s imperative that telecom operators build autonomous driving networks.”

Juniper CEO Rami Rahim said in his keynote at the company’s virtual AI event: (CRN, 2020)

“The goal now is a self-driving network. The call to action is to embrace the change. We can all benefit from putting more time into higher-layer activities, like keeping distributors out of the business. The future, I truly believe, is about getting the network out of the way. It is time for the infrastructure to take a back seat to the self-driving network.”

Is This Vision Achievable?

If you asked me this question 15 years ago, my answer would be “no chance” as I could not imagine an autonomous driving vehicle was possible then. But now, the vision is not far-fetch anymore not only because of ML/AI technology rapid advancement but other key building blocks are made significant progress, just name a few key building blocks:

software-defined networking (SDN) control
industry-standard models and open APIs
Real-time analytics/telemetry
big data processing
cross-domain orchestration
programmable infrastructure
cloud-native virtualized network functions (VNF)
DevOps agile development process
everything-as-service design paradigm
intelligent process automation
edge computing
cloud infrastructure
programing paradigm suitable for building an autonomous system . i.e., teleo-reactive programs, which is a set of reactive rules that continuously sense the environment and trigger actions whose continuous execution eventually leads the system to satisfy a goal. (Nils Nilsson, 1996)
open-source solutions

#network-automation #autonomous-network #ai-in-network #self-driving-network #neural-networks