What have all these…

…in common with all these…

Before 2020 you will also be talking to the bottom half…

 Gatfol technology will make this possible. Appliance chips only need
to carry the bare minimum of operational instructions and Gatfol will
connect the rich tapestry of human everyday language to it.

Without Gatfol  “…it is sweltering in here…” will mean zero to your wall air conditioner,

With Gatfol  “down temp” – will be a cinch

Do not buy it if it is not Gatfol -enabled

 


Gatfol is currently in limited scope pre-beta testing.

Gatfol is a massively scalable algorithmic semantic analysis engine that can be used to flag posts in social media data streams containing word groups that semantically crystallises to general public safety “danger” words.

In random test runs, Gatfol flagged the following public post found on several web sites with an “Extreme caution” warning :

Roses-Thorns

February 28, 2012 – 2:10 pm

This action of shooting bullies is a new trend in U.S.America that proves this theory of subconscious counter domestic terrorism is a reflex response.

To add; Zealot Religions are creating failure, and being check-mated by National Security via acts of domestic terrorism bills passed. Therefore parenting and then zealot religion by-proxy is the cannon fodder of and or actions of domestic terrorism, bullying, a hate-crime and other acts same end. Bullying is a hate-crime, and hate-crimes are acts of domestic terrorism, check-mate.[period].

Some suggest we should have counter domestic terrorism response teams in public schools to stop bullying, and if bullying is not that serious then why are so many bullies being plucked like chicken feathers from society recently with bullets into their heads? Dead.

Perhaps this dead end result being that parenting, zealot religion influences, the school faculty, local, state, and federal law enforcement are idiots. End, parenting teaches and allows children bulling, true in fact, and have become a trend of irrational critical non-thinking, therefore parental child abuse and religion gets away Scotty Free.

Since this counter bullying by lethal violence and force is common these days reflects obvious society is out of touch with reality completely regards proper ethics, morales, and civil conduct. Blame parenting, we have dead children, obvious too. These similar public school bullying events is a hate-crime and children end up dead.

This also applies in reflection especially to DHS/Department of Homeland Security whom have become useless regards this fact in definition in which DHS by origin of creation made these laws to combat domestic terrorism, and by indirect recourse religion and parenting skill sets unethical and immoral targeted, thus perverse by definition.

The coast guard was to take DHS position which was the original plan till congress, senate, and lobbyists wanted their noses up everyones business making laws to serve who(?), and now a mess.

Increasing majority are supporting military only federal martial law, indefinite; -till the infection is cleansed. Because no one else appears to be doing the right thing about this, believe in this true.

Bullying is an act of domestic terrorism because it represents and is a hate-crime.

Hate-crimes are acts of domesticated terrorism tought by the father and mother as parenting -a single entity infecting the off-spring host child which is ethically and morally guided by religion; -So national Security translates.

Many Zealot Religions these days motivate, instruct passive/aggressive bullying, (acts of domesticated terrorism), to herd in followers into their idea of proper manhood, citizenry and as end result donations to the zealot religious organization(s).

Many in society consider this young man, (“TJ Lane”), have taken the real manhood approach to illuminate domestic terrorism from society/public schools, (an up coming popular recursive reflex response to hate-crimes in public schools, as typed – mentioned).

Many in society also consider his, (“TJ Lane”), actions should not to be formalized as a terrorist, but in fact a blessing waking up U.S. to bullying. Have mentioned by others and should the parenting parents be shot in the head as well for raising a domestic terrorists equally? No! Yes! Undecided!

“TJ Lane” is considered a hero in society by many many bullied children throughout our U.S.culture, (equally as other such past events), of those being tortured by bullies feel a mental release and pleasure in observation through these events of counter domesticated terrorist life-styles defeated. These victimized bullied citizens are babe children, teens and young adults who have many times in the past been murdered by these bullies in which these murder cases never solved and why never solved? Is parenting, religion a crime and their ethical moral instructional is terrorism; The -U.S.gov suggested(?).

Have parenting and crew lead society out of control into hideousness of murdering children by bullying. Bullies end result kill citizens as if a target in moral ethical reflection of parenting and religion, yes true, believe.

Parenting, zealot religion teaches bulling. “TJ Lane” felt his life was in danger, obviously. Civilian/Federal law enforcement push these events of bully domestic terrorism under the rug with a huge blind eye and why? -Obvious.

Many children, teens, young adults feel for all the children murdered by bullies, and that “TJ Lane” is an obvious law abiding citizen and to many a hero -vers- the domesticated homegrown terrorism, bullies, a hate-crime. As many children applaud “TJ Lane” and while Outside the box looking at society appears being baited that parenting and religion support child abuse and are terrorist cell-leaders; by National Security definition of Domestic Terrorism.[end-game].

(mashable.com/2012/02/27/ohio-school-shooter-facebook/)

(cbsnews.com/8601-201_162-57386781-2.html?assetTypeId..)

(rssrover.com/living/religion/)

(wytv.com/content/…/3kA2nY0TSkeqCyFvB1yEAg.cspx)

[Please note that Gatfol currently does not systematically scrub data streams for flagging purposes. Full Gatfol beta release will be before end first quarter 2012.]

 

Chardon Shooting: Third Student Dies After Ohio Rampage

28 February 2012 (BBC News)

Facebook Post By Chardon Ohio High School Shooting Suspect T.J. Lane
Posted on Dec. 30, 2011

In a time long since, a time of repent, The Renaissance. In a quaint lonely town, sits a man with a frown. No job. No family. No crown. His luck had run out. Lost and alone. The streets were his home. His thoughts would solely consist of “why do we exist?” His only company to confide in was the vermin in the street. He longed for only one thing, the world to bow at his feet. They too should feel his secret fear. The dismal drear. His pain had made him sincere. He was better than the rest, allthose ones he detests, within their castles, so vain. Selfish and conceited. They couldn’t care less about the peasents they mistreated. They were in their own world, it was a joyous one too. That castle, she stood just to do all she could to keep the peasents at bay, not the enemy away. They had no enemies in their filthy orgy. And in her, the castles every story, was just another chamber of Lucifer’s Laboratory. The world is a sandbox for all the wretched sinners. They simply create what they want and make themselves the winners. But the true winner, he has nothing at all. Enduring the pain of waiting for that castle to fall. Through his good deeds, the rats and the fleas. He will have for what he pleads, through the eradication of disease. So, to the castle he proceeds, like an ominous breeze through the trees. “Stay back!” The Guards screamed as they were thrown to their knees. “Oh God, have mercy, please!” The castle, she gasped and then so imprisoned her breath, to the shallow confines of her fragile chest. I’m on the lamb but I ain’t no sheep. I am Death. And you have always been the sod. So repulsive and so odd. You never even deserved the presence of God, and yet, I am here. Around your cradle I plod. Came on foot, without shod. How improper, how rude. However, they shall not mind the mud on my feet if there is blood on your sheet. Now! Feel death, not just mocking you. Not just stalking you but inside of you. Wriggle and writhe. Feel smaller beneath my might. Seizure in the Pestilence that is my scythe. Die, all of you.

 

Rich multi-level automatic image- and video tagging on a massive scale


Conservative estimates are that the billions of web images today have at most been semantically (word meaning) tagged to a mere 1% of completion.

The holy grail of photo search is to have unambiguous automatic machine recognition and tagging of images covering detailed image components.

Current technology “recognises” image detail to an accuracy level spanning substantial uncertainty around most- or all individual visual components. Gatfol provides a solution to “crystallise” individual image elements from vague collective descriptions using Semantic Intelligence Filter Technology (SIFT).

With current automatic machine tagging every component or discrete pixel-grouping in an image can at best be labeled with several ambiguous descriptions each.

At left we could be looking at a work bench with a painters cap and several containers with dark- and yellow paints and some metallic tools, perhaps a green picnic blanket with tea or coffee and vegetation against a light sky, perhaps a green corn field with white grain silo’s etc.

As long as ambiguous image content descriptions are available, Gatfol semantic intelligence is powerful enough to stepwise “crystallise” the ambiguities and provide large-volume detailed tags that all relate semantically.

This is the tag results for the image above from one of the only web auto-tagging services still available (ALIPR) :

Even though utterly vague and descriptively extremely wide, Gatfol can proceed successfully from this  (in all other ways) – incomplete start.

Gatfol semantic crystallisation is not only many-to-single down but also single-to-many upwards. Gatfol takes each of the supplied tags above and with SIFT iterates through wider and wider semantic word groupings, continuously checking back to available tags for matchings.

If the shiny object at the bottom of the image is not a small mirror, work tool, ball bearing, lightbulb or lens – but a teaspoon, the white object with dark contents is likely a tea or coffee cup with contents and not a bowl with soup – if coffee, then the white object with partial covering of  a “hat” at right is not the “best fit” semantically. If a teacup, then the green striped object is likely a tea cosy with the white object adjacent, a tea pot. Given these crystallisations, the yellow blob at back is likely not paint, but either jam, butter or coloured ice cream. Given a “tea pot and cups” element grouping, the dark bands at back right is likely a chair structure, and given this, the light green object is semantically unlikely to be a picnic blanket or green meadow, but rather a table cloth. In this iterative manner a large volume of detailed tagging is obtained from a limited initial set of ambiguous descriptions.

Gatfol provides the base for rich multi-level automatic image tagging on a massive scale.

Gatfol adds huge search power through web images being labeled with a hundred or more component text tags each, instead of the one, two or three of today :

 

 

Mr Bernard Fadmof
B. Comm, PG Dip. Tax Law, BA Psych, CA (MA), EMDA (GF)

You received an e-mail from Mr Fadmof :

Hi

I saw an article on your company and am very intrigued. I own WorldWideWeb.com and will be developing it very soon and would like to integrate your products on WorldWideWeb. Right now I am involved with a company that is going public in the next few months and I have several million of its shares. They are using a self distribution model and will be doing a second IPO and then that same team will be available to me and I will make WorldWideWeb.com public. I will be looking for companies like yours to purchase a percentage interest using the pre-IPO stock. Once WorldWideWeb is public you will then be able to start selling that stock for growing your company. I am open to investing some money from my existing stock once it goes public. I will find 6 to 12 companies that have great products like you and WorldWideWeb will buy a minority interest. Each company will continue to run their own operation and will be promoted under the WorldWideWeb Brand. As you know everyone knows the phrase ” WorldWideWeb ” so with a collection of services we will become a major factor in the Internet world.

You have 10 minutes available to reply and make a decision as to proceed…

Already spooked by the scary pre-IPO’s you have no idea who Mr Fadmof is or what he is connected to. A quick Google search just throws up endless spaghetti diagrams.

How do you quickly connect all the web concepts around Mr Fadmof? And – more seriously – how do you then separate the wheat from the chaff?

Gatfol is a massively parallel algorithmic system specifically developed to find concept connections on the web where ordinary keyword search proves too weak.

To quickly know what we are working with here, we feed Mr Fadmof’s name and website address into our Gatfol enabled Mozilla or IE browser.

The Gatfol results immediately show a concern. Gatfol could find nothing of substance behind a barely known Fadmof enterprise called Kaftan Tag Green (other than a lifter product, some reflective panels – these are even linked to WorldWideWeb itself through Slideshare.net – and KTG being really just a reseller of ExonTech Builders Inc products of whom Gatfol could not find a direct ownership link back to Mr Fadmof. Gatfol also posits what KTG’s exact connection is to Grown Builders Source regarding the Jetfreeze product that appears on the KTG website.

Of larger concern is WorldWideWeb itself. Other than a few self promoting paragraphs, Gatfol again brings up zero “substantiality” – no company info, no press releases, no sales data, no employee information, no presentations, no partner info, no customer statistics and no funding history.

Gatfol flags as a real concern the relative minor investments and startups in Mr Fadmof’s history of copy store, computer store, golf club manufacturer and electric bike distributor.

Gatfol highlights that – with several million shares in an IPO, how does re-selling an oil filter on the KTG site make business sense? Manufacturing of course – but reselling? Gatfol picked up Kaftan Tag Green carries as “satisfied customers” huge enterprises like Martin Marietta, Chevron-Texaco and Halliburton, but could not find any wider verification of business relationships between KTG and any of these firms.

With Kaftan Tag Resorts  (another Fadmof startup/spin-off) stating annual sales at $10-$50 million per Alibaba.com and with property deposits alone each up to $1 million, why is the web connection for Fadmof resort developments given as 4tpc.com with no further evidence of any actual property sales? Gatfol showed that KTR also listed as country of origin “Hong Kong” as per Tradeim.com. With no other equivalent web entries, how can this connection be explained? Is Spartum Resort Management independent from Mr Fadmof in terms of ownership (Gatfol links Spartum as another possible obscure Fadmof historic enterprise)? Gatfol could not find any publically trading entity by this name other than that belonging to Spartum Properties Ltd.

If you use Gatfol in every single web search interaction, you would appreciate an investment relationship with Mr Fadmof ONLY pending clarification of the above…

Gatfol makes business puzzles come together …

 

 

…the fact that he is planning to sell his Italian-based furniture manufacturing empire for $6.8 million in six months time and distribute $1.5 million to each of his three married sons currently all living in small apartments in Milan – all three of whom have young children approaching school-going age….

AND

….you as the banker are unaware of this intention….

The list of all the banking investment-, insurance, home-ownership, credit card-, personal loan-, online services-, business finance-, hire purchase- and vehicle finance products that can be brought to Mr Lampre and his sons’ attention before they shop around with the multitude of other equivalent service providers out there, is large.

You notice that Mr Lampre did in fact let the bank know about details of his selling intentions several months back when he applied for a new lending account :

In many instances the most important client information from a bank’s commercial perspective is unstructured data around a client’s broader life, and – in many instances – this information is already available in-house.

Even though the unstructured “Notes” relating to Mr Lampre was typed into the online database, it was never returned in any global searches, as the most relevant search query was “…all clients selling substantial assets..”. None of the crucial search keywords were hit with Mr Lampre’s “…receive large fund return re business decision…”.

Luckily there is a core solution…

Gatfol is the foremost technology to turn any vague- or initial search input into a massive parallel set of stealth queries that will hit almost all the desired keywords.

 With Gatfol – Mr Lampre finishes the race, sells his business, enriches his sons, buys
into several expensive bank products and loses some weight…(well…some of it…)

 

 

You have a large online catalogue of products…


I am a buyer looking for very specific soccer boots but I cannot remember the brand name or model. All I can remember is that I saw somewhere that they provided the fastest running ability. Amongst many thousands of brands and models your catalogue carries Adidas F50 Adizero II Prime FG Soccer Cleats, with the only description being “best regarding rate,speed…”.

I use your catalogue search facility with “soccer boots fast running ability..”.
I get no hits on the keywords for the specific product that I want and you carry.
I know that if I search with different keywords and combinations I might get my product, but I do not have 20 minutes to spend on this.
I decide to run by the sports store on the corner and you lose a sale.

 

Now let’s say your catalogue was Gatfol enabled…..

Gatfol has worked out a way to show our actual human world to machines in “image matrixes” that they can understand:


Because Gatfol “knows” billions of real-world concepts and their relationships with each other, and because Gatfol operates invisibly and seamlessly between customers and your online catalogues, my query of “soccer boots fast running ability” is within a fraction of a second cloned into hundreds of thousands of semantic (meaning) equivalent queries ranked according to the best query first that will hit your catalogue. Gatfol calculates that if “soccer boots” becomes “cleats” and “fast running ability” becomes “speed”, the chances of a hit to your catalogue is 92,78% compared to 24.31% for my original search query.

Gatfol catches all those lost hits and lost dollars…

 

 

Both Anders Breivik and Jared Lee Loughner posted substantial digital repositories before their brutal extremist acts. In an ideal world we should be able to track social network text traffic to pinpoint individuals and groups narrowing in on behaviour that can on many levels be severely harmful to society.

Currently this is very difficult.

As is almost always the case, online threats and circumstantial postings are ambiguous and contain little or no clear-cut hits against “danger word” listings. With hindsight it is unfortunately evident in most cases that enough online material did exist “pre-terror” to perhaps prevent or minimise the planned actions.

Gatfol technology is ideal in facilitating danger-word hits where none exist in the source data.

Let’s see how by looking at the above case studies :

Anders Breivik’s online manifesto contained the following sentence : “I simulate various future scenarios relating to resistance efforts, confrontations with police, future interrogation scenarios, future court appearances, future media interviews etc. “Brief skimming brings up possible danger-words in “scenarios”, “resistance”, “police” etc, but nothing that does not appear in many daily online Tweets, Facebook postings or blog utterances.

The Gatfol semantic engine picked up these danger- words but flagged with extreme sensitivity the word combination “media interviews”. Together with “scenarios”, “resistance”, and “police”, the phrase “media interviews” instantaneously and uniquely crystallised in the Gatfol SIFT matrixes as “public violence of newsworthy effect”.  Note that the latter semantic equivalent phrase hit totally different keywords that might be required by available danger-word lists. 

Gatfol processes millions of concept permutations per second and the volume of input under scrutiny can therefore be quite large. Amongst much online ramblings and “word-salad” postings by Jared Lee Loughner, the following excerpts were analysed by Gatfol :

Hello, my name is Jared Lee Loughner.

This video is my introduction to you! My favorite activity is conscience dreaming; the greatest inspiration for my political business information. Some of you don’t dream — sadly.

Firstly, the current government officials are in power for their currency, but I’m informing you for your new currency! If you’re treasuerer of a new money system, then you’re responsible for the distributing of a new currency. We now know — the treasurer for a new money system, is the distributor of the new currency. As a result, the people approve a new money system which is promising new information that’s accurate, and we truly believe in a new currency. Above all, you have your new currency, listener?

Secondly, my hope – is for you to be literate! If you’re literate in English grammar, then you comprehend English grammar. The majority of poeple, who reside in District 8, are illiterate — hilarious. I don’t control your English grammar structure, but you control your English grammar structure.

Thirdly, I know who’s listening: Government Officials, and the People. Nearly all the people, who don’t know this accurate information of a new currency, aren’t aware of mind control and brainwash methods. If I have my civil rights, then this message wouldn’t have happen.

 In conclusion, my ambition – is for informing literate dreamers about a new currency; in a few days, you know I’m conscience dreaming! Thank you!

 My Final Thoughts: Jared Lee Loughner!

 Most people, who read this text, forget in the next 2 second!
The population of dreamers in the United States of America is less than 5%!
If 987,123,478,961,876,341,234,098,601,978,618 is the year in B.C.E. then the previous year is 987,123,478,961,876,341,234,098,601,978,619 B.C.E.987,123,478,961,876,341,234,098,601,978,618 is the year in B.C.E.
Therefore, the previous year of 987,123,478,961,876,341,234,098,601,978,619 B.C.E.
If B.C.E. years are unable to start then A.D.E. years are unable to begin.
B.C.E. years are unable to start.
Thus, A.D.E. years are unable to begin.
If A.D.E. is endless in year then the years in A.D.E. don’t cease.
A.D.E. is endless in year.
Therefore, the years in A.D.E. don’t cease.

If I teach a mentally capable 8 year old for 20 consecutive minutes to replace an alphabet letter with a new letter and pronunciation then the mentally capable 8 year old writes and pronounces the new letter and pronunciation that’s replacing an alphabet letter in 20 consecutive minutes.
I teach a mentally capable 8 year old for 20 consecutive minutes to replace an alphabet letter with a new letter and pronunciation.
Thus, the mentally capable 8 year old writes and pronounces the new letter and pronunciation that replaces an alphabet letter in 20 consecutive minutes.
Every human who’s mentally capable is always able to be treasurer of their new currency.
If you create one new currency then you’re able to create a second new currency.
If you’re able to create second new currency then you’re able to create third new currency.
You create one new currency.
Thus, you’re able to create a third new currency.
You’re a treasurer for a new currency, listener?
You create and distribute your new currency, listener?
You don’t allow the government to control your grammar structure, listener?
If you create one new language then you’re able to create a second new language.
If you’re able to create a second new language then you’re able to create a third new language.
You create one new language.
Thus, you’re able to create a third new language.

All humans are in need of sleep.
Jared Loughner is a human.
Hence, Jared Loughner is in need of sleep.
Sleepwalking
If I define sleepwalking then sleepwalking is the act or state of walking, eating, or performing other motor acts while asleep, of which one is unaware upon awakening.
I define sleepwalking.
Thus, sleepwalking is the act or state of walking, eating, or performing other motor acts while asleep, of which one is unaware upon awakening.
I’m a sleepwalker – who turns off the alarm clock.

All conscience dreaming at this moment is asleep.
Jared Loughner is conscience dreaming at this moment.
Thus, Jared Loughner is asleep.
Terrorist
If I define terrorist then a terrorist is a person who employs terror or terrorism, especially as a political  weapon.
I define terrorist.
This, a terrorist is a person who employs terror or terrorism, especially as a political weapon.
If you call me a terrorist then the argument to call me a terrorist is Ad hominem.
You call me a terrorist.
Thus, the argument to call me a terrorist is Ad hominem.
Every United States Military recruit at MEPS in Phoenis is receiving one mini bible before the tests.
Jared Loughner is a United States Military recruit at MEPS in Phoenix.
Therefore, Jared Loughner is receiving one mini bible before the tests.
I didn’t write a belief on my Army application, and the recruiter wrote on the application; None.
The majority of citizens in the United States of America have never read the United States of America’s Constitution.
You don’t have to accept the federalist laws.
Nonetheless, read the United States of America’s Constituion to apprehend all of the current treasonous laws.
You’re literate, listener?
If the property owners and government officials are no longer in ownership of their land and laws from a revolution then the revolutionary’s from the revolution are in control of the land and laws.
The property owners and government officials are no longer in ownership of their land and laws from a revolution.
Thus, the revolutionary’s from the revolution are in control of the land and laws.

In conclusion, reading the second United States Constition, I can’t trust the current government because of the ratifications: The government is implying mind control and brainwash on the people by controlling grammar.
No! I won’t pay debt with a currency that’s not backed by gold and silver!
No! I won’t trust in God!
What’s government if words don’t have meaning?

Given the above – what would we as human filters uniquely flag as strongly pointing to a possible public assassination attempt ? – (eventually that of Congresswoman Giffords in Arizona not long after the above online postings).

Gatfol employs a massively parallel filtering technology called SIFT that looks at data from multiple simultaneous “focus points”. Any multiword grouping can be equivalently transformed into any other multiword group, keeping semantic- and grammar integrity intact. This enables Gatfol to “see” semantic perspective in large datasets that is not immediately evident to human analysts or investigators.

Gatfol flagged the following phrase excerpts “in a few days, you know I’m conscience dreaming”, “final thoughts”, “political weapon” and interestingly enough “all humans are in need of sleep” which Gatfol, given the preceding phrases, semantically transcribed to “permanent sleep”. Together with Loughner’s words that Gifford was “stupid & unintelligent” (he apparently met her in 2007) and the fact that she was appearing in person in Tucson not far from Lougner’s place of stay flags critically of planned personal-directed injury.

With Gatfol’s huge processing power, inline streaming analysis of real-time social media
- and other data – is critical on any local, national and international security analysis level.

 


Massively scalable hierarchical semantic complexity crystallization of unstructured data

(Multiple keyword views of text)

15 September 2012

(Application for non-cloud, offline, local machine simulated-Hadoop, scalable processing for military-, homeland security- and other classified, private and confidential data streams)

Table of Contents:

Part I: Field

Part II: Background

Part III: Objective

Part IV: Innovation

Part V: Technical summary

(i) Programmatic data

(ii) Programmatic structure

Part VI: Technical example (descriptive)

Part VII: Technical examples (illustrative)

Part VIII : Technical example (diagrams)

Part IX : Applications

Part X : Application differentiation and strengths

Part XI : Application performance

Part XII : Application status

Part XIII : Application updates

Part XIV : Contact information

Part XV : Addendum (USA focus)

Part XVI : General disclaimers

 

Part I: Field

This software application relates to information processing and more specifically relates to scalable stepwise analysis of unstructured natural language data to generate multiple alternative semantic views of text and assist with simplifying and speeding-up of real-time analysis of large volume inline data streams. The latter could be real-time security image- or video tagging”, inline analysis of Twitter, Facebook or security scanning of mobile phone SMS and voice data.

Part II: Background

Enabling computers to understand language remains one of the hardest problems in text analysis. Language is highly contextual. Often the same words have different meanings in different contexts and small differences in sentence structure can lead to totally different meanings. At the same time, a great number of different sentence structures can have the same meaning.

Most information analytics use text-based probing tools. To return accurate results, search- or summarization algorithms must be able to apply some form of language interpretation to query strings. Most of the time interpretation will be limited to simple keyword determination for extraction against data repositories.

One simple increase in extraction complexity is to perform keyword synonym replacements – usually on a single-to-single word basis. Thus the word “picture” may have the synonym “photo” so that analytical searches against “picture of Grand Canyon”, also extracts “photo of Grand Canyon”.

Merely applying synonyms can easily lead to wrong results. If a search is for “history of motion pictures” then the word “pictures” must not be substituted with “photos” because the string “history of motion photos” is meaningless. As another example, if an analysis includes a search for “HP wide screen monitor” and we operationally substitute the synonym “detector” for “monitor”, and “shutter” for “screen”, completely irrelevant returns would be delivered.

General text extraction analysis therefore also needs to be able to perform contextual (meaning or semantic) processing so that it “knows”, for example, that the string “HP wide screen monitor” has nothing to do with shutters or detectors and that the term “motion photo” is not the same as “motion picture”.

Even words which are normally interchangeable can lead to totally different meanings when used in different contexts. A search for “arm reduction” probably has to do with cosmetic surgery whereas “arms reduction” relates to reducing stockpiles of weaponry. When longer sentences are involved, erroneous permutations become exponentially more complex.

It is very difficult for machines to semantically interpret longer search queries so as to deliver meaningful extraction results. As illustration, a search on Google TM for “Software companies founded before 1990 with a current turnover of more than $100 million” yields a list of largely irrelevant references, even though the search query is perfectly clear to a human and the information is doubtless available on the Internet.

Because existing analytics rely primarily on keywords and basic synonym replacement rather than the semantic “context” of words, most extraction operations – through necessity – spirals down to what has been dubbed “caveman speak”, where for example, an extraction of popular seafood restaurants in Seattle might end up as a search for “seafood Seattle” rather than “provide a list of good affordable seafood restaurants in Seattle”.

Existing semantic analysis engines are weak at converting complex contextual meanings in search inputs to meaningful results.

Much of the web and proprietary datasets in which machine-readable data is available, also contains “meta data” (information about data) that guides language analytic tools on what a subset of text or a topic is about. This meta-data can be in the form of structuring (i.e. columns with textual column headers), instruction sets that act as processing “directors”, or purely be textual synopsis of following information – all to enable non-human analytic tools to understand the meaning of information directly, without the interpretation problems that plague unstructured text .

Currently, certain defined domains – for example, airline booking systems – operate in this way. Thus the term “JFK” in an airline booking system means only John F Kennedy International airport in New York, not to the former US president or other terms that may have these three letters as their acronym. Some hierarchical analytic engines identify higher-level groupings or categories and filter out irrelevant results by “vertically” applying selected categories only. Thus a search for “chicken” might identify categories of “animals” and “recipes” and allow the analytics for instance to filter so as to only search within one of the two categories.

The goal of all-encompassing semantic search has not yet fully been realized, despite ongoing efforts to index, categorize and associate concepts in multitudes of datasets worldwide. The main problem is the enormity of the task involved in performing such identification and association, which requires huge structured lexicons and ontologies as guides.

It would be advantageous to have a completely autonomous self-replicating system that is able to build a contextual language model so that search strings can be interpreted more accurately by language analytic engines, without the need to categorize or index existing content.

Part III: Objective

It is the object of the Gatfol software application to provide a massively scalable but easy-to-install system in the form of a simulated Hadoop method (distributed multiple redundant master and slave nodes) for the stepwise crystallization of natural language (English) text input from semantic complexity to semantic simplicity on single ordinary desktop computers to enable extremely fast searches of multi-keyword groups to be made into very large data streams.

Part IV: Innovation

This software application is subsumed under provisional patent number 61/476,917 lodged 19 April 2011 in New York USA under the international searching authority of the United States Patent and Trademark Office (USPTO) (ISA/US) with the title of “A SYSTEM AND A METHOD FOR GENERATING MULTIPLE ALTERNATIVE SEARCH STRINGS TO FACILITATE IMPROVED COMPUTERIZED SEARCH”, and also under PCT international application PCT/IB2012/051870 on 16 April 2012, submission number 44897 with the International Bureau of the World Intellectual Property Organization in Geneva Switzerland, with the title “A COMPUTERIZED SYSTEM AND A METHOD FOR PROCESSING AND BUILDING SEARCH STRINGS”.

Part V: Technical summary

(i) Programmatic data

The crux of the application is the comparison of left-right ambidextrous grammar signatures for all keywords in the search input and the application of Markov chain analysis to create multiword groups of similar semantics and intact grammar corresponding to the original input.

(ii) Programmatic structure

The application comprises multiple redundant local machine based master and slave software nodes to process input in parallel to ensure extremely high throughput speeds at very large input volumes, regardless of machine- and CPU hardware configurations . Any amount of nodes can be used with processing speed increases proportional to the volume of nodes applied.

The application engine as well as all data inflow into the application and all resultant outflow is fully contained on the local machine. No programmatic calls are made outside of the local machine for any reason at any time whatsoever. This characteristic is critical for application in security classified military- or other confidential data stream environments.

Part VI: Technical example (descriptive)

For ease of understanding a general natural language example is used below. Inferences and application to the military- and security contexts can be made quite easily:

At a first stage, popular words are removed from the input search string. Popular words are identified as those words with a total frequency in each software processing node word relationship database that is higher than a predetermined threshold – in other words, those words that appear very commonly in the total body of text as indexed by the node.

Consider the search string “Where can I get cool spring water?”. The words “where”, “can”, “I” and “get” will be identified as popular words, with the remaining words “cool spring water” being non-popular words. This keyword compression maximizes search speed.

At the next stage, the non-popular words are linked in two-word groups from left to right with the last word of any preceding two-word group forming the first word of the next two-word group. In this case, there are two two-word groups, namely “cool spring” and “spring water”.

Each two word group is then analyzed according to its ambidextrous grammar signature as follows: the reverse signature of the first word and the forward signature of the second word are obtained. The forward and reverse group signatures are combined into a single left-right  “word-group” signature. For example, if the forward signature of “spring” in the node word relationship database is the following:

42551 (“spring”): 2211 (“day”), 21 | 53342 (“was”), 15 | 3321 (“morning”)

and the reverse signature of “cool” is the following :

1221 (“cool”): 49923 (“very”), 19 | 3221 (“stay”), 13 | 9219 (“really”)

then the ambidextrous signature of “cool spring” could be the following :

(“cool spring”): 2211 (“day”) | 49923 (“very”) | 53342 (“was”) | 3221 (“stay”) | 9219

(“really”) | 3321 (“morning”)

Importantly, the final ambidextrous word relationship signature gives the forward and reverse relationship of the two words “cool spring” in combination, as if the word combination is a single (but natural language wise currently “non-existing”) word.

Next the node signature database is searched to look for close signature matches for the ambidextrous “word group” signature. By comparing the ambidextrous signature to the word signature database and looking for close matches, single words can be found that are semantically similar to the two word group, “cool spring”. In this manner a crystallization from high grammar- and semantic complexity to simplicity is achieved.

The previous stage is repeated for each of the other two-word groups in the search string, which in this example is the second two word group, “spring water”. In this way, one or more other words are identified that are semantically similar to “spring water”. Combining the results of both iterations yields a number of two word strings that are each semantically similar to “cool spring water”. For example, if one of the words identified as semantically similar to “cool spring” was “refreshing” and one of the words identified as semantically similar to “spring water” was “liquid”, then “refreshing liquid” would be identified as semantically similar to “cool spring water”.

Using the substitute word or words for “cool spring” and “spring water”, and repeating the procedure with the substitute two words (e.g. “refreshing liquid”) using a simplified Markov chain analysis algorithm, it is possible to repeat the preceding stages to find individual words that are semantically similar to the three words, “cool spring water”. In this example, the single word “juice” could, for example, be identified as semantically similar to “refreshing liquid”.

By repeating the substitution procedure in all the previous stages a specified number of times, it is possible to obtain multiple alternate words for the extracted non-popular words. The alternate words can be a string that has any number of words fewer than the extracted non-popular words. For example, if 5 non-popular words were extracted, then alternate word string of 4, 3, 2, or 1 word(s) can be generated. In the case of the three word string, “cool spring water”, the following alternatives could perhaps have been generated:

“refreshing water”

“cool spring liquid”

“refreshing liquid”

“aqua”

While the method described above enables the extracted non-popular portion of the search string to be substituted with semantically similar words, it does not necessarily follow that the semantically similar words will be grammatically correct when substituted back into the original search string. For example, in the search string, “Where can I get cool spring water?”, if the word “season” is identified as semantically similar to the two words “cool spring”, substituting “season” into the original string yields the phrase, “Where can I get season water?” which clearly is not grammatically correct.

In this case, the meaning is also not as originally intended because of the multiple meanings of the word “spring”. In most cases, where the substituted words yield a sentence that is grammatically incorrect, the meaning of the alternative string is different from the intended meaning of the original string, but where the substituted words yield a sentence that is grammatically correct, the meaning is generally consistent with the original meaning.

To overcome the problem of grammatically incorrect alternative search strings, each node application applies additional steps by means of which grammatically incorrect alternative strings can be excluded. To do this, the semantically substituted words are first substituted back into the original search string. Then each substituted word is analyzed within the original string to see whether the words preceding it and following it are words that are associated with the substituted word by a predefined degree. This is done by looking up the word in the node word relationship database and checking whether the word following it appears within the list of row fields with more than a predetermined frequency. Using the reverse signature of that word, a check is also made to see whether the word preceding it appears within the list of row fields with more than a predetermined frequency. Only if both the preceding and following words appear within the row of fields with more than a predetermined frequency is the word regarded as fitting grammatically within the string, otherwise they are rejected at the final stage.

For example, in the case of the alternative string, “Where can I get season water”, it is very unlikely that “get” will appear within the list of words that commonly precede “season” or that “water” will appear within the list of words that commonly follow “season”. This alternative string will therefore be rejected as grammatically incorrect.

If the word “fresh” is identified as semantically similar to “cool spring”, the string, “Where can I get fresh water?” would be checked for grammatical correctness by seeing whether the word “get” commonly precedes “fresh” and whether “water” commonly follows “fresh”. In both cases, the answer will be in the affirmative and, at the final stage, the string “Where can I get fresh water?” will be identified as an alternative string for “Where can I get cool spring water?”.

Once multiple alternative strings of diminishing grammar- and semantic complexity (crystallization) have been generated, they are simultaneously or in very rapid succession input into search streams (i.e. using “danger” word blacklists) and the results compared. The “search hits” that are found to be relevant in the aggregated results of multiple alternative crystallized search inputs can then be identified as more relevant than those hits which are only found to be relevant in the results of one search string – as is currently the case with most search engine input. The most relevant search hits are presented to the original user application first.

From the perspective of the user of search analytics all functioning described above is completely hidden and operates in stealth ‘in the background. The user interacts with the search analytics application(s) in exactly the same way as before, but receives output results based on the multiple stealth alternatives.

Part VII: Technical examples (illustrative)

Lets look at the following input phrase :

“…I am looking for a small coffee shop with red canopies in central
Copenhagen that serves many types of strawberry cheesecake…”

Gatfol crystallization “sees” keywords as negative semantic spaces :

Gatfol combines the negative spaces of two-word linkages i.e. “coffee shop” :

Note how the small “coffee cup” negative space adds “coffee” to the full “coffee shop” semantic image. We do not have “coffee” as a concept, neither do we have “shop”, and NEITHER DO WE HAVE “coffee” and “shop” as a combinational concept. We have a unique negative space of  “coffeeshop” that’s neither individually or in combination part of the original input concepts. Gatfol fluidly expands the “negative spaces” to contain almost any combination of concepts in an input dataset.

Negative space templates are multi-dimensionally compared for the closest fit :


giving us…

“….Maurice’s deli with the largest variety of strawberry
confectionary, in walking distance from Copernicus square…..”

Part VIII : Technical example (diagrams)


Part IX : Applications

Image/Video Analysis : Semantic Image Component Crystallization Through Gatfol SIFT (Semantic Intelligence Filter Technology)

(Rich multi-level automatic image- and video tagging on a massive scale)

When building the software node word relationship databases, Gatfol uses a proprietary technology called SIFT. Build words are forced through semantic matrix “filters” each with a different “focus”.

In the landscape images below a wide semantic “focus” sees only a lake with mountains, but a narrow focus additionally sees detail of a country house and village street:


Using SIFT, Gatfol is extremely effective as base technology in automatic machine tagging of images covering detailed image components. Current auto-tagging technology “recognizes” image detail to an accuracy level spanning substantial uncertainty around most- or all individual visual components or discrete pixel-groupings:

Above we could be looking at a work bench with a painters cap and several containers with dark- and yellow paints and some metallic tools, perhaps a green picnic blanket with tea or coffee and vegetation against a light sky, perhaps a green corn field with white grain silo’s etc. As long as ambiguous image content descriptions are available, Gatfol semantic intelligence is powerful enough to stepwise crystallize the ambiguities and provide large-volume detailed tags that all relate semantically.

Given the above vague and semantically wide tag results for the image above  (taken from a leading current image tagging application – ALIPR), Gatfol does the following :

Each of the supplied tags above is iterated with SIFT through wider and wider semantic word groupings, continuously checking back to available tags for matchings (Gatfol semantic crystallization is not only many-to-single down but also single-to-many upwards as well as multiword to multiword). If the shiny object at the bottom of the image is not a small mirror, work tool, ball bearing, light bulb or lens – but a teaspoon, the white object with dark contents is likely a tea or coffee cup with contents and not a bowl with soup – if coffee, then the white object with partial covering of a “hat” at right is not the “best fit” semantically. If a teacup, then the green striped object is likely a tea cozy with the white object adjacent, a tea pot. Given these crystallizations, the yellow blob at back is likely not paint, but either jam, butter or coloured ice cream. Given a “tea pot and cups” element grouping, the dark bands at back right is likely a chair structure, and given this, the light green object is semantically unlikely to be a picnic blanket or green meadow, but rather a table cloth. In this iterative manner a large volume of detailed tagging is obtained from a limited initial set of ambiguous descriptions.

Semantic scrubbing of media- and cell phone data streams covering – inter alia – aspects related to Terrorism, Weather/Natural Disasters/Emergency Management, Fire, Trafficking/Border Control Issues, Immigration, HAZMAT, Nuclear, Transportation Security, Infrastructure, National/International Security, Health Concerns, National/International, Public Safety and Cyber Security.

Gatfol’s massively scalable algorithmic semantic analysis engine can be used to flag posts in social media data streams containing word groups that semantically crystallizes to defined “danger” words :

Both Anders Breivik and Jared Lee Loughner posted substantial digital repositories before their extremist acts. In an ideal world we should be able to track ambiguous social network traffic to pinpoint individuals and groups narrowing in on behaviour that can be harmful to society.

Currently this is very difficult.

As is mostly the case, online threats and circumstantial postings contain little or no clear-cut hits against “danger word” listings. Anders Breivik’s online manifesto contained the following sentence : “I simulate various future scenarios relating to resistance efforts, confrontations with police, future interrogation scenarios, future court appearances, future media interviews etc. “Brief skimming brings up possible danger-words in “scenarios”, “resistance”, “police” etc, but nothing that does not appear in many daily online Tweets, Facebook postings or blog utterances.

The Gatfol semantic engine picked up these danger-words but flagged with extreme sensitivity the word combination “media interviews”. Together with “scenarios”, “resistance”, and “police”, the phrase “media interviews” uniquely crystallized in the Gatfol SIFT matrixes as “public violence of newsworthy effect”.  The latter semantic equivalent phrase hit totally different keywords than the original set of input words – Gatfol “sees” semantic perspective in large datasets that is not immediately evident to human analysts or investigators :

Part X : Application differentiation and strengths

Unlike almost all competing technology available today, Gatfol provides robust parallel processing power from even simple desktops or laptops. With all data streams staying local to the processing machine, field agents or operators do not require online access for operation in any way.

With a simulated Hadoop multiple master-and-slave node architecture built around simple but robust WindowsTM executable files and with multiple fallback redundancies around both master and slave functions, as well as all nodes individually carrying full word relationship databases, reliability of throughput is ensured – especially critical in large volume streaming functionality.

With a base in ordinary executable files, Gatfol also secures legacy hardware and OS (Windows XP and older) functionality and easy portability in instances of local machine OS upgrades.

Gatfol standalone architecture can be easily incorporated into wider distributed processing architecture including full Hadoop – with corresponding increases in throughput performance.

Part XI : Application performance

Current best performance of a 50-100 level deep crystallization stack on a standalone desktop (Intel Dual 2.93 GHZ  3.21GB RAM Windows XP) for text throughput is 3.6mb/hour for a single Gatfol cluster instance, 11.78mb/hour for a 10-cluster instance and 98mb/hour for a 100-cluster instance.

On a standalone desktop (Intel Quad 3.30 GHZ  2.91GB RAM Windows XP) best text throughput for a 50-100 level stack on a Gatfol 1000-cluster instance is 611mb/hour.

Total text throughput for a 50-100 level stack on an ordinary desktop Microsoft Networks-linked grouping of 20 desktops (Intel Single core 2.8GHZ  768MB RAM  Windows XP) each running a Gatfol 100-cluster instance is 1.9GB/hour – giving maximum text output volume of 140GB/hour.

Part XII : Application status

In private Beta since March 2012. Full field ready product to be available from October 2012.

Part XIII : Application updates

Gatfol operates a 24/7 software node word relationship database update service trawling through approximately 9TB of unique web text data per month.

To ensure absolute stealth and security on a local machine basis no online update calls are made by the application. Updates can be made freely and easily at any time on a manual basis from the Gatfol website by any user.

Part XIV : Contact information

Carl Greyling : Founder at Gatfol

Mobile             : ++27 82 5902993

Skype             : carl_greyling

Email               : carl@gatfol.com

Part XV : Addendum (USA focus)

Relevant Gatfol applicable data streams with specific Homeland Security focus :

1) Terrorism: Includes media reports on the activities of terrorist organizations both in the United States as well as abroad. This category also covers media articles that report on the threats, media releases by al Qaeda and other organizations, killing, capture, and identification of terror leaders and/or cells.

2) Weather/Natural Disasters/Emergency Management: Includes media reports on emergency and disaster management related issues. Reports include hurricanes, tornadoes, flooding, earthquakes, winter weather, etc. (all hazards). Reports outline the tracking of weather systems, reports on response and recovery operations, as well as the damage, costs, and effects associated with emergencies and disasters by area. Will also include articles regarding requests for resources, disaster proclamations, and requests for assistance at the local, state, and federal levels.

3) Fire: Includes reports on the ignition, spread, response, and containment of wildfires/industrial fires/explosions regardless of source.

4) Trafficking/Border Control Issues: Includes reports on the trafficking of narcotics, people, weapons, and goods into and out of the United States of an exceptional level.

5) Immigration: Includes reports on the apprehension of illegal immigrants and border control issues.

6) HAZMAT: Includes reports on the discharge of chemical, biological, and radiological hazardous materials as well as security and procedural incidents at nuclear facilities around the world, and potential threats toward nuclear facilities in the United States. Also included under this category are reports and responses to suspicious powder and chemical or biological agents.

7) Nuclear: Reports on international nuclear developments, attempts to obtain nuclear materials by terrorist organizations, and stateside occurrences such as melt downs, the mismanagement of nuclear weapons, releases of radioactive materials, illegal transport of nuclear materials, obtaining of weapons by terrorist organizations, and breaches in nuclear security protocol.

8 ) Transportation Security: Reports on security breaches, airport procedures, and other transportation related issues, and any of the above issues that affect transportation. Reports including threats toward and incidents involving rail, air, road, and water transit in the United States.

9) Infrastructure: Reports on national infrastructure including key assets and technical structures. Articles related to failures or attacks on transportation networks, telecommunications/ internet networks, energy grids, utilities, finance, domestic food and agriculture, government facilities, and public health.

10) National/International Security: Reports on threats or actions taken against United States national interests both at home and abroad. Reports including articles related to threats against American citizens, political figures, military installations, embassies, consulates, as well as efforts taken by local, state, and federal agencies to secure the homeland. Articles involving intelligence will also be included in this category.

11) Health Concerns, National/International: Includes articles on national and international outbreaks of infectious diseases and recalls of food or other items deemed dangerous to the public health.

12) Public Safety: Includes reports on public safety incidents, building lockdowns, bomb threats, mass shootings, and building evacuations.

13) Reports on DHS, Components, and other Federal Agencies: Includes both positive and negative reports on FEMA, CIS, CBP, ICE, etc. as well as organizations outside of DHS.

14) Cyber Security: Reports on cyber security matters that could have a national impact on other CIR Categories; internet trends affecting DHS missions such as cyber attacks, computer viruses; computer tools and techniques that could thwart local, state and federal law enforcement; use of IT and the internet for terrorism, crime or drug-trafficking; and Emergency Management use of social media strategies and tools that aid or affect communications and management of crises.

(Department of Homeland Security National Operations Center Media Monitoring Capability Desktop Reference Binder 2011)

 

U.S. Department of Homeland Security

Privacy Impact Assessment for the Office of Operations Coordination and Planning

Publicly Available Social Media Monitoring and Situational Awareness Initiative Update

January 6, 2011 :

Terms Used by the NOC When Monitoring Social Media Sites

This is a current list of terms that will be used by the NOC when monitoring social media sites to provide situational awareness and establish a common operating picture. As natural or manmade disasters occur, new search terms may be added.

DHS & Other AgenciesDepartment of Homeland Security (DHS)Federal Emergency Management Agency (FEMA)

Coast Guard (USCG)

Customs and Border Protection (CBP)

Border Patrol

Secret Service (USSS)

National Operations Center (NOC)

Homeland Defense

Immigration Customs Enforcement (ICE)

Agent

Task Force

Central Intelligence Agency (CIA)

Fusion Center

Drug Enforcement Agency (DEA)

Secure Border Initiative (SBI)

Federal Bureau of Investigation (FBI)

Alcohol Tobacco and Firearms (ATF)

U.S. Citizenship and Immigration Services (CIS)

Federal Air Marshal Service (FAMS)

Transportation Security Administration (TSA)

Air Marshal

Federal Aviation Administration (FAA)

National Guard

Red Cross

United Nations (UN)

 

Domestic Security

Assassination

Attack

Domestic security

Drill

Exercise

Cops

Law enforcement

Authorities

Disaster assistance

Disaster management

DNDO (Domestic Nuclear Detection Office)

National preparedness

Mitigation

Prevention

Response

Recovery

Dirty bomb

Domestic nuclear detection

Emergency management

Emergency response

First responder

Homeland security

Maritime domain awareness (MDA)

National preparedness initiative

Militia

Shooting

Shots fired

Evacuation

Deaths

Hostage

Explosion (explosive)

Police

Disaster medical assistance team (DMAT)

Organized crime

Gangs

National security

State of emergency

Security

Breach

Threat

Standoff

SWAT

Screening

Lockdown

Bomb (squad or threat)

Crash

Looting

Riot

Emergency Landing

Pipe bomb

Incident

Facility

 

HAZMAT & Nuclear

Hazmat

Nuclear

Chemical spill

Suspicious package/device

Toxic

National laboratory

Nuclear facility

Nuclear threat

Cloud

Plume

Radiation

Radioactive

Leak

Biological infection (or event)

Chemical

Chemical burn

Biological

Epidemic

Hazardous

Hazardous material incident

Industrial spill

Infection

Powder (white)

Gas

Spillover

Anthrax

Blister agent

Chemical agent

Exposure

Burn

Nerve agent

Ricin

Sarin

North Korea

 

Health Concern + H1N1

Outbreak

Contamination

Exposure

Virus

Evacuation

Bacteria

Recall

Ebola

Food Poisoning

Foot and Mouth (FMD)

H5N1

Avian

Flu

Salmonella

Small Pox

Plague

Human to human

Human to Animal

Influenza

Center for Disease Control (CDC)

Drug Administration (FDA)

Public Health

Toxic

Agro Terror

Tuberculosis (TB)

Agriculture

Listeria

Symptoms

Mutation

Resistant

Antiviral

Wave

Pandemic

Infection

Water/air borne

Sick

Swine

Pork

Strain

Quarantine

H1N1

Vaccine

Tamiflu

Norvo Virus

Epidemic

World Health Organization (WHO) (and components)

Viral Hemorrhagic Fever

E. Coli

 

Infrastructure Security

Infrastructure security

Airport

Airplane (and derivatives)

Chemical fire

CIKR (Critical Infrastructure & Key Resources)

AMTRAK

Collapse

Computer infrastructure

Communications infrastructure

Telecommunications

Critical infrastructure

National infrastructure

Metro

WMATA

Subway

BART

MARTA

Port Authority

NBIC (National Biosurveillance Integration Center)

Transportation security

Grid

Power

Smart

Body scanner

Electric

Failure or outage

Black out

Brown out

Port

Dock

Bridge

Cancelled

Delays

Service disruption

Power lines

Southwest Border ViolenceDrug cartelViolence

Gang

Drug

Narcotics

Cocaine

Marijuana

Heroin

Border

Mexico

Cartel

Southwest

Juarez

Sinaloa

Tijuana

Torreon

Yuma

Tucson

Decapitated

U.S. Consulate

Consular

El Paso

Fort Hancock

San Diego

Ciudad Juarez

Nogales

Sonora

Colombia

Mara salvatrucha

MS13 or MS-13

Drug war

Mexican army

Methamphetamine

Cartel de Golfo

Gulf Cartel

La Familia

Reynosa

Nuevo Leon

Narcos

Narco banners (Spanish equivalents)

Los Zetas

Shootout

Execution

Gunfight

Trafficking

Kidnap

Calderon

Reyosa

Bust

Tamaulipas

Meth Lab

Drug trade

Illegal immigrants

Smuggling (smugglers)

Matamoros

Michoacana

Guzman

Arellano-Felix

Beltran-Leyva

Barrio Azteca

Artistic Assassins

Mexicles

New Federation

 

Terrorism

Terrorism

Al Qaeda (all spellings)

Terror

Attack

Iraq

Afghanistan

Iran

Pakistan

Agro

Environmental terrorist

Eco terrorism

Conventional weapon

Target

Weapons grade

Dirty bomb

Enriched

Nuclear

Chemical weapon

Biological weapon

Ammonium nitrate

Improvised explosive device

IED (Improvised Explosive Device)

Abu Sayyaf

Hamas

FARC (Armed Revolutionary Forces Colombia)

IRA (Irish Republican Army)

ETA (Euskadi ta Askatasuna) Basque Separatists

Hezbollah

Tamil Tigers

PLF (Palestine Liberation Front)

PLO (Palestine Liberation Organization

Car bomb

Jihad

Taliban

Weapons cache

Suicide bomber

Suicide attack

Suspicious substance

AQAP (AL Qaeda Arabian Peninsula)

AQIM (Al Qaeda in the Islamic Maghreb)

TTP (Tehrik-i-Taliban Pakistan)

Yemen

Pirates

Extremism

Somalia

Nigeria

Radicals

Al-Shabaab

Home grown

Plot

Nationalist

Recruitment

Fundamentalism

Islamist

 

Weather/Disaster/Emergency

Emergency

Hurricane

Tornado

Twister

Tsunami

Earthquake

Tremor

Flood

Storm

Crest

Temblor

Extreme weather

Forest fire

Brush fire

Ice

Stranded/Stuck

Help

Hail

Wildfire

Tsunami Warning Center

Magnitude

Avalanche

Typhoon

Shelter-in-place

Disaster

Snow

Blizzard

Sleet

Mud slide or Mudslide

Erosion

Power outage

Brown out

 

Warning

Watch

Lightening

Aid

Relief

Closure

Interstate

Burst

Emergency Broadcast System

 

Cyber Security

Cyber security

Botnet

DDOS (dedicated denial of service)

Denial of service

Malware

Virus

Trojan

Keylogger

Cyber Command

2600

Spammer

Phishing

Rootkit

Phreaking

Cain and abel

Brute forcing

Mysql injection

Cyber attack

Cyber terror

Hacker

China

Conficker

Worm

Scammers

Social media

 

Other

Breaking News

 

 http://www.dhs.gov/xlibrary/assets/privacy/privacy_pia_ops_publiclyavailablesocialmedia_update.pdf

 

Part XVI : General disclaimers

This white paper and updates to it are made available for general information purposes only and is in no way binding upon Gatfol. By reading this white paper you understand that there is no supplier-client or advisory relationship created between you and Gatfol. Although the information in this white paper and updates is intended to be current and accurate, the information presented therein may not reflect the most current technical- or procedural developments, regulatory actions or software developments. These materials may be changed, improved, or updated without notice. Gatfol is not responsible for any errors or omissions in the content of this white paper or for damages arising from the use or performance of this white paper under any circumstances. We encourage you to contact us for specific feedback- or advice as to your particular matter.

The contents of this paper are protected by the patent laws of the United States and other jurisdictions. You may print a copy of any part of this blog for your own personal, noncommercial use, or for reasonable distribution to directly interested third parties, but you may not copy any part of the white paper for any other purposes, and you may not modify any part of the white paper. Inclusion of any part of the content of this paper in another work, whether in printed or electronic, or other form, or inclusion of any part hereof in another web site by linking, framing, or otherwise without the express written permission of Gatfol is prohibited.

Updates to this document will be published on the gatfol.com blog.

 

 
 
How does Gatfol work algorithmically?

 

 Lets look at the following input phrase.

“…I am looking for a small coffee shop with red canopies in central
Copenhagen that serves many types of strawberry cheesecake…”


Gatfol builds a mathematical world image through web
spidering and “sees” keywords as negative semantic spaces


Gatfol combines negative spaces of groups of words i.e. “coffee shop”

Note how the small “coffee cup” negative space adds “coffee” to the full “coffee shop” world image

We do not have “coffee” as a concept, neither do we have “shop”
as a concept and NEITHER DO WE HAVE “coffee” and “shop” as a combinational concept

We have a unique negative space of  “coffeeshop” that’s neither individually
or in combination part of the input concepts

Gatfol can easily and fluidly expand the “negative spaces” to contain
almost any combination of concepts in an input query

Negative space templates are multi-dimensionally compared for the closest fit: giving us…

“….Maurice’s deli with the largest variety of strawberry confectionary,
in walking distance from Copernicus square…..”

Gatfol additionally provides an error protection layer:

Semantic fits are re-fed into the world image to ensure
grammar accuracy in the search phrase equivalent

 

 

It’s all very confusing!

 
How does Gatfol fit in with search engines like Google?…

Let’s take our favorite example as Google search input :

“….the small coffee shop with red canopies in Central Copenhagen
that serves five types of strawberry cheesecake….”

What Google Sees

What Google "Sees" (with quotation marks)


Gatfol “sees” much more…

The Gatfol and the Google query universes…

 

 Gatfol generates hundreds to thousands of stealth
replacement search queries to the original

 

Semantic Intelligence Filter Technology

 

 What is the underlying technology driving Gatfol’s semantic analysis engine?

Gatfol drops input search words through patented matrix “filters” each with a different “focus”.

Think of the landscape images below: a wide “focus” sees only a lake with mountains,
but a narrow focus additionally sees a country house in the landscape.


 

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