No Hacker is a Bandit

When bankers in suits can lose billions, and be bailed out by tax payer money and pay themselves and each other millions- why should the common man , the middle class be left out of the gravy train that economic productivity created by digital revolutions.

A hacker is a highly skilled person, why should he be exploited for visas, billing rates, sub contracting but have no recourse in normal outdated legal processes

If spies can hack ala Stuxnet and get golden medals

If companies can hack and be rewarded by IPOs

Why cant humans hack and be rewarded for it

Hackers and malware creators are highly talented individuals who have been failed with the system, one in which corporations and governments collude for deliberate vulnerabilities on unsuspecting tax paying software buying people

No Linux user is complaining of a virus if you noticed. But you cant sue people for defective software. Yet!

A Healthy Routine for Hackers and Tech Workers

  • Meet people for dinner
  • Sleep
  • Do until

 

Heuristics and Occam’s razor for Counter Terrorism

When overworked analysts use shortcuts to search huge noisy dirty databases, they create trails which can be mined for actual heuristics

Heuristics –

A heuristic technique (/hjᵿˈrɪstk/; Ancient Greek: εὑρίσκω, “find” or “discover”), often called simply a heuristic, is any approach to problem solving, learning, or discovery that employs a practical method not guaranteed to be optimal or perfect, but sufficient for the immediate goals. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution

 

 

Example- A Police chief in Chicago may adopt different heuristics than in New York than in New Orleans for allocating human resources

Solution- Make a database of heuristics as actually in practice for that particular domain

Additional Solution- Search Companies to partner not just in giving data but also training and in some case search algorithms for database analysis and database design reviews of Homeland Security

Occam’s Razor-

Occam’s razor (also written as Ockham’s razor, and lex parsimoniae in Latin, which means law of parsimony) is a problem-solving principle attributed to William of Ockham (c. 1287–1347), who was an English Franciscan friar, scholastic philosopher and theologian. The principle can be interpreted as stating Among competing hypotheses, the one with the fewest assumptions should be selected.

https://en.wikipedia.org/wiki/Occam%27s_razor

https://en.wikipedia.org/wiki/Heuristic

Related – How to amplify noise in social media using other algorithms

https://decisionstats.com/2010/01/19/a-noisy-algorithm/

https://decisionstats.com/2015/04/14/random-thoughts-on-cryptography/

https://decisionstats.com/2013/12/14/play-color-cipher-and-visual-cryptography/

 

https://decisionstats.com/2010/11/25/increasing-views-to-youtube-videos/

http://www.cs.cornell.edu/~shmat/shmat_oak09.pdf

De-anonymizing Social Networks
Arvind Narayanan and Vitaly Shmatikov
The University of Texas at Austin
Abstract
Operators of online social networks are increasingly sharing potentially sensitive information about users and their relationships with advertisers, application developers,
and data-mining researchers. Privacy is typically protected by anonymization, i.e., removing names, addresses, etc.
We present a framework for analyzing privacy and anonymity in social networks and develop a new re-identification algorithm targeting anonymized social- network graphs. To demonstrate its effectiveness on real-
world networks, we show that a third of the users who can be verified to have accounts on both Twitter, a popular microblogging service, and Flickr, an online photo-sharing site, can be re-identified in the anonymous Twitter graph with only a 12% error rate.

 

 

 

 

Data Science for Olympics and lack of Reproducible Research

Despite the plethora of data generated in Sports, there is not much open data for Olympics and one wonders why if sharing best practices and data openly on what works and what does not can reduce the level of Russian athletes being banned in a cylical cold war era game.

Some links I found useful

http://www.kdnuggets.com/2014/01/data-mining-predict-sochi-winter-olympics-medal-counts.html

Could data mining techniques accurately predict the medal counts at the Olympics? A predictive model could give us an estimate of the number of medals each nation might win; but how close could we get to the actual outcomes? It was a tantalizing project …

Sochi-Ru By Dan Graettinger with Tim Graettinger

• Which nation will bring home the most medals at the upcoming Winter Olympics in Sochi, Russia?

• Will any nation from Africa, South America, or the Middle East finally break through and win a medal?

• Why do some nations win a bundle of medals while others win only a few?

• Can data mining give us the answers to these questions?

and
https://www.ibm.com/developerworks/community/blogs/jfp/entry/data_science_is_hard?lang=en

the Graettinger brothers do? They used a seemingly  standard methodology: learn from the past to predict the future.  More precisely, they used past Olympics results to build a predictive model.  Each country is represented by a feature vector, i.e. a set of quantities drawn form several categories:

  • Economic
  • Population
  • Human Development
  • Geography
  • Religion
  • Politics and Freedom

Then they used a standard technique known as linear regression to find which set of features were best for predicting medal count.  I was reading their blog post with great interest until I saw what were the most meaningful features found by the linear regression algorithm:

  • Geographic area
  • GDP per capita
  • Value of Exports
  • Latitude of Nation’s Capital

and

http://www.discoverycorpsinc.com/winter-olympic-medal-predict_1/?cm_mc_uid=41778113867514699665668&cm_mc_sid_50200000=1469966566

I was able to find data in many categories:

  • Economic
  • Population
  • Human Development
  • Geography
  • Religion
  • Politics and Freedom

Thankfully, there were some good sources out there[f3], and I collected enough data that I felt I had a good chance to predict some meaningful outcomes.  But would it be enough?  There is more than one way to go about predicting the medal count at the Olympics, and the route before me was the “30,000 feet” approach.

So any takers?

Hackers for Hacking the Olympics 🙂

 

 

Racism causes Terrorism

An uncomfortable fact that policy makers and intelligence analysts do not want to confront is that lack of integration and disillusionment is caused by inbred racism in Western societies to non-conformance of the Caucasian or Judeo-Christian mould. Thanks to regulation, explicit racism is banned, but implicit racism exists and is enabled by both economics as well as technology. Unless you confront racism inherent in some societies or geographies, you will be doing post mortems on events rather than pre-emptive cures. Why does India have much lower cases of home grown terror with 150 million Muslims. It is because they fit well here. Muslim males are not fitting well in Florida or California or on the French Riviera. The golden age of surveillance and the cooperation between technology service providers and government agencies cannot solve the problems of lack of integration due to racism.

Related

https://www.solidarity-us.org/node/1265

https://www.theguardian.com/commentisfree/2015/jun/19/american-mass-shootings-terrorism-racism

http://www.jcpa.org/jl/vp468.htm

https://www.quora.com/Is-terrorism-racism

https://wikiislam.net/wiki/72_Virgins