Month: May 2011

Movie Review -Source Code

Jake Gyllenhaal has always been a dear chap from his Donny Darko days. So when you mash some quantum physics (parabolic calculus as per movie), with science fiction to capture terrorists (a very topical topic)- you get Source Code– an investigative and recursive logical thriller. Lead actress Vera Farmiga from the Departed (remember the scene of making love to Comfortably Numb) seems a bit bored today and the tension never crackles. This is a movie for science fiction or action thrillers not geeks- and the name source code is a bit of a misleading title- as it should probably be called Complex Event Processing. It is also a terrible name to search for in Google Image Search- you dont get movie images at all.

The movie is very watchable, but it wont be winning any Hugo awards yet.

Also stars Michelle Monaghan of MI3 ,

with the cute upturned nose made famous by

Nicole not Kid(ding)man

Related articles

Analyzing Judas of Lady Gaga

Youtube, the big tube of the fat pipes of the internet’s shallow side (we shall discuss the deep Dark Net later)-

well Youtube enhanced video analytics greatly.

Have a look at Lady Gaga Judas video analytics data viz- do you think you can pack more or better data viz here.

Analytics is great- but Youtube please be a dear and hire some graphics designers once in a while. Nopes- not the ones your engineers are dating, but real graphics designers.

Related articles

Cognitive Biases exploited by Spammers and Phishers

"Keep Walking"

Since they day you arrive on this planet, you are programmed into accepting reality as good and bad.

Beautiful people good. Ugly people not good.

Fellow countrymen good. Fellow earthling not so good.

Same religion is good. Different religion is awkward.

These cognitive biases are exploited in social media in the following manner-

1) Same Name Bias- You like people of the same name as you. or people who remind you of your brothers name. or uncles name.

All that information is already known. Esp true on Linkedin.

2) Same Orientation Bias- People tend to react better to photos considered attractive of opposite sex / opposite preference. Especially true on Twitter and Facebook.

3) Nationality Bias- Israeli Americans tend to respond better to Jewish looking phishers who claim to be from Israel but are not. Ditto for Indians- Arabs etc. E|sp true on Linkedin and Facebook.

You are positively biased to people of same country or of friendly nation states and will likely accept invites/friend/poke

4) Same organization/ alumni bias- People at end of phishing attack will have higher response rate if proxy identity claims familiarity with organizations or schools attended. Especially true on Facebook and Linkedin.

5) Same interests/movies/books bias- Your likely response rate is higher to someone who has seen your profile page on Facebook for interests, and checked the RSS stream of your tweets for stuff you like.

Bias is just maths. Period.

Related articles

Tom Davenport to Keynote at PAW New York

Unidentified building, Babson College - IMG 0443
Image via Wikipedia

message from Predictive Analytics World. If you are NY based you may want to drop in and listen.———————————————————————————-Tom Davenport to Keynote at
Predictive Analytics World New York
Take advantage of Super Early Bird Pricing by May 20th and recognize savings of $400. Additional savings when you bring the team*

Announcing Tom Davenport Keynote:
Thomas Davenport Every Day Analytics:
Making Leading Edge CommonplaceThomas Davenport
President’s Distinguished Prof, Babson College
Author, Competing on Analytics & Analytics at Work

Join your peers October 17-21, 2011 at the Hilton New York for Predictive Analytics World, the business event for predictive analytics professionals, managers and commercial practitioners, covering today’s commercial deployment of predictive analytics, across industries and across software vendors.

PAW NYC promises to once again break records as the biggest cross-vendor predictive analytics event ever. The conference program is packed with the top predictive analytics experts, practitioners, authors and business thought leaders, including keynote addresses from Thomas Davenport, author of Competing on Analytics: The New Science of Winning, and PAW Program Chair Eric Siegel, plus special sessions from industry heavy-weights Usama Fayyad and John Elder.
RAVE REVIEWS:“I came to PAW because it provides case studies relevant to my industry. It has lived up to the expectation and I think it’s the best analytics conference I’ve ever attended!“

Shaohua Zhang, Senior Data Mining Analyst
Rogers Telecommunications

“Hands down, best applied analytics conference I have ever attended. Great exposure to cutting-edge predictive techniques and I was able to turn around and apply some of those learnings to my work immediately. I’ve never been able to say that after any conference I’ve attended before!”

Jon Francis, Senior Statistician
T-Mobile

PAW NYC’s agenda covers black box trading, churn modeling, crowdsourcing, demand forecasting, ensemble models, fraud detection, healthcare, insurance applications, law enforcement, litigation, market mix modeling, mobile analytics, online marketing, risk management, social data, supply chain management, targeting direct marketing, uplift modeling (net lift), and other innovative applications that benefit organizations in new and creative ways.

Take advantage of Super Early Bird Pricing and realize
$400 in savings before May 20, 2011.

Note:  Each additional attendee from the same company registered at the same time receives an extra $200 off the Conference Pass.

Register Now!

eMetrics New York
Related articles

Chromebooks for enterprise BI

From-

http://googleblog.blogspot.com/

Chromebooks will be available online June 15 in the U.S., U.K., France, Germany, Netherlands, Italy and Spain. More countries will follow in the coming months. In the U.S., Chromebooks will be available from Amazon and Best Buy and internationally from leading retailers.

Even with dedicated IT departments, businesses and schools struggle with the same complex, costly and insecure computers as the rest of us. To address this, we’re also announcing Chromebooks for Business and Education.

and

http://www.google.com/chromebook/business-education.html#

Chromebooks: work better.

Crashes, long boot times, application conflicts, endless updates, viruses, security issues and obsolete hardware all frustrate IT managers and end users – and most users don’t need or want the complexity and annoyance of their current PCs.

Increasingly the browser is the only tool users need, making a new and better computing model possible. Chromebooks can instantly run your browser-based apps, whether in the cloud or behind your firewall, and apps virtualized through technologies like Citrix®. And an entire fleet of Chromebooks can be managed from one web-based console – making life better for users and IT admins alike.

Contact Sales

Related articles

East loves Gold and USD. and chokes on it

A brief analysis shows how Eastern Hemisphere loves gold and USD so much

I did the graph in JMP since it is an easier GUI for me to use (I do have some learning disabilities).

https://www.cia.gov/library/publications/the-world-factbook/rankorder/2188rank.html

RANK
 COUNTRY RESERVES OF FOREIGN EXCHANGE AND GOLD DATE OF INFORMATION
1 China
$ 2,622,000,000,000
31 December 2010 est.
2 Japan
$ 1,096,000,000,000
31 December 2010 est.
3 Russia
$ 483,100,000,000
30 November 2010
4 Saudi Arabia
$ 456,200,000,000
31 December 2010 est.
5 Taiwan
$ 387,200,000,000
31 December 2010 est.
6 Brazil
$ 290,900,000,000
31 December 2010 est.
7 India
$ 284,100,000,000
31 December 2010 est.
8 Korea, South
$ 274,600,000,000
31 December 2010 est.
9 Hong Kong
$ 268,900,000,000
31 December 2010 est.
10 Switzerland
$ 236,600,000,000
31 December 2010
11 Singapore
$ 225,800,000,000
31 December 2010 est.
12 Thailand
$ 176,100,000,000
31 December 2010 est.
13 Algeria
$ 150,100,000,000
31 December 2010 est.
14 Mexico
$ 116,400,000,000
31 December 2010 est.
15 Libya
$ 107,300,000,000
31 December 2010 est.
16 Malaysia
$ 106,500,000,000
31 December 2010 est.
17 Poland
$ 99,760,000,000
31 December 2010 est.
18 Indonesia
$ 96,210,000,000
31 December 2010 est.
19 Turkey
$ 78,000,000,000
31 December 2010 est.
20 Iran
$ 75,060,000,000
31 December 2010 est.
21 Israel
$ 66,980,000,000
31 December 2010 est.
22 Philippines
$ 62,370,000,000
31 December 2010 est.
23 Argentina
$ 53,610,000,000
31 December 2010 est.
24 Romania
$ 50,510,000,000
31 December 2010 est.
25 Iraq
$ 45,680,000,000
31 December 2010 est.
26 South Africa
$ 45,520,000,000
31 December 2010 est.
27 Hungary
$ 44,990,000,000
31 December 2010 est.
28 Peru
$ 44,110,000,000
31 December 2010
29 Nigeria
$ 43,360,000,000
31 December 2010 est.
30 Czech Republic
$ 42,340,000,000
31 December 2010 est.
31 Lebanon
$ 41,570,000,000
31 December 2010 est.
32 United Arab Emirates
$ 39,100,000,000
31 December 2010 est.
33 Australia
$ 38,620,000,000
31 December 2010 est.
34 Egypt
$ 35,720,000,000
31 December 2010 est.
35 Ukraine
$ 32,910,000,000
31 December 2010 est.
36 Kazakhstan
$ 32,440,000,000
31 December 2010 est.
37 Venezuela
$ 29,490,000,000
31 December 2010 est.
38 Colombia
$ 28,500,000,000
31 December 2010 est.
39 Chile
$ 26,080,000,000
31 December 2010 est.
40 Morocco
$ 24,570,000,000
31 December 2010 est.
41 Macau
$ 23,730,000,000
42 Kuwait
$ 22,420,000,000
31 December 2010 est.
43 Qatar
$ 22,410,000,000
31 December 2010 est.
44 Austria
$ 21,890,000,000
31 December 2010 est.
45 Syria
$ 17,960,000,000
31 December 2010 est.
46 New Zealand
$ 17,850,000,000
31 December 2010 est.
47 Bulgaria
$ 17,270,000,000
31 December 2010 est.
48 Angola
$ 16,890,000,000
31 December 2010 est.
49 Pakistan
$ 16,100,000,000
31 December 2010 est.
50 Serbia
$ 15,100,000,000
30 November 2010 est.
51 Oman
$ 14,000,000,000
31 December 2010 est.
52 Croatia
$ 13,790,000,000
31 December 2010 est.
53 Vietnam
$ 13,000,000,000
31 December 2010 est.
54 Jordan
$ 12,640,000,000
31 December 2010 est.
55 Tunisia
$ 11,230,000,000
31 December 2010 est.
56 Turkmenistan
$ 10,810,000,000
31 December 2010 est.
57 Bangladesh
$ 10,790,000,000
31 December 2010 est.
58 Uzbekistan
$ 10,500,000,000
31 December 2010 est.
59 Bolivia
$ 9,730,000,000
31 December 2010 est.
60 Trinidad and Tobago
$ 9,659,000,000
31 December 2010 est.
61 Finland
$ 9,128,000,000
31 December 2010 est.
62 Botswana
$ 7,834,000,000
31 December 2010 est.
63 Uruguay
$ 7,700,000,000
31 December 2010 est.
64 Latvia
$ 7,170,000,000
31 December 2010 est.
65 Lithuania
$ 6,418,000,000
31 December 2010 est.
66 Azerbaijan
$ 6,330,000,000
31 December 2010 est.
67 Belarus
$ 5,755,000,000
31 December 2010 est.
68 Yemen
$ 5,744,000,000
31 December 2010 est.
69 Guatemala
$ 5,709,000,000
31 December 2010 est.
70 Sri Lanka
$ 5,630,000,000
31 December 2010 est.
71 Cuba
$ 4,847,000,000
31 December 2010 est.
72 Kenya
$ 4,585,000,000
31 December 2010 est.
73 Costa Rica
$ 4,584,000,000
31 December 2010 est.
74 Iceland
$ 4,206,000,000
31 December 2010 est.
75 Bosnia and Herzegovina
$ 4,200,000,000
31 December 2010 est.
76 Paraguay
$ 4,130,000,000
31 December 2010 est.
77 Congo, Republic of the
$ 4,123,000,000
31 December 2010 est.
78 Equatorial Guinea
$ 4,086,000,000
31 December 2010 est.
79 Cameroon
$ 4,023,000,000
31 December 2010 est.
80 Cote d’Ivoire
$ 3,985,000,000
31 December 2010 est.
81 Cambodia
$ 3,840,000,000
31 December 2010 est.
82 Ghana
$ 3,800,000,000
31 December 2010 est.
83 Bahrain
$ 3,766,000,000
31 December 2010 est.
84 Burma
$ 3,762,000,000
31 December 2010 est.
85 Uganda
$ 3,743,000,000
31 December 2010 est.
86 Tanzania
$ 3,687,000,000
31 December 2010 est.
87 Estonia
$ 3,641,000,000
31 December 2010 est.
88 Ecuador
$ 3,590,000,000
31 December 2010 est.
89 Panama
$ 3,525,000,000
31 December 2010 est.
90 Papua New Guinea
$ 3,017,000,000
31 December 2010 est.
91 El Salvador
$ 2,882,000,000
31 December 2010 est.
92 Dominican Republic
$ 2,705,000,000
31 December 2010 est.
93 Gabon
$ 2,602,000,000
31 December 2010 est.
94 Mauritius
$ 2,360,000,000
31 December 2010 est.
95 Georgia
$ 2,350,000,000
31 December 2010 est.
96 Honduras
$ 2,302,000,000
31 December 2010 est.
97 Zambia
$ 2,287,000,000
31 December 2010 est.
98 Armenia
$ 2,247,000,000
31 December 2010 est.
99 Macedonia
$ 2,217,000,000
30 November 2010 est.
100 Senegal
$ 2,200,000,000
31 December 2010 est.
101 Ireland
$ 2,104,000,000
31 December 2010
102 Sudan
$ 2,063,000,000
31 December 2010 est.
103 Albania
$ 1,992,000,000
31 December 2010 est.
104 Mozambique
$ 1,982,000,000
31 December 2010 est.
105 Namibia
$ 1,961,000,000
31 December 2010 est.
106 Ethiopia
$ 1,880,000,000
31 December 2010 est.
107 Jamaica
$ 1,850,000,000
31 December 2010 est.
108 Moldova
$ 1,710,000,000
31 December 2010 est.
109 Kyrgyzstan
$ 1,615,000,000
31 December 2010 est.
110 Burkina Faso
$ 1,588,000,000
31 December 2010 est.
111 Haiti
$ 1,587,000,000
31 December 2010 est.
112 Nicaragua
$ 1,580,000,000
31 December 2010 est.
113 Benin
$ 1,254,000,000
31 December 2010 est.
114 Slovakia
$ 1,160,000,000
31 January 2010 est.
115 Madagascar
$ 1,038,000,000
31 December 2010 est.
116 Congo, Democratic Republic of the
$ 1,010,000,000
March 2010 est.
117 Lesotho
$ 893,000,000
31 December 2010 est.
118 Chad
$ 868,000,000
31 December 2010 est.
119 Rwanda
$ 816,000,000
31 December 2010 est.
120 Laos
$ 756,000,000
31 December 2010 est.
121 Swaziland
$ 708,000,000
31 December 2010 est.
122 Togo
$ 686,000,000
31 December 2010 est.
123 Barbados
$ 620,000,000
2007
124 Malta
$ 522,000,000
31 December 2010 est.
125 Guyana
$ 506,000,000
31 December 2010 est.
126 Zimbabwe
$ 376,000,000
31 December 2010 est.
127 Burundi
$ 320,000,000
31 December 2010 est.
128 Tajikistan
$ 303,000,000
31 December 2010 est.
129 Malawi
$ 301,000,000
31 December 2010 est.
130 Cape Verde
$ 296,000,000
31 December 2010 est.
131 Suriname
$ 263,300,000
2006
132 Belize
$ 219,000,000
31 December 2010 est.
133 Gambia, The
$ 203,000,000
31 December 2010 est.
134 Seychelles
$ 193,000,000
31 December 2010 est.
135 Eritrea
$ 104,000,000
31 December 2010 est.
136 Samoa
$ 70,150,000
FY03/04
137 Sao Tome and Principe
$ 46,000,000
31 December 2010 est.
138 Tonga
$ 40,830,000
FY04/05
139 Vanuatu
$ 40,540,000
2003

Topic Models in R- search documents for similarity by frequency

Zombie-process
Image via Wikipedia

From the marvelous lovely Journal of Statistical Software, ignored by mainstream corporatia, but beloved to academia. here is one more interesting and very timely paper.

Can be used to grade stdudents homework, catch terrorists as in plagiarists , search engine spam linkers. Enjoy!