A Farewell to Orkut

Youve probably never heard of Orkut (unless youre Brazilian) and I didnt even recall having an account with them, but yesterday I received an email from Google telling me they were closing the service down. Orkut was Googles version of Facebook; launched in 2004 it became one of India and Brazils most popular websites. However, Facebook e clipsed it in the rest of the Western World and if you live in the West (outside of Brazil) then youve probably never heard of it. Google say that the growth in YouTube and Google+ means that Orkut is no longer needed. It lasted longer than Google Buzz though, which barely lasted a year.

from The Universal Machine http://universal-machine.blogspot.com/

Put the internet to work for you.

Turn off or edit this Recipe

Recommended for you

Read More..

Why We Need to Tame Our Algorithms Like Dogs

Algorithms control our daily lives, wether were aware of it or not. Algorithms run riot in financial markes; they predict the weather and electricity demand; they price insurance and decide how many doctors to schedule to the emergency room on any given night. They even decide what groceries to stock in your local supermarket. Given algorithms (hidden) importance it therefore makes sense that they work for  us. An interesting article in Wired makes the point that our algorithms need to evolve alongside us, much as dogs have, to become useful servants.

from The Universal Machine http://universal-machine.blogspot.com/

Put the internet to work for you.

Turn off or edit this Recipe

Recommended for you

Read More..

PowerShell v2 Get WinEvent Filtering Performance Testing and Analysis

Having been on an optimization and performance testing kick for the past week or so I stumbled across one I hadnt seen yet. Get-WinEvent can be a hog at times, so, I avoid it unless I must, particularly when hunting for specific conditions. To date I have used Get-WinEvent with a pipelined Where approach. Slow to say the least. While reading Get-Help I found an example that highlighted some filtering approaches that can significantly improve performance:

• -FilterHashTable
• -FilterXML
• -FilterXPath

Not being familiar with any of these approaches I ran example 14 in the v2 Get-Help for Get-WinEvent and did some testing. I did make a minor modification to the on-the-box help examples to have data I could use to get results. Here is my approach:

Clear-Host

$test1 = {

       # Use the Where-Object cmdlet

       $yesterday = (get-date) - (new-timespan -day 1);

       $where = get-winevent -logname "application" | where {$_.timecreated -ge $yesterday}

}

$test2 = {

       # Uses FilterHashTable

       $yesterday = (get-date) - (new-timespan -day 1)

       $filtertable = get-winevent -FilterHashTable @{LogName=application; StartTime=$yesterday}

}

$test3 =

{

       # Use FilterXML

       $filterxml = get-winevent -FilterXML "*[System[TimeCreated[timediff(@SystemTime) <= 86400000]]]"

}

$test4 =

{

       # Use FilterXPath

       $filterxpath = get-winevent -LogName "application" -FilterXPath "*[System[TimeCreated[timediff(@SystemTime) <= 86400000]]]"

}

$tests = @(

       $test1,

       $test2,

       $test3,

       $test4

)

1..10 | % {

       "Iteration $_";

       $tests |

       Foreach-Object {

              (Measure-Command -Expression { & $_  }).Ticks

       }

       ""

}

When I run it I get a nice set of results. Here they are in a relatively pretty format:

Iteration	Where	FilterHashTable	FilterXml	FilterXpath
1	493014972	3749254	4855395	5986625
2	593577895	3861254	5235727	4911358
3	520402676	7081213	6600362	6964718
4	617752316	4202329	5188378	5189118
5	496873977	4888834	4751030	5445491
6	511961827	3755366	5036335	5184823
7	818546428	6821936	12815080	13361985
8	566443409	3830192	5188369	5606259
9	492985827	4348790	4967646	5845117
10	492653208	4150840	5108303	5153839
Average	560421253.5	4669000.8	5974662.5	6364933.3

 Based on these figures, -FilterHashTable is significantly faster. Assuming Where as a baseline, the other three are faster by 90-120 times. In fact, relative to the Where, each is listed below as a percentage and a figure representing the number of times faster than Where each approach happens to process:

• FilterHashTable: 0.833% - 120 times faster than Where
• FilterXml: 1.066% - 94 times faster than Where
• FilterXPath: 1.1336% - 88 times faster than Where

Gaining improvements in performance like this should have people jumping off the Where ship like the Titanic. For sysadmins searching logs on regular basis drops in processing time like this could be significant. Plus, the -FilterHashTable option, to me at least, seems to be the easiest by far. You dont need to know XPath OR be able to hack XML. Just create a hashtable with your parameters/argument pairs and let it run.

NOTE: In the help the -FilterXml has a typo. Instead of using <= it should use <= since the < character is interpreted as XML and the parser yells when you try to execute the command.

Read More..

Using Kinect with Unity 3D Game Engine

How to use the Kinect (Microsoft Drivers v. 1.5) with Unity 3D

Here is my current result for fun (The beginning is my own C++ version that doesnt involve unity) in a game where you can throw fireballs:


After spending a while developing a little C++ library to use with the Microsoft Kinect Drivers (which Ive been meaning to release), I decided to try to integrate the Kinect functionality into the popular Unity 3D Game Development software.

I was pretty excited after finding the handy CMU package here. However, it only worked with the beta Microsoft SDK and didnt have some of the more advanced functionality that I needed. Im running version 1.5 hacked together a quick DLL and modified some code.

I use the Microsoft Kinect SDK Version 1.5 which I have available for download here.

The DLL source code can be found here in case you want to modify it for a different version.

The DLL itself (needed for the Kinect to work with Unity) can be found here and should be placed in your Microsoft SDK/Kinect folder like below:
C:Program FilesMicrosoft SDKsKinectv1.5AssembliesUnity

The scripts to control a player and receive information from the Kinect can be found here.

They are used the same way as the CMU ones but are slightly modified with a bit more error checking and some extra flags. All you have to do is follow the same instructions on the CMU page linked above which just involves enabling the Kinect and telling the KinectModelControllerV2 where your joints are. The movement flag is still in beta as Im trying to make it so the character can walk around.

I have a version that is entirely in C++ using OpenGL, FreeGlut, my own version of Blepo, and my own development libraries that is previewed in the beginning of the video that can be found here. It is what I do most of my testing in before I port it over to Unity.

Enjoy!

Consider donating to further my tinkering.

Places you can find me

• Facebook
• Google+
• Linkedin
• Blogspot
• Google-Code
• Github
• YouTube
• Twitter

Read More..

Natural Language Understanding focused awards announced

Posted by Massimiliano Ciaramita, Research Scientist and David Harper, Head University Relations (EMEA)

Some of the biggest challenges for the scientific community today involve understanding the principles and mechanisms that underlie natural language use on the Web. An example of long-standing problem is language ambiguity; when somebody types the word “Rio” in a query do they mean the city, a movie, a casino, or something else? Understanding the difference can be crucial to help users get the answer they are looking for. In the past few years, a significant effort in industry and academia has focused on disambiguating language with respect to Web-scale knowledge repositories such as Wikipedia and Freebase. These resources are used primarily as canonical, although incomplete, collections of “entities”. As entities are often connected in multiple ways, e.g., explicitly via hyperlinks and implicitly via factual information, such resources can be naturally thought of as (knowledge) graphs. This work has provided the first breakthroughs towards anchoring language in the Web to interpretable, albeit initially shallow, semantic representations. Google has brought the vision of semantic search directly to millions of users via the adoption of the Knowledge Graph. This massive change to search technology has also been called a shift “from strings to things”.

Understanding natural language is at the core of Googles work to help people get the information they need as quickly and easily as possible. At Google we work hard to advance the state of the art in natural language processing, to improve the understanding of fundamental principles, and to solve the algorithmic and engineering challenges to make these technologies part of everyday life. Language is inherently productive; an infinite number of meaningful new expressions can be formed by combining the meaning of their components systematically. The logical next step is the semantic modeling of structured meaningful expressions -- in other words, “what is said” about entities. We envision that knowledge graphs will support the next leap forward in language understanding towards scalable compositional analyses, by providing a universe of entities, facts and relations upon which semantic composition operations can be designed and implemented.

So we’ve just awarded over $1.2 million to support several natural language understanding research awards given to university research groups doing work in this area. Research topics range from semantic parsing to statistical models of life stories and novel compositional inference and representation approaches to modeling relations and events in the Knowledge Graph.

These awards went to researchers in nine universities and institutions worldwide, selected after a rigorous internal review:

• Mark Johnson and Lan Du (Macquarie University) and Wray Buntine (NICTA) for “Generative models of Life Stories”

• Percy Liang and Christopher Manning (Stanford University) for “Tensor Factorizing Knowledge Graphs”

• Sebastian Riedel (University College London) and Andrew McCallum (University of Massachusetts, Amherst) for “Populating a Knowledge Base of Compositional Universal Schema”

• Ivan Titov (University of Amsterdam) for “Learning to Reason by Exploiting Grounded Text Collections”

• Hans Uszkoreit (Saarland University and DFKI), Feiyu Xu (DFKI and Saarland University) and Roberto Navigli (Sapienza University of Rome) for “Language Understanding cum Knowledge Yield”

• Luke Zettlemoyer (University of Washington) for “Weakly Supervised Learning for Semantic Parsing with Knowledge Graphs”

We believe the results will be broadly useful to product development and will further scientific research. We look forward to working with these researchers, and we hope we will jointly push the frontier of natural language understanding research to the next level.

Read More..

change your processor name computer tips with tricks

computer tips with tricks















todays i am going to show u how to make ur processor core i3,core i5,core i7, ..its just a change ur processor name ok  just have a fun lets go........

if you dont understand my image instruction then watch below this video

Read More..

Chat With Command Prompt

If you want personal chat with a friend

you dont need to download any yahoo messenger
All you need is your friends IP address and Command Prompt.

Firstly, open Notepad and enter:

@echo off

:A

Cls

echo MESSENGER

set /p n=User:

set /p m=Message:

net send %n% %m%

Pause

Goto A

Now save this as "Messenger.bat". Open the .bat file and in Command

Prompt you should see:

MESSENGER

User:

After "User" type the IP address of the computer you want to contact.

After this, you should see this:

Message:

Now type in the message you wish to send.Before you press "Enter" it should look like this:

MESSENGER

User:27.196.391.193

Message: Hi

Now all you need to do is press "Enter", and start chatting!

Read More..