Remove Windows shutdown button

How to remove shutdown button from your friends computer. If your friends have windows 7 operating system then you are applying this tricks. So, follow my image instruction...

 

click here and download this file


#Follow my image instruction:

#OK Done now check your shutdown button..........

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How to create a table in blogspot com

Sometimes we try to list some content in a table but there is no tool to create a table in blogger. Here is the code to create the table in blogger.

<table><tbody>
<tr><td>---row1 col1---</td><td>---row1 col2---</td><td>---row1 col3---</td></tr>
<tr><td>---row2 col1---</td><td>---row2 col2---</td><td>---row3 col3---</td></tr>
<tr><td>---row3 col1---</td><td>---row3 col2---</td><td>---row3 col3---</td></tr>
</tbody></table>

• Paste this code in Edit Html and configure according to your needs
• The above code creates a table of 3 rows and 3 columns.
• The code <td>----</td> creates a cell and <tr>---</tr> creates a row
• You can put anything like a JavaScript, flash etc inside each cell.

Once you paste the above code the output will be as below:

---row1 col1------row1 col2-------row1 col3---
---row2 col1------row2 col2------row3 col3---
---row3 col1------row3 col2------row3 col3---

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A PPT Presentation on Digital Cash e Commerce

Hey friends!!!
Again back with another ppt on Digital Cash submitted by Jamsheer Kutty of KC College, Mumbai.
Nice topic and Nice content. Download and enjoy!!!

Few Points which are covered in this PPT are as fallows:

• What is E-commerce ?
• What is Digital Cash ?
• Relation between e commerce and digital cash
• Key Properties of Digital Cash
• Digital cash VS Credit Card
• The Online Model
• Pros And Cons of the online model
• The Offline Model
• Pros And Cons of the offline model
• Traceable Signature Protocol and it’s problem
• A typical coin
• How can we catch the user
• Reusability
• Advantages
• Global disadvantages
• Legal Issues with Digital cash

Submitted by : Jamsheer Kutty
College : KC College, Mumbai

Team Members :

• Jamsheer kutty
• Satyam Dwivedi
• Gaurav Sharma
• Ganesh Balraj

Download the presentation file below:

• digital_cash_ecommerce_ppt.zip

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11 Billion Clues in 800 Million Documents A Web Research Corpus Annotated with Freebase Concepts

Posted by Dave Orr, Amar Subramanya, Evgeniy Gabrilovich, and Michael Ringgaard, Google Research

“I assume that by knowing the truth you mean knowing things as they really are.”

- Plato

When you type in a search query -- perhaps Plato -- are you interested in the string of letters you typed? Or the concept or entity represented by that string? But knowing that the string represents something real and meaningful only gets you so far in computational linguistics or information retrieval -- you have to know what the string actually refers to. The Knowledge Graph and Freebase are databases of things, not strings, and references to them let you operate in the realm of concepts and entities rather than strings and n-grams.

We’ve previously released data to help with disambiguation and recently awarded $1.2M in research grants to work on related problems. Today we’re taking another step: releasing data consisting of nearly 800 million documents automatically annotated with over 11 billion references to Freebase entities.

These Freebase Annotations of the ClueWeb Corpora (FACC) consist of ClueWeb09 FACC and ClueWeb12 FACC. 11 billion phrases that refer to concepts and entities in Freebase were automatically labeled with their unique identifiers (Freebase MID’s). For example:



Since the annotation process was automatic, it likely made mistakes. We optimized for precision over recall, so the algorithm skipped a phrase if it wasn’t confident enough of the correct MID. If you prefer higher precision, we include confidence levels, so you can filter out lower confidence annotations that we did include.

Based on review of a sample of documents, we believe the precision is about 80-85%, and recall, which is inherently difficult to measure in situations like this, is in the range of 70-85%. Not every ClueWeb document is included in this corpus; documents in which we found no entities were excluded from the set. A document might be excluded because there were no entities to be found, because the entities in question weren’t in Freebase, or because none of the entities were resolved at a confidence level above the threshold.

The ClueWeb data is used in multiple TREC tracks. You may also be interested in our annotations of several TREC query sets, including those from the Million Query Track and Web Track.

If you would prefer a human-annotated set, you might want to look at the Wikilinks Corpus we released last year. Entities there were disambiguated by links to Wikipedia, inserted by the authors of the page, which is effectively a form of human annotation.

You can find more detail and download the data on the pages for the two sets: ClueWeb09 FACC and ClueWeb12 FACC. You can also subscribe to our data release mailing list to learn about releases as they happen.

Special thanks to Jamie Callan and Juan Caicedo Carvajal for their help throughout the annotation project.

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PowerShell v2 Get Content ReadCount Reading Really Large Files

At work we deal with really large files...as in multi-gibibyte files. I have tried in the past to swallow these whole only to run into OutOfMemory exceptions on x64 machines. After posting on Twitter for some help mjolinor hinted an answer he gave to someone in a similar predicament:

How can I make this PowerShell script parse large files faster?

Near the end of Robs solution was this:

Get-Content $path$infile -ReadCount $batch |
foreach {
  $_ -match $match_regex -notmatch $header_regex -Replace $replace_regex ,$1 | Out-File $path$outfile -Append
}

The  magic bullet here is the -ReadCount parameter. $batch is simple an int set to 1000.  In plain English, this parameter instructs Get-Content to read the $path$infile value in chunks of 1,000 lines.  So, if I have a 5 million line file, no problem, I only do chunks of 1,000 at a time. In a way, this is like streaming, except, the data being worked upon is collections of 1,000 lines, not streams. Using the approach shown above, you can segment large data segments into small, bite-sized segments.

To get more background on the parameter I went to

get-help get-content -parameter readcount

and got this:

-ReadCount <Int64>
Specifies how many lines of content are sent through the pipeline at a time. The default value is 1. A value of 0 (zero) sends all of the content at one time.

This parameter does not change the content displayed, but it does affect the time it takes to display the content. As the value of ReadCount increases, the time it takes to return the first line increases, but the total time for the operation decreases. This can make a perceptible difference in very large items.

Required? false
Position? named
Default value
Accept pipeline input? true (ByPropertyName)
Accept wildcard characters? false

When you pass a collection to the pipelined foreach {} this automatically assumes a process block and iterates over the objects without special manipulation being required.  Note that, as listed above, larger read values (the number after -ReadCount) slows the initial time to the first processing as it has to gulp up the initial chunk of content.

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Create Your Own Logon Message

1 Click start
click run
type regedit,
then click ok!

2 In The registry editor, drill down to the following key:
hklmsoftwareMicrosoftWindows NTCurrent versionWinlogon.

3 Right click LegalNoticeCaption,
click modify,
Type: THIS IS NEXT TRICK,and then click ok!

4 Right click legalNoticeText,
click modify, and then Close your message!

5 Restart Your Computer.

6 The message will appear every time you logon!

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