http://rapidshare.com/files/22970405/sql_01.zip
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Wednesday, April 1, 2009
Tuesday, March 31, 2009
Overview Of Facial Recognition System
Facial recognition systems are used for identification solutions, by translating the characteristics of a face into a unique set of numbers. Facial recognition technology introduces a new era of internet and e-commerce security. Using this it has been able to move to the forefront in using new modern security solutions for prisons. This technology has been used by Tampa police department, which allows snapshots of faces from the crowd to be compared to a database of criminal mug shots. People have an amazing ability to recognize and remember thousands of faces; in this edition you’ll learn how computers are turning your face into computer code so it can be compared to thousands, if not millions, of other faces. We'll also look at how facial recognition software is being used in elections, criminal investigations and to secure your personal computer.
The face-recognition technology both enhances existing identification solutions and offers opportunities for a variety of new applications. Using a sophisticated algorithm based on Principle Component Analysis (PCA) developed at the Massachusetts Institute Technology's Media Lab, the Company's software translates the characteristics of a face into a unique set of numbers, which is referred to as the eigenface.
The eigenface is used by both identification and verification systems for face comparisons made in real-time. Identification involves a one-to-many comparison of an individual's face against all faces in a database in order to determine identity; and verification is characterized as a one-to-one match of an individual's face to his or her stored image for the purpose of confirming identity. The Company's face-recognition technology is unique because of its capabilities of both rapid and accurate real-time acquisition as well as its scalability to databases containing millions of faces. Therefore, the software can instantly calculate an individual's eigenface from either live video or a still digital image, and then search a database of millions in only a few seconds in order to find similar or matching images.
The Face
The face-recognition technology both enhances existing identification solutions and offers opportunities for a variety of new applications. Using a sophisticated algorithm based on Principle Component Analysis (PCA) developed at the Massachusetts Institute Technology's Media Lab, the Company's software translates the characteristics of a face into a unique set of numbers, which is referred to as the eigenface.
The eigenface is used by both identification and verification systems for face comparisons made in real-time. Identification involves a one-to-many comparison of an individual's face against all faces in a database in order to determine identity; and verification is characterized as a one-to-one match of an individual's face to his or her stored image for the purpose of confirming identity. The Company's face-recognition technology is unique because of its capabilities of both rapid and accurate real-time acquisition as well as its scalability to databases containing millions of faces. Therefore, the software can instantly calculate an individual's eigenface from either live video or a still digital image, and then search a database of millions in only a few seconds in order to find similar or matching images.
The Face
Your face is an important part of who you are and how people identify you. Imagine how hard it would be to recognize an individual if all faces looked the same. Except in the case of identical twins, the face is arguably a person's most unique physical characteristic. While humans have had the innate ability to recognize and distinguish different faces for millions of years, computers are just now catching up.Visionics, a company based in New Jersey, is one of many developers of facial recognition technology. The twist to its particular software is that it can pick someone's face out of a crowd, extract that face from the rest of the scene and compare it to a database full of stored images. In order for this software to work, it has to know what a basic face looks like. Facial recognition software is based on the ability to first recognize faces, which are a technolog
ical feat in itself, and then measure the various features of each face.
Facial recognition software is designed to pinpoint a face and measure its features.
If you look in the mirror, you can see that your face has certain distinguishable landmarks. These are the peaks and valleys that make up the different facial features. These landmarks are defined as nodal points. There are about 80 nodal points on a human face. Here are a few of the nodal points that are measured by the software:
Distance between eyes
Width of nose
Depth of eye sockets
Cheekbones
Jaw line
Chin
These nodal points are measured to create a numerical code, a string of numbers that represents the face in a database. Only 14 to 22 nodal points are needed for the software to complete the recognition process. In the next section, we'll look at how the system goes about detecting, capturing and storing faces.
The SoftwareFacial recognition software falls into a larger group of technologies known as biometrics. Biometrics uses biological information to verify identity. The basic idea behind biometrics is that our bodies contain unique properties that can be used to distinguish us from others. Besides facial recognition, biometric authentication methods also include:
Fingerprint scan
Retina scan
Voice identification
Facial recognition methods may vary, but they generally involve a series of steps that serve to capture, analyze and compare your face to a database of stored images. Here is the basic process that is used by the system to capture and compare images:
To identify someone, facial recognition software compares newly captured images to databases of stored images.
1. Detection - When the system is attached to a video surveillance system, the recognition software searches the field of view of a video camera for faces. If there is a face in the view, it is detected within a fraction of a second. A multi-scale algorithm is used to search for faces in low resolution. (An algorithm is a program that provides a set of instructions to accomplish a specific task). The system switches to a high-resolution search only after a head-like shape is detected.
2. Alignment - Once a face is detected, the system determines the head's position, size and pose. A face needs to be turned at least 35 degrees toward the camera for the system to register it.
3. Normalization -The image of the head is scaled and rotated so that it can be registered and mapped into an appropriate size and pose. Normalization is performed regardless of the head's location and distance from the camera. Light does not impact the normalization process.
4. Representation - The system translates the facial data into a unique code. This coding process allows for easier comparison of the newly acquired facial data to stored facial data.
5. Matching - The newly acquired facial data is compared to the stored data and (ideally) linked to at least one stored facial representation.
The heart of the facial recognition system is the Local Feature Analysis (LFA) algorithm. This is the mathematical technique the system uses to encode faces. The system maps the face and creates a faceprint, a unique numerical code for that face. Once the system has stored a faceprint, it can compare it to the thousands or millions of faceprints stored in a database. Each faceprint is stored as an 84-byte file.
Eigenface-based facial recognition
How does it work?
The task of facial recognition is discriminating input signals (image data) into several classes (persons). The input signals are highly noisy (e.g. the noise is caused by differing lighting conditions, pose etc.), yet the input images are not completely random and in spite of their differences there are patterns which occur in any input signal. Such patterns, which can be observed in all signals could be – in the domain of facial recognition– the presence of some objects (eyes, nose, mouth) in any face as well as relative distances between these objects. These characteristic features are called eigenfaces in the facial recognition domain (or principal components generally). They can be extracted out of original image data by means of a mathematical tool called Principal Component Analysis (PCA).
By means of PCA one can transform each original image of the training set into a corresponding eigenface. An important feature of PCA is that one can reconstruct any original image from the training set by combining the eigenfaces. Remember that eigenfaces are nothing less than characteristic features of the faces. Therefore one could say that the original face image can be reconstructed from eigenfaces if one adds up all the eigenfaces (features) in the right proportion. Each eigenface represents only certain features of the face, which may or may not be present in the original image.
If the feature is present in the original image to a higher degree, the share of
the corresponding eigenface in the”sum” of the eigenfaces should be greater. If, contrary, the particular feature is not (or almost not) present in the original image, then the corresponding eigenface should contribute a smaller (or not at all) part to the sum of eigenfaces. So, in order to reconstruct the original image from the eigenfaces, one has to build a kind of weighted sum of all eigenfaces. That is, the reconstructed original image is equal to a sum of all eigenfaces, with each eigenface having a certain weight.
This weight specifies, to what degree the specific feature (eigenface) is present in the original image. If one uses all the eigenfaces extracted from original images, one can reconstruct the
original images from the eigenfaces exactly. But one can also use only a part of the eigenfaces. Then the reconstructed image is an approximation of the original image.
However, one can ensure that losses due to omitting some of the eigenfaces can be minimized. This happens by choosing only the most important features (eigenfaces).
Omission of eigenfaces is necessary due to scarcity of computational resources. How does this relate to facial recognition? The clue is that it is possible not only to extract the face from eigenfaces given a set of weights, but also to go the opposite way. This opposite way would be to extract the weights from eigenfaces and the face to be recognized. These weights tell nothing less, as the amount by which the face in question differs from “typical” faces represented by the eigenfaces. Therefore, using this
weights one can determine two important things:
1. Determine, if the image in question is a face at all. In the case the weights of the image differs too much from the weights of face images (i.e. images, from which we know for sure that they are faces), the image probably is not a fac2. Similar faces (images) possess similar features (eigenfaces) to similar degrees (weights). If one extracts weights from all the images available, the images could be grouped to clusters. That is, all images having similar weights are likely to be similar face
Applications
ical feat in itself, and then measure the various features of each face.Facial recognition software is designed to pinpoint a face and measure its features.
If you look in the mirror, you can see that your face has certain distinguishable landmarks. These are the peaks and valleys that make up the different facial features. These landmarks are defined as nodal points. There are about 80 nodal points on a human face. Here are a few of the nodal points that are measured by the software:
Distance between eyes
Width of nose
Depth of eye sockets
Cheekbones
Jaw line
Chin
These nodal points are measured to create a numerical code, a string of numbers that represents the face in a database. Only 14 to 22 nodal points are needed for the software to complete the recognition process. In the next section, we'll look at how the system goes about detecting, capturing and storing faces.
The SoftwareFacial recognition software falls into a larger group of technologies known as biometrics. Biometrics uses biological information to verify identity. The basic idea behind biometrics is that our bodies contain unique properties that can be used to distinguish us from others. Besides facial recognition, biometric authentication methods also include:
Fingerprint scan
Retina scan
Voice identification
Facial recognition methods may vary, but they generally involve a series of steps that serve to capture, analyze and compare your face to a database of stored images. Here is the basic process that is used by the system to capture and compare images:
To identify someone, facial recognition software compares newly captured images to databases of stored images.
1. Detection - When the system is attached to a video surveillance system, the recognition software searches the field of view of a video camera for faces. If there is a face in the view, it is detected within a fraction of a second. A multi-scale algorithm is used to search for faces in low resolution. (An algorithm is a program that provides a set of instructions to accomplish a specific task). The system switches to a high-resolution search only after a head-like shape is detected.
2. Alignment - Once a face is detected, the system determines the head's position, size and pose. A face needs to be turned at least 35 degrees toward the camera for the system to register it.
3. Normalization -The image of the head is scaled and rotated so that it can be registered and mapped into an appropriate size and pose. Normalization is performed regardless of the head's location and distance from the camera. Light does not impact the normalization process.
4. Representation - The system translates the facial data into a unique code. This coding process allows for easier comparison of the newly acquired facial data to stored facial data.
5. Matching - The newly acquired facial data is compared to the stored data and (ideally) linked to at least one stored facial representation.
The heart of the facial recognition system is the Local Feature Analysis (LFA) algorithm. This is the mathematical technique the system uses to encode faces. The system maps the face and creates a faceprint, a unique numerical code for that face. Once the system has stored a faceprint, it can compare it to the thousands or millions of faceprints stored in a database. Each faceprint is stored as an 84-byte file.
Eigenface-based facial recognition
How does it work?
The task of facial recognition is discriminating input signals (image data) into several classes (persons). The input signals are highly noisy (e.g. the noise is caused by differing lighting conditions, pose etc.), yet the input images are not completely random and in spite of their differences there are patterns which occur in any input signal. Such patterns, which can be observed in all signals could be – in the domain of facial recognition– the presence of some objects (eyes, nose, mouth) in any face as well as relative distances between these objects. These characteristic features are called eigenfaces in the facial recognition domain (or principal components generally). They can be extracted out of original image data by means of a mathematical tool called Principal Component Analysis (PCA).
By means of PCA one can transform each original image of the training set into a corresponding eigenface. An important feature of PCA is that one can reconstruct any original image from the training set by combining the eigenfaces. Remember that eigenfaces are nothing less than characteristic features of the faces. Therefore one could say that the original face image can be reconstructed from eigenfaces if one adds up all the eigenfaces (features) in the right proportion. Each eigenface represents only certain features of the face, which may or may not be present in the original image.
If the feature is present in the original image to a higher degree, the share of
the corresponding eigenface in the”sum” of the eigenfaces should be greater. If, contrary, the particular feature is not (or almost not) present in the original image, then the corresponding eigenface should contribute a smaller (or not at all) part to the sum of eigenfaces. So, in order to reconstruct the original image from the eigenfaces, one has to build a kind of weighted sum of all eigenfaces. That is, the reconstructed original image is equal to a sum of all eigenfaces, with each eigenface having a certain weight.
This weight specifies, to what degree the specific feature (eigenface) is present in the original image. If one uses all the eigenfaces extracted from original images, one can reconstruct the
original images from the eigenfaces exactly. But one can also use only a part of the eigenfaces. Then the reconstructed image is an approximation of the original image.
However, one can ensure that losses due to omitting some of the eigenfaces can be minimized. This happens by choosing only the most important features (eigenfaces).
Omission of eigenfaces is necessary due to scarcity of computational resources. How does this relate to facial recognition? The clue is that it is possible not only to extract the face from eigenfaces given a set of weights, but also to go the opposite way. This opposite way would be to extract the weights from eigenfaces and the face to be recognized. These weights tell nothing less, as the amount by which the face in question differs from “typical” faces represented by the eigenfaces. Therefore, using this
weights one can determine two important things:
1. Determine, if the image in question is a face at all. In the case the weights of the image differs too much from the weights of face images (i.e. images, from which we know for sure that they are faces), the image probably is not a fac2. Similar faces (images) possess similar features (eigenfaces) to similar degrees (weights). If one extracts weights from all the images available, the images could be grouped to clusters. That is, all images having similar weights are likely to be similar face
Applications
The primary users of facial recognition software have been law enforcement agencies, which use the system to capture random faces in crowds. These faces are compared to a database of criminal mug shots. In addition to law enforcement and security surveillance, facial recognition software has several other uses, including: Eliminating voter fraud Check-cashing identity verification Computer security One of the most innovative uses of facial recognition is being employed by the Mexican government, which is using the technology to weed out duplicate voter registrations. To sway an election, people will register several times under different names so they can vote more than once. Conventional methods have not been very successful at catching these people. Using the facial recognition technology, officials can search through facial images in the voter database for duplicates at the time of registration. New images are compared to the records already on file to catch those who attempt to register under aliases. The technology was used in the country's 2000 presidential election and is expected to be used in local elections soon. Potential applications even include ATM and check-cashing security. The software is able to quickly verify a customer's face. After the user consents, the ATM or check-cashing kiosk captures a digital photo of the customer. The software then generates a faceprint of the photograph to protect customers against identity theft and fraudulent transactions. By using facial recognition software, there's no need for a picture ID, bank card or personal identification number (PIN) to verify a customer's identity.
Many people who don’t use check cashing machines. Facial recognition eliminates possible criminal activity
This Biometric technology could also be used to secure your computer files. By mounting a Web cam to your computer and installing the facial recognition software, your face can become the password you use to get into your computer. IBM has incorporated the technology into a screensaver for it’s A, T and X series ThinkPad laptops.
While facial recognition can be used to protect your private information, it can just as easily be used to invade your privacy by taking you picture when you are entirely unaware of the camera. As with many developing technologies, the incredible potential of facial recognition comes with drawbacks.
Facial recognition software can be used to lock your computer.
Advantages
· Non-Intrusive - requires no physical extraction/invasion
Accurate - high enrollment and verification rates
Uses Existing Infrastructure - cost effective and quick to deploy
Discussion & future works
Biometric Systems Integration Services providing new solutions by combining face recognition software with other biometrics, such as iris, voice, signature and fingerprint technology as well as with existing identification card systems. Other applications include PC networks, retail point-of-sale and virtually any other application that requires identification or verification of an individual. This will lead to a day when society could be free from cards, keys, Pin's and signatures. A person's face will be the private, secure and convenient password of choice.
Conclusion
Further advancement in face-recognition technologies are going on. These patented and patent pending proprietary face recognition innovations have enabled this technology to achieve outstanding performance across a variety of environments and real world conditions for e-commerce and home workers, large database fraud reduction, casino surveillance, airport and other security and law enforcement applications. Various company’s are leading the way to bring face recognition applications and products to market.
References
T. M. Mitchell. Machine Learning. McGraw-Hill International Editions.
D. Pissarenko. Neural networks for financial time series prediction: Overview over
recent research. BSc thesis.
L. I. Smith. A tutorial on principal components analysis, February 2002. URL
http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_
components.pdf. (URL accessed on November 27, 2008).
M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience,
3(1), 1991a. URL http://www.cs.ucsb.edu/~mturk/Papers/jcn.
Pdf. (URL accessed on November 27, 2002).
M. A. Turk and A. P. Pentland. Face recognition using eigenfaces. In Proc. of Computer
Vision and Pattern Recognition, pages 586–591. IEEE, June 1991b. URL http:
//www.cs.wisc.edu/~dyer/cs540/handouts/mturk-CVPR91.pdf. (URL accessed
on November 27, 2002).
Many people who don’t use check cashing machines. Facial recognition eliminates possible criminal activity
This Biometric technology could also be used to secure your computer files. By mounting a Web cam to your computer and installing the facial recognition software, your face can become the password you use to get into your computer. IBM has incorporated the technology into a screensaver for it’s A, T and X series ThinkPad laptops.
While facial recognition can be used to protect your private information, it can just as easily be used to invade your privacy by taking you picture when you are entirely unaware of the camera. As with many developing technologies, the incredible potential of facial recognition comes with drawbacks.
Facial recognition software can be used to lock your computer.
Advantages
· Non-Intrusive - requires no physical extraction/invasion
Accurate - high enrollment and verification rates
Uses Existing Infrastructure - cost effective and quick to deploy
Discussion & future works
Biometric Systems Integration Services providing new solutions by combining face recognition software with other biometrics, such as iris, voice, signature and fingerprint technology as well as with existing identification card systems. Other applications include PC networks, retail point-of-sale and virtually any other application that requires identification or verification of an individual. This will lead to a day when society could be free from cards, keys, Pin's and signatures. A person's face will be the private, secure and convenient password of choice.
Conclusion
Further advancement in face-recognition technologies are going on. These patented and patent pending proprietary face recognition innovations have enabled this technology to achieve outstanding performance across a variety of environments and real world conditions for e-commerce and home workers, large database fraud reduction, casino surveillance, airport and other security and law enforcement applications. Various company’s are leading the way to bring face recognition applications and products to market.
References
T. M. Mitchell. Machine Learning. McGraw-Hill International Editions.
D. Pissarenko. Neural networks for financial time series prediction: Overview over
recent research. BSc thesis.
L. I. Smith. A tutorial on principal components analysis, February 2002. URL
http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_
components.pdf. (URL accessed on November 27, 2008).
M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience,
3(1), 1991a. URL http://www.cs.ucsb.edu/~mturk/Papers/jcn.
Pdf. (URL accessed on November 27, 2002).
M. A. Turk and A. P. Pentland. Face recognition using eigenfaces. In Proc. of Computer
Vision and Pattern Recognition, pages 586–591. IEEE, June 1991b. URL http:
//www.cs.wisc.edu/~dyer/cs540/handouts/mturk-CVPR91.pdf. (URL accessed
on November 27, 2002).
SOCCER VS FOOTBALL
Soccer is one of the most popular games in the world. It is the National game of European and most of Latin Americans and other Countries. The
Soccer W
orld Cup is held every four years. Soccer is one of the famous International Sports.Soccer is known world wide and it is played in the Olympic. Soccer contains two teams having each team containing eleven players, Each Member of the team tries to kick the ball to the opponents net, only Goal keeper can touch the ball in his hand and rest will be playing by leg and body. Today Soccer is the fastest growing sport. Soccer
can help you to stay fit and healthy. Soccer is fun and very much exciting sport.There are lots of leagues played in European and other countries, some of the popular leagues are English Premiere league, Spanish League, Italian League, French League etc. Every league contains 20 to 30 clubs playing 38 matches, Team having maximum wins will be the champions of League. Real Madrid is the Richest Sports club in the world.At the end of every league their will be a transfer season, Where players will be transferred from one league to another league by bidding top class players for millions of dollars. The biggest transfer Record ever made in the history of football was for Zinedine Zidane to a Spanish club called Real Madrid for £46.5m.
Soccer Coaching
Start Automation Testing with WATIR
WATIR
I would like to share my knowledge on WATIR(Web Application Testing In Ruby language).It is pronounced water
What is WATIR?
WATIR is an open source automated testing tool in ruby language. This tool helps us in automated testing for any web application. It allows us to write Test cases .So that we can avoid the drawbacks of manual testing like time complexity , human resource, document management ,simplicity ,flexibility etc .We can easily write Test Cases by learning few commands of Ruby Language. Ruby also a object oriented programming language like JAVA.I feel this is one of very efficient and interesting Automated Testing tool. Also Watir is too much magic and is too confusing for people to understand.
WATIR Installation
->Download ruby advanced version (Ruby 1.8.*.*) and install it by just clicking setup file
->Go to command command prompt and type gem install watir .This step will completes your installation process !!!.
How it works ?
->Go to command prompt and type irb .irb is interactive ruby shell for ruby.
We can run the ruby command here .
I would like to share my knowledge on WATIR(Web Application Testing In Ruby language).It is pronounced water
What is WATIR?
WATIR is an open source automated testing tool in ruby language. This tool helps us in automated testing for any web application. It allows us to write Test cases .So that we can avoid the drawbacks of manual testing like time complexity , human resource, document management ,simplicity ,flexibility etc .We can easily write Test Cases by learning few commands of Ruby Language. Ruby also a object oriented programming language like JAVA.I feel this is one of very efficient and interesting Automated Testing tool. Also Watir is too much magic and is too confusing for people to understand.
WATIR Installation
->Download ruby advanced version (Ruby 1.8.*.*) and install it by just clicking setup file
->Go to command command prompt and type gem install watir .This step will completes your installation process !!!.
How it works ?
->Go to command prompt and type irb .irb is interactive ruby shell for ruby.
We can run the ruby command here .
require 'watir' # use watir gem
test_site = 'http://www.google.com' # set a variable
ie = Watir::IE.new # open the IE browser,here ie is the variable
ie.goto(test_site) # load url, go to site
ie.text_field(:name, "q").set("some Text") # load text "pickaxe" into search field named"q"
ie.button(:name, "btnG").click #btnG" is the name of the Search button, click it
if ie.text.include?("Programming Ruby")
puts "Test Passed. Found the test string: 'Programming Ruby'."
else
puts "Test Failed! Could not find: 'Programming Ruby'"
end
test_site = 'http://www.google.com' # set a variable
ie = Watir::IE.new # open the IE browser,here ie is the variable
ie.goto(test_site) # load url, go to site
ie.text_field(:name, "q").set("some Text") # load text "pickaxe" into search field named"q"
ie.button(:name, "btnG").click #btnG" is the name of the Search button, click it
if ie.text.include?("Programming Ruby")
puts "Test Passed. Found the test string: 'Programming Ruby'."
else
puts "Test Failed! Could not find: 'Programming Ruby'"
end
I reffered this example from http://wtr.rubyforge.org/.Save this code in a file with .rb extension . And to execute this file go to the parent directory via command prompt and type ruby file.rb
For more detail please refer wtr.rubyforge.org/watir_user_guide.html
I will end with a list of most common beginner problems:
-> Capitalizing Watir in the require statement: require ‘watir’ NOT require ‘Watir’.
-> Not putting an “end” for the class statement in the test case file.
-> Leaving out the “class” statement in the test case file.
-> Not specifying that your test class inherits from Watir::TestCase.
-> Not starting the test case definition with def “test_”
-> Capitalizing of test names instead of keeping them lower case, “test_01_test_to_see_if this_works”.
Drawbacks
Watir is Ruby library that works only with Internet Explorer on Windows at present.Still trust me Watir is one of the most useful and very interesting framework.
If you have any doubt regarding WATIR please contact me on nitil84@gmail.com.
Thursday, January 10, 2008
A small touching story mainly for professionals...
Hey friends check it out na........................
A man came home from work late, tired and irritated, to find his 5-year old son waiting for him at the door.
SON: "Daddy, may I ask you a question?"
DAD: "Yeah sure, what is it?" replied the man.
SON: "Daddy, how much do you make an hour?"
DAD: "That's none of your business. Why do you ask such a thing?" the man said angrily.
SON: "I just want to know. Please tell me, how much do you make an hour?"
DAD: "If you must know, I make Rs.100 an hour."
SON: "Oh," the little boy replied, with his head down.
SON: "Daddy, may I please borrow Rs.50?"
The father was furious, "If the only reason you asked that is so you can borrow some money to buy a silly toy or some other nonsense, then you march yourself straight to your room and go to bed. Think aboutwhy you are being so selfish. I work hard everyday for such this childish behavior."
The little boy quietly went to his room and shut the door.
The man sat down and started to get even angrier about the little boy's questions. How dare he ask such questions only to get some money? After about an hour or so, the man had calmed down, and started to think: Maybe there was something he really needed to buy with that Rs.50 and he really didn't ask for money very often.
The man went to the door of the little boy's room and opened the door. "Are you asleep, son?" He asked. "No daddy, I'm awake," replied the boy.
"I've been thinking, maybe I was too hard on you earlier" said the man.
"It's been a long day and I took out my aggravation on you. Here's the Rs.50 you asked for." The little boy sat straight up, smiling. "Oh, thank you daddy!" He yelled.
Then, reaching under his pillow he pulled out some crumpled up bills. The man saw that the boy already had money, started to get angry again. The little boy slowly counted out his money, and then looked up at his father."Why do you want more money if you already have some?"
the father grumbled. "Because I didn't have enough, but now I do,"
the little boy replied. "Daddy, I have Rs.100 now. Can I buy an hour of your time?Please come home early tomorrow. I would like to have dinner with you."
The father was crushed. He put his arms around his little son, and he begged for his forgiveness.
It's just a short reminder to all of you working so hard in life. We should not let time slip through our fingers without having spent some time with those who really matter to us, those close to our hearts. Do remember to share that Rs.100 worth of your time with someone you love. If we die tomorrow, the company that we are working for could easilyreplace us in a matter of days. But the family & friends we leave behind will feel the loss for the rest of their lives. And come to think of it, we pour ourselves more into work than to our family. coooooooooooool
A man came home from work late, tired and irritated, to find his 5-year old son waiting for him at the door.
SON: "Daddy, may I ask you a question?"
DAD: "Yeah sure, what is it?" replied the man.
SON: "Daddy, how much do you make an hour?"
DAD: "That's none of your business. Why do you ask such a thing?" the man said angrily.
SON: "I just want to know. Please tell me, how much do you make an hour?"
DAD: "If you must know, I make Rs.100 an hour."
SON: "Oh," the little boy replied, with his head down.
SON: "Daddy, may I please borrow Rs.50?"
The father was furious, "If the only reason you asked that is so you can borrow some money to buy a silly toy or some other nonsense, then you march yourself straight to your room and go to bed. Think aboutwhy you are being so selfish. I work hard everyday for such this childish behavior."
The little boy quietly went to his room and shut the door.
The man sat down and started to get even angrier about the little boy's questions. How dare he ask such questions only to get some money? After about an hour or so, the man had calmed down, and started to think: Maybe there was something he really needed to buy with that Rs.50 and he really didn't ask for money very often.
The man went to the door of the little boy's room and opened the door. "Are you asleep, son?" He asked. "No daddy, I'm awake," replied the boy.
"I've been thinking, maybe I was too hard on you earlier" said the man.
"It's been a long day and I took out my aggravation on you. Here's the Rs.50 you asked for." The little boy sat straight up, smiling. "Oh, thank you daddy!" He yelled.
Then, reaching under his pillow he pulled out some crumpled up bills. The man saw that the boy already had money, started to get angry again. The little boy slowly counted out his money, and then looked up at his father."Why do you want more money if you already have some?"
the father grumbled. "Because I didn't have enough, but now I do,"
the little boy replied. "Daddy, I have Rs.100 now. Can I buy an hour of your time?Please come home early tomorrow. I would like to have dinner with you."
The father was crushed. He put his arms around his little son, and he begged for his forgiveness.
It's just a short reminder to all of you working so hard in life. We should not let time slip through our fingers without having spent some time with those who really matter to us, those close to our hearts. Do remember to share that Rs.100 worth of your time with someone you love. If we die tomorrow, the company that we are working for could easilyreplace us in a matter of days. But the family & friends we leave behind will feel the loss for the rest of their lives. And come to think of it, we pour ourselves more into work than to our family. coooooooooooool
BOND ADAPT V11
BOND ADAPT V11
Adapt is a specialist portfolio of recruitment software applications which has earned Bond the reputation for enhancing business growth and profitability throughout the global staffing industry. Adapt goes further than any other staffing software. It provides recruitment consultancies of all sizes with the optimum tools to manage their present business and confidently plan future success.
Business Need:
Manpower develops Front Office Applications using Bond AdaptV11 software. It enables the recruiter to manage and track companies, candidates, jobs, interviews, resumes, assignments etc. It is used mainly by the staffing industry. Some of the staffing companies are Manpower, Kelly, Robert-Half, and Spherion
Services Provided:
Adapt V11 is a recruitment software specialises in the provision of e-recruitment, talent management and payroll software solutions to corporate companies across the globe. It provides recruitment consultancies of all sizes with the optimum tools to manage their present business and confidently plan future success. It allows the users to develop Web application as well as standalone application.
Adapt provides tools which enable the configurator to create DB design , create screens , configure users, user groups, permissions, logins, access, user profiles, email, calendar ,task settings and write programs called business objects etc.
Manpower develops Front Office Applications using Bond AdaptV11 software. It enables the recruiter to manage and track companies, candidates, jobs, interviews, resumes, assignments etc. It is used mainly by the staffing industry. Some of the staffing companies are Manpower, Kelly, Robert-Half, and Spherion
Services Provided:
Adapt V11 is a recruitment software specialises in the provision of e-recruitment, talent management and payroll software solutions to corporate companies across the globe. It provides recruitment consultancies of all sizes with the optimum tools to manage their present business and confidently plan future success. It allows the users to develop Web application as well as standalone application.
Adapt provides tools which enable the configurator to create DB design , create screens , configure users, user groups, permissions, logins, access, user profiles, email, calendar ,task settings and write programs called business objects etc.
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