Time travel with Google Street View

I always knew Google would do this, it was so obvious. Google Street View now has a new feature that lets you go back in time with Street View to see how any location looked right back to when Googles cameras first captured the view. Now when you start Street View youll see a time stamp on the pop-up Street View window and a time-line with a slider. You can select from any of the points on the slider.
Your street, like mine, probably hasnt changed much. Time magazine has put together a series of time-lapse sequences that show iconic buildings like NYCs One World Trade Center rising out of the ground over the years. This feature will be fascinating to explore in 20 to 30 years time. 

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

Put the internet to work for you.

via Personal Recipe 895909

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Making online learning even easier with a re envisioned Course Builder

Posted by Adam Feldman, Product Manager and Pavel Simakov, Technical Lead, Course Builder Team

(Cross-posted on the Google for Education blog)

The Course Builder team believes in enabling new and better ways to learn (for both the instructor and learner). Todays release of Course Builder v1.10 furthers these goals in three ways, by being easier to use, embeddable and applicable to more types of content.

Easier to use
We took a step back and re-envisioned the menus and navigation of the administrative interface based on the steps instructors take as they create a course. These are designed to help you through the process of creating, styling, publishing and managing your courses. This re-imagined design gives a solid foundation for future versions of Course Builder.

A completely redesigned navigation simplifies content authoring and configuration.

To support this redesign, we’ve also completely revamped our documentation. There’s now one home for all of Course Builder’s materials: Google Open Online Education. Here, you’ll find everything you need to conceptualize and construct your content, create a course using Course Builder, and even develop new modules to extend Course Builder’s capabilities. The content now reflects the latest features and organization. This re-imagined design gives a solid foundation for future versions of Course Builder.

Embeddable assessment support
What if you want to use some of Course Builder’s features but already have an existing learning site? To help with these situations, Course Builder now supports embeddable assessments (graded questions and answers with an optional due date). Simply create your assessments in Course Builder, copy the JavaScript snippet and paste it on any site. Your users will be able to complete the assessments from the comfort of your existing site and you’ll be able to benefit from Course Builder’s per-question feedback, auto-grading and analytics with just two short lines of code that are automatically generated for you.

We started with embeddable assessments because evaluation is so important to learning, but we don’t plan to stop there. Watch for additional embeddable components in the future.

Applicable to more types of content
Many types of online learning content, like tutorials, exercises and documentation, are a lot like online courses. For instance, they might involve presenting content to users, having them do exercises or assessments and allowing them to stop and return later. Yet, you might not think of them as traditional courses.

To make Course Builder a better fit for a broader set of online content, we’ve added a new “guides” experience. Guides are a new way for students to browse and consume your content. Compared to typical online courses -- which can enforce a strict linear path (from unit 1 to unit 2, etc.) -- guides present your content as a non-numbered list. Users are free to enter and exit in any order. It also allows you to show the content for many courses together.

You could imagine each guide being a documentation page or tutorial section. Guides also work with any existing Course Builder units and can be made available by simply enabling that feature in the dashboard. Here are a couple of our courses, when viewed as guides:

Within each guide, the user is guided through the steps, which could be portions of a docs page or lessons in a unit, as in this example from the “Power Searching with Google” sample course:

By letting users jump in and out of the content as they like, guides are ideally suited to the on-the-go learner and look great on phones and tablets. It’s our first foray into responsive mobile design... but it won’t be our last.

Guides currently support public courses, but we’ll be adding registration, enhanced statefulness and interface customization, as well as elements of dynamic learning (think of a personalized list of guides).

This release has focused on making Course Builder easier to use and more relevant. It sets up the framework to give future features a natural home. It adds embeddable assessments to make Course Builder useful in more places. And it introduces guides, a new, less linear format for consuming content.

For a full list of features, see the release notes, and let us know what you think. Keep on learning!

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PC user does not know the password changed despite his previous password WITH VIDEO TUTORIAL

Follow my image instruction:

 if you have facing any problems then watch this videos

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Improving YouTube video thumbnails with deep neural nets

Posted by Weilong Yang and Min-hsuan Tsai, Video Content Analysis team and the YouTube Creator team

Video thumbnails are often the first things viewers see when they look for something interesting to watch. A strong, vibrant, and relevant thumbnail draws attention, giving viewers a quick preview of the content of the video, and helps them to find content more easily. Better thumbnails lead to more clicks and views for video creators.

Inspired by the recent remarkable advances of deep neural networks (DNNs) in computer vision, such as image and video classification, our team has recently launched an improved automatic YouTube "thumbnailer" in order to help creators showcase their video content. Here is how it works.

The Thumbnailer Pipeline

While a video is being uploaded to YouTube, we first sample frames from the video at one frame per second. Each sampled frame is evaluated by a quality model and assigned a single quality score. The frames with the highest scores are selected, enhanced and rendered as thumbnails with different sizes and aspect ratios. Among all the components, the quality model is the most critical and turned out to be the most challenging to develop. In the latest version of the thumbnailer algorithm, we used a DNN for the quality model. So, what is the quality model measuring, and how is the score calculated?

The main processing pipeline of the thumbnailer.

(Training) The Quality Model

Unlike the task of identifying if a video contains your favorite animal, judging the visual quality of a video frame can be very subjective - people often have very different opinions and preferences when selecting frames as video thumbnails. One of the main challenges we faced was how to collect a large set of well-annotated training examples to feed into our neural network. Fortunately, on YouTube, in addition to having algorithmically generated thumbnails, many YouTube videos also come with carefully designed custom thumbnails uploaded by creators. Those thumbnails are typically well framed, in-focus, and center on a specific subject (e.g. the main character in the video). We consider these custom thumbnails from popular videos as positive (high-quality) examples, and randomly selected video frames as negative (low-quality) examples. Some examples of the training images are shown below.

Example training images.

The visual quality model essentially solves a problem we call "binary classification": given a frame, is it of high quality or not? We trained a DNN on this set using a similar architecture to the Inception network in GoogLeNet that achieved the top performance in the ImageNet 2014 competition.

Results

Compared to the previous automatically generated thumbnails, the DNN-powered model is able to select frames with much better quality. In a human evaluation, the thumbnails produced by our new models are preferred to those from the previous thumbnailer in more than 65% of side-by-side ratings. Here are some examples of how the new quality model performs on YouTube videos:

Example frames with low and high quality score from the DNN quality model, from video “Grand Canyon Rock Squirrel”.

Thumbnails generated by old vs. new thumbnailer algorithm.

We recently launched this new thumbnailer across YouTube, which means creators can start to choose from higher quality thumbnails generated by our new thumbnailer. Next time you see an awesome YouTube thumbnail, don’t hesitate to give it a thumbs up. ;)

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Controlling music with your mind

Last year I bought an EEG headset (the Mindwave Mobile) to play with my Raspberry Pi and then ended up putting it down for a while. Luckily, this semester I started doing some more machine learning and decided to try it back out. I thought it might be possible to have it recognize when you dislike music and then switch the song on Pandora for you. This would be great for when you are working on something or moving around away from your computer.

So using the EEG headset, a Raspberry Pi, and a bluetooth module, I set to work on recording some data. I listened to a couple songs I liked and then a couple songs I didnt like with labeled data. The Mindwave gives you the delta, theta, high alpha, low alpha, high beta, low beta, high gamma, and mid gamma brainwaves. It also approximates your attention level and meditation level using the FFT (Fast Fourier Transform) and gives you a skin contact signal level (with 0 being the best and 200 being the worst).

Since I know very little about brainwaves, I cant make an educated decision on what changes to look at to detect this; thats where machine learning comes in. I can use Bayesian Estimation to construct two multivariate Gaussian models, one that represents good music and one that represents bad music.



----TECHNICAL DETAILS BELOW----
We construct the model using the parameters below (where ? is the mean of the data and ? is the standard deviation of the data):

Now that we have the model above for both good music and bad music, we can use a decision boundary to detect what kind of music you are listening to at each data point.

where:

The boundary will be some sort of quadratic (hyper ellipsoid, hyper parabola, etc) and it might look something like below (though ours is a 10 dimensional function):

 

----END TECHNICAL DETAILS----

The result is an algorithm that is accurate about 70% of the time, which isnt reliable enough. However, since we have temporal data, we can utilize that information, and we wait until we get 4 bad music estimations in a row, then we skip the song.

Ive created a short video (dont worry, I skip around so you dont have to watch me listen to music forever) as a proof of concept. Then end result is a way to control what song is playing with only your brainwaves.

This is an extremely experimental system and only works because there are only two classes to choose and it is not even close to good accuracy. I just thought it was cool. Im curious to see if training using my brainwaves will work for other people as well but I havent tested it yet. There is a lot still to refine but its cool to have a proof of concept. You cant buy one of these off the shelf and expect it to change your life. Its uncomfortable and not as accurate as an expensive EEG but it is fun to play with. Now I need to attach one to Google Glass.

NOTE: This was done as a toybox example as fun. You probably arent going to see EEG controlled headphones in the next couple years. Eventually maybe, but not due to work like this.

How to get it working

HERE IS THE SOURCE CODE

I use pianobar to stream Pandora and have a modified version of the control-pianobar.sh control scripts I have put in the github repository below.
I have put the code on Github here but first you need to make sure you have python >= 3.0, bluez, pybluez, and pianobar installed to use it. You will also need to change the home directory information, copy the control-pianobar.sh script to /usr/bin, change the MAC address (mindwaveMobileAddress) in  mindwavemobile/MindwaveMobileRawReader.py to the MAC address of your mindwave mobile device (which I got the python code from here), and run sudo python setup.py install.

I start pianobar with control-pianobar.sh p then I start the EEG program with python control_music.py, it will tell you what it thinks the song is in real time and then will skip it if it detects 4 bad signals in a row. It will also tell you whether the headset is on well enough with a low signal warning.

Thanks to Dr. Aaron Bobick (whose pictures and equations I used), robintibor (whose python code I used), and Daniel Castro (who showed me his code for Bayesian Estimation in python since my implementation was in Matlab).

Consider donating to further my tinkering.

Places you can find me

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

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Circumventing 6 Strikes Rule with Transmission

Here are some easy steps to help circumvent the new "Six Strikes Rule" the ISPs have put in to place (I dont know if I would go as far as calling it the Six Strikes Law since its just a company agreement).

If you really want to get crazy about it, use TOR or set up an SSL server somewhere using something like Amazon Web Services.

In the mean time, here are some simple little things you can do to try to prevent your ISP spying on your files and prevent those letters from coming in. We will do this two ways, once using the web client and once with the command line

Note: I would also recommend using VPN or a proxy but we wont go into that.
And if you are on a college network, you should definitely consider enabling LPD.

Editing Transmission Options Using the Web App

Go to the appropriate transmission web page. If you dont know this. See below:

http://stevenhickson.blogspot.com/2012/10/using-raspberry-pi-as-web-server-media.html

Click the wrench to open up the options. Then change require encryption to true and click enable blocklist. You can pick whichever blocklist you want but I use the following blocklist:
http://iblocklist.charlieprice.org/f/tagqfxtteucbuldhezkz/bt_level1.gz

Simply paste that in the box as shown below and you are ready to go.

Editing Transmission Options Using the Command Line

Open up your terminal and stop transmission by typing:

sudo service transmission-daemon stop

then edit the settings file by typing:

sudo nano /etc/transmission-daemon/settings.json

Change the following settings:

"blocklist-enabled": true,
"blocklist-url": "http://list.iblocklist.com/?list=bt_level1&fileformat=p2p&archiveformat=gz",
"encryption": 2,

Then simply start transmission again with the command:

sudo service transmission-daemon start


To automatically download things and use your transmission to your fullest potential, see this page.

Consider donating to further my tinkering.

Places you can find me

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

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Collection of SQL queries with Answer and Output Set 3

Here is a collection or a list of 38 SQL Queries with Answers as well as output. You can write your answer at the text box below each query any time you can see the table structure by clicking on Table Structure. And check your Answer by clicking on Answer. You can test your Skill in SQL. You can also go for an online Quiz in SQL in one of my previous posts: Click here for Quiz. More queries will be added to this post within few days, visit again!!!

Happy learning!!!
Carry on....
You can also share your queries in this site. Use this Link to share your part with the visitors like you.

SQL Query collection: Set1 Set2 Set3 Set 4

Below is the Table Structure using which you have to form the queries:


1) Who is the highest paid C programmer?

Table Structure

Write your answer hereAnswer

SELECT * FROM PROGRAMMER
WHERE SALARY=(SELECT MAX(SALARY)
FROM PROGRAMMER
WHERE PROF1 LIKE C OR PROF2 LIKE C)

2) Who is the highest paid female cobol programmer?

Table Structure

Write your answer hereAnswer

SELECT * FROM PROGRAMMER
WHERE SALARY=(SELECT MAX(SALARY)
FROM PROGRAMMER
WHERE (PROF1 LIKE COBOL OR PROF2 LIKE COBOL))
AND SEX LIKE F

3) Display the name of the HIGEST paid programmer for EACH language (prof1)

Table Structure

Write your answer hereAnswer

SELECT DISTINCT NAME, SALARY, PROF1
FROM PROGRAMMER
WHERE (SALARY,PROF1) IN (SELECT MAX(SALARY),PROF1
FROM PROGRAMMER
GROUP BY PROF1)

4) Who is the LEAST experienced programmer?

Table Structure

Write your answer hereAnswer

SELECT FLOOR((SYSDATE-DOJ)/365) EXP,NAME
FROM PROGRAMMER
WHERE FLOOR((SYSDATE-DOJ)/365) = (SELECT MIN(FLOOR((SYSDATE-DOJ)/365))
FROM PROGRAMMER)

5) Who is the MOST experienced programmer?

Table Structure

Write your answer hereAnswer

SELECT FLOOR((SYSDATE-DOJ)/365) EXP,NAME,PROF1,PROF2
FROM PROGRAMMER
WHERE FLOOR((SYSDATE-DOJ)/365) = (SELECT MAX(FLOOR((SYSDATE-DOJ)/365))
FROM PROGRAMMER)
AND (PROF1 LIKE COBOL OR PROF2 LIKE COBOL)

6) Which language is known by ONLY ONE programmer?

Table Structure

Write your answer hereAnswer

SELECT PROF1
FROM PROGRAMMER
GROUP BY PROF1
HAVING PROF1 NOT IN
(SELECT PROF2 FROM PROGRAMMER)
AND COUNT(PROF1)=1
UNION
SELECT PROF2
FROM PROGRAMMER
GROUP BY PROF2
HAVING PROF2 NOT IN
(SELECT PROF1 FROM PROGRAMMER)
AND COUNT(PROF2)=1;

7) Who is the YONGEST programmer knowing DBASE?

Table Structure

Write your answer hereAnswer

SELECT FLOOR((SYSDATE-DOB)/365) AGE, NAME, PROF1, PROF2
FROM PROGRAMMER
WHERE FLOOR((SYSDATE-DOB)/365) = (SELECT MIN(FLOOR((SYSDATE-DOB)/365))
FROM PROGRAMMER
WHERE PROF1 LIKE DBASE OR PROF2 LIKE DBASE)

8) Which institute has MOST NUMBER of students?

Table Structure

Write your answer hereAnswer

SELECT SPLACE
FROM STUDIES
GROUP BY SPLACE
HAVING COUNT(SPLACE)= (SELECT MAX(COUNT(SPLACE))
FROM STUDIES GROUP BY SPLACE)

9) Who is the above programmer?

Table Structure

Write your answer hereAnswer

SELECT NAME
FROM PROGRAMMER
WHERE PROF1 IN (SELECT PROF1
FROM PROGRAMMER
GROUP BY PROF1
HAVING PROF1 NOT IN (SELECT PROF2 FROM PROGRAMMER)
AND COUNT(PROF1)=1
UNION
SELECT PROF2
FROM PROGRAMMER
GROUP BY PROF2
HAVING PROF2 NOT IN (SELECT PROF1 FROM PROGRAMMER)
AND COUNT(PROF2)=1))
UNION
SELECT NAME
FROM PROGRAMMER
WHERE PROF2 IN (SELECT PROF1
FROM PROGRAMMER
GROUP BY PROF1
HAVING PROF1 NOT IN (SELECT PROF2 FROM PROGRAMMER)
AND COUNT(PROF1)=1
UNION
SELECT PROF2
FROM PROGRAMMER
GROUP BY PROF2
HAVING PROF2 NOT IN (SELECT PROF1 FROM PROGRAMMER)
AND COUNT(PROF2)=1))

10) Which female programmer earns MORE than 3000/- but DOES NOT know C, C++, Oracle or Dbase?

Table Structure

Write your answer hereAnswer

SELECT * FROM PROGRAMMER
WHERE SEX LIKE F
AND SALARY >3000
AND (PROF1 NOT IN(C,C++,ORACLE,DBASE)
OR PROF2 NOT IN(C,C++,ORACLE,DBASE))

11) Which is the COSTLIEST course?

Table Structure

Write your answer hereAnswer

SELECT COURSE
FROM STUDIES
WHERE CCOST = (SELECT MAX(CCOST) FROM STUDIES)

12) Which course has been done by MOST of the students?

Table Structure

Write your answer hereAnswer

SELECT COURSE
FROM STUDIES
GROUP BY COURSE
HAVING COUNT(COURSE)= (SELECT MAX(COUNT(COURSE))
FROM STUDIES
GROUP BY COURSE)

13) Display name of the institute and course Which has below AVERAGE course fee?

Table Structure

Write your answer hereAnswer

SELECT SPLACE,COURSE
FROM STUDIES
WHERE CCOST < (SELECT AVG(CCOST) FROM STUDIES)

14) Which institute conducts COSTLIEST course?

Table Structure

Write your answer hereAnswer

SELECT SPLACE
FROM STUDIES
WHERE CCOST = (SELECT MAX(CCOST) FROM STUDIES)

15) Which course has below AVERAGE number of students?

Table Structure

Write your answer hereAnswer

SELECT COURSE
FROM STUDIES
HAVING COUNT(NAME)<(SELECT AVG(COUNT(NAME))
FROM STUDIES
GROUP BY COURSE)
GROUP BY COURSE;

16) Which institute conducts the above course?

Table Structure

Write your answer hereAnswer

SELECT SPLACE
FROM STUDIES
WHERE COURSE IN (SELECT COURSE
FROM STUDIES
HAVING COUNT(NAME) < (SELECT AVG(COUNT(NAME))
FROM STUDIES
GROUP BY COURSE)
GROUP BY COURSE);

17) Display names of the course WHOSE fees are within 1000(+ or -) of the AVERAGE fee.

Table Structure

Write your answer hereAnswer

SELECT COURSE
FROM STUDIES
WHERE CCOST < (SELECT AVG(CCOST)+1000 FROM STUDIES)
AND CCOST > (SELECT AVG(CCOST)-1000 FROM STUDIES)

18) Which package has the HIGEST development cost?

Table Structure

Write your answer hereAnswer

SELECT TITLE,DCOST
FROM SOFTWARE
WHERE DCOST = (SELECT MAX(DCOST) FROM SOFTWARE)

19) Which package has the LOWEST selling cost?

Table Structure

Write your answer hereAnswer

SELECT TITLE,SCOST
FROM SOFTWARE
WHERE SCOST = (SELECT MIN(SCOST) FROM SOFTWARE)

20) Who developed the package, which has sold the LEAST number of copies?

Table Structure

Write your answer hereAnswer

SELECT NAME,SOLD
FROM SOFTWARE
WHERE SOLD = (SELECT MIN(SOLD) FROM SOFTWARE)

21) Which language was used to develop the package WHICH has the HIGEST sales amount?

Table Structure

Write your answer hereAnswer

SELECT DEV_IN,SCOST
FROM SOFTWARE
WHERE SCOST = (SELECT MAX(SCOST) FROM SOFTWARE)

22) How many copies of the package that has the LEAST DIFFRENCE between development and selling cost were sold?

Table Structure

Write your answer hereAnswer

SELECT SOLD,TITLE
FROM SOFTWARE
WHERE TITLE = (SELECT TITLE
FROM SOFTWARE
WHERE (DCOST-SCOST)=(SELECT MIN(DCOST-SCOST) FROM SOFTWARE))

23) Which is the COSTLIEAST package developed in PASCAL?

Table Structure

Write your answer hereAnswer

SELECT TITLE
FROM SOFTWARE
WHERE DCOST = (SELECT MAX(DCOST)
FROM SOFTWARE
WHERE DEV_IN LIKE PASCAL)

24) Which language was used to develop the MOST NUMBER of package?

Table Structure

Write your answer hereAnswer

SELECT DEV_IN FROM SOFTWARE
GROUP BY DEV_IN
HAVING MAX(DEV_IN) = (SELECT MAX(DEV_IN) FROM SOFTWARE)

25) Which programmer has developed the HIGEST NUMBER of package?

Table Structure

Write your answer hereAnswer

SELECT NAME FROM SOFTWARE
GROUP BY NAME
HAVING MAX(NAME) = (SELECT MAX(NAME) FROM SOFTWARE)

26) Who is the author of the COSTLIEST package?

Table Structure

Write your answer hereAnswer

SELECT NAME,DCOST
FROM SOFTWARE
WHERE DCOST = (SELECT MAX(DCOST) FROM SOFTWARE)

27) Display names of packages WHICH have been sold LESS THAN the AVERAGE number of copies?

Table Structure

Write your answer hereAnswer

SELECT TITLE
FROM SOFTWARE
WHERE SOLD < (SELECT AVG(SOLD) FROM SOFTWARE)

28) Who are the female programmers earning MORE than the HIGEST paid male programmers?

Table Structure

Write your answer hereAnswer

SELECT NAME
FROM PROGRAMMER
WHERE SEX LIKE F
AND SALARY > (SELECT(MAX(SALARY))
FROM PROGRAMMER
WHERE SEX LIKE M)

29) Which language has been stated as prof1 by MOST of the programmers?

Table Structure

Write your answer hereAnswer

SELECT PROF1
FROM PROGRAMMER
GROUP BY PROF1
HAVING PROF1 = (SELECT MAX(PROF1)
FROM PROGRAMMER)

30) Who are the authors of packages, WHICH have recovered MORE THAN double the development cost?

Table Structure

Write your answer hereAnswer

SELECT NAME distinct
FROM SOFTWARE
WHERE SOLD*SCOST > 2*DCOST

31) Display programmer names and CHEAPEST package developed by them in EACH language?

Table Structure

Write your answer hereAnswer

SELECT NAME,TITLE
FROM SOFTWARE
WHERE DCOST IN (SELECT MIN(DCOST)
FROM SOFTWARE
GROUP BY DEV_IN)

32) Who is the YOUNGEST male programmer born in 1965?

Table Structure

Write your answer hereAnswer

SELECT NAME
FROM PROGRAMMER
WHERE DOB=(SELECT (MAX(DOB))
FROM PROGRAMMER
WHERE TO_CHAR(DOB,YYYY) LIKE 1965)

33) Display language used by EACH programmer to develop the HIGEST selling and LOWEST selling package.

Table Structure

Write your answer hereAnswer

SELECT NAME, DEV_IN
FROM SOFTWARE
WHERE SOLD IN (SELECT MAX(SOLD)
FROM SOFTWARE
GROUP BY NAME)
UNION
SELECT NAME, DEV_IN
FROM SOFTWARE
WHERE SOLD IN (SELECT MIN(SOLD)
FROM SOFTWARE
GROUP BY NAME)

34) Who is the OLDEST female programmer WHO joined in 1992

Table Structure

Write your answer hereAnswer

SELECT NAME
FROM PROGRAMMER
WHERE DOJ=(SELECT (MIN(DOJ))
FROM PROGRAMMER
WHERE TO_CHAR(DOJ,YYYY) LIKE 1992)

35) In WHICH year where the MOST NUMBER of programmer born?

Table Structure

Write your answer hereAnswer

SELECT DISTINCT TO_CHAR(DOB,YYYY)
FROM PROGRAMMER
WHERE TO_CHAR(DOJ,YYYY) = (SELECT MIN(TO_CHAR(DOJ,YYYY))
FROM PROGRAMMER)

36) In WHICH month did MOST NUMBRER of programmer join?

Table Structure

Write your answer hereAnswer

SELECT DISTINCT TO_CHAR(DOJ,MONTH)
FROM PROGRAMMER
WHERE TO_CHAR(DOJ,MON) = (SELECT MIN(TO_CHAR(DOJ,MON))
FROM PROGRAMMER)

37) In WHICH language are MOST of the programmers proficient?

Table Structure

Write your answer hereAnswer

SELECT PROF1
FROM PROGRAMMER
GROUP BY PROF1
HAVING COUNT(PROF1)=(SELECT MAX(COUNT(PROF1))
FROM PROGRAMMER
GROUP BY PROF1)
OR COUNT(PROF2)=(SELECT MAX(COUNT(PROF2))
FROM PROGRAMMER
GROUP BY PROF2)
UNION
SELECT PROF2
FROM PROGRAMMER
GROUP BY PROF2
HAVING COUNT(PROF1)=(SELECT MAX(COUNT(PROF1))
FROM PROGRAMMER
GROUP BY PROF1)
OR COUNT(PROF2)=(SELECT MAX(COUNT(PROF2))
FROM PROGRAMMER
GROUP BY PROF2)

38) Who are the male programmers earning BELOW the AVERAGE salary of female programmers?

Table Structure

Write your answer hereAnswer

SELECT NAME
FROM PROGRAMMER
WHERE SEX LIKE M
AND SALARY < (SELECT(AVG(SALARY))
FROM PROGRAMMER
WHERE SEX LIKE F)

SQL Query collection: Set1 Set2 Set3 Set 4

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