First Divine Skill Under Heaven

Recently, while I was walking on the street, I met an incredibly skilled pugilist. The pugilist was dressed in rags and had unkempt hair. He was clearly from the Beggar Sect, Bukit Panjang Branch. Perhaps he was even the leader of the preeminent Sect.

The pugilist stared at me, and then spoke, "To that young hero over there, I observe that you have the bone structure of a martial arts genius which is only seen once every 1000 years ! It will be a pity if you do not practice martial arts and uphold justice ! Let me transfer all my skills and internal power to you !!!".

After speaking, he grabbed me, and transferred his internal power to me. After the process was complete, the man laughed loudly, and said, "Now I have transferred all my internal power to you ! Don't underestimate this power, for I myself received all the internal power of my master, who received all the internal power of his master !!! You are now very powerful, powerful enough to defeat 2 pugilists !"

I remarked that 2 was a seemingly low number for the combined internal power of 3 generations of experts. Perhaps they were quite lousy in combat. That would also explain why they were beggars.

The master sighed, shaking his head. "You do not understand, young hero. The power transference skill is not perfect ! As power transference goes against the natural laws of the world, only 50% of the internal power is transfered, and the rest wasted ! Hence, even though power transference is an amazing skill, ultimately the maximum power attainable has an upper ceiling."

"Consider a pugilist of generation n + 1. Assuming that he is an average pugilist, his total power, X(n+1), will be X(n+1) = 1 + r * X(n), where r is the percentage of internal power transferred. For the pugilist of generation n, his power is X(n) = 1 + r * X(n-1).

Hence, the power of the pugilist in generation n + 1 is:

X(n+1) = r * ( 1 + r * X(n -1)) + 1 = (r^2) X(n-1) + r + 1

Continuing the process until the original pugilist, X(0),

X(n+1) = (r^(n+1))*X(0) + (1-r^(n+2))/(1-r)

Assuming that there are many many generations, or n large, and since r less than 1, hence,

X(n+1) = 1 / (1-r)

Since my Sect's energy transference skill transfers power at 50% loss, the power of a pugilist (assuming he trains) is 2 !"

After explaining the situation to me, the pugilist fell over and died. I was slightly saddened, since I had lost a comrade in upholding justice.

Not all was lost, however. I discovered a secret manual hidden in his rags. The manual was titled, "First Divine Skill Under Heaven", which arguably meant that it was either the most powerful skill in the world, or (inclusive or) that it was the most primitive skill in the world.

In order to ensure that everyone is also able to uphold justice, I'm sharing part of the manual here.

Manual for the First Divine Skill Under Heaven

---

Note : I really think that for power transference skills, there should be an attenuation factor, otherwise everyone would be extremely powerful after a few generations.

Secret Research Project 1: Interesting Facts

Now that the research report has been completed and submitted, I have some time to share some interesting facts about the research.

1. When people ask what I do for my secret research, I say I download images of apples, bananas, irons, scientific calculators, suitcases, crayons, dustbins, pears, plastic bottles, tissue boxes, forks, spoons, laptops, clocks, pillows and blenders.
2. I now possess a large collection of images of apples, bananas, irons, scientific calculators, suitcases, crayons, dustbins, pears, plastic bottles, tissue boxes, forks, spoons, laptops, clocks, pillows and blenders. Much time was spent to collect and download these images.
3. For the preliminary versions of the classifier, the classifier achieved a classification rate of 70%. Coincidentally, the training set consisted of 70% natural images and 30% synthetic images.
4. For the uninformed, point 3 was a consequence of the classifier labeling everything as a natural image, hence achieving a 70% performance. This approach is excellent. By increasing the set of natural images to 99%, the performance can be boosted to 99% !
5. Point 4 was a joke.
6. The trusty Microsoft Paint was used to perform many tasks for the project. These tasks include converting GIF, PNG, and BMP images into JPEG format, and creating diagrams for the final report.
   
7. Two computers were used for the project. The project could be run exclusively on either the desktop or the laptop computer, but using two computers greatly increase rate of work.
8. The increase in rate of work was not due to being able to run multiple simulations simultaneously. Rather, the simulations were mostly done on one computer (the laptop), while the other (the desktop) was used for net research and report writing. Useful work was done on the desktop while the MATLAB program ran on the laptop.
9. Using two computers was also cool for report writing. All the data and related papers were displayed on the laptop screen, while the desktop ran only Word. Information could be directly read from the laptop and entered into the report without ALT-TABBING and changing windows constantly.
   
10. I want a dual monitor setup after learning the advantages of point 9.
11. I want a dual core system to be able to run my (hypothetical) dual monitor system properly without lagging. This is also to make running simulations less of a pain.
12. MATLAB should perform some checks on code integrity before running. Many times, MATLAB would return an error after it had run much of the simulation processing. The error was a simple formatting error located at the last few lines of the code.

More information on the image classifier will be released if anyone is interested.

Secret Research Project 1: Quick Update

I'm just dropping by to release some of the latest results for the graphics classifier.

Classifier	SR	NR	AR	Time Taken
C1	85.44 %	69.86 %	77.65 %	37.4 s
C2	87.67%	72.69 %	80.14 %	38.2 s
C3	86.95 %	74.51 %	80.73 %	38.5 s
C4	86.00 %	76.21 %	81.11 %	38.4 s

Notes:
SR, NR = Recall rates for synthetic and natural images. AR = average recall rate.
C1~C4 are classifiers employing some set of metrics.

I'm going to sleep soon. The only reason I'm awake at this hour is to finish my research report, which is now at 37 pages. I'm still missing the discussion and conclusion chapters, as well as a third of the introduction. Work also has to be done on the formatting of the report.

I'm hoping I can mop up the remaining work by noon tomorrow later, since I still have another secret research report to complete by Friday.

I'll release more information and results on the Synthetic and Natural Image Classifier (that's the official name of the thingy) when I'm extremely free. That would be next Tuesday.

[25/04/08] Errors in the calculations were found and corrected.

Secret Research Project 1: Graphics Classifier

One of the 'secret research projects' I'm working on currently is a graphics classifier. Basically, the aim of the research is to build a classifier capable of differentiating between a graphics image and a realistic image.

To be clearer, a graphics image is an image which is artificial. This type of image is not directly captured from the physical environment. Hence, drawings, paintings, cartoons and clipart are considered to be graphics images.

On the other hand, realistic images are images which are directly captured from the physical environment. In other words, these are photographs of real objects.

A sample of a realistic image and a graphics image is shown below.

Left : Graphics Image
Below: Realistic Image

I'm now using simple metrics to metrify the images. According to intuition, graphics and realistic images will have different metrics, hence enabling me to clasify them. Some metrics that I have adopted, and the assumptions behind them, are:

Saturation Metric: Graphics, especially computer generated ones, tend to have higher saturation values compared to photographs, which are invariably less saturated (more faded/dull) due to natural effects.

Number of Colors Metric: Graphics tend to be composed of a small palette of colors compared to realistic images, which tend to occupy much of the spectrum.

I've also used a number of other metrics, but I'll share those at a later point of time. In any case, the effectiveness of the existing metrics are reasonable, but not spectacular, achieving only about a 60~70% correct classification rate.

However, by combining the different metrics into a single classifier system (aka boosting), I expect the performance of the system to improve.

More updates on the secret project will come later.