Hung Truong: The Blog!

Nacho Libre: Movie Review

I borrowed and watched the Jack Black movie from a few years ago, Nacho Libre, last night. I’m not a huge fan of Jack Black but I guess I do find him generally funny. I figured this movie would be pretty good since the Napoleon Dynamite guy directed it and Jack Black stars in it.

I have to agree with Ebert. The movie just isn’t that great. Or rather, it doesn’t reach the potential that it obviously has. There are so many untied loose ends that at the end of the movie, I was really sure there had to be more.

What about the skinny guy’s relationship with the fat woman? What about the skinny guy’s love of science, and not God? Wasn’t Nacho supposed to convert him or something? And wasn’t Nacho going to get married to the hot nun (who I thought was Penelope Cruz through the entire movie but is apparently a different actress)?

None of the characters were really fleshed out that well. The hot nun was just sort of a token hot nun who just looked confused for most of the movie. The skinny dude sort of became a companion to Nacho, but his character really didn’t go anywhere either.

I think part of the problem is that you lose a lot of Jack Black’s funniness when he has to use a fake Mexican accent. Instead of talking like Jack Black, he just does a lame impression of a Mexican dude. It’s like they put really heavy ankle weights on Jack Black’s comedy ankles and they never took them off! Except maybe for the scene where he sang. That was pretty funny.

Also, the movie was a “Nickelodeon” production, which explained the lack of cruder humor and the abundance of fart noises.

PowerSet: Good For Searching Wikipedia!

There have been quite a few blogs today comparing the newly released preview of PowerSet (an alternative search engine) Vs. Google. The line we’ve been reading for a while now is that PowerSet uses NLP (Natural Language Processing) to figure out what you want to find, not just stuff that sort of has what you want to find in its body text.

Previous comparisons have been throwing out things like “what does an orange taste like?” or “what is the capital of New Zealand?” No one asks those kinds of questions. Therefore, I will just search “who is Hung Truong?” since I’m sure lots of people want to know. It’s probably the most popular search term of all time, really.

The Google search does pretty well. 6/10 links on the first page are actually attributable to me, Hung Truong (the most popular Hung Truong on Google today!).

Hmmm… Powerset just returns a bunch of stuff from Wikipedia. Unfortunately, I don’t have a Wikipedia article (yet)! 0/10 of the first 10 results are actually about me.

So there you have it. Google beats PowerSet because PowerSet is only good for searching Wikipedia at the moment. Usually, when I want to search Wikipedia, I use Wikipedia.

The Meijer-Garfield Connection

Meijer, the grocery store I usually frequent, uses Garfield the cat as a sort of product mascot. I hate Garfield. Garfield is probably the least funny comic in existence. The comics are so bad that removing all traces of Garfield makes them funnier.

So it’s strange to see Meijer use such a hated cat as a mascot. Personally, seeing Garfield on a product makes me want to buy it less. I used to like Garfield when I was a kid. It’s probably because back then, my brain wasn’t fully developed or something.

Anyway, I bought some Meijer String Cheese today. It’s pretty good, despite the existence of Garfield as a mascot.

Stats Project: Sociability of Musical Instruments Using Facebook Data

For my SI 544: Stats class this semester, I worked with two cool dudes, Jim Laing and Sameer Halai. Our project involved using data gathered from a Facebook application to test a hypothesis about the perceived sociability of certain musical instruments.

If you recall, I wrote a blog post a few months ago about the viral vs. non-viral growth of Facebook applications that I had developed. One of those apps, Musical Instruments, lets you list which musical instruments you play. It’s kind of fun because some people play really whacked-out instruments (I play pianica and soprano trombone). I think playing instruments is typically a pretty social experience, which sort of led me to think about comparing the “sociability” of certain instruments to each other with the data gathered from this app.

Users input their instruments via an autocompleting text field. If an instrument already exists in the database (and at least 3 or so users have claimed it), it will autocomplete. In the above screenshot, I’ve typed “Trumpet” and you can see there’s many different types of trumpet to choose from. A user can also type an instrument that doesn’t yet exist in the database and it’ll be added automatically. This kind of free vocabulary is nice because it doesn’t require an administrator to continuously accept new instruments.

The data that the application has access to are:

• The user’s FBID (Their unique Facebook user ID in the form of a number)
• The instruments that the user claims to play
• The number of friends that the user has

We ended up getting 8603 rows of data (user/instrument pairs). After getting a bunch of free text instruments, we went to work classifying many of them into groups and subgroups. So Piccolo is in the group “Flute” and subgroup “Woodwind.”

We then generated a survey for people to rank 16 instruments in order of sociability. That is, people who play x instrument probably have more friends than y instrument.

The survey results showed that people thought Vocalists had the most friends and that Guitar was pretty popular too.

From the application data and survey results, we formed a hypothesis. We hypothesized that the instruments given high sociability rank would also have statistically higher mean numbers of friends. So people who played Guitar would have more friends than people who played Flute.

First, we did some basic analysis of the data using R, the free stats program that we were using for class assignments and labs.

This figure shows the histogram of frequency of number of friends. Basically, many people have 0-100 friends, less people have 101-200 friends, etc. This probably follows a power law curve, but we didn’t think it would be really important to find the alpha or anything for our purposes.

This is a graph of the mean number of friends, by instrument. This looks like a pretty standard normal distribution and it shows off the central limit theorem that Lada is always talking about in class.

This is just a boxplot of all of the classified instruments and their # of friends. There are some crazy outliers; people who have 1000 friends. From this boxplot, it’s hard to make out whether or not any of the means are actually statistically significant.

Finally, we ran pairwise t-tests on each set of instruments. We could see that there was a significant difference in the mean number of friends for certain instruments. For example, Guitar and Horn, Guitar and Oboe, and Guitar and Saxophone. Looking at the mean number of friends for these instruments, Saxophone players had on average 20 more friends than Guitar players. This is interesting because Saxophone was ranked 10.7 (not very sociable) and Guitar was ranked very sociable.

The scope of this project was pretty small, and given some more time, I think we could’ve come up with some more interesting conclusions. Stuff like “is flute really a girly instrument?” by looking at the average number of female flute players vs male flute players and “do guitar players get more chicks?” by looking at relationship status of guitar players vs. something like trumpet players (personal burn!).

I was glad my Facebook app actually provided some interesting data. I’ve always been sort of skeptical to the ability of Facebook apps to be profitable. I think the data that the apps provide is very valuable in the context of social network research. Anyway, I hope you found this post to be somewhat entertaining. I’ve also uploaded the project report and presentation slides in PDF if you want to check them out.

Many thanks to Jim and Sameer for sharing much of the work in this project. I ended up providing data, formatting it, and presenting the final presentation, so props to my teammates!

KVM DVI Switches: Why So Expensive!?

Right now I’m using a Dell 24-inch screen with my hand-built Windows box. But I also have a 13-inch Macbook that I’ve been using for school and software development (Unix is simply a nicer environment than Windows). I’ve been wanting to get the Mac on the 24-inch screen, but I only have a mini-dvi to DVI adapter. So I was thinking of getting one of those nifty KVM switches that let you use one set of input devices for multiple computers.

A KVM switch that supports DVI video inputs costs like, $175! Why so expensive!? I’m not an electrical engineer (or computer engineer even), but it doesn’t make sense that the hardware can cost so much. Couldn’t you just rig something up that just physically “switches” the wiring? Maybe the switch requires some power, but I can’t imagine the logic being that terribly complex. In comparison, a lot of the VGA KVM switches I’ve looked at cost maybe $25. What’s the difference, besides a few extra signals being re-routed?

So my short term cheapo fix will probably be to buy a $19 Mini-dvi to VGA adapter for my Macbook. My monitor has multiple inputs (VGA, DVI, Component, Composite, S-Video) so I can just switch from VGA to DVI for Mac to PC. The video quality might suffer, but I probably won’t be able to tell the difference anyway (my older brother claims he can).