• 残像 – Zanzou

    残像 – Zanzou

    Zanzou is alive. =)

    Zanzou an e-learning tool I made as my computer science master’s degree thesis project at the Faculty of electrical engineering, Osijek, Croatia. Its goal is to help people memorize kana using flashcards. Based on unsuccessful answering, Zanzou will repeat certain symbols more frequently and (depending on the chosen difficulty level) it will group and present some of the similar symbols together to make it harder to “guess the answer”. Global user success statistics are also available.

    Features

    • memorize hiragana and katakana using flashcards
    • three difficulty levels
    • three types of exercises
      • kana to roumaji
      • roumaji to kana
      • voice to kana
    • customize your kana practice set
    • flashcard frequency based on correct answers
    • free

    Thanks to

    For any feedback or bug reports, feel free to contact me through email or just drop a comment bellow. =)

  • cmik.fm at build.last.fm + source at github

    cmik.fm at build.last.fm + source at github

    cmik.fm made it into build.last.fm a few days ago, and not only that, but last.fm team rewarded me with a jolly good one year of free last.fm subscription as well, yaaay! thanks last.fm!

    I also decided to release the source-code for cmik.fm under GPL3 over at github for anyone interested (not that I see a good reason for anyone to look at it, but it just feels good to leave it out there). heh :D

  • cmik.fm

    cmik.fm

    Some time ago I noticed I often fall into the habit of repeating a single song over and over – until I can’t listen to it any more that is. I have also often been wondering how much more or less do I do it compared to my friends. Curious enough to want to find out, having a few years old last.fm account, and (so it happens) being on a side-quest for finding myself a small web project to work on in my free time and learn something new, I started working on cmik.fm. Finally it’s done, I learned quite a lot in the process and it was quite a joyful experience coding it.

    So, if you ever wanted to know this useless piece of information about your life (like I did :D), you could give cmik.fm a try. If you like it, perhaps show it to your friends who use last.fm.

    Special thanks to:

    Feel free to leave a comment with your feedback, any suggestions you might have, a bug you noticed or whatever! =)

    Read more:
    cmik.fm at build.last.fm + source at github

  • Da ne zaboravim _\\//

    Da ne zaboravim _\\//

    craptop
    [2009-03-04 19:08] installed filesystem (2009.01-1)
    [2009-03-04 20:35] synchronizing package lists
    [11.09.2012] R.I.P. (3y6m7d / 1287 days)

    kanta
    [2009-03-07 20:18] installed filesystem (2009.01-1)
    [2009-03-07 21:15] synchronizing package lists

    Long live Arch.

  • Swarm Intelligence in HTML5 Canvas

    Swarm Intelligence in HTML5 Canvas

    Swarm Intelligence – I wanted to visualize different ways of particles following each other for some time now but have never really gotten to the point of doing it until one day, I was browsing through lots of amazing things people have made using HTML5 Canvas. One of these things was Daniel Puhes Liquid Particles canvas thingy which immediately reminded me of my idea and I thought – why not try and do it using HTML5 canvas? I wanted to try canvas out a little bit anyway. So, I asked Daniel for permission to use his code as an “engine” for my idea and he was ok with that. Thanks Daniel! He also made some more pretty cool stuff so go and check that out if you want.

    So I took his code, refactored it a fair bit, made it so that particles follow each other instead of following the mouse and that’s how Swarm Intelligence was born. I do know that this is not real AI but I don’t care, I just like to call it that way anyways. :)

    At the moment there are three different algorithms that make particles follow each other:

    • swarm – each particle targets a random particle from the swarm and follows it for next 5000-10000 canvas refresh cycles. after that, it randomly finds a new one and follows it for next 5000-10000 refresh cycles and so on
    • tail – each particle targets a different random particle on each canvas refresh cycle and follows it for a single refresh cycle after which it targets a new random particle and then follows than one for a single refresh cycle and so on
    • lead – all particles follow the same (single) particle which moves randomly inside the canvas a bit faster than all the other particles

    Algorithms change randomly over time but can also be changed manually by clicking  the left mouse button somewhere inside the canvas. I also introduced some chaos into the system so it wouldn’t stabilize itself over time. For example, every now and then some particles get randomly blown away from the swarm a little bit. Also, after some time all the particles will get blown away at the same time. I plan to add two or three more following algorithms when I get the time. One of them will be following the nearest particle but I will have to come up with one bad-ass algorithm to not kill the performance. Soon I guess. Or not. :D

    Algorithm indicator in the top left corner of the canvas introduced some performance problems. Apparently, drawing text inside canvas is very, very poorly implemented in all browsers except Chromium. The problem I had was that I would redraw a line of text inside canvas on each refresh cycle (every 70ms) which slowed it down a fair bit if you tried to run it in anything else except Chromium. Redrawing the text on each cycle wasn’t really necessary since the text stays the same most of the time so what I did was – on top of the old canvas frame I added a new one which redraws text only when the following algorithm gets changed. That turned out to be a pretty good solution.

    Nothing more to say about this small experiment I guess.. except – enjoy it! :)

  • My first open-source code contribution – spilp

    My first open-source code contribution – spilp

    Spilp is a simple Python script that takes IIS logs, parses them and creates statistical reports which can be used to discover unusual IP activity more easily.”

    ‘Been working on this one for some time now and finally everything I wanted to implement is in place. The whole thing is released under GNU GPL v3 license for everybody to enjoy. You can download the script here. Below is a list of spilp features with links to sample reports.

    Features

    • extracts a list of IP addresses with number of hits they made sorted by number of hits
    • extracts a list of “close” IP addresses that made a certain number of hits
    • extracts a list of user agents sorted by number of hits
    • extracts a list of cs-method hits (GET method excluded)
    • extracts a list of file hits sorted by number of hits
    • extracts extended information for document and web file hits
      • includes timestamps, client IP addresses, methods, ports, user agent details and http status codes
    • extracts a list of “unusual” http status code hits sorted by number of hits
      • client IP address list
      • a list of files hit by an IP and number of hits for that file
    • filtering results (include or exclude filtering – works in “either-or” way)
      • ability to auto-generate an IP range list as a filter
    • reverse DNS country lookup using MaxMinds? GeoIP country downloadable database
      • additional info in certain reports
      • filtering results by country of origin (as a separate filtering option using spilpconf.py file)
    • ability to process large amount of IIS log files
    • CONFIG file for performance and output tweaking
    Rejoice!
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