Latif Anshori Kurniawan

Faedah Berbahasa Indonesia

Category: General (Page 1 of 2)

KDE

I use KDE software since a couple years ago. Even, I had been started with Linux-based system at the beginning, played on my cousin’s old computer, when I was kid (actually, when I have had my first year at junior high school). I have ever tried KDE on any various Linux distributions (distro), and it was run smoothly. I could say that KDE more suitable and fit on Linux distros that polished very well with KDE, like SUSE/openSUSE & KDE neon (Ubuntu-based), though everything works fine on other distros. Actually, I only use Linux of non-DEB family.

KDE is based on Qt framework. I following Qt progress and history time after time, from Troltech (in Oslo, Norway) then the Qt company stand still in Finland. Yes, better you understand Qt first, then develop or deploy a tiny patch of a KDE app.

KDE is an open-source software community project. Everyone can contribute in KDE. Not only as developer, no need to be able to code or having skill in programming. Whatever your main jobs and hobbies, whatever you are, you can join the game. KDE is great, is about the people, the patrons, the passions, the community.

The Powerful of Qt
Qt is C++, one of the the strongest programming languages. All codes of Qt based on C++, but added so many rich features, libraries, modules, and some more. Even if you familiar with other programming languages, you still could play with Qt. Whatever your other lovely languages and scripts, like Python, Java, HTML5, XML, etc. Indeed, you can develop several apps on several platforms, such as: Linux-based kernel, macOS, Windows, even Android.

For mobile apps development, you can touch your BlackBerry, Sailfish (by Jolla) with Qt. It is fantastic framework because you can code easily on cross-platform, any infrastructure. No need to change your codes more between platforms, you “just” compile them then play.

If you care about open-source, try KDE, and welcome to the freedom of software development!

V-geometrically Ergodic Markov Data

I found nice references about machine learning in Cornell University Library’s arXiv.org. One–of the references–is an article that tell about “Generalization and Robustness of Batched Weighted Average Algorithm with V-geometrically Ergodic Markov Data”. This topic written by Nguyen Viet Cuong, Lam Si Tung Ho, and Vu Dinh.

Their article was published in Proceedings of the 24th International Conference on Algorithmic Learning Theory (ALT) 2013. Please visit here for more information.

arXiv.org

Cornell University Library created an open access library, especially in Physics, Mathematics, Computer Science, Quantitative Biology, Quantitative Finance, and Statistics. Those subjects can browse at arXiv.org.

arXiv.org is an electronic print (e-print) service by Cornell University, owned and operated by them as a private non-profit educational institution. arXiv.org has supported by Cornell’s Library, the Simons Foundation, and the member institutions.

You can find several e-prints that you can take benefits from. All e-prints are great, you can refer them to strengthen the arguments, theoretical things, and even data, of your papers. Let’s take a look.

SINGA Fund Ph.D.

Singapore International Graduate Award (SINGA) give opportunities for Ph.D. research areas, they will fund in several subjects. Learn more about that on their official web about.

The good news is everyone can join the opportunities. You can apply one of the subjects from several affiliations. It is better for you to read carefully first before you send your own application. I suggest you to get in touch with SINGA team and the professors (that you want they read your ideas) firstly. Give it a try!

Machine Learning Conferences

When we talk about machine learning, we can not forget about how artificial intelligence concept works. The ML topic is quite super-cool nowadays. About machine learning, a brief start that you can read on Springer.

People will always talk with this topic, even more at conference. Several conferences about machine learning held well done at National University of Singapore (NUS) for a couple years ago, such as: MLSS 2011, ACML 2012, and more.

Several proceedings of machine learning research can be found on PMLR. It is very fantastic collection of proceedings that available online.

Any of you learn, research or study about machine learning? Please let me know.

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