Tuesday, January 20, 2015

Boxplots grouped by categories

Simulating self-organizing systems often requires a compact representation of numerical data. One way to achieve this is via Boxplots,which indicate statisical distributions of data series through their quartiles. Usually, a box shows the median, the lower and the upper quartile values of a data series. The whiskers depict the lowest datum still withing 1.5 IQR (interquartile range) of the lower quartile and the highest datum still within 1.5 IQR of the upper quartile. Boxplots depict a good deal of information for statistical interpretation of data. Most of the tools for statistical computing and graphics can easily build boxplots, e.g., the boxplot function in R, the boxplot function in MATLAB, and the boxplot function in Python. As you can see, there are many affordable tools to display boxplots, but things get tricky if there is a need to group in categories. To achieve this, Sergii Zhevzhyk wrote a Python program using the matplotlib library which supports customization and adaptation of graphs. Data are loaded from the given csv files. One boxplot sample is shown below. The source code of our implementation can be found at GitHub.         
The image above shows the results of two measurements for different type of the candies. The comparison of two measurements can be done without problem, because they placed close to each other and have different colors. Two files (first file, second file) contain the data for this boxplot.

Links:

Call for Papers 10th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2015)



In conjunction with:
CISIS 2015
and
ICEUTE 2015

The 10th International Conference on Soft Computing Models in Industrial and Environmental Applications will take place in Burgos, Spain. Soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate, and analyze very complex issues and phenomena. This conference is mainly focus on its industrial and environmental applications.

Topics of interest include, but are not limited to:
• Green Computing
• Evolutionary Computing
• Neuro Computing
• Probabilistic Computing
• Immunological Computing
• Hybrid Methods
• Causal Models
• Case-based Reasoning
• Chaos Theory Fuzzy Computing
• Intelligent Agents and Agent Theory
• Interactive Computational Models

The application fields of interest cover, but are not limited to:
• Decision Support
• Process and System Control
• System Identification and Modelling
• Optimization
• Signal or Image Processing
• Vision or Pattern Recognition
• Condition Monitoring
• Fault Diagnosis
• Systems Integration
• Internet Tools
• Human Machine Interface
• Time Series Prediction
• Robotics
• Motion Control & Power Electronics
• Biomedical Engineering
• Virtual Reality
• Reactive Distributed AI
• Telecommunications
• Consumer Electronics
• Industrial Electronics
• Manufacturing Systems
• Power and Energy
• Data Mining
• Data Visualisation
• Intelligent Information Retrieval
• Bio-inspired Systems
• Autonomous Reasoning
• Intelligent Agents

Important Dates

Paper submission deadline: 24th January, 2015
Acceptance notification: 13th February, 2015
Submission of final papers: 3rd March, 2015
Final version submission: 13th March, 2015
Conference dates: 15th-17th June, 2015

Monday, December 1, 2014

Advent Programming Contest 2014

Still in the flow from the IEEEXtreme programming challenge? Looking for a daily new programming problems to train your skills?
The Advent Programming Contest 2014, organized by the IEEE Student Branch Klagenfurt will provide a new problem every day from December 1st to December 24th. On Saturdays and Sundays, new problems will appear at 12:00 Central European Time, on workdays at 18:00 CET. You can submit solutions any day until the contest ends on December 26. You can choose to use C, C++, Java, Python or Perl as programming language. The programming tasks can be solved with short programs (typically less than 100 lines of code). Until a solution is correct you can submit your program as often as you want (but please don't spam our server). Your score depends on the number of correct solutions and the time and trials you needed to solve the problem. Winners will be announced after closing of the contest.

The event is open to everyone. If you want to participate, please register at http://mooshak.nes.aau.at/ When you register please indicate if you belong to the group University, Pupils or other.
This is an individuals competition, not a team contest - be fair!
You can also join the contest after 1st December, registration is possible until December 24.

Monday, November 24, 2014

Scalability in Self-Organizing Systems

One of the properties of self-organizing systems is scalability. It means that system keeps its working capabilities even if we remove some of its components or add more of them. In our reseach, we employ different evolutionary algorithms (EAs) to create a self-organizing system. In particular, algorithms like a simple evolutionary algorithm or a two dimensional cellular EA are used  for adjusting the synaptic weights of an neural controller. The best solutions are identified based on simulations of the target application. Typically, the simulation parameters limit the applicability of the solution - there is no guarantee that an evolved solution is adaptable or scalable to situations not specified in the simulation parameters. On the other hand, there are many examples in nature where solutions could be successfully employed in other contexts. We decided to check how our soccer teams, which consist of evolved neural controllers, can scale.

For the FIFA World Cup in Brazil we organized our own tournament between evolved self-organized soccer teams. This is an exciting show - to see how simple agents having only partial information about the environment around them are reaching its goal (score a goal) as a team. Will they be able to play in the same manner if we take the contoller, trained in the simulation with 10 players per team, and increase or decrease the number of players? This question has remained open until today.

In our first scenario, we assume that we invited two soccer teams to show us a fantastic game, but due to some circumstances, only 4 players per teamshow up.
Thus our first experiment can be seen in the video below.
Despite the players being evolved in a context of 11 players on each side, reducing the number of players did not affect the ability of players to show good game.


To check the other extreme, we settled a very dangerous experiment - each team consisting of 40 players! The results were stunning (see video below). These soccer heroes could play as a team even with significantly increased number of players. Unfortunately, they could not play for a long time in this mode: Marco Materazzi headbutted Zinadine Zidane in the chest and shouted "Revenge!"; Luis Suarez bit two players in order to show his perfect teeth; Diego Maradona scored the goal by striking the ball with his hand and this time he was disqualified for this trick. We didn't care about these incidents since we got the results of our experiment:


Links:

Sunday, October 19, 2014

On the Road to your PhD

In the blog Between a rock and a hard place, James Hickey posted a valuable list of tipps for succeeding in your PhD:

1. Learn Latex
2. Use Bibtex
3. Keep your papers organised
4. Keep a formatted list of your own publications and conference abstracts as you go along
5. Always give conference abstracts different titles
6. Keep on top of your emails
7. Manage time
8. Hypothesis testing
9. Keep detailed notes
10. Avoid perfectionism
11. Always give deadlines when you want feedback
12. Source additional funding
13. Write as you go
14. Don’t be scared of your supervisors
15. Log out of Facebook
16. Keep an eye on your budget
17. Diversify yourself
18. Music
19. Get your workstation set up
20. Take notes in meetings
21. Read around your subject
22. Write a literature review
23. Socialise!
24. Sport and/or hobbies
25. Go to conferences and workshops
26. Network
27. Establish a routine
28. Take the lead
29. Practice presenting your work
30. Be prepared for the worse
31. Back up, and back up again
32. Small steps to success
33. Keep on top of admin
34. ENJOY IT!

Being somebody who already has his PhD, I find the list very useful, the title "Things I wish I knew when I started my PhD..." definitely holds some truth, although I accidently did many of the good things mentioned there. See the original post for a detailed explanation of every item!

Sunday, October 12, 2014

Self-organizing Processes in Physical Geography

In this talk, Marco van der Wiel presents some ideas on self-organization and self-organized criticality, and how these relate to physical geography and (explanatory and exploratory) modelling in physical geography.


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The talk is in english, you might want to skip the german introduction until 1:40. Video by AAU Campus TV/Egmont Sparouz.

Tuesday, August 19, 2014

DarwinTunes - Evolution of Music by Public Choice

http://game.darwintunes.org/REST/population/1/individual/573a5f10/audio.mp3
The natural world – creatures, plants, infections – is the result of Darwinian evolution by natural selection, i.e., the gradual process of (biological) traits become either more or less common in a population based on the success of the organism carrying these traits. This process, repeated for two billion years, has created the vast diversity of life on earth.
The same process can be also observed in human society where cultural artifacts – words, songs, images, ideas – are constantly remixed and reinterpreted by people. A reinterpretation is an imperfect copy and therefore a “mutation”. Thus, the variety of our culture is the result of a cultural evolution.
In order to examine the underlying mechanism of cultural evolution, Robert M. MacCalluma, Matthias Mauch, Austin Burta, and Armand M. Leroia constructed a Darwinian music engine consisting of a population of short audio loops that sexually reproduce and mutate.
The selection is based on human feedback via a webpage that implements the remixing of tunes as a game. By remixing your tune with others, the other parent gets a score point. Your goal is to make an attractive tune - the more often you get remixed, the more points you have.

In 2010, researchers from the Alpen-Adria-Universität Klagenfurt released a similar system where people could vote for recombinations of music tunes.  

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