Summer School

We are very happy to announce that we will have a summer school in the coming week!
If you are a member of IEEE SMC Society, you are able to attend the school WITHOUT CHARGE!
You may attend part of the following lectures. When you complete all the lectures,
we will send you the certification of the attendance of SMCS Summer School.

You can join the school after signing up in the following Google Form.

Registration Fee for Summer School

You can become IEEE and IEEE SMC Society member through the following webpage.
You can pay for the school through the following payment site.

SMC Society Student MemberFree (0 JPY)
SMC Society MemberFree (0 JPY)
IEEE Student Member 1,000 JPY
IEEE Member 1,000 JPY
JSAI Student Member1,000 JPY
JSAI Member1,000 JPY
Non IEEE Student Member 5,000 JPY
Non IEEE Member 8,000 JPY

SMCS Cybernetics Summer School tentative timetable

June 10 Thursday to June 12 Saturday

DateThu, June 10Fri, June 11Sat, June 12 or
Fri, June 11
Sat, June 12
Fri, June 11
Sat, June 12
Fri, June 11
Peng Shi
Prof. Hideyuki TakagiProf.
Vladik Kreinovich
Ljiljana Trajkovic
TimeJapan 7:20PM-9:20PM
GMT 10:20AM- 12:20PM
EST 6:20AM- 8:20AM
PST 3:20AM- 5:20AM
Japan 6:20PM-9:20PM
GMT 9:20AM-12:20PM
EST 5:20AM-8:20AM
PST 2:20AM-5:20AM
Japan 8:30AM-10:30AM
GMT 11:30PM-1:30AM
EST (Fri) 7:30PM-9:30PM
PST (Fri) 4:30PM-6:30PM
Japan 10:45AM-12:45AM
GMT 1:45AM-3:45AM
EST (Fri) 9:45PM-11:45PM
PST (Fri) 6:45PM-8:45PM
GMT 4:45AM-6:45AM
EST (Sat) 12:45AM-2:45AM
PST (Fri) 9:45PM-11:45PM
Time Allocation70min: Introduction and Lecture
20min: Exercise
20min: Presentation
10min: Conclusion
90min: Lecture
Some short exercises of total about 15 minutes for some statistical tests are prepared during this lecture.
Exercises: Examples of interval computation techniques45 min: Introduction and Lecture
45 min: Exercise
20 min: Presentation
10 min: Conclusion
30min: Introduction and Lecture
30min: Exercise
30min: Lecture
20min: Presentation
10min: Conclusion

June 10 Thursday in Tokyo, Japan
GMT 10:20AM-12:20AM, Tokyo 7:20PM-9:20PM, EST 6:20AM-8:20AM, PST 3:20AM-5:20AM
Lecturer: Peng Shi (Professor at University of Adelaide, Australia)
Theme: Collaborative Control Design for Multi-agent Systems

Abstract: Multi-agent Systems (MASs) are systems with characteristics of cooperation and decentralization. As the agents often work under complex circumstances, limitations of the hardware that include limited passive sensing and active communication capabilities are likely to be present. As a result of the localization conditions above, the agents need to collaborate in a distributed manner to achieve a common goal. In this lecture, the analysis, design, and potential applications of a variety of collaborative control including consensus, formation control and collision avoidance will be presented for MASs. As for different collaborative tasks, examples of system design frameworks with functional modules are illustrated for people to understand how the MASs achieve assigned tasks in an optimal way. Simulation and Lab experimental results are given to demonstrate the effectiveness of some design schemes proposed in our group.

June 11 Friday in Tokyo, Japan
GMT 9:20AM-12:20AM, Tokyo 6:20PM-9:20PM, EST 5:20AM-8:20AM, PST 2:20AM-5:20AM
Lecturer: Hideyuki Takagi (Professor at Kyushu University, Japan)
Theme 1: Humanized Technologies: It is not just about performance but cooperation with human KANSEI

Abstract: It is important to recognize that the objective of researching / developing technologies is not to increase their performance but to increase the quality of our life. Their high performance is not the goal but the result of this approach. Firstly, we learn what happened when technologies were developed without taking care of their users, humans.
Secondary, we show how human factors are important for developing techniques. The approach of artificial intelligence is to model human functions, such as handling knowledge, learning, reasoning, and others, and use the models in computer instead of humans. Contrastively, we emphasize the importance of good cooperation between humans and computer with some examples.
Thirdly, we introduce KANSEI engineering and interactive evolutionary computation (IEC) as a tool for Humanized Computational Intelligence. We show several concrete ways of cooperation through IEC in robotics, signal processing, and others.
Finally, we shortly introduce new research direction: IEC for human science. We can analyze human characteristics or find new knowledge on humans by analyzing the system optimized using IEC and the IEC user.

Theme 2: Statistical Tests for Computational Intelligence

Abstract: When we compare convergence speed of multiple methods, for example, conclusions without statistical supports cannot be accepted. However, it is true that there are several students and researchers whose good research was not accepted due to wrong or no supports of statistical tests for their conclusions.
We explain how to use what kind of statistical tests for which cases with exercise. Statistical tests that we study here include standard t-test, analysis of variance, sign test, Mann-Whitney U-test, Wilcoxon’s signed-ranks test, Kruskal-Wallis test, Friedman test, and multiple comparisons. You become able to choose a correct test for your data by checking only three aspects of the data, which is the biggest fruit of your attendance to this talk.
One of features of this tutorial talk is to show how to handle human subjective tests that cannot be avoided for interactive evolutionary computation (IEC) research and other human-related applications. We study Sheffe’s method of paired comparison that is one kind of ANOVA for this purpose.

June 12 Saturday Morning in Tokyo, Japan
GMT 11:30PM-1:30AM, Tokyo 8:30AM-10:30AM, EST 7:30PM-9:30PM on Friday, PST 4:30PM-6:30PM on Friday
Lecturer: Vladik Kreinovich (Professor at University of Texas at El Paso, USA)
Theme: Dealing with Uncertainties in Data Processing:
From Probabilistic and Interval Uncertainty to Combination of Different Approaches,
with Application to Geoinformatics, Bioinformatics, and Engineering

Abstract: Most data processing techniques traditionally used in scientific and engineering practices are statistical. These techniques are based on the assumption that we know the probability distributions of measurement errors etc. In practice, often, we do not know the distributions, we only know the bound D on the measurement accuracy – hence, after the get the measurement result X, the only information that we have about the actual (unknown) value x of the measured quantity is that x belongs to the interval [X-D, X+D]. Techniques for data processing under such interval uncertainty are called interval computations; these techniques have been developed since the 1950s. In many practical problems, we have a combination of different types of uncertainty: interval, fuzzy, probabilistic. The purpose of this talk is to describe the theoretical background for interval and combined techniques, to describe the existing practical applications, and ideally, to come up with a roadmap for such techniques.

June 12 Saturday Morning in Tokyo, Japan
GMT 1:45AM-3:45AM, Tokyo 10:45AM-12:45PM, EST 9:45PM-11:45PM on Friday, PST 6:45PM-8:45PM on Friday
Lecturer: Ljiljana Trajkovic (Professor at Simon Fraser University, Canada)
Theme: Data mining and machine learning for detecting traffic anomalies and intrusions

Abstract: In this lecture, we will review traffic traces collected from deployed communication networks. These traces will be used to detect network anomalies and intrusions including worms, denial of service attacks, ransomware, and blackouts. Machine learning techniques will be applied to analyze collected datasets, predict anomalous Internet traffic behavior, and classify various traffic routing anomalies. We will then evaluate performance of various machine learning models developed for detecting malicious intentions of network users.

June 12 Saturday Afternoon in Tokyo, Japan
GMT 4:45AM-6:45AM, Tokyo 1:45PM-3:45PM, EST 12:45AM-2:45AM on Saturday, PST 9:45PM-11:45PM on Friday
Lecturer: Tadahiko Murata (Professor at Kansai University, Japan)
Theme: Social Simulations in the Post COVID-19 Era

Abstract: In this lecture, a tentative social simulation is explained and students will have an exercise on that typical simulation problem. Since 2020 onward, COVID-19 are spreading in the world and social simulations are attempted to see effects of means to prevent the COVID-19 infections. To apply social simulation approaches to real world matters, target areas of the simulations should be appropriately prepared. Geographical information is one of such critical information on the target areas. Another critical information is populations of the target areas. The lecturer has prepared a nation-wide synthesized population for Japan since 2012. Since 2019, whole population in Japan are synthesized using supercomputers in Japan. The synthesized population includes compositions of each household using the national census in 2000, 2005, 2010 and 2015. Currently synthesized populations in 2000, 2005, 2010, and 2015 according to the national census are released under the national supercomputing project in Japan. In this lecture, the way how to synthesize the population and how to use the synthesized populations in the social simulations for a specific target area. We also show the relation of synthesized population and privacy protection.

School Chair:
Dr. Emi Yuda, Tohoku University, Japan
School Coordinator:
Professor Tadahiko Murata, Kansai University, Japan
Scientia Associate Professor Daoyi Dong, University of New South Wales, Australia