Objectives
The primary goal of this summer school on Quantum Computational Intelligence for Pre-University and Undergraduate Students is to promote STEM education with computational intelligence at the pre-university and undergraduate student levels. As such, our target audience includes undergraduates, high-school, and elementary school students. Computational Intelligence (CI) is considered one of the most promising and yet-to-be-fully-explored fields. This summer school introduces CI techniques and the Artificial Intelligence-Fuzzy Markup Language (CI&AI-FML) learning model to undergraduate or pre-university students. We aim to motivate undergraduate or pre-university students around the world to study CI techniques and experience quantum CI learning tools with hardware/software and their applications. In the future, we hope to further encourage young students and researchers to engage with quantum CI techniques and methodologies in real-world scenarios.
Our motivations for proposing this summer school are: (1) to bring more participants to our IEEE CIS conferences and competitions, (2) to increase outreach to undergraduate and high school students through IEEE CIS, and (3) to educate young students (high school, middle school, and undergraduates) through our STEM activities or a fun competition. The idea is to create a workshop for young students, associated with a competition, during IEEE CIS-sponsored conferences. Similar workshops and competitions have been carried out at IEEE CEC 2023 in the USA and IEEE International Conference FUZZ-IEEE 2023 in Korea.
In addition, we will hold a competition titled Quantum Computational Intelligence for Pre-University and Undergraduate Students at IEEE WCCI 2024 (Competition Website). Hence, we plan to have a summer school before the competition of the IEEE WCCI 2024 to attract more young students to join the IEEE CIS-sponsored conferences. Before or during the summer school, participants will receive learning materials and guidance from tutors to learn about a CI-related topic. Later, participants will have the opportunity to apply the CI-related materials they have learned in a simple real-world application using the CI&AI-FML Learning Tool (Hardware/Software) provided by IEEE CIS sponsored in 2023, and IEEE R10 sponsored in 2023 and 2024. Specifically, participants will use the CI&AI-FML learning tools to solve various tasks. On the day of the competition at IEEE WCCI 2024, participants will be encouraged to form teams with other participants to foster cooperation and showcase their learning achievements.
Six-Step Co-Learning Process for Human and Machine
The six-step co-learning procedure for human intelligence and machine learning inspired by the Heart Sutra is described as follows. Humans and machines learn together through a process of
observing and attending the learning environment (Step 1),
studying the detailed knowledge based on the learner's intelligence (Steps 2 and 3),
utilizing what they have learned (Step 4),
understanding specific domain knowledge (Step 5), and finally
explaining it to new learners (Step 6).
This interaction is repeated continuously to achieve the goal of co-learning between humans and machines.
03252023-IEEE R10 EAC & 2023 CIS Education Portal Event (No. 5) (3:45)
10062023-PSO-based CI Agent with Learning Tool for Student Experience in Real-World Application (2:54)
Registration Deadline
Registration must be received before June 16, 2024 via the website (Register Form).
Available Software Tools
VisualFMLTool : It can be executed on platforms containing the Java Runtime Environment. The Java Software Development Kit, including JRE, compiler and many other tools can be found at here. The VisualFMLTool can download from here and then to extract it. Then it is only needed to click the file VisualFMLTool.bat included in the zip to execute the tool.
QCI&AI-FML Learning Platform : It is developed by KWS center/OASE Lab., NUTN, Taiwan and can be executed on different platforms online. After registering the competition, we can provide an account for the participants.
ZAI-FML Learning Platform : It is developed by Zsystem Co. Ltd., Taiwan and can be executed on different platforms online. The participants can apply for a trial account online.
JFML : A spanish research group (Jose Manuel Soto Hidalgo, Giovanni Acampora, Jesus Alcala Fernandez, Jose Alonso Moral) has released a library for FML programming that is very simple to use and compliant with IEEE 1855. JFML can download from here. Additional information about the library is here.
Some References associated to JFML
J. M. Soto-Hidalgo, Jose M. Alonso, G. Acampora, and J. Alcala-Fdez, "JFML: A Java library to design fuzzy logic systems according to the IEEE Std 1855-2016," IEEE Access, vol. 6, pp. 54952-54964, 2018.
J. M. Soto-Hidalgo, A. Vitiello, J. M. Alonso, G. Acampora, J. Alcala-Fdez, "Design of fuzzy controllers for embedded systems with JFML," International Journal of Computational Intelligence Systems, vol. 12, no. 1, pp. 204-214, 2019.
Reference
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C. S. Lee, M. H. Wang, L. W. Ko, Y. Hsiu Lee, H. Ohashi, N. Kubota, Y. Nojima, and S. F. Su, "Human intelligence meets smart machine: a special event at the IEEE International Conference on Systems, Man, and Cybernetics 2018," IEEE Systems, Man, and Cybernetics Magazine, vol. 6, no. 1, pp. 23-31, Jan. 2020.
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C. S. Lee, M. H. Wang, Y. Nojima, M. Reformat, and L. Guo, "AI-Fuzzy Markup Language with Computational Intelligence for High-School Student Learning," arXiv, Cornell University, Nov. 2021.
C. S. Lee, M. H. Wang, W. K. Kuan, S. H. Huang, Y. L. Tsai, Z. H. Ciou, C. K. Yang, and N. Kubota, "BCI-based hit-loop agent for human and AI robot co-learning with AIoT application," Journal of Ambient Intelligence and Humanized Computing, vol. 14, pp. 3583–3607, Oct. 2021
C. S. Lee, Y. L. Tsai, M. H. Wang, S. H. Huang, M. Reformat, and N. Kubota, "Adaptive fuzzy neural agent for human and machine co-learning," International Journal of Fuzzy Systems, vol. 24, pp. 778–798, Nov. 2021.
C. S. Lee, M. H. Wang, Z. H. Ciou, R. P. Chang, C. H. Tsai, S. C. Chen, T. X. Huang, E. Sato-Shimokawara, and T. Yamaguchi, "Robotic assistant agent for student and machine co-learning on AI-FML practice with AIoT application," 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021), Luxembourg, Luxembourg, Jul. 11-14, 2021.
C. S. Lee, M. H. Wang, M. Reformat, S. H. Huang, "Human intelligence-based Metaverse for co-learning of students and smart machines," Journal of Ambient Intelligence and Humanized Computing, 2023.
C. S. Lee, M. H. Wang, S. H. Huang, F. J. Yang, C. H. Tsai, and L. Q. Wang, "Fuzzy ontology-based intelligent agent for high-school student learning in AI-FML Metaverse," 2022 IEEE World Congress on Computational Intelligence (IEEE WCCI 2022), Padua, Italy, Jul. 18-23, 2022.
C. S. Lee, M. H. Wang, R. P. Chang, H. C. Liu, S. C. Chiu, Y. C. Chang, L. A. Lin, and S. C. Chen, "Computational intelligence and AI-FML experience model for pre-university student learning and practice," The 18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP 2022), Kitakyushu, Japan, Dec. 16-18, 2022.
C. S. Lee, M. H. Wang, C. Y. Chen, M. Reformat, Y. Nojima, and N. Kubota, "Knowledge graph-based genetic fuzzy agent for human intelligence and machine co-learning," 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo International City, Korea, Aug. 13-17, 2023. (Accepted).
G. Acampora, R. Schiattarella, and A. Vitiello, "On the implementation of fuzzy inference engines on quantum computers," IEEE Transactions on Fuzzy Systems, vol. 31, no. 5, pp. 1419-1433, 2023.
C. S. Lee, M. H. Wang, M. H. Wang, P. Y. Wu, R. Schiattarella, G. Acampora, and A. Vitiello, "Fuzzy markup language-based quantum FIE for student and robot co-learning model assessment," 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo International City, Korea, Aug. 13-17, 2023. (Late Breaking paper)
A. Pourabdollah, C. Wilmott, R. Schiattarella, and G. Acampora, "Fuzzy inference on quantum annealers," 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo International City, Korea, Aug. 13-17, 2023.
G. Acampora, M. Grossi, and R. Schiattarella, "A comparison of quantum computer architectures in running fuzzy inference engines," 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo International City, Korea, Aug. 13-17, 2023.
G. Acampora, A. Massa, R. Schiattarella, and A. Vitiello, "Distributing fuzzy inference engines on quantum computers," 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo International City, Korea, Aug. 13-17, 2023.