Keynote Addresses

Keynote 4

Brain-inspired Networking and QoE Control

9:00 AM — 10:00 AM JST
Jun 26 Sat, 8:00 PM — 9:00 PM EDT

Brain-inspired Networking and QoE Control

Masayuki Murata (Osaka University, Japan)

Abstract: Machine learning is now actively applied to the “Industry 4.0” and “smart city” for establishing the next ICT-enabled world.  However, since the neural network was “invented” in the mid-1980s, brain science has much progressed by advancements of high-precision measurement devices such as EEG and fMRI.  We are now ready to develop the next-generation machine learning approaches.  It is known that the most striking feature of the human brain is the ability to handle uncertainty under the dynamic environment, instead of pursuing optimality.  Also, it accumulates “confidence” for reaching a final decision on the target task, which gives the flexibility of handling decisions against various sources of uncertainty. Furthermore, the environment may be changed by the decision itself, and the human may face the new environment.  It can be viewed as a feedback control system, which must be utilized in artificial control systems. In this talk, brain-inspired approaches for networking problems are introduced by taking two steps.  First, the “Yuragi” (meaning fluctuation in Japanese) concept is introduced.  It is a universal feature of adaptability found in the natural systems including various biological systems and the human brain.  It is formulated as the Yuragi theory as a simple canonical formula and can be used for network control methods in several situations where adaptability is much more important than optimality.  Second, Yuragi theory is extended to the machine learning approach (which we call Yuragi Learning) by incorporating the Bayesian attractor model.  Then, it is applied to a real-time QoE control in the video-streaming service, in which the user’s current emotional status is obtained by utilizing the recently developed lightweight device such as the headset EEG, and the agent controls the video quality instead of the user.  Of course, the human brain is not perfect.  One famous example is a cognitive bias. Problems for dealing with the cognitive bias in the case of QoE control are finally addressed.

Biography: Professor Masayuki Murata received the M.E. and D.E. degrees in Information and Computer Science from Osaka University, Japan, in 1984 and 1988, respectively. In April 1984, he joined Japan Science Institute (currently Tokyo Research Laboratory), IBM Japan, as a Researcher. He moved to Osaka University as an Assistant Professor in September 1987. In April 1999, he became a Full-Professor with the Graduate School of Engineering Science, Osaka University.  Since April 2004, he is a Full-Professor with the Graduate School of Information Science and Technology, Osaka University.  His research interests include computer communication network architecture inspired by biology and the human brain.  He is a member of IEICE, IEEE, and ACM. He is now the Dean of Graduate School of Information Science and Technology, Osaka University, and the Vice-Director of the Center for Information and Neural Networks (CiNet), co-founded by Osaka University and National Institute of Information and Communications (NICT), Japan.  In April 2021, he published the book entitled “Fluctuation-Induced Network Control and Learning: Applying the Yuragi Principle of Brain and Biological Systems of Brain and Biological Systems” co-edited with Dr. Kenji Leibniz from Springer.

Session Chair

Hitoshi Asaeda, NICT, Japan

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