MATSUO, Yoichi

- Yoichi Matsuo received his Ph.D. in applied mathematics from Keio University in 2012 and 2015. He is currently a researcher at the NTT Service System Laboratories, Tokyo, Japan. Since he joined NTT, he had been engaged in researches on network management.

Network operation

Machine Learning, Deep Learning

- 2011, May. Student Encouragement Award of IPSJ National Convention, information processing society of japan

[Journal Papers]

- Y. Matsuo and T. Nodera, "The Optimal Block-Size for the Block Gram-Schmidt Orthogonalization," J. Sci. Tech., Vol. 49, pp. 569-584, 2011.
- Y. Matsuo and T. Nodera, "An Efficient Implementation of the Block Gram-Schmidt Method," Anziam J., Vol. 154, pp. C476-C491, 2013.
- Y. Matsuo, H. Guo and P. Arbenz, "Experiments on a Parallel Nonlinear Jacobi-Davidson Algorithm," Procedia Computer Science, Vol. 29, pp. 565-575, 2014.
- A. Watanabe, K. Ishibashi, T. Toyono, K. Watanabe, T. Kimura, Y. Matsuo, K. Shiomoto, and R. Kawahara, "Workflow Extraction for Service Operation using Multiple Unstructured Trouble Tickets," IEICE Transactions on Information and Systems, Vol.E101-D,No.4,pp.-,Apr. 2018.
- Y. Matsuo, T. Kimura, K. Nishimatsu, "DeepSIP: A System for Predicting Service Impact of Network Failure by Temporal Multimodal CNN", IEICE Transactions on Information and Systems, Vol.E104-B, No.10, pp.-, 2021

[International Conferences]

- Y. Matsuo and T. Nodera, "The Optimal Block-Size for the Block Gram-Schmidt Orthogonalization," J. Sci. Tech., Vol. 49, pp. 569-584, 2011.
- Y. Matsuo and T. Nodera, "The Optimal Block-Size for the Block Gram-Schmidt Orthogonalization," Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam, October, 19-21, 2011.
- Y. Matsuo and T. Nodera, "Parallel Block Gram-Schmidt Method with Optimal Block-size", Keio-Yonsei workshop, Keio, Japan, May, 2012.
- Y. Matsuo and T. Nodera, "Parallel Block Gram-Schmidt Orthogonalization with Optimal Block-size," VECPAR2012, RIKEN AICS, Kobe, Japan, July, 2012.
- Y. Matsuo and T. Nodera, "An Efficient Implementation of the Block Gram-Schmidt Method,"
- P. Arbenz, H. Guo and Y. Matsuo, "3-dimensional Eigenmodal Analysis of Electromagnetic Structures," Efficient Solution of Large Systems of Non-linear PDEs in Science, Lyon, France, October, 2013.
- P. Arbenz, H. Guo and Y. Matsuo, "3-dimensional Eigenmodal Analysis of Electromagnetic Structures," EPASA2014, Tsukuba, Japan, March 7-9, 2014.
- Y. Matsuo, H. Guo and P. Arbenz, "Experiments on a Parallel Nonlinear Jacobi-Davidson Algorithm," ICCS, Cairns, Australia, June 10-12, 2014.
- Y. Matsuo, H. Guo and P. Arbenz, "Experiments on a Parallel Nonlinear Jacobi-Davidson Algorithm," Keio-Yonsei workshop, Keio, Japan, November, 2014.
- Matsuo and T. Nodera, "A New Approach to SR Algorithm for Solving Eigenvalue Problems," CTAC2014, Australian National University, Canberra, Australia, December, 2014.
- A. Watanabe, K. Ishibashi, T. Toyono, T. Kimura, K. Watanabe, Y. Matsuo and K. Shiomoto, "Workflow Extraction for Service Operation using Multiple Unstructured Trouble Tickets," IEEE/IFIP NOMS 2016 (mini-conf.), pp. 652-658, Apr. 2016.
- Y. Matsuo, Y. Nakano, A. Watanabe, K. Watanabe, K. Ishibashi, and K. Kawahara, "Root-cause diagnosis for rare failures using Bayesian network with dynamic modification," Proc. IEEE, ICC, 2018.
- H. Ikeuchi, A. Watanabe, T. Hirao, M. Morishita, M. Nishino, Y. Matsuo and K. Watanabe, "Recovery command generation towards automatic recovery in ICT systems by Seq2Seq learning,", Proc. IEEE/IFIP, NOMS 2020(mini-conf), 2020.
- Y. Matsuo, T. Kimura and K. Nishimatsu, "DeepSIP: A System for Predicting Service Impact of Network Failure by Temporal Multimodal CNN,", Proc. IEEE/IFIP AnNet, 2020.
- K. Tajiri, T. Iwata, Y. Matsuo and K. Watanabe, "Fault Detection of ICT systems with Deep Learning Model for Missing Data," 2021 IFIP/IEEE International Symposium on Integrated Network Management (IM), 2021, pp. 445-451.
- Y. Matsuo and K. Yamagishi, "Shapley-value-based Quality Degradation Analysis Method for Adaptive Bitrate Streaming Services," Proc. MMSP 2021, Oct. 2021.
- Y. Matsuo and D. Ikegami, "Performance Analysis of Anomaly Detection Methods for Application System on Kubernetes with Auto-scaling and Self-healing", CNSM, 2021.
- K. Tajiri, R. Kawahara, and Y. Matsuo, "Optimizing Edge-Cloud Cooperation for Machine Learning Accuracy Considering Transmission Latency and Bandwidth Congestion", Proc. NOMS 2022.
- Y. Matsuo, Y. Nakano and K. Watanabe, "CMRCV: Causal Modeling to Localize Failed Equipment by Representative Nodes and Contribution Values," ICNC, 2023, pp. 403-408.
- Y. Matsuo, J. Singh, S. Verma and G. Fraysse, "Integrating state prediction into the Deep Reinforcement Learning for the Autoscaling of Core Network Functions," Proc. NOMS 2023, pp. 1-5.
- J. Singh, S. Verma, Y. Matsuo, F. Fossati and G. Fraysse, "Autoscaling Packet Core Network Functions with Deep Reinforcement Learning," Proc. AnNet, 2023, pp. 1-6.
- Y. Matsuo, "Empirical Analysis of the Fine-Tuning for Unsupervised Anomaly Detection in the ICT System," Proc. CNSM, 2023, pp. 1-7.

[NTT Technical Review]

- T. Kawata, Y. Matsuo, H. Ikeuchi, and Y. Hashimoto, "Automatic Generation of Recovery-command Sequences," NTT Technical Review, Vol. 17, No. 7, Jul. 2019
- K. Kazuhisa and Y. Matsuo, "Recent Activities of QoE-related Standardization in ITU-T SG12," NTT Technical Review, Vol.18 No.10, Oct. 2020.
- Y. Matsuo, K. Kazuhisa and M. Koike, "Recent Activities of QoE-related Standardization in ITU-T SG12," NTT Technical Review, Vol.21 No.6, pp.66-69, June 2023.

[Contribution]

- Y. Matsuo and K. Yamagishi, "Identifying the main factor of deterioration for estimated quality of experience in adaptive audiovisual streaming services," ITU-T Contribution COM 12 C465R1, Apr. 2020.
- Y. Matsuo and K. Yamagishi, "P.DiAQoSE Terms of Reference (ToR)," ITU-T Contribution COM 12 C499, Sept. 2020.
- Y. Matsuo and K. Yamagishi, "P.DiAQoSE Terms of Reference (ToR)," ITU-T Contribution COM 12 C517, Apr. 2021.
- Y. Matsuo and K. Yamagishi, "P.DiAQoSE model and experiment results," ITU-T Contribution COM 12 C567, Oct. 2021.
- Y. Matsuo and K. Yamagishi, “Confirmation of P.DiAQoSE model,” ITU-T Contribution COM 12 C9, June 2022.
- Y. Matsuo and K. Yamagishi, “Confirmation of P.DiAQoSE model,” ITU-T Contribution COM 12 C9, June 2022. Y. Matsuo and K. Yamagishi, “Confirmation of P.DiAQoSE model using P.1203 mode 3 and P.1204.4,” ITU-T Contribution COM 12 C80, Jan. 2023.
- Y. Matsuo and K. Yamagishi, “New Recommendation ITU-T P.DiAQoSE,” ITU-T Contribution COM 12 C128, Sep. 2023.