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Name

MATSUO, Yoichi

Biography

  • 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.

Research Interests

Network operation
Machine Learning, Deep Learning

Academic Awards

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

Publication

[Journal Papers]

  1. Y. Matsuo and T. Nodera, "The Optimal Block-Size for the Block Gram-Schmidt Orthogonalization," J. Sci. Tech., Vol. 49, pp. 569-584, 2011.
  2. Y. Matsuo and T. Nodera, "An Efficient Implementation of the Block Gram-Schmidt Method," Anziam J., Vol. 154, pp. C476-C491, 2013.
  3. Y. Matsuo, H. Guo and P. Arbenz, "Experiments on a Parallel Nonlinear Jacobi-Davidson Algorithm," Procedia Computer Science, Vol. 29, pp. 565-575, 2014.
  4. 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.
  5. 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]

  1. Y. Matsuo and T. Nodera, "The Optimal Block-Size for the Block Gram-Schmidt Orthogonalization," J. Sci. Tech., Vol. 49, pp. 569-584, 2011.
  2. 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.
  3. Y. Matsuo and T. Nodera, "Parallel Block Gram-Schmidt Method with Optimal Block-size", Keio-Yonsei workshop, Keio, Japan, May, 2012.
  4. Y. Matsuo and T. Nodera, "Parallel Block Gram-Schmidt Orthogonalization with Optimal Block-size," VECPAR2012, RIKEN AICS, Kobe, Japan, July, 2012.
  5. Y. Matsuo and T. Nodera, "An Efficient Implementation of the Block Gram-Schmidt Method,"
  6. 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.
  7. P. Arbenz, H. Guo and Y. Matsuo, "3-dimensional Eigenmodal Analysis of Electromagnetic Structures," EPASA2014, Tsukuba, Japan, March 7-9, 2014.
  8. Y. Matsuo, H. Guo and P. Arbenz, "Experiments on a Parallel Nonlinear Jacobi-Davidson Algorithm," ICCS, Cairns, Australia, June 10-12, 2014.
  9. Y. Matsuo, H. Guo and P. Arbenz, "Experiments on a Parallel Nonlinear Jacobi-Davidson Algorithm," Keio-Yonsei workshop, Keio, Japan, November, 2014.
  10. Matsuo and T. Nodera, "A New Approach to SR Algorithm for Solving Eigenvalue Problems," CTAC2014, Australian National University, Canberra, Australia, December, 2014.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. Y. Matsuo and K. Yamagishi, "Shapley-value-based Quality Degradation Analysis Method for Adaptive Bitrate Streaming Services," Proc. MMSP 2021, Oct. 2021.
  17. Y. Matsuo and D. Ikegami, "Performance Analysis of Anomaly Detection Methods for Application System on Kubernetes with Auto-scaling and Self-healing", CNSM, 2021.
  18. K. Tajiri, R. Kawahara, and Y. Matsuo, "Optimizing Edge-Cloud Cooperation for Machine Learning Accuracy Considering Transmission Latency and Bandwidth Congestion", Proc. NOMS 2022.
  19. 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.
  20. 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.
  21. 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.
  22. 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]

  1. T. Kawata, Y. Matsuo, H. Ikeuchi, and Y. Hashimoto, "Automatic Generation of Recovery-command Sequences," NTT Technical Review, Vol. 17, No. 7, Jul. 2019
  2. K. Kazuhisa and Y. Matsuo, "Recent Activities of QoE-related Standardization in ITU-T SG12," NTT Technical Review, Vol.18 No.10, Oct. 2020.
  3. 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]

  1. 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.
  2. Y. Matsuo and K. Yamagishi, "P.DiAQoSE Terms of Reference (ToR)," ITU-T Contribution COM 12 C499, Sept. 2020.
  3. Y. Matsuo and K. Yamagishi, "P.DiAQoSE Terms of Reference (ToR)," ITU-T Contribution COM 12 C517, Apr. 2021.
  4. Y. Matsuo and K. Yamagishi, "P.DiAQoSE model and experiment results," ITU-T Contribution COM 12 C567, Oct. 2021.
  5. Y. Matsuo and K. Yamagishi, “Confirmation of P.DiAQoSE model,” ITU-T Contribution COM 12 C9, June 2022.
  6. 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.
  7. Y. Matsuo and K. Yamagishi, “New Recommendation ITU-T P.DiAQoSE,” ITU-T Contribution COM 12 C128, Sep. 2023.
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