Shogo Shimada
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Shogo Shimada has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Peerawat Pannattee
Tokyo Metropolitan University
4 shared publications - Vibol Yem
University of Tsukuba, Tsukuba, Japan
4 shared publications - Nobuyuki Nishiuchi
Tokyo Metropolitan University
3 shared publications - Kazuhiro Saito
KDDI Research, Inc.,Fujimino-shi, Saitama,Japan
1 shared publication Nobuyuki Nishiuchi
Graduate School of Systems Design, Tokyo Metropolitan University, Tokyo, Japan
1 shared publication- Pulak Ranjan Giri
KDDI Research, Inc., Fujimino-shi, Saitama, Japan
1 shared publication
Latest Publications
- 6 April 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-6
Quantum Walk-Based Hash Function: Scalable Readout for Proof of Quantum Work
We investigate quantum walk-based hash functions, which are generated from the count distribution generated by a quantum walker on graphs. The method is expected to be one of the promising approaches for generating robust hash functions; however, the quantum computation in small systems is susceptible to spoofing via classical imitation. To address this issue, it is necessary to perform calculations in a quantum space size that classical algorithms are difficult to simulate. In such cases, simply increasing the quantum space size leads to an increase in the number of quantum circuit measurements, thereby increasing the time required to generate the ideal measurement distribution. Based on these facts, we propose a quantum hybrid algorithm combining a quantum walk with a shot-based random projection architecture. This method compresses the output into a fixed-length 256-bit hash by weighting and binarizing the measurement counts of each node. We investigate the proposed method through the numerical simulations of quantum walks on the Hanoi Network (HN4) with $N=4096$. The results demonstrate no hash collisions and an avalanche effect of approximately 49%, which is close to the ideal value of 50%. Additionally, we achieved the consensus robustness of up to 94%, compared to the ideal value of 100%. These results suggest the feasibility of a scalable quantum hash function for Proof of Quantum Work (PoQ) on quantum devices.- 6 April 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-6
Quantum Walk-Based Hash Function: Scalable Readout for Proof of Quantum Work
We investigate quantum walk-based hash functions, which are generated from the count distribution generated by a quantum walker on graphs. The method is expected to be one of the promising approaches for generating robust hash functions; however, the quantum computation in small systems is susceptible to spoofing via classical imitation. To address this issue, it is necessary to perform calculations in a quantum space size that classical algorithms are difficult to simulate. In such cases, simply increasing the quantum space size leads to an increase in the number of quantum circuit measurements, thereby increasing the time required to generate the ideal measurement distribution. Based on these facts, we propose a quantum hybrid algorithm combining a quantum walk with a shot-based random projection architecture. This method compresses the output into a fixed-length 256-bit hash by weighting and binarizing the measurement counts of each node. We investigate the proposed method through the numerical simulations of quantum walks on the Hanoi Network (HN4) with $N=4096$. The results demonstrate no hash collisions and an avalanche effect of approximately 49%, which is close to the ideal value of 50%. Additionally, we achieved the consensus robustness of up to 94%, compared to the ideal value of 100%. These results suggest the feasibility of a scalable quantum hash function for Proof of Quantum Work (PoQ) on quantum devices.- 30 July 2025
- Published by Springer Nature
- p. 19-33
Investigating the Use of Deep Neural Networks for Predicting Perceived Realism in VR Scenes
- 30 July 2025
- Published by Springer Nature
- p. 19-33
Investigating the Use of Deep Neural Networks for Predicting Perceived Realism in VR Scenes
- Open Access
- 11 January 2025
- Published by Springer Nature in Neural Computing and Applications
- Vol. 37 (8) , 6283-6303
A deep learning framework for automatic assessment of presence in virtual reality using multimodal behavioral cues
- 11 January 2025
- Published by Springer Nature in Neural Computing and Applications
- Vol. 37 (8) , 6283-6303
Open AccessA deep learning framework for automatic assessment of presence in virtual reality using multimodal behavioral cues
- 15 September 2023
- Published by Springer Nature
- p. 481-495
Investigating the Use of Machine Learning Methods for Levels of Sense of Presence Classification Based on Eye Tracking Data
- 15 September 2023
- Published by Springer Nature
- p. 481-495
Investigating the Use of Machine Learning Methods for Levels of Sense of Presence Classification Based on Eye Tracking Data
- Open Access
- 5 September 2023
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 11, 95825-95839
High-Frequency Cybersickness Prediction Using Deep Learning Techniques With Eye-Related Indices
Cybersickness is a growing concern in the field of virtual reality (VR). It is characterized by symptoms, such as headache, sweating, disorientation, and nausea. These symptoms can considerably hinder the users’ immersive experience in VR environments, leading to a pressing need for effective solutions to combat cybersickness. In this study, we aim to tackle cybersickness by presenting a novel high-frequency approach for detecting the timing at which users experience cybersickness. Our approach uses 1-, 5-, or 10-s time-series eye-related indices processed by deep learning algorithms to predict cybersickness severity. In five-fold cross-validation, we achieved 71.09% accuracy in classifying four classes of cybersickness severity when individuals were not distinguished. Furthermore, with individualized cross-validation, we achieved an accuracy of up to approximately 80%. Our approach outperforms other cybersickness prediction studies as it provides the highest frequency in predicting cybersickness. It is anticipated that our approach will be valuable not only for immediate evaluation by researchers investigating cybersickness mitigation but also for early detection and notification of users experiencing cybersickness symptoms. By predicting cybersickness, our approach has the potential to promote the future advancement of VR technology.
- 5 September 2023
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 11, 95825-95839
Open AccessHigh-Frequency Cybersickness Prediction Using Deep Learning Techniques With Eye-Related Indices
Cybersickness is a growing concern in the field of virtual reality (VR). It is characterized by symptoms, such as headache, sweating, disorientation, and nausea. These symptoms can considerably hinder the users’ immersive experience in VR environments, leading to a pressing need for effective solutions to combat cybersickness. In this study, we aim to tackle cybersickness by presenting a novel high-frequency approach for detecting the timing at which users experience cybersickness. Our approach uses 1-, 5-, or 10-s time-series eye-related indices processed by deep learning algorithms to predict cybersickness severity. In five-fold cross-validation, we achieved 71.09% accuracy in classifying four classes of cybersickness severity when individuals were not distinguished. Furthermore, with individualized cross-validation, we achieved an accuracy of up to approximately 80%. Our approach outperforms other cybersickness prediction studies as it provides the highest frequency in predicting cybersickness. It is anticipated that our approach will be valuable not only for immediate evaluation by researchers investigating cybersickness mitigation but also for early detection and notification of users experiencing cybersickness symptoms. By predicting cybersickness, our approach has the potential to promote the future advancement of VR technology.
- Open Access
- 12 March 2023
- Published by Association for Computing Machinery (ACM)
- p. 298-300
Comparative Evaluation of Joystick and Leaning-Based Locomotion in Immersive Telepresence with Body Motion Feedback
Some previous studies have focused on the effects of leaning-based methods and motion feedback for reducing cybersickness and improving usability during locomotion in virtual environments. However, it is actually challenging to combine of these technologies for practical use in the real environments using telepresence robots. In this study, we developed an immersive telepresence system with body motion feedback and comparatively evaluated the effectiveness of using a joystick and a leaning-based method in terms of reducing cybersickness and improving usability during locomotive tasks. The results of the user study (N=8) showed that leaning-based method with body motion feedback is effective for improving comfort and reducing cybersickness compared with the cases of using the joystick. Moreover, the use of body motion display to generate walking like sensation was shown to be effective for improving the telepresence experience.
- 12 March 2023
- Published by Association for Computing Machinery (ACM)
- p. 298-300
Open AccessComparative Evaluation of Joystick and Leaning-Based Locomotion in Immersive Telepresence with Body Motion Feedback
Some previous studies have focused on the effects of leaning-based methods and motion feedback for reducing cybersickness and improving usability during locomotion in virtual environments. However, it is actually challenging to combine of these technologies for practical use in the real environments using telepresence robots. In this study, we developed an immersive telepresence system with body motion feedback and comparatively evaluated the effectiveness of using a joystick and a leaning-based method in terms of reducing cybersickness and improving usability during locomotive tasks. The results of the user study (N=8) showed that leaning-based method with body motion feedback is effective for improving comfort and reducing cybersickness compared with the cases of using the joystick. Moreover, the use of body motion display to generate walking like sensation was shown to be effective for improving the telepresence experience.