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かねむら あつのり
兼村 厚範

KANEMURA Atsunori, Ph.D.

Representative Director
Mirai Hoshu K.K.
Tsukuba, 305-0031, Japan
atsu-kan@mirai-hoshu.co.jp

Sub Program Director, Cabinet Office, Government of Japan, Tokyo, Japan
Invited Senior Scientist, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
Invited Scientist, Advanced Telecommunications Research Institute International (ATR), Kyoto, Japan
Advisor, DG Lab, Digital Garage, Inc., Tokyo, Japan

Education

  • Ph.D. in Informatics, 2009, Kyoto University

  • M.Eng. in Information Science, 2006, Nara Institute of Science and Technology

Publications

"☆" indicates top venues (i.e., top 20 in Google Scholar Metrics).

Journals

  • [21] ☆K. Yamada and A. Kanemura, "Simulating bout-and-pause patterns with reinforcement learning," PLOS ONE, vol. 15, no. 11, e0242201 (21 pp.), 2020.

  • [20] ☆Y. Li, A. Kanemura, H. Asoh, T. Miyanishi, and M. Kawanabe, "Multi-sensor integration for key-frame extraction from first-person videos," IEEE Access, vol. 8, pp. 122281–122291, 2020.

  • [19] ☆S. Kanoga, A. Kanemura, and, H. Asoh, "Are armband-type sEMG sensors dense enough for long-term use?—Sensor position shifts cause significant recognition accuracy drops," Biomedical Signal Processing and Control, vol. 60, 101981 (12 pp.), 2020.

  • [18] ☆S. Kanoga, M. Nakanishi, A. Murai, M. Tada, and A. Kanemura, "Robustness analysis of decoding SSVEPs in humans with head movements using a moving visual flicker," Journal of Neural Engineering, vol. 17, no. 1, 016009 (12 pp.), 2019.

  • [17] ☆I. Nakamura, A. Kanemura, T. Nakaso, R. Yamamoto, and T. Fukuhara, "Non-standard trajectories found by machine learning for evaporative cooling of 87Rb atoms," Optics Express, vol. 27, no. 15, pp. 20435–20443, 2019.

  • [16] ☆Y. Li, Z. Li, and A. Kanemura, "Residual-network-based supervised gaze prediction for first-person videos," IEEE Access, vol. 7, pp. 56208–56216, 2019.

  • [15] ☆S. Kanoga, A. Kanemura, and H. Asoh, "Multi-scale dictionary learning for ocular artifact reduction from single-channel electroencephalograms," Neurocomputing, vol. 347, pp. 240–250, 2019.

  • [14] ☆Y. Li, Z. Li, and A. Kanemura, "An l1/2-norm regularizer-based sparse coding framework for gaze prediction in first-person videos," IEEE Access, vol. 7, pp. 42472–42481, 2019.

  • [13] ☆R. Sugimoto, T. Kouyama, A. Kanemura, S. Kato, N. Imamoglu, and R. Nakamura, "Automated attitude determination for pushbroom sensors based on robust image matching," Remote Sensing, vol. 10, no. 10, article no. 1629 (18 pp.), 2018.

  • [12] ☆B. Tan, Y. Li, S. Ding, I. Park, and A. Kanemura, "DC programming for solving a sparse modeling problem of video key frame extraction," Digital Signal Processing, vol. 83, pp. 214–222, 2018.

  • [11] Y. Li, B. Tan, A. Kanemura, S. Ding, and W. Chen, "Analysis sparse representation based on determinant measures by DC programming," Complexity, vol. 2018, article ID 2685745, 12 pp., 2018.

  • [10] ☆T. Murakami, A. Kanemura, and H. Hino, "Group sparsity tensor factorization for re-identification of open mobility traces," IEEE Transactions on Information Forensics and Security, vol. 12, no. 3, pp. 689–704, 2017.

  • [9] ☆T. Kouyama, A. Kanemura, S. Kato, N. Imamoglu, T. Fukuhara, and R. Nakamura, "Satellite attitude determination and map projection based on robust image matching," Remote Sensing, vol. 9, no. 1, article no. 90 (20 pp.), 2017.

  • [8] ☆H. Morioka, A. Kanemura, J. Hirayama, M. Shikauchi, T. Ogawa, S. Ikeda, M. Kawanabe, and S. Ishii, "Learning a common dictionary for subject-transfer decoding with resting calibration," NeuroImage, vol. 111, pp. 167–178, 2015.

  • [7] ☆H. Morioka, A. Kanemura, S. Morimoto, T. Yoshioka, S. Oba, M. Kawanabe, and S. Ishii, "Decoding spatial attention by using cortical currents estimated from electroencephalography with near-infrared spectroscopy prior information," NeuroImage, vol. 90, pp. 128–139, 2014.

  • [6] T. Takeuchi, S. Sakano, K. Umakoshi, A. Kanemura, M. Kawanabe, T. Kawano, T. Kambayashi, M. Takemoto, M. Matsuo, and R. Kakinuma, "Proposal and evaluation of agent-based service platform by applying on BMI-enabled services (in Japanese)," IPSJ Journal, vol. 55, no. 2, pp. 681–694, 2014.

  • [5] ☆M. Fukushima, O. Yamashita, A. Kanemura, S. Ishii, M. Kawato, and M. Sato, "A state-space modeling approach for localization of focal current sources from MEG," IEEE Transactions on Biomedical Engineering, vol. 59, no. 6, pp. 1561–1571, 2012.

  • [4] ☆A. Kanemura, S. Maeda, and S. Ishii, "Sparse Bayesian learning of filters for efficient image expansion," IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1480–1490, 2010.

  • [3] A. Kanemura, S. Maeda, W. Fukuda, and S. Ishii, "Bayesian image superresolution and hidden variable modeling," Journal of Systems Science and Complexity, vol. 23, no. 1, pp. 116–136, 2010.

  • [2] A. Kanemura, S. Maeda, and S. Ishii, "Hyperparameter estimation in image superresolution with a compound Markov random field prior (in Japanese)," IEICE Transactions on Information and Systems (Japanese Edition), vol. J92–D, no. 10, pp. 1802–1811, 2009.

  • [1] ☆A. Kanemura, S. Maeda, and S. Ishii, “Superresolution with compound Markov random fields via the variational EM algorithm,” Neural Networks, vol. 22, no. 7, pp. 1025–1034, 2009. [JNNS Best Paper Award 2010]

Conferences

  • [34] L. Torres-Valverde, N. Imamoglu, A. Gonzàlez-Torres, T. Kouyama, and A. Kanemura, "Evaluation of neural networks with data quantization in low power consumption devices," IEEE Latin American Symposium on Circuits and Systems (LASCAS), 5 pp., San Josè, Costa Rica, February 2020.

  • [33] G. Yoshimura, A. Kanemura, and H. Asoh, "Enumerating hub motifs in time series based on the matrix profile," Workshop on Mining and Learning from Time Series (MiLeTS), ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 11 pp., Anchorage, AL, USA, August 2019.

  • [32] ☆Y. Shindoh, A. Kanemura, and Y. Miyao, "A simple method to remove reviews against guideline for online review services," IEEE International Conference on Big Data (BigData), 9 pp., Seattle, WA, USA, December 2018.

  • [31] K. Nozawa, K. Kimura, and A. Kanemura, "Analyzing centralities of embedded nodes," ICDM Workshop on Large Scale Graph Representation Learning and Applications (GRLA), IEEE International Conference on Data Mining (ICDM), 4 pp., Singapore, November 2018.

  • [30] S. Kanoga, A. Kanemura, and H. Asoh, "A comparative study of features and classifiers in single-channel EEG-based motor imagery BCI," IEEE Global Conference on Signal and Information Processing (GlobalSIP), 6 pp., Anaheim, CA, USA, November 2018.

  • [29] Y. Li, A. Kanemura, H. Asoh, T. Miyanishi, and M. Kawanabe, "Supervised saliency maps for first-person videos based on sparse coding," Asia-Pasific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 6 pp., Honolulu, HI, USA, November 2018.

  • [28] T. Hoshino, S. Kanoga, A. Kanemura, and T. Ogawa, "A no-reference metric of cerebral blood flow extraction for fNIRS data," International Conference Systems, Man, Cybernetics (SMC), pp. 83–89, Miyazaki, Japan, October 2018.

  • [27] ☆T. Miyanishi, J. Hirayama, A. Kanemura, and M. Kawanabe, "Answering mixed-type questions about daily living episodes," International Joint Conference on Artificial Intelligence (IJCAI), pp. 4265–4271, Stockholm, Sweden, July 2018.

  • [26] A. Kanemura, Y. Cheng, T. Kaneko, K. Nozawa, and S. Fukunaga, "Imputing missing values in EEG with multivariate autoregressive models," International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 4 pp., Honolulu, HI, USA, July 2018.

  • [25] S. Kanoga, M. Matsuoka, and A. Kanemura, "Transfer learning over time and position in wearable myoelectric control systems," International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 4 pp., Honolulu, HI, USA, July 2018.

  • [24] S. Kanoga, M. Nakanishi, A. Murai, T. Tada, and A. Kanemura, "Semi-simulation experiments for quantifying the performance of SSVEP-based BCI after reducing artifacts from Trapezius muscles," International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 4 pp., Honolulu, HI, USA, July 2018.

  • [23] ☆Y. Li, A. Kanemura, H. Asoh, T. Miyanishi, and M. Kawanabe, "A sparse coding framework for gaze prediction in egocentric video," IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1313–1317, Calgary, Canaca, April 2018.

  • [22] S. Kanoga and A. Kanemura, "Assessing the effect of transfer learning on myoelectric control systems with three electrode positions," IEEE International Conference on Industrial Technology (ICIT), 6 pp., Lyon, France, February 2018.

  • [21] M. Tsubaki, M. Shimbo, A. Kanemura, and H. Asoh, "End-to-end learning of graph neural networks for latent molecular representations," NIPS Workshop on Machine Learning for Molecules and Materials, Neural Information Processing Systems (NIPS), 9 pp., Long Beach, CA, USA, December 2017. [Best Paper Award]

  • [20] G. Yoshimura, A. Kanemura, and H. Asoh, "Reconstructable and interpretable representations for time series with time-skip sparse dictionary learning," ACM MM Thematic Workshops, ACM Multimedia Conference (ACM MM), 8 pp., Mountain View, CA, USA, October 2017.

  • [19] ☆Y. Li, A. Kanemura, H. Asoh, T. Miyanishi, and M. Kawanabe, "Extracting key frames from first-person videos in the common space of multiple sensors," IEEE International Conference on Image Processing (ICIP), 5 pp., Beijing, China, September 2017.

  • [18] Y. Li, B. Tan, S. Ding, I. Park, and A. Kanemura, "Key frame extraction from video based on determinant-type of sparse measure and DC programming," IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), 7 pp., Seoul, Korea, September 2017.

  • [17] S. Koyamada, Y. Kikuchi, A. Kanemura, S. Maeda, and S. Ishii, "Neural sequence model training via α-divergence minimization," ICML Workshop on Learning to Generate Natural Language (LGNL), International Conference on Machine Learning (ICML), 7 pp., Sydney, Australia, August 2017.

  • [16] A. Kanemura, H. Sawada, T. Wakisaka, and H. Hano, "Experimental exploration of the performance of binary networks," IEEE International Conference on Signal and Image Processing (ICSIP), pp. 451–455, Singapore, August 2017.

  • [15] ☆A. Kanemura, T. Kouyama, S. Kato, N. Imamoglu, T. Fukuhara, and R. Nakamura, "Turning a two-dimensional image sensor to an attitude sensor: Image matching for determining satellite attitudes," IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2748–2751, Fort Worth, TX, USA, July 2017.

  • [14] ☆Y. Li, A. Kanemura, H. Asoh, T. Miyanishi, and M. Kawanabe, "Key frame extraction from first-person video with multi-sensor integration," IEEE International Conference on Multimedia and Expo (ICME), pp. 1303–1308, Hong Kong, July 2017.

  • [13] K. Uchihashi and A. Kanemura, "Modeling the propensity score with statistical learning," International Conference on Neural Information Processing (ICONIP), Kyoto, Japan, in Lecture Notes in Computer Science, vol. 9950, pp. 261–269, October 2016.

  • [12] A. Kanemura, G. Lipowski, H. Komine, and S. Akaho, "Automatic categorization of health indices for risk quantification," International Conference on Information and Communication Technologies in Healthcare (ICTH), Berlin, Germany, in Procedia Computer Science, vol. 63, pp. 325–331, September 2015.

  • [11] T. Murakami, A. Kanemura, and H. Hino, "Group sparsity tensor factorization for de-anonymization of mobility traces," IEEE International Conference on Trust, Security and Privacy (TrustCom), pp. 621–629, Helsinki, Finland, August 2015. [Best Paper Award]

  • [10] ☆A. Kanemura, Y. Morales, M. Kawanabe, H. Morioka, N. Kallakuri, T. Ikeda, T. Miyashita, N. Hagita, and S. Ishii, "A waypoint-based framework in brain-controlled smart home environments: Brain interfaces, domotics, and robotics integration," IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 865–870, Tokyo, Japan, November 2013.

  • [9] S. Maeda, W. Fukuda, A. Kanemura, and S. Ishii, "Maximum a posteriori X-ray computed tomography using graph cuts," International Workshop on Statistical-Mechanical Informatics (IW-SMI), Kyoto, Japan, in Journal of Physics: Conference Series, vol. 233, 11 pp., March 2010.

  • [8] ☆W. Fukuda, S. Maeda, A. Kanemura, and S. Ishii, "Bayesian X-ray computed tomography using material class knowledge," IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 2126–2129, Dallas, TX, USA, March 2010.

  • [7] W. Fukuda, S. Maeda, A. Kanemura, and S. Ishii, "X-ray computed tomography using material-class modeling by Markov random field energy minimization," International Symposium on Artificial Life and Robotics (AROB), pp. 662–665, Beppu, Japan, February 2010.

  • [6] W. Fukuda, A. Kanemura, S. Maeda, and S. Ishii, "Superresolution from occluded scenes," International Conference on Neural Information Processing (ICONIP), Bangkok, Thailand, in Lecture Notes in Computer Science, vol. 5863, pp. 19–27, December 2009.

  • [5] ☆A. Kanemura, S. Maeda, and S. Ishii, "Learning color image expansion filters," IEEE International Conference on Image Processing (ICIP), pp. 357–360, Cairo, Egypt, November 2009.

  • [4] A. Kanemura, S. Maeda, and S. Ishii, "Image superresolution under spatially structured noise," IEEE International Symposium on Information Technology and Signal Processing (ISSPIT), pp. 279–284, Cairo, Egypt, December 2007.

  • [3] A. Kanemura, S. Maeda, and S. Ishii, "Edge-preserving Bayesian image superresolution based on compound Markov random fields," International Conference on Artificial Neural Networks (ICANN), Porto, Portugal, in Lecture Notes in Computer Science, vol. 4669, pp. II-611–620, September 2007.

  • [2] A. Kanemura, S. Maeda, and S. Ishii, "Hyperparameter estimation in Bayesian image superresolution with a compound Markov random field prior," IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 181–186, Thessaloniki, Greece, August 2007.

  • [1] A. Kanemura, S. Maeda, and S. Ishii, "Bayesian super-resolution with a smooth-gap model," ECCV Workshop on Statistical Methods in Multi-Image and Video Processing (SMVP), European Conference in Computer Vision (ECCV), pp. 85–93, Graz, Austria, May 2006.

Other publications include five review articles/tutorials, 82 presentations at local or non-refereed international conferences, one book chapter translation, eight informal talks, and seven patent applications.