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JACIII Best Paper and Young Researcher Awards 2023
JACIII BEST PAPER AWARD 2023 Visualization Method Corresponding to Regression Problems and Its Application to Deep Learning-Based Gaze Estimation Model Daigo Kanda, Shin Kawai, and Hajime Nobuhara |
Daigo Kanda University of Tsukuba | Shin Kawai University of Tsukuba | Hajime Nobuhara University of Tsukuba |
Message from the Winner
We are truly humbled and deeply honored to have been chosen as recipients of the JACIII Best Paper Awards for 2023. This recognition is not only a testament to our team’s dedication and hard work but also a reflection of the collective efforts and encouragement from the wider research community. We would like to extend our profound gratitude to the esteemed members of the JACIII Editorial Committee, the dedicated administrative staff, the meticulous reviewers, and every individual who has supported and championed this journal.
The awarded papers focus on adapting the widely-used techniques in explainable AI, Gradient-weighted Class Activation Mapping (Grad-CAM), to address regression problems. Moreover, they address significant challenges of deep learning-based gaze estimation algorithms: the black-box problem.
Through these studies, we believe we have expanded the applicability of explainable AI and contributed to the development of AI systems that humans can trust.
We anticipate that the domain of human-centered AI will continue to expand, and the fields exploring these areas will become increasingly diverse. Journals such as the JACIII, which cover a broad range of topics from foundational to applied research, will undoubtedly grow in significance for researchers like us.
We are committed to advancing research of the highest caliber and meeting the expectations of all stakeholders. We would like to express our heartfelt gratitude for these prestigious awards.
JACIII YOUNG RESEARCHER AWARD 2023 LSTM Network Classification of Dexterous Individual Finger Movements Christopher Millar, Nazmul Siddique, and Emmett Kerr |
Christopher Millar Faculty of Computing, Engineering and Built Environment, Ulster University | Nazmul Siddique Faculty of Computing, Engineering and Built Environment, Ulster University | Emmett Kerr Faculty of Computing, Engineering and Built Environment, Ulster University |
Message from the Winner
Hello fellow researchers and academics,
I would like to take this opportunity to thank the JACIII and Fuji Technology Press for publishing my paper and giving me the prestigious award of “Young Researcher Award 2023.” It is a privilege and an honor for my work to be showcased in the JACIII and to present at the associated CCS conference. Our paper, “LSTM Network Classification of Dexterous Individual Finger Movements,” was the first that was produced as part of my research into using LSTM networks to classify sEMG signals. Typically, this type of network has been applied to natural language processing or other sequential data sequences, but we have applied it to bio-signal classification. This was the first step toward achieving our goal of developing a system that can classify complex hand gestures and other grasping movements for potential application with anthropomorphic robotic hands.
Furthermore, I would like to thank the team of editors that provided me with crucial feedback throughout the submission process and helped me to refine my submission. I would also like thank everyone who read my paper and found it helpful or of some interest. Finally, I wish to thank my family for supporting me throughout this process and my supervisors for helping me write a paper worthy of such an award.
JACIII YOUNG RESEARCHER AWARD 2023 Automatic Neonatal Alertness State Classification Based on Facial Expression Recognition Kento Morita, Nobu C. Shirai, Harumi Shinkoda, Asami Matsumoto, Yukari Noguchi, Masako Shiramizu, and Tetsushi Wakabayashi |
Kento Morita Graduate School of Engineering, Mie University | Nobu C. Shirai Center for Information Technologies and Networks, Mie University | Harumi Shinkoda Kagoshima Immaculate Heart University | Asami Matsumoto Suzuka University of Medical Science | Yukari Noguchi St. Mary College | Masako Shiramizu Kyushu University Hospital | Tetsushi Wakabayashi Graduate School of Engineering, Mie University |
Message from the Winner
I am immensely proud to have received the JACIII Young Researcher Award 2023. I would like to express my gratitude to the JACIII editorial board, award committee members, and co-authors.
Our paper, entitled “Automatic Neonatal Alertness State Classification Based on Facial Expression Recognition,” proposes a video image analysis and machine learning classification system, based sleep-wake states, for neonates in neonatal intensive care unit.
Based on Brazelton’s alertness definitions, the proposed method uses machine learning to categorize sleep-wake states into four or six classes. Since the input data are the video images of neonates, the proposed method extracts the histogram of oriented gradients (HoG) or the gradient feature from each slice, and these are then merged in an average merge or a standard deviation merge. The experimental results show that the weighted support vector machine classifier using the HoG feature and average merging achieves the highest classification performance.
We are currently working on developing a hybrid model that combines facial expression and body motion for the alertness state classification. Lastly, I am considering continuing our collaborative research in the medical engineering field in the future.