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IEEE International Conference on Artificial Intelligence in engineering and Technology (IICAIET)

作者： Hiro Okamoto Sinan Chen Masahide Nakamura Sachio Saiki Faculty of Engineering Graduate School of Engineering Kobe University Kobe Japan RIKEN Center for Advanced Intelligence Project Tokyo Japan School of Data and Innovation Kochi University of Technology Kochi Japan

The increase in single-person households and the onset of “COVID-19 frailty” due to COVID-19 have become problems. Our research group focuses on technology support and develops virtual agents using speech recognition technology. In this service, we examined how to promote self-care by having the agent listen to the older person and increase conversation opportunities. However, this service has the problem that it cannot increase opportunities for dialogue with family and friends, which leads to mutual support. In this study, we aim to propose and implement a service called “Easy Video Chat Service” that increases the opportunities for conversation with others through a virtual agent listening service for the elderly person at home.

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Long - Term Forecast of Emergency Demand Using EMS Big Data and Population Estimates by Age

Long - Term Forecast of Emergency Demand Using EMS Big Data ...

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IEEE International Conference on Artificial Intelligence in engineering and Technology (IICAIET)

作者： Masaki Kaneda Sinan Chen Masahide Nakamura Sachio Saiki Faculty of Engineering Graduate School of Engineering Kobe University Nada Japan RIKEN Center for Advanced Intelligence Project Tokyo Japan School of Data and Innovation Kochi University of Technology Kochi Japan

In recent years, Japan has been facing a super- aging society. The problems of tight emergency medical care and an increase in the number of emergency medical transports have become ¶uite serious. In response to this situation, our research group has been conducting joint research with the Kobe City Fire Department. This study aims to propose and establish a method for medium to long-term prediction of the number of emergency medical transports and to provide an indicator for the strategic deployment of Emergency Medical Services (EMS) and the expansion and contraction of the scale of medical services in the field. This medium to long-term prediction of the number of ambulance transports is achieved by analyzing big data, population data, and future population estimates in each region without machine learning. The proposed method was evaluated in Kobe City, Japan. The results showed that the predicted number of transports is reasonable based on the actual number of transports in the past.

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Hybrid Data-Free Knowledge Distillation 39

Hybrid Data-Free Knowledge Distillation

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Tang, Jialiang Chen, Shuo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)157735897X

Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called Hybrid Data-Free Distillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, i.e., the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Personnel training

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Beyond Click to Cognition: Effective Interventions for Promoting Examination of False Beliefs in Misinformation 25

Beyond Click to Cognition: Effective Interventions for Promo...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Tanaka, Yuko Arai, Hiromi Inuzuka, Miwa Takahashi, Yoichi Kukita, Minao Iseki, Ryuta Inui, Kentaro Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Center for Advanced Intelligence Project RIKEN Tokyo Japan Department of Education Tokyo Gakugei University Tokyo Japan Graduate School of Informatics Nagoya University Nagoya Japan Department of Human Sciences Taisho University Tokyo Japan Natural Language Processing Department MBZUAI Abu Dhabi United Arab Emirates

ISBN: (纸本)9798400713941

In the digital information ecosystem, clicks serve as a crucial gateway to fact-checking, yet the essential challenge extends beyond this to fostering cognitive shifts that update entrenched false beliefs. This study investigates effective interventions aimed at encouraging users vulnerable to misinformation, particularly those who tend to avoid incongruent facts, to examine their false beliefs. We conducted an online experiment with 627 participants, comparing metacognitive and ranking interventions. Both interventions successfully improved click behavior, with the metacognitive intervention increasing belief-examining clicks by 14 percentage points and the ranking intervention by 33 percentage points. However, only the metacognitive intervention significantly promoted users examination of misinformation. This finding underscores the importance of interventions that go beyond merely influencing easily measurable clicks to facilitating thoughtful engagement with factchecking content. We discuss implications for designing strategies to enhance online fact-checking engagement and mitigate misinformation's societal impact. © 2025 Copyright held by the owner/author(s).

关键词： Misinformation

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UAV-assisted Microservice Mobile Edge Computing Architecture: Addressing Post-Disaster Emergency Medical Rescue

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IEEE Transactions on Computers 2025年

作者： Li, Ji He, Qiang Wang, Xingwei Hawbani, Ammar Yu, Keping Bi, Yuanguo Zhao, Liang Northeastern University College of Medicine and Biological Information Engineering Shenyang110169 China Northeastern University College of Computer Science and Engineering Shenyang110169 China Shenyang Aerospace University School of Computer Science Shenyang110136 China Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan RIKEN RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan

In post-disaster emergency medical rescue operations, rapidly establishing an adaptive and flexible edge computing (EC) network, balancing data offloading with energy consumption, and ensuring the stable operation of the network have become urgent priorities. To address these challenges, we proposed an unmanned aerial vehicle (UAV)-assisted microservice mobile edge computing (MEC) architecture. The architecture can be rapidly deployed to provide temporary network coverage and EC services in disaster-stricken areas. A transformer-based resource management (TBRM) approach is utilized to optimize data offloading efficiency and reduce energy consumption, thereby maximizing the service time of the architecture. To enhance the security and reliability of the architecture, four microservices are designed to manage the full UAV lifecycle, and UAV identity authentication is implemented through dual digital signature certificates. Large-scale simulation experiments have demonstrated the effectiveness of the architecture in complex rescue scenarios, providing strong technical support for postdisaster medical rescue efforts. © 1968-2012 IEEE.

关键词： Micro air vehicle (MAV)

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A Dynamic Distributed Queueing-Based Random Access Protocol for Softwarized Internet of Things

A Dynamic Distributed Queueing-Based Random Access Protocol ...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Zhen, Li Li, Yanwen Yu, Keping Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an China Hosei University Graduate School of Science and Engineering Tokyo Japan RIKEN Center for Advanced Intelligence Project Tokyo Japan

ISBN: (纸本)9781665459754

In this paper, we propose an efficient random access (RA) protocol based on the dynamic distributed queueing (DQ) mechanism to support massive bursty access for softwarized Internet of Things (IoT). Different from the existing DQ-based protocols, it can flexibly adjust the number of machine-type devices (MTDs) in the divided subgroup according to the occupancy condition of RA preambles, so as to effectively enhance both the resource utilization and access efficiency in the late stage of contention resolution. In addition, a two-phase theoretical model is developed to analyze the average access delay and the number of preamble retransmissions of the proposed protocol. Simulation results verify the validity of mathematical analysis, and demonstrate that our protocol can achieve a remarkably reduced access delay in comparison with the conventional ones, while maintaining a consistently high access success probability. © 2022 IEEE.

关键词： Internet of things

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PRACH Sequence Design for NR Unlicensed Spectrum

PRACH Sequence Design for NR Unlicensed Spectrum

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Zhen, Li Li, Shuchang Wang, Yue Su, Jingrui Yuyz, Keping Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an China Hosei University Graduate School of Science and Engineering Tokyo Japan RIKEN Center for Advanced Intelligence Project Tokyo Japan

ISBN: (纸本)9781665459754

The employment of additional spectrum is considered the most simple and effective way to boost network capacity for next-generation wireless communications. Towards this end, ongoing research work has been undertaken by the 3rd Generation Partnership project (3GPP) on enabling 5G new radio (NR) in the unlicensed band, which is called NR-U. In particular, some key physical layer technologies based on the NR air interface must be enhanced, by reason of the different regulatory requirements in the unlicensed band. In this paper, a comprehensive physical random access channel (PRACH) sequence design scheme is presented for the NR-U system, which is characterized by low cubic metric (CM), enhanced coverage, resistance to carrier frequency offset (CFO), and low-complexity detection. For the proposed sequence, a joint timing offset and CFO estimation scheme is proposed, furthermore, the theoretical derivation of CM and maximum coupling loss (MCL) is conducted. Results show that the proposed sequence can maintain a comparatively low CM value with an increment of the number of frequency-domain sequence repetitions while having remarkably improved PRACH coverage and detection performance. © 2022 IEEE.

关键词： Frequency domain analysis

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Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications

arXiv

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arXiv 2023年

作者： Hanada, Hiroyuki Hashimoto, Noriaki Taji, Kouichi Takeuchi, Ichiro Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Department of Mechanical Systems Engineering Nagoya University Nagoya464-8603 Japan

In this study, we have developed an incremental machine learning (ML) method that efficiently obtains the optimal model when a small number of instances or features are added or removed. This problem holds practical importance in model selection, such as cross-validation (CV) and feature selection. Among the class of ML methods known as linear estimators, there exists an efficient model update framework called the low-rank update that can effectively handle changes in a small number of rows and columns within the data matrix. However, for ML methods beyond linear estimators, there is currently no comprehensive framework available to obtain knowledge about the updated solution within a specific computational complexity. In light of this, our study introduces a method called the Generalized Low-Rank Update (GLRU) which extends the low-rank update framework of linear estimators to ML methods formulated as a certain class of regularized empirical risk minimization, including commonly used methods such as SVM and logistic regression. The proposed GLRU method not only expands the range of its applicability but also provides information about the updated solutions with a computational complexity proportional to the amount of dataset changes. To demonstrate the effectiveness of the GLRU method, we conduct experiments showcasing its efficiency in performing cross-validation and feature selection compared to other baseline methods. © 2023, CC BY.

关键词： Computational complexity

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Recognizing Real-World Intentions using A Multimodal Deep Learning Approach with Spatial-Temporal Graph Convolutional Networks

Recognizing Real-World Intentions using A Multimodal Deep Le...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jiaqi Shi Chaoran Liu Carlos Toshinori Ishi Bowen Wu Hiroshi Ishiguro Graduate School of Engineering Science Osaka University Japan Guardian Robot Project RIKEN Japan Advanced Telecommunications Research Institute International (ATR) Japan

Identifying intentions is a critical task for comprehending the actions of others, anticipating their future behavior, and making informed decisions. However, it is challenging to recognize intentions due to the uncertainty of future human activities and the complex influence factors. In this work, we explore the method of recognizing intentions alluded under human behaviors in the real world, aiming to boost intelligent systems' ability to recognize potential intentions and understand human behaviors. We collect data containing real-world human behaviors before using a hand dispenser and a temperature scanner at the building entrance. These data are processed and labeled into intention categories. A questionnaire is conducted to survey the human ability in inferring the intentions of others. Skeleton data and image features are extracted inspired by the answer to the questionnaire. For skeleton-based intention recognition, we propose a spatial-temporal graph convolutional network that performs graph convolutions on both part-based graphs and adaptive graphs, which achieves the best performance compared with baseline models in the same task. A deep-learning-based method using multimodal features is proposed to automatically infer intentions, which is demonstrated to accurately predict intentions based on past behaviors in the experiment, significantly outperforming humans.

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Implementation of Object Tracking Using YOLOv7 Pose Estimation for Human Activity Recognition in Fabrication Yard via CCTV Videos

Implementation of Object Tracking Using YOLOv7 Pose Estimati...

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International Conference on Smart Sensors and Application (ICSSA)

作者： Sharitta Madiahlagan Farid Azhari Norzaidi Ammar Zakaria Ahmad Shakaff Ali Yeon Latifah Munirah Kamarudin Syed Muhammad Mamduh Syed Zakaria Muhammad Ismail Hafizal Khalid Muhammad Azri Ahmad Baharom Ahmad Helmi M Amin Khairul Hafiz Zolkipli Asrul Sani Abdullah Faculty of Electrical Engineering and Technology University Malaysia Perlis Arau Malaysia Centre of Excellence for Advanced Sensor Technology University Malaysia Perlis Arau Malaysia Project Delivery and Technology PETRONAS Kuala Lumpur Malaysia Project Excellence PETRONAS Kuala Lumpur Malaysia

ISBN: (数字)9798350354294

ISBN: (纸本)9798350354300

A new dataset is created in a fabrication yard environment for human activity classification. YOLOv7 object detection model is used to identify 5 classes which are welding, grinding, standing, sitting and walking. Five experiments were conducted, and Experiment 5 achieved the highest mean Average Precision (mAP_ 0.5) of 0.621. The mAP@0.5 value is the most critical metric as it reflects the model's ability to balance precision and recall across all confidence thresholds. This higher mAP@0.5 suggests that Experiment 5's model is better at accurately identifying. However, YOLOv7 object detection posed limitation to differentiate classes for sitting, standing and walking. The implementation of pretrained models YOLOv7 Object Tracking and YOLOv7 Pose Estimation is done respectively with YOLOv7 Object Detection to improve the detections. The YOLOv7 Object Tracking showed significant improvement in the detections of standing and walking but had limitation for sitting. The YOLOv7 Pose Estimation which uses the heuristic method showed significant improvement in the detections of standing, sitting and walking compared to YOLOv7 Object Tracking.

关键词： Legged locomotion Training YOLO Welding Pose estimation Object detection Cameras Object tracking Long short term memory Videos

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