Updated on 2025/04/15

写真a

 
NAKAMURA YUGO
 
Organization
Faculty of Information Science and Electrical Engineering Department of Advanced Information Technology Assistant Professor
School of Engineering Department of Electrical Engineering and Computer Science(Concurrent)
Graduate School of Information Science and Electrical Engineering Department of Information Science and Technology(Concurrent)
Title
Assistant Professor
Contact information
メールアドレス
Profile
人を理解し、人を動かすIoT技術(IoTナッジ)の研究に従事
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Degree

  • Doctor of Engineering, Ph.D

Research History

  • 2020年4月から2021年4月 奈良先端科学技術大学院大学 特任助教   

Research Interests・Research Keywords

  • Research theme: Research on realtime detection and attention control of digital distraction

    Keyword: Digital Distraction, Personality, Multimodal Sensing, Tailored Intervention, Digital Wellbeing

    Research period: 2024.4 - 2027.3

  • Research theme: Empowerment ICT Platform for Health Behavior Security

    Keyword: Behavior recognition, behavior transformation, nudge, health behavior security, empowerment ICT

    Research period: 2021.10 - 2024.3

Awards

  • 優秀論文賞

    2024.4   情報処理学会 マルチメディア通信と分散処理ワークショップ (DPSWS2023)   イアラブルデバイスのマイクを用いた食事内容と咀嚼回数の推定手法の提案

  • 異能ジェネレーションアワード 分野賞 食に関する分野

    2024.3   異能ベーション   食べて、塗って、健やかに「eat2pic」

  • The 13th International Conference on the Internet of Things (IoT 2023)

    2023.11   The 13th International Conference on the Internet of Things (IoT 2023)   Kaolid: a Lid-type Olfactory Interface to Present Retronasal Smell towards Beverage Flavor Augmentation

  • Best Demonstration Runner-up Award

    2023.11   The 13th International Conference on the Internet of Things (IoT 2023)   Counting Nods from Chair Rocking

    Toshiki Hayashida, Yugo Nakamura, Hyuckjin Choi, Yutaka Arakawa

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  • Best Paper Candidate Award

    2023.11   The 13th International Conference on the Internet of Things (IoT 2023)   Kaolid: a Lid-type Olfactory Interface to Present Retronasal Smell towards Beverage Flavor Augmentation

    Daiki Mayumi, Yugo Nakamura, Yuki Matsuda, Shinya Misaki, Keiichi Yasumoto

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Papers

  • Privacy-Preserving Federated Learning With Resource Adaptive Compression for Edge Devices Reviewed International journal

    Muhammad Ayat Hidayat, Yugo Nakamura, Yutaka Arakawa

    IEEE Internet of Things Journal   2024.3

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    Language:English   Publishing type:Research paper (scientific journal)  

    Federated learning (FL) has gained widespread attention as a distributed machine learning (ML) technique that offers data protection when training on local devices. Unlike conventional centralized training in traditional ML, FL incorporates privacy and security measures as it does not share raw data between the client and server, thereby safeguarding potentially sensitive information. However, there are still vulnerabilities in the FL field, and commonly used approaches, such as encryption and blockchain technologies, often result in significant computational and communication costs, making them impractical for devices with restricted resources. To tackle this challenge, we present a privacy-preserving FL system specifically designed for resource-constrained devices, leveraging compressive sensing and differential privacy (DP) techniques. We implemented the weight-pruning-based compressive sensing method with an adaptive compression ratio based on resource availability. In addition, we employ DP to introduce noise to the gradient before sending it to a central server for aggregation, thereby protecting the gradient’s privacy. Evaluation results demonstrate that our proposed method achieves slightly better accuracy when compared to state-of-the-art methods like DP-federated averaging, DP-FedOpt, and adaptive Gaussian clipping-DP (AGC-DP) for the MNIST, Fashion-MNIST, and Human Activity Recognition data sets. Furthermore, our approach achieves this higher accuracy with a lower total communication cost and training time than the current state-of-the-art methods. Moreover, we comprehensively evaluate our method’s resilience against poisoning attacks, revealing its better resistance than existing state-of-the-art approaches.

    DOI: 10.1109/JIOT.2023.3347552

  • Kaolid: A Lid-Type Olfactory Interface to Present Retronasal Smell towards Beverage Flavor Augmentation Reviewed International journal

    Daiki Mayumi, Yugo Nakamura, Yuki Matsuda, Shinya Misaki, Keiichi Yasumoto

    ACM International Conference Proceeding Series   1 - 8   2023.11   ISBN:9798400708541

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:ACM International Conference Proceeding Series  

    In this paper, we introduce Kaolid, an olfactory interface that uses a lid mechanism to augment the flavor of beverages by delivering scents as retronasal smell. Kaolid aims to promote the consumption of healthier beverages by intensifying their perceived taste through the release of scents during drinking. The system features a compact olfactory display and an IMU sensor, triggering scents in response to drinking movements. It comes in two models: A straw-Type for cold beverages and a cup-Type for hot drinks. We tested the interface using sparkling and hot water and measured its efficacy in enhancing perceived sweetness when paired with scents. Results showed significant enhancements in all evaluation metrics (taste satisfaction, perceived sweetness, and preference) with the straw-Type device. Notably, the perceived sweetness increased by an amount equivalent to about 2.88 grams of sugar when a retronasal smell was introduced compared to when no scent was present. This innovative interface holds promise in elevating the flavor of sugar-free drinks and could support those aiming to limit sugar consumption. Furthermore, this research contributes to the future of IoT systems for health support by harnessing the power of scent, opening avenues for novel approaches in sensory-driven well-being advancements.

    DOI: 10.1145/3627050.3627056

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    Other Link: https://dblp.uni-trier.de/db/conf/iot/iot2023.html#MayumiN0MY23

  • eat2pic: An Eating-Painting Interactive System to Nudge Users into Making Healthier Diet Choices Reviewed International journal

    Yugo Nakamura, Rei Nakaoka, Yuki Matsuda, Keiichi Yasumoto

    Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies   7 ( 1 )   1 - 23   2023.3   ISSN:2474-9567 eISSN:2474-9567

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Association for Computing Machinery ({ACM})  

    Given the complexity of human eating behaviors, developing interactions to change the way users eat or their choice of meals is challenging. In this study, we propose an interactive system called eat2pic designed to encourage healthy eating habits such as adopting a balanced diet and eating more slowly, by refraining the task of selecting meals into that of adding color to landscape pictures. The eat2pic system comprises a sensor-equipped chopstick (one of a pair) and two types of digital canvases. It provides fast feedback by recognizing a user's eating behavior in real time and displaying the result on a small canvas called "one-meal eat2pic."Moreover, it also provides slow feedback by displaying the number of colors of foods that the user consumed on a large canvas called "one-week eat2pic."The former was designed and implemented as a guide to help people eat more slowly, and the latter to encourage people to select more balanced menus. Through two user studies, we explored the experience of interaction with eat2pic, in which users' daily eating behavior was reflected in a series of "paintings,"that is, images produced by the automated system. The experimental results suggest that eat2pic may provide an opportunity for reflection in meal selection and while eating, as well as assist users in becoming more aware of how they are eating and how balanced their daily meals are. We expect this system to inspire users' curiosity about different diets and ways of eating. This research also contributes to expanding the design space for products and services related to dietary support.

    DOI: 10.1145/3580784

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  • Unsupervised Learning of Domain-Independent User Attributes Reviewed International journal

    Ishikawa, Y., Legaspi, R., Yonekawa, K., Nakamura, Y., Ishida, S., Mine, T., Arakawa, Y.

    IEEE Access   10   119649 - 119665   2022.11

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    Language:English   Publishing type:Research paper (scientific journal)  

    Learning user attributes is essential for providing users with a service. In particular, for e-commerce portals which deal in variety of goods ranging from clothes to foods to home electronics, it is especially important to learn 'domain-independent' attributes such as age, gender, and personality that affect people's behavior across various domains of daily life (e.g., clothing, eating and housing) because these attributes can be used for personalization in diverse domains their service covers. Thus far, researchers have proposed approaches to learn user representation (UR) from user-item interactions, trying to embed rich information about user attributes in UR. However, very few can learn URs that are domain-independent without confounding them with domain-specific attributes (e.g., food preferences). This could consequently undermine the former's utility for personalizing services in other domains from which the URs are not learned. To address this, we propose an approach to learn URs that exclusively reflect domain-independent attributes. Our approach introduces a novel multi-layer RNN with two types of layers: Domain Specific Layers (DSLs) for modeling behavior in individual domains and a Domain Independent Layer (DIL) for modeling attributes that affect behavior across multiple domains. By exchanging hidden states between these layers, the RNNs implement the process of domain-independent attributes affecting domain-specific behavior and makes the DIL learn URs that capture domain-independence. Our evaluation results confirmed that the URs learned by our approach have greater utility in predicting behavior in the other domains from which these URs were not learned thereby demonstrating adaptability to various domains.

    DOI: 10.1109/ACCESS.2022.3220781

  • Large-Scale Evacuation Shelter Selection Method Through Iterations of Pedestrian Simulations With Dynamic Congestion Reproduction Reviewed International journal

    Kazuhito Umeki, Tomoki Tanaka, Yugo Nakamura, Manato Fujimoto, Teruhiro Mizumoto, Hirohiko Suwa, Yutaka Arakawa, Keiichi Yasumoto

    IEEE Access   10   89387 - 89401   2022.8   ISSN:2169-3536 eISSN:2169-3536

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:IEEE Access  

    It is necessary to optimize evacuation guidance to shelters in short evacuation time. The state-of-the-art method based on an idea of combinatorial optimization problems related to evacuees' locations and the capacities of nearby shelters has been developed, while it cannot mitigate the effect of congestion on roads/streets after evacuation starts. In this study, to cover this problem, we develop a new method that utilizes simulations for estimating the effect of congestion on roads/streets during evacuation and reassigning shelters to evacuees based on the simulation results. By iterating this step, our method derives the congestion-aware solutions for shelter selection that can realize more smooth evacuation. To evaluate our method, we conducted multi-agent simulations assuming a disaster situation in a sightseeing spot. Specifically, we examined a hypothetical case scenario involving the evacuation of 30,000 visitors from the Gion Festival. We compared the proposed method with conventional methods, such as the nearest shelter selection method and our previous method. We found that our proposed method reduced average and total evacuation time and congestion on roads compared to the conventional methods including the nearest shelter selection method and our previous method that only employs combinatorial optimization without estimating congestion. From this result, our idea of simulation-based congestion estimation has an impact of easing congestion during evacuation and preventing overcapacity of shelters at the same time. It shows the possibilities of help in developing congestion-aware evacuation strategies in emergency situations of crowded areas like huge cities or sightseeing spots.

    DOI: 10.1109/ACCESS.2022.3194874

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Presentations

  • IoTセンサを用いたロバストな行動認識技術 Invited

    中村優吾

    応用物理学会「トータルバイオミメティクス領域グループ」シンポジウム  2020.11 

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  • 健康行動セキュリティのためのエンパワメントICTの実現に向けて Invited

    中村 優吾

    情報処理学会・第85回全国大会 企画セッション「Society 5.0時代の安心・安全・信頼を支える基盤ソフトウェア技術の構築」  2023.3 

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  • 健康行動セキュリティに基づくエンパワメントICTの実現に向けて Invited

    中村 優吾

    第104回モバイルコンピューティングと新社会システム・第75回ユビキタスコンピューティングシステム・第35回コンシューマ・デバイス&システム・第24回高齢社会デザイン合同研究発表会 若手研究者招待講演  2022.9 

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  • Short Stick Exercise Tracking System for Elderly Rehabilitation using IMU Sensor

    Kazuki Oi, Yugo Nakamura, Yuki Matsuda, Manato Fujimoto, Keiichi Yasumoto

    2022 2nd International Workshop on Cyber-Physical-Human System Design and Implementation (CPHS)  2022.5 

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    Stick exercises, which have been attracting attention for improving the health of the elderly, are usually performed in nursing homes under the guidance of nursing staff. However, in the current pandemic in which the elderly are advised to refrain from going out unnecessarily, it is desirable for each individual to be able to perform the stick exercises alone. In this study, we aim to develop a stick exercise support system that can automatically record the number of times an elderly person performs each type of stick exercise and provide feedback to improve the movement for each exercise. As a first step toward the realization of this stick exercise support system, we investigated a method for recognizing exercise movements using inertial measurement unit (IMU) sensors. In the evaluation experiment, 21 subjects performed 3 sets (10 times per set) of eight basic stick exercises. The exercise movements were classified based on the linear acceleration and quaternion data obtained from the IMU. As a result, 90% of F-measure was achieved when using Light GBM as the learning algorithm.

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  • QoS-Aware Point Cloud Streaming of Wild Animals/Humans for Interactions in Virtual Space

    Hiroki Ishimaru, Yugo Nakamura, Manato Fujimoto, Hirohiko Suwa, Keiichi Yasumoto

    2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023  2023 

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    3D applications such as VR and AR are attracting increasing commercial attention, and point cloud video is expected to be one of the most suitable representations for real-time applications due to its simplicity and versatility. However, point cloud data is large in size and difficult to stream in a mobile network environment with limited bandwidth. Therefore, a method for streaming point clouds with low bandwidth consumption while maintaining the quality of the user experience is needed. In this paper, we present a point cloud streaming method of real-space objects such as humans and animals for real-time 3D reconstruction in VR space. The system uses a depth camera to scan a human or animal, divides the point cloud into parts of the body, and then controls the quality of the point cloud (i.e., resolution and frame rate) for each part in real-time according to the object's motion and context. This enables point cloud streaming with limited resources (computational and network resources) and maximizes the user's quality of experience. We exhibit a series of systems that enhance the user experience in remote communication in realistic environments and scenarios while maintaining interactivity between real-space objects and remote users.

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    Other Link: https://dblp.uni-trier.de/rec/conf/percom/2023w

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MISC

  • C-AAE: Study of Privacy-Aware Activity Recognition Methods Integrating Code Modulation and Anonymizing AutoEncoder

    藤本, 隆晟, 中村, 優吾, 荒川, 豊

    マルチメディア,分散,協調とモバイルシンポジウム2024論文集   2024   348 - 358   2024.6

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    Language:Japanese  

    加速度やジャイロセンサから取得できる人の行動データにはそのユーザのプライバシー情報を多く含んでいる.そのため,それらを特定しようとする外部の脅威から行動データを守る必要がある.また,プライバシー情報を守るためには,それらが推定できないような形式で利用・保存することが求められる.つまり,そのデータが利用されるアプリケーションでの機能を提供するための有用性は保ちつつ,プライバシーリスクを最小限にするためのデータ処理が求められる.そこで,本研究では,Adaptive Differential Pulse Code(ADPCM, 適応的差分パルス符号変調)とAnonymizing AutoEncoder(AAE, 匿名化オートエンコーダ)を組み合わせた行動データの匿名化手法であるCompressive-AAE(C-AAE)を提案し,実際に匿名化したデータのプライバシーリスクとアプリケーションでの有用性を評価した.結果としては先行研究のAAEと比較し,よりプライバシーリスクを抑えた上で精度を維持した行動認識を行うことに成功した.

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  • 5分で分かる!? 有名論文ナナメ読み:Caraban, Ana et al. : 23 Ways to Nudge : A Review of Technology-Mediated Nudging in Human-Computer Interactions

    中村 優吾

    情報処理   65 ( 5 )   268 - 270   2024.4   ISSN:0447-8053

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    Language:Japanese   Publisher:情報処理学会  

    ナッジとは,人々の選択の自由を奪うことなく,特定の方向に微妙にかつ効果的に促すことを目的とした,行動の促進や意思決定の支援手段の総称である.その核心は,人々が常に合理的な選択をするわけではなく,しばしば予測可能な方法で非合理的な決定を下すという人間の行動の側面に着目している点にある.つまり,人々の思考のクセを捉えて,環境や情報提示の仕方を工夫することで,人々がより良い選択をするよう後押しすることが可能であると考えられてきた.HCIの分野では,このアイディアが早い段階から採用され,さまざまなインタラクティブシステムの研究開発に役立てられてきた.本稿では,HCI領域におけるナッジの活用事例を体系的にレビューした論文を取り上げ,特定された23種類のナッジ・メカニズムを概説する.

    DOI: 10.20729/00233650

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  • ChatGPT-based Application of Text Message Integration and Prioritization for Smartphone Push Notifications

    楊, 子毅, 崔, 赫秦, 中村, 優吾, 荒川, 豊

    第86回全国大会講演論文集   2024 ( 1 )   197 - 198   2024.3

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    Language:English  

    In this study, we develop a mobile application that collects push notifications from text messengers, social media, and email apps, then prioritizes the messages based on their sender, content, and application name using ChatGPT. By this application, users can check the messages in a single interface without opening multiple applications and displaying read receipts. Additionally, this application provides the capability to sort messages by priority, thus users can view more important content and filter advertisements. Moreover, this application supports content analyzation in multiple languages, allowing simultaneous prioritization of messages in different languages. We found that our application can give the higher priority to work-related messages, contrastively, the lower priority to advertisements.

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  • Chew-Drawインタラクションの実現に向けた食事中の咀嚼音に基づくeat2picシステムの検討

    大平, 祐大, 中村, 優吾, 荒川, 豊

    第31回マルチメディア通信と分散処理ワークショップ論文集   278 - 280   2023.10

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    Language:Japanese  

    本稿では,先行研究で提案したイヤラブルデバイスを用いた食事中の音から食品推定と咀嚼検出をシステムへの組み込み例として,我々の研究グループで提案している eat2pic への適用を紹介する.従来の eat2pic システムでは,センサを搭載した箸からユーザが一口ずつ何をどれくらいのスピードで食べたかを自動追跡し,食行動の良し悪しをキャンバス上に描かれた絵を用いてリアルタイムの視覚的フィードバックを提供する.しかし,eat2pic のセンシングアプローチは,カメラと IMU を搭載した専用の箸型センサを使用する必要があるため,日常生活での利便性や社会全体への普及可能性という観点で課題が残っている.そこで本稿では,センシングアプローチとして市販のワイヤレスイヤホンを用いた手法を提案する.提案手法では,市販のワイヤレスイヤホンとスマートフォンのみで完結するため,従来の eat2pic と比較して普及可能性の観点で大きく優れており,ユーザの噛む動作に応じて絵を描くという新たなインタラクションを実現することが可能となる.最近ではワイヤレスイヤホンで音楽を聴きながら食事を行う人も増えてきているため,ごく自然な形でセンシングを行うことができる.

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  • ゴミ捨て行動における誘因を活用した介入による行動変容に関する調査

    大園, 咲奈, 甲斐, 貴一朗, 織, 睦樹, 中村, 優吾, 荒川, 豊, 山崎, 悠大, 曹, 蓮, 柏本, 幸俊, 上坂, 大輔

    行動変容と社会システム vol.09   2023.3

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    Language:Japanese  

    Research on behavioral change by an inducement using an inducement in trash dumping behavior

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Industrial property rights

Patent   Number of applications: 1   Number of registrations: 0
Utility model   Number of applications: 0   Number of registrations: 0
Design   Number of applications: 0   Number of registrations: 0
Trademark   Number of applications: 0   Number of registrations: 0

Professional Memberships

  • ACM

    2020.4

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  • IEEE

    2020.4

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  • IEEE

  • 情報処理学会

  • 電子情報通信学会

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Committee Memberships

  • 情報処理学会 ユビキタスコンピューティングシステム研究会   Organizer   Domestic

    2023.4 - 2025.3   

  • 情報処理学会   IoT行動変容学研究グループ 運営委員  

    2022.4 - Present   

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    Committee type:Academic society

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  • 情報処理学会 IoT行動変容学研究グループ   Steering committee member   Domestic

    2022.4 - 2025.3   

  • 電子情報通信学会 センサネットワークとモバイルインテリジェンス研究会   Steering committee member   Domestic

    2022.4 - 2025.3   

  • 情報処理学会 モバイルコンピューティングと新社会システム研究会   Steering committee member   Domestic

    2022.4 - 2025.3   

Academic Activities

  • Technical Program Co-Chairs International contribution

    Eighth IEEE International Workshop on Smart Service Systems SmartSys 2024  ( Japan ) 2024.6 - 2024.7

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  • Publication Chair International contribution

    The 22nd ACM International Conference on Mobile Systems, Applications, and Services (MobiSys 2024)  ( Japan ) 2024.6

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  • プログラム副委員長

    第31回 マルチメディア通信と分散処理ワークショップ (DPSWS2023)  ( Japan ) 2023.10

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  • Technical Program Committee International contribution

    IEEE PerCom Workshop WristSense 2023  ( Japan ) 2023.3

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  • プログラム副委員長

    第30回 マルチメディア通信と分散処理ワークショップ (DPSWS2022)  ( Japan ) 2022.10

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Research Projects

  • デジタル・ディストラクションの即時検知とアテンション制御に関する研究

    Grant number:24K15227  2024 - 2026

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (C)

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    Authorship:Principal investigator  Grant type:Scientific research funding

  • デジタル・ディストラクションの即時検知とアテンション制御に関する研究

    Grant number:24K15227  2024 - 2026

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (C)

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    Grant type:Scientific research funding

  • デジタルウェルビーイングに向けた情報選択行動支援

    Grant number:JP23H00216  2023 - 2028

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (A)

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    Authorship:Coinvestigator(s)  Grant type:Scientific research funding

  • デジタルウェルビーイングに向けた情報選択行動支援

    Grant number:23H00216  2023 - 2027

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (C)

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    Grant type:Scientific research funding

  • 健康行動セキュリティのためのエンパワメントICT基盤

    2021 - 2024

    JST Strategic Basic Research Program (Ministry of Education, Culture, Sports, Science and Technology)

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    Authorship:Principal investigator  Grant type:Contract research

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Class subject

  • 基幹教育セミナー

    2024.6 - 2024.8   Summer quarter

  • 電気情報工学実験Ⅰ(CM)

    2024.4 - 2024.9   First semester

  • 電気情報工学実験Ⅰ(C)

    2024.4 - 2024.9   First semester

  • (IUPE)Lab. of Electrical Eng and Computer ScienceⅠ(CM)

    2024.4 - 2024.6   Spring quarter

  • (IUPE)System Programming Lab(for C)

    2024.4 - 2024.6   Spring quarter

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Social Activities

  • エシカル消費研究会

    CCCマーケティング株式会社  オンライン  2022.6

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    Audience:General, Scientific, Company, Civic organization, Governmental agency

    Type:Lecture

  • DX Tech Play Edge Computingハンズオンセミナー

    デル・テクノロジーズ株式会社  オンライン  2021.9

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    Audience:General, Scientific, Company, Civic organization, Governmental agency

    Type:Seminar, workshop

Media Coverage

  • 大学院進学に関するインタビュー記事 Newspaper, magazine

    月刊高専  2021.12

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    大学院進学に関するインタビュー記事