Tokushima University / Educator and Researcher Directory --- Takeuchi, Hironori

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Research

Field of Study

Subject of Study

Book / Paper

Academic Paper (Judged Full Paper):

1.	Hironori Takeuchi, Kenji Matsuura, Tetsushi Ueta and Tomohito Wada : Development of a Support System for Recalling 3D Vision from a 2D Plane, Journal of Educational Multimedia and Hypermedia, Vol.32, No.1, (inpress), 2024. (Summary) Basketball tactical patterns are typically taught using tools such as 2D tactical boards. Rapid decision-making in a team depends on the ability to connect two-dimensional (2D) third-person positions with three-dimensional (3D) first-person perspectives. This study develops a support system that offers a virtual environment to enhance the efficient recall of 3D vision from a 2D board. The proposed system generates static or dynamic 3D visualizations based on 2D inputs. A total of 30 volunteers participated in this study and were randomly assigned to groups that received either 2D images, static 3D images, or dynamic 3D images. The study evaluated participants ability to accurately recall a 3D vision from a 2D board under both constrained and unconstrained time conditions. The results indicate that under constrained time conditions, a learning method that uses static 3D images in response to 2D operations is more effective than traditional learning methods based only on 2D images. (Keyword) team sports / basketball / Web3D / virtual reality / spatial skills / training
2.	Hironori Takeuchi, Kyota FUTAGAMI and Kenji Matsuura : Attempt to Divide Old/New OS Based on HTTPS Request without User-Agent Infomation, Journal for Academic Computing and Networking, No.27, 106-111, 2023. (Summary) HTTP ヘッダに含まれる User-Agent 文字列(以下，UA)は，利用者のクライアント情報に応じた適切なコンテンツ配信など様々な用途で活用される．徳島大学においても，これを学内サービスへアクセスする脆弱なクライアント検出に利用し，サポート対象外である OS からのアクセスを利用制限する仕組みを実現したところである．しかし，個人情報の取り扱い厳格化を背景に UA 情報の更新は停止され，信頼性を失いつつある．代替技術として提案される UA Client Hints 対応は一部のブラウザに限定されており，制約されないクライアント機器を扱う国立大学法人での活用には適さない．これに依存しない新たな手段を検討する必要がある．本稿では，UA 情報に依存しない脆弱な Web クライアント推測を目的として，収集可能なクライアント情報を用いて回帰分析による OS の新旧推定を試みる．重回帰モデルを選択して推定性能を検証した結果，Windows，macOS，iOS それぞれ 80%，70%，58% の正確度で OS の新旧推定が可能であった． (Keyword) ユーザエージェント / 回帰分析 / OS推定 (Link to Publication Site) ● Publication site (DOI): 10.24669/jacn.27.1_106 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390298278371636352 ● Search Scopus @ Elsevier (DOI): 10.24669/jacn.27.1_106 (DOI: 10.24669/jacn.27.1_106, CiNii: 1390298278371636352)
3.	Takafumi Bando, Hironori Takeuchi, Tetsushi Ueta and Kenji Matsuura : A management of the support life cycle for client operating systems in Tokushima University, Journal for Academic Computing and Networking, No.24, 48-57, 2020. (Summary) 大学など研究機関は，常にサイバー攻撃の脅威にさらされており，水際であるPC，および搭載OSのセキュリティ対策の需要が高まっている．本学では，構成員が使用しているクライアント機器の OSについて，新たな方針の策定と，既存の情報システムを利用したOS更新の促進のための施策を実施した．これら施策は，大学組織の構成員の多様性を考慮して設計されている．学内の認証基盤の網羅性に着目し，そのログを用いて利用者と利用OSを推定，対象となるOSの利用者に対しては個別メールによるOS更新の周知促進を行った．本論文では，その実践事例と効果，今後の課題について述べる． (Keyword) OS / Windows / macOS / Shibboleth (Tokushima University Institutional Repository) ● Metadata: 115736 (Link to Publication Site) ● Publication site (DOI): 10.24669/jacn.24.1_48 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.24669/jacn.24.1_48 (Tokushima University Institutional Repository: 115736, DOI: 10.24669/jacn.24.1_48)

Proceeding of International Conference:

1.	Taketo Shibasaki, Kenji Matsuura, Hironori Takeuchi and Tetsushi Ueta : Visualization for easier recognition of low-risk and successful passes in a Basketball match, Proceedings of IIAI-AAI2024, 6pgs, Takamatsu, Jul. 2024.
2.	Kenji Matsuura, Hironori Takeuchi, Kazuki Urushihara, Hiroki Tanioka, Stephen Githinji Karungaru and Tomohito Wada : Multiplayer Tracking with Diagonal Video to Support Basketball Tactical Learning, 18th International Conference on Ubiquitous Information Management and Communication, 5pgs, Kuala Lumpur, Malaysia, Jan. 2024. (Summary) Player tracking has become a crucial technique for getting strategic advantage in competitive team sports. Movies captured by one or a few cameras are easy to use for this purpose at any level including amateur. This study describes a design issue that integrates up-to-date technologies of multi-object tracking for tactics detection from the actual game data to bring affordability, feasibility, and accessibility. (Keyword) multi-object-tracking / basketball / tactics / learning (Link to Publication Site) ● Publication site (DOI): 10.1109/IMCOM60618.2024.10418315 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85186143229 (DOI: 10.1109/IMCOM60618.2024.10418315, Elsevier: Scopus)
3.	Taisei Matsuo, Kenji Matsuura and Hironori Takeuchi : Awareness and learning for initial configuration of an webserver, 27th International Conference in Knowledge Based and Intelligent Information & Engineering Systems - KES2023, Vol.225, 2516-2525, Athens, Greece, Sep. 2023. (Summary) An webserver should be maintained by its experienced administrator under the firm knowledge and conviction on vulnerability measures. General administrators of novice learn the vulnerabilities with public literatures to get knowledge while unlearnt people sometimes try by the Internet articles for its affordability. However, the configuration of vulnerability countermeasures can be represented structurally. Therefore, even novices should learn more efficiently by using a learning support system originally developed those structures with the elements of vulnerability countermeasures than by learning with the literature. The system asks questions based on the learner's understanding. In the experiment, learners were divided into two groups of studying by the literature and studying by the system. Through the experiment consisted of a pre-test, learning, and post-test, the average score indicates the advantage of the system while the literature-based study shows partially advantageous. The explanatory text and feedback for items that the learners could not answer correctly need to be revised. (Keyword) Webserver / System administrator / Vulnerability (Link to Publication Site) ● Publication site (DOI): 10.1016/j.procs.2023.10.243 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85183533055 (DOI: 10.1016/j.procs.2023.10.243, Elsevier: Scopus)
4.	Yuri Lucas Direbieski, Hiroki Tanioka, Kenji Matsuura, Hironori Takeuchi, Masahiko Sano and Tetsushi Ueta : Security Impact Analysis of Degree of Field Extension in Lattice Attacks on Ring-LWE Problem, COMPSAC 2023 Workshop: The 1st IEEE International Workshop on Data Science & Machine Learning for Cybersecurity, IoT & Digital Forensics (DSML), 1441-1446, Turin, Jun. 2023. (Summary) Modern information communications use cryptography to keep the contents of communications confidential. RSA (RivestShamirAdleman) cryptography and elliptic curve cryptography, which are public-key cryptosystems, are widely used cryptographic schemes. However, it is known that these cryptographic schemes can be deciphered in a very short time by Shors algorithm when a quantum computer is put into practical use. Therefore, several methods have been proposed for quantum computer-resistant cryptosystems that cannot be cracked even by a quantum computer. A simple implementation of LWE-based lattice cryptography based on the LWE (Learning With Errors) problem requires a key length of O(n2) to ensure the same level of security as existing public-key cryptography schemes such as RSA and elliptic curve cryptography. In this paper, we attacked the Ring-LWE (RLWE) scheme, which can be implemented with a short key length, with a modified LLL (Lenstra-Lenstra-Lova sz) basis reduction algorithm and investigated the trend in the degree of field extension required to generate a secure and small key. Results showed that the lattice-based cryptography may be strengthened by employing Cullen or Mersenne prime numbers as the degree of field extension. (Keyword) lattice-cipher / ring-LWE / LLL-basis-reduction (Link to Publication Site) ● Publication site (DOI): 10.1109/COMPSAC57700.2023.00221 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/COMPSAC57700.2023.00221 (DOI: 10.1109/COMPSAC57700.2023.00221)
5.	Kenji Matsuura, Kohta Jobe, Hiroki Tanioka, Hironori Takeuchi and Tetsushi Ueta : Performed-Tactics Detection of a Basketball Match using Multi-Object Tracking Technology Applicable for a Movie, Elizabeth Langran (Ed.), Proceedings of Society for Information Technology & Teacher Education International Conference, 676-680, New Orleans, LA, US, Mar. 2023.
6.	Hironori Takeuchi, Kenji Matsuura and Lei He : Enhancing online learning focusing on the relationship between gaze and browsing materials, Proceedings of CELDA2022, 155-161, Lisbon, Nov. 2022. (Keyword) Microsoft Teams / Online Classes / Gazing point / Online Learning / Online Seminars (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85147533292 (Elsevier: Scopus)
7.	Taketo Yamada, Kenji Matsuura, Hironori Takeuchi, Akihiro Kashihara, Kenichi Yamasaki and Genta Kurita : Toward better driving with gaze awareness environment supported by area segmentation, Proceedings of CELDA2022, 49-56, Lisbon, Nov. 2022. (Keyword) Driving Simulator / U-Net / Grid Segmentation / Eye Tracker (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85147530930 (Elsevier: Scopus)

Proceeding of Domestic Conference:

1.	MATSUO Taisei, Kenji Matsuura, Hironori Takeuchi and Masahiko Sano : Learning support for access-control configuration of webserver focusing on logical structure, 教育システム情報学会2024年全国大会講演論文集, 69-70, Aug. 2024. (Keyword) アクセス制御 / Apache / 論理構造 / LMS
2.	SHIBASAKI Taketo, Kenji Matsuura, Hironori Takeuchi and Tetsushi Ueta : Extended approach based on low-risk pass learning support system for novice basketball players, 教育システム情報学会2024年全国大会講演論文集, 31-32, Aug. 2024. (Keyword) バスケットボール / シミュレータ / パス / 支援学習
3.	TAKAGI Shota, Kenji Matsuura and Hironori Takeuchi : Gaze-awareness for learning what's focused in basketball match using MR, 教育システム情報学会2024年全国大会講演論文集, 65-66, Aug. 2024. (Keyword) バスケットボール / 学習支援 / 注視動向 / HoloLens2 / アイトラッキング
4.	Hironori Takeuchi and Kenji Matsuura : Step-by-step notice for errors of semantics in tracing tasks, 教育システム情報学会2024年全国大会講演論文集, 269-270, Aug. 2024. (Keyword) 生成AI / プログラミング / トレース問題 / ヒント
5.	Kenji Matsuura, YASHIRO Ryo and Hironori Takeuchi : A Discussion for Surmounting AI Hallucination in Learning Support Systems, 教育システム情報学会2023年全国大会講演論文集, 215-216, Aug. 2023. (Keyword) 生成AI / API / AI Hallucination
6.	Hironori Takeuchi, ONO Kentaro, Kenji Matsuura and Tetsushi Ueta : Study of designing a tactical learning system by mathematical modeling, 教育システム情報学会2023年全国大会講演論文集, 225-226, Aug. 2023. (Keyword) フィールドスポーツ / 戦術学習 / 学習支援
7.	Hironori Takeuchi and Kenji Matsuura : A Discussion on Promoting Recognition for Recalling Team Tactics, Proceedings of the 2022 IEICE General Conference, 112, Mar. 2022.

Et cetera, Workshop:

1.	MATSUO Taisei, Kenji Matsuura, Hironori Takeuchi and Masahiko Sano : Learning support for access-control configuration of an webserver focusing on logical structure, IPSJ SIG Technical Report, Vol.2024-CLE-42, No.16, 7pgs, Mar. 2024. (Keyword) アクセス制御 / Apache / 論理構造 / LMS
2.	YASHIRO Ryo, Kenji Matsuura and Hironori Takeuchi : Awareness-support environment for learning error debugging with generative AI, JSiSE Resaeach Report, 2pgs, Mar. 2024. (Keyword) プログラミング初学者 / デバッグ支援 / 生成AI / ChatGPT
3.	URUSHIHARA Kazuki, Kenji Matsuura, Hironori Takeuchi and Tomohito Wada : Generating Top-Down View Animations from Real Footage Using Player Detection, JSiSE Resaeach Report, 2pgs, Mar. 2024. (Keyword) バスケットボール / corpus linguistics / 初学者支援 / シミュレータ
4.	TAKAGI Shota, Kenji Matsuura and Hironori Takeuchi : Support for learning gaze trend in baske tball using MR environment, JSiSE Resaeach Report, 2pgs, Mar. 2024. (Keyword) バスケットボール / 学習支援 / 注視動向 / HoloLens2 / アイトラッキング
5.	SHIBASAKI Taketo, Kenji Matsuura, Hironori Takeuchi and ONO Kentaro : Beginner assistance in identifying low risk pass areas for basketball, JSiSE Resaeach Report, 2pgs, Mar. 2024. (Keyword) バスケットボール / corpus linguistics / 初学者支援 / シミュレータ
6.	YAMADA Taketo, Hironori Takeuchi, Kenji Matsuura, KASHIHARA Akihiro and MURAKAMI Ryo : Support for learning the gaze home position during train operation., JSiSE Resaeach Report, 2pgs, Mar. 2024. (Keyword) 鉄道運転 / 運転シミュレータ / 視線計測 / 画面分割
7.	Takumi Mari, Hironori Takeuchi, Kenji Matsuura, Gaku Hirose and Akihiro Kashihara : A Study on Learning Support for Mastering Fundamental Gaze Behavior of Train Drivers, JSiSE Resaeach Report, 233-234, Mar. 2023. (Keyword) 鉄道車両運転士 / 視線振る舞い / 鉄道運転シミュレータ / アイトラッキング
8.	Kota Jobe, Hironori Takeuchi and Kenji Matsuura : Multiplayer-tracking from actual video to support understanding of basic tactics for a basketball match, JSiSE Resaeach Report, 231-232, Mar. 2023. (Keyword) バスケットボール / 戦術理解支援 / 実映像 / PoseNet / トラッキング
9.	Rin Yamaoka, Hironori Takeuchi and Kenji Matsuura : Support for improvement of gaze point in a basketball match using the MR environment, JSiSE Resaeach Report, 229-230, Mar. 2023. (Keyword) バスケットボール / 学習支援 / 注視点 / HoloLens2 / アイトラッキング / ハンドトラッキング
10.	Hironori Takeuchi, Lei He and Kenji Matsuura : Design an online learning environment focusing on the relationship between eye gaze and learning materials, JSiSE Resaeach Report, Vol.37, No.4, 1-4, Nov. 2022. (Keyword) Microsoft Teams / オンライン授業 / 注意力向上 / 視線検出 / ページ遷移
11.	Kenji Matsuura, Hironori Takeuchi, Akihiro Kashihara, Kenichi Yamasaki and Genta Kurita : Improvement of a driving simulator for better awareness and recognition of a driver as a learner, JSiSE Resaeach Report, Vol.37, No.4, 97-103, Nov. 2022. (Keyword) 運転シミュレータ / 安全意識 / シナリオ制御 / 視線誘導
12.	Taketo Yamada, Kenji Matsuura, Hironori Takeuchi, Akihiro Kashihara, Kenichi Yamasaki and Genta Kurita : Learning driving with improving gaze through area division methods, JSiSE Resaeach Report, 215-216, Mar. 2022.
13.	Rin Yamaoka, Kenji Matsuura and Hironori Takeuchi : Learning support environment for a player's gaze awareness in basketball, JSiSE Resaeach Report, 213-214, Mar. 2022.

Grants-in-Aid for Scientific Research (KAKEN Grants Database @ NII.ac.jp)

データサイエンスに基づくチームスポーツ戦術モデルとスキル学習支援 (Project/Area Number: 22K12314 )
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