論文一覧 | 人間情報システム研究グループ（荒川・峯・福嶋研究室）

2024

論文誌

11.

Menna Fateen, Bo Wang, Tsunenori Mine

Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with Feedback Journal Article

In: IEEE Access, vol. 12, pp. 1 - 1, 2024.

Abstract | BibTeX

10.

Gangkai Li, Yugo Nakamura, Hyuckjin Choi, Shogo Fukushima, Yutaka Arakawa

WatchLogger: Keystroke Detection and Typing Words Recognition based on Smartwatch Journal Article

In: Sensors and Materials, vol. 36, iss. 10, no. 3, pp. 4519-4534, 2024.

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Bin Chen, Yugo Nakamura, Shogo Fukushima, Yutaka Arakawa

Retrieval-augmented-generation-enhanced Dense Video Caption for Human Indoor Activities: Disambiguating Caption Using Spatial Information Beyond Field of View Constraints Journal Article

In: Sensors and Materials, vol. 36, no. 10, pp. 4549-4566, 2024.

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Shogo Fukushima, Keigo Sakamoto, Yugo Nakamura

NariTan: Enhancing Second Language Vocabulary Learning Through Non-Human Avatar Embodiment in Immersive Virtual Reality Journal Article

In: Multimodal Technologies and Interaction, vol. 8, iss. 10, no. 93, 2024.

Links | BibTeX

Bo Wang, Tsunenori Mine

One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive Learning Journal Article

In: IEEE Access, vol. 12, pp. 148488 - 148501, 2024.

Abstract | BibTeX

Chang-Hong Lee, Hyuckjin Choi, Yutaka Arakawa, Dong-Hyun Kim, Jong-Deok Kim

Bilatangulation: A Novel Measurement Error Compensation Method for Wi-Fi Indoor Positioning with Two Anchors Journal Article

In: IEEE Access, vol. 12, pp. 128652-128661, 2024, ISSN: 2169-3536.

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Muhammad Ayat Hidayat, Yugo Nakamura, Yutaka Arakawa

Enhancing Efficiency in Privacy-preserving Federated Learning for Healthcare : Adaptive Gaussian Clipping with DFT Aggregator Journal Article

In: IEEE Access, pp. 1-1, 2024.

Links | BibTeX

Yuki Nishi, Yugo Nakamura, Shogo Fukushima, Yutaka Arakawa

LightSub: Unobtrusive Subtitles with Reduced Information and Decreased Eye Movement Journal Article

In: Multimodal Technologies and Interaction, vol. 8, no. 6, 2024, ISSN: 2414-4088.

Abstract | Links | BibTeX

@article{mti8060051,

title = {LightSub: Unobtrusive Subtitles with Reduced Information and Decreased Eye Movement},

author = {Yuki Nishi and Yugo Nakamura and Shogo Fukushima and Yutaka Arakawa},

url = {https://www.mdpi.com/2414-4088/8/6/51},

doi = {10.3390/mti8060051},

issn = {2414-4088},

year  = {2024},

date = {2024-06-01},

journal = {Multimodal Technologies and Interaction},

volume = {8},

number = {6},

abstract = {Subtitles play a crucial role in facilitating the understanding of visual content when watching films and television programs. In this study, we propose a method for presenting subtitles in a way that considers cognitive load when viewing video content in a non-native language. Subtitles are generally displayed at the bottom of the screen, which causes frequent eye focus switching between subtitles and video, increasing the cognitive load. In our proposed method, we focused on the position, display time, and amount of information contained in the subtitles to reduce the cognitive load and to avoid disturbing the viewer’s concentration. We conducted two experiments to investigate the effects of our proposed subtitle method on gaze distribution, comprehension, and cognitive load during English-language video viewing. Twelve non-native English-speaking subjects participated in the first experiment. The results show that participants’ gazes were more focused around the center of the screen when using our proposed subtitles compared to regular subtitles. Comprehension levels recorded using LightSub were similar, but slightly inferior to those recorded using regular subtitles. However, it was confirmed that most of the participants were viewing the video with a higher cognitive load using the proposed subtitle method. In the second experiment, we investigated subtitles considering connected speech form in English with 18 non-native English speakers. The results revealed that the proposed method, considering connected speech form, demonstrated an improvement in cognitive load during video viewing but it remained higher than that of regular subtitles.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Yuichi Hattori, Yutaka Arakawa, Sozo Inoue

Evaluation of Functional Estimation Methods in IoT Devices at Intervals of a Few Seconds by Communication Traffic Analysis Journal Article

In: International Journal of Activity and Behavior Computing, vol. 2024, no. 1, pp. 5, 2024.

Links | BibTeX

Sopicha Stirapongsasuti, Francis Jerome Tiausas, Yugo Nakamura, Keiichi Yasumoto

Preserving Data Utility in Differentially Private Smart Home Data Journal Article

In: IEEE Access, vol. 12, pp. 56571-56581, 2024, ISBN: 2169-3536.

Links | BibTeX

Shaowen Peng, Kazunari Sugiyama, Tsunenori Mine

Less is More: Removing Redundancy of Graph Convolutional Networks for Recommendation Journal Article

In: Transactions on Information Systems, vol. 42, iss. 3, no. 85, pp. 1-26, 2024.

Abstract | Links | BibTeX

@article{transactions2024-peng,

title = {Less is More: Removing Redundancy of Graph Convolutional Networks for Recommendation},

author = {Shaowen Peng, Kazunari Sugiyama, Tsunenori Mine},

doi = {10.1145/3632751},

year  = {2024},

date = {2024-01-22},

urldate = {2024-01-22},

journal = {Transactions on Information Systems},

volume = {42},

number = {85},

issue = {3},

pages = {1-26},

abstract = {While Graph Convolutional Networks (GCNs) have shown great potential in recommender systems and collaborative filtering (CF), they suffer from expensive computational complexity and poor scalability. On top of that, recent works mostly combine GCNs with other advanced algorithms which further sacrifice model efficiency and scalability. In this work, we unveil the redundancy of existing GCN-based methods in three aspects: (1) Feature redundancy. By reviewing GCNs from a spectral perspective, we show that most spectral graph features are noisy for recommendation, while stacking graph convolution layers can suppress but cannot completely remove the noisy features, which we mostly summarize from our previous work; (2) Structure redundancy. By providing a deep insight into how user/item representations are generated, we show that what makes them distinctive lies in the spectral graph features, while the core idea of GCNs (i.e., neighborhood aggregation) is not the reason making GCNs effective; and (3) Distribution redundancy. Following observations from (1), we further show that the number of required spectral features is closely related to the spectral distribution, where important information tends to be concentrated in more (fewer) spectral features on a flatter (sharper) distribution. To make important information be concentrated in as few features as possible, we sharpen the spectral distribution by increasing the node similarity without changing the original data, thereby reducing the computational cost. To remove these three kinds of redundancies, we propose a Simplified Graph Denoising Encoder (SGDE) only exploiting the top-K singular vectors without explicitly aggregating neighborhood, which significantly reduces the complexity of GCN-based methods. We further propose a scalable contrastive learning framework to alleviate data sparsity and to boost model robustness and generalization, leading to significant improvement. Extensive experiments on three real-world datasets show that our proposed SGDE not only achieves state-of-the-art but also shows higher scalability and efficiency than our previously proposed GDE as well as traditional and GCN-based CF methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

While Graph Convolutional Networks (GCNs) have shown great potential in recommender systems and collaborative filtering (CF), they suffer from expensive computational complexity and poor scalability. On top of that, recent works mostly combine GCNs with other advanced algorithms which further sacrifice model efficiency and scalability. In this work, we unveil the redundancy of existing GCN-based methods in three aspects: (1) Feature redundancy. By reviewing GCNs from a spectral perspective, we show that most spectral graph features are noisy for recommendation, while stacking graph convolution layers can suppress but cannot completely remove the noisy features, which we mostly summarize from our previous work; (2) Structure redundancy. By providing a deep insight into how user/item representations are generated, we show that what makes them distinctive lies in the spectral graph features, while the core idea of GCNs (i.e., neighborhood aggregation) is not the reason making GCNs effective; and (3) Distribution redundancy. Following observations from (1), we further show that the number of required spectral features is closely related to the spectral distribution, where important information tends to be concentrated in more (fewer) spectral features on a flatter (sharper) distribution. To make important information be concentrated in as few features as possible, we sharpen the spectral distribution by increasing the node similarity without changing the original data, thereby reducing the computational cost. To remove these three kinds of redundancies, we propose a Simplified Graph Denoising Encoder (SGDE) only exploiting the top-K singular vectors without explicitly aggregating neighborhood, which significantly reduces the complexity of GCN-based methods. We further propose a scalable contrastive learning framework to alleviate data sparsity and to boost model robustness and generalization, leading to significant improvement. Extensive experiments on three real-world datasets show that our proposed SGDE not only achieves state-of-the-art but also shows higher scalability and efficiency than our previously proposed GDE as well as traditional and GCN-based CF methods.

国際会議

23.

Shusaku Kita, Tsunenori Mine, Yutaka Arakawa

Examine the Filter Bubble with a Focus on Emotion and Content Proceedings Article

In: 2024 8th International Conference on Natural Language Processing and Information Retrieval , 2024.

BibTeX

22.

Toshihiko Sakai, Nobuhiko Chiwata, Tsunenori Mine

Named Entity Recognition from Materials Science Patent Documents Using Clue Word Tags Proceedings Article

In: 2024 8th International Conference on Natural Language Processing and Information Retrieval , 2024.

BibTeX

21.

Takuya Matsumoto,Kenta Abe,Tsunenori mine

Identification of Meta-Cognitive Behavior in Trainees’ Reflection Reports using Contrastive Learning Proceedings Article

In: 2024 8th International Conference on Natural Language Processing and Information Retrieval, 2024.

BibTeX

20.

Abe Kenta, Takuya Matsumoto, Tsunenori Mine

Identifying cognitive evaluation statements in reflection documents on the PDCA cycle Proceedings Article

In: 2024 8th International Conference on Natural Language Processing and Information Retrieval, 2024.

BibTeX

19.

Sho Futamura,Isshin Nakao,Shusaku Kita,Ryusei Fujimoto,Tsunenori Mine

iTOChat2024: Development and Evaluation of a Department Recommendation Bot for Open Campus Proceedings Article

In: 2024 International Conference on Intelligent Computing and Its Emerging Application (ICEA 2024), 2024.

BibTeX

18.

Bin Chen, Yugo Nakamura, Shogo Fukushima, Yutaka Arakawa

Generating Spatially-Aware Dense Video Captions for Indoor Human Behavior Analysis with Position-Based Scene Knowledge Proceedings Article

In: 2024 International Conference on Intelligent Computing and Its Emerging Application (ICEA 2024), 2024.

BibTeX

17.

Wataru Aiura, Masanori Yamada, Shogo Fukushima

The Development and Formative Evaluation of an AR-Based Picture-Book Storytelling Training System Proceedings Article

In: eLearn2024, 2024.

BibTeX

16.

Akihisa Takiguchi, Tsunenori Mine, Yutaka Arakawa

Investigation of the Diversity of News Reading Articles and Browsing Trends Using Sentence-BERT Proceedings Article

In: 16th International Congress on Advanced Applied Informatics (IIAI AAI 2024), 2024.

Links | BibTeX

15.

Kensuke Taninaka, Yoshiki Furuya, Keigo Sakamoto, Tsunenori Mine, Shogo Fukushima

Analyzing User Behavior in Scenario-based Virtual Reality Environments using Knowledge Graphs Proceedings Article

In: 16th International Congress on Advanced Applied Informatics (IIAI AAI 2024), 2024.

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14.

Ryosuke Takizawa, Isshin Nakao, Kensuke Taninaka, Akihisa Takiguchi, Toshiki Hayashida, Hiromu Motomatsu, Yutaka Arakawa

Development of a Communication Analysis System for Detecting Isolated Users in Slack Proceedings Article

In: 16th International Congress on Advanced Applied Informatics (IIAI AAI 2024), 2024.

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13.

Hyuckjin Choi, Yugo Nakamura, Shogo Fukushima, Yutaka Arakawa

Poster: Desk Activity Recognition Using On-desk Low-cost WiFi Proceedings Article

In: The 22nd ACM International Conference on Mobile Systems, Applications, and Services (MobiSys2024), 2024.

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12.

Kiichiro Kai, Hyuckjin Choi, Yugo Nakamura, Yutaka Arakawa

Poster: Annotation Assist System Using Backscatter Tags for WiFi CSI-based Indoor Activity Recognition Proceedings Article

In: The 22nd ACM International Conference on Mobile Systems, Applications, and Services (MobiSys2024), 2024.

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11.

Muhammad Ayat Hidayat, Yugo Nakamura, Yutaka Arakawa

Demo: Privacy-Preserving Decentralized Machine Learning Framework for Clustered Resource-Constrained Devices Proceedings Article

In: The 22nd ACM International Conference on Mobile Systems, Applications, and Services (MobiSys2024), 2024.

Links | BibTeX

10.

Koushi Hiraoka,Yugo Nakamura,Yutaka Arakawa

Automatic Annotation Method for Human Activity Recognition Using Wearable Devices with Micro-Camera Proceedings Article

In: ASSET Symposium in conjunction with ACM MobiSys2024, 2024.

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Lee Liu Cheng, Yutaka Arakawa

Improving English Teaching using Deep Directed Graph Knowledge Tracking LLM Models Proceedings Article

In: ASSET Symposium in conjunction with ACM MobiSys2024, 2024.

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Chen Bin, Yutaka Arakawa

RAG-Enhanced Dense Video Captioning For Indoor Human Action with Environment Information Proceedings Article

In: ASSET Symposium in conjunction with ACM MobiSys2024, 2024.

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Toshiki Hayashida, Yugo Nakamura, Hyuckjin Choi, Shogo Fukushima, Yutaka Arakawa

Estimating Psychological States during Communication through Non-invasive Multimodal Sensing Methods Proceedings Article

In: ASSET Symposium in conjunction with ACM MobiSys2024 , 2024.

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Lixin Pang, Jianyu Huang, Yutaka Arakawa

A Case-based Reward Function Design for Enhanced Performance in Reinforcement Learning-based Pure Pursuit Path Tracking Proceedings Article

In: ASSET Symposium in conjunction with ACM MobiSys2024, 2024.

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Yugo Nakamura, Yutaka Arakawa

Color Psychology-Based Persuasive Interaction Design for Health Behavior Change Proceedings Article

In: The 19th International Conference on Persuasive Technology 2024, 2024.

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Toshiki Hayashida, Yugo Nakamura, Hyuckjin Choi, Yutaka Arakawa

Privacy-aware Quantitative Measurement of Psychological State in Meetings based on Non-verbal Cues Proceedings Article

In: 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2024.

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Naoki Eguchi, Hyuckjin Choi, Yugo Nakamura, Shogo Fukushima, Yutaka Arakawa

Design and Implementation of Persuasive Public Wi-Fi to Derive Prosocial Network Usage Proceedings Article

In: The 38th International Conference on Information Networking, 2024.

Links | BibTeX

Bin Chen, Yugo Nakamura, Shogo Fukushima, Yutaka Arakawa

A Space Information-Enhanced Dense Video Caption for Indoor Human Action Recognition Proceedings Article

In: The 8th International Conference on Robotics, Control and Automation (ICRCA 2024), 2024.

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Jianyu Huang, Yutaka Arakawa

Faster Trajectory Planning for Lane Change Scenarios with Dynamic Environment Proceedings Article

In: The 8th International Conference on Robotics, Control and Automation (ICRCA 2024), 2024.

Links | BibTeX

研究会・シンポジウム

40.

平岡滉司, 中村優吾, 荒川豊

ウェアラブルカメラを用いたプライバシアウェアな行動認識のためのエッジVLMの適用可能性の検討 Conference

第84回ユビキタスコンピューティングシステム（UBI）研究発表会, 2024.

BibTeX

39.

羽田野武蔵, 藤本隆晟, 中村優吾, 荒川豊

LiteAAE: ウェアラブル行動認識におけるプライバシ保護とリソース効率化のための符号変調を組み合わせた匿名化オートエンコーダの提案 Conference

第84回ユビキタスコンピューティングシステム（UBI）研究発表会, 2024.

BibTeX

38.

福田航, 荒川豊

[WiP] スマートホームにおける複数IoT機器の自動連携アーキテクチャの検討 Conference

第113回モバイルコンピューティングと新社会システム・第99回高度交通システムとスマートコミュニティ合同研究発表会, 2024.

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37.

平岡滉司, 中村優吾, 荒川豊

[デモ] 小型ウェアラブルカメラとエッジ検出によるプライバシーアウェアな行動認識システムの提案 Conference

第32回マルチメディア通信と分散処理ワークショップ (DPSWS 2024), 2024.

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36.

片桐凜太郎,中村優吾,荒川豊

[デモ] バランスボールに取り付けたピン型磁石とスマートフォンを用いた初心者向けトレーニングの評価 Conference

第32回マルチメディア通信と分散処理ワークショップ (DPSWS 2024), 2024.

BibTeX

35.

Lixin Pang, Jianyu Huang, Yutaka Arakawa

A Case-based Reward Function Design for Reinforcement Learning-based Pure Pursuit Hybrid Controller Conference

第32回マルチメディア通信と分散処理ワークショップ (DPSWS 2024), 2024.

BibTeX

34.

曹蓮, 池田直樹, 柏本幸俊, 多屋優人, 上坂大輔, 須藤路真, 西優己, 中村優吾, 荒川豊

チャルディーニの法則と報酬の説得方略を用いた向社会的行動の促進効果の検証 Conference

第32回マルチメディア通信と分散処理ワークショップ (DPSWS 2024), 2024.

BibTeX

33.

平岡滉司，中村優吾，荒川豊

小型ウェアラブルカメラを用いたプライバシーアウェアな行動認識の実現に向けた基礎調査 Conference

第83回ユビキタスコンピューティングシステム(UBI)研究会, 2024.

Links | BibTeX

32.

片桐凜太郎，中村優吾，荒川豊

磁気センサを用いたバランスボールトレーニングの評価手法に関する提案と実装 Conference

第112回モバイルコンピューティングと新社会システム(MBL)研究会, 2024.

Links | BibTeX

31.

谷中健介，中村優吾，崔赫秦，福嶋政期

歩行時の前庭動眼反射が光学透過型HMD上のテキスト可読性に与える影響 Conference

第29回日本バーチャルリアリティ学会大会, 2024.

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30.

森登志樹，古谷佳輝，崔赫秦，中村優吾，ハウタサーリアリ，福嶋政期

漫符を顔の周りに提示する椅子型空中像インタフェースの基礎検討 Conference

第29回日本バーチャルリアリティ学会大会, 2024.

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29.

本田祐大，谷澤健太，正井克俊，中村優吾，崔赫秦，福嶋政期

香り体験を言葉に接地させた単語学習法の検討 Conference

第29回日本バーチャルリアリティ学会大会 , 2024.

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28.

古谷佳輝，矢作優知，菊池知世，余合彩子，清川清，福嶋政期

仰臥位でのVR体験向け非装着型空中浮遊HMDの基礎検討 Conference

第29回日本バーチャルリアリティ学会大会 , 2024.

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27.

喜田周作，峯恒憲，荒川豊

ニュースサイトにおけるフィルターバブル状態の事前検知モデルの検討 Conference

第21回テキストアナリティクス・シンポジウム, 2024.

Links | BibTeX

26.

清瀬一志，福嶋政期，荒川豊

対話型進化計算法を使用したドラムワンショット音源選択支援システム Conference

第141回音楽情報科学研究発表会, 2024.

Links | BibTeX

25.

甲斐貴一朗,崔赫秦,中村優吾, 荒川豊

WiFi CSIを用いた屋内行動認識のためのバックスキャッタータグを用いたアノテーション手法の検討 Conference