| 前田 泰一 | M, 1回目発表 | サイバネティクス・リアリティ工学 | 清川 清, 中村 哲, 内山 英昭, Perusquia Hernandez Monica, 平尾 悠太朗 |
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title: Investigation of Independent Tactile Stimulus Placement for Interfinger Tactile Synthesis
abstract: In physiology, the sense of touch is considered to be composed of five senses: pressure, vibration, temperature, cold, and pain. The concept of "Touch Blend" was proposed to generate a synthetic sense of touch by presenting these five tactile sensations at the same location, but no research has been conducted to synthesize tactile sensations by presenting different tactile sensations at two different locations. In this study, we attempted to present synthetic tactile sensation to the middle finger by presenting different tactile sensations to the index and ring fingers. language of the presentation: Japanese 発表題目: 指間触覚合成に向けた独立触覚刺激配置の検討 発表概要: 生理学において、触覚は圧覚・振動覚・温覚・冷覚・痛覚の5つによって構成されるとされている。この5つの触覚を同箇所に提示をして合成触覚を生起させるTouch Blendという考え方が提唱されたが、２点の異なる位置に異なる触覚を提示することで触覚を合成させる研究は行われていない。本研究では人差し指と薬指に異なる触覚を提示することで、中指に合成触覚を提示させることを試みる。 | |||
| FERREIRA DA SILVA LUCAS | M, 1回目発表 | 光メディアインタフェース | 向川 康博, 和田 隆広, 舩冨 卓哉, 藤村 友貴, 北野 和哉 |
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title: Transparent Scene Reconstruction with Neural Radiance Fields and Transient Data
abstract: 3D reconstruction of complex scenes is crucial for the advancement of imaging technologies like LiDAR and microscopy. This research aims to enhance the accuracy of 3D scene reconstruction in the presence of transparent materials. Current imaging methods struggle with such materials due to the way light bends when interacting with refractive objects (e.g., glass or water). This study leverages transient data captured from Single Photon Avalanche Diodes (SPAD) sensors combined with Neural Radiance Fields (NeRF) to refine the reconstruction process. This method is expected to yield better 3D images and contribute to the creation of a high-quality dataset. language of the presentation: English | |||
| 川端 祐也 | M, 1回目発表 | ソフトウェア工学 | 松本 健一, 笠原 正治, Raula Gaikovina Kula, 嶋利 一真 |
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title: Issue-based SATD and Comment Inconsistency Detection Methodology
abstract: Self-admitted technical debt (SATD) is technical debt that developers themselves are aware of. SATD that is mentioned in code comments is called SATD comment (SATD-C), and SATD that is mentioned in the Issue Tracking System is called issue-based SATD (SATD-I). Previous studies have shown that 29% of SATD-I is tracked with SATD-C. Normally, these SATDs should be consistent with the source code, but developers sometimes neglect or forget to delete SATD-C or SATD-I even though they have modified the code, resulting in a divergence in their relationship. In this study, we propose a method to detect and notify when these SATD-C, SATD-I, and codes are inconsistent. Based on previous work on comment and code consistency using machine learning, we extend this method to SATD-I consistency. language of the presentation: Japanese 発表題目: Issueに基づいたSATDとコメントの非一貫性を検出する手法 発表概要: Self-admitted technical debt (SATD) は，開発者自身が自覚している技術的負債のことである．SATDの中でもコードコメントとして言及しているSATDをSATD comment (SATD-C) ，Issue Tracking Systemの中で言及されるSATDをissue-based SATD (SATD-I) という．SATD-Iの内29%はSATD-Cに関連付けできていることが先行研究でわかっている．通常，これらのSATDとソースコードは一貫性が保たれるべきであるが，開発者はコードを修正したのにも関わらずSATD-CやSATD-Iの削除を怠ったり，消し忘れたりすることによってそれらの関係に乖離が生じてしまうことがある．本研究では，これらのSATD-C，SATD-I，コードの一貫性が欠如した時に検出し通知する手法を提案する．機械学習を用いたコメントとコードの一貫性に関する先行手法に基づいて，この手法をSATD-Iとの一貫性にも拡張する． | |||
| MOHALI MARCANO ANDRES MANUEL | M, 1回目発表 | 数理情報学 | 池田 和司, 松本 健一, 久保 孝富, 日永田 智絵 |
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title: Behavioral Analysis of Mice with Disomy Deletion of Chromosome 15: Base for Pharmacological Approach
abstract: Disomy deletion of chromosome 15 is known to induce metabolic and endocrine abnormalities, along with psychiatric disorders, especially autism spectrum disorders. While short-term tests in mouse models have suggested differences in social behavior within minutes, the long-term behavior in social group settings remains largely unexplored. This study employs established concepts from mice isolation studies to analyze the impact of disomy deletion on social behavior over extended periods. Utilizing DeepLabCut (DLC), an AI-oriented tool for pose estimation, we aim to assess these behavioral changes. The results obtained will be used to evaluate the potential restoration of mouse behavioral phenotypes through genetic pharmacology or optogenetics approaches in the future. language of the presentation: English | |||