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2020 Fiscal Year Final Research Report

Enhancement of Integrated Environment for Computational Forensics Data Analysis and Visualization

Research Project

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Project/Area Number 17H00737
Research Category

Grant-in-Aid for Scientific Research (A)

Allocation TypeSingle-year Grants
Section一般
Research Field Multimedia database
Research InstitutionKeio University

Principal Investigator

Fujishiro Issei  慶應義塾大学, 理工学部(矢上), 教授 (00181347)

Co-Investigator(Kenkyū-buntansha) 清木 康  慶應義塾大学, 環境情報学部(藤沢), 教授 (10169956)
茅 暁陽  山梨大学, 大学院総合研究部, 教授 (20283195)
竹島 由里子  東京工科大学, メディア学部, 教授 (20313398)
安達 登  山梨大学, 大学院総合研究部, 教授 (60282125)
猩々 英紀  山梨大学, 大学院総合研究部, 准教授 (60284626)
豊浦 正広  山梨大学, 大学院総合研究部, 准教授 (80550780)
杉浦 篤志  山梨大学, 大学院総合研究部, 助教 (90755480)
Project Period (FY) 2017-04-01 – 2021-03-31
Keywords計算法科学 / 構造化文書 / 出自管理 / 分析可視化
Outline of Final Research Achievements

As a follow-up to the previous study JSPS KAKENHI under the Grants-in-Aid for Scientific Research (A) No. 26240015 (AY2014-AY2016), this study reformulated the LMML (legal medicine markup language) based on an extended application ontology of computational forensics that encompasses the entire lifecycle of stabbing incident forensics from autopsy to court. We also built on the extended LMML (e-LMML) to design the basic architecture of an integrated computational forensics platform, which is composed of FORESTI, which allows one to author and browse x-LMML documents with AR juxtaposition-based visual analytics functionalities; THEMIS, which takes advantage of the Mathematical Modeling of Meaning to standardize a context-sensitive visual similarity analysis for wound imagery described in X-LMML; and a designated repository with provenance management of x-LMML encoded precedents.

Free Research Field

情報科学

Academic Significance and Societal Importance of the Research Achievements

刑事裁判の本質は,様々な状況や物的証拠をもとに案件の核心を捉え,的確な量刑を決定することである.これを支援する統合計算法科学拡張基盤の基本アーキテクチャは,計算法科学のライフサイクル全体に渡る一連の文書処理の透明性を確保するとともに,文書間の導出過程を系統的に記述し,説明責任を与える出自管理機能と,量刑の妥当性に効果的な過去の事案の類似検索機能を提供している.一方,要素技術としての出自管理や,ARベース並置可視化,意味の数学モデル応用は,通常診療,標本展示,文化人類学発掘等,幅広い視覚情報探索問題の解決へ向けた利活用も可能である.

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Published: 2022-01-27  

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