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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

Unlike studies of the evolutionary relationship between languages, the dialect-level variation within a language has seldom been studied within the framework of a phylogenetic tree, because frequent lexical borrowing muddles the evidence of shared ancestry. The phonological history of Japanese is an exceptional case study where the phenomenon called accentual class merger enables the phylogenetic analysis of dialectal pitch-accent systems in a way that is not subject to borrowing. However, previous studies have lacked statistical analysis and failed to evaluate the relative credence of alternative hypotheses. Here we developed a novel substitution model that describes the mutation of pitch-accent systems driven by accentual class merger and integrated the model into the framework of Bayesian phylogenetic inference with geographical diffusion. Applying the method to data on the pitch-accent variation in modern Japanese dialects and historical documents collected from literature, we reconstructed the evolutionary history and spatial diffusion of pitch-accent systems. Our result supports the monophyly of each of three groups of pitch-accent systems in conventional categorization, namely Tokyo type, Keihan type, and N-kei (N-pattern) type of Kyushu, whereas the monophyly of the Tokyo type has been highly controversial in previous studies. The divergence time of the mainland pitch-accent systems was estimated to be from mid-Kofun to early Heian period. Also, it is suggested that the modern Kyoto dialect did not inherit its accent patterns from Bumoki but from an unrecorded lineage which survived from the Muromachi period. Analyses on geographical diffusion suggest that the most recent common ancestor (MRCA) of all the taxa and that of Keihan type were located in or around the Kinki region, whereas the MRCA of N-kei type was located in northern to central Kyushu. The geographical location of the MRCA of Tokyo type remains unclear, but the Kinki and Kanto regions are the most plausible candidates.

DOI： 10.1093/jole/lzae004

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Language：Japanese   Publishing type：Research paper (bulletin of university, research institution)   Publisher：Center for Statistics and Information  

This study explores the application of AI technology in the evolution of education, proposing a novel method for creating educational content using conversational AI models and AI voice technology. Education is essential for societal development and personal growth, and the accessibility and efficiency of education are critical challenges. This research focuses specifically on the efficient development of educational content using ChatGPT and VOICEPEAK. The technology significantly reduces the time and cost associated with creating educational content, with an expected improvement in the quality and accessibility of education. The significance of the on-demand nature of educational content is also discussed in detail, pointing to the potential for enhancing the flexibility of learning according to student needs, reducing teacher workload, and improving educational effectiveness through diversification of teaching methods. Furthermore, the process of creating educational content by combining conversational AI models and AI voice technology is elaborated, examining a practical approach and its effectiveness. This process includes recording face-to-face lessons, automatic transcription, text reconstruction by AI, AI voice conversion, and video editing. Overall, this research underscores the importance and potential of AI technology in the efficient creation of educational content, proposing a new direction for shaping the future of education. This technological innovation suggests the possibility of effectively utilizing educational resources, enabling the provision of flexible education tailored to learners.
Key words : Conversational AI Models, AI Voice Technology, Educational Content Creation, On-Demand Accessibility, Technological Innovation in Education

File： 2024対話型AI モデル並びにAI 音声を活用した教育コンテンツ作成―AI 技術の新たな展開―.pdf

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Language：Japanese  

J-GLOBAL

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Authorship：Lead author,　Corresponding author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Language change is affected by various factors such as environmental and social factors. In this study, a linear mixed model was applied to estimate that the gain or loss rate of language
change which associated with various factors in local area. This result indicates that rates of gain or loss words are lower in larger population area that is, the large population cause hardly language
change This result showed opposing t endency to previous studies. So it's concluded that it is important to consider about the time and space scale.

File： P40.pdf

J-GLOBAL

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Authorship：Lead author   Language：Japanese   Publishing type：Doctoral thesis  

言語変化の原因については、比較言語学や言語地理学双方の立場から、様々な議論がなされてきたが、この問題に対し、変化の原因と変化の結果という関係を多変量的に捉えて知見を得る方法は、十分な議論があるとはいえない。そこで本研究では、これまでの言語変化の議論を踏まえつつ、具体的な事例について、系統推定や重回帰分析、対応分析といった数理的な手法を用いて多変量的な分析を行い、言語変化の原因や原因別の変化の特徴を明らかにした。

DOI： 10.14988/di.2017.0000016166

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Language：Japanese  

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Language：Japanese   Publisher：The association of japanese geographers  

In <i>Chiri Sougou</i> (Geography), which has become a compulsory subject in high schools and one of the pillars is the use of GIS. In addition, the importance of geography in data science sector is increasing. In this presentation, we report on Geography practice contents using Python for promoting GIS in Geography and data science methods. We used Google Colaboratory for practice. A topic of contents was selected from the textbook of the Geography. The contents are published in Interface magazine. For utilizing the content in education, teachers can obtain the digital version of the content as a free.

DOI： 10.14866/ajg.2023s.0_133

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Language：English   Publishing type：Research paper, summary (international conference)  

DOI： 10.17617/2.3398549

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Language：English   Publisher：Copernicus GmbH  

Abstract. Currently, several types of geospatial data are provided as open data and/or map tile data. This implies that geospatial data have become more barrier-free than older data with traditional licenses and formats. In this study, we used open geospatial data to develop a client-side web mapping system for assessment of site suitability for vineyards. As the research area for testing the system, we selected Nagano Prefecture, where many farmers recently started viticulture. The data used in the system are geology, soil, slope gradient, slope aspect, annual mean temperature, and annual maximum and minimum temperatures. These data were converted to a Grid PNG tile, which was developed by The Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology. FOSS4G tools, such as QGIS, TileMill, and MBUtil were used to prepare Grid PNG tile data. Map algebra functions were implemented using the WebGL. Web mapping systems developed in this research do not require server-side systems and/or middleware and can be operated using only a web browser. This means that various entities can be operated on the same system at low cost, or on a free web service such as GitHub pages.

DOI： 10.5194/isprs-archives-xlviii-4-w1-2022-223-2022

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Language：English   Publisher：FOSS4G  

DOI： 10.7275/R5PN93TQ

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Total pages：6  

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Event date： 15 12 2024 - 18 12 2024

Language：English   Presentation type：Oral presentation (general)  

Venue：Bangkok  

In recent years, the availability of geospatial data has significantly increased, with aerial photographs and Digital Elevation Models (DEMs) becoming widely accessible as open data. Additionally, the acquisition of high-resolution image data through drones has become more feasible and commonplace. However, extracting meaningful information from these vast datasets remains a labor-intensive process, often requiring significant time and resources.

While various deep learning techniques have been employed to address this challenge, they typically demand extensive effort in collecting and preparing training data. In light of these constraints, the Segment Anything Model (SAM) has emerged as a promising solution. SAM, a recent development in the field of fundamental models, offers the advantage of zero-shot classification without the need for specific training. Moreover, its Apache-2.0 license ensures accessibility for a wide range of applications.

This presentation aims to demonstrate the potential of SAM in the realm of geospatial information processing. We will showcase practical applications of SAM in analyzing geospatial data, with a focus on two critical areas: landslide detection and forest canopy mapping. These case studies will illustrate how SAM can efficiently process and extract valuable insights from complex geospatial datasets, potentially enhancing the efficiency and effectiveness of our approach to environmental monitoring and disaster risk assessment.

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Event date： 7 12 2024 - 8 12 2024

Language：Japanese   Presentation type：Poster presentation  

Venue：広島  

本研究は、ヒトがニッチ構築した環境と⾳声に含まれる感情との関係を解明することを⽬的とする。具体的には、⾳声感情分析ソフトウェア（Open Smile など）を⽤いて実験参加者の発声から感情を分析し、その結果に基づき、場所に関する⼝コミ情報（Yahoo!ローカルサーチ API など）を活⽤して、感情に基づく最適な場所を推奨するシステムを構築する。参加者には提案された場所が⾃⾝の感情状態に適しているかどうかを評価してもらい、その評価結果に基づき感情と環境の関係性を明らかにする。また、Odling-Smee らのニッチ構築理論に基づき、ニッチ構築環境がネガティブな感情反応を引き起こさないことも検証する予定である。本発表を通じて、研究⽅法の妥当性について議論を深めたい。

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Event date： 28 10 2023 - 29 10 2023

Language：Japanese   Presentation type：Poster presentation  

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Event date： 25 3 2023 - 27 3 2023

Language：Japanese   Presentation type：Poster presentation  

File： AJG2023_20230323.pdf

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Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

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Type：Academic society, research group, etc. 

本セミナーでは、Google colaboratoryを用いてe-statのAPIの 利用方法について解説します。今回は地理情報データを扱います が、その他のデータにも利用できる普遍性の高い方法です。API を用いることで、これまで取り扱いが難しかった大量のデータを 簡単に処理できるようになります。 また、セミナーではGoogle colaboratoryの記述言語である Pythonに触れるため、より高度なデータ分析や機械学習への架け 橋となるような知識を習得することが可能です。

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Type：Academic research 

調査人数
108人（男：55人、女：53人）
13歳～34歳：38人(男 : 女 = 21 : 17)
35歳～57歳：35人(男 : 女 = 14 : 21)
56歳～：35人(男 : 女 = 20 : 15)

柏原地区全体の0.06%(柏原総人口1918人)

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Type：Academic research 

調査人数18人

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Type：Academic research 

地元の市民グループ「はびろネット」が調査主体。分析協力。調査人数873人（対象地域全人口の 4.27％）。

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Type：Academic society, research group, etc. 

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Audience： General

Type：Citizen’s meeting/Assembly

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Audience： General

Type：Investigation, survey

pp. 1-31

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Audience： Governmental agency

Type：Other

pp. 1-34

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『人文情報学月報』 第14号

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『人文情報学月報』 第13号（後編）

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『人文情報学月報』 第6号

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