Details of a Researcher

LU MIN

写真a

Affiliation	Graduate School of Engineering Science Department of Mathematical Science and Electrical-Electronic-Computer Engineering Human-Centered Computing Course
Date of Birth	1986

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Research Interests 【 display / non-display 】

地図
地図学
地理情報システム
学習支援システム
Geographic Information Science

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Graduating School 【 display / non-display 】

2004.09

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2008.07

Peking University School of Space and Earth Sciences Geographic Information System Graduated

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Graduate School 【 display / non-display 】

2011.10

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2014.09

The University of Tokyo Graduate School of Frontier Sciences Socio-Cultural Environmental Studies Doctor's Course Completed
2008.09

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2011.07

Peking University School of Space and Earth Sciences Cartology and Geographyic Information System Master's Course Completed

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Campus Career 【 display / non-display 】

2022.09

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Now

Akita University Graduate School of Engineering Science Department of Mathematical Science and Electrical-Electronic-Computer Engineering Human-Centered Computing Course Assistant Professor

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Research Areas 【 display / non-display 】

Natural Science / Human geosciences
Humanities & Social Sciences / Geography
Informatics / Learning support system
Informatics / Human interface and interaction

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Thesis for a degree 【 display / non-display 】

Human-Centered Mapping in Mobile Environments

Min Lu

2014.09 [Refereed]

Single author

DOI

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Research Achievements 【 display / non-display 】

◆Original paper【 display / non-display 】

Cross-language font style transfer

Chenhao Li, Yuta Taniguchi, Min Lu, Shin’ichi Konomi, Hajime Nagahara

Applied Intelligence ( Springer Science and Business Media LLC ) 2023.02 [Refereed]

Research paper (journal)

Abstract

In this paper, we propose a cross-language font style transfer system that can synthesize a new font by observing only a few samples from another language. Automatic font synthesis is a challenging task and has attracted much research interest. Most previous works addressed this problem by transferring the style of the given subset to the content of unseen ones. Nevertheless, they only focused on the font style transfer in the same language. In many cases, we need to learn font style from one language and then apply it to other languages. Existing methods make this difficult to accomplish because of the abstraction of style and language differences. To address this problem, we specifically designed the network into a multi-level attention form to capture both local and global features of the font style. To validate the generative ability of our model, we constructed an experimental font dataset of 847 fonts, each containing English and Chinese characters with the same style. Results show that our model generates 80.3% of users’ preferred images compared with state-of-the-art models.

DOI

Proposal and Demonstration Experiment of a Simple Framework for Realizing Indoor AR Navigation

Masatoshi Arikawa, Kohei Oba, Shinpei Ito, Ryo Sato, Min Lu

63 ( 12 ) 1821 - 1829 2022.12 [Refereed]

Research paper (journal) Domestic Co-author

DOI

Exploring jump back behavior patterns and reasons in e-book system

Boxuan Ma, Min Lu, Yuta Taniguchi, Shin’ichi Konomi

Smart Learning Environments ( Springer Science and Business Media LLC ) 9 ( 2 ) 1 - 23 2022.01 [Refereed]

Research paper (journal) Domestic Co-author

With the increasing use of digital learning materials in higher education, the accumulated operational log data provide a unique opportunity to analyzing student learning behaviors and their effects on student learning performance to understand how students learn with e-books. Among the students’ reading behaviors interacting with e-book systems, we find that jump-back is a frequent and informative behavior type. In this paper, we aim to understand the student’s intention for a jump-back using user learning log data on the e-book materials of a course in our university. We at first formally define the “jump-back” behaviors that can be detected from the click event stream of slide reading and then systematically study the behaviors from different perspectives on the e-book event stream data. Finally, by sampling 22 learning materials, we identify six reading activity patterns that can explain jump backs. Our analysis provides an approach to enriching the understanding of e-book learning behaviors and informs design implications for e-book systems.

DOI

CourseQ: the impact of visual and interactive course recommendation in university environments

Boxuan Ma, Min Lu, Yuta Taniguchi, Shin’ichi Konomi

Research and Practice in Technology Enhanced Learning ( Springer ) 16 ( 1 ) 1 - 24 2021.12 [Refereed]

Research paper (journal) Domestic Co-author

The abundance of courses available in a university often overwhelms students as they must select courses that are relevant to their academic interests and satisfy their requirements. A large number of existing studies in course recommendation systems focus on the accuracy of prediction to show students the most relevant courses with little consideration on interactivity and user perception. However, recent work has highlighted the importance of user-perceived aspects of recommendation systems, such as transparency, controllability, and user satisfaction. This paper introduces CourseQ, an interactive course recommendation system that allows students to explore courses by using a novel visual interface so as to improve transparency and user satisfaction of course recommendations. We describe the design concepts, interactions, and algorithm of the proposed system. A within-subject user study (N=32) was conducted to evaluate our system compared to a baseline interface without the proposed interactive visualization. The evaluation results show that our system improves many user-centric metrics including user acceptance and understanding of the recommendation results. Furthermore, our analysis of user interaction behaviors in the system indicates that CourseQ could help different users with their course-seeking tasks. Our results and discussions highlight the impact of visual and interactive features in course recommendation systems and inform the design of future recommendation systems for higher education.

DOI

Investigating course choice motivations in university environments

Boxuan Ma, Min Lu, Yuta Taniguchi, Shin’ichi Konomi

Smart Learning Environments ( Springer Science and Business Media LLC ) 8 ( 31 ) 1 - 18 2021.11 [Refereed]

Research paper (journal) Domestic Co-author

<title>Abstract</title>Recommendation systems need a deeper understanding of users and their motivations to improve recommendation quality and provide more personalized suggestions. This is especially true in the education domain, the more about the student is known, the more useful recommendations can be made. However, although many studies on the course recommendation exist, studies on the students’ course selection motivations in universities are limited. This study investigates the factors that contribute to students’ choice when selecting courses in universities to better understand student perceptions, attitudes, and needs and leverage data-driven approaches for recommending and explaining the recommendations in university environments. A qualitative interview for university students (N = 10) comprised of open-ended questions as well as a questionnaire for students (N = 81) was conducted, aiming to investigate the main reasons behind their choices. The results of this study show that students highly value the course contents and the benefits of the course towards their future careers. Furthermore, students are influenced by other reasons such as the possibility of obtaining a higher grade, the popularity of professors, and recommendations from peers. Next, we extract the main categories of students’ motivations and analyzed the questionnaire data by employing statistical analysis methods as well as the k-means clustering algorithm to identify different types of students in terms of course selection. Based on our findings, we discuss implications for designing more personalized course recommendation systems.

DOI

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◆International conference proceedings【 display / non-display 】

Adaptive visualization of tourists' preferred spots and streets using trajectory articulation

Iori Sasaki, Masatoshi Arikawa, Min Lu

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility ( ACM ) 27 - 32 2022.11 [Refereed]