Kyushu University Academic Staff Educational and Research Activities Database
List of Presentations
Daiki Suehiro Last modified date:2019.06.17

Assistant Professor / Practical Data Science / Department of Advanced Information Technology / Faculty of Information Science and Electrical Engineering


Presentations
1. Heon Song, Daiki Suehiro, Seiichi Uchida, Online People-flow Prediction, 電気・情報関係学会九州支部連合大会, 2018.09.
2. Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, Akiko Takeda, Learning theory and algorithms for shapelets and other local features, NIPS Time Series Workshop 2017,, 2017.12.
3. Daiki Suehiro, kohei hatano, Eiji Takimoto, Approximate reduction from AUC maximization to 1-norm soft margin optimization, 22nd International Conference on Algorithmic Learning Theory, ALT 2011, 2011.10, Finding linear classifiers that maximize AUC scores is important in ranking research. This is naturally formulated as a 1-norm hard/soft margin optimization problem over pn pairs of p positive and n negative instances. However, directly solving the optimization problems is impractical since the problem size (pn) is quadratically larger than the given sample size (p+n). In this paper, we give (approximate) reductions from the problems to hard/soft margin optimization problems of linear size. First, for the hard margin case, we show that the problem is reduced to a hard margin optimization problem over p+n instances in which the bias constant term is to be optimized. Then, for the soft margin case, we show that the problem is approximately reduced to a soft margin optimization problem over p+n instances for which the resulting linear classifier is guaranteed to have a certain margin over pairs..
4. Daiki Suehiro, kohei hatano, Shuji Kijima, Eiji Takimoto, Kiyohito Nagano, Online prediction under submodular constraints, 23rd International Conference on Algorithmic Learning Theory, ALT 2012, 2012.10, We consider an online prediction problem of combinatorial concepts where each combinatorial concept is represented as a vertex of a polyhedron described by a submodular function (base polyhedron). In general, there are exponentially many vertices in the base polyhedron. We propose polynomial time algorithms with regret bounds. In particular, for cardinality-based submodular functions, we give O(n 2)-time algorithms..
5. Xinyu Fu, Atsushi Shimada, Hiroaki Ogata, Yuta Taniguchi, Daiki Suehiro, Real-time learning analytics for C programming language courses, 7th International Conference on Learning Analytics and Knowledge, LAK 2017, 2017.03, Many universities choose the C programming language (C) as the first one they teach their students, early on in their program. However, students often consider programming courses difficult, and these courses often have among the highest dropout rates of computer science courses offered. It is therefore critical to provide more effective instruction to help students understand the syntax of C and prevent them losing interest in programming. In addition, homework and paper-based exams are still the main assessment methods in the majority of classrooms. It is difficult for teachers to grasp students' learning situation due to the large amount of evaluation work. To facilitate teaching and learning of C, in this article we propose a system-LAPLE (Learning Analytics in Programming Language Education)-that provides a learning dashboard to capture the behavior of students in the classroom and identify the different difficulties faced by different students looking at different knowledge. With LAPLE, teachers may better grasp students' learning situation in real time and better improve educational materials using analysis results. For their part, novice undergraduate programmers may use LAPLE to locate syntax errors in C and get recommendations from educational materials on how to fix them..
6. Yuta Taniguchi, Daiki Suehiro, Atsushi Shimada, Revealing Hidden Impression Topics in Students' Journals Based on Nonnegative Matrix Factorization, 17th IEEE International Conference on Advanced Learning Technologies, ICALT 2017, 2017.08, Students' reflective writings are useful not only for students themselves but also teachers. It is important for teachers to know which concepts were understood well by students and which concepts were not, to continuously improve their classes. However, it is difficult for teachers to thoroughly read the journals of more than one hundred students. In this paper, we propose a novel method to extract common topics and students' common impressions against them from students' journals. Weekly keywords are discovered from journals by scoring noun words with a measure based on TF-IDF term weighting scheme, and then we analyze co-occurrence relationships between extracted keywords and adjectives. We employs nonnegative matrix factorization, one of the topic modeling techniques, to discover the hidden impression topics from the co-occurrence relationships. As a case study, we applied our method on students' journals of the course 'Information Science' held in our university. Our experimental results show that conceptual keywords are successfully extracted, and four significant impression topics are identified. We conclude that our analysis method can be used to collectively understand the impressions of students from journal texts..
7. Daiki Suehiro, Yuta Taniguchi, Atsushi Shimada, Face-to-Face Teaching Analytics
Extracting Teaching Activities from E-Book Logs via Time-Series Analysis, 17th IEEE International Conference on Advanced Learning Technologies, ICALT 2017, 2017.08, To discover teaching knowledge efficiently, we must extract the various teaching activities from educational data. In this paper, through the use of e-book logs and techniques of time-series analysis, we describe a method of practicing teaching analytics in face-to-face classes, one which enable us to extract the teaching activity efficiently and accurately..