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Graduate School of Informatics and Engineering,
The University of Electro-Communications
Japanese

International Conference

  • Shouta Sugahara, Koya Kato and Maomi Ueno: Learning Bayesian Network Classifiers to Minimize Class Variable Parameters. In the 38th AAAI Conference on Artificial Intelligence (AAAI 2024). (PDF)
  • Wakaba Kishida, Kazuma Fuchimoto, Yoshimitsu Miyazawa and Maomi Ueno: Item difficulty constrained uniform adaptive testing. Artificial Intelligence in Education – 24th International Conference, AIED 2023 (PDF)
  • Shouta Sugahara, Wakaba Kishida, Koya Kato, Ueno Maomi: Recursive autonomy identification-based learning of augmented naive Bayes classifiers, Proceedings of the 11th International Conference on Probabilistic Graphical Models (PGM),2022, volume 186, pages 265–276, 2022
  • Emiko Tsutsumi, Yiming Guo, Maomi Ueno: Deep knowledge tracing in corporating a hypernetwork with independent student and item networks, Proceedings of the 15th International Conference on Educational Data Mining (EDM), 2022
  • Maomi Ueno, Yoshimitsu Miyazawa: Two-Stage Uniform Adaptive Testing to Balance Measurement Accuracy and Item Exposure. Artificial Intelligence in Education – 23rd International Conference,　AIED (1) 2022, 626-632, 2022(PDF)
  • Maomi Ueno: AI based e-Testing as a common yardstick for measuring human abilities, 18th International Joint Conference on Computer Science and Software Engineering (JCSSE), IEEE, 2021(PDF)
  • Itsuki Aomi, Emiko Tsutsumi, Masaki Uto, Maomi Ueno: Integration of Automated Essay Scoring Models using Item Response Theory, Artificial Intelligence in Education – 22th International Conference, AIED 2021(2), 54-59, 2021 (PDF)
  • Emiko Tsutsumi, Ryo Kinoshita, Maomi Ueno: Deep-IRT with independent student and item networks, Proceedings of the 14th International Conference on Educational Data Mining (EDM), 2021 (PDF)
  • Shouta Sugahara, Itsuki Aomi, Maomi Ueno: Bayesian Network Model Averaging Classifiers by Subbagging, Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM2020), Proceedings of Machine Learning Research(PMLR) 138:461-472(PDF)
  • Masaki Uto, Yikuan Xie, Maomi Ueno:Neural Automated Essay Scoring Incorporating Handcrafted Features. COLING 2020:The 28th International Conference on Computational Linguistics, pp, 6077-6088(PDF)
  • Yoshimitsu Miyazawa, Maomi Ueno:Computerized Adaptive Testing Method Using Integer Programming to Minimize Item Exposure. JSAI 2019: Advances in Artificial Intelligence, Springer, pp 105-113, 2019
  • Maomi Ueno and Yoshimitsu Miyazawa： Uniform adaptive testing using maximum clique algorithm, Artificial Intelligence in Education – 20th International Conference, Lecture Notes in Artificial Intelligence, LNAI11625, AIED 2019, 482-493,(PDF)
  • Shouta Sugahara, Masaki Uto,Maomi Ueno: Exact learning augmented naïve Bayes classifier, International conference on Probabilistic Graphical Models, PGM 2018, Proceedings of Machine Learning Research (PMLR)72 , 439-450
  • Masaki Uto and Maomi Ueno: Item Response Theory Without Restriction of Equal Interval Scale for Rater’s Score, Artificial Intelligence in Education – 19th International Conference, AIED 2018(2), 363-368
  • Kazuki Natori, Masaki Uto and Maomi Ueno: Consistent Learning Bayesian Networks with Thousands of Variables, The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2017, Proceedings of Machine Learning Research (PMLR) 73:57-68
  • Takatoshi Ishii and Maomi Ueno: Algorithm for Uniform Test Assembly Using a Maximum Clique Problem and Integer Programming, Artificial Intelligence in Education – 18th International Conference, AIED 2017, 102-112
  • Masaki Uto, Nguyen Duc Thien and Maomi Ueno: Group Optimization to Maximize Peer Assessment Accuracy Using Item Response Theory, Artificial Intelligence in Education – 18th International Conference, AIED 2017, 393-405
  • Thien Nguyen, Masaki Uto, Yu Abe and Maomi Ueno: Reliable Peer Assessment for Team-project-based Learning using Item Response Theory, International Conference on Computers in Education, ICCE 2015, 144-153
  • Chao Li and Maomi Ueno: A Fast Clique Maintenance algorithm for Optimal Triangulation of Bayesian Networks, The 2nd Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015, LNAI 9505, 152-167
  • Kazuki Natori, Masaki Uto, Yu Nishiyama, Shuichi Kawano and Maomi Ueno: Constraint-based learning Bayesian networks using Bayes factor, The 2nd Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015, LNAI 9505, 15-31
  • Maomi Ueno and Yoshimitsu Miyasawa: Probability Based Scaffolding System with Fading, Artificial Intelligence in Education – 17th International Conference, AIED 2015, 237-246
  • Takatoshi Ishii and Maomi Ueno: Clique Algorithm to Minimize Item Exposure for Uniform Test Forms Assembly, Artificial Intelligence in Education – 17th International Conference, AIED 2015, 638-641
  • Masaki Uto andMaomi Ueno: Item Response Model with Lower Order Parameters for Peer Assessment, Artificial Intelligence in Education – 17th International Conference, AIED 2015, 800-803
  • Sébastien Louvigné, Yoshihiro Kato, Neil Rubens, Maomi Ueno:
    SNS Messages Recommendation for Learning Motivation, Artificial Intelligence in Education – 17th International Conference, AIED 2015, 237-246
  • Masaki Uto, Maomi Ueno: Academic Writing Support System Using Bayesian Networks, IEEE International Conference on Advanced Learning Technologies, ICALT 2015: 385-387
  • Sébastien Louvigné, Yoshihiro Kato, Neil Rubens,  Maomi Ueno:
    Goal-Based Messages Recommendation Utilizing Latent Dirichlet Allocation, IEEE International Conference on Advanced Learning Technologies, 2014: 464-468
  • Takatoshi Ishii, Pokpong Songmuang, Maomi Ueno:
    Maximum Clique Algorithm for Uniform Test Forms Assembly, Artificial Intelligence in Education – 16th International Conference, AIED 2013,451-462
  • Maomi Ueno:Adaptive Testing Based on Bayesian Decision Theory Artificial Intelligence in Education – 16th International Conference, AIED 2013, 712-716
  • Yoshimitsu Miyasawa, Maomi Ueno:Mobile Testing for Authentic Assessment in the Field. Artificial Intelligence in Education – 16th International Conference, AIED 2013,619-623
  • Maomi Ueno and Masaki Uto: Non-Informative Dirichlet Score for learning Bayesian networks, Proc. The Sixth European Workshop on Probabilistic Graphical Models(PGM), 331-338 (2012)
  • Chao Li and Maomi Ueno: A Depth-First Search Algorithm for Optimal Triangulation of Bayesian Network, Proc. The Sixth European Workshop on Probabilistic Graphical Models(PGM), 187-194 (2012)
  • Maomi Ueno: Robust learning Bayesian networks for prior belief, AUAI Press (UAI) Proc. The Twenty-Seventh Conference of Uncertainty in Artificial Intelligence, 698-707（2011）
  • Maomi Ueno: Learning networks determined by the ratio of prior and data, AUAI Press (UAI) Proc. The Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, 598-605（2010）
  • Maomi Ueno: Advanced technologies for e-testing, Proc. The 18th International Conference on Computers in Education, (ICCE) (2010),(Invited speech)
  • Takashi Isozaki and Maomi Ueno:  Minimum Free Energy Principle for Constraint-Based Learning Bayesian Networks, ECML PKDD 2009, Machine Learning and Knowledge Discovery in Databases, European Conference, LNAI 5789, 612-627（2009）
  • Takashi Isozaki and Maomi Ueno: Minimum Free Energies with “Data Temperature” for Parameter Learning of Bayesian Networks,  Proc. The 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2008),371-378 (2008): Best Paper Award
  • Maomi Ueno, Toshio Okamoto: Item Response Theory for Peer Assessment, Proc. The 8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008 554-558（2008）
  • Masahiro Ando, Maomi Ueno: Effect of pointer presentation on multimedia e-learning materials, Proc.World Conference on Educational Multimedia, Hypermedia &Telecommunications(ED-MEDIA 2008), 5549-5559: Outstanding Paper Award
  • Maomi Ueno, Toshio Okamoto: System for Online Detection of Aberrant Responses in E-Testing, Proc. The 8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008, 824-828 (2008)
  • Maomi Ueno: Learning Bayesian networks from an empirical Bayes approach, Proc. Int. Conf. on International Association for Statistical Computing, Invited Session on Bayesian statistics (2008) (invited)
  • Maomi Ueno and Toshio Okamoto, Bayesian Agent in e-Learning, Proc.The 7th IEEE International Conference on Advanced Learning Technologies, ICALT 2007, 282-284（2007）
  • Yasuhiko Morimoto, Maomi Ueno, Isao Kikukawa, Setsuo Yokoyama, Youzou Miyadera, SALMS: SCORM-compliant Adaptive LMS, Proc. the 12th World Conference on E-Learning in Corporate, Government, Healthcare, & Higher Education (E-Learn2007), 7287-7296 (2007) : Outstanding Paper Award
  • Maomi Ueno and Toshio Okamoto, Intelligent Bayesian agent as a facilitator in e-Learning. Proc. E-Learn2006, 3084-3092 (2006)
  • Maomi Ueno and Toshio Okamoto, Online MDL-Markov analysis of a discussion process in CSCL, ICALT ’06： Proc. the Sixth IEEE International Conference on Advanced Learning Technologies, 764-768（2006）
  • Maomi Ueno: Evaluation of e-Learning contents presentation methods using an eye mark recorder, Proc.the 2nd Joint Workshop of Cognition and Learning through Media-Communication for Advanced e-Learning, 207-212（2005）
  • Maomi Ueno: Intelligent LMS with an agent that learns from log data, Proc. e-Learn2005, 3169-3176（2005）: Outstanding Paper AwardMaomi Ueno: New pedagogies and vocational education, Proc. UNISCO-UNEVOC /JSISE International Seminar,  Invited Speach, pp.153-166（2005）(invited)
  • Maomi Ueno: Animated Pedagogical Agent based on Decision Tree for e-Learning, Proc.IEEE conference (Computer Science), ICALT（2005）
  • Maomi Ueno, Keizo Nagaoka, On-Line Analysis of e-Learning Time based on Gamma Distributions, Proc. ED-Media(full paper),3629-3637（2005）
  • Maomi Ueno: An Unified Derivation of Various IRT Models from Bayesian Approach, Proc.The 82nd Symposium of the Behaviormetric Society of Japan, Recent Developments in Latent Variables Modeling,83-99（2004）(invited)
  • Maomi Ueno: Data mining and text mining technologies for collaborative learning in LMS “SAMURAI”, Proc.IEEE International Conference, Special Panel “Collaborative Technology and New e-Pedagogy, Proc.IEEE conference (Computer Science), ICALT2004 2004, 1052-1053 (2004) (invited)
  • Maomi Ueno: On-Line Contents Analysis System for e-Learning, Proc.IEEE conference (Computer Science) ICALT2004, 762-764 (2004)
  • Maomi Ueno: Animated agent to maintain learner’s attention in e-learning , Proc. E-Learn2004 (2004) :Outstanding Paper Award
  • Yasuhiko Morimoto, Maomi Ueno, Nobuyuyoshi Yonezawa, Setsuo Yokoyama, Youzou Miyadera: A Meta-Language for Portfolio Assessment, Proc.IEEE conference (Computer Science) ICALT2004,  2004, 46-50 (2004)
  • Maomi Ueno, Tetsuya Kimura,  Alfred Neudorfer, Rupert Maclean: e-learning on TVET between Japan and Germany, Proc. ITHET 2004(Full paper), in Istanbul（2004）
  • Maomi Ueno: Evaluation of E-Learning Contents Presentation methods using Eye Mark Recorder, Proc. ED-Media(Full paper) in Lugano（2004）
  • Maomi Ueno: Technical and Vocational Education based on ICT, Proc.International Research Conference Education and Training（2004）(invited speech)
  • Maomi Ueno: Online Outlier Detection System for Learning Time Data in E-Learning and It’s evaluation, Proc. Computers and Advanced Technology in Education(CATE2004)（2004）
  • Maomi Ueno: Learning Log Database and Data Mining system for e-Learning -On-Line Statistical Outlier Detection of irregular learning processes-, Invited talk, Proc. The 6th Sanken ISIR International Symposium, New Trends in Knowledge Proceedings, 147-150（2003）(invited)
  • Maomi Ueno: LMS with irregular learning processes detection system, Proc. E-learn2003, pp.2486-2493（2003）
  • Maomi Ueno: Online statistical outlier detection of irregular learning processes for e-learning, Proc. ED-Media(Full paper) in Hawaii pp.227-234（2003）
  • Maomi Ueno: Technical and Vocational Education in Japan, Invited Speech in UNESCO TVE seminar, Mongolia(2003)
  • Maomi Ueno: E-learning between Universities and Japanese National Colleges of Technology, Proc. ITHET2003 (Full Paper) in Morocco , 121-129（2003）
  • Keizo Nagaoka, Hiroshi Kato, Toshihisa Nishimori, Maomi Ueno: Distant IT Course and IT Counseling System over a City-based Broadband Area Network connected via Laser Beam Transmitter, Proc. ITHET2003 (Full Paper)in Morocco　 91-97（2003）
  • Maomi Ueno & Keizo Nagaoka: Web based response analyzer for distance, education(full paper), Proc. Intertech 2002, Santos-Brazil（2002）
  • Maomi Ueno, Keizo Nagaoka: Learning Log Database and Data Mining system for e-Learning -On-Line Statistical Outlier Detection of irregular learning processes, Proc. International Conference on Advanced Learning Technologies 2002, IEEE Computer Science, 436-438（2002）
  • Maomi Ueno, Fumio Yoshida: Web based Computerized Testing System, Proc. International Conference on Advanced Learning Technologies 2002, IEEE Computer Science, 534-538（2002）
  • Maomi Ueno: Joint discrete probabilities distribution, Invited Lecture, in KU Laven in Berugium（2001）(invited)
  • Maomi Ueno ： An unified derivation of various IRT models from Bayesian approach, Proc. International Meeting of the Psychometric Society, 196-197（2001）
  • Maomi Ueno: Student models Construction by using Information Criteria(as a full paper), Proc. IEEE International Conference on Advanced Learning Technologies (published by IEEE Computer Society),331-334, (2001)
  • Maomi Ueno & Keizo Nagaoka: Web based Computerized Testing System for Distance Education(as a full paper), Proc. ICCE 2001, 547-554, (2001)
  • Yoshiki Mikami, Maomi Ueno, Yoshida Fumio, Ishibashi Takazumi, Suzuki Izumi:  Distance Learning and Web Based Learning in Technical Education ： A case at Nagaoka University of Technology Japan, Proc. Saudi Technical Conference and Exhibition（2000）
  • Maomi Ueno: Intelligent Tutoring System based on belief networks, Proc. IEEE International Conference on Advanced Learning Technologies, Computer Science（2000）
  • Maomi Ueno and Peter Bearse: A unified Approach to Information-Theoretic and Bayesian Model Selection Criteria, INTERNATIONAL SOCIETY for BAYESIAN ANALYSIS, Proc. 6th WORLD MEETING (2000)(Hersonissos-Heraklion, Crete) (Invited)
  • Maomi Ueno: derivation of discrete joint probability, Proc. Joint Statistical Meetings, American Statistical Association（1999）
  • Maomi Ueno: An asymptotic analysis of log-likelihood of Bayesian networks, Proc. Information-Based Induction Science(1999)
  • Maomi Ueno ：Expanded Bayesian Model Selection, Proc. The 6th conference of the International Federation of Classification Societies 98（1998）
  • Maomi Ueno ：Open Testing System, Proc. Open Learning International Conference 98（1998）
  • Maomi Ueno ：Bias-Corrected Bayesian Model Selection, Proc.The 6th Japan China Statistical Symposium（1997）
  • Maomi Ueno and Nagaoka, K.: The development of a computer-assisted test construction system in consideration of evaluation of learner’s response speed, Proc.’ICOMMET’ 91, 13-15（1991）
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