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The University of Electro-Communications, Japan

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5 Papers

Hair Shading Style Transfer for Manga with cGAN

Topics: AI and Creativity; Deep Learning

Authors: Masashi Aizawa , Ryohei Orihara , Yuichi Sei , Yasuyuki Tahara and Akihiko Ohsuga

Affiliation: The University of Electro-Communications, Tokyo, Japan

Digital Object Identifier: 10.5220/0008961405870594

Proceedings of ICAART 2020, ISBN: 978-989-758-395-7

Quick Abstract

Coloring line drawings is an important process in creating artwork. In coloring, a shading style is where an artist’s style is the most noticeable. Artists spend a great deal of time and effort creating art. It is thus difficult for beginners to draw specific shading styles, and even experienced people have a hard time trying to draw many different styles. Features of a shading styles appear prominently in the hair region of human characters. In many cases, hair is drawn using a combination of the base color, the highlights, and the shadows. In this study, we propose a method for transferring the shading style used on hair in one drawing to another drawing. This method uses a single reference image for training and does not need a large data set. This paper describes the framework, transfer results, and discussions. The transfer results show the following: when transferring the shading style to the line drawing by the same artist, the method can detect the hair region relatively well, and the transfer result is indistinguishable from the transfer target in some shading styles. In addition, the evaluation results show that our method has higher scores than an existing automatic colorizing service.

Model Smoothing using Virtual Adversarial Training for Speech Emotion Estimation using Spontaneity

Topics: Machine Learning; Conversational Agents; Data Science; Deep Learning

Authors: Toyoaki Kuwahara , Ryohei Orihara , Yuichi Sei , Yasuyuki Tahara and Akihiko Ohsuga

Affiliation: Graduate School of Information and Engineering, University of Electro-Communications, Tokyo, Japan

Digital Object Identifier: 10.5220/0008958405700577

Proceedings of ICAART 2020, ISBN: 978-989-758-395-7

Quick Abstract

Speech-based emotion estimation increases accuracy through the development of deep learning. However, most emotion estimation using deep learning requires supervised learning, and it is difficult to obtain large datasets used for training. In addition, if the training data environment and the actual data environment are significantly different, the problem is that the accuracy of emotion estimation is reduced. Therefore, in this study, to solve these problems, we propose a emotion estimation model using virtual adversarial training (VAT), a semi-supervised learning method that improves the robustness of the model. Furthermore, research on the spontaneity of speech has progressed year by year, and recent studies have shown that the accuracy of emotion classification is improved when spontaneity is taken into account. We would like to investigate the effect of the spontaneity in a cross-language situation. First, VAT hyperparameters were first set by a preliminary experiment using a single corpus. Next, the robustness of the model generated by the evaluation experiment by the cross corpus was shown. Finally, we evaluate the accuracy of emotion estimation by considering spontaneity and showed improvement in the accuracy of the model using VAT by considering spontaneity.

Generation of Multiple Choice Questions Including Panoramic Information using Linked Data

Topics: Semantic Web; Natural Language Processing; Knowledge-Based Systems

Authors: Fumika Okuhara , Yuichi Sei , Yasuyuki Tahara and Akihiko Ohsuga

Affiliation: Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo and Japan

Digital Object Identifier: 10.5220/0007259301100120

Proceedings of ICAART 2019, ISBN: 978-989-758-350-6

Quick Abstract

In recent years, just about all subjects require students to learn panoramic information. Because the need exists for cross-curriculum learning aimed at relating subject areas, it is useful for multiple-choice questions to include panoramic information for learners. A question including panoramic information refers to content that includes transverse related information and makes respondents grasp the whole knowledge. However, it is costly to manually generate and collect appropriate multiple-choice questions for questioners and learners. Therefore, in this research, we propose a method for the automatic generation of multiple-choice questions including panoramic information using Linked Data. Linked Data is graphical data that can link structured data, and it is used as a technology for data integration and utilization. Some attempts have been made to use Linked Data as a resource for creating teaching material, and the possibility of using Semantic Web technology in education has been verified. In this paper, we aim to realize a system for automatically generating two types of multiple-choice questions by implementing an approach to generating questions and choices. An evaluation method for the generation of questions and choices involves setting indicators for each evaluation item, such as validity and the degree of the inclusion of panoramic information.

Related Ontology Subjects/Areas/Topics (12)

Applications	Enterprise Information Systems	Pattern Recognition
Artificial Intelligence	Knowledge Engineering and Ontology Development	Semantic Web
Collaboration and e-Services	Knowledge-Based Systems	Soft Computing
e-Business	Natural Language Processing	Symbolic Systems

New Indicator for Centrality Measurements in Passing-network Analysis of Soccer

Topics: Data Mining; Big Data; Data Science

Authors: Masatoshi Kanbata , Ryohei Orihara , Yuichi Sei , Yasuyuki Tahara and Akihiko Ohsuga

Affiliation: Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo and Japan

Digital Object Identifier: 10.5220/0007377506160623

Proceedings of ICAART 2019, ISBN: 978-989-758-350-6

Quick Abstract

A number of fields including business, science, and sports, make use of data analytics. The evaluation of players and teams affect how tactics, training, and scouting are conducted in soccer teams. Data such as the number of shots and goals in match results are often used to evaluate players and teams. However, this is not enough to fully understand the potential of the players and teams. In this paper, we describe a new analysis method using passing-distribution data from soccer games. To evaluate the performance of players and teams, we applied graph mining. We also used an index called centrality, which evaluates individual contributions with an organization. In this research, we propose a new centrality model to improve existing conventional models. In the calculating the centrality of a given player pair, we consider not only the shortest sequence of passing but also longer ones. In this research, we verified the significance of these indicators by applying the data of UEFA EURO 2008, 2012, and 2016. As a result, we found our method to be more consistent with game results than conventional methods.

Related Ontology Subjects/Areas/Topics (10)

Artificial Intelligence	Data Mining	Sensor Networks
Big Data	Databases and Information Systems Integration	Signal Processing
Data Engineering	Enterprise Information Systems	Soft Computing
Data Management and Quality

Transforming the Emotion in Speech using a Generative Adversarial Network

Topics: Neural Networks; Deep Learning

Authors: Kenji Yasuda , Ryohei Orihara , Yuichi Sei , Yasuyuki Tahara and Akihiko Ohsuga

Affiliation: Graduate School of Information and Engineering, University of Electro-Communications, 1–5–1 Chofugaoka, Chofu-shi, Tokyo, 182–8585 and Japan

Digital Object Identifier: 10.5220/0007258504270434

Proceedings of ICAART 2019, ISBN: 978-989-758-350-6

Quick Abstract

In recent years, natural and highly accurate outputs in domain transfer tasks have been achieved by deep learning techniques. Especially, the advent of Generative Adversarial Networks (GANs) has enabled the transfer of objects between unspecified domains. Voice conversion is a popular example of speech domain transfer, which can be paraphrased as domain transfer of speakers. However, most of the voice conversion studies have focused only on transforming the identities of speakers. Understanding other nuances in the voice is necessary for natural speech synthesis. To resolve this issue, we transform the emotions in speech by the most promising GAN model, CycleGAN. In particular, we investigate the usefulness of speech with low emotional intensity as training data. Such speeches are found to be useful when the training data contained multiple speakers.

Related Ontology Subjects/Areas/Topics (16)

Artificial Intelligence	Methodologies and Methods	Physiological Computing Systems
Biomedical Engineering	Neural Networks	Sensor Networks
Biomedical Signal Processing	Neurocomputing	Signal Processing
Computational Intelligence	Neurotechnology, Electronics and Informatics	Soft Computing
Health Engineering and Technology Applications	Pattern Recognition	Theory and Methods
Human-Computer Interaction