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Paper Abstract and Keywords
Presentation 2020-12-10 14:20
Slide Design Assessment Featuring Visual and Structural Analysis
Shengzhou Yi (UTokyo), Junichiro Matsugami (Rubato), Xueting Wang, Toshihiko Yamasaki (UTokyo) AI2020-3
Abstract (in Japanese) (See Japanese page) 
(in English) Appealing design of presentation slides is a great way to make the presentation more attractive and easier to understand. However, the design of slides is difficult to novices and there are limited support systems to help them by evaluating their slides. In this paper, the design problems of the presentation slides are recognized by proposed neural network based on the visual features and the structural features. The created dataset contains 856 slide pairs. For each slide pair, one slide was created by a novice and the other one was improved by the advices from professional consultants. Ten check points with high frequencies were summarized by the consultants, which are set as the prediction targets in this study. For the binary classification of each check point, the class distribution is very imbalanced, since only a small part of samples has the responding design problem. Therefore, recent machine learning methods for addressing class imbalance were applied to prediction and proved to be effective for improving the performance of the proposed model. The proposed neural network can achieve the average accuracies of 80.9% and 80.0% on the balanced and imbalanced dataset, respectively.
Keyword (in Japanese) (See Japanese page) 
(in English) Presentation Slide / Design Assessment / Feature Fusion / Class Imbalance / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 281, AI2020-3, pp. 13-18, Dec. 2020.
Paper # AI2020-3 
Date of Issue 2020-12-03 (AI) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF AI2020-3

Conference Information
Committee AI  
Conference Date 2020-12-10 - 2020-12-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Online and HAMAMATSU ACT CITY 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Foundations and application technologies for AI systems on the new normal 
Paper Information
Registration To AI 
Conference Code 2020-12-AI 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Slide Design Assessment Featuring Visual and Structural Analysis 
Sub Title (in English)  
Keyword(1) Presentation Slide  
Keyword(2) Design Assessment  
Keyword(3) Feature Fusion  
Keyword(4) Class Imbalance  
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Keyword(6)  
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1st Author's Name Shengzhou Yi  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Junichiro Matsugami  
2nd Author's Affiliation Rubato Co., Ltd. (Rubato)
3rd Author's Name Xueting Wang  
3rd Author's Affiliation The University of Tokyo (UTokyo)
4th Author's Name Toshihiko Yamasaki  
4th Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2020-12-10 14:20:00 
Presentation Time 25 minutes 
Registration for AI 
Paper # AI2020-3 
Volume (vol) vol.120 
Number (no) no.281 
Page pp.13-18 
#Pages 6 
Date of Issue 2020-12-03 (AI) 

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