Pub Date : 2016-07-01DOI: 10.1109/IIAI-AAI.2016.87
Hiroshi Imahashi, K. Gemba, K. Uenishi
We analyzed the brand-building of small-and medium-sized enterprises in the food manufacturing industry. Specifically, we analyzed the strategy and capabilities of traditional tofu production. The results of our research and study indicate, the need for management to have capabilities in sales-targeting, manufacturing, and distribution. Furthermore, management should be able to integrate, construct, and relocate capabilities.
{"title":"Strategies for the Brand-Building of Small-and Medium-Sized Enterprises","authors":"Hiroshi Imahashi, K. Gemba, K. Uenishi","doi":"10.1109/IIAI-AAI.2016.87","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2016.87","url":null,"abstract":"We analyzed the brand-building of small-and medium-sized enterprises in the food manufacturing industry. Specifically, we analyzed the strategy and capabilities of traditional tofu production. The results of our research and study indicate, the need for management to have capabilities in sales-targeting, manufacturing, and distribution. Furthermore, management should be able to integrate, construct, and relocate capabilities.","PeriodicalId":272739,"journal":{"name":"2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125489687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Analysis of fMRI data is very useful for studying relationship between neural activity and a variety of brain functions. For many years, a number of brain image analysis techniques using machine learning were proposed. However, this task is still challenging due to the unique characteristics of the brain data with very small samples but extremely high dimensionality, reducing generalization performance. This paper presents a novel analysis method for fMRI data. It consists of three major steps: (1) Identifying informative voxels, (2) extracting feature space by analyzing semantic relationships among voxels and (3) learning fMRI classifier from the extracted features. Preliminary experimental results conducted on the task of image prediction from fMRI data confirmed that the proposed method achieves improvements of classification accuracy more than 20% in mean accuracy in comparing with current neuroimaging methods.
{"title":"Learning Representation for fMRI Data Analysis Using Autoencoder","authors":"Suwatchai Kamonsantiroj, Parinya Charoenvorakiat, Luepol Pipanmaekaporn","doi":"10.1109/IIAI-AAI.2016.66","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2016.66","url":null,"abstract":"Analysis of fMRI data is very useful for studying relationship between neural activity and a variety of brain functions. For many years, a number of brain image analysis techniques using machine learning were proposed. However, this task is still challenging due to the unique characteristics of the brain data with very small samples but extremely high dimensionality, reducing generalization performance. This paper presents a novel analysis method for fMRI data. It consists of three major steps: (1) Identifying informative voxels, (2) extracting feature space by analyzing semantic relationships among voxels and (3) learning fMRI classifier from the extracted features. Preliminary experimental results conducted on the task of image prediction from fMRI data confirmed that the proposed method achieves improvements of classification accuracy more than 20% in mean accuracy in comparing with current neuroimaging methods.","PeriodicalId":272739,"journal":{"name":"2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131373574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-01DOI: 10.1109/IIAI-AAI.2016.97
Yutaka Arakawa, K. Yasumoto, Ken-ichi Matsumoto, Hideaki Hata, H. Suwa, Akinori Ihara, Manato Fujimoto
Project IS^3 is the leading-edge project in Nara Institute of Science and Technology, a national graduate school in Japan, started from February 2016. This project aims to utilize a human's behavior change for solving the social problems and maintaining our society sustainably. In order to cause the behavior change intentionally, various information technologies are required. In this paper, we explain the concept and goal of our project and figure out what we will do in this project.
{"title":"Project IS^3: Incentive-Based Intelligent Intervention for Smart and Sustainable Society","authors":"Yutaka Arakawa, K. Yasumoto, Ken-ichi Matsumoto, Hideaki Hata, H. Suwa, Akinori Ihara, Manato Fujimoto","doi":"10.1109/IIAI-AAI.2016.97","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2016.97","url":null,"abstract":"Project IS^3 is the leading-edge project in Nara Institute of Science and Technology, a national graduate school in Japan, started from February 2016. This project aims to utilize a human's behavior change for solving the social problems and maintaining our society sustainably. In order to cause the behavior change intentionally, various information technologies are required. In this paper, we explain the concept and goal of our project and figure out what we will do in this project.","PeriodicalId":272739,"journal":{"name":"2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131954930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-01DOI: 10.1109/IIAI-AAI.2016.26
N. Bouhmala
Data Mining is concerned with the discovery of interesting patterns and knowledge in data repositories. Cluster Analysis which belongs to the core methods of data mining is the process of discovering homogeneous groups called clusters. Given a data-set and some measure of similarity between data objects, the goal in most clustering algorithms is maximizing both the homogeneity within each cluster and the heterogeneity between different clusters. In this work, test cases are used to demonstrate that the Euclidean Distance widely in literature is not a suitable metric for capturing the quality of the clustering.
{"title":"How Good is the Euclidean Distance Metric for the Clustering Problem","authors":"N. Bouhmala","doi":"10.1109/IIAI-AAI.2016.26","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2016.26","url":null,"abstract":"Data Mining is concerned with the discovery of interesting patterns and knowledge in data repositories. Cluster Analysis which belongs to the core methods of data mining is the process of discovering homogeneous groups called clusters. Given a data-set and some measure of similarity between data objects, the goal in most clustering algorithms is maximizing both the homogeneity within each cluster and the heterogeneity between different clusters. In this work, test cases are used to demonstrate that the Euclidean Distance widely in literature is not a suitable metric for capturing the quality of the clustering.","PeriodicalId":272739,"journal":{"name":"2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134565661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-01DOI: 10.1109/IIAI-AAI.2016.253
Ting-Chia Hsu
This study developed a novel peer assessment system from the approach of knowledge engineering. Gathering information from experts, a dynamic repertory grid was created as a peer assessment form to be used in a computer skills class taught in a vocational school. This form is dynamic in that it allows student reviewers to select personalized assessment criteria from a bank of items, causing students to reflect on the purpose of the assignment being reviewed and how to best characterize the performance of the student completing the assignment as well as providing ample feedback for students. The results compared the performance of the students using the proposed system with that of the ones using conventional approaches, and found that those who applied the proposed approach learned better and made significant improvement.
{"title":"The Effects of a Dynamic Repertory Grid for Peer Assessment: Peer Assessment of Computer Software Application Certificate Practice","authors":"Ting-Chia Hsu","doi":"10.1109/IIAI-AAI.2016.253","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2016.253","url":null,"abstract":"This study developed a novel peer assessment system from the approach of knowledge engineering. Gathering information from experts, a dynamic repertory grid was created as a peer assessment form to be used in a computer skills class taught in a vocational school. This form is dynamic in that it allows student reviewers to select personalized assessment criteria from a bank of items, causing students to reflect on the purpose of the assignment being reviewed and how to best characterize the performance of the student completing the assignment as well as providing ample feedback for students. The results compared the performance of the students using the proposed system with that of the ones using conventional approaches, and found that those who applied the proposed approach learned better and made significant improvement.","PeriodicalId":272739,"journal":{"name":"2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131432339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When consumers purchase products through online, products information such as ratings, product reviews, product descriptions given by sellers are very useful for consumers to optimise their purchasing decisions. However, when a consumer purchases used products via online e-commerce sites, the consumer may consider much more attributes about the products than that for purchasing new products. This is due to the need for understanding instance-specific conditions before purchasing a used product and thus the available descriptions for a used product may differ in each other. In this paper, we proposed a design and implementation of a system that supports users to investigate instance-specific attribute values by extracting and predicting attributes and values of used items that are selling on e-commerce sites. Our key idea is preparing a system to identify instance-specific attributes as well as their values from the descriptions of items while browsing the e-commerce sites. Our system can also apply various machine learning methods to predict missing attributes values.
{"title":"Toward Extracting and Predicting Instance-Specific Attribute Values from E-Commerce Sites for Used Products","authors":"Hettiarachchige Dona Nidhana Harshika, Naoki Yamada, Masahiro Nishi, Kihaya Sugiura, Naoki Fukuta","doi":"10.1109/IIAI-AAI.2016.60","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2016.60","url":null,"abstract":"When consumers purchase products through online, products information such as ratings, product reviews, product descriptions given by sellers are very useful for consumers to optimise their purchasing decisions. However, when a consumer purchases used products via online e-commerce sites, the consumer may consider much more attributes about the products than that for purchasing new products. This is due to the need for understanding instance-specific conditions before purchasing a used product and thus the available descriptions for a used product may differ in each other. In this paper, we proposed a design and implementation of a system that supports users to investigate instance-specific attribute values by extracting and predicting attributes and values of used items that are selling on e-commerce sites. Our key idea is preparing a system to identify instance-specific attributes as well as their values from the descriptions of items while browsing the e-commerce sites. Our system can also apply various machine learning methods to predict missing attributes values.","PeriodicalId":272739,"journal":{"name":"2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127873400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-01DOI: 10.1109/IIAI-AAI.2016.101
Yan-Zhi Wang, Fang-Mei Tseng, Ju-yin Weng
We analyze consumer upgrade behavior through the theories of inertia and learning effect. The mobile usage behavior from 3G upgrade to 4G in Taiwan is suitable for our study and we obtain the real data set from one telecom service provider in Taiwan. We find the impact of inertia makes consumer keep same behavior in voice usage. The learning effect lead consumer gradually used more data usage and decrease voice usage.
{"title":"Consumer Upgrade Behavior Analysis in Mobile Telecommunication Services","authors":"Yan-Zhi Wang, Fang-Mei Tseng, Ju-yin Weng","doi":"10.1109/IIAI-AAI.2016.101","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2016.101","url":null,"abstract":"We analyze consumer upgrade behavior through the theories of inertia and learning effect. The mobile usage behavior from 3G upgrade to 4G in Taiwan is suitable for our study and we obtain the real data set from one telecom service provider in Taiwan. We find the impact of inertia makes consumer keep same behavior in voice usage. The learning effect lead consumer gradually used more data usage and decrease voice usage.","PeriodicalId":272739,"journal":{"name":"2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124313958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-01DOI: 10.1109/IIAI-AAI.2016.44
Y. Jiang, W. Weng, S. Fujimura
This article proposes a novel method to solve the manufacturing scheduling problems in multi-agent system (MAS) for dynamic environment. The study focuses on the machine selection work for jobs and proposes a remaining time prediction method, which will help a job choose machines based on estimated finishing time. In this way, we can keep the job finishing just-in-time, which aims to support a customer-oriented lead time policy, and can provide better manufacturing performance and increase customer service level. This article provides experimental results compared with another approach in previous research, through which we can find that in some case if we put the Just-in-time philosophy into consideration, the proposed method will deliver competitive performance.
{"title":"Multi-agent Just-in-Time Manufacturing Scheduling System for Dynamic Environment","authors":"Y. Jiang, W. Weng, S. Fujimura","doi":"10.1109/IIAI-AAI.2016.44","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2016.44","url":null,"abstract":"This article proposes a novel method to solve the manufacturing scheduling problems in multi-agent system (MAS) for dynamic environment. The study focuses on the machine selection work for jobs and proposes a remaining time prediction method, which will help a job choose machines based on estimated finishing time. In this way, we can keep the job finishing just-in-time, which aims to support a customer-oriented lead time policy, and can provide better manufacturing performance and increase customer service level. This article provides experimental results compared with another approach in previous research, through which we can find that in some case if we put the Just-in-time philosophy into consideration, the proposed method will deliver competitive performance.","PeriodicalId":272739,"journal":{"name":"2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114735419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-01DOI: 10.1109/IIAI-AAI.2016.38
Kiyota Hashimoto, T. Soonklang, S. Hirokawa
Extraction of structure from texts is a key issue of text mining. The rhetorical structure of move in scientific articles is useful for assisting in the reading and writing. In this paper, we classify move structure in the abstract of research articles with a small number of characteristic words that determine five moves of including background (B), purpose(P), method(M), result(R) and discussion(D). Eleven measures were introduced and used to select features of moves. Exhaustive parameter search were conducted to get the optimal combination of measure and the number of features. We applied support vector machine and evaluated 10 fold cross validations. The accuracies with optimal feature selections are 0.9022, 0.8322, 0.8442, 0.8820 and 0.8354 for B, P, M, R and D, respectively. They are 10% better than the baseline performance that use all keywords. This study surprisedly found that the negative feature words play central role for prediction performance improvement.
{"title":"Feature Words of Moves in Scientific Abstracts","authors":"Kiyota Hashimoto, T. Soonklang, S. Hirokawa","doi":"10.1109/IIAI-AAI.2016.38","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2016.38","url":null,"abstract":"Extraction of structure from texts is a key issue of text mining. The rhetorical structure of move in scientific articles is useful for assisting in the reading and writing. In this paper, we classify move structure in the abstract of research articles with a small number of characteristic words that determine five moves of including background (B), purpose(P), method(M), result(R) and discussion(D). Eleven measures were introduced and used to select features of moves. Exhaustive parameter search were conducted to get the optimal combination of measure and the number of features. We applied support vector machine and evaluated 10 fold cross validations. The accuracies with optimal feature selections are 0.9022, 0.8322, 0.8442, 0.8820 and 0.8354 for B, P, M, R and D, respectively. They are 10% better than the baseline performance that use all keywords. This study surprisedly found that the negative feature words play central role for prediction performance improvement.","PeriodicalId":272739,"journal":{"name":"2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114895585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-01DOI: 10.1109/IIAI-AAI.2016.112
Hui-Chun Chu, Yu-Hsuan Sung
Engaging students in constructing their knowledge during real-world observation and inquiry-based learning activities has been recognized as an important issue for improving students' learning achievement and motivation. However, researchers have indicated that due to a lack of effectively connection between observation in real-world and inquiry problems, it is a challenge to lead an effective outdoor inquiry-based learning activity. In this study, a progressive inquiry-based approach with augmented reality technology is proposed for improving students' high-level thinking and problem solving abilities by asking students to answer different layer of questions in in-field inquiry learning activities. An experiment will be conducted to evaluate its performance on high school geography courses. A total of 60 first-grade high school students from two classes will participant in this experiment. The students in the experimental group will learn with the progressive inquiry-based augmented reality system, while those in the control group will learn with the conventional AR contextual u-learning approaches. The students' learning achievements, learning attitudes, learning motivation, and problem solving ability will be investigated after the experiment. It is expected that the proposed approach will effectively enhance the students' learning achievement in comparison with the control group.
{"title":"A Context-Aware Progressive Inquiry-Based Augmented Reality System to Improving Students' Investigation Learning Abilities for High School Geography Courses","authors":"Hui-Chun Chu, Yu-Hsuan Sung","doi":"10.1109/IIAI-AAI.2016.112","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2016.112","url":null,"abstract":"Engaging students in constructing their knowledge during real-world observation and inquiry-based learning activities has been recognized as an important issue for improving students' learning achievement and motivation. However, researchers have indicated that due to a lack of effectively connection between observation in real-world and inquiry problems, it is a challenge to lead an effective outdoor inquiry-based learning activity. In this study, a progressive inquiry-based approach with augmented reality technology is proposed for improving students' high-level thinking and problem solving abilities by asking students to answer different layer of questions in in-field inquiry learning activities. An experiment will be conducted to evaluate its performance on high school geography courses. A total of 60 first-grade high school students from two classes will participant in this experiment. The students in the experimental group will learn with the progressive inquiry-based augmented reality system, while those in the control group will learn with the conventional AR contextual u-learning approaches. The students' learning achievements, learning attitudes, learning motivation, and problem solving ability will be investigated after the experiment. It is expected that the proposed approach will effectively enhance the students' learning achievement in comparison with the control group.","PeriodicalId":272739,"journal":{"name":"2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116892851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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