{"title":"Mid-level Feature Extraction Method Based Transfer Learning to Small-Scale Dataset of Medical Images with Visualizing Analysis","authors":"Dong-Ho Lee, Yan Li, B. Shin","doi":"10.3745/JIPS.04.0194","DOIUrl":"https://doi.org/10.3745/JIPS.04.0194","url":null,"abstract":"","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128349819","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}
A graph is a data structure consisting of nodes and edges between these nodes. Graph embedding is to generate a low dimensional vector for a given graph that best represents the characteristics of the graph. Recently, there have been studies on graph embedding, especially using deep learning techniques. However, until now, most deep learning-based graph embedding techniques have focused on unweighted graphs. Therefore, in this paper, we propose a graph embedding technique for weighted graphs based on long short-term memory (LSTM) autoencoders. Given weighted graphs, we traverse each graph to extract node-weight sequences from the graph. Each node-weight sequence represents a path in the graph consisting of nodes and the weights between these nodes. We then train an LSTM autoencoder on the extracted node-weight sequences and encode each nodeweight sequence into a fixed-length vector using the trained LSTM autoencoder. Finally, for each graph, we collect the encoding vectors obtained from the graph and combine them to generate the final embedding vector for the graph. These embedding vectors can be used to classify weighted graphs or to search for similar weighted graphs. The experiments on synthetic and real datasets show that the proposed method is effective in measuring the similarity between weighted graphs.
{"title":"A Graph Embedding Technique for Weighted Graphs Based on LSTM Autoencoders","authors":"Minji Seo, Ki Yong Lee","doi":"10.3745/JIPS.04.0197","DOIUrl":"https://doi.org/10.3745/JIPS.04.0197","url":null,"abstract":"A graph is a data structure consisting of nodes and edges between these nodes. Graph embedding is to generate a low dimensional vector for a given graph that best represents the characteristics of the graph. Recently, there have been studies on graph embedding, especially using deep learning techniques. However, until now, most deep learning-based graph embedding techniques have focused on unweighted graphs. Therefore, in this paper, we propose a graph embedding technique for weighted graphs based on long short-term memory (LSTM) autoencoders. Given weighted graphs, we traverse each graph to extract node-weight sequences from the graph. Each node-weight sequence represents a path in the graph consisting of nodes and the weights between these nodes. We then train an LSTM autoencoder on the extracted node-weight sequences and encode each nodeweight sequence into a fixed-length vector using the trained LSTM autoencoder. Finally, for each graph, we collect the encoding vectors obtained from the graph and combine them to generate the final embedding vector for the graph. These embedding vectors can be used to classify weighted graphs or to search for similar weighted graphs. The experiments on synthetic and real datasets show that the proposed method is effective in measuring the similarity between weighted graphs.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124328769","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}
{"title":"A Method for Tree Image Segmentation Combined Adaptive Mean Shifting with Image Abstraction","authors":"Ting-ting Yang, Su Zhou, Ai-jun Xu, Jianhang Yin","doi":"10.3745/JIPS.02.0151","DOIUrl":"https://doi.org/10.3745/JIPS.02.0151","url":null,"abstract":"","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125988033","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}
{"title":"Suggestion for Collaboration-Based UI/UX Development Model through Risk Analysis","authors":"Seong-Hwan Cho, Seung-Hee Kim","doi":"10.3745/JIPS.04.0200","DOIUrl":"https://doi.org/10.3745/JIPS.04.0200","url":null,"abstract":"","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"493 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120863175","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}
S. Singh, Abir El Azzaoui, Mikail Mohammed Salim, and Jong Hyuk Park
{"title":"Quantum Communication Technology for Future ICT - Review","authors":"S. Singh, Abir El Azzaoui, Mikail Mohammed Salim, and Jong Hyuk Park","doi":"10.3745/JIPS.03.0154","DOIUrl":"https://doi.org/10.3745/JIPS.03.0154","url":null,"abstract":"","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123145525","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}
{"title":"A Study on the Fault Process and Equipment Analysis of Plastic Ball Grid Array Manufacturing Using Data-Mining Techniques","authors":"H. Sim","doi":"10.3745/JIPS.04.0195","DOIUrl":"https://doi.org/10.3745/JIPS.04.0195","url":null,"abstract":"","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123503347","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}
In recent years, future information and communication technology (ICT) has influenced and changed our lives. Without various ICT-based applications, we would have difficulty in securely storing, efficiently processing, and conveniently communicating information. In the future, ICT will play a very important role in the convergence of computing, communication, and all other computational sciences and application. ICT will also influence various fields including communication, science, engineering, industry, business, law, politics, culture, and medicine. In this paper, we investigate the latest algorithms, processes, and services in future fields.
{"title":"Algorithms, Processes, and Services for Future ICT","authors":"Y. Jeong, J. Park","doi":"10.3745/JIPS.01.0061","DOIUrl":"https://doi.org/10.3745/JIPS.01.0061","url":null,"abstract":"In recent years, future information and communication technology (ICT) has influenced and changed our lives. Without various ICT-based applications, we would have difficulty in securely storing, efficiently processing, and conveniently communicating information. In the future, ICT will play a very important role in the convergence of computing, communication, and all other computational sciences and application. ICT will also influence various fields including communication, science, engineering, industry, business, law, politics, culture, and medicine. In this paper, we investigate the latest algorithms, processes, and services in future fields.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126614295","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}
More and more cloud computing services are being applied in various fields; however, it is difficult for users and cloud computing service platforms to establish trust among each other. The trust value cannot be measured accurately or effectively. To solve this problem, we design a service-oriented cloud trust assessment model using a cloud model. We also design a subjective preference weight allocation (SPWA) algorithm. A flexible weight model is advanced by combining SPWA with the entropy method. Aiming at the fuzziness and subjectivity of trust, the cloud model is used to measure the trust value of various cloud computing services. The SPWA algorithm is used to integrate each evaluation result to obtain the trust evaluation value of the entire cloud service provider.
{"title":"Service Oriented Cloud Computing Trusted Evaluation Model","authors":"Hongqiang Jiao, Xinxin Wang, Wanning Ding","doi":"10.3745/JIPS.03.0153","DOIUrl":"https://doi.org/10.3745/JIPS.03.0153","url":null,"abstract":"More and more cloud computing services are being applied in various fields; however, it is difficult for users and cloud computing service platforms to establish trust among each other. The trust value cannot be measured accurately or effectively. To solve this problem, we design a service-oriented cloud trust assessment model using a cloud model. We also design a subjective preference weight allocation (SPWA) algorithm. A flexible weight model is advanced by combining SPWA with the entropy method. Aiming at the fuzziness and subjectivity of trust, the cloud model is used to measure the trust value of various cloud computing services. The SPWA algorithm is used to integrate each evaluation result to obtain the trust evaluation value of the entire cloud service provider.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"362 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133069113","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 a picture book is photographed with a smart device, the text is analyzed for meaning and associated images are created. Image creation is the first step in learning DCGAN using class lists and images. In this study, DCGAN was trained with 11 classes and images of 1688 bears, which were collected by ImageNet for design. The second step is to shoot the image and text of the picture book on a smart device, and convert the text part of the shot image into a system readable character. We use the morpheme analyzer to classify nouns and verbs in text, and Discriminator learn to recognize the classified parts of speech as latent vectors of images. The third step is to create an associated image in the text. In the picture book, take the text of the part without the image and extract nouns and verbs. The extracted parts of speech and the learned latent vector are used as Generator parameters to generate images associated with the text.
{"title":"Design of Image Generation System for DCGAN-Based Kids' Book Text","authors":"Jaehyeong Cho, Nammee Moon","doi":"10.3745/JIPS.02.0149","DOIUrl":"https://doi.org/10.3745/JIPS.02.0149","url":null,"abstract":"When a picture book is photographed with a smart device, the text is analyzed for meaning and associated images are created. Image creation is the first step in learning DCGAN using class lists and images. In this study, DCGAN was trained with 11 classes and images of 1688 bears, which were collected by ImageNet for design. The second step is to shoot the image and text of the picture book on a smart device, and convert the text part of the shot image into a system readable character. We use the morpheme analyzer to classify nouns and verbs in text, and Discriminator learn to recognize the classified parts of speech as latent vectors of images. The third step is to create an associated image in the text. In the picture book, take the text of the part without the image and extract nouns and verbs. The extracted parts of speech and the learned latent vector are used as Generator parameters to generate images associated with the text.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124463952","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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