Pub Date : 2018-09-01DOI: 10.1109/ICAWST.2018.8517192
Israel Mendonça, Antoine Trouvé, Akira Fukuda, K. Murakami
In this paper, we address the performance problems inherited when we use word embedding for recommendation. Free-text documents has no structural constructing rules, and are hard to model. Hence, the problem of having an accurate model, that conveys all the important information is a nontrivial problem. We convert the document to a numeric structure using word-embedding and test two document representations: one based in the center of this numeric representation and the other one based on pre-defined set of topics. We build a free text recommendation system and study how the performance, in terms of precision and recommendation time, is affected by both representations. We then vary the number of topics used to represent documents and verify the tradeoffs inherited from having a compact representation. The more compact the recommendation, the shorter the recommendation time, however more information is lost in the compactation process. We empirically test different possibilities for the topics and find an optimal point that is 3 times faster than a baseline and almost as accurate as it.
{"title":"Exploring a Topical Representation of Documents for Recommendation Systems","authors":"Israel Mendonça, Antoine Trouvé, Akira Fukuda, K. Murakami","doi":"10.1109/ICAWST.2018.8517192","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517192","url":null,"abstract":"In this paper, we address the performance problems inherited when we use word embedding for recommendation. Free-text documents has no structural constructing rules, and are hard to model. Hence, the problem of having an accurate model, that conveys all the important information is a nontrivial problem. We convert the document to a numeric structure using word-embedding and test two document representations: one based in the center of this numeric representation and the other one based on pre-defined set of topics. We build a free text recommendation system and study how the performance, in terms of precision and recommendation time, is affected by both representations. We then vary the number of topics used to represent documents and verify the tradeoffs inherited from having a compact representation. The more compact the recommendation, the shorter the recommendation time, however more information is lost in the compactation process. We empirically test different possibilities for the topics and find an optimal point that is 3 times faster than a baseline and almost as accurate as it.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114403094","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 : 2018-09-01DOI: 10.1109/ICAWST.2018.8517218
Yujia Wang, Chalermpon Kongjit, T. Hara, Daichi Amagata
Properly managing knowledge workers greatly impacts and influences the contemporary globalized world. Although, much research has emphasized what kinds of competency knowledge workers should have, it is difficult to reach a consensus on what constitutes a competent lecturer in the knowledge management (KM) domain. Due to the importance of lecturers’ competencies in the context of the student quality assurance, industry requirements, existing problems in the management process and operational functions in changing business environments, it is necessary to design and validate an effective competency model and implement it into practical systems. In this paper, we develop a competency-based knowledge management system which employs a database and competency model. That is, based on a KM lecturers’ competency model, we set up a database so that the internal management process can be easily handled (e.g., administrators can assign the lecture for a particular class, and students can find their advisors). This system demonstrates how a competency model practically supports decision-making and work processes.
{"title":"Developing a Competency-based System to Enhance Knowledge Management Program","authors":"Yujia Wang, Chalermpon Kongjit, T. Hara, Daichi Amagata","doi":"10.1109/ICAWST.2018.8517218","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517218","url":null,"abstract":"Properly managing knowledge workers greatly impacts and influences the contemporary globalized world. Although, much research has emphasized what kinds of competency knowledge workers should have, it is difficult to reach a consensus on what constitutes a competent lecturer in the knowledge management (KM) domain. Due to the importance of lecturers’ competencies in the context of the student quality assurance, industry requirements, existing problems in the management process and operational functions in changing business environments, it is necessary to design and validate an effective competency model and implement it into practical systems. In this paper, we develop a competency-based knowledge management system which employs a database and competency model. That is, based on a KM lecturers’ competency model, we set up a database so that the internal management process can be easily handled (e.g., administrators can assign the lecture for a particular class, and students can find their advisors). This system demonstrates how a competency model practically supports decision-making and work processes.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122121496","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 : 2018-09-01DOI: 10.1109/ICAWST.2018.8517196
Yuh-Min Tseng, Jui-Di Wu, Ruo-Wei Hung, H. Chien
Now, Internet of Things (IoT) brings people innovative experiences and applications through connectivity of numerous computing devices. In these applications, computing devices generate and exchange a large number of critical and sensitive data. Typically, these computing devices are putted on some unprotected environments that make them to be attractive attack targets while easily suffering from a new kind of threat, called “side-channel attacks By side-channel attacks, an adversary could obtain partial information of secret values (or internal states) stored in these devices by observing execution timing or energy consumption. However, most adversary models of previous cryptographic schemes/protocols do not concern with such side-channel attacks. Indeed, leakage-resilient cryptography is a flexible solution for resisting to side-channel attacks. So far, little work focuses on the design of leakage-resilient certificate-based encryption (LR-CBE) schemes. In the article, we propose the first LR-CBE scheme resilient to continuous key leakage of user's private keys, system secret key and random values. In the generic bilinear group model, security analysis is given to show that the proposed LR-CBE scheme is provably secure against chosen cipher-text attacks under the continual leakage model. Performance evaluation is made to demonstrate that our scheme is suitable for embedded devices.
如今，物联网(Internet of Things, IoT)通过连接众多计算设备，为人们带来创新的体验和应用。在这些应用程序中，计算设备生成并交换大量关键和敏感数据。通常，这些计算设备被放置在一些未受保护的环境中，使它们成为有吸引力的攻击目标，同时容易遭受一种新的威胁，称为“侧信道攻击”。通过侧信道攻击，攻击者可以通过观察执行时间或能量消耗来获取存储在这些设备中的秘密值(或内部状态)的部分信息。然而，以前的加密方案/协议的大多数对手模型都不关心这种侧信道攻击。事实上，防泄漏加密技术是一种抵抗侧信道攻击的灵活解决方案。到目前为止，很少有人关注基于证书的防泄漏加密(LR-CBE)方案的设计。本文提出了首个抗用户私钥、系统私钥和随机值连续密钥泄露的LR-CBE方案。在一般双线性群模型下，对所提出的LR-CBE方案进行了安全性分析，证明了该方案在连续泄漏模型下对所选密文攻击的安全性。性能评估表明，该方案适用于嵌入式设备。
{"title":"Leakage-Resilient Certificate-based Encryption Scheme for IoT Environments","authors":"Yuh-Min Tseng, Jui-Di Wu, Ruo-Wei Hung, H. Chien","doi":"10.1109/ICAWST.2018.8517196","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517196","url":null,"abstract":"Now, Internet of Things (IoT) brings people innovative experiences and applications through connectivity of numerous computing devices. In these applications, computing devices generate and exchange a large number of critical and sensitive data. Typically, these computing devices are putted on some unprotected environments that make them to be attractive attack targets while easily suffering from a new kind of threat, called “side-channel attacks By side-channel attacks, an adversary could obtain partial information of secret values (or internal states) stored in these devices by observing execution timing or energy consumption. However, most adversary models of previous cryptographic schemes/protocols do not concern with such side-channel attacks. Indeed, leakage-resilient cryptography is a flexible solution for resisting to side-channel attacks. So far, little work focuses on the design of leakage-resilient certificate-based encryption (LR-CBE) schemes. In the article, we propose the first LR-CBE scheme resilient to continuous key leakage of user's private keys, system secret key and random values. In the generic bilinear group model, security analysis is given to show that the proposed LR-CBE scheme is provably secure against chosen cipher-text attacks under the continual leakage model. Performance evaluation is made to demonstrate that our scheme is suitable for embedded devices.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129633624","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}
The global consumption trend of facial skin care products market is gradually changing. With the concept of preventing aging from becoming more common, the age level of using facial skin care products is gradually reduced, so that the demand of young consumer groups gradually increases. This paper used a deep learning algorithm based on the combination of a smart phone and facial skin detection to develop a facial skin image classification system using Convolutional Neural Networks (CNN) deep learning algorithm. In this system, it can recognize three classes facial skin problem, good facial skin quality, bad facial skin quality and face makeup, which helps people quickly understand their facial skin problem. We proposed two different CNN architectures. One has two convolutional layers, two pooling layers and three fully connected layer and the other has three convolution layers, three pooling layers, and four fully connected layer. Finally, we compare the result of our proposed architecture with LeNet-5. From the experimental result, we understand that the architecture which has three convolution layers, three pooling layers, and four fully connected layer, has the highest recognition rate, and we use it as a baseline to build a framework for detecting facial skin problems.
{"title":"Facial skin image classification system using Convolutional Neural Networks deep learning algorithm","authors":"Chiun-Li Chin, Ming-Chieh Chin, Ting-Yu Tsai, Wei-En Chen","doi":"10.1109/ICAWST.2018.8517246","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517246","url":null,"abstract":"The global consumption trend of facial skin care products market is gradually changing. With the concept of preventing aging from becoming more common, the age level of using facial skin care products is gradually reduced, so that the demand of young consumer groups gradually increases. This paper used a deep learning algorithm based on the combination of a smart phone and facial skin detection to develop a facial skin image classification system using Convolutional Neural Networks (CNN) deep learning algorithm. In this system, it can recognize three classes facial skin problem, good facial skin quality, bad facial skin quality and face makeup, which helps people quickly understand their facial skin problem. We proposed two different CNN architectures. One has two convolutional layers, two pooling layers and three fully connected layer and the other has three convolution layers, three pooling layers, and four fully connected layer. Finally, we compare the result of our proposed architecture with LeNet-5. From the experimental result, we understand that the architecture which has three convolution layers, three pooling layers, and four fully connected layer, has the highest recognition rate, and we use it as a baseline to build a framework for detecting facial skin problems.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130098603","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 : 2018-09-01DOI: 10.1109/ICAWST.2018.8517226
Shing-Hong Liu, Shao-Heng Lai, Tai-Shen Huang
Multi-channel physiological signal measurement systems that are available at the moment are usually wired ones, such as the BioPack MP150 system. The latter, however, is known for its huge size, required connection to alternated current power, lack of an independent data storage unit, and lack of wireless transmission. This study aims to develop a portable wireless physiological signal measurement system that consists of 8 channels. With TI MSP430 F5438A at its microcontroller unit, it had a compact size, lithium battery to supply the needed power, Bluetooth 3.0 data transmission, and built-in 2G flash memory, and the signals were showed on a tablet, smart phone or a notebook computer concurrently. Meanwhile, it also supported the extra power supply, ± 3V for the other measurement modules. Researcher could use a self-made sensor circuit, and combined with this system to do the ubiquitous healthcare studies.
{"title":"A Study on the Development of Portable Wireless Multi-channel Physiological Signal Measurement System","authors":"Shing-Hong Liu, Shao-Heng Lai, Tai-Shen Huang","doi":"10.1109/ICAWST.2018.8517226","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517226","url":null,"abstract":"Multi-channel physiological signal measurement systems that are available at the moment are usually wired ones, such as the BioPack MP150 system. The latter, however, is known for its huge size, required connection to alternated current power, lack of an independent data storage unit, and lack of wireless transmission. This study aims to develop a portable wireless physiological signal measurement system that consists of 8 channels. With TI MSP430 F5438A at its microcontroller unit, it had a compact size, lithium battery to supply the needed power, Bluetooth 3.0 data transmission, and built-in 2G flash memory, and the signals were showed on a tablet, smart phone or a notebook computer concurrently. Meanwhile, it also supported the extra power supply, ± 3V for the other measurement modules. Researcher could use a self-made sensor circuit, and combined with this system to do the ubiquitous healthcare studies.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127906198","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 : 2018-09-01DOI: 10.1109/ICAWST.2018.8517222
Chowdhury Md Intisar, Y. Watanobe
Computer programming is one of the most important and vital skill in the current generation. In order to encourage and enable programmers to practice and sharpen their skills, there exist many online judge programming platforms. Estimation of these programmers’ strength and progress has been an important research topic in educational data mining in order to provide adaptive educational contents and early prediction of ‘at risk’ learner. In this paper, we trained a Kohonen Self organizing feature map (KSOFM) neural network on programmers’ performance log data of Aizu Online Judge (AOJ) database. Propositional rules and knowledge was extracted from the U-matrix diagram of the trained network which partitioned AOJ programmers into three distinct clusters ie. ‘expert’, ‘intermediate’ and ‘at risk’. The proportional rules performed classification with an accuracy of 94% on a testing set. For validation and comparison, three more predicting models were trained on the same dataset. Among them, feedforward multilayer neural network and decision tree have scored accuracy of 97% and 96% respectively. In contrast, the precision score for support vector machine was about 88%, but it scored the highest recall score of 99% in terms of identifying ‘at risk’ students.
{"title":"Classification of Online Judge Programmers based on Rule Extraction from Self Organizing Feature Map","authors":"Chowdhury Md Intisar, Y. Watanobe","doi":"10.1109/ICAWST.2018.8517222","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517222","url":null,"abstract":"Computer programming is one of the most important and vital skill in the current generation. In order to encourage and enable programmers to practice and sharpen their skills, there exist many online judge programming platforms. Estimation of these programmers’ strength and progress has been an important research topic in educational data mining in order to provide adaptive educational contents and early prediction of ‘at risk’ learner. In this paper, we trained a Kohonen Self organizing feature map (KSOFM) neural network on programmers’ performance log data of Aizu Online Judge (AOJ) database. Propositional rules and knowledge was extracted from the U-matrix diagram of the trained network which partitioned AOJ programmers into three distinct clusters ie. ‘expert’, ‘intermediate’ and ‘at risk’. The proportional rules performed classification with an accuracy of 94% on a testing set. For validation and comparison, three more predicting models were trained on the same dataset. Among them, feedforward multilayer neural network and decision tree have scored accuracy of 97% and 96% respectively. In contrast, the precision score for support vector machine was about 88%, but it scored the highest recall score of 99% in terms of identifying ‘at risk’ students.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124419562","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 : 2018-09-01DOI: 10.1109/ICAWST.2018.8517194
Takumi Yoshino, Arisa Takura, Y. Shirota
–Stock prices are likely to reflect the reputation of the company’s sales products. When the company sells a new product, if the stock price increases, we can think that the new product is welcome on the market. In the paper, we shall propose a detection model for the correlation between an SNS (Social Network Service) spike concerning the product and stock price movement. If we find an SNS spike, firstly, topic extraction is conducted on the SNS text data to remove the noise data to extract a purely breaking topic. Then, from the breaking topic distribution and period, we make the differential equation. Finally, we determine whether the solution data matches the actual stock price data. If we find the correlation between the SNS spike and the stock price change, we can predict the future stock price movement.
{"title":"Marketing Awareness by Social Network: A Case Study of HeatTech Products","authors":"Takumi Yoshino, Arisa Takura, Y. Shirota","doi":"10.1109/ICAWST.2018.8517194","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517194","url":null,"abstract":"–Stock prices are likely to reflect the reputation of the company’s sales products. When the company sells a new product, if the stock price increases, we can think that the new product is welcome on the market. In the paper, we shall propose a detection model for the correlation between an SNS (Social Network Service) spike concerning the product and stock price movement. If we find an SNS spike, firstly, topic extraction is conducted on the SNS text data to remove the noise data to extract a purely breaking topic. Then, from the breaking topic distribution and period, we make the differential equation. Finally, we determine whether the solution data matches the actual stock price data. If we find the correlation between the SNS spike and the stock price change, we can predict the future stock price movement.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126884618","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}
This paper proposed a low cost worm-shape robot that can mimic the locomotion of a caterpillar to crawl by arching and stretching its body. Based on the process of implementing the caterpillar robot, the participated students can built up their interdisciplinary skills. Students have to integrate the technologies of micro-controllers, sensors, materials and mechanisms. We used a Motoduino U1 module to drive 4 servomotors and Bluetooth module to communicate between robot and users. In order to imitate gaits of caterpillars, the body of the robot was made by using discrete PVC rings to flexibly arch. The robot also can be remotely controlled to crawl forward/back or right/left, and change its height of arched back. The weight and total length of the robot were bout 470g and 65cm, respectively. The experiment shows that when the caterpillar robot moves in line, the averaged speed of robot can be 25m/hr. The maximum rotating angle is 30° and the minimum rotating diameter is 120cm.
{"title":"Design and Implementation of a Caterpillar Robot","authors":"Li-Chun Liao, Gwo-Liang Liao, Yen-Yu Lin, Chen-Yu Huang, Yun-Chen Tsai","doi":"10.1109/ICAWST.2018.8517166","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517166","url":null,"abstract":"This paper proposed a low cost worm-shape robot that can mimic the locomotion of a caterpillar to crawl by arching and stretching its body. Based on the process of implementing the caterpillar robot, the participated students can built up their interdisciplinary skills. Students have to integrate the technologies of micro-controllers, sensors, materials and mechanisms. We used a Motoduino U1 module to drive 4 servomotors and Bluetooth module to communicate between robot and users. In order to imitate gaits of caterpillars, the body of the robot was made by using discrete PVC rings to flexibly arch. The robot also can be remotely controlled to crawl forward/back or right/left, and change its height of arched back. The weight and total length of the robot were bout 470g and 65cm, respectively. The experiment shows that when the caterpillar robot moves in line, the averaged speed of robot can be 25m/hr. The maximum rotating angle is 30° and the minimum rotating diameter is 120cm.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130463677","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 : 2018-09-01DOI: 10.1109/ICAWST.2018.8517169
Van Quan Dang, Yan Pei
We use kernel method-based autoencoder in feature extraction application and evaluate its performance with a public handwriting database. Neural network-based autoencoder is an unsupervised algorithm and model that tries to learn an approximation function so as to extract features from data. Kernel method-based autoencoder has the same function compared with neural network-based autoencoder, but uses kernel methods to implement linear and non-linear data transformation. We use a handwriting dataset to evaluate kernel-based autoencoder, and examine the result by mean square error estimator, structural similarity index and peak signal-to-noise ratio for measuring image quality. We also investigate parameters of kernel functions to observe changes in the performance of the autoencoder. We found that effectiveness of kernel method-based autoencoder depends on the selection of kernel function and its parameter.
{"title":"A Study on Feature Extraction of Handwriting Data Using Kernel Method-Based Autoencoder","authors":"Van Quan Dang, Yan Pei","doi":"10.1109/ICAWST.2018.8517169","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517169","url":null,"abstract":"We use kernel method-based autoencoder in feature extraction application and evaluate its performance with a public handwriting database. Neural network-based autoencoder is an unsupervised algorithm and model that tries to learn an approximation function so as to extract features from data. Kernel method-based autoencoder has the same function compared with neural network-based autoencoder, but uses kernel methods to implement linear and non-linear data transformation. We use a handwriting dataset to evaluate kernel-based autoencoder, and examine the result by mean square error estimator, structural similarity index and peak signal-to-noise ratio for measuring image quality. We also investigate parameters of kernel functions to observe changes in the performance of the autoencoder. We found that effectiveness of kernel method-based autoencoder depends on the selection of kernel function and its parameter.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122384617","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 : 2018-09-01DOI: 10.1109/ICAWST.2018.8517232
Da Li, Rafal Rzepka, M. Ptaszynski, K. Araki
Pictograms (emoticons/emojis) have been widely used in social media as a mean for graphical expression of emotions. People can express delicate nuances through textual information when supported with emoticons, and the effectiveness of computer-mediated communication (CMC) is also improved. Therefore it is important to fully understand the influence of emoticons on CMC. In this paper, we propose an emoticon polarity-aware recurrent neural network method for sentiment analysis of Weibo, a Chinese social media platform. In the first step, we analyzed the usage of 67 emoticons with racial expression used on Weibo. By performing a polarity annotation with a new “humorous type” added, we have confirmed that 23 emoticons can be considered more as humorous than positive or negative. On this basis, we applied the emoticons polarity in a Long Short-Term Memory recurrent neural network (LSTM) for sentiment analysis of undersized labelled data. Our experimental results show that the proposed method can significantly improve the precision for predicting sentiment polarity on Weibo.
{"title":"Emoticon-Aware Recurrent Neural Network Model for Chinese Sentiment Analysis","authors":"Da Li, Rafal Rzepka, M. Ptaszynski, K. Araki","doi":"10.1109/ICAWST.2018.8517232","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517232","url":null,"abstract":"Pictograms (emoticons/emojis) have been widely used in social media as a mean for graphical expression of emotions. People can express delicate nuances through textual information when supported with emoticons, and the effectiveness of computer-mediated communication (CMC) is also improved. Therefore it is important to fully understand the influence of emoticons on CMC. In this paper, we propose an emoticon polarity-aware recurrent neural network method for sentiment analysis of Weibo, a Chinese social media platform. In the first step, we analyzed the usage of 67 emoticons with racial expression used on Weibo. By performing a polarity annotation with a new “humorous type” added, we have confirmed that 23 emoticons can be considered more as humorous than positive or negative. On this basis, we applied the emoticons polarity in a Long Short-Term Memory recurrent neural network (LSTM) for sentiment analysis of undersized labelled data. Our experimental results show that the proposed method can significantly improve the precision for predicting sentiment polarity on Weibo.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127480708","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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