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2017 9th International Conference on Knowledge and Smart Technology (KST)最新文献

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Polysemy detection in distributed representation of word sense 词义分布表示中的多义检测
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886073
Kana Oomoto, Haruka Oikawa, E. Yamamoto, Mitsuo Yoshida, Masayuki Okabe, Kyoji Umemura
{"title":"Polysemy detection in distributed representation of word sense","authors":"Kana Oomoto, Haruka Oikawa, E. Yamamoto, Mitsuo Yoshida, Masayuki Okabe, Kyoji Umemura","doi":"10.1109/KST.2017.7886073","DOIUrl":"https://doi.org/10.1109/KST.2017.7886073","url":null,"abstract":"In this paper, we propose a statistical test to determine whether a given word is used as a polysemic word or not. The statistic of the word in this test roughly corresponds to the fluctuation in the senses of the neighboring words a nd the word itself. Even though the sense of a word corresponds to a single vector, we discuss how polysemy of the words affects the position of vectors. Finally, we also explain the method to detect this effect.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"57 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132900382","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}
引用次数: 2
Social network user identification 社交网络用户识别
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886120
Laikhram Jamjuntra, Pantakan Chartsuwan, Peerapong Wonglimsamut, Kriengkrai Porkaew, Umaporn Supasitthimethee
{"title":"Social network user identification","authors":"Laikhram Jamjuntra, Pantakan Chartsuwan, Peerapong Wonglimsamut, Kriengkrai Porkaew, Umaporn Supasitthimethee","doi":"10.1109/KST.2017.7886120","DOIUrl":"https://doi.org/10.1109/KST.2017.7886120","url":null,"abstract":"Many people use more than one social network services (SNS). They create accounts for sharing both private and public information. These information are digital footprints that can be used to identify the owners. To identify the account identities, it is necessary to gather user information regarding their online behaviors. It can be used for marketing and customer interaction. In this paper, the architecture for matching accounts across multiple SNSs was proposed. It is designed for extensibility and configurability so that, given an account of a person on an SNS, it can be used to find other accounts belonging to the same person on any SNS. The system collects account information, such as username, friends, and interests from accounts on various SNSs and compare them using Jaro-Winkler distance algorithm. To be specific, this system focuses on finding Twitter accounts of a person given his/her Facebook account. An experiment of 138 user accounts are tested and its accuracy was 55%, which is almost twice better than a previous research on a similar situation.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132918978","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}
引用次数: 14
Multiclass support vector machine for classification spatial data from satellite image 基于多类支持向量机的卫星图像空间数据分类
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886107
Kanita Tangthaikwan, N. Keeratipranon, Adshariya Agsornintara
{"title":"Multiclass support vector machine for classification spatial data from satellite image","authors":"Kanita Tangthaikwan, N. Keeratipranon, Adshariya Agsornintara","doi":"10.1109/KST.2017.7886107","DOIUrl":"https://doi.org/10.1109/KST.2017.7886107","url":null,"abstract":"This paper presents a method for the classification of Landsat Multi-Spectral Scanner (MSS) satellite images to identify the areas of land use. The image is pre-processed and classified using Support Vector Machine (SVM) with the Radial Basis Function (RBF) Kernel as it is an efficient supervised-classification technique. In this research, pixel — base classification method is performed according to the value of spectral pixels with Multi-Spectral Scanner satellite image and used data corresponds to a 3×3 square neighborhoods. The research work consists of two main stages. At the first stage, the optimal parameter, sigma value of RBF kernel, for SVM is studied. At the second stage, the obtained classification result is compared with other classification methods. In this study, sigma value, a parameter of RBF kernel, is varied between 1.0 and 2.0. The sigma value at 1.7 lead to the best classification result which has over 90% accuracy. The result from this SVM method has a higher accuracy compared to other methods.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114784891","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}
引用次数: 9
Dog cough sound classification using artificial neural network and the selected relevant features from discrete wavelet transform 利用人工神经网络对狗咳嗽声进行分类，并从离散小波变换中选取相关特征
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886118
Panchana Kakabutr, K. Chen, Viewpaka Wangvisavawit, P. Padungweang, Olarn Rojanapornpun
{"title":"Dog cough sound classification using artificial neural network and the selected relevant features from discrete wavelet transform","authors":"Panchana Kakabutr, K. Chen, Viewpaka Wangvisavawit, P. Padungweang, Olarn Rojanapornpun","doi":"10.1109/KST.2017.7886118","DOIUrl":"https://doi.org/10.1109/KST.2017.7886118","url":null,"abstract":"Coughing is one of the important signs of several diseases in dogs. There are two types of dog cough: dry cough and productive cough. The latter is most often associated with an infectious condition. It is difficult to differentiate between the two types even by experienced practitioners. In this paper, an automatic cough sound classification using neural network is introduced. A discrete wavelet transform is employed to decompose the cough sound into low frequency and high frequency components. The statistical features of these components are used as the sound features. The discrimination power of these features in classification are evaluated. Finally, the artificial neural network is used to classify dog cough sound using a subset of discriminant features. The experimental results show that classifying dog cough sounds needs only one fourth of all features and an average accuracy as high as 90% is achievable.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122195665","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}
引用次数: 3
A search for geometric-shape objects in a vector image: Scalable Vector Graphics (SVG) file format 在矢量图像中搜索几何形状对象:可缩放矢量图形(SVG)文件格式
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886098
Chanon Seel-audom, Wassana Naiyapo, Varin Chouvatut
{"title":"A search for geometric-shape objects in a vector image: Scalable Vector Graphics (SVG) file format","authors":"Chanon Seel-audom, Wassana Naiyapo, Varin Chouvatut","doi":"10.1109/KST.2017.7886098","DOIUrl":"https://doi.org/10.1109/KST.2017.7886098","url":null,"abstract":"This paper proposes a model for detecting objects with geometric shapes in an image whose file format is SVG (Scalable Vector Graphics). The SVG is known as a type of vector images. A major interesting feature of SVG is that components in victor images are created using mathematical theories and equations, in addition, vector images have many more advantages over raster images in various aspects. Moreover, an object's details in a human's sight, a triangle for example, is looked as it has three corners. But the true shape created in SVG of the object may be different from the human's limited sight. Observing these gaps or limitation, we thus propose alternatively simple methods for processing on objects with geometric shapes directly in a vector image without loss of any details in its original form.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123320278","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}
引用次数: 4
Human fall detection by using the body vector 基于身体向量的人体跌倒检测
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886075
Siriporn Pattamaset, T. Charoenpong, Patsama Charoenpong, C. Chianrabutra
{"title":"Human fall detection by using the body vector","authors":"Siriporn Pattamaset, T. Charoenpong, Patsama Charoenpong, C. Chianrabutra","doi":"10.1109/KST.2017.7886075","DOIUrl":"https://doi.org/10.1109/KST.2017.7886075","url":null,"abstract":"Due to a problem of current research occurring when detecting a human falling in a camera direction, we propose a new method for detecting human fall detection by using body vector technique. This method consists of three steps. Two image sequence is used as the input of the system. Firstly markers are affixed on sixteen joints. These markers are extracted by using mahalanobis distance. Secondly, the stereo vision technique is used to construct human joints in three dimensional space. Finally, joint coordinates are used to compute principal component vector. Human falling is detected from an angle between the human body vector and the vertical axis and the human center velocity. To test the performance of the proposed method, subject walks to the cameras. Falling down in the camera direction by twenty subjects is used. Accuracy is 100%. This method perform effectively for detecting human falling in the camera direction.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131468664","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}
引用次数: 3
Thai local product recommendation using ontological content based filtering 使用基于本体内容过滤的泰国本地产品推荐
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886079
Natedao Thotharat
{"title":"Thai local product recommendation using ontological content based filtering","authors":"Natedao Thotharat","doi":"10.1109/KST.2017.7886079","DOIUrl":"https://doi.org/10.1109/KST.2017.7886079","url":null,"abstract":"This paper presents ontology based recommender system using similarity of properties of Thai local products. With the designed ontology, products are mapped to the schema to realize their properties. The application is designed for users to search a product with criteria, and the selected item is recognized as a base for recommendation expansion. The recommendation is made by comparing ontology properties of the base item with other items, and the items with common properties are recommended to the user. User profiles are asked to prevent recommending products with unwanted properties while positive preference is designed to rank the recommendation result. From experiment, we found that the recommendation result is able to gain users' interest with their view count on the most of generated recommended item.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125925519","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}
引用次数: 9
Mining high-utility itemsets with irregular occurrence 挖掘不规则出现的高效用项集
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2016-08-31 DOI: 10.5614/ITBJ.ICT.RES.APPL.2016.10.2.5
Supachai Laoviboon, Komate Amphawan
{"title":"Mining high-utility itemsets with irregular occurrence","authors":"Supachai Laoviboon, Komate Amphawan","doi":"10.5614/ITBJ.ICT.RES.APPL.2016.10.2.5","DOIUrl":"https://doi.org/10.5614/ITBJ.ICT.RES.APPL.2016.10.2.5","url":null,"abstract":"High-utility itemsets mining (HUIM) is proposed to discover itemsets giving high utilities (such as high profit, low cost/risk and other factors). This can help to extract hidden-knowledge from buying behavior of customers. However, HUIM may not sufficiently give hidden-knowledge and observe occurrence behavior of itemsets in some applications, since it only considers utilities of items/itemsets. Thus, we propose to mine high utility itemsets with irregular occurrence (also called High Utility-Irregular Itemsets, HUIIs). HUIIs can help to gain knowledge about “products giving high profits even if customers do not regularly purchase them together” and to improve marketing strategies and sale profit. To mine HUIIs, an efficient single-pass algorithm based on the use of new modified utility-list structure, called HUIIM (HUIIs-Miner), is designed. Experiments on real and synthetic datasets were done to investigate computational time and memory consumption of HUIIM.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129284037","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}
引用次数: 8
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