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2018 9th International Conference on Awareness Science and Technology (iCAST)最新文献

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A New Filter Evaluation Function for Feature Subset Selection with Evolutionary Computation 一种基于进化计算的特征子集选择滤波器评价函数
2018 9th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2018-09-01 DOI: 10.1109/ICAWST.2018.8517241
Atsushi Kawamura, B. Chakraborty
{"title":"A New Filter Evaluation Function for Feature Subset Selection with Evolutionary Computation","authors":"Atsushi Kawamura, B. Chakraborty","doi":"10.1109/ICAWST.2018.8517241","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517241","url":null,"abstract":"Feature subset selection is an optimization problem to achieve high classification accuracy with low number of features and low computational cost in the area of pattern classi- fication or data mining. There are various approaches to obtain this. Basically a search algorithm is used with a fitness function either based on intrinsic characteristics of the data, known as filter type, or based on classification accuracy of the classifier used, known as the wrapper type, to find out the optimum feature subset. Both the approaches have respective merits and demerits. Though lots of algorithms are developed so far, none of them works equally well for all the data sets, specially for very high dimensional data sets. In this work, a new feature evaluation measure based on the concept borrowed from topic modelling in text processing, has been developed. The proposed measure is used as a fitness function of evolutionary computational search techniques for designing filter type feature subset selection approach. Simulation experiments with various benchmark data sets have been done for assessing the efficiency of the proposed approach in comparison to the popular conventional filter type feature selection algorithms mRMR and CFS. It is found that the proposed approach is better in terms of selecting lesser number of features with comparable classification accuracy. The proposed algorithms work better for higher dimensional features and can be proved as an effective solution of feature selection for very high dimensional data.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"21 2 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":"133089108","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: 1
Which Source Code Plagiarism Detection Approach is More Humane? 哪种源代码抄袭检测方法更人性化?
2018 9th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2018-09-01 DOI: 10.1109/ICAWST.2018.8517170
Oscar Karnalim, Lisan Sulistiani
{"title":"Which Source Code Plagiarism Detection Approach is More Humane?","authors":"Oscar Karnalim, Lisan Sulistiani","doi":"10.1109/ICAWST.2018.8517170","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517170","url":null,"abstract":"This paper contributes in developing source code plagiarism detection that is more aligned with human perspective. Three evaluation mechanisms that directly relate human perspective with evaluated approaches are proposed: think-aloud, aspectoriented, and empirical mechanism. Using those mechanisms, a comparative study toward attribute-and structure-based plagiarism detection approach (i.e., two popular approach categories in source code plagiarism detection) is conducted. According to that study, structure-based approach is more effective than the attribute-based one; its signature aspect and resulted similarity degrees are more related to human preferences. In addition, such approach is related to most human-oriented aspects for suspecting source code plagiarism.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"38 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":"132955029","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
A Data Reconstruction Method for The Big-Data Analysis 面向大数据分析的数据重构方法
2018 9th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2018-09-01 DOI: 10.1109/ICAWST.2018.8517197
Masataka Mito, K. Murata, Daisuke Eguchi, Yuichiro Mori, M. Toyonaga
{"title":"A Data Reconstruction Method for The Big-Data Analysis","authors":"Masataka Mito, K. Murata, Daisuke Eguchi, Yuichiro Mori, M. Toyonaga","doi":"10.1109/ICAWST.2018.8517197","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517197","url":null,"abstract":"In recent years, the big-data approach has become important within various business operations and salesjudgment tactics. Contrarily, numerous privacy problems limit the progress of their analysis technologies. To mitigate such problems, this paper proposes several privacy-preserving methods, i.e., anonymization, extreme value record elimination, fully encrypted analysis, and so on. However, privacy-cracking fears still remain that prevent the open use of big-data by other, external organizations. We propose a big-data reconstruction method that does not intrinsically use privacy data. The method uses only the statistical features of big-data, i.e., its attribute histograms and their correlation coefficients. To verify whether valuable information can be extracted using this method, we evaluate the data by using Self Organizing Map (SOM) as one of the big-data analysis tools. The results show that the same pieces ofinformation are extracted from our data and the big-data.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"11 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":"132444650","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
Logic Error Detection Algorithm for Novice Programmers based on Structure Pattern and Error Degree 基于结构模式和错误程度的初级程序员逻辑错误检测算法
2018 9th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2018-09-01 DOI: 10.1109/ICAWST.2018.8517171
Yuto Yoshizawa, Y. Watanobe
{"title":"Logic Error Detection Algorithm for Novice Programmers based on Structure Pattern and Error Degree","authors":"Yuto Yoshizawa, Y. Watanobe","doi":"10.1109/ICAWST.2018.8517171","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517171","url":null,"abstract":"In recent years, the importance of programming skills is increasing due to advances in information and communication technologies. However, the difficulty involved in learning programming is a major problem for novices. Therefore, we propose a logic error detection algorithm based on structure pattern and error degree. Structure pattern is an index of similarity based on abstract syntax trees, and error degree is a measure of appropriateness for feedback. In the present paper, we define structure pattern and error degree and present the proposed algorithm method. Implementation and experimentation using actual data are also considered.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"13 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":"126343496","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: 10
Aspect Aware Optimized Opinion Analysis of Online Product Reviews 面向方面的在线产品评论优化意见分析
2018 9th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2018-09-01 DOI: 10.1109/ICAWST.2018.8517172
S. Das, B. Chakraborty
{"title":"Aspect Aware Optimized Opinion Analysis of Online Product Reviews","authors":"S. Das, B. Chakraborty","doi":"10.1109/ICAWST.2018.8517172","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517172","url":null,"abstract":"Now-a-days social media and micro blogging sites are the most popular form of communication. The most useful application on these platforms is Opinion mining or Sentiment classification of the users. Here, in this work an automated method has been proposed to analyze and summarize opinions on a product in a structured, product aspect based manner. The proposed method will help future potential buyers to acquire complete idea, from a comprehensible representation of the reviews, without going through all the reviews manually.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"26 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":"125970124","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
The Assistance for Drug Dispensing Using LED Notification and IR Sensor-based Monitoring Methods 使用LED通知和基于红外传感器的监测方法协助药品调剂
2018 9th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2018-09-01 DOI: 10.1109/ICAWST.2018.8517168
Chin-Chuan Han, Hao-Pu Lin, Chao-Hsu Chang, Chang-Hsing Lee, Jau-Ling Shih, Chunlan Hsu, Jen-Chih Chang
{"title":"The Assistance for Drug Dispensing Using LED Notification and IR Sensor-based Monitoring Methods","authors":"Chin-Chuan Han, Hao-Pu Lin, Chao-Hsu Chang, Chang-Hsing Lee, Jau-Ling Shih, Chunlan Hsu, Jen-Chih Chang","doi":"10.1109/ICAWST.2018.8517168","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517168","url":null,"abstract":"In this study, an assistant system is developed for pharmacist to improve the dispensing quality by two functions: notification in time and monitoring in real time. During drug dispensation, the system gets the patient’s prescription issued from doctors, and drives the light-emitting diode (LED) for notification. Since some drug titles, shapes, colors, or packages are very similar, pharmacists waste lots of time to find the correct drugs. With LED notification, pharmacists pick up the drugs from the correct cabinets, and save the dispensing time. Second, the system monitors pharmacist actions by the infrared (IR) sensors. An alarm is given if pharmacists pick up the incorrect drugs or lost the drug items, even the correct LEDs are turn on. In addition, a web-based information system is designed for drug dispensing and inventory management. During the dispensation, patient information and drug data are displayed on the screen for notification.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"28 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114025312","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: 1
Video Summarization: How to Use Deep-Learned Features Without a Large-Scale Dataset 视频摘要:如何在没有大规模数据集的情况下使用深度学习的特征
2018 9th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2018-08-24 DOI: 10.29007/21Q3
Didik Purwanto, Yie-Tarng Chen, Wen-Hsien Fang, Wen-Chi Wu
{"title":"Video Summarization: How to Use Deep-Learned Features Without a Large-Scale Dataset","authors":"Didik Purwanto, Yie-Tarng Chen, Wen-Hsien Fang, Wen-Chi Wu","doi":"10.29007/21Q3","DOIUrl":"https://doi.org/10.29007/21Q3","url":null,"abstract":"This paper proposes a framework incorporating deep-learned features with the conventional machine learning models within which the objective function is optimized by using quadratic programming or quasi-Newton methods instead of an end-to-end deep learning approach which uses variants of stochastic gradient descent algorithms. A temporal segmentation algorithm is first scrutinized by using a learning to rank scheme to detect the abrupt changes of frame appearances in a video sequence. Afterward, a peak-searching algorithm, statisticssensitive non-linear iterative peak-clipping (SNIP), is employed to acquire the local maxima of the filtered video sequence after rank pooling, where each of the local maxima corresponds to a key frame in the video. Simulations show that the new approach outperforms the main state-of-the-art works on four public video datasets.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126686345","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
Modeling Non-Compositional Expressions using a Search Engine 使用搜索引擎建模非组合表达式
2018 9th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2018-08-09 DOI: 10.29007/4JL9
Cheikh M. Bamba Dione, Christer Johansson
{"title":"Modeling Non-Compositional Expressions using a Search Engine","authors":"Cheikh M. Bamba Dione, Christer Johansson","doi":"10.29007/4JL9","DOIUrl":"https://doi.org/10.29007/4JL9","url":null,"abstract":"Non-compositional multi-word expressions present great challenges to natural language processing applications. In this paper, we present a method for modeling non-compositional expressions based on the assumption that the meaning of expressions depends on context. Therefore, context words can be used to select documents and separate documents where the expression has different meanings. Deviation from a baseline is measured using serendipity (i.e. the pointwise effect size). We used this statistical measure to mark which patterns are over-and under-represented and to take a decision if the pattern under scrutiny belongs to the meaning selected by the context words or not. We used the Google search engine to find document frequency estimates. When used with Google document frequency estimates, the serendipity measure closely mirrors some human intuitions on the preferred alternative.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127070778","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: 1
An Enhanced Hybrid MobileNet 增强型混合移动网络
2018 9th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2017-12-13 DOI: 10.1109/ICAWST.2018.8517177
Hong-Yen Chen, Chung-Yen Su
{"title":"An Enhanced Hybrid MobileNet","authors":"Hong-Yen Chen, Chung-Yen Su","doi":"10.1109/ICAWST.2018.8517177","DOIUrl":"https://doi.org/10.1109/ICAWST.2018.8517177","url":null,"abstract":"Complicated and deep neural network models can achieve high accuracy for image recognition. However, they require a huge amount of computations and model parameters, which are not suitable for mobile and embedded devices. Therefore, MobileNet was proposed, which can reduce the number of parameters and computational cost dramatically. The main idea of MobileNet is to use a depthwise separable convolution. Two hyper-parameters, a width multiplier and a resolution multiplier are used to the trade-off between the accuracy and the latency. In this paper, we propose a new architecture to improve the MobileNet. Instead of using the resolution multiplier, we use a depth multiplier and combine with either Fractional Max Pooling or the max pooling. Experimental results on CIFAR database show that the proposed architecture can reduce the amount of computational cost and increase the accuracy simultaneously 1.This work is partly supported by Ministry of Science and Technology, R.O.C. under Contract No. MOST 106-2221-E-003-011.","PeriodicalId":277939,"journal":{"name":"2018 9th International Conference on Awareness Science and Technology (iCAST)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133152471","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: 51
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