Pub Date : 2017-11-01DOI: 10.1109/ICAWST.2017.8256450
J. Teh, Yu-Huei Cheng, Ching-Ming Lai
As the penetration of wind power into the power system increases, the ability to assess the reliability impact of such interaction becomes more important. The composite reliability evaluations involving wind energy provide ample opportunities for assessing the benefits of different wind farm connection points. A connection to the weak area of the transmission network will require network reinforcement for absorbing the additional wind energy. Traditionally, the reinforcements are performed by constructing new transmission corridors. However, a new state-of-art technology such as the dynamic thermal rating (DTR) system, provides new reinforcement strategy and this requires new reliability assessment method. This paper demonstrates a methodology for assessing the cost and the reliability of a network reinforcement strategy by considering the DTR systems when large scale wind farms are connected to existing power network. Sequential Monte Carlo simulations were performed and all DTRs and wind speed were simulated using the auto-regressive moving average (ArMA) models.
{"title":"A framework for transmission network planning","authors":"J. Teh, Yu-Huei Cheng, Ching-Ming Lai","doi":"10.1109/ICAWST.2017.8256450","DOIUrl":"https://doi.org/10.1109/ICAWST.2017.8256450","url":null,"abstract":"As the penetration of wind power into the power system increases, the ability to assess the reliability impact of such interaction becomes more important. The composite reliability evaluations involving wind energy provide ample opportunities for assessing the benefits of different wind farm connection points. A connection to the weak area of the transmission network will require network reinforcement for absorbing the additional wind energy. Traditionally, the reinforcements are performed by constructing new transmission corridors. However, a new state-of-art technology such as the dynamic thermal rating (DTR) system, provides new reinforcement strategy and this requires new reliability assessment method. This paper demonstrates a methodology for assessing the cost and the reliability of a network reinforcement strategy by considering the DTR systems when large scale wind farms are connected to existing power network. Sequential Monte Carlo simulations were performed and all DTRs and wind speed were simulated using the auto-regressive moving average (ArMA) models.","PeriodicalId":378618,"journal":{"name":"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115429766","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 : 2017-11-01DOI: 10.1109/ICAWST.2017.8256482
Kuo-Lung Hung, Shih-Che Lai
Digital inpainting is a technique used to remove some specified objects or to repair damaged area in an image or a video file. It has a wide used range in many applications such as in film and entertainment production, digital archives repairing, and satellite photography making. The main challenge in video inpainting is the patched video sequence ought to remain as much visual quality as the original one. To meet this requirement, video inpainting must have a robust object tracking algorithm with considering factors of the continuity of temporal relationship between frames, especially for camera-moving cases. In this paper, we propose a video inpainting algorithm based on the exemplar-based method [2]. In the proposed method, we first use Harris corner detection to extract feature points of each frame and match them between consecutive frames. The frames with same feature points are aligned by using affine transformation, and the median filter method is then used to establish a dynamic panoramic background. Next, we propose a robust object tracking method by using three-steps searching algorithm wherein moving objects can be find in different frames. Then, the foreground objects of each frame are removed by using background subtraction. Finally, we use an exemplar-based video inpainting method to patch the video. Experimental results showed that the proposed method can not only accurately track the moving objects but also can repair the video without losing linear structure of image (frame) and will not produce blur. In sum, the proposed method is an efficient and effective video patching method that repairs the video of acceptable quality.
{"title":"Exemplar-based video inpainting approach using temporal relationship of consecutive frames","authors":"Kuo-Lung Hung, Shih-Che Lai","doi":"10.1109/ICAWST.2017.8256482","DOIUrl":"https://doi.org/10.1109/ICAWST.2017.8256482","url":null,"abstract":"Digital inpainting is a technique used to remove some specified objects or to repair damaged area in an image or a video file. It has a wide used range in many applications such as in film and entertainment production, digital archives repairing, and satellite photography making. The main challenge in video inpainting is the patched video sequence ought to remain as much visual quality as the original one. To meet this requirement, video inpainting must have a robust object tracking algorithm with considering factors of the continuity of temporal relationship between frames, especially for camera-moving cases. In this paper, we propose a video inpainting algorithm based on the exemplar-based method [2]. In the proposed method, we first use Harris corner detection to extract feature points of each frame and match them between consecutive frames. The frames with same feature points are aligned by using affine transformation, and the median filter method is then used to establish a dynamic panoramic background. Next, we propose a robust object tracking method by using three-steps searching algorithm wherein moving objects can be find in different frames. Then, the foreground objects of each frame are removed by using background subtraction. Finally, we use an exemplar-based video inpainting method to patch the video. Experimental results showed that the proposed method can not only accurately track the moving objects but also can repair the video without losing linear structure of image (frame) and will not produce blur. In sum, the proposed method is an efficient and effective video patching method that repairs the video of acceptable quality.","PeriodicalId":378618,"journal":{"name":"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)","volume":"229 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130896230","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 : 2017-11-01DOI: 10.1109/ICAWST.2017.8256469
C. Lee, Incheon Paik
Due to the rapid development of the web, services of social media and Internet of Things (IoT) are producing a huge volume of data in every second. This data is not only large, but also grows quickly and is difficult to analyze. Most of traditional big data framework can't process such data in real-time. For processing the data in real-time, many companies and researchers have started to develop new big data frameworks. The Apache Spark, Apache Flink and Apache Storm have been introduced for real-time data processing. With the new processing frameworks, it has become more efficient to analyze the streaming data. Stock market analysis is a hot issued domain to analyze the big streaming data. In this paper, we build a real-time processing system to analyze tweets for finding correlation with the stock market. System configuration, performance of our system is explained. With 77% accuracy of Twitter data classification, we got 80% of separation of increase/decrease of stock value.
{"title":"Stock market analysis from Twitter and news based on streaming big data infrastructure","authors":"C. Lee, Incheon Paik","doi":"10.1109/ICAWST.2017.8256469","DOIUrl":"https://doi.org/10.1109/ICAWST.2017.8256469","url":null,"abstract":"Due to the rapid development of the web, services of social media and Internet of Things (IoT) are producing a huge volume of data in every second. This data is not only large, but also grows quickly and is difficult to analyze. Most of traditional big data framework can't process such data in real-time. For processing the data in real-time, many companies and researchers have started to develop new big data frameworks. The Apache Spark, Apache Flink and Apache Storm have been introduced for real-time data processing. With the new processing frameworks, it has become more efficient to analyze the streaming data. Stock market analysis is a hot issued domain to analyze the big streaming data. In this paper, we build a real-time processing system to analyze tweets for finding correlation with the stock market. System configuration, performance of our system is explained. With 77% accuracy of Twitter data classification, we got 80% of separation of increase/decrease of stock value.","PeriodicalId":378618,"journal":{"name":"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121586512","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 : 2017-11-01DOI: 10.1109/ICAWST.2017.8256485
Fu-Shiung Hsieh
A Cyber-Physical System (CPS) for IoT-enabled manufacturing environment consists two parts, the Physical World and the Cyber World. Modeling and managing operations of CPS for manufacturing sector are challenging issues due to the complex interaction between entities in the system. Recent progress in artificial intelligence and context-aware computing technology provides a solid background to develop a framework to provide a context-aware guidance information system for guiding resources in IoT enabled CPS. To achieve the goal to design context-aware guidance information system, a method is proposed to combine a scheduler in multi-agent systems (MAS), an algorithm to construct execution model that can be applied to guide the operation of CPS. The proposed method is illustrated by an example.
{"title":"Context-generation for workflows in IoT-enabled cyber-physical systems","authors":"Fu-Shiung Hsieh","doi":"10.1109/ICAWST.2017.8256485","DOIUrl":"https://doi.org/10.1109/ICAWST.2017.8256485","url":null,"abstract":"A Cyber-Physical System (CPS) for IoT-enabled manufacturing environment consists two parts, the Physical World and the Cyber World. Modeling and managing operations of CPS for manufacturing sector are challenging issues due to the complex interaction between entities in the system. Recent progress in artificial intelligence and context-aware computing technology provides a solid background to develop a framework to provide a context-aware guidance information system for guiding resources in IoT enabled CPS. To achieve the goal to design context-aware guidance information system, a method is proposed to combine a scheduler in multi-agent systems (MAS), an algorithm to construct execution model that can be applied to guide the operation of CPS. The proposed method is illustrated by an example.","PeriodicalId":378618,"journal":{"name":"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115344291","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}
With e-Commerce activities burgeoning over the last decades, consumers have seemingly been switching their purchases from the physical marketplace to the Internet marketspace. However, it remains skeptical as to why the consumers are able to make purchase decisions in lack of personally experiencing the products. This study, on the basis of the Engel-Kollat-Blackwell Consumer Purchase Behavior Model (the EKB Model), attempted to explore the effects of the information search behavior in the context of the digital age on consumer decision making when buying apparel via the Internet. The sample consists of 344 with experience of buying apparel products via the Internet, and they were recruited from one of the most popular local online forums, in answer to the questionnaire by recall method. The study found that the information search behavior is related to the decision making, particularly in terms of fame information, visual information, comparison information, e-word-of-mouth information, and promotional information. Moreover, the results proved that personality traits, to some extent, affected the information search and decision making, except for the effects of the performance risk and the personal risk on the decision making.
{"title":"Online apparel shopping behavior: Effects of consumer information search on purchase decision making in the digital age","authors":"Yueh-Chin Chen, Yen-His Lee, Hsiao-Chun Wu, Yu-Chin Sung, Hung-Yi Chen","doi":"10.1109/ICAWST.2017.8256434","DOIUrl":"https://doi.org/10.1109/ICAWST.2017.8256434","url":null,"abstract":"With e-Commerce activities burgeoning over the last decades, consumers have seemingly been switching their purchases from the physical marketplace to the Internet marketspace. However, it remains skeptical as to why the consumers are able to make purchase decisions in lack of personally experiencing the products. This study, on the basis of the Engel-Kollat-Blackwell Consumer Purchase Behavior Model (the EKB Model), attempted to explore the effects of the information search behavior in the context of the digital age on consumer decision making when buying apparel via the Internet. The sample consists of 344 with experience of buying apparel products via the Internet, and they were recruited from one of the most popular local online forums, in answer to the questionnaire by recall method. The study found that the information search behavior is related to the decision making, particularly in terms of fame information, visual information, comparison information, e-word-of-mouth information, and promotional information. Moreover, the results proved that personality traits, to some extent, affected the information search and decision making, except for the effects of the performance risk and the personal risk on the decision making.","PeriodicalId":378618,"journal":{"name":"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122658008","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 : 2017-11-01DOI: 10.1109/ICAWST.2017.8256426
Enrico Laoh, I. Surjandari, Zulkarnain
Determination of optimal distribution route is one of the keys to increase supply chain efficiency. Looking for an optimal distribution route that belongs to the type of vehicle routing problem (VRP) can be solved by modeling the entire boundary of the problem as the constraints and finding the solution with the objective of minimizing the total distance. The problem is the complexity of solving the model will be increased in line with a number of constraints that exist. In addition, some dynamic constraints and unidentifiable boundaries can make the optimal route obtained is not suitable with the actual current condition. In this study, historical-based VRP (HbVRP) method which consists of graph partitioning and graph optimization is used to solve the problem. In the case study, the HbVRP method can build optimal route with 97.98% similarity level to the actual route and reduce the total distance from 572.217 to 120.913 which is better than existed method.
{"title":"Reconfiguring oil distribution route using graph partitioning and graph optimization","authors":"Enrico Laoh, I. Surjandari, Zulkarnain","doi":"10.1109/ICAWST.2017.8256426","DOIUrl":"https://doi.org/10.1109/ICAWST.2017.8256426","url":null,"abstract":"Determination of optimal distribution route is one of the keys to increase supply chain efficiency. Looking for an optimal distribution route that belongs to the type of vehicle routing problem (VRP) can be solved by modeling the entire boundary of the problem as the constraints and finding the solution with the objective of minimizing the total distance. The problem is the complexity of solving the model will be increased in line with a number of constraints that exist. In addition, some dynamic constraints and unidentifiable boundaries can make the optimal route obtained is not suitable with the actual current condition. In this study, historical-based VRP (HbVRP) method which consists of graph partitioning and graph optimization is used to solve the problem. In the case study, the HbVRP method can build optimal route with 97.98% similarity level to the actual route and reduce the total distance from 572.217 to 120.913 which is better than existed method.","PeriodicalId":378618,"journal":{"name":"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123364342","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 : 2017-11-01DOI: 10.1109/ICAWST.2017.8256436
Anggita Larasati, I. Surjandari
Retail industries in Indonesia are experiencing rapid growth every year. As a consequence of this growth, the level of competitiveness and challenges among these industries is rising. Some of the challenges that have to face are the shift of consumers' purchasing pattern, weakening of people's purchasing power, and expansion of foreign retailers in Indonesia. To understand consumers' purchasing pattern, Market Basket Analysis was applied to extract an association between sets of products that purchased together. The Apriori Algorithm and Speaker-Listener Label Propagation Algorithm (SLPA) were proposed in this study. By using the Apriori Algorithm, there were thirteen rules generated, meanwhile the SLPA resulted in 41 communities of product categories as the research outcomes. Therefore, the outcome obtained from using the SLPA algorithm is considered to give a broader view of consumers' purchasing pattern.
{"title":"Consumer behaviour analysis using speaker-listener label propagation algorithm (SLPA)","authors":"Anggita Larasati, I. Surjandari","doi":"10.1109/ICAWST.2017.8256436","DOIUrl":"https://doi.org/10.1109/ICAWST.2017.8256436","url":null,"abstract":"Retail industries in Indonesia are experiencing rapid growth every year. As a consequence of this growth, the level of competitiveness and challenges among these industries is rising. Some of the challenges that have to face are the shift of consumers' purchasing pattern, weakening of people's purchasing power, and expansion of foreign retailers in Indonesia. To understand consumers' purchasing pattern, Market Basket Analysis was applied to extract an association between sets of products that purchased together. The Apriori Algorithm and Speaker-Listener Label Propagation Algorithm (SLPA) were proposed in this study. By using the Apriori Algorithm, there were thirteen rules generated, meanwhile the SLPA resulted in 41 communities of product categories as the research outcomes. Therefore, the outcome obtained from using the SLPA algorithm is considered to give a broader view of consumers' purchasing pattern.","PeriodicalId":378618,"journal":{"name":"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116222692","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 : 2017-11-01DOI: 10.1109/ICAWST.2017.8256430
Shih-Shuo Tung, H. Shao, W. Hwang
A robust depth of field (DOF) extension algorithm was proposed based on the refocusing property of a light field photograph and the depth-from-defocus approach of multi-focus image fusing. The main techniques of the algorithm are depth estimation and all-in-focus image estimation. By making use of the redundancy of a light field photograph, we leverage both estimations in a noisy environment. For the noise level smaller than 25dB, the proposed algorithm is still robust and the performance is better than other methods both in PSNR and SSIM. We conclude that the algorithm is robust to high noise.
{"title":"Extending depth of field in noisy light field photography","authors":"Shih-Shuo Tung, H. Shao, W. Hwang","doi":"10.1109/ICAWST.2017.8256430","DOIUrl":"https://doi.org/10.1109/ICAWST.2017.8256430","url":null,"abstract":"A robust depth of field (DOF) extension algorithm was proposed based on the refocusing property of a light field photograph and the depth-from-defocus approach of multi-focus image fusing. The main techniques of the algorithm are depth estimation and all-in-focus image estimation. By making use of the redundancy of a light field photograph, we leverage both estimations in a noisy environment. For the noise level smaller than 25dB, the proposed algorithm is still robust and the performance is better than other methods both in PSNR and SSIM. We conclude that the algorithm is robust to high noise.","PeriodicalId":378618,"journal":{"name":"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126856790","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 : 2017-11-01DOI: 10.1109/ICAWST.2017.8256455
Y. Yaguchi, Keisuke Moriuchi, K. Amma
In this research, we investigate what camera settings are effective for an indoor automatic search system. We recommend installing RGB cameras with depth sensors like the Kinect and show how they should be installed to facilitate searches in indoor environments such as buildings with multiple floors. To validate camera configurations, the RTA∗ algorithm is used for automatic searching and we also measured how fast a drone could move to goal points in a simulation of a 3D-building model. We also studied various patterns of restart points because a drone has limited battery life, which restricts the available flight time. In the experiment, we allowed six batteries and each flight could last 600 seconds. This experiment showed that we should use three cameras positioned on the forward, upward, and backward of a drone to conduct a 3D building floor search because drones can easily rotate in the yaw direction, but cannot rotate in the pitch direction. We also showed that once the drone had returned to its start position for a battery replacement, it should restart from that point for effective searching.
{"title":"Comparison of camera configuration for real-time drone route planning in 3D building maze","authors":"Y. Yaguchi, Keisuke Moriuchi, K. Amma","doi":"10.1109/ICAWST.2017.8256455","DOIUrl":"https://doi.org/10.1109/ICAWST.2017.8256455","url":null,"abstract":"In this research, we investigate what camera settings are effective for an indoor automatic search system. We recommend installing RGB cameras with depth sensors like the Kinect and show how they should be installed to facilitate searches in indoor environments such as buildings with multiple floors. To validate camera configurations, the RTA∗ algorithm is used for automatic searching and we also measured how fast a drone could move to goal points in a simulation of a 3D-building model. We also studied various patterns of restart points because a drone has limited battery life, which restricts the available flight time. In the experiment, we allowed six batteries and each flight could last 600 seconds. This experiment showed that we should use three cameras positioned on the forward, upward, and backward of a drone to conduct a 3D building floor search because drones can easily rotate in the yaw direction, but cannot rotate in the pitch direction. We also showed that once the drone had returned to its start position for a battery replacement, it should restart from that point for effective searching.","PeriodicalId":378618,"journal":{"name":"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133959857","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 : 2017-11-01DOI: 10.1109/ICAWST.2017.8256508
Y. Lai, Fen-Fen Huang, P. Chiou
Application distribution platforms (or mobile app stores) provide a space for users to submit feedback or ratings for downloaded software. These platforms have become very important for both software providers and users in communication. The really image of what users' demands can be obtained from the analysis of large-scale data of user feedback. This study analyzes 4,480 user feedbacks from a health and fitness-tracking app in the Google Play with text mining. The result of this study shows that the users of health and fitness-related apps are concerned about their physical activity records and physiological records. The records include track, distance, time, and calories burned during jogging or walking…etc. Besides, the connection between the mobile device and wearable devices is very important for users.
{"title":"Analysis of user feedback in the mobile app store using text mining: A case study of Google Fit","authors":"Y. Lai, Fen-Fen Huang, P. Chiou","doi":"10.1109/ICAWST.2017.8256508","DOIUrl":"https://doi.org/10.1109/ICAWST.2017.8256508","url":null,"abstract":"Application distribution platforms (or mobile app stores) provide a space for users to submit feedback or ratings for downloaded software. These platforms have become very important for both software providers and users in communication. The really image of what users' demands can be obtained from the analysis of large-scale data of user feedback. This study analyzes 4,480 user feedbacks from a health and fitness-tracking app in the Google Play with text mining. The result of this study shows that the users of health and fitness-related apps are concerned about their physical activity records and physiological records. The records include track, distance, time, and calories burned during jogging or walking…etc. Besides, the connection between the mobile device and wearable devices is very important for users.","PeriodicalId":378618,"journal":{"name":"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131505918","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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