2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA)最新文献

{"title":"AutoCoach: Driving Behavior Management Using Intelligent IoT Services","authors":"Zahraa Marafie, Kwei-Jay Lin, Daben Wang, Haoyu Lyu, Yu Meng, Takayuki Ito","doi":"10.1109/SOCA.2019.00023","DOIUrl":"https://doi.org/10.1109/SOCA.2019.00023","url":null,"abstract":"AutoCoach is an intelligent agent intended for improving automobile drivers' performance by applying persuasive technology. System models like Advanced driver-assistance (ADAS) and some Usage-based-Insurance (UBI) share an aim to increase car and road safety. However, most prior models do not consider the differences between driving habits. The AutoCoach design includes two unique components to build an effective persuasive system. The first component is the personality classification, which recognizes drivers' personalities by analyzing driving behavior patterns. The second component is the rewarding system, which determines the current driving behavior's risk score based on some immediate past behavior. We propose the idea of memory factor, which decides when to provide feedback to drivers based on their personality. This memory factor identifies the most critical behaviors within a flexible time-period. AutoCoach then decides on feedback to maintain safe driving or improve the level of awareness for risky driving habits.","PeriodicalId":113517,"journal":{"name":"2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114911651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sitting Posture Prediction and Correction System using Arduino-Based Chair and Deep Learning Model","authors":"H. Cho, Hee-Joe Choi, Chae-eun Lee, C. Sir","doi":"10.1109/SOCA.2019.00022","DOIUrl":"https://doi.org/10.1109/SOCA.2019.00022","url":null,"abstract":"In this paper, we propose a system that applies deep learning to classify different postures of the users of our specially designed chair composed of Arduino hardware. The system provides users with information of their real-time posture and analytics for a given period of time, hence users can figure out their sitting habits. Moreover, the system suggests guidance videos of stretching body parts that help the users to correct their sitting postures.","PeriodicalId":113517,"journal":{"name":"2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122550871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metalworking Industry: Quality Management via Edge Computing-Based Cloud Monitoring System","authors":"Zheng-Wei Wu, Shang-Chih lin, Po-Chun Hu, S. Su, Yennun Huang","doi":"10.1109/SOCA.2019.00027","DOIUrl":"https://doi.org/10.1109/SOCA.2019.00027","url":null,"abstract":"This study uses the edge computing system for fast quality screening in the metal processing industry, and the Internet of Things technology is responsible for delivering data to the cloud for visualization and analysis. First, the system consists of optical components and embedded systems. Further, a fast Fourier transform is used to make the image have frequency characteristics. However, the convolution operation between the random kernel model and the image is the main means of feature extraction. In order to evaluate the performance and convergence of the proposed method, a rapid screening mechanism for good/bad products is defined. Finally, the data is passed to the cloud (ThingSpeak) platform for visualization through the MQTT protocol, and the content is subscribed to the content by the background host to perform the quality decision of the fuzzy inference system. The result is released back to the cloud. The experimental results in the industrial example show that the proposed method can accurately and quickly complete the quality inspection of surface roughness, and the feature distribution is easy to understand. At the same time, the edge computing system has the advantages of instant response and low cost, while the Internet of Things technology brings more management and analysis convenience. In future research, the unsupervised learning algorithm based on convolutional neural networks is a potential application, which can learn the quality of good or bad through a large amount of data.","PeriodicalId":113517,"journal":{"name":"2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA)","volume":"13 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114044457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring Data Management Services on the Edge Using Enhanced TSDBs","authors":"Wenxi Zeng, Shuai Zhang, I. Yen, F. Bastani, San-Yih Hwang","doi":"10.1109/SOCA.2019.00010","DOIUrl":"https://doi.org/10.1109/SOCA.2019.00010","url":null,"abstract":"Many IoT systems are data intensive and are for the purpose of monitoring of critical systems. In these monitoring systems, a large volume of data steadily flow out of a large number of sensors which monitor the physical systems and environments. Thus, first of all, we need to consider how to store and manage these IoT data. Also, data sharing can greatly enhance the quality of data analytics and help with cold start of similar systems. Thus, the data storage and management solutions should consider how to help discover useful data in order to facilitate data sharing. Time series databases (TSDBs) have been developed in recent years for storing IoT data, but they have some deficiencies. One problem is that they are not very effective in supporting data sharing due to the lack of a good semantic model for proper data specifications, which is critical in data discovery. To resolve this problem, we develop a monitoring data annotation (MDA) model to guide the systematic specification of monitoring data streams. To support the realization of the MDA model, we also develop an external tool suite, which stores the additional MDA-based specifications for the data streams and interfaces with queries to perform preliminary processing to allow effective monitoring data discovery based on the MDA specifications. Another problem with current TSDBs is their focus on storing time series data that arrive at a fixed rate, but not on storing and retrieval of event data, which may come sporadically with irregular timing patterns. When storing such event data in existing TSDBs, the retrieval may have performance problems. Also, existing TSDBs do not have specific query language defined for event analysis. We develop a model for event specifications and use it to specify abnormal system states to be captured to allow timely mitigation. The event model is integrated into the TSDB by translating them to continuous queries defined in some TSDBs. Also, we develop an event storage scheme and incorporate it in TSDBs to facilitate efficient event retrieval. Experimental results show that our event solution for the TSDB is effective and efficient.","PeriodicalId":113517,"journal":{"name":"2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130076091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Web Service Selection with Correlations: A Feature-Based Abstraction Refinement Approach","authors":"Kaustabha Ray, A. Banerjee, S. Mohalik","doi":"10.1109/SOCA.2019.00013","DOIUrl":"https://doi.org/10.1109/SOCA.2019.00013","url":null,"abstract":"In this paper, we address the web service selection problem for linear workflows. Given a linear workflow specifying a set of ordered tasks and a set of candidate services providing different features for each task, the selection problem deals with the objective of selecting the most eligible service for each task, given the ordering specified. A number of approaches to solving the selection problem have been proposed in literature. With web services growing at an incredible pace, service selection at the Internet scale has resurfaced as a problem of recent research interest. In this work, we present our approach to the selection problem using an abstraction refinement technique to address the scalability limitations of contemporary approaches. Experiments on web service benchmarks show that our approach can add substantial performance benefits in terms of space when compared to an approach without our optimization.","PeriodicalId":113517,"journal":{"name":"2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122010973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Publisher's Information","authors":"","doi":"10.1109/soca.2019.00036","DOIUrl":"https://doi.org/10.1109/soca.2019.00036","url":null,"abstract":"","PeriodicalId":113517,"journal":{"name":"2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130140379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PicPose: Using Picture Posing for Localization Service on IoT Devices","authors":"Yu Meng, Kwei-Jay Lin, Bo-Lung Tsai, C. Shih, Bin Zhang","doi":"10.1109/SOCA.2019.00020","DOIUrl":"https://doi.org/10.1109/SOCA.2019.00020","url":null,"abstract":"Device self-localization is an important capability for many IoT applications that require mobility in service capabilities. In our previous work, we have designed the ArPico method for robot indoor localization. By placing and recognizing pre-installed pictures on walls, robots can use low-cost cameras to identify their positions by referencing to pictures' precise locations. However, using ArPico, all pictures need to have clear rectangular borders for the pose computation. But some real-world pictures does not have clear thick borders. Moreover, some pictures may have odd shapes or are only partially visible. To address these problems, a new picture-based localization service PicPose is presented. PicPose relies on the feature points extracted from a camera-captured image and conducts feature point matching with the original wall picture to conduct pose calculation. Using PicPose, even partially visible pictures can be used for localization, which is impossible for ArPico and ArUco. We present our implementation and experiment results in this paper.","PeriodicalId":113517,"journal":{"name":"2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126288341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised Online Anomaly Detection on Multivariate Sensing Time Series Data for Smart Manufacturing","authors":"Ruei-Jie Hsieh, Jerry Chou, Chih-Hsiang Ho","doi":"10.1109/SOCA.2019.00021","DOIUrl":"https://doi.org/10.1109/SOCA.2019.00021","url":null,"abstract":"The emergence of IoT and AI has brought revolutionary change in various application domains. One of them is Industry 4.0, also called Smart Manufacturing, which aims to achieve highly flexible and automated production processes. In this paper, we study a use case of anomaly detection in smart manufacturing using the real data collected from the sensing devices of a factory production line. Our goal is to improve the anomaly detection accuracy at an earlier stage of production line, so that cost and time wasted by possible production failures can be reduced. To overcome the limited and irregular anomaly patterns found from our multivariate sensor dataset, we proposed an unsupervised real-time anomaly detection algorithm based on LSTM-based Auto-Encoder. Our evaluations show that our approach achieved almost 90% accuracy for both precision and recall while other classification or regression based methods only reached 70%~85%.","PeriodicalId":113517,"journal":{"name":"2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA)","volume":"4 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120920023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT Application in Sports to Support Skill Acquisition and Improvement","authors":"Kazunari Ishida","doi":"10.1109/SOCA.2019.00034","DOIUrl":"https://doi.org/10.1109/SOCA.2019.00034","url":null,"abstract":"This paper proposes a sport-service-oriented architecture framework and describes an example of design and implementation of an IoT application in sports based on sport-skills researches. Especially, the focus is on wearable applications with inertial sensors because sports applications require simple and durable motion-sensing systems. Skateboarding is introduced as a target sport to understand skill components and to support skill acquisition and improvement. Two types of skills (i.e., basic and advanced) are introduced to show different requirements for IoT applications in sports. Design factors to implement a sports application are discussed in terms of two aspects (i.e., analysis and service phases) concerning sports activities. To implement these designs, technology selection and implementation alternatives are discussed in terms of skill types and purpose of sensing (i.e., analysis or service phase).","PeriodicalId":113517,"journal":{"name":"2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133042567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review of Cybersecurity Research Topics, Taxonomy and Challenges: Interdisciplinary Perspective","authors":"Hatma Suryotrisongko, Y. Musashi","doi":"10.1109/SOCA.2019.00031","DOIUrl":"https://doi.org/10.1109/SOCA.2019.00031","url":null,"abstract":"As cybersecurity is a growing field of science, there is not complete agreement across the scope of cybersecurity research topics. This paper proposes a grouping and classifications of cybersecurity researches to introduce an easily referenceable taxonomy of the cybersecurity research topics. A literature survey was conducted to collect published literature review papers about various cybersecurity researches during the past five years periods. Taxonomic analysis of 99 selected papers, which were grouped based on research topic similarities, produced sets of research categories. It categorized the cybersecurity research topics into 8 areas: (1) Applied cybersecurity, (2) Cybersecurity data science, (3) Cybersecurity education and training, (4) Cybersecurity incidents, (5) Cybersecurity management and policy, (6) Cybersecurity technology, (7) Human and social cybersecurity and (8) Theories in cybersecurity. Although cybersecurity grew out of the computer science field, this paper argues that its interdisciplinary nature (not only technical computer security, but also data, system/technology, and human/social) can attract researcher from various disciplines, such as management, policy, psychology, and so on, to contribute to cybersecurity advancement.","PeriodicalId":113517,"journal":{"name":"2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132705286","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}