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2020 IEEE Conference on Open Systems (ICOS)最新文献

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An Investigation of Learning Analytics in an Online Video-based Learning Platform 基于在线视频学习平台的学习分析研究
2020 IEEE Conference on Open Systems (ICOS) Pub Date : 2020-11-17 DOI: 10.1109/ICOS50156.2020.9293629
E. Sim, Sian Lun Lau
{"title":"An Investigation of Learning Analytics in an Online Video-based Learning Platform","authors":"E. Sim, Sian Lun Lau","doi":"10.1109/ICOS50156.2020.9293629","DOIUrl":"https://doi.org/10.1109/ICOS50156.2020.9293629","url":null,"abstract":"Using an online platform as a teaching and learning tool has been more common since the last decade. An advantage of this approach is the ability to capture implicit information, and hence learning analytics may be implemented to assist improvement in learning experience and efficiency. Thus, this paper aims to investigate and identify what features are needed by stakeholders to be included in an online video-based learning platform in order to implement learning analytics. A prototype learning platform has been developed. It has been evaluated through a survey by a group of testers to obtain feedback and review regarding their learning and usage experience. The majority of the participants of the survey have shown interest in the features implemented by the system and would like to see these features implemented in a working video learning platform.","PeriodicalId":314692,"journal":{"name":"2020 IEEE Conference on Open Systems (ICOS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128264237","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
[Copyright notice] (版权)
2020 IEEE Conference on Open Systems (ICOS) Pub Date : 2020-11-17 DOI: 10.1109/icos50156.2020.9293622
{"title":"[Copyright notice]","authors":"","doi":"10.1109/icos50156.2020.9293622","DOIUrl":"https://doi.org/10.1109/icos50156.2020.9293622","url":null,"abstract":"","PeriodicalId":314692,"journal":{"name":"2020 IEEE Conference on Open Systems (ICOS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131764306","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: 0
A Survey on Surface Reconstruction Techniques for Structured and Unstructured Data 结构化和非结构化数据表面重构技术综述
2020 IEEE Conference on Open Systems (ICOS) Pub Date : 2020-11-17 DOI: 10.1109/ICOS50156.2020.9293685
C. C. You, Seng Poh Lim, Seng Chee Lim, Joi San Tan, Chen Kang Lee, Y. Khaw
{"title":"A Survey on Surface Reconstruction Techniques for Structured and Unstructured Data","authors":"C. C. You, Seng Poh Lim, Seng Chee Lim, Joi San Tan, Chen Kang Lee, Y. Khaw","doi":"10.1109/ICOS50156.2020.9293685","DOIUrl":"https://doi.org/10.1109/ICOS50156.2020.9293685","url":null,"abstract":"Surface reconstruction of real-world objects is a commonly discussed topic in reverse engineering. Generally, 3-D scanning technologies are used to scan the objects through multiple angles and represent them using point cloud. The point cloud can be either in structured or unstructured form which may contain problems such as noise, outliers and incomplete points. The point cloud is considered as unstructured form when it does not contain any connectivity information between adjacent points and structure information. Various types of surface reconstruction techniques are proposed to overcome the problems of point cloud and the limitations of existing techniques. Besides, soft computing techniques are also employed to enhance the performance and overcome the downsides of existing techniques. Therefore, the objective of this paper is to conduct a survey towards the existing techniques in the surface reconstruction on structured or unstructured data. Generally, this paper will only focus on the interpolation and approximation techniques, learning-based techniques, and soft computing techniques. Based on the analysis, it shows that learning-based techniques performed better compared to other techniques as they are able to handle the problem of unstructured point clouds. It can also form as hybrid techniques by integrating with other techniques which can improve its accuracy. The outcome of this paper can be used to assist the researchers in understanding and finding suitable surface reconstruction techniques in representing the objects and solving their case studies.","PeriodicalId":314692,"journal":{"name":"2020 IEEE Conference on Open Systems (ICOS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133234563","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
Automatic Segmentation And Classification Of Eczema Skin Lesions Using Supervised Learning 基于监督学习的湿疹皮损自动分割与分类
2020 IEEE Conference on Open Systems (ICOS) Pub Date : 2020-11-17 DOI: 10.1109/ICOS50156.2020.9293657
H. Nisar, Y. Ch'ng, Yeap Kim Ho
{"title":"Automatic Segmentation And Classification Of Eczema Skin Lesions Using Supervised Learning","authors":"H. Nisar, Y. Ch'ng, Yeap Kim Ho","doi":"10.1109/ICOS50156.2020.9293657","DOIUrl":"https://doi.org/10.1109/ICOS50156.2020.9293657","url":null,"abstract":"In this paper our aim is to develop a fully automated eczema skin lesion segmentation method. We have studied three supervised learning methods for segmentation of lesions: Support Vector Machine (SVM), Naïve Bayesian Classifier (NBC) and K-Nearest Neighbor (KNN). Two sets of images that are different in erythema severity levels (mild, moderate) are used for training the supervised classifiers. From the training images 108 features are extracted that are ranked using four feature ranking methods (standard deviation, T-statistical score, fisher scoring, and correlation coefficient) to obtain the most significant features. The performance of classification is investigated using green channel of RGB and CSN-I RGB color space. The performance of the different methods is assessed by comparing the segmented lesions with the gold standard segmented images. Based on these comparisons, it is observed that SVM classifier shows the best segmentation result having an accuracy of 84.43% whereas the accuracy of NBC and KNN is 82.77% and 83.53% respectively.","PeriodicalId":314692,"journal":{"name":"2020 IEEE Conference on Open Systems (ICOS)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128641598","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: 7
Classification for iOS Mobile Malware Inspired by Phylogenetic: Proof of Concept 基于系统发育的iOS手机恶意软件分类:概念验证
2020 IEEE Conference on Open Systems (ICOS) Pub Date : 2020-11-17 DOI: 10.1109/ICOS50156.2020.9293666
M. A. Husainiamer, M. Saudi, Azuan Ahmad
{"title":"Classification for iOS Mobile Malware Inspired by Phylogenetic: Proof of Concept","authors":"M. A. Husainiamer, M. Saudi, Azuan Ahmad","doi":"10.1109/ICOS50156.2020.9293666","DOIUrl":"https://doi.org/10.1109/ICOS50156.2020.9293666","url":null,"abstract":"There are raising cases of mobile malwares exploiting iOS users across the world such as FinSpy and Exodus that were able to steal credential information from the victims and affect loss of victims’ productivity. Yet, not many solutions were able to encounter iOS malware attacks. Hence, this paper presents a new iOS mobile malware classification based on mobile behaviour, vulnerability exploitation inspired by phylogenetic concept. The experiment was conducted by using hybrid analysis. Proof of concept (POC) was conducted and based on the POC it indicated that this proposed classification is significant to detect the malware attacks. In future, this proposed classification will be the input for iOS mobile malware detection.","PeriodicalId":314692,"journal":{"name":"2020 IEEE Conference on Open Systems (ICOS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127345037","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
The Adoption of Cloud Computing CRM in SME’s, Southland, New Zealand 云计算CRM在中小企业中的应用，南国，新西兰
2020 IEEE Conference on Open Systems (ICOS) Pub Date : 2020-11-17 DOI: 10.1109/ICOS50156.2020.9293682
Oras Baker, Prabhjot Kaur
{"title":"The Adoption of Cloud Computing CRM in SME’s, Southland, New Zealand","authors":"Oras Baker, Prabhjot Kaur","doi":"10.1109/ICOS50156.2020.9293682","DOIUrl":"https://doi.org/10.1109/ICOS50156.2020.9293682","url":null,"abstract":"Customer Relationship Management (CRM) solutions are helping businesses to grow by maintaining healthy relationships with the existing customers and building strong relationships with new customers. However, SaaS-CRM solutions are more desirable options for SME’s as these solutions are less expensive and unsophisticated to implement as compared to on-premise CRM. The main aim of this research is to identify to what extent the SMEs in Southland are aware of SaaS-CRM and what are the factors that affect their decision for adoption. A survey was used to collect data from 35 SME’s operating in the region of Southland, New Zealand. The results showed that only 10% of the SME’s use cloud CRM solutions and different factors such as relative advantage, perceived ease of use, perceived usefulness, technology readiness, top management support, trust in vendor, and regulatory support affect their cloud CRM adoption decisions.","PeriodicalId":314692,"journal":{"name":"2020 IEEE Conference on Open Systems (ICOS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121277606","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: 5
ICOS 2020 Committees ICOS 2020委员会
2020 IEEE Conference on Open Systems (ICOS) Pub Date : 2020-11-17 DOI: 10.1109/icos50156.2020.9293678
{"title":"ICOS 2020 Committees","authors":"","doi":"10.1109/icos50156.2020.9293678","DOIUrl":"https://doi.org/10.1109/icos50156.2020.9293678","url":null,"abstract":"","PeriodicalId":314692,"journal":{"name":"2020 IEEE Conference on Open Systems (ICOS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128480518","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: 0
Intelligent Automatic Door System based on Supervised Learning 基于监督学习的智能自动门系统
2020 IEEE Conference on Open Systems (ICOS) Pub Date : 2020-11-17 DOI: 10.1109/ICOS50156.2020.9293673
M. Kiru, B. Belaton, S. Mohamad, Gana Usman, Abdullahi Aminu Kazaure
{"title":"Intelligent Automatic Door System based on Supervised Learning","authors":"M. Kiru, B. Belaton, S. Mohamad, Gana Usman, Abdullahi Aminu Kazaure","doi":"10.1109/ICOS50156.2020.9293673","DOIUrl":"https://doi.org/10.1109/ICOS50156.2020.9293673","url":null,"abstract":"The widespread adoption of automatic sliding doors in both commercial and non-commercial environments globally has necessitated the need to improve their efficiency, safety, and mode of operation. The automatic door gives access to go into or outside a building by sensing the approaching individual using sensors. However, it does not have the intuition to understand when a person is not authorized to go outside based on their age limit, for example, children. To address this problem, researchers have proposed solutions ranging from the use of fuzzy logic to rule-based approaches to make automatic doors better than the previous ones. In this study, an AI-based automatic door system is proposed, which uses a supervised machine learning approach to train classifiers using human body measurement. Our evaluation of different classifiers indicates that SVM is capable of classifying the instances correctly while achieving about 88.9% F-score. Thus, the proposed approach is expected to improve the safety of automatic doors, thereby making them smarter and more intelligent.","PeriodicalId":314692,"journal":{"name":"2020 IEEE Conference on Open Systems (ICOS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124691365","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: 5
ICOS 2020 Index ICOS 2020指数
2020 IEEE Conference on Open Systems (ICOS) Pub Date : 2020-11-17 DOI: 10.1109/icos50156.2020.9293684
{"title":"ICOS 2020 Index","authors":"","doi":"10.1109/icos50156.2020.9293684","DOIUrl":"https://doi.org/10.1109/icos50156.2020.9293684","url":null,"abstract":"","PeriodicalId":314692,"journal":{"name":"2020 IEEE Conference on Open Systems (ICOS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124323324","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: 0
Estimation of Ammonia in Water Samples Using Image Analysis 利用图像分析估计水样中的氨
2020 IEEE Conference on Open Systems (ICOS) Pub Date : 2020-11-17 DOI: 10.1109/ICOS50156.2020.9293648
L. Kim, Ooi Zi Xen, Ho Hooi Eng, Tan Xin Yee, Wong Vin Yean, H. Nisar
{"title":"Estimation of Ammonia in Water Samples Using Image Analysis","authors":"L. Kim, Ooi Zi Xen, Ho Hooi Eng, Tan Xin Yee, Wong Vin Yean, H. Nisar","doi":"10.1109/ICOS50156.2020.9293648","DOIUrl":"https://doi.org/10.1109/ICOS50156.2020.9293648","url":null,"abstract":"Ammonia plays an important role in the stability of the ecosystem. However, high concentration of ammonia in the water is toxic to the ecosystem. Hence it is important to monitor the amount of ammonia in water bodies. In this paper we use image processing and analysis to detect the amount of ammonia in water by identifying the color of the water. 7 different ammonia concentrations equal to 0.0, 0.25, 0.5, 1.0, 2.0, 4.0 and 8.0 ppm were used for testing purposes. Two color models RGB (Red, Green, Blue) and HSV (Hue, Saturation, Value) are used in the analysis. Three features are extracted from the images which are mean intensity, standard deviation and skewness. It has been observed that the proposed method using mean intensity and three color channels R, G, and B is able to identify the correct ammonia concentration in the test samples with an accuracy of 100 %.","PeriodicalId":314692,"journal":{"name":"2020 IEEE Conference on Open Systems (ICOS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115253373","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: 0
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