2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...最新文献
Pub Date : 2020-08-01DOI: 10.1109/IRI49571.2020.00048
Maria E. Presa-Reyes, B. Bogosian, Bradley Schonhoff, Christopher Jerauld, Christian Moreyra, P. Gardinali, Shu‐Ching Chen
Maintaining environmental sustainability relies on continuously monitoring environmental conditions. Water is an environmental component essential to the survival of all living organisms; hence, to prevent contamination and ensure proper water treatment, persistent observations and measurements of water quality are crucial. Traditionally, the procedure for testing the quality of water involved traveling to designated testing sites, manually collecting surface samples, transporting said samples to a laboratory for analysis, analyzing chemicals and microbial contaminants, and publishing the findings with the community. The technological advances in wireless sensor networks bring forth the opportunity for remote measurement and monitoring of water samples. Not only is the presence of the scientist no longer mandatory on the testing site, but the data can also be automatically collected, visualized, monitored, and shared through sensor recordings. These transitions exhibit a much fine-grained level of spatio-temporal information collection and allow for more comprehensive and long-term studies. Three research buoys, designed to be deployed in both shallow freshwater ecosystems and near-shore marine environments, were launched in different locations of South Florida to tackle complex challenges of environmental contamination. The research presented here designs and deploys a water quality monitoring platform for allowing the scientists to analyze better the near-real-time data collected by the buoys and generate insights. We further demonstrate two engaging near-real-time visualization methods developed to disseminate data trends and findings to a wide range of audiences from diverse backgrounds.
{"title":"A Water Quality Research Platform for the Near-real-time Buoy Sensor Data","authors":"Maria E. Presa-Reyes, B. Bogosian, Bradley Schonhoff, Christopher Jerauld, Christian Moreyra, P. Gardinali, Shu‐Ching Chen","doi":"10.1109/IRI49571.2020.00048","DOIUrl":"https://doi.org/10.1109/IRI49571.2020.00048","url":null,"abstract":"Maintaining environmental sustainability relies on continuously monitoring environmental conditions. Water is an environmental component essential to the survival of all living organisms; hence, to prevent contamination and ensure proper water treatment, persistent observations and measurements of water quality are crucial. Traditionally, the procedure for testing the quality of water involved traveling to designated testing sites, manually collecting surface samples, transporting said samples to a laboratory for analysis, analyzing chemicals and microbial contaminants, and publishing the findings with the community. The technological advances in wireless sensor networks bring forth the opportunity for remote measurement and monitoring of water samples. Not only is the presence of the scientist no longer mandatory on the testing site, but the data can also be automatically collected, visualized, monitored, and shared through sensor recordings. These transitions exhibit a much fine-grained level of spatio-temporal information collection and allow for more comprehensive and long-term studies. Three research buoys, designed to be deployed in both shallow freshwater ecosystems and near-shore marine environments, were launched in different locations of South Florida to tackle complex challenges of environmental contamination. The research presented here designs and deploys a water quality monitoring platform for allowing the scientists to analyze better the near-real-time data collected by the buoys and generate insights. We further demonstrate two engaging near-real-time visualization methods developed to disseminate data trends and findings to a wide range of audiences from diverse backgrounds.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91284171","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 : 2020-08-01DOI: 10.1109/IRI49571.2020.00076
Safwa Ameer, James O. Benson, R. Sandhu
The Internet of Things (IoT) is enabling smart houses, where multiple users with complex social relationships interact with smart devices. This requires sophisticated access control specification and enforcement models, that are currently lacking. In this paper, we introduce the extended generalized role based access control (EGRBAC) model for smart home IoT. We provide a formal definition for EGRBAC and illustrate its features with a use case. A proof-of-concept demonstration utilizing AWS-IoT Greengrass is discussed in the appendix. EGRBAC is a first step in developing a comprehensive family of access control models for smart home IoT.
{"title":"The EGRBAC Model for Smart Home IoT","authors":"Safwa Ameer, James O. Benson, R. Sandhu","doi":"10.1109/IRI49571.2020.00076","DOIUrl":"https://doi.org/10.1109/IRI49571.2020.00076","url":null,"abstract":"The Internet of Things (IoT) is enabling smart houses, where multiple users with complex social relationships interact with smart devices. This requires sophisticated access control specification and enforcement models, that are currently lacking. In this paper, we introduce the extended generalized role based access control (EGRBAC) model for smart home IoT. We provide a formal definition for EGRBAC and illustrate its features with a use case. A proof-of-concept demonstration utilizing AWS-IoT Greengrass is discussed in the appendix. EGRBAC is a first step in developing a comprehensive family of access control models for smart home IoT.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73549919","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 : 2020-08-01DOI: 10.1109/IRI49571.2020.00049
Felipe Cordeiro de Paula, Aline Vasconcelos, R. Santos, P. Lago
Understanding and analyzing relationships among information systems (IS) and different stakeholders can be challenging to decision-making in IS management. In this context, accountability becomes critical, since it comprises responsible actions as well as their results considering regulations and sanctions. To cope with such a requirement, systems thinking (ST) can be used for conducting analysis, synthesis, and inquiry into the system's parts and the whole. We argue that applying ST lens to IS management may improve accountability and then decision-making. Therefore, we propose the accountability suggestion map (ASM) as an approach to cover users’ feedbacks aiming to promote accountability in IS management. This paper (1) presents ASM as a structure for representing systemic behaviors over time and then accountability suggestions, and (2) reports the application of ASM in an organizational scenario in the education domain. It was chosen since it encompasses management challenges, e.g., different IS must interoperate, each one with its own objective and within organizational units. As such, we analyzed two diagrams addressing daily issues regarding the absence of teachers and school evasion rates, respectively. We claim that ASM contributes to describe the environment and provide a set of suggestions to mitigate daily issues.
{"title":"Towards an Accountability Suggestion Map for Supporting Information Systems Management Based on Systems Thinking","authors":"Felipe Cordeiro de Paula, Aline Vasconcelos, R. Santos, P. Lago","doi":"10.1109/IRI49571.2020.00049","DOIUrl":"https://doi.org/10.1109/IRI49571.2020.00049","url":null,"abstract":"Understanding and analyzing relationships among information systems (IS) and different stakeholders can be challenging to decision-making in IS management. In this context, accountability becomes critical, since it comprises responsible actions as well as their results considering regulations and sanctions. To cope with such a requirement, systems thinking (ST) can be used for conducting analysis, synthesis, and inquiry into the system's parts and the whole. We argue that applying ST lens to IS management may improve accountability and then decision-making. Therefore, we propose the accountability suggestion map (ASM) as an approach to cover users’ feedbacks aiming to promote accountability in IS management. This paper (1) presents ASM as a structure for representing systemic behaviors over time and then accountability suggestions, and (2) reports the application of ASM in an organizational scenario in the education domain. It was chosen since it encompasses management challenges, e.g., different IS must interoperate, each one with its own objective and within organizational units. As such, we analyzed two diagrams addressing daily issues regarding the absence of teachers and school evasion rates, respectively. We claim that ASM contributes to describe the environment and provide a set of suggestions to mitigate daily issues.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75513992","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 : 2020-08-01DOI: 10.1109/IRI49571.2020.00028
Paromita Nitu, P. Madiraju, F. Pintar
In motor vehicle crash study, spine injury investigation has a greater impact due to the serious physical, mental and financial consequences. Even though spine fracture deteriorates the quality of life significantly, to the best of our knowledge, there is no study that searched for the exhaustive thoracolumbar spine fracture(TL-fx) feature space to discover potential feature pattern in the motivation of illustrating the increasing risk phenomenon as a function of vehicle model year. This study investigates National Automotive Sampling System Crashworthiness (NASS-CDS) database, year 2000 to 2015. Each year, approximately 4000 to 6000(weighted) occupants are diagnosed with one or multiple TL-fx in road crashes. Even though the TL-fx data support is less than 1.6%, a two-fold feature selection model in a combination of random forest and lift measure based Apriori algorithm generates insightful association rules yielding prominent feature patterns and promotes further investigation to build causal model for the TL-fx study.
{"title":"Identifying Feature Pattern for Weighted Imbalance Data: A Feature Selection Study for Thoracolumbar Spine Fractures in Crash Injury Research","authors":"Paromita Nitu, P. Madiraju, F. Pintar","doi":"10.1109/IRI49571.2020.00028","DOIUrl":"https://doi.org/10.1109/IRI49571.2020.00028","url":null,"abstract":"In motor vehicle crash study, spine injury investigation has a greater impact due to the serious physical, mental and financial consequences. Even though spine fracture deteriorates the quality of life significantly, to the best of our knowledge, there is no study that searched for the exhaustive thoracolumbar spine fracture(TL-fx) feature space to discover potential feature pattern in the motivation of illustrating the increasing risk phenomenon as a function of vehicle model year. This study investigates National Automotive Sampling System Crashworthiness (NASS-CDS) database, year 2000 to 2015. Each year, approximately 4000 to 6000(weighted) occupants are diagnosed with one or multiple TL-fx in road crashes. Even though the TL-fx data support is less than 1.6%, a two-fold feature selection model in a combination of random forest and lift measure based Apriori algorithm generates insightful association rules yielding prominent feature patterns and promotes further investigation to build causal model for the TL-fx study.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83751510","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 : 2020-08-01DOI: 10.1109/IRI49571.2020.00015
Khaled Rabieh, Suat Mercan, K. Akkaya, Vashish Baboolal, R. S. Aygün
Unmanned Aerial Vehicles (UAVs) also known as drones are being used in many applications where they can record or stream videos. One interesting application is the Intelligent Transportation Systems (ITS) and public safety applications where drones record videos and send them to a control center for further analysis. These videos are shared by various clients such as law enforcement or emergency personnel. In such cases, the recording might include faces of civilians or other sensitive information that might pose privacy concerns. While the video can be encrypted and stored in the cloud that way, it can still be accessed once the keys are exposed to third parties which is completely insecure. To prevent such insecurity, in this paper, we propose proxy re-encryption based sharing scheme to enable third parties to access only limited videos without having the original encryption key. The costly pairing operations in proxy re-encryption are not used to allow rapid access and delivery of the surveillance videos to third parties. The key management is handled by a trusted control center, which acts as the proxy to re-encrypt the data. We implemented and tested the approach in a realistic simulation environment using different resolutions under ns-3. The implementation results and comparisons indicate that there is an acceptable overhead while it can still preserve the privacy of drivers and passengers.
{"title":"Privacy-Preserving and Efficient Sharing of Drone Videos in Public Safety Scenarios using Proxy Re-encryption","authors":"Khaled Rabieh, Suat Mercan, K. Akkaya, Vashish Baboolal, R. S. Aygün","doi":"10.1109/IRI49571.2020.00015","DOIUrl":"https://doi.org/10.1109/IRI49571.2020.00015","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) also known as drones are being used in many applications where they can record or stream videos. One interesting application is the Intelligent Transportation Systems (ITS) and public safety applications where drones record videos and send them to a control center for further analysis. These videos are shared by various clients such as law enforcement or emergency personnel. In such cases, the recording might include faces of civilians or other sensitive information that might pose privacy concerns. While the video can be encrypted and stored in the cloud that way, it can still be accessed once the keys are exposed to third parties which is completely insecure. To prevent such insecurity, in this paper, we propose proxy re-encryption based sharing scheme to enable third parties to access only limited videos without having the original encryption key. The costly pairing operations in proxy re-encryption are not used to allow rapid access and delivery of the surveillance videos to third parties. The key management is handled by a trusted control center, which acts as the proxy to re-encrypt the data. We implemented and tested the approach in a realistic simulation environment using different resolutions under ns-3. The implementation results and comparisons indicate that there is an acceptable overhead while it can still preserve the privacy of drivers and passengers.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80178776","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 : 2020-08-01DOI: 10.1109/IRI49571.2020.00042
Tianyi Wang, Shu‐Ching Chen
Data collected from real-world environments often contain multiple objects, scenes, and activities. In comparison to single-label problems, where each data sample only defines one concept, multi-label problems allow the co-existence of multiple concepts. To exploit the rich semantic information in real-world data, multi-label classification has seen many applications in a variety of domains. The traditional approaches to multi-label problems tend to have the side effects of increased memory usage, slow model inference speed, and most importantly the under-utilization of the dependency across concepts. In this paper, we adopt multi-task learning to address these challenges. Multi-task learning treats the learning of each concept as a separate job, while at the same time leverages the shared representations among all tasks. We also propose a dynamic task balancing method to automatically adjust the task weight distribution by taking both sample-level and task-level learning complexities into consideration. Our framework is evaluated on a disaster video dataset and the performance is compared with several state-of-the-art multi-label and multi-task learning techniques. The results demonstrate the effectiveness and supremacy of our approach.
{"title":"Multi-Label Multi-Task Learning with Dynamic Task Weight Balancing","authors":"Tianyi Wang, Shu‐Ching Chen","doi":"10.1109/IRI49571.2020.00042","DOIUrl":"https://doi.org/10.1109/IRI49571.2020.00042","url":null,"abstract":"Data collected from real-world environments often contain multiple objects, scenes, and activities. In comparison to single-label problems, where each data sample only defines one concept, multi-label problems allow the co-existence of multiple concepts. To exploit the rich semantic information in real-world data, multi-label classification has seen many applications in a variety of domains. The traditional approaches to multi-label problems tend to have the side effects of increased memory usage, slow model inference speed, and most importantly the under-utilization of the dependency across concepts. In this paper, we adopt multi-task learning to address these challenges. Multi-task learning treats the learning of each concept as a separate job, while at the same time leverages the shared representations among all tasks. We also propose a dynamic task balancing method to automatically adjust the task weight distribution by taking both sample-level and task-level learning complexities into consideration. Our framework is evaluated on a disaster video dataset and the performance is compared with several state-of-the-art multi-label and multi-task learning techniques. The results demonstrate the effectiveness and supremacy of our approach.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80631748","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}
To predict users’ interests, the traditional recommendation system (RS) relies on exploring the explicit user-item ratings and macro implicit feedbacks (e.g., whether or not a user clicks the item). In this work, fine-grained post-click behaviors (e.g., mouse behaviors, keyboard events, and page scrolling events) are integrated to alleviate the data sparsity problem of explicit feedback and the data accuracy problem of macro implicit feedback. In the deployed article recommendation pipeline, a variety of post-click behaviors are combined to create a reading pattern model. The reading patterns are leveraged by the recommendation system to estimate users’ preference levels. As compared with existing click-based (macro implicit feedback) and dwell time-based (single micro implicit feedback) recommendation systems, the test performance of our designed reading pattern-based RS has been significantly improved in terms of rating prediction and ranking.
{"title":"Post-Click Behaviors Enhanced Recommendation System","authors":"Zhenhua Liang, Siqi Huang, Xueqing Huang, Rui Cao, Weize. Yu","doi":"10.1109/IRI49571.2020.00026","DOIUrl":"https://doi.org/10.1109/IRI49571.2020.00026","url":null,"abstract":"To predict users’ interests, the traditional recommendation system (RS) relies on exploring the explicit user-item ratings and macro implicit feedbacks (e.g., whether or not a user clicks the item). In this work, fine-grained post-click behaviors (e.g., mouse behaviors, keyboard events, and page scrolling events) are integrated to alleviate the data sparsity problem of explicit feedback and the data accuracy problem of macro implicit feedback. In the deployed article recommendation pipeline, a variety of post-click behaviors are combined to create a reading pattern model. The reading patterns are leveraged by the recommendation system to estimate users’ preference levels. As compared with existing click-based (macro implicit feedback) and dwell time-based (single micro implicit feedback) recommendation systems, the test performance of our designed reading pattern-based RS has been significantly improved in terms of rating prediction and ranking.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83189307","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 the popularized of Internet and smart mobile devices, people nowadays can complete shopping process in seconds. Hence, consumers’ reliance toward online shopping is getting stronger. Yet, contrasting to the shopping experience in physical store, the chance of impulse buying and product return increases when consumers shops online. Subsequently, problems like post-purchase dissonance (e.g. emotion dissonance, product dissonance) arise as well. Therefore, in order to Figure out what causes impulse buying behavior and the relationship with cognitive dissonance, we adopt the concept from Powers and Jacks [1]. Also, despite of two shopping values as antecedents for impulse buying, we supplement time pressure from Tıgli and Kaytaz Yigit [2] as well. Consequently, we attach return tendency, which are to explore the effect of post-purchase dissonance. In this study, we conduct a survey and receive 155 valid response in total, and the result shows that time pressure and emotion dissonance are the main factors influence impulse buying. To sum up. our findings may be useful for retailers attempting to operate websites in e-commerce.
随着互联网和智能移动设备的普及，现在人们可以在几秒钟内完成购物过程。因此，消费者对网上购物的依赖越来越强。然而，与实体店的购物体验相比，消费者在网上购物时冲动购买和退货的机会增加了。随后，诸如购后失调(如情绪失调、产品失调)等问题也会出现。因此，为了弄清冲动购买行为的成因及其与认知失调的关系，我们采用了Powers and Jacks[1]的概念。此外，尽管有两个购物价值作为冲动购买的先决条件，我们也补充了Tıgli和Kaytaz Yigit[2]的时间压力。因此，本研究附加回归倾向，以探讨购后失调的影响。在本研究中，我们进行了一项调查，共收到155份有效回复，结果显示时间压力和情绪失调是影响冲动购买的主要因素。总而言之。我们的研究结果可能对试图在电子商务中运营网站的零售商有用。
{"title":"Research on Online Impulsive Buying and Post-Purchase Dissonance","authors":"Wen-Kuo Chen, Yen-Ling Lin, Hua-Sheng Pan, Cheng-Kun Chen","doi":"10.1109/IRI49571.2020.00071","DOIUrl":"https://doi.org/10.1109/IRI49571.2020.00071","url":null,"abstract":"With the popularized of Internet and smart mobile devices, people nowadays can complete shopping process in seconds. Hence, consumers’ reliance toward online shopping is getting stronger. Yet, contrasting to the shopping experience in physical store, the chance of impulse buying and product return increases when consumers shops online. Subsequently, problems like post-purchase dissonance (e.g. emotion dissonance, product dissonance) arise as well. Therefore, in order to Figure out what causes impulse buying behavior and the relationship with cognitive dissonance, we adopt the concept from Powers and Jacks [1]. Also, despite of two shopping values as antecedents for impulse buying, we supplement time pressure from Tıgli and Kaytaz Yigit [2] as well. Consequently, we attach return tendency, which are to explore the effect of post-purchase dissonance. In this study, we conduct a survey and receive 155 valid response in total, and the result shows that time pressure and emotion dissonance are the main factors influence impulse buying. To sum up. our findings may be useful for retailers attempting to operate websites in e-commerce.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83324052","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 : 2020-08-01DOI: 10.1109/IRI49571.2020.00073
Long Cheng, Zhaoqi Wu, Bo-Ya Lai, Qiang Yang, Anguo Zhao, Yuanting Wang
High-precision indoor positioning has nowadays emerged as a critical function for many applications. However, many existing indoor positioning systems either fail to achieve a high positioning accuracy or are very easily affected by indoor environments composed of many obstacles, preventing them from satisfying many application requirements. Ultra wideband (UWB) has recently drawn extensive attention in the field of indoor positioning due to its great ability to achieve high ranging and localization accuracy while minimizing the effect of multipath interference. Meanwhile, advanced artificial intelligence and signal processing techniques have been explored to improve the precision and performance of indoor positioning system. In this paper, a high-precision UWB indoor positioning system integrating artificial intelligence and signal processing techniques is designed. And field tests are also conducted to validate the design of the system.
{"title":"Ultra Wideband Indoor Positioning System based on Artificial Intelligence Techniques","authors":"Long Cheng, Zhaoqi Wu, Bo-Ya Lai, Qiang Yang, Anguo Zhao, Yuanting Wang","doi":"10.1109/IRI49571.2020.00073","DOIUrl":"https://doi.org/10.1109/IRI49571.2020.00073","url":null,"abstract":"High-precision indoor positioning has nowadays emerged as a critical function for many applications. However, many existing indoor positioning systems either fail to achieve a high positioning accuracy or are very easily affected by indoor environments composed of many obstacles, preventing them from satisfying many application requirements. Ultra wideband (UWB) has recently drawn extensive attention in the field of indoor positioning due to its great ability to achieve high ranging and localization accuracy while minimizing the effect of multipath interference. Meanwhile, advanced artificial intelligence and signal processing techniques have been explored to improve the precision and performance of indoor positioning system. In this paper, a high-precision UWB indoor positioning system integrating artificial intelligence and signal processing techniques is designed. And field tests are also conducted to validate the design of the system.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82753677","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 : 2020-08-01DOI: 10.1109/IRI49571.2020.00072
Wen-Kuo Chen, Ying-Hsun Hung, Jun-Yu Zhong
The aim of this study is to identify structural relationships between aspects of body image and behavioral intention. The study uses the theory of planned behavior to identify the latent antecedents for individual intention to improve their body image by adopting a cross-sectional design and using questionnaires to collect data. Responses from 460 participants were analyzed using structural equation modeling (SEM) to examine the research hypotheses. The results demonstrate that internal and external factors were positively related to the belief formation mechanism, and that self-identity was positively related to body image dissatisfaction. Additionally, the belief formation mechanism influenced behavioral intention through the theory of planned behavior. These results provide some body image strategy priorities for the health and beauty industries. Moreover, it is important for the health and beauty industries to manage intentions through internal and external factors of body image, such as self-identity and the healthcare climate. Strong support was found for the proposed theoretical model. Implications for research and practice are also discussed.
{"title":"Building the Body Image Conceptual Framework Based on the Theory of Planned Behavior (TPB)","authors":"Wen-Kuo Chen, Ying-Hsun Hung, Jun-Yu Zhong","doi":"10.1109/IRI49571.2020.00072","DOIUrl":"https://doi.org/10.1109/IRI49571.2020.00072","url":null,"abstract":"The aim of this study is to identify structural relationships between aspects of body image and behavioral intention. The study uses the theory of planned behavior to identify the latent antecedents for individual intention to improve their body image by adopting a cross-sectional design and using questionnaires to collect data. Responses from 460 participants were analyzed using structural equation modeling (SEM) to examine the research hypotheses. The results demonstrate that internal and external factors were positively related to the belief formation mechanism, and that self-identity was positively related to body image dissatisfaction. Additionally, the belief formation mechanism influenced behavioral intention through the theory of planned behavior. These results provide some body image strategy priorities for the health and beauty industries. Moreover, it is important for the health and beauty industries to manage intentions through internal and external factors of body image, such as self-identity and the healthcare climate. Strong support was found for the proposed theoretical model. Implications for research and practice are also discussed.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90101235","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}
2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...
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