The improvement of modern communication technology has made the Internet of Vehicles (IoV) advance by leaps and bounds, and promotes the progress of many technologies, such as mobile sensing, vehicular edge computing, sensor networks, satellite positioning, data analysis, etc. Vehicular edge computing (VEC) is an innovative computing paradigm which can provide flexible and reliable computation services for intelligent and connected vehicles. An optimized problem is formulated to minimize the total task offloading time delay by making a tradeoff between vehicle mobility and task nature. To tackle the optimization problem, we proposed the Delay-sensitive half-Determined atomic Search algorithm, called DeshDaS, in which we regard each intelligent vehicle as an atom and strategy as electron and consider electron transition process. Experimental results validate the effectiveness and superior of our algorithm compared with several existed offloading strategy, and the larger average amount of data waiting to be processed, the more significant our advantage is.
{"title":"Dynamic resource allocation on Vehicular edge computing and communication","authors":"Senyu Yu, Yan Guo, Ning Li, Duan Xue, Cuntao Liu","doi":"10.1145/3571662.3571696","DOIUrl":"https://doi.org/10.1145/3571662.3571696","url":null,"abstract":"The improvement of modern communication technology has made the Internet of Vehicles (IoV) advance by leaps and bounds, and promotes the progress of many technologies, such as mobile sensing, vehicular edge computing, sensor networks, satellite positioning, data analysis, etc. Vehicular edge computing (VEC) is an innovative computing paradigm which can provide flexible and reliable computation services for intelligent and connected vehicles. An optimized problem is formulated to minimize the total task offloading time delay by making a tradeoff between vehicle mobility and task nature. To tackle the optimization problem, we proposed the Delay-sensitive half-Determined atomic Search algorithm, called DeshDaS, in which we regard each intelligent vehicle as an atom and strategy as electron and consider electron transition process. Experimental results validate the effectiveness and superior of our algorithm compared with several existed offloading strategy, and the larger average amount of data waiting to be processed, the more significant our advantage is.","PeriodicalId":235407,"journal":{"name":"Proceedings of the 8th International Conference on Communication and Information Processing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122148237","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}
Usage control (UCON) model realizes the usage control of resources by integrating authorization, obligations and conditions and providing characteristics of decision continuity and attribute mutability. In order to better adapt to the data interaction demand in the industrial Internet environment, the enhanced UCON(EN-UCON) model is proposed to extend the UCON model to maintain the persistent control of obligations in the lifecycle of resources usage. Firstly, the continuous monitoring of obligations is implemented through the post obligation model. And then, the performance of the obligation is recorded through the trust level, which will be incorporated into the subsequent authorization strategy as an important factor. Finally, the application of EN-UCON model in the industrial Internet interaction scenario is described through a specific case.
{"title":"The Enhanced Usage Control for data sharing in Industrial Internet","authors":"Zhong Na, Kai Li, Wei Liu, Zhifeng Gao","doi":"10.1145/3571662.3571689","DOIUrl":"https://doi.org/10.1145/3571662.3571689","url":null,"abstract":"Usage control (UCON) model realizes the usage control of resources by integrating authorization, obligations and conditions and providing characteristics of decision continuity and attribute mutability. In order to better adapt to the data interaction demand in the industrial Internet environment, the enhanced UCON(EN-UCON) model is proposed to extend the UCON model to maintain the persistent control of obligations in the lifecycle of resources usage. Firstly, the continuous monitoring of obligations is implemented through the post obligation model. And then, the performance of the obligation is recorded through the trust level, which will be incorporated into the subsequent authorization strategy as an important factor. Finally, the application of EN-UCON model in the industrial Internet interaction scenario is described through a specific case.","PeriodicalId":235407,"journal":{"name":"Proceedings of the 8th International Conference on Communication and Information Processing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124925138","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}
The diffusion function with large branch number is a fundamental building block in the construction of many block ciphers to achieve provable bounds against differential and linear cryptanalysis. Conventional diffusion functions, which are constructed based on linear error-correction code, has the undesirable side effect that a linear diffusion function by itself is “transparent” (i.e., has transition probability of 1) to differential and linear cryptanalysis. Nonlinear diffusion functions are less studied in cryptographic literature, up to now. In this paper, we propose a practical criterion for nonlinear optimal diffusion functions. Using this criterion we construct generally a class of nonlinear optimal diffusion functions over finite field. Unlike the previous constructions, our functions are non-linear, and thus they can provide enhanced protection against differential and linear cryptanalysis.
{"title":"Construction of Nonlinear Optimal Diffusion Functions over Finite Fields","authors":"B. Shen, Yu Zhou","doi":"10.1145/3571662.3571679","DOIUrl":"https://doi.org/10.1145/3571662.3571679","url":null,"abstract":"The diffusion function with large branch number is a fundamental building block in the construction of many block ciphers to achieve provable bounds against differential and linear cryptanalysis. Conventional diffusion functions, which are constructed based on linear error-correction code, has the undesirable side effect that a linear diffusion function by itself is “transparent” (i.e., has transition probability of 1) to differential and linear cryptanalysis. Nonlinear diffusion functions are less studied in cryptographic literature, up to now. In this paper, we propose a practical criterion for nonlinear optimal diffusion functions. Using this criterion we construct generally a class of nonlinear optimal diffusion functions over finite field. Unlike the previous constructions, our functions are non-linear, and thus they can provide enhanced protection against differential and linear cryptanalysis.","PeriodicalId":235407,"journal":{"name":"Proceedings of the 8th International Conference on Communication and Information Processing","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125953023","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}
Lingling Tu, Gaoyan Cai, Bingji Liang, Weining Mao
Aiming at the problem that the load identification accuracy of non-intrusive load monitoring (NILM) is greatly affected by the power of loads and the number of background loads, a non-intrusive load identification method based on the current complex spectrum and support vector machine (SVM) is proposed. Through the high-frequency sampling of the load's voltage and current, the complex spectrum of the current is extracted by the fast Fourier transform (FFT), and the multi-class SVM load identification model is established and optimized to realize the non-intrusive load identification. The algorithm is verified using the PLAID datasets, and the load identification accuracy of the algorithm is compared with SVM classifiers based on total harmonic distortion rate (THD), harmonic component ratio and harmonic amplitude. The results of the experiments show that the proposed method not only improves the identification accuracy of low-power loads, but also has higher identification accuracy and better identification robustness of switching load in multi-load scenarios.
{"title":"Non-Intrusive Load Identification Based on Complex Spectrum and Support Vector Machine","authors":"Lingling Tu, Gaoyan Cai, Bingji Liang, Weining Mao","doi":"10.1145/3571662.3571665","DOIUrl":"https://doi.org/10.1145/3571662.3571665","url":null,"abstract":"Aiming at the problem that the load identification accuracy of non-intrusive load monitoring (NILM) is greatly affected by the power of loads and the number of background loads, a non-intrusive load identification method based on the current complex spectrum and support vector machine (SVM) is proposed. Through the high-frequency sampling of the load's voltage and current, the complex spectrum of the current is extracted by the fast Fourier transform (FFT), and the multi-class SVM load identification model is established and optimized to realize the non-intrusive load identification. The algorithm is verified using the PLAID datasets, and the load identification accuracy of the algorithm is compared with SVM classifiers based on total harmonic distortion rate (THD), harmonic component ratio and harmonic amplitude. The results of the experiments show that the proposed method not only improves the identification accuracy of low-power loads, but also has higher identification accuracy and better identification robustness of switching load in multi-load scenarios.","PeriodicalId":235407,"journal":{"name":"Proceedings of the 8th International Conference on Communication and Information Processing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130551754","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}
Mingkang Yuan, Ye Li, Jiaxi Sun, Baokun Shi, Jinzhong Xu, Lele Xu, Yisu Wang
Remote sensing images are usually distributed in different departments and contain private information, so they normally cannot be available publicly. However, it is a trend to jointly use remote sensing images from different departments, because it normally enables the model to capture more information and remote sensing image analysis based on deep learning generally requires lots of training data. To address the above problem, in this paper, we apply a distributed asynchronized discriminator GAN framework (DGAN) to jointly learn remote sensing images from different client nodes. The DGAN is composed of multiple distributed discriminators and a central generator, and only the synthetic remote sensing images generated by the DGAN are used to train a semantic segmentation model. Based on DGAN, we establish an experimental platform composed of multiple different hosts, which adopts socket and multi-process technology to realize asynchronous communication between hosts, and visualize the training and testing process. During DGAN training, instead of original remote sensing images or convolutional network model information, only synthetic images, losses and labeled images are exchanged between nodes. Therefore, the DGAN well protects the privacy and security of the original remote sensing images. We verify the performance of the DGAN on three remote sensing image datasets (City-OSM, WHU and Kaggle Ship). In the experiments, we take different distributions of remote sensing images in client nodes into consideration. The experiments show that the DGAN has a great capacity for distributed remote sensing image learning without sharing the original remote sensing images or the convolutional network model. Moreover, compared with a centralized GAN trained on all remote sensing images collected from all client nodes, the DGAN can achieve almost the same performance in semantic segmentation tasks for remote sensing images.
{"title":"Distributed Learning based on Asynchronized Discriminator GAN for remote sensing image segmentation","authors":"Mingkang Yuan, Ye Li, Jiaxi Sun, Baokun Shi, Jinzhong Xu, Lele Xu, Yisu Wang","doi":"10.1145/3571662.3571668","DOIUrl":"https://doi.org/10.1145/3571662.3571668","url":null,"abstract":"Remote sensing images are usually distributed in different departments and contain private information, so they normally cannot be available publicly. However, it is a trend to jointly use remote sensing images from different departments, because it normally enables the model to capture more information and remote sensing image analysis based on deep learning generally requires lots of training data. To address the above problem, in this paper, we apply a distributed asynchronized discriminator GAN framework (DGAN) to jointly learn remote sensing images from different client nodes. The DGAN is composed of multiple distributed discriminators and a central generator, and only the synthetic remote sensing images generated by the DGAN are used to train a semantic segmentation model. Based on DGAN, we establish an experimental platform composed of multiple different hosts, which adopts socket and multi-process technology to realize asynchronous communication between hosts, and visualize the training and testing process. During DGAN training, instead of original remote sensing images or convolutional network model information, only synthetic images, losses and labeled images are exchanged between nodes. Therefore, the DGAN well protects the privacy and security of the original remote sensing images. We verify the performance of the DGAN on three remote sensing image datasets (City-OSM, WHU and Kaggle Ship). In the experiments, we take different distributions of remote sensing images in client nodes into consideration. The experiments show that the DGAN has a great capacity for distributed remote sensing image learning without sharing the original remote sensing images or the convolutional network model. Moreover, compared with a centralized GAN trained on all remote sensing images collected from all client nodes, the DGAN can achieve almost the same performance in semantic segmentation tasks for remote sensing images.","PeriodicalId":235407,"journal":{"name":"Proceedings of the 8th International Conference on Communication and Information Processing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117190787","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}
RF stealth waveform design is an essential technology in RF stealth radar. LPI performance evaluation of waveforms becomes more and more critical. Several radars transmit waveforms are designed through compound modulation, and the relative entropy between the signal and Gaussian White Noise is used as an index to evaluate the LPI performance of the waveform. At the same time, two methods of ambiguity function and interception factor are used to compare and verify them. The final simulation realizes the quantitative evaluation of waveform RF stealth performance based on relative entropy.
{"title":"Evaluation of Waveform RF Stealth Performance Based on Relative Entropy","authors":"Min Zhao, Siyu Xu, Bing-Gang Sun","doi":"10.1145/3571662.3571685","DOIUrl":"https://doi.org/10.1145/3571662.3571685","url":null,"abstract":"RF stealth waveform design is an essential technology in RF stealth radar. LPI performance evaluation of waveforms becomes more and more critical. Several radars transmit waveforms are designed through compound modulation, and the relative entropy between the signal and Gaussian White Noise is used as an index to evaluate the LPI performance of the waveform. At the same time, two methods of ambiguity function and interception factor are used to compare and verify them. The final simulation realizes the quantitative evaluation of waveform RF stealth performance based on relative entropy.","PeriodicalId":235407,"journal":{"name":"Proceedings of the 8th International Conference on Communication and Information Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131055161","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}
Li-wei Guo, Xinglin Shen, Shanzhu Xiao, Huanzhang Lu
Under low signal-to-noise ratio (SNR) target tracking, poor target information and high clutter limit the tracking effect. Extended targets potentially generate more than one measurement per time step. Multiple extended targets tracking is therefore can be used to improve tracking performance with low SNR, due to the expanded data than point targets tracking. Based on the classical probability hypothesis density (PHD) filter, the extended target PHD (ET- PHD) filter is proposed to track multiple extended targets. The main contribution of this paper is the improvement of the classical extended target Gaussian-mixture probability hypothesis density (ET-GM-PHD) filter. A method based on the ET-GM-PHD filter is proposed for decreasing false alarms and improving measurement set partition performance under low SNR cases. The optimized method is shown a better tracking performance in estimation accuracy of the targets number and targets state in comparison with a point PHD filter.
{"title":"Optimization Tracking Algorithm Based on Extended Target Gaussian Mixture PHD Filter","authors":"Li-wei Guo, Xinglin Shen, Shanzhu Xiao, Huanzhang Lu","doi":"10.1145/3571662.3571687","DOIUrl":"https://doi.org/10.1145/3571662.3571687","url":null,"abstract":"Under low signal-to-noise ratio (SNR) target tracking, poor target information and high clutter limit the tracking effect. Extended targets potentially generate more than one measurement per time step. Multiple extended targets tracking is therefore can be used to improve tracking performance with low SNR, due to the expanded data than point targets tracking. Based on the classical probability hypothesis density (PHD) filter, the extended target PHD (ET- PHD) filter is proposed to track multiple extended targets. The main contribution of this paper is the improvement of the classical extended target Gaussian-mixture probability hypothesis density (ET-GM-PHD) filter. A method based on the ET-GM-PHD filter is proposed for decreasing false alarms and improving measurement set partition performance under low SNR cases. The optimized method is shown a better tracking performance in estimation accuracy of the targets number and targets state in comparison with a point PHD filter.","PeriodicalId":235407,"journal":{"name":"Proceedings of the 8th International Conference on Communication and Information Processing","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125881878","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}
The blockchain technology has developed rapidly in recent years and has been widely used in all walks of life. However, most of the authentication systems adopted by the current blockchain technology are public key infrastructure based on large integer decomposition or discrete logarithm difficulties, and these cryptosystems are not secure in the quantum environment. Therefore, this paper considers an identity based post quantum authentication system applicable to the blockchain, which provides anti quantum protection and eliminates the dependence on public key certificates. Under the control of the supervision node, the authentication system has the key revocation function.
{"title":"Post quantum identity authentication mechanism in blockchain","authors":"Peng Duan, Bo Zhou","doi":"10.1145/3571662.3571682","DOIUrl":"https://doi.org/10.1145/3571662.3571682","url":null,"abstract":"The blockchain technology has developed rapidly in recent years and has been widely used in all walks of life. However, most of the authentication systems adopted by the current blockchain technology are public key infrastructure based on large integer decomposition or discrete logarithm difficulties, and these cryptosystems are not secure in the quantum environment. Therefore, this paper considers an identity based post quantum authentication system applicable to the blockchain, which provides anti quantum protection and eliminates the dependence on public key certificates. Under the control of the supervision node, the authentication system has the key revocation function.","PeriodicalId":235407,"journal":{"name":"Proceedings of the 8th International Conference on Communication and Information Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129736909","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}
In recent years, Cover Song Identification (CSI) based on Siamese Network and music representation learning has achieved good performance, however, there are still many problems such as limited feature fusion, missing decision threshold and single data label. In this paper, we propose a novel fully fused cover song identification model via feature fusing and clustering. In our proposed model, there are a fusion feature extraction structure, a channel separation decision structure, and a music feature clustering structure. First, we combine the pre-processing features of the dual input along the channel dimension to achieve full feature fusion and increase the fusion degree of the two songs in the feature extraction process. Secondly, we introduce channel separation to calculate multi-channel cross-features to improve the ability of the model to learn the difference between feature channels, and combined with the binary decision network to avoid the shortcomings of lack of decision thresholds in music representation learning. Finally, feature clustering generates invisible feature labels to enriches the types of cover data labels and reduces the difficulty of training. The model is trained in stages to optimize the clustering loss and the classification loss for cover and non-cover pairs, respectively. The model is validated on three public datasets, and experiments show that our model could achieve competitive results.
{"title":"Fully Fused Cover Song Identification Model via Feature Fusing and Clustering","authors":"Qiang Yuan, Shibiao Xu, Li Guo","doi":"10.1145/3571662.3571672","DOIUrl":"https://doi.org/10.1145/3571662.3571672","url":null,"abstract":"In recent years, Cover Song Identification (CSI) based on Siamese Network and music representation learning has achieved good performance, however, there are still many problems such as limited feature fusion, missing decision threshold and single data label. In this paper, we propose a novel fully fused cover song identification model via feature fusing and clustering. In our proposed model, there are a fusion feature extraction structure, a channel separation decision structure, and a music feature clustering structure. First, we combine the pre-processing features of the dual input along the channel dimension to achieve full feature fusion and increase the fusion degree of the two songs in the feature extraction process. Secondly, we introduce channel separation to calculate multi-channel cross-features to improve the ability of the model to learn the difference between feature channels, and combined with the binary decision network to avoid the shortcomings of lack of decision thresholds in music representation learning. Finally, feature clustering generates invisible feature labels to enriches the types of cover data labels and reduces the difficulty of training. The model is trained in stages to optimize the clustering loss and the classification loss for cover and non-cover pairs, respectively. The model is validated on three public datasets, and experiments show that our model could achieve competitive results.","PeriodicalId":235407,"journal":{"name":"Proceedings of the 8th International Conference on Communication and Information Processing","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125514153","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}
While MOOC brings a great impact to higher education, there is also the problem of low course completion rate, and it is important to analyze the factors influencing learners' continuous learning and participation in Massive Open Online Course (MOOC) for improving the teaching quality of MOOC. This paper constructs a model to predict and explain learners' MOOC continuous learning intention based on expectation confirmation model, technology acceptance model, planned behavior theory and flow theory, and carries out a questionnaire survey on students of our university who participate in the general elective courses on the platform of Chinese University MOOC. The results based on structural equation modeling show that expected confirmation and perceived ease of use significantly influence learners' perceived usefulness of MOOC; perceived usefulness and perceived ease of use significantly influence learners' attitudes towards MOOC; expected confirmation and perceived usefulness significantly influence learning satisfaction; perceived ease of use, satisfaction, attitude, focus, Perceived behavior control and subjective norms significantly influence learners' MOOC continuous learning intention. Based on the data analysis, the researcher discusses the theoretical and practical significance of this study and proposes the follow-up research plan.
MOOC在给高等教育带来巨大影响的同时，也存在着课程完成率低的问题，分析影响学习者持续学习和参与大规模在线开放课程(Massive Open Online course, MOOC)的因素，对于提高MOOC的教学质量具有重要意义。本文基于期望确认模型、技术接受模型、计划行为理论和流理论构建了预测和解释学习者MOOC持续学习意愿的模型，并对我校在中国大学MOOC平台上参加普通选修课的学生进行了问卷调查。基于结构方程模型的研究结果表明，期望确认和感知易用性显著影响学习者对MOOC的感知有用性;感知有用性和感知易用性显著影响学习者对MOOC的态度;期望确认和感知有用性显著影响学习满意度;感知易用性、满意度、态度、关注点、感知行为控制和主观规范显著影响学习者的MOOC持续学习意愿。在数据分析的基础上，探讨了本研究的理论意义和现实意义，并提出了后续的研究计划。
{"title":"Analysis of MOOC's Continuous Learning Intention and Its Influencing Factors of Higher Vocational Students","authors":"Fengmei Zhao, Yong Hu","doi":"10.1145/3571662.3571681","DOIUrl":"https://doi.org/10.1145/3571662.3571681","url":null,"abstract":"While MOOC brings a great impact to higher education, there is also the problem of low course completion rate, and it is important to analyze the factors influencing learners' continuous learning and participation in Massive Open Online Course (MOOC) for improving the teaching quality of MOOC. This paper constructs a model to predict and explain learners' MOOC continuous learning intention based on expectation confirmation model, technology acceptance model, planned behavior theory and flow theory, and carries out a questionnaire survey on students of our university who participate in the general elective courses on the platform of Chinese University MOOC. The results based on structural equation modeling show that expected confirmation and perceived ease of use significantly influence learners' perceived usefulness of MOOC; perceived usefulness and perceived ease of use significantly influence learners' attitudes towards MOOC; expected confirmation and perceived usefulness significantly influence learning satisfaction; perceived ease of use, satisfaction, attitude, focus, Perceived behavior control and subjective norms significantly influence learners' MOOC continuous learning intention. Based on the data analysis, the researcher discusses the theoretical and practical significance of this study and proposes the follow-up research plan.","PeriodicalId":235407,"journal":{"name":"Proceedings of the 8th International Conference on Communication and Information Processing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129267912","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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