In recent years,research has revealed that in comparison to studies on other information and communication technologies,individuals'behaviour towards interact based learning tools has not been investigated and und...In recent years,research has revealed that in comparison to studies on other information and communication technologies,individuals'behaviour towards interact based learning tools has not been investigated and understood thoroughly.Consequently,this paper has investigated the factors of availability of information and communication technologies(ICTs)and other university context factors that have influence on students'intentions to use Internet-based learning tools(ILTs).The result showed that availability of ICTs,university readiness,perceived usefulness(PU),subjective norm,vice chancellor characteristics,university support and top management support significantly and directly impacted students'intention to use ILTs.The integration of a university's environment perspective into the technology acceptance model(TAM),resulted in our extended TAM model that captured both extrinsic motivation and university's characteristics for explaining students'intention to use new internet based learning tools.展开更多
The widespread use of the Internet of Things(IoTs)and the rapid development of artificial intelligence technologies have enabled applications to cross commercial and industrial band settings.Within such systems,all pa...The widespread use of the Internet of Things(IoTs)and the rapid development of artificial intelligence technologies have enabled applications to cross commercial and industrial band settings.Within such systems,all participants related to commercial and industrial systems must communicate and generate data.However,due to the small storage capacities of IoT devices,they are required to store and transfer the generated data to third-party entity called“cloud”,which creates one single point to store their data.However,as the number of participants increases,the size of generated data also increases.Therefore,such a centralized mechanism for data collection and exchange between participants is likely to face numerous challenges in terms of security,privacy,and performance.To address these challenges,Federated Learning(FL)has been proposed as a reasonable decentralizing approach,in which clients no longer need to transfer and store real data in the central server.Instead,they only share updated training models that are trained over their private datasets.At the same time,FL enables clients in distributed systems to share their machine learning models collaboratively without their training data,thus reducing data privacy and security challeges.However,slow model training and the execution of additional unnecessary communication rounds may hinder FL applications from operating properly in a distributed system.Furthermore,these unnecessary communication rounds make the system vulnerable to security and privacy issues,because irrelevant model updates are sent between clients and servers.Thus,in this work,we propose an algorithm for fully homomorphic encryption called Cheon-Kim-Kim-Song(CKKS)to encrypt model parameters for their local information privacy-preserving function.The proposed solution uses the impetus term to speed up model convergence during the model training process.Furthermore,it establishes a secure communication channel between IoT devices and the server.We also use a lightweight secure transport protocol to mitigate the communication overhead,thereby improving communication security and efficiency with low communication latency between client and server.展开更多
文摘In recent years,research has revealed that in comparison to studies on other information and communication technologies,individuals'behaviour towards interact based learning tools has not been investigated and understood thoroughly.Consequently,this paper has investigated the factors of availability of information and communication technologies(ICTs)and other university context factors that have influence on students'intentions to use Internet-based learning tools(ILTs).The result showed that availability of ICTs,university readiness,perceived usefulness(PU),subjective norm,vice chancellor characteristics,university support and top management support significantly and directly impacted students'intention to use ILTs.The integration of a university's environment perspective into the technology acceptance model(TAM),resulted in our extended TAM model that captured both extrinsic motivation and university's characteristics for explaining students'intention to use new internet based learning tools.
基金supported by the National Key Research and Development Program of China(No.2018YFB0803403)the Fundamental Research Funds for the Central Universities(Nos.FRF-AT-20-11 and FRF-AT-19-009Z)from the Ministry of Education of China.
文摘The widespread use of the Internet of Things(IoTs)and the rapid development of artificial intelligence technologies have enabled applications to cross commercial and industrial band settings.Within such systems,all participants related to commercial and industrial systems must communicate and generate data.However,due to the small storage capacities of IoT devices,they are required to store and transfer the generated data to third-party entity called“cloud”,which creates one single point to store their data.However,as the number of participants increases,the size of generated data also increases.Therefore,such a centralized mechanism for data collection and exchange between participants is likely to face numerous challenges in terms of security,privacy,and performance.To address these challenges,Federated Learning(FL)has been proposed as a reasonable decentralizing approach,in which clients no longer need to transfer and store real data in the central server.Instead,they only share updated training models that are trained over their private datasets.At the same time,FL enables clients in distributed systems to share their machine learning models collaboratively without their training data,thus reducing data privacy and security challeges.However,slow model training and the execution of additional unnecessary communication rounds may hinder FL applications from operating properly in a distributed system.Furthermore,these unnecessary communication rounds make the system vulnerable to security and privacy issues,because irrelevant model updates are sent between clients and servers.Thus,in this work,we propose an algorithm for fully homomorphic encryption called Cheon-Kim-Kim-Song(CKKS)to encrypt model parameters for their local information privacy-preserving function.The proposed solution uses the impetus term to speed up model convergence during the model training process.Furthermore,it establishes a secure communication channel between IoT devices and the server.We also use a lightweight secure transport protocol to mitigate the communication overhead,thereby improving communication security and efficiency with low communication latency between client and server.
