To improve the quality of computation experience for mobile devices,mobile edge computing(MEC)is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network,which s...To improve the quality of computation experience for mobile devices,mobile edge computing(MEC)is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network,which supports both traditional communication and MEC services.However,this kind of intensive computing problem is a high dimensional NP hard problem,and some machine learning methods do not have a good effect on solving this problem.In this paper,the Markov decision process model is established to find the excellent task offloading scheme,which maximizes the long-term utility performance,so as to make the best offloading decision according to the queue state,energy queue state and channel quality between mobile users and BS.In order to explore the curse of high dimension in state space,a candidate network is proposed based on edge computing optimize offloading(ECOO)algorithm with the application of deep deterministic policy gradient algorithm.Through simulation experiments,it is proved that the ECOO algorithm is superior to some deep reinforcement learning algorithms in terms of energy consumption and time delay.So the ECOO is good at dealing with high dimensional problems.展开更多
This study investigated how the mode in which the reading-writing integrated continuation task was conducted modulates the effects of second language(L2) syntactic alignment, through the English motion event construct...This study investigated how the mode in which the reading-writing integrated continuation task was conducted modulates the effects of second language(L2) syntactic alignment, through the English motion event construction with manner verbs. Ninety Chinese students were assigned to either of the two experimental groups or a control group, and they all experienced a pretest, an alignment phase and a posttest. In the alignment phase, the two experimental groups completed a reading-writing integrated continuation task but in different modes. For the multi-turn mode,participants reconstructed a picture story by continuing the episodes extracted from the story with one episode presented and continued at a time;for the single-turn mode, the first half of the same picture story was presented as a chunk, and then participants read and continued it. Results show that L2 learners aligned with the target structure in completing the story, and the alignment effect was retained in the posttest conducted after a delay of two weeks. Moreover, syntactic alignment was modulated by task mode with the multi-turn group exhibiting stronger immediate and longterm alignment effects. We conclude that the continuation task is a fruitful context for L2 structural alignment, and the magnitude of alignment effect hinges on interactive intensity.展开更多
This paper is on the study of motivational changes of Chinese non-English major students in their first two years of college English learning. The current study was carried out through the quantitative research method...This paper is on the study of motivational changes of Chinese non-English major students in their first two years of college English learning. The current study was carried out through the quantitative research method. The research question to be addressed is：During the first two years of college English learning, does non-English majors＇ learning motivation （i.e. types of motivation and motivational intensity） change？ The analyses of the data reveal that motivational changes do exist in the first two years of college English learning. Generally, most types of motivation （i.e. intrinsic interest motivation, information medium motivation, personal development motivation, and social responsibility motivation） decrease significantly except for the slight increase of immediate achievement motivation, and the decline of motivational intensity is also obvious.展开更多
Women have been stereotyped as better multitaskers when compared to their male counterparts. The purpose of this study is to investigate whether there are differences in gender performance when performing cognitive co...Women have been stereotyped as better multitaskers when compared to their male counterparts. The purpose of this study is to investigate whether there are differences in gender performance when performing cognitive combined tasks. Twenty-four graduate students （twelve females and twelve males） volunteered to participate in the study. The task requires participants to indicate when they perceive a change in the intensity of an auditory signal while simultaneously solving algebraic problems. Multivariate Analysis of Variance （MANOVA） results reveal no significant differences between genders when performing the combined tasks （p = 0.1831 and 2 = 0.7891） although the average number of false alarms made during the combined tasks by males is nearly 11% higher than the average number of false alarms made by females. However, （Multivariate Analysis of Variance） ANOVA results for the combined tasks show that males outperform females on the computational task while listening for changes in the auditory signal F（1, 22） - 5.09, p 〈 0.03, but there are no significant differences in their ability to detect noise intensity variation or in the number of false alarms made while multitasking. For the single task analysis the ANOVAs indicate no significant differences in signal detection task performance, computational task performance, or the number of false alarms made by males and females.展开更多
With the growth of maritime activities,the number of computationally complex applications is growing exponentially.Mobile edge computing(MEC)is widely recognized as a viable option to address the substantial need for ...With the growth of maritime activities,the number of computationally complex applications is growing exponentially.Mobile edge computing(MEC)is widely recognized as a viable option to address the substantial need for wireless communications and compute-intensive operations in maritime environments.To reduce the processing load and meet the demands of mobile terminals for high bandwidth,low latency and multiple access,MEC systems with unmanned aerial vehicles(UAVs)have been proposed and extensively explored.In this paper,a maritime MEC network that employs a top-UAV(T-UAV)for task offloading supported by digital twin(DT)is considered.To explore the task offloading strategy employed by the edge server,the flight trajectory and resource allocation strategy of the T-UAV is studied in detail.The objective of this study is to minimize latency costs while ensuring that the energy of the T-UAV is sufficient to fulfill services.In order to accomplish this objective,the joint optimization problem is described as a Markov decision process(MDP).To overcome this problem,the priority-based reinforcement learning(RL)algorithm for computation offloading and trajectory planning(PRL-COTP)is developed.The simulation results demonstrate that the proposed approach can significantlyreduce the overall cost of the system in comparison to other benchmarks.展开更多
基金National Natural Science Foundation of China(No.11461038)Science and Technology Support Program of Gansu Province(No.144NKCA040)。
文摘To improve the quality of computation experience for mobile devices,mobile edge computing(MEC)is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network,which supports both traditional communication and MEC services.However,this kind of intensive computing problem is a high dimensional NP hard problem,and some machine learning methods do not have a good effect on solving this problem.In this paper,the Markov decision process model is established to find the excellent task offloading scheme,which maximizes the long-term utility performance,so as to make the best offloading decision according to the queue state,energy queue state and channel quality between mobile users and BS.In order to explore the curse of high dimension in state space,a candidate network is proposed based on edge computing optimize offloading(ECOO)algorithm with the application of deep deterministic policy gradient algorithm.Through simulation experiments,it is proved that the ECOO algorithm is superior to some deep reinforcement learning algorithms in terms of energy consumption and time delay.So the ECOO is good at dealing with high dimensional problems.
文摘This study investigated how the mode in which the reading-writing integrated continuation task was conducted modulates the effects of second language(L2) syntactic alignment, through the English motion event construction with manner verbs. Ninety Chinese students were assigned to either of the two experimental groups or a control group, and they all experienced a pretest, an alignment phase and a posttest. In the alignment phase, the two experimental groups completed a reading-writing integrated continuation task but in different modes. For the multi-turn mode,participants reconstructed a picture story by continuing the episodes extracted from the story with one episode presented and continued at a time;for the single-turn mode, the first half of the same picture story was presented as a chunk, and then participants read and continued it. Results show that L2 learners aligned with the target structure in completing the story, and the alignment effect was retained in the posttest conducted after a delay of two weeks. Moreover, syntactic alignment was modulated by task mode with the multi-turn group exhibiting stronger immediate and longterm alignment effects. We conclude that the continuation task is a fruitful context for L2 structural alignment, and the magnitude of alignment effect hinges on interactive intensity.
文摘This paper is on the study of motivational changes of Chinese non-English major students in their first two years of college English learning. The current study was carried out through the quantitative research method. The research question to be addressed is：During the first two years of college English learning, does non-English majors＇ learning motivation （i.e. types of motivation and motivational intensity） change？ The analyses of the data reveal that motivational changes do exist in the first two years of college English learning. Generally, most types of motivation （i.e. intrinsic interest motivation, information medium motivation, personal development motivation, and social responsibility motivation） decrease significantly except for the slight increase of immediate achievement motivation, and the decline of motivational intensity is also obvious.
文摘Women have been stereotyped as better multitaskers when compared to their male counterparts. The purpose of this study is to investigate whether there are differences in gender performance when performing cognitive combined tasks. Twenty-four graduate students （twelve females and twelve males） volunteered to participate in the study. The task requires participants to indicate when they perceive a change in the intensity of an auditory signal while simultaneously solving algebraic problems. Multivariate Analysis of Variance （MANOVA） results reveal no significant differences between genders when performing the combined tasks （p = 0.1831 and 2 = 0.7891） although the average number of false alarms made during the combined tasks by males is nearly 11% higher than the average number of false alarms made by females. However, （Multivariate Analysis of Variance） ANOVA results for the combined tasks show that males outperform females on the computational task while listening for changes in the auditory signal F（1, 22） - 5.09, p 〈 0.03, but there are no significant differences in their ability to detect noise intensity variation or in the number of false alarms made while multitasking. For the single task analysis the ANOVAs indicate no significant differences in signal detection task performance, computational task performance, or the number of false alarms made by males and females.
基金Foundation items:National Natural Science Foundation of China(Nos.62301307 and 62072096)Shanghai Pujiang Program,China(No.23PJD041)Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission,China(No.CGA60)。
文摘With the growth of maritime activities,the number of computationally complex applications is growing exponentially.Mobile edge computing(MEC)is widely recognized as a viable option to address the substantial need for wireless communications and compute-intensive operations in maritime environments.To reduce the processing load and meet the demands of mobile terminals for high bandwidth,low latency and multiple access,MEC systems with unmanned aerial vehicles(UAVs)have been proposed and extensively explored.In this paper,a maritime MEC network that employs a top-UAV(T-UAV)for task offloading supported by digital twin(DT)is considered.To explore the task offloading strategy employed by the edge server,the flight trajectory and resource allocation strategy of the T-UAV is studied in detail.The objective of this study is to minimize latency costs while ensuring that the energy of the T-UAV is sufficient to fulfill services.In order to accomplish this objective,the joint optimization problem is described as a Markov decision process(MDP).To overcome this problem,the priority-based reinforcement learning(RL)algorithm for computation offloading and trajectory planning(PRL-COTP)is developed.The simulation results demonstrate that the proposed approach can significantlyreduce the overall cost of the system in comparison to other benchmarks.
