Oral English Autonomous Learning Ability of English Majors Under the Background of the Internet and Its Improvement

The Internet is an important means of communication for contemporary college students,especially those majoring in English,to acquire knowledge about information and improve their oral proficiency.However,research on the relevant oral English autonomous learning ability of English majors shows that the overall learning situation is not satisfying.Based on the development of the concept of autonomous learning,this article explores the current situation and existing problems in oral English autonomous learning of English majors under the context of the Internet,and proposes corresponding autonomous learning strategies for improving their oral English skill.

Cultivation and Improvement of Autonomous Learning Ability

In the age of network information,autonomous learning has become an important way of learning. Improving autonomous learning ability will help to cultivate students’ autonomous learning habits,innovation consciousness and practical ability. This paper analyzes the development,characteristic connotation,advantages and current status of autonomous learning and puts forward strategies for cultivating college students’ autonomous learning ability. This paper is of guiding significance for enhancing learners’ awareness of autonomous learning,developing learners’ autonomous learning strategies and methods and improving learners’ autonomous learning ability,and it will lay a foundation for cultivating students’ lifelong learning consciousness.

Cultivation of Autonomous Learning Ability and Intercultural Communication Competence in Foreign Language Teaching

Learner Autonomy has been a hot topic in foreign language learning and teaching since 1960s,especially in relation to life-long skills.As the globalization develops,intercultural communication becomes more and more significant for college students.This essay attempts to explore main approaches to cultivate and improve students' autonomous learning ability and intercultural communication competence in foreign language teaching.

On fostering the autonomous learning ability in college English teaching

Autonomous learning is one of the objectives of multi-media college English teaching. On basis of the test of students＇ autonomous learning ability and the analysis of the results, this paper attempts to explore the feasibility of fostering the autonomous learning ability in college English teaching.

On the Cultivation Strategy of Autonomous Learning Ability of College English in Minority Areas

For a long time, due to the influence of many factors, College English Teaching in China's colleges and universities has attached to much importance to knowledge imparting, ignoring the cultivation of students' autonomous learning ability, the teaching effect is unsatisfactory, which is particularly prominent in remote minority areas. This paper analyzes the problems faced by College English Teaching in ethnic minority areas, arguing that it is imperative to cultivate autonomous learning ability of College English in ethnic minority areas. The strategies of cultivating students' autonomous learning ability are discussed in this paper and some suggestions on the scope of application of these strategies are put forward.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Results of the virtual simulation teaching method on autonomous learning competencies of undergraduate nursing students

Background:With the continuous development of information technology,most universities use mobile teaching platforms for classroom teaching.With the help of the national virtual simulation experimental teaching project sharing platform,students can enhance self-directed learning through the virtual simulation operations of the project.Purpose:To explore the application of virtual simulation experiment in teaching the fundamentals of nursing practice based on the Platform of the National Virtual Simulation Experiment Teaching Project during the COVID-19 pandemic analyze the impact of this teaching method on the autonomous learning ability of undergraduate nursing students.Methods:Convenience sampling was used to select 121 nursing undergraduates from Y University’s School of Nursing;the online teaching of fundamentals of nursing practice was conducted to the students.After taking the course,questionnaires were distributed to the undergraduate nursing students to collect their perceptions regarding the use of the virtual simulation experiment platform and autonomous learning competencies.Results:Most students expressed their preference for the virtual simulation teaching platform,and their satisfaction with the project evaluation was high 83.05%.They hoped to promote the application in future experimental teaching.Undergraduate nursing students believed that the virtual simulation teaching platform was conducive to cultivating clinical thinking ability,could stimulate learning interest,enhanced autonomous learning competencies.Conclusion:During the pandemic,the virtual simulation teaching platform for a lecture on in nursing education has achieved good results in both the aspects of teaching and student learning.Teachers efficiently used their training time and reduced their teaching burden.Moreover,the laboratory cost was also reduced.For undergraduate nursing students,the system was conducive to cultivating clinical thinking ability,stimulating their interest in learning,enhancing their learning and comprehension abilities and learning initiative.

To Perform Road Signs Recognition for Autonomous Vehicles Using Cascaded Deep Learning Pipeline

Autonomous vehicle is a vehicle that can guide itself without human conduction.It is capable of sensing its environment and moving with little or no human input.This kind of vehicle has become a concrete reality and may pave the way for future systems where computers take over the art of driving.Advanced artificial intelligence control systems interpret sensory information to identify appropriate navigation paths,as well as obstacles and relevant road signs.In this paper,we introduce an intelligent road signs classifier to help autonomous vehicles to recognize and understand road signs.The road signs classifier based on an artificial intelligence technique.In particular,a deep learning model is used,Convolutional Neural Networks(CNN).CNN is a widely used Deep Learning model to solve pattern recognition problems like image classification and object detection.CNN has successfully used to solve computer vision problems because of its methodology in processing images that are similar to the human brain decision making.The evaluation of the proposed pipeline was trained and tested using two different datasets.The proposed CNNs achieved high performance in road sign classification with a validation accuracy of 99.8%and a testing accuracy of 99.6%.The proposed method can be easily implemented for real time application.

Research on the Training Mode of “the Combination of College Students’ Extracurricular English Autonomous Learning and Chinese Culture” Based on WeChat Platform

In today’s college English extracurricular learning,college students often neglect the combination of English language learning and the input of Chinese traditional cultural knowledge,paying too much attention to Western culture learning,ignoring their traditional culture and lead to be speechless when encountering cultural exchange activities.The author believes that college students should pay attention to the input of Chinese traditional cultural content in the process of independent English learning outside the classroom.The creative team of college students led by the author through the WeChat platform,in Xinjiang Agricultural University,conducted a combination of Chinese culture input and English autonomous learning,aiming at strengthening the effectiveness of English learning,improving the self-learning ability and intercultural communication skills.

Reinforcement Learning Based Obstacle Avoidance for Autonomous Underwater Vehicle

Obstacle avoidance becomes a very challenging task for an autonomous underwater vehicle(AUV)in an unknown underwater environment during exploration process.Successful control in such case may be achieved using the model-based classical control techniques like PID and MPC but it required an accurate mathematical model of AUV and may fail due to parametric uncertainties,disturbance,or plant model mismatch.On the other hand,model-free reinforcement learning(RL)algorithm can be designed using actual behavior of AUV plant in an unknown environment and the learned control may not get affected by model uncertainties like a classical control approach.Unlike model-based control model-free RL based controller does not require to manually tune controller with the changing environment.A standard RL based one-step Q-learning based control can be utilized for obstacle avoidance but it has tendency to explore all possible actions at given state which may increase number of collision.Hence a modified Q-learning based control approach is proposed to deal with these problems in unknown environment.Furthermore,function approximation is utilized using neural network(NN)to overcome the continuous states and large statespace problems which arise in RL-based controller design.The proposed modified Q-learning algorithm is validated using MATLAB simulations by comparing it with standard Q-learning algorithm for single obstacle avoidance.Also,the same algorithm is utilized to deal with multiple obstacle avoidance problems.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

Impact of self-directed learning readiness and learning attitude on problem-solving ability among Chinese undergraduate nursing students

Objective: To explore the effects of self-directed learning readiness and learning attitude on problem-solving ability among Chinese undergraduate nursing students. Methods: A convenience sampling of 460 undergraduate nursing students was surveyed in Tianjin, China. Students who participated in the study completed a questionnaire that included social demographic questionnaire, Self-directed Learning Readiness Scale, Attitude to Learning Scale, and Social Problem-Solving Inventory. Pearson’s correlation analysis was performed to test the correlations among problem-solving ability, self-directed learning readiness, and learning attitude. Hierarchical linear regression analyses were performed to explore the mediating role of learning attitude. Results: The results showed that learning attitude (r=0.338, P<0.01) and self-directed learning readiness (r=0.493, P<0.01) were positively correlated with problem-solving ability. Learning attitude played a partial intermediary role between self-directed learning readiness and problem-solving ability (F=74.227, P<0.01). Conclusions: It is concluded that nursing educators should pay attention on students’ individual differences and take proper actions to inspire students’ self-directed learning readiness and learning attitude.

AI Safety Approach for Minimizing Collisions in Autonomous Navigation

Autonomous agents can explore the environment around them when equipped with advanced hardware and software systems that help intelligent agents minimize collisions.These systems are developed under the term Artificial Intelligence(AI)safety.AI safety is essential to provide reliable service to consumers in various fields such asmilitary,education,healthcare,and automotive.This paper presents the design of an AI safety algorithmfor safe autonomous navigation using Reinforcement Learning(RL).Machine Learning Agents Toolkit(ML-Agents)was used to train the agentwith a proximal policy optimizer algorithmwith an intrinsic curiositymodule(PPO+ICM).This training aims to improve AI safety and minimize or prevent any mistakes that can cause dangerous collisions by the intelligent agent.Four experiments have been executed to validate the results of our research.The designed algorithmwas tested in a virtual environment with four differentmodels.A comparison was presented in four cases to identify the best-performing model for improvingAI safety.The designed algorithmenabled the intelligent agent to perform the required task safely using RL.A goal collision ratio of 64%was achieved,and the collision incidents were minimized from 134 to 52 in the virtual environment within 30min.

Autonomous Development Strategies for College English Teachers in a Mixed-mode Learning Community

At present,the improvement of the quality of higher education and the improvement of the professional ability and comprehensive quality of college students all depend on the education level and discipline professional ability of teachers in colleges and universities.In the new era of educational reform and development,university teachers also need to continuously learn and progress in order to adapt to changes in the educational environment and the update of the knowledge system.Nowadays,the construction and development of the mixed-mode learning community environment is becoming one of the effective ways for college teachers to improve their learning.From the perspective of the significance of autonomous development of college English teachers in the context of mixed-mode learning community,the current situation and other issues,this article expounds the strategies that promote the autonomous development of college English teachers in the context of mixed-mode learning community to improve the professional quality of college English teachers.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Metacognitive Ability and its Relationship with CET4 Score——A Study on Science Students under Online College English Autonomous Learning

By using 162 third-year science students from the Independent College in Shandong University of Science and Tech nology,this paper investigated the relationship between their metacognitive ability and their CET4 score.The results indicated that their metacognitive ability,and the three subcategories have positive significant correlations with the students'CET4 score.

Research into college students＇ English autonomous learning capability and the necessity of cultivating metacognitive strategies

Through the research into college students＇ English autonomous learning ability of the non-English major students. That the cause why university students＇ English autonomous learning ability is weak is proved to be that they do not value the use of learning strategies. The use of learning strategies can promote the formation and enhancement of autonomous learning ability of the learners. Metacognitive strategy is a high-level management skill which can enable the learners to plan, regulate, monitor and evaluate actively their own learning process. Massive researches have proved whether metacognitive strategy is used successfully or not can directly affect the student learning result. So, it is necessary for teachers to cultivate and train the students to use metacogitive strategy in the university English teaching.

An Autonomous Incremental Learning Algorithm for Radial Basis Function Networks

In this paper, an incremental learning model called Resource Allocating Network with Long-Term Memory (RAN-LTM) is extended such that the learning is conducted with some autonomy for the following functions: 1) data collection for initial learning, 2) data normalization, 3) addition of radial basis functions (RBFs), and 4) determination of RBF cen-ters and widths. The proposed learning algorithm called Autonomous Learning algorithm for Resource Allocating Network (AL-RAN) is divided into the two learning phases: initial learning phase and incremental learning phase. And the former is further divided into the autonomous data collection and the initial network learning. In the initial learning phase, training data are first collected until the class separability is converged or has a significant dif-ference between normalized and unnormalized data. Then, an initial structure of AL-RAN is autonomously determined by selecting a moderate number of RBF centers from the collected data and by defining as large RBF widths as possible within a proper range. After the initial learning, the incremental learning of AL-RAN is conducted in a sequential way whenever a new training data is given. In the experiments, we evaluate AL-RAN using five benchmark data sets. From the experimental results, we confirm that the above autonomous functions work well and the efficiency in terms of network structure and learning time is improved without sacrificing the recognition accuracy as compared with the previous version of AL-RAN.

Cultivation Path of Nursing Undergraduates’Scientific Research and Innovation Ability

Objective: The cultivation of the innovation ability and scientific research is one of the nursing learning objectives for undergraduate students. To explore the method and effect of training system of scientific research innovation ability of nursing undergraduates based on “3332”. Methods: Three course learning modules are constructed: stage-based course learning module, systematic project practice training module and comprehensive practice training module. A practical training platform for scientific research innovation projects is built, and undergraduate scientific research innovation ability training is carried out from both in-class and out-of-class lines. Results: Since 2017, the students have obtained 7 national innovation and entrepreneurship training programs, 52 university-level undergraduate scientific research projects, published more than 10 academic papers, and obtained 2 patent authorization. Conclusions: The training system of scientific research innovation ability of nursing undergraduates based on “3332” is conducive to the development of scientific research innovation ability of nursing students, and to cultivate nursing talents who can adapt to the development of the new era and have better post competence.

成功老龄者听觉词汇学习特征初步研究

目的:探索成功老龄者的记忆特征。方法:采用中国老年成套神经心理测验中听觉词汇学习测验评估65岁以上社区老人421名,其中成功老龄(SA)组230名,常态老龄(UA)组137名,轻度认知损害(MCI)组54名。结果:(1)控制年龄、性别、教育程度后,SA 与MCI、UA 与MCI 组间即刻记忆、延迟回忆保持率、倒摄抑制有显著性差异(P<0.01);但三组间学习效应指数、前摄抑制无显著性差异。(2)对三组间七次回忆正确数进行重复测量协方差分析:SA 与UA 组组别及时间与组别交互作用均无显著性差异,但时间*组别变化趋势有显著性差异(P<0.05)。SA 与MCI、UA 与MCI 比较,组别及时间与组别交互作用、变化趋势均有显著性差异(P<0. 05)。结论:(1)延迟回忆可能是鉴别SA 较敏感的指标。(2)UA、MCI 仍具一定的学习能力,为进行成功老龄化干预提供了相关理论依据。