Effects and Causes of VR-Supported Interpreting Learning Environment on the Interpretation Classroom Anxiety of Student Interpreter

Interpreting activity is considered a high-anxiety activity due to its immediacy, multitasking, complexity of cognitive processing, and uncertainty of cognitive processing. Research has shown that interpreting anxiety, as the biggest emotional obstacle in the interpreting process, is the main emotional factor that leads to individual differences in interpreting. Students often claim to have fear or anxiety behaviors in interpreting exams, interpreting competitions, and interpreting classes. However, the research on interpreting teaching attaches importance to the cultivation of language knowledge, cultural knowledge, and interpreting skills, and does not pay enough attention to emotional factors such as motivation and anxiety in interpreting learning, which makes it difficult for the cultivated interpreters to meet the requirements of professional practice. In recent years, virtual reality technology (VR) has been gradually applied in the field of foreign language and interpreting teaching for creating a real, interactive and experiential language learning environment. Situated Learning Theory stresses that the fundamental mechanism for learning to take place is for individuals to participate in the real context in which knowledge is generated, and to realize the construction of knowledge through the interaction with the community of practice and the environment. Virtual reality technology can satisfy the needs of language learners for real contexts by providing learners with immersive, imaginative and interactive scenario simulations, and has a certain positive effect on alleviating learning anxiety. Therefore, relying on the virtual simulation course “United Nations Kubuqi International Desert Ecological Science and Technology Innovation International Volunteer Language Service Practical Training System”, this paper adopts a combination of quantitative and qualitative analyses to investigate the interpretation anxiety level of the interpreter trainees and the factors affecting them in the VR situation to help them discover effective responses to interpreter anxiety.

Implications of The Gift of the Magi for Moral Education of Teenagers From the Perspective of Interaction Ritual Chains

Adolescence is a pivotal moment in shaping one’s character and holds great implications for one’s values and personal growth.The Gift of the Magi by O.Henry portrays how an ordinary young couple in difficult financial circumstances managed to show their love for each other,eulogizing truth,goodness,and beauty in humanity.We conducted a thorough examination of the significance of moral values conveyed in The Gift of the Magi for teenager education using the“situation-emotion-symbol”model that is based on Randall Collins’theory of interaction ritual chains in order to establish an interaction ritual chain model for moral education of teenagers.Our study reveals that situated cognition is useful in constructing the focus of attention for the moral education of teenagers;that shared emotion is the driving force for achieving moral education of teenagers;and that symbolic connections help teenagers put the virtues they’ve learned from The Gift of the Magi into action and provides effective measures that can significantly contribute to the cultivation and implementation of moral education among teenagers.

STABC-IR:An air target intention recognition method based on bidirectional gated recurrent unit and conditional random field with space-time attention mechanism

The battlefield environment is changing rapidly,and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage.The current Intention Recognition(IR)method for air targets has shortcomings in temporality,interpretability and back-and-forth dependency of intentions.To address these problems,this paper designs a novel air target intention recognition method named STABC-IR,which is based on Bidirectional Gated Recurrent Unit(Bi GRU)and Conditional Random Field(CRF)with Space-Time Attention mechanism(STA).First,the problem of intention recognition of air targets is described and analyzed in detail.Then,a temporal network based on Bi GRU is constructed to achieve the temporal requirement.Subsequently,STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements.Finally,an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment.The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%,which is higher than other latest intention recognition methods.STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability,which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.

Adaptive level of autonomy for human-UAVs collaborative surveillance using situated fuzzy cognitive maps

Collaborating with a squad of Unmanned Aerial Vehicles(UAVs)is challenging for a human operator in a cooperative surveillance task.In this paper,we propose a cognitive model that can dynamically adjust the Levels of Autonomy(LOA)of the human-UAVs team according to the changes in task complexity and human cognitive states.Specifically,we use the Situated Fuzzy Cognitive Map(Si FCM)to model the relations among tasks,situations,human states and LOA.A recurrent structure has been used to learn the strategy of adjusting the LOA,while the collaboration task is separated into a perception routine and a control routine.Experiment results have shown that the workload of the human operator is well balanced with the task efficiency.

Situation reasoning for an adjustable autonomy system

Purpose–The purpose of this paper is to provide a more capable and holistic adjustable autonomy system,involving situation reasoning among all involved information sources,to make an adjustable autonomy system which knows what the situation is currently,what needs to be done in the present situation,and how risky the task is in the present situation.This will enhance efficiency for calculating the level of autonomy.Design/methodology/approach–Situation reasoning methodologies are present in many autonomous systems which are called situation awareness.Situation awareness in autonomous systems is divided into three levels,situation perception,situation comprehension and situation projection.Situation awareness in these systems aims to make the tactical plans cognitive,but situation reasoning in adjustable autonomous systems aim to communicate mission assessments to unmanned vehicle or humans.Thus,in solving this problem,it is important to design a new situation reasoning module for the adjustable autonomous system.Findings–The contribution of this paper is presenting the Situation Reasoning Module(SRM)for an adjustable autonomous system,which encapsulates event detection,cognitive situations,cognitive tasks,performance capacity assessment and integrated situation reason.The paper concludes by demonstrating the benefits of the SRM in a real-world scenario,a situation reasoning simulation in unmanned surface vehicles(USV)while performing a navigation mission.Originality/value–The method presented in this paper represents a new SRM to reason the situation for adjustable autonomous system.While the results presented in the paper are based on fuzzy logic and Bayesian network methodology.The results of this paper can be applicable to land,sea and air robotics in an adjustable autonomous system.