Situational Language Teaching Approach to Oral The English Teaching in Primary Schools

This paper aims at analyzing some applications of Situational Language teaching to the oral English learning in primary schools. Through this study, teachers could get some advice and improve their oral English teaching efficiency

Lis tening Anxiety in EFL Learning Taking “Middle School Students” As an Example

Among the fundamental skills of English language learning such as listening, speaking, reading and writing, noticeably, listening is regarded as the most important part by Second Language researchers. This thesis will mainly reports the new findings of a survey on middle school student’s anxiety in English listening. The new findings of this survey indicate that importance should be attached to the possibility and potential besides students poor performance when English teachers attempt to come up withcountermeasures on reducing English listening anxiety and assist students listen more effectively

A hybrid representation based simile component extraction

Simile, a special type of metaphor, can help people to express their ideas more clearly. Simile component extraction is to extract tenors and vehicles from sentences. This task has a realistic significance since it is useful for building cognitive knowledge base. With the development of deep neural networks, researchers begin to apply neural models to component extraction. Simile components should be in cross-domain. According to our observations, words in cross-domain always have different concepts. Thus, concept is important when identifying whether two words are simile components or not. However, existing models do not integrate concept into their models. It is difficult for these models to identify the concept of a word. What’s more, corpus about simile component extraction is limited. There are a number of rare words or unseen words, and the representations of these words are always not proper enough. Exiting models can hardly extract simile components accurately when there are low-frequency words in sentences. To solve these problems, we propose a hybrid representation-based component extraction (HRCE) model. Each word in HRCE is represented in three different levels: word level, concept level and character level. Concept representations (representations in concept level) can help HRCE to identify the words in cross-domain more accurately. Moreover, with the help of character representations (representations in character levels), HRCE can represent the meaning of a word more properly since words are consisted of characters and these characters can partly represent the meaning of words. We conduct experiments to compare the performance between HRCE and existing models. The experiment results show that HRCE significantly outperforms current models

A Case Study of Phonics among Primary School Students

Phonics is a widely implemented teaching approach in primary schools in many English speaking countries. Through a flexural development, the teaching approach has been proved to be an efficient way of improving children’s decoding, spelling and general reading ability. This paper reports case study of presenting phonics to 10 students in Grade 3 in China. The study shows that phonics teaching can help the students to form a connection between words and their pronunciation, hence help students to acquire the ability to decode and spell new words in their further reading

Progress on Silicone Packaging Materials for Power LED

AbstractEncapsulation materials are very vital to the power light emitting diode packaging and become a hot topic for worldwide researchers because the devices packaging and assembly yield, and the devices reliability and lifespan are determined by the quality of packaging and assembly materials as well as their processing. In this paper, the functions requirements and properties of power LED packaging materials were introduced. In addition, the research progress on traditional LED packaging materials, especially high refractive index silicone encapsulants were discussed in detail. Meanwhile, the direction of further development of encapsulation materials was pointed out

Ultrasonic Image's Annotation Removal: A Self-supervised Noise2Noise Approach

Accurately annotated ultrasonic images are vital components of a high-quality medical report. Hospitals often have strict guidelines on the types of annotations that should appear on imaging results. However, manually inspecting these images can be a cumbersome task. While a neural network could potentially automate the process, training such a model typically requires a dataset of paired input and target images, which in turn involves significant human labour. This study introduces an automated approach for detecting annotations in images. This is achieved by treating the annotations as noise, creating a self-supervised pretext task and using a model trained under the Noise2Noise scheme to restore the image to a clean state. We tested a variety of model structures on the denoising task against different types of annotation, including body marker annotation, radial line annotation, etc. Our results demonstrate that most models trained under the Noise2Noise scheme outperformed their counterparts trained with noisy-clean data pairs. The costumed U-Net yielded the most optimal outcome on the body marker annotation dataset, with high scores on segmentation precision and reconstruction similarity. We released our code at https://github.com/GrandArth/UltrasonicImage-N2N-Approach.Comment: 10 pages, 7 figure