Mental Image and Its Application in Language Learning

Mental image has often been believed to play a very large, even pivotal, role in both memory and motivation. However, with the lateralization of the brain, the treasure of mental image, a function dominated by the right hemisphere of the brain, is often neglected in the realm of language learning, a function dominated by the left-brain. Thus, educational systems have largely, if not exclusively, catered to the logic, analytical left-brain, and unwisely phase out the imagery elements of the right brain. In this paper, therefore, the author argues that mental image should not be a property confined to children, who have more imagination. This kind of human treasure should be appropriately deployed through the process of language teaching and learning, for it can provide a strong impetus for language learning, thus making language-learning process more enjoyable and beneficial.

中西日文化中的Image/形象/姿

全球化多元文化的互动框架表现为一元/多元的冲突。中西日文化中的Image/形象/姿作为表情达意的艺术符号,呈现出三种不同的美的形态。古希腊时代的Image以外在的纯形为主,由"形"上升到"体"并在其中突显出几何形状的"Form",在"Form"中体现宇宙的大美。中国的"形象"轻形而重象,由外在之"象"直抵内在之"神",以主客合一的"神"为媒介达到象外之象,是人在天地之中的到场。日本的"姿"并非理性之形,而是直观经验触摸到的形;是动中之静、静中有动的无形之"姿",由瞬间之"姿"来暗示自然万物无限的流动性。

Loss of Image in Translating Chinese Poem into English

The process of translating Chinese poem into English is actually the process of the transference of image.Image is the soul of poetry.In translating the poem there often exists a phenomenon that is loss of image.So there are some questions to be discussed,such as:the definition of image,the reasons of loss of image,and the way to avoid losing image.This thesis will discuss the questions from three aspects:language difference,culture difference and factors of translator.

基于CLIP模型和知识数据库的零样本动作识别

零样本动作识别旨在从已知类别的动作样本数据中学习知识,并将其迁移到未知的动作类别上,从而实现对未知动作样本的识别和分类.现有的零样本动作识别模型依赖有限的训练数据,可学习到的先验知识有限,难以将视觉特征准确地映射到语义标签上,是限制零样本学习性能提升的关键因素.针对上述问题,本文提出了一种引入外部知识数据库和CLIP模型的零样本学习框架,利用多模态CLIP模型通过自监督对比学习方式积累的知识,来扩充零样本动作识别模型的先验知识.同时,设计了时序编码器,以弥补CLIP模型时序建模能力的欠缺.为了使模型学习到更丰富的语义特征,缩小视觉特征和语义标签之间的语义鸿沟,本文扩展了已知动作类别的语义标签,用更为详细的描述语句代替简单的文本标签,丰富了文本表示的语义信息;在此基础上,在模型外部构建了一个知识数据库,在不增加模型参数规模的条件下为模型提供额外的辅助信息,强化视觉特征与文本特征表示之间的关联关系.最后,本文遵循零样本学习规范,对模型进行微调,使其适应零样本动作识别任务,提高了模型的泛化能力.所提方法在HMDB51和UCF101两个主流数据集上进行了广泛实验,实验数据表明,该方法的识别性能相比目前的先进方法在上述两个数据集上分别提升了3.8%和2.3%,充分体现了所提方法的有效性.

Cultural Image of Animal Words

This paper,after introducing the definition and forms of cultural image,focuses on the detailed comparison and analysis of cultural image of animal words both in English and in Chinese from four aspects,that is,same animal word,same cultural image;same animal word,different cultural images;different animal words,same cultural image;different animal words,different cultural images.

Shape characterization of sand particles based on digital image processing technology

To characterize the shape of sand particles for concrete,a new method is proposed based on digital image processing(known as the DIP method).By analyzing sand particles projection,the length,width and thickness of sand were measured to characterize particle form.The area and perimeter were measured to characterize particle angularity.The results of the DIP method and Vernier caliper were compared to examine the accuracy of the DIP method.The sample size test was conducted to show the statistical significance of shape results measured by the DIP method.The practicality of the DIP method was verified by instance analysis.The results show that aspect ratios and roundness measured by the DIP method are equal to ones by the Vernier caliper.Results by DIP are dependent on the sand particle number,and at least 350 particles should be measured to represent the overall shape property of sand.The results show that the DIP method is able to distinguish the differences in the shape of sand particles.It achieves the direct measurement of sand particle thickness,and the characterization results of sand aspect ratios and roundness are accurate,statistically significant and practical.Therefore,the DIP method is suitable for sand particle shape characterization.

In situ monitoring methods for selective laser melting additive manufacturing process based on images-A review

Selective laser melting(SLM)has been widely used in the fields of aviation,aerospace and die manufacturing due to its ability to produce metal components with arbitrarily complex shapes.However,the instability of SLM process often leads to quality fluctuation of the formed component,which hinders the further development and application of SLM.In situ quality control during SLM process is an effective solution to the quality fluctuation of formed components.However,the basic premise of feedback control during SLM process is the rapid and accurate diagnosis of the quality.Therefore,an in situ monitoring method of SLM process,which provides quality diagnosis information for feedback control,became one of the research hotspots in this field in recent years.In this paper,the research progress of in situ monitoring during SLM process based on images is reviewed.Firstly,the significance of in situ monitoring during SLM process is analyzed.Then,the image information source of SLM process,the image acquisition systems for different detection objects(the molten pool region,the scanned layer and the powder spread layer)and the methods of the image information analysis,detection and recognition are reviewed and analyzed.Through review and analysis,it is found that the existing image analysis and detection methods during SLM process are mainly based on traditional image processing methods combined with traditional machine learning models.Finally,the main development direction of in situ monitoring during SLM process is proposed by combining with the frontier technology of image-based computer vision.

An infrared and visible image fusion method based upon multi-scale and top-hat transforms

The high-frequency components in the traditional multi-scale transform method are approximately sparse, which can represent different information of the details. But in the low-frequency component, the coefficients around the zero value are very few, so we cannot sparsely represent low-frequency image information. The low-frequency component contains the main energy of the image and depicts the profile of the image. Direct fusion of the low-frequency component will not be conducive to obtain highly accurate fusion result. Therefore, this paper presents an infrared and visible image fusion method combining the multi-scale and top-hat transforms. On one hand, the new top-hat-transform can effectively extract the salient features of the low-frequency component. On the other hand, the multi-scale transform can extract highfrequency detailed information in multiple scales and from diverse directions. The combination of the two methods is conducive to the acquisition of more characteristics and more accurate fusion results. Among them, for the low-frequency component, a new type of top-hat transform is used to extract low-frequency features, and then different fusion rules are applied to fuse the low-frequency features and low-frequency background; for high-frequency components, the product of characteristics method is used to integrate the detailed information in high-frequency. Experimental results show that the proposed algorithm can obtain more detailed information and clearer infrared target fusion results than the traditional multiscale transform methods. Compared with the state-of-the-art fusion methods based on sparse representation, the proposed algorithm is simple and efficacious, and the time consumption is significantly reduced.

Investigating the tourism image of mountain scenic spots in China through the lens of tourist perception

A favorable tourism image of high-quality mountain scenic spots(HQMSS)is crucial for tourism prosperity and sustainability.This paper establishes a framework for investigating the tourism image based on cognitive-emotion theory and uses natural language processing(NLP)tools to clarify the cognition,emotion,and overall tourist image of the HQMSS in China from the perspective of tourist perception.This paper examines the multi-dimensional spatial differentiation of China's overall image,including province,scenic spot scales,as well as the spatial pattern of the overall comprehensive tourism image.Strategies for comprehensively improving HQMSS's tourism image are also formulated.The results show that:(1)The cognitive image of Chinese HQMSS is categorized into core and marginal images,and the core images such as scenery and cable car are the expression of the uniqueness of mountainous scenic spots.Additionally,the cognitive image is classified into six dimensions:tourism environment,tourism supporting facilities,tourism experience,tourism price,tourism service,and tourism safety.(2)Positive emotions are the dominant mood type of HQMSS in China,followed by neutral emotions,with negative emotions being the least frequent.Emotional images vary across dimensions,with tourism environment and tourism experience evoking relatively higher emotion.(3)The spatial pattern of HQMSS for each dimension at the national,provincial,and scenic scales is diversifying.This article provides a multidimensional perspective for investigating the tourism image of mountainous scenic spots,proposes targeted recommendations to improve the overall image of HQMSS in China,and can greatly contribute to the sustainable development of mountain tourism.

Visual processing of word and image in the fusiform gyrus

BACKGROUND： The role of the left midfusiform gyrus as a target for visual word processing has been a topic of discussion. Numerous studies have utilized alphabetic writing for subject matter. However, few have addressed visual processing of Chinese characters in the left midfusiform gyrus. OBJECTIVE： To verify visual processing of Chinese characters and images in the left midfusiform gyrus using functional magnetic resonance imaging. DESIGN, TIME AND SETTING： A blocked design paradigm study. Experiments were performed at the Room of Magnetic Resonance, Guangdong Provincial Second People＇s Hospital, China from May to June 2009. PARTICIPANTS： A total of eight undergraduate students were recruited from Guangzhou University of China, comprising two females and six males, aged 20-23 years. The subjects were right-handed which was determined by a Chinese standard questionnaire. None of the subjects had a history of psychoneurosis, familial disease, color blindness, or color weakness. METHODS： A total of eight undergraduates were enrolled as subjects. Picture-naming and verb generation tasks were employed through the use of functional magnetic resonance imaging. Analysis of Functional Neurolmages software was used to process the data. MAIN OUTCOME MEASURES： Visual processing of Chinese characters and images in the left midfusiform gyrus was measured. RESULTS： Picture-naming and verb generation tasks were shown to significantly activate the bilateral midfusiform gyrus. Activation occurred in the visual word form area of the left midfusiform gyrus. CONCLUSION： The left midfusiform gyrus plays a general role in visual processing of Chinese characters and images.

GEOMETRIC PROPERTIES FOR GAUSSIAN IMAGE OF SUBMANIFOLDS IN S^(n+p)(1)

The geometric properties for Gaussian image of submanifolds in a sphere are investigated. The computation formula, geometric equalities and inequalities for the volume of Gaussian image of certain submanifolds in a sphere are obtained.

Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System

The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so on.If an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be identified.The aim of the image captioning process is to generate a caption for image.In next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct.In this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the image.The current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input image.To attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding processes.Initially,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)model.This model represents the input data appropriately by inserting it into a predefined length vector.Besides,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the method.The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively.The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets.Awidespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.

Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of Computer Vision(CV)and Natural Language Processing(NLP)for generating the image descriptions.Itfinds use in several application areas namely recommendation in editing applications,utilization in virtual assistance,etc.The development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual semantics.In this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)technique.The OHHO-DLIC technique involves the design of distinct levels of pre-processing.Moreover,the feature extraction of the images is carried out by the use of EfficientNet model.Furthermore,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as decoder.At last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM models.The experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

Research and Practice of Visual Image in Task-Based Foreign Language Teaching

Task-based language teaching(TBLT)emphasizes the relevance of classroom tasks to real-life scenarios,while focusing on the learner’s personal life experiences as an important resource for classroom learning.This article is a teaching experiment based on task-based teaching method,which requires learners to complete a picture story about their daily life.The pictures are taken in real life scenes.Teachers plan to combine different task phases with visual images to closely link the tasks to the learner’s personal life and enhance the authenticity of the task.After the task is completed,the teacher understands the learner’s attitude and evaluation of the whole task through questionnaires,and analyzes the feasibility of the visual image applied to the foreign language classroom,the problems worthy of reflection,and suggestions for improvement.

Free form deformation and symmetry constraint‐based multimodal brain image registration using generative adversarial nets

Multi‐modal brain image registration has been widely applied to functional localisation,neurosurgery and computational anatomy.The existing registration methods based on the dense deformation fields involve too many parameters,which is not conducive to the exploration of correct spatial correspondence between the float and reference images.Meanwhile,the unidirectional registration may involve the deformation folding,which will result in the change of topology during registration.To address these issues,this work has presented an unsupervised image registration method using the free form deformation(FFD)and the symmetry constraint‐based generative adversarial networks(FSGAN).The FSGAN utilises the principle component analysis network‐based structural representations of the reference and float images as the inputs and uses the generator to learn the FFD model parameters,thereby producing two deformation fields.Meanwhile,the FSGAN uses two discriminators to decide whether the bilateral registration have been realised simultaneously.Besides,the symmetry constraint is utilised to construct the loss function,thereby avoiding the deformation folding.Experiments on BrainWeb,high grade gliomas,IXI and LPBA40 show that compared with state‐of‐the‐art methods,the FSGAN provides superior performance in terms of visual comparisons and such quantitative indexes as dice value,target registration error and computational efficiency.

On the Construction of Literary Images from Vague Language

Literary image is the universal phenomenon in literary works. The construction of literary images depends on the vague indication and allusive function in language. This paper mainly probes into the construction of literary images from vague language in the following aspects： the use of polysemy, homonymy and pun; the application of indefiniteness; through divergence of language; and the exaggeration of numerals with vague indication.

Form and Image Expression of Traditional Village Texture in Southern Hebei

The image expression of traditional villages is studied through the analysis of their forms.With the famous historical and cultural villages in southern Hebei as the research objects,the relationship between the form and image of traditional villages is deeply analyzed through the survey of village layout and courtyard layout,which can deeply explain the characteristics of regional architectural culture.

A Multi-Classifier Based Prediction Model for Phishing Emails Detection Using Topic Modelling, Named Entity Recognition and Image Processing

Phishing is the act of attempting to steal a user’s financial and personal information, such as credit card numbers and passwords by pretending to be a trustworthy participant, during online communication. Attackers may direct the users to a fake website that could seem legitimate, and then gather useful and confidential information using that site. In order to protect users from Social Engineering techniques such as phishing, various measures have been developed, including improvement of Technical Security. In this paper, we propose a new technique, namely, “A Prediction Model for the Detection of Phishing e-mails using Topic Modelling, Named Entity Recognition and Image Processing”. The features extracted are Topic Modelling features, Named Entity features and Structural features. A multi-classifier prediction model is used to detect the phishing mails. Experimental results show that the multi-classification technique outperforms the single-classifier-based prediction techniques. The resultant accuracy of the detection of phishing e-mail is 99% with the highest False Positive Rate being 2.1%.

基于形色筛选的苹果园羽化害虫粘连图像分割方法

针对苹果园害虫识别过程中的粘连问题,提出了一种基于形色筛选的害虫粘连图像分割方法。首先,采集苹果园害虫图像,聚焦于羽化害虫。害虫在羽化过程中已完成大部分生长发育,其外部形态、颜色、纹理更为稳定显著。因此,基于不同种类害虫的形色特征信息分析,来获取害虫HSV分割阈值和模板轮廓。其次,利用形状因子判定分割粘连区域,通过颜色分割法和轮廓定位分割法来实现非种间与种间粘连害虫的分割。最后,对采集的苹果园害虫图像进行了试验分析,采用基于形色筛选的分割法对单个害虫进行分割,结果表明,本文方法的平均分割率、平均分割错误率和平均分割有效率分别为101%、3.14%和96.86%,分割效果优于传统图像分割方法。此外,通过预定义的颜色阈值,本文方法实现了棉铃虫、桃蛀螟与玉米螟的精准分类,平均分类准确率分别为97.77%、96.75%与96.83%。同时,以Mask R-CNN模型作为识别模型,平均识别精度作为评价指标,分别对已用本文方法和未用本文方法分割的害虫图像进行识别试验。结果表明,已用本文方法分割的棉铃虫、桃蛀螟和玉米螟害虫图像平均识别精度分别为96.55%、94.80%与95.51%,平均识别精度分别提高16.42、16.59、16.46个百分点。这表明该方法可为果园害虫精准识别提供理论和方法基础。

现代绘画艺术中的形式美与情感表达

现代绘画艺术注重形式美与情感表达的融合,通过独特的艺术语言和表现手法,传递艺术家的情感和思想。形式美和情感表达显示了现代绘画艺术的特点,艺术家运用形式元素和表现手法来表达情感,能更好地理解和欣赏现代绘画艺术。