A large language model-powered literature review for high-angle annular dark field imaging

High-angle annular dark field(HAADF)imaging in scanning transmission electron microscopy(STEM)has become an indispensable tool in materials science due to its ability to offer sub-°A resolution and provide chemical information through Z-contrast.This study leverages large language models(LLMs)to conduct a comprehensive bibliometric analysis of a large amount of HAADF-related literature(more than 41000 papers).By using LLMs,specifically ChatGPT,we were able to extract detailed information on applications,sample preparation methods,instruments used,and study conclusions.The findings highlight the capability of LLMs to provide a new perspective into HAADF imaging,underscoring its increasingly important role in materials science.Moreover,the rich information extracted from these publications can be harnessed to develop AI models that enhance the automation and intelligence of electron microscopes.

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.

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.

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.

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.

Plasticity of language pathways in patients with low-grade glioma A diffusion tensor imaging study

Knowledge of the plasticity of language pathways neurosurgeons to achieve maximum resection wh n patients with low-grade glioma is important for e preserving neurological function. The current study sought to investigate changes in the ventral language pathways in patients with low-grade glioma located in regions likely to affect the dorsal language pathways. The results revealed no significant difference in fractional anisotropy values in the arcuate fasciculus between groups or between hemispheres. However, fractional anisotropy and lateralization index values in the left inferior longitudinal fasciculus and lateralization index values in the left inferior fronto-occpital fasciculus were higher in patients than in healthy subjects. These results indicate plasticity of language pathways in patients with low-grade glioma. The ventral language pathways may perform more functions in patients than in healthy subjects. As such, it is important to protect the ventral language pathways intraoperatively.

Evidence of cortical reorganization of language networks after stroke with subacute Broca's aphasia:a blood oxygenation level dependent-functional magnetic resonance imaging study

Aphasia is an acquired language disorder that is a common consequence of stroke.The pathogenesis of the disease is not fully understood,and as a result,current treatment options are not satisfactory.Here,we used blood oxygenation level-dependent functional magnetic resonance imaging to evaluate the activation of bilateral cortices in patients with Broca＇s aphasia 1 to 3 months after stroke.Our results showed that language expression was associated with multiple brain regions in which the right hemisphere participated in the generation of language.The activation areas in the left hemisphere of aphasia patients were significantly smaller compared with those in healthy adults.The activation frequency,volumes,and intensity in the regions related to language,such as the left inferior frontal gyrus（Broca＇s area）,the left superior temporal gyrus,and the right inferior frontal gyrus（the mirror region of Broca＇s area）,were lower in patients compared with healthy adults.In contrast,activation in the right superior temporal gyrus,the bilateral superior parietal lobule,and the left inferior temporal gyrus was stronger in patients compared with healthy controls.These results suggest that the right inferior frontal gyrus plays a role in the recovery of language function in the subacute stage of stroke-related aphasia by increasing the engagement of related brain areas.

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.

语义增强图像-文本预训练模型的零样本三维模型分类

目前,基于对比学习的图像-文本预训练模型(CLIP)在零样本3维模型分类任务上表现出了巨大潜力,然而3维模型和文本之间存在巨大的模态鸿沟,影响了分类准确率的进一步提高。针对以上问题,该文提出一种语义增强CLIP的零样本3维模型分类方法。该方法首先将3维模型表示成多视图;然后为了增强零样本学习对未知类别的识别能力,通过视觉语言生成模型获得每张视图及其类别的语义描述性文本,并将其作为视图和类别提示文本之间的语义桥梁,语义描述性文本采用图像字幕和视觉问答两种方式获取;最后微调语义编码器将语义描述性文本具化为类别的语义描述,其拥有丰富的语义信息和较好的可解释性,有效减小了视图和类别提示文本的语义鸿沟。实验表明,该文方法在ModelNet10和ModelNet40数据集上的分类性能优于现有的零样本分类方法。

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.

Reconstructing the landing trajectory of the CE-3 lunar probe by using images from the landing camera

An accurate determination of the landing trajectory of Chang＇e-3 （CE-3） is significant for verifying orbital control strategy, optimizing orbital planning, accu- rately determining the landing site of CE-3 and analyzing the geological background of the landing site. Due to complexities involved in the landing process, there are some differences between the planned trajectory and the actual trajectory of CE-3. The land- ing camera on CE-3 recorded a sequence of the landing process with a frequency of 10 frames per second. These images recorded by the landing camera and high-resolution images of the lunar surface are utilized to calculate the position of the probe, so as to reconstruct its precise trajectory. This paper proposes using the method of trajectory reconstruction by Single Image Space Resection to make a detailed study of the hov- ering stage at a height of 100 m above the lunar surface. Analysis of the data shows that the closer CE-3 came to the lunar surface, the higher the spatial resolution of im- ages that were acquired became, and the more accurately the horizontal and vertical position of CE-3 could be determined. The horizontal and vertical accuracies were 7.09 m and 4.27 m respectively during the hovering stage at a height of 100.02 m. The reconstructed trajectory can reflect the change in CE-3＇s position during the powered descent process. A slight movement in CE-3 during the hovering stage is also clearly demonstrated. These results will provide a basis for analysis of orbit control strategy, and it will be conducive to adjustment and optimization of orbit control strategy in follow-up missions.

Mexican Sign Language Recognition Using Jacobi-Fourier Moments

The present work introduces a system for recognizing static signs in Mexican Sign Language (MSL) using Jacobi-Fourier Moments (JFMs) and Artificial Neural Networks (ANN). The original color images of static signs are cropped, segmented and converted to grayscale. Then to reduce computational costs 64 JFMs were calculated to represent each image. The JFMs are sorted to select a subset that improves recognition according to a metric proposed by us based on a ratio between dispersion measures. Using WEKA software to test a Multilayer-Perceptron with this subset of JFMs reached 95% of recognition rate.

Population Spatial Distribution Based on Luojia 1-01 Nighttime Light Image:A Case Study of Beijing

With the continuous development of urbanization in China,the country’s growing population brings great challenges to urban development.By mastering the refined population spatial distribution in administrative units,the quantity and agglomeration of population distribution can be estimated and visualized.It will provide a basis for a more rational urban planning.This paper takes Beijing as the research area and uses a new Luojia1-01 nighttime light image with high resolution,land use type data,Points of Interest(POI)data,and other data to construct the population spatial index system,establishing the index weight based on the principal component analysis.The comprehensive weight value of population distribution in the study area was then used to calculate the street population distribution of Beijing in 2018.Then the population spatial distribution was visualize using GIS technology.After accuracy assessments by comparing the result with the WorldPop data,the accuracy has reached 0.74.The proposed method was validated as a qualified method to generate population spatial maps.By contrast of local areas,Luojia 1-01 data is more suitable for population distribution estimation than the NPP/VIIRS(Net Primary Productivity/Visible infrared Imaging Radiometer)nighttime light data.More geospatial big data and mathematical models can be combined to create more accurate population maps in the future.

The overview of the language acquisition for Chinese city children aged 0-3 years

This paper examines the language development of Chinese children aged 0-3years in city family. About baby＇s language development by the time stages accordingly. Paying special attention to the efforts for the parents or caregivers. Infants receive more talking education and speak earlier. Most parents can hardly wait for their baby to say its first word and communicate with their baby. From about 2 years old, the child should be able to use simple phrases, retell simple story and even to sing song. Parents play an important role in this stage. By the 3 years children begin to use most of function words rather than omit them. And acquisition for the second language will help children＇s language development. The children have a gift for learning language.

BACKGROUND RECTIFICATION AND FEATURE EXTRACTION OF IMAGE IN A SPOT WELD OF AL ALLOY X-RAY DETECTION

A primary study on Processing in X - ray inspection of spot weld for aluminum alloy spot welding,in- cluding for background simulation,acquisition of ideal binary image, and extraction and identifi- cation of defect features was presented.

Example-based super-resolution for single-image analysis from the Chang'e-1 Mission

Due to the low spatial resolution of images taken from the Chang＇e-1 （CE-I） orbiter, the details of the lunar surface are blurred and lost. Considering the limited spatial resolution of image data obtained by a CCD camera on CE-1, an example-based super-resolution （SR） algorithm is employed to obtain high- resolution （HR） images. SR reconstruction is important for the application of image data to increase the resolution of images. In this article, a novel example-based algorithm is proposed to implement SR reconstruction by single-image analysis, and the computational cost is reduced compared to other example-based SR methods. The results show that this method can enhance the resolution of images using SR and recover detailed information about the lunar surface. Thus it can be used for surveying HR terrain and geological features. Moreover, the algorithm is significant for the HR processing of remotely sensed images obtained by other imaging systems.

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%.

基于生成式大模型的老年友好城市设计--以杭州市街道为例

中国正面临深度老龄化的挑战,建设老年友好城市已成为迫切目标。本研究利用百度“文心一格”大模型,结合居民对街道的老年友好需求生成改造方案,进一步统计分析各群体的老年友好需求差异及整体特征。研究发现:第一,生成式大模型可以有效地应用在老年友好城市设计;第二,女性更关注老年友好街道的环境设施功能,男性更关注街道形态;受教育水平越高,老年友好街道的绿化率需求越高;第三,当前街道的天空开敞度、围墙(栏)、步行道、路灯、车流和车行道现状与老年友好街道需求之间仍存在差距。本研究以期为老年友好理念下的城市规划和设计提供创新方法和实践参考。

结合视觉文本匹配和图嵌入的可见光-红外行人重识别

对于可见光-红外跨模态行人重识别(Re-ID),大多数方法采用基于模态转换的策略,通过对抗网络生成图像,以此建立不同模态间的相互联系。然而这些方法往往不能有效降低模态间的差距,导致重识别性能不佳。针对此问题,该文提出一种基于视觉文本匹配和图嵌入的双阶段跨模态行人重识别方法。该方法通过上下文优化方案构建可学习文本模板,生成行人描述作为模态间的关联信息。具体而言,在第1阶段基于图片-文本对的预训练(CLIP)模型实现同一行人不同模态间的统一文本描述作为先验信息辅助降低模态差异。同时在第2阶段引入基于图嵌入的跨模态约束框架,设计模态间自适应损失函数,提升行人识别准确率。为了验证所提方法的有效性,在SYSU-MM01和Reg DB数据集上进行了大量实验,其中SYSU-MM01数据集上的首次命中(Rank-1)和平均精度均值(m AP)分别达到64.2%,60.2%。实验结果表明,该文所提方法能够提升可见光-红外跨模态行人重识别的准确率。

Adaptability of language-related brain network in a low-grade glioma patient

Because functional magnetic resonance imaging can be used for dynamic observation of functional cortical changes after brain injuries, we followed up functional magnetic resonance imaging manifestations of a language-related brain network in a low-grade glioma patient. Disease progression and therapy during a 3-year period were followed up at different time points： before and after reoperation, after radiation therapy, and 1 year after irradiation. During the whole 3-year follow-up period, the patient exhibited no neurological deficits while functional magnetic resonance imaging revealed different topologies of the language-related brain network. During disease progression and after irradiation, the language-related brain network was extended or completely transferred to the nondominant （right） hemisphere. In addition, after reoperation and 1 year after irradiation, language areas were primarily found in the language dominant （left） hemisphere. Our results suggest a high level of adaptability of the language-related cortical network of the bilateral hemispheres in this low-grade glioma patient.