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.

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.

IQAGPT:computed tomography image quality assessment with vision-language and ChatGPT models

Large language models(LLMs),such as ChatGPT,have demonstrated impressive capabilities in various tasks and attracted increasing interest as a natural language interface across many domains.Recently,large vision-language models(VLMs)that learn rich vision–language correlation from image–text pairs,like BLIP-2 and GPT-4,have been intensively investigated.However,despite these developments,the application of LLMs and VLMs in image quality assessment(IQA),particularly in medical imaging,remains unexplored.This is valuable for objective performance evaluation and potential supplement or even replacement of radiologists’opinions.To this end,this study intro-duces IQAGPT,an innovative computed tomography(CT)IQA system that integrates image-quality captioning VLM with ChatGPT to generate quality scores and textual reports.First,a CT-IQA dataset comprising 1,000 CT slices with diverse quality levels is professionally annotated and compiled for training and evaluation.To better leverage the capabilities of LLMs,the annotated quality scores are converted into semantically rich text descriptions using a prompt template.Second,the image-quality captioning VLM is fine-tuned on the CT-IQA dataset to generate qual-ity descriptions.The captioning model fuses image and text features through cross-modal attention.Third,based on the quality descriptions,users verbally request ChatGPT to rate image-quality scores or produce radiological qual-ity reports.Results demonstrate the feasibility of assessing image quality using LLMs.The proposed IQAGPT outper-formed GPT-4 and CLIP-IQA,as well as multitask classification and regression models that solely rely on images.

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.

Sign Language Recognition and Classification Model to Enhance Quality of Disabled People

Sign language recognition can be considered as an effective solution for disabled people to communicate with others.It helps them in conveying the intended information using sign languages without any challenges.Recent advancements in computer vision and image processing techniques can be leveraged to detect and classify the signs used by disabled people in an effective manner.Metaheuristic optimization algorithms can be designed in a manner such that it fine tunes the hyper parameters,used in Deep Learning(DL)models as the latter considerably impacts the classification results.With this motivation,the current study designs the Optimal Deep Transfer Learning Driven Sign Language Recognition and Classification(ODTL-SLRC)model for disabled people.The aim of the proposed ODTL-SLRC technique is to recognize and classify sign languages used by disabled people.The proposed ODTL-SLRC technique derives EfficientNet model to generate a collection of useful feature vectors.In addition,the hyper parameters involved in EfficientNet model are fine-tuned with the help of HGSO algorithm.Moreover,Bidirectional Long Short Term Memory(BiLSTM)technique is employed for sign language classification.The proposed ODTL-SLRC technique was experimentally validated using benchmark dataset and the results were inspected under several measures.The comparative analysis results established the superior performance of the proposed ODTL-SLRC technique over recent approaches in terms of 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.

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.

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.

Identification of risk factors for poor language outcome in surgical resection of glioma involving the arcuate fasciculus:an observational study

The arcuate fasciculus is a critical component of the neural substrate of human language function.Surgical resection of glioma adjacent to the arcuate fasciculus likely damages this region.In this study,we evaluated the outcome of surgical resection of glioma adjacent to the arcuate fasciculus under the guidance of magnetic resonance imaging and diffusion tensor imaging,and we aimed to identify the risk factors for postoperative linguistic deficit.In total,54 patients with primary glioma adjacent to the arcuate fasciculus were included in this observational study.These patients comprised 38 men and 16 women(aged 43±11 years).All patients underwent surgical resenction of glioma under the guidance of magnetic resonance imaging and diffusion tensor imaging.Intraoperative images were updated when necessary for further resection.The gross total resection rate of the 54 patients increased from 38.9%to 70.4%by intraoperative magnetic resonance imaging.Preoperative language function and glioma-to-arcuate fasciculus distance were associated with poor language outcome.Multivariable logistic regression analyses showed that glioma-to-arcuate fasciculus distance was the major independent risk factor for poor outcome.The cutoff point of glioma-to-arcuate fasciculus distance for poor outcome was 3.2 mm.These findings suggest that intraoperative magnetic resonance imaging combined with diffusion tensor imaging of the arcuate fasciculus can help optimize tumor resection and result in the least damage to the arcuate fasciculus.Notably,glioma-to-arcuate fasciculus distance is a key independent risk factor for poor postoperative language outcome.This study was approved by the Ethics Committee of the Chinese PLA General Hospital,China(approval No.S2014-096-01)on October 11,2014.

Lateralization of Receptive Language Function Using near Infrared Spectroscopy

In recent decades, functional magnetic resonance imaging (fMRI) has proven to be more effective than the Wada test in the evaluation of language lateralization in special populations such as epileptic patients and children. However, fMRI requires that subjects remain motionless during data acquisition, making the assessment of receptive and expressive language difficult in young children and population with special needs. Near-Infrared spectroscopy (NIRS) is a non- invasive technique that has proven to be more tolerant to motion artifacts. The aim of the present study was to investigate the use of NIRS to assess receptive language patterns using a story listening paradigm. Four native French-speakers listened to stories read aloud by a bilingual speaker in both French and Arabic. To determine if the signal recorded was affected by episodic memory processes, a familiar story and an unknown story were presented. Results showed that listening to stories in French elicited a significantly higher left lateralized response than listening to stories in Arabic, independently of the familiarity of the story. These results confirm that NIRS is a useful non-invasive technique to assess receptive language in adults and can be used to investigate language lateralization among children and epileptic patients slated for epilepsy surgery.

Sign Language to Sentence Formation:A Real Time Solution for Deaf People

Communication is a basic need of every human being to exchange thoughts and interact with the society.Acute peoples usually confab through different spoken languages,whereas deaf people cannot do so.Therefore,the Sign Language(SL)is the communication medium of such people for their conversation and interaction with the society.The SL is expressed in terms of specific gesture for every word and a gesture is consisted in a sequence of performed signs.The acute people normally observe these signs to understand the difference between single and multiple gestures for singular and plural words respectively.The signs for singular words such as I,eat,drink,home are unalike the plural words as school,cars,players.A special training is required to gain the sufficient knowledge and practice so that people can differentiate and understand every gesture/sign appropriately.Innumerable researches have been performed to articulate the computer-based solution to understand the single gesture with the help of a single hand enumeration.The complete understanding of such communications are possible only with the help of this differentiation of gestures in computer-based solution of SL to cope with the real world environment.Hence,there is still a demand for specific environment to automate such a communication solution to interact with such type of special people.This research focuses on facilitating the deaf community by capturing the gestures in video format and then mapping and differentiating as single or multiple gestures used in words.Finally,these are converted into the respective words/sentences within a reasonable time.This provide a real time solution for the deaf people to communicate and interact with the society.

Continuous Arabic Sign Language Recognition in User Dependent Mode

Arabic Sign Language recognition is an emerging field of research. Previous attempts at automatic vision-based recog-nition of Arabic Sign Language mainly focused on finger spelling and recognizing isolated gestures. In this paper we report the first continuous Arabic Sign Language by building on existing research in feature extraction and pattern recognition. The development of the presented work required collecting a continuous Arabic Sign Language database which we designed and recorded in cooperation with a sign language expert. We intend to make the collected database available for the research community. Our system which we based on spatio-temporal feature extraction and hidden Markov models has resulted in an average word recognition rate of 94%, keeping in the mind the use of a high perplex-ity vocabulary and unrestrictive grammar. We compare our proposed work against existing sign language techniques based on accumulated image difference and motion estimation. The experimental results section shows that the pro-posed work outperforms existing solutions in terms of recognition accuracy.

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.

Design of the Intelligent Foreign Language Multimedia Teaching System Based on the "New liberal arts" Paradigm

The main features of the foreign language education in the information age are to realize the modernization, informatization, intellectualization and diversification of the foreign language teaching, and to promote the modernization of the foreign language teaching through the informatization of the foreign language teaching. The new concept of the leapfrogging development in the foreign language teaching should be a hot topic and trend in the study of the foreign language teaching theories and practice in the new era. Under the "New liberal arts" paradigm, students are expected to take part in various activities to promote the transformation of the classroom theoretical knowledge into the specific practical skills and enhance their abilities to solve the practical problems in the real world.

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.

Sign, Image and Language in The Book of Changes CYijing 易经）

It is challenging to estimate the degree to which the system of the Trigrams and Hexagrams in The Book of Changes （Yijing） had an impact on the whole history of Chinese thought. The universal paradigm from which it was derived formed the basis of a semiotic theory of evolution which, because of structural analogies, was applied to all fields and aspects of human life where decision making and action in correspondence with a cosmic principle was required. To achieve that goal, countless commentaries on and interpretations of the Yijing have been written. They can be divided into two schools. The first used the Yijing as a book for divination, in combination with manifestations of the universe and nature. The second interpreted it with a philosophical background, making it part of the tradition of Confucian thought. Modem scholars have also contributed some new approaches to the Yijing. My paper is based on the assumption that the Trigrams and Hexagrams of the Yijing cannot be understood in a purely representational way. They do not represent things apart from their relation to human needs or consciousness. Because of the co-determination of text and reader as a task without determinate end-points, it proves to be a unique case of effective-history. In the Yijing, there is no real line between culture and nature, sign/image/language and fact, the universe of semiosis and other universes. With its use of signs, images and language, the Yijing confirms that the universe of semiosis is the universe of heaven, earth and man. Against this background, my explanations will not only focus on the Trigrams and Hexagrams. My paper will also deal with the following topics： （1） interpenetration of linguistic meaning and objective reality and （2） the social nature of verbal or literary expression.

Asymmetry of brain functional activation: fMRI study under language and music stimulation

Objective To determine the asymmetry of the human brain functional activation Methods With the help of GE Signa Horizon MRI cystem, 14 cases of right handed volunteers were examined and the blood oxygenation level dependent method was used The T1 weighted images were obtained with spin echo pulse sequence and the functional imaging(T2 * weighted) was performed using a single shot echo planar imaging pulse sequence Data analysis was done with Sun Sparc Workstation and by the method of student t test or correlation analysis Results Most of activation areas were in the left hemisphere under language stimulation, while they were in the right side under music stimulation Besides, a few brain areas in the contralateral cerebral cortex were also activated under both stimulations Conclusion The present study supported the hypothesis of the asymmetry of brain functional activation and many brain areas of the cerebral cortex as well as both hemispheres worked in coordination In addition, it also proved that fMRI is a feasible method in the study of human brain in vivo

Automatic extraction of imaging observation and assessment categories from breast magnetic resonance imaging reports with natural language processing

Background:Structured reports are not widely used and thus most reports exist in the form of free text.The process of data extraction by experts is time-consuming and error-prone,whereas data extraction by natural language processing (NLP) is a potential solution that could improve diagnosis efficiency and accuracy.The purpose of this study was to evaluate an NLP program that determines American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) descriptors and final assessment categories from breast magnetic resonance imaging (MRI) reports.Methods:This cross-sectional study involved 2330 breast MRI reports in the electronic medical record from 2009 to 2017.We used 1635 reports for the creation of a revised BI-RADS MRI lexicon and synonyms lists as well as the iterative development of an NLP system.The remaining 695 reports that were not used for developing the system were used as an independent test set for the final evaluation of the NLP system.The recall and precision of an NLP algorithm to detect the revised BI-RADS MRI descriptors and BI-RADS categories from the free-text reports were evaluated against a standard reference of manual human review.Results:There was a high level of agreement between two manual reviewers,with a κ value of 0.95.For all breast imaging reports,the NLP algorithm demonstrated a recall of 78.5% and a precision of 86.1% for correct identification of the revised BI-RADS MRI descriptors and the BI-RADS categories.NLP generated the total results in <1 s,whereas the manual reviewers averaged 3.38 and 3.23 min per report,respectively.Conclusions:The NLP algorithm demonstrates high recall and precision for information extraction from free-text reports.This approach will help to narrow the gap between unstructured report text and structured data,which is needed in decision support and other applications.

Can Language Skills Help Government’s Image?

From August this year, civil servants under the age of 50 in Kunming, capital of southwest China’s Yunnan Province, have been told to attend training in five foreign languages, as well as common spoken Chinese and