Contextual Comprehension Research Articles

Exploring the Impact of ChatGPT on Scientific Research: Assessing Strengths, Weaknesses, Opportunities, and Threats

ChatGPT’s adaptability spans various fields, notably scientific research. This research investigates the transformative possibilities of incorporating ChatGPT into scientific enquiry, employing a strengths, weaknesses, opportunities, and threats (SWOT) analysis to examine its merits and drawbacks. The analysis highlights the model’s strengths, encompassing an extensive knowledge base, linguistic proficiency, information-retrieval capabilities, and continuous learning capacity. Conversely, it uncovers weaknesses such as a lack of contextual comprehension, potential dependence on training data, limitations in information verification, and constrained critical thinking abilities. Amidst these considerations, opportunities emerge, including support for literature reviews, fostering collaborative ideation, facilitating seamless language translation, interpretation, and enhancing knowledge dissemination. However, a range of threats looms, encompassing concerns about plagiarism, ethical dilemmas, the dissemination of misinformation, and the potential erosion of higher-order cognitive skills. These multifaceted elements warrant comprehensive examination. Recommendations for researchers incorporating ChatGPT advocate for a balanced approach that harmonises artificial intelligence with human creativity to maintain research integrity. The potential of ChatGPT to reshape scientific exploration hinges on judicious use and ongoing oversight.

TSMD-Net: A two-stage mixed dehazing network with feature fusion and multi-window self attention on Jetson board

Single image dehazing is a pivotal endeavor focused on enhancing the quality of images by removing haze-induced noise. Achieving this goal requires a delicate equilibrium between retaining spatial intricacies and contextual understanding, particularly crucial in real-world scenarios where complexity is amplified. In response to this challenge, we present the Two-Stage Mixed Dehazing Network (TSMD-Net), designed to reconcile these conflicting objectives by leveraging the foundational U-Net architecture. Our approach entails a dual-phase strategy: initially assimilating contextual comprehension through encoder-decoder frameworks, followed by amalgamating it with high-resolution branches to uphold spatial integrity. To streamline the analysis and comparison of this architecture, we opt to substitute or eliminate nonlinear activation functions with multiplication operations, effectively mitigating system complexity. Furthermore, to foster seamless information exchange across hierarchical layers of the encoder-decoder at varying scales, we introduce the Feature Fusion (FF) mechanism, facilitating the integration of insights from higher layers progressively down to the base layer. This amalgamated data significantly enhances the fidelity of the original image transformation by harmonizing the inclusion of contextual feature maps with the preservation of spatial details, resulting in noise-free, high-quality images. Additionally, we propose the “Multi-Window Self Attention” (MWSA) mechanism, characterized by linear time complexity, as the central block of the encoder-decoder. This module mitigates the confined receptive fields inherent in convolutional neural networks (CNNs), enabling the aggregation of more comprehensive feature-map data. Subsequently, our TSMD-Net undergoes rigorous training and testing on both synthetic and real-world datasets, showcasing superior performance compared to current state-of-the-art (SOTA) networks while demanding fewer processing resources. To validate the robustness and practical utility of our methodology, we implement TSMD-Net on a low-end Jetson Nano board, confirming its effectiveness and efficiency in real-world settings.

Artificial Intelligent Agent Architecture and Clinical Decision-Making in the Healthcare Sector.

This paper examines the decision-making processes of physicians and intelligent agents within the healthcare sector, particularly focusing on their characteristics, architectures, and approaches. We provide a theoretical insight into the evolving role of artificial intelligence (AI) in healthcare, emphasizing its potential to address various healthcare challenges. Defining features of intelligent agents are explored, including their perceptual abilities and behavioral properties, alongside their architectural frameworks, ranging from reflex-based to general learning agents, and contrasted with the rational decision-making structure employed by physicians. Through data collection, hypothesis generation, testing, and reflection, physicians exhibit a nuanced approach informed by adaptability and contextual understanding. A comparative analysis between intelligent agents and physicians reveals both similarities and disparities, particularly in adaptability and contextual comprehension. While intelligent agents offer promise in enhancing clinical decisions, challenges with types of dataset biases pose significant hurdles. Informing and educating physicians about AI concepts can build trust and transparency in intelligent programs. Such efforts aim to leverage the strengths of both human and AI toward improving healthcare delivery and outcomes.

Financial Report Sentiment Analysis Using Loughran-mcdonald Dictionary and BERT

In the ever-changing world of financial markets, understanding investor behavior and making informed decisions relies heavily on sentiment analysis. This study delves into the integration of traditional techniques, such as the Loughran- McDonald dictionary, with advanced natural language processing (NLP) methods utilizing BERT (Bidirectional Encoder Representations from Transformers). The goal is to enhance the accuracy and depth of sentiment analysis in financial reports.To begin, we employ the specialized Loughran-McDonald dictionary designed for financial sentiment analysis. This lexicon includes domainspecific word lists for positive and negative sentiments, forming a solid foundation for sentiment scoring. Expanding on this foundation, we incorporate BERT, an advanced transformerbased NLP model. BERT’s contextual understanding of language and ability to capture intricate semantic relationships within financial texts aim to overcome the limitations of rule-based sentiment analysis. The methodology involves preprocessing financial reports, integrating Loughran-McDonald sentiment scores, and fine-tuning BERT for financial sentiment classification. This hybrid approach leverages both the domain expertise encoded in the dictionary and BERT’s contextual comprehension of financial jargon and nuances. We validate and evaluate our implementation using a diverse dataset comprising quarterly earnings releases, annual reports, and other relevant disclosures. Performance metrics such as precision, recall, and F1 score are analyzed to assess the effectiveness of our hybrid approach compared to individual methods. The findings have significant implications for financial analysts, investors, and policymakers by providing a more nuanced understanding of sentiment in financial reports. Our hybrid approach aims to offer improved accuracy in capturing sentiment polarity while facilitating more informed decision-making in today’s complex and dynamic realm of financial markets.

ABNet: An Aggregated Backbone Network Architecture for Fine Landcover Classification

High-precision landcover classification is a fundamental prerequisite for resource and environmental monitoring and land-use status surveys. Imbued with intricate spatial information and texture features, very high spatial resolution remote sensing images accentuate the divergence between features within the same category, thereby amplifying the complexity of landcover classification. Consequently, semantic segmentation models leveraging deep backbone networks have emerged as stalwarts in landcover classification tasks owing to their adeptness in feature representation. However, the classification efficacy of a solitary backbone network model fluctuates across diverse scenarios and datasets, posing a persistent challenge in the construction or selection of an appropriate backbone network for distinct classification tasks. To elevate the classification performance and bolster the generalization of semantic segmentation models, we propose a novel semantic segmentation network architecture, named the aggregated backbone network (ABNet), for the meticulous landcover classification. ABNet aggregates three prevailing backbone networks (ResNet, HRNet, and VoVNet), distinguished by significant structural disparities, using a same-stage fusion approach. Subsequently, it amalgamates these networks with the Deeplabv3+ head after integrating the convolutional block attention mechanism (CBAM). Notably, this amalgamation harmonizes distinct scale features extracted by the three backbone networks, thus enriching the model’s spatial contextual comprehension and expanding its receptive field, thereby facilitating more effective semantic feature extraction across different stages. The convolutional block attention mechanism primarily orchestrates channel adjustments and curtails redundant information within the aggregated feature layers. Ablation experiments demonstrate an enhancement of no less than 3% in the mean intersection over union (mIoU) of ABNet on both the LoveDA and GID15 datasets when compared with a single backbone network model. Furthermore, in contrast to seven classical or state-of-the-art models (UNet, FPN, PSPNet, DANet, CBNet, CCNet, and UPerNet), ABNet evinces excellent segmentation performance across the aforementioned datasets, underscoring the efficiency and robust generalization capabilities of the proposed approach.

Enhancing Aspect Category Detection Through Hybridised Contextualised Neural Language Models: A Case Study In Multi-Label Text Classification

Abstract Recently, the field of Natural Language Processing (NLP) has made significant progress with the evolution of Contextualised Neural Language Models (CNLMs) and the emergence of large LMs. Traditional and static language models exhibit limitations in tasks demanding contextual comprehension due to their reliance on fixed representations. CNLMs such as BERT and Semantic Folding aim to produce feature-rich representations by considering a broader linguistic context. In this paper, Deep Learning-based Aspect Category Detection approaches are introduced to perform text classification. The study extensively assesses classification model performance, emphasising enhanced representativeness and optimised feature extraction resolution using CNLMs and their hybridised variants. The effectiveness of the proposed approaches is evaluated on benchmark datasets of 4500 reviews from the laptop and restaurant domains. The results show that the proposed approaches using hybridised CNLMs outperform state-of-the-art methods with an f-score of 0.85 for the laptop and f-scores higher than 0.90 for the restaurant dataset. This study represents a pioneering work as one of the initial research efforts aiming to jointly evaluate the representation performance of CNLMs with different architectures to determine their classification capabilities. The findings indicate that the proposed approaches can enable the development of more effective classification models in various NLP tasks.

Unlocking the Black Box? A Comprehensive Exploration of Large Language Models in Rehabilitation.

Rehabilitation is a vital component of health care, aiming to restore function and improve the well-being of individuals with disabilities or injuries. Nevertheless, the rehabilitation process is often likened to a " black box ," with complexities that pose challenges for comprehensive analysis and optimization. The emergence of large language models offers promising solutions to better understand this " black box ." Large language models excel at comprehending and generating human-like text, making them valuable in the healthcare sector. In rehabilitation, healthcare professionals must integrate a wide range of data to create effective treatment plans, akin to selecting the best ingredients for the " black box. " Large language models enhance data integration, communication, assessment, and prediction.This article delves into the ground-breaking use of large language models as a tool to further understand the rehabilitation process. Large language models address current rehabilitation issues, including data bias, contextual comprehension, and ethical concerns. Collaboration with healthcare experts and rigorous validation is crucial when deploying large language models. Integrating large language models into rehabilitation yields insights into this intricate process, enhancing data-driven decision making, refining clinical practices, and predicting rehabilitation outcomes. Although challenges persist, large language models represent a significant stride in rehabilitation, underscoring the importance of ethical use and collaboration.

Assessing the Acceptance and Utilization of ChatGPT by Chinese University Students in English Writing Education

As an advanced large language model, ChatGPT has garnered significant attention from academic researchers and educators due to its capabilities in generating coherent natural-language responses based on user input, facilitating extended dialogues, and contextual comprehension. Numerous scholarly investigations delve into the merits and potential shortcomings of ChatGPT within educational contexts, emphasizing both the model’s inherent attributes and the scarcity of end-user feedback. Furthermore, the Technology Acceptance Model and the Information System Success Model serve as prevalent frameworks in assessing user receptivity and satisfaction with novel technologies. This research employed an online questionnaire to probe Chinese university students’ perceptions and adoption of ChatGPT in English writing instruction, assessing five distinct dimensions using a Likert scale. The findings suggest a favorable inclination among students towards ChatGPT concerning its utility and effectiveness. However, ChatGPT has yet to supplant the indispensable role of educators entirely, and apprehensions persist about potential privacy infringements and associated technological issues.

GastroBot: a Chinese gastrointestinal disease chatbot based on the retrieval-augmented generation.

Large Language Models (LLMs) play a crucial role in clinical information processing, showcasing robust generalization across diverse language tasks. However, existing LLMs, despite their significance, lack optimization for clinical applications, presenting challenges in terms of illusions and interpretability. The Retrieval-Augmented Generation (RAG) model addresses these issues by providing sources for answer generation, thereby reducing errors. This study explores the application of RAG technology in clinical gastroenterology to enhance knowledge generation on gastrointestinal diseases. We fine-tuned the embedding model using a corpus consisting of 25 guidelines on gastrointestinal diseases. The fine-tuned model exhibited an 18% improvement in hit rate compared to its base model, gte-base-zh. Moreover, it outperformed OpenAI's Embedding model by 20%. Employing the RAG framework with the llama-index, we developed a Chinese gastroenterology chatbot named "GastroBot," which significantly improves answer accuracy and contextual relevance, minimizing errors and the risk of disseminating misleading information. When evaluating GastroBot using the RAGAS framework, we observed a context recall rate of 95%. The faithfulness to the source, stands at 93.73%. The relevance of answers exhibits a strong correlation, reaching 92.28%. These findings highlight the effectiveness of GastroBot in providing accurate and contextually relevant information about gastrointestinal diseases. During manual assessment of GastroBot, in comparison with other models, our GastroBot model delivers a substantial amount of valuable knowledge while ensuring the completeness and consistency of the results. Research findings suggest that incorporating the RAG method into clinical gastroenterology can enhance the accuracy and reliability of large language models. Serving as a practical implementation of this method, GastroBot has demonstrated significant enhancements in contextual comprehension and response quality. Continued exploration and refinement of the model are poised to drive forward clinical information processing and decision support in the gastroenterology field.

EFFECTIVE LEADERSHIP: Building a Positive Academic Culture

In the organizational context, leadership assumes a pivotal role as a foundational element in executing functions and wielding authority. Occupying positions of prominence and wielding significant authority, leaders possess the capacity to effectively oversee and guide the organization. The leader's strategic positioning is indispensable, rendering them a key ally in advancing the collective interests of the organization's various echelons, ranging from the grassroots to the uppermost tiers, with a steadfast commitment to objective-oriented pursuits. This research endeavors to scrutinize the responses, perceptions, and evaluations of lecturers regarding the leadership exhibited by heads of study programs, thereby furnishing a comprehensive and contextual comprehension of the intricacies of leadership dynamics. The study was conducted at Pamulang University, focusing on the Undergraduate Management study program within the Faculty of Economics and Business. Primary respondents comprised level one lecturers who concurrently served as developers of the study program. Employing a simple random sampling technique facilitated by the distribution of direct questionnaires, the research data underwent analysis through qualitative descriptive methods. Findings from the research reveal that the leadership style demonstrated by the head of the Undergraduate Management study program at the Faculty of Economics and Business embodies democratic characteristics. This is evidenced by the active involvement of developer lecturers in all facets of the tri dharma activities and the decision-making processes inherent to the study program.

The Use of Visual Aids to Improve Deaf Students’ English Vocabulary: A Literature Review

This literature review examines the efficacy of employing visual aids to enhance the acquisition of English vocabulary among deaf students. It investigates the vital role of visual aids in conveying meaning, enhancing memory and cognitive functions, and fostering engagement. The review discusses their effectiveness in promoting contextual comprehension, improving learning outcomes, and the integration of visual aids with sign language for deaf learners. The study also addresses the challenges that deaf students encounter, such as learning English as a foreign language, lack of exposure, and the complexities of abstract concepts. The implications of this research highlight the need for collaborative efforts between ESL teachers and sign language experts to develop accurate and culturally relevant visual aids. The review concludes by emphasizing the importance of context-based learning, hands-on experiences, and multisensory approaches to cater to the diverse learning needs of deaf students. This synthesis contributes valuable insights for educators, researchers, and policymakers seeking to enhance vocabulary learning experiences for deaf students through the effective integration of visual aids.

Feature-enhanced fusion of U-NET-based improved brain tumor images segmentation

The field of medical image segmentation, particularly in the context of brain tumor delineation, plays an instrumental role in aiding healthcare professionals with diagnosis and accurate lesion quantification. Recently, Convolutional Neural Networks (CNNs) have demonstrated substantial efficacy in a range of computer vision tasks. However, a notable limitation of CNNs lies in their inadequate capability to encapsulate global and distal semantic information effectively. In contrast, the advent of Transformers, which has established their prowess in natural language processing and computer vision, offers a promising alternative. This is primarily attributed to their self-attention mechanisms that facilitate comprehensive modeling of global information. This research delineates an innovative methodology to augment brain tumor segmentation by synergizing UNET architecture with Transformer technology (denoted as UT), and integrating advanced feature enhancement (FE) techniques, specifically Modified Histogram Equalization (MHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Modified Bi-histogram Equalization Based on Optimization (MBOBHE). This integration fosters the development of highly efficient image segmentation algorithms, namely FE1-UT, FE2-UT, and FE3-UT. The methodology is predicated on three pivotal components. Initially, the study underscores the criticality of feature enhancement in the image preprocessing phase. Herein, techniques such as MHE, CLAHE, and MBOBHE are employed to substantially ameliorate the visibility of salient details within the medical images. Subsequently, the UT model is meticulously engineered to refine segmentation outcomes through a customized configuration within the UNET framework. The integration of Transformers within this model is instrumental in imparting contextual comprehension and capturing long-range data dependencies, culminating in more precise and context-sensitive segmentation. Empirical evaluation of the model on two extensively acknowledged public datasets yielded accuracy rates exceeding 99%.

Cognitive Robotics: Integrating Artificial Intelligence and Embodied Intelligence for Advanced Problem Solving

Artificial intelligence (AI) and embodied intelligence are combined in cognitive robotics, which marks a paradigm change in highly developed problem-solving skills. This study offers a comprehensive review of the theoretical underpinnings, practical difficulties, practical applications, and moral implications of cognitive robots. The study explores machine learning methods, natural language processing tools, and computer vision technologies after introducing the fundamental concepts. It clarifies embodied intelligence further by emphasizing the important roles that sensory data fusion, robotic actuation, and adaptive behaviours play in the cognitive capabilities of robots. The study then moves on to discuss cognitive robotics' integration difficulties, including issues in sensory data fusion, cognitive dissonance in AI-driven robotics, and human-robot interaction. Applications in autonomous cars, healthcare, manufacturing, and assistive technologies demonstrate how cognitive robotics is transforming many different industries. The ethical obligations involved in the creation and use of cognitive robotic systems are highlighted, along with ethical issues relating to employment, privacy, and ethical AI practises. In-depth analysis is done of the present and foreseeable difficulties in cognitive robotics. Along with cognitive difficulties like contextual comprehension and moral conundrums, technical obstacles linked to sensor limits and processing restrictions are also studied. The study highlights cutting-edge research projects and multidisciplinary partnerships that provide answers to these problems, paving the path for the development of cognitive robots in the future. The article envisions a future where cognitive robots would play a part in space exploration, disaster response, and personalised robotics. It also surveys integration possibilities with cutting-edge technologies like augmented reality and the Internet of Things. The study summarises the key findings and presents the implications for future research, and emphasises the important part cognitive robotics plays in resolving difficult issues. To ensure that cognitive robots continue to have an impact on a future that is smarter, more connected, and understanding, ethical issues, multidisciplinary collaboration, and responsible development practises are necessary.

Relevance of Machine Learning Techniques in Water Infrastructure Integrity and Quality: A Review Powered by Natural Language Processing

Water infrastructure integrity, quality, and distribution are fundamental for public health, environmental sustainability, economic development, and climate change resilience. Ensuring the robustness and quality of water infrastructure is pivotal for sectors like agriculture, industry, and energy production. Machine learning (ML) offers potential for bolstering water infrastructure integrity and quality by analyzing extensive data from sensors and other sources, optimizing treatment protocols, minimizing water losses, and improving distribution methods. This study delves into ML applications in water infrastructure integrity and quality by analyzing English-language articles from 2015 onward, compiling a total of 1087 articles. Initially, a natural language processing approach centered on topic modeling was adopted to classify salient topics. From each identified topic, key terms were extracted and utilized in a semi-automatic selection process, pinpointing the most relevant articles for further scrutiny, while unsupervised ML algorithms can assist in extracting themes from the documents, generating meaningful topics often requires intricate hyperparameter adjustments. Leveraging the Bidirectional Encoder Representations from Transformers (BERTopic) enhanced the study’s contextual comprehension in topic modeling. This semi-automatic methodology for bibliographic exploration begins with a broad topic categorization, advancing to an exhaustive analysis of each topic. The insights drawn underscore ML’s instrumental role in enhancing water infrastructure’s integrity and quality, suggesting promising future research directions. Specifically, the study has identified four key areas where ML has been applied to water management: (1) advancements in the detection of water contaminants and soil erosion; (2) forecasting of water levels; (3) advanced techniques for leak detection in water networks; and (4) evaluation of water quality and potability. These findings underscore the transformative impact of ML on water infrastructure and suggest promising paths for continued investigation.

Advances in Sentiment Analysis in Deep Learning Models and Techniques

The article investigates the advantages, disadvantages, and areas of research that need more exploration regarding deep learning architectures used in sentiment analysis. These architectures let models learn complex language features from data without explicit feature engineering, changing sentiment analysis. The models' capacity to capture long-range dependencies has improved their context and nuanced expression interpretation, especially in long or metaphorical texts. Deep learning sentiment analysis algorithms have improved, yet they still face obstacles. The complexity of these models raises ethical questions about bias and transparency. They also require huge, annotated datasets and computational resources, which limits their use in resource-constrained contexts. Adopting deep learning models requires balancing performance and practicality. Explore critical deep learning sentiment analysis research gaps. Cross-domain and cross-lingual sentiment analysis requires context- and language-specific models. Textual and non-textual multimodal sentiment analysis offers untapped potential for complex sentiment interpretation. Responsible AI deployment requires model interpretability, robustness against adversarial assaults, and domain consistency. Finally, deep learning and sentiment analysis have changed our knowledge of human emotion. Accuracy and contextual comprehension have improved, but model transparency, data prerequisites, and practical applicability remain issues. Overcoming these restrictions and exploring research gaps will enable responsible sentiment analysis AI innovation.

Bi-LS-AttM: A Bidirectional LSTM and Attention Mechanism Model for Improving Image Captioning

The discipline of automatic image captioning represents an integration of two pivotal branches of artificial intelligence, namely computer vision (CV) and natural language processing (NLP). The principal functionality of this technology lies in transmuting the extracted visual features into semantic information of a higher order. The bidirectional long short-term memory (Bi-LSTM) has garnered wide acceptance in executing image captioning tasks. Of late, scholarly attention has been focused on modifying suitable models for innovative and precise subtitle captions, although tuning the parameters of the model does not invariably yield optimal outcomes. Given this, the current research proposes a model that effectively employs the bidirectional LSTM and attention mechanism (Bi-LS-AttM) for image captioning endeavors. This model exploits the contextual comprehension from both anterior and posterior aspects of the input data, synergistically with the attention mechanism, thereby augmenting the precision of visual language interpretation. The distinctiveness of this research is embodied in its incorporation of Bi-LSTM and the attention mechanism to engender sentences that are both structurally innovative and accurately reflective of the image content. To enhance temporal efficiency and accuracy, this study substitutes convolutional neural networks (CNNs) with fast region-based convolutional networks (Fast RCNNs). Additionally, it refines the process of generation and evaluation of common space, thus fostering improved efficiency. Our model was tested for its performance on Flickr30k and MSCOCO datasets (80 object categories). Comparative analyses of performance metrics reveal that our model, leveraging the Bi-LS-AttM, surpasses unidirectional and Bi-LSTM models. When applied to caption generation and image-sentence retrieval tasks, our model manifests time economies of approximately 36.5% and 26.3% vis-a-vis the Bi-LSTM model and the deep Bi-LSTM model, respectively.

Gender Discourses within Pesantren in Cirebon: Understanding the Typologies of Kyais’ Interpretations of the Concept of Qawwām

The gender discourse within pesantren is a significant scientific subject due to its reliance on classical fiqh literature that often maintains patriarchal ideology. One such issue is the interpretation of the concept of “qawwām”, a fragment of the Qur’anic verse (An-Nisā’: 34), meaning supporter or protector, in which the kyai (the spiritual leaders of pesantren) predominantly associates the role with men and their responsibilities. This understanding is influenced by the traditional (salaf) paradigm and results in a patriarchal interpretation. This study applies descriptive qualitative analysis to answer how pesantren leaders interpret the concept of “qawwām” in relation to gender ideology. Data was conducted through interviews with kyais in Cirebon and observations of their family life. The article analyses their understanding of “qawwām” based on the trichotomy of gender understanding: extreme patriarchy, moderate patriarchy, and liberal. The article argues that differences in the kyais’ viewpoints regarding the distribution of roles within the family can be traced back to their textual, eclectic, and contextual comprehension of religious teachings. The study recommends that an intricate understanding of religious teachings, taking into account the evolving social context and the diverse range of interpretations, may pave the way for a more balanced and egalitarian allocation of responsibilities between spouses in the family.Keywords: Kyai; pesantren, qawwām, gender ideology, Cirebon

대학 영문독해 수업에 기계번역(post-editing)의 활용 및 오류분석

This paper aims to explore the possibility of using machine translation in English reading classes. For this purpose, 38 subjects were divided into two groups according to the TOEIC score and required to translate and submit a 386-word English text through post-editing translation after using a machine translation tool. This analysis shows the four major error types of contextual comprehension, sentence structures, pronouns, title nouns. In addition, the subjects were required to participate in the questionnaire survey related to the effectiveness of reading comprehension classes using machine translation. The research also proved how effective English reading classes using machine translation were in comparison with English grammar-based reading classes. We can find the similarities between machine translation and students’ post-editing translation in error ratio and characteristics, and the differences between the two English levels in terms of post-editing correction methods and the number of translation errors. This paper is meaningful in that this study will contribute to diversification of teaching methods and improvement of the teaching quality in English reading classes by employing machine translation in English reading classes.

Advancements in Natural Language Processing for Text Understanding

Natural language processing (NLP) developments have made it possible for robots to read and analyze human language with astounding precision, revolutionizing the field of text understanding. An overview of current advancements in NLP approaches and their effects on text comprehension are provided in this abstract. It examines significant developments in fields including named entity identification, sentiment analysis, semantic analysis, and question answering, highlighting the difficulties encountered and creative solutions put forth. To sum up, recent developments in natural language processing have raised the bar for text comprehension. Deep learning models and extensive pre-training have changed methods including semantic analysis, sentiment analysis, named entity identification, and question answering. These developments have produced text comprehension systems that are increasingly precise and complex. However, issues with prejudice, coreference resolution, and contextual comprehension still need to be resolved. The future of NLP for text understanding has considerable potential with continuing study and innovation, opening the door for increasingly sophisticated applications in numerous sectors.

Doing the right thing, in the right place, with the right people, at the right time; a study of the development of judgment and decision making in mid-career outdoor instructors

ABSTRACT In recent years, increased interest in the Professional judgment and Decision Making (PJDM) of outdoor instructors has focused on experts. This paper builds on earlier work, exploring the components and development of PJDM in mid-career outdoor instructors. The study conducted and thematically analysed semi-structured interviews with nine senior instructors working for Outward Bound Trust (the Trust) in the UK. Findings included two over-arching themes; (1) a Hahnian approach to development, in line with the philosophies that underpin the Trust’s work, and (2) practical wisdom: the synergy of contextual comprehension and appropriate options. We conclude that these mid-career outdoor instructors’ decision making is dependent on high levels of situational awareness, situational demands, and availability of appropriate options as a course of action. We further identify a philosophical chain between the Trust’s ethos, instructor development, and adaptive expertise. These skills develop through a process akin to a cognitive apprenticeship within the Trust’s community of practice.