Extensible Language Research Articles

Developing a Graphical Domain-Specific Modeling Language for Efficient Lightweight Block Cipher Schemas Configuration: LWBCLang

The lightweight block cipher is an encryption technique with negligible computational overhead. Despite its advantages, it faces a substantial challenge. Correct handwriting of the script code for the cipher scheme is a challenge for programmers. In this research, we suggest a new graphical domain-specific modeling language to make it easier for both non-technical users and domain specialists to implement lightweight block cipher schemes. The proposed language, called LWBCLang, is a modular and extensible language that offers graphical components for constructing three essential types of inner block cipher structures. Seven different methods of keystream generation and all the tests of the NIST suite with performance analysis are provided. In the context of its meta-model, LWBCLang's abstract and concrete syntaxes are specified. LWBCLang has been implemented as an internal DSML with Python as the host language. The evaluation of LWBCLang is based on qualitative analysis to demonstrate the language's effectiveness and efficiency. Further benefits of this proposed language are evaluated and discussed in depth in this research.

Analyzing Morphological Errors and Contextual Influence in Senior High School Students' Written Works: A Qualitative Study

Morphological skills are essential for developing linguistic competence, as they underpin various aspects of language acquisition, including reading comprehension and effective writing. This study examined morphological errors in the written works of 25 Grade 11 students in the Oral Communication course at Northern Abra National High School, exploring how these errors reflected their understanding of word formation rules and the influence of contextual factors like educational background and language exposure. Using a document analysis design, the research involved evaluating students’ assignments and essays to identify error types and frequencies. A systematic coding process categorized errors through qualitative content analysis, revealing themes related to inflectional, derivational, and compounding errors. The study identified common morphological errors in the written works of 25 Grade 11 students enrolled in the Oral Communication course. Inflectional errors were the most prevalent, comprising 40% of the total, with students frequently misusing verb conjugations and plural forms, indicating a fundamental misunderstanding of grammatical rules. Derivational errors accounted for 30%, highlighting confusion around suffix and prefix application. Compounding errors, at 20%, involved incorrect separation and formation of compound words, leading to clarity issues in writing. Affixation errors made up 10%, reflecting overgeneralization and misuse of affixes. Also, students from well-resourced families made 12% fewer morphological errors than those from under-resourced families, with dedicated language programs leading to a 20% reduction. Teacher expertise contributed to a 15% decrease in errors. Students exposed to multiple languages had 30% fewer errors, with those speaking three or more languages showing a 40% reduction, highlighting the benefits of multilingualism on language skills. Moreover, students with both quality education and extensive language exposure made 35% fewer errors than those lacking either factor. These findings emphasize the need for educational equity and multilingualism to enhance morphological skills. A combined approach integrating quality instruction with diverse language experiences is recommended to improve writing proficiency. Generally, the educational background and language exposure significantly impact Grade 11 students' morphological accuracy. Students from well-resourced schools made fewer errors, especially with effective language instruction, while multilingual students demonstrated enhanced writing proficiency. The synergy between quality education and diverse linguistic exposure is essential for improving students' linguistic skills. To address morphological challenges, educational policies should focus on integrating robust language programs and promoting multilingualism, fostering better writing outcomes for all students.

Research on the Application Status of Generative AI Tools among College Students--Taking ChatGPT as an Example

In recent years, artificial intelligence has continuously overcome technological challenges and swept the world at a rapid pace of development. Generative AI tools represented by ChatGPT have undergone extensive language training and made a qualitative leap in capacity algorithms. Although the application status of ChatGPT has received widespread attention, there is still a limited amount of related research and shortcomings. This article takes ChatGPT as an example to analyze the current application status of generative AI tools among college students. Through the collection and organization of survey data, research, and analysis have found that college students have some problems with the actual use of ChatGPT, including insufficient familiarity, inconvenient use of ChatGPT, and lack of autonomy in using ChatGPT among college students. Based on this, this article proposes the following suggestions. Firstly, draw on mature experience and promote digital education. Secondly, offer artificial intelligence courses such as ChatGPT pilot courses. Thirdly, build and improve the digital moral education system and regulatory mechanism.

The Origin of North Mesopotamian Arabic

This research paper explores the origin and distinct characteristics of the Noarth Mesopotamian Arabic dialect, specifically the Moslawi dialect spoken in Mosul, northern Iraq. The study begins with tracing the roots of the Arabic language within the Semitic language family tree and examines the emergence of Modern Arabic Dialects. It highlights Ferguson's theory that these dialects originated from a asimplified Koine as the most plausible explanation. The paper then delves into the Mesopotamian dialect group, detailing the division into qeltu and gelet varieties through bedouinization. The phonological and lexical uniqueness of Moslawi is analyzed, emphasizing the persistent use of the phoneme /q/ by Moslawi speakers and the systematic vowel-raising rule that differentiates it from other dialects. Extensive language contact, with Persian, Turkish, Hindi, Kurdish, and English, is also discussed, which explains the numerous borrowed words in Moslawi. This research underscores the complex historical and sociolinguistic factors contributing to the distinctiveness of the Moslawi dialect within the broader context of Arabic dialects.

Word learning in the wild: App-based evidence for valence and concreteness effects

AbstractSecond language (L2) learners need to acquire large vocabularies to approach native-like proficiency. Many controlled experiments have investigated the factors facilitating and hindering word learning; however, few studies have validated these findings in real-world learning scenarios. We use data from the language learning app Lingvist to explore how L2 word learning is affected by valence (positivity/negativity) and concreteness of target words and their linguistic contexts. We found that valence, but not concreteness, affects learning. Users learned positive and negative words better than neutral ones. Moreover, positive words are learned best in positive contexts and negative words in more negative contexts. Word and context valence effects are strongest on the learner’s second encounter with the target word and diminish across subsequent encounters. These findings provide support for theories of embodied cognition and the lexical quality hypothesis and point to the linguistic factors that make learning words, and by extension languages, faster.

Exploration of Blended Teaching Mode in Python Programming Course

Python is a free, cross-platform, easily extensible, and concise programming language, as well as a suitable tool for data analysis, mining, and visualization. In response to the teaching situation of Python programming courses with strong practical experience, limited class hours, and differences in student levels, various teaching methods such as online learning, group projects, and case teaching are integrated to enable students to acquire more comprehensive knowledge and skills in their learning. The practical results show that blended teaching mode helps promote teachers' auxiliary "teaching" and students' active "learning", effectively stimulates students' learning interest, cultivates their practical application ability and teamwork ability, and achieves good teaching results.

EFL Teachers’ expectations and students’ reading-related difficulties at the university level

The ability to read English is essential in academic settings, but it can pose challenges for EFL students, potentially impacting their academic performance. This study investigates the expectations that EFL university teachers have for their English major students’ reading and the challenges that the students encounter while reading English. Findings from interviews with teachers and students, as well as class observations indicate that teachers expect their students to read for different academic purposes beyond main idea comprehension. However, these expectations are not always clearly conveyed through classroom discourse and reading activities. While students have some awareness of what is expected of them, they still believed they lack necessary skills to read effectively. This research provides discipline-specific insights into the demands and difficulties English major students face when reading English for general and academic purposes. It highlights the need for more extensive language and reading support in the higher education settings.

AI-Augmented LangChain: Facilitating Natural Language SQL Queries for Non-Technical Users

This study explores the potential of LangChain, a framework for constructing applications with advanced language models, to translate natural language queries into executable SQL code. Study propose an innovative LangChain-based architecture that receives a natural language query, analyzes it with a language model, and generates the corresponding SQL statement for database querying. This approach aims to empower non-technical users, facilitate inter-team collaboration, and enable data-informed decision-making. However, challenges persist, including managing complex queries and grasping domain-specific terminology. This research investigates the methodology and system design of our proposed natural language interface for databases, leveraging LangChain and extensive language models. Study also explore the possibilities and potential applications of this system, as well as future research avenues for enhancing its functionalities and addressing current constraints. By integrating advanced natural language processing with database technologies, this research aims to enable inclusive and powerful data querying experiences.

Measuring the linguistic similarity of discourse from open-world role-playing games to the real world through an additive multidimensional analysis

Abstract Digital games can provide rich sources of second language (L2) input; however, the extent to which gaming discourse is similar to real-world discourse has been a topic of debate in the computer-assisted language learning community. To quantitatively measure the extent to which gaming discourse shares linguistic similarity with real-world discourse, this study reports the findings of an additive multidimensional (MD) analysis comparing registers in open-world role-playing games to real-world registers using Biber’s (1988) Dimension 1: ‘Involved versus Informational Production.’ Results indicate that gaming discourse provides extensive language exposure that shares much linguistic similarity across a wide range of real-world contexts. Importantly, however, these similarities only become salient when the situational characteristics of gaming discourse are considered and parsed appropriately into register categories.

Speech Cloning: Text-To-Speech Using VITS

Voice is one of the most common and natural communication methods for humans. Voice is becoming the primary interface for AI voice assistants like Amazon Alexa, as well as in autos and smart home devices. Homes and so on. As human-machine communication becomes more common, researchers are exploring technology that mimics genuine speech. Speech cloning is the practice of copying or mimicking another person's speech, usually utilizing modern technology and artificial intelligence (AI). This entails producing a synthetic or cloned version of someone's voice that sounds very similar to the actual speaker. The objective is to produce speech that is indistinguishable from the genuine person, both in tone and intonation. Instant Voice Cloning (IVC) in text-to-speech (TTS) synthesis refers to the TTS model's capacity to copy the voice of any reference speaker based on a short audio sample, without requiring extra speaker-specific training. This method is usually referred to as zero-shot TTS. IVC provides users with the flexibility to tailor the generated voice, offering significant value across diverse real-world applications. Examples include media content creation, personalized chatbots, and multi-modal interactions between humans and computers or extensive language models.

Dubify: Multilingual Video Translation Platform

In an increasingly globalized society, effective cross-linguistic communication is essential. This brief presents a new approach. Dubify is a feature-rich application created to facilitate smooth translation of video information between languages. Dubify uses advanced machine learning algorithms to provide real-time translation services, ensuring that video content is understandable and accessible to viewers around the world. Strong speech recognition, accurate translation, and natural language generation are some of Dubify's main strengths. Thanks to the platform's extensive language support, users can easily translate video content into multiple languages. In addition, Dubify offers customization tools that allow users to adjust translations to suit their particular taste and dialect. Dubify's easy-to-use interface and advanced translation skills make it a flexible choice for individuals, businesses, and organizations looking to translate

An Exaggeration? Reality?: Can ChatGPT Be Used in Neonatal Nursing?

Artificial intelligence (AI) represents a system endowed with the ability to derive meaningful inferences from a diverse array of datasets. Rooted in the advancements of machine learning models, AI has spawned various transformative technologies such as deep learning, natural language processing, computer vision, and robotics. This technological evolution is poised to witness a broadened spectrum of applications across diverse domains, with a particular focus on revolutionizing healthcare services. Noteworthy among these innovations is OpenAI's creation, ChatGPT, which stands out for its profound capabilities in intricate analysis, primarily facilitated through extensive language modeling. In the realm of healthcare, AI applications, including ChatGPT, have showcased promising outcomes, especially in the domain of neonatal nursing. Areas such as pain assessment, feeding processes, and patient status determination have witnessed substantial enhancements through the integration of AI technologies. However, it is crucial to approach the deployment of such applications with a judicious mindset. The accuracy of the underlying data must undergo rigorous validation, and any results lacking a solid foundation in scientific insights should be approached with skepticism. The paramount consideration remains patient safety, necessitating that AI applications, like ChatGPT, undergo thorough scrutiny through controlled and evidence-based studies. Only through such meticulous evaluation can the transformative potential of AI be harnessed responsibly, ensuring its alignment with the highest standards of healthcare practice.

ACCEPT OR REJECT? WHAT DO ACADEMICS THINK ABOUT UTILISING CHATGPT PUBLICATIONS?

The Chat Generative Pre-Trained Transformer (ChatGPT), a chatbot with artificial intelligence, made its debut in the United States in 2022. The platform created by incorporating human feedback has garnered millions of interactions. ChatGPT can generate a response autonomously, drawing from a vast pool of online sources and frequently without requiring additional input from the user. It has been reported that individuals have utilised ChatGPT to generate academic essays and scholarly publications. AI tools utilising extensive language models, such as ChatGPT, heavily rely on pre-existing textual data available on the internet. Consequently, ensuring their results’ authenticity, credibility, and precision poses a challenging and uncertain task. This study aims to examine the viewpoints of scholars holding various academic titles and affiliated with universities located in diverse regions of Turkey regarding the utilisation of ChatGPT in scholarly publications. A total of 121 academicians participated in the study, and data were gathered in both quantitative and qualitative formats. The findings have indicated that while scholars did not perceive any issue with incorporating ChatGPT into their manuscript drafting procedures, they remained hesitant to embrace this technology by establishing an academic persona and collaborating on scholarly works.

Evaluations of Large Language Models a Bibliometric analysis

The development of artificial intelligence (AI) and the increased curiosity about how large language models (LLMs) may maximize an organization's opportunities and the ethical implications of LLMs, such as the ability to generate human-like text, give rise to concerns regarding disinformation and fake news. As a result, it is crucial to develop evaluation benchmarks that take into account the social and ethical implications involved. The great challenges of LLMs lack awareness of their own limitations, yet they persist in producing responses to the best of their capabilities. This often results in seemingly plausible but ultimately incorrect answers, posing challenges to the implementation of reliable generative AI in industry. This paper aims to delve into the evaluation metrics of machine-learning models' performance, specifically focusing on LLM. Therefore, bibliometric analysis utilized to explore and analyze various techniques and methods used in evaluating large language models. Additionally, it sheds light on the specific areas of focus when evaluating these models. The results show that natural language processing systems, classification of information, and computational linguistics are some of the techniques used to evaluate large language models. This work paves the way for future investigations employing extensive language models.

Kazakhstani Gansu Dungan as a Contact Language: An Analysis of Russian Influence

This paper discusses extensive language contact and its results in Kazakhstani Gansu Dungan, a divergent variety of Mandarin Chinese. Based primarily on recorded conversational source materials, this study offers a contact linguistic overview of the language, introducing both phonological and morphosyntactic contact phenomena. It is shown that Kazakhstani Gansu Dungan is currently under extensive Russian influence. The influence permeates all layers of the language and exceeds lexical borrowing mentioned in earlier Dungan studies. For instance, clause combining and complex clauses in Dungan have shifted to the direction of a Russian model, which makes the language stand out among other Sinitic varieties. This study demonstrates that, in addition to introducing new structures, extensive Russian influence on Dungan also reinforces earlier development that has led the language further away from the Sinitic prototype. In all, Kazakhstani Gansu Dungan forms its own kind of divergent Russianized Sinitic variety and thus offers a contribution to both researching language contact in the Russophone world and to understanding the typological diversity of Sinitic languages.

New words and meanings arising from literature in modern Chinese: A case study of English loanwords in Lu Xun's novel Call to Arms

In the period of the May Fourth New Culture Movement, under the background of the Europeanization of Chinese with Western learning in the East, a large number of loanwords were absorbed into Chinese in this period due to the extensive language contact. The use of loanwords in Lu Xun's representative vernacular novel Call to Arms embodies Lu Xun's vocabulary concept of constructing modern vernacular. This paper focuses on the study of English loanwords in Call to Arms, and classifies them into four categories: pure transliteration, transliteration and free translation, letter words and Japanese borrowing form words. These words mainly involve the fields of science, technology, politics, humanities and society, at the same time, new affixes and word formation have appeared in modern Chinese. After being introduced and transformed into the Chinese language, they have been continuously integrated into the specific meanings in the Chinese cultural background during the development process, and gradually localized, forming a unique Chinese vocabulary form.

Large language model may assist diagnosis of SAPHO syndrome by bone scintigraphy.

In this study, we employed a large language model to evaluate the diagnostic efficacy of radiology reports of bone scintigraphy in the context of identifying SAPHO syndrome, and further examined the potential of such a model to augment the diagnostic procedure. Imaging data and clinical information of 151 patients (105/46 women/men, mean age: 53.5 years) who underwent bone scintigraphy for suspected Synovitis, Acne, Pustulosis, Hyperostosis, and Osteitis (SAPHO) syndrome between January 2007 and December 2022 were retrospectively reviewed. ChatGPT-4.0 was used as the large language model. The diagnostic performance of the large language model was verified by comparing the cases judged to have SAPHO syndrome that fulfilled Kahn's classification criteria based on a combination of concise radiology reports and skin lesions such as palmoplantar pustulosis, with cases diagnosed with SAPHO syndrome by rheumatologists based on all clinical information. The diagnostic accuracy of a large language model for analysing bone scintigraphy radiology reports in conjunction with information about skin symptoms, such as palmoplantar pustulosis, achieved a sensitivity of 83.5%, specificity of 69.4%, and an overall accuracy of 76.8%. This research indicates the prospective value of extensive language models in scrutinizing radiology accounts from bone scintigraphy for the diagnosis of SAPHO syndrome.

USING BIG DATA IN SEMANTIC RESEARCH: PERSPECTIVES AND APPROACHES

Statement of the problem. Digitization and technology are rapidly taking over various fields of humanitarian sciences, including cognitive linguistics that is developing new algorithms and methods of language research, with linguists being encouraged to acquire new skills in data collection and processing as well as to expand their sources of language materials. In cognitive semantics and discourse studies, the biggest game-changer has been ever increasing (to the points of apparent limitlessness) volumes of various language data from digitalized texts available on the Internet that effectively accumulate in and function as Big Data – data sets that are too multiple, large and complex to be processed by traditional, ‘by-hand’ methods. Extensive language data requires specific mining and analysis tools and techniques and imposes certain restrictions on its processing and interpretation. In this paper, the authors discuss approaches to processing (‘intellectualization’) of empirical linguistic data coming from various online sources via search engines and their overall reliability for cognitive semantics research. Results. The main argument is that to ensure the validity of the digital language data under study and credible study results, researchers should employ several data collection methods and tools, i.e., opt for methodological triangulation. The authors describe their methodology on a practical example and conclude that big data systems such as indexed digital texts can effectively replace native speakers as a source of credible information on a natural language semantics as well as improve the overall quality and verifiability of semantics studies. Conclusion. Considering the national segment of the Internet in a certain language (English, Russian, etc.) as a natural text corpus of high representativeness, the researcher can use it as a tool for testing various linguistic hypotheses. At the same time, it is necessary to reflect on the intrinsic qualities of big data, such as volume, velocity (swiftness of change), variability, lack of structure, valuableness, and the presence of “noise”, as well as carefully consider, design and test procedures for using search engines to collect big data in order to ensure the quality and verifiability of the results obtained.

The Impact of ChatGPT on English Language Teaching, Learning, and Assessment: A Rapid Review of Literature

This study aimed to explore the impact of ChatGPT on English language teaching, learning, and assessment. Specifically, it aimed to answer the following questions: 1) How can ChatGPT enhance English language learning, teaching, and assessment? and 2) What are the issues associated with ChatGPT in terms of language teaching, learning, and assessment? Utilizing Rapid Literature Review as a methodology guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this study found that ChatGPT can support and enhance English language learning by providing meaning-focused inputs, offering scaffoldings during the production of meaning-focused outputs, giving feedback on the accuracy of learners’ language outputs, and facilitating fluency development through extensive language practice. Moreover, this study also found that ChatGPT can enhance English language teaching by assisting teachers in designing bespoke lesson plans, facilitating language learning inside and outside language classrooms, developing customized instructional materials, assessing L2 learning, and giving immediate, individualized feedback. However, despite the benefits it can provide to English language teachers and learners, its use in the classroom is faced with many issues such as inaccurate responses, academic dishonesty and plagiarism, skills deterioration, generic responses, inherent biases, privacy breaches, non-emotionality, technical limitations, educational inequity, and teacher job security threat. Detailed results and implications for policymaking and language teacher development are discussed.

Utilizing an Attention-Based LSTM Model for Detecting Sarcasm and Irony in Social Media

Sarcasm and irony represent intricate linguistic forms in social media communication, demanding nuanced comprehension of context and tone. In this study, we propose an advanced natural language processing methodology utilizing long short-term memory with an attention mechanism (LSTM-AM) to achieve an impressive accuracy of 99.86% in detecting and interpreting sarcasm and irony within social media text. Our approach involves innovating novel deep learning models adept at capturing subtle cues, contextual dependencies, and sentiment shifts inherent in sarcastic or ironic statements. Furthermore, we explore the potential of transfer learning from extensive language models and integrating multimodal information, such as emojis and images, to heighten the precision of sarcasm and irony detection. Rigorous evaluation against benchmark datasets and real-world social media content showcases the efficacy of our proposed models. The outcomes of this research hold paramount significance, offering a substantial advancement in comprehending intricate language nuances in digital communication. These findings carry profound implications for sentiment analysis, opinion mining, and an enhanced understanding of social media dynamics.