Model Of Language Learning Research Articles

Intelligent computing for electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification: Levenberg–Marquardt backpropagation algorithm

The Levenberg–Marquardt (LM) backpropagation optimization algorithm, an artificial neural network algorithm, is used in this study to perform integrated numerical computing to evaluate the electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification. The model is then reduced to a collection of boundary value problems, which are solved with the help of a numerical technique and the proposed scheme, i.e., the LM algorithm, which is an iterative approach to determine the minimum of a nonlinear function defined as the sum of squares. As a blend of the steepest descent and the Gauss–Newton method, it has become a typical approach for nonlinear least-squares problems. Furthermore, the stability and consistency of the algorithm are ensured. For validation purposes, the results are also compared with those of previous research and the MATLAB bvp4c solver. Neural networking is also utilized for velocity, temperature, and concentration profile mapping from input to output. These findings demonstrate the accuracy of forecasts and optimizations produced by artificial neural networks. The performance of the bvp4c solver, which is used to reduce the mean square error, is used to generalize a dataset. The artificial neural network-based LM backpropagation optimization algorithm operates using data based on the ratio of testing (13%), validation (17%), and training (70%). This stochastic computing work presents an activation log-sigmoid function based LM backpropagation optimization algorithm, in which tens of neurons and hidden and output layers are used for solving the learning language model. The overlapping of the results and the small computed absolute errors, which range from 10−3 to 10−10 and from 106 to 108 for each model class, indicate the accuracy of the artificial neural network-based LM backpropagation optimization algorithm. Furthermore, each model case’s regression performance is evaluated as if it were an ideal model. In addition, function fitness and histogram are used to validate the dependability of the algorithm. Numerical approaches and artificial neural networks are an excellent combination for fluid dynamics, and this could lead to new advancements in many domains. The findings of this research could contribute to the optimization of fluid systems, resulting in increased efficiency and production across various technical domains.

Research on the Construction of Mobile Terminal Assisted Language Learning Model Based on Artificial Intelligence Technology

Research on the Construction of Mobile Terminal Assisted Language Learning Model Based on Artificial Intelligence Technology

Using computational models to study accent adaptation: A tutorial

One of the limitations in accent perception research is the difficulty of quantifying how much exposure is needed to observe adaptation to a novel accent. While numerous studies have investigated this issue (Baese-Berk et al., 2013; Clarke & Garrett, 2004), it has been impossible to control for individual-level differences and solely focus on linguistic differences with human participants. However, computational models can be intentionally set in terms of these features to better understand the process of adaptation to novel accents. Here we discuss how machine learning models can be implemented using PyTorch, a framework in Python made by Meta. PyTorch allows for the easy creation of language learning models that can be used in speech-recognition experiments (Paszke et al., 2019). Here we use the Wav2Vec2 model created by Meta, pre-trained on 960 hours of the Librispeech ASR corpus (Panayotov et al., 2015). We discuss importing the model and fine-tuning it using samples of different accents. Next, we demonstrate building a decoder using a greedy algorithm to transform the model's classification into a transcript. Our goal is to compare simulations against data from human participants and tease apart the direct effects of individual variability on accent perception.

Online Network Protection Firmware for Malware Identification Utilizing Transformer Learning

Abstract: Malware ID expects a critical part in network security with the expansion in malware improvement. What more, kinds of progress in cutting edge assaults. Noxious programming applications, or malware, are the principal wellspring of different security issues. For different reasons, including the taking of state of the art developments and insightful properties, regulative exhibitions of retaliation, and the modification of sensitive information, to give some examples, these pernicious applications plan to perform unapproved exercises on the host machines to assist their makers. More valuable assistance systems are required because of the quick expansion of noxious programming on the web and their self changing skills, as in polymorphic and remarkable malware. This task proposes to support the MalFree Sandbox with stacked bidirectional long transient memory (Stacked BiLSTM) and generative prepared transformer based (GPT2) critical learning language models for recognizing pernicious code isolated. The proposed computations, specifically the bidirectional long transient memory (BiLSTM) model and the generative prepared transformer 2 (GPT-2) method, employ gathering rules derived from Minimal Executable (PE) Records static examination results to identify harmful code pieces. To comprehend malwares through MalFree Sandbox, care should be taken to sandbox the malwares in a climate that considers an encapsulation and exhaustive evaluation while in addition keeping on propelling spread from being gifted.

The Project-Based Learning Model for Javanese Language in Kurikulum Merdeka

The purpose of this research is to present the implementation of a project-based learning model in Javanese language subjects. Descriptive qualitative method is used as the procedural steps in this research. Data in the research are collected through interview techniques, observations, and documentation. The data analysis technique used is Miles and Huberman's, which involves the steps of 1) data collection, 2) data reduction, 3) data presentation, and 4) data conclusion. Informants include the school principal, teachers, and students. The results of this research show that the project-based learning model is implemented for the wayang (traditional Javanese puppet) material. The findings indicate that the Project-Based Learning Model is very suitable as an alternative Javanese language learning model in the Merdeka Curriculum. This Project-Based Learning Model for Javanese language is implemented by preparing students of class VII F, arranging activity schedules, supervising the implementation, assessing students, and conducting evaluations.

Cyberbullying Text Identification based on Deep Learning and Transformer-based Language Models

In the contemporary digital age, social media platforms like Facebook, Twitter, and YouTube serve as vital channels for individuals to express ideas and connect with others. Despite fostering increased connectivity, these platforms have inadvertently given rise to negative behaviors, particularly cyberbullying. While extensive research has been conducted on high-resource languages such as English, there is a notable scarcity of resources for low-resource languages like Bengali, Arabic, Tamil, etc., particularly in terms of language modeling. This study addresses this gap by developing a cyberbullying text identification system called BullyFilterNeT tailored for social media texts, considering Bengali as a test case. The intelligent BullyFilterNeT system devised overcomes Out-of-Vocabulary (OOV) challenges associated with non-contextual embeddings and addresses the limitations of context-aware feature representations. To facilitate a comprehensive understanding, three non-contextual embedding models GloVe, FastText, and Word2Vec are developed for feature extraction in Bengali. These embedding models are utilized in the classification models, employing three statistical models (SVM, SGD, Libsvm), and four deep learning models (CNN, VDCNN, LSTM, GRU). Additionally, the study employs six transformer-based language models: mBERT, bELECTRA, IndicBERT, XML-RoBERTa, DistilBERT, and BanglaBERT, respectively to overcome the limitations of earlier models. Remarkably, BanglaBERT-based BullyFilterNeT achieves the highest accuracy of 88.04% in our test set, underscoring its effectiveness in cyberbullying text identification in the Bengali language.

Is ChatGPT taking over the language classroom?

ChatGPT generated much dialogue on the implications of large language models (LLMs) for language teaching and learning. Since language teachers are uniquely positioned to teach metalinguistic awareness, they can support their learners’ understanding of how LLMs are shaped by language ideologies and how their outputs are indexical of social power. This awareness would help learners be more conscientious in using LLMs, deciding how to interact with them and adapt their outputs for their purposes. This article introduces LLMs as statistical systems that predict linguistic forms. It surfaces two language ideologies that have shaped their development: the belief in the separability of language from its social contexts and the belief in the value of larger text corpora. It also highlights some ideological effects including uneven language performance, text outputs that reflect biases, privacy violations, circulation of copyrighted materials, misinformation, and hallucinations. Some suggestions for mitigating these effects are offered.

FACTORS ARISING FROM THE UTILIZATION OF ARTIFICIAL INTELLIGENCE AND LARGE LANGUAGE MODELS IN SPECIAL EDUCATION AND TRAINING

This literature review aims to create a connection between Special Education and Training (SET) with Artificial Intelligence (ΑΙ), specifically focusing on machine learning language models. These systems analyze language structures and rapidly generate text that closely resembles human expression. While AI tools have been present since the last century, global technological and research interest in AI experienced resurgence in November 2022 with the release of ChatGPT, a machine learning language model –“chatbot”. The introduction of this tool raised expectations for its potential in educating individuals with Special Educational Needs and/or Disabilities (SENDs). However, the literature also highlights concerns about potential risks and challenges associated with the widespread use of such tools in education. This paper explores the intersection of Special Education and Training goals and Large Language Models (LLM), presenting in the results the potential benefits and risks that may emerge from this interaction.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0758/a.php" alt="Hit counter" /></p>

Chinese Cyberbullying Detection Using XLNet and Deep Bi-LSTM Hybrid Model

The popularization of the internet and the widespread use of smartphones have led to a rapid growth in the number of social media users. While information technology has brought convenience to people, it has also given rise to cyberbullying, which has a serious negative impact. The identity of online users is hidden, and due to the lack of supervision and the imperfections of relevant laws and policies, cyberbullying occurs from time to time, bringing serious mental harm and psychological trauma to the victims. The pre-trained language model BERT (Bidirectional Encoder Representations from Transformers) has achieved good results in the field of natural language processing, which can be used for cyberbullying detection. In this research, we construct a variety of traditional machine learning, deep learning and Chinese pre-trained language models as a baseline, and propose a hybrid model based on a variant of BERT: XLNet, and deep Bi-LSTM for Chinese cyberbullying detection. In addition, real cyber bullying remarks are collected to expand the Chinese offensive language dataset COLDATASET. The performance of the proposed model outperforms all baseline models on this dataset, improving 4.29% compared to SVM—the best performing method in traditional machine learning, 1.49% compared to GRU—the best performing method in deep learning, and 1.13% compared to BERT.

Confronting the Disruption of the Infectious Diseases Workforce by Artificial Intelligence: What This Means for Us and What We Can Do About It.

With the rapid advancement of artificial intelligence (AI), the field of infectious diseases (ID) faces both innovation and disruption. AI and its subfields including machine learning, deep learning, and large language models can support ID clinicians' decision making and streamline their workflow. AI models may help ensure earlier detection of disease, more personalized empiric treatment recommendations, and allocation of human resources to support higher-yield antimicrobial stewardship and infection prevention strategies. AI is unlikely to replace the role of ID experts, but could instead augment it. However, its limitations will need to be carefully addressed and mitigated to ensure safe and effective implementation. ID experts can be engaged in AI implementation by participating in training and education, identifying use cases for AI to help improve patient care, designing, validating and evaluating algorithms, and continuing to advocate for their vital role in patient care.

Synthetic Genres: Expert Genres, Non-Specialist Audiences, and Misinformation in the Artificial Intelligence Age

Drawing on rhetorical genre studies, we explore research article abstracts created by generative artificial intelligence (AI). These synthetic genres—genre-ing activities shaped by the recursive nature of language learning models in AI-driven text generation—are of interest as they could influence informational quality, leading to various forms of disordered information such as misinformation. We conduct a two-part study generating abstracts about (a) genre scholarship and (b) polarized topics subject to misinformation. We conclude with considerations about this speculative domain of AI text generation and dis/misinformation spread and how genre approaches may be instructive in its identification.

GMean—a semi-supervised GRU and K-mean model for predicting the TF binding site

The transcription factor binding site is a deoxyribonucleic acid sequence that binds to transcription factors. Transcription factors are proteins that regulate the transcription gene. Abnormal turnover of transcription factors can lead to uncontrolled cell growth. Therefore, discovering the relationships between transcription factors and deoxyribonucleic acid sequences is an important component of bioinformatics research. Numerous deep learning and machine learning language models have been developed to accomplish these tasks. Our goal in this work is to propose a GMean model for predicting unlabelled deoxyribonucleic acid sequences. The GMean model is a hybrid model with a combination of gated recurrent unit and K-mean clustering. The GMean model is developed in three phases. The labelled and unlabelled data are processed based on k-mers and tokenization. The labelled data is used for training. The unlabelled data are used for testing and prediction. The experimental data consists of deoxyribonucleic acid experimental of GM12878, K562 and HepG2. The experimental results show that GMean is feasible and effective in predicting deoxyribonucleic acid sequences, as the highest accuracy is 91.85% in predicting K562 and HepG2. This is followed by the prediction of the sequence between GM12878 and K562 with an accuracy of 89.13%. The lowest accuracy is the prediction of the sequence between HepG2 and GM12828, which is 88.80%.

Continually adapting pre-trained language model to universal annotation of single-cell RNA-seq data.

Cell-type annotation of single-cell RNA-sequencing (scRNA-seq) data is a hallmark of biomedical research and clinical application. Current annotation tools usually assume the simultaneous acquisition of well-annotated data, but without the ability to expand knowledge from new data. Yet, such tools are inconsistent with the continuous emergence of scRNA-seq data, calling for a continuous cell-type annotation model. In addition, by their powerful ability of information integration and model interpretability, transformer-based pre-trained language models have led to breakthroughs in single-cell biology research. Therefore, the systematic combining of continual learning and pre-trained language models for cell-type annotation tasks is inevitable. We herein propose a universal cell-type annotation tool, called CANAL, that continuously fine-tunes a pre-trained language model trained on a large amount of unlabeled scRNA-seq data, as new well-labeled data emerges. CANAL essentially alleviates the dilemma of catastrophic forgetting, both in terms of model inputs and outputs. For model inputs, we introduce an experience replay schema that repeatedly reviews previous vital examples in current training stages. This is achieved through a dynamic example bank with a fixed buffer size. The example bank is class-balanced and proficient in retaining cell-type-specific information, particularly facilitating the consolidation of patterns associated with rare cell types. For model outputs, we utilize representation knowledge distillation to regularize the divergence between previous and current models, resulting in the preservation of knowledge learned from past training stages. Moreover, our universal annotation framework considers the inclusion of new cell types throughout the fine-tuning and testing stages. We can continuously expand the cell-type annotation library by absorbing new cell types from newly arrived, well-annotated training datasets, as well as automatically identify novel cells in unlabeled datasets. Comprehensive experiments with data streams under various biological scenarios demonstrate the versatility and high model interpretability of CANAL. An implementation of CANAL is available from https://github.com/aster-ww/CANAL-torch. dengmh@pku.edu.cn. Supplementary data are available at Journal Name online.

Evaluation of a Large Language Model to Identify Confidential Content in Adolescent Encounter Notes

This cross-sectional study assesses the ability of a language learning model to classify whether a progress note contains confidential information and to identify the specific confidential content in the note.

Application of GPT-4 foundation model for risk prediction and stratification in colorectal cancer.

219 Background: Deep Learning Language Models (LLMs) show significant potential in analyzing complex data, particularly in oncology, where they can predict and stratify patient risks, detect non-linear interactions, and weigh multiple variables. This study applies LLMs to stage II/III localized colon cancer patients to assess their effectiveness in identifying risk subgroups. Methods: In this retrospective study, we examined a cohort of stage II and III localized colorectal cancer patients diagnosed from September 2016 to December 2022 at La Paz University Hospital. Utilizing the GPT-4 API beta version, patients were stratified into high and low-risk groups for relapse based on various risk parameters outlined in the table. Notably, adjuvant treatment data was excluded from the input. The model was prompted with the question, "Given the following risk parameters for localized colorectal cancer {param}, guess whether the patient is in low risk or high risk for relapse," and the model provided dichotomic responses categorized as either 1 (high risk) or 0 (low risk). Results: Patient characteristics are depicted in the table. During a median follow-up of 23.68 months, 63 recurrence events and 73 deaths were observed. The GPT-4 model stratified patients into low (37.9%) and high-risk (62.1%) groups, with the survival curves showing a significant difference in outcomes between the groups. Low-risk patients had a relapse-free rate of 93% at 36 months vs 63% in the high-risk group. In univariate analysis, the high-risk variable had a hazard ratio (HR) of 4.26 (95% CI: 2.03-8.95; p < 0.005). When stratified by adjuvant treatment, no significant difference in relapse probability was observed for those receiving therapy. However, without adjuvant therapy, high-risk patients had an HR of 12.41 (95% CI: 3.72-41.97; p < 0.005) for relapse. Conclusions: GPT-4 demonstrated its potential in predicting oncology outcomes by accurately understanding colorectal cancer risk factors. To fully harness the benefits of LLMs in the future of oncology, they must be fine-tuned on extensive oncological datasets to improve patient risk stratification and treatment selection, ultimately leading to better patient outcomes.[Table: see text]

Participation as relational investment: thinking beyond social capital in culturally and linguistically diverse educational contexts

Departing from normative notions of participation as passive or responsive vis-à-vis active or agentic, this article conceptualises participation as ‘relational investment’. Drawing from a qualitative case study of a culturally and linguistically diverse (CALD) Australian primary classroom, I argue that students’ decisions about how to participate are shaped by the nature of their social relationships. Thus, I illuminate all participatory strategies as agentic and subject to the relational conditions of the field. Building on Bourdieu’s (1977, 1990, 1992) theorisations of ‘social capital’ and how this has been mobilised in Norton’s (1995) concept of ‘investment’ (used as a model for language learning), I illustrate the limits of the economic logic of capital embedded in these theoretical tools. Instead, I propose engaging with the notion of ‘sociality’ (Bottero’s 2009) to capture how the characteristics of classroom social relationships shape the ways students invest in participating.

Research on the Evaluation Model of Students' Foreign Language Learning Situation based on Oriented Online Teaching Collaboration Platform

Internet + Education makes online teaching gradually penetrate the education industry, and makes the industry enter a great revolution based on information technology. The traditional student learning evaluation system cannot satisfy the actual demand of current learning evaluation. This paper constructs an evaluation model for the foreign language learning of online students. Firstly, the DBSCAN algorithm with distance optimization is used to conduct cluster analysis on the description indicators of student behavior, and the student groups with different behavior characteristics are obtained. Then the ANOVA F-test was used to extract the features of different student groups. Finally, a novel N-Adaboost algorithm based on multiple classifiers is proposed and a model is constructed to evaluate students’ foreign language learning. The experimental results show that the accuracy of the evaluation model is 74.02% in the pass and fail groups and 73.74% in the excellent and non-excellent groups. Students’ listening, speaking, and reading abilities are in a state of upward development overall through the online teaching collaboration platform, but their writing ability is obviously declining. There is a great improvement in foreign language vocabulary. This study provides a new perspective of thinking for the improvement of the quality of school teaching management, the analysis of students’ behavior, and the evaluation of learning situations, and provides a new solution for the problem of students’ learning situations in modern information teaching.

Democratizing Knowledge Creation Through Human-AI Collaboration in Academic Peer Review

In the rapidly evolving landscape of academic research, artificial intelligence (AI) is poised to revolutionize traditional academic peer review processes and knowledge evaluation systems. We believe that the growing collaboration between humans and AI will disrupt how academics assess scholarly manuscripts and disseminate published works in a way that facilitates the closing of gaps among diverse scholars as well as competing scholarly traditions. Such human-AI collaboration is not a distant reality but is unfolding before us, in part, through the development, application, and actual use of AI, including language learning models (LLMs). This opinion piece focuses on the academic peer review process. It offers preliminary ideas on how human-AI collaboration will likely change the peer review process, highlights the benefits, identifies possible bottlenecks, and underscores the potential for democratizing academic culture worldwide.

Enhancing Financial Inclusion and Regulatory Challenges: A Critical Analysis of Digital Banks and Alternative Lenders Through Digital Platforms, Machine Learning, and Large Language Models Integration

Enhancing Financial Inclusion and Regulatory Challenges: A Critical Analysis of Digital Banks and Alternative Lenders Through Digital Platforms, Machine Learning, and Large Language Models Integration

Detection and Prediction of Future Mental Disorder From Social Media Data Using Machine Learning, Ensemble Learning, and Large Language Models

Detection and Prediction of Future Mental Disorder From Social Media Data Using Machine Learning, Ensemble Learning, and Large Language Models