Multiple Languages Research Articles

The (re)construction of the multilingual identity of a Chilean graduate student in South Korea: a narrative inquiry

ABSTRACT A growing number of students studying abroad in Asian countries face a common linguistic experience: the language of instruction in the courses they follow is English, which is not the language of the host country and international students whose L1 is neither English nor Korean must thus need to (re)construct their academic and personal identities in various multiple second languages (L2). This study presents the narrative of a Chilean student who had recently completed postgraduate studies in an English-taught programme at a South Korean university. It explores how she constructed a life for herself in South Korea and built a multilingual identity. It highlights in particular the tension she experienced between the two L2s involved: English and Korean. The participant was interviewed four times, and her answers were reconstituted into a narrative. Analysis of the narrative highlights four key themes: language learning enthusiasm, the learner’s investment in language learning, the process of identity reconstruction, and maintaining multilingualism.

A multiscale distributed neural computing model database (NCMD) for neuromorphic architecture

Distributed neuromorphic architecture is a promising technique for on-chip processing of multiple tasks. Deploying the constructed model in a distributed neuromorphic system, however, remains time-consuming and challenging due to considerations such as network topology, connection rules, and compatibility with multiple programming languages. We proposed a multiscale distributed neural computing model database (NCMD), which is a framework designed for ARM-based multi-core hardware. Various neural computing components, including ion channels, synapses, and neurons, are encompassed in NCMD. We demonstrated how NCMD constructs and deploys multi-compartmental detailed neuron models as well as spiking neural networks (SNNs) in BrainS, a distributed multi-ARM neuromorphic system. We demonstrated that the electrodiffusive Pinsky–Rinzel (edPR) model developed by NCMD is well-suited for BrainS. All dynamic properties, such as changes in membrane potential and ion concentrations, can be easily explored. In addition, SNNs constructed by NCMD can achieve an accuracy of 86.67% on the test set of the Iris dataset. The proposed NCMD offers an innovative approach to applying BrainS in neuroscience, cognitive decision-making, and artificial intelligence research.

Transformer-based approach for symptom recognition and multilingual linking.

This paper presents a transformer-based approach for symptom Named Entity Recognition (NER) in Spanish clinical texts and multilingual entity linking on the SympTEMIST dataset. For Spanish NER, we fine tune a RoBERTa-based token-level classifier with Bidirectional Long Short-Term Memory and conditional random field layers on an augmented train set, achieving an F1 score of 0.73. Entity linking is performed via a hybrid approach with dictionaries, generating candidates from a knowledge base containing Unified Medical Language System aliases using the cross-lingual SapBERT and reranking the top candidates using GPT-3.5. The entity linking approach shows consistent results for multiple languages of 0.73 accuracy on the SympTEMIST multilingual dataset and also achieves an accuracy of 0.6123 on the Spanish entity linking task surpassing the current top score for this subtask. Database URL: https://github.com/svassileva/symptemist-multilingual-linking.

Early Detection of Mental Health Crises through Artifical-Intelligence-Powered Social Media Analysis: A Prospective Observational Study.

The early detection of mental health crises is crucial for timely interventions and improved outcomes. This study explores the potential of artificial intelligence (AI) in analyzing social media data to identify early signs of mental health crises. We developed a multimodal deep learning model integrating natural language processing and temporal analysis techniques. The model was trained on a diverse dataset of 996,452 social media posts in multiple languages (English, Spanish, Mandarin, and Arabic) collected from Twitter, Reddit, and Facebook over 12 months. Its performance was evaluated using standard metrics and validated against expert psychiatric assessments. The AI model demonstrated a high level of accuracy (89.3%) in detecting early signs of mental health crises, with an average lead time of 7.2 days before human expert identification. Performance was consistent across languages (F1 scores: 0.827-0.872) and platforms (F1 scores: 0.839-0.863). Key digital markers included linguistic patterns, behavioral changes, and temporal trends. The model showed varying levels of accuracy for different crisis types: depressive episodes (91.2%), manic episodes (88.7%), suicidal ideation (93.5%), and anxiety crises (87.3%). AI-powered analysis of social media data shows promise for the early detection of mental health crises across diverse linguistic and cultural contexts. However, ethical challenges, including privacy concerns, potential stigmatization, and cultural biases, need careful consideration. Future research should focus on longitudinal outcome studies, ethical integration of the method with existing mental health services, and developing personalized, culturally sensitive models.

Global invasion and biosecurity risk from the online trade in ornamental crayfish.

Trade in undomesticated ornamental animals has rapidly expanded beyond brick-and-mortar retail stores to now include growing numbers of internet marketplaces. The growing volume, diversity, and origins of invasive non-native species in trade challenge already weak national biosecurity policies. Despite widespread focus on vertebrates, many knowledge gaps exist regarding the online global trade of ornamental invertebrates. We conducted the first global assessment of the online trade in and associated invasion risk of freshwater crayfishes, which are increasingly popular aquarium animals. We systematically examined e-commerce marketplaces in multiple languages, scrapping information that included species identity, price, quantity, and shipping designation from each crayfish listing. Next, we combined geographic pathways of shipping associated with online trade (introduction risk) and environmental suitability modeling (establishment risk) to estimate global risk of non-native crayfish invasion risk. We identified hundreds of online marketplaces and thousands of sale listings in 33 countries (5 continents) involving 60 species and representing a selling value of ∼US$1.5 million. Invasion risk of non-native crayfish in trade was widespread, with geographic hotspots coinciding with both elevated opportunities for introduction (greater shipping offerings) and establishment. Precise characterization of the online species trade is fundamental to support new and reformed biosecurity policies, build industry partnerships, and design educational campaigns to prevent species invasions through trade. We found that the taxonomy, geography, and economics of the global online ornamental crayfish trade are vast and require greater attention.

Studying word meaning evolution through incremental semantic shift detection

AbstractThe study of semantic shift, that is, of how words change meaning as a consequence of social practices, events and political circumstances, is relevant in Natural Language Processing, Linguistics, and Social Sciences. The increasing availability of large diachronic corpora and advance in computational semantics have accelerated the development of computational approaches to detecting such shift. In this paper, we introduce a novel approach to tracing the evolution of word meaning over time. Our analysis focuses on gradual changes in word semantics and relies on an incremental approach to semantic shift detection (SSD) called What is Done is Done (WiDiD). WiDiD leverages scalable and evolutionary clustering of contextualised word embeddings to detect semantic shift and capture temporal transactions in word meanings. Existing approaches to SSD: (a) significantly simplify the semantic shift problem to cover change between two (or a few) time points, and (b) consider the existing corpora as static. We instead treat SSD as an organic process in which word meanings evolve across tens or even hundreds of time periods as the corpus is progressively made available. This results in an extremely demanding task that entails a multitude of intricate decisions. We demonstrate the applicability of this incremental approach on a diachronic corpus of Italian parliamentary speeches spanning eighteen distinct time periods. We also evaluate its performance on seven popular labelled benchmarks for SSD across multiple languages. Empirical results show that our results are comparable to state-of-the-art approaches, while outperforming the state-of-the-art for certain languages.

HeLP: The Hebrew Lexicon project

Lexicon projects (LPs) are large-scale data resources in different languages that present behavioral results from visual word recognition tasks. Analyses using LP data in multiple languages provide evidence regarding cross-linguistic differences as well as similarities in visual word recognition. Here we present the first LP in a Semitic language—the Hebrew Lexicon Project (HeLP). HeLP assembled lexical decision (LD) responses to 10,000 Hebrew words and nonwords, and naming responses to a subset of 5000 Hebrew words. We used the large-scale HeLP data to estimate the impact of general predictors (lexicality, frequency, word length, orthographic neighborhood density), and Hebrew-specific predictors (Semitic structure, presence of clitics, phonological entropy) of visual word recognition performance. Our results revealed the typical effects of lexicality and frequency obtained in many languages, but more complex impact of word length and neighborhood density. Considering Hebrew-specific characteristics, HeLP data revealed better recognition of words with a Semitic structure than words that do not conform to it, and a drop in performance for words comprising clitics. These effects varied, however, across LD and naming tasks. Lastly, a significant inhibitory effect of phonological ambiguity was found in both naming and LD. The implications of these findings for understanding reading in a Semitic language are discussed.

Widespread Associations between Behavioral Metrics and Brain Microstructure in ASD.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and repetitive behaviors. A diagnosis of ASD is provided by a clinician following cognitive and behavioral evaluations, but there is currently no biomarker associating these metrics with neurological changes. Our lab has previously found that g-ratio, the proportion of axon width to myelin diameter, and axonal conduction velocity, which is associated with the capacity of an axon to carry information, are both decreased in ASD individuals. By associating these differences with performance on cognitive and behavioral tests, we can evaluate which tests most reveal changes in the brain. Analyzing 273 participants (148 with ASD) ages 8-to-17 (49% female) through an NIH-sponsored Autism Centers of Excellence (ACE) network (Grant#: MH100028), we observe widespread associations between behavioral and cognitive evaluations of autism and between behavioral and microstructural metrics. Analyzing data from all participants, conduction velocity but not g-ratio was significantly associated with many behavioral metrics. However, this pattern was reversed when looking solely at ASD participants. This reversal may suggest that the mechanism underlying differences between autistic and non-autistic individuals may be distinct from the mechanism underlying ASD behavioral severity. Two additional machine learning cluster analyses applied to neuroimaging data reinforce the association between neuroimaging and behavioral metrics and suggest that age-related maturation of brain metrics may drive changes in ASD behavior. By associating neuroimaging metrics with ASD, it may be possible to measure and identify individuals at high risk of ASD before behavioral tests can detect them.

Spam Review Detection Using Weighted Swarm Support Vector Machines and Pre-Trained Word Embedding for Multiple Languages

Before making a purchase, many find internet reviews to be a vital source of information. On top of that, companies may learn a lot about their products and services via these reviews. Having faith in these reviews was especially important during the COVID-19 pandemic, when many stayed inside and read reviews at a dizzying pace. The pandemic altered the atmosphere and people's preferences in addition to increasing the number of evaluations. Spam reviewers keep an eye on these changes and try to improve their sneaky techniques. In order to deceive customers or harm competitors, reviews that are deemed spam may include inaccurate, misleading, or dishonest information. Consequently, this work introduces a WSVM plus an HHO to identify spam reviews. The HHO is similar to an algorithm in that it optimises hyperparameters and uses feature weights. Using English, Spanish, and Arabic language corpora as datasets, the multilingual difficulty in spam reviews has been tackled. Ngram-3, TFIDF, whereas One-hot encoding are three methods for representing words, while pre-trained word incorporation (BERT) is another one that has been used. Each of the four such studies has shed light on and provided a solution to a different facet. From start to finish, the proposed technique beat rival cutting-edge algorithms in every test. For the Multi dataset, the WSVM-HHO achieved a success rate of 84.270 percent; for the English information set, 89.565 percent; for the Spanish information set, 71.913 percent; and for the Arabic dataset, 88.565 percent. Furthermore, we have extensively researched the review environment before to and during the COVID-19 event. To further enhance detection performance, it has been designed to merge its existing textual attributes with statistical information to build a new dataset.

Psychological and Educational Approach in Teaching and Learning Foreign Languages

Schools play a central role in the holistic development of children, providing a structured environment that fosters their intellectual, social, emotional, and physical growth, and equipping them with essential skills and knowledge for their future through the experiences each child will have at school. These contribute to the cognitive and academic development of children in several ways. First and foremost, the primary objective of school is to provide a pedagogical education to children. In this regard, the school promotes intellectual development through a structured and organized learning environment where children can acquire knowledge, develop cognitive skills, and build their academic journey. Teachers play a crucial role in delivering effective instruction, facilitating learning experiences, and providing individualized support to meet the diverse needs of students. Furthermore, schools provide resources, programs, and assessments that help children progress academically and prepare them for their future studies. Moreover, children are also exposed to several languages from a very young age, which promotes the acquisition and mastery of languages. The learning of multiple languages in a multilingual school will lead children to develop cognitive skills such as attention, memory, and executive functions. These cognitive skills are transferable and can support academic abilities in areas such as reading, comprehension, mathematics, and problem-solving. As the basis of all educational activities, whether they involve listening, speaking, reading or writing, language plays a crucial role in the fulfilment of many important tasks and goals of a school. Language in school is not only a subject to be taught, but a source of study for other subjects as well, as it enables students to gather data and gain knowledge in a variety of subject areas. As a result, primary education provides all students with a basic education as well as access to other languages, such as French or English. This initiative aims to give students an appropriate linguistic approach that enables them to communicate, express themselves and use their language skills in their academic, personal and social activities, as well as direct access to information and knowledge. Foreign language education aims as well as to give learners the ability to communicate, express themselves, and use their language skills in their school work, personal lives, and social interactions. It also helps them have direct access to information and knowledge. From this point of view, we seek through the following scientific paper to discuss the purpose of the need for children to acquire foreign languages and the most appropriate age to start learning them. We will also try to present the scientific opinions of the most important thinkers and researchers on how languages are learnt and taught and the proposed teaching methods in this field.

Insights into maternal pertussis vaccination counselling: a qualitative study on perspectives and experiences among midwives and gynaecologists in the Netherlands

BackgroundHealthcare professionals (HCPs) play a significant role in the decision-making process of pregnant women on maternal vaccinations. Whereas a high proportion of HCPs discuss maternal vaccinations with pregnant women, confidence in discussing maternal vaccinations is lacking and HCPs experience inadequate training to discuss maternal vaccinations with pregnant women. Furthermore, different practical barriers might influence the consultation process, such as lack of time. More studies on the barriers, as well as facilitators, to discussing maternal vaccinations is needed and will help us to better understand and support HCPs in discussing maternal vaccinations.MethodsThis qualitative study involved semi-structured interviews with fourteen HCPs working as midwives or gynaecologists in the Netherlands. An integrated theoretical approach was used to inform data collection and analysis. Thematic analysis was conducted using inductive and deductive approaches. This study followed the COnsolidated criteria for REporting Qualitative research (COREQ) guidelines.ResultsThe thematic analysis of the data pointed to the following five themes of HCP counselling: the consultation process, attitude, perceived norm, perceived control and improvement ideas. Most HCPs follow a similar approach in maternal pertussis vaccination consultations, beginning by assessing clients’ understanding, providing basic information, and addressing questions. However, consultation timing and prioritization vary among HCPs. Challenges in consultations include client requests for clear advice, with HCPs trained to remain neutral, emphasizing client autonomy in decision-making. Most HCPs acknowledge the importance of their consultations in informing pregnant women about maternal pertussis vaccination.ConclusionsThis study offers a confirmation of the awareness of the pivotal role of HCPs in informing pregnant women about the maternal pertussis vaccination. HCPs stress the importance of neutral counselling, enabling pregnant women to make well-informed decisions independently. Because of upcoming vaccine hesitancy nowadays, HCPs must be equipped with the knowledge and confidence to navigate difficult conversations. Continuous education and training might help to increase HCPs’ confidence in handling difficult consultations. Additionally, making the information materials for pregnant women available in multiple languages and incorporating more visuals to enhance comprehension could support HCPs in reaching a broader group of pregnant women.

Multilingual media repertoires of young people in the migration society: A plea for a language and culture-aware approach to media education

Digital media provide easy access to media content in multiple languages from local, national, and transnational contexts. This accessibility of diverse content enables young people in the migration society to develop multilingual and transnational media repertoires: they have the option to continuously and strategically navigate between different platforms, between different contexts and multiple languages. In this paper, we discuss how multilingualism and transnationalism can be used as key concepts for understanding the cross-media practices of young people and their participation in the migration society. Based on focus groups with young people living in a culturally diverse and socio-economically disadvantaged neighborhood of Bremen, Germany, we discuss how and why young people choose specific media content in a specific language in their daily media practices. Our discussion of the findings then focuses on the question of how media education in the migration society can take the multilingual and transnational media repertoires into account and benefit from it.

Encoder-decoder approach for describing health of cauliflower plant in multiple languages

Physically examining each plant to determine its state of health and determining the disease if plant is affected due to it, is challenging. The encoder - decoder approach is proposed for describing health of cauliflower plant in English, Hindi and Marathi languages from aerial images. Experiments are performed with different CNN models and LSTM combinations. The Multilanguage Cauliflower Captions Dataset (MCCD) is developed to evaluate the performance of the model. The dataset contains 1213 images where each image is described in 3 different languages. The dataset contains images of cauliflower plant affected due to bacterial spot rot, black rot and downy mildew diseases. It also contains images of healthy plant.<strong> </strong>The objective metrics such as BLEU scores and subjective criteria are used to decide the quality of the generated description.<br /><strong></strong>

Clustering of clinical symptoms using large language models reveals low diagnostic specificity of proposed alternatives to consensus mast cell activation syndrome criteria

The rate of diagnosis of mast cell activation syndrome (MCAS) has increased since the disorder's original description as a mastocytosis-like phenotype. While a set of consortium MCAS criteria is well described and widely accepted, this increase occurs in the setting of a broader set of proposed alternative MCAS criteria. Effective diagnostic criteria must minimize the range of unrelated diagnoses that can be erroneously classified as the condition of interest. We sought to determine if the symptoms associated with alternative MCAS criteria result in less concise or consistent diagnostic alternatives, reducing diagnostic specificity. We used multiple large language models, including ChatGPT, Claude, and Gemini, to bootstrap the probabilities of diagnoses that are compatible with consortium or alternative MCAS criteria. We utilized diversity and network analysis to quantify diagnostic precision and specificity compared to control diagnostic criteria including systemic lupus erythematosus (SLE), Kawasaki disease, and migraines. Compared to consortium MCAS criteria, alternative MCAS criteria are associated with more variable (Shannon diversity 5.8 vs. 4.6, respectively; p-value=0.004) and less precise (mean Bray-Curtis similarity 0.07 vs 0.19, respectively; p-value=0.004) diagnoses. The diagnosis networks derived from consortium and alternative MCAS criteria had lower between-network similarity compared to the similarity between diagnosis networks derived from two distinct SLE criteria (cosine similarity 0.55 vs. 0.86, respectively; p-value=0.0022). Alternative MCAS criteria are associated with a distinct set of diagnoses compared to consortium MCAS criteria and have lower diagnostic consistency. This lack of specificity is pronounced in relation to multiple control criteria, raising the concern that alternative criteria could disproportionately contribute to MCAS overdiagnosis, to the exclusion of more appropriate diagnoses.

BERT-based models for classifying multi-dialect Arabic texts

<p><a name="_Hlk133997549"></a><a name="_Hlk127607325"></a><span lang="EN-US">The area of natural language processing (NLP) is presently a rapidly developing field characterized by innovation and research. Despite this progress, several </span><a name="_Hlk148089340"></a><span lang="EN-US">dialects of Arabic</span><span><span lang="EN-US"> (DA) are classified as low-resource languages, making it challenging for NLP systems to process DA data. One approach to address this issue is to train NLP models on social media data sets containing DA texts. </span><span lang="IN">Therefore, these open-access social media datasets, as outlined in our paper, can serve as a valuable resource for developers and researchers involved in the processing of DA.</span><span lang="EN-US">To create our multilingual corpus, we gathered data from various datasets containing different versions of DA. These datasets will be used to classify texts in terms of sentiment classification, topic classification, and dialect identification. Our study contributes to the automated analysis of the classification of Arabic dialects. We aim to investigate and assess various machine learning and deep learning techniques, with a specific focus on utilizing the BERT model. </span><span lang="EN-US">The results of our experiments on our datasets show that DarijaBERT and DziriBERT trained on a similar DA outperform traditional machine learning methods and previous more general pre-trained models that were trained on multiple dialects or languages.</span></span></p>

Translanguaging space through pointing gestures: Multilingual family literacy at a science museum

Translanguaging theory highlights the dynamic use of multiple languages and communication modes by multilingual people in their daily experiences. Museums are informal family learning spaces where multilingual families use languages and other semiotic resources to create learning opportunities for their children. Using a microethnographic approach to discourse analysis and multimodal interaction analysis, I examined how a multilingual family uses translanguaging practices to organize their family learning in museums and the role of pointing gestures as part of their translanguaging repertoires in multilingual family learning. The analysis of two literacy events highlights that a child and his mother translanguaged with various semiotic resources to organize museum performances, joint attention, and telling, and that pointing gestures played a role in constructing a translanguaging space as they organized the two performances. Pointing involved the family in reading signage texts and allowed the mother to translate them for the child. Viewing translation as part of the translanguaging repertoire, this study recognizes the importance of the role of pointing gestures in constructing family learning at museums, enriching children’s schooling and literacy learning in classrooms. I argue that recognizing pointing as a critical component of translanguaging allows educators to develop strategies that leverage families’ unique repertoires to support multilingual students’ language and literacy learning.

Enhancing Diabetes Management Education in Community Health Centers: Empowering Patients and Improving Health Outcomes

Background: Diabetes poses significant public health challenges globally, with South Africa experiencing high prevalence rates, particularly in underserved regions. The lack of tailored diabetes management programs in community health centers (CHCs) exacerbates poor health outcomes and increases healthcare costs. Methods and Materials: This project aimed to enhance diabetes management education at Pholela Community Health Centre in Durban, South Africa, through comprehensive educational interventions and healthcare provider training. Culturally sensitive educational materials were developed and delivered in multiple languages, targeting patient self-management and healthcare provider competence. Results : The intervention led to a 20% increase in the proportion of diabetic patients achieving target HbA1c levels within one year. Healthcare providers showed improved proficiency in delivering culturally sensitive, evidence-based diabetes care. Patient education workshops and peer support groups significantly improved patient knowledge and self-management practices. Conclusion: The project successfully addressed critical gaps in diabetes education and care at Pholela CHC, resulting in improved patient outcomes and enhanced healthcare practices. Continued community engagement and resource allocation are essential for sustaining these improvements and reducing the disease burden in underserved populations.

Conversion of Speech to Text: Transcribe and Translate Using Whisper AI on iOS

Abstract: This thesis investigates the implementation and efficiency of Whisper AI for transcribing and translating speech to text on iOS devices. Leveraging a large-scale weakly supervised dataset, Whisper AI demonstrates robust performance across multiple languages and tasks. The study explores its architecture, implementation on iOS, and performance comparisons with existing models. Findings indicate significant potential for real-world applications, despite some computational and accuracy challenges.

Artificial Intelligence in Multilingual Interpretation and Radiology Assessment for Clinical Language Evaluation (AI-MIRACLE).

The AI-MIRACLE Study investigates the efficacy of using ChatGPT 4.0, a large language model (LLM), for translating and simplifying radiology reports into multiple languages, aimed at enhancing patient comprehension. The study assesses the model's performance across the most spoken languages in the U.S., emphasizing the accuracy and clarity of translated and simplified radiology reports for non-medical readers. This study employed ChatGPT 4.0 to translate and simplify selected radiology reports into Vietnamese, Tagalog, Spanish, Mandarin, and Arabic. Hindi was used as a preliminary test language for validation of the questionnaire. Performance was assessed via Google form surveys distributed to bilingual physicians, which assessed the translation accuracy and clarity of simplified texts provided by ChatGPT 4. Responses from 24 participants showed mixed results. The study underscores the model's varying success across different languages, emphasizing both potential applications and limitations. ChatGPT 4.0 shows promise in breaking down language barriers in healthcare settings, enhancing patient comprehension of complex medical information. However, the performance is inconsistent across languages, indicating a need for further refinement and more inclusive training of AI models to handle diverse medical contexts and languages. The study highlights the role of LLMs in improving healthcare communication and patient comprehension, while indicating the need for continued advancements in AI technology, particularly in the translation of low-resource languages.

“Addressing barriers to digital health readiness among a Latino population”

ObjectiveThere are many potential barriers to an individual's readiness and ability to use telehealth, including technology access, trust and knowledge. This qualitative study was a partnership between Jefferson and Esperanza Health Center (EHC), focused on addressing barriers to digital readiness among the Latino population served by EHC. MethodsWe conducted semi-structured interviews with Latino patients to assess their experiences with technology and willingness to learn more about technology for healthcare. Interviews were transcribed, coded, and analyzed using a content analysis approach. ResultsWe completed 28 interviews. Most interviews (n = 23, 82 %) were conducted in Spanish. Barriers to using health technology included forgetting passwords, platforms not being available in multiple languages, and lacking digital skills. ConclusionParticipants identified a need and interest in receiving support to develop technological skills needed to access health information and engage in digital health services. Future work is needed to develop patient-centered interventions to support the digital health readiness needs of underserved populations. InnovationHealthcare systems and community health organizations can partner to build the capacity of community members to identify barriers in digital health use. These partnerships have the potential to empower communities to create culturally sensitive interventions that aim to increase digital health literacy.