Language Information Research Articles

Design of Unified Modeling Language Information System for Motorcycle Unit Selling and Buying Transactions using the Waterfall Method

This research aims to design an information system for buying and selling motorbike units using the Waterfall method. The information system for buying and selling motorbike units has an important role in supporting motorbike sales business activities, from ordering to the payment process. The design of this system was carried out by following the Waterfall method approach which consists of several stages, namely requirements analysis, system design, implementation, testing and maintenance. The needs analysis stage involves identifying business and system user needs, including required functions and system requirements. Next, system design is carried out by designing the structure, interface and transaction process flow using the notation and methods provided by the Waterfall method. In implementation, an information system for buying and selling motorbike units is developed based on the design that has been created. Then, testing is carried out to verify the success of the system in carrying out its functions and ensure conformity with the specified needs. After that, the system is ready for use and maintenance is carried out to maintain the performance and reliability of the system on an ongoing basis. By applying the Waterfall method in designing a motor unit buying and selling transaction information system, it is hoped that it can produce a system that is structured, has functions that suit business needs, and is tested overall. The Waterfall method also allows developers to have a clear understanding of each design stage before moving on to the next, thereby minimizing the risk of errors and deficiencies in the system.

Hybrid Prompt Recommendation Explanation Generation combined with Graph Encoder

Recommendation systems have been effectively utilized in various fields, but their internal decision-making methods are still largely unknown. This opaque decision-making method can greatly affect users’ trust in the recommendation system. Therefore, finding a way to explain the reasons for model decisions has become an urgent task. Previous studies often used LSTM and other models to generate recommendation explanations and explain the reasons for recommendations in text form. However, traditional methods cannot effectively use the ID information of users and items, and the text generated is highly repetitive. To solve this problem, this paper uses the method of prompt learning combined with a graph encoder to design a recommendation explanation generation model. In order to narrow the semantic gap between the ID information of users and items and natural language and capture high-level interaction information, this paper designs a graph encoder based on user similarity to learn the interactive semantic information of user and item IDs, and to construct a continuous prompt. Then, the discrete prompt composed of discrete features of users and items is combined with the continuous prompt to construct a hybrid prompt to input into the pre-trained model to generate the recommended explanation. This paper experiments on three publicly available datasets and compares them with several state-of-the-art methods to demonstrate the personalization and text quality of the generated explanations.

Unraveling the Potential of ChatGPT: Investigating the Efficacy of Reading Text Adaptation

The purpose of this study is to investigate the effectiveness of ChatGPT in reading text adaptation. Its primary focus is on examining ChatGPT's ability to adapt texts with particular attention to its contextual understanding and limitations. Employing a mixed-methods approach, the research combines quantitative analysis and qualitative evaluation. Quantitative measures assess the consistency of ChatGPT's responses across various text adaptation scenarios, whereas qualitative evaluations involve the assessment of language teachers' attitudes and perceptions towards the use of ChatGPT for text adaptation in their teaching practices. Ethical considerations pertaining to potential biases and misinformation in the model's output are also discussed. The outcomes of this investigation contribute to understanding ChatGPT's strengths and limitations in reading text adaptation. The research has practical implications for domains such as language education, content creation, and information retrieval, where accurate and adaptable text comprehension is crucial.

COVID-19 and local community resilience in the Westfjords of Iceland

ABSTRACT Remote Arctic communities have often been depicted as being particularly vulnerable to the challenges of disasters, with their location and lack of infrastructure exacerbating risk. This study explores the characteristics of local resilience in the Arctic using the case study of the communities of the north-western Westfjords. A total of 42 semi-structured interviews were carried out with various community members, seeking to uncover the features of inbuilt resilience that contribute to successes and vulnerabilities. These were transcribed, coded, and categorised in relation to an integrated framework for assessing community resilience in disaster management, which groups topics via the themes of environmental, social, governance, economic, and infrastructure. All themes played a role in the success of local coping strategies, with easy access to the natural environment central to physical and mental well-being. Despite this, vulnerabilities of the community were evident, including insufficient local healthcare workers during a severe COVID-19 outbreak in a care home, the absence of a local quarantine hotel, and insufficient information in foreign languages for non-natives of Iceland. The general trend of following rules and expert advice was demonstrative of strong social capital, with locals trusting those in charge, nationally and locally, to manage the pandemic.

Syntax-aware bidirectional decoding Neural Machine Translation model

The mainstream model in neural machine translation, the Transformer, relies heavily on self-attention mechanisms for translation operations. This approach has significantly improved both accuracy and speed. However, there are still some challenges. For instance, it lacks the incorporation of linguistic knowledge and the ability to leverage syntactic structure information in natural language for translation, leading to issues such as mistranslation and omission. Addressing the limitations of the Transformer's autoregressive decoding, which decodes from left to right without fully utilizing context information and is prone to exposure bias, this paper proposes a syntax-aware bidirectional decoding neural machine translation model. By employing both forward and backward decoders, the generated decoding results can encompass contextual information. Additionally, the model integrates dependency syntax to generate target language sentences with syntactic guidance. Finally, an optimization strategy involving the Teacher Forcing mechanism is introduced to balance the discrepancies between the Teacher Forcing training phase and the autoregressive testing phase, thus alleviating exposure bias issues.

'It's quite a balancing act': A qualitative study of parents'experiences and information needs related to the COVID-19 pandemic.

Throughout the coronavirus disease 2019 (COVID-19) pandemic, parents and children faced significant challenges as a result of prevention measures implemented to control the spread of the disease. Ensuring that families have access to essential health information is critical for improving health outcomes and adherence to public health recommendations. Understanding parents' experiences and information needs related to the pandemic and associated health measures (e.g., vaccination, mask wearing, social distancing, etc.)will inform the development and dissemination of resources tailored to parents' needs to support informed decision making. We conducted a qualitative descriptive study. Between September and November 2021, parents across Canada were recruited online via social media and community organisation newsletters and listservs to participate in focus groups via Zoom. Focus groups were audio-recorded and transcribed verbatim. Data were coded and analysed using thematic analysis. Participants completed a demographic questionnaire before the focus groups (via SimpleSurveys). Sixty-seven parents participated in 12 focus groups between October and November 2021. In relation to experiences, parents felt they were (1) constantly trying to balance everything, and (2) trying to do their best with the information they had at the time when making decisions. Regarding information needs, parents reflected on (1) how difficult it was navigating copious amounts of changing information and finding credible sources to rely on, (2) the need for resources that were easily accessible, credible and in plain language and (3) the need for resources that were tailored to their needs to support them and their children make informed decisions. Trying to mitigate the risk of COVID-19 infection and adhere to public health recommendations, while balancing various factors (work, online learning, and social interactions) and navigating changing information, was overwhelming for many parents. Reflecting on their needs, parents suggested tailored resources that provided concise, credible information in plain language to help them make informed decisions and navigate conflicting information. These findings reveal important knowledge gaps and highlight areas that need to be addressed to support parents during the pandemic period and beyond. Members of our established Paediatric Parent Advisory Group (P-PAG)were involved as collaborators throughout the planning (grant proposal), development and execution of the study. P-PAG members gave input on the design of the questionnaire, interview guide, recruitment strategyand interpretation of findings.

Template for a Hypothesis Description paper

Hypothesis Descriptions are a type of manuscript dedicated to the formal description of a hypothesis, as introduced in an accompanying editorial and an examplary Hypothesis Description for the Enemy Release Hypothesis that is used in invasion biology. This questionnaire provides a template for such a Hypothesis Description manuscript. The template's format was designed for simplicity to facilitate adoption, and it can be easily extended to capture additional information, e.g. instructions for falsification or generalization, taxonomic or geographic scope, etymology, or relevant information in other research fields or other languages. The template reflects the recommended structure for a Hypothesis Description manuscript in that each of its sections provides the title for a section in a Hypothesis Description manuscript and indicates whether that section is mandatory or optional. Four sections - Keywords (mandatory), Conflicts of interest (optional), Acknowledgments (optional) and References (mandatory) - are in this template filled in for the template itself but should otherwise be adjusted for the hypothesis at hand. Comments to guide authors who work on a Hypothesis Description manuscript are provided as well.

Doing Being on Social Media

The present study set in Denmark explores how social media use may help to facilitate English as a second language (L2) development by means of social media literacy socialization. The data consisted of English language Ariana Grande (AG) fan TikToks made over the course of a year by a Danish girl, aged 11–12, and semi-structured interviews. Findings show that through the participant’s focus on creating a popular fan persona, she was socialized into the fan community and thereby encountered affordances for language learning and opportunities to develop her critical and digital social media skills. She communicated in English daily with fellow fans, sought English language information related to content and editing, developed a growing awareness of the language conventions in the space, and was very invested in her engagement (Norton, 2013). The study also showed that while the focus on obtaining a popular identity guided much of the participant’s engagement and thereby provided language learning affordances, it eventually resulted in a decrease in her postings and, by extension, in fewer learning opportunities. Implications for the L2 classroom are suggested.

Language inclusion in ecological systematic reviews and maps: Barriers and perspectives.

Systematic reviews and maps are considered a reliable form of research evidence, but often neglect non-English-language literature, which can be a source of important evidence. To understand the barriers that might limit authors' ability or intent to find and include non-English-language literature, we assessed factors that may predict the inclusion of non-English-language literature in ecological systematic reviews and maps, as well as the review authors' perspectives. We assessed systematic reviews and maps published in Environmental Evidence (n = 72). We also surveyed authors from each paper (n = 32 responses), gathering information on the barriers to the inclusion of non-English language literature. 44% of the reviewed papers (32/72) excluded non-English literature from their searches and inclusions. Commonly cited reasons included constraints related to resources and time. Regression analysis revealed that reviews with larger author teams, authors from diverse countries, especially those with non-English primary languages, and teams with multilingual capabilities searched in a significantly greater number of non-English languages. Our survey exposed limited language diversity within the review teams and inadequate funding as the principal barriers to incorporating non-English language literature. To improve language inclusion and reduce bias in systematic reviews and maps, our study suggests increasing language diversity within review teams. Combining machine translation with language skills can alleviate the financial and resource burdens of translation. Funding applications could also include translation costs. Additionally, establishing language exchange systems would enable access to information in more languages. Further studies investigating language inclusion in other journals would strengthen these conclusions.

Research on Public Engineering Emergency Decision-Making Based on Multi-Granularity Language Information

To effectively deal with fuzzy and uncertain information in public engineering emergencies, an emergency decision-making method based on multi-granularity language information is proposed. Firstly, decision makers select the appropriate language phrase set according to their own situation, give the preference information of the weight of each key indicator, and then transform the multi-granularity language information through consistency. On this basis, the sequential optimization technology of the approximately ideal scheme is introduced to obtain the weight coefficient of each key indicator. Subsequently, the weighted average operator is used to aggregate the preference information of each alternative scheme with the relative importance of decision-makers and the weight of key indicators in sequence, and the comprehensive evaluation value of each scheme is obtained to determine the optimal scheme. Lastly, the effectiveness and practicability of the method are verified by taking the earthwork collapse accident in the construction of a reservoir as an example.

Quranic Studies Evolution: A Bibliometric Analysis from 1880 to 2023

This analysis focuses on the study of the Quran, from 1880 to 2023 using data obtained from Scopus. Made accessible through a BibTeX file on GitHub. The examination covers aspects of research trends including the number of citations per year the influence of sources notable authors and their affiliations the countries of corresponding authors the production trends in different countries over time and a network illustrating how key concepts are interconnected. By conducting an analysis several significant findings were discovered. The data demonstrated an increase in citations per year indicating a growing importance and scholarly interest in Quranic Studies. Noteworthy authors such as Rippin A and Reynolds GS as prominent affiliations like the University of Malaya and International Islamic University Malaysia made substantial contributions to this field. The distribution of corresponding authors countries highlighted Malaysias, Indonesias and Irans roles. Furthermore when examining production trends over time for countries it became evident that contributions from the United Kingdom, USA,Malaysia,Iran and Indonesia had seen growth. Additionally the interconnectedness between concepts such as natural language processing systems, computational linguistics,semantics and information retrieval were emphasized in the cooccurrence network, within Quranic Studies. This, in depth analysis provides insights into the development significant contributors and intersections of themes in the study of the Quran. It establishes a foundation for research endeavors, in this area.

A security and privacy preserving approach based on social IoT and classification using DenseNet convolutional neural network

This method is able to synthesize fine-detailed images by the use of a global attention that gives more attention to the words in the textual descriptions. Also we have the deep attention multimodal similarity model (DAMSM) that calculates the matching loss in the generator. Though this work produced images of high quality, there was some loss while training the system and it takes enough time for training. Although there has been little study on applying character-level Dense Net algorithms for text classification tasks; the Dense Net structures we suggested in this paper have shown outstanding performance in image classification tasks. Extensive testing has revealed that they perform better when it comes to their ability to withstand interruption and that they can influence exerted many organizations implementing information usage and language information on the specifications of user privacy protection, framework implies, and regulatory requirements.

Research on Optimization Strategies for Accuracy of English Automatic Language Translation under Machine Learning Framework

Abstract The mainstream machine translation model Transformer is completely based on the self-attention mechanism for translation operation, but there are still some problems, such as not being able to combine the syntactic structure information of the natural language for translation, which leads to problems such as mistranslation and omission. In this paper, for the problem that the position encoding obtained by traditional RNN and attention mechanism machine translation models using a fixed formula does not contain contextual information, the source language sequences containing contextual positional information are obtained by introducing a bidirectional long-short-term memory network and a tree-shaped long-short-term memory network, trained horizontally and vertically, respectively, and the self-attention mechanism is used in Tree-LSTM for the prediction of the contribution of the decision that The relative position information between words is preserved to the maximum extent, and finally, the Bi-Tree-LSTM translation model based on positional encoding optimization is constructed. The performance of the model is tested on four datasets: general, legal, business, film, and television, and the BLEU value of the model translation is analyzed under low data resources and increased sentence length, and then a 4000-sentence long English text is translated to check the wrong sentences and analyze the translation quality. It was found that the BLEU values of this paper’s model are 33.5, 35.2, 31.7, and 34.4 in the four types of text tests, which are the highest among the models. The BLEU of this paper’s model at 5K data volume has been as high as 26.14 points, which is 2.72 points higher than the highest score of the rest of the machine translation models at 50K data volume. The BLEU scores for 8-18 word sentences consistently remain above 45, and the peak performance is superior to that of other models. 4000 sentences of English long text translation, the total number of error sentences is 54, accounting for 1.39% of the whole text, which is lower than that of the Transformer model’s 7.15%, and the performance is in line with the expectation of the optimization design. This paper provides a new idea and useful exploration for optimizing English machine translation accuracy.

Gaps in cancer care in a multi-ethnic population in Sarawak, Borneo: A central referral centre study.

The state of Sarawak on the island of Borneo in East Malaysia, in working towards developing and strengthening cancer services through a holistic patient-centred approach, must focus on the comprehensive needs of cancer patients by taking into account the psycho-social, cultural and spiritual aspects of Sarawak's multi-ethnic, multi-cultural population. A 42-item survey questionnaire was developed and validated with a total of 443 patients. The perceived importance of information provided and level of patient satisfaction were assessed with a 5-point Likert scale in 10 domains (Diagnosis, Surgery, Radiotherapy, Systemic therapy, Clinical trials, Pain management, Treatment monitoring, Psychosocial support, Sexual care and fertility issues, and Financial support). A Spearman's rank correlation test was applied to determine the correlation between response in both item and domain categories for perceived importance and satisfaction. Overall, patients were more satisfied with information related to cancer diagnosis, treatment and surgery but less satisfied with information pertaining to sexual aspects of care and family planning, psycho-social support and financial support. The majority of patients were satisfied with the level of treatment-related information received but preferred the information to be delivered in more easily comprehendible formats. Sexual aspects of care and family planning, psychosocial support and treatment monitoring post-discharge were perceived as important but seldom addressed by health care professionals due to lack of professional counsellors, social workers and clinical nurse specialists. Many patients face financial toxicity following a cancer diagnosis, particularly when diagnosed with advanced cancer requiring complex multi-modality treatment. Cancer patients in Sarawak have various unmet information needs. Written information and educational videos in local indigenous languages may be more suitable for Sarawak's multi-ethnic population. Sexual aspects of care and family planning are challenging but essential topics to discuss, in particular due to the high prevalence of breast and cervical cancer amongst young women of reproductive age in Sarawak. Financial assessment and information on support services offered by government and non-government organisations should be provided to eligible patients. A holistic needs assessment of each patient at time of diagnosis and support through their cancer journey requires a multi-disciplinary team of medical, nursing and allied health professionals including clinical nurse specialists, pharmacists, counsellors, physiotherapists, occupational therapists, speech and language therapists, dieticians and social workers.

Development of an error correction algorithm for Kazakh language

This article discusses a method for correcting spelling errors in the Kazakh language using the advantages of morphological analysis and a model based on noisy channels. To achieve this goal, modern problems of automatic processing of Kazakh textual information were analyzed, existing linguistic resources and processing systems of the Kazakh language were systematized, the basic requirements for the development of a system for analyzing Kazakh textual information based on machine learning were determined, and models and algorithms for extracting facts from unstructured and poorly structured text arrays were developed. The search function, an enhanced spelling correction algorithm, was utilized in this work and has the ability to recommend the proper spelling of the input text. The maximum editing distance, whether to include the original word when near matches are not found, and how to handle case sensitivity and exclusion based on regular expressions are all easily adjustable features of this functionality. Because of their adaptability, algorithms can be applied to a wide range of problems, from straightforward spell checks in user interfaces to intricate natural language processing assignments. Because of the way it’s designed, the search function finds possible corrections and verifies the context of words while accounting for user preferences like verbosity and ignore markers. Most modern multilingual natural language processing programs use only the graphical stage of text processing. On the other hand, semantic text analysis or analysis of the meaning of natural language is still an important problem in the theory of artificial intelligence and computational linguistics. But in order to process the grammar and semantics of multilingual information, precreated semantic and grammatical corpora of each natural language are necessary. To solve this problem, several tasks were considered and solved. These tasks included the analysis of research in the field of machine learning methods used in the processing of textual information, the existing problems of formalization and modeling of the Kazakh language, as well as the development and implementation of models, methods and algorithms for morphological and semantic analysis of texts of the Kazakh language.

A Semi-Automated Approach to Process Discovery from Multiple Natural Language Documents

The discovery of process information in natural language documents is hindered by the innate ambiguity of natural language. This issue is even more challenging when process information is spread throughout several documents, each with the possibility of different formats, writers, and original purposes, that must be read and interpreted individually by a business analyst before their information can be uncovered. This work presents a semi-automated approach to process discovery from multiple documents simultaneously, making use of semantic similarity measures and natural language processing techniques to identify, gather, and extract process information from these documents. An experiment is conducted with this approach to demonstrate its use and results.

Development of a New Tool for Writing Research Key Information in Plain Language.

The complexity of research informed consent forms makes it hard for potential study participants to make informed consent decisions. In response, new rules for human research protection require informed consent forms to begin with a key information section that potential study participants can read and understand. This research study builds on exiting guidance on how to write research key information using plain language. The aim of this study was to develop a valid and reliable tool to evaluate and improve the readability, understandability, and actionability of the key information section on research informed consent forms. We developed an initial list of measures to include on the tool through literature review; established face and content validity of measures with expert input; conducted four rounds of reliability testing with four groups of reviewers; and established construct validity with potential research participants. We identified 87 candidate measures via literature review. After expert review, we included 23 items on the initial tool. Twenty-four raters conducted 4 rounds of reliability testing on 10 informed consent forms. After each round, we revised or eliminated items to improve agreement. In the final round of testing, 18 items demonstrated substantial inter-rater agreement per Fleiss' Kappa (average = .73) and Gwet's AC1 (average = .77). Intra-rater agreement was substantial per Cohen's Kappa (average = .74) and almost perfect per Gwet's AC1 (average = 0.84). Focus group feedback (N = 16) provided evidence suggesting key information was easy to read when rated as such by the Readability, Understandability and Actionability of Key Information (RUAKI) Indicator. The RUAKI Indicator is an 18-item tool with evidence of validity and reliability investigators can use to write the key information section on their informed consent forms that potential study participants can read, understand, and act on to make informed decisions. [HLRP: Health Literacy Research and Practice. 2024;8(1):e29-e37.].

A qualitative study on informed consent decision-making at two tertiary hospitals in Uganda: Experiences of patients undergoing emergency surgery and their next of kin.

In emergency situations, patients and their next of kin must make complex medical and ethical decisions in a quick and timely way. To describe the decision-making process during informed consent for emergency surgery among patients and the next of kin of patients who have undergone emergency surgery. Consecutive sampling of 39 participants and in-depth semi-structured interviews were conducted at two tertiary teaching hospitals in Uganda. There were 22 patients and 17 next of kin of patients who had undergone emergency surgery within 24-72 h. Responses about decision-making were coded into themes using the social constructivist theory and phenomenological approach. There were four emergent themes; decision-makers, people consulted, documentation of the consent and factors influencing decision-making. Most patients and next of kin made decisions on their own and documented the consent for themselves. Other family members and doctors were consulted during the decision-making process. Decision-making was influenced by reassurance of good outcomes of surgery and disclosure by the doctors. Decisions were made collaboratively with the patient at the center but with input of health personnel, the next of kin and other family members. A communitarian approach combined with shared decision-making between the doctor and the patient and next of kin with adequate discussion and disclosure of information in simple language would improve decision-making for patients and their next of kin.

Reading of ingroup politicians' smiles triggers smiling in the corner of one's eyes.

Spontaneous smiles in response to politicians can serve as an implicit barometer for gauging electorate preferences. However, it is unclear whether a subtle Duchenne smile-an authentic expression involving the coactivation of the zygomaticus major (ZM) and orbicularis oculi (OO) muscles-would be elicited while reading about a favored politician smiling, indicating a more positive disposition and political endorsement. From an embodied simulation perspective, we investigated whether written descriptions of a politician's smile would trigger morphologically different smiles in readers depending on shared or opposing political orientation. In a controlled reading task in the laboratory, participants were presented with subject-verb phrases describing left and right-wing politicians smiling or frowning. Concurrently, their facial muscular reactions were measured via electromyography (EMG) recording at three facial muscles: the ZM and OO, coactive during Duchenne smiles, and the corrugator supercilii (CS) involved in frowning. We found that participants responded with a Duchenne smile detected at the ZM and OO facial muscles when exposed to portrayals of smiling politicians of same political orientation and reported more positive emotions towards these latter. In contrast, when reading about outgroup politicians smiling, there was a weaker activation of the ZM muscle and no activation of the OO muscle, suggesting a weak non-Duchenne smile, while emotions reported towards outgroup politicians were significantly more negative. Also, a more enhanced frown response in the CS was found for ingroup compared to outgroup politicians' frown expressions. Present findings suggest that a politician's smile may go a long way to influence electorates through both non-verbal and verbal pathways. They add another layer to our understanding of how language and social information shape embodied effects in a highly nuanced manner. Implications for verbal communication in the political context are discussed.

Decoding Consciousness in Artificial Intelligence

The exploration of whether artificial intelligence (AI) can evolve to possess consciousness is an intensely debated and researched topic within the fields of philosophy, neuroscience, and artificial intelligence. Understanding this complex phenomenon hinges on integrating two complementary perspectives of consciousness: the objective and the subjective. Objective perspectives involve quantifiable measures and observable phenomena, offering a more scientific and empirical approach. This includes the use of neuroimaging technologies such as electrocorticography (ECoG), EEG, and fMRI to study brain activities and patterns. These methods allow for the mapping and understanding of neural representations related to language, visual, acoustic, emotional, and semantic information. However, the objective approach may miss the nuances of personal experience and introspection. On the other hand, subjective perspectives focus on personal experiences, thoughts, and feelings. This introspective view provides insights into the individual nature of consciousness, which cannot be directly measured or observed by others. Yet, the subjective approach is often criticized for its lack of empirical evidence and its reliance on personal interpretation, which may not be universally applicable or reliable. Integrating these two perspectives is essential for a comprehensive understanding of consciousness. By combining objective measures with subjective reports, we can develop a more holistic understanding of the mind.