Semantic Issues Research Articles

Breathing patterns and associated cardiovascular changes in intermittently breathing animals: (Partially) correcting a semantic quagmire.

Many animal species do not breathe in a continuous, rhythmic fashion, but rather display a variety of breathing patterns characterized by prolonged periods between breaths (inter-breath intervals), during which the heart continues to beat. Examples of intermittent breathing abound across the animal kingdom, from crustaceans to cetaceans. With respect to human physiology, intermittent breathing-also termed 'periodic' or 'episodic' breathing-is associated with a variety of pathologies. Cardiovascular phenomena associated with intermittent breathing in diving species have been termed 'diving bradycardia', 'submersion bradycardia', 'immersion bradycardia', 'ventilation tachycardia', 'respiratory sinus arrhythmia' and so forth. An examination across the literature of terminology applied to these physiological phenomena indicates, unfortunately, no attempt at standardization. This might be viewed as an esoteric semantic problem except for the fact that many of the terms variously used by different authors carry with them implicit or explicit suggestions of underlying physiological mechanisms and even human-associated pathologies. In this article, we review several phenomena associated with diving and intermittent breathing, indicate the semantic issues arising from the use of each term, and make recommendations for best practice when applying specific terms to particular cardiorespiratory patterns. Ultimately, we emphasize that the biology-not the semantics-is what is important, but also stress that confusion surrounding underlying mechanisms can be avoided by more careful attention to terms describing physiological changes during intermittent breathing and diving.

What is a trait? Lessons from the human chin.

The chin, a distinguishing feature of Homo sapiens, has sparked ongoing debates regarding its evolutionary origins and adaptive significance. We contend that these controversies stem from a fundamental disagreement about what constitutes a well-defined biological trait, a problem that has received insufficient attention despite its recognized importance in biology. In this paper, we leverage paleoanthropological research on the human chin to investigate the general issue of character or trait identification. First, we examine four accounts of the human chin from the existing literature: the mandibular differential growth byproduct, the bony prominence, the inverted T-relief, and the symphyseal angle. We then generalize from these accounts and propose a three-stage framework for the process of character identification: description, detection, and justification. We use this framework to reinterpret the four accounts, elucidating key points of contention surrounding the chin as well as other morphological characters. We show that debates over the chin carry broad and important biological implications that extend beyond this trait and that are not mere semantic issues of definition.

Deep Learning-Based Medical Image Registration Algorithm: Enhancing Accuracy with Dense Connections and Channel Attention Mechanisms

In critical clinical medical image analysis applications, such as surgical navigation and tumor monitoring, image registration is crucial. Recognizing the potential for enhanced accuracy in existing unsupervised image registration techniques for single-modal imagery, this research introduces an innovative deep learning-based image registration algorithm. Its novelty resides in integrating short and long connections to create a densely connected structure, markedly refining the feature map interconnectivity within the U-Net architecture. This advancement addresses the significant semantic gap issues arising from disparities in feature map sampling depths. Moreover, the algorithm incorporates a channel attention mechanism within the U-shaped network's decoder, significantly mitigating image noise and facilitating the generation of smoother deformation fields. This enhancement not only boosts the model's detail sensitivity but also markedly increases image registration precision, particularly evident when processing single-modal brain MRI datasets, thereby proving the algorithm's efficacy and utility. Extensive clinical application-based training and testing have underscored this algorithm's substantial contributions to medical image registration accuracy enhancement. Overall, by leveraging deep learning technologies and innovative algorithmic structures, this study addresses pivotal challenges in medical image registration, offering more precise and dependable support for clinical applications like surgical navigation and tumor surveillance.

Семиотическое прочтение сценического костюма

The aim of the study is to identify cause-and-effect relations in semiotic interpretation of stage costume. The article explores the history of stage costume development, briefly presents its periodization with characteristic features of each stage, and socio-cultural forms of encoding. The scientific novelty of the research lies in focusing on the issue of costume semantics as an important component of the expressive system of modern music and theater productions. The research establishes that stage (theatrical) costume has its own developmental regularities and periodization caused by many factors. It is found that in the 20th and 21st centuries, there is an increasing interaction of two main trends: 1) stage costume is characterized by a high degree of codification and symbolism, with significant transformations in the specific content of symbols; 2) a variety of techniques are observed in achieving various aspects of “authenticity” in relation to theatrical action as a whole and theatrical costumes in particular.

A formalization of constraint interval: A precussor to fuzzy interval analysis

This presentation outlines some aspects of interval analysis from the constraint interval point of view, focusing on some issues associated with mathematical analysis, especially those that are fundamental to fuzzy mathematical analysis. We begin by reviewing interval representations from the standard interval point of view and the study of intervals as functions of parameters varying in hypercubes, called constraint interval representation (CI). These concepts are used to define arithmetic operations between intervals, as well, interval-valued and interval functions for rational and transcedental functions. Issues of semantics as they apply to interval analysis and fuzzy interval analysis are discussed. The definition of constrained interval function extension is used to prove that each CI arithmetic operation is an isotonic inclusion.

Multi-professional screening instrument for risk of broncho-aspiration in a hospital environment for the elderly population: validity evidence based on response processes.

The objective of this study was to validate the Multiprofessional Screening Instrument for Broncho-aspiration Risk in Hospital Environment, which is aimed at the elderly population, based on response processes. Judges applied the instrument to different patients and randomly selected. After the application, the judges were interviewed so that it was possible to verify their impression regarding the relevance of the items about their interpretation of the written content, as well as grammatical and semantic issues. Suggestions for adding alternative questions and answers were considered, as well as proposals for adapting the questions that made up the instrument. Non-verbal reactions, such as facial expressions that suggested doubts or hesitations, by the judges concerning the instrument were also analyzed. The agreement of the judges concerning each item of the device was calculated by the Content Validity Index (CVI) and by the Intraclass Correlation Coefficient (ICC), and their results showed a high level of agreement. Through the suggestions of the judges, a new version of the Multi-professional Screening Instrument for the Risk of Broncho-aspiration in a Hospital Environment in the Elderly was elaborated. The results obtained showed that the validity of the Multi-professional Instrument for Screening the Risk of Broncho-aspiration in the Hospital Environment with the elderly population, based on the response processes, was achieved and makes it a promising device to assist professionals in hospital care for the elderly.

Instrumento multiprofissional de rastreio para risco de broncoaspiração em ambiente hospitalar à população idosa: evidências de validade baseada nos processos de resposta

ABSTRACT Purpose The objective of this study was to validate the Multiprofessional Screening Instrument for Broncho-aspiration Risk in Hospital Environment, which is aimed at the elderly population, based on response processes. Methods Judges applied the instrument to different patients and randomly selected. After the application, the judges were interviewed so that it was possible to verify their impression regarding the relevance of the items about their interpretation of the written content, as well as grammatical and semantic issues. Suggestions for adding alternative questions and answers were considered, as well as proposals for adapting the questions that made up the instrument. Non-verbal reactions, such as facial expressions that suggested doubts or hesitations, by the judges concerning the instrument were also analyzed. Results The agreement of the judges concerning each item of the device was calculated by the Content Validity Index (CVI) and by the Intraclass Correlation Coefficient (ICC), and their results showed a high level of agreement. Through the suggestions of the judges, a new version of the Multi-professional Screening Instrument for the Risk of Broncho-aspiration in a Hospital Environment in the Elderly was elaborated. Conclusion The results obtained showed that the validity of the Multi-professional Instrument for Screening the Risk of Broncho-aspiration in the Hospital Environment with the elderly population, based on the response processes, was achieved and makes it a promising device to assist professionals in hospital care for the elderly.

The semantics of taste in interaction

Abstract Tasting sessions constitute a perspicuous setting that reveals how a community of practice uses and shapes specialized lexicons and semantics within a situated and embodied activity. The activity aims at associating words and sensations: Participants engage with material objects (samples to taste), and utter/write down words corresponding to the way they experience them through their senses. This association between words and sensorial qualities constitutes an endogenous semantic task. This task can be seen as a respecification of various semantic problems, addressing within social interaction several semantic issues, such as the embodied grounding of sensory semantics, qualia, sensory lexicons, and specialized terminological repertoires. The paper is based on video recordings of training tasting sessions for professional cheese tasters in Italy and Italian Switzerland. The analyses show how participants engage not only in describing sensorial features, but also in normatively assessing the descriptors used, categorizing them as well as the features described as more or less standard. Moreover, the descriptive task is also guided by the use of several artefacts, such as tasting sheets to fill in and official repertoires of terminology available to read, which further socialize the participants. The analysis shows the reflexive mutual shaping of lexicons and sensations as well as the way participants address the semantics of taste in situ.

Empowering Public Engagement in Spatial Planning: A Collaborative Mapping Approach

This paper explores collaborative mapping as an approach to enhance public engagement in spatial planning. It provides insights into the current state of the art in collaborative mapping for practitioners and policymakers aiming to utilize its potential for better spatial planning. Through a systematic analysis of relevant literature, it investigates the evolution, application, and challenges of collaborative mapping. A comprehensive search of relevant sources on Google Scholar reveals the growing interest in collaborative mapping, particularly since 2018, with a focus on topics like land use and accessibility. The review underscores the importance of democratizing spatial data collection and highlights the role of user-generated content in participatory approaches. Public participation in collaborative mapping is examined, emphasizing the inclusion of diverse stakeholders and the need for tailored approaches to accommodate varied motivations for engagement. Advantages such as cost reduction and data provision are balanced against challenges like data quality, semantic issues and accessibility barriers. Key dimensions of collaborative mapping, including cost reduction, data accuracy, semantic issues and stakeholder importance, are addressed in more detail.

The critical dimension of Brandom’s normative pragmatism

For all of Brandom’s self-professed allegiance to Hegel, there is something perplexing about his fixation on semantic and epistemological issues at the expense of the type of social and political considerations that are at the heart of Hegel’s system. However, and although Brandom himself concedes that his work is circumscribed to a number of highly specialized and technical issues in the philosophy of mind and language, the truth is that his views often radiate to other philosophical fields, if not always explicitly. My claim in this article is that at the heart of Brandom’s semantic theory, there are elements of a critical project, one that offers a normative standpoint to judge and improve our current practices. Moreover, these progressive features of Brandom’s normative pragmatics should be seen in the light of his adoption of a series of hermeneutic themes, ultimately culminating in his recollective conception of rationality and his edifying view of semantics.

Enhancing health-care data integration via automated semantic mapping

PurposeThis paper aims to delve into the complexities of terminology mapping and annotation, particularly within the context of the COVID-19 pandemic. It underscores the criticality of harmonizing clinical knowledge organization systems (KOS) through a cohesive clinical knowledge representation approach. Central to the study is the pursuit of a novel method for integrating emerging COVID-19-specific vocabularies with existing systems, focusing on simplicity, adaptability and minimal human intervention.Design/methodology/approachA design science research (DSR) methodology is used to guide the development of a terminology mapping and annotation workflow. The KNIME data analytics platform is used to implement and test the mapping and annotation techniques, leveraging its powerful data processing and analytics capabilities. The study incorporates specific ontologies relevant to COVID-19, evaluates mapping accuracy and tests performance against a gold standard.FindingsThe study demonstrates the potential of the developed solution to map and annotate specific KOS efficiently. This method effectively addresses the limitations of previous approaches by providing a user-friendly interface and streamlined process that minimizes the need for human intervention. Additionally, the paper proposes a reusable workflow tool that can streamline the mapping process. It offers insights into semantic interoperability issues in health care as well as recommendations for work in this space.Originality/valueThe originality of this study lies in its use of the KNIME data analytics platform to address the unique challenges posed by the COVID-19 pandemic in terminology mapping and annotation. The novel workflow developed in this study addresses known challenges by combining mapping and annotation processes specifically for COVID-19-related vocabularies. The use of DSR methodology and relevant ontologies with the KNIME tool further contribute to the study’s originality, setting it apart from previous research in the terminology mapping and annotation field.

The cross-cultural adaptation and psychometric properties of the menstrual symptom questionnaire (MSQ) among Chinese women of reproductive age

ObjectiveThis study reports on a translation of the Menstrual Symptom Questionnaire (MSQ) into Chinese, a cross-cultural adaptation among Chinese women of reproductive age, and an assessment of its reliability and validity. MethodsPreviously published translation guidelines were followed to translate and cross-culturally adapt the English version of MSQ to produce a Chinese version. This Chinese version was then administered to 2800 Chinese women of reproductive age recruited by convenience sampling method. The reliability of the Chinese MSQ was tested for internal consistency and test-retest reliability. The concurrent and construct validity of the questionnaire was evaluated using correlation and factor analysis. ResultsThe Chinese version of the MSQ showed no linguistic or semantic issues. The internal consistency of the Chinese MSQ Cronbach'α = 0.912, and the test-retest reliability r = 0.911. The exploratory factor analysis identified four factors. The confirmatory factor analysis demonstrated that the four factor structure of the Chinese version of the MSQ (Pain Experience, Emotional Changes, Pain Coping Strategies, and Other Physical Symptoms) is reasonable among Chinese women of reproductive age. There was a significant correlation found between these four factors and both the Pittsburgh Sleep Quality Index and the SF-8 Health Survey. ConclusionThe Chinese version of the MSQ achieved semantic equivalence in translation and demonstrated good reliability and validity among Chinese women of reproductive age. Thus, it can serve as an effective tool to assess the experience of menstrual symptoms among Chinese women.

LATIN RENAISSANCE TRANSLATIONS OF SOPHOCLES’ OEDIPUS AT COLONUS: AD VERBUM AND AD SENSUM

This article discusses ad verbum and ad sensum translation techniques in the Middle Ages and in the Renaissance, with special attention paid to the literal translation of Sophocles’ Oedipus at Colonus made by an Italian humanist, Giovanni Battista Gabia, in the 16th century as an example of ad verbum (word-by-word) translation. It can be seen that trying to translate Greek into Latin involves some grammatical and semantic issues; as a result, the translation becomes hard to perceive. However, the ad verbum type of translation was used for parallel studying of ancient languages that was widespread in the 16th century. Another translation mentioned in the article was produced by a German humanist Veit Winsheimius. It is characterized by simplified grammar and vocabulary. As a result, the translation becomes easier to perceive and understand. The article shows the variety of forms of reception that can be explained not only by usage of certain translation techniques, but also by certain goals of the translators: though both Giovanni Gabia and Veit Winsheim made their translations to use them for teaching ancient languages, the former preferred the method of parallel texts and the latter wanted to make the Latin translation itself clear enough for students.

Exploiting Emotions via Composite Pretrained Embedding and Ensemble Language Model

Decisions in the modern era are based on more than just the available data; they also incorporate feedback from online sources. Processing reviews known as Sentiment analysis (SA) or Emotion analysis. Understanding the user's perspective and routines is crucial now-a-days for multiple reasons. It is used by both businesses and governments to make strategic decisions. Various architectural and vector embedding strategies have been developed for SA processing. Accurate representation of text is crucial for automatic SA. Due to the large number of languages spoken and written, polysemy and syntactic or semantic issues were common. To get around these problems, we developed effective composite embedding (ECE), a method that combines the advantages of vector embedding techniques that are either context-independent (like glove & fasttext) or context-aware (like XLNet) to effectively represent the features needed for processing. To improve the performace towards emotion or sentiment we proposed stacked ensemble model of deep lanugae models.ECE with Ensembled model is evaluated on balanced dataset to prove that it is a reliable embedding technique and a generalised model for SA.In order to evaluate ECE, cutting-edge ML and Deep net language models are deployed and comapared. The model is evaluated using benchmark datset such as MR, Kindle along with realtime tweet dataset of user complaints . LIME is used to verify the model's predictions and to provide statistical results for sentence.The model with ECE embedding provides state-of-art results with real time dataset as well.

Computed tomography image segmentation of irregular cerebral hemorrhage lesions based on improved U-Net

ObjectiveThis paper aims to improve U-Net for more accurate segmentation of irregular intracranial hemorrhage lesions in CT images. MethodsThe residual octave convolution (ResOctConv) module was introduced to overcome the semantic gap issue in U-Net, and a hybrid attention mechanism called mixed attention mechanism (MAM) was proposed to further enhance the performance of U-Net. 40 patients with irregular cerebral hemorrhage images were selected from head CT scans performed between August and December 2022. Two radiologists independently traced the edge of each selected image three times, and the final segmentation boundary was determined by consensus. The effectiveness of the lesion segmentation was measured using the Dice coefficient, Jaccard Index, and Relative volume difference. ResultsBased on the box plot of the Dice coefficient for all 40 patients, the improved U-Net demonstrated higher accuracy in segmenting irregular intracranial hemorrhage lesions in CT images compared to the original U-Net. Moreover, the comparison of Dice coefficient, Jaccard coefficient, and RVD indicates that the improved U-Net outperforms both the original U-Net and the region growing algorithm in segmenting irregular cerebral hemorrhage lesions. ConclusionsThe proposed improved U-Net outperforms both the original U-Net and the region growing algorithm in segmenting irregular cerebral hemorrhage lesions, providing an advanced toolset for radiologists to accurately identify and diagnose irregular cerebral hemorrhage lesions.

On solving textual ambiguities and semantic vagueness in MRC based question answering using generative pre-trained transformers.

Machine reading comprehension (MRC) is one of the most challenging tasks and active fields in natural language processing (NLP). MRC systems aim to enable a machine to understand a given context in natural language and to answer a series of questions about it. With the advent of bi-directional deep learning algorithms and large-scale datasets, MRC achieved improved results. However, these models are still suffering from two research issues: textual ambiguities and semantic vagueness to comprehend the long passages and generate answers for abstractive MRC systems. To address these issues, this paper proposes a novel Extended Generative Pretrained Transformers-based Question Answering (ExtGPT-QA) model to generate precise and relevant answers to questions about a given context. The proposed architecture comprises two modified forms of encoder and decoder as compared to GPT. The encoder uses a positional encoder to assign a unique representation with each word in the sentence for reference to address the textual ambiguities. Subsequently, the decoder module involves a multi-head attention mechanism along with affine and aggregation layers to mitigate semantic vagueness with MRC systems. Additionally, we applied syntax and semantic feature engineering techniques to enhance the effectiveness of the proposed model. To validate the proposed model's effectiveness, a comprehensive empirical analysis is carried out using three benchmark datasets including SQuAD, Wiki-QA, and News-QA. The results of the proposed ExtGPT-QA outperformed state of art MRC techniques and obtained 93.25% and 90.52% F1-score and exact match, respectively. The results confirm the effectiveness of the ExtGPT-QA model to address textual ambiguities and semantic vagueness issues in MRC systems.

How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVEN

AbstractCurrent language models can generate high-quality text. Are they simply copying text they have seen before, or have they learned generalizable linguistic abstractions? To tease apart these possibilities, we introduce RAVEN, a suite of analyses for assessing the novelty of generated text, focusing on sequential structure (n-grams) and syntactic structure. We apply these analyses to four neural language models trained on English (an LSTM, a Transformer, Transformer-XL, and GPT-2). For local structure—e.g., individual dependencies—text generated with a standard sampling scheme is substantially less novel than our baseline of human-generated text from each model’s test set. For larger-scale structure—e.g., overall sentence structure—model-generated text is as novel or even more novel than the human-generated baseline, but models still sometimes copy substantially, in some cases duplicating passages over 1,000 words long from the training set. We also perform extensive manual analysis, finding evidence that GPT-2 uses both compositional and analogical generalization mechanisms and showing that GPT-2’s novel text is usually well-formed morphologically and syntactically but has reasonably frequent semantic issues (e.g., being self-contradictory).

Machine Translation on Dravidian Languages

The Dravidian languages are spoken all over the world. Despite their distinctiveness, Dravidian languages haven’t gotten much attention because there aren’t enough resources to handle tasks like translation that require language technology. Since Dravidian languages are largely spoken in southern India, machine translation is necessary. For those who speak these regional languages, this would improve information creation and access. It can be challenging to translate between languages, particularly that of Dravidian, because of lexical divergence, ambiguity, and other, lexical, syntactic and semantic issues. This research looks into a number of machine translation models for different languages, conducts a thorough literature review on the various machine translation techniques from earlier studies, and analyses their methodology. The major objective of this research is to evaluate the viability and effectiveness of a machine translation process for Dravidian languages.

Latinate terminology in Modern Greek: An “intruder” or an “asset”?

Abstract This article discusses Latinate Modern Greek (MG) terminology in the light of language planning, language contact, transliteration, registers, and speakers’ attitudes. It starts by describing sociolinguistic aspects of MG with respect to borrowing and proceeds with case studies of Latinate MG terms and their Greek-based synonyms from various science, arts, social, and technical fields. Focusing on phonological, morphological, semantic, and terminological issues such as semantic intra- and inter-linguistic transparency and consistency in the light of the long-standing cultural ties between Greece and the West, an updated approach to the form and utility of MG Latinate terms is attempted, aiming at influencing eventual language planning in Greek-speaking areas.

Semantic issues of hitting women in the Quran Methodology: Resolving reading discrepancies

Referring to the Quranic verse that allows physical discipline of women has caused embarrassment to the Sharia law, as many scholars, imams, preachers, and intellectuals have cited it. The term "hitting" has created semantic issues, especially when combining Quranic texts that promote good deeds and kindness, which fall under the objectives of building a stable family, with prophetic texts that explicitly prohibit mistreatment of wives and completely forbid hitting them. Furthermore, the actual actions of the Prophet do not demonstrate the use of hitting as a form of discipline. Can the tools of Islamic jurisprudence resolve this reading discrepancy?