Semantic Meaning Research Articles

ФОРМУВАННЯ ІНФОРМАЦІЙНОЇ КУЛЬТУРИ НАУКОВИХ ДОСЛІДЖЕНЬ ЗДОБУВАЧІВ ВИЩОЇ ОСВІТИ

In the conditions of the information society, the modern education system must prepare conscious citizens of their country who are able to navigate in the information field, critically interpret what they hear, draw their own conclusions and seek answers to the questions posed. That is why higher education must contribute to the development of these skills and abilities. The purpose of the article is to find out the semantic meaning of the concept of “information culture” and to determine the methodology of its formation among students of higher education during scientific activity. During the writing of the article, general scientific methods of analysis and synthesis were used. The worldview and methodological principles of democratization, innovation, and flexibility of the educational process were also observed. The article defines the main approaches to understanding the essence of the concept of “information culture”. The importance of careful selection of information sources, the need for a critical understanding of the received information, and the importance of observing these principles in the course of scientific activity are emphasized. The obtained results of the research can be taken into account when planning training sessions, especially practical work. Conclusions. Thus, the concept of information culture has different definitions and is used for different fields of knowledge. The key feature of this concept is a critical attitude to the received information and its proper verification. The modern education system pays considerable attention to the formation of information culture, introducing special courses, including media education. Observance of information hygiene by students of higher education is an integral part of their educational process, especially during scientific activities. Completing a number of practical tasks for the development of critical thinking should contribute to the formation of the information culture of higher education graduates. Key words: information culture, information society, media education, critical thinking, scientific activity.

FAPM: Functional Annotation of Proteins using Multi-Modal Models Beyond Structural Modeling.

Assigning accurate property labels to proteins, like functional terms and catalytic activity, is challenging, especially for proteins without homologs and "tail labels" with few known examples. Previous methods mainly focused on protein sequence features, overlooking the semantic meaning of protein labels. We introduce FAPM, a contrastive multi-modal model that links natural language with protein sequence language. This model combines a pretrained protein sequence model with a pretrained large language model to generate labels, such as Gene Ontology (GO) functional terms and catalytic activity predictions, in natural language. Our results show that FAPM excels in understanding protein properties, outperforming models based solely on protein sequences or structures. It achieves state-of-the-art performance on public benchmarks and in-house experimentally annotated phage proteins, which often have few known homologs. Additionally, FAPM's flexibility allows it to incorporate extra text prompts, like taxonomy information, enhancing both its predictive performance and explainability. This novel approach offers a promising alternative to current methods that rely on multiple sequence alignment for protein annotation. The online demo is at: https://huggingface.co/spaces/wenkai/FAPM_demo. Supplementary data are available at Bioinformatics online.

Categorization of Mujarrad Verbs Beginning with the Letter ‘ba’ in Al Munawwir Arabic-Indonesian Dictionary and Mazīd Verbs in Duleim Masoud Al Qahtani Arabic-English Dictionary: A Morphosemantic Analysis

Objective: This research discusses the categorization of Arabic verbs, which hold a significant importance in word formation and meaning determination. Therefore, the study of verbs in Arabic becomes one of the important theoretical foundations to continue to be studied and developed. One of the important studies of Arabic verbs is the study of mujarrad verbs and mazīd verbs. Method: Employing a qualitative approach, data were gathered from two dictionaries: Al-Munawwir Arabic – Indonesian dictionary and Al-Qahtani Arabic – English dictionary. Content analysis was employed to analyze the collected data. Data were collected from two Arabic dictionary corpora using morphological analysis of verbs. After collecting the data, the morphological data were categorized into three groups based on the semantic meanings of verbs: process verbs, action verbs and state verbs. In the final stage, the researcher investigated the relationship between morphological and semantic analysis in Arabic verbs (mujarrad and mazīd). Results: Morphosemantic analysis reveals that action verbs dominate “Arabic verbs”. Mujarrad verbs in Arabic are classified as action verbs, process verbs, and state verbs. The binary analysis indicates that 77,89 % of the mazīd verbs in this study are action verbs, 20% are process verbs, and 25,26% are state verbs. In this case, action verbs are the most common. Conclusions: This study contributes to the optimization of multidisciplinary analysis in linguistic studies, namely the combination of morphological and semantic analysis. In terms of Arabic dictionary development, this study suggests that verb analysis be supplemented by the categorization of actions, states, and processes

How does contextual information affect aesthetic appreciation and gaze behavior in figurative and abstract artwork?

Numerous studies have investigated how providing contextual information with artwork influences gaze behavior, yet the evidence that contextually triggered changes in oculomotor behavior when exploring artworks may be linked to changes in aesthetic experience remains mixed. The aim of this study was to investigate how three levels of contextual information influenced people's aesthetic appreciation and visual exploration of both abstract and figurative art. Participants were presented with an artwork and one of three contextual information levels: a title, title plus information on the aesthetic design of the piece, or title plus information about the semantic meaning of the piece. We measured participants liking, interest and understanding of artworks and recorded exploration duration, fixation count and fixation duration on regions of interest for each piece. Contextual information produced greater aesthetic appreciation and more visual exploration in abstract artworks. In contrast, figurative artworks were highly dependent on liking preferences and less affected by contextual information. Our results suggest that the effect of contextual information on aesthetic ratings arises from an elaboration effect, such that the viewer aesthetic experience is enhanced by additional information, but only when the meaning of an artwork is not obvious.

An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication

Semantic communication (SC) is a communication paradigm that has gained significant attention, as it offers a potential solution to move beyond Shannon’s formulation in bandwidth-limited communication channels by delivering the semantic meaning of the message rather than its exact form. In this paper, we propose an autoencoder-based SC system for transmitting images between two machines over error-prone channels to support emerging applications such as VIoT, XR, M2M, and M2H communications. The proposed autoencoder architecture, with a semantically modeled encoder and decoder, transmits image data as a reduced-dimension vector (latent vector) through an error-prone channel. The decoder then reconstructs the image to determine its M2M implications. The autoencoder is trained for different noise levels under various channel conditions, and both image quality and classification accuracy are used to evaluate the system’s efficacy. A CNN image classifier measures accuracy, as no image quality metric is available for SC yet. The simulation results show that all proposed autoencoders maintain high image quality and classification accuracy at high SNRs, while the autoencoder trained with zero noise underperforms other trained autoencoders at moderate SNRs. The results further indicate that all other proposed autoencoders trained under different noise levels are highly robust against channel impairments. We compare the proposed system against a comparable JPEG transmission system, and results reveal that the proposed system outperforms the JPEG system in compression efficiency by up to 50% and in received image quality with an image coding gain of up to 17 dB.

Fake News Detection Using Machine Learning

Abstract: The identification of fake news has grown in importance as false information circulates quickly via digital channels. The goal of this project is to combine deep learning and machine learning to create an automated system that can identify news stories as legitimate or fraudulent. The preprocessed text data in the dataset, which comprises of tagged news items, was done using methods such tokenization, stop word removal, and padding. A Convolutional Neural Network (Conv1D), which is based on deep learning, was combined with more conventional machine learning models, such as Naive Bayes, in a hybrid approach. After converting the text input into a series of tokens, semantic meanings were captured by using word embeddings. Important patterns from the word sequences were intended to be captured by the Conv1D model with global max pooling, whereas the Naive Bayes also used in the project.

Syntax in animal communication: its study in songbirds and other taxa

Many building blocks of human language can be found within the vocal communication systems of other species, most notably songbirds. One of the most prominent of these building blocks is syntax. While studies of syntax are abundant, a lack of consensus on the definition of syntax in non‐human animal communication studies has led to much debate. Consistent and deliberate use of terminology is needed to facilitate understanding across disciplines. In addition, new terminology may better describe syntactic structure found in vocal signals that are devoid of semantic associations, such as birdsong. Here, we propose two terms to describe the types of syntax commonly found in birdsong: sequential syntax and dialectical syntax. Sequential syntax can be defined as the rules that govern the patterns of sound without regard to semantic meaning. Dialectic syntax can be defined as sequential syntax that is distinct among different populations or groups with behavioral significance for those groups. Taken together, these two terms can describe the type of syntax seen in ornamental signals, such as birdsong.

Deep‐Learning‐Based Facial Retargeting Using Local Patches

AbstractIn the era of digital animation, the quest to produce lifelike facial animations for virtual characters has led to the development of various retargeting methods. While the retargeting facial motion between models of similar shapes has been very successful, challenges arise when the retargeting is performed on stylized or exaggerated 3D characters that deviate significantly from human facial structures. In this scenario, it is important to consider the target character's facial structure and possible range of motion to preserve the semantics assumed by the original facial motions after the retargeting. To achieve this, we propose a local patch‐based retargeting method that transfers facial animations captured in a source performance video to a target stylized 3D character. Our method consists of three modules. The Automatic Patch Extraction Module extracts local patches from the source video frame. These patches are processed through the Reenactment Module to generate correspondingly re‐enacted target local patches. The Weight Estimation Module calculates the animation parameters for the target character at every frame for the creation of a complete facial animation sequence. Extensive experiments demonstrate that our method can successfully transfer the semantic meaning of source facial expressions to stylized characters with considerable variations in facial feature proportion.

Средства выражения итеративной неоднократности с семантикой «цикличность» в произведении «Темные аллеи» И. А. Бунина

The aim of the research is to identify the specifics of the functioning of iterative repetition with the semantics of cyclicity in the individual style of I. A. Bunin. The article considers iterative repetition as a functional grammatical category that constitutes the functional-semantic field of aspectuality; details the ways of expressing iterative repetition in a literary text at the lexical, morphological and syntactic levels; creates a semantic model of iterative repetition with the meaning of cyclicity in the work ‘Dark Alleys’ by I. A. Bunin. The scientific novelty of the research lies in the fact that for the first time the category of iteration is considered in I. A. Bunin’s work from the point of view of the functional approach. The iterative meaning is studied taking into account not only the paradigmatics, but also the syntagmatics of verb forms. As a result, it has been found that the category of iterative repetition in the work of I. A. Bunin is presented as a complex and branched system of multi-level linguistic means. It has been revealed that the category of iterative repetition as an expressive means is actively used in Bunin’s texts when describing everyday habits, lifestyle and activities of characters. It has also been determined that the semantic meaning of iterativity is combined with other components of the functional-semantic field of aspectuality: duration, phase and statality.

ONTO-TDM domain ontology population for a specific discipline

Ontologies play a vital role in organizing and constructing knowledge across various domains, enabling effective knowledge management and sharing. The development of domain-specific ontologies, such as the ONTO-TDM ontology for teaching domain modeling, is essential for providing a comprehensive and standardized representation of knowledge within a given discipline. However, to maximize the usefulness and relevance of such ontologies, it is crucial to automate their population with domain-specific information, reducing manual work and ensuring scalability. This paper presents a novel method for ontology population by extracting and integrating relevant information from diverse sources. The method combines the TextRank algorithm with Word2Vec to enhance keyword extraction, capturing both semantic meaning and textual importance. Keywords are then annotated and used to train a machine learning classifier, which aids in integrating new instances into the ontology. Experiments show that the proposed method achieves a precision of 63.33%, a recall of 61.29% and an F1-score of 62.28%, significantly improving keyword extraction and ontology population accuracy compared to existing methods. This validates the method’s effectiveness in semi-automatically extracting relevant instances from diverse data sources, enhancing the efficiency and accuracy of ontology population, and advancing automated knowledge management in domain-specific contexts.

Decomposed Prototype Learning for Few-Shot Scene Graph Generation

Today's scene graph generation (SGG) models typically require abundant manual annotations to learn new predicate types. Therefore, it is difficult to apply them to real-world applications with massive uncommon predicate categories whose annotations are hard to collect. In this paper, we focus on Few-Shot SGG (FSSGG) , which encourages SGG models to be able to quickly transfer previous knowledge and recognize unseen predicates well with only a few examples. However, current methods for FSSGG are hindered by the high intra-class variance of predicate categories in SGG: On one hand, each predicate category commonly has multiple semantic meanings under different contexts. On the other hand, the visual appearance of relation triplets with the same predicate differs greatly under different subject-object compositions. Such great variance of inputs makes it hard to learn generalizable representation for each predicate category with current few-shot learning (FSL) methods. However, we found that this intra-class variance of predicates is highly related to the composed subjects and objects. To model the intra-class variance of predicates with subject-object context, we propose a novel Decomposed Prototype Learning (DPL) model for FSSGG. Specifically, we first construct a decomposable prototype space to capture diverse semantics and visual patterns of subjects and objects for predicates by decomposing them into multiple prototypes. Afterwards, we integrate these prototypes with different weights to generate query-adaptive predicate representation with more reliable semantics for each query sample. We conduct extensive experiments and compare with various baseline methods to show the effectiveness of our method.

Towards a Pedagogy of Ancient Egyptian Language and Hieroglyphs

This paper discusses the challenges of teaching the Middle Egyptian language and Egyptian hieroglyphs to adult anglophone learners outside university settings. These challenges include the lack of L1 speakers; the large volume of signs in the script; the difficulty in reconstructing pronunciation due to the paucity of written vowels; and the lack of accessible learning tools such as readers and textbooks for the intermediate stage. As spoken proficiency in this extinct language is not a feasible goal, it is argued here that teaching should revolve around achieving reading competence. The paper advocates for a pedagogical approach that centres on using vocabulary, including variant spellings, as the fundamental building blocks of learning. This entails reading words, sentences and paragraphs of increasing complexity, which over time aids memorisation of vocabulary and builds confidence. Grammar and syntax can be gradually introduced and contextualised by reading practice sentences. It is also argued here that producing digitised versions of ancient Egyptian texts using hieroglyphic font software such as JSesh allows for the creation of practice texts in a standardised and legible format. This, in turn, makes it possible to use pedagogical aids such as adding spaces between words and adding signs omitted by the ancient scribes. Finally, it is argued that learning vocabulary and grammar is enhanced by discussions of the wider semantic and cultural meaning(s) of the ancient text in question.

Better together: Automated app review analysis with deep multi-task learning

Context:User reviews of mobile apps provide an important communication channel between developers and users. Existing approaches to automated app review analysis mainly focus on one task (e.g., bug classification task, information extraction task, etc.) at a time, and are often constrained by the manually defined patterns and the ignorance of the correlations among the tasks. Recently, multi-task learning (MTL) has been successfully applied in many scenarios, with the potential to address the limitations associated with app review mining tasks. Objective:In this paper, we propose MABLE, a deep MTL-based and semantic-aware approach, to improve app review analysis by exploiting task correlations. Methods:MABLE jointly identifies the types of involved bugs reported in the review and extracts the fine-grained features where bugs might occur. It consists of three main phases: (1) data preparation phase, which prepares data to allow data sharing beyond single task learning; (2) model construction phase, which employs a BERT model as the shared representation layer to capture the semantic meanings of reviews, and task-specific layers to model two tasks in parallel; (3) model training phase, which enables eavesdropping by shared loss function between the two related tasks. Results:Evaluation results on six apps show that MABLE outperforms ten commonly-used and state-of-the-art baselines, with the precision of 79.76% and the recall of 79.24% for classifying bugs, and the precision of 79.83% and the recall of 80.33% for extracting problematic app features. The MTL mechanism improves the F-measure of two tasks by 3.80% and 4.63%, respectively. Conclusion:The proposed approach provides a novel and effective way to jointly learn two related review analysis tasks, and sheds light on exploring other review mining tasks.

Symbol ungrounding: what the successes (and failures) of large language models reveal about human cognition.

Large language models can handle sophisticated natural language processing tasks. This raises the question of how their understanding of semantic meaning compares to that of human beings. Supporters of embodied cognition often point out that because these models are trained solely on text, their representations of semantic content are not grounded in sensorimotor experience. This paper contends that human cognition exhibits capabilities that fit with both the embodied and artificial intelligence approaches. Evidence suggests that semantic memory is partially grounded in sensorimotor systems and dependent on language-specific learning. From this perspective, large language models demonstrate the richness of language as a source of semantic information. They show how our experience with language might scaffold and extend our capacity to make sense of the world. In the context of an embodied mind, language provides access to a valuable form of ungrounded cognition.This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.

The problem of the many: a view from the semantics of numerals and countability

AbstractThis paper addresses both semantic issues of countability in linguistics, and philosophical issues arising from the problem of the many. I argue (i) that the problem of the many is orthogonal to vagueness and we should look to the semantics of count nouns and numerals for its solution; (ii) that the problem of the many is a challenge for contemporary mereological analyses of count nouns in semantics; but (iii) that the count criterion in these theories can be weakened to avoid the problem. Specifically, weak quantization is proposed as the criterion: a sum, x, counts as nPs if and only if x has n disjoint parts, each of which is in the extension of P, but where the sum of no two of them is also in the extension of P. I show how importing this idea into the semantics of numerals provides a semantic means of dissolving the paradox.

Localisation as Sociocultural Adaptation of Modern Design of Visual Communications

The aim of the article is to ground and formulate the definition of the term “localisation” in design using the example of visual communications. Results. A review of definitions and interpretations of localisation in different scientific and industrial spheres highlights that the term has different semantic meanings. In the sphere of visual communications, the concept appeared in web design, programming interfaces of programmes and games in the context of language, as well as technical, normative (State Standards) and legal parameters. Nowadays, the use of the term has expanded to the majority of graphic design, branding and marketing product. Its content is associated with sociocultural characteristics of the target groups. Interviews conducted with experts in the re-publishing and redesign of English-language books in the Ukrainian language, advertising communications, curatorship in mural placement and social poster display confirm the relevance of the content and design adчaptation to the specifics of target groups. Localisation in design is defined as a strategy in increasing the functional and communicative efficiency of the project through the adaptation of the content and design to the specifics of sociocultural, legislative, technological, geographical and historical conditions of its use within the country, region, etc. Scientific novelty. For the first time in the theory of design, the term “localisation” is grounded and introduced (using the example of visual communications); the specifics of using the concept of “local” in the context of consideration of these phenomena are clarified; the results of interviews of Ukrainian experts with experience in the localisation of visual communications are presented; the necessity in sociocultural adaptation of the content and design of visual communications to the requests of target groups is emphasised. Conclusions. In conditions of globalisation, the significance of localisation of visual communication projects is increasing. Its implementation requires immersion in the local culture and/or close contact with their representatives during the design process. However, the most effective localisation can be carried out directly by a representative of the target group as a carrier of its local culture.

Importance of Investigating Linguacultural Variability in Learning Foreign Languages

The aim of the article is to investigate and prove the importance of linguocultural variability in the study of foreign languages. Results. In general, the question “Does language determine culture or culture determins language?” is still definitive as a fundamental question of new studies and investigations. Results show that these and other similar questions, as well as forms of interaction of language and culture, have recently become serious in a wider context, and have been a subject of discussion. As we know, language and culture both give distribution to the process of communication, and this interconnection helps foreign language learners to declare pragmatic, semantic and syntactic meanings in order to cooperate. Scientific novelty. This article is the author’s contribution to the study of the interaction of language and culture as two interrelated social phenomena that develop under conditions of mutual influence. Conclusions. Anyone who prefers to intercommunicate with success through language and culture must be aware of the differences between various cultures. In this example, anyone who evolves such communication should consider sociolinguistic, discourse, grammatical and strategic skills. As a rule, certain concepts may be specific to one culture, and might not be found in other cultures. This case can be considered as one of the crucial reasons of investigating cultural variability of certain language, and this is primary to success in learning foreign languages. While borderlines between languages are very clearly observed and followed, cultural boundaries only originate as a consequence of speakers’ cultural “collision”. Observations give us a chance to make a conclusion that in most countries, multiculturalism exists as a form of equal coexistence of different cultures within the same country. As far as people are related to various cultures, their cultural values are quite different. One can consider that numbers of values exist simply because they “disagree” from each other.

Creating Future Stations: The Application of Large Generative Models in Metro Design

New technologies, including the use of generative models, are beginning to actively influence design decisions in the decoration of metro stations. The study of the possibilities of using generative artificial intelligence as an art design tool in the decoration of metro stations is relevant, because it can significantly accelerate the design process, reducing time and labour costs for the development and optimization of projects. The novelty of the article lies in the fact that for the first time the process of interaction between the designer and software and hardware of large generative models is considered in detail on the example of designing the interior of a metro station on the theme “ocean”.The problem of creation of original design project in conditions of generative models at the moment is not solvable without direct control on the part of a human, as this technology, for lack of human perception, first of all orientate on expressive means contained in training databases. Therefore, when designing metro stations using generative models, images generated through text often lose the semantic and emotional meanings that the designer put into them. As a result, the generated design project does not fully conform to the designer’s intent and requires further manual adjustment and optimization. Thus, as long as there is a need for conscious and purposeful communication of ideas and feelings through art and design, the problem of endowing a work with meaning using generative models will require human involvement.At the same time, it should be noted that in the artist’s work, the use of generative pre-trained transformers is manifested in the ability to easily create realistic visual effects, to execute images in different styles, focusing on the creative individual manner of famous masters, and to improve design ideas. The advantage of this technology is that it can reduce the designer’s workload while improving the efficiency and accuracy of the design.

SYMBOLIC CONNOTATIONS AS AN INPUT TO THE DESIGN OF THE ADVERTISING POSTER

The research dealt with the background of the research, the research problem, the importance of the research, the research objectives, the research hypotheses, the research limits, the research methodology, and the research terms. The first chapter dealt with the visual perception of shapes, the symbolic semantic formulation of the shape, the formulation of visually created shapes, the visual perception of shapes, the physiology of shape perception, the visual formulation, and the structural composition of the visual message, including the representative level, the abstract level, and the symbolic level, the semantic properties of images. The second chapter included the semantic meaning of shapes, the stages of the emergence of semantics, and the types of semantic meanings, which represent the basic cognitive meaning, the additional or secondary meaning, the stylistic meaning, the psychological meaning, and the suggestive meaning. Forms of meaning change include the specialization of meaning, generalization, and transfer of meaning. The third chapter dealt with the research experience, the intellectual aspect of the experience, the technical aspect of the experience, and the technical analysis of the results of the experience. The fourth chapter dealt with the results, recommendations, and references.

A least-squares framework for developing interval type-2 fuzzy semantics

The developing of IT2 fuzzy semantics is critical for computing with words (CWW), but exiting approaches lack flexibility and fail to adapt user’s diversified demands. The study proposes a least-squares framework for designing CWW encoders that construct interval type-2 fuzzy sets (IT2 FSs) to represent the semantic meanings of linguistic words. In the least-squares framework, an CWW encoder is characterized by two elements: an intra-uncertain semantic mapping and an inter-uncertain semantic family. The intra-uncertain semantic mapping transforms data intervals into type-1 fuzzy sets (T1 FSs), then an optimal IT2 FS is derived from the inter-uncertain semantic family using the least-squares method. Furthermore, several intra-uncertain semantic mappings are introduced, and a compatibility measure is defined to facilitate model selection. The least-squares framework benefits from the flexible selection of intra-uncertain semantic mappings and least-squares optimization-based construction of IT2 FSs. In experiments, the least-squares framework is applied to handle real-world online survey data and the large-scale online review data set of a Chinese life service review site, Dianping.com. Compared to the enhanced interval approach and the Hao-Mendel approach, the least-squares framework shows its favorable efficiency in experiments and statistical tests, and adapts to user-defined intra- and inter-uncertain semantic families.