Semantic Concepts Research Articles

АНАЛІЗ ДОХОДІВ НАСЕЛЕННЯ ЯК ЧИННИКА ФОРМУВАННЯ ТУРИСТИЧНИХ ПОТОКІВ

Стаття присвячена питанням використання різновидів показників доходу населення з метою визначення головних ознак та ємності споживчого ринку туристичних послуг у межах одної країни та у групі країн, об'єднаних на основі попередньо обраного критерію, з метою подальшої деталізації стратегії виходу на цільові ринки. Надається розширена характеристика різновидів середньодушового показника доходу, наводяться приклади використання змістової концепції кожної підгрупи у конкретних підходах, що можуть використовуватися підприємствами галузі туризму. Проведена класифікація країн із рівнем доходів нижче за середній (у відповідності до методики Світового банку) на основі міри розбіжності номінального доходу та доходу за паритетом купівельної спроможності. Надано опис типової структури туристичних потоків (міжнародного та внутрішнього туризму) для кожної підгрупи.

LINGUOCULTURAL NATURE OF MENTALITY

The article is devoted to the linguocultural explaining of the concept "mentality". The object of research is the concept of "mentality", which is used in modern linguistics in two semantic aspects: first, when talking about ethnic or social conditioning of our mind and, secondly, when they try to justify the origins of the spiritual unity and integrity of the people. The mental symptoms of the national consciousness are objectified in language, and in this regard the following categories of cognitive semantics are in demand in the linguistics of language in their synergistic and cognitive understanding: the value and meaning, the inner form of nominative units of language, means of secondary and indirect derivative nomination, cultural connotations etc. The author's aim is to explain the concept of linguocultural mentality. The author concludes that the mentality can be described as a synergistic perception of the world through the prism of the interaction of the language, consciousness and culture; linguoculture is a way of expressing ethnic mentality as a special vision of the world in concrete chronotopic terms of artistic and creative life of a people.

The lexical semantics of blaguer: French ways of bringing people together through persuasion, deception and laughter

This study presents a lexical semantic analysis of the French verb blaguer and related expressions. This verb belongs to a suite of “French humour practices”, and French-English dictionaries translate it as ‘to joke’. However, Anglo-specific terminology such as “joke” does not match the conceptual semantics of blaguer and its related noun blague. Relying on Anglo-specific terms to categorise culture-specific practices perpetuates conceptual and terminological Anglocentrism. This study furthers the call to avoid the dangers of sustaining Anglocentrism in the theoretical vocabulary of humour studies (Goddard & Mullan 2020; Goddard 2018; Wierzbicka 2014a). Working from the assumption that semantic categories reflect particular ways of speaking, thinking, and behaving, this study’s goal is to capture the insider perspective that French speakers have about the meaning of the verb blaguer and the noun blague. Making local understandings more obvious and accessible to cultural and linguistic outsiders will increase cross-cultural understanding and foster appreciation for the different ways that speakers construct and interpret their world with words (Levisen & Waters 2017). The analytical tool for this study is the technique of semantic explication couched in the simple cross-translatable and culture-neutral words of the Natural Semantic Metalanguage (Goddard & Wierzbicka 2014). Carefully chosen example sentences are drawn from Google searches (google.fr) of authentic language use of the verb blaguer and the noun blague. Comparative reference is made to the verb ‘to joke’ from Australian English to highlight the differences in the conversational humour cultures of French and English speakers (Goddard & Mullan 2020; Béal & Mullan 2013, 2017).

Individual Differences in the Neural Localization of Relational Networks of Semantic Concepts.

Semantic concepts relate to each other to varying degrees to form a network of zero-order relations, and these zero-order relations serve as input into networks of general relation types as well as higher order relations. Previous work has studied the neural mapping of semantic concepts across domains, although much work remains to be done to understand how the localization and structure of those architectures differ depending on various individual differences in attentional bias toward different content presentation formats. Using an item-wise model of semantic distance of zero-order relations (Word2vec) between stimuli (presented both in word and picture forms), we used representational similarity analysis to identify individual differences in the neural localization of semantic concepts and how those localization differences can be predicted by individual variance in the degree to which individuals attend to word information instead of pictures. Importantly, there were no reliable representations of this zero-order semantic relational network when looking at the full group, and it was only through considering individual differences that a stable localization difference became evident. These results indicate that individual differences in the degree to which a person habitually attends to word information instead of picture information substantially affects the neural localization of zero-order semantic representations.

En begreppsanalys om lidande vid livets slutskede

This study examines the concept of suffering to gain a deeper understanding of the dimensions of suffering at the end of life. Suffering is a central concept in the caring communion at the end of life. To develop an understanding of the concept of suffering, we used semantic concept analysis techniques developed by Koort and analyzed testability using quality content analysis developed by Graneheim and Lundman. The materials analyzed included 14 dictionaries and an autobiography. The concept analysis of suffering revealed three dimensions related to end-of-life care: tolerance, endurance, and agony. These dimensions were continuously referred to throughout the testability analysis. The dimensions can give a deeper understanding for the patients’ total suffering in end-of-life care and their relatives.

Person Retrieval in Surveillance Videos Via Deep Attribute Mining and Reasoning

Person retrieval largely relies on the appearance features of pedestrians. This task is rather more difficult in surveillance videos due to the limitations of extracting robust appearance features brought by the cross-view and cross-camera data with lower image resolution, motion blur, occlusion and other kinds of image degradation. To build up a more reliable person retrieval system, recent works introduced appearance attribute models to describe and distinguish different persons with high-level semantic concepts. Despite the progress of previous works, the value of utilizing appearance attributes is still under-explored. On one hand, existing methods lack for concise and precise attribute representations that are specific for each attribute category and, in the meantime, are able to filter noisy information in irrelevant spatial locations and useless patterns. On the other hand, correlation and reasoning between different attributes are neglected, which could generate more useful information and add more robustness to the retrieval system. In this paper, we propose an Attribute Mining and Reasoning (AMR) framework which is capable to handle the issues in question. The AMR makes better use of appearance attributes with two main components. First, the AMR disentangles the representations of different attributes by localizing their spatial positions and identifying their effective patterns in a weakly supervised manner. To achieve more reliable localization, we propose the Attribute Localization Ensemble (ALE) module that is consisted of multiple localization heads and a voting mechanism. Second, we introduce the Attribute Reasoning (AR) module to correlate different attributes together with the global appearance features and discover their latent relations to generate more comprehensive descriptions of pedestrians. Extensive experiments on DukeMTMC-ReID and Market-1501 datasets demonstrate the effectiveness of the proposed AMR framework as well as its superiority over the existing state-of-the-art methods. The AMR model also shows great generalization ability on the unseen CUHK03 dataset when it is only trained on Market-1501 dataset.

Знання-орієнтований інструментальний комплекс обробки баз даних наукових публікацій

Introduction. Nowadays, numerous applications and tools are known that implement information retrieval technologies in various text sources in accordance with specified parameters. Moreover, the search results are provided to the user for each search parameter individually and not related to each other. And the application of Semantic Web technologies for the purpose of multi-parameter and related information retrieval in various sources in Ukraine is at the initial stage of development. A separate problem is the multimedia presentation of search results and their comparison with the conceptual structure of the domain of interest (Knowledge Domain) with the goal of extracting new knowledge. From this point of view, it is relevant for scientific research to process the scientific publications of one author, authors of a scientific unit and the academic institute as a whole, using the Semantic Web technologies, multimedia presentation of information, and effective support for the process of extracting new knowledge. Purpose. Designing the architecture and functioning algorithms of the instrumental complex for processing databases of scientific publications, as well as developing examples of using a formal description of a scientific article with a number of queries. Methods. The methods and models used in this work are based on Semantic Web information technologies focused on the development and use of subject ontologies. Ontologies are the basic components of these technologies both for conducting scientific research and creating large databases, including scientific publications of the authors. Results. The architecture of the instrumental complex for processing databases of scientific publications and the algorithms for its functioning at the preparatory and main stages have been developed. Examples of queries to the database of scientific publications that demonstrate the performance of IR are given. Conclusion. The article discusses the architecture of the instrumental complex for processing databases of scientific publications and the algorithms for its functioning at the preparatory and main stages. The steps of the preparatory phase, which are implemented by the knowledge engineer, are examined in detail. At the same time, the creation of two ontology models of the scientific article with the presentation of the corresponding ontographs was highlighted: the CRF-model describes the concepts contained in the article, and the OWL-model describes the structural components of the article. In conclusion, examples of queries to the databases of scientific publications are presented, demonstrating the performance of the instrumental complex. Further, it is necessary to expand the use in the development of IR technologies, such as cognitive semantics and graphics, multimedia presentation of information, focused on the effective support of the processes of extraction and/or generation of new knowledge.

Neuropsychological and biochemical features of cognitive resilient octogenarians and nonagenarians

AbstractBackgroundAlzheimer’s dementia has evolved into a biological construct based on fluid and imaging biomarkers to aid in early diagnosis. Biomarkers are expensive and not widely available. It is therefore important to define signatures of cognitive resiliency to better identify this group in the prodromal stages of dementia. Our AIM was to determine neuropsychological and biochemical characteristics of cognitively intact patients older than age 80.MethodSingle center, cross‐sectional study involving forty patients older than 80, admitted to a community hospital. Participants with diagnoses of dementia, delirium, history of falls, dependency on cognitively focused IADL, and those residing in residential care were excluded. Recruited patients were subjected to neurocognitive evaluation, with emphasis on episodic/working memory, semantic cognition, and visuoperceptual/executive spheres, using the Rey‐Osterrieth figure copy/recall (ROSC) test. Regression analysis was used to establish correlation.ResultThe mean age was 85 years (SD 4.06). The mean level of education was 12.12 years (SD 2.93). Only 28% of participants lived alone. Semantic evaluation was the most difficult task, with only 48% of patients able to pass. Patients who answered 3 memory tasks right, had a mean score of 21 (SD 4.6) on the ROSC recall. Those who pass 2 memory tasks, had a mean score of 18.75 (SD 5.20) on the ROSC recall, and those who pass only one, or failed all of them, had a mean score of 9. We identified the ROSC recall as the most powerful predictor of cognitive resiliency. Age correlated well with ROSC recall (p 0.0017, CI 45‐126), but no years of education (p 0.21, CI 2.32‐21). Hemoglobin had a significant effect on ROSC copy (p 0.0019, CI 5‐26), but no ROSC recall (p 0.84, CI ‐14‐12). No correlation was found between ROSC recall and MCV, RDW, or albumin level.ConclusionWe corroborate that age is the single most important factor for cognitive decay, whereas a normal hemoglobin level appears to be protective. Level of education seems to lose its protective effect in the old‐old and oldest‐old population. Patients able to perform well on the three types of memory, showed great proficiency on the ROSC recall.

Discrete Semantic Alignment Hashing for Cross-Media Retrieval.

Cross-media hashing, which maps data from different modalities to a low-dimensional sharing Hamming space, has attracted considerable attention due to the rapid increase of multimodal data, for example, images and texts. Recent cross-media hashing works mainly aim at learning compact hash codes to preserve the class label-based or feature-based similarities among samples. However, these methods ignore the unbalanced semantic gaps between different modalities and high-level semantic concepts, which generally results in less effective hash functions and unsatisfying retrieval performance. Specifically, the key words of texts contain semantic meanings, while the low-level features of images lack of semantic meanings. That means the semantic gap in image modality is larger than that in text modality. In this paper, we propose a simple yet effective hashing method for cross-media retrieval to address this problem, dubbed discrete semantic alignment hashing (DSAH). First, DSAH formulates to exploit collaborative filtering to mine the relations between class labels and hash codes, which can reduce memory consumption and computational cost compared to pairwise similarity. Then, the attribute of image modality is employed to align the semantic information with text modality. Finally, to further improve the quality of hash codes, we propose a discrete optimization algorithm to learn discrete hash codes directly, and each bit has a closed-form solution. Extensive experiments on multiple public databases show that our model can seamlessly incorporate attributes and achieve promising performance.

Poetic Themes and Ideas of Faqe Qader Hamawand

Abdul Qadir Rasool was one of the poets of the nineteenth century. He is known as Faqe. He was a talented poet but his works are still being disregarded and unknown to others. Searching and understanding his poems will clarify his new innovations in poetry. By analyzing the collected works and poems of Persian poets and Quranic verses, he could utilize conceptual semantics in his poetical works. His poems are the symbol of desire, sympathy, using the persuasive of metaphors and dominating words to spread splendid compound literary works. He was able to combine the impacts of both his life experience and the complicated community issues of sultan Aziz Ottoman era in his works intelligently. In this research, the researcher attempted to indicate the notions of the poet to give the ability to understand his concepts and his views, and they are important to understand ingenuity he innovated in his poems. Key words: Faqe Qader, Elegy, Saki poem, Blaming poem, Prison poems.

Adaptive Attention-based High-level Semantic Introduction for Image Caption

There have been several attempts to integrate a spatial visual attention mechanism into an image caption model and introduce semantic concepts as the guidance of image caption generation. High-level semantic information consists of the abstractedness and generality indication of an image, which is beneficial to improve the model performance. However, the high-level information is always static representation without considering the salient elements. Therefore, a semantic attention mechanism is used for the high-level information instead of conventional of static representation in this article. The salient high-level semantic information can be considered as redundant semantic information for image caption generation. Additionally, the generation of visual words and non-visual words can be separated, and an adaptive attention mechanism is employed to realize the guidance information of image caption generation switching between new fusion information (fusion of image feature and high-level semantics) and a language model. Therefore, visual words can be generated according to the image features and high-level semantic information, and non-visual words can be predicted by the language model. The semantics attention, adaptive attention, and previous generated words are fused to construct a special attention module for the input and output of long short-term memory. An image caption can be generated as a concise sentence on the basis of accurately grasping the rich content of the image. The experimental results show that the performance of the proposed model is promising for the evaluation metrics, and the captions can achieve logical and rich descriptions.

Metaphorical Analyses of Russian Verbs of Thinking Activity Meaning

The study of cognitive semantics of verbs is an important topic in contemporary linguistics. Taking Russian metaphorical thinking meaning verbs as the object, this article discusses the cognitive semantics of verbs. First of all, the paper combs out the subconscious thinking activity verbs derived from the metaphors of prototype actions of volitional and non volitional activities, and the autonomous thinking activity verbs from the prototype action metaphor of will activity; then focusing on the cognitive similarity and semantic violation, the article separately analyses the metaphorical semantic derivation of these two kinds of thinking activity, meanwhile, it correspondingly discusses the problem of polysemy of Russian verbs. Relevant research will be helpful to further investigate the metaphorical performance and mechanism of the unique thinking activity verbs, at the same time, it is beneficial to promote the theoretical exploration of semantic derivation of Russian polysemous verbs. The relevant study will be helpful to further investigate the cognitive semantic attributes and operational characteristics of Russian verbs with metaphorical thinking meaning, and it is beneficial to promote the research of lexical semantic theory of Russian language on the cognitive level.

The Spread of Chinese Virus in the Internet Discourse: A Cognitive Semantic Analysis

In the present paper we examine the use of non-technical terms used to refer to the SARS-Cov2 virus, which – in a deliberate or non-deliberate fashion – stress the place of the virus’ origin. In particular, we consider the three following terms: Chinese virus , Wuhan virus , and Asian virus . Drawing on the cognitive semantics methodology, in particular, the notion of meaning potentials (Noren & Linell, 2007) and conceptual blending theory (Fauconnier & Turner, 2003; Brandt & Brandt, 2005; Oakley & Coulson, 2008) we aim to investigate the dynamics of meaning construal of the selected expressions in English-language Internet discourse. The main objective is to account for different interpretations of the same expressions as they are used in different contexts with, presumably, various intentions. Complex expressions such as compounds, tend to exhibit various meanings and thus, can be said to possess different meaning potentials which are activated differently depending on the contextual circumstances in the process of meaning construal, which – as we want to show in our analysis – is not always so intuitive. In this paper we thus argue that only a pragmatically-enriched model of dynamic meaning construal can account for the polysemy and frequent ambiguity of compound expressions, including semantic extensions with generally derogatory or even outright racist connotations.

Application of linked data technologies in libraries: Pakistani information professionals’ attitudes and perceptions

Purpose This study aims to ascertain the information professionals’ perceptions and attitudes towards the application of linked data (LD) technologies in libraries in the context of a developing country. It also explores the Pakistani information professionals’ level of familiarity with LD concepts, plans and efforts in its application, along with its potential benefits. Design/methodology/approach An online questionnaire survey was used to identify the information professionals’ perceived level of familiarity with LD concepts and benefits to implement LD in libraries, and their plans and efforts to adopt LD technologies. A questionnaire was developed to meet the objective of the study and collect data from the purposefully selected professionals (N = 86) working in university libraries in Pakistan. SPSS was used to analyse the data set. Findings Findings show that LD application in libraries is still not common among Pakistani information professionals. They have a moderate level of understanding with semantic Web (SW) and LD key concepts. The respondents were positive about the potential benefits of implementing LD technology in libraries, such as assisting patrons to discover relevant information, enriching traditional bibliographic records, enhancing the visibility and discoverability of library data and improving the users’ overall search experiences that may foster future developments. Overall, the respondents’ institutions were on different levels of planning – from working on a plan to having a finalized plan. The findings emphasized establishing the SW’s potential in library services. Research limitations/implications There are certain limitations of this study. Firstly, the population is Pakistani information professionals who are purposefully selected. Secondly, the findings reveal that the respondents’ understanding with LD concepts, opinions about LD, plans and efforts to its application along with its potential benefits in libraries are self-perceived and not tested in any way and they were not actually implementing these technologies in their institutions because of a lack of best practices. It is a quantitative study and it will serve as a baseline study, even though there is a need to conduct more in-depth qualitative studies in the information environment to explore the phenomenon. Social implications This study will guide information professionals and policymakers to take initiatives towards the implementation of LD technology in libraries. It may also lead to further research to understand the perceptions of information professionals so that appropriate training and advocacy programmes can be developed to promote LD initiatives in libraries. Originality/value To the best of the authors’ knowledge, this is the first known empirical study in the context of a developing country. It clearly demonstrates the Pakistani information professionals’ perceptions regarding the library LD and associated plans along with potential benefits to implement it.

Стилістичні особливості односкладних речень репрезентації у творах Сергія Жадана

Absract. The proposed article analyzes the stylistic peculiarities of simple sentences of representation in the poetic and prose works of modern Ukrainian writer Serhiy Zhadan. It was found that the author often uses these short in structure and at the same time semantically capacious units to reflect the realities of actuality in the literary text. It was discovered that within the context, the nominative sentences can take different position: preposition, interposition or postposition. The text-forming properties of simple sentences of representation in the monologue and dialogue speech of the persons are determined. Attention is paid to a special combination by the method of stringing of different in semantic content concepts in one sentence or group of sentences (the spectrum of the image of the artist of the word includes a variety of subjects and objects of the surrounding reality). The most basic syntactic functions of nominative sentences in the text are described, in particular, ascertaining, descriptive, evaluative, etc. The role and significance of simple sentences of representation in the outline of the author's text are established.

A Visual Saliency-Based Approach for Content-Based Image Retrieval

During the past two decades an enormous amount of visual information has been generated; as a result, content-based image retrieval (CBIR) has received considerable attention. In CBIR the image is used as a query to find the most similar images. One of the biggest challenges in CBIR system is to fill up the “semantic gap,” which is the gap between low-level visual features and the high-level semantic concepts of an image. In this paper, the authors have proposed a saliency-based CBIR system that utilizes the semantic information of image and users search intention. In the proposed model, firstly a significant region is identified with the help of method structured matrix decomposition (SMD) using high-level priors that highlight the prominent area of the image. After that, a two-dimensional principal component analysis (2DPCA) is used as a feature, which is compact and effectively used for fast recognition. Experiment results are validated on different image dataset having an extensive collection of semantic classifications.

Fronto-temporal brain activity and connectivity track implicit attention to positive and negative social words in a novel socio-emotional Stroop task

Previous inconsistencies on the effects of implicitly processing positively - vs. negatively - connotated emotional words might reflect the influence of uncontrolled psycholinguistic dimensions, and/or social facets inherent in putative “emotional” stimuli. Based on the relevance of social features in semantic cognition, we developed a socio-emotional Stroop task to assess the influence of social vs. individual (non-social) emotional content, besides negative vs. positive valence, on implicit word processing. The effect of these variables was evaluated in terms of performance and RTs, alongside associated brain activity/connectivity. We matched conditions for several psycholinguistic variables, and assessed a modulation of brain activity/connectivity by trial-wise RT, to characterize the maximum of condition- and subject-specific variability. RTs were tracked by insular and anterior cingulate activations likely reflecting implicit attention to stimuli, interfering with task-performance based on condition-specific processing of their subjective salience. Slower performance for negative than neutral/positive words was tracked by left-hemispheric structures processing negative stimuli and emotions, such as fronto-insular cortex, while the lack of specific activations for positively-connotated words supported their marginal facilitatory effect. The speeding/slowing effects of processing positive/negative individual emotional stimuli were enhanced by social words, reflecting in specific activations of the right anterior temporal and orbitofrontal cortex, respectively. RTs to social positive and negative words modulated connectivity from these regions to fronto-striatal and sensorimotor structures, respectively, likely promoting approach vs. avoidance dispositions shaping their facilitatory vs. inhibitory effect. These results might help assessing the neural correlates of impaired social cognition and emotional regulation, and the effects of rehabilitative interventions.

Deploying spatial-stream query answering in C-ITS scenarios1

Cooperative Intelligent Transport Systems (C-ITS) play an important role for providing the means to collect and exchange spatio-temporal data via V2X-based communication between vehicles and the infrastructure, which will become a central enabler for road safety of (semi)-autonomous vehicles. The Local Dynamic Map (LDM) is a key concept for integrating static and streamed data in a spatial context. The LDM has been semantically enhanced to allow for an elaborate domain model that is captured by a mobility ontology, and for queries over data streams that cater for semantic concepts and spatial relationships. Our approach for semantic enhancement is in the context of ontology-mediated query answering (OQA) and features conjunctive queries over DL-LiteA ontologies that support window operators over streams and spatial relations between spatial objects. In this paper, we show how this approach can be extended to address a wider range of use cases in the three C-ITS scenarios traffic statistics, traffic events detection, and advanced driving assistance systems. We define for the mentioned use cases requirements derived from necessary domain-specific features and report, based on them, on extensions of our query language and ontology model. The extensions include temporal relations, numeric predictions and trajectory predictions as well as optimization strategies such as caching. An experimental evaluation of queries that reflect the requirements has been conducted using the real-world traffic simulation tool PTV Vissim. It provides evidence for the feasibility/efficiency of our approach in the new scenarios.

Connecting Semantic Situation Descriptions with Data Quality Evaluations—Towards a Framework of Automatic Thematic Map Evaluation

A continuing question in the geospatial community is the evaluation of fitness for use of map data for a variety of use cases. While data quality metrics and dimensions have been discussed broadly in the geospatial community and have been modelled in semantic web vocabularies, an ontological connection between use cases and data quality expressions allowing reasoning approaches to determine the fitness for use of semantic web map data has not yet been approached. This publication introduces such an ontological model to represent and link situations with geospatial data quality metrics to evaluate thematic map contents. The ontology model constitutes the data storage element of a framework for use case based data quality assurance, which creates suggestions for data quality evaluations which are verified and improved upon by end-users. So-created requirement profiles are associated and shared to semantic web concepts and therefore contribute to a pool of linked data describing situation-based data quality assessments, which may be used by a variety of applications. The framework is tested using two test scenarios which are evaluated and discussed in a wider context.

ASTS: attention based spatio-temporal sequential framework for movie trailer genre classification

Automatic movie trailer genre classification is a challenging task because trailers have more diverse content and high-level sequential semantic concepts within the movie storyline, which can help for multimedia search and personalized movie recommendation. Traditional methods generally extract the low-level features or consider the local sequential dependencies among trailer frames, ignoring the global high-level sequential semantic concepts. In this manuscript, we propose a novel and effective Attention based Spatio-temporal Sequential Framework (ASTS) for movie trailer genre classification. The proposed framework mainly consists of two modules, respectively the spatio-temporal descriptive module and the attention-based sequential module. The spatio-temporal descriptive module adopts some advanced convolution neural networks to extract the spatio-temporal features of key trailer frames, which can capture the local spatio-temporal semantic features. The attention-based sequential module is designed to process the extracted spatio-temporal feature representation sequence for capturing the global high-level sequential semantic concepts within the movie storyline. We crawl 14,415 labeled movie trailers from YouTube and integrate them into the public dataset MovieLens. Experiment results show that our proposed framework is superior to state-of-the-art methods.