Semantic Concepts Research Articles

Global motion estimation with iterative optimization-based independent univariate model for action recognition

Motion information used in the existed video action recognition schemes is mixing of global motion(GM) and local motion(LM). In fact, GM & LM have their respective semantic concepts. Thus, it is promising to decouple GM and LM from the mixed motions. Numerous efforts have been made on the design of global motion models for video encoding, video dejittering, video denoising, and so on. Nevertheless, some of the models are too basic to cover the camera motions in action recognition while others are over-complicated. In this paper, we focus on the characteristic of the action recognition and propose a novel independent univariate GM model. It ignores camera rotation, which appears rarely in action recognition videos, and represents the GM in x and y direction respectively. Furthermore, GM is position invariant because it is from the universal camera motion. Pixels with global motions are subjected to the same parametric model and pixels with mixed motion can be seen as outliers. Motivated by this, we develop an iterative optimization scheme for GM estimation which removes the outlier points step by step and estimates global motions in a coarse-to-fine manner. Finally, the LM is estimated through a Spatio-temporal threshold-based method. Experimental results demonstrate that the proposed GM model makes a better trade-off between the model complexity and the robustness. And the iterative optimization scheme is more effective than the existed algorithms. The compared experiments using four popular action recognition models on UCF-101 (for action recognition) and NCAA (for group activity recognition) demonstrate that local motions are more effective than the mixed motions.

DOG – IMAGE PAREMIAS IN TRANSLATION (BASED ON NIKITA KHRUSHCHEV’S MEMOIRS)

Animalistic metaphors are widely used in political discourse. The paper deals with the comparative analysis of Russian paremias with the constituent element “dog” employed in Nikita Khrushchev’s memoirs and their English translations. The etymology and cultural connotations of the phraseological units are explored as well. The corpus linguistics methodology is used to identify the expressions containing the word “dog.” By applying Conceptual Metaphor Theory and frame semantics, the mappings that serve to recreate the author’s view of “dog” cognition and communication for the reader are investigated. The research contributes to the study of phraseology and translation as it provides an insight into challenges caused by linguistic and cultural differences while transferring metaphorical expressions from one language and culture to another.

Enhancing vs. inhibiting semantic performance with transcranial magnetic stimulation over the anterior temporal lobe: Frequency- and task-specific effects

Accumulating, converging evidence indicates that the anterior temporal lobe (ATL) appears to be the transmodal hub for semantic representation. A series of repetitive transcranial magnetic stimulation (rTMS) investigations utilizing the ‘virtual lesion’ approach have established the brain-behavioural relationship between the ATL and semantic processing by demonstrating that inhibitory rTMS over the ATL induced impairments in semantic performance in healthy individuals. However, a growing body of rTMS studies suggest that rTMS might also be a tool for cognitive enhancement and rehabilitation, though there has been no previous exploration in semantic cognition. Here, we explored a potential role of rTMS in enhancing and inhibiting semantic performance with contrastive rTMS protocols (1 Hz vs. 20 Hz) by controlling practice effects. Twenty-one healthy participants were recruited and performed an object category judgement task and a pattern matching task serving as a control task before and after the stimulation over the ATL (1 Hz, 20 Hz, and sham). A task familiarization procedure was performed prior to the experiment in order to establish a ‘stable baseline’ prior to stimulation and thus minimize practice effect. Our results demonstrated that it is possible to modulate semantic performance positively or negatively depending on the ATL stimulation frequency: 20 Hz rTMS was optimal for facilitating cortical processing (faster RT in a semantic task) contrasting with diminished semantic performance after 1 Hz rTMS. In addition to cementing the importance of the ATL to semantic representation, our findings suggest that 20 Hz rTMS leads to semantic enhancement in healthy individuals and potentially could be used for patients with semantic impairments as a therapeutic tool.

A MORPHOSYNTACTIC AND SEMANTIC ANALYSIS OF PLACENAMES IN KWAHU IN THE EASTERN REGION OF GHANA

African names, as signs of language, can be divided into two morpho-syntactic categories: they are either nominal, which means they constitute single words, or syntagmatic, implying they are made up of sentences or parts thereof. Therefore, understanding place names in general hugely depends on a critical look at the internal structure of the word(s) or placename(s). This research uses complete semi-structured interviews and documents to critically investigate 20 randomly selected placenames from Kwahu South and Kwahu East districts in the Eastern Region of Ghana. It analyses the syntax, morphology and meaning of these placenames. It applies the Frame Semantic (Fillmore, 1982) concept to ascertain whether the meaning-making elements in the individual names reflect the views or purposes of their ‘namers’ or otherwise, as (oral) histories have them. The selected placenames are then grouped using Tent and Blair (2011) model of Motivations for Naming. The semantic and morphological analysis reveal that some Kwahu habitation names could be realized from non-morphemic parts of words, single stems/roots, phrases, inflections (affixations), etc. Again, vowel elision, coinage, and backformation are used by the name givers, with the topography, environment and other factors being key in the naming process. The paper concludes that Kwahu placenames are useful linguistic sources. The study contributes to the body of knowledge on Kwahu placenames in particular, and Ghanaian toponymy in general. It is recommended that ‘Kwahu’, which also stands as an Akan dialect type, spoken by this speech community, be explored by future researchers.
 
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Training flexible conceptual retrieval in post-stroke aphasia

ABSTRACT Semantic therapy in post-stroke aphasia typically focusses on strengthening links between conceptual representations and their lexical-articulatory forms to aid word retrieval. However, research has shown that semantic deficits in this group can affect both verbal and non-verbal tasks, particularly in patients with deregulated retrieval as opposed to degraded knowledge. This study, therefore, aimed to facilitate semantic cognition in a sample of such patients with post-stroke semantic aphasia (SA) by training the identification of both strong and weak semantic associations and providing explicit pictorial feedback that demonstrated both common and more unusual ways of linking concepts together. We assessed the effects of this training on (i) trained and untrained items; and (ii) trained and untrained tasks in eleven individuals with SA. In the training task, the SA group showed improvement with practice, particularly for trained items. A similar untrained task using pictorial stimuli (Camel and Cactus Test) also improved. Together, these results suggest that semantic training can be beneficial in patients with SA and may show some degree of generalization to untrained situations. Future research should seek to understand which patients are most likely to benefit from this type of training.

Explication functions in the conditions of professionalization of thinking

The article substantiates that the explication method in conditions of professionalization of the subject’s thinking performs various functions. Explication allows for: firstly, the translation of unclear and uncertain intuitive representations (explicands) to the form of strictin categorical, semantic and logical form concepts (explicans); secondly, the conceptualization of a certain area of professional knowledge, bringing it into a coherent, effective system; thirdly, the detection and accounting of implicit dependencies between various knowable and transformable phenomena; fourthly, the implementation of supra-situational thinking that ensures the acquisition of valuable personal and professional neoformations by the subject. Explication (expression, distribution) of the selected professional thinking featuresof the subject as anbeingexplicatedphenomenonis carried out by differentiating the knowable phenomena: spiritual as a manifestation of the power and content of inner life; social as a refraction of social requirements in professional activity; environmental as the integration and understanding of those environmental conditions that become the means of operational coping with the difficulties encountered; resource as the implementation and multiplication of resources, primarily internal as the basis for professional and personal self-development of the subject.

Смыслы экранной культуры как педагогического феномена

The purpose of the study is to determine theoretically (including from the standpoints of sciences related to pedagogy) and empirically (through perceptions of pedagogical students) the interpretative characteristics of screen culture as a personal phenomenon generated by the information society. The article uses a comparative analysis of various scientific approaches to screen culture to form its semantic concepts, which are subsequently subjected to experimental verification. Scientific novelty of the study lies in determining essential statuses-meanings of screen culture through the lens of pedagogy, taking into account interdisciplinary positions of its understanding, and in documenting corroborative empirical facts. As a result, basing on culturological, communicative, competency-based approaches, the researchers have identified the primary meanings of the studied phenomenon in pedagogical science: formation of an individual image of the world in students; a way of transmitting and assimilating the content of education; development of communicative and information competencies through the use of screens; integrative quality of a personality in the structure of its basic culture.

Integrating semantic features in fruit recognition based on perceptual color and semantic template

Many fruit recognition works have applied statistical approaches to make an exact correlation between low-level visual feature information and high-level semantic concepts given by predefined text caption or keywords. Two common fruit recognition models include bag-of-features (BoF) and convolutional neural network (ConvNet), which achieve high-performance results. In most cases, the overfitting problem is unavoidable. This problem makes it difficult to generalize new instances with only a slightly different appearance, although belonging to the same category. This article proposes a new fruit recognition model by associating an object's low-level features in an image with a high-level concept. We define a perceptual color for each fruit species to construct a relationship between fruit color and semantic color name. Furthermore, we develop our model by integrating the perceptual color and semantic template concept to solve the overfitting problem. The semantic template concept as a mapping between the high-level concept and the low-level visual feature is adopted in this model. The experiment was conducted on three different fruit image datasets, with one dataset as train data and the two others as test data. The experimental results demonstrate that the proposed model, called perceptual color on semantic template (PCoST), is significantly better than the BoF and ConvNet models in reducing the overfitting problem.

Names of places

Abstract The thesis advanced in this paper is that the proper names of cities or countries inherit the linguistic types of the nouns which denote the basic category of the objects the names refer to. As a result, in the case of the proper names of cities or countries, a reference by those names may select particular aspects of those objects, in the same way that book or newspaper selects the physical or informational aspects of objects in the extension of the nouns. This view is based on Asher’s and Pustejovsky’s conception of dot type semantics.

The emergence of richly organized semantic knowledge from simple statistics: A synthetic review

As adults, we draw upon our ample knowledge about the world to support such vital cognitive feats as using language, reasoning, retrieving knowledge relevant to our current goals, planning for the future, adapting to unexpected events, and navigating through the environment. Our knowledge readily supports these feats because it is not merely a collection of stored facts, but rather functions as an organized, semantic network of concepts connected by meaningful relations. How do the relations that fundamentally organize semantic concepts emerge with development? Here, we cast a spotlight on a potentially powerful but often overlooked driver of semantic organization: Rich statistical regularities that are ubiquitous in both language and visual input. In this synthetic review, we show that a driving role for statistical regularities is convergently supported by evidence from diverse fields, including computational modeling, statistical learning, and semantic development. Finally, we identify a number of key avenues of future research into how statistical regularities may drive the development of semantic organization.

Translation and cultural adaptation of a romanian version of the communication assessment tool (CAT_Ro)

BackgroundThe communication between health providers and patients influences the quality of medical care. The Communication Skills Assessment (CAT) is a reliable, validated tool, which was developed to assess interpersonal communication skills between physicians and patients. The purpose of this study was to obtain a Romanian version of the CAT (CAT_Ro), using a controlled and systematic process to translate and cross-culturally adapt the original questionnaire, since there are no validated instruments to assess healthcare professionals’ communication capability in Romania.MethodsThe study was conducted in two Departments of Internal Medicine and Rheumatology from Bucharest, Romania, using a rigorous scientific methodology for the translation process, according to literature recommendations, implicating conceptual evaluation, semantics, and cultural adaptation, which involved several steps. The updated version was pre-tested in a pilot study, which included 89 outpatients.ResultsThe results showed a narrow range of variability in item interpretation, without differences in patients’ responses according to variables such as age, gender, education, disease type, number of previous visits with the same doctor.ConclusionCAT-Ro is the result of a comprehensive process study. It represents the first translation and cultural adaptation in Romanian of an instrument able to assess the health providers’ communication skills, which was validated in a pilot study and is to be used in more extensive studies with patients from several specialties.

Semantic Relatedness Emerges in Deep Convolutional Neural Networks Designed for Object Recognition.

Human not only can effortlessly recognize objects, but also characterize object categories into semantic concepts with a nested hierarchical structure. One dominant view is that top-down conceptual guidance is necessary to form such hierarchy. Here we challenged this idea by examining whether deep convolutional neural networks (DCNNs) could learn relations among objects purely based on bottom-up perceptual experience of objects through training for object categorization. Specifically, we explored representational similarity among objects in a typical DCNN (e.g., AlexNet), and found that representations of object categories were organized in a hierarchical fashion, suggesting that the relatedness among objects emerged automatically when learning to recognize them. Critically, the emerged relatedness of objects in the DCNN was highly similar to the WordNet in human, implying that top-down conceptual guidance may not be a prerequisite for human learning the relatedness among objects. In addition, the developmental trajectory of the relatedness among objects during training revealed that the hierarchical structure was constructed in a coarse-to-fine fashion, and evolved into maturity before the establishment of object recognition ability. Finally, the fineness of the relatedness was greatly shaped by the demand of tasks that the DCNN performed, as the higher superordinate level of object classification was, the coarser the hierarchical structure of the relatedness emerged. Taken together, our study provides the first empirical evidence that semantic relatedness of objects emerged as a by-product of object recognition in DCNNs, implying that human may acquire semantic knowledge on objects without explicit top-down conceptual guidance.

Review of Levin & Pinker (1991): Lexical and Conceptual Semantics

Review of Levin & Pinker (1991): Lexical and Conceptual Semantics

Scene Complexity: A New Perspective on Understanding the Scene Semantics of Remote Sensing and Designing Image-Adaptive Convolutional Neural Networks

Scene understanding of remote sensing images is of great significance in various applications. Its fundamental problem is how to construct representative features. Various convolutional neural network architectures have been proposed for automatically learning features from images. However, is the current way of configuring the same architecture to learn all the data while ignoring the differences between images the right one? It seems to be contrary to our intuition: it is clear that some images are easier to recognize, and some are harder to recognize. This problem is the gap between the characteristics of the images and the learning features corresponding to specific network structures. Unfortunately, the literature so far lacks an analysis of the two. In this paper, we explore this problem from three aspects: we first build a visual-based evaluation pipeline of scene complexity to characterize the intrinsic differences between images; then, we analyze the relationship between semantic concepts and feature representations, i.e., the scalability and hierarchy of features which the essential elements in CNNs of different architectures, for remote sensing scenes of different complexity; thirdly, we introduce CAM, a visualization method that explains feature learning within neural networks, to analyze the relationship between scenes with different complexity and semantic feature representations. The experimental results show that a complex scene would need deeper and multi-scale features, whereas a simpler scene would need lower and single-scale features. Besides, the complex scene concept is more dependent on the joint semantic representation of multiple objects. Furthermore, we propose the framework of scene complexity prediction for an image and utilize it to design a depth and scale-adaptive model. It achieves higher performance but with fewer parameters than the original model, demonstrating the potential significance of scene complexity.

Conceptual Metaphor about Corona Virus: Cognitive Semantic Analysis

Abstract. Conceptual metaphors are the result of mental construction, conceptualization of the experience of human life. In Japanese, metaphorical features are often found in conveying information so that information can be easily understood. This study aimed to describe the conceptual metaphors found at the Japanese Prime Minister's Press Conference, Shinzo Abe on March 14 and 28, 2020 through the official website kantei.go.jp. The research method used was descriptive qualitative analysis. Data were collected by taking text that contained metaphorical elements and then selected. Data selection was based on the basic principle of metaphor, which was the mapping from the source domain to the target domain. Then the data were classified based on the type of metaphor by Lakoff and Johnson and the type of image scheme by Cruse and Croft. Based on the study done, there were three types of conceptual metaphors, 1) Structural metaphors with conceptuals meaning of enemy, medical treatment, control, and mind; 2) Orientational metaphors with conceptual meaning of disadvantage, and approval; 3) Ontological metaphors with conceptuals meaning of finance, and emotion. In addition, there were also six types of image schemes, namely the image scheme of Strength, Existence, Identity, Scale, Space, and Unity.Keywords: Conceptual Meaning, Press Conference, Cognitive Semantic, Image Scheme

The phenomenon of data diplomacy: key terms, research methodology, and current challenges

The key goal of this research lies in presenting the basic terminology and structure of the phenomenon of data diplomacy, as well as in forming the methodological framework based on the empirical manifestations of data diplomacy in the modern context. The subject of this article is the examination of the three semantic concepts of data diplomacy: “data in diplomacy”, “diplomacy for data”, and “data for diplomacy”, as well as the possibilities of their intersection and interaction for achieving political goals. Using the Russian and international political examples, the author describes the manifestations of these concepts in diplomatic practice. The relevance of data diplomacy emerges due to the ongoing technological changes, which provide new instruments and capabilities for traditional diplomacy. The existing gap between the emergence of new technological instruments, namely those related to big data, and their implementation in the traditional diplomacy of any country, requires deliberate attention and development of methodology that allows determining the key trends in transition from traditional diplomacy to data diplomacy. The main results of the conducted research consists in the formation of a framework for studying data diplomacy, as well as outlining the key legal, ethical, and technological challenges and problems of using big data and their impact upon the role and work of a diplomat, as well as interaction of various actors in the international relations. This brings novelty into the Russian scope of works on digital diplomacy, since the aforementioned framework of data and definition of the key challenges based on it have not been previously reflected in the Russian research.

Impact of Teaching Cohesive Devices to Iranian EFL learners: Case of IELTS writing task 2 examination

The notion of “textuality” encouraged Halliday and Hasan in 1976 to present their model of discourse analysis through raising questions about whether “cohesion” was a semantic concept or a structural relation, whether a text was a structural unit or not or even if there were semantic or structural relationships within a text. Cohesion was like the glue that unified the meaning within a text through binding the textual elements. Tackling relations of meaning and references in a text was often related to cohesion. A text can be cohesive if its units were bound together with explicit or implicit relations. Cohesion was often defined as the network of lexical, grammatical, and other relations which provide links between various parts of a text. The units of a cohesive text were not just a random set of sentences. Writing section on IELTS was commonly considered one of the most difficult parts of test. The test takers cannot even understand what to do with the tasks provided. They eventually wrote without knowing the expected direction. Therefore, there should be a fast way to equip students well to successfully cope with such hindrances. This research was an action research report of 10 students to make them better understand and answer writing tasks on IELTS test by employing probing techniques as one of test taking strategies. As the result, teaching material about cohesive devices, according to criteria and references to IELTS writing task 2, could help students to understand of their logic on writing proper materials.

Reverse-engineering the cortical architecture for controlled semantic cognition.

We employ a ‘reverse-engineering’ approach to illuminate the neurocomputational building blocks that combine to support controlled semantic cognition: the storage and context-appropriate use of conceptual knowledge. By systematically varying the structure of a computational model and assessing the functional consequences, we identified the architectural properties that best promote some core functions of the semantic system. Semantic cognition presents a challenging test case as the brain must achieve two seemingly contradictory functions: abstracting context-invariant conceptual representations across time and modalities, whilst producing specific context-sensitive behaviours appropriate for the immediate task. These functions were best achieved in models possessing a single, deep multimodal hub with sparse connections from modality-specific regions, and control systems acting on peripheral rather than deep network layers. The reverse-engineered model provides a unifying account of core findings in the cognitive neuroscience of controlled semantic cognition, including evidence from anatomy, neuropsychology, and functional brain imaging.

Vision-to-Language Tasks Based on Attributes and Attention Mechanism.

Vision-to-language tasks aim to integrate computer vision and natural language processing together, which has attracted the attention of many researchers. For typical approaches, they encode image into feature representations and decode it into natural language sentences. While they neglect high-level semantic concepts and subtle relationships between image regions and natural language elements. To make full use of these information, this paper attempt to exploit the text-guided attention and semantic-guided attention (SA) to find the more correlated spatial information and reduce the semantic gap between vision and language. Our method includes two-level attention networks. One is the text-guided attention network which is used to select the text-related regions. The other is SA network which is used to highlight the concept-related regions and the region-related concepts. At last, all these information are incorporated to generate captions or answers. Practically, image captioning and visual question answering experiments have been carried out, and the experimental results have shown the excellent performance of the proposed approach.

Class-Balanced Text to Image Synthesis With Attentive Generative Adversarial Network

Although the text-to-image synthesis task has shown significant progress, it still remains a challenge in generating high-quality images. In this article, we first propose an attention-driven, cycle-refinement generative adversarial network, AGAN-v1, to bridge the domain gap between visual contents and semantic concepts by constructing spatial configurations of objects. The generation of image contours is the core component, in which an attention mechanism is developed to refine local details of images by focusing on the objects that complement one subregion. Second, an advanced class-balanced generative adversarial network, AGAN-v2, is proposed to address the problem of long-tailed data distribution. Importantly, it is the first method to solve this problem in the text-to-image synthesis task. Our AGAN-v2 introduces a reweighting scheme, which adopts the effective number of samples for each class to rebalance the generative loss. Extensive quantitative and qualitative experiments on CUB and MS-COCO datasets demonstrate that the proposed AGAN-v2 significantly outperforms the state-of-the-art methods.