Personal User’s Communicative Competence of “Mediatized World” Construction

The relevance of this article is determined by the growing ubiquitous digitalization of mediatized communications, including under the influence of the COVID-19 pandemic in 2020-2021. The consequence of this process is the transformation of the structure of social space and approval of virtual interaction as a basic method of communication - instead of interpersonal. These transformations entail institutional changes, manifested in axiological and normative transitions of communicative space, semantic restructuring of communications under the influence of changing social reality. Induced by the technological infrastructure of communication, a mediatized social reality emerges, which also entails semantic changes in communication. All these circumstances actualize the phenomenon of communicative competence of an individual, which determines the effectiveness of interactions in the context of technological, semantic and institutional changes. The article presents some outcomes of empirical verification of communicative competence in contemporary Russian interaction practices. The phenomenon of hybridization of communicative competence during the transition of a person from the environment of real communications to the environment of virtual communications, contributes to the construction of mediatized social reality and expands his social reality. This ability of an individual and his communicative competence for transgression, combined with hybridity, is a factor in the integration of the communicative space of society with its contradictory characteristics.

Looking for Semantic Similarity: What a Vector-Space Model of Semantics Can Tell Us About Attention in Real-World Scenes

The visual world contains more information than we can perceive and understand in any given moment. Therefore, we must prioritize important scene regions for detailed analysis. Semantic knowledge gained through experience is theorized to play a central role in determining attentional priority in real-world scenes but is poorly understood. Here, we examined the relationship between object semantics and attention by combining a vector-space model of semantics with eye movements in scenes. In this approach, the vector-space semantic model served as the basis for a concept map, an index of the spatial distribution of the semantic similarity of objects across a given scene. The results showed a strong positive relationship between the semantic similarity of a scene region and viewers’ focus of attention; specifically, greater attention was given to more semantically related scene regions. We conclude that object semantics play a critical role in guiding attention through real-world scenes.

Diagrams, Conceptual Space and Time, and Latent Geometry

The “origins” of (geometric) space is examined from the perspective of the so-called “conceptual space” or “semantic space”. Semantic space is characterized by its fundamental “locality” that generates an “implicit” mode of geometrizing. This view is examined from within three perspectives. First, the role that various diagrammatic entities play in the everyday life and pragmatic activities of selected ethnic groups is illustrated. Secondly, it is shown how conceptual spaces are fundamentally linked to the meaning effects of particular natural languages and these are very different from the global and universal aspects of Euclidean spaces. Thirdly, it is contended that these modes of creating body and culture-based spatial frameworks and related cosmogonies and cosmologies can be described as forms of “latent geometry” that initially appear unexplainable in any rational way. Nonetheless, and thanks to the deep mathematical reflections provided by René Thom, it is illustrated how the various ways of generating space can be further analyzed as distortions of mainstream spatialization furnished by Euclidean geometry that established the dominant universality of the ideas of space (and time).

Looking for Semantic Similarity: What a Vector Space Model of Semantics Can Tell Us About Attention in Real-world Scenes

The visual world contains more information than we can perceive and understand in any given moment. Therefore, we must prioritize important scene regions for detailed analysis. Semantic knowledge gained through experience is theorized to play a central role in determining attentional priority in real- world scenes but is poorly understood. Here we examined the relationship between object semantics and attention by combining a vector space model of semantics with eye movements in scenes. Within this approach, the vector space semantic model served as the basis for a concept map, an index of the spatial distribution of the semantic similarity of objects across a given scene. The results showed a strong positive relationship between the semantic similarity of a scene region and viewers’ focus of attention, with greater attention to more semantically related scene regions. We conclude that object semantics play a critical role in guiding attention through real-world scenes.

Development of an Indoor Space Semantic Model and Its Implementation as an IndoorGML Extension

Geospatial data capture and handling of indoor spaces is increasing over the years and has had a varied history of data sources ranging from architectural and building drawings to indoor data acquisition approaches. While these have been more data format and information driven primarily for the physical representation of spaces, it is important to note that many applications look for the semantic information to be made available. This paper proposes a space classification model leading to an ontology for indoor spaces that accounts for both the semantic and geometric characteristics of the spaces. Further, a Space semantic model is defined, based on this ontology, which can then be used appropriately in multiple applications. To demonstrate the utility of the model, we also present an extension to the IndoorGML data standard with a set of proposed classes that can help capture both the syntactic and semantic components of the model. It is expected that these proposed classes can be appropriately harnessed for use in diverse applications ranging from indoor data visualization to more user customised building evacuation path planning with a semantic overtone.

RESEARCH ON EXTENSION OF SPARQL ONTOLOGY QUERY LANGUAGE CONSIDERING THE COMPUTATION OF INDOOR SPATIAL RELATIONS

A method suitable for indoor complex semantic query considering the computation of indoor spatial relations is provided According to the characteristics of indoor space. This paper designs ontology model describing the space related information of humans, events and Indoor space objects (e.g. Storey and Room) as well as their relations to meet the indoor semantic query. The ontology concepts are used in IndoorSPARQL query language which extends SPARQL syntax for representing and querying indoor space. And four types specific primitives for indoor query, "Adjacent", "Opposite", "Vertical" and "Contain", are defined as query functions in IndoorSPARQL used to support quantitative spatial computations. Also a method is proposed to analysis the query language. Finally this paper adopts this method to realize indoor semantic query on the study area through constructing the ontology model for the study building. The experimental results show that the method proposed in this paper can effectively support complex indoor space semantic query.

A Cloud Support Vector Machine Model Based on Image Semantics

In allusion to randomness and fuzziness of digital image semantic, we propose a new semantic representation of digital image based on cloud model and construct a semantic vector space. In this space, semantic classifications of digital images are completed by calculating the semantic class certainty degree (SCCD). In addition, we propose cloud support vector machine based on image semantics (CSVM-IS) model. Experimental results show that CSVM-IS can accomplish target classification and has good classification accuracy.

A Semantic Classification Method of Digital Image Based on Cloud Model

In view of randomness and fuzziness of digital image semantics, a new semantic representation of digital image based on cloud model is proposed and a semantic vector space is constructed. In the space, semantic classifications of digital images are completed by calculating the semantic class certainty degree (SCCD). In addition, we propose cloud support vector machine based on image semantics (CSVM-IS) model, which can effectively utilize the knowledge of SCCD. This method can effectively classify the multi-semantic information and eliminate the rejection of the classification samples. Experimental results show that CSVM-IS is superior to the Nesting Algorithm in terms of classification performance.