Abstract:

One of the Common Digital Space of Scientific Knowledge (CDSSK) main components are the subject ontologies of individual thematic subspaces, which include the basic concepts related to this scientific area. The constructing subject ontologies task at the initial phase requires the array of key terms formation in a given scientific are with the subsequent establishment of links between them. A similar task is in the encyclopedias formation in terms of the articles (slots) list generating that determines their content. One of the sources for the formation of the key terms array can be the metadata of articles published in the leading scientific journals. Namely, the author's key terms ("keywords" in the terminology of the journals editors) quoted by the article. To make a conclusion about the possibility of using this approach to the subject ontologies formation, it is necessary to conduct the author's key terms array preanalysis, both in terms of real correspondence to the main areas of research in this science branch and in terms of the distribution of the certain terms occurrence frequency. This article presents the results of the occurrence frequency analysis of the author's key terms in Russian and English, carried out on the software processing basis of several thousand articles from leading Russian journals in mathematics, computer science and physics, reflected in the MathNet database. An assessment was made of the distribution of key terms correspondence (as phrases) and individual words to the Bradford's law, and the key terms cores within the thematic direction were identified.

Abstract:

The paper analyzes the statistical and temporal seasonal and decadal variability of the atmospheric pressure field in the Arctic region of Russia. Schemes for the frequency analysis of probability transitions for characteristics of stochastic-diffusion processes were used as the main research method. On the basis of the given series of 60 years long from 1948 to 2008, such parameters of diffusion processes as the mean (drift process) and variance (diffusion process) were calculated and their maps and time curves were constructed. The seasonal and long-term variability of calculated fields was studied as well as their dependencies on a discretization of the frequency intervals. These characteristics were analyzed and their geophysical interpretation was carried out. In particular, the known cycles of solar activity in 11 and 22 years were revealed. Numerical calculations were performed on the Lomonosov-2 supercomputer of the Lomonosov Moscow State University.

Abstract:

This paper presents a digital platform for complex monitoring data of dangerous geodynamic, engineering-geological and hydrogeological processes occurring in the region of intensive nature management of the central ecological zone of the Baikal natural territory (CEZ BNT). The platform is intended for integration and analysis of data coming from several polygons located within the CEZ BPT in order to assess the state of the geological environment and forecasting of hazardous processes manifestation.

The platform is built on a client-server architecture. Storage, processing and analysis of data is carried out on the server, which users can access via the Internet using a web browser. Several data filtering methods (linear frequency, Savitsky-Goley and others), various methods of spectral and wavelet analysis, multifractal and entropy analysis, and spatial data analysis are currently available. The digital platform has been tested on real data.

Abstract:

Emotion recognition in text represents an important task in natural language processing. This study investigates the influence of verb morphological form on emotion classification performance by encoder models and identifies factors that may determine morphological sensitivity in language models. Three models (ruBERT-tiny2, ruBERT-base, ruRoBERTa-large), fine-tuned on a combined corpus of four Russian-language datasets (22,852 examples), were employed in the experiment. Each model classified 5,676 morphological forms of 473 verbs from the Russian Emotion Lexicon. Results revealed systematic morphological influence: differences in classification performance between forms, as measured by F1-score, reached 20–25% across all models. To account for the observed patterns, training data characteristics were analysed. Frequency of morphological forms in the corpus was shown to partially correlate with classification performance, though it does not appear to be the sole determining factor. Analysis of emotion distribution across morphological forms revealed that in the training corpus, imperative forms occur predominantly in contexts associated with Joy. However, Sadness-labelled verbs in imperative mood are systematically classified by encoders as Anger. This discrepancy between training data characteristics and classification outcomes suggests that imperatives combined with sadness semantics are interpreted by models as expressions of aggression, indicating an interaction between grammatical form and verb semantics in determining emotion class.

Abstract: Today the Virtual Observatory tools provide easy access to astronomical data and improve the efficiency of researches. Based on the existing web technologies the astronomical community is developing on realization greater data semantic connectivity. This will put the virtual observatory services to a new level of exchange of not only data but also knowledge. However, there is no development of tools for actualization information collected from web resource, and a means of organizing heterogeneous data prepared by the user for scientific analysis, which could improve further work. We consider the approach to organization of diverse information for a studying object, based on the development of the existing formats of astronomical data and tools to support semantic data connectivity.