The concept of stop words introduced by H. P. Lun in the mid-20th century plays a huge role in today’s NLP practice. Stop words are used to reduce noisy text data, remove uninformative words, speed up text processing, and minimize the amount of memory required to store data.The Kyrgyz language is an agglutinative Turkic language for which no scientific study of stop words has been previously published in English. In our study, we combined frequency analysis with rule-based linguistic analysis. First, we found the most frequently used words, set a threshold, and removed words below the threshold. This way we got a list of the most frequently used words. Then we reduced the list by excluding from the list all words that do not belong to the category of function words of the Kyrgyz language. Finally, we got a list of 50 words that can be considered stop words in the Kyrgyz language. In our analysis, we used a single corpus of sentences collected and posted as an open source project by one of the local broadcasters.

Holographic Intellectual Voice Assistant (HIVA) aims to facilitate human computer interaction using audiovisual effects and 3D avatar. HIVA provides complete information about the university, including requests of various nature: admission, study issues, fees, departments, university structure and history, canteen, human resources, library, student life and events, information about the country and the city, etc. There are other ways for receiving the data listed above: the university's official website and other supporting apps, HEI (Higher Education Institution) official social media, directly asking the HEI staff, and other channels. However, HIVA provides the unique experience of "face-to-face" interaction with an animated 3D mascot, helping to get a sense of 'real-life' communication. The system includes many sub-modules and connects a family of applications such as mobile applications, Telegram chatbot, suggestion categorization, and entertainment services. The Voice assistant uses Russian language NLP models and tools, which are pipelined for the best user experience.

Cyberthreat awareness and the timely exchange of threat intelligence among stakeholders are critical components of effective cybersecurity. However, despite ongoing efforts to strengthen cyber defenses, many countries continue to face significant challenges in facilitating the rapid and efficient sharing of relevant threat data. This deficiency often leads to severe consequences, including financial losses, reputational damage, and even threats to national security. This study investigates the feasibility of establishing an effective platform for cyberthreat data sharing, with a particular focus on organizations and insti- tutions that lack access to costly commercial threat intelligence solutions. The proposed approach leverages open-source tools, such as the Malware Information Sharing Platform (MISP), to provide a cost-effective and scalable solution for threat informa- tion exchange. The research presents a hybrid data collection model in which threat intelligence is aggregated through two primary methods: 1- manual input derived from structured reports and, 2- automated ingestion via scripts designed to process and sanitize raw threat data. This dual approach ensures both flexibility and efficiency in compiling actionable intelligence. The findings suggest that a freely available and user-friendly platform, such as MISP, can serve as a viable foundation for structured cyberthreat information sharing among stakeholders. By improving the accessibility and reliability of threat data, such a system can enhance incident response capabilities and support the implementation of proactive cybersecurity measures.

Accurate forecasting of exchange rates plays a huge role in the lives of the vast majority of the population. The work of various businesses, policymakers and economists is extremely dependent on the right tools for forecasting exchange rates, due to their significant implications for domestic and foreign trade, investment strategies, and economic planning. This paper focuses on modelling daily exchange rates between Kyrgyz som (KG som) and US dollar, and compares the actual data with developed forecasts using time series analysis over the period from 2010 to 2023. The official daily currency exchange data of National Bank of the Kyrgyz Republic is processed in the study. Three ML algorithms, Random Forest regressor, CatBoost regressor and multiple linear regression (MLR), are compared with triple exponential smoothing (Holt-Winters) and Prophet models. The goal of the study is to define how effectively the above methods predict changes in the exchange rate of the KG som to the US dollar. The accuracy of the forecasts is evaluated by Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). This study can be considered as the first academic study on time series forecasting of the national currency of Kyrgyzstan. The implicit idea of the work is to open up this area to researchers. Therefore, the study can be extended in the future by adding other methods and models.