ABSTRACT. Ten years ago, Ukrainian was considered a low-resource language, with no open-source tools or resources available — but that was a decade ago.

In this talk, we will showcase the latest achievements in Ukrainian language processing, while also addressing the unique challenges that this language continues to face. We will observe how shared tasks and workshops, industry and academia, as well as independent communities, are shaping the dynamics of language development and driving innovation in natural language processing (NLP).

We will discuss the limitations of open-source, multilingual large language models (LLMs) in tackling various tasks related to the Ukrainian language and elaborate on the necessity of developing a sovereign LLM specifically designed for Ukrainian.

This talk will be of interest to professionals in academia and industry who want to gain insights into the current state and prospects of Ukrainian NLP, and researchers working on low-resource languages.

ABSTRACT. It is well-known that hybrid automata are mathematical models for cyber-physical systems. Unfortunately, the most of problems in behavior analysis for stochastic and probabilistic hybrid automata of general form are algorithmically unsolvable. For this reason, much effort has been focused on the development of probabilistic high-level models intended to solve specific problems in behavior analysis for fairly narrow classes of cyber-physical systems. Among them, models based on Markov processes are widely used. An important, and the easiest to analyze, subclass of these models is formed by Markov chains. In the given paper we illustrate the application of the following three classes of such models intended for solving problems of the behavior analysis of cyber-physical systems: models based on iterating random functions on the state space, models based on finite discrete-time Markov chains, and models based on finite continuous-time Markov chains.

ABSTRACT. Nowadays, most of the data obtained using image and video sensors are discrete in nature and represent mappings of continuous processes onto metric spaces with a given dimension. The discretized data can be naturally represented as point arrays on regular integer grids of the corresponding dimension. The obtained arrays of points in a given metric space can be studied from the point of view of their spatial characteristics and relations between the points of the set, which, together with the growth of the amount of data obtained, lead to the need to develop specialized geometric data structures and computer algorithms for their effective processing. The paper presents a unified approach to the processing of discrete point data, which is based on their indexing in a space of a given dimension for solving computational geometry and image processing tasks. A time-efficient bijective geometric hashing method for grid-based data and an approach to organizing interaction of indexed structures have been developed. The proposed techniques have been implemented as computer software in the Python language, and computer experiments have been conducted to assess the effectiveness of the developed approach.

ABSTRACT. We present a monadic library in Scala that extends the capabilities of logical programming by integrating machine learning feedback into declarative search. Built upon the dotty-cps-async implementation of the Logic monad, our approach introduces result-reordering mechanisms for logical streaming operations. This enhancement allows more flexible and efficient evaluation strategies in logical computations. A key contribution of our work is the extension of the LogicalMonad interface with facilities for directed search—enabling the scoring of results during backtracking. This scoring mechanism serves as a bridge between logic-based search and machine learning models, which typically output evaluation metrics rather than deterministic answers. By combining this scoring with a monadic unification framework, we develop a universal and composable architecture for declarative programming in Scala. Our framework enables developers to guide logical search processes based on external or learned preferences, making it well-suited for applications where probabilistic reasoning or adaptive feedback is required. Moreover, the modular design of the system allows for the gradual integration of machine learning components into existing traditional software systems without full rewrites. This approach opens new possibilities for hybrid systems that leverage both symbolic reasoning and statistical learning within a unified and expressive programming paradigm.

ABSTRACT. This study introduces DMM DigSME, a digital maturity model to support SMEs in their digital transformation journey. The model enables businesses to evaluate their digital readiness, receive personalized recommendations, and develop strategic roadmaps for sustainable growth. By addressing digital inequality, DMM DigSME helps SMEs integrate into the global digital economy and enhance their long-term competitiveness.

ABSTRACT. The article highlights the problem of using numerous artificial intelligence (AI) services effectively in educators’ and academic staff’s research activities. The Aixploria catalogue has over 5,000 tools, but academic professionals often lack time to view, test and select them for specific research needs. To overcome this problem, a tool for personalised selection of AI services using the author's WPadV4 software has been proposed. On its basis, educational packages are created that make it possible to form structured collections of services according to selected criteria. The work implements the selection and classification of AI tools from the Aixploria catalogue following the main stages of scientific and pedagogical research: literature analysis and problem statement, data collection and processing, analysis and interpretation of results, peer review, and dissemination. For each stage, relevant services are selected, their brief descriptions are created and presented in the form of tables that are automatically converted into web resources – personalised educational packages ready for direct use by educators, academic researchers, and professional development providers. This avoids the routine processing of large amounts of unstructured information and focuses on meaningful aspects of research. The described solution was tested within the framework of international educational and scientific events, particularly in the V4+ EDUPORT project (2022–2023) and at the AISE 2024 conference. To further assess the effectiveness of the developed approach, an expert survey among specialists in the field of ICT in education was conducted.

ABSTRACT. Industry 4.0 is characterized by increasing automation of processes and the intro-duction of intelligent technologies into industry and production. The Internet of Things, cloud computing, artificial intelligence and machine learning, cybersecuri-ty, virtual reality, etc., are all examples of the fourth industrial revolution. However, the presence and implementation of technologies in production do not guarantee the quality of the process. Future specialists must be ready for the chal-lenges of the labor market, new technologies and, in particular, have the neces-sary set of skills and competencies for their success and demand. The purpose of the study is to analyze the readiness of young people for changes in the labor market of Ukraine and Poland in the context of Industry 4.0. The study was based on a survey of more than 1,000 future specialists from the two countries in various specialties regarding their readiness to work in the era of the fourth industrial revolution, the difficulties they face in finding a job, as well as their vision of the necessary skills and competencies to meet the modern realities of the labor market. Job analysis shows that a modern specialist, in addition to mastering digital tools and subject area orientation, must also have a certain set of soft skills. Our findings show the readiness of young people and the market to cooperate in the context of employment and the difficulties and gaps in the context of Industry 4.0.

ABSTRACT. The paper discusses applying RFM-D analysis for customer segmentation using modern machine-learning methods. The authors focus on integrating three main approaches: reinforcement learning methods, unsupervised ML methods, and supervised ML methods. The first approach, based on reinforcement learning, allows to adaptively adjusting segmentation strategies based on variable customer characteristics and preferences and can continuously improve the process of selecting optimal strategy to achieve the objective function, which is essential for businesses operating in a dynamic market environment. For unsupervised learning, the K-means clustering method is considered, which helps to identify more homogeneous groups of customers based on characteristics such as purchase history, frequency and other factors, allowing businesses to customize their unique customer offers and related marketing strategies more accurately. At the same time, machine learning methods, such as classification algorithms, can predict customer behavior based on training data, ensuring high prediction accuracy and improving the quality of management decisions regarding unique offers to each customer segment. The study results show that combining these approaches allows for effective customer segmentation and optimized customer interaction, critical for increasing loyalty and overall efficiency of business strategies.

ABSTRACT. Climatic factors play a primary role in crop‐yield fluctuations, with their effects exhibiting significant nonlinearity. This study examines the influence of air temperature throughout the growing season on wheat yield in the Steppe zone of Ukraine. Weather and climatic conditions in April, May, and June are critical for determining the subsequent wheat harvest. From an initial set of 132 climatic data records, 24 observations associated with high yield values were selected. Using machine‐learning techniques, a statistically significant quadratic regression model was constructed to relate yield to temperature indicators. The resulting quadratic model served as the objective function, defined over a constrained domain of admissible temperature values. To locate the maximum of this yield function, optimization methods for multivariate nonlinear functions were employed. The coordinates of the maximum define the optimal temperature trajectory that yields the highest wheat output in the Steppe zone of Ukraine. The findings enable the development of early dynamic yield forecasts with a lead time of three to four months. The proposed methodology can also be applied to forecast the yields of other agricultural crops.

ABSTRACT. We discuss the timing analysis of Software Defined Vehicle using both a Hardware Abstraction Layer (HAL) and Service-Oriented Architecture Components. With an availability of a common interface, both manufacturers and customers can install and update compatible off-the-shelf software, such as driving assistance and infotainment. In this context, both critical and non-critical components share the same communication mechanism and access to hardware, and for many - the same execution platform. To analyze and verify the safety requirements (e.g., reaction time), the timing behavior of the components and the hardware must be explicitly available. Such timing information must account for the uncertainties arising from the dynamicity of the software architecture and the inherent uncertainty of physical devices. As existing standards and frameworks related to SDVs often overlook the importance of timing or fail to provide a complete description, we propose a minimal set of annotations that capture the timing, communication patterns at the component interface, and other relevant properties of SDV components and HAL. Using this description, we provide reaction time analysis focusing on its understandability.