Georgi Dimitrov

The final phase of the backgammon game, known as "races", is considered. This phase is characterized by minimizing the interaction of opponent's checkers and reducing the game process mainly to assessing the rate of moving checkers to the "home" and their subsequent withdrawal from the board. The delivered analysis covers the specifics of this stage in various backgammon variants, in particular in classic backgammon, using mathematical models and probability theory. The paper proposes methods for optimizing strategies in the final phase of the game, which are based on a comprehensive analysis of possible game positions and calculating the probabilities of the dices values. Special attention is paid to algorithmic approaches to decision-making, which allow calculating the most effective game actions to achieve the optimal result. The results of the study are valuable both for improving the game strategies of practicing players and beginners, and for developing theoretical approaches to game analysis based on modeling and assessing the effectiveness of decision-making in conditions of limited interaction between participants.

This paper explores AI-based image processing techniques to enhance the readability of 12th-century palimpsests from the Bulgarian National Library. Combining UV/IR imaging with methods like denoising, CLAHE, and sharpening, the study demonstrates significant improvements in recovering obscured texts for research and educational purposes.

This paper presents a methodology for enhancing palimpsest readability using image processing. Applying CLAHE, noise reduction, and adaptive thresholding enables the extraction of hidden text layers. The results show effective separation of scriptio inferior and superior, supporting further analysis through digital reconstruction and OCR.

Currently, high-tech assistive technologies (ATs), particularly Socially Assistive Robots (SARs), virtual reality (VR) and conversational AI (ConvAI), are considered very useful in supporting professionals in Speech and Language Therapy (SLT) for children with communication disorders. However, despite a positive public perception, therapists face difficulties when integrating these technologies into practice due to technical challenges and a lack of user-friendly interfaces. To address this gap, a design-based research approach has been employed to streamline the integration of SARs, VR and ConvAI in SLT, and a new software platform called “ATLog” has been developed for designing interactive and playful learning scenarios with ATs. ATLog’s main features include visual-based programming with graphical interface, enabling therapists to intuitively create personalized interactive scenarios without advanced programming skills. The platform follows a subprocess-oriented design, breaking down SAR skills and VR scenarios into microskills represented by pre-programmed graphical blocks, tailored to specific treatment domains, therapy goals, and language skill levels. The ATLog platform was evaluated by 27 SLT experts using the Technology Acceptance Model (TAM) and System Usability Scale (SUS) questionnaires, extended with additional questions specifically focused on ATLog structure and functionalities. According to the SUS results, most of the experts (74%) evaluated ATLog with grades over 70, indicating high acceptance of its usability. Over half (52%) of the experts rated the additional questions focused on ATLog’s structure and functionalities in the A range (90–100), while 26% rated them in the B range (80–89), showing strong acceptance of the platform for creating and running personalized interactive scenarios with ATs. According to the TAM results, experts gave high grades for both perceived usefulness (44% in the A range) and perceived ease of use (63% in the A range).

Advancements in portable EEG headsets have accelerated Brain-Computer Interface (BCI) applications, particularly in smart home automation and user well-being. However, few BCI platforms support cross-disciplinary users with limited programming skills. To address this, we propose a visual, node-based BCI programming framework using BrainFlow and the OpenBCI toolkit within Node-RED. This approach enables users to stream, process, and extract EEG features, while leveraging BrainFlow APIs as backend child processes to ensure adaptability and seamless updates. The framework was validated through a case study with 14 participants, where an OpenBCI headset was used to control a robotic arm. Results revealed that specific bursts in Event-Related Synchronization (ERS) within certain frequency bands were crucial for attention-based control. θ (theta) and γ (gamma) frequency bands in the frontal lobe were highly significant, as these regions are associated focus and decision-making. Similarly, high β (beta) activity in the left central and parietal lobes demonstrated a strong correlation with motor control and sustained attention. The study also evaluated the BrainFlow 'Mindfulness' metric to assess mental state during task engagement. The average value of this metric was 0.31, with a standard deviation of 0.11, indicating a moderate relative variability with a coefficient of variation ≈ 0.364. The results also highlight the key electrodes and frequency bands involved during attention and concentration, emphasizing the potential of using EEG-based metrics and ERS burst patterns as reliable neural markers to distinguish these states.

The article presents work on developing a software application for test creation using artificial intelligence and large language models. Its main goal is to optimize the educators’ work by automating the process of test generation and evaluation, with the tests being stored for subsequent analysis and use. The application can generate test questions based on specified criteria such as difficulty level, Bloom’s taxonomy level, question type, style and format, feedback inclusion, and more, thereby providing opportunities to enhance the adaptability and efficiency of the learning process. It is developed on the Google Firebase platform, utilizing the ChatGPT API, and also incorporates cloud computing to ensure scalability and data reliability.

The consortium of “The Future is in Applied Artificial Intelligence” Project designed the first competency-based applied artificial intelligence curriculum at the higher-education institution level. The development was based on advanced system research on existing artificial intelligence-related resources and surveying target groups of teachers, information technology students, and employers, which should enhance the performance of implementing artificial intelligence education. A review of applied artificial intelligence was prepared in the form of keyword clustering. The initial data were collected with the help of surveying by identifying job offers, existing artificial intelligence training courses, scientific projects, and real cases. A synthetic analysis of the textual information from the studies was conducted using the word clouds technique. A tensor-based approach was used for the presentation of the competency-based course. The specific numerical requirements for the course in the form of priorities followed from the solution to decision-making problems using the analytic hierarchy process technique. Based on a comprehensive study of surveys, educational experience, scientific projects, and business requirements, and a meta-analysis of the recent references, we specified the criteria for a training course in the form of a tensor-based representation of competencies in relation to content and educational modules.

The area of cybersecurity problems has reached the stage of becoming a science. This raises questions about the connection between the mathematical theories used in cybersecurity research and their relation to the methodology for experiments and conceptual models synthesized from the academic community. This research proposes an analytical review of the mathematical ideas used in applied cyber-security and theoretical explorations. This meta viewpoint is dedicated to standard mathematical theories applied in cybersecurity issues. The ground of the work is methodological problems relating to the validation of experiments and models with mathematical ideas in the cybersecurity exploration of digital space. This research emphasizes the application of game theory, catastrophe theory, queuing systems, and Markov chains. The methods are shown without claiming to be exhaustive. The goal is to review the currently established implementation of mathematical approaches to cybersecurity. A spectrum of possibilities for applying mathematical apparatus in future research for cybersecurity is given. After a review of the literature for each presented mathematical approach, we expose a list of problematic areas in which this has already been implemented.