Oct 2026· Izvestia Journal of the Union of Scientists - Varna Economic Sciences Series· 0 citations
Abstract
The rapid expansion of artificial intelligence (AI) in education and scientific research presents both significant opportunities and substantial challenges for higher education institutions (HEIs). Despite widespread adoption, a large proportion of AI initiatives fail to deliver sustainable value, underscoring the need for structured, ethical, and human- centered implementation models. This paper presents the modern achievements of the University of Economics – Varna in the application of AI across education, scientific research, and institutional processes. The study outlines an institutional framework for AI integration based on the Plan–Do–Check–Act (PDCA) cycle, emphasizing strategic vision,stakeholder collaboration, ethical governance, and continuous improvement. The presented experience contributes
practical insights into the transition from AI toward hybrid intelligence, where technological advancement is balanced with human values, trust, and institutional responsibility
Investigating how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments finds that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes.
This systematic review evaluates 55 studies from 2017 to 2023 on the application of machine learning techniques to ASD, highlighting key challenges and opportunities, particularly the need for models that can integrate complex data to improve diagnostic accuracy and treatment outcomes.
Rafael Muñoz-Terol, Jesús Peral, Sandra Amador et al.· Heliyon· 4 citations· ⚡1
This paper identifies two different routes through which models can acquire geometrically separable features: they can learn them from complementary co-occurrence signals in general language data, including text-number co-occurrence and cross-number interaction, or from multi-token addition problems.
This work explores image generation using flow matching using flow matching and proposes an iterative process that can be integrated into virtually any generative modeling technique, thereby enhancing the performance and robustness of image synthesis systems.
Eldad Haber, Shadab Ahamed, Md Shahriar Rahim Siddiqui et al.· SIAM Journal on Scientific C...· 3 citations
Tensor networks are powerful formats for compressing large-scale data. However, their application to general data processing has been limited by the difficulty of performing nonlinear operations. Here, we introduce iterative tensor network transformations (ITNTs), a general algorithmic framework for the element-wise evaluation of elementary and nonlinear filtering functions on data encoded as tensor trains (TTs), a class of tensor networks. Our approach operates entirely in the compressed domain, enabling efficient computation on exponentially large datasets while maintaining a controlled computational cost. We demonstrate its power in two key areas: (I) evaluating highly nonlinear elementary and filtering functions on a 3D reactive flow field, enabling high-fidelity reaction rate computation and region filtering, and (II) finding extrema in complex optimization problems, such as solving Max-SAT instances on spaces up to $2^{70}$ configurations. These results establish ITNT as a foundational tool that provides tensor network methods with the capability for general-purpose data science and large-scale optimization.
Xiao Wang, Tomohiro Hashizume, Pia Siegl et al.· 2 citations
