?
Explainable AI for Industry 5.0: Shedding light on the black box
Business Informatics. 2026. Vol. 20. No. 1. P. 7–28.
The rapid development of artificial intelligence (AI) is accompanied by increasing computational
complexity and decreasing model transparency, which significantly limits its adoption in critical
domains that require a high level of trust, interpretability, and justification of decisions. Under these
conditions, the field of Explainable Artificial Intelligence (XAI) has gained particular importance as it
focuses on approaches and technologies that enable understanding of AI system logic and interpretation
of their outputs. This article examines the timely topic of implementing XAI in the context of Industry
5.0. Special attention is given to practical application scenarios: the authors present concrete industrial
cases from IBM, Siemens, and other companies demonstrating how XAI contributes to enhancing
the reliability, safety, efficiency, and trustworthiness of AI systems. The study includes a systematic
search and analysis of the literature in this domain and proposes well-grounded key criteria for
comparing existing XAI approaches. The article also outlines the advantages, current limitations, and
promising directions for the development of XAI, highlighting the opportunities it opens for improving
effectiveness, transparency, and trust in business.
Neal N. X., Weiqing L., Dacheng H. et al., Algorithms 2026 Vol. 19 No. 5 P. 1–22
In the era of data-driven education, educational social networks generate large volumes of high-dimensional and complex-structured data through learner interactions, collaborative activities, and resource-sharing behaviors, posing significant challenges to traditional unsupervised learning methods. Such data often exhibit non-convex distributions, heterogeneity, and noise sensitivity, making conventional clustering approaches insufficient for capturing their intrinsic structural relationships. To ...
Added: May 13, 2026
Velichkov B., Nikolova-Koleva I., Slavcheva M., Shumen: INCOMA Ltd, 2025.
The RANLP 2025 Student Research Workshop (RANLPStud’2025) is a special track of the established international conference Recent Advances in Natural Language Processing (RANLP’2025).
The RANLPStud is being organised for the 9th time and this year is running in parallel with the other tracks of the main RANLP 2025 conference. The target of RANLPStud’25 is to be a ...
Added: May 12, 2026
Stepanyants V., Долгов И. М., Хорошилов Г. С. et al., Труды Института системного программирования РАН 2026 Т. 38 № 3 С. 95–110
Highly automated and connected vehicles are gradually entering the market. Currently, solutions are being proposed that allow these technologies to be used for cooperative driving automation, which can significantly improve traffic safety. Such technologies and their software should be tested to ensure safety before being implemented in real systems. Verification and validation of vehicular control ...
Added: May 12, 2026
Tikhonov R., Efendiev M. T., Fedotenkov A. A., 2026 International Russian Smart Industry Conference (SmartIndustryCon) 2026 P. 542–547
High-fidelity simulation environments like CARLA and ROS are essential for connected and automated vehicle research. They allow researchers to verify and validate new software and technology without the time, financial, and safety overheads of real-world testing. However, their operation requires considerable expertise for creating platform-specific scenario configuration files, which complicates the research workflow. This paper ...
Added: May 11, 2026
Los Alamitos: IEEE Computer Society, 2026.
It is a great pleasure for us to welcome you on behalf of the conference committees, to the 11th IEEE International Conference on Smart Cloud (IEEE SmartCloud 2026), we are glad that we can have this international conference in New York city, USA. Now, please allow us to introduce the IEEE SmartCloud 2026 conference. The ...
Added: May 10, 2026
Avdoshin S. M., Pesotskaya E. Y., Информационные технологии 2026 Т. 32 № 4 С. 185–194
With the rapid advancement of artificial intelligence, and deep learning in particular, models have emerged that are capable of delivering highly accurate predictions. However, the internal logic of such models remains difficult to interpret—an issue of critical importance, especially in domains where the correctness of an algorithm directly affects high-stakes decision-making. One promising avenue for ...
Added: May 8, 2026
Varnavsky A., IEEE Access 2026 Vol. 14 P. 37487–37508
Puzzles are an excellent tool for learning computer science and programming, fostering increased interest, engagement, and motivation among students, as well as developing logical, critical, and computational thinking. Among beginner programmers, Parson's Programming Puzzles are quite popular, aimed at mastering the basic syntactic and logical constructs of programming languages. However, as students' skills grow, their ...
Added: May 7, 2026
Ismagilov T., Mukosey A., Smirnov F. et al., International Journal of High Performance Computing Applications 2026 Vol. 40 No. 2 P. 240–253
One of the most important aspects of supercomputer development in the post-Moore era is the interconnect technologies that allow one to unite a multitude of processing elements into a well-synchronized computing system. Novel types of supercomputer interconnect require careful benchmarking and compliance with the requirements of modern hardware trends. GPU-based heterogeneous computing is one of ...
Added: May 7, 2026
Yasnitsky L., Голдобин М. А., Мезенцев А. С., Прикладная математика и вопросы управления 2025 № 2 С. 99–116
Представлен обзор современных методов и основанных на них программных
инструментах, применяемых для математического моделирования серийных производственных процессов с целью снижения брака и повышения качества производимых изделий. Перечисляются группы работ, нацеленных на обнаружение и классификацию дефектов, работ, в которых решаются задачи прогнозирования образования дефектов и определения значимости параметров, работ направленных на поиск
оптимального сочетания технологических параметров изготовления изделий, ...
Added: May 5, 2026
Монахова Э. А., Монахов О. Г., Rzaev E. et al., Прикладная дискретная математика 2026 Т. 71 С. 112–127
В настоящей работе исследовано совместное конструирование топологий семейств оптимальных по диаметру циркулянтных сетей $C(N; \pm 1, \pm s_2)$ и реализуемых для них оптимальных алгоритмов маршрутизации сложности $O(1)$. Предлагаемый алгоритм маршрутизации основан на использовании масштабируемых параметров $L$-образных шаблонов плотной укладки графов на плоскости для семейств оптимальных сетей.
Определены аналитические формулы зависимости этих параметров от диаметра графов семейств ...
Added: May 4, 2026
Sosnin E. I., Vasil’ev Y. L., Solovyev R. A. et al., Computer Optics 2025 Vol. 49 No. 6 P. 1129–1137
In this article, we present a new unique dataset for dental research – AlphaDent. This dataset is based on the DSLR camera photographs of the teeth of 295 patients and contains over 1200 images. The dataset is labeled for solving the instance segmentation problem and is divided into 9 classes. The article provides a detailed ...
Added: May 4, 2026
Назаренко А. Г., Федоров М. В., Moshkin A. et al., Вестник Росздравнадзора 2026 № 1 С. 14–29
Multimodal foundation models and medical multimodal large language models are establishing a new class of diagnostic clinical decision support systems capable of operating on heterogeneous data sources, including medical imaging (X-ray, CT, MRI, ultrasound, histopathology), physiological signals (ECG, EEG), clinical text (electronic health records, reports, discharge summaries), laboratory measurements, molecular profiling data, and related modalities. ...
Added: May 4, 2026
Honolulu: IEEE, 2025.
International Conference on Computer Vision Workshops (ICCVW), Honolulu, HI, USA, 2025 ...
Added: May 3, 2026
Vasilev A., Kapitanov A., Solovyev Roman A. et al., PeerJ Computer Science 2026 Vol. 12 Article 3724
This article introduces MinMAE, a novel activation calibration method for Post-Training Quantization (PTQ) that significantly reduces accuracy loss in Convolutional Neural Networks (CNN). Motivated by the need for high-fidelity quantization without costly retraining, MinMAE directly minimizes the Mean Absolute Error (MAE) between original and dequantized activations, making it robust to outliers that degrade standard methods. ...
Added: May 3, 2026
Solovyev Roman A., Telpukhov Dmitry, Shafeev I. et al., Technologies 2026 Vol. 14 No. 3 Article 169
With the continuous scaling of semiconductor design technologies, evaluating static IR drop has become a critical bottleneck in the physical synthesis flow. This paper presents a machine learning-based framework that transforms the power delivery network (PDN) analysis problem into an image-to-image translation task using a U-Net architecture with MaxViT and EfficientNet encoders. By implementing a ...
Added: May 3, 2026
Taletskii D., / Series arXiv "math". 2026.
A vertex subset of a graph is called a \textit{distance-$k$ independent set} if the distance between any two of its distinct vertices is at least $k + 1$. For all $n,k \geq 1$, we determine the minimum possible number of inclusion-wise maximal distance-$k$ independent sets among all $n$-vertex trees. It equals~$n$ if $n \leq k ...
Added: May 1, 2026
Dayoub A., Suleiman E., IEEE, 2026.
2026 8th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)
1-3 April 2026 ...
Added: April 30, 2026
М.: ООО «Геомодель Развитие», 2024.
Интелшектуальный анализ данных в нефтегазовой отрасли, Калининград, Россия, 2024, ООО «Геомодель Развитие» ...
Added: April 29, 2026
Karpova Irina Petrovna, Pattern Recognition and Image Analysis 2025 Vol. 35 No. 4 P. 1138–1144
A solution to the problem of redistributing agents between groups based on simulating a form of social parasitism in ants known as slave-making is considered. To provide a comprehensive solution, the problem is integrated with a method of orientation based on visual landmarks and a compass, including route memorization and return. The models and mechanisms ...
Added: April 29, 2026
Derkacheva A., Sakirkina M., Kraev G. et al., /. 2026.
Comprehensive data on natural hazards and their consequences are crucial for effective for risk assessment, adaptation planning, and emergency response. However, many countries face challenges with fragmented, inconsistent, and inaccessible data, particularly regarding local-scale events. To address this data gap in Russia, we developed an end-to-end processing pipeline that scrapes news from various online sources, ...
Added: April 28, 2026
Ramenskaya A., Чудинова О. С., Первицкая Л. А., Индустриальная экономика 2026 № 1 С. 65–78
This article is devoted to the development of an algorithm for analyzing news information using machine learning methods implemented in Python libraries. The choice of tools used at each stage of the algorithm is justified by calculating metrics for the quality of the solution to the corresponding machine learning problems. The algorithm’s results are presented ...
Added: April 20, 2026
Makarov D. M., Kalikin N., Gurikov P. et al., Journal of Supercritical Fluids 2026 Vol. 235 Article 106979
Supercritical CO2 (scCO2 ) is an environmentally friendly solvent, but its low polarity limits the solubility of polar compounds. Cosolvents are commonly used to enhance solvation capability, yet comprehensive datadriven studies are scarce. We compiled the largest dataset to date — 4401 experimental solubility records with 22 cosolvents for 93 nonionic solutes, plus 4855 records ...
Added: April 19, 2026
Lysenok N., Фундаментальная и прикладная математика 2026 Т. 26 № 3 С. 33–42
This study examines the impact of realized volatility forecasts on the performance of active trading strategies in the Russian equity market. Using a sample of 17 liquid stocks over the period 2014–2026, a hybrid forecasting model is developed that combines HAR-J with gradient boosting; its superiority over the baseline HAR-J specification is confirmed by the ...
Added: April 17, 2026
Efremov A., Timofeev A., Ilyasov Y. et al., , in: ПАРАЛЛЕЛЬНЫЕ ВЫЧИСЛИТЕЛЬНЫЕ ТЕХНОЛОГИИ (ПаВТ’2025).: Издательский центр Южно-Уральского государственного университета, 2025. P. 56–81.
Accurate benchmarking is critical for selecting computing architectures optimized for machine learning (ML) tasks. Conventional benchmarks such as High-Performance Linpack (HPL) and High Performance Conjugate Gradients (HPCG) often fail to capture the diversity and complexity of modern ML workloads. This study investigates the correlation between hardware parameters (e.g., processor architecture, cache size, frequency) and ML ...
Added: April 4, 2026