The constant growth of data volumes requires the development of effective methods for managing, processing, and storing information. Additionally, it is advisable to apply multimodal approaches for knowledge aggregation to extract additional knowledge. Usually, the problem of efficient processing of multimodal data is associated with high-quality data preprocessing. One of the most critical preprocessing steps is synchronizing multimodal data stre- ams to analyze complex interactions in different data types. In this article, we evaluate existing approaches to synchronization, focusing on strategies based on real-time classifiers, which are based on comprehensive platforms for data integration and management. After the synchronization of multimodal sets, the key stage is data fusion, data identification in different channels, such as text, video, and audio. The results demonstrate the feasibility of the proposed synchronization approach for revealing subtle relationships between various data sets. An architectural solution was also suggested to integrate the proposed method into existing multimodal data processing pipelines. This work contributes to developing synchronization tools for multimodal data analysis in dynamic real- world scenarios.

The article considers the possibility and priority of using the Internet of Things, especially its implementation in the surface water monitoring system. The feasibility of developing a complex system of interactive monitoring of surface water using IoT technologies has been substantiated, such a system will significantly improve water monitoring in real-time and ensure the gradual implementation of new sensor capabilities, such as collecting data on the deviation of parameters from the specified normative indicators of water quality in natural reservoirs. An interactive system for intelligent monitoring of water quality in natural reservoirs using Internet of Things technologies and tools has been developed, among others, the Node MCU 1.0 Wi-Fi microcontroller based on the ESP8266 microcontroller was used, as well as PH4502s analog sensor, the DHT-11 water and environmental temperature sensor, the DFRobot water turbidity and signal conversion board V2. The results were displayed on a 2.2- inch QVGA TFT LCD. The microcontroller unit (MCU) is connected to the sensors and further processing is performed on the server unit. The choice of a cloud server was justified, and the transfer of received data was transferred to the cloud using IoT-based ThingSpeak open-source software for water quality monitoring. The computer design environment Autodesk was used to increase the efficiency of design, in particular, the arrangement of elements, ensuring functionality, and ergonomics. The software and hardware of the device were designed with open-source software Fritzing and Arduino (IDE). Based on the obtained statistical data about the quality of water in natural reservoirs, a modern network of smart devices was implemented, such a network is a monitoring and notification system, which considers the linking of data to the time and place of positioning. Features of obtaining data on the results of water quality monitoring in natural reservoirs in real time for consumers were presented, with such monitoring, it is possible to predict and take the necessary measures to prevent possible negative impacts.

Штучні нейронні мережі (НМ) стають все більш перспективним інструментом для криптоаналізу сучасних шифрів. У статті розглянуто підхід «нейронного криптоаналізу», що полягає у використанні глибоких НМ для автоматизованого пошуку вразливостей у симетричних криптосистемах. Наведено мету та задачі дослідження – продемонструвати здатність НМ виявляти приховані статистичні відхилення у шифрованих даних та на їх основі відновлювати секретну інформацію (ключі або відкритий текст). Для цього були розроблені та протестовані кілька моделей, зокрема: нейронний класифікатор-дистингвішер для розрізнення правильних шифротекстів від випадкових послідовностей, автоенкодер для виявлення внутрішніх структурних особливостей шифрованих даних та трансформерна модель для відновлення відкритого тексту за шифротекстом. Отримані результати підтвердили ефективність нейромережевого підходу на прикладі спрощених варіантів блокових шифрів (зокрема Speck). Глибока нейронна мережа-різницевий розрізнювач успішно відрізняла шифровані дані від випадкових та перевершила класичні диференційні методи за точністю, що дозволило здійснити більш глибокий злам меншого обсягу раундів шифру. Крім того, нейромережа змогла частково відновити ключову інформацію для алгоритму AES, використовуючи значно менше вибірок пар «відкритий текст – шифротекст», ніж потрібно для повного перебору ключа. Також проаналізовано різні архітектури НМ: встановлено, що у задачах класифікації шифртекстів рекурентні мережі можуть перевершувати трансформери, оскільки останні не отримують переваг на послідовностях біт без явної семантичної структури. Загалом отримані результати підтверджують, що НМ можуть знаходити нетривіальні шаблони, пов’язані з криптографічною стійкістю, й автоматизувати фрагменти криптоаналітичних атак. Зазначено, що успішність такого підходу залежить від великих масивів даних для навчання та потужних обчислювальних ресурсів. У висновках окреслено перспективи розвитку нейро-асистованого криптоаналізу, зокрема комбінування НМ з класичними методами та вдосконалення інтерпретованості моделей для кращого розуміння виявлених вразливостей.

In today's world, cloud services play an important role in the success of any global business. Evaluating the quality of these services is of paramount importance for companies and individuals who use them for their operations and data management. This article is an analysis of various methods for assessing the quality of cloud services. Reliability, performance, and security metrics are discussed, and different assessment models are compared. In the introduction, the authors describe the importance of cloud services and the need to assess their quality. This article provides an overview of cloud service quality assessment methods and shows how they can be applied to different aspects of these services. In the main part of the article, the author describes different methods of assessing the quality of cloud services and compares different assessment models. The author also provides examples of how these methods are used. The methods for assessing the quality of cloud services are discussed, covering reliability, performance, and security. While these metrics provide valuable insights into the performance of cloud services, some counterarguments highlight the potential limitations of using standardized metrics for evaluation. The author also recommends choosing a cloud provider and optimizing cloud services. It is emphasized that reliability metrics, such as uptime and disaster recovery time, are important to ensure the smooth operation of cloud services. Performance metrics, such as response time and throughput, indicate how quickly and efficiently cloud services can process data and requests. Security metrics, such as data encryption and access control, are important to protect data from unauthorized access and cyber threats. The author also emphasizes the importance of considering subjective factors such as user experience and customer support when assessing the quality of cloud services. Finally, the author discusses various risk assessment models that can be used to assess the security of cloud services. The author proposes a new approach to assessing the quality of cloud services for educational institutions. This approach is based on using a combination of quantitative and qualitative indicators that consider the reliability, performance, security, and usability of cloud services. In conclusion, the author recommends choosing a cloud provider and optimizing cloud services. Assessing the quality of cloud services is a complex process that requires a comprehensive and systematic approach. You need to consider various factors such as reliability, performance, and security, as well as your organization's individual needs and requirements.

Prototyping is an important component of user interface development, during which key decisions are made regarding methods of information presentation and the organization of user interaction. Assessing inclusivity at this stage makes it possible to account for relevant factors before the final implementation of the interface and to adjust design decisions accordingly. The goal of this study is to develop a model for fuzzy multi-criteria assessment of user interface inclusivity at the prototyping stage, taking into account the relative importance of the criteria and the preference degree of alternatives. The relative priorities of the criteria are established by the pairwise comparison method using the Saaty scale. To demonstrate the application of the model, three alternative interface prototypes are considered. For their comparison, fuzzy preference relations are formed for each criterion, and their intersections and weighted convolutions are performed. Sets of non-dominated alternatives and the resulting membership function are determined. A structural–functional model and pseudocode of the assessment algorithm are developed. The proposed approach makes it possible to quantitatively compare prototypes based on a set of inclusivity factors and determine the optimal interface solution.

Prototyping is an important component of user interface development, during which key decisions are made regarding methods of information presentation and the organization of user interaction. Assessing inclusivity at this stage makes it possible to account for relevant factors before the final implementation of the interface and to adjust design decisions accordingly. The goal of the study is to develop a model for fuzzy multi-criteria assessment of the user interface inclusivity at the prototyping stage, taking into account the relative importance of factors and the preference degree of alternatives. The relative priority of factors is established by the pairwise comparison method using the Saaty scale. To demonstrate the application of the model, three different options for the interface solution at the prototyping stage are considered. For their comparison, fuzzy preference relations are formed for each factor, their intersection and weighted convolution are performed. Sets of non-dominated alternatives and the resulting membership function are determined. A structural-functional model and pseudocode of the assessment algorithm are developed. The proposed approach makes it possible to quantitatively compare prototypes based on a set of inclusivity factors and determine the optimal interface solution.