Human resource management during project implementation in a multi-project environment requires addressing the resource-constrained project scheduling problem. Agile methodologies allow for greater management flexibility, necessitating an agile transformation of human resource management processes. Changes occurring in human resource management lead to modifications in the initial project team and alterations in the state of the resource pool in a multi-project environment. To ensure controllable changes in the project team and address the task of allocating (reallocating) limited resources among project tasks in a multi-project environment with subsequent optimization based on a selected criterion, it is proposed to use configuration management of human resources. Depending on the chosen level of detail, project specifics, and the implementation environment, configuration elements can be an executor, project team, or intact team. Types of equivalence applied to the set of configuration elements have been classified. A model of the configuration management process for human resources has been considered. Using the proposed model will allow for formalizing the process of implementing human resource configuration management in a multi-project environment. Constructive enumeration of configuration elements in a multi-project environment has been examined. Identifying a typical representative of the configuration and considering the given equivalence, followed by selecting a resource allocation/reallocation option that meets the specified constraints, enhances team adaptability. An example of configuration management in addressing team composition management tasks has been discussed. The proposed approach can be applied in managing human resources for agile transformation projects of critical infrastructure, particularly in the healthcare sector, during the establishment of hospital clusters and supercluster medical institutions. This is because implementing such projects necessitates continuous monitoring of changes and requirements for resource provisioning.

Relevance. Cryptographic algorithms and protocols are important tools in modern cybersecurity. They are used in various applications, from simple software for encrypting computer information to complex information and telecommunications systems that implement various electronic trust services. Developing complete biometric cryptographic systems will allow using personal biometric data as a unique secret parameter instead of needing to remember cryptographic keys or using additional authentication devices. The object of research the process of generating cryptographic keys from biometric images of a person's face with the implementation of fuzzy extractors. The subject of the research is the means and methods of building a neural network using modern technologies. The purpose of this paper to study new methods for generating cryptographic keys from biometric images using convolutional neural networks and histogram of oriented gradients. Research results. The proposed technology allows for the implementation of a new cryptographic mechanism - a technology for generating reliable cryptographic passwords from biometric images for further use as attributes for access to secure systems, as well as a source of keys for existing cryptographic algorithms.

Topicality. The concept of fog computing made it possible to transfer part of the data processing and storage tasks from the cloud to fog nodes to reduce latency. But in batch processing of integrated data streams from IoT sensors, it is sometimes necessary to distribute the tasks of the batch between the fog and cloud layers. For this, it is necessary to decompose the formed package. But the existing methods of decomposition do not meet the requirements for efficiency in high-density IoT systems. The subject of study in the article are methods of decomposition of integrated data streams. The purpose of the article is to develop a method of decomposition of an integrated data stream in a dense high-density Internet of Things fog environment. This will reduce the processing time of operational transactions. The following results were obtained. The concept of decomposition of integrated information flows in the foggy layer was implemented to transition from the batch mode to the flow mode of task processing. Within the framework of the concept, a method of selecting elementary task flows from an integrated flow is proposed. An algorithm for decomposition of the integrated flow of tasks is proposed. Conclusion. A comparison of the proposed method of processing information flows in the foggy environment of high-density IoT with the existing approach is carried out. The results of the comparison showed that the proposed method is more suitable for deployment in conditions of limited network and computing resources. It is advisable to use it on nodes of fog computing systems with a high density of IoT sensors.

Topicality. Recent studies confirm the growing trend to implement emotional feedback and sentiment analysis to improve the performance of recommender systems. In this way, a deeper personalization and current emotional relevance of the user experience is ensured. The subject of study in the article is a hybrid recommender system with a component of video sentiment analysis. The purpose of the article is to investigate the possibilities of improving the effectiveness of the results of the hybrid recommender system of virtual art compositions by implementing a component of video sentiment analysis. Used methods: matrix factorization methods, collaborative filtering method, content-based method, knowledge-based method, video sentiment analysis method. The following results were obtained. A new model has been created that combines a hybrid recommender system and a video sentiment analysis component. The average absolute error of the system has been significantly reduced. Added system reaction to emotional feedback in the context of user interaction with virtual art compositions. Conclusion. Thus, the system can not only select the most suitable virtual art compositions, but also create adaptive and dynamic content, which will increase user satisfaction and improve the immersive aspects of the system. A promising direction of further research may be the addition of a subsystem with a generative neural network, which will create new virtual art compositions based on the conclusions of the developed recommendation system.

Topicality. Nowadays, the concept of the Internet of Things (IoT) is developing rapidly. In recent years, mobile devices have been used as elements of the IoT. But when using mobile devices, a number of problems arise. The main ones are the following: – limited computing resources; the need to maintain an energy-saving mode. Therefore, there is a need to balance the distribution of resources between all layers of the IoT support network. In this case, it is necessary to comply with all the time and structural constraints imposed by the IoT system with mobile devices. The subject of study in the article is methods of distributing IoT tasks between support network layers. The purpose of the article is to reduce the energy consumption of mobile IoT devices. The reduction occurs by transferring part of the load of mobile devices from the edge layer of the IoT support network. Time limits on IoT transactions must also be enforced. The following results were obtained. A model of the mobile computing unloading process has been developed. Proposed approach to calculating the average response time for components of different layers of the IoT support network: mobile device, fog node, cloud computing node. The task of choosing the optimal energy consumption option for mobile devices in the IoT support network is formulated. Conclusion. The dependence of the probability of unloading tasks from mobile IoT devices on the unloading threshold was analyzed. The conditions under which the minimum energy consumption is obtained when meeting time requirements were determined.

Relevance. The growing use of client-side AR applications increases the demands on the speed of loading and rendering 3D models. Network conditions and the resource constraints of client devices make it necessary to develop simple yet adequate mathematical models of the loading process. Such models enable the identification of bottlenecks and the development of effective optimization methods aimed at reducing waiting time and improving the user experience. Object of the study: the process of loading and rendering 3D models in a client application with augmented reality elements. Purpose of the article: to develop a simplified mathematical model of the 3D model loading process in the client-side web application implemented with Angular, which takes into account key parameters – the size of the model data, network bandwidth, and processing delays – and serves as a foundation for the development of methods to optimize loading time. Research results. The study presents a mathematical model that describes the total time T required for loading and rendering 3D models in a client-side AR application. The model formalizes the relationships between the model data size, network bandwidth, processing delays, the overhead constant t₀, and the optimization coefficient δ. The derived analytical dependencies make it possible to evaluate the influence of each parameter on the total time T and to use them as a basis for methods of optimizing the transmission and preprocessing of 3D content. The model has been verified, which confirmed its adequacy within the defined assumptions and parameter ranges. Conclusions. A simplified model is proposed that demonstrates both flexibility and ease of application: through the introduction of the overhead constant and the optimization coefficient δ, it can be adapted to different scenarios of client application operation and varying network conditions. The derived analytical dependencies may be applied in the development of practical optimization methods aimed at reducing the loading time of 3D models and improving the user experience in AR clients. Scope of application of the obtained results: client-side AR applications (mobile and web-based), systems for preprocessing and delivery of 3D content, and optimization tools for frontend development projects (including those implemented with Angular).