This paper present a novel methodological approach in the design of integrated tactical telecommunications and computer networks for command information system and defining the elements for the OPNET simulation model. The elements proposed for design of network information architecture. For that purpose, the way established to set up relationships between elements that uses for network topologies design, building of network elements and network traffic. They are integrated with its parameters in the preliminary model defined by an OPNET simulation model. We propose the measure of efficiency for accomplishment of tactical command requirements to use the design model network. This measure of efficiency is mean time delay that described with mathematical relations, involving the communication parameters speed of generating traffic, packet size, link capacity and adequately reflects the projected network. Furthermore, the applications of the proposed approach and given instructions allow to designers full examination of the network architecture, and provide translation into preliminary model and design OPNET simulation model.

The burgeoning field of Machine Learning (ML) has revolutionized various aspects of our lives. However, the reliance on vast amounts of data, often containing personal information, raises concerns about individual privacy. Striking a balance between effective ML model training and protecting sensitive data is crucial for responsible development and ethical implementation. This paper explores the challenges and potential solutions for preserving privacy in ML training, focusing on differential privacy (DP). The advantages of implementing DP in ML training include robust protection of individual data, enabling meaningful insights from large datasets while maintaining privacy. This is essential for ethical and responsible data usage in machine learning applications. However, DP in ML training presents challenges including scalability issues and trade-offs between utility and privacy. The paper also covers the mathematical mechanisms of Laplace and Gaussian and their noise addition, followed by a comparative analysis of their efficiency within the dataset.

Social Human Behaviour algorithms are the next step in nature inspired algorithms development. In the past decade these are proved to be useful for various optimisation tasks. The paper provided a global preview of existing algorithms of this kind and focused on two specific algorithms, inspired by teaching and learning process: Teaching-Learning Based Optimization and Group Teaching Optimisation algorithms. The algorithms’ structure and flow are thoroughly explained and illustrated. A preview of algorithms’ application is reported, based on the recent research. It is concluded that this kind of algorithms can be aplied in various industry areas and that further research in this field is reqired.

Companies are moving towards the cloud more than ever, and startups are getting full support from cloud providers in terms of consulting and credits, with developers adopting new programming models shifting further away from traditional technologies. However, teaching cloud computing is still a major undertaking, as it is still difficult choosing the right tools, learning platforms, and creating curriculums to cover such a wide body of knowledge. One of the newest additions to teaching cloud is the AWS Academy curriculum and learner labs and long-running labs. In this paper, the results of implementing the AWS Academy with a hybrid model at Faculty of Technical Sciences are observed. Post-semester evaluation showed up that use of this initiative’s resources can fully support an introductory course in cloud computing. However the proper adaptation is needed to fully utilise the platform and achieve the learning goals

Development of the blended learning environment integrating online and hands-on laboratory practices together with learning of theoretical concepts within engineering course in DSP is described in this paper. The student surveys concerning to Kolb's inventory of learning styles and preferred type of lab exercises are carried out. Survey results were discussed from the point of matching different learning styles with preferred type of DSP lab exercises.

The Industry 4.0 trend has brought significant transformation in the manufacturing process through digitalization. In Intelligent Manufacturing Systems (IMS) there is an increase in the complexity of scheduling jobs on machines. The scheduling aims to collect data through the support of novel and emerging technologies such as: inclusion of machine sensors, cloud computing, artificial intelligence, big data analytics, scheduling software, etc. In this paper, we present an example of open-source job shop scheduling software (LEKIN) to solve real-time engineering problems in a manufacturing company. With using optimization algorithms and scheduling software there is likely to be a reduction of production costs and minimization of the total order completion time (make span). A test problem is running to evaluate the difference between the implementation of Shifting Bottleneck Heuristic (SBH) and some dispatching rules, such as Earliest Due Date (EDD), First Come First Served (FCFS), and Shortest Processing Time (SPT). The evaluation criteria used were the make span and the total weighted tardiness. The results have shown that the SBH outstripped the dispatching rules.