Edge or fog computing are being used to reduce latency between end devices and traditional cloud computing. The latest developments that have improved the hardware aspects of wireless sensors and mobile networks, gave the opportunity for other enhancements if such technologies are integrated between them. Regarding this enhanced heterogeneous environment, the approach of placing services needs to reconsider the security aspects. We have developed a baseline method of a solution which fulfills the cybersecurity requirements converging with the proposed multi-tier integrated model, that enhances the K-means algorithm with added processing steps supplying the criteria of the CIA triad.

In this paper, we propose an enhanced algorithm based on the Chio-like method for calculating non-square determinants, optimized for content-based video retrieval (CBVR) systems. The algorithm accelerates the computation of determinant kernels used for similarity score generation, which is critical for efficient video indexing and retrieval. While the classical Chio-like method reduces the determinant order by one at each step, our improved approach reduces the order by four, providing notable computational benefits. Although the asymptotic time complexity remains the same, considering the fact that the resulting determinant is decreased by four orders compared to one and two orders, respectively, from existing Chio-like methods, the proposed method demonstrates clear practical performance improvements. The computer implementation of the proposed algorithm in MATLAB shows an average execution time reduction of approximately 24.5% compared to the standard Chio-like method and 3.2% compared to its modified version. These enhancements make the method well-suited for large-scale or real-time CBVR applications, where fast and accurate similarity evaluation is essential.

Technology plays a very important role in virtually all areas, and has become an inseparable part of the industry. Currently, industry and technology are at a high point of development and research, but there is an ever increasing gap between the market needs and the skills that universities deliver to students. There is an increasing need for consolidation between university curricula and the industry needs in terms of qualifications. In this paper we will present a description of the current state of the labor market in the field of technology, including the needs that arise in improving the existing curricula of the Universities. We review the different technologies that can be used, in order to automatically gather information about the market needs in terms of job offers, and how they can be compared against University curricula. We will also present the latest achievements on these methods, and the suggestions that the researchers provide.