Informatica

The open data movement has led to the widespread sharing of data across all sectors, offering great potential for innovation and informed decision-making. Nevertheless, open data quality remains a key challenge. This study provides a systematic overview of 16 recent methodologies for data quality assessment, emphasizing their alignment with ISO/IEC 25012 and ISO 8000 standards, FAIR principles, 5-Star Linked Open Data System, and DCAT vocabulary. We also highlight foundational work and identify adaptable methods suitable for the Slovenian open data portal. By recommending practical approaches, this work provides a strategic basis for improving data quality in regional and national platforms, supporting improved data utilization and transparency for end users.

Human Action Recognition (HAR) is an important task in computer vision with diverse applications. However, most existing methods rely on all frames of an action video for classification, which leads to high computational cost and low efficiency. In many cases, a compact set of key keyframes can effectively encode the essence of a complete action. Therefore, this study proposes an efficient HAR method that combines a new keyframe extraction algorithm with a lightweight neural network. Our contribution is three-fold. Firstly, an accurate and efficient key frame algorithm is proposed to alleviate the issue of frame-order confusion in classical clustering methods. Secondly, a key-frame-based multi-feature fusion matrix is constructed to address information loss from spatio-temporal trajectory overlap and the sensitivity issue of viewpoint changes in classical models. Thirdly, a lightweight neural network model is designed to achieve effective convergence within a short training period. The proposed method was evaluated on two public datasets (UTKinect-Action3D and Florence-3D) and a self-collected dataset (HanYue-3D). The experiment results show the advantages of our method in both accuracy and efficiency.

Blockchain technology is transforming the healthcare sector by addressing challenges such as data security and interoperability. This systematic literature review, conducted using the PRISMA 2020 methodology, synthesises findings from 25 peer-reviewed studies to examine the applications, challenges, and future directions of blockchain in healthcare. Results reveal that most studies are theoretical or review-based, followed by case studies, with a small proportion of experimental research. This distribution highlights the early stage of blockchain research in healthcare and emphasises the need for empirical and experimental investigations. By synthesising findings, this study provides an overview of the current state of art regarding blockchain in healthcare.

In this paper, we consider the multi-Weber problem with polyhedral barriers. For this problem, a set of obstacles are introduced where travelling or placement is prohibited, which makes the distance metric non-convex and requires constructing a special graph for calculating the distances between pairs of points. For obtaining the global solution of the problem, we build a branch and bound algorithm with pruning criteria based on dividing clients into groups and analysing them separately. We have managed to obtain global solutions to several multi-Weber with polyhedral barriers problem size instances which to our knowledge have not been reported before.

In this paper, we present an enhanced version of a previously published symmetric block cipher implemented for the encryption of digital images. We introduce an additional step of using Arnold’s cat map prior to encryption to improve its quality. After inspecting the statistical characteristics of the ciphertexts for the electronic codebook (ECB) and cipher block chaining (CBC) modes, we found that with this additional step, our schemes produce high entropy ciphertexts for both regular and monochromatic images. Therefore, the results obtained in this paper show that our schemes are indifferent to the Advanced Encryption Standard (AES) cipher. Moreover, due to an effective parallelization of matrix operations, we think that our proposal can be executed reasonably fast.

When it comes to building and sustaining a company’s financial base, financial officers (FOs) are indispensable. Consequently, hiring FOs should be fair and efficient to guarantee continuous economic growth. Evaluating their performance is crucial. The main objective of this research is to find the best financial officer. The research developed an innovative method based on the parametric representation of interval numbers to handle the uncertainty in real-life multi-criteria decision-making (MCDM) scenarios. This research considers all the essential characteristics of an FO to find the best candidate. We provide a new approach to determining the weight of each criterion and sub-criterion, the Parametric Interval Number-Analytic Hierarchy Process (PIVN-AHP). The next step in finding the best FO is to use a hybrid algorithm called PIVN-TOPSIS, which stands for Parametric Interval Number-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Several MCDM approaches, such as Simple Additive Weighting (SAW), Weighted Aggregated Sum Product Assessment (WASPAS), and the Weighted Sum Model (WSM), were used in a comparative study to confirm the ranks. We could also conduct a sensitivity study by shifting the weight of specific criteria. An FO’s evaluation focuses on key criteria and sub-factors, with PIVN-AHP used to calculate weights. “Accounts Knowledge” (C5) is the most significant criterion, while “Growth of Customer” (CW31) holds the highest sub-criterion weight.

Spatial Global Optimization branch and bound (B&B) methods aim at enclosing global minimum points in a guaranteed way with a certain accuracy. We extend simplicial B&B (sBB) concepts to polytopal B&B (pBB), with polytope subsets. The main challenges are: polytope division and extension of monotonicity tests theoretically and algorithmically. We compare the performance of interval B&B with linear constraints (iBBLC), sBB and pBB algorithms, to determine the most efficient B&B algorithm for different types of instances.

Traditional loss functions such as mean squared error (MSE) are widely employed, but they often struggle to capture the dynamic characteristics of high-dimensional nonlinear systems. To address this issue, we propose an improved loss function that integrates linear multistep methods, system-consistency constraints, and prediction-phase error control. This construction simultaneously improves training accuracy and long-term stability. Furthermore, the introduction of recursive loss and interpolation strategies brings the model closer to practical prediction scenarios, broadening its applicability. Numerical simulations demonstrate that this construction significantly outperforms both mean square error and existing custom loss functions in terms of performance.

A steganographic scheme based on perfect coverings of dichotomous shares with sparse observation windows is presented in this paper. The manipulations with pixels are based on the number of different colours in the sparse cells of the current observation window. The conditions for the existence of perfect coverings for different architectures of sparse observation windows are derived. The number and distribution of active cells in the current observation window contribute to the additional security of the proposed scheme. This paper also provides performance measures, statistical features, and demonstrates the robustness of the proposed steganographic scheme.