Big data, artificial intelligence and the Internet of things (IoT) are still very popular areas in current research and industrial applications. Processing...

Poland’s efforts to combat COVID-19 were hindered by endemic vaccination hesitancy and the prevalence of opponents to pandemic restrictions. In this environment, the policy of a COVID-19 vaccination mandate faces strong resistance in the public debate. Exploring the discourse around this resistance could help uncover the motives and develop an understanding of vaccination hesitancy in Poland. This paper aims to conduct a social network analysis and content analysis of Twitter discussions around the intention of the Polish Ministry of Health to introduce mandatory vaccinations for COVID-19. Twitter was chosen as a platform to study because of the critical role it played during the global health crisis. Twitter data were retrieved from 26 July to 9 December 2021 through the API v2 for Academic Research, and analysed using NodeXL and Gephi. When conducting social network analysis, nodes were ranked by their betweenness centrality. Clustering analysis with the Clauset–Newman–Moore algorithm revealed two important groups of users: advocates and opponents of mandatory vaccination. The temporal trends of tweets, the most used hashtags, the sentiment expressed in the most popular tweets, and correlations with epidemiological data were also studied. The results reveal a substantial degree of polarisation, a high intensity of the discussion, and a high degree of involvement of Twitter users. Vaccination mandate advocates were consistently more numerous, but less engaged and less mobilised to “preach” their own stances. Vaccination mandate opponents were vocal and more mobilised to participate: either as original authors or as information diffusers. Our research leads to the conclusion that systematic monitoring of the public debate on vaccines is essential not only in counteracting misinformation, but also in crafting evidence-based as well as emotionally motivating narratives.

The domain of policymaking, which used to be limited to small groups of specialists, is now increasingly opening up to the participation of wide collectives, which are not only influencing government decisions, but also enhancing citizen engagement and transparency, improving service delivery and gathering the distributed wisdom of diverse participants. Although collective intelligence has become a more common approach to policymaking, the studies on this subject have not been conducted in a systematic way. Nevertheless, we hypothesized that methods and strategies specific to different types of studies in this field could be identified and analyzed. Based on a systematic literature review, as well as qualitative and statistical analyses, we identified 15 methods and revealed the dependencies between them. The review indicated the most popular approaches, and the underrepresented ones that can inspire future research.

Inconsistency, lacking values of attributes or parameters, as well as discrepancies between records caused by insufficient precision cannot always be managed in the initial phases of knowledge discovery, i.e., data preparation and refinement. The theory of rough sets aims to overcome problems that are caused by uncertainty and lack of precision within the gathered data sets. This approach is a useful tool that operates on a formal model using relational algebra, elementary operations on finite sets and first-order logic. In this paper, we present an analysis of existing rough set tools, namely: Rough Set Exploration System, Rough Sets Data Explorer, Rough Set Data Analysis Framework, Waikato Environment for Knowledge Analysis and Rough Set Toolkit for Analysis of Data. Our comparison is performed only theoretically and covers the available algorithms, preparation of input data, licensing, as well as installation requirements.

Scanning real 3D objects face many technical challenges. Stationary solutions allow for accurate scanning. However, they usually require special and expensive equipment. Competitive mobile solutions (handheld scanners, LiDARs on vehicles, etc.) do not allow for an accurate and fast mapping of the surface of the scanned object. The article proposes an end-to-end automated solution that enables the use of widely available mobile and stationary scanners. The related system generates a full 3D model of the object based on multiple depth sensors. For this purpose, the scanned object is marked with markers. Markers type and positions are automatically detected and mapped to a template mesh. The reference template is automatically selected for the scanned object, which is then transformed according to the data from the scanners with non-rigid transformation. The solution allows for the fast scanning of complex and varied size objects, constituting a set of training data for segmentation and classification systems of 3D scenes. The main advantage of the proposed solution is its efficiency, which enables real-time scanning and the ability to generate a mesh with a regular structure. It is critical for training data for machine learning algorithms. The source code is available at https://github.com/SATOffice/improved_scanner3D.

Dimensionality reduction is a key stage in data processing. Eliminating irrelevant attributes from the input dataset enables more efficient processing and supports the development of robust Machine Learning models. In particular, when dealing with large datasets, attribute reduction algorithms can significantly reduce computational complexity while minimizing the loss of valuable information contained in the data. This paper explores the potential application of a Feature selection method based on Rough Set Theory for multivariate data. The experiments were conducted on the publicly available Taiwan Credit dataset. The results show that limiting the input feature space to a minimal set of attributes generated by a reduct selection algorithm not only reduces training and inference time for Machine Learning or Deep Learning models but may also improve their classification performance. The models achieved higher Accuracy and better adaptability to the input data, as confirmed by increased or comparable Precision and Recall scores. Additionally, the findings indicate that applying Feature selection using a reduct set can lead to more efficient data processing. These results suggest that integrating Rough Set Theory with Deep Learning models still represents a promising approach to analyzing attribute-value data.