Land administration systems are of great importance for a large number of stakeholders. One of the key problems related to land administration systems is the problem of the correctness of their state, meaning that data stored in land administration systems are not in concordance with the actual legal, spatial and topographic situation. The main causes of land administration systems’ incorrect state are data collection and compilation, data processing, and data misuse. In this paper, we discuss the problems of data tampering, the long time needed for registering land administration system’s transactions, and the possibility of double spending, which all can add incorrectness in a land administration system. Our research is based on the hypothesis that these problems may be addressed by means of distributed ledger technology, or to be more precise, by means of blockchain technology. The solution is presented in a form of a smart contract written in Solidity programing language that can cover even those more specific use cases in land administration systems such as sharing of ownership, transferring part of ownership, splitting or merging of real estate, and limiting the possibility of trading a real estate. The proposed smart contract represents an implementation of a programming interface that was created based on both ERC-20 and ERC-721 token standards, to satisfy the specific needs of land administration systems.

Probability calibration is commonly utilized to enhance the reliability and interpretability of probabilistic classifiers, yet its potential for reducing algorithmic bias remains under-explored. In this study, the role of probability calibration techniques in mitigating bias associated with sensitive attributes, specifically country of origin, within binary classification models is investigated. Using a real-world lead-generation 2853 × 8 matrix dataset characterized by substantial class imbalance, with the positive class representing 1.4% of observations, several binary classification models were evaluated and the best-performing model was selected as the baseline for further analysis. The evaluated models included Binary Logistic Regression with polynomial degrees of 1, 2, 3, and 4, Random Forest, and XGBoost classification algorithms. Three widely used calibration methods, Platt scaling, isotonic regression, and temperature scaling, were then used to assess their impact on both probabilistic accuracy and fairness metrics of the best-performing model. The findings suggest that post hoc calibration can effectively reduce the influence of sensitive features on predictions by improving fairness without compromising overall classification performance. This study demonstrates the practical value of incorporating calibration as a straightforward and effective fairness intervention within machine learning workflows.

The aim of this paper is to investigate the effectiveness of combining mobile technologies and sensors to detect harmful particles in the air and address the problem of air pollution caused by traffic and exhaust emissions. The paper contains a systematic literature review of information technology research related to pollution detection in order to point out the main obstacles in the field and propose solutions. Furthermore, the paper presents the development of an Android smartphone-based real-time monitoring system that utilizes an external analog sensor board to acquire and evaluate physical measurements. The proposed system is calibrated for CO gas measurements, and it is compared with a commercial gas analyzer instrument. Results imply that the developed system is capable of detecting concentration levels in the air and that the accuracy is within the range of the industrial device’s accuracy.

Static code analysis tools are being increasingly used to improve code quality. The source code’s quality is a key factor in any software product and requires constant inspection and supervision. Static code analysis is a valid way to infer the behavior of a program without executing it. Many tools allow static analysis in different frameworks, different programming languages, and detecting different defects in the source code. Different strategies of using static code analysis tools are often used, and these strategies are not classified. In this paper, an experiment was conducted on different tools and their use in relation to the standard code review cycle. The identified strategies for using static code analysis tools and the steps required to implement them are presented. When using the tool, users should choose one of the identified strategies to implement following the defined steps for successful implementation.

The role of static code analysis in enhancing the quality of software codes is widely acknowledged. Static code analysis facilitates the examination of code for irregularities without program execution, which significantly impacts project quality. Furthermore, tools for static code analysis serve as educational aids, imparting essential lessons on coding practices. Motivated by the growing complexity of software projects and the pivotal role of code quality in academic performance within computing disciplines, this research examines over 500 student projects using static code analysis tools. The aim is to determine metrics that influence the code quality of student projects. The study investigates how metrics, such as project setup, influence code quality and students’ academic performances. By adopting a broad approach, the investigation determines the overall impact of these metrics on the technical integrity of software engineering projects and academic outcomes. Insights derived from this study are anticipated to enhance teaching strategies and curriculum development, aiming to improve academic performance by promoting better code quality.