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.

Large number of stakeholders is interested in data stored in land administration systems (LASs), so it is understandable that data quality in LASs is of utmost importance. Data in LASs are often in incorrect state since they are not in accordance with reality. Since data stored in LASs are considered to be correct in all legal affairs, these incorrectnesses could be a cause for large number of problems. Blockchain technology, more precisely smart contracts, could bring improvements to process of registering transactions in LASs. In this paper, two presented case studies propose improvements that would result in faster process of executing transactions and elimination of possibility of “double spending”.

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.

During the last couple of years, non-fungible tokens became the most prominent implementation of blockchain technology apart from cryptocurrencies. This is mainly due to their recent association with digital art, but the application of non-fungible tokens has been in the focus of researchers since the appearance of Blockchain 2.0. It was usually tightly coupled with the research on possible applications of blockchain technology in some real-life applications, such as land administration, healthcare, or supply chain management. Since the initial release of the Ethereum blockchain in 2015, until 2022, more than 44 million smart contracts have been created, and out of those that are still active, more than 70% are based on some prominent templates. In the Ethereum blockchain, the creation of nonfungible tokens is usually based on Ethereum Request for Comments 721. In this paper, the authors are proposing the creation of a new standard that would support fractional ownership of non-fungible tokens. Fractional ownership is necessary so non-fungible tokens and blockchain technology could be applied to an even wider number of use cases. This paper also presents an example of a possible implementation of the newly proposed standard in the Solidity programming language.

The Land Administration System (LAS) records real estates, owners, and rights information. Changes that take place in the real world are recorded as transactions in LAS. This paper discusses various data-integrity constraints that have to be taken into account so that LAS data will be correct and consistent after the execution of LAS transactions. Those transactions are executed by system users, typically through some graphical user interface (GUI) applications. Domain-specific languages (DSLs) provide the possibility for domain experts to write statements that can be interpreted and executed on respective software systems. In the case of LAS, DSL for LAS transactions could enable land administration experts to write statements that would execute transactions and keep LAS data up to date with real world changes. Two types of LAS transactions are considered: legal transactions, which result in ownership changes, and survey transactions, which change the real estate geometry data. In this paper, a possible DSL solution for transactions in the LAS domain is proposed. A system architecture that could enable the efficient writing, validation, verification, execution, and storage of DSL statements is also proposed. A possible DSL for LAS transaction implementation is presented, and examples of legal and survey transactions are explained. The advantages and possible challenges of the proposed solution’s implementation are also discussed in this paper.

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.