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This paper discusses a soft sample clustering problem for multivariate independent random data satisfying the mixture model of the Gaussian distribution. The theory recommends to estimate the parameters of model by the maximum likelihood method and to use “plug-in” approach for data clustering. Unfortunately, the calculation problem of the maximum likelihood estimate is not completely solved in multivariate case. This work proposes a new constructive a few stage procedure to solve this task. This procedure includes statistical distribution analysis of a large number of the univariate projections of observations, geometric clustering of a multivariate sample and application of EM algorithm. The results of the accuracy analysis of the proposed methods is made by means of Monte-Carlo simulation.

In this paper we establish equivalence between a theory of fuzzy functional dependences and a fragment of fuzzy logic. We give away to interpret fuzzy functional dependences as formulas in fuzzy logic. This goal is realized in four steps. Truth assignment of attributes is defined in terms of closeness between two tuples in a fuzzy relation. A corresponding fuzzy formula is associated to a fuzzy functional dependence. It is proved that if a relation satisfies a fuzzy functional dependence, then the corresponding fuzzy formula is satisfied and vice verse. Finally, equivalence of a fuzzy formulas and a set fuzzy functional dependence is demonstrated. Thus we are in position to apply the rule of resolution from fuzzy logic, while calculating fuzzy functional dependences.

In 1995, Wu proposed a remote login authentication scheme based on geometric approach. However, Chien, Jan and Tseng presented a cryptanalysis of Wu’s scheme to show that it is not secure. Moreover, they proposed a modified version of Wu’s scheme. This paper presents there is a serious weakness in this modified remote login authentication scheme. We show that an illegal user can easily forge a valid login request in the modified version proposed previously.

The design complexity of systems on a chip drives the need to reuse legacy or intellectual property cores, whose gate-level implementation details are unavailable. In this paper we consider the realization-independent testing and the impact of circuit realization on the fault coverage. We investigated two fault models (input-output pin pair fault and input-input-output pin triplet fault) that are used by test generation for circuits described at system description level. The test generation on the system-level model is preferable if the efforts and the duration of the test supplement activities are less than the efforts and the duration of the test generation on gate-level model. The test set for the black-box model is larger as compared to the test set for the particular realization of the circuit. However, large test sets for the black-box model can be compacted by analysis not only according to the stuck-at faults, but also according to various defects for the particular realization.

The general concept of probabilistic argumentation systems PAS is restricted to the two types of variables: assumptions, which model the uncertain part of the knowledge, and propositions, which model the rest of the information. Here, we introduce a third kind into PAS: so-called decision variables. This new kind allows to describe the decisions a user can make to react on some state of the system. Such a decision allows then possibly to reach a certain goal state of the system. Further, we present an algorithm, which exploits the special structure of PAS with decision variables.

Innovations in technology emerged with digitalization affect all sectors, including supply chain and logistics. The term “digital supply chain” has arisen as a relatively new concept in the manufacturing and service sectors. Organizations planning to utilize the benefits of digitalization, especially in the supply chain area, have uncertainties on how to adapt digitalization, which criteria they will evaluate, what kind of strategies should be developed, and which should be given more importance. Multi-criteria decision making (MCDM) approaches can be addressed to determine the best strategy under various criteria in digital transformation. Because of the need to capture this uncertainty, fermatean fuzzy sets (FFSs) have been preferred in the study to widen the definition domain of uncertainty parameters. Interval-valued fermatean fuzzy sets (IVFFSs) are one of the most often used fuzzy set extensions to cope with uncertainty. Therefore, a new interval-valued fermatean fuzzy analytic hierarchy process (IVFF-AHP) method has been developed. After determining the main criteria and sub-criteria, the IVFF-AHP method has been used for calculating the criteria weights and ranking the alternatives. By determining the most important strategy and criteria, the study provides a comprehensive framework of digital transformation in the supply chain.

Maps are a common tool for visualizing various statistical figures that describe development in our society. Domain experts, journalists, and general public can pose questions on how to emphasize regions where, for instance, most young patients have long stayed in hospitals. One of the visualization’s problems is expressing validities of short-quantified sentences for regions on maps. The truth value of a summary assigns a value from the unit interval, which makes it suitable for interpretation on maps by hues of a selected colour, but it does not reflect the data distribution among regions. To meet this goal, a new quality measure covering data distribution among districts and its aggregation by the ordinal sums of conjunctive and disjunctive functions with the truth value is proposed and documented on examples. The next proposal is a relative quantifier expressing significant proportion of entities. This model is applied to the interpretation of COVID-19 cases development in the Slovak Republic on real data from one health insurance company. Finally, this article discusses the applicability of the proposed approach in other areas where the interpretation of summarized sentences on maps is beneficial.

Blockchain is gaining traction for improving the security of healthcare applications, however, it does not become a silver bullet as various security threats are observed in blockchain-based applications. Moreover, when performing the security risk management (SRM) of blockchain-based applications, there are conceptual ambiguities and semantic gaps that hinder from treating the security threats effectively. To address these issues, we present a blockchain-based healthcare security ontology (HealthOnt) that offers coherent and formal information models to treat security threats of traditional and blockchain-based applications. We evaluate the ontology by performing the SRM of a back-pain patient’s healthcare application case. The results show that HealthOnt can support the iterative process of SRM and can be continually updated when new security threats, vulnerabilities, or countermeasures emerge. In addition, the HealthOnt may assist in the modelling and analysis of real-world situations while addressing important security concerns from the perspective of stakeholders. This work can help blockchain developers, practitioners, and other associated stakeholders to develop secure blockchain-based healthcare applications in the early stages.

The paper presents a secure and usable variant of the Game Changer Password System, first proposed by McLennan, Manning, and Tuft. Unlike the initial proposal based on inadequately secure Monopoly and Chess, we propose an improved version based on a layered “Battleship” game resilient against brute force and dictionary attacks. Since the initially proposed scheme did not check for the memorability and usability of a layered version, we conducted an experiment on the usability and memorability aspects. Surprisingly, layered passwords are just as memorable as single ones and, with an 80% recall rate, comparable to other graphical password systems. The claim that memorability is the most vital aspect of game-based password systems cannot be disproved. However, the experiment revealed that the usability decreased to such a low level that users felt less inclined to use such a system daily or recommend it to others.

Knowledge graphs are commonly represented by ontology-based databases. Tracking the provenance of ontological changes and ensuring ontology consistency is important. In this work, we propose a transaction manager for ontology-based database manipulation that combines blockchain and Semantic Web technologies. The latter is used for the efficient querying and modification of data, whereas the blockchain is used for the secure storage and tracking of changes. The blockchain enables a decentralized setup and data restoration. We evaluate our solution by measuring cost and time. Our solution introduces some overhead for updates whereas querying works at the same speed as the underlying ontology database.