In this paper, we introduce Interpolative Boolean algebra (IBA) as a suitable algebra for intuitionistic fuzzy sets (IFSs). IBA is [0,1]-valued realization of Boolean algebra, consistent with Boolean axioms and theorems. IFS theory takes into account both membership and non-membership function, so it can be viewed as a generalization of the traditional fuzzy set theory. We propose a realization of IFS conjunction and disjunction operations based on IBA. This may be viewed as a generalized framework for IFS-IBA calculus. Finally, we investigate the validity of the laws of contradiction and excluded middle in our approach.

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The authors discuss why the current conceptual base of project management research and practice continues to attract criticism since it does not adequately address the complexity that leads to software-project failure. To do so, the study explores systems thinking and artificial neural networks to shed light on complexity in software-project behavior using nonlinear functional relationships between critical success factors and project success to utilize their connectedness as an approach in order to create projectoutcome prediction models. The artificial neural networks were used to create two project-outcome prediction models: one for a binary classification task to discriminate failed from successful projects using a multi-input-single-output configuration and one for a multi-task binary classification to discriminate success from failure in multiple project-success dimensions using a multi-input multi-output configuration. The results yielded high-performance values for a binary classification task, performed to predict overall project success, and slightly lower performance values for the multi-task binary classification, which was also performed to predict success in project-success dimensions. It was found that the nonlinear behavior of critical success factors may be used to create prediction models, by embedding equifinality and connectedness constructs that prove to be useful to understand projects as complex, multi-loop, and nonlinear systems. Further research is needed to investigate the causality between critical success factors in order to explore the possible propagation of critical success factors within a project system network and its implications on project success. INDEX TERMS Artificial neural networks, critical success factors, project success, prediction models, systems thinking.

In this paper, we introduce a logic-driven framework for modeling similarity based on interpolative Boolean algebra (IBA). It consists of two main steps: data preprocessing and similarity measuring by means of IBA similarity measure and logical aggregation. The purpose of these steps is to detect dependencies and model interactions among attributes and/or similarities using an appropriate operator. The proposed framework is general, providing different approaches to multi-attribute object comparison: attribute-by-attribute comparison, object-level comparison and their combination. It is also a generic framework since various similarity measures can be easily derived. The proposed IBA-based similarity framework has a solid mathematical background, which ensures all necessary properties of similarity measure are satisfied. It is interpretable and close to human perception. The framework's applicability is illustrated by two numerical examples that confirm the need for a different level of aggregations. Furthermore, the example of similarity-based classification demonstrates the descriptive power and transparency of the framework on real financial data.

In order to improve quality-of-service of distributed applications, we propose a multi-criteria algorithm based on interpolative Boolean algebra for routing in an overlay network.We use a mesh topology because its implementation is easy and it quite simple addresses the cores during routing. In this paper, we consider four criteria: buffer usage, distance between peers, bandwidth, and remaining battery power. The proposed routing algorithm determines the path by using interpolative Boolean algebra, which satisfies quality-of-service requirements. The decision is made at each node, based on the ranking of available options and considering multiple constraints. The simulation shows that the proposed approach provides better results than the standard shortest path routing algorithm.

Digitization, digitalization and digital transformation represent one of the primary incentives of today's development. To successfully implement these changes, countries need to create smart digital policies which are evidence-and databased. The study presented in this paper uses the logical clustering approach for grouping countries according to five dimensions of the Digital Economy and Society Index (DESI). Logical clustering employs Interpolative Boolean Algebra (IBA) as a consistent fuzzy approach, which means that all Boolean axioms are fulfilled. To measure proximity among countries, logical clustering uses IBAbased exclusive disjunction and logical aggregation. The general aim is to provide help in identifying directions for defining smart digital policies for achieving digital competitiveness of nations, based on the analysis of similarities among countries.The results indicate that logical clustering enables more comprehensive differentiation between clusters than the original composite index methodology does, and determination of the primary areas of action in clusters, among similar countries. Some interesting cases where logical clustering results differ from the original methodology are discussed.

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