In this paper, we argue that the ability to accurately spot random and social relationships in dynamic networks is essential ton e twork applications that rely on human routines, such as, e.g.,o pportunistic routing. We thus propose a strategy to analyze users' interactions in mobile networks where users act according totheir interests and activity dynamics. Our strategy, named Random rElationship ClASsifier sTrategy (RECAST),allowsclassifyingusers' wireless interactions, separating random interactions from different kinds of social ties. To that end, RECAST observes how the real system differs from an equivalent one where entities' decisions are completely random. We evaluate the effectiveness of the RE-CAST classification on real-world user contact datasets collected in diverse networking contexts. Our analysis unveils significant differences among the dynamics of users' wireless interactionsinthe datasets, which we leverage to unveil the impact of social ties on opportunistic routing.We consider social networks composed of individuals who are wirelessly connected over time. Such wireless encounters inthese networks are driven by behaviors that tend: (i) to be regular and to repeat periodically; (ii) to build persistent communities of individuals or to generate commons acquaintances between them. The classification strategy we present in this paper leverages these two behaviors to efficiently distinguish social from random encounters in DCWNs. In the following, we detail our methodology and present the real world datasets considered in our analysis. Social vs. random interactions:I nD C W N s ,i n t e r a c t i o n sa m o n g the system entities are usually a consequence of semi-rational decisions. We say "usually" and "semi-rational" decisions because any system is subject to random events and irrational choices. Nevertheless, because most of the interactions still arise from conscious decisions made by their entities, the evolution of DCWNs is significantly different from the evolution of random networks, e.g., Erdős and Rényi networks [9]. Indeed, while in DCWNs the edges are created from semi-rational decisions, which tend to be regular and to repeat over time, in a random network the edges are created independently of the attributes of the network entities, i.e., the probability of connecting any two entities is always the same.More formally, an individual may execute a social decision,o r a random decision.I n t u i t i v e l y ,i fi t s p r o b a b i l i t yo fp e r f o r m i n ga social decision is greater than its probability of a random one, the network evolves to a well-structured social network. If the opposite it true, the network evolves as a random network, such as the Erdös and Rényi one. Differentiating social from random network:T h es e c o n dm ajor feature of DCWNs that we exploit in our study is the presence of communities, i.e., groups of individuals who are stronglyc o nnected to each other because they share the same interests or activity dynamics [5]. In contrast, communities can not b...

Traditional clustering algorithms, such as K-Means, perform clustering with a single goal in mind. However, in many real-world applications, multiple objective functions must be considered at the same time. Furthermore, traditional clustering algorithms have drawbacks such as centroid selection, local optimal, and convergence. Particle Swarm Optimization (PSO)-based clustering approaches were developed to address these shortcomings. Animals and their social Behaviour, particularly bird flocking and fish schooling, inspire PSO. This paper proposes the Multi-Objective Clustering Framework (MOCF), an improved PSO-based framework. As an algorithm, a Particle Swarm Optimization (PSO) based Multi-Objective Clustering (PSO-MOC) is proposed. It significantly improves clustering efficiency. The proposed framework's performance is evaluated using a variety of real-world datasets. To test the performance of the proposed algorithm, a prototype application was built using the Python data science platform. The empirical results showed that multi-objective clustering outperformed its single-objective counterparts.

Introduction In the world economy, QR codes became very popular, and their prominence is expanding rapidly. The QR-codes look a bit like barcodes, but are made up of square patterns. As businesses are increasingly embracing these technologies, QR codes are becoming more popular, QR code readers are being integrated into smartphones. Apple released iOS 11 to search QR codes using the smartphone camera back in 2017 which is now a game-changing marketing strategy for businesses and retailers. Objective: The objective of the paper is to conduct an extensive theoretical review on the growth of QR codes in the digital era and QR codes' reach as contactless payment solutions. Methodology: A bibliometric review by refereeing quality articles published in highly ranked journal. Conclusion: When the QR code reader was integrated into the new Android smartphone camera, it proved to be a key differentiator.  Following the global COVID-19 contagion, there has been a nudge for contactless activities and remote resource allocation, such as online work, payments and online classes among others. QR-codes have seen a spectacular increase in usage across all aspects of life.

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