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Purpose: Due to the increased research funding which academic institutions receive to perform advanced R&D, there is an indispensable need to have a systematic approach for selecting competent academicians capable of leading such projects. This paper aims to propose an approach to develop a system for Research Project Leadership Stipulation in finding such academicians. Design/methodology/approach: The system was developed using a decision tree model for leader selection and a neural network model for leader performance prediction, and validated through quantitative and empirical analysis by exercising it on a Research University's Human Resource dataset. Findings: In contrast with common perception, the results showed that the level of an academic leadership expertise does not alone determine R&D project success. Managerial and intellectual competencies complemented by soft skills are more influencing factors on the success of a research project. Originality/value: This paper provides a comparative analysis of selection criteria and influencing factors on research project leader's performance in terms of their hard and soft skills. The developed system by this research, selects and "intelligently" predicts the performance of an academician who possesses optimum ability to lead projects with a high confidence level of successful delivery.

This paper introduces a novel blockchain-based decentralized federation model that embodies quality verification for cloud providers who lease computing resources from each other. The blockchain structure removes the barriers of a traditional centralized federation and offers a fully distributed and transparent administration by enforcing the involved agents to maintain consensus on the data. For a blockchain-based federation, it is vital to avoid blind-trust on the claimed SLA guarantees and monitor the quality of service which is highly desirable considering the multi-tenancy characteristic of cloud services. Due to the fact that the blockchain network is unable to access the outside world, it cannot handle, by its own, providers misbehavior in terms of SLA violations. Thus, we introduce oracle as a verifier agent to monitor the quality of the service and report to the smart contract agents deployed on the blockchain. Oracle is a trusted third-party agent who can communicate with the outside world of the blockchain network. The interaction between cloud service providers (either providing a service or requesting it from another provider) and the oracle through smart contracts comprises a system of autonomous and utility maximizer agents. Cloud requesters seek to receive high quality services with constant monitoring at cheap prices or even with no charge, while cloud providers aim to have a balanced work-load with less preserved capacity, and the oracle tends to charge higher for their monitoring services. Therefore, to model this conflicting situation, we formulate a dynamic Stackelberg differential game to optimize the cost of using the oracle and maximize the profit of the agents with the role of provider agent as a leader, and the requester and verifier agents as followers. Our designed Stackelberg differential game can seize the dynamicity of users’ demand and resource provisioning in a competitive cloud market. We implemented our proposed decentralized model using the Solidity language in the remix IDE on the Ethereum network. We further evaluated the optimal controls and agents’ profit with real-world data simulated for three concrete cloud providers. The results revealed that the requester agent initiates most of the quality verification requests at the beginning to the middle time of the contract. Thus, the provider agent could reserve less computing resources considering the fact that it could share the workload among other customers’ computing resources during the peak-time. Moreover, imposing a higher penalty on the provider agent increased the capacity and decreased the number of requests for quality verification at the equilibrium. The evaluation also disclosed that the impact of timing in the dynamic pricing strategy of the verifier agent is very minimal, and the provisioning capacity of the provider is strongly correlated with the monitoring price. [ABSTRACT FROM AUTHOR]

Promoting recommender systems in real-world applications requires deep investigations with emphasis on their next generation. This survey offers a comprehensive and systematic review on recommender system development lifecycles to enlighten researchers and practitioners. The paper conducts statistical research on published recommender systems indexed by Web of Science to get an overview of the state of the art. Based on the reviewed findings, we introduce taxonomies driven by the following five phases: initiation (architecture and data acquisition techniques), design (design types and techniques), development (implementation methods and algorithms), evaluation (metrics and measurement techniques) and application (domains of applications). A layered framework of recommender systems containing market strategy, data, recommender core, interaction, security and evaluation is proposed. Based on the framework, the existing advanced humanized techniques emerged from computational intelligence and some inspiring insights from computational economics and machine learning are provided for researchers to expand the novel aspects of recommender systems. [ABSTRACT FROM AUTHOR]