A Recommender System for Robust Smart Contract Template Classification.

IoT environments are becoming increasingly heterogeneous in terms of their distributions and included entities by collaboratively involving not only data centers known from Cloud computing but also the different types of third-party entities that can provide computing resources. To transparently provide such resources and facilitate trust between the involved entities, it is necessary to develop and implement smart contracts. However, when developing smart contracts, developers face many challenges and concerns, such as security, contracts' correctness, a lack of documentation and/or design patterns, and others. To address this problem, we propose a new recommender system to facilitate the development and implementation of low-cost EVM-enabled smart contracts. The recommender system's algorithm provides the smart contract developer with smart contract templates that match their requirements and that are relevant to the typology of the fog architecture. It mainly relies on OpenZeppelin, a modular, reusable, and secure smart contract library that we use when classifying the smart contracts. The evaluation results indicate that by using our solution, the smart contracts' development times are overall reduced. Moreover, such smart contracts are sustainable for fog-computing IoT environments and applications in low-cost EVM-based ledgers. The recommender system has been successfully implemented in the ONTOCHAIN ecosystem, thus presenting its applicability.

Measuring how motivation affects information quality assessment: A gamification approach.

PURPOSE: Existing research on the measurability of information quality (IQ) has delivered poor results and demonstrated low inter-rater agreement measured by Intra-Class Correlation (ICC) in evaluating IQ dimensions. Low ICC could result in a questionable interpretation of IQ. The purpose of this paper is to analyse whether assessors' motivation can facilitate ICC. METHODOLOGY: To acquire the participants' views of IQ, we designed a survey as a gamified process. Additionally, we selected Web study to reach a broader audience. We increased the validity of the research by including a diverse set of participants (i.e. individuals with different education, demographic and social backgrounds). FINDINGS: The study results indicate that motivation improved the ICC of IQ on average by 0.27, demonstrating an increase in measurability from poor (0.29) to moderate (0.56). The results reveal a positive correlation between motivation level and ICC, with a significant overall increase in ICC relative to previous studies. The research also identified trends in ICC for different dimensions of IQ with the best results achieved for completeness and accuracy. PRACTICAL IMPLICATIONS: The work has important practical implications for future IQ research and suggests valuable guidelines. The results of this study imply that considering raters' motivation improves the measurability of IQ substantially. ORIGINALITY: Previous studies addressed ICC in IQ dimension evaluation. However, assessors' motivation has been neglected. This study investigates the impact of assessors' motivation on the measurability of IQ. Compared to the results in related work, the level of agreement achieved with the most motivated group of participants was superior.