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Optimized reviewer assignment can effectively utilize limited intellectual resources and significantly assure review quality in various scenarios such as paper selection in conference or journal, proposal selection in funding agencies and so on. However, little research on reviewer assignment of software peer review has been found. In this study, an optimization approach is proposed based on students preference matrix and the model of asymmetric traveling salesman problem (ATSP). Due to the most critical role of rule matrix in this approach, we conduct a questionnaire to obtain students preference matrices and convert them to rule matrices. With the help of software ILOG CPLEX, the approach is accomplished by controlling the exit criterion of ATSP model. The comparative study shows that the assignment strategies with both reviewers preference matrix and authors preference matrix get better performance than the random assignment. Especially, it is found that the performance is just a little better than that of random assignment when the reviewers and authors preference matrices are merged. In other words, the majority of students have a strong wish of harmonious development even though high-level students are not willing to do that.
We consider three important challenges in conference peer review: (i) reviewers maliciously attempting to get assigned to certain papers to provide positive reviews, possibly as part of quid-pro-quo arrangements with the authors; (ii) torpedo reviewi
A `peer-review system in the context of judging research contributions, is one of the prime steps undertaken to ensure the quality of the submissions received, a significant portion of the publishing budget is spent towards successful completion of t
A semi-supervised model of peer review is introduced that is intended to overcome the bias and incompleteness of traditional peer review. Traditional approaches are reliant on human biases, while consensus decision-making is constrained by sparse inf
This paper designs a market platform for Peer-to-Peer (P2P) energy trading in Transactive Energy (TE) systems, where prosumers and consumers actively participate in the market as seller or buyer to trade energy. An auction-based approach is used for
One of the outstanding challenges of cross-identification is multiplicity: detections in crowded regions of the sky are often linked to more than one candidate associations of similar likelihoods. We map the resulting maximum likelihood partitioning