PoIR: A Node Selection Mechanism in Reputation-Based Blockchain Consensus Using Bidirectional LSTM Regression Model

This research presents a reputation-based blockchain consensus mechanism called Proof of Intelligent Reputation(PoIR)as an alternative to traditional Proof of Work(PoW).PoIR addresses the limitations of existing reputationbased consensus mechanisms by proposing a more decentralized and fair node selection process.The proposed PoIR consensus combines Bidirectional Long Short-Term Memory(BiLSTM)with the Network Entity Reputation Database(NERD)to generate reputation scores for network entities and select authoritative nodes.NERD records network entity profiles based on various sources,i.e.,Warden,Blacklists,DShield,AlienVault Open Threat Exchange(OTX),and MISP(Malware Information Sharing Platform).It summarizes these profile records into a reputation score value.The PoIR consensus mechanism utilizes these reputation scores to select authoritative nodes.The evaluation demonstrates that PoIR exhibits higher centralization resistance than PoS and PoW.Authoritative nodes were selected fairly during the 1000-block proposal round,ensuring a more decentralized blockchain ecosystem.In contrast,malicious nodes successfully monopolized 58%and 32%of transaction processes in PoS and PoW,respectively,but failed to do so in PoIR.The findings also indicate that PoIR offers efficient transaction times of 12 s,outperforms reputation-based consensus such as PoW,and is comparable to reputation-based consensus such as PoS.Furthermore,the model evaluation shows that BiLSTM outperforms other Recurrent Neural Network models,i.e.,BiGRU(Bidirectional Gated Recurrent Unit),UniLSTM(Unidirectional Long Short-Term Memory),and UniGRU(Unidirectional Gated Recurrent Unit)with 0.022 Root Mean Squared Error(RMSE).This study concludes that the PoIR consensus mechanism is more resistant to centralization than PoS and PoW.Integrating BiLSTM and NERD enhances the fairness and efficiency of blockchain applications.

A Discrimination Index Based on Jain's Fairness Index to Differentiate Researchers with Identical H-index Values

Purpose:This paper proposes a discrimination index method based on the Jain’s fairness index to distinguish researchers with the same H-index.Design/methodology/approach:A validity test is used to measure the correlation of D-offset with the parameters,i.e.H-index,the number of cited papers,the total number of citations,the number of indexed papers,and the number of uncited papers.The correlation test is based on the Saphiro-Wilk method and Pearson’s product-moment correlation.Findings:The result from the discrimination index calculation is a two-digit decimal value called the discrimination-offset(D-offset),with a range of D-offset from 0.00 to 0.99.The result of the correlation value between the D-offset and the number of uncited papers is 0.35,D-offset with the number of indexed papers is 0.24,and the number of cited papers is 0.27.The test provides the result that it is very unlikely that there exists no relationship between the parameters.Practical implications:For this reason,D-offset is proposed as an additional parameter for H-index to differentiate researchers with the same H-index.The H-index for researchers can be written with the format of“H-index:D-offset”.Originality/value:D-offset is worthy to be considered as a complement value to add the H-index value.If the D-offset is added in the H-index value,the H-index will have more discrimination power to differentiate the rank of the researchers who have the same H-index.