## 4 Implementation of the Proposed UniChain System
### Blockchain Initialization--Part II: Computing the Degree of a University Node  
The process of computing the degree of a node is performed by a node that has a \"university\" role listed in the NC. In this stage, the REM installed in each university node computes the degree of the associated node within the network. Since an EAR is a student's asset and not a cryptocurrency or a digital currency to be exchanged, unlike previously proposed blockchain-based systems for EARs, a new incentive mechanism is proposed. The mechanism leverages the degree of university nodes from the perspective of EARs systems by measuring their efforts regarding maintaining academic records and creating new blocks. The degree of a node is calculated based on the quantity and the quality of the EARs stored in its database. Based on the purposes and the perspective of the designed system, various methods and several attributes could be used to define the quality of academic records. Generally, the quality of an academic record should be judged by whether it serves the purpose for which it was intended. To define a measurable standard for the quality in academic records, the proposal considers five key attributes that should be evaluated for each item included in the record. Thus, quality in academic records is defined as having the attributes of legibility, completeness, consistency, correctness, and non-redundancy. To demonstrate the structure of an EAR, Figures 1-3 in Appendix A present the entity relation diagrams of the university registration office, student marks, and examination scheduling. * Legibility (L): Any entry in an academic record has to be legible, dated, timed and authenticated by the university. * Completeness (CM): An academic record is considered complete if it has all items that all universities have agreed on during the previous stage. * Correctness (CR): The academic record's correctness refers to the accuracy of its collected data. * Consistency (CN): An academic record is considered consistent when the included data are reliable, and the data integrity has not been corrupted regardless of how often or in what way the data have been retrieved, viewed, stored, or processed. * Non-redundancy (NR): Redundancy in an academic record indicates that the data of a record may be repeated by several universities. Thus, by taking the previous attributes into consideration, the degree of a university is defined as the total quality of all EARs for all students stored in the database of that university. \[\delta_{i}=\sum_{EAR=1}^{m}Q_{EAR} \tag{1}\]  
where the quality of an EAR is defined as the product of its L, CM, CR, CN and NR attributes. To compute the Legibility indicator, each item in the EAR will be checked for whether it is considered as a legal item or not. The classification process will be performed via the REM component installed on the university node. Legal items will be tagged with \(i1\), while illegal items will be tagged with \(i2\). Thus, \(L_{EAR}=1\) if all items of the EAR are considered as legal items; otherwise,  
\[L_{EAR}=\frac{\sum i1}{\sum i1+i2} \tag{2}\]For the NR indicator, \(NR_{I}=1\) if all data stored in an academic record are unique and not repeated by any other university, otherwise the non-redundancy indicator will be divided among the universities that share that item. For the correctness, completeness, and consistency indicators, each item in the EAR will be classified via the REM as: \(n1\), correct element; \(n2\), incorrect element; \(n3\), missing element; \(n4\), extra element; and, \(n5\), conflict and reduction element. Equations (3)-(5) measure the completeness, correctness, and consistency of an EAR.