# ExCrowd: A Blockchain Framework for Exploration-Based Crowdsourcing
## 5 Communication and Work Flow of the Proposed System
### Task Evaluation  
In this section, we discuss the process of scoring the workers by the evaluators. Evaluators are miners on the blockchain network who are selected to assess submissions sent in by workers. Our suggested technique is based on the jury concept, which states that a group of less competent persons is more likely to make the right choice than an individual with more competence. As a result, rather than relying on user history to accept a worker's contribution, we rely on the consensus of a group of workers who are miners on the blockchain network. We denote the set of evaluators on the platform as \(e_{1},\cdots e_{n}\). There are two criteria for scoring the workers. The workers are scored based on the completeness and quality of their work. The completeness score is denoted as \(C_{i}\), and the quality score is denoted as \(Q_{i}\). \(C_{i}\) and \(Q_{i}\) represent the completeness and quality scores of worker \(W_{i}\), respectively. We represent the mean of the completeness and quality scores as \(comp_{m}\) and \(qual_{m}\), respectively. Some evaluators can act maliciously and collude to score a worker unjustly. Therefore, the mean score of the completeness and quality scores cannot be the accurate score of the workers. To solve the above problem, we use the standard deviation of the completeness score and quality scores. \(comp_{s}\) and \(qual_{s}\) denote the standard deviation of the completeness score and quality scores.  
Furthermore, the evaluators whose scores are beyond a specific limit away from the mean are considered outliers and therefore are not regarded as part of the score. This principle helps prevent evaluators from acting maliciously and scoring the workers unfairly. If the evaluators disagree on a particular submission, they resign and repeat the whole process. After the evaluators have reached an agreement, their scores for the two factors are put together. We denote the consensus score of a submission's completeness and quality scores as \(C_{avg}\) and \(Q_{avg}\), respectively, where \(w\) represents the worker and \(y\) represents the submission. Equations (4) and (5) depict the computation for the completeness and quality scores, respectively.  
\[Q_{avg}=\frac{\sum_{i=0}^{n}C_{iy}\cdot\alpha_{i}}{\sum_{i=0}^{n}\alpha_{i}} \tag{4}\]  
Here, (\(n=ec\)) denotes the number of consensus evaluators, and \(C_{avg}\) and \(Q_{avg}\) are the scores related to the completeness of the work and the work quality, respectively. The evaluator's credibility is used to buttress the scores assigned by the evaluators. The credibility of the evaluators are computed based on their reputation and denoted by \(\alpha_{i}\) for the \(i\)th worker.  
The final score \(\sigma_{w}y\) assigned by an evaluator to a worker is the weighted average of the score obtained in two occurrences. Here, \(w\) represents the worker, and \(y\) represents the submission. The task poster can decide on the weights of these factors, which adds up to 1.  
Let \(\beta_{c}\) and \(\beta_{q}\) represent the weight assigned to the completeness score \(C_{wy}\) and the quality score \(Q_{wy}\) of the work, respectively. We compute the final score as shown in Equation (3).  
\[\sigma_{wy}=\frac{\beta_{c}*C_{wy}+\beta_{q}*Q_{wy}}{\beta_{c}+\beta_{q}} \tag{5}\]  
After computing \(\sigma_{wy}\), the worker is notified about the transaction. Sending the worker a transaction is a way for us to keep track of all of the reviews they've earned on the platform. This makes it easier for anyone to establish the worker's previous reputation. The reputation score cannot be purposefully manipulated by a central entity in this situation, and no worker may intentionally fabricate its own reputation on the platform. The worker's reputation is then enhanced by the score he or she obtained. The worker's reputation is only updated if he or she makes a submission; it is not changed if the worker accepts the assignment but does not submit.