# ExCrowd: A Blockchain Framework for Exploration-Based Crowdsourcing
## 5 Communication and Work Flow of the Proposed System
### Worker Selection and Task Assignment  
Choosing the best applicant from a pool of candidates is a time-consuming exercise. Low-performing workers are not favored in the reputation-based worker selection approach since they may never be allowed to execute tasks because of their low reputation rating. This can also lead to requester-worker collusion to fraudulently boost workers' ratings. We first categorize the workers based on their expertise, such as I.T. specialists, health workers, economists, etc. A requester states the task's category before posting the job. This enhances task matching to the appropriate worker category on the platform. These worker matching tasks take place in the exploration-based environment based on the outcome of the worker selection decision algorithm. Once the requester has finalized the task, he/she uses the agreement contract to construct a contract that includes the details of the chosen category. The agreement in this case is a binding contract between the requester and worker for specific tasks, which may reinforce the task's system norms, such as acceptability, submission, and reward. While producing the agreement, the requester is prompted to pay the incentive for that job towards this contract. This payment may be retrieved only when the worker has never accepted the assignment, and thus, the requester wishes to terminate the job, or it may be granted once the work is completed. Following the proper generation of the agreement, the chosen worker is prompted with updates in relation to the commencement of the task. These transaction logs are processed using the processing nodes and kept on the blockchain. Workers maintain a consistent level of performance throughout time, even if it fluctuates across activities. We then investigate whether or not employees display learning behavior when undertaking tasks and whether or not we can estimate and anticipate their progress by employing learning curves based on their conduct. As a further step, we'll analyze existing workers' performance patterns using a decision tree technique. To back up our claim, we'll present evidence in this section. Consider \(w\) to be a worker with a rate of learning \(l_{w}\) and a background knowledge \(k_{w}\). Next,we use the hyperbolic learning curve model, which depicts that the worker \(w\) correct judgement percentage he has achieved up till the \(z\)th task is defined as:  
\[H_{w}(z)=\frac{z+k_{w}}{z+k_{w}+l_{w}} \tag{1}\]  
The quality of the worker, \(h_{w}(z)\), is estimated given \(H_{w}(z)\) at task \(z\) as:  
\[h_{w}(z)=zH_{w}(z)-(z-1)H_{w}(z-1) \tag{2}\]  
\(H_{w}(z)\) and \(h_{w}(z)\) would be simple to estimate if \(l_{w}\) and \(k_{w}\) were predetermined. As an alternative, we approximate \(H_{w}(z)\) by calculating the number of right responses that each worker has submitted so far. We map a linear model onto the data in order to learn the \(H_{w}(z)\) model. \[M_{w}(z)=f_{w}z+g_{w} \tag{3}\]  
where \(M_{w}(z)=\frac{1}{1-H_{w}(z)}\), \(f_{w}=\frac{1}{l_{w}}\), and \(g_{w}=\frac{k_{w}}{l_{w}}+1\), and using linear regression techniques to determine the parameters of \(M_{w}(z)\), so estimating \(H_{w}(z)\) workers' learning curves may be modeled in two ways. As a first step, we may utilize the projected \(l_{w}\)s to rate workers based on their learning rate and pick the most promising workers for training. This knowledge may be used in our recruiting decisions since we can calculate the likelihood that a worker will make the accurate prediction on upcoming tasks using \(H_{w}(z)\) and \(h_{w}(z)\). In the second phase, we use decision tree classification algorithms to pick and categorize employees from the pool of the existing workforce based on their learning rate performance using a decision tree classifier. Classification is performed via the decision tree-based C4.5 classifier. This classifier utilizes a \"divide-and-conquer\" strategy from a collection of independent occurrences.