Sentence examples for trustworthiness values from inspiring English sources

In addition, the location and time of report generation change fast and are important in assigning trustworthiness values to events.

We also assume that each peer periodically calculates the trustworthiness value for each of its neighbor peer by requesting other neighbor peers to send the subject trustworthiness values.

This is because of the false trustworthiness values computed by the recommender which is high in the existing methodology that increases the convergence time.

The final prediction with trust is calculated according to Eq. 5, i.e. the standard k-NN prediction of the user ratings together with the trustworthiness values of all selected neighbours.

The peer p r calculates the trustworthiness T r (p i ) for each neighbor peer p i by collecting the trustworthiness values T k (p i ) on the peer p i from every neighbor peer p k in N(p r ) which can communicate with both p i and p r, i.e. p k ∈ N (p r ) ∩ N(p i ).

After collecting the subjective trustworthiness ST k (p i ) on the target peer p i from every neighbor peer p k, the source peer p r calculates the trustworthiness T r (p i ) on the neighbor peer p i by calculating the average value of the subjective trustworthiness values: T r ( p i ) = ∑ p k ∈ T N ( p r ) - { p i } S T k ( p i ) | T N ( p r ) - { p i } | (2).

Therefore, each peer holds an up-to-date subjective trustworthiness value and trustworthiness value to each of its neighbor peers.

Since the trustworthiness value of p01 is six and the trustworthiness value of p02 is eight as shown in Figure 11 so that a more trustworthy peer p02 is selected to forward messages to the peer p13.

Since the peer p02 has a greater trustworthiness value 8 than the trustworthiness value 7 of the peer p01, the initiator peer p i selects the peer p02 to forward messages to the peers p10, p11, and p12.

In [97], trust management system is based on behaviour analysis of neighbour nodes to assign trustworthiness value which is additionally disseminated in network.

We assume each peer dynamically updates the subjective trustworthiness value of each neighbor peer on completion of each transaction with the corresponding neighbor peer.