In the big data era, data are envisioned as critical resources with various values, e.g., business intelligence, management efficiency, and financial evaluations. Data sharing is always mandatory for value exchanges and profit promotion. Currently, certain big data markets have been created for facilitating data dissemination and coordinating data transaction, but we have to assume that such centralized management of data sharing must be trustworthy for data privacy and sharing fairness, which very likely imposes limitations such as joining admission, sharing efficiency, and extra costly commissions. To avoid these weaknesses, in this paper, we propose a blockchain-based fair data exchange scheme, called FaDe. FaDe can enable de-centralized data sharing in an autonomous manner, especially guaranteeing trade fairness, sharing efficiency, data privacy, and exchanging automation. A fairness protocol based on bit commitment is proposed. An algorithm based on blockchain script architecture for a smart contract, e.g., by a bitcoin virtual machine, is also proposed and implemented. Extensive analysis justifies that the proposed scheme can guarantee data exchanging without a trusted third party fairly, efficiently, and automatically.
The replacement of used-up ink cartridges is unavoidable, but it makes the existing characterization model far from accurate, while recharacterization is labor intensive. In this study, we propose a new correction method for cellular Yule-Nielsen spectral Neugebauer (CYNSN) models based on principal component analysis (PCA). First, a small set of correction samples are predicted, printed using new ink cartridges, and then measured. Second, the link between the predicted and measured reflectance weights, generated by PCA, is determined. The experimental results show that the proposed method provides a significant and robust improvement, since not only the color change between original and new inks but also the systemic error of CYNSN modelsis taken into account in the method.
A spectral-based 8-ink characterization model is developed to accurately predict the recipe for a multi-ink printer. The 8-ink color separation method is a union of five 3-ink and six 4-ink combinations based on the cellular Yule-Nielsen spectral Neugebauer model with a recipe selection strategy. The performance levels of the forward and backward models are evaluated for individual ink combinations using printed testing samples. Furthermore, the spectral-based method performs better compared with the XYZ-based approach. On the basis of the backward model performance, a novel fast recipe selection strategy is proposed and estimated.
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