This paper proposes a conception Design environment that intends to support service collaboration. In this platform, technology independence is a very important goal to be achieved. Model Driven Architecture and metamodels are some of the resources to provide such independence. This article offers a modified software development process that would leverage MDA, and studies a web application case.
Figure 1: Cricoid object reconstruction.
AbstractA reconstruction method from contours is provided based on bicubic Bezier spline surface. At first, we interpolate every contour by the cubic Bezier spline curve. For corresponding points of different contours, we interpolate them by the cubic Bezier spline curve too, so the whole object can be reconstructed by the bi-cubic Bezier spline surface. The advantage of this method is that the reconstructed surface is smooth in that every Bezier surface is patched with G2 continuity, the reconstruction speed is fast because we can use the forward elimination and backward substitution method to solve the system of tridiagonal equations. The experiments show that the method is effective and applicable.
In the big data era, machine learning has developed prominently and is widely used in real-world systems. Yet, machine learning raises fairness concerns, which incurs discrimination against groups determined by sensitive attributes such as gender and race. Many researchers have focused on developing fairness audit technique of machine learning model that enable users to protect themselves from discrimination. Existing solutions, however, rely on additional external trust assumptions, either on third-party entities or external components, that significantly lower the security. In this study, we propose a trustless verifiable fairness audit framework that assesses the fairness of ML algorithms while addressing potential security issues such as data privacy, model secrecy, and trustworthiness. With succinctness and non-interactive of zero knowledge proof, our framework not only guarantees audit integrity, but also clearly enhance security, enabling fair ML models to be publicly auditable and any client to verify audit results without extra trust assumption. Our evaluation on various machine learning models and real-world datasets shows that our framework achieves practical performance.
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