This paper proposes a distributed deep learning framework for privacy-preserving medical data training. In order to avoid patients' data leakage in medical platforms, the hidden layers in the deep learning framework are separated and where the first layer is kept in platform and others layers are kept in a centralized server. Whereas keeping the original patients' data in local platforms maintain their privacy, utilizing the server for subsequent layers improves learning performance by using all data from each platform during training.

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This demo abstract introduces a new light-weight programming language koa which is suitable for blockchain system design and implementation. In this abstract, the basic features of koa are introduced including working system (with playground), architecture, and virtual machine operations. Rum-time execution of software implemented by koa will be presented during the session.

In this paper, we strengthen the properties of approximation by points (AP) and weak approximation by points (WAP) considered by A. Pultr and A. Tozzi in 1993 to define κ-AP and κ-WAP for an infinite cardinal κ. We also strengthen the properties of radial and pseudoradial to define κ-radial and κ-pseudoradial for an infinite cardinal κ. These allow us to consider new cardinal functions related to almost closed sets; AP-number, WAP-number, radial number, and pseudoradial number. We study their properties and show the relationships between them. We also provide some examples around κ-AP and κ-WAP which are closely connected with κ-radial and κ-pseudoradial.

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