An effective knowledge graph entity alignment model based on multiple information.

Entity alignment refers to matching entities with the same realistic meaning in different knowledge graphs. The structure of a knowledge graph provides the global signal for entity alignment. But in the real world, a knowledge graph provides insufficient structural information in general.

Towards More Accurate and Complete Heterogeneous Iris Segmentation Using a Hybrid Deep Learning Approach.

Accurate iris segmentation is a crucial preprocessing stage for computer-aided ophthalmic disease diagnosis. The quality of iris images taken under different camera sensors varies greatly, and thus accurate segmentation of heterogeneous iris databases is a huge challenge. At present, network architectures based on convolutional neural networks (CNNs) have been widely applied in iris segmentation tasks.

Towards Application-Driven Task Offloading in Edge Computing Based on Deep Reinforcement Learning.

Edge computing is a new paradigm, which provides storage, computing, and network resources between the traditional cloud data center and terminal devices. In this paper, we concentrate on the application-driven task offloading problem in edge computing by considering the strong dependencies of sub-tasks for multiple users. Our objective is to joint optimize the total delay and energy generated by applications, while guaranteeing the quality of services of users.