The existing deep-learning-based image inpainting algorithms often suffer from local structure disconnections and blurring when dealing with large irregular defective images. To solve these problems, an structure-induced semantic pyramid network for is proposed. model consists of two parts: the edge content-filling network. U-Net-based restores defect residual blocks. map input into together in...

The research community has turned to create Semantic Overlay Networks for information retrieval in large scale peer-to-peer networks. Much research work in semantic overlay protocols and searching algorithms is done and their results indicate that semantic overlay is powerful for content searching in peer-to-peer network. However, very limited work has been done in analyzing and evaluating char...

This paper presents a new semantic relatedness measure on semantic networks (SN) that uses both hierarchical and non-hierarchical relations. Our approach relies on two assumptions. Firstly, in a given SN, only a few numbers of paths can be considered as “semantically correct” and these paths obey to a given set of rules. Secondly, following a given edge in a path has a cost (which depends on it...

The contents and structure of semantic networks have been the focus of much recent research, with major advances in the development of distributional models. In parallel, connectionist modeling has extended our knowledge of the processes engaged in semantic activation. However, these two lines of investigation have rarely brought together. Here, starting from a standard textual model of semanti...

Child semantic development includes learning the meaning of words as well as the semantic relations among words. A presumed outcome of semantic development is the formation of a semantic network that reflects this knowledge. We present an algorithm for simultaneously learning word meanings and gradually growing a semantic network, which adheres to the cognitive plausibility requirements of incr...