The prosperity of knowledge graphs (KG), as well related downstream applications, have raised the urgent request graph completion techniques for fully supporting KG reasoning tasks, especially under circumstance training data scarcity. Though large efforts been made on solving this challenge via few-shot learning tools, they mainly focus simply aggregating entity neighbors to represent referenc...

Abstract Semantic segmentation of parts objects is a marginally explored and challenging task in which multiple instances within those must be recognized an image. We introduce novel approach (GMENet) for this combining object-level context conditioning, part-level spatial relationships, shape contour information. The first target achieved by introducing class-conditioning module that enforces ...

The aim of this paper is to dene the abstract domain, abstract operator, abstract semantic, the environments and states of disassembled executable codes as well as a way to analysis the disassembled executable codes. Nowadays, static analysis on disassembled code going to grow. Reverse engineering and malware analysis use this technique. Thus, we tried to perform pluralization the requirements ...

Over the last several years, in parallel with general global advancement mobile technology and a rise social media network content consumption, multimedia production reproduction has increased exponentially. Therefore, enabled by rapid recent advancements deep learning technology, research on scene graph generation is being actively conducted to more efficiently search for classify images desir...

Recommender systems have changed the way people find products, points of interest, services or even new friends. The technology behind recommender systems has evolved to provide user preferences and social influences. In this paper, we present a first approach to develop a recommendation engine based on social metrics applied to graphs that represent object’s characteristics, user profiles and ...