This paper investigate Iranian tourism demand to Malaysia using the recently developed autoregressive distributed lag (ARDL) ‘Bound test’ approach to cointegration for 2000:Q1 to 2013:Q4. The demand for tourism has been explained by macroeconomic variables, including income in Iran, tourism prices in Malaysia, tourism price substitute, travel cost and trade value between Iran and Malaysia. In a...

The advent of cloud computing has ushered in an era of mass data storage in remote servers. Remote data storage offers reduced data management overhead for data owners in a cost effective manner. Sensitive documents, however, need to be stored in encrypted format due to security concerns. But, encrypted storage makes it difficult to search on the stored documents. Therefore, this poses a major ...

Recently, the surge in popularity of Internet Things (IoT), mobile devices, social media, etc., has opened up a large source for graph data. Graph embedding been proved extremely useful to learn low-dimensional feature representations from graph-structured These can be used variety prediction tasks node classification link prediction. However, existing methods do not consider users' privacy pre...

Recent studies show that Graph Neural Networks (GNNs) are vulnerable and easily fooled by small perturbations, which has raised considerable concerns for adapting GNNs in various safety-critical applications. In this work, we focus on the emerging but critical attack, namely, Injection Attack (GIA), adversary poisons graph injecting fake nodes instead of modifying existing structures or node at...

Graph embedding learns low-dimensional representations for nodes or edges on the graph, which is widely applied in many real-world applications. Excessive graph mining promotes research of attack methods embedding. Most generate perturbations that maximize deviation prediction confidence. They are difficult to accurately misclassify instances into target label, and nonminimized more easily dete...