Most of the aspect based sentiment analysis research aims at identifying the sentiment polarities toward some explicit aspect terms while ignores implicit aspects in text. To capture both explicit and implicit aspects, we focus on aspect-category based sentiment analysis, which involves joint aspect category detection and category-oriented sentiment classification. However, currently only a few simple studies have focused on this problem. The shortcomings in the way they defined the task make their approaches difficult to effectively learn the inner-relations between categories and the inter-relations between categories and sentiments. In this work, we re-formalize the task as a category-sentiment hierarchy prediction problem, which contains a hierarchy output structure to first identify multiple aspect categories in a review sentence, and jointly predict the sentiment for each of the identified categories. Specifically, we propose a Hierarchical Graph Convolutional Network (Hier-GCN), where a lower-level GCN is to model the inner-relations among multiple categories, and the higher-level GCN is to capture the inter-relations between aspect categories and sentiments. Extensive evaluations demonstrate that our hierarchy output structure is superior over existing ones, and the Hier-GCN model consistently achieves the best results on four benchmarks.

With the development of the Internet of Things (IoT) technology, various attacks and threats have emerged. The advanced persistent threat (APT) refers to a class of advanced multiple-steps attacks among diverse attack activities, which brings severe threats to the IoT systems ascribe to its pertinence, concealment, and permeability.However, the existing technologies and methods fail to timely recognize the APT attack activities (especially the zero-day exploits) in a comprehensive scope. To address this problem, we propose a novel method of cyber situation perception for IoT systems, which based on zero-day attack activity recognition within APT (CSPAPTM). Moreover, we also design an edge computing framework for applying CSPAPTM to the typical IoT systems. Specifically, we first provide a cyber situation perception ontology construction module for describing the APT attack activities. Then, a malicious C&C DNS mining method (MCCDRM) is proposed to control the APT malicious activity correlation analysis trigger, which can effectively decrease the computing overhead. Finally, we propose a zero-day attack activity recognition method within APT (ZDAARA), which acts on system call instances to recognize the malicious activities, which cannot be detected by IDS. A relatively mature access control mechanism PO-SAAC is also applied to our method. Through the coalescent of these methods, CSPAPTM can accomplish the cyber situation perception effectively by the zero-day attack activities recognition in the IoT systems. The exhaustive experimental results demonstrate that the two kernel modules, that is, MCCDRM and ZDAARA in our CSPAPTM, can achieve both higher F 1 score and acceptable false positive rate.

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