DL2Fence: Integrating Deep Learning and Frame Fusion for Enhanced Detection and Localization of Refined Denial-of-Service in Large-Scale NoCs

This study introduces a refined Flooding Injection Rate-adjustable Denial-of-Service (DoS) model for Network-on-Chips (NoCs) and more importantly presents DL2Fence, a novel framework utilizing Deep Learning (DL) and Frame Fusion (2F) for DoS detection and localization. Two Convolutional Neural Networks models for classification and segmentation were developed to detect and localize DoS respectively. It achieves detection and localization accuracies of 95.8\% and 91.7\%, and precision rates of 98.5\% and 99.3\% in a 16x16 mesh NoC. The framework's hardware overhead notably decreases by 76.3\% when scaling from 8x8 to 16x16 NoCs, and it requires 42.4\% less hardware compared to state-of-the-arts. This advancement demonstrates DL2Fence's effectiveness in balancing outstanding detection performance in large-scale NoCs with extremely low hardware overhead.

Ontology Revision based on Pre-trained Language Models

Ontology revision aims to seamlessly incorporate new information into an existing ontology and plays a crucial role in tasks such as ontology evolution, ontology maintenance, and ontology alignment. Similar to repair single ontologies, resolving logical incoherence in the task of ontology revision is also important and meaningful since incoherence is a main potential factor to cause inconsistency and reasoning with an inconsistent ontology will obtain meaningless answers. To deal with this problem, various ontology revision methods have been proposed to define revision operators and design ranking strategies for axioms in an ontology. However, they rarely consider axiom semantics which provides important information to differentiate axioms. On the other hand, pre-trained models can be utilized to encode axiom semantics, and have been widely applied in many natural language processing tasks and ontology-related ones in recent years. Therefore, in this paper, we define four scoring functions to rank axioms based on a pre-trained model by considering various information from a rebuttal ontology and its corresponding reliable ontology. Based on such a scoring function, we propose an ontology revision algorithm to deal with unsatisfiable concepts at once. If it is hard to resolve all unsatisfiable concepts in a rebuttal ontology together, an adapted revision algorithm is designed to deal with them group by group. We conduct experiments over 19 ontology pairs and compare our algorithms and scoring functions with existing ones. According to the experiments, it shows that our algorithms could achieve promising performance. The adapted revision algorithm could improve the efficiency largely, and at most 96% time could be saved for some ontology pairs. Some of our scoring functions help a revision algorithm obtain better results in many cases, especially for the challenging pairs.