Neurodegenerative diseases target large-scale neural networks. Four competing mechanistic hypotheses have been proposed to explain network-based disease patterning: nodal stress, transneuronal spread, trophic failure, and shared vulnerability. Here, we used task-free fMRI to derive the healthy intrinsic connectivity patterns seeded by brain regions vulnerable to any of five distinct neurodegene...

In today’s business environment, harsher and more frequent natural and man-made disasters make supply chains more vulnerable. Supply chain disruptions now seem to occur more frequently and with more serious consequences. During and after supply chain disruptions, companies may lose revenue and incur high recovery costs. If supply chain managers were more capable of measuring and managing supply...

Security on Grid computing is often an afterthought. However assessing security of middleware systems is of the utmost importance because they manage critical resources owned by different organizations. To fulfill this objective we use First Principles Vulnerability Assessment (FPVA), an innovative analystic-centric (manual) methodology that goes beyond current automated vulnerability tools. FP...

The safety of people and the security of the vital functions of society are among the core tasks of governments. Various networks, especially transportation networks, are important for human life. Much research has been done to analyse the vulnerability of road networks and most of the methods were based on analysing the topological structure of the network using graph theory. For instance, Dem...

Abstract With the widespread use of blockchain, more and smart contracts are being deployed, their internal logic is getting sophisticated. Due to large false positive rate low detection accuracy most current methods, which heavily rely on already established criteria, certain additionally call for human secondary detection, resulting in efficiency. In this study, we propose HGAT, a hierarchica...

This paper presents FUNDED (Flow-sensitive vUl-Nerability coDE Detection), a novel learning framework for building vulnerability detection models. Funded leverages the advances in graph neural networks (GNNs) to develop graph-based method capture and reason about program's control, data, call dependencies. Unlike prior work that treats program as sequential sequence or an untyped graph, learns ...

With the flourishing of open-source software community, problem vulnerabilities is becoming more and serious. Hence, it urgent to come up with an effective efficient code vulnerability detection method. Source techniques used in practice today like symbolic execution fuzz testing suffer from high false positives low coverage, respectively. Traditional machine-learning-based solutions fail cope ...

Device vulnerabilities emerge one after another in the Internet of thing environment, attackers attack on several low-level devices simultaneously by multi-step method to trigger other high-level damage or control information system. Considering correlation between device vulnerabilities, we proposed a based graph evaluate vulnerability risk order ensure network security. First, according type,...