At present, convolutional neural networks (CNNs) have become popular in visual classification tasks because of their superior performance. However, CNN-based methods do not consider the correlation data to be classified. Recently, graph (GCNs) mitigated this problem by modeling pairwise relationship data. Real-world typically must address numerous complex relationships data, which are fit for structure using GCNs. Therefore, it is vital explore underlying Regarding issue, we propose a...

Ethereum Virtual Machine (EVM) is the run-time environment for smart contracts and its vulnerabilities may lead to serious problems ecology. With lots of techniques being continuously developed validation contracts, testing EVM remains challenging because special test input format absence oracles. In this paper, we propose EVMFuzzer, first tool that uses differential fuzzing technique detect EVM. The core idea generate seed feed them target benchmark EVMs, so as find many inconsistencies...

Ethereum Virtual Machine (EVM) is the run-time environment for smart contracts and its vulnerabilities may lead to serious problems ecology. With lots of techniques being developed validation contracts, security EVM have not been well-studied. In this paper, we propose EVMFuzz, aiming detect EVMs with differential fuzz testing. The core idea EVMFuzz continuously generate seed different EVMs' execution, so as find many inconsistencies among execution results possible, eventually discover...

Privacy-preserving data aggregation (PPDA) enables availability and privacy preservation simultaneously in smart grid. However, existing methods, such as masking homomorphic encryption, cannot offer strong preservation, fault tolerance for both meters aggregators, verifiable aggregation, lightweight encryption. To tackle these challenges, we design HTV-PRE, a threshold proxy re-encryption scheme with verifiability. HTV-PRE involves only linear operations resists quantum attacks after being...

Due to the material variations of lithium-ion cells and fluctuations in their manufacturing precision, differences exist electrochemical characteristics cells, which inevitably lead a reduction available capacity premature failure battery pack with multiple configured series, parallel, series–parallel. Screening that have similar overcome inconsistency among is challenging problem. This paper proposes an approach for lithium -ion cell screening using convolutional neural networks (CNNs)...

Learning-based clone detection is widely exploited for binary vulnerability search. Although they solve the problem of high time overhead traditional dynamic and static search approaches to some extent, their accuracy limited, need manually identify true positive cases among top-M results during industrial practice. This paper presents VulSeeker-Pro, an enhanced seeker that integrates function semantic emulation at back end learning, release engineers from manual identification work. It...

Abstract Background Suan-Zao-Ren Decoction (SZRD) has been widely used to treat neurological illnesses, including dementia, insomnia and depression. However, the mechanisms underlying SZRD’s improvement in cognitive function remain unclear. In this study, we examined effect on APP/PS1 transgenic mice associated with action alleviating neuroinflammation improving synaptic plasticity. Methods The were treated different dosages of SZRD (12.96 25.92 g/kg/day, L-SZRD H-SZRD groups, respectively)...

While massive efforts have been investigated in adversarial testing of convolutional neural networks (CNN), for recurrent (RNN) is still limited and leaves threats vast sequential application domains. In this paper, we propose an framework RNN-Test RNN systems, focusing on sequence-to-sequence (seq2seq) tasks widespread deployments, not only classification First, design a novel search methodology customized models by maximizing the inconsistency states against their inner dependencies to...

Introduction Existing evidence suggests an association between certain vitamins and metabolic syndrome (MetS), but few epidemiological studies have focused on the effects of multivitamin co-exposure MetS. This study aims to investigate associations individual or multiple water-soluble (i.e., vitamin C (VC), B9 (VB9), B12 (VB12)) with MetS, as well dose-response relationships among them. Methods A cross-sectional was conducted by employing National Health Examination Surveys (NHANESs)...

Automatic protocol reverse engineering is essential for various security applications.While many existing techniques achieve this task by analyzing static network traces, they face increasing challenges due to their dependence on high-quality samples.This paper introduces DYNPRE, a tool that exploits the interactive capabilities of servers obtain more semantic information and additional traffic dynamic inference.DYNPRE first processes initial input traces learns rules interacting with server...

Coverage-guided kernel fuzzing is a widely-used technique that has helped developers and testers discover numerous vulnerabilities. However, due to the high complexity of application hardware environment, there little study on deploying enterprise-level Linux kernel. In this paper, collaborating with enterprise developers, we present industry practice deploy four different distributions are responsible for internal business external services company. We have addressed following outstanding...

Vulnerable code clones in the operating system (OS) threaten safety of smart industrial environment, and most vulnerable OS clone detection approaches neglect correlations between functions that limits effectiveness. In this article, we propose a two-phase framework to find by learning on functions. On training phase, as set are extracted from latest repository function features derived their AST structure. Then, external internal explored graph modeling Finally, convolutional network for...

Electricity Theft Detection (ETD) based on deep learning can detect abnormal electricity consumption behaviors by analyzing user historical data. However, existing ETD schemes conduct neural network inferencing in plaintext without strong privacy guarantees, moreover, none has considered the of proprietary models from electric utilities. To address those issues, we introduce p2Detect, a privacy-preserving scheme via over encrypted p2Detect works two-party setting where model is held Server...

The spread of misinformation on social media is a serious issue that can have negative consequences for public health and political stability. While detecting identifying be challenging, many attempts been made to address this problem. However, traditional models focus pairwise relationships propagation paths may not effective in capturing the underlying connections among multiple tweets. To limitation, proposed “Conversation-Branch-Tweet” hypergraph convolutional network (CBT-HGCN) uses...

Embedded operating systems (Embedded OSs) are extensively deployed in many mission-critical industrial scenarios. Any defects within these may result unacceptable losses. Therefore, it is imperative to develop tools detect bugs OSs, thus minimizing potential impacts on infrastructures. Coverage-guided fuzzing a vulnerability detection technique that has found numerous real-world vulnerabilities both application programs as well kernels. However, state-of-the-art kernel fuzzers, e.g.,...

Quantum Neural Network (QNN) combines the Deep Learning (DL) principle with fundamental theory of quantum mechanics to achieve machine learning tasks acceleration. Recently, QNN systems have been found manifest robustness issues similar classical DL systems. There is an urgent need for ways test their correctness and security. However, differ significantly from traditional software systems, posing critical challenges testing. These include inapplicability testing methods due differences in...