Smart contract vulnerability detection has attracted increasing attention due to billions of economic losses caused by vulnerabilities. Existing smart contract vulnerability detection methods have high false negative and high false positive rates. To address these issues, we present ByteEye, a bytecode level smart contract vulnerability detection framework with Graph Neural Networks (GNNs). ByteEye first constructs an edge-enhanced Control Flow Graph (CFG) to maintain rich information from the low-level bytecode with low latency. ByteEye also designs and incorporates both general information and vulnerability-specific information into its detection method as bytecode level features. Furthermore, ByteEye flexibly supports machine/deep learning models, especially with graph neural networks, which can facilitate vulnerability detection precisely. The extensive experimental results highlight that ByteEye outperforms the state-of-the-art approaches on all three types of vulnerability detection. ByteEye can achieve an average of 35.29%, 43.95%, and 6.38% higher on F1 than the bytecode level best-performed baseline on reentrancy vulnerability, timestamp dependency vulnerability, and integer overflow/underflow vulnerability, respectively. Moreover, ByteEye can detect 361 new vulnerabilities in real-world smart contracts, which are reported for the first time. ByteEye enhances control flow information, designs general bytecode-level features with expert knowledge, and flexibly supports deep learning models, particularly GNNs, thus achieving high detection effectiveness.

State access is a critical part of smart contract execution which seriously affects the efficiency of smart contract execution in the mainstream Ethereum blockchain. To reduce state access latency, existing studies typically require manual source code modifications, which in practice may deliver limited performance gains and shift the burden to developers. In this paper, we propose CROSC to reduce state access latency and improve smart contract execution efficiency by a compilation-runtime joint optimization approach. CROSC consists of three key parts: 1) a runtime memory management mechanism named Fast State Memory (FastSM) to fully utilize the working memory and provide the context for the contract compiler; 2) a State Variable Address Relocation (SVAR) strategy to minimize costly persistent storage operations by precisely redirecting state variable access targets during compilation; 3) a one-shot unpacking design that eliminates frequent decoding overhead for low-bitwidth state variables. Preliminary experimental results highlight that, compared with the baseline compilation and runtime system of Ethereum, CROSC can achieve 2.5× and 7.5× speedups for single state load and store operations, respectively. CROSC reduces state access latency by up to 81.3%, and overall contract execution latency by 32.9% on average across 14 typical types of smart contracts. Extended evaluations on ERC20 and ERC721 token standard contracts show that CROSC delivers significant benefits in critical areas while remaining unobtrusive for less intensive state operations.

The performance bottleneck of blockchain has shifted from consensus to serial smart contract execution in transaction validation. Previous works predominantly focus on inter-contract parallel execution, but they fail to address the inherent limitations of each smart contract execution performance. In this paper, we propose PaVM, the first smart contract virtual machine that supports both inter-contract and intra-contract parallel execution to accelerate the validation process. PaVM consists of (1) key instructions for precisely recording entire runtime information at the instruction level, (2) a runtime system with a re-designed machine state and thread management to facilitate parallel execution, and (3) a read/write-operation-based receipt generation method to ensure both the correctness of operations and the consistency of blockchain data. We evaluate PaVM on the Ethereum testnet, demonstrating that it can outperform the mainstream blockchain client Geth. Our evaluation results reveal that PaVM speeds up overall validation performance by 33.4×, and enhances validation throughput by up to 46×.

Blockchain-based artificial intelligence (BC-AI) has been applied for protecting deep neural network (DNN) data from being tampered with, which is expected to further boost trusted distributed AI applications in many fields. However, due to smart contract execution environment architectural defects, it is challenging for previous BC-AI systems to support computing-intensive tasks on-chain performing such as DNN convolution operations. They have to offload computations and a large amount of data from blockchain to off-chain platforms to execute smart contracts as native code. This failure to take advantage of data locality has become one of the major critical performance bottlenecks in BC-AI system. To this end, in this article, we propose SmartVM with optimization methods to support on-chain DNN inference for BC-AI system. The key idea is to design and optimize the computing mechanism and storage structure of smart contract execution environment according to the characteristics of DNN such as high computational parallelism and large data volume. We decompose SmartVM into three components: 1) a compact DNN-oriented instruction set to describe computations in a short number of instructions to reduce interpretation time. 2) a memory management mechanism to make SmartVM memory dynamic free/allocated according to the size of DNN feature maps. 3) a block-based weight prefetching and parallel computing method to organize each layer's computing and weights prefetching in a pipelined manner. We perform the typical image classification in a private Ethereum blockchain testbed to evaluate SmartVM performance. Experimental results highlight that SmartVM can support DNN inference on-chain with roughly the same efficiency against the native code execution. Compared with the traditional off-chain computing, SmartVM can speed up the overall execution by70×,16×,11×, and12×over LeNet5, AlexNet, ResNet18, and MobileNet, respectively. The memory footprint can be reduced by84%,90.8%,94.3%, and93.7%over the above four models, while offering the same level model accuracy. This article sheds light on the design space of the smart contract virtual machine for DNN computation and is promising to further boost BC-AI applications.

Person re-identification (re-ID) has drawn attention significantly in the computer vision society due to its application and research significance. It aims to retrieve a person of interest across different camera views. However, there are still several factors that hinder the applications of person re-ID. In fact, most common data sets either assume that pedestrians do not change their clothing across different camera views or are taken under constrained environments. Those constraints simplify the person re-ID task and contribute to early development of person re-ID, yet a person has a great possibility to change clothes in real life. To facilitate the research toward conquering those issues, this paper mainly introduces a new benchmark data set for person re-identification. To the best of our knowledge, this data set is currently the most diverse for person re-identification. It contains 107 persons with 9,738 images, captured in 15 indoor/outdoor scenes from September 2019 to December 2019, varying according to viewpoints, lighting, resolutions, human pose, seasons, backgrounds, and clothes especially. We hope that this benchmark data set will encourage further research on person re-identification with clothes variation. Moreover, we also perform extensive analyses on this data set using several state-of-the-art methods. Our dataset is available at https://github.com/nkicsl/NKUP-dataset.