Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bu...
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Pre-training a language model and then fine-tuning it has shown to be an efficient and effective technique for a wide range of code intelligence tasks, such as code generation, code summarization, and vulnerability de...
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As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone de...
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Mainstream blockchain systems such as Bitcoin and Ethereum are revolutionizing the financial industry by adopting the Nakamoto consensus protocol, i.e., Proof-of-Work (PoW). Only nodes with sufficient computing resour...
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Mainstream blockchain systems such as Bitcoin and Ethereum are revolutionizing the financial industry by adopting the Nakamoto consensus protocol, i.e., Proof-of-Work (PoW). Only nodes with sufficient computing resources can work out the PoW difficulties, thereby increasing the mining cost of malicious attackers and ensuring the security of blockchain systems. Such an assumption of having abundant resources leads to drawbacks of low throughput and risk of centralization. In this article, we present Dispatcher, a novel distributed consensus protocol that takes resource heterogeneity into account to ensure resource-aware PoW with high efficiency. Dispatcher introduces a hierarchical topology to offer flexible PoW difficulties tailored for different nodes’ resources. In particular, it utilizes the limited resource of each node to jointly maximize the performance by concurrent mining. Moreover, we design an adaptive incentive mechanism to fit the available resource of blockchain nodes to rewards. Our experiments show that Dispatcher enjoys a substantial performance margin over the state-of-the-art. We can achieve a 50% throughput improvement compared with OHIE.
Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversar...
Graph Neural Networks (GNNs) have been increasingly adopted for graph analysis in web applications such as social networks. Yet, efficient GNN serving remains a critical challenge due to high workload fluctuations and...
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Federated learning (FL) has been widely acknowledged as a promising solution to training machine learning (ML) model training with privacy preservation. To reduce the traffic overheads incurred by FL systems, edge ser...
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作者:
Wang, HongfeiWan, CaixueJin, HaiHuazhong University of Science and Technology
National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology
National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China
The Physical Unclonable Function (PUF) is valued for its lightweight nature and unique functionality, making it a common choice for securing hardware products requiring authentication and key generation mechanisms. In...
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Knowledge representation learning is a key step required for link prediction tasks with knowledge graphs (KGs). During the learning process, the semantics of each entity are embedded by a vector or a point in a featur...
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Automatically generating webpage code from webpage designs can significantly reduce the workload of front-end developers, and recent Multimodal Large Language Models (MLLMs) have shown promising potential in this area...
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