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Analyzing Ethereum bytecode, rather than the source code from which it was generated, is a necessity when: (1) the source code is not available (e.g., the blockchain only stores the bytecode), (2) the information to be gathered in the…
Poorly designed smart contracts are particularly vulnerable, as they may allow attackers to exploit weaknesses and steal the virtual currency they manage. In this study, we train a model using unsupervised learning to identify…
Large language models (LLMs) have recently been applied to binary decompilation, yet they still treat code as plain text and ignore the graphs that govern program control flow. This limitation often yields syntactically fragile and…
When source code or the original toolchain is unavailable, patching binaries is difficult because it requires editing low-level assembly code directly. As an alternative, one can decompile the binary, apply the patch at the source level,…
State-of-the-art methods for Transformer-based semantic segmentation typically adopt Transformer decoders that are used to extract additional embeddings from image embeddings via cross-attention, refine either or both types of embeddings…
Binary decompilation aims to recover binaries into high-level source code, but existing evaluations mainly rely on syntactic similarity or single-axis readability metrics, which fail to capture practical reusability. We propose a…
Smart contracts have transformed decentralized finance by enabling programmable, trustless transactions. However, their widespread adoption and growing financial significance have attracted persistent and sophisticated threats, such as…
The current large blockchain systems (BTC Lightning network, Ethereum, etc.) are generally facing the problems of low persistence rates and high storage costs. Therefore, users tend to store single modal (textual) information on the…
Recent attacks exploiting errors in smart contract code had devastating consequences thereby questioning the benefits of this technology. It is currently highly challenging to fix errors and deploy a patched contract in time. Instant…
Parallel execution has become a key approach to improving blockchain scalability, but the lack of formal semantics for smart contract languages in such settings makes rigorous reasoning difficult. Crystality is a smart contract language…
Bitcoin has attracted everyone's attention and interest recently. Ethereum (ETH), a second generation cryptocurrency, extends Bitcoin's design by offering a Turing-complete programming language called Solidity to develop smart contracts.…
The execution of large deep neural networks (DNN) at mobile edge devices requires considerable consumption of critical resources, such as energy, while imposing demands on hardware capabilities. In approaches based on edge computing the…
Smart contracts are susceptible to being exploited by attackers, especially when facing real-world vulnerabilities. To mitigate this risk, developers often rely on third-party audit services to identify potential vulnerabilities before…
Smart contracts are full-fledged programs that run on blockchains (e.g., Ethereum, one of the most popular blockchains). In Ethereum, gas (in Ether, a cryptographic currency like Bitcoin) is the execution fee compensating the computing…
Decompilation is a well-studied area with numerous high-quality tools available. These are frequently used for security tasks and to port legacy code. However, they regularly generate difficult-to-read programs and require a large amount of…
Over the last few years, neural image compression has gained wide attention from research and industry, yielding promising end-to-end deep neural codecs outperforming their conventional counterparts in rate-distortion performance. Despite…
Deep learning-based image compression has made great progresses recently. However, many leading schemes use serial context-adaptive entropy model to improve the rate-distortion (R-D) performance, which is very slow. In addition, the…
Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling. However, when processing high-dimensional data, RNNs typically require very large model sizes, thereby bringing a series of deployment challenges.…
Encoder-decoder models have made great progress on handwritten mathematical expression recognition recently. However, it is still a challenge for existing methods to assign attention to image features accurately. Moreover, those…
We introduce the DeTerministic Virtual Machine (DTVM) Stack, a next-generation smart contract execution framework designed to address critical performance, determinism, and ecosystem compatibility challenges in blockchain networks. Building…