However, analyzing blockchain data requires domain expertise and computational resources, which pose a significant barrier and hinder advancement in this field.

This approach enables evaluation of the model's performance within a time-sensitive

Machine learning applied to blockchain graphs offers significant opportunities for enhanced data analysis and applications. However, the potential of this field is constrained by the lack of a large-scale, cross-chain dataset that includes hierarchical graph-level data. To address this issue, we present novel datasets that provide detailed label information at the token level and integrate interactions between tokens across multiple blockchain platforms.

Due to the decentralized nature of blockchain, this makes it possible for everyone to access all the transaction information.

Plus: A secret FBI anti-ransomware task force gets exposed, the mystery of the CIA's Kryptos sculpture is finally solved, North Koreans busted hiding malware in the Ethereum blockchain, and more. In a stunning new study, researchers at UC San Diego and the University of Maryland revealed this week that satellites are leaking a wealth of sensitive data completely unencrypted, from calls and text messages on T-Mobile to in-flight Wi-Fi browsing sessions, to military and police communications. And they did this with just $800 in off-the-shelf equipment. Face recognition systems are seemingly everywhere. But what happens when this surveillance and identification technology doesn't recognize your face as a face?

This approach enables evaluation of the model's performance within a time-sensitive

This study tackles the computational challenges of solving Markov Decision Processes (MDPs) for a restricted class of problems. It is motivated by the Last Revealer Attack (LRA), which undermines fairness in some Proof-of-Stake (PoS) blockchains such as Ethereum (\$400B market capitalization). We introduce pseudo-MDPs (pMDPs) a framework that naturally models such problems and propose two distinct problem reductions to standard MDPs. One problem reduction provides a novel, counter-intuitive perspective, and combining the two problem reductions enables significant improvements in dynamic programming algorithms such as value iteration. In the case of the LRA which size is parameterized by $κ$ (in Ethereum's case $κ$= 325), we reduce the computational complexity from $O(2^κκ^{2^{κ+2}})$ to $O(κ^4)$ (per iteration). This solution also provide the usual benefits from Dynamic Programming solutions: exponentially fast convergence toward the optimal solution is guaranteed. The dual perspective also simplifies policy extraction, making the approach well-suited for resource-constrained agents who can operate with very limited memory and computation once the problem has been solved. Furthermore, we generalize those results to a broader class of MDPs, enhancing their applicability. The framework is validated through two case studies: a fictional card game and the LRA on the Ethereum random seed consensus protocol. These applications demonstrate the framework's ability to solve large-scale problems effectively while offering actionable insights into optimal strategies. This work advances the study of MDPs and contributes to understanding security vulnerabilities in blockchain systems.

Due to the decentralized nature of blockchain, this makes it possible for everyone to access all the transaction information.