Smart contracts play a vital role in blockchain applications, supporting an expanding array of services as the number of blockchains rises. As service requirements become increasingly complex, the need for access and collaboration among multiple smart contracts becomes more prevalent. However, achieving access between smart contracts on different blockchains presents a significant challenge in the Internet of Blockchain scenario comprising numerous heterogeneous blockchains. In this paper, we first explore the problem of smart contract access in cross-heterogeneous blockchain scenarios. Then, an Oracle gateway-based cross-chain smart contract access architecture and a subscription-based cross-chain smart contract active access mechanism are proposed. Finally, a prototype is implemented to show that our architecture and mechanism can support cross-chain smart contract access for heterogeneous blockchains and reduce the complexity and latency of cross-chain smart contract access.

Millions of smart contracts have been deployed onto the Ethereum platform, posing potential attack subjects. Therefore, analyzing contract binaries is vital since their sources are unavailable, involving identification comprising function entry identification and detecting its boundaries. Such boundaries are critical to many smart contract applications, e.g. reverse engineering and profiling. Unfortunately, it is challenging to identify functions from these stripped contract binaries due to the lack of internal function call statements and the compiler-inducing instruction reshuffling. Recently, several existing works excessively relied on a set of handcrafted heuristic rules which impose several faults. To address this issue, we propose a novel neural network-based framework for EVM bytecode Function Entries and Boundaries Identification (neural-FEBI) that does not rely on a fixed set of handcrafted rules. Instead, it used a two-level bi-Long Short-Term Memory network and a Conditional Random Field network to locate the function entries. The suggested framework also devises a control flow traversal algorithm to determine the code segments reachable from the function entry as its boundary. Several experiments on 38,996 publicly available smart contracts collected as binary demonstrate that neural-FEBI confirms the lowest and highest F1-scores for the function entries identification task across different datasets of 88.3 to 99.7, respectively. Its performance on the function boundary identification task is also increased from 79.4% to 97.1% compared with state-of-the-art. We further demonstrate that the identified function information can be used to construct more accurate intra-procedural CFGs and call graphs. The experimental results confirm that the proposed framework significantly outperforms state-of-the-art, often based on handcrafted heuristic rules.

EOSIO smart contracts are programs that can be collectively executed by a network of mutually untrusted nodes. As EOSIO smart contracts manage valuable assets, they become high-value targets and are subjected to more and more attacks. Tools for protecting EOSIO smart contracts are imperative. This paper proposes EOSIOAnalyzer, an effective static secu-rity analysis framework for EOSIO smart contracts to counter the three most common attacks. The framework consists of three components, the control flow graph builder, the static analyzer and the vulnerability detector. This paper implements an approach to transforming low-level Wasm bytecode into a high-level intermediate representation (Register Transfer Language). Besides, this paper also implements vulnerability detection speci-fications for three popular EOSIO smart contracts vulnerabilities, including Fake EOS Transfer, Forged Transfer Notification and Block Information Dependency. As a proof of concept, this paper conducts experiments to evaluate the effectiveness and efficiency of the EOSIOAnalyzer. The experiment results show that the detection accuracy of the three vulnerabilities is 100 %, 98.8 % and 100%, respectively.

BACKGROUND: In medicine, chromosome karyotyping analysis plays a crucial role in prenatal diagnosis for diagnosing whether a fetus has severe defects or genetic diseases. However, chromosome instance segmentation is the most critical obstacle to automatic chromosome karyotyping analysis due to the complicated morphological characteristics of chromosome clusters, restricting chromosome karyotyping analysis to highly depend on skilled clinical analysts. METHOD: In this paper, we build a clinical dataset and propose multiple segmentation baselines to tackle the chromosome instance segmentation problem of various overlapping and touching chromosome clusters. First, we construct a clinical dataset for deep learning-based chromosome instance segmentation models by collecting and annotating 1,655 privacy-removal chromosome clusters. After that, we design a chromosome instance labeled dataset augmentation (CILA) algorithm for the clinical dataset to improve the generalization performance of deep learning-based models. Last, we propose a chromosome instance segmentation framework and implement multiple baselines for the proposed framework based on various instance segmentation models. RESULTS AND CONCLUSIONS: segmentation precision, and 95.38% accuracy, which exceeds results reported in current chromosome instance segmentation methods. The quantitative evaluation results demonstrate the effectiveness and advancement of the proposed method for the chromosome instance segmentation problem. The experimental code and privacy-removal clinical dataset can be found at Github.

Bounded model checking (BMC) has played an important role in verification of software, embedded systems and protocols. The idea of BMC is to encode finite state machine (FSM) and linear temporal logic (LTL) verification specification into satisfiability (SAT) instances, and then to search for a counterexample via various SAT tools. Improving encoding technology of BMC can generate a SAT instance easy to solve, and therefore is essential to improve the efficiency of BMC. In this paper, we improve the encoding of BMC by combining the characteristic of FSM state transition and semantics of LTL, get a simple and efficient recursion formula which is useful to efficiently generate SAT instances. We present an efficient algorithm to encode the modal operator (safety formula) in BMC. The experiments for comparative analysis shows that this encoding algorithm is more powerful than the existing two mainstream encoding algorithms in both the scale of generated SAT instances and the solving efficiency. The methodology presented in this paper is also valuable for optimization of other modal operator encodings in BMC.