Smart contracts typically store crucial application state data. Methods like SPV proofs are commonly used to verify the authenticity of cross-chain transactions. However, the heterogeneity between blockchains makes common cross-chain data validation methods impractical for smart contract state data. To address this challenge, this paper proposes MSCV to achieve cross-chain verification of smart contract state data. MSCV utilizes MTC to provide a universal verification method for state data and preserves the chronological order of state data. Furthermore, MSCV introduces zero-knowledge proof technology to reduce the additional verification overhead of MTC. Finally, this paper implements smart contracts containing MTC and a batch verification zero-knowledge proof circuit and evaluates their performance and effectiveness.

Nowadays, the integration of blockchain and MANET improves both MANET security and blockchain scalability. Hashgraph can better support the MANET-based blockchain by its inherent features, i.e., gossip about gossip, Byzantine fault tolerance, etc. In order to understand the limitations and performance of hashgraph in MANETs, this work investigate the theoretical model with two metrics. The metrics include the degree of node isolation and witness confirmation delay. By using the differential equations and markov chain, this work quantitatively analyze the general hashgraph with an optimal contact probability and the improved hashgraph resisting eclipse attacks, as well as identifying the expressions of these performance metrics. Extensive experiments support the analysis and reveal the impact of moving nodes and Byzantine nodes on the hashgraph performance in MANETs. To the best of our knowledge, our work is the first to apply DAG (Directed Acyclic Graph) blockchain for the MANETs from a methodological perspective.

Smart contracts, as a critical application of blockchain technology, play a pivotal role in automating contract rules and enhancing the transparency of transactions. They are programmed to define rules and automate protocols on the blockchain, ensuring that transactions are decentralized, efficient, and immutable. Once deployed, smart contracts cannot be altered, and any existing vulnerabilities can be maliciously exploited, leading to potential financial losses or data breaches. Consequently, the security of smart contracts has become a critical focus in blockchain security. This paper presents the XLNET-HyBA model for Ethereum smart contract vulnerability detection based on the fusion of heterogeneous data. Initially, the source code is transformed into Abstract Syntax Tree (AST) serialized information by the SmartConvert preprocessing algorithm, which effectively integrates the scattered control flow elements. Subsequently, a novel information fusion embedding technique is proposed to optimize the contribution weights of source code and AST serialized code, aiming for a more accurate localization of potential vulnerabilities. Finally, a hybrid loss function strategy based on metric learning is constructed, enhancing the model’s sensitivity to subtle differences. The experimental results show that the accuracy of all four vulnerabilities is over 96

Mobile crowdsourcing (MCS) takes advantage of widely distributed mobile devices to complete some temporal-spatial tasks. Edge computing is integrated into MSC to reduce the service delay from a remote cloud, and enhance the quality of services (QoS) through preprocessing data. However, the profit-driven edge nodes (workers) may provide fake or low-quality answers for lowering their data processing cost, which results in serious QoS challenges. We propose a reputation-based accountability mechanism, in which workers are accountable for their answer provision through reputation values, and edge nodes take on different responsibilities of blockchain management based on reputation values and some key factors. Specifically, the combination of the data-centric method and entity-centric method is utilized for precisely evaluating the quality of answers and managing the worker’s reputation. Storage nodes, mining candidate nodes, relay nodes, and verification nodes are mainly responsible for transaction retrieval, block generation, block forwarding and block verification, respectively. It reduces the latency of blockchain maintenance in a large-scale network. Finally, the experimental results show that our scheme achieves high-level reliability and reasonable efficiency for large-scale services in MCS.

As a vital solution for data trading, blockchain often carries a substantial amount of critical data. Consequently, data quality, as a crucial evaluation metric, plays a key role in facilitating data trading on the blockchain. In the process of comprehensively evaluating the quality of multi-field datasets, it is crucial to determine the importance coefficient of each data item. The importance evaluation methods of data items include subjective methods that depend on expert scoring, as well as objective methods based on statistical methods. Objective methods evaluate field importance based on the laws of data itself. However, such methods start from the quality of data to be evaluated, and cannot reflect the mutual dependency between data items and the resulting field importance. To solve this problem, this article starts from the business dependency between data items, constructs a directed graph, namely dependency network. On this basis, according to the characteristics and quantity of data items that depend on fields, we propose the FieldRank algorithm. The algorithm objectively quantifies and evaluates the importance of each field, so that the field importance weight obtained based on the algorithm is directly related to the actual business and is more close to the essence of data business. Therefore, this algorithm can provide objective data quality evaluation results for data use. Practice has proved that in the case of large amounts of big data and closely related businesses, the data quality evaluation results obtained by this method are more close to actual business practice.

The evaluation of outputs from large language models (LLMs) is an important part of LLMs’ born. A comprehensive evaluation for LLMs requires substantial human and material resources. This work proposes a crowdsourcing evaluation framework based on blockchain to comprehensively evaluate the toxicity of the outputs from LLMs. The framework offers LLM service to users and collects their evaluation scores for the outputs from the LLM. The evaluation scores are kept on blockchain. During this, the framework allocates and updates the reputation scores based on users’ contributions to the overall evaluation, in order to mitigate the impact of individual users’ subjective biases on the results. This framework lowers the cost of evaluation and enhances the objectivity and reliability of the results. Experiments demonstrate that this framework provides robust support for the objective evaluation of LLMs and offers a feasible and efficient idea for future works in evaluation for LLMs.

With the emergence of digital currencies, blockchain systems have been committed to the efficient and trusted storage of data, building decentralized, tamper-proof, persistent, and anonymous digital ledger. Researchers have devoted considerable attention to query methods on blockchain platforms, leading to numerous developments and advancements. There is an imperative need for a comprehensive investigation of all these efforts, as well as their most recent progress and results. This study strives to provide a comprehensive and thorough survey of notable works and recent progress related to query technologies and theories. In general, blockchain query methods rely on utilizing distributed databases, data indexing structures, and cryptographic algorithms to achieve efficient, verifiable, and secure queries. Beyond the above, we examine existing issues of blockchain query technologies and theories on query efficiency, reliability, and security. In the end, this work concludes with a summarization on typical scenarios of blockchain query schemes, as well as a discussion of future challenges to be addressed in future research.

Blockchain establishes security and trust in mobile ad hoc networks (MANETs). Due to the decentralized and opportunistic communication characteristics of MANETs, hashgraph consensus is more applicable to the MANET-based blockchain. Sharding scales the consensus further through disjoint nodes in multiple shards simultaneously updating ledgers. However, the dynamic addition and deletion of nodes in a shard pose challenges regarding robustness and efficiency. Particularly, the shard is vulnerable to Sybil attacks and targeted attacks, and dishonest gossip reduces the efficiency of hashgraph consensus. Therefore, we proposed a behavior-based sharding hashgraph scheme. First, dishonest behaviors of nodes are recorded in a decentralized blacklist. Gossip information is sent to a reliable neighbor, and gossip information from another reliable neighbor is received. Second, a tree-assisted inter-sharding consensus is proposed to prevent Sybil attacks. The combination of shard recovery and reconfiguration based on node state is devised to prevent targeted attacks. Finally, we conducted the performance evaluation including security analysis and experimental evaluation to reveal the security and efficiency of the proposed scheme.

The process of chromosome karyotype analysis is a highly time-consuming and error-prone task heavily relying on the experience of the cytogeneticists and influenced by factors such as fatigue and decrease of attention. Many efforts have dedicated to automatic chromosome karyotype analysis using various computer vision techniques based on geometric morphology and deep learning. However, few of them have paid attention to selections of high-suitability medical cell images for chromosome karyotype analysis. High-suitability cell images not only can significantly decrease the difficulty of manual chromosome karyotype analysis, but also can boost the analysis performance of automatic chromosome karyotype analysis algorithms. This paper proposes a suitability assessment framework for evaluating the suitabilities of cell images to address the issue of selecting high-suitability medical cell images for the inputs of chromosome karyotype analysis. The quantitative experimental results show that using the proposed suitability assessment framework to select suitable inputs can significantly boost chromosome segmentation performance by 5.06 percentage points of mAP, 2.4 percentage points of $$AP^{50}$$ , and 3.58 percentage points of $$AP^{75}$$ . The qualitative experiments with a group of cell images show that the corresponding suitability results evaluated by the proposed framework are highly in accordance with results evaluated by the experienced analysts, demonstrating the effectiveness of the proposed method to address the selection issue of suitable medical cell images.

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.