Isolatability and scalability are two critical issues faced by blockchain. Blockchain interoperability addresses isolatability between heterogeneous blockchains, while sharding-based blockchain achieves scalability by solving isolatability of homogeneous blockchains running in different shards. To ensure atomicity between different blockchains, they both need to overcome two significant problems: 1) how to handle cross-chain transactions without trusting any third parties; 2) how to enhance the resistance to double-spending attacks of participating blockchains. To this end, this work presents a two-tier multi-zone architecture, Distributed and Parallel Blockchain (DP-Chain), whereConsensus Zonein tier-1 features blockchain interoperability and sharding-based blockchain by allowing homogeneous or heterogeneous blockchains run in different zones, whileCoordination Layerin tier-2 allows them to interoperate as a Direct Acyclic Graph (DAG) without trusting any third parties. Meanwhile, the coordination scheme between the two tiers is designed to resist potential double-spending attacks. Then, stochastic models are used to captureDP-Chainconsensus process and analyze the probability of successful double-spending attacks. These analyses can help understandDP-Chaineasily and offer theoretical guidelines for further implementations. Finally, based on the proposed architecture, a practical system is realized with C++ and open in Github for testing. Extensive experiments show correctness and effectiveness of this design.

Semantic text matching models have achieved outstanding performance, but traditional methods may not solve Few-shot learning problems and data augmentation techniques could suffer from semantic deviation. To solve this problem, we propose STMAP, which is implemented from the perspective of data augmentation based on Gaussian noise and Noise Mask signal. We also employ an adaptive optimization network to dynamically optimize the several training targets generated by data augmentation. We evaluated our model on four English datasets: MRPC, SciTail, SICK, and RTE, with achieved scores of 90.3%, 94.2%, 88.9%, and 68.8%, respectively. Our model obtained state-of-the-art (SOTA) results on three of the English datasets. Furthermore, we assessed our approach on three Chinese datasets, and achieved an average improvement of 1.3% over the baseline model. Additionally, in the Few-shot learning experiment, our model outperformed the baseline performance by 5%, especially when the data volume was reduced by around 0.4. Our ablation experiments further validated the effectiveness of STMAP.2

To securely integrate Mobile Edge Computing (MEC) into Wireless Blockchain Network (WBN), this paper proposes a framework for blockchain based offloading strategy, where blockchain nodes are categorized as blockchain users and blockchain miners from a motivation perspective. Particularly, aiming at improving the motivation ability, a block generation process is first designed for blockchain miners’ short transaction processing time lower bound. Then, to further maximize the utilities of both blockchain users and blockchain miners, an optimization problem is formulated to determine an optimal strategy which involves a trade-off between the fast transaction confirmation rate required by blockchain users and the transaction fees obtained by blockchain miners. A Stackelberg game is introduced to model the interaction between the blockchain users and miners. Meanwhile, a distributed algorithm is designed to converge this strategy in an iterative manner based on the buyer-seller negotiation. Additionally, double-spending attack and selfish mining attack are analysed to examine their impact on the system performance in terms of confirmation delay and throughput while guaranteeing the high security level. Finally, extensive experiments have been conducted to show the rightness and effectiveness of the proposed equilibrium-based strategy and mathematical analysis, and some insights are discussed for the further guide as well.

Keeping patients' sensitive information secured and untampered in the e-Health system is of paramount importance. Emerging as a promising technology to build a secure and reliable distributed ledger, blockchain can protect data from being falsified, which has attracted much attention from both academia and industry. However, with limited computational resources, medical IoT devices do not have efficient ability to fulfill the functionalities as a full node in wireless blockchain network (WBN). Facing this dilemma, Mobile Edge Computing (MEC) brings us dawn and hope through offloading the high resource demanding blockchain functionalities at the IoT devices to the MEC. However, aiming to maximize the mining profit, most of existing offloading strategies have ignored the other needs of wireless devices, e.g., faster transaction writing. In this paper, according to different needs, blockchain nodes are firstly divided into two categories. One is blockchain users whose needs are faster transaction uploading, the other is blockchain miners whose goals are maximum revenue. Then, to maximize both the utilities of blockchain users and blockchain miners, a Stackelberg game is introduced to formulate the interaction between them. From the simulation results, this game is proved to converge to a unique optimal equilibrium.