Transfer learning enhances model performance in financial time series by leveraging data from related domains. The selection of appropriate source domains is crucial to avoid negative transfer. We propose using Gramian Angular Field (GAF) transformations to improve time series similarity functions for better domain alignment. Extensive experiments with DNN and LSTM models show that GAF-based similarity functions, specifically Coral (GAF) for DNN and CMD (GAF) for LSTM, significantly reduce prediction errors, demonstrating their effectiveness in complex financial environments.

In financial analysis, time series modeling is often hampered by data scarcity, limiting neural network models’ ability to generalize. Transfer learning mitigates this by leveraging data from similar domains, but selecting appropriate source domains is crucial to avoid negative transfer. This study enhances source domain selection in transfer learning by introducing Gramian Angular Field (GAF) transformations to improve time series similarity functions. We evaluate a comprehensive range of baseline similarity functions, including both basic and state-of-the-art (SOTA) functions, and perform extensive experiments with Deep Neural Networks (DNN) and Long Short-Term Memory (LSTM) networks. The results demonstrate that GAF-based similarity functions significantly reduce prediction errors. Notably, Coral (GAF) for DNN and CMD (GAF) for LSTM consistently deliver superior performance, highlighting their effectiveness in complex financial environments.

Empowered by blockchain technology, smart contracts have attracted considerable interest from Web3 users due to their distinct advantages. Nevertheless, it is challenging to address problems caused by the dramatic expansion of the Web3 ecosystem. This article introduces the smart contract-as-a-service (SCaaS) paradigm to mitigate smart contracts’ redundant deployment via their composability and reusability. Moreover, we design trust and incentive schemes to ensure project security and developer engagement in SCaaS. Specifically, we first introduce a reputation filter by leveraging the authentic on-chain data, aiming to eliminate high-risk contracts. We then design a contract-based incentive mechanism to help the foundation attract heterogeneous developers with multidimensional private information, and maximize the foundation’s utility by inducing developers to undertake projects of differing complexities based on their ability. We further differentiate between veteran and newcome developers and examine their influences on foundational strategies. Finally, extensive experimental results demonstrate that our proposed contracts can efficiently remove high-risk smart contracts, maximize the foundation’s utility, and ensure that developers select contracts honestly and participate in the SCaaS ecosystem actively.

Decentralized Finance (DeFi), propelled by Blockchain technology, has revolutionized traditional financial systems, improving transparency, reducing costs, and fostering financial inclusion. However, transaction activities i n these systems fluctuate significantly and the throughput can be effected. To address this issue, we propose a Dynamic Mining Interval (DMI) mechanism that adjusts mining intervals in response to block size and trading volume to enhance the transaction throughput of Blockchain platforms. Besides, in the context of public Blockchains such as Bitcoin, Ethereum, and Litecoin, a shift towards transaction fees dominance over coin-based rewards is projected in near future. As a result, the ecosystem continues to face threats from deviant mining activities such as Undercutting Attacks, Selfish Mining, and Pool Hopping, among others. In recent years, Dynamic Transaction Storage (DTS) strategies were proposed to allocate transactions dynamically based on fees thereby stabilizing block incentives. However, DTS’ utilization of Merkle tree leaf nodes can reduce system throughput. To alleviate this problem, in this paper, we propose an approach for combining DMI and DTS. Besides, we also discuss the DMI selection mechanism for adjusting mining intervals based on various factors.