JointCloud computing is a new computing paradigm that supports cloud services to achieve mutually beneficial outcomes. A key component of this model is the JointCloud computing distributed ledger, which ensures trust among cloud entities with diverse interests by providing digital space evidence. Blockchain forms the foundation of this framework, and its adoption is growing in various fields to optimize workflows. However, challenges related to blockchain interoperability and cross-chain data authentication remain unresolved, hindering the broader establishment of trust among cloud entities. This paper focuses on the specific scenario of cross-chain data authentication and provides a secure and easy-to-deploy solution. Drawing on the concepts of relay chain and notary mechanism in cross-chain methods, our solution leverages smart contracts and on-chain proofs to ensure security, thereby reducing the reliance on relay nodes. In cases of data falsification, our method offers mechanisms for accountability and data revocation, further enhancing security. Additionally, participating blockchains do not need to alter their data structures or network compositions for authentication, which makes the solution easy to deploy. Experimental results demonstrate that our design can be easily deployed on both homogeneous and heterogeneous blockchains. While maintaining performance comparable to existing solutions, it significantly enhances system security. In the event of data falsification, the solution can quickly hold individuals accountable and eliminate the impact of falsified data, reducing the time to mitigate the impact by half compared with other solutions.

Communication and coordination between open source software (OSS) developers who do not work physically in the same location have always been the challenging issues. The pull-based development model, as the state-of-the-art collaborative development mechanism, provides high openness and transparency to improve the visibility of contributors’ work. However, duplicate contributions may still be submitted by more than one contributor to solve the same problem due to the parallel and uncoordinated nature of this model. If not detected in time, duplicate pull-requests can cause contributors and reviewers to waste time and energy on redundant work. In this paper, we propose an approach combining textual and change similarities to automatically detect duplicate contributions in the pull-based model at submission time. For a new-arriving contribution, we first compute textual similarity and change similarity between it and other existing contributions. And then our method returns a list of candidate duplicate contributions that are most similar to the new contribution in terms of the combined textual and change similarity. The evaluation shows that 83.4% of the duplicates can be found in average when we use the combined textual and change similarity compared with 54.8% using only textual similarity and 78.2% using only change similarity.