Few-shot continual relation extraction aims to continually train a model on incrementally fewshot data to learn new relations while avoiding forgetting old ones.However, current memory-based methods are prone to overfitting memory samples, resulting in insufficient activation of old relations and limited ability to handle the confusion of similar classes.In this paper, we design a new N-way-K-shot Continual Relation Extraction (NK-CRE) task and propose a novel few-shot continual relation extraction method with Consistent Prototype Learning (ConPL) to address the aforementioned issues.Our proposed ConPL is mainly composed of three modules: 1) a prototypebased classification module that provides primary relation predictions under few-shot continual learning; 2) a memory-enhanced module designed to select vital samples and refined prototypical representations as a novel multi-information episodic memory; 3) a consistent learning module to reduce catastrophic forgetting by enforcing distribution consistency.To effectively mitigate catastrophic forgetting, ConPL ensures that the samples and prototypes in the episodic memory remain consistent in terms of classification and distribution.Additionally, ConPL uses prompt learning to extract better representations and adopts a focal loss to alleviate the confusion of similar classes.Experimental results on two commonly-used datasets show that our model consistently outperforms other competitive baselines 1 .

Blockchain and smart contracts are being embraced by more and more industrial practitioners in multiple domains including agriculture, manufacturing, and healthcare. As a distributed, immutable, and partly public ledger, the consortium blockchain demonstrates its potential to enable trustworthy interoperability and collaboration between organizations. However, the mismatch between the unruled software engineering practices and the increased interest of the consortium blockchain technology may pose threats to the quality of systems implemented. To mitigate the possible threats, this study takes the angle of software engineering to systematically understand the challenges and possible solutions in terms of developing and operating a consortium blockchain-based system. For this purpose, we conducted a case study on a typical consortium blockchain-based system and exhaustively collected the data by two rounds in-depth interviews on practitioners of different roles in the case project. Based on the data analysis, eight pairs of challenges and potential solutions were identified, which cover the phases of the development and operation of consortium blockchains. Moreover, we also captured two implications after further analysis of the findings, which worth the special attention of researchers in the near future, i.e. DevOps and microservices for blockchain or smart contracts.