NatSpec comments play an essential role in smart contracts. Their clear and informative format helps users gain an accurate understanding of smart contract functions and diminish financial risk. However, widespread non-adherence to NatSpec standards currently causes confusion for both end-users and developers. Current research often neglects the importance of NatSpec formats or solely emphasizes user-centric comments in smart contract generation. This oversight can hinder contract trustworthiness, code reusability, maintenance efficiency, and ultimately, the development of the community ecosystem. To bridge this gap, this paper presents the first empirical study on 253 verified contracts encompassing 16,620 functions from Etherscan, uncovering that 87 % of the smart contract functions have Comment Errors (CE) and pinpointing prevalent deviation patterns. Based on our findings, we propose CETerminator, an automated approach for detecting and rectifying CE in smart contract functions. Due to the scarcity of NatSpec-compliant comments for collected smart contract functions, CETerminator employs in-context learning on a large language model to generate NatSpec comments. The approach then compares the original and the generated comments, utilizing corpus-driven heuristic rules to identify and correct diverse error categories in the original comments. In our evaluation, CETerminator demonstrates a high token overlap rate for addressing missing comments. In addition, the average precision, recall, and F1-scores for handling inconsistency comments are 85.28 %, 86.48 %, and 85.85%, respectively, outperforming the baseline by 39.79%, 39.53%, and 39.84%.

The received signal strength (RSS) is an important indicator of the strength of a received signal. The signal propagation model is crucial for calculating the RSS. However, the RSS can vary and be unstable even in the same location, so it requires a more flexible calculation method to adapt to complex scenarios. In this article, we propose an automatic calculation method that can adjust the parameters according to the measured RSS. First, some outliers are removed from the measurements. Second, to ensure the accurate calculation of indoor signals, the propagation parameters are adjusted based on the actual measurement data from different indoor scenarios. Finally, segmental correction of the RSS is used to account for faster signal attenuation at closer ranges and slower attenuation at farther ranges. The performance of the corrected algorithm has been verified under various conditions. Experiments demonstrate that the proposed method can enhance the accuracy of signal calculations.

Decentralized storage systems such as blockchain storage applications adopt the distributed storage technology and use distributed storage nodes to store the persistent data. For each off-chain storage node, key-value (KV) stores are normally used to manage data. As the most common data structure for KV store, Log Structured Merge Tree (LSM-Tree) eliminates random write operations and keeps acceptable read performance. Although LSM-Tree-based decentralized storage system can provide a secure and reliable storage platform, the unique feature of blockchain applications is not fully exploited. In blockchain storage applications, the generation of keys for KV stores is based on the encrypted data, and the key determines the allocation of data. Since the granularity for a read/write request at the level of blockchain storage platform is much smaller than that at the level of LSM-Tree or flash memory, a physical block in a solid-state drive (SSD) could be filled with data from different system users. This mixture of workloads will lead to the inefficient usage of physical spaces in the SSD and cause extra compaction operations for LSM-Tree. This paper presentsMeta-Block, a cross-layer and efficient storage management strategy for decentralized blockchain storage applications. Meta-Block utilizes rich functionalities provided by the system infrastructure of open-channel SSD to provide direct storage accesses for the off-chain storage node. The objective is to capture the features of blockchain storage applications and reduce unnecessary read and write operations across different storage layers. As a cross-layer design, Meta-Block redesigns the organization of LSM-Tree, which can effectively reduce the write amplification. We also design a data prefetching strategy to speed up the indexing and enable direct storage access. We demonstrate the viability of the proposed technique using a set of extensive experiments. Experimental results show that Meta-Block can effectively reduce the write amplification and extend the lifetime of SSDs in comparison with representative schemes.

Convolutional neural network (CNN) is a power-hungry and resource-consuming application, which makes it hard to deploy on end devices. We propose a method to perform convolution operations in NOR flash memory. Experiment results show that our method has great performance and high energy efficiency.