Abstract With the widespread application of smart contracts, there is a growing concern over the quality assurance of smart contracts. The data flow testing is an important technology to ensure the correctness of smart contracts. We propose an approach named IABC‐TCG (Improved Artificial Bee Colony‐Test Case Generation) to generate test cases for the data flow testing of smart contracts. With a dominance relations‐based fitness function, an improved artificial bee colony algorithm is used to generate test cases, in which the bee colony search coefficient is adaptively adjusted to improve the effectiveness and efficiency of the search. In addition, an improved test case selection and updation strategy is used to avoid unnecessary test cases. The experimental results show that IABC‐TCG achieves 100% coverage for all the test requirements on a dataset of 30 smart contracts and outperforms the baseline approaches in terms of the number of test cases and the execution time. Performing tests with the generated test cases, IABC‐TCG can find more errors with less test cost.

With the development of blockchain technology, an increasing number of users are adopting cross-blockchain smart contracts, which necessitates effective testing methods due to digital asset security concerns. However, few work specifically tailored for cross-chain smart contracts. In this paper, we propose a test case generation method for cross-chain contracts based on the ant colony algorithm. This method conducts data flow analysis on the cross-chain smart contract to identify the critical information related statements. These statements are then subjected to mutation operations to create mutants. Subsequently, test cases are generated with an improved ant colony algorithm to kill as many mutants as possible. Theoretically, this method can effectively detect faults in critical information related handling in cross-chain smart contracts.

Visible-Infrared Person Re-Identification (VI-ReID) is a challenging person matching problem and is also a practical solution for intelligent surveillance systems at night. Due to the heterogeneity between visible and infrared modalities, the retrieval performance is seriously damaged. To address the issue of the discrepancy of the information between visible and infrared modalities, many works have been proposed. However, the relationship between cross-modality samples has rarely been mined. In this paper, we propose a Cross-modality Interaction and Alignment (CIA) module to solve the discrepancy problem. Through transforming the information between different modalities, the module guides the network to capture the modality-shared feature, which is beneficial to address the cross-modality discrepancy. Meanwhile, to better supervise the network, an enhanced contrastive loss is introduced. Contributed by the further optimization in the distance between intra-class samples, the network gains more effective supervision. Extensive experiments on two benchmark datasets show that our method achieves an excellent performance in VI-ReID.

In this paper, we solve the sample shortage problem in the human parsing task. We begin with the self-learning strategy, which generates pseudo-labels for unlabeled data to retrain the model. However, directly using noisy pseudo-labels will cause error amplification and accumulation. Considering the topology structure of human body, we propose a trainable graph reasoning method that establishes internal structural connections between graph nodes to correct two typical errors in the pseudo-labels, i.e., the global structural error and the local consistency error. For the global error, we first transform category-wise features into a high-level graph model with coarse-grained structural information, and then decouple the high-level graph to reconstruct the category features. The reconstructed features have a stronger ability to represent the topology structure of the human body. Enlarging the receptive field of features can effectively reducing the local error. We first project feature pixels into a local graph model to capture pixel-wise relations in a hierarchical graph manner, then reverse the relation information back to the pixels. With the global structural and local consistency modules, these errors are rectified and confident pseudo-labels are generated for retraining. Extensive experiments on the LIP and the ATR datasets demonstrate the effectiveness of our global and local rectification modules. Our method outperforms other state-of-the-art methods in supervised human parsing tasks.