Virtual streamers, as a typical application of AI-enabled digital humans, are increasingly being utilized in live-streaming commerce due to technological advancements and industry innovations. Although virtual streamers present several benefits, there is potential for adverse effects when they do not align with consumer expectations. Drawing upon expectancy violations theory, this study developed a theoretical model to explore whether and how consumers’ expectation violations during human–virtual streamer interactions affect consumers’ discontinuance behavior. Through an online questionnaire survey of 307 Chinese consumers with prior experience interacting with virtual streamers, this study used a partial least squares structural equation model to analyze the research model. The empirical results indicated that professionalism expectation violation, empathy expectation violation, and responsiveness expectation violation positively influenced consumers’ distrust and dissatisfaction, which subsequently led to discontinuance behavior. This study contributes to the literature on live-streaming commerce, human–AI interaction, and expectancy violation theory. Furthermore, the findings offer valuable insights for practitioners in the field of live-streaming commerce by enabling them to formulate preventive or remedial strategies to mitigate potential negative outcomes when implementing virtual streamers.

Segmentation of pulmonary nodule in thoracic computed tomography (CT) plays an important role in the computer-aided diagnosis (CAD) and clinical practices. However, segmentation of pulmonary nodules still remains a challenging task due to the presence of intrinsic noise, low contrast, intensity-profile inhomogeneity, variable sizes and shapes. Many variants and extensions of fuzzy C-mean (FCM) clustering algorithm have been developed to preserve image details as well as suppress image noises. However, these variants overemphasize the importance of the spatial information and neglect the role of the prior knowledge. To address this problem, a GMM fuzzy C-means (GMMFCM) algorithm is proposed for the segmentation of pulmonary nodules in this paper. A novel local similarity measure is defined by using local spatial information and GMM statistical information. A neighboring term is added to the energy function of traditional fuzzy C-mean algorithm. A superpixel-based random walker is proposed to segment pulmonary parenchyma, which reduces the computational complexity and improves the segmentation performance. Experiments performed on the LIDC dataset and the GHGZMCPLA dataset demonstrate that the segmentation performance of proposed GMMFCM algorithm is superior to the state-of-the-art algorithms.

Automatic and accurate segmentation of ground glass opacity (GGO) nodules still remains challenging due to inhomogeneous interiors, irregular shapes, and blurred boundaries from different patients. Despite successful applications in the image processing domains, the random walk has some limitations for segmentation of GGO pulmonary nodules. In this paper, an improved random walker method is proposed for the segmentation of GGO nodules. To calculate a new affinity matrix, intensity, spatial, and texture features are incorporated. It strengthens discriminative power between two adjacent nodes on the graph. To address the problem of robustness in seed acquisition, the geodesic distance is introduced and a novel local search strategy is presented to automatically acquire reliable seeds. For segmentation, a label constraint term is introduced to the energy function of original random walker, which alleviates the accumulation of errors caused by the initial seeds acquisition. Massive experiments conducted on Lung Images Dataset Consortium (LIDC) demonstrate that the proposed method achieves visually satisfactory results without user interactions. Both qualitative and quantitative evaluations also demonstrate that the proposed method obtains better performance compared with conventional random walker method and state-of-the-art segmentation methods in terms of the overlap score and F-measure.

With the explosive growth of data, personalized recommendation systems have become an essential component in digitally-driven society. However, one of the main challenges faced by traditional recommendation algorithms is the difficulty in obtaining accurate user preferences, especially for users with limited historical interaction data., which ultimately impacts the performance of these methods. To address this challenge, the paper proposes a recommendation method, named STRGAN (Social trust relationships Generative Adversarial Network), it leverages the advantages of Generative Adversarial Networks (GANs) to tackle the data sparsity problem, by integrating user ratings and social relationships. By incorporating both types of information, STRGAN aims to improve the accuracy and quality of recommendations provided to users. Moreover, the proposed STRGAN model employs negative sampling techniques to ensure that the generated recommendations align with the real data. To evaluate the effectiveness of STRGAN, extensive experiments were conducted on the real world dataset FilmTrust. The empirical results demonstrate that STRGAN outperforms other GAN-based models in various evaluation metrics, such as precision, recall, normalized discounted cumulative gain (NDCG), and mean reciprocal rank (MRR). STRGAN offers a robust and efficient solution for personalized recommendation tasks. The results of experiment support the efficacy of STRGAN, indicating its potential to significantly improve recommendation accuracy in real-world applications.