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Explore how the degree of humanization affects user misconduct, and provide effective misconduct prevention measures for the wide application of artificial intelligence in the future.

Based on the “Uncanny Valley theory”, three experiments were conducted to explore the relationship between the degree of humanization of service machines and user misbehavior, and to analyze the mediating role of cognitive resistance and the moderating role of social class.

There is a U-shaped relationship between the degree of humanization of service machines and user misbehavior; Social class not only regulates the main effect of anthropomorphism on misbehavior, but also regulates the intermediary effect of anthropomorphism on cognitive resistance, thus affecting misbehavior.

The design of the service robot can be from the user’s point of view, combined with the user’s social class, match different user types, and provide the same preferences as the user’s humanoid service robot.

This study is an important reference value for enterprises and governments to provide intelligent services in public places. It can prevent the robot from being vandalized and also provide users with a comfortable human-computer interaction experience, expanding the positive effects of providing smart services by government and enterprises.

This study avoids and reduces users' misbehavior towards intelligent service robots, improves users' satisfaction in using service robots, and avoids service robots being damaged, resulting in waste of government, enterprise and social resources.

From the perspective of product factors to identify the inducing factors of improper behavior, from the perspective of social class of users to analyze the moderating effect of humanization degree and user improper behavior.

This study examines the phenomenon of “knowledge influencers,” individuals who convey expert information to non-expert audiences and attract users to subscribe to their self-created knowledge products. It seeks to address two research questions: (1) What are the antecedents that promote perceived attractiveness of knowledge influencers? and (2) How does perceived attractiveness of knowledge influencers affect users’ willingness to subscribe to knowledge products?

Guided by self-branding theory, which suggests that individuals strategically shape user perceptions and interactions to create an appealing image, the study employed a sequential mixed-methods approach. Qualitative interviews were conducted with knowledge influencers and their subscribers, followed by a quantitative survey of users with knowledge subscription experience to validate the findings.

Results suggested that knowledge influencers could enhance their attractiveness to users by promoting perceived professionalism, perceived familiarity, and perceived connectedness. Perceived attractiveness of knowledge influencers could directly affect users’ willingness to subscribe or indirectly through the role of users’ attachment to knowledge influencers.

By understanding the factors driving users’ subscription intentions, platform operators and influencers can refine their strategies to enhance user attachment and optimize monetization opportunities through personalized interactions and tailored content offerings.

This study contributes to the literature by elucidating the relationship between perceived attractiveness and users’ subscription intentions, offering new insights into the dynamics of online knowledge consumption.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

The spectrum resources are becoming increasingly scarce and underutilized, and cooperative spectrum sensing (CSS) in cognitive wireless sensor networks (CWSNs) offers many solutions with good results, but this paper aims to address the significant issue of CSS in the context of low signal-to-noise ratio (SNR).

This study proposes Pearson Correlation Coefficient (PCC) to obtain value feature values under the Rayleigh channel model, which are then used for Memorial K-means Clustering (MKC) analysis of CSS in CWSNs at low SNR regimes. In addition, MKC algorithm is used for training and converted it into supervised model.

A series of numerical simulation results demonstrate that the correctness and effectiveness of the proposed MKC, especially the detection and false alarm probabilities in a low SNR condition. The detection probability is increased by 5%–12% at low SNR compared with other methods.

The MKC algorithm can reduce the impact of randomness on the clustering centers for multiple groups, which combined with PCC can effectively reduce the influence of noise at low SNR, and the unsupervised transformed model effectively reducing the complexity of re-discrimination.