Disentangling the Factors Driving Friendship Formation: An LLM-Enhanced Graph Convolutional Approach for Friend Recommendation

The global proliferation of social media has provided a unique platform for cross-cultural exchange, greatly enhancing interactions between users from different cultural backgrounds through friend recommendation systems. However, the highly complex and intrinsically coupled nature of factors driving friendship formation makes it difficult for traditional methods to effectively predict and recommend genuinely deep social connections. Therefore, this study proposes leveraging emerging information technologies, specifically deep learning, to optimize and improve friend recommendation systems on social media platforms. This paper introduces a novel personality trait disentanglement method. By using large language models to extract personality factors from user text, we constructed a multi-subgraph convolutional method driven by personality traits. This enables the model to clearly distinguish the mechanisms of different personality factors. Additionally, we designed a shared attention layer to adaptively learn the importance weights of different personality traits, and implicit representations to capture non-personality-driven factors. Our research combines deep learning with personality trait analysis to foster deeper interpersonal understanding and cultural exchange, thereby enhancing the quality and breadth of interactions on social networks globally

Debiased Gaussian Process-based Machine Learning with Partially Observed Information

Widely applicable machine learning and artificial intelligence technologies have resulted in an increasing demand for reliable models. Due to the ubiquitous data scarcity in the real world, model training can often be challenging and face limitations. Although various data augmentation techniques can efficiently alleviate this dilemma, bias is unavoidable, causing trade-offs in prediction accuracy. As this general dilemma is addressed, we discuss a Two-Stage Debiased Gaussian Process (TSDGP)-based machine learning model capable of providing robust and accurate predictions across various fields, even with partially observed information. Given the partially observed information in input data, the latent variable model was leveraged to enhance heterogeneous data utilization by reconstructing the unavailable information in stage one. Subsequently, the model and uncertainties from the first stage were refined within the Bayesian framework using the augmented dataset in stage two. By demonstrating the consistency and first and second moments of the proposed two-stage model, we are confident in the accuracy and robustness of the results. Supported by solid theoretical proof, we further evaluate the results of TSDGP through numerical and empirical experiments, showing the premium performances of the proposed approach. In conclusion, TSDGP can solve the dilemma caused by data scarcity in the real world—enabling a reliable high-fidelity predictive model to be trained on partially observed datasets without a significant trade-off in accuracy

Data Analytics based on MCDM Methods for Business Sustainability – What's Behind and What Lies Ahead?

Data analytics plays a key role in promoting and implementing sustainable development in business. Thanks to advanced data analysis techniques, enterprises can make more informed decisions, optimize their operations and achieve sustainable development goals. Due to the complex nature of the issue of enterprise sustainability, a particularly useful class of data analysis methods are multi-criteria decision making methods (MDCM). In order to indicate the usefulness of this approach, a bibliometric analysis was carried out in the article in the period 2007-2024. Thematic maps were developed and analyzed, and the thematic evolution was analyzed. The results of the analysis indicated the AHP, ANP and TOPSIS methods as the leading MCDM methods in the implementation of sustainable business development. It also identified the increasing importance of fuzzy modifications of these methods proposed to account for uncertainty in business decision making

Would You Preserve Your Privacy or Enhance it? How to Best Frame Privacy Interventions for Older and Younger Users

In this paper, we examine the role of personalized communication in promoting the effective use of privacy measures for different age groups. Research has shown that due to differences in cognitive processing, older and younger adults respond differently to rationally identical presentations of the same message (i.e., the framing effect). Therefore, messages that are designed to nudge users towards more privacy protective behaviors should be tailored according to the age of the user groups. We conducted a controlled experiment where we presented a privacy and security technology with a gain framing of “Privacy Enhancing Technology” vs. a loss framing of “Privacy Preserving Technology.” Our results show that older adults are more motivated to protect themselves by a loss-framed message than a gain-framed message, while younger adults’ responsiveness to either a gain- or loss-framed message depends on their level of privacy concern. The findings highlight the importance of personalized communication in promoting privacy and security measures among different age groups

Clarity in Complexity: Advancing AI Explainability through Sensemaking

This paper explores Explainable Artificial Intelligence (XAI) through a sensemaking lens, addressing the complexity in the extant literature and providing a comprehensive understanding of the process of explainability. Through an exhaustive review of relevant research, we develop a novel framework highlighting the dynamic interactions between AI systems and users in the co-construction of explanations. We conducted a thorough analysis and theoretical synthesis of the extant literature. Based on the results, we developed a framework that shows how explainability emerges as a shared process between humans and machines, rather than a one-sided output. The proposed framework offers valuable insights for enhancing human-AI interactions and contributes to the theoretical foundation of XAI. The findings pave the way for future research avenues, with implications for both academic investigation and practical applications in designing more transparent and effective AI systems

Effective GUI Generation: Leveraging Large Language Models for Automated GUI Prototyping

GUI prototyping is a common technique for requirements elicitation during software development and is essential in visualizing and communicating user requirements. However, creating GUI prototypes can be resource-intensive in terms of time and cost. This paper explores the innovative approach of integrating Large Language Models (LLMs) into the development of Graphical User Interface (GUI) prototypes to address this challenge. The primary objective of this work is to convert high-level text descriptions of mobile app interfaces into precise and detailed GUI prototypes. Various prompting strategies are adapted and evaluated within a structured GUI prototyping framework. The findings of this work highlight the feasibility of combining a state-of-the-art LLM with structured prompting approaches. This combination has successfully created high-quality GUI prototypes from textual descriptions, showcasing the significant potential of LLMs in the realm of GUI prototyping

Investigating the Factors Influencing ChatGPT Adoption Intention among Chinese Higher Education Personnel: An Empirical Study Based on the Extended UTAUT2 Model

This study explores the factors influencing the intent of Chinese higher education professionals to use ChatGPT, expanding our understanding to contexts where the technology is not officially accessible. We conducted an online survey involving 317 Chinese students and higher education staff, drawing insights from the UTAUT2 model. We established a structural equation model for data analysis. Our findings reveal direct influences of performance expectancy, social influence, price value, personal innovativeness, and technology fear on usage intention. We also observed indirect effects stemming from effort expectancy, context awareness, and perceived risk on usage intention. Furthermore, our results highlight that personal innovativeness moderates the impact of technology fear on usage intention. This study deepens our understanding of attitudes toward ChatGPT in higher education, especially in situations where official access is restricted, and extends the UTAUT2 model by establishing a connection between the psychological traits of higher education personnel and their technology acceptance

Mobilizing Health Monitoring: The Development and Integration of a Health-eScooter System

In preventive medicine, continuous health monitoring through technology is essential. This paper presents an innovative approach using an eScooter equipped with sensors for electrocardiography and photoplethysmography to monitor vital signs during commutes. Integrating rental identity management with biomedical analytics, we ensure secure and private health data collection from shared eScooters. Our study involved 20 participants and demonstrated the feasibility of acquiring health data using a convolutional neural network (CNN) combined with a long short-term memory (LSTM) model-based algorithm and a user interface. The results show that around 65 percent of the driving time is utilizable for medical analysis. Additionally, we develop a user-friendly interface for the iOS app. The Health-eScooter exemplifies how everyday transport can serve as an effective tool for health monitoring, offering convenience and mobility, thereby paving the way for mobile and everyday health technology