Introduction to Decision Support for Smart City and Digital Services Minitrack

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Exploring Trust in Online Ride-sharing Platform in China: A Perspective of Time and Location

Trust is a key issue to be considered deliberately in the online ride-sharing platform to reduce risk and ensure transactions. In this paper, trust-in-platform is explored from these two perspectives to fill the research gaps. A ride-sharing platform in China was investigated. Results show that trust-in-platform in economically developing districts is slightly higher than that in economically developed districts. At the same time, trust-in-platform level differs in time, trust-in-platform levels are obviously lower between 19’o clock and 23’o clock. Moreover, machine learning is employed to predict the relationships between time/location and trust-in-platform. The result is that recall is 78.3%, precision is 57.3%, and F1 is 66.2%. The result shows trust-in-platform has an obvious correlation with time and location, thus further consolidates the findings. This study contributes to the existing knowledge on trust in the ride-sharing platforms and has practical implications for platform operators

Operational Mechanism of Digital Humanistic Crowdsourcing Project Based on Actor Network Theory

This article is to promote the development of digital humanity-related crowdsourcing projects based on actor network theory (ANT). A case study on Shengxuanhuai Documents from Shanghai Library is selected as our research object. The article employs qualitative research approach to investigate core concepts, namely Problematization, Obligatory Passage Point, Interestment, and Mobilisation involved in the underway of the digital humanity-related crowdsourcing project. This study conducts interviews with 32 respondents, including the 10 contractees and 22 users. The crowdsourcing actors in humanity-related projects are mainly the organizers from public libraries, museums, archives, and other digital humanity institutions. Based on the project development documents and semi-structured interview data, we find that the main obstacles to prevent actors engaging in crowdsourcing projects include task guidance, user motivations, platform designs, and competition evaluations. The paper demonstrates the usefulness of ANT’s concepts and explores the contribution of each ANT analytical concept

IEEE Access Special Section Editorial: Artificial Intelligence and Cognitive Computing for Communication and Network

With the rapid development of communication and network technologies, novel information services and applications are rapidly growing worldwide. Advanced communications and networks greatly enhance the user experience, and have a major impact on all aspects of people's lifestyles in terms of work, society, and the economy. Although advanced techniques have extensively improved users' quality of experience (QoE), they are not adequate to meet the various requirements of seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability, and other scenarios. Therefore, it is a great challenge to develop smart communications and networks that support optimized management, dynamic configuration, and feasible services

Sampling Adaptive Learning Algorithm for Mobile Blind Source Separation

Learning rate plays an important role in separating a set of mixed signals through the training of an unmixing matrix, to recover an approximation of the source signals in blind source separation (BSS). To improve the algorithm in speed and exactness, a sampling adaptive learning algorithm is proposed to calculate the adaptive learning rate in a sampling way. The connection for the sampled optimal points is described through a smoothing equation. The simulation result shows that the performance of the proposed algorithm has similar Mean Square Error (MSE) to that of adaptive learning algorithm but is less time consuming

Individual Trust Development in Business Virtual Teams: An Experimental Study

This paper presents a longitudinal study of individual trust development in virtual collaboration in China. We review the concept of trust, trust factors, and examine the development of individual trust and explore why individual trust changes over time. Risk, benefit, and interest are main trust factors that influence the development of individual trust. Survey data were collected at three points to observe the development of individual trust. In addition, we took semi-structured interviews to verify the development of individual trust and explore why individual trust changes in business virtual teams. We found that individual trust was improved over time and three main individual trust factors changed in different patterns. Moreover, conflict of option, interpersonal communication, information sharing and team working were found to be related with individual trust by the relationship with risk, benefit or interest. The use of specific thinkLets is also found to have a moderate positive relationship to individual trust

Leveraging Cross Domain Recommendation Models to Alleviate Filter Bubble Problems

With the development of Web-based social networks, recommender systems have become prevalent and integral to how users are interacting with the Internet. They filter out redundant information and personalize relevant and interesting items to online users. However, the positive reinforcement effect of recommender systems narrows users’ information experiences and cause filter bubble problems. How to provide relevant and diversified items for online users are becoming a challenging issue. In this study, we develop a novel cross domain matrix factorization model with adaptive diversity regularization to tackle the above challenges. We leverage the social tags and adaptive diversity regularization to im-prove recommendation performance. We conducted a comprehensive experiment on a real social media site to verify the effectiveness of the proposed method. The results show that the proposed method is able to achieve a decent balance between the accuracy and diversity of recommendation

UNDERSTANDING RESEARCHERS’ META-KNOWLEDGE CONTRIBUTION BEHAVIOR IN RESEARCH SOCIAL NETWORK: A SOCIAL CAPITAL PERSPECTIVE

The rapid proliferation of information technologies especially Web 2.0 techniques have changed how researchers conduct research, especially how they contribute academic knowledge online. Research social network constructed based on Web 2.0 techniques as a particular academic platform helps researchers create and find knowledge easily. Research social network helps researchers to share academic knowledge via tagging it. However, the reasons for researchers’ meta-knowledge (knowledge about knowledge, e.g., tags given by researchers to annotate academic knowledge) contribution in research social network context and the extent to which this may differ from traditional knowledge contribution behavior in conventional virtual communities remain largely unexplored. To address this issue, this research use social capital theory to further understand researchers’ meta-knowledge contribution behavior based on data collected from a particular research social network: CiteULike. The findings provide new perspective to understand researchers’ meta-knowledge contribution behavior in research social network