Browsing by Author "Li, Xiang"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Effects of homophily and heterophily on preferred-degree networks: mean-field analysis and overwhelming transition
    Li, Xiang; Mobilia, Mauro; Rucklidge, Alastair M.; Zia, R. K. P. (IOP Publishing, 2022-01-01)
    We investigate the long-time properties of a dynamic, out-of-equilibrium network of individuals holding one of two opinions in a population consisting of two communities of different sizes. Here, while the agents' opinions are fixed, they have a preferred degree which leads them to endlessly create and delete links. Our evolving network is shaped by homophily/heterophily, a form of social interaction by which individuals tend to establish links with others having similar/dissimilar opinions. Using Monte Carlo simulations and a detailed mean-field analysis, we investigate how the sizes of the communities and the degree of homophily/heterophily affect the network structure. In particular, we show that when the network is subject to enough heterophily, an 'overwhelming transition' occurs: individuals of the smaller community are overwhelmed by links from the larger group, and their mean degree greatly exceeds the preferred degree. This and related phenomena are characterized by the network's total and joint degree distributions, as well as the fraction of links across both communities and that of agents having fewer edges than the preferred degree. We use our mean-field theory to discuss the network's polarization when the group sizes and level of homophily vary.
  • Thumbnail Image
    GesMessages: Using Mid-air Gestures to Manage Notifications
    Li, Xiang; Chen, Yuzheng; Tang, Xiaohang (ACM, 2023-10-13)
    This paper introduces GesMessages, an innovative mid-air interactive application that uses simple gestures to manage real-time message notifications on laptops and large displays. Leveraging cameras on computers or smart devices, the application offers three distinct gestures: expanding notifications for immediate attention, hiding non-urgent messages, and deleting spam messages. We present the technical setup and system design. Additionally, we explore potential applications in context-awareness systems, contributing to gestural interaction research. Our work fosters a deeper understanding of mid-air interaction’s impact on message management and future interactive systems.
  • Thumbnail Image
    Predicting Spatial Visualization Problems’ Difficulty Level from Eye-Tracking Data
    Li, Xiang; Younes, Rabih; Bairaktarova, Diana; Guo, Qi (MDPI, 2020-03-31)
    The difficulty level of learning tasks is a concern that often needs to be considered in the teaching process. Teachers usually dynamically adjust the difficulty of exercises according to the prior knowledge and abilities of students to achieve better teaching results. In e-learning, because there is no teacher involvement, it often happens that the difficulty of the tasks is beyond the ability of the students. In attempts to solve this problem, several researchers investigated the problem-solving process by using eye-tracking data. However, although most e-learning exercises use the form of filling in blanks and choosing questions, in previous works, research focused on building cognitive models from eye-tracking data collected from flexible problem forms, which may lead to impractical results. In this paper, we build models to predict the difficulty level of spatial visualization problems from eye-tracking data collected from multiple-choice questions. We use eye-tracking and machine learning to investigate (1) the difference of eye movement among questions from different difficulty levels and (2) the possibility of predicting the difficulty level of problems from eye-tracking data. Our models resulted in an average accuracy of 87.60% on eye-tracking data of questions that the classifier has seen before and an average of 72.87% on questions that the classifier has not yet seen. The results confirmed that eye movement, especially fixation duration, contains essential information on the difficulty of the questions and it is sufficient to build machine-learning-based models to predict difficulty level.
  • Thumbnail Image
    Sensory Lexicons and Formation Pathways of Off-Aromas in Dairy Ingredients: A Review
    Su, Xueqian; Tortorice, Monica; Ryo, Samuel; Li, Xiang; Waterman, Kim M.; Hagen, Andrea; Yin, Yun (MDPI, 2020-01-28)
    Consumers are becoming increasingly aware of the health benefits of dairy ingredients. However, products fortified with dairy proteins are experiencing considerable aroma challenges. Practices to improve the flavor quality of dairy proteins require a comprehensive understanding of the nature and origins of off-aroma. Unfortunately, existing information from the literature is fragmentary. This review presents sensory lexicons and chemical structures of off-aromas from major dairy ingredients, and it explores their possible precursors and formation mechanisms. It was found that similar chemical structures often contributed to similar off-aroma descriptors. Lipid degradation and Maillard reaction are two primary pathways that commonly cause aroma dissatisfaction. Traditional and novel flavor chemistry tools are usually adopted for off-aroma measurements in dairy ingredients. Strategies for improving aroma quality in dairy derived products include carefully selecting starting materials for formulations, and actively monitoring and optimizing processing and storage conditions.