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Fuzzy Feature Representation for Digital Twin-Oriented Social Network Simulators

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Fuzzy Feature Representation for Digital Twin-Oriented Socia...

IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Jiaqi Wen Bogdan Gabrys Katarzyna Musial Complex Adaptive Systems Lab Data Science Institute University of Technology Sydney Sydney Australia

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

Building Social Network Simulators (SNSs) that can accurately represent reality forms an important research area. To be able to reflect real world systems the modelling needs to consider not only the intensity of interactions between the entities but also features of all the elements of the system. This study aims to improve the expressive power of node features in Digital Twin-Oriented Social Network Simulators (DT-SNSs) with heterogeneous feature representation principles. This involves representing features with crisp feature values and fuzzy sets, each describing the objective and the subjective inductions of the nodes' features and feature differences. Our empirical analysis builds DT-SNSs to recreate realistic physical contact networks in different countries from real node feature distributions based on various representation principles and an optimised feature preference. The results suggest that the increasing flexibility of feature representation with fuzzy sets improves the expressive power and enables more accurate modelling. In addition, the heterogeneous features influence the network structure, where nodes with preferred features and similar features cluster.

关键词： Learning systems Fuzzy sets Accuracy Social networking (online) Sensitivity analysis Prevention and mitigation Reflection

The Two Fundamental Shapes of Sleep Heart Rate Dynamics and Their Connection to Mental Health in College Students

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Digital Biomarkers 2024年第1期8卷 120-131页

作者： Fudolig, Mikaela Irene Bloomfield, Laura S.P. Price, Matthew Bird, Yoshi M. Hidalgo, Johanna E. Kim, Julia N. Llorin, Jordan Lovato, Juniper McGinnis, Ellen W. McGinnis, Ryan S. Ricketts, Taylor Stanton, Kathryn Dodds, Peter Sheridan Danforth, Christopher M. Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT United States Department of Mathematics and Statistics University of Vermont BurlingtonVT United States Computational Story Lab MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT United States Gund Institute for Environment University of Vermont BurlingtonVT United States Department of Psychological Science University of Vermont BurlingtonVT United States Project Lemurs University of Vermont BurlingtonVT United States Department of Electrical and Biomedical Engineering University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Wake Forest University School of Medicine Winston-SalemNC United States Rubenstein School of Environment and Natural Resources University of Vermont BurlingtonVT United States Santa Fe Institute Santa FeNM United States

Introduction: Wearable devices are rapidly improving our ability to observe health-related processes for extended durations in an unintrusive manner. In this study, we use wearable devices to understand how the shape of the heart rate curve during sleep relates to mental health. Methods: As part of the Lived Experiences Measured Using Rings Study (LEMURS), we collected heart rate measurements using the Oura ring (Gen3) for over 25,000 sleep periods and self-reported mental health indicators from roughly 600 first-year university students in the USA during the fall semester of 2022. Using clustering techniques, we find that the sleeping heart rate curves can be broadly separated into two categories that are mainly differentiated by how far along the sleep period the lowest heart rate is reached. Results: Sleep periods characterized by reaching the lowest heart rate later during sleep are also associated with shorter deep and REM sleep and longer light sleep, but not a difference in total sleep duration. Aggregating sleep periods at the individual level, we find that consistently reaching the lowest heart rate later during sleep is a significant predictor of (1) self-reported impairment due to anxiety or depression, (2) a prior mental health diagnosis, and (3) firsthand experience in traumatic events. This association is more pronounced among females. Conclusion: Our results show that the shape of the sleeping heart rate curve, which is only weakly correlated with descriptive statistics such as the average or the minimum heart rate, is a viable but mostly overlooked metric that can help quantify the relationship between sleep and mental health. © 2024 The Author(s). Published by S. Karger AG, Basel.

关键词： Wearable technology

Stochastic Block Models for complex Network Analysis: A Survey

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ACM Transactions on Knowledge Discovery from data 2025年第3期19卷 1-35页

作者： Liu, Xueyan Song, Wenzhuo Musial, Katarzyna Li, Yang Zhao, Xuehua Yang, Bo College of Computer Science and Technology Jilin University Changchun China School of Information Science and Technology Northeast Normal University Changchun China Complex Adaptive Systems Lab Data Science Institute University of Technology Sydney Sydney Australia Aviation University of the Air Force Changchun China School of Digital Media Shenzhen Institute of Information Technology Shenzhen China Key Laboratory of Symbolic Computation and Knowledge Engineer Jilin University Ministry of Education Changchun China

complex networks enable to represent and characterize the interactions between entities in various complex systems which widely exist in the real world and usually generate vast amounts of data about all the elements, their behaviors and interactions over time. The studies concentrating on new network analysis approaches and methodologies are vital because of the diversity and ubiquity of complex networks. The stochastic block model (SBM), based on Bayesian theory, is a statistical network model. SBMs are essential tools for analyzing complex networks since SBMs have the advantages of interpretability, expressiveness, flexibility and generalization. Thus, designing diverse SBMs and their learning algorithms for various networks has become an intensively researched topic in network analysis and data mining. In this article, we review, in a comprehensive and in-depth manner, SBMs for different types of networks (i.e., model extensions), existing methods (including parameter estimation and model selection) for learning optimal SBMs for given networks and SBMs combined with deep learning. Finally, we provide an outlook on the future research directions of SBMs. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： complex Networks Structural Pattern Discovery Stochastic Block models Learning Methods of SBMs

Digital Twin-Oriented complex Networked systems based on Heterogeneous Node Features and Interaction Rules

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arXiv 2023年

作者： Wen, Jiaqi Gabrys, Bogdan Musial, Katarzyna Complex Adaptive Systems Lab Data Science Institute University of Technology Sydney Australia

This study proposes an extendable modelling framework for Digital Twin-Oriented complex Networked systems (DT-CNSs) with a goal of generating networks that faithfully represent real systems. Modelling process focuses on (i) features of nodes and (ii) interaction rules for creating connections that are built based on individual node's preferences. We conduct experiments on simulation-based DT-CNSs that incorporate various features and rules about network growth and different transmissibilities related to an epidemic spread on these networks. We present a case study on disaster resilience of social networks given an epidemic outbreak by investigating the infection occurrence within specific time and social distance. The experimental results show how different levels of the structural and dynamics complexities, concerned with feature diversity and flexibility of interaction rules respectively, influence network growth and epidemic spread. The analysis revealed that, to achieve maximum disaster resilience, mitigation policies should be targeted at nodes with preferred features as they have higher infection risks and should be the focus of the epidemic control. Copyright © 2023, The Authors. All rights reserved.

关键词： complex networks

Heterogeneous Feature Representation for Digital Twin-Oriented complex Networked systems

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arXiv 2023年

作者： Wen, Jiaqi Gabrys, Bogdan Musial, Katarzyna Complex Adaptive Systems Lab Data Science Institute University of Technology Sydney Australia

Building models of complex Networked systems (CNS) that can accurately represent reality forms an important research area. To be able to reflect real world systems, the modelling needs to consider not only the intensity of interactions between the entities but also features of all the elements of the system. This study aims to improve the expressive power of node features in Digital Twin-Oriented complex Networked systems (DT-CNSs) with heterogeneous feature representation principles. This involves representing features with crisp feature values and fuzzy sets, each describing the objective and the subjective inductions of the nodes’ features and feature differences. Our empirical analysis builds DT-CNSs to recreate realistic physical contact networks in different countries from real node feature distributions based on various representation principles and an optimised feature preference. We also investigate their respective disaster resilience to an epidemic outbreak starting from the most popular node. The results suggest that the increasing flexibility of feature representation with fuzzy sets improves the expressive power and enables more accurate modelling. In addition, the heterogeneous features influence the network structure and the speed of the epidemic outbreak, requiring various mitigation policies targeted at different people. Copyright © 2023, The Authors. All rights reserved.

关键词： Fuzzy sets

Review and Assessment of Digital Twin-Oriented Social Network Simulators

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arXiv 2023年

作者： Wen, Jiaqi Gabrys, Bogdan Musial, Katarzyna Complex Adaptive Systems Lab Data Science Institute University of Technology Sydney Australia

The ability to faithfully represent real social networks is critical from the perspective of testing various what-if scenarios which are not feasible to be implemented in a real system as the system’s state would be irreversibly changed. High fidelity simulators allow one to investigate the consequences of different actions before introducing them to the real system. For example, in the context of social systems, an accurate social network simulator can be a powerful tool used to guide policy makers, help companies plan their advertising campaigns or authorities to analyse fake news spread. In this study we explore different Social Network Simulators (SNSs) and assess to what extent they are able to mimic the real social networks. We conduct a critical review and assessment of existing Social Network Simulators under the Digital Twin-Oriented Modelling framework proposed in our previous study. We subsequently extend one of the most promising simulators from the evaluated ones, to facilitate generation of social networks of varied structural complexity levels. This extension brings us one step closer to a Digital Twin Oriented SNS (DT Oriented SNS). We also propose an approach to assess the similarity between real and simulated networks with the composite performance indexes based on both global and local structural measures, while taking runtime of the simulator as an indicator of its efficiency. We illustrate various characteristics of the proposed DT Oriented SNS using a well known Karate Club network as an example. While not considered to be of sufficient complexity, the simulator is intended as one of the first steps on a journey towards building a Digital Twin of a social network that perfectly mimics the reality. Copyright © 2023, The Authors. All rights reserved.

关键词： Ability testing

SLEEPWISE: A Continuous Sleep Monitoring and Cognitive Evaluation Platform in Ambient Residential Environment 46

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SLEEPWISE: A Continuous Sleep Monitoring and Cognitive Evalu...

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Jin, Shuwen Wang, Danqiong Ma, Xiao Li, Tao Lin, Feng Yu, Lei Zhang, Qing Zhejiang Lab Research Center for Frontier Fundamental Studies Hangzhou China Third Hospital of Shanxi Medical University Shanxi Bethune Hospital Shanxi Academy of Medical Sciences Tongji Shanxi Hospital Taiyuan China Huazhong University of Science and Technology Tongji Hospital Tongji Medical College Wuhan China Complex Systems Research Center Shanxi University Key Laboratory of Complex Systems and Data Science of Ministry of Education Shanxi China

ISBN: (纸本)9798350371499

Age-related cognitive decline presents a significant health challenge for older adults, with no specific medication available to counteract it. Early detection of cognitive decline is crucial for timely intervention and effective management. Sleep status plays a vital role in evaluating cognitive function, as disrupted sleep patterns are associated with cognitive decline and increased risk of neurodegenerative conditions. Existing methods for assessing sleep status, such as self-report questionnaires, actigraphy, and polysomnography, lack the ability to consistently monitor sleep status over a long period. In this paper we introduced SLEEPWISE, an AI-powered platform deployed in residential environments for continuous ambient sleep monitoring to assess the cognitive well-being of older adults. SLEEPWISE utilizes non-contact pads with highly sensitive piezoelectric sensors to monitor sleep parameters such as duration and efficiency, which are transmitted daily to the server. Moreover, we have also developed a gamified module for SLEEPWISE, allowing an objective assessment of daily cognitive functions. This module aids SLEEPWISE in establishing the correlation models between sleep and cognitive health. To evaluate the effectiveness of SLEEP-WISE, a pilot study was conducted with participants recruited from a local hospital. During the 30-days trials, SLEEPWISE identified a negative impact on congnitive performance associated with increased nocturnal body movements and getting out of bad. These findings emphasize the potential of SLEEPWISE as an early screening tool for evaluating daily cognitive health and well-being. © 2024 IEEE.

关键词： Sleep research

Dtcns: A Python Toolbox for Digital Twin-Oriented complex Networked systems

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SSRN

SSRN 2024年

作者： Wen, Jiaqi Gabrys, Bogdan Musial, Katarzyna Complex Adaptive Systems Lab Data Science Institute University of Technology Sydney PO Box 123. Broadway Sydney Australia

Digital Twin-Oriented complex Networked System (DT-CNS) is a modelling paradigm aiming at the representation and analysis of real-world networked systems. DT-CNS models network and the dynamics of and on the networks, including network growth dynamics and the dynamic process on networks. DT-CNS can be built and extended with increasing complexity towards the ultimate goal, a Digital Twin (DT) of reality. DT-CNS, as it approaches a DT, faithfully represents the temporal changes in network and processes, together with their interrelations, based on real-time information acquisition and feedback with reality. To build a DT-CNS with increasing complexity, one of the possible development paths is to increase the heterogeneity of nodes' features (characteristics of each specific node) and their preferences to create relationships while allowing the networks to evolve with the preference changes under the influence of dynamic process. To achieve that, we created a novel open-source Python tool named \texttt{\ DTCNS} to implement functionalities including feature representation, network growth based on heterogeneous node features and preferences to create relationships based on features, and the spreading process on networks. The source code, documentation and the full descriptions of the API are available at https://***/UTS-CASlab/DT-CNS. © 2024, The Authors. All rights reserved.

关键词： complex networks

WTU-EVAL: A Whether-or-Not Tool Usage Evaluation Benchmark for Large Language Models

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arXiv 2024年

作者： Ning, Kangyun Su, Yisong Lv, Xueqiang Zhang, Yuanzhe Liu, Jian Liu, Kang Xu, Jinan Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China College of Computer and Data Science Fuzhou University China The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation CAS China

Although Large Language Models (LLMs) excel in NLP tasks, they still need external tools to extend their ability. Current research on tool learning with LLMs often assumes mandatory tool use, which does not always align with real-world situations, where the necessity for tools is uncertain, and incorrect or unnecessary use of tools can damage the general abilities of LLMs. Therefore, we propose to explore whether LLMs can discern their ability boundaries and use tools flexibly. We then introduce the Whether-or-not tool usage Evaluation benchmark (WTU-Eval) to assess LLMs with eleven datasets, where six of them are tool-usage datasets, and five are general datasets. LLMs are prompted to use tools according to their needs. The results of eight LLMs on WTU-Eval reveal that LLMs frequently struggle to determine tool use in general datasets, and LLMs’ performance in tool-usage datasets improves when their ability is similar to ChatGPT. In both datasets, incorrect tool usage significantly impairs LLMs’ performance. To mitigate this, we also develop the finetuning dataset to enhance tool decision-making. Fine-tuning Llama2-7B results in a 14% average performance improvement and a 16.8% decrease in incorrect tool usage. We will release the WTU-Eval benchmark. © 2024, CC BY.

关键词： Decision making