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FedECCR: Federated Learning Method with Encoding Comparison and Classification Rectification 19th

FedECCR: Federated Learning Method with Encoding Comparison...

19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yan Zheng, Hui Wang, Xin Zhang, Beibei Zhou, Mingyao Zhang, Jilin Ren, YongJian School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Zhejiang Lab Hangzhou311100 China Hangzhou Huawei Communication Technology Co. Ltd. Hangzhou310052 China

ISBN: (纸本)9783031545306

Federated learning is a distributed training method that integrates multi-party data information using privacy-preserving technologies through dispersed client data sets to jointly construct a global model under the coordination of a central server. However, in practical applications, there is a high degree of data distribution skewness among clients, which causes the optimization direction of the client models to diverge, resulting in model bias and reducing the accuracy of the global model. Existing methods require the calculation and transmission of much information to correct the optimization direction of the client models, or only roughly limit the deviation of the client models end-to-end, ignoring targeted processing of the internal structure of the model, resulting in unclear improvement effects. To address these problems, we propose a federated optimization algorithm FedECCR based on encoding contrast and classification correction. This algorithm divides the model into an encoder and a classifier. It utilizes prototype contrastive training of the model encoder and unbiased classification correction of the classifier. This approach notably improves the accuracy of the global model while maintaining low communication costs. We conducted experiments on multiple data sets to evaluate the validity of our method, and the quantified results showed that FedECCR can improve the global model classification accuracy by approximately 1% to 6% compared to FedAvg, FedProx, and MOON. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Classification (of information)

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Applicability of the Minimal Dominating Set for Influence Maximisation in Multilayer Networks

arXiv

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arXiv 2025年

作者： Czuba, Michal Jia, Mingshan Bródka, Piotr Musial, Katarzyna Department of Artificial Intelligence Wroclaw University of Science and Technology 27 wybrzeze Wyspiańskiego st Wroclaw50-370 Poland Complex Adaptive Systems Lab Data Science Institute School of Computer Science University of Technology Sydney UltimoNSW2007 Australia

The minimal dominating set (MDS) is a well-established concept in network controllability and has been successfully applied in various domains, including sensor placement, network resilience, and epidemic containment. In this study, we adapt the local-improvement MDS routine and explore its potential for enhancing seed selection for influence maximisation in multilayer networks (MLN). We employ the Linear Threshold Model (LTM), which offers an intuitive representation of influence spread or opinion dynamics by accounting for peer influence accumulation. To ensure interpretability, we utilise rank-refining seed selection methods, with the results further filtered with MDS. Our findings reveal that incorporating MDS into the seed selection process improves spread only within a specific range of situations. Notably, the improvement is observed for larger seed set budgets, lower activation thresholds, and when an"AND" strategy is used to aggregate influence across network layers. This scenario reflects situations where an individual does not require the majority of their acquaintances to hold a target opinion, but must be influenced across all social circles. Copyright © 2025, The Authors. All rights reserved.

关键词： Multilayers

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hyperbox-brain: A Toolbox for Hyperbox-based Machine Learning Algorithms

arXiv

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arXiv 2022年

作者： Khuat, Thanh Tung Gabrys, Bogdan Complex Adaptive Systems Lab The Data Science Institute University of Technology Sydney SydneyNSW2007 Australia

Hyperbox-based machine learning algorithms are an important and popular branch of machine learning in the construction of classifiers using fuzzy sets and logic theory and neural network architectures. This type of learning is characterised by many strong points of modern predictors such as a high scalability, explainability, online adaptation, effective learning from a small amount of data, native ability to deal with missing data and accommodating new classes. Nevertheless, there is no comprehensive existing package for hyperbox-based machine learning which can serve as a benchmark for research and allow non-expert users to apply these algorithms easily. hyperbox-brain is an open-source Python library implementing the leading hyperbox-based machine learning algorithms. This library exposes a unified API which closely follows and is compatible with the renowned scikit-learn and numpy toolboxes. The library may be installed from Python Package Index (PyPI) and the conda package manager and is distributed under the GPL-3 license. The source code, documentation, detailed tutorials, and the full descriptions of the API are available at https://***/*** Codes 68T05, 68T30, 68T37, 68W27 © 2022, CC BY-NC-SA.

关键词： Machine learning

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The Technological Emergence of AutoML A Survey of Performant Software and Applications in the Context of Industry

arXiv

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arXiv 2022年

作者： Scriven, Alexander Kedziora, David Jacob Musial, Katarzyna Gabrys, Bogdan Complex Adaptive Systems Lab Data Science Institute University of Technology Sydney SydneyNSW2007 Australia

With most technical fields, there exists a delay between fundamental academic research and practical industrial uptake. Whilst some sciences have robust and well-established processes for commercialisation, such as the pharmaceutical practice of regimented drug trials, other fields face transitory periods in which fundamental academic advancements diffuse gradually into the space of commerce and industry. For the still relatively young field of Automated/Autonomous Machine Learning (AutoML/AutonoML), that transitory period is under way, spurred on by a burgeoning interest from broader society. Yet, to date, little research has been undertaken to assess the current state of this dissemination and its uptake. Thus, this review makes two primary contributions to knowledge around this topic. Firstly, it provides the most up-to-date and comprehensive survey of existing AutoML tools, both open-source and commercial. Secondly, it motivates and outlines a framework for assessing whether an AutoML solution designed for real-world application is’performant’;this framework extends beyond the limitations of typical academic criteria, considering a variety of stakeholder needs and the human-computer interactions required to service them. Thus, additionally supported by an extensive assessment and comparison of academic and commercial case-studies, this review evaluates mainstream engagement with AutoML in the early 2020s, identifying obstacles and opportunities for accelerating future uptake. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Caspase-1 and Gasdermin D Afford the Optimal Targets with Distinct Switching Strategies in NLRP1b Inflammasome-Induced Cell Death

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Research 2022年第4期2022卷 463-479页

作者： Xiang Li Peipei Zhang Zhiyong Yin Fei Xu Zhang-Hua Yang Jun Jin Jing Qu Zhilong Liu Hong Qi Chenggui Yao Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials Research Xiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling NetworkSchool of Life SciencesXiamen UniversityXiamen 361102China Complex Systems Research Center Shanxi UniversityShanxiTaiyuan 030006China College of Data Science Jiaxing UniversityJiaxing 314000China Oujiang Laboratory(Zhejiang Lab for Regenerative Medicine Vision and Brain Health)and Wenzhou InstituteUniversity of ChineseAcademyof SciencesWenzhouZhejiang325001China

Inflammasomes are essential complexes of innate immune system, which form the first line of host defense against pathogens. Mounting evidence accumulates that inflammasome signaling is highly correlated with coronavirus disease 2019 (COVID-19). However, there remains a significant gap in our understanding of the regulatory mechanism of inflammasome signaling. Combining mathematical modeling with experimental analysis of NLRP1b inflammasome signaling, we found that only the expression levels of caspase-1 and GSDMD have the potential to individually switch cell death modes. Reduction of caspase-1 or GSDMD switches cell death from pyroptosis to apoptosis. Caspase-1 and GSDMD present different thresholds and exert distinct pathway choices in switching death modes. Pyroptosis switches to apoptosis with an extremely low threshold level of caspase-1, but with a high threshold of GSDMD. Caspase-1-impaired cells employ ASC-caspase-8-dependent pathway for apoptosis, while GSDMD-impaired cells primarily utilize caspase-1-dependent pathway. Additionally, caspase-1 and GSDMD can severally ignite the cooccurrence of pyroptosis and apoptosis. Landscape topography unravels that the cooccurrence is dramatically different in caspase-1- and GSDMD-impaired cells. Besides pyroptosis state and apoptosis state, a potential new “coexisting” state in single cells is proposed when GSDMD acts as the driving force of the landscape. The “seesaw model” is therefore proposed, which can well describe the death states that are controlled by caspase-1 or GSDMD in single cells. Our study sheds new light on NLRP1b inflammasome signaling and uncovers the switching mechanisms among various death modes, providing potential clues to guide the development of more rational control strategies for diseases.

关键词： impaired utilize rational

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Network Disruption via Continuous Batch Removal: The Case of Sicilian Mafia

arXiv

引用

arXiv 2023年

作者： Jia, Mingshan De Meo, Pasquale Gabrys, Bogdan Musial, Katarzyna The Complex Adaptive Systems Lab Data Science Institute School of Computer Science University of Technology Sydney UltimoNSW2007 Australia The Department of Ancient and Modern Civilizations University of Messina Messina Italy

Network disruption is pivotal in understanding the robustness and vulnerability of complex networks, which is instrumental in devising strategies for infrastructure protection, epidemic control, cybersecurity, and combating crime. In this paper, with a particular focus on disrupting criminal networks, we proposed to impose a within-the-largest-connected-component constraint in a continuous batch removal disruption process. Through a series of experiments on a recently released Sicilian Mafia network, we revealed that the constraint would enhance degree-based methods while weakening betweenness-based approaches. Moreover, based on the findings from the experiments using various disruption strategies, we propose a structurally-filtered greedy disruption strategy that integrates the effectiveness of greedy-like methods with the efficiency of structural-metric-based approaches. The proposed strategy significantly outperforms the longstanding state-of-the-art method of betweenness centrality while maintaining the same time complexity. © 2023, CC BY-NC-SA.

关键词： Cybersecurity

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

引用

IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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Curating corpora with classifiers: A case study of clean energy sentiment online

arXiv

引用

arXiv 2023年

作者： Arnold, Michael V. Dodds, Peter Sheridan Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States

Well curated, large-scale corpora of social media posts containing broad public opinion offer an alternative data source to complement traditional surveys. While surveys are effective at collecting representative samples and are capable of achieving high accuracy, they can be both expensive to run and lag public opinion by days or weeks. Both of these drawbacks could be overcome with a real-time, high volume data stream and fast analysis pipeline. A central challenge in orchestrating such a data pipeline is devising an effective method for rapidly selecting the best corpus of relevant documents for analysis. Querying with keywords alone often includes irrelevant documents that are not easily disambiguated with bag-of-words natural language processing methods. Here, we explore methods of corpus curation to filter irrelevant tweets using pre-trained transformer-based models, fine-tuned for our binary classification task on hand-labeled tweets. We are able to achieve F1 scores of up to 0.95. The low cost and high performance of fine-tuning such a model suggests that our approach could be of broad benefit as a pre-processing step for social media datasets with uncertain corpus boundaries. © 2023, CC BY.

关键词： Tweets

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

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

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

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.

关键词： Correlation Hospitals Sensors Servers Biomedical monitoring Older adults Engineering in medicine and biology Monitoring

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Uncovering multi-order popularity and similarity mechanisms in link prediction by graphlet predictors

arXiv

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arXiv 2024年

作者： He, Yong-Jian Ran, Yijun Di, Zengru Zhou, Tao Xu, Xiao-Ke Department of Systems Science Faculty of Arts and Sciences Beijing Normal University Zhuhai519087 China International Academic Center of Complex Systems Beijing Normal University Zhuhai519087 China School of Systems Science Beijing Normal University Beijing100875 China Computational Communication Research Center Beijing Normal University Zhuhai519087 China Department of Systems Science Faculty of Artsand Sciences Beijing Normal University Zhuhai519087 China CompleX Lab University of Electronic Science and Technology of China Chengdu611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China

Link prediction has become a crucial problem in network science and has thus attracted increasing research interest. Popularity and similarity are two primary mechanisms in the link formation of real networks. However, the roles of these mechanisms in link prediction across various networks in domains remain poorly understood. Here, we use orbit degrees of graphlets to construct lower- and higher-order predictors and demonstrate that multi-order popularity and similarity indices can be efficiently represented in terms of orbit degrees. We further propose a supervised learning model that fuses multiple orbit-degree-based features and validate its enhanced performance. We observe that lower-order features, particularly lower-order similarity, serve as the primary role in the link prediction, while higher-order features tend to play a complementary role. Our research improves the accuracy and interpretability of link prediction and provides new perspectives and tools for analyzing complex networks. © 2024, CC BY.

关键词： Prediction models

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