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2023 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2023

作者： Zhou, Ying Song, Rui Zhuo Xia, Li Na School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education Beijing100083 China

ISBN: (纸本)9798350309003

The number of diabetic patients is predicted to increase and the age of diabetic patients is slowly becoming younger. Diabetes is generally categorized into the following types: type I diabetes, type II diabetes, gestational diabetes, and other types of diabetes caused by pancreatic disease, endocrine disease, medications, and other factors. The traditional method of treating diabetes involves injecting insulin using a device outside the body. This method is physically and psychologically harmful to the diabetic. Therefore, this paper focuses on the glycemic control of patients with type I diabetes who have completely lost their insulin secretion, and whose pancreas is unable to secrete insulin. Therefore, it is necessary to use a blood glucose sensor to measure the blood glucose concentration in the patient's body. Then the measured blood glucose concentration in the diabetic patient's body is fed back to the computer terminal. Finally the controller is utilized for regulation. This paper focuses on the regulation of blood glucose in the body of type I diabetic patients and combines a fuzzy controller and a PID controller to regulate the blood glucose concentration in type I diabetic patients. In this paper, the blood glucose-insulin metabolism models of healthy people and diabetic patients are firstly established. Then the parameter values of the PID controller are adjusted through several trials to build a fuzzy rule base. Then the fuzzy controller is designed so that the diabetic's blood glucose can be successfully tracked to the healthy person's blood glucose changes. In this way, the purpose of regulating the blood glucose concentration of diabetic patients is realized. In addition, on the basis of realizing the control of blood glucose concentration. In this paper, the GUI toolbox of MATLAB is also used to design the visualization interface for the simulation process and the results. © 2023 IEEE.

关键词： Insulin

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Pulsed polarized vortex beam enabled by metafiber lasers

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PhotoniX 2024年第1期5卷 110-120页

作者： Chenxi Zhang Lei Zhang He Zhang Bo Fu Jiyong Wang Min Qiu Key Laboratory of Precision Opto-Mechatronics Technology of Education Ministry School of Instrumentation and Optoelectronic EngineeringBeihang UniversityBeijing 100191China Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province School of EngineeringWestlake University18 Shilongshan RoadHangzhou 310024Zhejiang ProvinceChina Institute of Advanced Technology Westlake Institute for Advanced Study18 Shilongshan RoadHangzhou 310024Zhejiang ProvinceChina QianYuan National Laboratory Hangzhou 310000China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology School of Engineering MedicineBeihang UniversityBeijing 100191China Ministry of Education Engineering Research Center of Smart Microsensors and Microsystems School of Electronics and InformationHangzhou Dianzi UniversityHangzhou 310018China Westlake Institute for Optoelectronics FuyangHangzhou 311421China

Pulsed polarized vortex beams,a special form of structured light,are generated by tailoring the light beam spatiotemporally and witness the growing application demands in nonlinear optics such as ultrafast laser processing and surface plasma ***,existing techniques for generating polarized vortex beams suffer from either low compactness due to the use of bulky components or limited controlment of pulse ***,an all-fiber technique combining plasmonic metafibers with mode conversion method is harnessed to generate high-performance pulsed polarized vortex *** metafibers are utilized as saturable absorbers to produce Q-switched pulses with micro-second duration,while the offset splicing method is employed to partially convert the fundamental transverse mode(LP_(01))to higher-order mode(LP_(11)).Eventually,a polarized vortex beams laser is achieved at the telecom band with a repetition frequency of 116.0 *** impact of geometrical parameters including period of metafibers and offset of splicing on the spatiotemporal properties of pulsed polarized vortex beams is systematically *** findings could pave the way for design,control and generation of all-fiber pulsed polarized vortex beams,and also offer insights into the development of other types of structured laser sources.

关键词： Metafiber Q-switched pulse Polarized vortex beam

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Research on a quantification model of online learning cognitive load based on eye-tracking technology

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Multimedia Tools and Applications 2025年第18期84卷 18993-19007页

作者： Xue, Yaofeng Zhu, Fangqing Li, Jiaxuan Department of Educational Information Technology and Shanghai Engineering Research Center of Digital Educational Equipment East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal University Shanghai China

Online learning is characterized by a high degree of complexity and a wealth of information when compared to traditional classroom learning. This can have an adverse influence on the learning outcomes of online learners. The paper builds a quantification model of online learning cognitive load based on non-invasive eye-tracking technology by combining three eye-movement indicators: fixation time, fixation count, and pupil diameter. This is based on the analysis of cognitive load and eye-tracking technology. The study then uses a significant amount of eye movement experimental data in conjunction with the cognitive load test that students take in an online learning environment to confirm the viability and effectiveness of the quantification methodology. The paper builds a quantification model of online learning cognitive load based on non-invasive eye-tracking technology by combining three eye-movement indicators: fixation time, fixation count, and pupil diameter. This is based on the analysis of cognitive load and eye-tracking technology. The study then uses a significant amount of eye movement experimental data in conjunction with the cognitive load test that students take in an online learning environment to confirm the viability and effectiveness of the quantification methodology. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Eye movements

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A Cycling Friendliness Measurement System for Dockless Bike-sharing Dynamic Scheme

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1557-1569页

作者： Li, Ben Huang, Dejuan Liao, Lyuchao Xiao, Hankun Ma, Huankun Fujian Provincial Universities Key Laboratory of Industrial Control Data Analysis Fujian Provincial Universities Engineering Research Center for Intelligent Driving Technology School of Transportation Fujian University of Technology Fuzhou350118 China

The rapid growth of Dockless Bike-sharing (DLBS) provides an excellent opportunity to meet the strong demand for convenient green travel services. However, with the increasing number of cyclists and DLBS, the issue of DLBS friendliness faces several challenges: (1) the hardness of renting in empty fences;(2) the difficulty of parking in overload fences;(3) different road conditions have different effects on cycling friendliness. To deal with these challenges, we design a DLBS friendliness (DLBSF) sys-tem, consisting of DLBS recommendation (DLBSR) system and DLBS scoring (DLBSS) system to comprehensively alleviate the problem of decreasing friendliness. The former optimizes cycling friendliness by first identifying the overload fences where the morning peak tide phenomenon is most prominent, and then provides guidance for cyclists to park the nearest idle fences. The latter gives the riding friendliness scheme by scoring and analyzing each road section to improve friendliness. Experiments on real-world DLBS datasets verify the accuracy, effectiveness, and feasibility of the proposed DLBSF system. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Fences

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LBFL: A Lightweight Blockchain-based Federated Learning Framework with Proof-of-Contribution Committee Consensus

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IEEE Transactions on Big data 2024年

作者： Qiao, Shaojie Jiang, Yuhe Han, Nan Hua, Wei Lin, Yufeng Min, Shengjie Wu, Xindong Chengdu University of Information Technology School of Software Engineering Chengdu610225 China Sichuan University NMPA Key Laboratory of Human Cosmetic Evaluation and Big Data West China Hospital Chengdu610041 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230009 China

Blockchain technology makes it possible to design robust decentralized federated learning (FL). Minimizing the communication cost and storage consumption incurred is one of the essential challenges. In addition, maintaining the security and privacy of Big data raises to be a difficult problem. Aiming to tackle these challenges, this paper presents LBFL (a Lightweight Blockchain-based FL framework) that offers three novel features. First, it employs a new committee consensus mechanism called Proof-of-Contribution, which is used to avoid the selection latency from the competition of miners and alleviate the congestion in cross-validation of parameters in an asynchronous fashion. Second, LBFL employs a role-adaptive incentive mechanism to estimate devices' workloads and identify malicious nodes effectively. Third, to cope with the excessive storage overheads incurred in full-replication, LBFL applies a new storage partition mechanism that distributes triple redundant chunks in ReedSolomon coding (RSC) evenly to participating devices with high fault tolerance and recovery efficiency. To evaluate LBFL, empirical studies are performed on the famous MNIST dataset and LBFL is compared with the state-of-the-art FL frameworks. The results demonstrate that LBFL can reduce evaluation latency and storage consumption by 69.2% and 72.1%, respectively, and the learning efficiency of LBFL is higher than the state-ofthe-art methods. In particular, important findings are obtained: the proposed role-adaptive incentive mechanism can properly identify malicious devices and switch the roles of legitimate devices to achieve good decentralization © 2015 IEEE.

关键词： Federated learning

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Generative-Contrastive Graph Learning for Recommendation

arXiv

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arXiv 2023年

作者： Yang, Yonghui Wu, Zhengwei Wu, Le Zhang, Kun Hong, Richang Zhang, Zhiqiang Zhou, Jun Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China Ant Group China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center China

By treating users’ interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering (CF) based recommendation. Recently, researchers have introduced Graph Contrastive Learning (GCL) techniques into CF to alleviate the sparse supervision issue, which first constructs contrastive views by data augmentations and then provides self-supervised signals by maximizing the mutual information between contrastive views. Despite the effectiveness, we argue that current GCL-based recommendation models are still limited as current data augmentation techniques, either structure augmentation or feature augmentation. First, structure augmentation randomly dropout nodes or edges, which is easy to destroy the intrinsic nature of the user-item graph. Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph. To tackle the above limitations, we propose a novel Variational Graph Generative-Contrastive Learning (VGCL) framework for recommendation. Specifically, we leverage variational graph reconstruction to estimate a Gaussian distribution of each node, then generate multiple contrastive views through multiple samplings from the estimated distributions, which builds a bridge between generative and contrastive learning. The generated contrastive views can well reconstruct the input graph without information distortion. Besides, the estimated variances are tailored to each node, which regulates the scale of contrastive loss for each node on optimization. Considering the similarity of the estimated distributions, we propose a cluster-aware twofold contrastive learning, a node-level to encourage consistency of a node’s contrastive views and a cluster-level to encourage consistency of nodes in a cluster. Finally, extensive experimental results on three public datasets clearly demonstrate the effectiveness of the proposed model. Copyright © 2023, The Authors.

关键词： Collaborative filtering

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Corrosion-Resistant Polymer-Derived SiOC Membrane for Effective Organic Removal via Synergistic Adsorption and Peroxymonosulfate Activation

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Transactions of Tianjin University 2024年第3期30卷 238-249页

作者： Jiankun Ji Yarong Gu Jianning Zhang Chongwen Yu Xiao Hu Yueping Bao Yujie Song School of Materials Science and Chemical Engineering Ningbo UniversityNingbo 315211China Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Key Laboratory of Special Energy Materials and ChemistryCenter for Advanced Organosilicon PrecursorsNingbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China Qianwan Institute of CNITECH Ningbo 315336China University of Chinese Academy of Sciences Beijing 100049China School of Materials Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore College of Environmental Science and Engineering Nankai UniversityTianjin 300350China Advanced Energy Science and Technology Guangdong Laboratory Huizhou 516029China

A major challenge is to construct ceramic membranes with tunable structures and functions for water ***,a novel corrosion-resistant polymer-derived silicon oxycarbide(SiOC)ceramic membrane with designed architectures was fabricated by a phase separation method and was applied in organic removal via adsorption and oxidation for the first *** pore structure of the as-prepared SiOC ceramic membranes was well controlled by changing the sintering temperature and polydimethylsiloxane content,leading to a pore size of 0.84–1.62μm and porosity of 25.0–43.8%.Corrosion resistance test results showed that the SiOC membranes sustained minimal damage during 24 h exposure to high-intensity acid–base conditions,which could be attributed to the chemical inertness of *** rhodamine 6G(R6G)as the model pollutant,the SiOC membrane demonstrated an initial eff ective removal rate of 99%via adsorption;however,the removal rate decreased as the system approached adsorption *** peroxymonosulfate was added into the system,efficient and continuous degradation of R6G was observed throughout the entire period,indicating the potential of the as-prepared SiOC membrane in oxidation-related ***,this work provides new insights into the construction of novel polymer-derived ceramic membranes with well-defined structures and functions.

关键词： Polymer-derived ceramics Membrane Corrosion resistance Adsorption Oxidation

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Privacy-Preserving Fingerprint Recognition via Federated Adaptive Domain Generalization

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Computers, Materials & Continua 2025年第3期82卷 5035-5055页

作者： Yonghang Yan Xin Xie Hengyi Ren Ying Cao Hongwei Chang Henan Key Laboratory of Big Data Analysis and Processing Computer and Information EngineeringHenan UniversityKaifeng475004China College of Information Science and Technology Nanjing Forestry UniversityNanjing210037China Henan Branch China Life Insurance Co.Ltd.Zhengzhou450000China

Fingerprint features,as unique and stable biometric identifiers,are crucial for identity ***,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data *** Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security ***,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across *** heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse *** address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated *** algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature ***,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model *** conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.

关键词： Fingerprint recognition privacy protection federated learning adaptive weight adjustment

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A Graph with Adaptive AdjacencyMatrix for Relation Extraction

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Computers, Materials & Continua 2024年第9期80卷 4129-4147页

作者： Run Yang YanpingChen Jiaxin Yan Yongbin Qin Engineering Research Center of Text Computing&Cognitive Intelligence Ministry of EducationGuizhou UniversityGuiyang550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China College of Computer Science and Technology Guizhou UniversityGuiyang550025China

The relation is a semantic expression relevant to two named entities in a *** a sentence usually contains several named entities,it is essential to learn a structured sentence representation that encodes dependency information specific to the two named *** related work,graph convolutional neural networks are widely adopted to learn semantic dependencies,where a dependency tree initializes the adjacency ***,this approach has two main ***,parsing a sentence heavily relies on external toolkits,which can be ***,the dependency tree only encodes the syntactical structure of a sentence,which may not align with the relational semantic *** this paper,we propose an automatic graph learningmethod to autonomously learn a sentence’s structural *** of using a fixed adjacency matrix initialized by a dependency tree,we introduce an Adaptive Adjacency Matrix to encode the semantic dependency between *** elements of thismatrix are dynamically learned during the training process and optimized by task-relevant learning objectives,enabling the construction of task-relevant semantic dependencies within a *** model demonstrates superior performance on the TACRED and SemEval 2010 datasets,surpassing previous works by 1.3%and 0.8%,*** experimental results show that our model excels in the relation extraction task,outperforming prior models.

关键词： Relation extraction graph convolutional neural network adaptive adjacency matrix

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Empowering Biomedical Named Entity Recognition Through Multi-Tagger Collaboration 27

Empowering Biomedical Named Entity Recognition Through Multi...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhao, Jin Xie, Jian Zhu, Tinghui Guo, Qian Li, Zhixu Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Department of Computer Science and Engineering East China University of Science and Technology China

ISBN: (纸本)9781643685489

Biomedical Named Entity Recognition (BioNER) plays a crucial role in automatically identifying specific categories of entities from biomedical texts. Currently, region-based methods have shown promising performance in BioNER. However, existing paradigms in the region-based methods suffer from inherent limitations, including the generation of negative samples, and the ignorance of token dependencies. To overcome these limitations, we propose a new paradigm, implemented as Token Cascade Tagger (TCT), which combines span identification and category classification. The TCT utilizes category information to enhance the correlation between the heads and tails of entities, effectively reducing the generation of negative samples. Additionally, we introduce a Token Dependency Tagger (TDT) that captures token dependencies within entity spans by identifying the longest span in a sentence. The TDT filters out incorrect spans and further improves the accuracy of span detection obtained from the TCT. Furthermore, we employ a multi-task learning framework to optimize both the TCT and TDT, leading to superior performance in BioNER. Extensive experiments on publicly available biomedical datasets demonstrate our method outperforms the previous state-of-the-art methods, achieving 92.44%, 92.54%, and 81.26% on NCBI-Disease, BC5CDR, and GENIA, respectively, in terms of F1 score. © 2024 The Authors.

关键词： Multi-task learning

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