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SBOOSP for Massive Devices in 5G WSNs Using Conformable Chaotic Maps

Computers, Materials & Continua 2022年第6期71卷 4591-4608页

作者： Chandrashekhar Meshram Agbotiname Lucky Imoize Sajjad Shaukat Jamal Amer Aljaedi Adel R.Alharbi Department of Post Graduate Studies and Research in Mathematics Jayawanti Haksar Government Post-Graduation CollegeCollege of Chhindwara UniversityBetul460001M.P.India Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkoka100213LagosNigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr University44801BochumGermany Department of Mathematics College of ScienceKing Khalid UniversityAbhaSaudi Arabia College of Computing and Information Technology University of TabukTabuk 71491Saudi Arabia

The commercialization of the fifth-generation(5G)wireless network has *** devices are being integrated into 5G-enabled wireless sensor networks(5GWSNs)to deliver a variety of valuable services to network ***,there are rising fears that 5GWSNs will expose sensitive user data to new security *** secure end-to-end communication,key agreement and user authentication have been ***,when billions of massive devices are networked to collect and analyze complex user data,more stringent security approaches are *** integrity,nonrepudiation,and authentication necessitate special-purpose subtree-based signature mechanisms that are pretty difficult to create in *** address this issue,this work provides an efficient,provably secure,lightweight subtreebased online/offline signature procedure(SBOOSP)and its aggregation(Agg-SBOOSP)for massive devices in 5G WSNs using conformable chaotic *** SBOOSP enables multi-time offline storage access while reducing processing *** a result,the signer can utilize the pre-stored offline information in polynomial *** feature distinguishes our presented SBOOSP from previous online/offline-signing procedures that only allow for one ***,the new procedure supports a secret key during the pre-registration process,but no secret key is necessary during the offline *** suggested SBOOSP is secure in the logic of unforgeability on the chosen message attack in the random ***,SBOOSP and Agg-SBOOSP had the lowest computing costs compared to other contending ***,the suggested SBOOSP outperforms several preliminary security schemes in terms of performance and computational overhead.

关键词： Subtree-based online/offline signature procedure(SBOOSP) 5G WSNs provably secure scheme massive devices conformable chaotic maps

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Guest Editorial: Special issue on computational methods and artificial intelligence applications in low-carbon energy systems

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IET Renewable Power Generation 2024年第3期18卷 303-305页

作者： Wang, Yishen Zhou, Fei Guerrero, Josep M. Baker, Kyri Chen, Yize Wang, Hao Xu, Bolun Xu, Qianwen Zhu, Hong Agwan, Utkarsha State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China Department of Energy Technology Aalborg University Aalborg Denmark Department of Civil Environmental and Architectural Engineering University of Colorado-Boulder BoulderCO United States Guangzhou China Department of Data Science and AI Monash University ClaytonVIC Australia Department of Earth and Environmental Engineering Columbia University New YorkNY United States Electric Power and Energy Systems Division KTH Royal Institute of Technology Stockholm Sweden State Grid Jiangsu Electric Power Co. Ltd. Nanjing China Department of Electrical Engineering and Computer Sciences University of California - Berkeley BerkeleyCA United States

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An Automatic Tooth Position and Dental Disease Detection Algorithm Based on YOLOv8

An Automatic Tooth Position and Dental Disease Detection Alg...

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International Conference on Computer and Communications (ICCC)

作者： Hongliang Wang Ke Diao Lin Wu Xuerao Li Xin Zhou Xiaozhou Liu Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School and Hospital of Stomatology China Medical University Liaoning Provincial Key Laboratory of Oral Diseases Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

With the development of digital dental imaging technology, panoramic tomographic images have become an effective tool for diagnosing dental diseases. However, due to the diversity and complexity of dental lesions, accurately identifying tooth positions and pathological conditions from these images remains challenging. To address the issue of low target detection accuracy caused by the high similarity and complex textures of teeth, this paper introduces YOLOv8-Teeth, an enhanced teeth detection algorithm designed for accurate identification of teeth positions and dental diseases from panoramic radiographs. Key innovations include integrating the C2f_ConvNeXtv2 module for improved feature extraction, employing the ASPP module for optimized multi-scale feature capture, and utilizing Focal-EloU Loss for refined bounding box regression. Experimental results on a custom dataset of 476 panoramic radiographs demonstrate significant performance gains, achieving a mean average precision (mAP) of 91.3%, precision of 93.6%, and recall of 88.7%. These improvements offer promising potential for early diagnosis and treatment planning in dental care.

关键词： Technological innovation Accuracy Computational modeling Teeth Tomography Feature extraction Real-time systems Dentistry Detection algorithms Diseases

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Scalable Network Emulation on Analog Neuromorphic Hardware

arXiv

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arXiv 2024年

作者： Arnold, Elias Spilger, Philipp Straub, Jan V. Müller, Eric Dold, Dominik Meoni, Gabriele Schemmel, Johannes European Institute for Neuromorphic Computing Kirchhoff-Institute for Physics Heidelberg University Germany Advanced Concepts Team European Space Research and Technology Centre European Space Agency Netherlands Faculty of Aerospace Engineering Delft University of Technology Netherlands

We present a novel software feature for the BrainScaleS-2 accelerated neuromorphic platform that facilitates the partitioned emulation of large-scale spiking neural networks. This approach is well suited for deep spiking neural networks and allows for sequential model emulation on undersized neuromorphic resources if the largest recurrent subnetwork and the required neuron fan-in fit on the substrate. The ability to emulate and train networks larger than the substrate provides a pathway for accurate performance evaluation in planned or scaled systems, ultimately advancing the development and understanding of large-scale models and neuromorphic computing architectures. We demonstrate the training of two deep spiking neural network models —using the MNIST and EuroSAT datasets— that exceed the physical size constraints of a single-chip BrainScaleS-2 system. Copyright © 2024, The Authors. All rights reserved.

关键词： Network architecture

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Biogas Fermentation Monitoring research Based on Borderline-SMOTE Algorithm and Stacking Ensemble Learning

Biogas Fermentation Monitoring Research Based on Borderline-...

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Information and Communication Technology (ICTech), International Conference of

作者： Pengfei Xiu Lijun Fu Yukun Sun Hongliang Wang Xuerao Li Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Big Data Technology and Cognitive Intelligence Laboratory University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

Anaerobic fermentation is the most important link to gas production, this paper solves the current problem with the help of the current complete machine learning technology, combining perception and intelligent processing, intelligent processing of information, and derive a decision-making basis for its control or management [1]. Aiming at the current problems of the anaerobic fermentation process in biogas engineering, the system design was carried out, and the anaerobic fermentation process of biogas and its main influencing factors were investigated, and the monitoring parameters of the system were selected to be temperature, PH value, liquid level and redox potential [2]. Previous studies are mainly based on a single prediction model, which leads to poor generalization ability of the model. In this study, unbalanced samples are handled by Borderline-SMOTE algorithm. Based on the Stacking integrated learning approach, a common single prediction model is used to construct a combined prediction model. The accuracy, precision, recall, F1 score and area under the ROC curve are used to evaluate the advantages and disadvantages of the combined prediction model over the single prediction model.

关键词： Accuracy Biological system modeling Stacking Process control Production Predictive models Prediction algorithms Biogas Fermentation Monitoring

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Speaker-Independent Depression Detection based on Adversarial Training Method

Speaker-Independent Depression Detection based on Adversaria...

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European Signal Processing Conference (EUSIPCO)

作者： Minggang WANG Shohei KATO Wen GU Jun YAN Fenghui REN Department of Computer Science and Engineering Nagoya Institute of Technology Nagoya Japan School of Computing and Information Technology University of Wollongong Wollongong Australia Center for Innovative Distance Education and Research Japan Advanced Institute of Science and Technology Nomi Japan

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Depression, a worldwide mental health problem, significantly affects people's lives and may even lead to suicide in severe cases. As speech contains a large number of features that can reflect the speaker's mental condition, research has been conducted on depression detection based on speech analysis recently. However, personalized features from various speakers in speech can be mingled with depression-related features, causing a personalization bias in the features and reducing feature generalization. In this paper, we propose an adversarial training method based on the gradient reversal technique to remove speaker characteristics from speech signals, eliminating the personalization bias caused by speaker characteristics on depression features. Experiments carried out on the Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WoZ) dataset, demonstrate that our method improved the detection performance through more generalized depression feature extraction.

关键词： Training Analytical models Speech analysis Europe Mental health Signal processing Depression Feature extraction Interviews

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Guidance on Dataspaces: Development of a Learning Environment for Industrial SMEs

Guidance on Dataspaces: Development of a Learning Environmen...

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International Conference on Control and Robots (ICCR)

作者： Lick Jonas Lamarz Jessica Dohmann Friederike Kulkarni Pranav Jayant Zerbin Julian Koldewey Christian Department for Digital Transformation Fraunhofer Institute for Mechatronic Systems Design Paderborn Germany Department for Corporate Innovation Fraunhofer Institute for Mechatronic Systems Design Paderborn Germany Research group for Advanced Systems Engineering University of Paderborn - Heinz Nixdorf Institute Paderborn Germany

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

The rapid advancements in digital transformation have led to the emergence of dataspaces as a pivotal element for industry-wide and cross-industry data integration and interoperability across various businesses. Despite their potential, the adoption and effective utilization of dataspaces by business stakeholders remain challenging. This paper aims to address this gap by developing a comprehensive learning environment tailored for business stakeholders. Through an interview study and an analysis of the current state of research, we identify problem fields and derive key requirements for the development of the learning environment. The proposed environment includes a demonstrator and a training concept designed to enhance stakeholders' understanding and capabilities in managing and leveraging dataspaces. Our findings contribute to the body of knowledge by providing practical guidance through learning environments for the deployment of dataspaces in business contexts and highlighting areas for future research.

关键词： Training Service robots digital transformation Data integration Stakeholders Interviews Interoperability Business

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MVEB: Self-Supervised Learning with Multi-View Entropy Bottleneck

arXiv

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arXiv 2024年

作者： Wen, Liangjian Wang, Xiasi Liu, Jianzhuang Xu, Zenglin The School of Computing and Artificial Intelligence Research Institute for Digital Economy and Interdisciplinary Sciences Southwestern University of Finance and Economics Chengdu China The Hong Kong University of Science and Technology Hong Kong Shenzhen Institute of Advanced Technology Shenzhen China The Harbin Institute of Technology Shenzhen Shenzhen China The Pengcheng Laboratory Shenzhen China

Self-supervised learning aims to learn representation that can be effectively generalized to downstream tasks. Many self-supervised approaches regard two views of an image as both the input and the self-supervised signals, assuming that either view contains the same task-relevant information and the shared information is (approximately) sufficient for predicting downstream tasks. Recent studies show that discarding superfluous information not shared between the views can improve generalization. Hence, the ideal representation is sufficient for downstream tasks and contains minimal superfluous information, termed minimal sufficient representation. One can learn this representation by maximizing the mutual information between the representation and the supervised view while eliminating superfluous information. Nevertheless, the computation of mutual information is notoriously intractable. In this work, we propose an objective termed multi-view entropy bottleneck (MVEB) to learn minimal sufficient representation effectively. MVEB simplifies the minimal sufficient learning to maximizing both the agreement between the embeddings of two views and the differential entropy of the embedding distribution. Our experiments confirm that MVEB significantly improves performance. For example, it achieves top-1 accuracy of 76.9% on ImageNet with a vanilla ResNet-50 backbone on linear evaluation. To the best of our knowledge, this is the new state-of-the-art result with ResNet-50. Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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A Diffuse-Interface Lattice Boltzmann Method for the Dendritic Growth with Thermosolutal Convection

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Communications in Computational Physics 2023年第4期33卷 1164-1188页

作者： Chengjie Zhan Zhenhua Chai Baochang Shi Ping Jiang Shaoning Geng Dongke Sun School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan430074P.R.China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and TechnologyWuhan 430074P.R.China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan430074P.R.China The State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074P.R.China School of Mechanical Engineering Southeast UniversityNanjing 211189P.R.China

In this work,we proposed a diffuse-interface model for the dendritic growth with thermosolutal *** this model,the sharp boundary between the fluid and solid dendrite is firstly replaced by a thin but nonzero thickness diffuse interface,which is described by the order parameter,and the diffuse-interface based governing equations for the dendritic growth are *** solve the model for the dendritic growth with thermosolutal convection,we also developed a diffuse-interface multirelaxation-time lattice Boltzmann(LB)*** this method,the order parameter in the phase-field equation is combined into the force caused by the fluid-solid interaction,and the treatment on the complex fluid-solid interface can be *** addition,four LB models are designed for the phase-field equation,concentration equation,temperature equation and the Navier-Stokes equations in a unified ***,we performed some simulations of the dendritic growth to test the present diffuse-interface LB method,and found that the numerical results are in good agreements with some previous works.

关键词： Dendritic growth diffuse interface lattice Boltzmann method phase-field method.

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An Overview of Organs-on-Chips Based on Deep Learning

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research 2022年第2期2022卷 141-160页

作者： Jintao Li Jie Chen Hua Bai Haiwei Wang Shiping Hao Yang Ding Bo Peng Jing Zhang Lin Li Wei Huang Frontiers Science Center for Flexible Eletronrics Xi'an Institu te of Flexible Electronics(IFE)and Xr'an Institute of Biomedical Materials EngineeringNorthwesterm Polytechnical UniversityXi'an 710072China Key Laboratory of Ietlieant Computing and Signal Processing of Minsry of Educaton School of Electronics and Information EngineeringAnhui UriversityHefei 230601China 38th Research Institute of China Eilectronics Technology Grouep Corporation Hiefei 230088 China Collge of Biomedical Engimeering Sichuan UntiversityChengdu 610065China Key Laboratory of Flexible Elecronics(KLOFE)and Institute of Advanced Materials(IAM)Nanjing Tech University(NanjingTech)J Nanjing 211800 China

Microfluidic-based organs-on-chips(OoCs)are a rapidly developing technology in biomedical and chemical research and have emerged as one of the most advanced and promising in vitro *** miniaturization,stimulated tissue mechanical *** microenvironment of OoCs offer unique properties for biomedical ***,the large amount of data generated by the high parallelization of OoC systems has grown far beyond the scope of manual analysis by researchers with biomedical *** learning,an emerging area of research in the field of machine learning,can automatically mine the inherent characteristics and laws of"big data"and has achieved remarkable applications in computer vision,speech recognition,and natural language *** integration of deep learning in OoCs is an emerging field that holds enormous potential for drug development,disease modeling,and personalized *** review bricfly describes the basic concepts and mechanisms of microfluidics and deep learning and summarizes their successful *** then analyze thecombination of OoCs and deep learning for image digitization,data analysis,and ***,the problems faced in current applications are discussed,and future perspectives and suggestions are provided to further strengthen this integration.

关键词： Deep holds beyond

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