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A Novel & Innovative Blockchain-Empowered Federated Learning Approach for Secure Data Sharing in Smart City Applications 1

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International Conference on Advances in Communication technology and computer Engineering, ICACTCE 2023

作者： Hai, Tao Wang, Dan Seetharaman, Tamizharasi Amelesh, M. Sreejith, P.M. Sharma, Vandana Ibeke, Ebuka Liu, Hong School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guizhou Guizhou550025 China School of Information and Artificial Intelligence Nanchang Institute of Science and Technology Nanchang China School of Mathematics and Statistics Qiannan Normal University for Nationalities Guizhou Duyun558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province Guizhou Duyun558000 China Department of AI & ML Acharya Institute of Technology Bangalore India Amity Institute of Information Technology Amity University Noida Campus Noida India School of Creative and Cultural Business Robert Gordon University AberdeenAB10 7AQ United Kingdom

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

The very existence of smart cities forms the stepping stone in the evolution of many technological advancements in the future era. While smart cities have already grown in their way, the tremendous amount of data generated from them paves the way for new perspectives of development. This is because security and privacy remain to be the major constraint across smart city applications. Further, smart city applications such as smart homes, smart transportation, and smart healthcare are generating a huge amount of data every day and it is often complex to collect and manage all the data together at a single location. To address these constraints, this paper presents a novel and innovative blockchain-assisted federated learning approach for secure data sharing in IoT Smart Cities. Here, we implement a federated learning approach, where the process of learning is made in a distributed fashion. The use of blockchain in turn adds more security and resilience to smart city applications. The security analysis proves that the proposed approach offers comparatively better performance and remains more resistant to various security threats and vulnerabilities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Graph Prompt Learning: A Comprehensive Survey and Beyond

arXiv

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arXiv 2023年

作者： Sun, Xiangguo Zhang, Jiawen Wu, Xixi Cheng, Hong Xiong, Yun Li, Jia Department of Systems Engineering and Engineering Management Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong Hong Kong University of Science and Technology Guangzhou China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration with graph data, a cornerstone in our interconnected world, remains nascent. This paper presents a pioneering survey on the emerging domain of graph prompts in AGI, addressing key challenges and opportunities in harnessing graph data for AGI applications. Despite substantial advancements in AGI across natural language processing and computer vision, the application to graph data is relatively underexplored. This survey critically evaluates the current landscape of AGI in handling graph data, highlighting the distinct challenges in cross-modality, cross-domain, and cross-task applications specific to graphs. Our work is the first to propose a unified framework for understanding graph prompt learning, offering clarity on prompt tokens, token structures, and insertion patterns in the graph domain. We delve into the intrinsic properties of graph prompts, exploring their flexibility, expressiveness, and interplay with existing graph models. A comprehensive taxonomy categorizes over 100 works in this field, aligning them with pre-training tasks across node-level, edge-level, and graph-level objectives. Additionally, we present, ProG, a Python library, and an accompanying website, to support and advance research in graph prompting. The survey culminates in a discussion of current challenges and future directions, offering a roadmap for research in graph prompting within AGI. Through this comprehensive analysis, we aim to catalyze further exploration and practical applications of AGI in graph data, underlining its potential to reshape AGI fields and beyond. ProG and the website can be accessed by https: //***/WxxShirley/Awesome-Graph-Prompt, and https://***/sheldonresearch/ProG, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Websites

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A Framework on Fully Distributed State Estimation and Cooperative Stabilization of LTI Plants

arXiv

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arXiv 2023年

作者： Duan, Peihu Lv, Yuezu Wen, Guanghui Ogorzalek, Maciej School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China The Department of Systems Science School of Mathematics Southeast University Nanjing211189 China The Department of Information Technologies Jagiellonian University Krakow30-348 Poland

How to realize high-level autonomy of individuals is one of key technical issues to promote swarm intelligence of multi-agent (node) systems with collective tasks, while the fully distributed design is a potential way to achieve this goal. This paper works on the fully distributed state estimation and cooperative stabilization problem of linear time-invariant (LTI) plants with multiple nodes communicating over general directed graphs, and is aimed to provide a fully distributed framework for each node to perform cooperative stabilization tasks. First, by incorporating a novel adaptive law, a consensus-based estimator is designed for each node to obtain the plant state based on its local measurement and local interaction with neighbors, without using any global information of the communication topology. Subsequently, a local controller is developed for each node to stabilize the plant collaboratively with performance guaranteed under mild conditions. Specifically, the proposed method only requires that the communication graph be strongly connected, and the plant be collectively controllable and observable. Further, the proposed method can be applied to pure fully distributed state estimation scenarios and modified for noise-bounded LTI plants. Finally, two numerical examples are provided to show the effectiveness of the theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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A Reinforcement Learning Based Dynamic Multi-Objective Constrained Evolutionary Algorithm for Open-Pit Mine Truck Scheduling

A Reinforcement Learning Based Dynamic Multi-Objective Const...

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Chinese Automation Congress (CAC)

作者： Junxiang Qiu Changhe Li Shengxiang Yang Engineering Research Center of Intelligent Technology for Geo- Exploration Ministry of Education School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Computer Science and Informatics De Montfort University Leicester UK

Aiming at the truck scheduling problem in the open-pit mine scenario, a truck scheduling model based on real-time ore blending is established, and an adaptive evolution algorithm for truck scheduling based on DCNSGA-III is proposed. In the established scheduling model, the real-time grade variance of the crushing plant is minimized as one of the optimization objectives, and the Q-learning algorithm is introduced to adaptively select one of the most effective operators during the search process. Experiments show that the proposed method can effectively control the grade fluctuation of the ore flow and better scheduling schemes are obtained in comparison with algorithms equipped with the traditional search operator selection methods.

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Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis

arXiv

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arXiv 2023年

作者： Dong, Yanjie Wang, Luya Chi, Yuanfang Wang, Jia Zhang, Haijun Yu, Fei Richard Leung, Victor C.M. Hu, Xiping The Faculty of Engineering Shenzhen MSU-BIT University Shenzhen China The College of Computer Science and Software Engineering Shenzhen University Shenzhen China Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China The Department of Systems and Computer Engineering Carleton University Ottawa Canada The Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently upload local gradients to the server via bandwidth-limited channels, the uplink transmission from the workers to the server becomes a communication bottleneck. Therefore, a one-shot distributed principle component analysis (PCA) is leveraged to reduce the dimension of uploaded gradients such that the communication bottleneck is relieved. A PCA-based wireless federated learning (PCA-WFL) algorithm and its accelerated version (i.e., PCA-AWFL) are proposed based on the low-dimensional gradients and the Nesterov's momentum. For the non-convex loss functions, a finite-time analysis is performed to quantify the impacts of system hyper-parameters on the convergence of the PCA-WFL and PCA-AWFL algorithms. The PCA-AWFL algorithm is theoretically certified to converge faster than the PCA-WFL algorithm. Besides, the convergence rates of PCA-WFL and PCA-AWFL algorithms quantitatively reveal the linear speedup with respect to the number of workers over the vanilla gradient descent algorithm. Numerical results are used to demonstrate the improved convergence rates of the proposed PCA-WFL and PCA-AWFL algorithms over the benchmarks. © 2023, CC BY.

关键词： Momentum

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CancerAI: A Deep Learning Framework for Ovarian Cancer Prediction

CancerAI: A Deep Learning Framework for Ovarian Cancer Predi...

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Electronics and Sustainable Communication systems (ICESC), 2020 International Conference on

作者： G. Revathy P. Uma Maheswari S. Madhavi Vijay Anand P R. Vajubunnisa Begum H. Jasmin Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Computer science and Business systems K. Ramakrishnan college of Engineering Trichy India Department of Information Technology Vel tech Multi tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Artificial Intelligence and Data Science CMR Institute of Technology Bengaluru India Department of Electronics and Communication Science Justice Basheer Ahmed Sayeed College for Women Affiliated by University of Madras Chennai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Ovarian cancer is the fifth most common cause of death from cancer in women. This is mainly because it is often diagnosed at a late stage, as the earliest symptoms are unclear and inconsistent. Existing diagnostic techniques, such as biomarkers, biopsies, and imaging tests, have notable drawbacks such as reliance on subjective interpretation, inconsistency across different observers, and time-consuming testing procedures. This research study introduces an innovative deep learning framework that employs a convolutional neural network (CNN) algorithm to accurately predict and diagnose ovarian cancer, thereby overcoming the existing constraints. The CNN was trained using a dataset of histopathology images. The dataset was partitioned into test and training subsets to enhance the model's performance. The algorithm demonstrated an impressive accuracy rate of 96%, successfully detecting 97.12% of malignant instances and precisely categorizing 95.02% of healthy cells. This method effectively mitigates the challenges related to human expert evaluation, including elevated rates of misclassification and variability across different observers, while also reducing the time required for analysis. The results underscore the capability of this CNN-based technique to offer a more precise, effective, and dependable strategy for forecasting and detecting ovarian cancer. Subsequent investigations will prioritize the integration of new breakthroughs in deep learning to further amplify the efficacy of the suggested approach.

关键词： Deep learning Training Accuracy Histopathology Transfer learning Observers Prediction algorithms Partitioning algorithms Convolutional neural networks Ovarian cancer

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Robust Wireless Power Transfer by Self-Oscillating Controlled Inverter and Circular Pads

Robust Wireless Power Transfer by Self-Oscillating Controlle...

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Electrical Machines and Drives (ICEMD), International Conference on

作者： Alireza Eikani Mohammad Amirkhani Hossein Jafari Sadegh Vaez-Zadeh Mojtaba Mirsalim Davood Arab Khaburi Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical Engineering Electrical Machines and Transformers Research Laboratory (EMTRL) Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Electrical Engineering Iran University of Science and Technology Tehran Iran

Inductive wireless power transfer (WPT) faces significant challenges in achieving high efficiency and stable output power for charging mobile devices like EVs. Using circular pads (CP) as the transmitter (Tx) and receiver (Rx) and a self-oscillating controlled inverter, this paper demonstrates a robust WPT system that maintains high constant transfer efficiency and output power in a defined region regardless of coupling coefficient variations without communication between Tx and Rx or control circuits for the receiver side. In contrast to couplers with non-identical Tx and Rx pads and multiple decoupled coils in the Rx pad, the compact CP-CP magnetic coupler with a single identical coil on the Tx and Rx is more robust at a wider transfer distance (air gap) and lateral and rotational misalignment between the Tx and Rx. The proposed 1-kW robust WPT system with up to a 20 cm air gap between the Tx and Rx and up to 12 cm lateral misalignment in all directions at a 15 cm air gap and all degrees of rotational misalignment can tolerate coupling variations and achieve constant output power with 93% constant transfer efficiency.

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COph100: A comprehensive fundus image registration dataset from infants constituting the "RIDIRP" database

arXiv

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arXiv 2025年

作者： Hu, Yan Gong, Mingdao Qiu, Zhongxi Liu, Jiabao Shen, Hongli Yuan, Mingzhen Zhang, Xiaoqing Li, Heng Lu, Hai Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Beijing China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database1. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

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Spontaneous emission decay and excitation in photonic temporal crystals

arXiv

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arXiv 2024年

作者： Park, Jagang Lee, Kyungmin Zhang, Ruo-Yang Park, Hee-Chul Ryu, Jung-Wan Cho, Gil Young Lee, Min Yeul Zhang, Zhaoqing Park, Namkyoo Jeon, Wonju Shin, Jonghwa Chan, C.T. Min, Bumki Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong Department of Physics Pukyong National University Busan48513 Korea Republic of Center for Theoretical Physics of Complex Systems Institute for Basic Science Daejeon34126 Korea Republic of Department of Physics Pohang University of Science and Technology Pohang37673 Korea Republic of Department of Material Sciences and Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of

Over the last few decades, the prominent strategies for controlling spontaneous emission has been the use of resonant or space-periodic photonic structures [1–18]. This approach, initially articulated by Purcell [19] and later expanded by Bykov [1, 2] and Yablonovitch [3] in the context of photonic crystals, leverages the spatial surroundings to modify the spontaneous emission decay rate of atoms or quantum emitters. However, the rise of time-varying photonics [20–33] has compelled a reevaluation of the spontaneous emission process within dynamically changing environments, especially concerning photonic temporal crystals where optical properties undergo time-periodic modulation [20, 23]. Here, we apply classical light-matter interaction theory along with Floquet analysis to reveal a substantial enhancement in the spontaneous emission decay rate at the momentum gap frequency in photonic temporal crystals. This enhancement is attributed to time-periodicity-induced loss and gain mechanisms, as well as the non-orthogonality of Floquet eigenstates that are inherent to photonic temporal crystals. Intriguingly, our findings also suggest that photonic temporal crystals enable a non-equilibrium light-matter interaction process: the spontaneous excitation of an atom from its ground state to an excited state, accompanied by the concurrent emission of a photon, referred to as spontaneous emission excitation. © 2024, CC BY.

关键词： Spontaneous emission

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Predicting financial performance for listed companies in Thailand during the transition period: A class-based approach using logistic regression and random forest algorithm

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Journal of Open Innovation: technology, Market, and Complexity 2023年第3期9卷

作者： Supsermpol, Pornpawee Huynh, Van Nam Thajchayapong, Suttipong Chiadamrong, Navee School of Manufacturing Systems and Mechanical Engineering Sirindhorn International Institute of Technology Thammasat University Pathum Thani 12120 Thailand School of Knowledge Science Japan Advanced Institute of Science and Technology Ishikawa 923-1292 Japan National Electronics and Computer Technology Center (NECTEC) NSTDA Pathum Thani 12120 Thailand

This study presents a class-based approach developed to evaluate the financial performance of companies that have undergone public listing on the stock market. By employing both statistical analysis and machine learning methods, the models consider two important determinants, which are the company's internal capability determinants prior to going public and the amount of funds raised through the Initial Public Offering (IPO) to predict the company's financial performance after joining the stock market. The study demonstrates that the machine learning method (random forest algorithm) outperforms the statistical method (logistic regression) in predicting financial performance. The findings also reveal that certain determinants significantly influence the predicted financial performance in a specific period. Furthermore, the study examines the impact of IPO funds on financial performance and observes that while the first year after listing does not exhibit a significant effect, a subsequent positive correlation emerges in the subsequent two to three years, up to a certain threshold, with excessive funds potentially leading to adverse effects. Overall, the predictive models provide valuable insights for companies, enabling them to prioritize resources towards significant determinants in a specific relative year, make informed decisions, and enhance their long-term success in the stock market. © 2023 The Authors

关键词： Financial performance Initial Public Offering Internal capability Logistic regression Predictive model Random forest

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