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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

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arXiv 2025年

作者： Hennebelle, Alain Dieng, Qifan Ismail, Leila Buyya, Rajkumar Abu Dhabi United Arab Emirates Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates Emirates Center for Mobility Research United Arab Emirates National Water and Energy Center UAE University Abu Dhabi Al-Ain United Arab Emirates

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model *** Codes 68T01, 68T09, 68M14, 68W10, 68W15 © 2025, CC BY.

关键词： Electronic health record

A novel quantum visual secret sharing scheme

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arXiv 2023年

作者： Liu, Wenjie Xu, Yinsong Zhang, Maojun Chen, Junxiu Yang, Ching-Nung Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Division Department of Computer Science and Information Engineering National Dong Hwa University Taiwan97441 Taiwan

Inspired by Naor et al.’s visual secret sharing (VSS) scheme, a novel n out of n quantum visual secret sharing (QVSS) scheme is proposed, which consists of two phases: sharing process and recovering process. In the first process, the color information of each pixel from the original secret image is encoded into an n-qubit superposition state by using the strategy of quantum expansion instead of classical pixel expansion, and then these n qubits are distributed as shares to n participants, respectively. During the recovering process, all participants cooperate to collect these n shares of each pixel together, then perform the corresponding measurement on them, and execute the n-qubit XOR operation to recover each pixel of the secret image. The proposed scheme has the advantage of single-pixel parallel processing that is not available in the existing analogous quantum schemes, and perfectly solves the problem that in the classic VSS schemes the recovered image has the loss in resolution. Moreover, its experiment implementation with IBM Q is conducted to demonstrate the practical feasibility. © 2023, CC BY.

关键词： Pixels

CLDG: Contrastive Learning on Dynamic Graphs

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arXiv 2024年

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2024, CC BY.

关键词： Contrastive Learning

Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications

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IFAC-PapersOnLine 2023年第2期56卷 5233-5239页

作者： Dániel Horváth Kristóf Bocsi Gábor Erdős Zoltán Istenes Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Eőtvős Loránd Research Network Budapest Hungary CoLocation Center for Academic and Industrial Cooperation Eőtvős Loránd University Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

Adaptive robotics plays an essential role in achieving truly co-creative cyber physical systems. In robotic manipulation tasks, one of the biggest challenges is to estimate the pose of given workpieces. Even though the recent deep-learning-based models show promising results, they require an immense dataset for training. In this paper, two vision-based, multi-object grasp pose estimation models (MOGPE), the MOGPE Real-Time and the MOGPE High-Precision are proposed. Furthermore, a sim2real method based on domain randomization to diminish the reality gap and overcome the data shortage. Our methods yielded an 80% and a 96.67% success rate in a real-world robotic pick-and-place experiment, with the MOGPE Real-Time and the MOGPE High-Precision model respectively. Our framework provides an industrial tool for fast data generation and model training and requires minimal domain-specific data.

关键词： adaptive robotics robot vision sim2real knowledge transfer smart manufacturing cyber physical production systems

An efficient network operation automation scheme using network status information for local 5G networks

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An efficient network operation automation scheme using netwo...

International Conference on Information and Communication Technology Convergence (ICTC)

作者： Mahnsuk Yoon Jihun Seo Kyucheol Cho Future Mobile Communication Research Center GERI(Gumi Electronics & Information Technology Research Institute) Gumi Korea Center for Connected & Automated Driving Research Department of The Fourth Industrial Revolution & Transport Korea Transport Institute Sejong Korea School of Computer Secience and Engineering Software Education Center Kyungpook National University Daegu Korea

5G SA communication technology is being recognized as a crucial technology shaping the future of wireless communication. Compared to previous generations of networks, 5G offers ultra-high-speed, low latency, and massive connectivity, presenting both enterprises and individual users with opportunities for new business models and an enhanced quality of life. One of the most significant changes between the conventional mobile communication environment and 5G is the activation of various application cases in B2B services based on the local 5G network. The local 5G network, tailored to specific sites, requires effective management of high-volume data generated in dense areas and necessitates minimizing manual intervention by administrators. The ZSM (Zero-touch Service Management) technology is gaining attention to meet these requirements. ZSM focuses on automated operations for managing large systems like commercial mobile communication networks. It is anticipated that ZSM will also be applied to the local 5G network for efficient management and operation of tailored services. This paper proposes a network management technique that minimizes administrator intervention in the local 5G network environment. The proposed network management technique collects statistical logs from local 5G network base stations and core systems, supports real-time network status checks as well as traffic predictions using machine learning. Additionally, it offers automated operation techniques for the local 5G network by incorporating functions such as disconnecting network access for devices that are factors of malfunction within the specialized network.

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Consensus-Based Algorithms for Controlling Swarms of Unmanned Aerial Vehicles 19th

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Consensus-Based Algorithms for Controlling Swarms of Unmann...

19th International Conference on Ad-Hoc Networks and Wireless, ADHOC-NOW 2020

作者： Carli, Raffaele Cavone, Graziana Epicoco, Nicola Di Ferdinando, Mario Scarabaggio, Paolo Dotoli, Mariagrazia Department of Electrical and Information Engineering Polytechnic of Bari Bari Italy Center of Excellence DEWS Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy

ISBN: (纸本)9783030617455

Multiple Unmanned Aerial Vehicles (multi-UAVs) applications are recently growing in several fields, ranging from military and rescue missions, remote sensing, and environmental surveillance, to meteorology, logistics, and farming. Overcoming the limitations on battery lifespan and on-board processor capabilities, the coordinated use of multi-UAVs is indeed more suitable than employing a single UAV in certain tasks. Hence, the research on swarm of UAVs is receiving increasing attention, including multidisciplinary aspects, such as coordination, aggregation, network communication, path planning, information sensing, and data fusion. The focus of this paper is on defining novel control strategies for the deployment of multi-UAV systems in a distributed time-varying set-up, where UAVs rely on local communication and computation. In particular, modeling the dynamics of each UAV by a discrete-time integrator, we analyze the main swarm intelligence strategies, namely flight formation, swarm tracking, and social foraging. First, we define a distributed control strategy for steering the agents of the swarm towards a collection point. Then, we cope with the formation control, defining a procedure to arrange agents in a family of geometric formations, where the distance between each pair of UAVs is predefined. Subsequently, we focus on swarm tracking, defining a distributed mechanism based on the so-called leader-following consensus to move the entire swarm in accordance with a predefined trajectory. Moreover, we define a social foraging strategy that allows agents to avoid obstacles, by imposing on-line a time-varying formation pattern. Finally, through numerical simulations we show the effectiveness of the proposed algorithms. © 2020, Springer Nature Switzerland AG.

关键词： Unmanned aerial vehicles (UAV)

Estimation of continuous environments by robot swarms: Correlated networks and decision-making

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Estimation of continuous environments by robot swarms: Corre...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Mohsen Raoufi Pawel Romanczuk Heiko Hamann Science of Intelligence Research Cluster of Excellence Berlin Germany Department of Biology Institute for Theoretical Biology Humboldt Universität zu Berlin Berlin Germany Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Bernstein Center for Computational Neuro-science Berlin Germany Department of Computer and Information Science University of Konstanz Konstanz Germany

Collective decision-making is an essential capability of large-scale multi-robot systems to establish autonomy on the swarm level. A large portion of literature on collective decision-making in swarm robotics focuses on discrete decisions selecting from a limited number of options. Here we assign a decentralized robot system with the task of exploring an unbounded environment, finding consensus on the mean of a measurable environmental feature, and aggregating at areas where that value is measured (e.g., a contour line). A unique quality of this task is a causal loop between the robots' dynamic network topology and their decision-making. For example, the network's mean node degree influences time to convergence while the currently agreed-on mean value influences the swarm's aggregation location, hence, also the network structure as well as the precision error. We propose a control algorithm and study it in real-world robot swarm experiments in different environments. We show that our approach is effective and achieves higher precision than a control experiment. We anticipate applications, for example, in containing pollution with surface vehicles.

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Two-Stage Inverse Design of Superchiral Dielectric Metasurfaces

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Two-Stage Inverse Design of Superchiral Dielectric Metasurfa...

International Review of Progress in Applied Computational Electromagnetics (ACES)

作者： Omer Yesilyurt Vahagn Mkhitaryan Jer-Shing Huang Ilya Shadrivov Alexander V. Kildishev Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette IN USA Department of Energy (DOE) The Quantum Science Center (QSC) a National Quantum Information Science Research Center of the U.S. Oak Ridge TN USA Leibniz Institute of Photonic Technology Jena Germany ARC Centre of Excellence for Transformative Meta-Optical Systems (TMOS) Research School of Physics the Australian National University Canberra Australia

ISBN: (数字)9781733509671

ISBN: (纸本)9798350362978

Chirality is a fundamental property of asymmetry and is crucial in distinguishing molecular enantiomers, with profound implications across chemistry, biology, and materials science. Circular dichroism (CD) spectroscopy, which measures the differential absorption of left-handed (LCP) and right-handed (RCP) circularly polarized light, serves as a conventional method for identifying and separating chiral enantiomers. Nevertheless, due to the inherently weak chiral-optical interactions, the CD signals of numerous chiral molecules are minimal, typically spanning 10– 3 to 10– 6 within the ultraviolet spectrum. Superchiral metasurfaces, producing fields with enhanced optical chirality that surpasses the chirality of circularly polarized light, emerge as a solution to this challenge. Such metasurfaces, with enhanced interaction between light and chiral molecules, offer a significant increase in CD signal, underscoring the necessity for their advanced development for more accurate and sensitive analysis. This study presents a novel, two-stage inverse design methodology for superchiral metasurfaces with iterative optimization of the topology of a dielectric metasurface unit cell to enhance optical chiral density in the near field. In the first stage, a neural network-based optimizer works in tandem with a rigorous coupled wave analysis (RCWA) solver. In the second stage, adjoint gradients of the optical chirality density are derived from the first principles and calculated with a Finite Difference Time Domain (FDTD) scheme. Our results offer a new platform for high-sensitivity CD spectroscopy applications, allowing for fast, constrained optimization of the unit cells.

关键词： Spectroscopy Optical polarization Biomedical optical imaging Metasurfaces Optical network units Optical coupling Optical sensors

Collaborative Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud Network

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arXiv 2023年

作者： Shen, Shihao Han, Yiwen Wang, Xiaofei Wang, Shiqiang Leung, Victor C.M. The College of Intelligence and Computing Tianjin University Tianjin300350 China IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States The College of Computer Science and Software Engineering Shenzhen University Shenzhen518052 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Kubernetes (k8s) has the potential to coordinate distributed edge resources and centralized cloud resources, but currently lacks a specialized scheduling framework for edge-cloud networks. Besides, the hierarchical distribution of heterogeneous resources makes the modeling and scheduling of k8soriented edge-cloud network particularly challenging. In this paper, we introduce KaiS, a learning-based scheduling framework for such edge-cloud network to improve the long-term throughput rate of request processing. First, we design a coordinated multiagent actor-critic algorithm to cater to decentralized request dispatch and dynamic dispatch spaces within the edge cluster. Second, for diverse system scales and structures, we use graph neural networks to embed system state information, and combine the embedding results with multiple policy networks to reduce the orchestration dimensionality by stepwise scheduling. Finally, we adopt a two-time-scale scheduling mechanism to harmonize request dispatch and service orchestration, and present the implementation design of deploying the above algorithms compatible with native k8s components. Experiments using real workload traces show that KaiS can successfully learn appropriate scheduling policies, irrespective of request arrival patterns and system scales. Moreover, KaiS can enhance the average system throughput rate by 15.9% while reducing scheduling cost by 38.4% compared to baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning