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A Novel Federated Learning system with Privacy Protection and Blockchain Consensus Incentive Mechanisms in Cloud-Edge Collaboration Scenarios

A Novel Federated Learning System with Privacy Protection an...

引用

2024 ieee international conference on systems, Man, and Cybernetics, smc 2024

作者： Liu, Longyi Hu, Yi Zhao, Yanqing Zhang, Xiyang Ma, Yongze Chang, Guiyong Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665410205

The data-driven transformation of industrial intelligence is becoming a key focus for the future of smart manufacturing. However, due to concerns about data privacy, the industry often faces a prevalent 'data island' problem. Traditional federated learning ensures data privacy and usability to some extent, yet the C/S architecture's design model is susceptible to poisoning attacks, denial-of-service threats, and lacks consensus mechanisms and incentives among participants to ensure fairness in learning. This paper integrates federated learning into blockchain consensus protocols, proposing an FRConsensus algorithm based on model evaluation and stake election, which overcomes issues of passive participation and resource wastage during model training. Simultaneously, it introduces model watermarking and ECC public-key encryption mechanisms to secure parameter transmission. The experimental results demonstrate that the proposed federated learning system achieves superior model convergence. Furthermore, it is more effective in defending against poisoning attacks. In comparison to mainstream federated learning mechanisms based on consensus algorithms, it reduces the election latency by 20%, shortens the consensus latency by 8%, and increases the throughput by 12%. © 2024 ieee.

关键词： Federated learning

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Safety and Comfort in Autonomous Braking system with Deep Reinforcement Learning

Safety and Comfort in Autonomous Braking System with Deep Re...

引用

2022 ieee international conference on systems, Man, and Cybernetics, smc 2022

作者： Fanti, Maria Pia Mangini, Agostino Marcello Martino, Daniele Olivieri, Ignazio Parisi, Fabio Popolizio, Francesco Polytechnic University of Bari Electrical and Information Engineering Bari Italy

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Safety issues related to autonomous vehicles are of great concern both in the academy and industry, identifying the braking system performance as a crucial research field. In this work, an autonomous braking system based on deep reinforcement learning is proposed, employing an intelligent agent trained in city scenarios to manage both pedestrians' safety and passengers' comfort. The agent is modelled via the deep deterministic policy gradient algorithm in a software environment and its performance is tested showing good results in maximizing both pedestrians' safety and passengers' comfort. © 2022 ieee.

关键词： Autonomous vehicles

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Using Situational Awareness and Situative Criticality for Adaptive Planning Assistance in MUM-T Missions

Using Situational Awareness and Situative Criticality for Ad...

引用

2024 ieee international conference on systems, Man, and Cybernetics, smc 2024

作者： Bautz, Julian Schwerd, Simon Schulte, Axel Institute of Flight Systems University of the Bundeswehr Munich München Germany

ISBN: (纸本)9781665410205

This study examines the effect of adaptive assistance in online mission planning for military aircraft pilots in Manned-Unmanned-Teaming (MUM-T) missions. We evaluated two adaptive approaches to select from three levels of assistance in a cockpit simulator experiment. Both approaches used the criticality of the situation to choose an assistance level, with one approach additionally integrating an eye-tracking-based measure of Situational Awareness (SA). Based on these triggers, the adaptive system used different levels of assistance for mission planning support. We compared these two adaptive conditions to a baseline setup without assistance and evaluated performance, workload, usability, and subjective SA. Performance was assessed by flight data and mission completion times, while workload, usability, and SA were assessed with standard questionnaires. Both assistance systems exceeded the non-assisted baseline in terms of mission time, workload management, SA, and usability. Pilots supported by adaptive assistance integrating both, criticality, and SA, showed improvements in subjective SA over the criticality-only-condition while demonstrating faster mission completion. The findings suggest that adaptive assistance, particularly when incorporating SA, can enhance pilot performance in MUM-T operations. © 2024 ieee.

关键词： Cockpits (aircraft)

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Navigating Innovation within a Contractor's Business Model: A system Dynamics Approach 19

Navigating Innovation within a Contractor's Business Model: ...

引用

19th Annual international ieee conference on system of systems engineering (SoSE)

作者： Elazhary, Mariam Dagli, Cihan El-Adaway, Islam H. Missouri Univ Sci & Technol Dept Civil Architectural & Environm Engn Rolla MO 65409 USA Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Rolla MO 65409 USA Missouri Univ Sci & Technol Dept Civil Architectural & Environm Engn Dept Engn Management & Syst Engn Rolla MO 65409 USA

ISBN: (纸本)9798350365924;9798350365917

Innovation is essential in the construction industry, but current practices often focus narrowly on specific aspects rather than taking a comprehensive approach. To address this, systematic innovation through business model innovation (BMI) is proposed to enhance overall performance. Despite increasing interest in BMI research, it remains in its early stages, lacking quantitative analysis of its impact on business model components. This paper develops a framework using system dynamics to assess changes to a construction company's BM when faced with a variety of innovations across various projects. This shall be accomplished by (1) establishing factors and their relationships that define a contractor's BM over time, (2) modeling the said factors using AnyLogic using synthetic data, and (3) evaluating the emergent behavior of BM's value creation. The study found that the impact of innovation on a contractor's long-term value creation is not linear;it varies based on when and what type of innovation is implemented. This complexity arises from the trade-offs between cost reductions and additional expenses associated with innovation. The paper introduces a comprehensive framework to assess how innovation affects a contractor's business model, enabling practitioners to understand better and manage their long-term value-creation strategies.

关键词： Construction Innovation system Dynamics Business Model

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Ship Control of Approach Maneuvering under Wind Disturbance Using a Deep Neural Network

Ship Control of Approach Maneuvering under Wind Disturbance ...

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2023 ieee international conference on systems, Man, and Cybernetics, smc 2023

作者： Kashiwagi, H. Okazaki, T. Graduate School of Marine Science and Technology Tokyo University of Marine Science and Technology Tokyo Japan Tokyo University of Marine Science and Technology Department of Maritime Systems Engineering Tokyo Japan

ISBN: (纸本)9798350337020

The effect of wind disturbance is one of the major factors that complicate heading control in ship control at low speeds. Approach maneuvering while decreasing speed and approaching the pier requires a control adopted to changing maneuverability and wind conditions. In this study, a control scheme using a deep neural network (DNN) was developed to output command values corresponding to wind conditions by training the DNN on maneuvering patterns of approach maneuvering under various wind conditions. The developed control scheme was validated through experiments using an actual ship. © 2023 ieee.

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Enhancing Cyber-Physical Security: Integrating Virtual Fences Within Digital Twins

Enhancing Cyber-Physical Security: Integrating Virtual Fence...

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4th ieee Annual international conference on Cyber Security and Resilience (ieee CSR)

作者： Aghazadeh Ardebili, Ali Longo, Antonella Ficarella, Antonio Univ Salento Dept Engn Innovat Lecce Italy HSPI SpA Roma Rome Italy Univ Salento Data Lab Dept Engn Innovat Lecce Italy Univ Salento Green Engine Lab Dept Engn Innovat Lecce Italy

ISBN: (纸本)9798350375367

Virtual fencing exploits the transformation of a dynamic environment into a cyber-physical traceable system to restrict dangerous movements and create early warning zones. While most real-life applications rely on GPS systems, this article explores the use of computer vision to regulate the distribution of human crowds. The proposal includes utilizing Unmanned Aircraft systems (UAS) to create risk-based ranked early warning zones to enhance the resilience of critical infrastructures and citizen safety/security in smart cities. The proposed system conducts surveillance of labeled areas comprising infrastructure and/or populated by citizens. In the current research, a physical testbed is constructed to monitor human mobility, which simulates human behavior as a part of the cyber-physical-social dynamic environment, triggering an alert whenever a person enters a warning zone. These warning zones are categorized based on their level of risk, ranging from low to high depending on the distance from the risky area. The different warning levels can be dynamically set to leave sufficient time to react and mitigate potential hazards. The experiments to simulate the crossing of various virtual fences in the testbed demonstrate the system's high efficiency for early warning purposes. Finally, the main challenges of bringing EWZ into action are identified and discussed.

关键词： Early Warning Zone Virtual Fence Digital Twin Cyber-Physical-Social system Urban Digital Twins Public Spaces Security Civil Safety Critical Infrastructure Protection Computer Vision UAS UAV Smart City Crowd Monitoring Remote Pilot Aircraft systems (RPAS)

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Multi-Kernel Broad Learning system Based on Elastic-Net with Random Fourier Features

Multi-Kernel Broad Learning System Based on Elastic-Net with...

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2024 ieee international conference on systems, Man, and Cybernetics, smc 2024

作者： Zhang, Qihuai Zhao, Xiaojie The School of Artificial Intelligence Beijing Normal University Beijing100875 China

ISBN: (纸本)9781665410205

The Broad Learning system (BLS) features a simple yet efficient network structure, with its core being the fast and random generation of hidden layers;however, this generation method not only fails to effectively capture the nonlinear characteristics in the task, but also generates certain 'redundant nodes', which can negatively affect its learning capabilities. In this study, we propose an improved version of BLS, named the KEFBLS, aimed at enhancing the feature extraction capability of the hidden layer through the integration of multi-kernel technology and network sparsification strategies, complemented by deeper feature extraction using random Fourier features. the KEFBLS first combines polynomial and wavelet kernels to boost the nonlinear mapping capabilities of data;then, it applies the elastic-net method to refine the BLS objective function, removing low-impact hidden layer nodes to reduce redundancy and create a more streamlined network;finally, KEFBLS employs random Fourier features to map the processed hidden layers, further enhancing the network's feature extraction capabilities, constructing a new learning model. Our experimental results on three UCI regression datasets demonstrate that KEFBLS surpasses other methods in terms of learning efficiency and model performance. © 2024 ieee.

关键词： Contrastive Learning

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A Novel Framework Combining VSL and Vehicle Platooning for Freeway Bottleneck

A Novel Framework Combining VSL and Vehicle Platooning for F...

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2024 ieee international conference on systems, Man, and Cybernetics, smc 2024

作者： Lu, Tong Qi, Liang Luan, Wenjing Liu, Kun Guo, Xiwang Ann Talukder, Qurra Tul College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Communication Engineering Liaoning Petrochemical University Fushun113001 China

ISBN: (纸本)9781665410205

Freeway bottlenecks caused by traffic incidents contribute significantly to large-scale traffic congestion. Traditional strategies, including variable speed limit (VSL) and ramp metering, are commonly used for freeway traffic congestion management. Recently, vehicle platooning has become a promising way to alleviate traffic bottlenecks. This work proposes a novel framework that combines VSL and vehicle platooning for freeway bottleneck, referred to as VSL-VP, in mixed traffic of connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs). First, the upstream road of a bottleneck is divided into two segments, called the former and the latter. VSL limits vehicle speed at the former segment, thereby reducing inflow traffic to the latter one. Then, deep reinforcement learning is employed for CAV platooning at the latter segment, where low traffic flow density and large car-following distance create conditions for smooth lane change and platoon formulation of CAVs. Simulation results demonstrate that VSL-VP significantly enhances the bottleneck throughput and reduces traffic congestion at elevated levels of CAV penetration rates. © 2024 ieee.

关键词： Traffic congestion

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Cost-Effective and Dynamic Migration for Microservices in Hybrid Cloud-Edge systems

Cost-Effective and Dynamic Migration for Microservices in Hy...

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2023 ieee international conference on systems, Man, and Cybernetics, smc 2023

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Mobile edge computing (MEC), as a promising paradigm, delivers computation and storage capacities at the edge of the network. It supports delay-sensitive services for mobile users (MUs). However, dynamic and stochastic characteristics of MEC networks necessitate constant migration of installed services across edge servers to keep up with the mobility of MUs. As a result, the cost of maintaining the network increases significantly. Existing studies of MEC rarely consider the cost of service migration due to MU mobility. To minimize the long-term cost for microservices in a hybrid cloudedge system comprising of MUs, small base stations (SBSs), and a cloud data center (CDC), the total cost minimization is formulated as a constrained mixed-integer nonlinear program. To solve it, this work designs a novel meta-heuristic optimization algorithm called Multi-swarm Grey-wolf-optimizer based on Genetic-learning (MGG), which effectively combines strong local search capabilities of grey wolf optimizer with superior global search capabilities of genetic algorithm. MGG simultaneously optimizes service request routing among MUs, SBSs, and CDC, CPU speeds of SBSs, service deployment of SBSs, service migration cost of SBSs, as well as MUs' transmission power and channel bandwidth allocation. Simulation results with Google cluster trace demonstrate that MGG outperforms several state-of-the-art peers with respect to the overall cost of the hybrid system. © 2023 ieee.

关键词： genetic algorithm grey wolf optimizer Mobile edge computing resource allocation service migration

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Incremental Learning Algorithms for Broad Learning system with Node and Input Addition

Incremental Learning Algorithms for Broad Learning System wi...

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2024 ieee international conference on systems, Man, and Cybernetics, smc 2024

作者： Chen, Guang-Ze Jin, Junwei Sun, Hai-Wei Philip Chen, C.L. Faculty of Science and Technology University of Macau Department of Mathematics 99999 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou510641 510335 China

ISBN: (纸本)9781665410205

The Broad Learning system (BLS) has been established as an effective flat network alternative to Deep Neural Networks (DNNs), delivering high efficiency while achieving competitive accuracy. Despite its advantages, the incremental learning methods of BLS face challenges in stability and computation when expanding with new nodes or input. We introduce two novel incremental learning algorithms based on factorization updates for BLS that optimize node and input additions to overcome these limitations. Our node addition algorithm utilizes QR decomposition and Cholesky factorization, using the update of the Cholesky factor instead of pseudo-inverse computations. For input addition, we propose an iterative Cholesky factor update algorithm. Our algorithms demonstrate not only faster computation compared to the existing BLS but also improved testing accuracy on the MNIST or Fashion-MNIST dataset. This work presents a significant step forward in the practical application and scalability of BLS in various data-dense environments. © 2024 ieee.

关键词： Adversarial machine learning

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