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arXiv 2024年

作者： Su, Xiaoxin Zhou, Yipeng Cui, Laizhong Guo, Song College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Shenzhen University Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology HKSAR Hong Kong

Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server via Internet. To accelerate the communication speed, it has been explored to deploy a programmable switch (PS) in lieu of the parameter server to coordinate clients. The challenge to deploy the PS in FL lies in its scarce memory space, prohibiting running memory consuming aggregation algorithms on the PS. To overcome this challenge, we propose Federated Learning in-network Aggregation with Compression (FediAC) algorithm, consisting of two phases: client voting and model aggregating. In the former phase, clients report their significant model update indices to the PS to estimate global significant model updates. In the latter phase, clients upload global significant model updates to the PS for aggregation. FediAC consumes much less memory space and communication traffic than existing works because the first phase can guarantee consensus compression across clients. The PS easily aligns model update indices to swiftly complete aggregation in the second phase. Finally, we conduct extensive experiments by using public datasets to demonstrate that FediAC remarkably surpasses the state-of-the-art baselines in terms of model accuracy and communication traffic. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning systems

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XMODOS: An Explainable Model for Denial of Service Attack Detection

XMODOS: An Explainable Model for Denial of Service Attack De...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Reginald C. Molokwu Bonaventure C. Molokwu Victor C. Molokwu Canadian Institute for Cybersecurity University of New Brunswick Department of Computer Science Software Engineering Concordia University School of Energy Geoscience Infrastructure and Society Hariot-Watt University Edinburgh

This paper proposes a Machine Learning-based approach for detecting Denial-of-Service (DoS) attacks using different datasets for training and testing. The study evaluates the performance of the proposed approach on a unique dataset; thereafter, compares it to existing approaches in a bid to demonstrate its superiority in terms of accuracy, false-positive rate, and computational efficiency. Furthermore, the impacts of different Machine Learning algorithms and hypertuning configurations on the performance of the proposed approach are investigated. Also, via Feature Importance Analysis, this study examines the influence of each feature (present in the dataset) on our proposed model. The contributions of our work herein demonstrate the potentials of the proposed approach in detecting DoS attacks; and our research highlights the importance of employing Machine Learning in this domain. Future research directions have been suggested based on insights acquired from the investigations of different algorithms and hyperparameters.

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An Overview of Blockchain-Based Application in Internet of Things (IoT)

An Overview of Blockchain-Based Application in Internet of T...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Reginald C. Molokwu Bonaventure C. Molokwu Victor C. Molokwu Canadian Institute for Cybersecurity University of New Brunswick Department of Computer Science and Software Engineering Concordia University School of Energy Geoscience Infrastructure and Society Hariot-Watt University Edinburgh

The Internet of Things (IoT) is a very crucial aspect of Computing, and it fosters the interconnection of physical nodes on the Internet via enabling them to interact and share data. However, the potentials for security and privacy breaches increase as the number of connected devices/nodes (in the network) rises. Blockchain technology, with its capacity to provide secure and tamper-proof data storage, possess the potentials to mitigate the aforementioned vulnerabilities in the IoT. Hence, this paper gives a detailed review of the present state of blockchain-based applications in the IoT as well as the prospective benefits of employing blockchain technology with the aim/goal of securing IoT devices and its infrastructure. Also mentioned in this paper are the obstacles, research gaps, etc., currently impeding the implementation of some blockchain-based technologies; and the potential solutions toward surmounting these challenges.

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Architecting Digital Twins for Intelligent Transportation Systems

Architecting Digital Twins for Intelligent Transportation Sy...

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IEEE International Conference on software Architecture Workshops (ICSAW)

作者： Hiya Bhatt Sahil Karthik Vaidhyanathan Rahul Biju Deepak Gangadharan Ramona Trestian Purav Shah Software Engineering Research Centre IIIT Hyderabad India Computer Systems Group IIIT Hyderabad India Faculty of Science and Technology Middlesex University London

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation Systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency.

关键词： Adaptation models software architecture Transportation computer architecture Predictive models Traffic control Real-time systems Digital twins Safety Predictive analytics

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Communication, Power, and Sla-Aware Virtual Machine Placement in Oversubscribed Cloud Data Center: A Monetary Approach

SSRN

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SSRN 2023年

作者： Kiani, Mohsen Khayyambashi, Mohammad Reza Department of Computer Science Khansar Campus University of Isfahan Isfahan Iran Department of Software Engineering Faculty of Computer Engineering University of Isfahan Isfahan Iran

In the present study, we propose an algorithm for mapping virtual machines (VMs) to physical machines (PMs) in cloud data centers. The proposed method models a dynamic system where VMs enter and terminate. The goal of the algorithm is to minimize the power consumption of the PMs and the network while preventing service level agreement violation (SLA). Moreover, oversubscription is leveraged to enhance PM utilization. The mapping problem is formulated as an optimization problem and solved using a heuristic and a meta-heuristic algorithm. For the latter, we used chemical reaction optimization. In addition, to convert various important metrics into one optimization goal, first, the raw revenue of executing the VMs is calculated. Then, all other measured parameters, including PM and network power consumption, migration cost, and SLA violation penalty, are converted to monetary measures to obtain a net revenue, which is considered as the optimization goal. The simulation results show that the implemented CRO algorithm outperforms heuristic methods by a significant margin in terms of SLA violation and power consumption. © 2023, The Authors. All rights reserved.

关键词： Electric power utilization

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Identifying COVID-19 through X-ray and CT scan images using Machine Learning 18

Identifying COVID-19 through X-ray and CT scan images using ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Shehzadi, Maham Arshad, Usama Abideen, Zainul Ali, Raja Hashim Khan, Ammad Aslam Ijaz, Ali Zeeshan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences Rawalpindi Pakistan National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350328172

In the current global scenario, marked by the COVID-19 pandemic, it has become imperative for healthcare systems around the globe to swiftly and accurately diagnose the disease. This is where cutting-edge approaches such as machine learning (ML) come into play, specifically, ML-based identification of COVID-19 from chest X-ray and CT-Scan imagery. Our research collective is devotedly working towards devising a robust, accurate ML model capable of deciphering these images to identify COVID-19 effectively. This involves the application of a variety of AI algorithms and vast data resources. Our work is grounded on a large, annotated image dataset that aids in training and refining the model. Our findings indicate that ML methodologies can discern COVID-19 from chest X-ray and CT scan images with a mean accuracy of 90%. Such a tool holds promise in aiding medical practitioners in prompt and precise patient diagnosis, thereby improving patient prognosis. The most notable contribution of our study lies in the creation of an ML model that can accurately detect COVID-19 in chest X-ray and CT-Scan images, potentially aiding in early disease detection and management. © 2023 IEEE.

关键词： COVID-19

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Unveiling Stress: A Comparative Analysis of Multimodal Sensor Fusion Techniques for Predictive Modeling

Unveiling Stress: A Comparative Analysis of Multimodal Senso...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Noha A. Saad Eldien Raghda Essam Ali Mustafa Ezzeldin Moamen Zaher Faculty of Computer Science October University for Modern Sciences and Arts (MSA) Giza Egypt Software Engineering Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

Stress in professional environments is a significant concern. Medical professionals are particularly vulnerable to stress, especially during emergencies. Nurses hold a vital position in delivering care within hospital settings. It's crucial to anticipate stress levels among nurses to help them perform their duties effectively and avoid the long-term impacts of stress. This study seeks to explore how body sensors and machine learning techniques can be employed to monitor physiological signs and identify stress levels among nurses. It utilizes a benchmark dataset collected from 15 different nurses, including signals such as heart rate (HR), Electrodermal Activity (EDA), and Skin Temperature alongside location data extracted by an accelerometer. This study explores several fusion strategies, such as data, model, and prediction fusion levels to improve the accuracy and reliability of stress prediction models. Through a comparative analysis, this paper highlights the strengths and limitations of diverse fusion techniques. shedding light on their efficacy in capturing the nuanced features of stress. The findings offer valuable insights into the optimization of multimodal sensor fusion for enhanced stress prediction, creating pathways for reliable frameworks in the healthcare domain. This research conducted a comparative study between 3 different levels: data-fusion, model-fusion, and prediction-fusion. Prediction-level fusion outperformed both model-level fusion by 1.97% and data-level fusion by 1.26%.

关键词： Temperature sensors Heart rate Temperature measurement Temperature distribution Multimodal sensors Predictive models Data models

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Reinforcement Learning based MIMO Controller for Virtual Inertia Control in Isolated Microgrids

Reinforcement Learning based MIMO Controller for Virtual Ine...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Skiparev, Vjatseslav Belikov, Juri Petlenkov, Eduard Levron, Yoash Tallinn University of Technology Department of Software Science Tallinn Estonia Tallinn University of Technology Department of Computer Systems Tallinn Estonia Technion Faculty of Electrical Engineering Haifa Israel

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

In this paper, we propose a multi-input multi-output controller for optimal control of nonlinear energy storage, using deep reinforcement learning (DRL) algorithm. This controller provides the frequency support in an isolated microgrid with high penetration of variable renewable energy sources and varying system inertia. To achieve an optimal control we redesigned neural network of actor and critic, simplified deep deterministic policy gradient (DDPG) rules, and reorganized the reward/punishment system. Simulation results show the efficiency of the proposed virtual inertia control architecture in several scenarios. © 2022 IEEE.

关键词： Reinforcement learning

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Coordinated PI-based frequency deviation control of isolated hybrid microgrid: An online multi-agent tuning approach via reinforcement learning

Coordinated PI-based frequency deviation control of isolated...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Nosrati, K. Tepljakov, A. Petlenkov, E. Levron, Y. Skiparev, V. Belikov, J. Tallinn University of Technology Department of Computer Systems Tallinn Estonia Technion Faculty of Electrical Engineering Haifa Israel Tallinn University of Technology Department of Software Science Tallinn Estonia

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

Numerous remote area applications welcome standalone renewable energy power generation systems or isolated microgrids (MGs). Due to the nature of solar and wind energy, the frequency deviation control (FDC) in hybrid MGs has become more complicated and critical than the conventional grid for power quality purposes. By using a coordination control strategy between a double-layered capacitor and a fuel cell, our mission here is to design a FDC system based on the PI controller which is tuned by an artificial neural network (ANN) in a multi-agent structure. To achieve this aim, a reinforcement learning technique is applied to train the ANN-based tuners. The performance of the proposed FDC system has been verified under different conditions by using real data to demonstrate the stability and robustness of the proposed controller. © 2022 IEEE.

关键词： Reinforcement learning

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MQTT based monitoring and control system for remote stations in automotive industry

MQTT based monitoring and control system for remote stations...

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Denis Ivanović Josip Job Marko Puntarić Marijan Herceg CX TTTech-Auto Osijek Croatia Department of Software Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia Department of Communications Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

In this paper is presented implementation of a lightweight supervisory control and data acquisition system for remote test stations as a part of a larger test house in the automotive industry. It is necessary to allow remote set up and monitoring of the test stations, which means defining the structure of a system with numerous test stations as endpoints. The test stations are meant to be virtual machines but can be both virtual and physical machines. A communication protocol must be used that enables this within the organization’s network, regardless of actual distance and location. The parameters of the test stations to be monitored can be defined according to the needs and purpose. Even when creating your own tool, this function is not unique, i.e. on one test station parameters of operating system resources such as memory usage can be monitored, while on another test station logged-in users or running programs, reported system errors and others must be monitored. Data storage is one of the mandatory functions of this system. This process allows viewing data throughout history and more detailed analysis of events. There are numerous benefits that can be drawn and used from the data stored in the database. It is necessary to pay attention to the data stored as well as the scope and retention throughout history. The correct and secure setup of a database is one of the prerequisites for any serious system that processes data. Appropriate views should be created for users involved in the data monitoring process using all the elements mentioned so far. Creating a visually acceptable and easy-to-use interface that serves the main purpose is the final presentation of the entire system to users. A complete system leads to higher productivity, profitability and better organization of the company. It also allows project managers to better plan the use and occupancy of test stations. At any time, they have insight into the status of individual test stations, their activitie

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