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Anomalous Behavior Identification with Visual Federated Learning in Multi-UAVs Systems 29

Anomalous Behavior Identification with Visual Federated Lear...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wang, Pengfei Yu, Xinrui Ye, Yefei Qi, Heng Yu, Shuo Yang, Leyou Zhang, Qiang Dalian University of Technology School of Computer Science and Technology Dalian China Ministry of Education Dalian116024 China Northeastern University School of Computer Science and Engineering Shenyang China

ISBN: (纸本)9798350330717

Anomaly detection aims to identify data or behav-iors that are different from the usual patterns. In traditional anomaly detection settings, edge devices collect the data and send it to a centralized server for model training, which faces two critical issues: (1) it risks data exposure during transmission;(2) it demands a large amount of network bandwidth for data transfer. To tackle these problems, we propose a Visual Federated Learning algorithm (VFLA) for anomalous behavior identification in the multi-UAVs system. To the best of our knowledge, we are the first to merge federated learning with video-based anomaly detection. VFLA consists of two phases: The initial phase is training a pseudo-label generator. UAVs collect a dataset and manually annotate it. This labeled data is then used to train the pseudo-label generator on the server, which is subsequently distributed back to the UAVs. The second phase is the federated learning-based anomaly detection model training. UAVs leverage the pseudo-label generator to automatically annotate the collected video footage. These annotated videos are fed into an anomaly detection network for training. Once the local training is completed, UAVs upload their local models to a server for federated aggregation. The global model is then redistributed to the UAVs for additional training rounds, until reach the target accuracy. Finally, we simulate the federated learning anomaly detection algorithm on the Shanghai-tech dataset, it demonstrates an average accuracy boost of 5.6% compared to baselines. © 2023 IEEE.

关键词： Anomaly detection Distributed training Federated learning UAVs

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A Novel Redundant Service Caching and Task Offloading Method in Mobile Edge Computing 31st

A Novel Redundant Service Caching and Task Offloading Metho...

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31st International Conference on Web Services, ICWS 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Ouyang, Zhaobin Xia, Yunni Li, Jingpeng Feng, Jiafeng Yu, Yang Zhang, Ke Xu, Xifeng Ma, Yong Chen, Peng Li, Xiaobo School of Computer Chongqing University Chongqing400030 China China Huadian Corporation Guangdong Branch Guangdong China Nanjing Nanzi Huadun Digital Technology Co. Ltd. Nanjing China School of Computer Science and Technology Beijing Institute of Technology Beijing100083 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330000 China School of Computer and Software Engineering Xihua University Chengdu China Chongqing Animal Husbandry Techniques Extension Center Chongqing China

ISBN: (纸本)9783031770715

Edge caching is a promising technologies, which enhances content delivery and reduces service latency by caching potentially interested content and services on edge nodes near users. Due to constraints such as limited edge server capacities and unstable communications in a highly dynamic MEC environment, caching and content delivery are prone to failures and inconsistencies. Consequently, redundant caching mechanisms are in high need as counter measures in this paper, we proposes a novel redundant service caching and task offloading Decision (FT-STD) method. Specifically, we utilize the primary backup (PB) approach for offloading fault tolerance and employ a learning-based algorithm for yielding redundant service caching schedules in MEC. Simulation results demonstrate thatclearly outperforms its peers across multiple performance metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mobile edge computing

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On the Stability of Random Access with Congestion Control 14th

On the Stability of Random Access with Congestion Control

引用

14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024

作者： Xu, Yu Cui, Chen Wang, Zhenyong Guo, Qing School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China Songjiang Laboratory Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9783031861956

The random access protocol is a critical technique widely used in various wireless networks. A significant challenge is analyzing the stability of the random access system when a large number of users transmit packets independently. Although the congestion control policy can enhance the stability of the system, there is currently a lack of quantitative analysis on the stability of random access with congestion control. In this paper, we analyze the random access system with congestion control and quantify its stability. Specifically, we consider a congestion control scheme to enhance the stability of the random access system and present the graphical analytical model. Based on this graphical model, we qualitatively discuss the behavioral variations of the system as it works and quantify its stability. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Congestion control (communication)

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Robot Hand-eye Calibration Method Incorporating Filtering Techniques 25

Robot Hand-eye Calibration Method Incorporating Filtering Te...

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25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Guo, Peng Si, Weiyong Yang, Chenguang School Of Automation Science And Engineering South China University Of Technology Guangzhou China School Of Computer Science And Electronic Engineering University Of Essex Colchester United Kingdom University Of Liverpool Department Of Computer Science Liverpool United Kingdom

ISBN: (纸本)9798350340266

Hand-eye calibration is the foundation of visual servo for robotic tasks, aiming to determine the rigid transfor-mation matrix between the robot and the vision system. The accuracy of hand-eye calibration affects the robot's ability to accurately perform tasks. The classical form of the hand-eye calibration is to solve the equation 'AX = XB'. The algorithms for solving this problem mainly divided into two categorie: step-wise and simultaneous solutions for the rotation and translation components. Due to the noise in the collected data, the calculation accuracy is inevitably affected. In this paper, we introduce a filtering approach for hand-eye calibration. First, we design the movement path of the robot arm end effector, then summarize the variation patterns of parameters, and finally use these patterns to filter the actually collected data. Experimental results on our custom dataset and the robot arm platform demonstrate that our proposed method can reduce reprojection error and improve the accuracy of hand-eye calibration. © 2024 IEEE.

关键词： Calibration

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Observations on Adaptive Refinement Using the Locally-corrected Nyström Method

Observations on Adaptive Refinement Using the Locally-correc...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023

作者： Peterson, Andrew F. School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9781733509633

The locally-corrected Nyström method evolved from the classical Nyström approach from the 1930s and offers efficiencies for high accuracy electromagnetic field computations. In this presentation we explore the approach in connection with the electric field integral equation while investigating p -refinement applied to the hollow linear dipole antenna problem. © 2023 ACES.

关键词： Integral equations

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A Deployment of Ensemble-Based IDS on Raspberry Pi 13

A Deployment of Ensemble-Based IDS on Raspberry Pi

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Musthafa, Muhammad Bisri Huda, Samsul Shamim, S.M. Kodera, Yuta Mwaura, Jedidah Araki, Shunsuke Nogami, Yasuyuki Okayama University Graduate School of Environmental Life Natural Science and Technology Japan Okayama University Green Innovation Center Japan Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Fukuoka Japan

ISBN: (纸本)9798350355079

Intrusion Detection Systems (IDS) are essential for safeguarding IoT networks against various attacks. Our previously developed ensemble-based IDS model, which combines stacked Long Short-Term Memory (LSTM) networks with ANOVA feature selection, achieved a remarkable accuracy of 97.46% and an overfitting value of 0.22% in classifying network attacks. However, deploying this powerful model on resource-constrained devices like Raspberry Pi poses challenges due to limited computational resources. In this paper, we tackle these challenges by converting the original model to TensorFlow Lite (TFLite) and applying quantization techniques to optimize the model size for deployment on a Raspberry Pi 3b+. Our approach significantly reduces the model size by 90.9% while maintaining an impressive accuracy of 96.96%, only a 0.5% reduction from the original model. The optimized model demonstrates efficient performance on the Raspberry Pi, with a loading time of 100.2ms and an inference time of 1.2ms. © 2024 IEEE.

关键词： Network intrusion

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Predicting Stress at Workplace using Machine Learning Techniques 14

Predicting Stress at Workplace using Machine Learning Techni...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kapoor, Anika Goel, Shivani Bennett University School of Computer Science Engineering and Technology Greater Noida201310 India

ISBN: (纸本)9798350335095

Work stress is a common issue nowadays among working professionals, especially with the changing environment, and culture. This type of stress occurs in a wide range of circumstances, but the majority are not aware of the 'Stress Management' techniques to deal with it. With this paper, the aim is to create awareness about such techniques and further work on the available treatment by using different machine learning algorithms like Support Vector Machine, Decision Tree, Gradient Boost, etc. A survey is done of working professionals regarding the level of stress, various stress management policies present in the workplace and the awareness about the role of artificial intelligence or machine learning for handling stress. This has indicated an important need of stress management at the workplace and the role of AI for its identification. Various machine learning algorithms are used to identify mental health issues. The result presented is based on the execution of these algorithms on data from the OSMI Mental Health Survey 2021 conducted among working professionals in the tech industry and the result is drawn as a comparison to the existing work on the same data from OSMI Mental Health Survey 2014 after training different ML models for accuracy. Best results are identified with decision tree algorithm. © 2023 IEEE.

关键词： Support vector machines

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Improving Image Captioning with Control Signal of Sentence Quality 48

Improving Image Captioning with Control Signal of Sentence Q...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhu, Zhangzi Wang, Shuai Qu, Hong University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9781728163277

In the dataset of image captioning, each image is aligned with several descriptions. Despite the fact that the quality of these descriptions varies, existing captioning models treat them equally in the training process. In this paper, we propose a new control signal of sentence quality, which is taken as an additional input to the captioning model. By integrating the control signal information, captioning models are aware of the quality level of the target sentences and handle them differently. Moreover, we propose a novel reinforcement training method specially designed for the control signal of sentence quality: Quality-oriented Self-Annotated Training (Q-SAT). Extensive experiments on MSCOCO dataset show that without extra information from ground truth captions, models controlled by the highest quality level outperform baseline models on accuracy-based evaluation metrics, which validates the effectiveness of our proposed methods. © 2023 IEEE.

关键词： Control Signal Image Captioning Quality-oriented Self-Annotated Training Sentence Quality

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Integrating Decision Trees with Cloud Computing for Predicting Cognitive Decline in Alzheimer's Patients 2

Integrating Decision Trees with Cloud Computing for Predicti...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Meenakshi, K. Ravinder, Bathini Pasha, Mohammed Rehaman Thinakaran, K. Jayanayudu, Dabbara Maniraj, S.P. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Vadapalani India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India Siddartha Institute of Science and Technology Department of Electronics and Communication Engineering Andhra Pradesh Puttur India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350317558

The proposed research work suggests a combined technique based on decision trees and cloud computing for predicting cognitive decline in Alzheimer's patients. Medical records and data from wearable devices are among the many sources that perform preprocessing and feature engineering. Decision tree models can make the most efficient use of computing resources. Decision tree topologies are tested for performance criteria, including accuracy and F1 score. The system's predictive powers are rather encouraging since it frequently achieves accuracy rates over 90%. However, limitations in data quality and the intricacy of decision tree interpretation provide challenges. The prediction of cognitive deterioration in Alzheimer's patients may be made more scalable and interpretable by integrating decision trees with cloud computing. This method has promise for early identification and intervention despite its limitations. To further improve prediction accuracy and clinical value, future research should increase data quality and make it easier to understand. © 2024 IEEE.

关键词： Alzheimer's disease Cloud computing Decision trees Early detection Healthcare

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A Comprehensive Review on Early Diagnosis of Alzheimer's Disease Detection

A Comprehensive Review on Early Diagnosis of Alzheimer's Dis...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Gayathri, G.S. Nachiyappan, S. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350305708

A progressive neurodegenerative Alzheimer's Disease (AD) shrinks (atrophy) the brain and kills brain cells. On a global scale, Finland and the United Kingdom have the highest prevalence of AD, with 54.7 and 42.7 cases per 100, 000 inhabitants respectively. In India, there are 14.60 AD cases for every 100, 000 people and it is ranked 137th in the world and 13th in India among the 50 causes of death. At the beginning of the disease, a person may forget things, or have trouble remembering recent conversations. As the disease progresses, a person may experience severe memory loss and difficulty performing everyday tasks. Consequently, the only way to predict AD at an early stage is to use treatments that prevent the disease from progressing. This paper provides an overview of the current approaches to AD diagnosis and detection, with a focus on the use of Biomarkers and discussed the advantages and disadvantages of Machine Learning (ML)/Deep Learning (DL) techniques in early detection of AD. It also reviews the collection of AD datasets and the pre-processing techniques used in the studies. Although DL methods gives the best outcome measures such as accuracy, precision, F-measures, recall and Area Under the Curve (AUC) still DL techniques have some limitations that can be overcome via Quantum Computing (QC). QC is currently emerging new technology based on quantum theory where qubits are used to store the data instead of bits. It accomplishes 1000 times faster and also solves the complex problem within a fraction of time than the classical computers. © 2023 IEEE.

关键词： Alzheimer's Disease Biomarkers Datasets Deep Learning Machine Learning Quantum Computing

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