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Deep Equilibrium Models Meet Federated Learning 31

Deep Equilibrium Models Meet Federated Learning

引用

31st European Signal Processing Conference, EUSIPCO 2023

作者： Gkillas, Alexandros Ampeliotis, Dimitris Berberidis, Kostas Dept. of Computer Engineering and Informatics University of Patras Greece Dept. of Digital Media and Communication Ionian University Greece

ISBN: (纸本)9789464593600

In this study the problem of Federated Learning (FL) is explored under a new perspective by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks. We claim that incorporating DEQ models into the federated learning framework naturally addresses several open problems in FL, such as the communication overhead due to the sharing large models and the ability to incorporate heterogeneous edge devices with significantly different computation capabilities. Additionally, a weighted average fusion rule is proposed at the server-side of the FL framework to account for the different qualities of models from heterogeneous edge devices. To the best of our knowledge, this study is the first to establish a connection between DEQ models and federated learning, contributing to the development of an efficient and effective FL framework. Finally, promising initial experimental results are presented, demonstrating the potential of this approach in addressing challenges of FL. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Deep learning

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Modeling Data Set of Indonesian Heart Sound Signals 7

Modeling Data Set of Indonesian Heart Sound Signals

引用

7th International Conference of computer and informatics engineering, IC2IE 2024

作者： Oktivasari, Prihatin Sinantya, F. Agitha Nasywa, A. Hk, Firmansyah Setiawan, Evan Sutarna, Nana Mohd Safuan, Syadia Nabilah Najah Saud Al Humairi, Safaa Politeknik Negeri Jakarta Dept. of Informatics and Computer Engineering Depok Indonesia Politeknik Negeri Jakarta Dept. of Electrical Engineering Depok Indonesia Msu University Faculty of Information Sciences and Engineering Selangor Malaysia

ISBN: (纸本)9798331505530

Vital signs, such as heart and respiratory rates, are the main parameters when assessing a person's physical condition. Advanced technologies for monitoring vital signs include electrocardiography (ECG), photoplethysmograph (PPG), or phonocardiography (PCG). While ECG utilizes electrical stimulation that triggers each heartbeat, PPG measures changes in absorbance caused by blood flow, and PCG measures acoustic waves resulting from the mechanical activity of the heart. In recent decades, analysis of heart sound signals, especially for automatic heart sound segmentation and classification, has been widely studied and reported to have potential value for accurately and clinically detecting pathology. However, comparative analysis of algorithms in the literature has been hindered by the lack of high-quality, rigorously validated, and standardized heart sound databases of recordings. In this research, a heart sound database will be created by the public, especially Indonesians, to represent the condition of Asian society, which in the future can be used as a research reference for other researchers as an open data set. It includes healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. Records are collected from a variety of clinical or nonclinical environments and equipment. Recording length varies from a few seconds to several seconds-minutes. This study will report detailed information about subjects/patients including demographics (number, age, and gender), recordings (number, location, state, and length of time), associated synchronously recorded signals, sampling frequency, and type of sensor used. This research aims to measure acoustic waves resulting from the mechanical activity of the heart and the results of heart sound recordings in various patient conditions, along with their pathologies, will be grouped in an open database based on normal and abnormal. In this study, a deep-ranging S1 pattern was

关键词： Electrocardiography

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Mechanical Design and Forward Kinematics Analysis of T-FLoW 3.0 Prosthetic Robot Hand: Lever-based Finger Movement Mechanism 24

Mechanical Design and Forward Kinematics Analysis of T-FLoW ...

引用

24th International Electronics Symposium, IES 2022

作者： Apriandy, Kevin Ilham Dewantara, Bima Sena Bayu Dewanto, Raden Sanggar Pramadihanto, Dadet Dept. of Informatics and Computer Engineering Indonesia Dept. of Energy Mechanics Engineering Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

In this research, a prosthetic robot hand that features a lever-based finger movement mechanism is proposed as the new approach to the T-FLoW 3.0 humanoid robot's hand development. The proposed approach performs both grasping and releasing movements by pushing or pulling the finger-attached lever. The lever is pushed or pulled by micro-servo, which uses a stiff bar to transfer the force from the servo-horn to the finger's lever. Our prosthetic robot hand is equipped with six joints, six SG92R micro-servos as actuators, and six force-sensitive resistors (FSR) as grasping feedback. 3D printing manufacturing technology is utilized to give the hand a realistic appearance, and PLA filament material is used in the manufacturing process to provide low-cost, lightweight, and easy maintenance. Static structural analysis simulation result lead to the conclusion that our prosthetic robot hand could sustain a load of around 30N. With the lever-based finger movement mechanism, the proposed approach is expected to overcome the mechanical slip issues from finger movements, which are often experienced in the old approach of the T-FLoW 3.0 humanoid robot's hand development. © 2022 IEEE.

关键词： 3D printers

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PhishGuard: A Convolutional Neural Network-Based Model for Detecting Phishing URLs with Explainability Analysis 3

PhishGuard: A Convolutional Neural Network-Based Model for D...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Islam, Md Robiul Islam, Md Mahamodul Afrin, Mst. Suraiya Antara, Anika Tabassum, Nujhat Amin, Al Uttara University Dept. of Computer Science and Engineering Dhaka Bangladesh University of Oslo Dept. of Informatics Oslo Norway Dept. of Building Engineering and Construction Management Khulna Bangladesh BRAC University Dept. of Electrical and Electronics Engineering Dhaka Bangladesh North South University Dept. of Electrical and Computer Engineering Dhaka Bangladesh Dept. of Computer Science American International Dhaka Bangladesh

ISBN: (纸本)9798350372120

Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black-box intelligent models' decision to detect Suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a 1 D Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85 %. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL. © 2024 IEEE.

关键词： Convolutional neural networks

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DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clustering and Deep Learning for Scalable Indoor Localization Using Wi-Fi RSSI 13

DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clusterin...

引用

Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Mahali, Muhammad Izzuddin Leu, Jenq-Shiou Achmad Sulistyo Putro, Nur Widyawan Prakosa, Setya Avian, Cries National Taiwan University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan Universitas Negeri Yogyakarta Dept. of Electronics and Informatics Engineering Education Yogyakarta Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350373332

This research introduces a novel method for indoor positioning by integrating Fuzzy C-Means clustering with deep learning autoencoders, leveraging Wi-Fi RSSI fingerprinting for precise location prediction and building and floor level classification within a unified framework. The approach combines the comprehensibility of Fuzzy C-Means (FCM) with the robust feature extraction capabilities of deep autoencoders through a dual-phase strategy: extensive offline training utilizing RSSI fingerprint data followed by real-time online position prediction. The model's performance was rigorously tested using 5-Fold cross-validation, examining its effectiveness both with and without the integration of FCM on various cluster numbers. Evaluation metrics, including Euclidean distance error for regression and accuracy, F1-score, precision, and specificity for classification effectiveness, were used for comparative analysis. The results were visualized through regression plots, confusion matrices, and learning curves, supplemented by t-SNE visualizations that provide profound insights into the feature space. These detailed analyses highlight the considerable promise of this integrated approach in enhancing the accuracy and reliability of indoor positioning systems. © 2024 IEEE.

关键词： Fuzzy clustering

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Increasing Hosting Capacity of Uncontrollable Distributed Energy Resources in Isolated Power Systems

Increasing Hosting Capacity of Uncontrollable Distributed En...

引用

2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Therapontos, Phivos Stavrou, Andreas Aristidou, Petros Electricity Authority of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Dept. of Electrical and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9798350396782

Due to the push for more sustainable generation, small-scale distributed energy resources (DERs) penetration has increased significantly. Most countries have no remote monitoring and control requirements for DERs below a certain size. Therefore, they can be labeled as uncontrollable DERs (UDERs). In isolated systems, the UDERs hosting capacity is limited due to frequency stability concerns, as it is impossible to modify or curtail their generation in a centralized manner. In this paper, we propose the utilization of the active power over-frequency reduction curve for increasing the hosting capacity of UDERs in isolated power systems. Cyprus’s power system has been used to evaluate the proposed solution’s performance. The optimal settings of the curve have been identified, and it was found that UDERs hosting capacity can be increased significantly with the proposed methodology. © 2023 IEEE.

关键词： frequency stability grid code requirements hosting capacity islanded systems uncontrollable distributed energy resources

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A Cluster-based Virtual Edge Computation Offloading Scheme for MEC-enabled Vehicular Networks 26

A Cluster-based Virtual Edge Computation Offloading Scheme f...

引用

26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Sotiriadis, Leontios Mamalis, Basilis Pantziou, Grammati University of West Attica Dept. of Informatics and Computer Engineering Greece

ISBN: (纸本)9781450398541

Internet of Vehicles (IoV) has received a great deal of attention in recent years from many researchers. Recently, vehicular edge computing has been a new paradigm to support computation-intensive and latency-sensitive services in IoV. Moreover, with the cellular-vehicle to everything technology, many tasks and applications can be efficiently offloaded to another node for processing. In this paper a novel cluster-based virtual edge computation offloading scheme is proposed, which has as its main objective to efficiently find the most suitable multi-hop neighbor to act as a virtual edge computing (VEC) server for task offloading. The proposed scheme is initially based on the formation and maintenance of multi-hop clusters with high stability, whereas its efficiency is further enhanced by the local/distributed computations taking place where it's possible. © 2022 ACM.

关键词： Computation offloading

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An Indicative Demanding Teaching Scenario for Primary Education with the Support of a Multi-layer Fog Computing Architecture 26

An Indicative Demanding Teaching Scenario for Primary Educat...

引用

26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Giannou, Vasiliki Mamalis, Basilis University of West Attica Dept. of Informatics and Computer Engineering Greece

ISBN: (纸本)9781450398541

Fog computing is a new, modern type of computing that distributes some of the storage, estimation, and processing constraints of cloud computing closer to the edge of the network. The impact of Cloud and Fog Computing in an educational context is now more important than ever. This paper presents innovative designed services of fog computing techniques in the educational sector. An original educational scenario is described, based on innovative fog computing techniques and the appropriate use of online collaborative learning tools. The aim of the paper is to propose an appropriate fog-based architecture to support education in Primary and Secondary Education as well as to provide appropriate recommendations and present some indicative education scenarios implemented based on the use of cloud and fog computing. The proposed scenario is properly documented, while specific implementation issues are presented and analyzed. © 2022 ACM.

关键词： Fog computing

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A Review and Implementation of Object Detection Models and Optimizations for Real-time Medical Mask Detection during the COVID-19 Pandemic 16

A Review and Implementation of Object Detection Models and O...

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16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022

作者： Gogou, Ioanna C. Koutsomitropoulos, Dimitrios A. University of Patras Dept. of Computer Engineering and Informatics Patras Greece

ISBN: (数字)9781665498104

ISBN: (纸本)9781665498104

Convolutional Neural Networks (CNN) are commonly used for the problem of object detection thanks to their increased accuracy. Nevertheless, the performance of CNN-based detection models is ambiguous when detection speed is considered. To the best of our knowledge, there has not been sufficient evaluation of the available methods in terms of the speed/accuracy trade-off in related literature. This work assesses the most fundamental object detection models on the Common Objects in Context (COCO) dataset with respect to this trade-off, their memory consumption, and computational and storage cost. Next, we select a highly efficient model called YOLOv5 to train on the topical and unexplored dataset of human faces with medical masks, the Properly-Wearing Masked Faces Dataset (PWMFD), and analyze the benefits of specific optimization techniques for real-time medical mask detection: transfer learning, data augmentations, and a Squeeze-and-Excitation attention mechanism. Using our findings in the context of the COVID-19 pandemic, we propose an optimized model based on YOLOv5s using transfer learning for the detection of correctly and incorrectly worn medical masks that surpassed more than two times in speed (69 frames per second) the state-of-the-art model SE-YOLOv3 on the PWMFD dataset while maintaining the same level of mean Average Precision (67%). © 2022 IEEE.

关键词： Object detection

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Don't Look Up: The Cost of Attention to Stimulus Phrases in Mobile Text Entry Evaluations 26

Don't Look Up: The Cost of Attention to Stimulus Phrases in ...

引用

26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Komninos, Andreas Tsiouma, Angeliki Gogoulou, Georgia Garofalakis, John University of Patras Computer Engineering & Informatics Dept. Patras Greece

ISBN: (纸本)9781450398541

Transcription tasks have been long used as the de-facto evaluation method in mobile text entry research. Evaluations use memorable phrase sets, in order to prevent participants from devoting more attention to the stimulus phrase than the bare minimum. We present evidence from an eye-tracking study, demonstrating that the attention devoted to the stimulus phrase is much higher than might be expected. In fact, attention to the stimulus phrase takes up almost 50% of participant attention spent outside the keyboard area, and overall 25% of participant attention throughout any single transcription task. We explore a modification to the transcription task aimed at reducing this level of visual attention, without finding any statistically significant differences. These findings raise important questions on the continued use of the transcription task as the mainstream evaluation method for mobile text entry research. © 2022 ACM.

关键词： Eye tracking

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