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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Karras, Christos Karras, Aristeidis Drakopoulos, Georgios Tsolis, Dimitrios Mylonas, Phivos Sioutas, Spyros Decentralized Systems Computing Group University of Patras Computer Engineering and Informatics Department Greece Ionian University Humanistic and Social Informatics Laboratory Department of Informatics Greece

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

Emotion detection is crucial in many IoT deployments from an operational perspective with examples ranging from digital health to smart cities. This is particularly true in smart homes where the interaction between the local IoT ecosystem and the inhabitants are continuous, pervasive, and nuanced. More specifically, emotion estimation from human speech attributes is an integral architectural component of such ecosystems. In this work, we survey the emerging LPWAN technologies and after selecting the most optimal for our use-case, we propose an emotion estimation scheme based on LoRa wireless technology for automated actions in smart home environments. In particular, a voice recognition module coupled with a transmitter are installed in a car. Then, depending on the estimation outcome, the smart home may undertake one or more preemptive actions according to its configuration as the car passenger approaches. The prototype LoRa system has been tested with the widely-used TESS dataset with encouraging results as expressed in the emotion confusion matrix obtained from a extreme learning machine with various kernels. The preceding paves the way for adaptive smart homes tailored to the needs of their inhabitants. © 2022 IEEE.

关键词： Automation

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Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

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computer systems Science & engineering 2023年第5期45卷 2033-2047页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Lubna A.Alharbi Mohamed K.Nour Abdullah Mohamed Ahmed S.Almasoud Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk47512Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia

Recent developments on Internet and social networking have led to the growth of aggressive language and hate *** provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of user generated content makes it difficult to recognize *** advancements in machine learning(ML),deep learning(DL),and natural language processing(NLP)tools enable to detect and classify CB in social *** this view,this study introduces a spotted hyena optimizer with deep learning driven cybersecurity(SHODLCS)model for *** presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the *** achieving this,the SHODLCS model involves data pre-processing and TF-IDF based feature *** addition,the cascaded recurrent neural network(CRNN)model is applied for the identification and classification of ***,the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier *** experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches.

关键词： Cybersecurity cyberbullying online social network deep learning spotted hyena optimizer

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Data-Driven Adaptive Control for Frequency Perturbations in Power systems with PV-SmartParks

Data-Driven Adaptive Control for Frequency Perturbations in ...

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Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES Companion), IEEE Symposia on

作者： Ali Arzani Ganesh K. Venayagamoorthy Holcombe Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Clemson University SC USA Department of Electrical Electronic and Computer Engineering University of Pretoria South Africa

ISBN: (数字)9798331519681

ISBN: (纸本)9798331519698

Seamless operation of PV plants during both transient and dynamic conditions is imperative for all stakeholders including energy control centers. Fast and dampened transient response, low steady-state error, and stability are sought for power system operation with PV plants. For PV power perturbation at point of interconnection (POI), this can be further supported by deploying auxiliary fast-acting non-synchronous generation such as SmartParks (SPs). SmartParks are parking lots with a large number of electric vehicles performing bidirectional power transfers, namely, vehicle-to-grid and grid-to-vehicle. In this study, a data-driven adaptive frequency controller (DD-AFC) was developed for SPs to compensate for frequency perturbations at PV-POI. The DD-AFC was incorporated into the controller of SPs to provide a fast response for frequency perturbations caused by PV-site power perturbations. The adaptiveness of the frequency droop comes from data-driven empirical characterization of the PV plant operation at the PV-site location using time-series data analytics. The DD-AFC supports fast-frequency control response by SPs for mitigating fast power perturbation at PV-POIs. The effectiveness of the DD-AFC was illustrated via real-time simulations of a bulk power system with a 210MW p PV plant and 100MW p of SmartParks in RTDS.

关键词： Automatic frequency control Time-frequency analysis Vehicle-to-grid Transient response Data analysis Perturbation methods Power system stability Adaptive control Transient analysis Vehicle dynamics

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BPVFL: A Bidirectional Privacy-Preserving Verifiable Federated Learning Framework with Homomorphic Encryption

BPVFL: A Bidirectional Privacy-Preserving Verifiable Federat...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liu, Jingwei Chen, Sijing Zhu, Junrong Sun, Rong Du, Xiaojiang Guizani, Mohsen Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Xidian University State Key Laboratory of Isn Xi'an710071 China Stevens Institute of Technology Department of Electrical and Computer Engineering Hoboken United States Department of Machine Learning Abu Dhabi999041 United Arab Emirates

ISBN: (纸本)9798350351255

Federated learning, while advancing data privacy, faces risks of sensitive information leakage through parameter updates, making it susceptible to inference and data reconstruction attacks. Fraudulent behaviors by central servers or clients can undermine the integrity of model training, thereby reducing accuracy and affecting decision-making quality. This paper introduces a bidirectional, privacy-preserving verifiable federated learning framework(BPVFL) built on homomorphic encryption and a novel three-party zero-knowledge protocol. This framework guarantees the integrity of server aggregation and the credibility of the information uploaded by clients. Experimental results demonstrate that BPVFL effectively protects client privacy, prevents fraud by servers and certain clients, and efficiently handles numerous client disconnections with minimal overhead. © 2024 IEEE.

关键词： Federated learning

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Enhancing Breast Cancer Diagnosis with Multi-Resolution Vision Transformers and Robust Decision-Making

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IEEE Access 2025年 13卷 89704-89722页

作者： Sabry, Margo Balaha, Hossam Magdy Ali, Khadiga M. Soliman, Tayseer Hassan A. Gondim, Dibson Ghazal, Mohammed Alghamdi, Norah Saleh El-Baz, Ayman Assiut University Information Systems Department Asyut71511 Egypt University of Louisville J. B. Speed School of Engineering Bioengineering Department LouisvilleKY40292 United States Mansoura University Faculty of Medicine Pathology Department Mansoura35511 Egypt University of Louisville Laboratory Medicine Department of Pathology LouisvilleKY40292 United States Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Riyadh11564 Saudi Arabia

This study aims to improve breast cancer (BC) diagnosis through a novel multi-resolution Vision Transformer (ViT)-based framework with ensemble decision-making, addressing limitations in traditional single-magnification models. The proposed framework uses multiscale feature extraction at three magnification levels (L0, L1, L2 or 16x, 4x, 2x) to capture both fine-grained and high-level tumor features. A stacking ensemble method combines predictions from ViT models trained at these levels, improving classification robustness. Postprocessing techniques, including region-growing and fast-marching level set algorithms, refine whole-slide image (WSI) prediction and postprocessing quality. Performance was evaluated via metrics such as precision, recall, the F1 score, accuracy, and specificity across 50 trials with perturbed conditions. The framework achieved a top accuracy of 97.08%, with precision and recall above 94%. The suggested stack configuration outperformed individual models and other stacking configurations, demonstrating balanced performance with minimal variability. Statistical analysis highlighted the reliability and consistency of the framework under perturbed conditions. The multi-resolution ViT-based framework significantly improves BC classification by integrating multiscale analysis and ensemble decision-making. Its high accuracy and robustness make it a valuable tool for reducing interobserver variability in digital pathology workflows. © 2013 IEEE.

关键词： Image segmentation

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HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach

HPC Application Parameter Autotuning on Edge Devices: A Band...

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International Conference on High Performance computing

作者： Abrar Hossain Abdel-Hameed A. Badawy Mohammad A. Islam Tapasya Patki Kishwar Ahmed Department of Electrical Engineering and Computer Science The University of Toledo Department of Electrical and Computer Engineering New Mexico State University Department of Computer Science and Engineering The University of Texas at Arlington Lawrence Livermore National Laboratory Center for Applied Scientific Computing

ISBN: (数字)9798331509095

ISBN: (纸本)9798331509101

The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autotuning of Scientific Application Parameters), a novel strategy designed to address the parameter search space challenge in edge devices. Our strategy employs a multi-armed bandit (MAB) technique focused on online exploration and exploitation. Notably, LASP takes a dynamic approach, adapting seamlessly to changing environments. We tested LASP with four HPC applications: Lulesh, Kripke, Clomp, and Hypre. Its lightweight nature makes it particularly well-suited for resource-constrained edge devices. By employing the MAB framework to efficiently navigate the search space, we achieved significant performance improvements while adhering to the stringent computational limits of edge devices. Our experimental results demonstrate the effectiveness of LASP in optimizing parameter search on edge devices.

关键词： Performance evaluation Analytical models Navigation High performance computing Computational modeling Data models Computational efficiency

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Boosting the Performance of Transformer Architectures for Semantic Textual Similarity

arXiv

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arXiv 2023年

作者： Rep, Ivan Čeperić, Vladimir Department of Electronics Microelectronics Computer and Intelligent Systems Faculty of Electrical Engineering and Computing Zagreb Croatia

Semantic textual similarity is the task of estimating the similarity between the meaning of two texts. In this paper, we fine-tune transformer architectures for semantic textual similarity on the Semantic Textual Similarity Benchmark by tuning the model partially and then end-to-end. We experiment with BERT, RoBERTa, and DeBERTaV3 cross-encoders by approaching the problem as a binary classification task or a regression task. We combine the outputs of the transformer models and use handmade features as inputs for boosting algorithms. Due to worse test set results coupled with improvements on the validation set, we experiment with different dataset splits to further investigate this occurrence. We also provide an error analysis, focused on the edges of the prediction range. © 2023, CC BY.

关键词： Semantics

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case 63

Optimal Linear Deception Attacks on Remote State Estimation ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Shang, Jun Zhang, Hanwen Zhou, Jing Chen, Tongwen Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai200092 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

ISBN: (纸本)9798350316339

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack's stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback-Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples. © 2024 IEEE.

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NS3-Based Communication Network Modeling for Multi-Microgrid systems: Design and Evaluation

NS3-Based Communication Network Modeling for Multi-Microgrid...

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IEEE Green Technologies Conference (IEEE-Green)

作者： Ola Ali Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami FL USA

ISBN: (数字)9798331520182

ISBN: (纸本)9798331520199

This paper presents a communication emulation model for enhancing the efficiency of multi-microgrid (MMG) networks, employing Network Simulator 3 (ns3). MMG systems require robust communication frameworks for coordination, particularly under varying traffic conditions. The introduced model accommodates multiple local area networks (LANs) that support efficient communication across multiple autonomous AC microgrids. The developed model facilitates seamless bidirectional information exchange between the microgrids' local controllers and the MMG control center (MMGCC), employing TCP/IP protocol to ensure high data transmission integrity and low latency. A detailed design in C++ is demonstrated, including the configuration of the communication nodes, topology, communication links, and quality of service (QoS) analysis. The network performance is rigorously assessed across low and high traffic conditions, with performance metrics such as throughput, delay, and percentage packet loss at each node. Results confirm the effectiveness of the introduced model in sustaining minimal packet loss, high throughput, and low delays under dynamic operating scenarios. These findings establish the developed model as a viable solution for meeting the real-time communication requirements of MMG energy management systems.

关键词： Emulation Packet loss TCPIP Quality of service Microgrids C++ languages Throughput Real-time systems Delays Load modeling

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