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International Conference on Developments in esystems engineering (DESE)

作者： Hamid R. Alsanad Mohanad A. Al-Askari Khaldoon A. Omar Yousif Almashhadany Sameer Algburi Taisir A. Yaseen Department of Electrical Engineering University of Anbar Anbar Iraq Information Systems Department University of Anbar Anbar Iraq College of Engineering Al-Kitab University Kurkuk Iraq Computer Network Department University of Anbar Anbar Iraq

The Internet of Things (IoT) has made a significant impact on improving human life in the modern era, offering convenience and ease. Smart appliances, which are connected to the Internet, have become increasingly popular due to their improved efficiency, robustness, power consumption, and connectivity. The main objective of this paper is to convert a conventional refrigerator into an intelligent appliance by integrating computational intelligence using a microcontroller and a range of sensors. The proposed approach aims to improve food management capabilities by introducing automated techniques and cost-effective intelligence to the refrigerator. The resulting smart refrigerator is designed to be user-friendly and improve the overall quality of life. The system includes a mobile or web application that acts as a bridge between the refrigerator and the user, allowing for remote control and monitoring. The smart refrigerator employs sensors to detect shortages of items and can automatically place orders online or send messages to grocery stores for item delivery. Additionally, the smart module continuously monitors temperature and humidity levels, uploading real-time updates to a cloud platform for user access. The integration of IoT technology into refrigerators offers numerous features and benefits, ultimately enhancing convenience and efficiency in managing food storage and consumption.

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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PVWA: Privacy-preserving and Verifiable Weighted Aggregation for Federated Learning

PVWA: Privacy-preserving and Verifiable Weighted Aggregation...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Zihan Li Xiaodong Wang Shuai Yuan Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and Computer Engineering North China Electric Power University China Department of Finance Operations and Information Systems (FOIS) Brock University Canada Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Federated learning provides clients with a means of collaboratively training a global model without sharing their local data, managed by a central server. However, this server cannot always be trusted, as it may act dishonestly and compromise the privacy of clients’ local data. Consequently, mechanisms for privacy preservation and aggregation verification become crucial components of a secure federated learning system. In addition, support for weighted aggregation is also essential to address the challenges posed by non-IID training data. In this article, we present the Privacy-Preserving and Verifiable Weighted Aggregation (PVWA) scheme. Our approach introduces a new privacy-preserving solution by leveraging masking and homomorphic encryption techniques to protect local and global models, respectively. The masking protocol facilitates secure weighted aggregation, whereas a verification mechanism based upon homomorphic hashing and bilinear aggregated signatures ensures the correctness of aggregated results. Experimental evaluations of the performance, compared against alternative methods on two datasets, demonstrate its effectiveness and efficiency.

关键词： Training Privacy Data privacy Protocols Federated learning Training data Servers Security Global communication Homomorphic encryption

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Analyzing α-divergence in Gaussian Rate-Distortion-Perception Theory

Analyzing α-divergence in Gaussian Rate-Distortion-Percepti...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Martha V. Sourla Giuseppe Serra Photios A. Stavrou Marios Kountouris School of Electrical & Computer Engineering Technical University of Crete Chania Greece Communication Systems Department EURECOM France Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

The problem of the estimation of the information rate-distortion-perception function (RDPF), which is a relevant information-theoretic quantity in goal-oriented lossy compression and semantic information reconstruction, is investigated here. Specifically, we study the RDPF tradeoff for Gaussian sources subject to a mean-squared error (MSE) distortion and a perception measure that belongs to the family of α-divergences. Assuming a jointly Gaussian RDPF, which forms a convex optimization problem, we characterize an upper bound for which we find a parametric solution. We show that evaluating the optimal parameters of this parametric solution is equivalent to finding the roots of a reduced exponential polynomial of degree α. Additionally, we determine which disjoint sets contain each root, which enables us to evaluate them numerically using the well-known bisection method. Finally, we validate our analytical findings with numerical results and establish connections with existing results.

关键词： Wireless communication Upper bound Semantics Measurement uncertainty Estimation Distortion Polynomials Convex functions Distortion measurement Information theory

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A State Feedback Controller for Mitigation of Continuous-Time Networked SIS Epidemics

arXiv

引用

arXiv 2022年

作者： Wang, Yuan Gracy, Sebin Uribe, César A. Ishii, Hideaki Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Electrical and Computer Engineering Rice University HoustonTX United States Department of Computer Science Tokyo Institute of Technology Yokohama Japan

The paper considers continuous-time networked susceptible-infected-susceptible (SIS) diseases spreading over a population. Each agent represents a sub-population and has its own healing rate and infection rate;the state of the agent at a time instant denotes what fraction of the said sub-population is infected with the disease at the said time instant. By taking account of the changes in behaviors of the agents in response to the infection rates in real-time, our goal is to devise a feedback strategy such that the infection level for each agent strictly stays below a pre-specified value. Furthermore, we are also interested in ensuring that the closed-loop system converges either to the disease-free equilibrium or, when it exists, to the endemic equilibrium. The upshot of devising such a strategy is that it allows health administration officials to ensure that there is sufficient capacity in the healthcare system to treat the most severe cases. We demonstrate the effectiveness of our controller via numerical examples. © 2022, CC BY.

关键词： Closed loop systems

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Wildfire Spread Prediction Model Calibration Using Metaheuristic Algorithms 48

Wildfire Spread Prediction Model Calibration Using Metaheuri...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Pereira, Jorge Mendes, Jerome Junior, Jorge S. S. Viegas, Carlos Paulo, Joao Ruivo University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Pólo Ii CoimbraPT-3030-290 Portugal University of Coimbra Association for the Development of Industrial Aerodynamics CoimbraPT-3030-289 Portugal

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Every year, wildfires cause significant losses and destruction around the globe. In order to attempt to reduce their damages, resources have been put into developing fire propagation prediction systems. In a real wildfire event, these systems provide the authorities with information about the fire propagation in the near future, thus allowing them to make better decisions. Wildfire spread prediction systems are based on fire propagation models, from which the most used and accepted model is the Rothermel model. However, given the complexity of the wildfire phenomena and the uncertainty of some of its input parameter values, the Rothermel model can produce misleading results of fire propagation. This paper uses 3 metaheuristic algorithms, genetic algorithm (GA), differential evolution (DE) and simulated annealing (SA), for calibration of input parameters from the Rothermel model. These algorithms were validated using 37 datasets containing data from controlled experimental fires. Results have shown that these algorithms provide a precise wildfire spread prediction accounting for the uncertainties in the model's selected parameters. © 2022 IEEE.

关键词： Forecasting

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Dirac nodal line and pseudo-gap in 1T-VS₂ single crystal

引用

Current Applied Physics 2025年 77卷 94-99页

作者： Kim, Mincheol Kim, Hyuk Jin Choi, Byoung Ki Rhee, Tae Gyu Jozwiak, Chris Bostwick, Aaron Rotenberg, Eli Lee, Sunghun Chang, Young Jun Department of Physics University of Seoul Seoul 02504 South Korea Department of Smart Cities University of Seoul Seoul 02504 South Korea Pohang Accelerator Laboratory POSTECH Pohang 37673 South Korea Department of Electrical and Computer Engineering Texas A&M University College Station 3127 TX United States Advanced Light Source (ALS) E. O. Lawrence Berkeley National Laboratory Berkeley 94720 CA United States Division of Nanotechnology and Sensorium Institute Convergence Research Institute Daegu Gyeongbuk Institute of Science & Technology (DGIST) Daegu 42988 South Korea Department of Intelligent Semiconductor Engineering University of Seoul Seoul 02504 South Korea

Vanadium disulfide (VS2), one of transition metal dichalcogenide (TMDC) family, attracts elevated interests for its charge-density wave phase transition, ferromagnetism, optoelectronic switching, and catalytic reactiv... 详细信息

Vanadium disulfide (VS₂), one of transition metal dichalcogenide (TMDC) family, attracts elevated interests for its charge-density wave phase transition, ferromagnetism, optoelectronic switching, and catalytic reactivity, but the topological Dirac surface states have not been observed yet. Here we report the Dirac nodal line states of VS₂ single crystals via angle-resolved photoemission spectroscopy (ARPES) measurements. ARPES analysis reveals that Dirac nodal line surface states are extended from the Μ point toward the Γ point with energy of −1.1 eV. Comparison with the other vanadium chalcogenides shows monotonic energy shift of the Dirac states depending on the spin-orbit coupling strength of the chalcogen atoms. Furthermore, we studied the temperature-dependence of pseudo gaps near both the M and Γ points. Our observations provide experimental evidence of topological electronic structures, contributing to the understanding of the correlated TMDC systems and their potential in switching devices, optical saturable absorber, and electrocatalysis. © 2025 Korean Physical Society

关键词： Angle-resolved photoemission spectroscopy Charge-density wave Topological Dirac nodal line Transition metal dichalcogenide Vanadium disulfide

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Benchmarking Cryptographic Protocols for (IoT) Malware Defence

Benchmarking Cryptographic Protocols for (IoT) Malware Defen...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Khalil Ben Khaled Dönìz Borsos Jozsef Katona Kandó Kalmán Faculty of Electrical Engineering Institute of Microelectronics and Technology Obuda University Budapest Hungary Kándó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This study presents a comprehensive benchmarking analysis of cryptographic protocols for Internet of Things (IoT) malware defense. The framework was specifically tailored to evaluate cryptographic protocols such as AES-128, AES-256, ChaCha20, RSA (1024-bit, 2048-bit, and 4096-bit), SHA256, SHA512, and HMAC-SHA256 were tested in both standalone and emulated environments. The primary objective was to evaluate the performance and resource consumption of these protocols, focusing on their encryption and hashing efficiency. Key innovations include the comparative analysis of resource consumption and performance efficiency across diverse cryptographic operations, under both real-world and emulated conditions. By identifying protocols like ChaCha20 for high efficiency and minimal resource usage, and RSA 4096-bit for enhanced security at higher computational costs, this study provides actionable insights into the trade-offs between security and performance. These findings offer a foundational reference for selecting optimized cryptographic protocols, advancing IoT malware defense strategies through informed decision-making.

关键词： Performance evaluation Technological innovation Benchmark testing Malware Encryption Internet of Things Reliability Informatics Machine intelligence Cryptographic protocols

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An effective approach for detecting deepfake videos using Long Short-Term Memory and ResNet

An effective approach for detecting deepfake videos using Lo...

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： S Keerthana N Deepika E Pooja I Nandhini M Shanthalakshmi G R Khanaghavalle Department of Computer Science and Engineering Sri Venkateswara College of Engineering Pennalur Sriperumbudur Tamilnadu India Department of Computer Science and Business Systems Rajalakshmi Institute of Technology Kuthambakkam Tamilnadu India Department of ECE School of Electrical and Communication Vel Tech Rangarajan Dr. Sakunthala R&D Institute of Science and Technology Avadi

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Deep learning algorithms are becoming more potent and producing human-synthesized, undifferentiated footage is a simple process thanks to advances in computing power. A method of synthesizing human images called deep fakes is built on neural network techniques like GAN. These technologies use deep learning algorithms to overlay target pictures onto the source films, producing realistic-looking deep-fake photos and videos. In this work, we provide a novel deep-learning approach that can reliably discriminate between actual and AI-generated fake videos. In this work, we test our method on a large number of balanced and mixed datasets created by mixing various publicly available datasets, such as Face-Forensic++, Deepfake detection, Celeb DF, and other publicly available datasets as images and videos, to emulate real-time scenarios and improve model performance with real-time data by using LSTM and ResNet.

关键词： Deep learning Deepfakes Films Neural networks Generative adversarial networks Real-time systems Telecommunication computing

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Rethinking Generalization in Few-Shot Classification

arXiv

引用

arXiv 2022年

作者： Hiller, Markus Ma, Rongkai Harandi, Mehrtash Drummond, Tom School of Computing and Information Systems The University of Melbourne Australia Department of Electrical and Computer Systems Engineering Monash University Australia

Single image-level annotations only correctly describe an often small subset of an image's content, particularly when complex real-world scenes are depicted. While this might be acceptable in many classification scenarios, it poses a significant challenge for applications where the set of classes differs significantly between training and test time. In this paper, we take a closer look at the implications in the context of few-shot learning. Splitting the input samples into patches and encoding these via the help of Vision Transformers allows us to establish semantic correspondences between local regions across images and independent of their respective class. The most informative patch embeddings for the task at hand are then determined as a function of the support set via online optimization at inference time, additionally providing visual interpretability of 'what matters most' in the image. We build on recent advances in unsupervised training of networks via masked image modelling to overcome the lack of fine-grained labels and learn the more general statistical structure of the data while avoiding negative image-level annotation influence, aka supervision collapse. Experimental results show the competitiveness of our approach, achieving new state-of-the-art results on four popular few-shot classification benchmarks for 5-shot and 1-shot scenarios. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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