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IEEE Conference on Global Communications (GLOBECOM)

作者： Wenxuan Qiao Yuyang Zhang Ping Dong Xiaojiang Du Chengxiao Yu Hongke Zhang Mohsen Guizani School of Electronic and Information Engineering Beijing Jiaotong University P. R. China Dept. of New Networks Peng Cheng Laboratory Shenzhen Guangdong China Dept. of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Dept. of Machine Learning Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

The multipath transmission structure decouples network services from a single transmission carrier, which has great potential for shaping a more secure and efficient 6G network. Existing multipath transmission schemes face challenges such as network heterogeneity, perception lag, and additional scheduling delay, which limits their ability to improve bandwidth aggregation capacity and information security. To address these issues, we propose the Sequential Reinforcement Evolution (SRE) scheme, which utilizes deep reinforcement learning to predict the value of future scheduling actions based on past network states. The SRE scheme regards improving bandwidth aggregation capacity and anti-eavesdropping ability as optimization goals, and designs a semi-symmetric attention recurrent neural network (SARNN) to better mine the sequential nature of the scheduling process. The SRE scheme utilizes approximately 500 million real network data points to pre-train the SARNN model, and performs cycle optimization during the actual deployment process. Experimental results show that SRE significantly outperforms state-of-the-art scheduling schemes with a 32% increase in bandwidth aggregation and a 117% increase in traffic security dispersion with minimal impact on latency.

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Novel ground reaction force-based parameters for monitoring rehabilitation in tibial fractures

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Gait & Posture 2024年 113卷 256-257页

作者： Christian Wolff Elke Warmerdam Tim Dahmen Tim Pohlemann Philipp Slusallek Bergita Ganse German Research Center for Artificial Intelligence Agents and Simulated Reality Saarbrücken Germany Saarland University Saarbrücken Graduate School of Computer Science Saarbrücken Germany Saarland University Werner Siemens-Endowed Chair for Innovative Implant Development Fracture Healing- Departments and Institutes of Surgery Homburg Germany Hochschule Aalen Chair for Computer Vision and Machine Learning Aalen Germany Saarland University Department of Trauma- Hand and Reconstructive Surgery- Departments and Institutes of Surgery Homburg Germany

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ID-Guard: A Universal Framework for Combating Facial Manipulation via Breaking Identification

arXiv

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arXiv 2024年

作者： Qu, Zuomin Lu, Wei Luo, Xiangyang Wang, Qian Cao, Xiaochun The School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Institute of Artificial Intelligence Sun Yat-sen University Guangzhou510006 China The State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China The School of Cyber Science and Engineering Wuhan University Wuhan430072 China The School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen518107 China

—The misuse of deep learning-based facial manipulation poses a potential threat to civil rights. To prevent this fraud at its source, proactive defense technology was proposed to disrupt the manipulation process by adding invisible adversarial perturbations into images, making the forged output unconvincing to the observer. However, their non-directional disruption of the output may result in the retention of identity information of the person in the image, leading to stigmatization of the individual. In this paper, we propose a novel universal framework for combating facial manipulation, called ID-Guard. Specifically, this framework requires only a single forward pass of an encoder-decoder network to generate a cross-model universal adversarial perturbation corresponding to a specific facial image. To ensure anonymity in manipulated facial images, a novel Identity Destruction Module (IDM) is introduced to destroy the identifiable information in forged faces targetedly. Additionally, we optimize the perturbations produced by considering the disruption towards different facial manipulations as a multitask learning problem and design a dynamic weights strategy to improve cross-model performance. The proposed framework reports impressive results in defending against multiple widely used facial manipulations, effectively distorting the identifiable regions in the manipulated facial images. In addition, our experiments reveal the ID-Guard’s ability to enable disrupted images to avoid face inpaintings and open-source image recognition systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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IoT Enabled Smart Metering for Domestic Electrical Distribution System Using Arduino 3

IoT Enabled Smart Metering for Domestic Electrical Distribut...

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3rd IEEE International Conference on Device intelligence, Computing and Communication Technologies, DICCT 2025

作者： Mohan, B. Mahalakshmi, S. Ananth, R. Prem Raj, J. Relin Francis Krishnan, R Santhana Sundaravadivel, P. Sarathkumar, D. P.S.R. Engineering College Department of Electronics and Communication Engineering Sivakasi India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Kongu Engineering College Department of Electrical and Electronics Engineering Perundurai India

ISBN: (纸本)9798331543358

The primary objective is to help consumers use electricity more efficiently from the power grid to minimize their electricity bills. The system continuously monitors power consumption by household appliances and calculates the total energy usage, alerting consumers when they are approaching the current slab rate. As the consumption nears the slab rate, the system automatically initiates a phone call and sends an SMS alert to notify the user to reduce power consumption. An auxiliary load line is included to connect non-essential loads;when power consumption is about to exceed the slab rate, the auxiliary line is automatically disconnected, and the status is communicated to the user via SMS. For enhanced scalability, overall power consumption is monitored through cloud-based tracking. © 2025 IEEE.

关键词： Energy utilization

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Sustainable Crop Yield Forecasting with the Dual-Branch Geometric Progressive Feedback Cosine Convolutional Network Model

Sustainable Crop Yield Forecasting with the Dual-Branch Geom...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： M. Siva Ramkumar Nivetha R Gokul Gopan Mohammad Kanan Rahmath Ulla Baig Jayant Giri Dept of ECE SNS College of Technology Computer Science and Engineering Kumaraguru College of Technology Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mechanical Engineering College of Engineering Zarqa University Zarqa Jordan Department of Artificial Intelligence and Machine Learning Cambridge Institute of Technology Bangalore Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Using deep learning algorithms to extract important agricultural traits has made crop production prediction based on environmental, soil, water, and crop parameters an important area of research. But conventional approaches have trouble creating a straight linear or non-linear mapping between yield values and raw data, and the quality of the characteristics that are extracted has a significant impact on how well they work. Deep reinforcement learning builds a strong prediction framework by combining the intelligence of deep learning with reinforcement learning to get beyond these drawbacks. In order to anticipate agricultural yields accurately and sustainably, this work aims to create a Dual-Branch Geometric Progressive Feedback Cosine Convolutional Network with Skill Optimization Algorithm (DB-GPFCNet-SOA). Robust Double-Weighted Guided Filtering (RDWGF) is used for data preprocessing in order to minimize noise and improve important components. To ensure a complete prediction model, the N-Branch retrieves local neighbor-based information while the C-Branch records global geometric properties. Moreover, the accuracy is enhanced by the SOA, which enhances loss parameters. The suggested model has RMSE of 0.25 and MAE of 0.15 with exceptional accuracy of 99.2%, precision of 99.1%, recall of 99%, and F1-score of 99%. These results validate the performance of the model, which renders it a suitable approach for real-time agricultural applications.

关键词： Accuracy Filtering Computational modeling Noise Predictive models Prediction algorithms Real-time systems Convolutional neural networks Optimization Crop yield

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CUTTLEFISH: LOW-RANK MODEL TRAINING WITHOUT ALL THE TUNING

arXiv

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arXiv 2023年

作者： Wang, Hongyi Agarwal, Saurabh U-Chupala, Pongsakorn Tanaka, Yoshiki Xing, Eric P. Papailiopoulos, Dimitris Machine Learning Department Carnegie Mellon University United States Department of Computer Sciences University of Wisconsin-Madison United States Sony Group Corporation Japan Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Petuum Inc. United States Department of Electrical and Computer Engineering University of Wisconsin-Madison United States

Recent research has shown that training low-rank neural networks can effectively reduce the total number of trainable parameters without sacrificing predictive accuracy, resulting in end-to-end speedups. However, low-rank model training necessitates adjusting several additional factorization hyperparameters, such as the rank of the factorization at each layer. In this paper, we tackle this challenge by introducing CUTTLEFISH, an automated low-rank training approach that eliminates the need for tuning factorization hyperparameters. CUTTLEFISH leverages the observation that after a few epochs of full-rank training, the stable rank (i.e., an approximation of the true rank) of each layer stabilizes at a constant value. CUTTLEFISH switches from full-rank to low-rank training once the stable ranks of all layers have converged, setting the dimension of each factorization to its corresponding stable rank. Our results show that CUTTLEFISH generates models up to 5.6× smaller than full-rank models, and attains up to a 1.2× faster end-to-end training process while preserving comparable accuracy. Moreover, CUTTLEFISH outperforms state-of-the-art low-rank model training methods and other prominent baselines. The source code for our implementation can be found at: https://***/hwang595/Cuttlefish. Copyright © 2023, The Authors. All rights reserved.

关键词： Factorization

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A Dual-Objective Bandit-Based Opportunistic Band Selection Strategy for Hybrid-Band V2X Metaverse Content Update

A Dual-Objective Bandit-Based Opportunistic Band Selection S...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Sherief Hashima Zubair Md Fadlullah Mostafa M. Fouda Kohei Hatano Eiji Takimoto Mohsen Guizani Computational Learning Theory Team RIKEN-AIP Fukuoka Japan Department of Computer Science Western University London ON Canada Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Informatics Kyushu University Fukuoka Japan Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE

As vehicular communication networks embrace metaverse beyond 5G/6G systems, the rich content update via the least interfered subchannel of the optimal frequency band in a hybrid band vehicle to everything (V2X) setting emerges as a challenging optimization problem. We model this problem as a tradeoff between multi-band VR/AR devices attempting to perform metaverse scenes and environmental updates to metaverse roadside units (MRSUs) while minimizing energy consumption. Due to the computational hardness of this optimization, we formulate an opportunistic band selection problem using a multi-armed bandit (MAB) that provides a good quality solution in real-time without computationally burdening the already stretched augmented/virtual reality (AR/VR) units acting as transmitting nodes. The opportunistic use of scheduling rich content updates at traffic signals and stand-still scenarios maps well with the formulated bandit problem. We propose a Dual-Objective Minimax Optimal Stochastic Strategy (DOMOSS) as a natural solution to this problem. Through extensive computer-based simulations, we demonstrate the effectiveness of our proposal in contrast to baselines and comparable solutions. We also verify the quality of our solution and the convergence of the proposed strategy.

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Deep learning and Blockchain-based Framework to Detect Malware in Autonomous Vehicles

Deep Learning and Blockchain-based Framework to Detect Malwa...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Dev Patel Dhairya Jadav Rajesh Gupta Nilesh Kumar Jadav Sudeep Tanwar Bassem Ouni Mohsen Guizani Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Technology Innovation Institute (TII) Abu Dhabi UAE Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE

The advancement in technology has brought to life the concept of Autonomous vehicles (AV). The primary goal of AV is to reduce driving stress and provide comfort to the occupants. Since AVs can drive themselves, it poses a question of passenger security. Furthermore, AVs are connected to an open network like a public Internet to communicate to the outer world, raising security and privacy concerns. Skillful attackers can effortlessly infiltrate the vehicle by injecting malware which can disrupt the regular operation of the entire AV system. A Deep learning (DL) and Blockchain framework is proposed for AV to resolve the aforementioned security challenges. The network traffic is continuously monitored, and the malware binaries are converted to grey-scale images, which are then classified by Convolutional Neural Network (CNN) employed in the DL model. The CNN architecture, ResNet50V2, has been tested and proves to be efficient in detecting malware with an accuracy of 97.56%.

关键词： Deep learning Wireless communication Prototypes computer architecture Telecommunication traffic Malware Blockchains

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Ultrasound-Based Kidney Disease Detection: Resampling Cubature Kalman Filter and Physics-Guided VGG Cross-Contextual Network

Ultrasound-Based Kidney Disease Detection: Resampling Cubatu...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Padmanayana N.S.L. Kumar Kurumeti Veena S Ch. Raja Vijay Jagdish Upadhye Ramya Maranan Department of Artificial Intelligence and Data Science Engineering AJ Institute of Engineering and Technology Mangaluru Karnataka India Department of Artificial Intelligence and Machine Learning Aditya University Surampalem Andhra Pradesh India Department of Electronics and Communication Engineering SJC Institute of Technology Chikkaballapur Karnataka India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Hyderabad Telangana India Department of Microbiology Dept of Microbiology Research and Development Cell (RDC) Parul Institute of Applied Sciences (PIAS) Parul University Post Limda Waghodia Gujarat India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Limitations of ultrasound-based identification of kidney disease include poor image resolution, operator reliance, and lack of detection of small tissue anomalies. Diagnostic accuracy may be affected by artefacts or noise, or changes in patient anatomy. It may also have difficulty detecting microscopic structural changes or renal disease in its incipiency. The limitations are overcome by the analysis of the Resampling Cubature Kalman Filter and Physics-Guided VGG Cross-Contextual Networks (RCKF-PG-VGG-CCNet) for kidney disease detection from ultrasound images based on the Kidney Ultrasound Image Dataset. The initial use of the Resampling Cubature Kalman Filter and the first use of Physics-Guided VGG Cross-Contextual Networks is to enhance image quality. Through identifying regions through deviations, the Scale-Aware Meet Transformer (SAMT) performs segmentation. While the Synergistic Fibroblast Optimization (SFO) enhances classification performance through optimization of the search for diseased regions, the Physics-Guided VGG Cross-Contextual Networks (PG-VGG-CCNet) classify kidney disease and identify features. Experimental output from a Python-based experiment demonstrates that RCKF-PG-VGG-CCNet is more efficient than other methods with 99.9% efficiency and 99.8% sensitivity and may enhance the accuracy of kidney disease detection.

关键词： Ultrasonic imaging Accuracy Sensitivity Noise Fibroblasts Transformers Kalman filters Kidney Optimization Diseases

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IoT Enabled Smart Metering for Domestic Electrical Distribution System Using Arduino

IoT Enabled Smart Metering for Domestic Electrical Distribut...

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Device intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： B Mohan S Mahalakshmi R Prem Ananth J Relin Francis Raj R Santhana Krishnan P Sundaravadivel D Sarathkumar Department of Electronics and Communication Engineering P.S.R. Engineering College Sivakasi India Department of Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering Kongu Engineering College Perundurai India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

The primary objective is to help consumers use electricity more efficiently from the power grid to minimize their electricity bills. The system continuously monitors power consumption by household appliances and calculates the total energy usage, alerting consumers when they are approaching the current slab rate. As the consumption nears the slab rate, the system automatically initiates a phone call and sends an SMS alert to notify the user to reduce power consumption. An auxiliary load line is included to connect non-essential loads; when power consumption is about to exceed the slab rate, the auxiliary line is automatically disconnected, and the status is communicated to the user via SMS. For enhanced scalability, overall power consumption is monitored through cloud-based tracking.

关键词： Home appliances Cloud computing Power demand Electricity Real-time systems Energy efficiency User experience Internet of Things Slabs Monitoring

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