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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Xue, Xiangdong Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Fog computing provides lower latency and higher bandwidth compared to cloud computing and is widely used in Internet of Vehicles (IoV). Vehicles cannot compute all tasks locally due to their limited computing power and battery capacity. Thus, it is a useful way to offload some tasks of vehicles to other resource-rich servers. However, due to the high mobility of vehicles, there may be a failure of returning computing results. Thus, it is a challenge to minimize the latency of tasks while meeting the constraint of energy consumption. This work proposes a vehicle-fog offloading system that offloads tasks to fog servers or idle vehicles which is a probabilistic offloading problem. So this work proposes an improved optimization algorithm called an adaptive Lévy flight-based Whale optimization algorithm with Hierarchical learning (LWH) to solve this problem. Simulation experiments show that LWH has strong global search capability and outperforms its five typical and widely used algorithms. © 2023 IEEE.

关键词： computation offloading fog computing Lévy flight Vehicular networks whale optimization algorithm

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Web Traffic Anomaly Detection Using a Hybrid Spatio-temporal Neural Network

Web Traffic Anomaly Detection Using a Hybrid Spatio-temporal...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Xu, Lifeng Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Engineering Dept. of Computer Science in Lyle Southern Methodist University DallasTX United States

ISBN: (纸本)9798350337020

Nowadays, rapid development of Internet has brought a sharp increase in traffic data. Abnormal traffic haS serious impact on network security. Traffic anomaly detection can be achieved by extracting characteristics of network traffic to detect anomalous intrusions, and therefore, anomaly detection algorithms are of great significance to maintenance of network security. This work proposes a hybrid spatio-temporal neural network with attention named CTGA to effectively identify anomalous traffic. CTGA combines a Convolutional neural network (CNN), a Temporal convolutional network (TCN), a bidirectional Gated recurrent unit network (BiGRU), and a self-Attention mechanism. It automatically extracts temporal and spatial features of sequences from raw data by sliding window preprocessing followed by CNN, TCN, BiGRU, and the self-attention mechanism to detect anomalous data. CNN is used to extract spatial features of time sequences and reduce the loss of spatial information. In the sequence, TCN obtains short-term features. Long-term dependencies in the data are captured by BiGRU, and the self-attention mechanism obtains important information in the sequence. Finally, experiments with the real-life Yahoo S5 dataset prove that CTGA outperforms other approaches substantially. © 2023 IEEE.

关键词： Anomaly detection bidirectional gated recurrent unit convolutional neural network self-attention temporal convolutional network

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SynthEcho Watermarking for Secure and Robust Image-to-Image Generation

SynthEcho Watermarking for Secure and Robust Image-to-Image ...

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International Conference on Distributed Computing Systems Workshop

作者： Xiang Xin Honggang Wang Hua Fang Graduate Computer Science and Engineering Department Katz School of Health and Science Yeshiva University Dept. of Computer and Information Science University of Massachusetts Dartmouth and Chan Medical

ISBN: (数字)9798350354713

ISBN: (纸本)9798350354720

The boundaries between human creativity and artificial intelligence are blurring, posing challenges for digital copyright protection. This article introduces a novel img-to-img invisible watermarking technology called SynthEcho Watermarking, which adds watermarks to the diffusion model and combines with the BLIP(Bootstrapping Language-Image Pre-training) model to generate the imgto-img(image-to-image) model with built-in robust watermarks that can be detected without training. By embedding these patterns into the initial noise vector of the diffusion model, we use Fourier transform to implement a watermark that maintains robustness to common image operations while being invisible to human *** utilized the COCO dataset to test our model’s effectiveness, demonstrating significant resilience against common image manipulations while maintaining high image fidelity. Our results underscore the potential of integrating robust watermarking within AI-driven content creation processes. This method demonstrates its application in various image-to-image generation tasks, not only ensuring the integrity and copyright of generated images, but also playing a key role in navigating the complexity of AI-generated content, ensuring transparent attribution, and ethical use.

关键词： Training Semantics Refining Watermarking Copyright protection Diffusion models Vectors

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Feature Distraction Based Backdoor Defense for Federated Trained Intrusion Detection System

Feature Distraction Based Backdoor Defense for Federated Tra...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Chen, Yu-Wen Ke, Bo-Hsu Wang, Yen-Xin Lin, Shih-Heng Tsai, Ming-Han Chen, Bo-Zhong Kuo, Jian-Jhih Hwang, Ren-Hung New York City College of Technology BrooklynNY United States National Yang-Ming Chiao Tung University College of Artificial Intelligence Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi Taiwan

ISBN: (纸本)9798350351255

Convolutional Neural Network (CNN)-based Intrusion Detection System (IDS) incorporated with Federated Learning (FL) facilitates collaborative model training to secure user privacy and enhance data diversity, emerging as a promising solution to defend malicious flows. Yet, FL is vulnerable to trigger backdoor attacks. Existing methods have limited ability on IDS with FL for detecting or repairing a converged attacked model as they either require access to the training process or are dedicated to specific applications. To tackle the limitations, we propose a novel Features Distraction Defense Framework (FDDF) that does not interfere with the model training and requires only a few additional model inferences to protect users from the aforesaid attack. The experiment results show that FDDF can recover the accuracy effectively and preserve the accuracy of normal models. © 2024 IEEE.

关键词： Network intrusion

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Optimizing Human Activity Recognition for Precision in Yoga Practice Analysis

Optimizing Human Activity Recognition for Precision in Yoga ...

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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Supriya Salian Preethi Salian K Maria Viola Rodrigues Ritesh Muhammed Elham P N Mohammed Abdul Mannan Dept. of Computer Science and Engineering St. Joseph Engineering College Vamanjoor Mangaluru India Dept. of Information Science and Engineering NMAM Institute of Technology Nitte (Deemed to be University) Udupi India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

The proposed system on enhancing human activity recognition in yoga presents an innovative solution using advanced technologies to accurately and quickly analyze data of yoga poses. Through meticulous data collection and preprocessing, including noise removal, normalization, and handling missing data, the system ensures a clean and standardized dataset. Pose estimation algorithms contribute to accurately mapping the body's posture, extracting essential features like joint angles and body orientation. The features form the basis for a powerful machine learning model, trained on a labelled dataset and employing neural networks, enabling the system to recognize yoga poses with remarkable precision. The study embraces an iterative enhancement process, continuously refining the model based on feedback and performance metrics, ensuring ongoing improvements in accuracy. Ultimately, the proposed system represents a cutting-edge solution for advancing human activity recognition in yoga, providing practitioners with real- time feedback and guidance, fostering improved techniques, and enhancing the overall yoga experience.

关键词： Measurement Machine learning algorithms Refining Pose estimation Noise Neural networks Very large scale integration Feature extraction Human activity recognition Robots

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Generation of Chinese classical poetry based on pretrained model 2

Generation of Chinese classical poetry based on pretrained m...

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2nd International Conference on Big Data, information and computer Network, BDICN 2023

作者： Wang, Ziyao Guan, Lujin Liu, Guanyu Ma, Jun Lanzhou University Dept. School of Information Science and Engineering Gansu Lanzhou China Huazhong University Line of Science and Technology College of Life Science Technology Hubei Wuhan China School of Computer and Cyberspace Security Communication University of China Beijing China

ISBN: (纸本)9798350321364

Chinese traditional poetry is an important art form, and it is a meaningful task to use artificial intelligence algorithm to generate poetry. In the past, researchers have proposed different generation algorithms, but these algorithms have some *** paper mainly tries to use BART and other pretrained models, proposes FS2TEXT and RR2TEXT to generate metrical poetry text and even specific style poetry text, and solves the problem that the user's writing intention gradually reduces the relevance of the generated poetry text. Finally, in order to test the effect of the algorithm, this paper imitates Turing test and finds more than 600 testers to distinguish the works generated by the algorithm from the works of real human poets, and the results show that they can't tell whether the work is from an algorithm or a *** shows that the algorithm proposed in this paper works well. © 2023 IEEE.

关键词： Deep learning

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Development of Framework to Find Lung Canker Using GAN Technique 5

Development of Framework to Find Lung Canker Using GAN Techn...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Ramakrishnan, Rakesh Muniandi, Balakumar Yennapusa, Haritha Jiwani, Nasmin Kiruthiga, T. University of the Cumberlands Department of Information Technology Union CityCA United States Lawrence Technological University Department of Electrical and Computer Engineering SouthfieldMI United States University of Central Missouri Department of Computer Science AustinTX United States University of the Cumberlands Dept. of Information Technology WilliamsburgKY United States Vetri Vinayaha College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798350383522

Lung cancer is one of the main reasons of lack of existence globally, accounting for approximately 1. Eight million deaths in past one year. Early prognosis of lung most cancers is crucial for a hit remedy, however it stays a task due to its subtle early signs and symptoms and the limitations of present-day diagnostic techniques. In recent years, the improvement of deep information of era has proven promising consequences in medical photograph analysis and ailment evaluation. On this context, the improved Generative adverse network (GAN) framework has emerged as a powerful device for improving the accuracy and overall performance of lung cancer evaluation. The stepped forward GAN framework is an extension of the conventional GAN, which incorporates a generator and a discriminator working collectively in a competitive gaining knowledge of the approach. The generator generates pics which might be then evaluated with the aid of the discriminator, which identifies if they may be actual or fake. This technique is repeated till the generator efficiently generates sensible photos, fooling the discriminator. Within the context of lung most cancers prognosis, the progressed GAN framework makes use of radiological snap shots, along with CT scans, to generate practical snap shots of lung nodules. Those generated photos are used to enhance the training dataset, developing the range and complexity of the facts, main to a miles higher deep getting-to-know version. This lets in higher differentiation between cancerous and non-cancerous nodules. © 2024 IEEE.

关键词： Better Differentiation Discriminator Extension Prognosis

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Position Aware 60 GHz mmWave Beamforming for V2V Communications Utilizing Deep Learning

arXiv

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arXiv 2024年

作者： Mollah, Muhammad Baqer Wang, Honggang Fang, Hua Dept. of Electrical and Computer Engineering University of Massachusetts Dartmouth MA02747 United States Dept. of Graduate Computer Science and Engineering Yeshiva University NY10016 United States Dept. of Computer and Information Science University of Massachusetts Dartmouth MA02747 United States

Beamforming techniques are considered as essential parts to compensate the severe path loss in millimeter-wave (mmWave) communications by adopting large antenna arrays and formulating narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over such narrow beams for efficient link configuration by traditional beam selection approaches, mainly relied on channel state information, typically impose significant latency and computing overheads, which is often infeasible in vehicle-to-vehicle (V2V) communications like highly dynamic scenarios. In contrast, utilizing out-of-band contextual information, such as vehicular position information, is a potential alternative to reduce such overheads. In this context, this paper presents a deep learning-based solution on utilizing the vehicular position information for predicting the optimal beams having sufficient mmWave received powers so that the best V2V line-of-sight links can be ensured proactively. After experimental evaluation of the proposed solution on real-world measured mmWave sensing and communications datasets, the results show that the solution can achieve up to 84.58% of received power of link status on average, which confirm a promising solution for beamforming in mmWave at 60 GHz enabled V2V communications. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Enhancing Industrial Load Scheduling through Demand Side Management and Advanced Optimization Techniques Using Neuro-Fuzzy and Deep Learning Approaches 4

Enhancing Industrial Load Scheduling through Demand Side Man...

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Agarwal, Shivani Sharma, Ritu Tamilselvi, M. Sharma, Harsh Mohan Sahu, Dinesh Prasad Singh, Jagendra Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India Gyan Ganga Institute of Technology and Science Dept. of Electrical and Electronics Engineering Jabalpur India Roever Engineering College Perambalur Deartment of Computer Science and Engineering Perambalur India Raj Kumar Goel Institute of Technology Department of Management Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350306118

This study aims to investigate whether physical factors like temperature, humidity, and pressure interact with current in Micro-electromechanical systems (MEMS). To study these interactions, a straightforward system with four spin-operated engines was employed. The pressure, humidity, and temperature may all change as a result of changing the motor's output current. The results are evaluated and predicted using LSTM and Neuro-Fuzzy models. Sensors are used to keep an eye on these circumstances. Neurofuzzy models employ fuzzy logic and artificial intelligence to capture interconnections, as opposed to LSTM models, which use their capacity to comprehend data connections to capture long-Term effects. The results demonstrate that the body's capacity to exert energy is enhanced by the effect of body energy on temperature, humidity, and pressure. The research could lead to improvements in temperature control, energy efficiency, and the functionality and design of massive industrial machinery. A straightforward design is used to launch controlled experiments, and the data collected is then used to train and assess the model. By offering helpful suggestions for the improvement of the motor and the operating electric economy, this study contributes to our understanding of the evolution of the electric motor. © 2023 IEEE.

关键词： Electric loads

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Performance Evaluation of Coherent MIMO Radar Assisted with Space-Time Coding 9

Performance Evaluation of Coherent MIMO Radar Assisted with ...

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9th International Conference on computer and Communication engineering, ICCCE 2023

作者： Barakat, Mohammed Elmogtaba Saeed, Rashid A. Barakat, Mohammed Edam, Salaheldin Khalifa, Othman O. Sudan University of Science and Technology School of Electronics Engineering Khartoum Sudan Taif University College of Computers and Information Technology Department of Computer Engineering P.O. Box 11099 Taif21944 Saudi Arabia International Islamic University Malaysia Dept. of Electrical and Computer Engineering Malaysia Libyan Center for Engineering Research and Information Technology Bani Waleed Libya

ISBN: (纸本)9798350325218

Using multiple-input multiple-output (MIMO) technology has revolutionized the radar field by providing higher resolution, improved target detection, and reduced interference. However, interrupting signals can significantly degrade the Performance of MIMO radar systems to transmit and receive signals simultaneously;MIMO radar systems employ multiple antennas at the transmitter and receiver. This allows for improved spatial resolution and increased target detection capabilities. However, MIMO radar systems are susceptible to fading and interference, degrading performance. STC is a technique to mitigate these effects by transmitting multiple signals simultaneously from different antennas. This article evaluates the performance of space-time coding (STC) with coherent multiple-input multiple-output (MIMO) radar. The paper investigates the impact of STC on the radar system's detection and estimation capabilities in various scenarios, including target detection in cluttered environments and target tracking in the presence of interference. The simulation results demonstrate that STC can significantly improve the radar system's performance by reducing interference effects and increasing the signal-to-noise ratio. Furthermore, the study shows that different STC schemes have varying degrees of effectiveness depending on the specific scenario. Overall, this research provides valuable insights into the potential benefits of using STC with coherent MIMO radar for various applications. © 2023 IEEE.

关键词： Target tracking

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