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Joint Optimization in MEC Incorporating MD Preference: A Hybrid GA and AFSA Scheme 100

Joint Optimization in MEC Incorporating MD Preference: A Hyb...

引用

100th IEEE Vehicular technology conference, VTC 2024-Fall

作者： Dong, Yunan Pei, Jianhua Wu, Kun Dong, Yuhan Zhang, Xiao-Ping Tsinghua University Shenzhen Key Laboratory of Ubiquitous Data Enabling Shenzhen International Graduate School Shenzhen518055 China

ISBN: (纸本)9798331517786

Mobile edge computing (MEC) is an efficient method to tackle computationally intensive tasks for mobile devices (MDs). However, current studies about MEC do not consider that different MDs have different preferences for delay and energy consumption. Thus, we propose a MD preference-based MEC computing model addressing three optimization goals: delay preference, energy preference, and their balance, tailored to diverse MD preference. This optimization problem is formulated as a mixed integer non-linear programming (MINLP) task with four optimization variables: offloading decisions, channel allocation, power allocation, and resource allocation. Additionally, we propose a novel genetic artificial fish swarm cooperative optimization algorithm (GAFSCOA) to solve this problem, which integrates genetic algorithm (GA) and artificial fish swarm algorithm (AFSA), respectively. Numerical results show our proposed model can achieve different optimization goals according to MDs' preferences. Compared with GA and AFSA, our proposed GAFSCOA demonstrates 30.71% faster convergence speed and 46.09% better optimization results. Furthermore, in comparison with other baseline algorithms, our algorithm yields superior optimization results. © 2024 IEEE.

关键词： Optimization algorithms

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CDQKL: Consensus-Based Distributed Quantum Kernel Learning 1

CDQKL: Consensus-Based Distributed Quantum Kernel Learning

引用

1st international conference on Quantum Communications, Networking, and computing, QCNC 2024

作者： Ma, Wenxuan Liu, Mengxiang Deng, Ruilong State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Zhejiang University Hangzhou310027 China University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (纸本)9798350366778

The field of quantum computing has developed rapidly in recent years due to its promising trend of surpassing traditional machine learning in terms of speed and effectiveness. Quantum kernel learning is one of the paradigms of quantum machine learning, but the training of quantum kernel is time consuming. Therefore, this work makes the first attempt to introduce a consensus-based distributed approach to quantum kernel learning - named CDQKL - that only requires to exchange model parameter information between adjacent nodes while avoiding the need of sharing local training data. Through comparative experimental studies, the advantages of CDQKL in classification accuracy and convergence speed are verified. Considering the popularization of quantum computing cloud service and miniaturization of quantum terminals, the CDQKL adapting to this trend is able to play a vital role in data security, which implies the far-reaching significance of this work. Our code is available at https://***/Leisurivan/CDOKL. © 2024 IEEE.

关键词： Quantum computers

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A Reinforcement Learning-Based Adaptive Learning Rate Scheduler for Optimizing Brain Tumor Detection 1

A Reinforcement Learning-Based Adaptive Learning Rate Schedu...

引用

1st international conference for Women in computing, InCoWoCo 2024

作者： Shekhar, Saakshi Dubey, Siddhanth Jothikumar, C. Ashokkumar, C. Shanmugam, S. SRM Institute of Science and Technology Department of Computing Technologies Chennai603203 India

ISBN: (纸本)9798331518943

One key factor that determines a properly trained deep learning model with the capability to determine medical images for brain tumor is the optimal learning rate. Traditional approaches usually are bounded with static or pre-defined adaptive learning rate schedules, thus failing to capture such problems in dynamic medical images data and not sufficient for getting the optimal performance level in the model. In this regard, we propose an innovative adaptive learning rate scheduler, which uses the concepts of reinforcement learning to adjust the learning rate during neural network training, specifically with a view to optimize the performance in the task of detection of brain tumors from MRI scans. In our proposal, the training process interacts continuously with the reinforcement learning agent to update the learning rate dynamically. It thus allows for intelligent adaptation toward the specific needs of a dataset, thus achieving quicker and more stable convergence. Implemented in a CNN framework and tested on standard brain MRI datasets, our scheduler thus demonstrates superior performance compared with traditional learning rate techniques by achieving higher accuracy and efficiency. This is the first proposed RL-based adaptive learning rate scheduler, one of the significant advancements applied to machine learning in the field of medical diagnostics so that model reliability and accuracy to detect brain tumors, as crucial for improving patient results. © 2024 IEEE.

关键词： Deep reinforcement learning

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Unifying GenerationandCompression: Ultra-low bitrate Image Coding Via Multi-stage Transformer

Unifying GenerationandCompression: Ultra-low bitrate Image C...

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IEEE international conference on Multimedia and Expo (ICME)

作者： Xuel, Naifu Mao, Qi Wang, Zijian Zhang, Yuan Mai, Siwei Commun Univ China Sch Informat & Commun Engn Beijing Peoples R China Commun Univ China State Key Lab Media Convergence & Commun Beijing Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China

ISBN: (纸本)9798350390155;9798350390162

Recent progress in generative compression technology has significantly improved the perceptual quality of compressed data. However, these advancements primarily focus on producing high-frequency details, often overlooking the ability of generative models to capture the prior distribution of image content, thus impeding further bitrate reduction in extreme compression scenarios (< 0.05 bpp). Motivated by the capabilities of predictive language models for lossless compression, this paper introduces a novel Unified Image Generation-Compression (UIGC) paradigm, merging the processes of generation and compression. A key feature of the UIGC framework is the adoption of vector-quantized (VQ) image models for tokenization, alongside a multi-stage transformer designed to exploit spatial contextual information for modeling the prior distribution. As such, the dual-purpose framework effectively utilizes the learned prior for entropy estimation and assists in the regeneration of lost tokens. Extensive experiments demonstrate the superiority of the proposed UIGC framework over existing codecs in perceptual quality and human perception, particularly in ultra-low bitrate scenarios (<= 0.03 bpp), pioneering a new direction in generative compression.

关键词： Generative Compression Extreme Compression Image Generation VQGANs Transformer

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Enhancing the Financial Sector with Quantum computing: A Comprehensive Review of Current and Future Applications 22nd

Enhancing the Financial Sector with Quantum Computing: A Com...

引用

22nd international conference on Informatics in Economy, IE 2023

作者： Claudiu, Brandas Cosmin, Enache Otniel, Didraga Andrei, Albu West University of Timisoara Timisoara Romania

ISBN: (纸本)9789819969593

Quantum computing is currently a disruptive and game-changing technology that challenges the development of various industries, and it is a new paradigm in business software development. In this paper, we explore the current and future applications and research of quantum computing in the financial sector and present a SWOT analysis to evaluate its potential impact. We begin by introducing the fundamental concepts of quantum computing. Next, we analyze existing use cases and research on quantum computing in the financial sector. Finally, the SWOT analysis highlights the strengths, weaknesses, opportunities, and threats associated with implementing quantum computing in the financial sector. Our study emphasizes the transformative potential of quantum computing to reshape the financial sector by enabling faster processing, faster communication, better security, new financial services, solving complex problems, and more accurate decision-making processes. Quantum computing can significantly impact the following financial areas: portfolio optimization, risk management, option pricing, high-frequency trading, blockchain and cryptocurrencies, and AI applications (such as fraud detection and credit scoring). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Risk management

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Ubiquitous Sensing the convergence of WSNs and IoT in Contemporary Scenarios 4

Ubiquitous Sensing the Convergence of WSNs and IoT in Contem...

引用

4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Manjula, M. Karnik, M.P. Ramesh, P.N. Suri, Sahil Vijayaraghavan, P. Sohal, Jagmeet School of Sciences Physics & Electronics Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Institute of Technology Department of Computer Science Engineering Coimbatore641105 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mech Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798331518981

Ubiquitous sensing, which combines WSNs and the IoT, makes settings smarter and more efficient for numerous procedures. For "Ubiquitous Sensing: The convergence of WSNs and IoT in Contemporary Scenarios,"this research examines a strategy to increase dependability, security, energy efficiency, and data accuracy. Recommended features include adaptive power management, data fusion methods, edge computing nodes, blockchain technology, and robust identification systems. These pieces improve the system and address common issues. The major research findings suggest that the strategy performs effectively in numerous success categories. Power management systems optimise energy consumption, reducing costs and environmental impact. Advanced data fusion and fault-tolerant designs improve data accuracy and reliability, allowing you to integrate data appropriately and keep the system functioning. Latency-sensitive applications may move and react to data in real time with reduced network delay and greater performance. Strong authentication and blockchain-based encryption maintain data accuracy, user privacy, and legal compliance while reducing hacking threats. The study describes the method's comprehensive approach to ubiquitous sensing, establishing a standard for IoT advances. Making systems more efficient and dependable, while prioritising data privacy and security, improves IoT infrastructure dependability and resilience. © 2024 IEEE.

关键词： Data accuracy

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A Wearable BTE-EEG Embedded Device for Emotion Monitoring with Quantum Machine Learning 10

A Wearable BTE-EEG Embedded Device for Emotion Monitoring wi...

引用

10th IEEE international conference on Communications and Electronics (IEEE ICCE)

作者： Mai, Ngoc-Dau Barki, Hika Chung, Wan-Young Pukyong Natl Univ Dept Artificial Intelligence Convergence Busan 48513 South Korea

ISBN: (纸本)9798350379808;9798350379792

Emotional disorders present significant challenges in both mental health research and clinical practice, underscoring the critical need for effective recognition and intervention strategies. This study introduces a groundbreaking approach that employs a behind-the-ear (BTE) EEG system for emotion recognition, utilizing quantum machine learning techniques. The research encompasses a comprehensive design framework addressing both hardware and software aspects of the BTE-EEG emotion recognition system for data acquisition and monitoring. By utilizing BTE-EEG technology, this study aims to improve comfort, mobility, and signal quality compared to traditional scalp EEGs, thus enabling continuous monitoring in real-world settings. Furthermore, the integration of quantum computing and machine learning represents a paradigm shift in emotion recognition and cognitive tasks, offering exponential speedups and superior pattern recognition capabilities compared to classical approaches. The findings underscore the efficacy of the proposed BTE EEG system in accurately detecting emotional states (positive and negative). This advancement shows promise for enhancing our understanding of emotional disorders and improving clinical interventions tailored to individual needs.

关键词： Emotion recognition Ear-EEG Quantum machine learning Variational quantum classifier

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A Hybrid Approach of BBO and PSO for Resource Management in Fog computing 13

A Hybrid Approach of BBO and PSO for Resource Management in ...

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13th international conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Bhakhar, Ruchika Chhillar, Rajender Singh Goyal, Archna Maharshi Dayanand University Department of Computer Science and Applications Haryana Rohtak India School of Engineering and Technology K.R. Mangalam University Sohna Rural Gurugram India

ISBN: (纸本)9798350380583

Efficient resource management is crucial for fog computing to achieve optimal performance and resource utilization over different cloud nodes. In order to overcome this issue, a hybrid BBO-PSO has been proposed. Inspired by species migration, BBO treats fog nodes as habitats with suitability indices specifying the performance of each in terms to tasks and adapt inferior convergence schedules dynamically based on resource load without locality-awareness while minimizing latency through migration and mutation ha1933b. The resulting system is akin to the PSO, which uses swarm intelligence for resource configuration (e.g., particles in a nearby cluster), and adjusts their position via historical information as well global best-known placements based on performance metrics such as increased resource utilization, reduced request latency and energy efficiency. This paper takes a hybrid approach by directly integrating these techniques through exchanging suitability indices and best-known positions, thereby combining the exploration ability of BBO with the targeted search capability of PSO. Migration and mutation techniques are optimized to cope with adapting mechanisms changes in environments thus accommodates workload patterns as well network conditions. This BBO-PSO hybrid approach holds significant promise for improving fog computing's scalability and robustness. © 2024 IEEE.

关键词： Swarm intelligence

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On the convergence acceleration of vanilla Generative Adversarial Network 2

On the convergence acceleration of vanilla Generative Advers...

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2nd international conference on computing Innovation and Applied Physics, CONF-CIAP 2023

作者： Si, Hang School of Physics Technology University of Wuhan Hubei Wuhan430072 China

Generative Adversarial Network (GAN) is very successful and widely used in both academic and industrial fields. It is inspired by game theory where two models, a generator and a critic, compete with each other, learn from each other, and make each other stronger and stronger. Most improvements focused on stability and there are already many different models such as deep convolutional GAN (DCGAN) and Wassertein GAN (WGAN) that have succeeded. But the speed of the convergence during training process is also an essential ability that needs to be enhanced. In this paper, a decreasing noise is added to the MNIST data and these new data are delivered to the discriminator of the vanilla GAN to accelerate the training process. The result shows that this change could accelerate the convergence and not affect the results' quality a lot. And this improvement should be general enough to apply to the other models. © Published under licence by IOP Publishing Ltd.

关键词： Generative adversarial networks

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A Efficient Method to Predicting Chronic Kidney Disease using Machine Learning 15

A Efficient Method to Predicting Chronic Kidney Disease usin...

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15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Shelake, Nitin L. Swati, Kene Deshmukh, Yogesh S. Priyanka, Maher Sakshi, Shingade Department of Information Technology Sanjivani College of Enginerring Kopargoan India

ISBN: (纸本)9798331300579

Chronic kidney disease (CKD), commonly referred to as chronic renal disease, is a group of disorders that affect your kidneys and reduce their ability to keep you healthy. Nerve harm, high blood pressure, anemic (a low blood count), weak bones, and inadequate nutrition could be symptoms. Early detection and treatment often prevents chronic renal disease from getting worse. Data mining is the technique used to gather knowledge from huge datasets. Data mining, which aims to use previous data to find recurring patterns and enhance decision-making moving forward, is a result of the convergence of several current trends: the decreasing price of massive data storage devices and the growing simplicity of data collection across networks;the development of reliable and effective machine learning algorithms to handle this data, as well as the declining cost of computing power, which makes it possible to apply computationally expensive techniques for data analysis. Machine learning has already produced useful applications in fields like analyzing results from medical research, spotting fraud, spotting bogus users, etc. For the purpose of predicting chronic diseases, various data mining and categorization methodologies and machine learning algorithms are used. The goal of this study is to develop a new decision-support system for forecasting chronic renal disease. This study compares support vector machine (SVM) classifier performance for CKD prediction based on its accuracy, precision, and execution time. © Grenze Scientific Society, 2024.

关键词： Support vector machines

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