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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是41-50 订阅

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Joint UAV Trajectory Optimization and Task Offloading in Integrated Air-Ground Networks 7th

Joint UAV Trajectory Optimization and Task Offloading in Int...

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8th Asia Pacific Web and Web-Age Information Management Joint international conference on Web and Big data

作者： Zhang, Yuanwei Shuai, Jiaqi Wen, Weichang Cui, Haixia South China Normal Univ Sch Elect & Informat Engn Foshan 528200 Peoples R China

ISBN: (纸本)9789819600540;9789819600557

As an essential component of the integrated air-ground-space networks, the unmanned aerial vehicle (UAV) is indispensable for the mobile edge computing (MEC) processing. In this paper, considering the highly dynamic nature of information exchange, we apply the multiagent reinforcement learning (MARL) for the UAV-based communication networks and introduce a low-complexity K-means algorithm for the precluster/partition users to improve the system performance. We devise a general reward and punishment strategy within the multi-agent reinforcement learning process to generate the optimal trajectories and promote the coverage maximization. Furthermore, we utilize NOMA technology to provide stable data rate gains and allocate more power to weaker users under optimizing energy consumption so as to maximize the overall system throughput. Simulation results demonstrate that the proposed joint optimization algorithm achieves excellent convergence performance in UAV based integrated air-ground-space networks.

关键词： UAV K-means algorithm multi-agent reinforcement learning

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Privacy-Preserving Asynchronous Vertically Federated learning against Batch Level Label Inference Attacks 24

Privacy-Preserving Asynchronous Vertically Federated Learnin...

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2024 international conference on Artificial Intelligence of Things and computing, AITC 2024

作者： Jin, Yu Liu, Ying College of Information Science & Electronic Engineering Zhejiang University Zhejiang Hangzhou China

ISBN: (纸本)9798400709869

Vertical Federated learning (VFL), which enables multiple parties that hold different features of the same data set to collaboratively train a global learning model without sharing their raw data, is an important paradigm in FL. However, VFL is vulnerable to privacy threats, particularly Label Inference Attacks (LIA), where passive participants try to deduce sensitive labels of the active party. Existing defense strategies, such as Homomorphic Encryption (HE) and Differential Privacy (DP), often face challenges related to high computational overhead or tradeoff between privacy and accuracy, especially in asynchronous settings. Considering this, in this paper, we propose a novel defense mechanism against batch-level label inference attacks (BLI) in vertical asynchronous federated learning (VAFL), where client-level differential noise is added to obscure batch-level gradients such that the passive participants cannot derive the label information from the active participant. Besides, to address party's asynchronicity, our approach incorporates a temporal weight decay strategy according to the staleness of each party. A series numerical simulations are then performed to verify the effectiveness and robustness in privacy protection for VAFL scenarios. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Optimal Scheduling for Integrated Energy System via Model-Based Reinforcement learning 24

Optimal Scheduling for Integrated Energy System via Model-Ba...

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2024 international conference on Artificial Intelligence of Things and computing, AITC 2024

作者： Cheng, Kexin Wan, Jianxiong Liu, Chuyi Sun, Pengfei Li, Leixiao Zhang, Huaqin School of Data Science and Application Inner Mongolia University of Technology Inner Mongolia Autonomous Region Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Inner Mongolia Autonomous Region Hohhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Republic of China Inner Mongolia Autonomous Region Hohhot China

ISBN: (纸本)9798400709869

The Integrated Energy System (IES) integrates energy consumption, conversion, and storage to increase the utilization of renewable energy sources and fossil fuels. When using heuristic algorithms and mathematical optimization methods for optimal scheduling of IES, these methods often face challenges of high computational cost and insufficient model accuracy, for which Reinforcement learning (RL) offers a promising solution. However, most studies have used Model-Free Reinforcement learning (MFRL) for optimal scheduling, which is limited by problems of low sampling efficiency and long training time. Model-Based Reinforcement learning (MBRL) is an effective alternative to accelerate learning and facilitate RL applications. Therefore, this paper compares the performance gap between MFRL and MBRL in the optimal scheduling problem of IES to determine the superiority of MBRL. Experimental results show that the MBPO algorithm significantly improves sampling efficiency and learning speed at high prediction accuracy, outperforming the Soft Actor-Critic (SAC) algorithm. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis

Physics-Informed Latent Diffusion for Multimodal Brain MRI ...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

作者： Lüpke, Sven Yeganeh, Yousef Adeli, Ehsan Navab, Nassir Farshad, Azade Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Stanford University Stanford United States

ISBN: (纸本)9783031845246

Recent advances in generative models for medical imaging have shown promise in representing multiple modalities. However, the variability in modality availability across datasets limits the general applicability of the synthetic data they produce. To address this, we present a novel physics-informed generative model capable of synthesizing a variable number of brain MRI modalities, including those not present in the original dataset. Our approach utilizes latent diffusion models and a two-step generative process: first, unobserved physical tissue property maps are synthesized using a latent diffusion model, and then these maps are combined with a physical signal model to generate the final MRI scan. Our experiments demonstrate the efficacy of this approach in generating unseen MR contrasts and preserving physical plausibility. Furthermore, we validate the distributions of generated tissue properties by comparing them to those measured in real brain tissue. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image denoising

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SACL: Sequential Augmentation with Curriculum learning in dataset Level 13th

SACL: Sequential Augmentation with Curriculum Learning in Da...

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13th international conference on Natural Language Processing and Chinese computing

作者： Mae, Biao Kong, Fang Soochow Univ Suzhou Peoples R China

ISBN: (纸本)9789819794362;9789819794379

Curriculum learning, as a deep learning training strategy, has proven its effectiveness across many Natural Language Processing (NLP) and Multi-modal tasks. However, traditional Curriculum learning is often instance-level, which requires manually classifying each instance in the data into different difficulty level. This progress is not only time-consuming but also costly in labor. To outcome this challenge, we introduce an innovative training approach named Sequential Augmentation with Curriculum learning (SACL). SACL is a curriculum learning approach in dataset-level, which utilizes various data augmentation techniques to generate data in various difficulty levels and feeds this data to the model in an order of increasing difficulty. This approach effectively avoids the process of manually classifying data in traditional curriculum learning which greatly reduces the consumption of time and resources. To validate our method, we conduct experiments on the MET-MEME dataset for a multimodal emotion classification task and deeply analyze the impact of curriculum learning on the training results. The results prove the effectiveness of our method.

关键词： Curriculum learning data Augmentation Knowledge System

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Context-Aware Selection of machine learning as a Service (MLaaS) in IoT Environments

Context-Aware Selection of Machine Learning as a Service ...

引用

PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web data Analytics, AIWDA 2024 form the 25th international conference on Web Information Systems Engineering, WISE 2024

作者： Patel, Keya Mistry, Sajib Kanneganti, Deepak Krishna, Aneesh School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia

ISBN: (纸本)9789819614820

The integration of machine learning as a Service (MLaaS) into the Internet of Things (IoT) environments presents considerable opportunities for enhancing decision-making and automation. We propose a novel framework for context-aware selection of MLaaS in IoT settings, aimed at optimising the interaction between IoT users’ activities and machine learning services. Our framework considers various contextual dimensions, such as user preferences, locations, IoT device capabilities, and application requirements, to develop a dynamic selection process. By employing context-aware algorithms, our approach seeks to enhance the efficiency, accuracy, and responsiveness of IoT systems. We propose a context change analysis algorithm based on support vector machines (SVM). We develop a contextual bandits algorithm along with skyline queries to achieve optimal mapping between abstract MLaaS services and concrete MLaaS services for quality of service (QoS) attributes. Experiments conducted with real-world and simulated datasets demonstrate the effectiveness of our proposed methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Application and research of particle swarm optimization algorithm in cloud data backup 2

Application and research of particle swarm optimization algo...

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2nd international conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Song, Tao Liu, Na Liaoning Economic Vocational Technological Institute Shenyang110122 China

ISBN: (纸本)9781510689053

With the rapid development of cloud computing technology, data security and backup have become an important guarantee for enterprises. The purpose of this study is to optimize the data allocation, storage strategy and resource management of cloud data backup through particle swarm optimization algorithm. In this paper, the basic principle and flow of Particle Swarm Optimization algorithm are introduced in detail, and a cloud data backup model based on Particle Swarm Optimization algorithm is proposed to solve the problems of resource allocation, unreasonable selection of storage nodes and optimization of backup time strategy in data backup. Through experimental comparison and performance analysis, this paper verifies the effectiveness and feasibility of Particle Swarm Optimization algorithm in improving cloud data backup efficiency, reducing backup cost and optimizing resource utilization. This study proves the important application value of Particle Swarm Optimization in cloud data backup optimization, which can improve backup efficiency and reduce resource consumption. This study also provides theoretical basis and practical guidance for further exploring the application of intelligent algorithms in cloud computing. © 2025 SPIE.

关键词： Particle swarm optimization data backup Clouds Particles data storage Mathematical optimization machine learning Cloud computing data modeling data processing

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Construction and realization of modern Chinese vocabulary base driven by big data 2

Construction and realization of modern Chinese vocabulary ba...

引用

2nd international conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Luo, Zhong Faculty of Language and Literature Anhui Sanlian University Anhui Hefei230601 China

ISBN: (纸本)9781510689053

With the rapid advancement of big data technology, the construction of large-scale databases has become a critical focus in computer science. Existing research primarily explores the integration of big data with various computational methods, such as machine learning, data mining, and cloud computing, to enhance data processing efficiency and analytical capabilities. However, there is still a gap in the development of specialized databases that can effectively leverage big data for specific applications. This study aims to address this gap by proposing a novel approach to constructing a modern Chinese vocabulary database driven by big data techniques. The research focuses on the application of advanced computer methods, including data preprocessing, indexing, and retrieval algorithms, to optimize database performance. The ultimate goal is to develop a robust and scalable database system that can efficiently handle large volumes of data and provide accurate and timely information for various computational tasks. © 2025 SPIE.

关键词： data assimilation

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Federated learning as a Service for Hierarchical Edge Networks with Heterogeneous Models 22nd

Federated Learning as a Service for Hierarchical Edge Networ...

引用

22nd international conference on Service Oriented computing

作者： Gao, Wentao Tavallaie, Omid Chen, Shuaijun Zomaya, Albert Univ Sydney Sch Comp Sci Sydney NSW Australia Univ Oxford Dept Engn Sci Oxford England

ISBN: (纸本)9789819608041;9789819608058

Federated learning (FL) is a distributed machine learning (ML) framework that is capable of training a new global model by aggregating clients' locally trained models without sharing users' original data. Federated learning as a service (FLaaS) offers a privacy-preserving approach for training machine learning models on devices with various computational resources. Most proposed FL-based methods train the same model in all client devices regardless of their computational resources. However, in practical Internet of Things (IoT) scenarios, IoT devices with limited computational resources may not be capable of training models that client devices with greater hardware performance hosted. Most of the existing FL frameworks that aim to solve the problem of aggregating heterogeneous models are designed for Independent and Identical Distributed (IID) data, which may make it hard to reach the target algorithm performance when encountering non-IID scenarios. To address these problems in hierarchical networks, in this paper, we propose a heterogeneous aggregation framework for hierarchical edge systems called HAF-Edge. In our proposed framework, we introduce a communication-efficient model aggregation method designed for FL systems with two-level model aggregations running at the edge and cloud levels. This approach enhances the convergence rate of the global model by leveraging selective knowledge transfer during the aggregation of heterogeneous models. To the best of our knowledge, this work is pioneering in addressing the problem of aggregating heterogeneous models within hierarchical FL systems spanning IoT, edge, and cloud environments. We conducted extensive experiments to validate the performance of our proposed method. The evaluation results demonstrate that HAF-Edge significantly outperforms state-of-the-art methods.

关键词： Federated learning as a Service (FLaaS) Model Heterogeneity Edge computing IoT Networks

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Research on data-driven Virtual Experimental Framework 24

Research on Data-driven Virtual Experimental Framework

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7th international conference on Educational Technology Management, ICETM 2024

作者： Li, Miaomiao He, Xiangchun College of Educational Technology Northwest Normal University Gansu Lanzhou China

ISBN: (纸本)9798400717468

With the rapid development of computing technology, data-driven method has become an indispensable part of scientific research and engineering design. Aiming at the problems of high cost and long cycle of traditional experimental methods, a data-driven virtual experimental framework is proposed. The framework utilizes advanced data analysis techniques and machine learning algorithms to predict and simulate the behavior of complex systems without the need for physical experiments. Based on the research of virtual experiment literature, this paper understands the current status of virtual experiment research, summarizes the key elements of virtual experiment, and proposes a virtual experiment framework including data layer, service layer and user layer by using design-based research method. The framework includes knowledge graph construction of data layer and behavior data construction based on xAPI. Service layer data collection, analysis and application and other basic functions;The design of specific virtual experiments in the application layer and the feedback of experiment reports, and the data collection, analysis and application methods for the overall experiment design through the experiment link. In the part of result report, it not only gives the overall results of the data analysis of the whole experiment process, but also gives the personalized resources and intelligent assistant database resources to provide data feedback for the improvement of the virtual experiment. © 2024 Copyright held by the owner/author(s).

关键词： data driven

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