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检索条件"任意字段=International Conference on Big Data Intelligence and Computing, DataCom 2022"

共 1359 条记录，以下是381-390 订阅

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AI-based Blockchain for the Metaverse: Approaches and Challenges 4

AI-based Blockchain for the Metaverse: Approaches and Challe...

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4th international conference on Blockchain computing and Applications (BCCA)

作者： Bouachir, Ouns Aloqaily, Moayad Karray, Fakhri Elsaddik, Abdulmotaleb Zayed Univ Coll Technol Innovat CTI Dubai U Arab Emirates Mohamed Bin Zayed Univ Artificial Intelligence MB Machine Learning Dept Abu Dhabi U Arab Emirates

ISBN: (数字)9781665499583

ISBN: (纸本)9781665499583

The recent advances in AI have enabled novel approaches and remarkable enhancements in the provision of existing services and applications. The metaverse is one of the emerging applications that got the advantages of this technological progression. Due to the cooperation between several cutting-edge technologies including IoT, digital twin (DT), and big data, the metaverse highly relies on the manipulation of an immense amount of gathered data. In this context, security is a major challenge, and as such, blockchain brings several advantages to the metaverse ecosystem as it allows secure and transparent data storage. However, with the dynamic nature of such an ecosystem and the fast variation in its AI-based services, the traditional blockchain features (e.g. smart contracts and consensus protocols) will face big challenges to meet the needed dynamic service requirements and scalability. This positioning paper proposes the concept of using a blockchain governed by AI to leverage the various features and advantages of blockchain services. The paper provides a review of the role of blockchain in the metaverse, challenges that arise, and then the role of AI in building intelligent blockchain features and their impact on the metaverse ecosystem.

关键词： Metaverse blockchain Immersive Services AI XR Autonomous Resource Management

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A Review of FPGA Accelerated computing Methods for YOLO Models 24

A Review of FPGA Accelerated Computing Methods for YOLO Mode...

引用

4th international conference on Internet of Things and Machine Learning

作者： Bian, Yifan Luo, Dongxiang Zhang, Menglong South China Normal Univ Sch Semicond Sci & Technol Foshan Guangdong Peoples R China Guangzhou Univ Coll Chem & Chem Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)9798400710353

In recent years, with the rapid evolution of deep learning and neural networks, combined with the advent of the big data and intelligence era, one of the models in the field of object detection, YOLO (You Only Look Once) has also become a hot research topic. Addressing the critical benchmarks of object detection-Timeliness Rate and Accuracy Rate-has prompted a surge in research dedicated to constructing an FPGA (Field-Programmable Gate Array)-based acceleration scheme. In this article, we first provide an overview of neural networks and hardware platforms, followed by an in-depth exploration of the implementation of the YOLO model on FPGA hardware platforms. Additionally, we consolidate and review the current state of FPGA acceleration for YOLO models. Subsequently, we undertake a thorough analysis of the performance of different acceleration techniques. Finally, we delve into the exploration and discussion of potential future directions for development.

关键词： YOLO (You Only Look Once) FPGA (Field-Programmable Gate Array) model acceleration hardware acceleration

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POTDP: Research GPU Performance Optimization Method based on Thread Dynamic Programming 4

POTDP: Research GPU Performance Optimization Method based on...

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4th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2022

作者： Wei, Xiong Hu, Qian Li, Li School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

GPU is widely used in high-performance computing such as big data and artificial intelligence because of its high concurrency and high throughput. With the development of VLSI technology, more and more processing units are integrated on chip. High power consumption increases the operating cost of equipment, reduces the battery life and reliability of integrated circuit chip, which seriously restricts the improvement of integrated circuit chip performance and restricts the expansion and application field of parallel systems. In view of the above problem, this paper proposes a data dependent GPU power management method- DDPM to reduce the power con-sumption of GPU system. The experimental results of DDPM show that compared with the shared aware data management method, DDPM improves the L1 cache hit rate by 2.8%, reduces DRAM data transmission capacity by 5%, and improves the average energy efficiency by 4.67% compared with MC-aware-ORI, MC-aware-LoSe and MC-aware-SiOb. © 2022 IEEE.

关键词： Dynamic programming

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A Systematic Review of Distributed Deep Learning Frameworks for big data 18th

A Systematic Review of Distributed Deep Learning Frameworks ...

引用

18th international conference on Intelligent computing (ICIC)

作者： Berloco, Francesco Bevilacqua, Vitoantonio Colucci, Simona Polytech Univ Bari I-70126 Bari Italy Apulian Bioengn Srl Via Violette 14 I-70026 Modugno BA Italy

ISBN: (纸本)9783031138324;9783031138317

Traditional Machine Learning and Deep Learning techniques (data acquisition, preparation, model training and evaluation) take a lot of computational resources and time to produce even a simple prediction model, especially when implemented on a single machine. Intuitively, the demand for computational requirements is higher in case of management of big data and training of complex models. Thus, a paradigm shift from a single machine to a BD-oriented approach is required for making traditional Machine Learning and Deep Learning techniques fit to big data. In particular, it emerges the need for developing and deploying big data Analytics Infrastructures on cluster of machines. In this context, main features and principles of Distributed Deep Learning frameworks are here discussed. The main contribution of this paper is a systematic review of proposed solutions, aimed at investigating under a unifying lens their foundational elements, functional features and capabilities, despite the inherent literature fragmentation. To this, we conducted a literature search in Scopus and Google Scholar. This review also compares Distributed Deep Learning approaches according to more technical facets: implemented of parallelism techniques, supported hardware, model parameters sharing modalities, computation modalities for stochastic gradient descent and compatibility with other frameworks.

关键词： big data Distributed Deep Learning Parallel computing Distributed Deep Learning Frameworks

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Proceedings - 2022 IEEE/ACIS 7th international conference on big data, Cloud computing, and data Science, BCD 2022

Proceedings - 2022 IEEE/ACIS 7th International Conference on...

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7th IEEE/ACIS international conference on big data, Cloud computing, and data Science, BCD 2022

ISBN: (纸本)9781665465823

The proceedings contain 73 papers. The topics discussed include: efficient scalable migrations in the cloud;a study on factors determining public acceptance of telemedicine in Malaysia’s private healthcare system;modeling customer satisfaction based on kano model from online reviews: focused on deep learning natural language processing;a study on big data utilization intention of small and medium-sized manufacturing companies;a study on the factors affecting customer satisfaction in delivery applications: focusing on sentiment analysis of review data;a study on the intention to use new innovative financial service;analysis of the predictive power of highway traffic flow with weather conditions;a case study on the continuous usage intention of artificial intelligence speaker in product service system perspective;a study on the factors affecting the intention to adopt of blockchain-based identity certification services in the defense sector;a study on the intention to use of smart home service based on AI - focusing on fine dust detection solution;and the effect of non-face-to-face collaboration system quality on business performance.

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splitDyn: Federated Split Neural Network for Distributed Edge AI Applications

splitDyn: Federated Split Neural Network for Distributed Edg...

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2022 IEEE international conference on big data, big data 2022

作者： Khoa, Tran Anh Nguyen, Do-Van Dao, Minh-Son Zettsu, Koji National Institute of Information and Communications Technology Big Data Integration Research Center Tokyo Japan

ISBN: (纸本)9781665480451

Split learning (SL) is a popular distributed machine learning (ML) method used to enable ML. It divides a neural network based model into subnetworks. Then, it separately trains the subnetworks on distributed parties (e.g., client and server). In distributed ML, data are generated and collected on the client-side. In contrast, the collected data are processed using an application deployed on the server side. However, when applied in practice using Internet of things systems and clients, numerous obstacles occur because of limited configuration and resources. Dividing neural networks in the SL is the biggest problem and an open question in numerous studies. This study introduces splitDyn, which is a new dynamic SL solution to solve the aforementioned problems. This method provides a solution for eliminating their inherent drawbacks. The main idea is to apply a Round-Robin schedule to select the client for the training process. Then, the next idea is to use the Hungarian optimization algorithm to assign a layer to a client and enhance the accuracy. The proposed method reasonably achieved better accuracy and reduced processing time than the other learning models. Furthermore, it applies the incident datasets to predict the incident event and in edge computing for edge artificial intelligence (AI) applications. © 2022 IEEE.

关键词： Edge computing

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Multi-UAV-Assisted Electromagnetic Jamming: Path Optimization Based on Deep Reinforcement Learning 8

Multi-UAV-Assisted Electromagnetic Jamming: Path Optimizatio...

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8th international conference on Smart Internet of Things

作者： Chai, Hongzheng Wan, Liangtian Sun, Lu Zheng, Jianbo Wang, Xianpeng Dalian Univ Technol Sch Software Dalian 116620 Peoples R China Dalian Maritime Univ Inst Informat Sci Technol Dalian 116026 Peoples R China Shenzhen MSU BIT Univ Artificial Intelligence Res Inst Shenzhen Peoples R China Hainan Univ Sch Informat & Commun Engn Haikou 570228 Hainan Peoples R China

ISBN: (纸本)9798350366457;9798350366440

Unmanned Aerial Vehicles (UAVs) have been widely applied in various fields due to their flexibility, low cost, and rapid deployment capabilities. UAVs can be equipped with electromagnetic jamming devices to disrupt target areas, even in complex or hazardous environments. In this paper, we propose a comprehensive optimization scheme for the problem of multi-UAV-assisted electromagnetic jamming. This scheme models the problem as a Markov game and employs a multi-agent reinforcement learning (MARL) algorithm to schedule UAVs, aiming to achieve superior electromagnetic jamming effects while ensuring safer positional deployment. We first construct a model for the UAV-assisted electromagnetic jamming scenario based on realistic conditions. Then, we address the problem of electromagnetic jamming on targets by a UAV swarm and propose an improved algorithm based on existing MARL algorithm. This improvement effectively mitigates the issue of agent inertia, making the algorithm more suitable for our specific problem. Experimental results show that our proposed algorithm outperforms existing algorithms in optimizing the electromagnetic jamming effectiveness and reducing the risk factor.

关键词： Multi-Agent Reinforcement Learning UAV Electromagnetic jamming

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MADDPG-Based Power Control Strategy for Unmanned Vehicles 8

MADDPG-Based Power Control Strategy for Unmanned Vehicles

引用

8th international conference on Smart Internet of Things

作者： Han, Zhenyuan Sun, Lu Wan, Liangtian Luo, Ruoheng Wang, Xianpeng Dalian Maritime Univ Inst Informat Sci Technol Dalian 116026 Peoples R China Dalian Univ Technol Sch Software Dalian 116620 Peoples R China Shenzhen MSU BIT Univ Artificial Intelligence Res Inst Guangdong Hong Kong Macao Joint Lab Emot Intellig Shenzhen Peoples R China Hainan Univ Sch Informat & Commun Engn Haikou 570228 Hainan Peoples R China

ISBN: (纸本)9798350366457;9798350366440

In the domain of unmanned vehicle networks, efficient spectrum utilization is crucial due to the scarcity of spectrum resources. This paper addresses the challenge of dynamic spectrum access by proposing an advanced strategy to enhance spectral efficiency in such networks. We present a novel approach that combines Multi-Agent Deep Deterministic Policy Gradient (MADDPG) with Echo State Networks (ESN) and prioritized experience replay mechanisms. Our approach focuses on optimizing the power control of unmanned vehicles to maximize overall spectral efficiency while maintaining communication quality for all vehicles. The proposed MADDPG_ESN algorithm integrates the ESN to effectively capture temporal dynamics and improve learning efficiency, and incorporates prioritized experience replay to optimize training stability and performance. This combination allows for rapid and accurate decision-making in the complex environment of unmanned vehicle communications. Our findings underscore the effectiveness of combining ESNs with prioritized experience replay in reinforcement learning for dynamic spectrum access, offering significant improvements in system performance and resource utilization.

关键词： Spectrum resource allocation power control MADDPG Echo State Networks prioritized experience replay mechanisms

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Toward Pervasive Computer Vision for Intelligent Transport System

Toward Pervasive Computer Vision for Intelligent Transport S...

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20th IEEE international conference on Pervasive computing and Communications (IEEE PerCom)

作者： Leone, Giuseppe Riccardo Carboni, Andrea Nardi, Simone Moroni, Davide Natl Res Council Italy Inst Informat Sci & Technol Pisa Italy Eikontech Srl Computat Sci Dept Pisa Italy

ISBN: (纸本)9781665416474

The Smart Passenger Center (SPaCe) is a fully integrated platform that aims to overcome the complexity of centralized management of public transport infrastructure and vehicles. The SPaCe artificial intelligence engine predicts threats and critical events and proposes countermeasures by examining the daily flows of people and correlating different data and events, thanks to machine learning and big data analytics. All this massive data comes from a pervasive smart camera network that constantly monitors activities in stations, trains, buses and other places of interest. In this work, we present the idea of this computer vision distributed sub-system, the state of the art of the techniques involved and the advanced functionalities that this intelligent surveillance system offers to the upper layers. Everything is developed following the privacy-by-design paradigm;namely, no real image is recorded or transmitted, but all the elaborations take place on the edge nodes of the system.

关键词： Intelligent Transport System pervasive computing edge computing privacy-by-design multiple people tracking

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Research and application of data-driven carbon emission analysis and prediction 2

Research and application of data-driven carbon emission anal...

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2nd international conference on Cloud computing, big data Application and Software Engineering, CBASE 2023

作者： Wei, Dongmei Meng, Dan Southwest University of Finance and Economics School of Management Science and Engineering Chengdu611130 China Xihua University School of Computer and Software Engineering Chengdu610039 China

ISBN: (纸本)9798350331448

The issue of carbon emissions has become an important policy direction for countries around the world to promote healthy socio-economic development and address climate change issues. This article conducts feature extraction and data analysis on various major energy data in China from 1997 to 2017, and constructs a model for verifying and predicting total carbon emissions. The model can not only accurately reflect China's carbon emissions over the past twenty years, but also predict future carbon emissions. This not only provides scientific basis for government decision-making, but also enriches the application of data-driven and artificial intelligence methods in this field. © 2023 IEEE.

关键词： Machine learning

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