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A Utility-Adjustable Reverse Auction Mechanism for UAV Data Collection Task Allocation 20th

A Utility-Adjustable Reverse Auction Mechanism for UAV Data...

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20th ifip WG 10.3 international conference on network and parallel computing, NPC 2024

作者： Chen, Peng Zhang, Jixian School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9789819628636

Due to their low cost, easy deployment, and high flexibility, unmanned aerial vehicles(UAV) have become widely utilized for various data collection tasks across different fields. In particular, virtual service providers(VSP) recruit UAVs to collect data in their regions of interest(ROI), and leverage this data to offer a range of services, such as metaverse, traffic management, etc. To attract UAV owners to participate in the recruitment activities, VSP need to provide participating users with payments as rewards to motivate their participation. However, the recruitment demands of different VSPs for UAVs can vary significantly. For instance, time-sensitive tasks require timely recruitment of sufficient UAVs to ensure prompt completion of data collection tasks, necessitating higher payments to increase user enthusiasm. Conversely, in other types of tasks, the VSP’s objective might be to maximize its own utility. However, designing a general incentive mechanism for VSPs with different tasks that satisfies incentive compatibility(IC) and individual rationality(IR) to address the issues of UAV task allocation and payment is challenging. If VSP offer insufficient payments to users, it will be difficult to attract enough users to share their UAVs;conversely, offering excessive payments to users will harm the VSP’ profits. To address this problem, we propose a utility-adjustable reverse auction mechanism based on reverse Vickrey-Clarke-Groves(VCG) auction and affine maximizer auction(AMA) theory to cope with different task scenarios. this incentive mechanism can maximize social welfare while flexibly adjusting the utility allocation between VSP and users, ensuring non-negative utility for both parties. Experimental results show that the mechanism has excellent performance. © ifip international Federation for Information Processing 2025.

关键词： Unmanned aerial vehicles (UAV)

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LogTrans: Providing Efficient Local-Global Fusion with Transformer and CNN parallel network for Biomedical Image Segmentation 24

LogTrans: Providing Efficient Local-Global Fusion with Trans...

引用

24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Nie, Xingqing Zhou, Xiaogen Li, Zhiqiang Wang, Luoyan Lin, Xingtao Tong, Tong College of Physics and Information Engineering Fuzhou University Fuzhou China Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology Fuzhou University Fuzhou China Imperial Vision Technology Fuzhou China

ISBN: (纸本)9798350319934

Accurate biomedical image segmentation is a prerequisite for excellent computer-aided diagnosis (CAD) systems. A series of researches have shown that convolutional neural networks (CNNs) have made impressive progress in segmentation tasks. Nevertheless, owing to the finite receptive field of CNN-based algorithms, such networks are focused too much on local area features rather than global context. While the Transformer architecture can encode global dependencies information through the self-attention mechanism, this mechanism typically ignores the local pixel-level structural information within each divided patch. therefore, a better solution is still needed for how to integrate CNN architecture with Transformer architecture efficiently. In this essay, we propose an originative parallel segmentation algorithm called LogTrans. First, in the encoder path, the local details and global contour dependencies on the entire image are captured by the CNN branch and the Transformer branch, respectively. then these two branches complement each other by a novel separate-combiner (SeCo) module, leading to better fused features. Moreover, we attempt to further enhance the segmentation properties by using a residual stackable dilated (ReSD) block, which applies residual shortcut connections to resolve dimension alterations in the target region and stacks dilated convolutions to capture more spatial information. the proposed LogTrans framework was evaluated on two biomedical datasets, including ISIC-2017 and UITNS-2022 datasets. Collectively, multiple results have indicated that our LogTrans performs superior with other state-of-the-art architectures in both visual comparison and quantitative appraisal. © 2022 IEEE.

关键词： Convolutional neural networks

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A Platform Independent Model Design and Adaptation for Edge Intelligence 29

A Platform Independent Model Design and Adaptation for Edge ...

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29th international conference on Automation and computing (ICAC)

作者： Zhang, Chaolong Zhou, Jiliu He, Jia Xu, Zhijie Jin, Jin Kong, Chao Xu, Yuanping Guo, Benjun Gai, Qiuyan Chengdu Univ Informat Technol Sch Software Engn Chengdu Peoples R China Chengdu Univ Informat Technol Sch Comp Sci Chengdu Peoples R China Univ Huddersfield Sch Comp & Engn Huddersfield W Yorkshire England

ISBN: (纸本)9798350360882;9798350360899

Along with the rapid developments in deep learning and edge computing technologies, deploying models on mobile devices is a trend for modern applications. However, the resource-constrained and heterogeneous edge devices fail to cope with complicated model inference. To tackle this challenge, this study explores the platform independent model design based on Open Neural network Exchange. then we quantize the existing heavy deep learning models into lightweight ones by applying low-precision arithmetic and partitioning the computational graph into sub-graphs for parallel execution and hardware-based acceleration on heterogeneous systems. this study evaluates the efficiency and effectiveness among different quantization settings and analyses the impacts for model acceleration. the experiments show that the quantized models with uint8 type computation perform faster inference while still preserve high performance. this result suggests a novel solution for the research on edge intelligence.

关键词： Edge computing Edge Intelligence ONNX Model Inference Quantization IoT

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ICSAI 2022 - 8th international conference on Systems and Informatics

ICSAI 2022 - 8th International Conference on Systems and Inf...

引用

8th international conference on Systems and Informatics, ICSAI 2022

ISBN: (纸本)9781665474481

the proceedings contain 56 papers. the topics discussed include: Chinese biomedical entity relation extraction based on directed graph convolutional network;facial expression emotion recognition based on transfer learning and generative model;learning a parallel network for emotion recognition based on small training data;a cognitive computing based system for human factors readiness level assessment in manned space product design;multi-channel contrastive learning for sequential recommendation;short text classification method with dual channel hypergraph convolution networks;research on vehicle parking position detection based on Swin transformer semantic segmentation;a novel ping-pong task strategy based on model-free multi-dimensional Q-function deep reinforcement learning;multi-target threat assessment method based on an effective reduction method;learning a bimodal emotion recognition system based on small amount of speech data;and an intelligent sensor for deep-sea sediment thermal conductivity.

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Incentivizing Crowdsensing for DT-Enabled Metaverse 20th

Incentivizing Crowdsensing for DT-Enabled Metaverse

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20th ifip WG 10.3 international conference on network and parallel computing, NPC 2024

作者： Ye, Dongdong Huang, Xumin Wu, Yuan Kang, Jiawen Zhong, Weifeng Niyato, Dusit School of Automation Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China Department of Computer and Information Science University of Macau Taipa China Collge of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819628292

Digital Twin (DT) has been proposed as a key enabling technology to produce a digital clone of the physical world and facilitate the convergence of the physical and virtual worlds for building a real-world Metaverse. For physical-virtual synchronization, crowdsensing is applied to employ Internet of things (IoT) devices to sense physical objects and collect status information for timely updating the associated DTs. However, an incentive mechanism is necessitated to stimulate the devices to perform sensing tasks while meeting task requirements such as Age of Information (AoI) requirements. thus, we adopt a contract theoretic approach to design an incentive mechanism for the devices that are classified into several types and the type parameters are unknown to others. the contract consists of a set of contract items with each contract item representing a device type, which specifies the required data updating frequency and monetary reward. We formulate an optimal contract design problem subject to feasible constraints including the average AoI requirement for each device type. Furthermore, we solve the problem by transforming it into a convex optimization problem that can be directly tackled by the existing methods. Finally, we provide numerical results to demonstrate the effectiveness and efficiency of our scheme. © ifip international Federation for Information Processing 2025.

关键词： Convex optimization

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Deep Reinforcement Learning for Dependent Task Offloading in Mobile Edge computing Systems 19

Deep Reinforcement Learning for Dependent Task Offloading in...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Gong, Bencan Jiang, Xiaowei Three Gorges University Hubei Key Laboratory of Intelligent Vision Monitoring for Hydropower Engineering Yichang China Three Gorges University College of Computer and Information Technology Yichang China

ISBN: (纸本)9798350346558

In mobile edge computing, there are usually relevant dependencies between different tasks, and traditional algorithms are inefficient in solving dependent task offloading problems and neglect the impact of the dynamic change of the channel on the offloading strategy. To solve the offloading problem of dependent tasks in dynamic network environment, this paper establishes the dependent task model as a directed acyclic graph. A Dependent Task Offloading Strategy (DTOS) based on deep reinforcement learning is proposed with minimizing the weighted sum of delay and energy consumption of network services as the optimization objective. the task-dependent offloading problem is transformed into an optimal policy problem under Markov decision processes. Multiple parallel deep neural networks (DNN) are used to generate offloading decisions, cache the optimal decisions for each round, and then optimize the DNN parameters using experience replay and incentive-driven mechanisms to obtain the optimal task offloading decisions. By using multiple parallel deep neural networks to generate offloading decisions, the impact of oscillations in the training results of individual neural networks on the algorithm is reduced. the experimental results show that, compared with the existing algorithms, the offloading decision generated by the algorithm can effectively reduce the delay and energy consumption of network services. © 2022 IEEE.

关键词： Reinforcement learning

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Violence Detection In Videos via Motion-Guided Global and Local Views 8

Violence Detection In Videos via Motion-Guided Global and Lo...

引用

8th international conference on Data Science in Cyberspace, DSC 2023

作者： Su, Ning Sun, Lijuan Gao, Yutong Wu, Jingchen Wu, Xu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education China School of Cyberspace Security Bupt China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Beijing Jiaotong University Beijing Key Lab of Traffic Data Analysis and Mining Beijing China Bupt China

ISBN: (纸本)9798350331035

Video violence detection aims to locate the time window in which violent behavior occurs. Most methods focus on utilizing RGB features directly or only fusing RGB and audio features, ignoring the effective exploitation of motion information carried in optical flow. this lack of emphasis on motion information may impact the overall accuracy of violence detection. Moreover, we observe that videos contain strong local correlations, so it is insufficient to analyze only from a holistic perspective without capturing finer details. therefore, in this paper, we design a novel Global-and-Local Cross-Modal network (GL-CMN) for violence detection, which effectively integrates motion information and multi-granularity features from target videos. Specifically, we first propose a Motion-Guided Attention Module (MGAM) to obtain enhanced visual features by calibrating RGB features through optical flow features. Secondly, the enhanced features are simultaneously fed into two parallel branches of the network. the global branch fuses the visual and audio features into holistic representations. the local branch extracts multi-scale temporal dependencies through dilated convolutions. Experiments demonstrate that our method exhibits significant improvement compared to previous state-of-the-art methods on the XD-Violence dataset. © 2023 IEEE.

关键词： Attention Mechanism Multimodality Multiple Instance Learning Violence Detection

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TreeOptimizer: A classifier-based task scheduling framework 23

TreeOptimizer: A classifier-based task scheduling framework

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24th international conference on Distributed computing and networking (ICDCN)

作者： Venkatesh, Pruthvi Raj Srinivas, M. Krishna, P. Radha Openstream Technol Bangalore Karnataka India Natl Inst Technol Warangal Andhra Pradesh India

ISBN: (纸本)9781450397964

Distributed computing (DC) involves a collection of tasks (or modules) executed in parallel on different compute nodes connected through a network. Cloud Service providers (CSP) such as Azure[1], Amazon[2], and Google[3] are providing DC platforms as PaaS (Platform As A Service) offerings. these cloud platforms reduce implementation costs but have a significant drawback as these services can be configured to spawn only a single type of compute node for executing all the tasks in the DC environment. these drawback lead to inefficiency in execution cost and time as each task will have specific compute node requirements. this paper presents a novel framework called TreeOptimizer(TO) to resolve these shortcomings. TO uses a classifier-based dynamic task scheduling to determine the best available node to perform the task. the framework has been tested in Azure Batch[1] for an Oil Industry use case for extracting data from scanned images. Experimental results indicate that TO significantly reduces the overall execution cost by 68% and processing time by 8%. Although this paper uses Batch Service to explain the proposed framework, it can be applied to other PaaS DC platforms.

关键词： Distributed computing Azure Batch Decision Tree PaaS CSP

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Performance and Energy Aware Training of a Deep Neural network in a Multi-GPU Environment with Power Capping

Performance and Energy Aware Training of a Deep Neural Netwo...

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29th international conference on parallel and Distributed computing (Euro-Par)

作者： Koszczal, Grzegorz Dobrosolski, Jan Matuszek, Mariusz Czarnul, Pawel Gdansk Univ Technol Fac Elect Telecommun & Informat Narutowicza 11-12 PL-80233 Gdansk Poland

ISBN: (纸本)9783031488023;9783031488030

In this paper we demonstrate that it is possible to obtain considerable improvement of performance and energy aware metrics for training of deep neural networks using a modern parallel multi-GPU system, by enforcing selected, non-default power caps on the GPUs. We measure the power and energy consumption of the whole node using a professional, certified hardware power meter. For a high performance workstation with 8 GPUs, we were able to find non-default GPU power cap settings within the range of 160-200W to improve the difference between percentage energy gain and performance loss by over 15.0%, EDP (Abbreviations and terms used are described in main text.) by over 17.3%, EDS with k = 1.5 by over 2.2%, EDS with k = 2.0 by over 7.5% and pure energy by over 25%, compared to the default power cap setting of 260W per GPU. these findings demonstrate the potential of today's CPU+GPU systems for configuration improvement in the context of performance-energy consumption metrics.

关键词： deep neural network training power capping multi GPU performance-energy optimization

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SCDNet: Real-time Semantic Segmentation network with Split Connection and Flexible Dilated Convolution 19

SCDNet: Real-time Semantic Segmentation Network with Split C...

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作者： Yao, Guangyu Tian, Shu Chen, Song-Lu Yin, Xu-Cheng University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350346558

Recently, semantic segmentation has been applied widely in various realistic scenarios. Many semantic segmentation backbone networks use various forms of dense connection to improve the efficiency of gradient propagation in the network. they achieve excellent segmentation accuracy but lack inference speed. therefore, we propose a backbone network SCDNet with a dual path structure and higher speed and accuracy. Firstly, we propose a split connection structure, which is a streamlined lightweight backbone with a parallel structure to increase inference speed. Secondly, we introduce a flexible dilated convolution using different dilation rates so the network can have richer receptive fields to perceive objects. Finally, we propose a three-level hierarchical module to effectively balance the feature maps with multiple resolutions. Our work achieves a trade-off of accuracy and speed on the Cityscapes and Camvid datasets. Specifically, we get a 20% improvement in FPS and a 0.5% improvement in mIoU on the Cityscapes test set. © 2022 IEEE.

关键词： Convolution

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