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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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MiDedup: A Restore-Friendly Deduplication Method on Docker Image Storage Systems 20th

MiDedup: A Restore-Friendly Deduplication Method on Docker ...

引用

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

作者： Qin, Lisha Tan, Haoliang Zou, Xiangyu Ou, Wenhao Wei, Yan Huang, Rubing Xia, Wen Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China Zhongruan International Technology Service Co. Ltd. Shenzhen China Macau University of Science and Technology Macao China

ISBN: (纸本)9789819628292

Docker image storage systems, like Docker registry, often employ deduplication to reduce storage overhead. Existing deduplication methods for these systems detect redundancy at either layer or file level, each with its own advantages and limitations. Layer-level deduplication offers much faster restoration but lower deduplication ratio, as it eliminates duplicate layers while ignoring duplicate files across layers. Conversely, file-level deduplication offers a higher deduplication ratio but slower restoration, as removing duplicate files scatters the files of a layer, leading to multiple disk read I/O during restoration. Our analysis reveals that the slow restoration in file-level deduplication partly stems from ignoring file-layer relationships. In this paper, we propose MiDedup, a new Docker deduplication method. MiDedup detects redundancy at file-level and organizes files into a restore-friendly layout by considering file-layer relationships to enhance restore speed. Our studies also show that duplicated files of one layer are not uniformly distributed across different layers. MiDedup uses this insight to skip deduplicating scattered files to further optimize the file layout for faster restoration. Evaluations indicate that MiDedup achieves restore speeds that are 8–19× faster than file-level deduplication (slightly slower than layer-level one), while maintaining 9–47% higher deduplication ratios than layer-level deduplication. © ifip international Federation for Information Processing 2025.

关键词： Redundancy

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Scalable High-Performance Community Detection Using Label Propagation in Massive networks 16th

Scalable High-Performance Community Detection Using Label Pr...

引用

16th international conference on Social networks Analysis and Mining

作者： Boddu, Sharon Khan, Maleq Texas A&M Univ Dept Elect Engn & Comp Sci Kingsville TX 78363 USA

ISBN: (纸本)9783031785405;9783031785412

Community detection is the problem of finding naturally forming clusters in networks. It is an important problem in mining and analyzing social and other complex networks. Community detection can be used to analyze complex systems in the real world and has applications in many areas, including network science, data mining, and computational biology. Label propagation is a community detection method that is simpler and faster than other methods such as Louvain, InfoMap, and spectral-based approaches. Some real-world networks can be very large and have billions of nodes and edges. Sequential algorithms might not be suitable for dealing with such large networks. This paper presents distributed-memory and hybrid parallel community detection algorithms based on the label propagation method. We incorporated novel optimizations and communication schemes, leading to very efficient and scalable algorithms. We also discuss various load-balancing schemes and present their comparative performances. These algorithms have been implemented and evaluated using large high-performance computing systems. Our hybrid algorithm is scalable to thousands of processors and has the capability to process massive networks. This algorithm was able to detect communities in the Metaclust50 network, a massive network with 282 million nodes and 42 billion edges, in 654 s using 4096 processors.

关键词： Community detection parallel graph algorithms network analysis graph mining

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A Multi-Location Enhanced Multi-Scale Feature Fusion Skin Lesion Segmentation network Based on U-shaped network 8

A Multi-Location Enhanced Multi-Scale Feature Fusion Skin Le...

引用

8th international conference on Video and Image Processing, ICVIP 2024

作者： Wu, Rongqing Song, Haixing Zeng, Xuqing Li, Guanglian Zhang, Junyu He, Fuyun Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China Key Laboratory of Integrated Circuits and Microsystems Guangxi Normal University Education Department of Guangxi Zhuang Autonomous Region Guilin China

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

With the continuous development of deep learning, computer-aided diagnosis reduces the pressure of dermatologists. Although many U-Net-based convolution neural networks can achieve effective segmentation, they still need to be improved when faced with challenging skin lesion segmentation tasks such as fuzzy and irregular lesions. To this end, we propose a skin lesion segmentation algorithm (MSMF-Net) based on U-shaped network architecture, which mainly includes the following aspects: the feature distribution of different encoding stages are fused and fully utilised by the EFM module. To better handle high-level semantic features, multiscale and attention parallel module (MS-AM) is introduced at the bottleneck of the network. Attention bridging module (ABM) is used at skip connection, which enables effective local and global information fusion and provides rich feature information for decoding. The decoding fusion module (DFM) processes the decoded information by flexibly embedding channel and spatial attention into the fusion process, so that the information is more concentrated in the lesion area. Our network is compared with other state-of-the-art methods on two public datasets and obtains the best results. © 2025 SPIE.

关键词： Convolutional neural networks

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Research on new energy grid-connected human-machine cooperative training system applying parallel control and transformer modeling 3

Research on new energy grid-connected human-machine cooperat...

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3rd international conference on Algorithms, network, and Communication Technology, ICANCT 2024

作者： Li, Miao Wang, Jing Zhuang, Yuan Training Center of Jilin Province State Grid Corporation of China Changchun China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

The purpose of this paper is to propose a panoramic human-machine collaborative training system that can adapt to new energy grid-connected operation conditions, simulate various complex situations, and provide regulators with simulation exercises, intelligent deductions, decision-making references, Q&A services, which provide intelligent reference solutions for precise regulation under new energy access scenarios with high percentage of new energy under a new type of power system. In order to enhance training effectiveness and operational efficiency, it is need to apply intelligent technology and data-driven models instead of experience and human labor. Interactive Q&A application such as Chat GPT has subversively changed the implementation mode of the training process, making the traditional teaching of theory and basic knowledge easier and faster. However, these commercial Q&A systems still have limitations in terms of accuracy, security, as well as professionalism, which makes them inefficient in professional learning area. In this paper, we use the idea of parallel control and the transformer model to construct a human-computer cooperative training system adapted to new energy grid-connected electric power systems, which realize a human-computer cooperative training model that highly integrates the Q&A services with the real trainer, the real trainees, the computer simulation system. By constructing a large model of human-computer system, a training computing experiment platform, as well as a system with a closed loop of training reality, the parallel training will help training program planning, training teaching design, training arrangements, teaching interaction and other key training links, and realize automated and intelligent training design and execution. As a training and management model adapted to the situation of artificial intelligence, parallel training will bring brand new possibilities for the development of the training industry in the intellige

关键词： Decision making

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Energy-Aware Node Selection for Cloud-Based parallel Workloads with Machine Learning and Infrastructure as Code 15

Energy-Aware Node Selection for Cloud-Based Parallel Workloa...

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15th international conference on Cloud computing and Services Science, CLOSER 2025

作者： Citadin, Denis B. Rossi, Fábio Diniz Luizelli, Marcelo C. Navaux, Philippe O.A. Lorenzon, Arthur F. Institute of Informatics Federal University of Rio Grande do Sul Brazil Federal Institute Farroupilha Campus Alegrete Brazil Federal University of Pampa Campus Alegrete Brazil

ISBN: (纸本)9789897587474

Cloud computing has become essential for executing high-performance computing (HPC) workloads due to its on-demand resource provisioning and customization advantages. However, energy efficiency challenges persist, as performance gains from thread-level parallelism (TLP) often come with increased energy consumption. To address the challenging task of optimizing the balance between performance and energy consumption, we propose SmartNodeTuner. It is a framework that leverages artificial intelligence and Infrastructure as Code (Iac) to optimize performance-energy trade-offs in cloud environments and provide seamless infrastructure management. SmartNodeTuner is split into two main modules: a BuiltModel Engine leveraging an artificial neural network (ANN) model trained to predict optimal TLP and node configurations;and AutoDeploy Engine using IaC with Terraform to automate the deployment and resource allocation, reducing manual efforts and ensuring efficient infrastructure management. Using ten well-known parallel workloads, we validate SmartNodeTuner on a private cloud cluster with diverse architectures. It achieves a 38.2% improvement in the Energy-Delay Product (EDP) compared to Kubernetes’ default scheduler and consistently predicts near-optimal configurations. Our results also demonstrate significant energy savings with negligible performance degradation, highlighting SmartNodeTuner ’s effectiveness in optimizing resource use in heterogeneous cloud environments. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Energy efficiency

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Large and parallel Human Sorting networks 7th

Large and Parallel Human Sorting Networks

引用

7th international conference on Creative Mathematical Sciences Communication, CMSC 2024

作者： Szeider, Stefan Algorithms and Complexity Group TU Wien Vienna Austria

ISBN: (纸本)9783031732560

This paper presents two innovative extensions of the classic Human Sorting network (HSN) activity from the CS Unplugged program. First, we describe the implementation of a large-scale HSN with 50 input nodes, realized with high school students in Vienna, Austria. We detail the logistical challenges and solutions for creating an HSN of this magnitude, including location selection, network layout, and participant coordination. Second, we report on using parallel 6-input HSNs, which introduce a competitive element and enhance engagement. This parallel setup allows for races between teams and can be adapted for various age groups and knowledge levels. Both extensions aim to increase the educational impact and enjoyment of the HSN activity. We provide comprehensive insights into our experiences, enabling other educators and researchers to replicate or further develop these HSN variants. © The Author(s) 2025.

关键词： algorithmic education Computational Thinking CS Unplugged Human sorting networks large-scale computing activities parallel sorting algorithms

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3rd international conference on Internet of Everything, IoECon 2024

3rd International Conference on Internet of Everything, IoEC...

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3rd international conference on Internet of Everything, IoECon 2024

ISBN: (纸本)9783031844256

The proceedings contain 10 papers. The special focus in this conference is on Internet of Everything. The topics include: parallelism Everywhere: The Internet of Everything and parallel computing;ioV Simulation Architecture for Software-Defined Vehicular Fog network Orchestration;toward Modeling of Flooding Attacks Targeting Massive IoT networks;using the Internet of Everything for Machine-Learning-Based Computer System Design and Optimization;enhancing Urban Freight Delivery: A Machine Learning Approach to Predicting Delivery Speed;soil-Based Thermoelectric Energy Harvesting System for IoT Devices;a Methodology for Measuring Selected Performance Parameters of Smartphone-Based Vehicle-to-Pedestrian and Pedestrian-to-Infrastructure Communication;wordPress Architecture Modernization Projects.

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Integrating More-Than-Human Perspectives in Speculative Prototyping Based on Actor-network Theory 23rd

Integrating More-Than-Human Perspectives in Speculative Pro...

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23rd ifip TC 14 international conference on Entertainment computing, ICEC 2024

作者： Li, Jiawei Fu, Zhiyong Wang, Jiayue Zhu, Lin Qian, Fengying Tsinghua University Beijing100084 China Beijing Institute of Fashion Technology Beijing China

ISBN: (纸本)9783031743528

This study is to develop educational methods that enable students to acquire practical skills that go beyond the realm of human design and solve real-world problems. Using a collection of speculative design cases within the Human-Computer Interaction (HCI) domain, including "The Pink Chicken Project," "Afterlife," and "Asunder," we introduce two core models: Actor-network Maps and Actor Portraits. Both models take into account human and non-human actors such as AI agents, artifacts, animals, and the natural environment. Through a workshop with 10 participants from diverse backgrounds, we validate the effectiveness of these methods in fostering a holistic understanding of Actor-network Theory (ANT). The workshop involves two phases: human-based participatory research and AI-assisted content generation using GPT-4. By comparing AI-generated content with human-created results, we demonstrate how AI can enrich interaction design education by integrating more-than-human perspectives. This approach guides students towards creating more equitable and sustainable solutions in their design practices. Furthermore, the study provides a comprehensive framework combining theoretical understanding and practical application, serving as a valuable resource for educators and students in interaction design disciplines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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parallel Digital VLSI Architecture for Combined SVM Training and Classification 5th

Parallel Digital VLSI Architecture for Combined SVM Trainin...

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5th international conference on computing and network Communications, CoCoNet 2023

作者： Priyadarshi, Sidhant Siddamal, Saroja V. KLE Technological University Hubballi India

ISBN: (纸本)9789819745395

The paper presents a parallel Digital Hardware architecture for Integrated Support Vector Machine Training and Classification. The architecture is designed with three layers of parallelism. As use case, 1000 data sets of diabetic patients are considered to classify diabetic and non-diabetic based on the Support Vector Machine (SVM) Algorithm. The design is synthesized using Cadence Genus with TSMC 180 nm technology. Design is much faster than software tools which are used to execute behavioral codes of AI/ML or faster than general-purpose CPU’s. The design takes 1024×1024 clock cycles of period 7.147 ns which is in total 7.494 ms to train raw data sets which is attractively much better than software. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： General purpose computers

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