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检索条件"任意字段=20th International Conference on Distributed Computing and Intelligent Technology, ICDCIT 2024"

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20th international conference on distributed computing and intelligent technology, icdcit 2024

20th International Conference on Distributed Computing and I...

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20th international conference on distributed computing and intelligent technology, icdcit 2024

ISBN: (纸本)9783031505829

the proceedings contain 24 papers. the special focus in this conference is on distributed computing and intelligent technology. the topics include: A Multi-class Classification for Detection of IoT Network Attacks Using Machine Learning Models;optimizing Sentiment Analysis on Twitter: Leveraging Hybrid Deep Learning Models for Enhanced Efficiency;identification of Onset and Progression of Alzheimer’s Disease Using Topological Data Analysis;enhancing Mario Gaming Using Optimized Reinforcement Learning;deep Learning in Industry 4.0: Transforming Manufacturing through Data-Driven Innovation;SASE: Sentiment Analysis with Aspect Specific Evaluation Using Deep Learning with Hybrid Contextual Embedding;a Genetic Algorithm-Based Heuristic for Rumour Minimization in Social Networks;a Machine Learning Driven Approach for Forecasting Parkinson’s Disease Progression Using Temporal Data;prediction of High-Resolution Soil Moisture Using Multi-source Data and Machine Learning;an Improved and Efficient distributed computing Framework with intelligent Task Scheduling;Evaluating Deep CNNs and Vision Transformers for Plant Leaf Disease Classification;intelligent Ensemble-Based Road Crack Detection: A Holistic View;online Payment Fraud Detection for Big Data;bursty Event Detection Model for Twitter;a General Variable Neighborhood Search Approach for the Clustered Traveling Salesman Problem with d-Relaxed Priority Rule;scheduling of Containerized Resources for Microservices in Cloud;ELITE: Energy and Latency-Optimized Task Offloading for DVFS-Enabled Resource-Constrained Devices in MEC;parking Problem by Oblivious Mobile Robots in Infinite Grids;the Computational Landscape of Autonomous Mobile Robots: the Visibility Perspective;coverage Criteria Based Testing of IoT Applications;An Online Algorithm for Cost Minimization of Amazon EC2 Burstable Resources;a Practical and Efficient Key-Aggregate Cryptosystem for Dynamic Access Control in Cloud Storage.

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Research on the Application of intelligent Education in Practical Teaching for Mechanical and Electrical Engineering Undergraduates 1

Research on the Application of Intelligent Education in Prac...

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1st international conference on intelligent Manufacturing and Cloud computing, ICIMCC 2024

作者： Xie, Yi Zheng, Liming Navy Submarine Academy Shangdong Qingdao China

ISBN: (纸本)9781643685779

With the rapid development of technology, intelligent education has gradually become a research hotspot in the field of education. the third Plenary Session of the 20th Central Committee of the Communist Party of China explicitly proposed to promote the digitalization of education and align with the global "Education 4.0". this article explores the application of intelligent education in practical teaching for undergraduate students majoring in mechanical and electrical engineering, aiming to improve teaching effectiveness and students’ operational skills. By analyzing relevant literature, this article proposes specific application strategies for establishing a complete practical teaching system for undergraduate students majoring in mechanical and electrical engineering, constructing an intelligent teaching platform for practical teaching in this field, enhancing teachers’ intelligent literacy in mechanical and electrical engineering, and innovating the intelligent teaching evaluation methods for practical teaching. these strategies can provide references for the cultivation of undergraduate students majoring in mechanical and electrical engineering in colleges and universities. © 2025 the Authors.

关键词： Students

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An Efficient Device Placement Method for distributed Training of Multi-branch Neural Network-Based Remote Sensing Interpretation 25th

An Efficient Device Placement Method for Distributed Traini...

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25th international conference on Web Information Systems Engineering, WISE 2024

作者： Long, Ao Wang, Yuewei Huang, Xiaohui Han, Wei Fan, Runyu Chen, Yunliang Li, Jianxin School of Computer Science China University of Geosciences Wuhan China School of Information Technology Deakin University Melbourne Australia

ISBN: (纸本)9789819605699

Multi-branch neural networks are widely used in remote sensing intelligent interpretation because they can fuse multi-modal remote sensing data to improve interpretation accuracy. Meanwhile, in the current "big data era" of remote sensing, the application of distributed deep learning has been steadily growing to improve the training efficiency of remote sensing interpretation models. However, prevailing distributed training systems necessitate users to manually devise a parallelization plan or automatically generate one from a restricted space of model parallelism configurations. At the same time, these techniques are not suitable for extending the application of multi-branch neural network-based remote sensing interpretation on computing devices. In this paper, considering both the data and the model parallelism, we design and implement a fast and near-optimal device placement method for automatically placing branches in multi-branch neural networks across multiple devices. the core idea is to consider the trade-off between the computation and the inter-device communication to minimize the total training time. through careful formulation of the problem as an integer program, our method can offer optimal placement and scheduling of the branches. We conduct experiments to deploy and evaluate the proposed method on two multi-branch networks. the results indicate that the method finds non-trivial device placements outperforming baselines. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mixed-integer linear programming

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intelligent Telemetry: P4-Driven Network Telemetry and Service Flow intelligent Aviation Platform 20th

Intelligent Telemetry: P4-Driven Network Telemetry and Serv...

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Gao, Jing Zhou, Fanqin Dong, Mianxiong Feng, Lei Ota, Kaoru Li, Zijian Fan, Jiawei State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Information and Electronic Engineering Muroran Institute of Technology Muroran050-8585 Japan Beijing University of Posts and Telecommunications Address Beijing Beijing100876 China

ISBN: (纸本)9789819628292

To address the current challenges in network monitoring and management, such as poor real-time performance, high cost, low accuracy, and slow fault detection and recovery, a P4-driven network telemetry and resource scheduling platform is designed and presented in this paper. this platform embeds telemetry information within data packets, achieving real-time monitoring and anomaly detection of network states. It significantly enhances the granularity and timeliness of network monitoring and employs a path rerouting mechanism for rapid network recovery. the platform boasts high scalability and customizability, allowing on-demand adjustment of telemetry strategies and parameters. It provides low-overhead, business-level telemetry information, offering comprehensive support for diverse services and advancing network management and service quality. this work provides a technical foundation for constructing the next generation of intelligent, efficient, and reliable networks through continuous research and practical application. © IFIP international Federation for Information Processing 2025.

关键词： Telemetering

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Efficient Task Offloading in MEC via UAV-UGV Collaboration 20th

Efficient Task Offloading in MEC via UAV-UGV Collaboration

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Su, Tianchi Ma, Qian School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen China

ISBN: (纸本)9789819628636

In UAV-assisted mobile edge computing (MEC) systems, the flexibility of unmanned aerial vehicles (UAVs) significantly extends service range and adaptability. However, UAVs are constrained by limited battery life and flight range, which restrict their ability to cover larger areas effectively. While some works consider vehicles as charging stations for UAVs, the potential for unmanned ground vehicles (UGVs) to collaborate with UAVs and serve as edge computing nodes remains largely unexplored. In this paper, we formulate an online joint task offloading and trajectory planning problem to minimize the total service delay in an MEC system with UAV-UGV collaboration, where UAVs not only land on UGVs for recharging but also use UGVs for long-distance transportation. the challenges of solving this problem include the long-term energy constraints, the time-coupled UAV-UGV collaboration decisions, and the non-convexity of the optimization problem. We propose a hierarchical solution framework to solve the problem. Specifically, we first employ Lyapunov optimization to handle energy constraints. We then use the sample average approximation method to handle the time-coupled UAV-UGV collaboration decisions, and utilize block coordinate descent combined with successive convex approximation to derive the task offloading and trajectory planning strategies. theoretical analysis demonstrates the near-optimal performance of our proposed solution framework. Experimental results validate the effectiveness of our framework in reducing service delay. © IFIP international Federation for Information Processing 2025.

关键词： Unmanned aerial vehicles (UAV)

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

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

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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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Adaptive Gradient Data Partition and Route Selection for distributed DNN Training 20th

Adaptive Gradient Data Partition and Route Selection for D...

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Chai, Bo Tan, Xiaobin Yuan, Shenzhi Jia, Guangge Meng, Qiushi Wang, Weifeng Zhu, Shiyin University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China H3C Technologies Co. Limited Beijing China

ISBN: (纸本)9789819628636

As the scale of distributed training for Deep Neural Network (DNN) increases, communication has become a critical performance bottleneck in data center networks. In-Network Aggregation (INA) can accelerate aggregating gradients process, the critical communication bottleneck in distributed DNN training. Due to the limited processing capacity of network switch, existing aggregation approaches have turned to a combination of INA and Parameter Server (PS) methods. However, these approaches employ a fixed partitioning of the gradient data, leading to suboptimal performance. this paper proposes GPRS, an adaptive gradient aggregation framework for distributed DNN training, which adaptively combines the line-rate processing of INA and large memory capacity of PS. We present an adaptive gradient data partition scheme, which divides the gradient data into INA and PS aggregation. We propose a heuristic algorithm that determines the aggregation route selection by assigning priority to the available network paths. To validate our approach, we build a real-world experimental testbed and implement the proposed framework. Experimental results show that it can overcome the limitations of traditional INA-only methods and reduce communication time by 15% ∼ 52% for DNN training of varying scales. © IFIP international Federation for Information Processing 2025.

关键词： Data aggregation

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SpecInF: Exploiting Idle GPU Resources in distributed DL Training via Speculative Inference Filling 20th

SpecInF: Exploiting Idle GPU Resources in Distributed DL Tr...

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Lv, Cunchi Shi, Xiao Liang, Dong Tan, Wenting Zhao, Xiaofang Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Nanjing China Zhongguancun Laboratory Haidian China Nanjing Institute of InforSuperbahn Nanjing China

ISBN: (纸本)9789819628292

Deep Learning (DL), especially with Large Language Models (LLMs), brings benefits to various areas. However, DL training systems usually yield prominent idling GPU resources due to many factors, such as resource allocation and collective communication. To improve GPU utilization, we present SpecInF, which adopts a Speculative Inference Filling method to exploit idle GPU resources. It collocates each primary training instance with additional inference instances on the same GPU, detects the training bubbles and adaptively fills with online or offline inference workloads. Our results show that SpecInF can effectively enhance GPU utilization under mainstream parallel training modes, delivering additional up to 14× offline inference throughputs than TGS and 67% reduction in online inference p95 latency than MPS, while guaranteeing collocated training throughput. © IFIP international Federation for Information Processing 2025.

关键词： Resource allocation

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A Knowledge Graph-Based Approach for Construction Safety Hazards Management and Rectification Measures intelligent Recommendation 20th

A Knowledge Graph-Based Approach for Construction Safety Haz...

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20th international conference on computing in Civil and Building Engineering, ICCCBE 2024

作者： Xiang, Yunfei Lin, Peng Luo, Yiming Xu, Houlei Ning, Zeyu Qiao, Yu Zhou, Mengxia Department of Hydraulic Engineering Tsinghua University Beijing100084 China Huaneng Clean Energy Research Institute Co. Ltd. Beijing102209 China China Three Gorges Corporation Beijing100084 China PowerChina Kunming Engineering Corporation Limited Kunming650000 China

ISBN: (纸本)9783031842238

Identification and rectification of safety hazards are crucial for the safety management of a construction site. However, the absence of structured knowledge necessitates a heavy reliance on experienced managers or experts for construction safety management. this dependence on manual formulation of rectification measures results in inefficient safety management and underutilization of data value. this study proposes a knowledge graph based approach for the safety hazards inspections and recommendation of rectification measures, which consists of four steps: (1) constructing the semantic expression framework of safety hazard ontology for information extraction;(2) extracting entities from textual and semi-structured data using a large language model (ChatGLM-6B);(3) knowledge fusion and inference;(4) storing structured knowledge and developing semantic retrieval and safety hazard rectification measures recommendation. the proposed method was validated in a hydropower project, where a knowledge graph of safety hazards was constructed, consisting of 108,000 entities and 121 relationships. the results demonstrate that this method can automatically provide targeted rectification measures for safety hazards such as fire, falls from heights, scaffold collapse and electric shock. this work can provide a practical reference for improving the effectiveness of the construction project safety management. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Knowledge graph

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MTEE: Multiscale Temporal Entropy Evaluation Paradigm for Heterogeneous Complex Datasets 20th

MTEE: Multiscale Temporal Entropy Evaluation Paradigm for H...

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： An, Ledong Wang, Chenyang Huang, Shaoyuan Guo, Yang Zhang, Cheng Qiu, Chao Wang, Xiaofei College of Intelligent and Computing Tianjin University Tianjin China College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China Tianjin University of Finance and Economics Tianjin China Software Technology Company Limited Suzhou215163 China

ISBN: (纸本)9789819628292

the rapid advancements in time-series forecasting have led to the emergence of datasets with increasingly diverse characteristics. Researchers typically focus on designing robust algorithms to handle these datasets. However, model performance can vary across different datasets. Existing studies commonly use Mean Absolute Error (MAE) and Mean Squared Error (MSE) to evaluate model performance. these metrics often need to fully account for the impact of dataset quality on forecast accuracy and reliability, leading to insufficient explanations of model forecasts and even dataset dependence. Furthermore, the lack of dataset evaluation makes it difficult to determine whether forecast results are influenced by data characteristics or model architecture, significantly hindering model improvement. To address this problem, this study proposes a new dataset evaluation paradigm-Multiscale Temporal Entropy Evaluation Paradigm. this paradigm aims to tackle the problem of data feature ambiguity affecting model forecast interpretability. By evaluating nine time-series datasets from domains such as weather, economics, transportation, and networks and comparing these evaluations with the forecast results from six different models, this study demonstrates the effectiveness of this method in explaining the relationship between dataset features and model performance. © IFIP international Federation for Information Processing 2025.

关键词： Spatio-temporal data

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