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检索条件"任意字段=Proceedings of the First International Conference on Parallel and Distributed Information Systems"

共 6615 条记录，以下是61-70 订阅

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Exploiting Fine-Grained Structured Pruning for Efficient Inference on CNN Model 29

Exploiting Fine-Grained Structured Pruning for Efficient Inf...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Wu, Cheng-Hung Hong, Ding-Yong Liu, Pangfeng Wu, Jan-Jan National Taiwan University Department of Computer Science and Information Engineering Taiwan Institute of Information Science Academia Sinica Taiwan

ISBN: (纸本)9798350330717

Weight pruning is a technique to remove redundant or unimportant weights from the network. It can help reduce the size and computational cost of neural networks while preserving their accuracy. In this paper, we aim to design efficient CNN models with N:M pruning on the CPU. We propose a dynamic programming algorithm to find a good sparsity ratio for every layer under a total time budget based on the execution times and L1 norm of layers. After deciding the sparsity ratio of each layer, we leverage the auto-tuner of the TVM compiler to search for an optimization schedule of the pruned convolution to accelerate fine-grained pruned models. Experimental results show that our scheme can achieve 0.35% accuracy improvement and a 1.55× speedup on VGG-16 with ImageNet than the dense model. © 2023 IEEE.

关键词： deep neural network weight pruning

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FedSKG: Self-supervised Federated Learning with Structural Knowledge of Global Model 29

FedSKG: Self-supervised Federated Learning with Structural K...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Li, Jun Luo, Dan Wei, Kang Mei, Zhen Shao, Yumeng Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (纸本)9798350330717

Federated self-supervised learning (FedSSL) is an emerging method in the domain of machine learning. It collaboratively learns a powerful feature extractor among multiple participants by utilizing distributed unlabeled data. However, conventional FedSSL suffers from statistical heterogeneity due to the non-independent and identically distributed (Non-IID) data among participants. In this work, we introduce a novel method to tackle the Non-IID data issue in FedSSL. first, the relation knowledge distillation is utilized to enhance the learning from global models. Then, we dynamically update the local model with divergence-aware update (DAU) method to preserve the client's knowledge of Non-IID data. Our experimental results demonstrate that the proposed approach outperforms other methods by up to 8% on linear evaluation, verifying the effectiveness of our approach. © 2023 IEEE.

关键词： Federated self-supervised learning knowledge distillation non-independent and identically distributed

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A QoE-Aware Adaptive Energy-Efficient Transmission Scheduling Method 30

A QoE-Aware Adaptive Energy-Efficient Transmission Schedulin...

引用

30th IEEE international conference on parallel and distributed systems, ICPADS 2024

作者： Yuan, Yankun Wang, Lin Xiao, Chonghui Liu, Zijuan Dang, Fan Wang, Xu Zhao, Haitian Miao, Xin Yanshan University School of Information Science and Engineering China National Computer Network Emergency Response Technical Team/Coordination Center of China China Tsinghua University Department of Automation and BNRist China Tsinghua University Global Innovation Exchange China Tsinghua University School of Software and BNRist China

ISBN: (纸本)9798331515966

In this paper, we propose a dynamic data transmission strategy for smart home environments that aims to optimize the Quality of Experience (QoE) by adaptively adjusting the data upload frequency based on the predicted trends in sensor data. Using the home wireless sensors monitoring dataset, we implement a deep learning model for accurate time series forecasting. In addition, an anomaly detection mechanism is used to identify critical events, requiring more frequent data uploads when important changes are detected. The QoE is quantified through a weighted average of several influencing factors, including data timeliness, timely upload of critical events, and transmission frequency. Our optimization objective is to maximize QoE while minimizing the number of transmissions, with an emphasis on reducing energy consumption through intelligent scheduling. The results demonstrate that our approach effectively balances data timeliness, transmission efficiency, and energy savings, leading to improved user satisfaction in smart home applications. © 2024 IEEE.

关键词： Adaptive algorithm Anomaly detection Energy conservation IoT LSTM Models QoE

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Effective Search Strategy for Moving Targets in Unknown Environments Using Multiple Robots 30

Effective Search Strategy for Moving Targets in Unknown Envi...

引用

30th IEEE international conference on parallel and distributed systems, ICPADS 2024

作者： Ye, Shujin Chang, Ming-Yui Chan, Tse-Tin Liu, Hai Wang, Yue Yu, Lu The Hang Seng University of Hong Kong Department of Computer Science Hong Kong University College Department of Computer Science London United Kingdom The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong

ISBN: (纸本)9798331515966

Robots are widely used for target search in applications such as search and rescue, environmental monitoring, and surveillance. Existing search algorithms typically rely on target signals or predictable movement patterns, which might not be available in real applications. In this paper, we address the practical challenge of a target search problem where targets do not emit signals and have unpredictable movement patterns. The problem is formulated as an area coverage problem: how to maximize the coverage of the robots' detection areas within a limited time. We propose an algorithm that divides the search area into multiple partitions and assigns specific partitions to robots for maximizing their coverage and the success rate of target detection. Within each partition, the random walk technique is adopted by the robots to handle robot failures and unknown obstacles. Through theoretical analysis and experiments, we explore the optimal number of partitions as well as the optimal partition shape to facilitate searching. Extensive simulations across different dynamic environments validate the effectiveness and adaptability of our proposed algorithm. © 2024 IEEE.

关键词： Area coverage autonomous robots dynamic environments moving targets target search

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Acceleration of BAM I/O on distributed file systems

Acceleration of BAM I/O on distributed file systems

引用

2023 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Ito, Satoshi Miyano, Satoru Ono, Kenji Tokyo Medical and Dental University Dept. of Integrated Analysis M&d Data Science Center Tokyo Japan Kyushu University Section of Applied Data Science Res. Inst. for Information Technology Fukuoka Japan

ISBN: (纸本)9798350337488

Rapid advances in high-throughput sequencers have made it possible to obtain large amounts of whole genome data quickly and inexpensively. As the amount of data increases, the increase in computation time has become a serious problem. One of the main causes of this problem is file I/O performance. Most pipelines do not implement file I/O suitable for distributed file systems, which are common storage systems in parallel computers used for large-scale analysis. In this study, we developed an I/O system adapted to distributed file systems. In the proposed system, storage access frequency was suppressed by I/O buffers, and thread parallelization by OpenMP was employed. We tested the developed system and a high parallel speedup was achieved. Availability: The developed system is freely available from https://***/SatoshiITO/GAPS © 2023 IEEE.

关键词： distributed File System High performance computing Whole genome sequencing analysis

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Optimization design of computer information processing systems in the big data perspective

Optimization design of computer information processing syste...

引用

2023 international conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Zheng, Zhaoxia Chi Feng Institute of Industrial Technology Inner Mongolia Chifeng024000 China

ISBN: (纸本)9781510677685

Computer information processing systems face numerous challenges in the context of big data, and optimization design provides an important avenue to enhance system performance in handling large datasets. This paper first analyzes the bottlenecks, inefficiencies, and poor scalability challenges that big data poses to existing systems. Subsequently, it proposes three system optimization design approaches: modular design, pipeline parallel processing, and elastic scaling mechanisms. Specific solutions such as modular segmentation, MapReduce pipeline implementation, and dynamic resource allocation are presented based on these optimization ideas. These optimization strategies and design solutions effectively enhance the system's scalability and processing performance in the face of big data. Therefore, this paper offers important insights and approaches to drive the evolution of computer information processing systems into the era of big data. © 2024 SPIE.

关键词： Digital storage

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A Truth Inference Algorithm Using Bidirectional Convolution Autoencoder for Crowdsourcing Image Segmentation 29

A Truth Inference Algorithm Using Bidirectional Convolution ...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Li, Yueyang Dong, Zhaoan Li, Guangshun Wang, Sifeng Wang, Xiaofan Liu, Zhibin Li, Zhenqiang Wang, Boyong Qufu Normal University School of Computer Science Rizhao China

ISBN: (纸本)9798350330717

In the paper, we propose a truth inference algorithm based on bidirectional convolutional autoencoder to capture and utilize the internal structure information underling complex tasks, e.g. image segmentation. firstly, the correlation information of adjacent pixels is extracted from the horizontal and vertical directions of the image by two encoders, and each pixel is encoded by the feature. Then, the embedding features of the two encoders are weighted and fused to obtain a new embedding feature, and the pixels are clustered according to this new embedding feature. Finally, determine whether the cluster is a background or an object based on what most people choose. Experiments are conducted on four real-world biomedical image datasets, and the experimental results prove the effectiveness and robustness of proposed algorithm. © 2023 IEEE.

关键词： crowdsourcing image segmentation the hidden internal structure information truth inference

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Early Exploration of Using ChatGPT for Log-based Anomaly Detection on parallel File systems Logs 23

Early Exploration of Using ChatGPT for Log-based Anomaly Det...

引用

32nd international Symposium on High-Performance parallel and distributed Computing (HPDC) part of the ACM Federated Computing Research conference (FCRC)

作者： Egersdoerfer, Chris Zhang, Di Dai, Dong Univ N Carolina Charlotte NC 27599 USA

ISBN: (纸本)9798400701559

Log-based anomaly detection has been extensively studied to help detect complex runtime anomalies in production systems. However, existing techniques exhibit several common issues. first, they rely heavily on expert-labeled logs to discern anomalous behavior patterns. But labelling enough log data manually to effectively train deep neural networks may take too long. Second, they rely on numeric model prediction based on numeric vector input which causes model decisions to be largely non-interpretable by humans which further rules out targeted error correction. In recent years, we have witnessed groundbreaking advancements in large language models (LLMs) such as ChatGPT. These models have proven their ability to retain context and formulate insightful responses over entire conversations. They also present the ability to conduct few-shot and in-context learning with reasoning ability. In light of these abilities, it is only natural to explore their applicability in understanding log content and conducting anomaly classification among parallel file system logs.

关键词： anomaly detection parallel file system large language model

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parallel Computing Optimization of Tourism Big Data based on Crawler Programs 1

Parallel Computing Optimization of Tourism Big Data based on...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Zhou, Xiaoyu Wuhan Donghu University School of Management Hubei Wuhan China

ISBN: (纸本)9798331529246

With the rapid development of the tourism industry, traditional tourism methods are undergoing significant transformation, and online tourism is gradually becoming a new highlight in the market. However, faced with the challenges of big data, online tourism data experiences tremendous pressure in terms of storage, computation, and management, and the issue of information overload seriously affects the user experience. Therefore, there is an urgent need for innovative platforms and recommendation algorithms to optimize the screening and recommendation of tourism information. This paper proposes an optimization scheme based on the Spark cloud computing platform to improve the efficiency of tourism data processing. first, utilizing Spark's distributed computing and memory management capabilities, tourism data can be stored and processed in a distributed manner, thus accelerating the computation speed of recommendation algorithms. Secondly, a Python-written crawler program is used to obtain real tourism data, including user ratings and comments on attractions, to evaluate the effectiveness of the improved algorithm. Finally, a weighted algorithm combining the LDA topic model is proposed. This algorithm analyzes the topic distribution of comment texts and weights the comments based on user ratings, calculating the predicted score for unrated attractions for the target user, thereby achieving accurate attraction recommendations. The experimental results show that the algorithm proposed in this study demonstrates significant advantages in prediction accuracy. © 2025 IEEE.

关键词： Cloud platforms

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Research on Medical information Retrieval Mechanism Based on distributed Storage and Computation 1

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16th international conference on Ubiquitous Computing and Ambient Intelligence

作者： Zhao Qigang Qiu Xiaoping Li Pengfei Wu Yongkang Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Southwest Jiaotong Univ Tangshan Inst Tangshan 063000 Peoples R China Sichuan Univ West China Hosp Dept Lab Med Chengdu 610041 Peoples R China Jintang First Peoples Hosp Chengdu 610499 Peoples R China

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

With the continuous advancement of medical technology and equipment, the healthcare domain is undergoing an unprecedented data explosion. Data from gene sequences, medical imaging to daily health monitoring offers invaluable insights for improving disease diagnosis and treatment efficacy. Concurrently, the challenge of how to effectively store, manage, access, and share this vast amount of data has emerged as a pressing issue. Traditional centralized data storage methods, with inherent flaws such as data silos, vulnerable single points of failure, and inefficiency in data transmission and access, are increasingly proving to be inadequate. To address these challenges, this research delves into a medical information access mechanism based on distributed computation and storage. This mechanism, considering the diversity, sensitivity, and continuity of medical data, introduces a novel data model. In this model, data is stored in shards across a distributed network. Each shard of data is encrypted to ensure security, and a hash chain structure is employed to ensure data integrity and continuity. Moreover, a mathematical model describing the process of data storage, retrieval, and sharing is established, and corresponding experiments are designed for validation. Experimental results indicate that the mechanism excels in enhancing data access efficiency, ensuring data safety, and maintaining data integrity, paving the way for a new solution in future medical data management.

关键词： distributed computation and storage medical information access mechanism mathematical model

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