检索结果-内蒙古大学图书馆

A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform, MERLIN, to Facilitate Healthcare AI Research

引用

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第6期27卷 3014-3025页

作者： Cohen, Raphael Y. Y. Kovacheva, Vesela P. P. Harvard Med Sch Brigham & Womens Hosp Dept Anesthesiol Perioperat & Pain Med Boston MA 02115 USA PathAI Boston MA 02215 USA

Healthcare artificial intelligence (AI) holds the potential to increase patient safety, augment efficiency and improve patient outcomes, yet research is often limited by data access, cohort curation, and tools for analysis. Collection and translation of electronic health record data, live data, and real-time high-resolution device data can be challenging and time-consuming. The development of clinically relevant AI tools requires overcoming challenges in data acquisition, scarce hospital resources, and requirements for data governance. These bottlenecks may result in resource-heavy needs and long delays in research and development of AI systems. We present a system and methodology to accelerate data acquisition, dataset development and analysis, and AI model development. We created an interactive platform that relies on a scalable microservice architecture. This system can ingest 15,000 patient records per hour, where each record represents thousands of multimodal measurements, text notes, and high-resolution data. Collectively, these records can approach a terabyte of data. The platform can further perform cohort generation and preliminary dataset analysis in 2-5 minutes. As a result, multiple users can collaborate simultaneously to iterate on datasets and models in real time. We anticipate that this approach will accelerate clinical AI model development, and, in the long run, meaningfully improve healthcare delivery.

关键词： Artificial intelligence Medical services Data models Bioinformatics Python Microservice architectures Brain modeling distributed computing electronic healthcare records medical information systems

来源：评论

学校读者我要写书评

暂无评论

Federated-Learning-Based Synchrotron X-Ray Microdiffraction Image Screening for Industry Materials

引用

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第2期19卷 2228-2237页

作者： Chen, Bo Xu, Kang Zhu, Yongxin Tian, Li Chang, Victor Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Aston Univ Aston Business Sch Dept Operat & InformationManagement Birmingham B47ET England

Synchrotron X-ray microdiffraction (mu XRD) services are conducted for industrial minerals to identify their crystal impurities in terms of crystallinity and potential impurities. mu XRD services generate huge loads of images that have to be screened before further processing and storage. However, there are insufficient effective labeled samples to train a screening model since service consumers are unwilling to share their original experimental images. In this article, we propose a physics law-informed federated learning (FL) based mu XRD image screening method to improve the screening while protecting data privacy. In our method, we handle the unbalanced data distribution challenge incurred by service consumers with different categories and amounts of samples with novel client sampling algorithms. We also propose hybrid training schemes to handle asynchronous data communications between FL clients and servers. The experiments show that our method can ensure effective screening for industrial users conducting industrial material testing while keeping commercially confidential information.

关键词： Client sampling distributed computing federated learning (FL) industrial inspection machine learning synchrotron radiation X-ray

来源：评论

学校读者我要写书评

暂无评论

RSP-gcForest: A distributed Deep Forest via Random Sample Partition

RSP-gcForest: A Distributed Deep Forest via Random Sample Pa...

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Li, Mark Junjie Cai, Wenzhu Lin, Yigang Huang, Sunjie Huang, Joshua Zhexue Peng, Patrick Xiaogang Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen Academy of Inspection and Quarantine China

ISBN: (纸本)9798350324457

Deep Forest, a powerful alternative to deep neural networks, has gained much attention due to its advantages, such as low complexity, minimal hyperparameter requirements, and strong application performance. In the current big data environment, where data volumes and model complexities are growing rapidly, distributed computing is needed to increase computational efficiency. Recently, a distributed deep forest approach, called BLB-gcForest (Bag of Little Bootstraps-gcForest), has been successful in reducing training instances within cascade forests, combining BLB and granularity segmentation, thus improving the computational efficiency and scalability of distributed deep forests. However, it still transmits the entire data set between layers and requires double sampling with BLB, limiting the amount of data and the scalability of resource utilization. This paper introduces a novel algorithm, RSP-gcForest, based on Random Sample Partition (RSP) to improve distributed deep forests computational efficiency and scalability. RSP-gcForest uses block-level samples that replace the full dataset, significantly reducing interlayer instance transmission and prediction within cascade forests. Additionally, RSP blocks are integrated with the segmentation granularity of cascade forests for ensemble learning, effectively addressing computational efficiency and resource constraints. We conducted experiments on four extensive datasets using Spark and evaluated performance across five key metrics. The results clearly show that RSP-gcForest, while maintaining high classification quality, surpasses state-of-the-art methods in terms of computational efficiency and resource utilization. Furthermore, it achieves superior load balancing, demonstrating its potential as a powerful tool in big data and distributed computing. © 2023 IEEE.

关键词： Deep forest distributed computing Ensemble Learning Random Sample Partition

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Machine Learning on Apache Spark

引用

IEEE ACCESS 2023年 11卷 127907-127930页

作者： Brito, Claudia V. Ferreira, Pedro G. Portela, Bernardo L. Oliveira, Rui C. Paulo, Joao T. INESC TEC P-4200465 Porto Portugal Univ Minho Dept Informat P-4710057 Braga Portugal Univ Porto Fac Sci P-4099002 Porto Portugal

The adoption of third-party machine learning (ML) cloud services is highly dependent on the security guarantees and the performance penalty they incur on workloads for model training and inference. This paper explores security/performance trade-offs for the distributed Apache Spark framework and its ML library. Concretely, we build upon a key insight: in specific deployment settings, one can reveal carefully chosen non-sensitive operations (e.g. statistical calculations). This allows us to considerably improve the performance of privacy-preserving solutions without exposing the protocol to pervasive ML attacks. In more detail, we propose Soteria, a system for distributed privacy-preserving ML that leverages Trusted Execution Environments (e.g. Intel SGX) to run computations over sensitive information in isolated containers (enclaves). Unlike previous work, where all ML-related computation is performed at trusted enclaves, we introduce a hybrid scheme, combining computation done inside and outside these enclaves. The experimental evaluation validates that our approach reduces the runtime of ML algorithms by up to 41% when compared to previous related work. Our protocol is accompanied by a security proof and a discussion regarding resilience against a wide spectrum of ML attacks.

关键词： Cluster computing Training Machine learning Hardware Task analysis Homomorphic encryption distributed computing Trusted computing Privacy-preserving machine learning distributed systems apache spark trusted execution environments Intel SGX

来源：评论

学校读者我要写书评

暂无评论

MHND: Multi-Homing Network Design Model for Delay Sensitive Applications

引用

IEICE TRANSACTIONS ON COMMUNICATIONS 2023年第11期E106B卷 1143-1153页

作者： Kawabata, Akio Chatterjee, Bijoy Chand Oki, Eiji Toyohashi Univ Technol Toyohashi 4418580 Japan South Asian Univ New Delhi India Kyoto Univ Kyoto 6068501 Japan

When mission -critical applications are provided over a network, high availability is required in addition to a low delay. This paper proposes a multi -homing network design model, named MHND, that achieves low delay, high availability, and the order guarantee of events. MHND maintains the event occurrence order with a multi -homing configuration using conservative synchronization. We formulate MHND as an integer linear programming problem to minimize the delay. We prove that the distributed server allocation problem with MHND is NP -complete. Numerical results indicate that, as a multi -homing number, which is the number of servers to which each user belongs, increases, the availability increases while increasing the delay. Noteworthy, two or more multi -homing can achieve approximately an order of magnitude higher availability compared to that of conventional single -homing at the expense of a delay increase up to two times. By using MHND, flexible network design is achieved based on the acceptable delay in service and the required availability.

关键词： delay sensitive service network design availability distributed computing conservative synchronization

来源：评论

学校读者我要写书评

暂无评论

Age-Aware Fairness in Blockchain Transaction Ordering for Reducing Tail Latency

引用

IEEE TRANSACTIONS ON NETWORKING 2025年第2期33卷 807-822页

作者： Sokolik, Yaakov Nassar, Mohammad Rottenstreich, Ori Technion Israel Inst Technol IL-3200003 Haifa Israel

In blockchain networks, transaction latency is crucial for determining the quality of service (QoS). The latency of a transaction is measured as the time between its issuance and its inclusion in a block in the chain. A block proposer often prioritizes transactions with higher fees or transactions from accounts it is associated with, to minimize their latencies. To maintain fairness among transactions, a block proposer is expected to select the included transactions randomly. The random selection might cause some transactions to experience high latency following the variance in the time a transaction waits until it is selected. We suggest an alternative, age-aware approach towards fairness so that transaction priority is increased upon observing a large waiting time. We explain that a challenge with this approach is that the age of a transaction is not absolute due to transaction propagation. Moreover, a node might present its transactions as older to obtain priority. We describe a new technique to enforce a fair block selection while prioritizing transactions that observed high latency. The technique is based on various declaration schemes in which a node declares its pending transactions, providing the ability to validate transaction age. By evaluating the solutions on Ethereum data and synthetic data of various scenarios, we demonstrate the advantages of the approach under realistic conditions and understand its potential impact to maintain fairness and reduce tail latency.

关键词： Peer-to-peer computing Tail Quality of service Recording Nonce Finance Computational modeling Bitcoin User experience distributed computing blockchains cryptocurrency cryptocurrency

来源：评论

学校读者我要写书评

暂无评论

Balancing local vs. remote state allocation for micro-services in the cloud-edge continuum

引用

PERVASIVE AND MOBILE computing 2023年 93卷

作者： Puliafito, Carlo Cicconetti, Claudio Conti, Marco Mingozzi, Enzo Passarella, Andrea Univ Pisa Dept Informat Engn Largo Lucio Lazzarino 1 I-56122 Pisa Italy CNR Inst Informat & Telemat Via Giuseppe Moruzzi 1 I-56124 Pisa Italy Univ Florence Dept Informat Engn Via S Marta 3 I-50139 Florence Italy

In the world of cloud technologies, serverless computing has now settled as a stable and promising resident. This gives a cloud provider the flexibility to provide its users with both Platform-as-a-Service (PaaS), i.e., the back-end application runs in a dedicated container, or Function-as-a-Service (FaaS), i.e., the back-end logic is offered as elementary functions that are invoked by the client applications. In parallel, edge computing has attracted a significant interest, due its enticing promises of reducing the outbound traffic of telco operators, while at the same time cutting down the user latency. As a result, in the near future, PaaS and FaaS containers are going to cohabit in a versatile computation infrastructure spanning from the far edge up to the cloud. In this paper we propose a mathematical formulation of a resource allocation problem that optimizes the assignment of both types of containers and can be solved efficiently by an edge orchestrator. We evaluate the proposed solution via extensive simulation experiments, which show that our approach, which takes into account the characteristics of PaaS vs. FaaS, provides significant performance benefits compared to less sophisticated strategies, despite its relatively low run-time complexity.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： FaaS Function-as-a-service distributed computing Edge computing Micro-services Stateful functions

来源：评论

学校读者我要写书评

暂无评论

OPTIMIZING HADOOP DATA LOCALITY: PERFORMANCE ENHANCEMENT STRATEGIES IN HETEROGENEOUS computing ENVIRONMENTS

引用

SCALABLE computing-PRACTICE AND EXPERIENCE 2024年第6期25卷 4558-4575页

作者： Kim, Si-Yeong Kim, Tai-Hoon Chonnam Natl Univ Sch Elect & Comp Engn Yeosu Campus Gwangju 59626 South Korea

As organizations increasingly harness big data for analytics and decision-making, the efficient processing of massive datasets becomes paramount. Hadoop, a widely adopted distributed computing framework, excels in processing large-scale data. However, its performance is contingent on effective data locality, which becomes challenging in heterogeneous computing environments comprising diverse hardware resources. This research addresses the imperative of enhancing Hadoop's data locality performance in heterogeneous computing environments. The study explores strategies to optimize data placement and task scheduling, considering the diverse characteristics of nodes within the infrastructure. Through a comprehensive analysis of Hadoop's data locality algorithms and their impact on performance, this work proposes novel approaches to mitigate challenges associated with disparate hardware capabilities. Weighted Extreme Learning Machine Technique (Weighted ELM) with the Firefly Algorithm (WELM-FF) is used in the proposed work. The integration of Weighted Extreme Learning Machine (WELM) with the Firefly Algorithm holds promise for enhancing machine learning models in the context of large-scale data processing. The research employs a combination of theoretical analysis and practical experiments to evaluate the effectiveness of the proposed enhancements. Factors such as network latency, disk I/O, and CPU capabilities are taken into account to develop a holistic framework for improving data locality and, consequently, overall Hadoop performance. The findings presented in this study contribute valuable insights to the field of distributed computing, offering practical recommendations for organizations seeking to maximize the efficiency of their Hadoop deployments in heterogeneous computing environments. By addressing the intricacies of data locality, this research strives to enhance the scalability and performance of Hadoop clusters, thereby facilitating more effective utilizatio

关键词： Hadoop Data Locality Performance Enhancement Heterogeneous computing distributed computing Big Data

来源：评论

学校读者我要写书评

暂无评论

FIDES: A Proposal for Federated Accountability in the Compute Continuum

引用

IEEE INTERNET computing 2023年第5期27卷 33-42页

作者： Duran, Amador Fernandez, Pablo Garcia, Jose Maria Dustdar, Schahram Univ Seville Seville 41004 Spain Vienna Univ Technol Comp Sci A-1040 Vienna Austria Vienna Univ Technol Res Div Distributed Syst A-1040 Vienna Austria

In this visionary article, we present the concept of federated accountability, an innovative approach that distributes accountability-related computation and data across the compute continuum. To demonstrate the feasibility and versatility of our approach, we developed a prototype using blockchain technology that serves as a tangible illustration of how federated accountability can be applied across various domains.

关键词： Privacy Computational modeling Biological system modeling Prototypes Blockchains Reliability Federated learning distributed computing

来源：评论

学校读者我要写书评

暂无评论

SQUID: subtrajectory query in trillion-scale GPS database

引用

VLDB JOURNAL 2023年第4期32卷 887-904页

作者： Zhang, Dongxiang Chang, Zhihao Yang, Dingyu Li, Dongsheng Tan, Kian-Lee Chen, Ke Chen, Gang Zhejiang Univ Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China Natl Univ Def Technol Changsha Peoples R China Natl Univ Singapore Singapore Singapore

Subtrajectory query has been a fundamental operator in mobility data management and useful in the applications of trajectory clustering, co-movement pattern mining and contact tracing in epidemiology. In this paper, we make the first attempt to study subtrajectory query in trillion-scale GPS databases, so as to support applications with urban-scale moving users and weeks-long historical data. We develop SQUID as a distributed subtrajectory query processing engine on Spark, with threefold technical contributions. First, we propose compact index and storage layers to handle massive trajectory datasets with trillion-scale GPS points. Second, we leverage hybrid partitioning, together with local indexes that are disk I/O friendly, to facilitate pruning. Third, we devise a novel filter-and-refine query processing framework to effectively reduce the number of trajectories for verification. Our experiments are conducted on huge trajectory datasets with up to 520 billion GPS points. The results validate the compactness of the storage mechanism and the scalability of the distributed query processing framework.

关键词： Subtrajectory query Subtrajectory join distributed computing Trillion-scale GPS databases

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：