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Research on performance optimization of virtual data space across WAN

Frontiers of computer science 2024年第6期18卷 167-187页

作者： Jiantong HUO Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China

For the high-performance computing in a WAN environment,the geographical locations of national supercomputing centers are scattered and the network topology is complex,so it is difficult to form a unified view of *** aggregate the widely dispersed storage resources of national supercomputing centers in China,we have previously proposed a global virtual data space named GVDS in the project of“High Performance Computing Virtual Data Space”,a part of the National Key Research and Development Program of *** GVDS enables large-scale applications of the high-performance computing to run efficiently across ***,the applications running on the GVDS are often data-intensive,requiring large amounts of data from multiple supercomputing centers across *** this regard,the GVDS suffers from performance bottlenecks in data migration and access across *** solve the above-mentioned problem,this paper proposes a performance optimization framework of GVDS including the multitask-oriented data migration method and the request access-aware IO proxy resource allocation *** a WAN environment,the framework proposed in this paper can make an efficient migration decision based on the amount of migrated data and the number of multiple data sources,guaranteeing lower average migration latency when multiple data migration tasks are running in *** addition,it can ensure that the thread resource of the IO proxy node is fairly allocated among different types of requests(the IO proxy is a module of GVDS),so as to improve the application’s performance across *** experimental results show that the framework can effectively reduce the average data access delay of GVDS while improving the performance of the application greatly.

关键词： storage aggregation across WANs large-scale applications GVDS data migration allocation of IO proxy resource

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CARE-EMRs: Efficient and Privacy-Preserving Data Sharing Framework for Electronic Medical Records

CARE-EMRs: Efficient and Privacy-Preserving Data Sharing Fra...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tao, Yu Zhou, Lu Zhu, Yi Li, Yuhang Ge, Chunpeng Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Jiangsu University College of Computer Science and Communication Engineering Zhenjiang China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China and Software School Jinan China

ISBN: (纸本)9798350386226

Cross-institutional Electronic Medical Records (EMRs) sharing significantly improves healthcare quality and reduces costs. Current EMRs are often stored on cloud servers and protected by Attribute-based Encryption (ABE) for access control. Cross-institutional sharing allows doctors to request EMRs access from target Healthcare Institutions (HIs) through attribute-based collaboration, enabling flexible and secure access. However, existing collaboration in practical EMRs sharing scenarios suffers from inefficiency, hindering timely access and treatment. This paper proposes an efficient and privacy-preserving data sharing framework for EMRs, called CARE-EMRs. CARE-EMRs enables efficient EMRs data access for doctors across different HIs through an outsourcing mechanism. Furthermore, we designed an improved cryptographic primitive PPD-CP-ABE to serve as the security foundation for CARE-EMRs framework, ensuring privacy of the outsourced process. We prove the security of CARE-EMRs. Performance analysis indicates that it significantly reduces the computation and communication overhead. © 2024 IEEE.

关键词： Electronic health record

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Managing Information Updating with Edge Computing: A Distributed and Learning Approach 48

Managing Information Updating with Edge Computing: A Distrib...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： He, Junyi Zhang, Di Liu, Shumeng Zhou, Yuezhi Zhang, Yaoxue Beijing Jiaotong University School of Software Engineering China The Chinese University of Hong Kong School of Science and Engineering Shenzhen China Tsinghua University Department of Computer Science and Technology China

ISBN: (纸本)9781728163277

The rapid proliferation of some real-time applications (e.g., video surveillance) has driven enormous interest in maximizing information freshness, quantified by the age of information (AoI). For some computation-intensive updates such as images or videos, the real-time update processing requires intensive resources, which edge servers can provide in mobile edge computing (MEC). In this paper, we investigate information updating scheduling with multiple users in MEC. Due to the centralized algorithms' limitations in distributed systems where users are self-interested, we investigate an efficient distributed scheduling algorithm. We model the information updating scheduling as an uncooperative game and propose a distributed algorithm to compute the unique Nash equilibrium. Considering the unavailability of some global network information, we propose a learning algorithm where each user learns how to make decisions based on observable information in a distributed manner. Extensive evaluation results show the efficiency of the proposed algorithms. © 2023 IEEE.

关键词： Age of information distributed updating scheduling mobile edge computing

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A Novel Heuristics for the Strong Generalized Minimum Label Spanning Tree Problem 3

A Novel Heuristics for the Strong Generalized Minimum Label ...

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3rd International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Long, Yinyan Li, Xiaoxia Long, Zhuming Wu, Fuying Long, Hao School of Computer Engineering and Science ShangHai University ShangHai200444 China School of Software Jiangxi Normal University Nanchang330022 China

ISBN: (纸本)9781643684444

The strong generalized minimum label spanning tree problem (SGMLSTP) is to search the minimum label spanning tree (MLST) from an Edge-labeled graph (ELG), in which each edge is associated with one or more labels. SGMLSTP is commonly existed in reality and proven NP-hard. In recent years, researchers have proposed some algorithms;however, high computational costs are still severe obstacle, especially for large size graphs. In this paper, we propose a novel heuristics to solve SGMLSTP. We decompose the problem into two sub-problems, one is to search a connected subgraph with minimum labels from the original graph, the other is to search a spanning tree from the subgraph. As the latter sub-problem is solved, we focus on the former sub-problem and propose a community-based zigzag piloting algorithm: Firstly a label graph is derived from the original edge-labeled graph;then the label graph is partitioned and some label community (or community combinations) is chosen to form an initial solution;finally, the zigzag piloting process is applied to refine the initial solution. Label partition finds the initial solution quickly, the zigzag piloting process improves solution refinement. Experimental results on typical benchmark datasets show better effectiveness and performance of our algorithm than that of the state-of-the-art algorithms. © 2023 The Authors.

关键词： Trees (mathematics)

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Research on Map Construction and Location Technology Based on Multi-line LiDAR 5th

Research on Map Construction and Location Technology Based o...

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5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022

作者： Liu, Fang Yang, Yang Su, Weixing School of Software Engineering Tiangong University Tianjin300387 China School of Computer Science and Technology Tiangong University Tianjin300387 China

ISBN: (纸本)9789819910267

Point cloud registration is an important part of 3D point cloud map construction based on multi-line lidar. At present, traditional iterative nearest point algorithm (ICP) is mainly used for point cloud registration. For the traditional iterative nearest point algorithm, point cloud registration is time-consuming and error-prone, which leads to the low accuracy of point cloud registration. Based on the principle of traditional iterative nearest point algorithm, this paper proposes an improved ICP algorithm combining PL-ICP algorithm and PP-ICP algorithm. The map construction is realized by this method, and on the basis of the map construction, NDT algorithm is used to further realize the positioning of unmanned vehicles. The experimental results show that the method can effectively construct the point cloud image, reduce the registration time of point cloud, and has good registration accuracy. Through the validation of the dataset, the positioning experiment can be completed by randomly placing the unmanned vehicle in a certain position on the constructed map. © 2023, Beijing Paike Culture Commu. Co., Ltd.

关键词： Optical radar

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A novel few-shot learning approach for the classification of histological images of liver tumors

A novel few-shot learning approach for the classification of...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Xu, Bo Wang, Qirui Li, Longjiao Yang, Jinqiao Lin, Hongfei Ding, Feng Dalian University of Technology School of Software Engineering Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798350337488

The liver is an internal organ that is most commonly affected by cancer metastasis. Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths worldwide. Identifying the histological category of this disease is a crucial step in determining the most appropriate treatment plan. Currently, the classification of HCC primarily relies on the observation of pathologists through a visual microscope, which is undoubtedly subjective. To address this issue, the present study conducted pathological examination of liver tumors based on few-shot learning. In order to achieve automatic identification of the three categories in HCC tissue, we propose a few-shot learning based HCC histological image classification method that combines contrastive learning (CL) using different backbone architectures with several well-known machine learning methods. We evaluate 12 backbone-classifier combinations, and the best performance is achieved by combining CL using DeiT-TinyR as the backbone architecture with support vector machine (SVM), with an accuracy of 74.42% and an F1 score of 74.58% in the 3-way-10-shot setting. Experimental results demonstrate the effectiveness of this method in NB histological image classification. © 2023 IEEE.

关键词： classification contrastive learning few-shot hepatocellular carcinoma hyperspectral image

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AI in the Shell: Towards an Understanding of Integrated Embodiment

AI in the Shell: Towards an Understanding of Integrated Embo...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Li, Zhuying Huang, Tianze Patibanda, Rakesh Mueller, Florian School of Computer Science and Engineering Southeast University Nanjing China Southeast University-Monash University Joint Graduate School Suzhou China Exertion Games Lab Monash University Melbourne Australia

ISBN: (纸本)9781450394222

With technologies becoming increasingly intelligent, the interaction paradigm of Human-computer Integration where computers and human form a partnership emerged. Most of these works considered computers as separate from users' embodiment. However, in recent years, technologies are becoming increasingly closer and even interwoven with the human body. Our work asks whether computers can incorporate into one's embodiment and form a partnership. We call this integrated embodiment. Such a paradigm might facilitate a more direct and intimate partnership between humans and computers. To exemplify the paradigm, we present AI-in-the-Shell, an exoskeleton-based system that enables users to experience having an AI residing in their body. The AI-powered system can make independent decisions and actuate the user's body to better support their daily tasks and experiences, e.g., to enhance their game performance. We hope this work can extend the current understanding of Human-computer Integration, and step towards a more complete understanding of integrated embodiment. © 2023 Owner/Author.

关键词： Mergers and acquisitions

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Optimization Performance Analysis of Different Initialization Methods in Single-Objective and Multi-Objective Chaotic Evolution 13

Optimization Performance Analysis of Different Initializatio...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Zhang, Xiaoyi Pei, Yan Jiang, Huiyan Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu965-8580 Japan University of Aizu Computer Science Division Aizuwakamatsu965-8580 Japan Northeastern University College of Software Shenyang110819 China

ISBN: (纸本)9798350373332

Chaotic Evolution (CE) is a significantly faster and more robust method for solving single-objective and multi-objective optimization problems. However, there are various factors that can impact the performance of CE, with population initialization being one of them. A well-executed initialization method can contribute to the discovery of better solutions and enhance both convergence rate and accuracy. This paper conducts a comparative experiment on CE algorithms using six different initialization methods. The performance of the CE algorithms utilizing these various initialization methods is analyzed and discussed. To assess convergence rate and accuracy, eight single-objective optimization benchmark functions and five multi-objective optimization benchmark functions are employed in our analysis. The evaluation results reveal significant disparities among the six initialization methods, indicating that different approaches to initialization have a substantial influence on the optimization performance of the CE algorithm. © 2024 IEEE.

关键词： Optimization algorithms

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Service Level Agreement Monitoring as a Service: An Independent Monitoring Service for Service Level Agreements in Clouds

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Big Data 2023年第5期11卷 339-354页

作者： Badshah, Afzal Jalal, Ateeqa Farooq, Umar Rehman, Ghani-Ur Band, Shahab S. Iwendi, Celestine Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan Department of Computer Science University of Science and Technology Bannu Pakistan Department of Computer Science and Bioinformatics Khushal Khan Khattak University Karak Pakistan Future Technology Research Center College of Future National Yunlin University of Science and Technology Douliou Taiwan School of Creative Technologies University of Bolton Bolton United Kingdom

The cloud network is rapidly growing due to a massive increase in interconnected devices and the emergence of different technologies such as the Internet of things, fog computing, and artificial intelligence. In response, cloud computing needs reliable dealings among the service providers, brokers, and consumers. The existing cloud monitoring frameworks such as Amazon Cloud Watch, Paraleap Azure Watch, and Rack Space Cloud Kick work under the control of service providers. They work fine;however, this may create dissatisfaction among customers over Service Level Agreement (SLA) violations. Customers’ dissatisfaction may drastically reduce the businesses of service providers. To cope with the earlier mentioned issue and get in line with cloud philosophy, Monitoring as a Service (MaaS), completely independent in nature, is needed for observing and regulating the cloud businesses. However, the existing MaaS frameworks do not address the comprehensive SLA for customer satisfaction and penalties management. This article proposes a reliable framework for monitoring the provider’s services by adopting third-party monitoring services with clearcut SLA and penalties management. Since this framework monitors SLA as a cloud monitoring service, it is named as SLA-MaaS. On violations, it penalizes those who are found in breach of terms and condition enlisted in SLA. Simulation results confirmed that the proposed framework adequately satisfies the customers (as well as service providers). This helps in developing a trustworthy relationship among cloud partners and increases customer attention and retention. © Mary Ann Liebert, Inc.

关键词： Sales

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A Tutorial on Federated Learning from Theory to Practice:Foundations,software Frameworks,Exemplary Use Cases,and Selected Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 824-850页

作者： M.Victoria Luzón Nuria Rodríguez-Barroso Alberto Argente-Garrido Daniel Jiménez-López Jose M.Moyano Javier Del Ser Weiping Ding Francisco Herrera Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Communications Engineering University of the Basque Country(UPV/EHU)and also with TECNALIABasque Research&Technology Alliance(BRTA)Spain School of Information Science and Technology Nantong UniversityNantong 226019China

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third *** paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data *** virtue of FL,models can be learned from all such distributed data sources while preserving data *** aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software ***,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL *** ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

关键词： Data privacy distributed machine learning federated learning software frameworks

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