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检索条件"任意字段=3rd International Conference on Computing and Data Science, CONF-CDS 2021"

共 909 条记录，以下是31-40 订阅

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Research on Elastic Parallel computing Environment Based on Cloud-Native Virtualization 3

Research on Elastic Parallel Computing Environment Based on ...

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3rd international conference on High Performance computing and Communication Engineering, HPCCE 2023

作者： Yin, Siyuan Hu, Menglong Cao, Wudi Lv, Zhuoheng Zhao, Huan Zhang, Tao Li, Zhu Jia, Handong Zhengzhou University 100 Science Avenue Gaoxin District Henan Zhengzhou450001 China Co. Ltd. Sugon Building No.36 Zhongguancun Software Park No.8 Dongbeiwang West Road Haidian District Beijing100193 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

Cloud-native virtualization technology combines virtualization technology with cloud-native computing to provide a more efficient, flexible, and scalable cloud computing environment. In the process of analysis and research in the field of bioinformatics, it is usually necessary to deal with large-scale data sets and complex computing tasks, and the demand for computing power throughout the research and development cycle is characterized by peaks and troughs. The elastic scalability of cloud-native virtualization technology allows for the expansion of computing resources according to demand, meeting the data processing and analysis requirements throughout the entire research and development cycle. By integrating virtualized InfiniBand high-speed NICs, data transfer and the execution of computational tasks are accelerated, further reducing the research and development cycle. In summary, cloud-native virtualization technology has significant application value in the field of bioinformatics, providing an efficient computing environment while saving time and costs. © 2024 SPIE.

关键词： Bioinformatics

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A Real-time Demand Response Strategy of Home Energy Management by Using Distributed Deep Reinforcement Learning 23

A Real-time Demand Response Strategy of Home Energy Manageme...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Weirong Wang, Yujie Jiang, Fu Cheng, Yijun Rong, Jieqi Wang, Chenglong Peng, Jun School of Computer Science and Engineering Central South University Changsha China School of Automation Central South University Changsha China

ISBN: (纸本)9781665494571

Home energy management system (HEMS) autonomously schedules the energy usage of home electricity consuming devices to reduce the electricity cost based on real-time electricity price. However, it is very challenging to design an optimal energy management strategy under the uncertainty of the resident's behavior, outdoor temperature and renewable generation. To solve this problem, a real-time demand response (DR) strategy based on deep reinforcement learning (DRL) is proposed. Firstly, the real-time DR management problem is modeled as a Markov game, and then a home energy management algorithm is proposed based on Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3). It is worth mentioning that the proposed algorithm builds the thermal dynamics model and takes energy storage system (ESS) into account, which improves the utilization of renewable power generation effectively. The simulation result based on real-world data demonstrates the effectiveness of the proposed algorithm. © 2021 IEEE.

关键词： Renewable energy sources Schedules Costs Uncertainty Heuristic algorithms Simulation Reinforcement learning

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Privacy Preserving Mechanism for Medical data Stored in Cloud During Health Emergencies 3rd

Privacy Preserving Mechanism for Medical Data Stored in Clou...

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3rd international conference on data science, Machine Learning and Applications, IcdsMLA 2021

作者： Bhaskar, R. Komala, M. Lekhana, B.M. Pooja, J. Don Bosco Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9789811959356

In this paper, for web-based cloud computing services, we provide a novel fine-grained two-factor authentication access control mechanism. Our suggested 2FA access control system, which requires both a user secret key and a lightweight security device, employs an attribute-based access control mechanism in particular. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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A Transpose-free Three-dimensional FFT Algorithm on ARM CPUs 23

A Transpose-free Three-dimensional FFT Algorithm on ARM CPUs

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作者： Chen, Tun Jia, Haipeng Li, Zhihao Li, Chendi Zhang, Yunquan Skl of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Huawei Technologies Co. Ltd Shenzhen China

ISBN: (纸本)9781665494571

According to the traditional multi-dimensional FFT, memory layouts of high-dimensional data are discontinuous. Transposition is introduced to keep high-dimensional data continuous in memory. However, transposition increases memory access and is a hot spot for multi-dimensional FFT. This paper proposes an optimization framework to eliminate explicit transpositions and optimize the three-dimensional (3D) FFT. This framework includes three research points. 1) combines the width-first and breadth-first search to optimize the butterfly network of one-dimensional (1D) FFT;2) adopts a column-order algorithm to eliminate data transposition;3) adopts a blocking algorithm of cache-aware to better use the hardware resources of ARM architecture. Based on this optimized framework, a multi-dimensional FFT library named MDFFT is implemented. The experiments demonstrate that MDFFT generally performs better than FFTW and ARMPL on ARM CPUs. © 2021 IEEE.

关键词： Three-dimensional displays Smart cities Layout Parallel processing Libraries Hardware Optimization

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BOBBLE: A Mixed Routing-Granularity Distributed Load Balancing for data Center Networks 23

BOBBLE: A Mixed Routing-Granularity Distributed Load Balanci...

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作者： Xu, Zhengzhi Lu, Yifei Ma, Xu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210096 China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi214215 China

ISBN: (纸本)9781665494571

In current datacenter Networks (DCNs), Equal-Cost Multipath (ECMP) is a default load balancing scheme. However, using ECMP may result in rapid growth of Flow Completion Time (FCT) due to its well-known drawbacks. In order to solve the problem of ECMP, load balancing schemes with per-packet granularity (such as RPS) and per-flowlet granularity (such as LetFlow) are proposed. These two different granularities have their own features, but there's no existing load balancing considering both their advantages. In this paper, we mix per-flowlet and per-packet in scheduling decisions and introduce the potential performance bottleneck in mixed granularities load balancing. Based on this idea, we propose a hybrid scheme for load balancing, referred to as BOBBLE. The key idea of BOBBLE is to distinguish elephant and mouse flows and schedule them with different granularity. We evaluate BOBBLE in simulations with NS-3. Experiment shows that BOBBLE slightly sacrifices elephant flows FCT but it is worthy because our results demonstrate that BOBBLE is a good trade-off which outperforms RPS and LetFlow in mouse flows FCT in both symmetric and asymmetric topology. © 2021 IEEE.

关键词： data centers Network topology Smart cities Load management Routing Mice Computer networks

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Cooperative Relationship Prediction between Scholars in Heterogeneous Academic Network 23

Cooperative Relationship Prediction between Scholars in Hete...

引用

作者： Shi, Jia Jin, Hai Xie, Xia National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9781665494571

The real academic network belongs to a heterogeneous network, therefore, for the link prediction tasks, some information on the network may be lost if only using homogeneous network methods. In order to make good use of the rich semantic information in a heterogeneous network for better predicting the cooperative relationship between scholars, this paper proposes a cooperative relationship prediction (CoRP) model. The CoRP model includes the following three steps. Firstly, a structural feature extraction module is designed based on meta path by using the similarity metric function to measure the meta path similarity between scholars. Secondly, to overcome the defects that the existing methods usually ignore the attribute information on nodes, three attribute feature presentation functions are constructed to fully utilize the attribute information of scholars in the academic network. In the end, a machine learning classifier which combines the structural similarity and attribute similarity is utilized to learn each attribute feature and complete the task of cooperative relationship prediction. The experimental results reveal that compared with the benchmark method, the proposed method has better performance and interpretability with improvement from 3% to 10% of AUC value for the AMiner academic data set. © 2021 IEEE.

关键词： Measurement Smart cities Semantics Machine learning Benchmark testing Predictive models Feature extraction

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The Study of Power Planning for Cyclists based on Genetic Algorithm 3

The Study of Power Planning for Cyclists based on Genetic Al...

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3rd international conference on Applied Mathematics, Modelling, and Intelligent computing, CAMMIC 2023

作者： Wu, Sixuan Tan, Qiancheng Xu, Dewei Mao, Rui School of Mathematics and Computing Science Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

This paper studies the power curve of two types riders (the Time Trial Specialist and the Sprinter) and set up a multi-objective optimization model by taking the total energy consumed and the total time used by a rider during the race as objective functions. By using the genetic algorithm to solve the multi-objective optimization model, the total energy and total time as far as possible to get the optimal solution under the condition of small number. The number of the optimal solutions corresponding to the rider in the process of competition has the opportunity to go beyond the opponent's corner number. The number of the optimal solutions can determine the relationship between the rider’s position on the course and the power the rider applies. Combining 2021 Tokyo Olympic Games, the model is applied to the individual time trials of two types of riders. © 2023 SPIE.

关键词： Genetic algorithms

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Optimization Model for Collaborative Overhaul Workshop Scheduling Problem of Multiple EMUs 23

Optimization Model for Collaborative Overhaul Workshop Sched...

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作者： Zhang, Weijiao Wang, Zhongkai Jia, Zhikai Wang, Hui Institute of Computing Technologies China Academy of Railway Sciences Beijing China

ISBN: (纸本)9781665494571

China's high-speed railway construction is making remarkable achievements. The increasing EMUs are approaching the overhaul cycle, which has brought great pressure to the existing 7 overhaul bases. With the increasing expansion of high-speed rail network, it is of significance to reasonably arrange the maintenance plan of EMU overhaul base under the joint scheduling mode between multiple EMUs. Aiming at improving the maintenance throughput and the utilization rate of key equipment, the current overhaul bases are adopting the parallel maintenance mode of multiple EMUs. To optimize the repair process of multiple parallel mode, this paper constructed an optimization model with the goal of minimizing the invalid maintenance fragment time of key equipment, under the dual constraints of the BOM of EMU parts to be repaired and the topology of maintenance process. In view of the nonlinearity of the model, the topology diagram of cooperative advanced repair of multiple EMUs was constructed, and then the max-min ant colony algorithm was designed to optimize the model, so as to obtain the optimal solution of the model. The model and algorithms were validated by an actual problem of the overhaul (Level 3) process of multiple EMUs. © 2021 IEEE.

关键词： Job shop scheduling Smart cities Bills of materials Organizations Maintenance engineering Inspection Throughput

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Improvement of SMOTE Based on Graph Neighborhood Structure and Latent Feature Learning and Its Application 3

Improvement of SMOTE Based on Graph Neighborhood Structure a...

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3rd international conference on Artificial Intelligence, Internet of Things and Cloud computing Technology, AIoTC 2024

作者： Wang, Xiaoxia Li, Leixiao Inner Mongolia University of Technology School of Data Science and Application Hohhot China

ISBN: (纸本)9798331530280

The SMOTE algorithm is a classical method for addressing imbalanced classification problems. In this paper, we improves the SMOTE algorithm by incorporating ideas from transfer learning. Initially, a k-nearest neighbor (kNN) graph of minority class samples is constructed to capture the structural relationships between samples. The graph convolutional network (GCN) is then employed to model the adjacency matrix and sample features jointly, generating node feature representations that include neighborhood information. These are used to generate samples in the first phase within the feature space;Subsequently, the generated samples are input into an Autoencoder-Generative Adversarial Network (AE-GAN) for a second phase of sample regeneration. The AE component extracts latent features to enhance the learning of features in the generated samples. The samples generated through these two phases are made closer to the real sample distribution. Finally, results on five imbalanced datasets indicate that the proposed method achieves the best classification performance, validating the effectiveness of the algorithm. © 2024 IEEE.

关键词： Generative adversarial networks

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Job Resource Prediction and Completion Status Prediction on Large-Scale computing Platforms 3

Job Resource Prediction and Completion Status Prediction on ...

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3rd international conference on High Performance computing and Communication Engineering, HPCCE 2023

作者： He, Jiale Zhang, Tao Zheng, Yu Zhengzhou University 100 Science Avenue Gaoxin District Henan Zhengzhou450001 China Dawning Information Industry Co. Ltd. Sugon Building No.36 Zhongguancun Software Park No.8 Dongbeiwang West Road Haidian District Beijing100193 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

Predicting the resource consumption and completion status of jobs is beneficial to improve the scheduling performance of the system. Many studies have shown that job name can effectively improve the accuracy of prediction. Therefore, by mining the structural semantic information of job name, this paper introduces new features of job name habit, including job name length, number of job name elements, editing distance, and analyzes each substructure of job name, adding classification features after clustering. The introduced new features can better characterize the similarity between jobs and provide strong support for model prediction. Based on the model trained by the new feature data set, the prediction accuracy is significantly improved compared with the model that only introduces the job name. © 2024 SPIE.

关键词： Forecasting

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