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检索条件"任意字段=12th Euromicro Conference on Parallel, Distributed and Network-Based Processing"

共 1249 条记录，以下是21-30 订阅

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A parallel software pipeline to select relevant genes for pathway enrichment 30

A parallel software pipeline to select relevant genes for pa...

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30th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Agapito, Giuseppe Cannataro, Mario Magna Graecia Univ Catanzaro Dept Law Econ & Social Sci Catanzaro Italy Magna Graecia Univ Catanzaro Data Analyt Res Ctr Catanzaro Italy Magna Graecia Univ Catanzaro Dept Surg & Med Sci Catanzaro Italy

ISBN: (纸本)9781665469586

the continuous technological development of experimental omics technologies such as microarrays, allows to perform large scale genomics studies. After the initial enthusiasm, it became pretty clear that even the results provided by microarrays in form of lists of differential expressed genes (DEGs), were mainly as enigmatic as the first sequence of the genome, because these lists of DEGs are detached from the influenced biological mechanisms. Pathway enrichment analysis (PEA) supports researchers to provide the clues necessary to link DEGs to the influenced biological pathways and consequently to the underlying biological mechanisms and processes. Putting DEGs data sets in a suitable format for the PEA can be a tedious error-prone and laborious process even for bioinformaticians, who needs to perform it manually before to be ready for the PEA. To fill this lack, we present a parallel software pipeline which uploads a list of DEGs and automatically provides as results the enriched pathways. the parallel software pipeline is implemented in Python and provides the following automated actions: i) parallel splitting of DEGs in groups;ii) parallel building of the similarity matrices related to the DEGs groups;iii) parallel mapping of similarity matrices in networks;iv) parallel pathway enrichment analysis for each group of identified DEGs. Preliminary results shown that the pipeline can help to analyze DEGs and easily generate in a few minutes a list of pathway enrichment results that otherwise would require numerous hours of manual work and several different scripts. the parallel software pipeline provides a two-fold benefits: first, it contributes to speed up the computation of pathway enrichment, automating several steps currently performed manually. Second, it provides a more peculiar list of DEGs to calculate pathway enrichment, contributing to improve the relevance and significance of the enriched pathways.

关键词： Pathway Enrichment Analysis parallel Computing Omics Sciences Statistical Analysis network Analysis

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ZePoP: A distributed Leader Election Protocol using the Delay-based Closeness Centrality for Peer-to-Peer Applications 12

ZePoP: A Distributed Leader Election Protocol using the Dela...

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12th International conference on Cloud networking (CloudNet)

作者： Hossain, Md Amjad Khan, Javed I. Emporia State Univ Sch Business & Technol Emporia KS 66801 USA Kent State Univ Dept Comp Sci Kent OH USA

ISBN: (纸本)9798350313062;9798350313079

this paper presents ZePoP, a leader election protocol for distributed systems, optimizing a delay-based closeness centrality. We design the protocol specifically for the Peer to Peer(P2P) applications, where the leader peer (node) is responsible for collecting, processing, and redistributing data or control signals satisfying some timing constraints. the protocol elects an optimal leader node in the dynamically changing network and constructs a Data Collection and Distribution Tree (DCDT) rooted at the leader node. the elected optimal leader is closest to all nodes in the system compared to other nodes. We validate the proposed protocol through theoretical proofs as well as experimental results.

关键词： Peer to Peer network Leader Election network Delay distributed Systems Closeness Centrality P2P Video Conferencing

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Towards parallel Data Stream processing on System-on-Chip CPU+GPU Devices 30

Towards Parallel Data Stream Processing on System-on-Chip CP...

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30th euromicro International conference on parallel, distributed and network-based processing, PDP 2022

作者： Mencagli, Gabriele Griebler, Dalvan Danelutto, Marco University of Pisa Department of Computer Science Italy Do Rio Grande Do Sul Pontificia Universidade Catolica Porto Alegre Brazil

ISBN: (纸本)9781665469586

Data Stream processing is a pervasive computing paradigm with a wide spectrum of applications. Traditional streaming systems exploit the processing capabilities provided by homogeneous Clusters and Clouds. Due to the transition to streaming systems suitable for IoT/Edge environments, there has been the urgent need of new streaming frameworks and tools tailored for embedded platforms, often available as System-onChips composed of a small multicore CPU and an integrated onchip GPU. Exploiting this hybrid hardware requires special care in the runtime system design. In this paper, we discuss the support provided by the WindFlow library, showing its design principles and its effectiveness on the NVIDIA Jetson Nano board. © 2022 IEEE.

关键词： System-on-chip

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2024 12th International conference on Intelligent Control and Information processing, ICICIP 2024

2024 12th International Conference on Intelligent Control an...

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12th International conference on Intelligent Control and Information processing, ICICIP 2024

ISBN: (纸本)9798350308020

the proceedings contain 33 papers. the topics discussed include: DWT-RT: a lightweight image deraining model based on discrete wavelet transforms;data-driven optimal traffic signal control with phase priority and switching cost;sonar object detection based on global context feature fusion and extraction;an image decomposition-based enhancement using a matrix iterative algorithm;tendency coefficient-based weighted distance measure for intuitionistic fuzzy sets with applications;higher-order link prediction based on message passing simplicial networks;short-term power load forecasting based on CEEMDAN-CNN-LSTM hybrid modeling;a method for large scale unconstrained binary quadratic programming problem based on graph neural network;encoding variable stiffness skills with interaction force and motion information for robot-environment interaction;and distributed Nash equilibrium seeking for high-order dynamics with event-triggered communication.

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Control of Unmanned Bicycle based on Tensor Product Model Transformation and Hammersley Sampling Method 12

Control of Unmanned Bicycle Based on Tensor Product Model Tr...

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12th International conference on Intelligent Control and Information processing, ICICIP 2024

作者： Xie, Depeng Wang, Degang Zhao, Guoliang Li, Hongxing School of Control Science and Engineering Dalian University of Technology Dalian China College of Electronic Information Engineering Inner Mongolia University Hohhot China

ISBN: (纸本)9798350308020

In this paper, path planning and balance control of unmanned bicycles are investigated. Firstly, the Deep Deterministic Policy Gradient (DDPG) algorithm is used to train the Artificial Potential Field (APF) algorithm to implement path planning in complex maps. Secondly, a dual closed-loop control strategy is adopted for the path tracking and the balance control. In the inner-loop control, the Hammersley sampling (HS) method is used to obtain the hyper-cube network model of the unmanned bicycle, and the balance controller is designed based on the tensor product (TP) model transformation and parallel distributed compensation (PDC) method for stabilization of the unmanned bicycle. the outer-loop control system uses the Ackermann steering geometry model to track the planned path. Finally, the simulation results demonstrate that the path planning algorithm plans the path that satisfies the constraints and the designed balance controller can keep the unmanned bicycle balanced well during trajectory tracking. © 2024 IEEE.

关键词： Motion planning

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Power Quality Analytical Methodology Using Signal processing and Machine Learning Techniques for Grid Fault Classification and Location Detection 12

Power Quality Analytical Methodology Using Signal Processing...

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12th International conference on Smart Energy Grid Engineering

作者： Jabbar, Abdullah A. Wanik, Mohd Zamri Che Sanfilippo, Antonio P. HBKU QEERI Doha Qatar

ISBN: (纸本)9798350377385;9798350377378

the increase in global energy demand with a growing dynamic power system has made today's electrical network a complex infrastructure where increased faults and grid violations are being reported at higher rates. Also, with an increased penetration of intermittent distributed Energy Resource (DER), the detection of transient events occurring in a large network is becoming challenging and therefore encouraging potential areas of research in this field. Furthermore, the deployment of high-resolution devices such as PMU gives access to vast amounts of data and carries vital information to ensure grid safety and protection. this paper analyzes a methodology to detect and predict power system disturbances and fault location in electrical power network (distribution & transmission) using data-driven & machine learning techniques. It uses signal processing techniques based on multi-level discrete wavelet transforms, with wavelet spectral energy/entropy-based feature extraction stage to train ANN (Artificial Neural network) classifiers to predict and classify faults in power system applications. Machine learning classification techniques are used based on a dataset which is generated with single measurement point in simulation environment where grid model is used with varying fault conditions such as alternating phase & neutral impedance, inception angle & distance. A comparative analysis is performed with ANN and other potential classifiers to identify potential limitation & improvements in feature extraction stage to better classify faults and location. Furthermore, PQ events such as voltage sag, voltage swell, and transient event have been analyzed and the propagating effect of faults in the network, medium voltage to low voltage network are also discussed.

关键词： Power quality discrete wavelet transform artificial neural network machine learning fault classification

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Towards Resilient and Efficient Big Data Storage: Evaluating a SIEM Repository based on HDFS 30

Towards Resilient and Efficient Big Data Storage: Evaluating...

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30th euromicro International conference on parallel, distributed and network-based processing, PDP 2022

作者： Saenko, Igor Kotenko, Igor Laboratory of Computer Security Problems St. Petersburg Russia

ISBN: (纸本)9781665469586

Building an efficient, scalable, distributed storage system is challenging in a variety of industries. Currently, the most promising and efficient way to organize this method of data storage is using the Hadoop distributed File System (HDFS). It is of interest to develop an approach to optimize the distribution of replicas across storage nodes, which allows one to provide the required resilience and efficiency of storing big data in distributed systems based on HDFS. the analysis showed that in the well-known works on processing big data, the issues of simultaneous provision of resilient and efficient data storage are practically not raised. the paper proposes an approach based on the application of the developed probabilistic models for assessing the resilience and efficiency of data storage. the models take into account the random and deterministic modes of distribution of data blocks across the network nodes and allow one to solve the task of providing resilient and efficient big data storage in three kinds of criteria: resilience maximization, efficiency maximization, and restrictions of resilience and efficiency. the objective of the experiment was to optimize the variables of the the security information and event management (SIEM) system. Experiments have confirmed the effectiveness of the proposed approach and the possibility of solving with its help the assigned tasks to satisfy the three selected species. At the same time, genetic algorithms (GAs) were used for the deterministic mode, in which some improvements were introduced regarding the construction of chromosomes and types of fitness functions. © 2022 IEEE.

关键词： Big data

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A Ray-based Asynchronous distributed parallel K-SVD Method 12

A Ray-based Asynchronous Distributed Parallel K-SVD Method

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12th International conference on Advanced Cloud and Big Data, CBD 2024

作者： Li, Yongkang Chen, Xiaodong Wan, Xiaohui Sun, Jin Zheng, Peng Wang, Yunchang Wu, Zebin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Quzhou Transportation Bureau Department of Industry Development Quzhou China Chang shan Inspection and Testing institute Department of Quality Control and Testing Quzhou China

ISBN: (纸本)9798331511074

K-SVD (K-Singular Value Decomposition)is a com-monly used dictionary learning method that progressively opti-mizes dictionaries to better represent data by iterating sparse coding and dictionary update steps. However, with the increasing data size, the traditional K -SVD method faces significant challenges in terms of computational efficiency. therefore, this paper proposes an asynchronous distributed parallel K-SVD method based on Ray framework to cope with the computational efficiency problem in large-scale data processing. this method achieves efficient parallel computation by decomposing the sparse coding and dictionary updating steps of the K-SVD method into multiple independent tasks and utilizing the distributed scheduling capability of the Ray framework. Since the zero elements of the coefficient matrix are unevenly distributed, we introduce a stride partitioning approach when performing the decomposition. In addition, in order to avoid the method falling into local optima and failing to find global optima due to parallelism, this paper synchronizes with the global at the same time as the atomic update of the dictionary. Meanwhile, for the purpose of avoiding the overhead caused by data synchronization, a parameter server is introduced to realize asynchronous updating of dictionary and coefficient matrix to reduce the overhead caused by communication. the experimental results show that, compared with the traditional K-SVD method, the method proposed in this paper has a significant advantage in computational efficiency and is able to efficiently process hyperspectral image datasets under the premise of guaranteeing the sparse representation accuracy. Compared with the synchronous parallel K-SVD method, the proposed method has faster error convergence and does not fall into local optima. © 2024 IEEE.

关键词： network security

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Selection of Deep Neural network Models for IoT Anomaly Detection Experiments 29

Selection of Deep Neural Network Models for IoT Anomaly Dete...

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29th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Gaifulina, Diana Kotenko, Igor Russian Acad Sci Russian Acad Sci SPC RAS St Petersburg Inst Informat & Automat St Petersburg Fed Res Ctr 14th Liniya St Petersburg Russia

ISBN: (纸本)9781665414555

this research is about selection of deep neural network models for anomaly detection in Internet of things network traffic. We are experimentally evaluating deep neural network models using the same software, hardware and the same subsets of the UNSW-NB 15 dataset for training and testing. the assessment results are quality metrics of anomaly detection and the time spent on training models.

关键词： deepp learning deep neural networks anomaly detection Internet of things information security

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WorkTrue: An Efficient and Secure Cloud-based Workflow Management System 29

WorkTrue: An Efficient and Secure Cloud-based Workflow Manag...

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29th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Fugkeaw, Somchart Thammasat Univ Sch Informat Comp & Cammunicat Technol ICT Sirindhorn Int Inst Technol Pathum Thani Thailand

ISBN: (纸本)9781665414555

this paper proposes an e-document workflow system called "WorkTrue" that supports secure, fine-grained access control and multiple digital signings for e-document workflow management implemented on cloud. We apply a ciphertext policy attribute-based encryption (CP-ABE) to enable the access control with data privacy-preserving for all signed documents stored in the cloud server. In addition, we present a dynamic workflow scheduling algorithm to dynamically schedule the workflow tasks to be run by VM instances (VMIs) from a single VM in a cloud. In our experiment, we developed a WorkTrue prototype as a proof of concept for validating its functionality. We also conducted performance evaluation to substantiate that our WorkTrue is efficient in practice.

关键词： cloud-based workflow data integrity access control digital signing CP-ABE

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