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Graph-based Multi-view Clustering for Web services

Graph-based Multi-view Clustering for Web services

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Wang Yang Yuan Zhenzhen Kang Guosheng Cao Buqing Liu JianXun Xiao Yong HNUST Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan China School of Computer Science and Engineering

The number of Web services on the Internet has been steadily increasing in recent years due to their growing popularity. Under the big data environment, how to effectively manage Web services is of significance for service discovery, service recommendation, etc. It is widely studied that Web services clustering is an effective way for service management. However, most of the current Web service clustering only extracts the information of Web services for clustering from one view, such as Web service content descriptions, networks in which Web services participate, and so on. Extracting information from Web services only unilaterally will not be able to provide a three-dimensional and comprehensive description of Web services, which may diminish the effect of Web service clustering. In addition, some Web service resources will be wasted if other information of Web services is not used at the same time. We find that multi-view clustering can simultaneously consider multiple information of a data at the same time, and multiple information can complement and enhance each other according to the characteristics of multiview clustering. Therefore, in this paper, we apply Web services to graph-based multi-view clustering in multi-view clustering to improve the performance of Web service clustering by simultaneously considering multiple feature information about Web services and distributing different weights to different information in the clustering process.

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distributed-Memory parallel Algorithms for Sparse Times Tall-Skinny-Dense Matrix Multiplication 21

Distributed-Memory Parallel Algorithms for Sparse Times Tall...

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35th ACM international conference on Supercomputing (ICS)

作者： Selvitopi, Oguz Brock, Benjamin Nisa, Israt Tripathy, Alok Yelick, Katherine Buluc, Aydin Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781450383356

Sparse times dense matrix multiplication (SpMM) finds its applications in well-established fields such as computational linear algebra as well as emerging fields such as graph neural networks. In this study, we evaluate the performance of various techniques for performing SpMM as a distributed computation across many nodes by focusing on GPU accelerators. We examine how the actual local computational performance of state-of-the-art SpMM implementations affect computational efficiency as dimensions change when we scale to large numbers of nodes, which proves to be an unexpectedly important bottleneck. We consider various distribution strategies, including A-Stationary, B-Stationary, and C-Stationary algorithms, 1.5D and 2D algorithms, and RDMA-based and bulk synchronous methods of data transfer. Our results show that the best choice of algorithm and implementation technique depends not only on the cost of communication for particular matrix sizes and dimensions, but also on the performance of local SpMM operations. Our evaluations reveal that with the involvement of GPU accelerators, the best design choices for SpMM differ from the conventional algorithms that are known to perform well for dense matrix-matrix or sparse matrix-sparse matrix multiplies.

关键词： Sparse linear algebra Sparse matrices Graphics accelerators parallel algorithms RDMA

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Proceedings of the 8th IEEE/ACM international conference on Big Data computing, Applications and Technologies, BDCAT 2021

Proceedings of the 8th IEEE/ACM International Conference on ...

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8th IEEE/ACM international conference on Big Data computing, Applications and Technologies, BDCAT 2021

ISBN: (纸本)9781450391641

The proceedings contain 15 papers. The topics discussed include: evaluating serverless architecture for big data enterprise applications;3D object recognition for virtual reality based digital twins;linking user accounts across social media platforms;crowd counting using deep learning in edge devices;multiscale clustering based diffusion representation learning method;a proactive data-parallel framework for machine learning;distributed orchestration of regression models over administrative boundaries;attribute network embedding method based on joint clustering of representation and network;and comparative analysis of pre-trained deep neural networks for vision-based security systems on a novel dataset.

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Data Structures for Routing Table of the distributed Key-Value Store Based on Order Preserving Linear Hashing and Skip Graph with the Load Balancing Method

Data Structures for Routing Table of the Distributed Key-Val...

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Summer Virtual conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing (SNPD-Summer), ACIS international

作者： Ken Higuchi Ami Miyazaki Kenya Hasegawa Tatsuo Tsuji Center for Data Science and Artificial Intelligence University of Fukui Fkui Japan School of Engineering University of Fukui Fkui Japan

ISBN: (纸本)9798350396386

In this paper, data structures for reducing redundancy of routing information on the distributed key-value store based on order preserving linear hashing and Skip Graph with the load balancing method are proposed and evaluated. In this system, data are divided by order preserving linear hashing and Skip Graph is used for overlay network. For load balancing, by storing many Skip Graph nodes in one physical node, any highest-load Skip Graph can be divided. By this method, load balancing can be done. But the number of Skip Graph nodes becomes very many and redundant routing information exists. But this redundant routing information has regularity. In this paper, two types of data structure for reducing redundant routing elements are proposed. They are tree structure and array structure. By calculating space for them and searching time, the proposed data structures are experimentally evaluated.

关键词： Overlay networks Redundancy Routing Load management Arrays Artificial intelligence Software engineering

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An MPI-based Algorithm for Mapping Complex networks onto Hierarchical Architectures 27th

An MPI-based Algorithm for Mapping Complex Networks onto Hie...

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27th international European conference on parallel and distributed computing (Euro-Par)

作者： Predari, Maria Tzovas, Charilaos Schulz, Christian Meyerhenke, Henning Humboldt Univ Berlin Germany Heidelberg Univ Heidelberg Germany

ISBN: (纸本)9783030856656;9783030856649

Processing massive application graphs on distributed memory systems requires to map the graphs onto the system's processing elements (PEs). This task becomes all the more important when PEs have non-uniform communication costs or the input is highly irregular. Typically, mapping is addressed using partitioning, in a two-step approach or an integrated one. parallel partitioning tools do exist;yet, corresponding mapping algorithms or their public implementations all have major sequential parts or other severe scaling limitations. In this paper, we propose a parallel algorithm that maps graphs onto the PEs of a hierarchical system. Our solution integrates partitioning and mapping;it models the system hierarchy in a concise way as an implicit labeled tree. The vertices of the application graph are labeled as well, and these vertex labels induce the mapping. The mapping optimization follows the basic idea of parallel label propagation, but we tailor the gain computations of label changes to quickly account for the induced communication costs. Our MPI-based code is the first public implementation of a parallel graph mapping algorithm;to this end, we extend the partitioning library PARHIP. To evaluate our algorithm's implementation, we perform comparative experiments with complex networks in the million- and billion-scale range. In general our mapping tool shows good scalability on up to a few thousand PEs. Compared to other MPI-based competitors, our algorithm achieves the best speed to quality trade-off and our quality results are even better than non-parallel mapping tools.

关键词： Load balancing Process mapping Hierarchical architectures parallel label propagation

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Thingspeak-Based Environmental Monitoring System Using IoT

Thingspeak-Based Environmental Monitoring System Using IoT

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international conference on parallel distributed and Grid computing (PDGC)

作者： Nidhi Sindhwani Rohit Anand Rashmi Vashisth Sudhir Chauhan Veera Talukdar Dharmesh Dhabliya AIIT Amity University Noida UP India DSEU New Delhi India AIAS Amity University Noida UP India Kaziranga University Jorhat Assam India Department of IT Vishwakarma Institute of Information Technology Pune Maharashtra India

ISBN: (纸本)9781665454025

Environmental safety is a significant feature necessary for the healthy life style. Any abnormality in the environment changes the atmospheric parameters such as humidity, temperature, smoke, level of carbon monoxide and sound. Fatal health issues such as loss or weakening of immunity & infertility occurs due to the different activities occurring in nuclear power plants such as recycling of nuclear fuel and leakage of radiation that add low levels of radiation to the atmosphere. Leakage of nuclear radiation gets mixed with fresh air in the atmosphere that results into the increase of temperature, humidity, smoke, level of carbon monoxide and sound. In this work, a wireless sensor network is used for measuring the changes in these parameters which in turn are displayed by the Thingspeak Linux based web server available freely. Monitoring of the parameters in the environment is based on Internet of Things (IoT) as radiation leakage is detected by using sensors and is transmitted to the remote location via IoT and displayed using Thingspeak web server.

关键词： Temperature measurement Temperature sensors Wireless sensor networks Atmospheric measurements Atmosphere Humidity Web servers

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Design and Implementation of AI aided Fruit Grading Using Image Recognition

Design and Implementation of AI aided Fruit Grading Using Im...

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed computing (SNPD)

作者： Hang Hong Kuo Daiby Sunandan Barik Jun You Zhou Yi Kai Hong Jun Juh Yan Meng Hua Yen Department of Electronics Engineering National Chin-Yi University of Technology Taichung Taiwan

This research is based on the framework of a fully automated smart fruit factory that builds a small and simple fruit grading device, and spots defects in the three fruit models of apples, lemons and oranges which were used as test target, and the entire process of detection is performed in a dark box. There is a ring-shaped LED light to regulate the light source inside the dark box. The fruits to be identified are moved into the dark box by a conveyor belt, an infrared sensor is used to judge whether the fruits are within the shooting range of the image capture area, and then the photos are sent to the SSD (Single Shot Multi Box Detector) neural network model to identify defects. This system screens the surface of apples, lemons and oranges for defects like damage, pest damage, dryness, bruises, etc. and removes them to preserve the fruits that are good in quality. It has been verified by several experiments that the identification accuracy rate can reach upto more than 95%.

关键词： Performance evaluation Image recognition Computational modeling Neural networks Transportation Belts Production facilities

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AAWAS: An Application and Workload-Aware Scheduling Technique for Efficient Task Allocation in Cloud computing Environments

AAWAS: An Application and Workload-Aware Scheduling Techniqu...

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Advancements and Key Challenges in Green Energy and computing (AKGEC), international conference on

作者： Vidhyasagar Bs M. Arvindhan J. Arunnehru H. Anwarbasha Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Amity School of Engineering & Technology Gwalior Madhya Pradesh India SRM Institute of Science and Technology Chennai India Rajalakshmi Institute of Technology Chennai India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

One interesting aspect of distributed systems is cloud computing. It offers its services on an as-needed, pay-per-use basis. Research challenges in cloud computing include the scheduling and balance of tasks. Scheduling tasks entails assigning them to available resources (Virtual Machines), whereas load balancing is spreading out the work over multiple available resources. We offer a job scheduling technique that takes into account anticipated demands on compute nodes. We begin by investigating the reasons for the disparity in workloads and the viability of redistributing computing resources. All of the complex simulations are run in clouds. The process consisted of two distinct parts. The simulation begins with a randomly generated workload. Second, we combine the workload prediction model with an application and workload-aware scheduling algorithm (AAWAS) [14]. We present a parallel job scheduling approach using computational node workload prediction AAWAS to simplify the AAWAS algorithm. Experimental results reveal that AAWA is superior to other algorithms in terms of minimizing makespan and optimizing resource utilization, and the proposed method is compared to FCFS, SJF, Min-Min, and EDF. To make Task scheduling in compute clouds faces various challenges due to the highly dynamic environment, such as availability, access policies, security, and reliability of computing resources. Furthermore, the collaborative nature of cloud computing projects on the internet adds complexity to task scheduling. From simulation results, it is evident that our proposed scheduler AAWAS outperforms A2C, DQN, and PPO across the board by 4.1%, 2.6%, and 3.4%, respectively. Because AAWS relies on policy for learning rather than Q-value, its superiority can be defended. Several environmental factors affect AAWAS and contribute to its consistent learning process and the high chance of accurate predictions. As a result, AAWASC may learn from experiences independently of target networks,

关键词： Cloud computing Protocols Scheduling algorithms Computational modeling Throughput Prediction algorithms Virtual machines Resource management Faces Load modeling

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A Trajectory Tracking System for Zebrafish Based on Embedded Edge Artificial Intelligence

A Trajectory Tracking System for Zebrafish Based on Embedded...

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international conference on parallel and distributed Systems (ICPADS)

作者： Chuanhao Zang Anming Dong Jiguo Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Trajectory tracking of zebrafish is an important requirement in studying neurological disorders and developing new psychoactive drug. However, many challenges emerge for stable tracking, since zebrafish are similar in appearance, occlusion, agile, non-linear in moving and easy to swarm, all of which will lead to mistrack for multiple fish. And there is no precedent for tracking zebrafish through embedded edge artificial intelligence device. To overcome these difficulties, we present a tracking system for zebrafish based on RK3588-S. First, We construct an embedded edge AI hardware system consisting of two cameras driven by the RK3588-S, one for the front view and the other for top view of the fish. Then, we develop a 2D tracking algorithm based on YOLOv5 and the Observation-Centric Simple online and real-time tracking (OC-SORT) algorithm, which are transplanted to the RK3588-S for tracking the top and front views of the fish at the edge device. Compared with the previous methods, our method has fewer ID exchanges and highly real-time. Moreover, we apply the MQTT mechanism to establish communication links between the edge and the cloud to reliably transmit data to the cloud. The correspondence between cloud server and embedded AI is 1: N. Finally, we design a multi-view data fusion association algorithm to fuse the data of the two views in the cloud, which are further utilized to build the 3D tracklets of the zebrafish.

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Balance Graphs: An Aid for Studying Convolutional Neural networks

Balance Graphs: An Aid for Studying Convolutional Neural Net...

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed computing (SNPD)

作者： Lyell Embery Eva Ignatious Sami Azam Miriam Jonkman Friso De Boer College of Engineering and IT Charles Darwin University Casuarina Australia

Deep Learning Neural networks offer a powerful tool to process visual data and to make decisions, but a limitation is its black box nature which offers low transparency to human examiners. This hurdle presents a particular challenge in using Neural networks in safety-critical systems, which require high performance and transparency such as medical diagnosis of life-threatening diseases. This paper seeks to build and test a neural network for grading gliomas of comparable function and complexity to others in the field, then to apply Data Visualisation techniques to render the internal workings of the NN more understandable to a human observer. The purpose is to develop a system that can classify brain tumors into low-grade gliomas (LGG) and high-grade gliomas (HGG), to aid with diagnosis and prognosis The Brain Tumor Segmentation Challenge 2020 (BraTS2020) data set was used, with data categorised based on a combination of grade assigned in BraTS2020, and the labels in the segmentation data. As some categories are over-represented, methods were employed to ensure a better balance between different categories. Data augmentation was used to expand the limited number of scans in the BraTS2020. A 3D convolution neural network (CNN) was constructed to grade gliomas. With the method developed in this paper, an accuracy of 94.1% was achieved. A newly devised method to visually represent the weights of a convolution is explored. These graphs, called ‘weight graphs’ allow convolutions to be condensed into a visual medium. The weight graph is designed for easy visual interpretation of the weights assigned within a particular convolution. To overcome the limitations of weight graphs, an alternate graph was devised, called a balance graph, because it shows the overall balance of weights in a kernel, allowing for a quick impression of what effect a single kernel has. It is demonstrated that Balance Graphs improve the accessibility and transparency of the of the weights in convolut

关键词： Training Visualization Three-dimensional displays Convolution Data visualization Convolutional neural networks Kernel

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