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Testing parallel evolution strategies on the quadratic assignment problem

Testing parallel evolution strategies on the quadratic assig...

引用

IEEE International conference on Systems, Man and Cybernetics

作者： I. de Falco R. del Balio E. Tarantino Istituto per la Ricerca sui Sistemi Informatici Paralleli (IRSIP) CNR Naples Italy

parallel evolution strategies are demonstrating to be worthwhile in a variety of contexts. In this paper, besides the classical genetic and evolutionary strategies, a hybrid evolutionary approach which incorporates memory of the search history within the structure is analyzed. The parallel evolution algorithms are mapped on a distributed memory MIMD multicomputer whose processors are configured in a torus topology. The simulations are conducted using the quadratic assignment problem as an artificial environment. The relationship between genetic representations and recombination operators is investigated. The experimental results obtained show the value of structures richer than bit strings and the effectiveness of memory for the evolution process.< >

关键词： Testing Evolutionary computation Optimization methods History Topology Genetic algorithms Delay Neural networks image processing Machine learning

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A New parallel Sorting Algorithm based on Odd-Even Mergesort

A New Parallel Sorting Algorithm based on Odd-Even Mergesort

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Ezequiel Herruzo Guillermo Ruiz J. Ignacio Benavides Oscar Plata Dept. Computer Architecture and Electronics University of Córdoba Spain Dept. Computer Architecture University of Málaga Spain

This paper describes a new parallel sorting algorithm, derived from the odd-even mergesort algorithm, named "partition and concurrent merging" (PCM). The proposed algorithm is based on a divide-and-conquer strategy. First, the data sequence to be sorted is decomposed in several pieces that are sorted in parallel using Quicksort. After that, all pieces are merged using a recursive procedure to obtain the final sorted sequence. In each iteration of this procedure pairs of sequence pieces are selected and sorted concurrently. The paper analyzes the computational complexity of the new algorithm and compares it with that of other well-known parallel sorting algorithms. We implemented the PCM algorithm on a SGI Origin2000 multiprocessor using OpenMP, sorting different benchmark sets of data sequences. Experimental results are compared with those of the Quicksort sequential algorithm and parallel implementations of other sorting algorithms, obtaining that our proposal outperforms the other solutions

关键词： Sorting Partitioning algorithms Phase change materials Merging Computational complexity Computer architecture Algorithm design and analysis Proposals Sampling methods Graph theory

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ROGC: Role-Oriented Graph Convolution Based Multi-Agent Reinforcement Learning

ROGC: Role-Oriented Graph Convolution Based Multi-Agent Rein...

引用

2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Liu, Yuntao Li, Yuan Xu, Xinhai Liu, Donghong Dou, Yong National University of Defense Technology National Laboratory for Parallel and Distributed Processing China Academy of Military Sciences China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

The role-oriented learning approach could improve the performance of multi-agent reinforcement learning by decomposing complex multi-agent tasks into different roles. However, due to the dynamic environment and interactions among agents, the role undertaken by an agent changes rapidly with time going on. Therefore, the roles of agents should be adapted to the varying situation during the learning process. In this paper, we propose a role-oriented graph convolution based multi-agent reinforcement learning framework (ROGC). Firstly, we design a role assigner based on samples generated from the environment to learn roles for classifying agents into different groups. To further enhance cooperation among agents in the same group for higher performance, we design a graph convolutional module to achieve intra-role communications based on discovered roles. With roles and extracted role features, we design a role-oriented policy learning module that embeds the role information into the algorithm and generates effective policies for individuals. Further, we introduce an auto-encoder to learn the intra-role cooperation knowledge in the graph convolutional module, which ensures our framework executes in a decentralized way. Extensive experiments show that our framework can learn dynamic roles and make full use of learned roles, which makes it outperform popular MARL methods. © 2022 IEEE.

关键词： Reinforcement learning

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Porting the ABAQUS Structural Analysis Code to Run on the iPSC/2

Porting the ABAQUS Structural Analysis Code to Run on the iP...

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distributed Memory Computing conference

作者： M.L. Barton E.J. Kushner Intel Scientific Computers Beaverton OR USA

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parallel image classification using multiscale Markov random fields

Parallel image classification using multiscale Markov random...

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1993 IEEE International conference on Acoustics, Speech, and Signal processing

作者： Z. Kato M. Berthod J. Zerubia INRIA Sophia-Antipolis France

The application of massively parallel multiscale relaxation algorithms to image classification is considered. First, a classical multiscale model applied to supervised image classification is presented. The model consists of a label pyramid and a whole observation field. The potential functions of the coarse grid are derived by simple computations. Then, a scheme which introduces a local interaction between two neighbor grids in the label pyramid is proposed. This is a way to incorporate cliques, with far-apart sites for a reasonable price. Finally, results on noisy synthetic data and on a SPOT image obtained by different relaxation methods using these models are presented.< >

关键词： image classification Markov random fields Grid computing Petroleum Relaxation methods image resolution Tin Mesh generation

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Advances in Information Systems - 4th International conference, ADVIS 2006, Proceedings

引用

4th International conference on Advances in Information Systems, ADVIS 2006

ISBN: (纸本)3540462910

The proceedings contain 42 papers. The topics discussed include: processing preference queries in standard database systems;from on-campus project organized problem based learning to facilitated work based learning in industry;a novel clustering-based approach to schema matching;three-level object-oriented database architecture based on virtual updateable views;data mining with parallel support vector machines for classification;comparative analysis of classification methods for protein interaction verification system;distributed architecture for association rule mining;automatic lung nodule detection using template matching;structural and event based multimodal video data modeling;knowledge management in different software development approaches;an architecture design process using a supportable meta-architecture and roundtrip engineering;and adaptive enumeration strategies and metabacktracks for constraints solving.

关键词： Information science

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Ultra-high speed real-time 4D display system installed in ultra-high speed parallel OCT system at a volume rate of 12 volumes/sec

Ultra-high speed real-time 4D display system installed in ul...

引用

conference on Optical Coherence Tomography and Coherence Domain Optical methods in Biomedicine XV

作者： Ohbayashi, K. Choi, D. Hiro-oka, H. Kubota, A. Ohno, T. Ikeda, R. Shimizu, K. Kitasato Univ Grad Sch Med Sci Sagamihara Si 2520373 Japan

ISBN: (纸本)9780819484260

Optical coherence tomography (OCT) has become a promising diagnostic method in many medical fields. Non-invasive real-time optical biopsy of internal organs is one of the most attractive applications of OCT enabling in-situ diagnostic of cancer in its early stage, i.e. optical biopsy. For the application, faster OCT methods are required to reduce the inspection time and motion artifacts in images. A criterion to satisfy the purpose is an endoscopic-OCT method capable to display volumetric tomography continuously in real-time at a rate of video movie like conventional endoscopes. In our previous work, we demonstrated ultra-high speed OCT at an A-scan rate of 60 MHz. However, movies were rendered after the data acquisition. In this work, we have developed an ultra-fast data processing system, installed it in the ultra-high speed OCT system, and enabled real time display of various 3D tomography images without limitation of diagnostic time, i.e. 4D OCT imaging, at an A-scan rate, B-scan rate and volume rate of 10 MHz, 4 kHz and 12 volumes/sec, respectively. Various image presentations in real-time are demonstrated such as continuous rendered 3D imaging and continuous 2D-slice scanning 3D imaging.

关键词： Optical coherence tomography OCT ultra-high speed OCT endoscopic OCT optical biopsy

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Collaborative Segmentation Model for Colonoscopy Ulcers based on Fuzzy Labeling 9

Collaborative Segmentation Model for Colonoscopy Ulcers base...

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9th IEEE International conference on Advanced Robotics and Mechatronics (ICARM)

作者： Lin, Yanning Chen, Jie Ding, Ziqian Chen, Linxu Li, Le Zhu, Zhongsheng Wang, Zhaoxia Li, Jianqiang Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518000 Peoples R China Shenzhen Childrens Hosp Gastroenterol Shenzhen 518000 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518000 Peoples R China Shenzhen Childrens Hosp Shenzhen 518000 Peoples R China

ISBN: (纸本)9798350385731;9798350385724

Inflammatory Bowel Disease (IBD) is a global chronic intestinal inflammatory disease, and its incidence rate increases year by year with the progress of economic globalization. Currently, the diagnosis of IBD in children mainly relies on endoscopic examination, but scoring endoscopic images is a challenging issue, especially in distinguishing different types of ulcers. To address this issue, this article designs a mobile application to accelerate data annotation processing and may provide reference for other unlabeled datasets. In the context of image segmentation, blurring labels has become an important issue. Deep learning methods are widely used in medical image segmentation, but their accuracy depends on high-quality annotated data. However, there are low-quality noise areas in the annotated data, and obtaining accurate and high-quality annotations becomes more time-consuming with limited annotation budgets. This article proposes a collaborative training framework to improve learning of noisy pixels. This framework determines the label confidence of an image by calculating the similarity between image pixels and surrounding pixels. Then, two parallel deep networks were constructed for semantic prediction, which aimed to guide each other on pixels that may have noise. By applying consistency in dual network prediction, the semantic information of uncertain pixels is corrected as much as possible. Experimental results have shown that this framework is slightly superior to models trained with pixel level precise labels, thus more effectively utilizing existing annotated data in the case of fuzzy labels.

关键词： image segmentation fuzzy label data annotation

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Conch: A Cyclic MapReduce Model for Iterative Applications

Conch: A Cyclic MapReduce Model for Iterative Applications

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Ran Zheng Genmao Yu Hai Jin Xuanhua Shi Qin Zhang Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

MapReduce programming model is a popular model to simplify but speed up data parallel applications. However, it is not efficient for iterative applications because of its repeated data transmission with HDFS (Hadoop distributed File System). Conch, a cyclic MapReduce model, is designed for efficient processing of iterative applications. In order to minimize network overhead, shared data is cached locally and a "map-shuffle" phase is presented with a combined transmission mechanism. Meanwhile, a prediction scheduler for iterative applications is brought out to achieve better data locality in terms of runtime information. The experiments show that Conch can support iterative applications transparently and efficiently. Compared with Hadoop and HaLoop in single-job environment, Conch can achieve 13%-17% improvements on K-Means and fuzzy C-Means. Especially in multi-job environment, 63.6% and 28.6% improvements can be obtained compared with Hadoop and HaLoop.

关键词： Delays Iterative methods Computational modeling Prediction algorithms Heart beat Data transfer Data models

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Analysis of Real-Time Hostile Activitiy Detection from Spatiotemporal Features Using Time distributed Deep CNNs, RNNs and Attention-Based Mechanisms 5

Analysis of Real-Time Hostile Activitiy Detection from Spati...

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5th IEEE International conference on image processing Applications and Systems (IPAS)

作者： Siddique, Labib Ahmed Junhai, Rabita Reza, Tanzim Khan, Salman Sayeed Rahman, Tanvir BRAC Univ Dept Comp Sci & Engn 66 Mohakhali Dhaka 1212 Bangladesh Univ Delaware Coll Engn Dept Comp & Informat Sci Newark DE 19716 USA

ISBN: (纸本)9781665462198

Real-time video surveillance, through CCTV camera systems has become essential for ensuring public safety which is a priority today. Although CCTV cameras help a lot in increasing security, these systems require constant human interaction and monitoring. To eradicate this issue, intelligent surveillance systems can be built using deep learning video classification techniques that can help us automate surveillance systems to detect violence as it happens. In this research, we explore deep learning video classification techniques to detect violence as they are happening. Traditional image classification techniques fall short when it comes to classifying videos as they attempt to classify each frame separately for which the predictions start to flicker. Therefore, many researchers are coming up with video classification techniques that consider spatiotemporal features while classifying. However, deploying these deep learning models with methods such as skeleton points obtained through pose estimation and optical flow obtained through depth sensors, are not always practical in an IoT environment. Although these techniques ensure a higher accuracy score, they are computationally heavier. Keeping these constraints in mind, we experimented with various video classification and action recognition techniques such as ConvLSTM, LRCN (with both custom CNN layers and VGG-16 as feature extractor) CNNTransformer and C3D. We achieved a test accuracy of 80% on ConvLSTM, 83.33% on CNN-BiLSTM, 70% on VGG16-BiLstm,76.76% on CNN-Transformer and 80% on C3D.

关键词： Deep learning Video classification Neural network Attention based encoder Violence detection LRCN ConvLSTM Transformer C3D

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