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2019 International conference on Machine Learning, Big Data, Cloud and parallel Computing, COMITCon 2019

作者： Asokan, Anju Anitha, J. ECE Department Karunya Institute of Technology and Sciences Coimbatore641114 India

ISBN: (纸本)9781728102115

This paper presents the detailed comparison of various image processing techniques for analyzing satellite images. The satellite images are large in size, acquired from long distances and are affected by noise and other environmental conditions. Hence it is necessary to process them so that they can be used by the researchers for analysis. Satellite images are widely used in many real time applications such as in agriculture land detection, navigation and in geographical information systems. In this paper, a review of some popular machine learning based image processing techniques is presented. Also a detailed comparison of various techniques is performed. Limitations in each image processing method are also described. In addition to reviewing of different methods, different metrics for performance evaluation in each of the image processing areas is studied. © 2019 IEEE.

关键词： Machine learning

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RANDOM PART LOCALIZATION MODEL FOR FINE GRAINED image CLASSIFICATION 26

RANDOM PART LOCALIZATION MODEL FOR FINE GRAINED IMAGE CLASSI...

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26th IEEE International conference on image processing (ICIP)

作者： Xin, Qi Lv, Tiejun Gao, Hui Beijing Univ Posts & Telecommun Minist Educ Key Lab Trustworthy Distributed & Serv Beijing Peoples R China

ISBN: (纸本)9781538662496

Fine-grained recognition is challenging due to its subtle local inter-class differences versus large intra-class variations. Finding those subtle traits that fully characterize the object is not straightforward. In this paper, we present a novel random part localization model, which first extracts the foreground object using the saliency map, and then localizes the discriminative parts through a set of potential regions in a random way based on their contribution to classification. We train three convolutional neural networks to capture the features that belong to different levels and average their classification results as our final prediction score. Experiments show that our approach achieves competitive performance compared with state-of-the-art methods on three publicly available fine-grained recognition datasets (CUB-200-2011, Stanford Cars and FGVC-Aircraft).

关键词： fine-grained convolutional neural network random part localization

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distributed Multi-objective Particle Swarm Optimization using Time-delayed Virtual Global Best Method 11th

Distributed Multi-objective Particle Swarm Optimization usin...

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11th International Joint conference on Computational Intelligence (IJCCI)

作者： Sato, Yuji Ueno, Shota Hirotsu, Toshio Hosei Univ Dept Comp & Informat Sci Tokyo Japan

ISBN: (纸本)9789897583841

To reduce the computational cost of particle swarm optimization (PSO) methods, research has begun on the use of Graphics processing Units (GPUs) to achieve faster processing speeds. However, since PSO methods search based on a global best value, they are hampered by the frequent need for communication with global memory. Even using a standard PSO that uses a local best value does not solve this problem. In this paper, we propose a virtual global best method that speeds up computations by defining a time-delayed global best as a virtual global best in order to reduce the frequency of communication with low-speed global memory. We also propose a method that combines decomposition-based multi-objective PSO (MOPSO/D) with a virtual global best method to speed up multi-objective particle swarm optimization by running it in parallel while maintaining search accuracy, and we demonstrate the effectiveness of this approach by using a number of unimodal/multimodal single objective benchmark test functions and three classical benchmark test functions with two objectives.

关键词： Particle Swarm Optimization parallel and distributed System Performance Improvement Multi-objective Optimization

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Design and Evaluation of an HPC-based Expert System to speed-up Retail Data Analysis using Residual Networks Combined with parallel Association Rule Mining and Scalable Recommenders

Design and Evaluation of an HPC-based Expert System to speed...

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Proceedings of the International Convention MIPRO

作者： C. Barakat M. Riedel S. Brynjólfsson G. Cavallaro J. Busch R. Sedona School of Engineering and Natural Sciences University of Iceland Iceland Jülich Supercomputing Centre Forschungszentrum Jülich Germany

Given the Covid-19 pandemic, the retail industry shifts many business models to enable more online purchases that produce large transaction data quantities (i.e., big data). Data science methods infer seasonal trends about products from this data and spikes in purchases, the effectiveness of advertising campaigns, or brand loyalty but require extensive processing power leveraging High-Performance Computing to deal with large transaction datasets. This paper proposes an High-Performance Computing-based expert system architectural design tailored for ‘big data analysis’ in the retail industry, providing data science methods and tools to speed up the data analysis with conceptual interoperability to commercial cloud-based services. Our expert system leverages an innovative Modular Supercomputer Architecture to enable the fast analysis by using parallel and distributed algorithms such as association rule mining (i.e., FP-Growth) and recommender methods (i.e., collaborative filtering). It enables the seamless use of accelerators of supercomputers or cloud-based systems to perform automated product tagging (i.e., residual deep learning networks for product image analysis) to obtain colour, shapes automatically, and other product features. We validate our expert system and its enhanced knowledge representation with commercial datasets obtained from our ON4OFF research project in a retail case study in the beauty sector.

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LADD: A Length-Adaptive Approach to Detecting Taxi Anomalous Detours 25

LADD: A Length-Adaptive Approach to Detecting Taxi Anomalous...

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25th IEEE International conference on parallel and distributed Systems (IEEE ICPADS)

作者： Lu, Xuansheng Cheng, Weiyu Shen, Yanyan Zhu, Yanmin Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781728125831

This paper focuses on the practical problem of detecting taxi anomalous detours. In this paper, we propose a length-adaptive detection approach called LADD to detect if a taxi conducted an anomalous detour by investigating the taxi journey's trajectory. We employ a similarity-based detection strategy, which takes trajectory length into consideration. Our approach can deal with both long and short trajectories by applying weighted similarity on trajectories and using trajectory concatenation or clipping. For quantitative performance comparison, we propose a performance metric, and compare our approach with the state-of-the-art methods on a real-world dataset. The results verify the superior performance of LADD on taxi detour detection.

关键词： Big data processing spatial databases detour detecting GPS trajectories intelligent transportation systems

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Lion Swarm Optimization Based on Balanced Local and Global Search with Different Distributions

Lion Swarm Optimization Based on Balanced Local and Global S...

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Symposium of image, Signal processing, and Artificial Vision (STSIVA)

作者： Keqin Jiang Mingyan Jiang School of Computer Science and Technology Nanjing Tech University Nanjing China School of Information Science and Engineering Shandong University Qingdao China

ISBN: (纸本)9781665426565

In view of the shortcomings of the basic lion swarm optimization, which is prone to local optimization and low convergence accuracy in partial optimization, this paper proposes a lion swarm optimization based on balanced local and global search with different distributions. The improved algorithm adds chaos search and different distributed perturbation strategies to the positions of lions in the earlier stage, which improves the optimization efficiency of the algorithm in the optimization process. These disturbance strategies include variations based on Cauchy mutation, t probability distribution, and levy flight. The simulation results of the test functions show that the optimization accuracy of the improved algorithm is much higher than that of the basic lion swarm optimization. The improved algorithm effectively prevents the swarm optimization from easily falling into the local optimization value in the extremely difficult optimization functions.

关键词： Chaos Perturbation methods Simulation conferences Robustness Probability distribution Particle swarm optimization

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基于并行计算的抛光轴表面缺陷检测研究

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机电工程 2020年第12期37卷 1540-1546页

作者：姜庆胜李研彪计时鸣浙江工业大学机械工程学院浙江杭州310023

针对在生产实践中抛光轴表面缺陷检测的实时性和缺陷分类需求问题,对抛光轴表面缺陷检测系统、缺陷图像采集、图像特征、传统检测方法等方面进行了研究,归纳了轴表面缺陷检测的处理方法。提出了两种基于并行计算的检测系统,即基于云的Sp... 详细信息

针对在生产实践中抛光轴表面缺陷检测的实时性和缺陷分类需求问题,对抛光轴表面缺陷检测系统、缺陷图像采集、图像特征、传统检测方法等方面进行了研究,归纳了轴表面缺陷检测的处理方法。提出了两种基于并行计算的检测系统,即基于云的Spark多机并行的分布式计算机集群检测系统和单机多核的CUDA并行处理检测系统;通过Spark架构多机分布式并行计算机理的分析,以及结合CUDA单机多核并行运算原理,从数学角度进行了并行化处理,最后进行了相应的实验。研究结果表明:基于CUDA的单机多核的并行处理检测系统适合实时检测,基于Spark的多机分布式并行检测系统不适合实时检测系统,但适合大数据处理系统。

关键词：图像处理 Spark 分布式并行计算图像处理单元 CUDA

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MilKBQA: A Complex Knowledge Base Question Answering Dataset on Chinese Military Field

MilKBQA: A Complex Knowledge Base Question Answering Dataset...

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IEEE International conference on Data Science in Cyberspace (DSC)

作者： Pike Xu Tiejun Li Zhen Huang Min Guo Jiezhong He Ding Sun Institute of Computer National University of Defense Technology Changsha China Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665418164

Facts in military field tend to involve elements of time, space, quantity, status, and so on. Existing methods of representing knowledge in the form of triples fail to adequately express these facts, and also cause obstacles to knowledge storage and updating. Furthermore, question answering on these facts introduces new complexity dimension, which are complicated to be supported by existing corpus. Thus, we construct a Chinese knowledge base for military field covering entities and events centric knowledge, referred as MilKB. It consists of 965 entities and 3,017 facts. Moreover, we classify the natural questions into 26 types and construct a complex question answering dataset derived from MilKB, referred as MilKBQA. It consists of 2,829 question-answer pairs, in which 600 are event-centric ones. Experiments with three recent strong baseline models demonstrate that MilKBQA requires further research.

关键词： Triples (Data structure) conferences Knowledge based systems Cyberspace Knowledge representation Data science Cognition

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19th Joint European conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019

19th Joint European Conference on Machine Learning and Princ...

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19th Joint European conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019

ISBN: (纸本)9783030438227

The proceedings contain 116 papers. The special focus in this conference is on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. The topics include: Learning and interpreting potentials for classical hamiltonian systems;automating common data science matrix transformations;finding interpretable concept spaces in node embeddings using knowledge bases;local interpretation methods to machine learning using the domain of the feature space;measuring unfairness through game-theoretic interpretability;lioNets: Local interpretation of neural networks through penultimate layer decoding;distributed generative modelling with sub-linear communication overhead;distributed learning of neural networks with one round of communication;decentralized learning with budgeted network load using gaussian copulas and classifier ensembles;decentralized recommendation based on matrix factorization: A comparison of gossip and federated learning;Ring-Star: A sparse topology for faster model averaging in decentralized parallel SGD;hardware acceleration of machine learning beyond linear Algebra;deepNotebooks: Deep probabilistic models construct python notebooks for reporting datasets;detecting stable communities in link streams at multiple temporal scales;a comparative study of community detection techniques for large evolving graphs;dynamic joint variational graph autoencoders;evolution analysis of large graphs with gradoop;MHDNE: Network embedding based on multivariate hawkes process;multimodal deep networks for text and image-based document classification;manifold mixing for stacked regularization;a wide and deep neural network for survival analysis from anatomical shape and tabular clinical data;deep generative multi-view learning;SDE-KG: A stochastic dynamic environment for knowledge graphs;HyperUCB: Hyperparameter optimization using contextual bandits;iterative representation learning for entity alignment leveraging textual information;preface.

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ADAPTIVE PARTICLE SAMPLING AND RESAMPLING IN parallel/distributed PARTICLE FILTERS

ADAPTIVE PARTICLE SAMPLING AND RESAMPLING IN PARALLEL/DISTRI...

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Spring Simulation conference (SpringSim)

作者： Zhang, Xudong Gu, Feng CUNY Grad Ctr Dept Comp Sci 365 5th Ave New York NY 10016 USA CUNY Coll Staten Isl Dept Comp Sci 2800 Victory Blvd Staten Isl NY 10314 USA

ISBN: (纸本)9781510883888

Particle filters have been widely used in estimating the states of dynamic systems by using Bayesian interference and stochastic sampling techniques. parallel computing techniques were introduced to improve the performance of sequential particle filters with multiple processing units (PUs). However, the unavoidable communications between Pus, lower the performance. The hybrid and adaptive resampling algorithms were proposed to improve the performance of parallel/distributed particle filters by reducing the communication costs without loss of estimation accuracy. In this paper, we propose an adaptive sampling and resampling technique in particle filters. In the proposed algorithm, the number of particle is dynamically adjustable based on the model convergence. As a result, less particles will be used if the current convergence is good and more particles will be used if the convergence is getting bad. The experimental results show the improved performance by using less particles and reducing the communication cost compared with other algorithms.

关键词： particle filters parallel/distributed particle filter adaptive particle filter Monte Carlo (SMC) methods resampling

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