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Where Does AlphaGo Go: From Church-Turing Thesis to AlphaGo Thesis and Beyond

IEEE/CAA Journal of Automatica Sinica 2016年第2期3卷 113-120页

作者： Fei-Yue Wang Jun Jason Zhang Xinhu Zheng Xiao Wang Yong Yuan Xiaoxiao Dai Jie Zhang Liuqing Yang IEEE the State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences (SKL-MCCS CASIA) Research Center of Computational Experiments and Parallel Systems The National University of Defense Technology the Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer Science University of Denver the Department of Computer Science and Engineering University of Minnesota the Qingdao Academy of Intelligent Industries (QAII) Qingdao Shandong China and SKL-MCCS CASIA the Department of Electrical and Computer Engineering Colorado State University

An investigation on the impact and significance of the AlphaGo vs. Lee Sedol Go match is conducted, and concludes with a conjecture of the AlphaGo Thesis and its extension in accordance with the Church-Turing Thesis in the history of computing. It is postulated that the architecture and method utilized by the AlphaGo program provide an engineering solution for tackling issues in complexity and intelligence. Specifically, the AlphaGo Thesis implies that any effective procedure for hard decision problems such as NP-hard can be implemented with AlphaGo-like approach. Deep rule-based networks are proposed in attempt to establish an understandable structure for deep neural networks in deep learning. The success of AlphaGo and corresponding thesis ensure the technical soundness of the parallel intelligence approach for intelligent control and management of complex systems and knowledge automation. © 2014 Chinese Association of Automation.

关键词： Religious buildings

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Scalable parallel Algorithms for Shared Nearest Neighbor Clustering

Scalable Parallel Algorithms for Shared Nearest Neighbor Clu...

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International Conference on High Performance Computing

作者： Sonal Kumari Saurabh Maurya Poonam Goyal Sundar S Balasubramaniam Navneet Goyal Department of Computer Science & Information Systems Advanced Data Analytics & Parallel Technologies Laboratory INDIA

ISBN: (纸本)9781509054121

Clustering is a popular data mining technique which discovers structure in unlabeled data by grouping objects together on the basis of a similarity criterion. Traditional similarity measures lose their meaning as the number of dimensions increases and as a consequence, distance or density based clustering algorithms become less meaningful. Shared Nearest Neighbor (SNN) is a solution to clustering high-dimensional data with the ability to find clusters of varying density. SNN assigns objects to a cluster, which share a large number of their nearest neighbors. However, SNN is compute and memory intensive for data of large size and/or dimensionality. Nearest neighbor queries are responsible for a major proportion of computations in SNN, resulting in lower efficiency for higher value of number of nearest neighbors (k). The main motivation of this work is to improve the efficiency of SNN and to parallelize it so that it can be used for clustering large high-dimensional datasets and for large values of k. Existing SNN algorithms become inefficient in these situations. In this paper, we present a new sequential SNN algorithm, R-SNN, which uses R-tree for executing neighborhood queries efficiently and exploiting spatial locality to minimize memory usage. R-SNN is benchmarked against the best available implementation of SNN and is found up to 77 times faster when tested on various real datasets. R-SNN is parallelized for distributed memory, shared memory, and hybrid systems. Significant speedup and scalability achieved can be attributed to parallelization and good load balancing strategies and also to exploitation of spatial locality. Experimental results demonstrate the same for datasets of varying dimensionality and size. The maximum speedup achieved for shared, distributed, and hybrid models are 427.19 using 48 threads, 394.24 using 32 processes, and 1380.69 on 32 nodes (with each node spawning 4 threads), respectively. Super-linear speedup for some datasets is attributed

关键词： Clustering algorithms Algorithm design and analysis Time complexity Spatial databases parallel algorithms Memory management Density measurement

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A parallel Framework for Grid-Based Bottom-Up Subspace Clustering

A Parallel Framework for Grid-Based Bottom-Up Subspace Clust...

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International Conference on Data science and Advanced Analytics (DSAA)

作者： Poonam Goyal Sonal Kumari Shubham Singh Vivek Kishore Sundar S. Balasubramaniam Navneet Goyal Department of Computer Science & Information Systems Advanced Data Analytics & Parallel Technologies Laboratory Pilani Campus INDIA

ISBN: (纸本)9781509052073

Clustering is a popular data mining and machine learning technique which discovers interesting patterns from unlabeled data by grouping similar objects together. Clustering high-dimensional data is a challenging task as points in high dimensional space are nearly equidistant from each other, rendering commonly used similarity measures ineffective. Subspace clustering has emerged as a possible solution to the problem of clustering high-dimensional data. In subspace clustering, we try to find clusters in different subspaces within a dataset. Many subspace clustering algorithms have been proposed in the last two decades to find clusters in multiple overlapping subspaces of high-dimensional data. Subspace clustering algorithms iteratively find the best subset of dimensions for a cluster from 2d-1 possible combinations in d-dimensional data. Subspace clustering is extremely compute intensive because of exhaustive search of subspaces, especially in the bottom-up subspace clustering algorithms. To address this issue, an efficient parallel framework for grid-based bottom-up subspace clustering algorithms is developed, considering popular algorithms belonging to this category. The framework is implemented for shared memory, distributed memory, and hybrid systems and is tested for three grid-based bottom-up subspace clustering algorithms: CLIQUE, MAFIA, and ENCLUS. All parallel implementations exhibit impressive speedup and scalability on real datasets.

关键词： Conferences

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GPU acceleration of subgraph isomorphism search in large scale graph

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Journal of Central South University 2015年第6期22卷 2238-2249页

作者：杨博卢凯高颖慧王小平徐凯 Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology College of Computer National University of Defense Technology Department of Electronic Science and Engineering National University of Defense Technology

A novel framework for parallel subgraph isomorphism on GPUs is proposed, named GPUSI, which consists of GPU region exploration and GPU subgraph matching. The GPUSI iteratively enumerates subgraph instances and solves the subgraph isomorphism in a divide-and-conquer fashion. The framework completely relies on the graph traversal, and avoids the explicit join operation. Moreover, in order to improve its performance, a task-queue based method and the virtual-CSR graph structure are used to balance the workload among warps, and warp-centric programming model is used to balance the workload among threads in a warp. The prototype of GPUSI is implemented, and comprehensive experiments of various graph isomorphism operations are carried on diverse large graphs. The experiments clearly demonstrate that GPUSI has good scalability and can achieve speed-up of 1.4–2.6 compared to the state-of-the-art solutions.

关键词： parallel graph isomorphism GPU backtrack paradigm

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Enhancing Temporal Alignment with Autoencoder Regularization

Enhancing Temporal Alignment with Autoencoder Regularization

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International Joint Conference on Neural Networks

作者： Liquan Nie Yuanyuan Wang Xiang Zhang Xuhui Huang Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Department of Basic Courses Army Officer Academy Department of Computer Science and Technology College of Computer National University of Defense Technology

ISBN: (纸本)9781509006212

Temporal alignment aligns two temporal sequences and is quite challenging due to drastic differences among temporal sequences and source data from different views. Canonical time warping (CTW) has shown great potential in temporal alignment tasks because it can reduce data redundancy by transforming high-dimensional data to a lower-dimensional subspace via canonical correlation analysis (CCA). However, CTW cannot uncover the underlying nonlinear structure embedded in the dataset. In this paper, we propose an autoencoder regularized canonical time warping method (AECTW) to overcome this drawback. Specifically, AECTW enhances lower-dimensional representation of each sequence by incorporating an autoencoder regularization, meanwhile reveals the nonlinear structure of features by explicit nonlinear transformation. By these strategies, AECTW significantly boosts CTW in temporal alignment tasks. Experiments on both synthetic data and two practical human action datasets demonstrate that AECTW outperforms the representative DTW-based methods.

关键词： NONLINEAR STRUCTURES data redundancy Dataset synthetic data Alignment Warping Structural properties Canonical Religious Missions Data sources

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GB-RC4: Effective brute force attacks on RC4 algorithm using GPU 7

GB-RC4: Effective brute force attacks on RC4 algorithm using...

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7th International Green and Sustainable Computing Conference, IGSC 2016

作者： Xue, Pei Li, Tao Dong, Han Liu, Chunbo Ma, Wenjing Pei, Songwen College of Computer and Control Engineering Nankai University Tianjin300071 China Information Security Evaluation Center Civil Aviation University of China Tianjin300300 China Laboratory of Parallel Software and Computational Science State Key Laboratory of Computing Science Institute of Software CAS Beijing China Shanghai Key Lab of Modern Optical Systems Department of Computer Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781509051175

Encryption algorithms are applied to a variety of fields and the security of encryption algorithms depends heavily on the computational infeasibility of exhaustive key-space search. RC4 algorithm has an extensive application for stream encryption, however, the disadvantages of traditional RC4 serial algorithm are large computational quantity and slow computation speed, which means a great challenge for throughput of computing system. Graphics processors recently have been widely used as accelerators for high throughput computing. In this paper, GPU is used to improve the efficiency and performance of attacking RC4 algorithm. First, we revised RC4 algorithm to make it easier to parallelize on a GPU. Then we take advantage of constant memory and shared memory in GPU to search the key space of RC4 algorithm efficiently. Besides, we solved the bank conflict problem in shared memory to further improve the performance. Our approach was evaluated in the whole key space of 40-bit, and the results show it can complete searching of the whole key space within 12.8 hours. © 2016 IEEE.

关键词： Graphics processing unit

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Towards Robust Ego-Centric Hand Gesture Analysis for Robot Control

Towards Robust Ego-Centric Hand Gesture Analysis for Robot C...

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2016 IEEE International Conference on Signal and Image Processing

作者： Hongyong Song Weijiang Feng Naiyang Guan Xuhui Huang Zhigang Luo Institute of Software College of Computer National University of Defense Technology Department of Computer Science and Technology College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

Wearable device with an ego-centric camera would be the next generation device for human-computer interaction such as robot *** gesture is a natural way of egocentric human-computer *** this paper, we present an ego-centric multi-stage hand gesture analysis pipeline for robot control which works robustly in the unconstrained environment with varying *** particular, we first propose an adaptive color and contour based hand segmentation method to segment hand region from the egocentric *** then propose a convex U-shaped curve detection algorithm to precisely detect positions of *** parallelly, we utilize the convolutional neural networks to recognize hand *** on these techniques, we combine most information of hand to control the robot and develop a hand gesture analysis system on an i Phone and a robot arm platform to validate its *** result demonstrates that our method works perfectly on controlling the robot arm by hand gesture in real time.

关键词： ego-centric vision hand detection and segmentation fingertips detection hand gesture recognition robot control human-computer interaction

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A fast, Scalable SLINK Algorithm for Commodity Cluster Computing Exploiting Spatial Locality

A fast, Scalable SLINK Algorithm for Commodity Cluster Compu...

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IEEE International Conference on High Performance Computing and Communications

作者： Poonam Goyal Sonal Kumari Sumit Sharma Dhruv Kumar Vivek Kishore Sundar Balasubramaniam Navneet Goyal Advanced Data Analytics & Parallel Technologies Laboratory Department of Computer Science & Information Systems BITS-Pilani Pilani Campus INDIA

ISBN: (纸本)9781509042982

Single linkage (SLINK) hierarchical clustering algorithm is a preferred clustering algorithm over traditional partitioning-based clustering as it does not require the number of clusters as input. But, due to its high time complexity and inherent data dependencies, it does not scale well for large datasets. To the best of our knowledge, all existing parallel SLINK algorithms are based on the traditional SLINK algorithm and thus require large number of computing resources. In this paper, we present a novel optimization of SLINK algorithm, GridSLINK, which is an order of magnitude faster than the existing state-of-the-art implementation. The optimization in GridSLINK comes from reduction in number of distance calculations required by SLINK. This reduction is achieved by exploiting spatial locality of data points and using an adaptive gridding technique. GridSLINK is parallelized for distributed memory systems. Scalable performance is achieved for increasing number of compute nodes. The proposed parallel algorithm, dGridSLINK, is benchmarked against the best existing parallel algorithm in literature and found to outperform the latter for all the real datasets considered. dGridSLINK can cluster millions of data points in few seconds/minutes using a small number of processing elements, without compromising the quality of clustering.

关键词： parallel computing multi-core multi-node clustering SLINK adaptive gridding

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DD-Rtree: A dynamic distributed data structure for efficient data distribution among cluster nodes for spatial data mining algorithms

DD-Rtree: A dynamic distributed data structure for efficient...

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IEEE International Conference on Big Data

作者： Jagat Sesh Challa Poonam Goyal S. Nikhil Aditya Mangla Sundar S. Balasubramaniam Navneet Goyal Department of Computer Science & Information Systems Birla Institute of Technology & Science Pilani Pilani Campus India Advanced Data Analytics & Parallel Technologies Laboratory Department of Computer Science & Information Systems Birla Institute of Technology & Science Pilani Pilani Campus India

ISBN: (纸本)9781467390064

parallelizing data mining algorithms has become a necessity as we try to mine ever increasing volumes of data. Spatial data mining algorithms like Dbscan, Optics, Slink, etc. have been parallelized to exploit a cluster infrastructure. The efficiency achieved by existing algorithms can be attributed to spatial locality preservation using spatial indexing structures like k-d-tree, quad-tree, grid files, etc. for distributing data among cluster nodes. However, these indexing structures are static in nature, i.e., they need to scan the entire dataset to determine the partitioning coordinates. This results in high data distribution cost when the data size is large. In this paper, we propose a dynamic distributed data structure, DD-Rtree, which preserves spatial locality while distributing data across compute nodes in a shared nothing environment. Moreover, DD-Rtree is dynamic, i.e., it can be constructed incrementally making it useful for handling big data. We compare the quality of data distribution achieved by DD-Rtree with one of the recent distributed indexing structure, SD-Rtree. We also compare the efficiency of queries supported by these indexing structures along with the overall efficiency of DBSCAN algorithm. Our experimental results show that DD-Rtree achieves better data distribution and thereby resulting in improved overall efficiency.

关键词： Data structures Clustering algorithms Data mining Indexing distributed databases Algorithm design and analysis

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Direct method-green's theory: From PDE to BIE in the geometric transformation

Direct method-green's theory: From PDE to BIE in the geometr...

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International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Li-Na Yang Tao-Shen Li Yuan Yan Tang Jia Xu Jian-Jia Pan Hui-Wu Luo Xian-Wei Zheng Electronics and Information Guangxi University Nanning China Department of Computer and Information Science University of Macau Macau Guangxi Colleges and Universities Key Laboratory of Parallel and Distributed Computing Nanning China

ISBN: (纸本)9781509029181

In this research, we apply the Green's theory for converting the partial differential equation to the boundary integral equation for geometric transformation. Green's theory is designed specifically for integral equation. It is efficient in detecting the singularity point to the geometric transformation that has been verified. Experimental results show that the Green's theory has good performance.

关键词： Mathematical model Integral equations Pattern recognition Image restoration Wavelet analysis Partial differential equations Boundary conditions

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