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Feature Selection and Intrusion Detection in Cloud Environment based on Machine Learning algorithms 15

Feature Selection and Intrusion Detection in Cloud Environme...

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15th IEEE international Symposium on parallel and Distributed processing with Applications (ISPA) / 16th IEEE international conference on Ubiquitous Computing and Communications (IUCC)

作者： Javadpour, Amir Abharian, Sanaz Kazemi Wang, Guojun Guangzhou Univ Guangzhou Guangdong Peoples R China Sharif Univ Technol Tehran Iran

ISBN: (纸本)9781538637906

Characteristics and way of behavior of attacks and infiltrators on computer networks are very difficult and need an expert. In addition;the advancement of computer networks, the number of attacks and infiltrations is also increasing. In fact, the knowledge coming from expert will lose its value over time and must be updated and made available to the system and this makes the need for expert person always felt. In machine learning techniques, knowledge is extracted from the data itself which has diminished the role of the expert. Various methods used to detect intrusions, such as statistical models, safe system approach, neural networks, etc., all weaken the fact that it uses all the features of an information packet rotating in the network for intrusion detection. Also, the huge volume of information and the unthinkable state space is also an important issue in the detection of intrusion. therefore, the need for automatic identification of new and suspicious patterns in attempt for intrusion with the use of more efficient methods (Lower cost and higher performance) is needed more than before. the purpose of this paper is to provide a new method based on intrusion detection systems and its various architectures aimed at increasing the accuracy of intrusion detection in cloud computing.

关键词： intrusion detection feature Selection classification Algorithm machine learning neural network cloud computing

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From Abstract Agents Models to Real-World AGI architectures: Bridging the Gap 10th

From Abstract Agents Models to Real-World AGI Architectures:...

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10th international conference on Artificial General Intelligence (AGI)

作者： Goertzel, Ben OpenCog Fdn Sha Tin Hong Kong Peoples R China

ISBN: (纸本)9783319637037;9783319637020

A series of formal models of intelligent agents is proposed, with increasing specificity and complexity: simple reinforcement learning agents;"cognit" agents with an abstract memory and processing model;hypergraph-based agents (in which "cognit" operations are carried out via hypergraphs);hypergraph agents with a rich language of nodes and hyperlinks (such as the OpenCog framework provides);"PGMC" agents whose rich hypergraphs are endowed with cognitive processes guided via Probabilistic Growth and Mining of Combinations;and finally variations of the PrimeAGI design, which is currently being built on top of the OpenCog framework.

关键词： Intelligent agents

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GPU-Accelerated Block-Max Query processing 17th

GPU-Accelerated Block-Max Query Processing

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17th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Huang, Haibing Ren, Mingming Zhao, Yue Stones, Rebecca J. Zhang, Rui Wang, Gang Liu, Xiaoguang Nankai Univ Coll Comp & Control Engn Nankai Baidu Joint Lab Tianjin 300350 Peoples R China

ISBN: (纸本)9783319654829;9783319654812

In this paper, we propose a method for parallel top-k query processing on GPU(s). We employ a novel partitioning strategy which splits the posting lists according to document ID numbers. Individual GPU threads simultaneously perform top-k query processing within their allocated subsets of posting lists, the results of the query are merged to give the final top-k results. We further design a CPU-GPU cooperative query processing method, where a majority of queries involving shorter posting lists are processed on the GPU side. We experiment with AND, OR, WAND, and Block-Max WAND (BMW) queries, with experimental results showing a promising improvement in query throughput, particularly in the case of BMW queries.

关键词： Information retrieval GPU Index partition Query assignment

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SRIANN: SPHERE RING INTERSECTION FOR APPROXIMATE NEAREST NEIGHBOR SEARCH IN VIDEOS

SRIANN: SPHERE RING INTERSECTION FOR APPROXIMATE NEAREST NEI...

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IEEE international conference on Image processing

作者： Yi-Ting Chen Wei-Chih Tu Shao-Yi Chien Graduate Institute of Electronics Engineering National Taiwan University

In the field of approximate nearest neighbor (ANN) search, rare of the existing approaches are tailored for video applications. the Ring Intersection Approximate Nearest Neighbor (RIANN) is the first ANN search algorithm for videos. It achieves real-time by performing the ANN search on the sparse grid and interpolating others. For some applications, the dense ANN search is needed to ensure the searching accuracy. To achieve dense ANN search in real-time, we consider the parallel computing as a solution. However, the RIANN algorithm is not suitable for parallel computing as the algorithm itself suffers from bad thread coherency. In this paper, we propose the Sphere Ring Intersection Approximate Nearest Neighbor (SRIANN), which solves the problem of bad thread coherency and improves the accuracy of ANN search compared to the original RIANN method. the experimental results show that the proposed method is the only one able to perform dense ANN search for CIF videos in real-time.

关键词： Instruction sets Videos Graphics processing units Real-time systems Databases Approximation algorithms parallel processing

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Power-Performance Evaluation of parallel Multi-objective EEG Feature Selection on CPU-GPU Platforms 17th

Power-Performance Evaluation of Parallel Multi-objective EEG...

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17th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Jose Escobar, Juan Ortega, Julio Francisco Diaz, Antonio Gonzalez, Jesus Damas, Miguel Univ Granada Dept Comp Architecture & Technol CITIC Granada Spain

ISBN: (纸本)9783319654829;9783319654812

Heterogeneous CPU-GPU platforms include resources to benefit from different kinds of parallelism present in many data mining applications based on evolutionary algorithms that evolve solutions with time-demanding fitness evaluation. this paper describes an evolutionary parallel multi-objective feature selection procedure with subpopulations using two scheduling alternatives for evaluation of individuals according to the number of subpopulations. Evolving subpopulations usually provides good diversity properties and avoids premature convergence in evolutionary algorithms. the proposed procedure has been implemented in OpenMP to distribute dynamically either subpopulations or individuals among devices and OpenCL to evaluate the individuals taking into account the devices characteristics, providing two parallelism levels in CPU and up to three levels in GPUs. Different configurations of the proposed procedure have been evaluated and compared with a master-worker approach considering not only the runtime and achieved speedups but also the energy consumption between both scheduling models.

关键词： Heterogeneous subpopulations scheduling Energy-aware computing EEG classification Multi-objective feature selection GPU

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A Hybrid parallel Search Algorithm for Solving Combinatorial Optimization Problems on Multicore Clusters 17th

A Hybrid Parallel Search Algorithm for Solving Combinatorial...

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17th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Sanz, Victoria De Giusti, Armando Naiouf, Marcelo Natl Univ La Plata III LIDI Sch Comp Sci La Plata Argentina CIC Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Buenos Aires DF Argentina

ISBN: (纸本)9783319654829;9783319654812

Multicore clusters are widely used to solve combinatorial optimization problems, which require high computing power and a large amount of memory. In this sense, Hash Distributed A* (HDA*) parallelizes A*, a combinatorial optimization algorithm, using the MPI library. HDA* scales well on multicore clusters and on multicore machines. Additionally, there exist several versions of HDA* that were adapted for multicore machines, using the Pthreads library. In this paper, we present Hybrid HDA* (HHDA*), a hybrid parallel search algorithm based on HDA* that combines message-passing (MPI) with shared-memory programming (Pthreads) to better exploit the computing power and memory of multicore clusters. We evaluate the performance and memory consumption of HHDA* on a multicore cluster, using the 15-puzzle as a case study. the results reveal that HHDA* achieves a slightly higher average performance and uses considerably less memory than HDA*. these improvements allowed HHDA* to solve one of the hardest 15-Puzzle instances.

关键词： parallel search algorithms Hybrid programming Multicore cluster Combinatorial optimization problems Hash Distributed A

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Puffin: Graph processing System on Multi-GPUs 10

Puffin: Graph Processing System on Multi-GPUs

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10th IEEE international conference on Service-Oriented Computing and Applications (SOCA)

作者： Zhao, Peng Luo, Xuan Xiao, Jiang Shi, Xuanhua Jin, Hai Huazhong Univ Sci & Technol Sch Comp Sci & Technol Big Data Technol & Syst Lab Serv Comp Technol & Syst Lab Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781538613269

Multi-GPM nodes are becoming the platform of choice for graph processing. However, in the multiple GPUs environment, there are two main challenges in designing a graph processing system. First, the system suffers from huge communication overhead. GPUs and CPUs are connected through PCIe, whose bandwidth is far smaller than that of GPU memory. Second, the system is developed based on BSP (Bulk Synchronous parallel) model, which results in heavy thread synchronization overhead and low resource utilization. Aiming to solve the above problems, we design a graph processing system based on multi-GPUs called Puffin. Puffin proposes a novel data representation, which meets our programming needs and has minimum storage space occupancy. Our system achieves overlap of communication and computation by developing an efficient task management scheme, which divides the task and manages the order of different kinds of tasks. Puffin also provides two different programming models and some optimization strategies to implement graph algorithms. Our experiment shows that Puffin gets significant performance improvement over other state-of-the-art approaches on GPU(s), such as Cusha and Gunrock. In addition, Puffin exhibits good scalability, specially for compute-intensive applications.

关键词： GPGPU Graph processing Communication Overhead

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A Machine Learning Approach for Efficient parallel Simulation of Beam Dynamics on GPUs 46

A Machine Learning Approach for Efficient Parallel Simulatio...

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46th international conference on parallel processing Workshops (ICPPW)

作者： Arumugam, Kamesh Godunov, Alexander Islam, Tunazzina Ranjan, Desh Terzic, Balsa Zubair, Mohammad Old Dominion Univ Dept Comp Sci Norfolk VA 23529 USA Old Dominion Univ Ctr Accelerator Sci Norfolk VA 23529 USA Old Dominion Univ Dept Phys Norfolk VA 23529 USA

ISBN: (纸本)9781538610428

parallel computing architectures like GPUs have traditionally been used to accelerate applications with dense and highly-structured workloads;however, many important applications in science and engineering are irregular and dynamic in nature, making their effective parallel implementation a daunting task. Numerical simulation of charged particle beam dynamics is one such application where the distribution of work and data in the accurate computation of collective effects at each time step is irregular and exhibits control-flow and memory access patterns that are not readily amenable to GPU's architecture. algorithms with these properties tend to present both significant branch and memory divergence on GPUs which leads to severe performance bottlenecks. We present a novel cache-aware algorithm that uses machine learning to address this problem. the algorithm presented here uses supervised learning to adaptively model and track irregular access patterns in the computation of collective effects at each time step of the simulation to anticipate the future control-flow and data access patterns. Access pattern forecast are then used to formulate runtime decisions that minimize branch and memory divergence on GPUs, thereby improving the performance of collective effects computation at a future time step based on the observations from earlier time steps. Experimental results on NVIDIA Tesla K40 GPU shows that our approach is effective in maximizing data reuse, ensuring workload balance among parallel threads, and in minimizing both branch and memory divergence. Further, the parallel implementation delivers up to 485 Gflops of double precision performance, which translates to a speedup of up to 2.5X compared to the fastest known GPU implementation.

关键词： Computational modeling Data models Heuristic algorithms Graphics processing units Predictive models Two dimensional displays Instruction sets

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A Survey and Recommendations for Distributed, parallel, Single Pass, Incremental Bayesian Classification based on MapReduce for Big Data

A Survey and Recommendations for Distributed, Parallel, Sing...

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IEEE 19th international conference on High Performance Computing and Communications (HPCC)

作者： Shafiq, M. Omair Yang, Yibing Fekri, Maryam Carleton Univ Sch Informat Technol Ottawa ON Canada

ISBN: (纸本)9781538659304

In the emerging digital age, massive production of data is occurred actively or passively by collecting data from users and environment via applications, sensor devices and so on. that makes it important and crucial to have the ability to process big data efficiently and effectively utilize it. the challenge to process big data is that it has high volume, velocity, variety, as well as veracity and value. In this paper, we present a survey of related work and prescribe our recommendations towards building Bayesian classification for big data environments. It is based on MapReduce and is distributed, parallel, single pass and incremental which makes it feasible to be deployed and executed on cloud computing platform We also carry out scalability analysis of the proposed solution that it can train Bayesian classifier to perform predictive analytics by processing big data with large volume, velocity and variety.

关键词： Distributed parallel Single-pass Incremental Bayesian Classification Big Data

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A New State Model for DRAMs Using Petri Nets 17

A New State Model for DRAMs Using Petri Nets

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17th Annual international conference on Embedded Computer Systems - architectures, Modeling, and Simulation (SAMOS)

作者： Jung, Matthias Kraft, Kira Wehn, Norbert Fraunhofer IESE D-67663 Kaiserslautern Germany Univ Kaiserslautern D-67663 Kaiserslautern Germany

ISBN: (纸本)9781538634370

the functionality of DRAMs, especially the state transitions are described in JEDEC standards. these standards contain a finite state machine, which intends to provide an overview of the possible state transitions and the commands to control them. However, today's DRAMs are highly concurrent devices as they provide bank parallelism. the state diagram used in JEDEC standards does not model this concurrency and furthermore it is misleading in several aspects. In this paper, for the first time we present an easily comprehensive model of the DRAM states and transitions, using a Petri Net, which covers also the DRAM concurrency.

关键词： Random access memory Petri nets Inhibitors Standards parallel processing Computer architecture Concurrent computing Petri nets random access machine Computer Architecture parallel processing (COMPUTERS) parallel programming DRAM1 gene Inhibitors Joint Electron Device Engineering Council State Change finite state machines

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