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检索条件"任意字段=10th International Conference on Algorithms and Architectures for Parallel Processing"

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Finding Minimum Locating Arrays Using a SAT Solver 10

Finding Minimum Locating Arrays Using a SAT Solver

引用

10th IEEE international conference on Software Testing, Verification and Validation (ICSTW)

作者： Konishi, Tatsuya Kojima, Hideharu Nakagawa, Hiroyuki Tsuchiya, Tatsuhiro Osaka Univ Grad Sch Infomat Sci & Technol Suita Osaka Japan

ISBN: (纸本)9781509066766

Locating arrays provide combinatorial test suites not only for detecting the presence of an interaction fault but also for locating the fault. Compared with test suites for ordinary combinatorial testing, however, the fault locating capability entails a substantial increase in the size of test suites. In this position paper, we consider the problem: how small can locating arrays be? To answer the question, we develop a method that finds a locating array of a given size using a SAT solver. We report the size of the smallest locating arrays discovered by using our method. this result provides the smallest known upper bound on the size of the minimum locating arrays.

关键词： Sugar Upper bound conferences parallel processing Software testing Electronic mail

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Secure NoSQL Based Medical Data processing and Retrieval: the Exposome Project 10

Secure NoSQL Based Medical Data Processing and Retrieval: Th...

引用

10th international conference on Utility and Cloud Computing (UCC) / 4th international conference on Big Data Computing, Applications and Technologies (BDCAT)

作者： Shetty, Roshan Ramprasad Dissanayaka, Akalanka Mailewa Mengel, Susan Gittner, Lisa Vadapalli, Ravi Khan, Hafiz Texas Tech Univ Lubbock TX 79409 USA Texas Tech Hlth Sci Ctr Lubbock TX USA

ISBN: (纸本)9781450351959

the transdisciplinary big data medical research of the Exposome project discussed in this paper can be best described by the old adage `Finding a needle in a haystack', in this case, of *** or other types of disparate files from various service providers, such as the US Census Bureau and the Center for Disease Control and Prevention of the US Department of Health and Human Services. the Exposome project aims to bring together such data files from different sources to draw previously unknown insights into medical and other types of issues, such as cardiovascular disease and infant mortality. Data from these and other providers, however, continues to grow with new data added frequently;so, the process of finding the correct or desired data is a quite cumbersome and time-consuming task. the data may also be unstructured causing relational databases to be less optimal for handling and processing such enormous data volumes. thus, NoSQL databases offer a promising means to organize and allow parallel access to the data as presented in this paper. the aim is to provide a retrieval system that deals with disparate files and unstructured data while providing an easy and efficient solution for data selection to the researchers. the paper discusses various processes that can be used and optimized to eliminate critical challenges haunting big data processing. the key features of the system include: auto data dictionary creation, conflict resolution, null data handling, and an assistive user interface.

关键词： MongoDB NoSQL Information Retrieval Medical Data

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DEEP IMAGE CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDDING WIth INCEPTION-LIKE BLOCK

DEEP IMAGE CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDD...

引用

IEEE international conference on Image processing

作者： Qiang Wang Jiaqing Xu Rongchun Li Peng Qiao Ke Yang Shijie Li Yong Dou National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China

Image clustering is one of the challenging tasks in machine learning, and has been extensively used in various applications. Recently, various deep clustering methods has been proposed. these methods take a two-stage approach, feature learning and clustering, sequentially or jointly. We observe that these works usually focus on the combination of reconstruction loss and clustering loss, relatively little work has focused on improving the learning representation of the neural network for clustering. In this paper, we propose a deep convolutional embedded clustering algorithm with inception-like block (DCECI). Specifically, an inception-like block with different type of convolution filters are introduced in the symmetric deep convolutional network to preserve the local structure of convolution layers. We simultaneously minimize the reconstruction loss of the convolutional autoencoders with inception-like block and the clustering loss. Experimental results on multiple image datasets exhibit the promising performance of our proposed algorithm compared with other competitive methods.

关键词： Convolutional codes Convolution Clustering algorithms Image reconstruction Decoding Clustering methods Task analysis

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Combining Two Different DNN architectures for Classifying Speaker's Age and Gender 10

Combining Two Different DNN Architectures for Classifying Sp...

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10th international Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC)

作者： Abu Mallouh, Arafat Qawaqneh, Zakariya Barkana, Buket D. Univ Bridgeport Comp Sci & Engn Dept Bridgeport CT 06604 USA Univ Bridgeport Elect Engn Dept Bridgeport CT 06604 USA

ISBN: (纸本)9789897582127

Speakers' age and gender classification is one of the most challenging problems in the field of speech processing. Recently, remarkable developments have been achieved in the neural network field, nowadays, deep neural network (DNN) is considered one of the state-of-art classifiers which have been successful in many speech applications. Motivated by DNN success, we jointly fine-tune two different DNNs to classify the speaker's age and gender. the first DNN is trained to classify the speaker gender, while the second DNN is trained to classify the age of the speaker. then, the two pre-trained DNNs are reused to tune a third DNN (AGender-Tuning) which can classify the age and gender of the speaker together. the results show an improvement in term of accuracy for the proposed work compared with the I-Vector and the GMM-UBM as baseline systems. Also, the performance of the proposed work is compared with other published works on a publicly available database.

关键词： Deep Neural Network GMM-UBM I-Vector Speaker Age and Gender Classification Fine-Tuning

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Optimization Scheme Based on parallel Computing Technology 8th

Optimization Scheme Based on Parallel Computing Technology

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8th international Symposium on parallel architectures, algorithms, and Programming (PAAP)

作者： Li, Xiulai Chen, Chaofan Luo, Yali Chen, Mingrui Hainan Univ Haikou Hainan Peoples R China

ISBN: (纸本)9789811064425;9789811064418

parallel computing is a high performance technology to solve problems, in order to improve computing efficiency, we use the processor to concurrent execute several parts divided from one problem. Based on the current issues in parallel computing area, both the data processing repetition rate and the parallel computing time depend on the time of the last thread in the task completing. this paper was written to take an overview of the existing parallel computing techniques and structures, and propose a solution of adding an advanced thread or advanced processor to make up the deficiency in parallel computing area.

关键词： parallel structure parallel computing parallel model parallel technology

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Last Level Collective Hardware Prefetching For Data-parallel Applications 24

Last Level Collective Hardware Prefetching For Data-Parallel...

引用

IEEE 24th international conference on High Performance Computing Workshops (HiPCW)

作者： Michelogiannakis, George Shalf, John Lawrence Berkeley Natl Lab 1 Cyclotron Rd Berkeley CA 94720 USA

ISBN: (纸本)9781538622933

With rapidly increasing parallelism, DRAM performance and power have surfaced as primary constraints from consumer electronics to high performance computing (HPC) for a variety of applications, including bulk-synchronous data-parallel applications which are key drivers for multi-core, with examples including image processing, climate modeling, physics simulation, gaming, face recognition, and many others. We present the last-level collective prefetcher (LLCP), a purely hardware last-level cache (LLC) prefetcher that exploits the highly correlated prefetch patterns of data-parallel algorithms that would otherwise not be recognized by a prefetcher that is oblivious to data parallelism. LLCP generates prefetches on behalf of multiple cores in memory address order to maximize DRAM efficiency and bandwidth, and can prefetch from multiple memory pages without expensive translations. Compared to well-established other prefetchers, LLCP improves execution time by 5.5% on average (10% maximum), increases DRAM bandwidth by 9% to 18%, decreases DRAM rank energy by 6%, produces 27% more timely prefetches, and increases coverage by 25% at minimum.

关键词： Prefetching Bandwidth Random access memory Memory management Kernel parallel processing Arrays

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Performance Evaluation of Two Load Balancing algorithms on a Hybrid parallel Architecture 14th

引用

14th international conference on parallel Computing Technologies (PaCT)

作者： do Nascimento, Tiago M. dos Santos, Rodrigo W. Lobosco, Marcelo Univ Fed Juiz de Fora Grad Program Computat Modeling Juiz De Fora Brazil

ISBN: (纸本)9783319629322;9783319629315

Accelerated processing Units (APUs) are an emerging architecture that integrates, in a single silicon chip, the traditional CPU and the GPU. Due to its heterogeneous architecture, APUs impose new challenges to data parallel applications that want to take advantage of all the processing units available on the hardware to minimize its execution time. Some standards help in the task of writing parallel code for heterogeneous devices, but it is not easy to find the data division between CPU and GPU that will minimize the execution time. In this context, this work further extends and details load balancing algorithms designed to be used in a data parallel problem. Also, a sensitivity analysis of the parameters used in our models was performed. the results have shown that the algorithms are effective in their purpose of improving the performance of an application on an heterogeneous environment.

关键词： Load balancing Hybrid parallel architectures APU HPC

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FiM: Performance Prediction for parallel Computation in Iterative Data processing Applications 10

FiM: Performance Prediction for Parallel Computation in Iter...

引用

10th IEEE international conference on Cloud Computing (CLOUD)

作者： Bhimani, Janki Mi, Ningfang Leeser, Miriam Yang, Zhengyu Northeastern Univ Dept Elect & Comp Engn 360 Huntington Ave Boston MA 02115 USA

ISBN: (纸本)9781538619933

Predicting performance of an application running on high performance computing (HPC) platforms in a cloud environment is increasingly becoming important because of its influence on development time and resource management. However, predicting the performance with respect to parallel processes is complex for iterative, multi-stage applications. this research proposes a performance approximation approach FiM to model the computing performance of iterative, multi-stage applications running on a master-compute framework. FiM consists of two key components that are coupled with each other: 1) Stochastic Markov Model to capture non-deterministic runtime that often depends on parallel resources, e.g., number of processes. 2) Machine Learning Model that extrapolates the parameters for calibrating our Markov model when we have changes in application parameters such as dataset. Our new modeling approach considers different design choices along multiple dimensions, namely (i) process level parallelism, (ii) distribution of cores on multi-core processors in cloud computing, (iii) application related parameters, and (iv) characteristics of datasets. the major contribution of our prediction approach is that FiM is able to provide an accurate prediction of parallel computation time for the datasets which have much larger size than that of the training datasets. Such calculation prediction provides data analysts a useful insight of optimal configuration of parallel resources (e.g., number of processes and number of cores) and also helps system designers to investigate the impact of changes in application parameters on system performance.

关键词： Performance Modeling Markov Model Regression Distributed Systems Cloud Computing Big Data Infrastructure

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10th international conference on Artificial General Intelligence, AGI 2017

10th International Conference on Artificial General Intellig...

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10th international conference on Artificial General Intelligence, AGI 2017

ISBN: (纸本)9783319637020

the proceedings contain 25 papers. the special focus in this conference is on Artificial General Intelligence. the topics include: From abstract agents models to real-world AGI architectures: bridging the gap;a formal model of cognitive synergy;generic animats;self-awareness and self-control in NARS;unified reasoning with integrative memory using global workspace theory;abstract representations and generalized frequent pattern discovery;on hierarchical compression and power laws in nature;from first-order logic to assertional logic;genetic algorithms with DNN-based trainable crossover as an example of partial specialization of general search;deductive and analogical reasoning on a semantically embedded knowledge graph;computational neuroscience offers hints for more general machine learning;generating single subject activity videos as a sequence of actions using 3D convolutional generative adversarial networks;one-shot ontogenetic learning in biomedical datastreams;towards an artificial general episodic learner;a game theoretic analysis of the off-switch game;malevolent cyborgization;a conceptual framework for artificial pedagogy;an information-theoretic predictive model for the accuracy of ai agents adapted from psychometrics;bandit models of human behavior: reward processing in mental disorders;analyzing human decision making process with intention estimation using cooperative pattern task;pursuing fundamental advances in human reasoning;a priori modeling of information and intelligence.

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algorithms and architectures for parallel processing: 15th international conference, ICA3PP 2015 zhangjiajie, China, November 18-20, 2015 proceedings, part IV 15th

Algorithms and architectures for parallel processing: 15th i...

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15th international conference on algorithms and architectures for parallel processing, ICA3PP 2015

作者： Wang, Guojun Zomaya, Albert Perez, Gregorio Martinez Li, Kenli Central South University Changsha China The University of Sydney SydneyNSW Australia University of Murcia Murcia Spain Hunan University Changsha China

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