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检索条件"任意字段=3rd International Conference on Soft Computing in Data Science, SCDS 2017"

共 696 条记录，以下是471-480 订阅

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Evaluation of Combining Bootstrap with Multiple Imputation Using R on Knights Landing Platform 4

Evaluation of Combining Bootstrap with Multiple Imputation U...

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4th IEEE international conference on Cyber Security and Cloud computing, CSCloud 2017 and 3rd IEEE international conference of Scalable and Smart Cloud, SSC 2017

作者： Zhou, Chuan Gao, Yuxiang Howard, Waylon Center for Child Health Behavior and Development Seattle Children's Research Institute Seattle United States Business Development Department Quanta Cloud Technology San Jose United States

ISBN: (纸本)9781509066438

Cloud computing and big data technologies are converging to offer a cost-effective delivery model for cloud-based big data analytics. Though impacts of size and scaling of big data on cloud have been extensively studied, the effects of complexity of underlying analytic methods on cloud performance have received less attention. This paper will develop and evaluate a computationally intensive statistical methodology to perform inference in the presence of both non-Gaussian data and missing data. Two well-established statistical approaches, bootstrap and multiple imputations (MI), will be combined to form the methodology. Bootstrap is a computer-based nonparametric resampling procedure that involves randomly selecting data many thousands of times to construct an empirical distribution, which is then used to construct confidence intervals for significance tests. This statistical technique enables scientists who conduct studies on data with known non-normality to obtain higher quality significance tests than is possible with a traditional asymptotic, normal-theory based significance test. However, the bootstrapping procedure only works when no data are missing or the data are missing completely at random (MCAR). Missing data can lead to biased estimates when the MCAR assumption is violated. It is unclear how to best implement a bootstrapping procedure in the presence of missing data. The proposed methods will provide guidelines and procedures that will enable researchers to use the technique in all areas of health, behavior and developmental science in which a study has missing data and cannot rely on parametric inference. Either bootstrapping or MI can be computationally expensive, and combining these two can lead to further computation costs in the cloud. Using carefully constructed simulation examples, we demonstrate that it is feasible to implement the proposed methodology in a high performance Knights Landing platform. However, the computation costs are substantial

关键词： Big data

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Treat a Weak Dependence Among Causes and Lives in Insurance by Means of Fuzzy Sets 8th

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8th international conference on soft Methods in Probability and Statistics (SMPS)

作者： Wang, Dabuxilatu Wang, Tengteng Guangzhou Univ Higher Educ Mega Ctr Sch Econ & Stat Dept Stat 230 Waihuanxi Rd Guangzhou 510006 Guangdong Peoples R China Univ N Carolina Gilling Sch Publ Hlth Dept Epidemiol Chapel Hill NC 27599 USA

ISBN: (纸本)9783319429724;9783319429717

In this paper, we apply the copulas and fuzzy sets to approximate the dependencies in causes and lives, where under each cause of decrement the decrement times of the lives are assumed to be weak dependence. We propose utilizing a mixture of both randomness and fuzziness to describe the concept of weak dependence. An application is considered for a general symmetric status of multiple life under dependent causes of decrement.

关键词： Weak dependency Survival function Copulas

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Quantum Computer Simulation on GPU Cluster Incorporating data Locality 3rd

Quantum Computer Simulation on GPU Cluster Incorporating Dat...

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3rd international conference on Cloud computing and Security (ICCCS)

作者： Li, Zhen Yuan, Jiabin Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9783319685052;9783319685045

Quantum computer simulation provides researchers with tools for verification of quantum algorithms. GPU (Graphics Processing Units) cluster is an advisable platform for this task. However, the high cost of communication between GPUs makes the simulation inefficiency. To overcome this drawback, we propose the following two methods. (1) A method for GPU cluster quantum simulation to improve the data locality is introduced, and two schemes for data exchanging are proposed. (2) A novel data distribution method for quantum computer simulation on GPU cluster is proposed. Experimental results show that the simulation of 33-qubit Quantum Fourier Transform algorithm using 4 nodes outperforms the serial program of the CPU cluster with a speedup of 129 times.

关键词： Quantum computer simulation GPU cluster High performance computing CUDA

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3rd international conference on Future Network Systems and Security, FNSS 2017

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3rd international conference on Future Network Systems and Security, FNSS 2017

ISBN: (纸本)9783319655475

The proceedings contain 15 papers. The special focus in this conference is on Future Network Systems and Security. The topics include: Efficient Certificate Verification for Vehicle-to-Grid Communications;The GENI Test Automation Framework for New Protocol Development;Computational Security and the Economics of Password Hacking;DNS DDoS Mitigation, via DNS Timer Design Changes;RFID-Based Non-repudiation Protocols for Supply Chains;Towards Realizing a Distributed Event and Intrusion Detection System;Adjusting Matryoshka Protocol to Address the Scalability Issue in IoT Environment;Is High Performance computing (HPC) Ready to Handle Big data?;Transportation at the Confluence of Engineering and Big data;Competing in a Rapidly Changing World;Persuasive Educational Platform Design for Underprivileged Children;Business Analytics Generated data Brokerage;Android Application Collusion Demystified and Acceptance of Technology-Driven Interventions for Improving Medication Adherence.

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Energy Efficient Dynamic Cooperating Set Planning for CoMP in LTE/LTE-A Systems 14

Energy Efficient Dynamic Cooperating Set Planning for CoMP i...

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14th international Symposium on Pervasive Systems, Algorithms and Networks (I-SPAN) / 11th international conference on Frontier of Computer science and Technology (FCST) / 3rd international Symposium of Creative computing (ISCC)

作者： Chen, Jen-Jee Liang, Jia-Ming Hsu, Po-Min Huang, Yi-Chan Natl Univ Tainan Dept Elect Engn Tainan Taiwan Chang Gung Univ Dept Comp Sci & Informat Engn Taoyuan Taiwan Natl Cheng Kung Univ Dept Elect Engn Tainan Taiwan

ISBN: (纸本)9781538608401

Coordinated Multi-Point (CoMP) is considered as one of the most important techniques in 3GPP LTE/LTE-A. In CoMP, several base stations can be grouped together to form the cooperating set, where the cooperating set is used to improve the system throughput as well as the throughput of cell edge users. Most of the studies discussed how to relieve the overload condition or maximize the system throughput by exploiting CoMP. In these studies, static cooperating set is adopted, i.e., the size of the cooperating set is fixed. Different from previous studies, this paper provides a complete discussion by considering both off-peak hours and overload conditions. In off-peak hours, we try to save the transmit power of base stations and utilize the idle radio resource to achieve energy-efficient green communications while guarantee users' service quality. On the contrary, when big activities occur with gathered crowds or during the peak hours, we propose to dynamically form the cooperating set according to the actual traffic demand condition and adapt the transmit power of base stations, so that the system can effectively offload the traffic demand to the member cells with free radio resource in the cooperating set. Our proposed approach is a decentralized-based method, which avoids excess computing being executed in a base station. Simulation results show that our proposed method achieves the best system throughput, radio resource utilization, and energy efficiency in all schemes.

关键词： Long Term Evolution-Advanced (LTE-A) energy efficiency CoMP cooperating set Dynamic Cell Selection (DCS) resource management soft Frequency Reuse (SFR)

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Family Relationship Inference Using Knights Landing Platform 4

Family Relationship Inference Using Knights Landing Platform

引用

4th IEEE international conference on Cyber Security and Cloud computing, CSCloud 2017 and 3rd IEEE international conference of Scalable and Smart Cloud, SSC 2017

作者： Gao, Yuxiang Chen, Wei-Min Department of Public Health Science Center for Public Health Genomics University of Virginia School of Medicine Charlottesville United States Business Development Department Quanta Cloud Technology San Jose United States

ISBN: (纸本)9781509066438

Using genetic data to infer relatedness has been crucial for genetics studies for decades. In a previously published paper together with the KING software, we demonstrated that the kinship coefficient, a measure of relatedness between a pair of individuals, can be accurately estimated using their genome-wide SNP data, without estimating the allele frequencies at each SNP in the whole dataset. The computational efficiency of this algorithm has been substantially improved in the second generation of KING. Three levels of computational speed-up are implemented in KING 2.0, including: 1) bit-level parallelism;2) multiple-core parallelism using OpenMP;and 3) a multi-stage procedure to eliminate unrelated or distantly related pairs of individuals. The efficient implementation in KING 2.0 allows instant relationship inference in a matter of seconds in a typical dataset (with 10,000s individuals). To demonstrate the computational performance and scalability of KING 2.0, we use the Knights Landing platform to infer relatedness in a dataset consisting of 303,750 individuals each typed at 168,749 autosome SNPs. The computational time to identify all first-degree relatives by scanning 46 billion pairs of individuals is ∼10 minutes using 256 threads, a noticeable speed-up comparing to the general-purpose CPU. Algorithm improvement in the second generation of KING and the use of the latest computing system such as the Knights Landing platform makes it feasible for researchers to infer relatedness in their genetic datasets in the largest size up-to-date on a single computer. © 2017 IEEE.

关键词： Computational efficiency

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The behavioral patterns of volunteer computing communities 3

The behavioral patterns of volunteer computing communities

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3rd international conference BOINC-Based High Performance computing: Fundamental Research and Development, BOINC:FAST 2017

作者： Tishchenko, Victor I. FRC Computer Science and Control RAS 9 prospekt 60-letya Oktyabrya Moscow117312 Russia

The article analyses the model of behavior of the Russian participants of volunteer computing (VC) using platform BOINC. In contrast to the literature data based on sociological surveys our study based on clustering technique shows that the thematic preferences are deceive of the Russian participants of VC motives. And practically there is no any effect on the behavior of a team or individual activity, quantitatively expressed as crédits. Copyright © by the paper's authors. Copying permitted for private and academic purposes.

关键词： Complex networks

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DynSGX: A privacy preserving toolset for dinamically loading functions into intel(R) SGX enclaves 9

DynSGX: A privacy preserving toolset for dinamically loading...

引用

9th IEEE international conference on Cloud computing Technology and science, CloudCom 2017

作者： Silva, Rodolfo Barbosa, Pedro Brito, Andrey Universidade Federal de Campina Grande Campina Grande PB Brazil

ISBN: (纸本)9781538606926

Intel(R) software Guard eXtensions (SGX) is a hardware-based technology for ensuring security of sensitive data from disclosure or modification that enables user-level applications to allocate protected areas of memory called enclaves. Such memory areas are cryptographically protected even from code running with higher privilege levels. This memory protection can be used to develop secure and dependable applications, but the technology has some limitations: (i) the code of an enclave is visible at load time, (ii) libraries used by the code must be statically linked, and (iii) the protected memory size is limited, demanding page swapping to be done when this limit is exceeded. We present DynSGX, a privacy preserving tool that enables users and developers to dynamically load and unload code to be executed inside SGX enclaves. Such a technology makes possible that developers use public cloud infrastructures to run applications based on sensitive code and data. Moreover, we present a series of experiments that assess how applications dynamically loaded by DynSGX perform in comparison to statically linked applications that disregard privacy of the enclave code at load time. © 2017 IEEE.

关键词： Sensitive data

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Computer Communication, Networking and Internet Security 1st ed. 2017

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丛书名： Lecture Notes in Networks and Systems

2017年

作者： Suresh Chandra Satapathy Vikrant Bhateja K. Srujan Raju B. Janakiramaiah

ISBN: (数字)9789811032264

ISBN: (纸本)9789811032257

The book is a compilation of high-quality scientific papers presented at the 3rd international conference on Computer & Communication Technologies (IC3T 2016). The individual papers address cutting-edge technologies and applications of soft computing, artificial intelligence and communication. In addition, a variety of further topics are discussed, which include data mining, machine intelligence, fuzzy computing, sensor networks, signal and image processing, human-computer interaction, web intelligence, etc. As such, it offers readers a valuable and unique resource.

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Big data Validation Case Study 3

Big Data Validation Case Study

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3rd IEEE international conference on Big data computing Service and Applications (BigdataService)

作者： Xie, Chunli Gao, Jerry Tao, Chuanqi Jiangsu Normal Univ Dept Comp Sci & Technol Xuzhou Peoples R China San Jose State Univ Dept Comp Engn San Jose CA 95192 USA Nanjing Univ Sci & Technol Coll Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781509063185

With the advent of big data, data is being generated, collected, transformed, processed and analyzed at an unprecedented scale. Since data is created at a fast velocity and with a large variety, the quality of big data is far from perfect. Recent studies have shown that poor quality can bring serious erroneous data costs on the result of big data analysis. data validation is an important process to recognize and improve data quality. In this paper, a case study that is relevant to big data quality is designed to study original big data quality, data quality dimension, data validation process and tools.

关键词： big data quality big data validation data checklist big data tool case study

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