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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441-450 订阅

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Filtering the big data based on volume, variety and velocity by using Kalman filter recursive approach 3

Filtering the big data based on volume, variety and velocity...

引用

3rd IEEE international conference on Engineering Technologies and Social sciences, ICETSS 2017

作者： Riaz, Fatima Alam, Muhammad Ali, Attra Department of Mathematics and Statistics Computer Science Institute of Business and Management Korangi Creek Karachi75190 Pakistan Institute of Post Graduate Studies University of Kuala Lumpur Jalan-Sultan-Ismail50250 Malaysia

ISBN: (纸本)9781538616116

For the past seven decades the term Big data is known, but due to the emerging technology shift of this era, it is captivating a lot of attention from the researchers of mathematics, computing, telecommunication, information technology, data warehousing, and mining. As this generation is living in the age of technology where data is playing a vital role and especially the Big data has lots of success stories, but at the same time it is becoming the biggest threat to network service provider, telecom industry, and homeland security. Every device such as smart phones, laptop, desktop, etc. connected with the network is contributing to add data to a Big data pool by using different applications. Social media such as Instagram, Facebook, WhatsApp, Apple, Google, Google+, Twitter, Flickr, etc. are few famous tools which are used to add redundant data. The question appears, is it mandatory to store and especially process all the data either useful or redundant? This research paper is focusing for filtering useful data from redundant data by using their parameters which are velocity, variety, and volume. In proposed architecture, Memcache DB (for velocity), Voldemort layers (for variety) and MapReduce (for volume) are linked with Hadoop to achieve filtered data. Kalman filter recursive approach is used to inject the data back into Hadoop Distributed File System to reduce processing cost of next iterations. © 2017 IEEE.

关键词： Velocity

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A Lightweight Traffic Anomaly Detection Model in SDN Based on Decision Tree

A Lightweight Traffic Anomaly Detection Model in SDN Based o...

引用

2018 3rd international conference on Communications, Information Management and Network Security(CIMNS2018)

作者： Dong Li Zizhun Li Network and Computing Center HuaZhong University of Science and Technology

This paper establishes a SDN-based network traffic anomaly detection model based on decision ***,five statistical indexes are presented to describe the behavior of network traffic,then normal and abnormal traffic data to train the machine learning model to detect traffic *** machine learning algorithms,decision tree,k-nearest neighbor,support vector machine and naive Bayes,are used to predict the detection rate,false alarm rate and other *** shows that decision tree algorithm is suitable for detecting traffic anomaly in SDN network.

关键词： SDN network security anomaly detection

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High-Speed FPGA-GPU Processing for 3D-OCT Imaging 3

High-Speed FPGA-GPU Processing for 3D-OCT Imaging

引用

3rd IEEE international conference on Computer and Communications (ICCC)

作者： Jin, Kyung-Chan Lee, Kye-Sung Kim, Geun-Hee Korea Inst Ind Tech Smart Mfg Tech Grp Cheonan Si South Korea Korea Basic Sci Inst Ctr Anal Inst Dev Daegeon Si South Korea

ISBN: (纸本)9781509063529

Three dimensional (3D) imaging using optical coherence tomography (OCT) is equipped with a field-programmable gate array and graphics processing unit (FPGA-GPU) acquisition and processing architecture, thereby making it highly advantageous in the domain of parallel computing. To realize the full benefit of the data acquisition and processing capabilities, it is preferable to increase the number of high-speed processing modules capable of running the complicated image processing algorithms at comparable speeds. In this paper, we propose the design of a real-time image acquisition and pre-processing FPGA via LabVIEW (National Instruments (NI)) with GPU-based acceleration that is capable of sustaining the rate of data acquisition. When using the NI LabVIEW FPGA to develop high-speed processing, FPGA cores are modeled as reusable code modules and comprise subVls, which are commonly implemented in the LabVIEW environment. The OCT pre-processing was implemented and performed via subVls in LabVIEW FPGA. Additionally, we utilized GPU-based acceleration, which employs the use of a general purpose GPU (GP-GPU) together with a CPU, to accelerate the operation at hand. Finally, we implemented the LabVIEW FPGA core to perform data acquisition and image pre-processing in the frame grabber. Results showed that, by applying GPU acceleration to the tomographic inspection of biological samples, SD-OCT imaging in excess of 40 frames/s (FPS) for the NVIDIA M6000 GPU-accelerated SD-OCT with frame size 4096 (axial) x 512 (lateral) becomes feasible, and more than 512 x 512 x 500 volumes can be reconstructed with a speed increase of at least 7x that of a non-GPU.

关键词： optical coherence tomography field-programmable gate array graphic processing unit tomographic inspection

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Finding the Best Box-Cox Transformation in Big data with Meta-Model Learning: A Case Study on QCT Developer Cloud 4

Finding the Best Box-Cox Transformation in Big Data with Met...

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 Zhang, Tongling Yang, Baijian Business Development Department Quanta Cloud Technology San Jose United States Computer Science Department Purdue University West Lafayette United States

ISBN: (纸本)9781509066438

Finding the best model to reveal potential relationships of a given set of data is not an easy job and often requires many iterations of trial and errors for model sections, feature selections and parameters tuning. This problem is greatly complicated in the big data era where the I/O bottlenecks significantly slowed down the time needed to finding the best model. In this article, we examine the case of Box-Cox transformation when assumptions of a regression model are violated. Specifically, we construct and compute a set of summary statistics and transformed the maximum likelihood computation into a per-role operational fashion. The innovative algorithms reduced the big data machine learning problem into a stream based small data learning problem. Once the Box-Cox information array is obtained, the optimal power transformation as well as the corresponding estimates of model parameters can be quickly computed. To evaluate the performance, we implemented the proposed Box-Cox algorithms on QCT developer cloud. Our results showed that by leveraging both the algorithms and the QCT cloud technology, find the fittest model from 101 potential parameters is much faster than the conventional approach. © 2017 IEEE.

关键词： Big data

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Research on Faceted Search Method for Water data Catalogue Service 4

Research on Faceted Search Method for Water Data Catalogue S...

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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： Feng, Jun Kong, Shengqiu Du, Bingshuai Lu, Jiamin College of Computer and Information Hohai University Nanjing China

ISBN: (纸本)9781509066438

Traditionally the data retrieval is achieved by searching the metadata with keywords, though it is often difficult for ordinary users to express professional and precise query demands in the water industry. Regarding this issue, this paper introduces an exploratory retrieval method called faceted search by gradually recommending relevant facets to the users. Firstly, a unified modeling algorithm is proposed to construct the unified metadata model in XML for heterogeneous water metadata. Based on this model, candidate facet terms can be extracted and filtered, in order to retrieve the various water metadata uniformly. At last, a facet recommendation algorithm is proposed to help the users to sharpen their queries by prompting less but more accurate facets as the search gets deeper, after excluding those 'irrelevant facets' and 'redundant facets'. The experimental results demonstrate that our facet recommendation algorithm can significantly improve the retrieval precision on querying the water data. © 2017 IEEE.

关键词： Metadata

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Preliminary analysis of the bio-mechanical characteristics for High-kitchen Municipal Solid Waste 3

Preliminary analysis of the bio-mechanical characteristics f...

引用

2017 3rd international conference on Energy, Environment and Materials science (EEMS)

作者： Li, He. Zhang, Jian. Guo. Lan, Ji. Wu. He, Haijie Zhejiang Univ MOE Key Lab Soft Soils & Geoenvironm Engn Hangzhou 310058 Zhejiang Peoples R China Shen Kan Engn & Technol Corp MCC SKET Shenyang Liaoning Peoples R China

Degradation of Municipal Solid Wastes (MSW) results in a change in solid skeleton, particle size and pore structure, inducing an alteration of compressibility and liquid/gas conductivity of the wastes. To investigate the complicated biological, hydraulic and mechanical coupled processes of the MSWs, a pilot-scale experimental device which is consist of waste column container, environment regulation system, vertical loading system and measuring system for liquid/gas conductivity is built. With the experimental systems, long-term tests were set up to investigate the biological, hydraulic and mechanical behaviour of the High-kitchen Municipal solid waste with high organic content and high water content. Different values of vertical stress and different degradation conditions (micro-aerobic and anaerobic) were simulated. Throughout the experiments, the changes in total volume, degree of saturation, leachate quantity and chemistry, LFG generation and composition, liquid and gas conductivity were measured. The experimental results will provide solid data for a development of the Bio-Hydro-Mechanical coupled characteristics for High-kitchen Municipal solid waste.

关键词： Particle size

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Survivable Virtual SDN Embedding Approach for Innovative Cloud data Network 14

Survivable Virtual SDN Embedding Approach for Innovative Clo...

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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： Tan, Yusong Li, Rongzhen Zhang, Jianfeng Li, Xiaoling Wu, Qingbo Yan, Yuejin Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781538608401

software-defined networking (SDN) has separated control and forwarding planes, which is a powerful approach to enable network virtualization by SDN hypervisors to share physical SDN networks among multiple virtual SDN in cloud data center. However, the centralized control and virtual network shared physical network are delicately weak and easy to failure if virtual topology embedding is not properly designed. This paper develops an enhanced virtual controller node mapping strategy to improve the survivability of virtual SDN. The strategy explicitly considers the network delay and the number of disjoint path between controller and forwarding devices to form survivable factor. Our proposed approach has been evaluated and the results show that the proposed technology has improved the survivability and network delay of virtual SDN while keeping the other within reasonable bounds.

关键词： Cloud datacenter Network Virtual Network Survivability SDN Virtual SDN Embedding Network Delay

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Brain-Based Computer Interfaces in Virtual Reality 4

Brain-Based Computer Interfaces in Virtual Reality

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： Li, Sukun Leider, Avery Qiu, Meikang Gai, Keke Liu, Meiqin Department of Computer Science Pace University New YorkNY10038 United States College of Electrical Engineering Zhejiang University 310027 China

ISBN: (纸本)9781509066438

Virtual Reality (VR) research is accelerating the development of inexpensive real-time Brain Computer Interface (BCI). Hardware improvements that increase the capability of Virtual Reality displays and Brain Computer wearable sensors have made possible several new software frameworks for developers to use and create applications combining BCI and VR. It also enables multiple sensory pathways for communications with a larger sized data to users' brains. The intersections of these two research paths are accelerating both fields and will drive the needs for an energy-aware infrastructure to support the wider local bandwidth demands in the mobile cloud. In this paper, we complete a survey on BCI in VR from various perspectives, including Electroencephalogram (EEG)-based BCI models, machine learning, and current active platforms. Based on our investigations, the main findings of this survey highlights three major development trends of BCI, which are entertainment, VR, and cloud computing. © 2017 IEEE.

关键词： Brain computer interface

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A Nonparametric Linearity Test for a Multiple Regression Model with Fuzzy data 8th

引用

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

作者： Wang, Dabuxilatu Guangzhou Univ Higher Educ Mega Ctr Dept Stat 230 Waihuanxi Rd Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9783319429724;9783319429717

A linearity test for a multiple regression model with LR-fuzzy responses and LR-fuzzy explanatory variables is considered. The regression model consists of severalmultiple regression models from response center or spreads to the explanatory centers and spreads. A multiple nonparametric regression model to be employed as a reference in the testing approach is estimated, and with which the linearity of the regression model is tested. Some simulation example is also presented.

关键词： LR-fuzzy random variables Nonparametric regression model Linearity test

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Performance analysis of real-time face detection system based on stream data mining frameworks 3

Performance analysis of real-time face detection system base...

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3rd international conference on Information Technology and Nanotechnology (ITNT)

作者： Kazanskiy, Nikolay Protsenko, Vladimir Serafimovich, Pavel Samara Natl Res Univ Moskovskoe Shosse 34 Samara 443086 Russia Russian Acad Sci Branch FSRC Crystallog & Photon Image Proc Syst Inst Molodogvardeyskaya 151 Samara 443001 Russia

This work describes performance analysis results of two face detection systems based on Apache Storm and IBM InfoSphere Streams frameworks. Profiling was evaluated on image sequences of four different sizes: 100 x 100, 640 x 640, 1920 x 1080, and 4096 x 3112. Face detection was performed by OpenCV cascade classifier. Experiment was held under five CentOS nodes cluster. It was investigated that system based on Apache Storm was able to operate in real-time at 24 frames per second on used hardware configuration. Apache Storm was more scalable and demonstrated advantage in throughput over its counterpart. Experiment helped to reveal configuration parameters of frameworks that played a major role in face detection task on image sequences. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： Real-time Image processing Stream processing Performance measurement Big data High performance computing

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