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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331-340 订阅

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A novel heuristic for data distribution in massively parallel phylogenetic inference using site repeats 19

A novel heuristic for data distribution in massively paralle...

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19th IEEE international conference on High Performance computing and Communications (HPCC) / 15th IEEE international conference on Smart City (SmartCity) / 3rd IEEE international conference on data science and Systems (DSS)

作者： Morel, Benoit Flouri, Tomas Stamatakis, Alexandros Heidelberg Inst Theoret Studies Sci Comp Grp Heidelberg Germany Karlsruhe Inst Technol Inst Theoret Informat Karlsruhe Germany

ISBN: (纸本)9781538625880

Continuous advances in molecular sequencing technologies now allow for inferring evolutionary trees (phylogenies) on supercomputers that comprise hundreds to thousands of species at the whole-transcriptome or whole-genome level. The phylogenetic likelihood function (PLF) consumes 90-95% of total execution time in such analyses and is therefore typically parallelized. Recently, the site repeats (SR) technique for substantially accelerating the PLF has been introduced. It identifies repeating patterns in parts of the likelihood computation and omits the respective redundant calculations to save time and space. However, the SR technique induces a parallel load imbalance. In this paper, we introduce a novel randomized data distribution algorithm to improve load balance (rdDA) for SR-based likelihood calculations. The algorithm is available as opensource code, induces minimal run-time overhead, and yields up to 25% run time improvements on empirical datasets and up to 50% for a synthetic, worst-case scenario. This improvement is substantial as current evolutionary data analyses may require tens of millions of core hours on supercomputer systems.

关键词： Vegetation Partitioning algorithms Phylogeny Tools Topology Computational efficiency conferences

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Robust Task Scheduling Strategy for Big data Clusters 3

Robust Task Scheduling Strategy for Big Data Clusters

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3rd international conference on Big data computing and Communications (BIGCOM)

作者： Wang, Zixiang Liu, Zhoubin Huan, Zhan Kong, Xiaoyun Yuan, Xiaolu State Grid Zhejiang Elect Power Res Inst Hangzhou Zhejiang Peoples R China Changzhou Univ Changzhou Peoples R China State Grid Zhejiang Elect Power Corp Hangzhou Zhejiang Peoples R China Xi An Jiao Tong Univ Xian Shaanxi Peoples R China

ISBN: (纸本)9781538633496

Task scheduling problem has been an active area in big data framework. Arriving tasks should be assigned to suitable nodes in a big data cluster for load balancing. The system can become more efficient when the workload is well distributed among different nodes. However, the existing researches consider little about the uncertainties in the resource and service provision. Since effective processor speed is changing over time, the computational ability of a node is not consistent. The load balancing may not be well achieved due to the computing uncertainty. In this paper, we propose a robust task scheduling algorithm for big data clusters. The computational uncertainty is modeled as perturbation on the processing speed, and our task scheduling approach is designed to deal with the potential computing uncertainties. The simulation results demonstrate that our scheduling strategy can reject perturbation and provide the stable computing service.

关键词： task scheduling big data robust uncertainty feedback

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data and Decision Intelligence for Internet of Things: Putting Human in the Loop 3

Data and Decision Intelligence for Internet of Things: Putti...

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IEEE 3rd international conference on Big data Security on Cloud (BigdataSecurity) / 3rd IEEE international conference on High Performance and Smart computing (HPSC) / 2nd IEEE international conference on Intelligent data and Security (IDS)

作者： Ma, Meng Lin, Weilan Pan, Disheng Wang, Ping Zhou, Yuchen Liang, Xiaoxing Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China Peking Univ Sch Software & Microelect Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Software Beijing Peoples R China Peking Univ Natl Engn Res Ctr Software Engn Beijing Peoples R China IBM Res China Beijing Peoples R China

ISBN: (纸本)9781509062966

The requirement and expanding needs of bridging the physical and cyber world promote the emergence of a new global internet-based information architecture, the Internet of Things. The main challenge in Internet of Things is how to make fast and accurate decisions based on multi-modal sensing data. In this paper, we put human into the process and formulate a closed-loop computing paradigm for IoT data and decision intelligence. We first propose a human-in-the-loop reference model, which extends the traditional cyber-physical interaction extended into a closed-loop process based on cyber, physical and human three factors. We defined the three-factor decision paradigm in the model. On the basis of the reference model, we analyze the feature requirement in the data intelligent process and summarize it as three key aspects: semantic, interactive and iterative. Further, we analyze several major challenges facing IoT data and decision intelligence from the perspective of data characteristics. We select and review the recent progress in three typical application domains and analyzed whether the current research has solved the target issues. According to the results of the review and analysis, the paper finally summarizes and addresses some key research directions in the future.

关键词： Internet of Things data intelligence data mining decision-making human-in-the-loop

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DCEIL: Distributed Community Detection with the CEIL Score 19

DCEIL: Distributed Community Detection with the CEIL Score

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作者： Jain, Akash Nasre, Rupesh Ravindran, Balaraman IIT Madras Dept Comp Sci & Engn Robert Bosch Ctr Data Sci & AI Madras Tamil Nadu India

ISBN: (纸本)9781538625880

Community detection in complex networks has a wide range of applications such as detection of cyber-communities in social networks, recommendations based on the interest group, and estimating hidden features in a social network. In distributed frameworks, the primary focus has been scalability. However, the accuracy of the algorithm's output is also critical. We propose the first distributed community detection algorithm based on the state-of-the-art CEIL scoring function. Our algorithm, named DCEIL, is fast, scalable and maintains the quality of communities. DCEIL outperforms the existing state-of-theart distributed Louvain algorithm by 180% on an average in Normalized Mutual Information (NMI) Index and 6.61% on an average in Jaccard Index metrics. DCEIL completes execution for 1 billion edges within 112 minutes and outperforms state-of-the-art distributed Louvain algorithm by 4.3x. DCEIL critically exploits three novel heuristics which address the existing issues with distributed community detection algorithms that have the hierarchical structure of CEIL or Louvain methods. Further, our proposed heuristics are generic as well as efficient, and we illustrate their efficacy by enhancing the accuracy of distributed Louvain algorithm by 22.91% on an average in Jaccard Index, and the average execution time by 1.68 x over popular datasets.

关键词： Image edge detection Measurement Detection algorithms Indexes Clustering algorithms data models Mutual information

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Keyword extraction using machine learning approaches 3

Keyword extraction using machine learning approaches

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3rd international conference on Advances in computing, Communication and Automation (Fall), ICACCA 2017

作者： Kaur, Bhavneet Jain, Sushma Department of Computer Science and Engineering Thapar University Patiala India

ISBN: (纸本)9781509064038

data Mining is the procedure to separate concealed prescient data from database and change it into a reasonable structure for sometime later. The varying areas in information mining are Web Mining, Text Mining, Sequence Mining, graph Mining, temporal data Mining, Spatial data Mining, Distributed data Mining and Multimedia Mining. A portion of the utilizations of information mining, it is utilized for the budgetary information examination, retail and media transmission ventures, science and designing and interruption location and counteractive action. In this exploration paper a calculation is proposed for ensembling and catchphrase extraction in content mining. In the proposition the above methods are upgraded which in turns, enhance the exactness, review, f-measure and accuracy. © 2017 IEEE.

关键词： Extraction

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Smart Meter data Analytics based on Modified Streaming k-Means 3

Smart Meter Data Analytics based on Modified Streaming k-Mea...

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3rd international conference on Big data computing and Communications (BIGCOM)

作者： Zhu, Wendong Yu, Weiqing Kan, Bowen Liu, Guangyi GEIRI North Amer San Jose CA 95134 USA

ISBN: (纸本)9781538633496

k-Means clustering is one of the central problems in data analysis and remains as one of the most popular data processing algorithms. There are streaming variant of k-Means clustering algorithms to address the continuous and evolving data. Stream computing allows users to capture and analyze all events and data, all the time and just in time. Streaming models have advantages over batch models due to the real time, dynamic and incremental features. However, streaming k-Means is sensitive to initial assignment of cluster centers, and the results are not accurate in the case that cluster centers drift significantly. This paper presents a Modified Streaming k-Means algorithm which is dynamic and incremental, as well as being able to take into consideration of long term patterns of data. Compared with streaming k-means clustering, the Modified Streaming k-Means clustering has better convergence ability and more stable results. We explore the application of our Modified Streaming k-Means algorithm in consumer segmentation problem, and demonstrate the effectiveness of our method using real-world smart meter data.

关键词： Clustering data Streams k-Means Big data Stream computing Smart Grid

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A new data collaboration service based on cloud computing security 3

A new data collaboration service based on cloud computing se...

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3rd international conference on Applied Materials and Manufacturing Technology (ICAMMT)

作者： Ren Ying Li Hua-Wei Wang Lina Naval Aviat Univ Yantai Shandong Peoples R China Shan Dong Business Inst Yantai 264001 Shandong Peoples R China

ISBN: (纸本)9781510848931

With the rapid development of cloud computing, the storage and usage of data have undergone revolutionary changes. data owners can store data in the cloud. While bringing convenience, it also brings many new challenges to cloud data security. A key issue is how to support a secure data collaboration service that supports access and updates to cloud data. This paper proposes a secure, efficient and extensible data collaboration service, which prevents data leaks in cloud storage, supports one to many encryption mechanisms, and also enables cloud data writing and fine-grained access control.

关键词： Clouds Cloud computing access control new data One to many Secure Access

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Analysis on Anomalous Short Term Load Forecasting Using Two Different Approaches 3

Analysis on Anomalous Short Term Load Forecasting Using Two ...

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3rd international conference on science in Information Technology (ICSITech)

作者： Abdullah, Ade Gafar Hasan, Bahtiar Mulyadi, Yadi Hakim, Dadang Lukman Hasbullah Riza, Lala Septem Univ Pendidikan Indonesia Dept Elect Engn Educ Bandung Indonesia Univ Pendidikan Indonesia Dept Comp Sci Educ Bandung Indonesia

ISBN: (纸本)9781509058662

The problem of optimizing Short Term Load Forecasting (STLF) is a task in the management of electrical power systems. STLF problem solving using soft computing approach has been becoming the interest of researchers. This study aims to compare two approaches on anomalous short term load forecasting. These approaches are one method based on soft computing, which is Fuzzy Subtractive Clustering (FSC) algorithm and Holt-Winter exponential smoothing, which is included in Time Series Analysis. Load forecasting is defined by the base load and peak load for weekdays and weekend days. The experimental results verify that the influences range optimization from FSC algorithm provided a better accuracy of forecasting results affecting on the efficiency of generation cost. Meanwhile, the exponential smoothing method produces a good result although they tend to lag behind the observed values a little bit.

关键词： optimization soft computing forecasting model time series analysis Holt-Winter exponential smoothing

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Feature extraction for image content retrieval in thai traditional painting with SIFT algorithms 3rd

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3rd international conference on soft computing in data science, scds 2017

作者： Prasomphan, Sathit Department of Computer and Information Science Faculty of Applied Science King Mongkut’s University of Technology North Bangkok 1518 Pracharat 1 Road Wongsawang Bangsue Bangkok10800 Thailand

ISBN: (纸本)9789811072413

This research presents a novel algorithm for feature extraction in Thai traditional painting by using keypoint generated from SIFT algorithms. The proposed algorithms aim to retrieve knowledge inside an image. Content Based Image Retrieval (CBIR) technique was applied. The generated keypoint and descriptors were used as input in neural network for training. Neural network technique was used for image classification to get details of image. The algorithms were tested with the corridor around the temple of the Emerald Buddha. The experimental results show that the proposed framework can efficiently give the correct descriptions to the image compared to using the traditional method. The proposed algorithms can be used to classify the type of Thai traditional painting which can provide the accuracy about 70–80% in average. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Feature extraction

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Fast implementation for SM4 cipher algorithm based on bit-slice technology 1

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3rd international conference on Smart computing and Communications, SmartCom 2018

作者： Zhang, Jingbin Ma, Meng Wang, Ping School of Electronics Engineering and Computer Science Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China School of Software and Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education Beijing China

ISBN: (数字)9783030057558

ISBN: (纸本)9783030057541

The SM4 block cipher algorithm used in IEEE 802.11i standard is released by the China National Cryptographic Authority and is one of the most important symmetric cryptographic algorithms in China. However, whether in the round encryption or key expansion phase of the SM4 algorithm, a large number of bit operations on the registers (e.g., circular shifting) are required. These operations are not effective to encryption in scenarios with large-scale data. In traditional implementations of SM4, different operands are assigned to different words and are processed serially, which can bring redundant operations in the process of encryption and decryption. Bit-slice technology places the same bit of multiple operands into one word, which facilitates bit-level operations in parallel. Bit-slice is actually a single instruction parallel processing technology for data, hence it can be accelerated by the CPU’s multimedia instructions. In this paper, we propose a fast implementation of the SM4 algorithm using bit-slice techniques. The experiment proves that the Bit-slice based SM4 is more efficient than the original version. It increases the encryption and decryption speed of the message by an average of 80%–120%, compared with the original approach. © 2018, Springer Nature Switzerland AG.

关键词： Cryptography

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