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31st ACM International Conference on Supercomputing, ICS 2017

作者： Liu, Haikun Chen, Yujie Liao, Xiaofei Jin, Hai He, Bingsheng Zheng, Long Guo, Rentong Services Computing Technology and System Lab/Big Data Technology System Lab/Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computing National University of Singapore Singapore117418 Singapore

ISBN: (纸本)9781450350204

Non-Volatile Memory (NVM) has recently emerged for its nonvolatility, high density and energy efficiency. Hybrid memory systems composed of DRAM and NVM have the best of both worlds, because NVM can offer larger capacity and have near-zero standby power consumption while DRAM provides higher performance. Many studies have advocated to use DRAM as a cache to NVM. However, it is still an open problem on how to manage the DRAM cache effectively and efficiently. In this paper, we propose a novel Hardware/Software Cooperative Caching (HSCC) mechanism that organizes NVM and DRAM in a flat address space while logically supporting a cache/memory hierarchy. HSCC maintains the NVMto-DRAM address mapping and tracks the access counts of NVM pages through a moderate extension to page tables and TLBs. It significantly simplifies the hardware design and offers several optimization opportunities for cache management in software layers. We thus propose utility-based cache filtering policies to improve the efficiency of DRAM cache. Experimental results show that HSCC improves system performance by up to 9.6X (77.2% on average) and reduces energy consumption by 34.3% on average, compared to a hardware-assisted DRAM/NVM memory system. HSCC also presents 15.4% and 14.5% performance improvement against a .ataddressable memory architecture and a Row *** Locality Aware (RBLA) caching policy for hybrid memories, respectively. © 2017 Association for Computing Machinery.

关键词： Dynamic random access storage

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On Predictability of Time Series

arXiv

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arXiv 2018年

作者： Xu, Paiheng Yin, Likang Yue, Zhongtao Zhou, Tao CompleX Lab University of Electronic Science and Technology of China Chengdu611731 China School of Hanhong Southwest University Chongqing400715 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu611731 China

The method to estimate the predictability of human mobility was proposed in [C. Song et al., science 327, 1018 (2010)], which is extensively followed in exploring the predictability of disparate time series. However, the ambiguous description in the original paper leads to some misunderstandings, including the inconsistent logarithm bases in the entropy estimator and the entropy-predictability-conversion equation, as well as the details in the calculation of the Lempel-Ziv estimator, which further results in remarkably overestimated predictability. This paper demonstrates the degree of overestimation by four different types of theoretically generated time series and an empirical data set, and shows the intrinsic deviation of the Lempel-Ziv estimator for highly random time series. This work provides a clear picture on this issue and thus helps researchers in correctly estimating the predictability of time series. Copyright © 2018, The Authors. All rights reserved.

关键词： Time series

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CSTG: An effective framework for cost-sensitive sparse online learning 17

CSTG: An effective framework for cost-sensitive sparse onlin...

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17th SIAM International Conference on data Mining, SDM 2017

作者： Chen, Zhong Fang, Zhide Fan, Wei Edwards, Andrea Zhang, Kun Department of Computer Science Xavier University of Louisiana United States Department of Biostatistics School of Public Health LSU Health Sciences Center United States Baidu Big Data Lab. United States

ISBN: (纸本)9781611974874

Sparse online learning and cost-sensitive learning are two important areas of machine learning and data mining research. Each has been well studied with many interesting algorithms developed. However, very limited published work addresses the joint study of these two fields. In this paper, to tackle the high-dimensional data streams with skewed distributions, we introduce a framework of cost-sensitive sparse online learning. Our proposed framework is a substantial extension of the influential Truncated Gradient (TG) method by for-mulating a new convex optimization problem, where the two mutual restraint factors, misclassification cost and sparsity, can be simultaneously and favorably balanced. We theoretically analyze the regret and cost bounds of the proposed algorithm, and pinpoint its theoretical merit compared to the existing related approaches. Large-scale empirical comparisons to five baseline methods on eight real-world streaming datasets demonstrate the encouraging performance of the developed method. Algorithm implementation and datasets are available upon request. Copyright © by SIAM.

关键词： data mining

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Analysis of Magnetoencephalography in Depression Based on DFA

Analysis of Magnetoencephalography in Depression Based on DF...

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2018 2nd International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2018)

作者： Rui Yan Chuchu Ding Wei Yan Jun Wang Jin Li Fengzhen Hou Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Department of Psychiatry The Affiliated Brain Hospital of Nanjing Medical University College of Physics and Information Technology Shaanxi Normal University School of Science China Pharmaceutical University

In this paper, we used the method of detrended fluctuation analysis(DFA) to study the self-similarity of MEG signals in the healthy subjects and the patients with depression. In the same negative emotional image stimulation, the DFA algorithm was used to calculate the scaling exponent of MEG signals in the patients with depression and the healthy subjects, respectively. The result show: Under the same stimulation, the scaling exponent of the patients with depression is higher than that of the healthy subjects. As a general phenomenon, the scaling exponent is used as a discriminant basis for identifying MEG signals in the healthy subjects and the patients with depression, and in the experiment obtained a more satisfactory results, this is an important indication of clinical diagnosis.

关键词： DFA MEG signals depression scaling exponent

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Theory and methodology of research on cloud security

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science China(Information sciences) 2016年第5期59卷 15-17页

作者： Hai JIN Weiqi DAI Deqing ZOU Services Computing Technology and System Lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

Cloud computing is a model for enabling ubiquitous,convenient,on-demand access to a shared pool of configurable computing *** to Gartner’s survey,cloud computing has been at the head of a list of the top ten IT strategic technologies for some *** computing is currently undergoing vigorous development and promotion by many leading Chinese and foreign enterprises,and it has become the basis for a new

关键词： TPM Theory and methodology of research on cloud security

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Visco-acoustic Wave-equation Traveltime Inversion with Correct and Incorrect Attenuation Profiles 87

Visco-acoustic Wave-equation Traveltime Inversion with Corre...

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Society of Exploration Geophysicists International Exposition and 87th Annual Meeting, SEG 2017

作者： Yu, Han Chen, Yuqing Guo, Bowen Schuster, Gerard T. Division of Physical Sciences and Engineering King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Jiangsu Key Lab of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210003 China

A visco-acoustic wave-equation traveltime inversion method is presented that inverts for a shallow subsurface velocity distribution with correct and incorrect attenuation profiles. Similar to the classical wave equation traveltime inversion, this method applies the misfit functional that minimizes the first break differences between the observed and predicted data. Although, WT can partly avoid the cycle skipping problem, an initial velocity model approaches to the right or wrong velocity models under different setups of the attenuation profiles. However, with a Q model far away from the real model, the inverted tomogram is obviously different from the true velocity model while a small change of the Q model does not improve the inversion quality in a strong manner if low frequency information is not lost.. © 2017 SEG.

关键词： Wave equations

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Pivot-based metric indexing 43

Pivot-based metric indexing

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43rd International Conference on Very Large data Bases, VLDB 2017

作者： Chen, Lu Gao, Yunjun Zheng, Baihua Jensen, Christian S. Yang, Hanyu Yang, Keyu College of Computer Science Zhejiang University Hangzhou China The Key Lab of Big Data Intelligent Computing of Zhejiang Province Zhejiang University Hangzhou China School of Information Systems Singapore Management University Singapore Department of Computer Science Aalborg University Denmark

The general notion of a metric space encompasses a diverse range of data types and accompanying similarity measures. Hence, metric search plays an important role in a wide range of settings, including multimedia retrieval, data mining, and data integration. With the aim of accelerating metric search, a collection of pivotbased indexing techniques for metric data has been proposed, which reduces the number of potentially expensive similarity comparisons by exploiting the triangle inequality for pruning and validation. However, no comprehensive empirical study of those techniques exists. Existing studies each offers only a narrower coverage, and they use different pivot selection strategies that affect performance substantially and thus render cross-study comparisons difficult or impossible. We offer a survey of existing pivot-based indexing techniques, and report a comprehensive empirical comparison of their construction costs, update efficiency, storage sizes, and similarity search performance. As part of the study, we provide modifications for two existing indexing techniques to make them more competitive. The findings and insights obtained from the study reveal different strengths and weaknesses of different indexing techniques, and offer guidance on selecting an appropriate indexing technique for a given setting. © 2017 VLDB Endowment.

关键词： data integration

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A new feature-based self-embedding image watermarking scheme

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Journal of Physics: Conference Series 2019年第4期1168卷

作者： Yang Liu Zhikun Chen Haocui Liang Shaoyuan Fu School of Computer Science China University of Geosciences Wuhan 430074 China Beibu Gulf Big Data Resources Utilisation Lab Qinzhou University Qinzhou 535000 China Guangxi Key Laboratory of Beibu Gulf Marine Biodiversity Conservation Qinzhou 535000 China

The performance of digital watermarking algorithm is mainly manifested in invisibility and robustness. At present, many algorithms improve these two aspects by sacrificing embedding capacity for the reason that if watermark embedding amount is small, the watermark has little influence on the visuality of the cover image, and its robustness is also higher. Therefore, how to improve the anti-attack performance of the algorithm without reducing the amount of embedding has become a major research direction. This paper proposed a new robust watermarking scheme based on feature extraction techniques. Firstly, the SIFT (scale invariant feature transform) algorithm is used to extract the stable feature points from cover image to form embedding region. Then watermark is composed of the cover's texture features obtained by LBP (local binary pattern) extracting method. Finally, embedding region is transformed by singular value decomposition (SVD) and the watermark is embedded into its singular value vector. The simulation results showed that the watermark embedding capacity of this algorithm is relatively high, and it has good resistance to common image processing and geometric transformation.

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Extraction of Interactions of Genes2Genes Related to Breast Cancer

Extraction of Interactions of Genes2Genes Related to Breast ...

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International Conference on Software Engineering Research, Management and Applications (SERA)

作者： Lejun Gong Daoyu Huang Shixing Sun Zhihong Gao Chuandi Pan Ronggen Yang Yongmin Li Geng Yang Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Zhejiang Engineering Research Center of Intelligent Medicine The First Affiliated Hospital of Wenzhou Medical University Wenzhou China College of Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing China

ISBN: (纸本)9781538658871

Breast cancer is the most prevalent disease to females in the worldwide. Its pathology remains unclear. Genetics factors is the ways to understand the molecular mechanism. This paper proposed a computational approach to explore the interactions of genes2genes related to breast cancer. We first defined the interactions of genes2genes, and described the representation of interactions of genes2genes. Using the experimental dataset, we implemented the proposed approach for extracting the interactions of genes2genes. Moreover, we also represented the interactions of genes2genes in two forms: relationship matrix and network visualization. By manual analysis, we extracted the interactions of top 10 genes2genes is related to breast cancer, which show the approach is promising for studying molecular mechanism related to breast cancer.

关键词： Breast cancer Pipelines data mining Manuals Genetics

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Emergency Events Classification Based on Minsheng Hotline Unbalanced Short-Text

Emergency Events Classification Based on Minsheng Hotline Un...

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IEEE International Conference on big Knowledge (ICBK)

作者： Li-Yuan Geng Wei Jin Han-Bing Qu Research Center for Big Data Beijing Institute of New Technology Applications Beijing China Key Lab. of Artificial Intell. & Big Data Anal. Beijing Academy of Science and Technology Beijing CN

ISBN: (纸本)9781538631218

In this work, a combined strategy is proposed to solve the unbalance problem in the classification of the short-text data sets. The improved K-means sampling method and category guide words are used to improve the classification accuracy of unbalanced data, and then VSM(vector space method) is used to express text. Finally, Naive Bayesian classifiers are used to classify the unbalanced short-text. Experiments show that this method is effective and feasible in the classification of small class events in unbalanced short-text data. The method can improve the small class classification accuracy and provide the decision basis for the government to respond quickly and precisely to emergencies.

关键词： Training Classification algorithms Feature extraction Government Text categorization Clustering algorithms Vocabulary

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