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International Conference on Computer science and Network Technology

作者： Zaid Alaa Hussien Hai Jin Zaid Ameen Abduljabbar Mohammed Abdulridha Hussain Salah H. Abbdal Deqing Zou Cluster and Grid Computing Lab Big Data Technology and System Lab Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781467381741

Cloud computing is a promising information technology service that allocates and reallocates resources when a client requires virtual data storage and network facility at any time and place. Cloud computing provides large data storage and management services at the lowest cost. Thus, it is crucial for many organizations and clients who seek such services. data security and integrity are necessary and play important roles. However, these two issues are facing significant challenges. Therefore, a robust scheme is required to ensure security and privacy while transmitting or storing data in the cloud storage environment. We propose an efficient and robust scheme to ensure data security in a semi-trusted third party auditor. Our scheme adopts an advanced encryption standard to support data owner privacy, a cryptography hash function to maintain data owner integrity, and elliptic curve cryptography to ensure data confidentiality, correctness, and security when transmitting data over unsecure channels. A security analysis confirms that our scheme can withstand man-in-the-middle attack and provides data correctness.

关键词： Cloud storage data security elliptic curve cryptography hash function integrity semi-trusted third party auditor data Security Elliptic curve cryptography cryptography Cloud Computing computer information security data Storage Scheme auditor Third party customers Management services Transmitting data

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Extortion under uncertainty: Zero-determinant strategies in noisy games

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Physical Review E 2015年第5期91卷 052803-052803页

作者： Dong Hao Zhihai Rong Tao Zhou CompleX Lab Web Sciences Center University of Electronic Science and Technology of China Chengdu 611731 P.R. China Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 P.R. China

Repeated game theory has been one of the most prevailing tools for understanding long-running relationships, which are the foundation in building human society. Recent works have revealed a new set of “zero-determinant” (ZD) strategies, which is an important advance in repeated games. A ZD strategy player can exert unilateral control on two players' payoffs. In particular, he can deterministically set the opponent's payoff or enforce an unfair linear relationship between the players' payoffs, thereby always seizing an advantageous share of payoffs. One of the limitations of the original ZD strategy, however, is that it does not capture the notion of robustness when the game is subjected to stochastic errors. In this paper, we propose a general model of ZD strategies for noisy repeated games and find that ZD strategies have high robustness against errors. We further derive the pinning strategy under noise, by which the ZD strategy player coercively sets the opponent's expected payoff to his desired level, although his payoff control ability declines with the increase of noise strength. Due to the uncertainty caused by noise, the ZD strategy player cannot ensure his payoff to be permanently higher than the opponent's, which implies dominant extortions do not exist even under low noise. While we show that the ZD strategy player can still establish a novel kind of extortions, named contingent extortions, where any increase of his own payoff always exceeds that of the opponent's by a fixed percentage, and the conditions under which the contingent extortions can be realized are more stringent as the noise becomes stronger.

关键词： Noise Game animals Histidine cyclic Players low noise Game theory Robustness

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Text Clustering Based on a Divide and Merge Strategy

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Procedia Computer science 2015年 55卷 825-832页

作者： Man Yuan Yong Shi China Huarong Asset Management CO. LTD. Beijing China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Univiersity of Chinese Academy of Sciences Beijing China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

A text clustering algorithm is proposed to overcome the drawback of division based clustering method on sensitivity of estimated class number. Complex features including synonym and co-occurring words are extracted to make a feature space containing more semantic information. Then the divide and merge strategy helps the iteration converge to a reasonable cluster number. Experimental results showed that the dynamically updated center number prevent the deterioration of clustering result when k deviates from the real class numbers. When k is too small or large, the difference of clustering results between FC-DM and k-means is more obvious and FC-DM also outperformed other benchmark algorithms.

关键词： Text clustering feature extension k-means

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Performance Evaluation of Enterprise big data Platforms with HiBench

Performance Evaluation of Enterprise Big Data Platforms with...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Todor Ivanov Raik Niemann Sead Izberovic Marten Rosselli Karsten Tolle Roberto V. Zicari Frankfurt Big Data Lab Goethe University Frankfurt Frankfurt am Main Germany Institute of Information Systems University of Applied Science Hof Hof Germany Accenture Germany

ISBN: (纸本)9781467379533

In this paper, we evaluate the performance of dataStax Enterprise (DSE) using the HiBench benchmark suite and compare it with the corresponding Cloudera's Distribution of Hadoop (CDH) results. Both systems, DSE and CDH were stress tested using CPU-bound (WordCount), I/O-bound (Enhanced DFSIO) and mixed (HiveBench) workloads. The experimental results showed that DSE is better than CDH in writing files, whereas CDH is better than DSE in reading files. Additionally, for DSE the read and write throughput difference is very minor, whereas for CDH the read throughput is much higher than the write throughput. The results we obtained show that the HiBench benchmark suite, developed specifically for Hadoop, can be successfully executed on top of the dataStax Enterprise (DSE).

关键词： Benchmark testing big data Cloud computing File systems Throughput Writing

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Robust crowd bias correction via dual knowledge transfer from multiple overlapping sources

Robust crowd bias correction via dual knowledge transfer fro...

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IEEE International Conference on big data

作者： Sihong Xie Qingbo Hu Jingyuan Zhang Jing Gao Wei Fan Philip S. Yu Department of Computer Science University of Illinois at Chicago Chicago IL USA Department of Computer Science University at Buffalo Buffalo NY USA Baidu Research Big Data Lab Sunnyvale CA USA

One of the largest constituents of big data is the crowdsourced or user-generated data which contain a wide range of valuable information. However, they are inherently biased and possibly spammed, making trustworthy information extraction an imperative task. As a special case, we study reviewer-posted ratings for products. The biased ratings can lead to disappointed customers due to overrated products, and reduced revenues of business owners caused by undeserved negative ratings. To distill objective product quality measurements, most existing methods try to infer unbiased ratings from the raw ratings alone, and may not overcome the inherent bias to recover the underlying true ratings. Though improved bias corrections have been achieved with domain expert helps, the overhead of expert efforts can be rather expensive in practice. We exploit the variety of big data and adopt a multiple source mining approach, which finds trustworthy measurements without domain expert, but with knowledge crowdsourced and transferred from external domains. We address the challenges that the multiple data sources are 1) inherently heterogeneous, 2) at most only partially overlapping and 3) biased by themselves. We explore and analyze the strengths and weaknesses of various knowledge transfer strategies. We then propose Consensus Ranking Dual Transfer (CRDT) to handle the above challenges by identifying "anchor reviewers" as a bridge for robust "dual transfer", and removing bias in individual sources via consensus ranking aggregation. Experiments on real-world rating datasets demonstrate that the proposed approach can deliver more robust bias correcting effects than the baselines and can identify abnormal reviewers.

关键词： Quality assessment Product design big data Robustness Motion pictures

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Mining Indecisiveness in Customer Behaviors

Mining Indecisiveness in Customer Behaviors

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IEEE International Conference on data Mining (ICDM)

作者： Qi Liu Xianyu Zeng Chuanren liu Hengshu Zhu Enhong Chen Hui Xiong Xing Xie School of Computer Science and Technology University of Science and Technology of China Decision Sciences & MIS Department Drexel University Big Data Lab Baidu Research Rutgers Business School Rutgers University Microsoft Research

ISBN: (纸本)9781467395052

In the retail market, the consumers' indecisiveness refers to the inability to make quick and assertive decisions when they choose among competing product options. Indeed, indecisiveness has been investigated in a number of fields, such as economics and psychology. However, these studies are usually based on the subjective customer survey data with some manually defined questions. Instead, in this paper, we provide a focused study on automatically mining indecisiveness in massive customer behaviors in online stores. Specifically, we first give a general definition to measure the observed indecisiveness in each behavior session. From these observed indecisiveness, we can learn the latent factors/reasons by a probabilistic factor-based model. These two factors are the indecisive indexes of the customers and the product bundles, respectively. Next, we demonstrate that this indecisiveness mining process could be useful in several potential applications, such as the competitive product detection and personalized product bundles recommendation. Finally, we perform extensive experiments on a large-scale behavioral logs of online customers in a distributed environment. The results reveal that our measurement of indecisiveness agrees with the common sense assessment, and the discoveries are useful in predicting customer behaviors and providing better recommendation services for both customers and online retailers.

关键词： Psychology data mining Decision making Indexes Probabilistic logic Manuals

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Consolidation: Metric+Active Learning and Its Applications for Cross-Domain Recommendation

Consolidation: Metric+Active Learning and Its Applications f...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Chu Guan Qi Liu Jingsong Lv Enhong Chen Hengshu Zhu Xin Li University of Science and Technology of China Hefei Anhui CN School of Computer Science and Technology University of Science and Technology of China Anhui USTC iFLYTEK Co. Ltd. China Baidu Research-Big Data Lab

How to discover potential users of new released products is an important task in marketing campaigns, especially for companies have many established users. Indeed, transfer learning is a natural choice for addressing such problem, which can leverage the user data from historical products (i.e., auxiliary domains) for recommendation of new product (i.e., target domain). However, although most of the existing solutions of transfer learning work well on predicting user ratings, few of them can be directly exploited for the top-k recommendation with respect to user preferences. To this end, in this paper, we propose a novel transfer learning approach based on metric learning and active learning to address the problem of top-k potential user recommendation. Specifically, by modeling the user behaviors on historical products, we first propose to learn a global distance metric for capturing the commonality of users and segmenting users into different neighborhoods. Furthermore, to enhance the local discriminability among different neighborhoods, we select one representative user for each neighborhood by a new type of active learning and also learn local distance metrics for better estimating the user preferences. With such representative selection and metric learning, a novel method is developed for recommending top-k potential users of new products. Finally, we perform extensive experiments on real-world datasets, and the results validate the effectiveness of the proposed methods.

关键词： Measurement Companies Classification algorithms Recommender systems Schedules Learning systems Intelligent agents

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GOMES: A group-aware multi-view fusion approach towards real-world image clustering

GOMES: A group-aware multi-view fusion approach towards real...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhe Xue Guorong Li Shuhui Wang Chunjie Zhang Weigang Zhang Qingming Huang Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Key Lab. of Intell. Info. Process Chinese Academy of Sciences School of Computer Science and Technology Harbin Institute of Technology at Weihai

Different features describe different views of visual appearance, multi-view based methods can integrate the information contained in each view and improve the image clustering performance. Most of the existing methods assume that the importance of one type of feature is the same to all the data. However, the visual appearance of images are different, so the description abilities of different features vary with different images. To solve this problem, we propose a group-aware multi-view fusion approach. Images are partitioned into groups which consist of several images sharing similar visual appearance. We assign different weights to evaluate the pairwise similarity between different groups. Then the clustering results and the fusion weights are learned by an iterative optimization procedure. Experimental results indicate that our approach achieves promising clustering performance compared with the existing methods.

关键词： Visualization Clustering methods Kernel Feature extraction Fuses Cost function

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Automatic Control Process Analysis of Gas Pressure in Electrostatic Discharge Measurement System

Automatic Control Process Analysis of Gas Pressure in Electr...

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The Seventh Asia-Pacific Conference on Environmental Electromagnetics (CEEM'2015)

作者： Zhang Chao Ruan Fangming Xiao Wenjun Yang Xiaohong David Pommerenke You Xiaojin Jiang Yuanyuan School of Big Data and Information Eng. Guizhou University IEEE Dept.of Electronic and Information Engineering Guizhou Normal University Guizhou Aerospace Institute of Metrology and Testing EMC Lab of Missouri University of Science&Technology

Based on requirement of new invented ESD measurement system,the automatic control system of pressure was designed to automatically control the pressure of experimental *** proposed system uses STM32 MCU as its ***,after received feedback signal of sensor,controls on-off state of solenoid valve with applying PID algorithm to output PWM *** of experimental box can set desire value and keep it *** design provides compensation to remove deficiency in original pressure control process,runs faster,more accurate and convenient operation than that *** design provides accurate pressure environment in the experimental box of the new ESD measurement system.

关键词： ESD measurement system automatic pressure control STM32 solenoid valve

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International Conference on Computer Vision (ICCV)

作者： Guoli Song Shuhui Wang Qingming Huang Qi Tian Key Lab of Intell. Info. Process. Inst. of Comput. Tech Chinese Academy of Sciences Beijing China Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing China Department of Computer Science University of Texas at San Antonio TX USA

ISBN: (纸本)9781467383929

data from real applications involve multiple modalities representing content with the same semantics and deliver rich information from complementary aspects. However, relations among heterogeneous modalities are simply treated as observation-to-fit by existing work, and the parameterized cross-modal mapping functions lack flexibility in directly adapting to the content divergence and semantic complicacy of multi-modal data. In this paper, we build our work based on Gaussian process latent variable model (GPLVM) to learn the non-linear non-parametric mapping functions and transform heterogeneous data into a shared latent space. We propose multi-modal Similarity Gaussian Process latent variable model (m-SimGP), which learns the nonlinear mapping functions between the intra-modal similarities and latent representation. We further propose multi-modal regularized similarity GPLVM (m-RSimGP) by encouraging similar/dissimilar points to be similar/dissimilar in the output space. The overall objective functions are solved by simple and scalable gradient decent techniques. The proposed models are robust to content divergence and high-dimensionality in multi-modal representation. They can be applied to various tasks to discover the non-linear correlations and obtain the comparable low-dimensional representation for heterogeneous modalities. On two widely used real-world datasets, we outperform previous approaches for cross-modal content retrieval and cross-modal classification.

关键词： Correlation Gaussian processes data models Semantics Analytical models data analysis Kernel

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