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Distributed Entropy Minimization Discretizer for Big data analysis under Apache Spark 14

Distributed Entropy Minimization Discretizer for Big Data An...

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Joint 14th IEEE Int Conf on Trust, Secur and Privacy in Comp and Commun / 13th IEEE Int symposium on Parallel and Distributed Proc with Applications / 9th IEEE Int Conf on Big data Science and Engineering (IEEE TrustCom-ISPA-BigdataSE)

作者： Ramirez-Gallego, Sergio Garcia, Salvador Mourino-Talin, Hector Martinez-Rego, David Bolon-Canedo, Veronica Alonso-Betanzos, Amparo Manuel Benitez, Jose Herrera, Francisco Univ Granada Dept Comp Sci & Artificial Intelligence CITIC UGR E-18071 Granada Spain Univ A Coruna Dept Comp Sci La Coruna 15071 Spain UCL Dept Comp Sci London WC1E 6BT England

ISBN: (纸本)9781467379526

the astonishing rate of data generation on the Internet nowadays has caused that many classical knowledge extraction techniques have become obsolete. data reduction techniques are required in order to reduce the complexity order held by these techniques. Among reduction techniques, discretization is one of the most important tasks in data mining process, aimed at simplifying and reducing continuous-valued data in large datasets. In spite of the great interest in this reduction mechanism, only a few simple discretization techniques have been implemented in the literature for Big data. thereby we propose a distributed implementation of the entropy minimization discretizer proposed by Fayyad and Irani using Apache Spark platform. Our solution goes beyond a simple parallelization, transforming the iterativity yielded by the original proposal in a single-step computation. Experimental results on two large-scale datasets show that our solution is able to improve the classification accuracy as well as boosting the underlying learning process.

关键词： big data ISOCHRONOUS CYCLOTRONS data generation data analysis data reduction entropy data mining Discretization

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Reducing Multi-Hop Calibration Errors in large-Scale Mobile Sensor Networks 14

Reducing Multi-Hop Calibration Errors in Large-Scale Mobile ...

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14th IEEE/ACM International Conference on Information Processing in Sensor Networks (IPSN)

作者： Saukh, Olga Hasenfratz, David thiele, Lothar Swiss Fed Inst Technol Comp Engn & Networks Lab Zurich Switzerland

ISBN: (纸本)9781450334754

Frequent sensor calibration is essential in sensor networks with low-cost sensors. We exploit the fact that temporally and spatially close measurements of different sensors measuring the same phenomenon are similar. Hence, when calibrating a sensor, we adjust its calibration parameters to minimize the differences between co-located measurements of previously calibrated sensors. In turn, freshly calibrated sensors can now be used to calibrate other sensors in the network, referred to as multi-hop calibration. We are the first to study multi-hop calibration with respect to a reference signal (micro-calibration) in detail. We show that ordinary least squares regression-commonly used to calibrate noisy sensors-suffers from significant error accumulation over multiple hops. In this paper, we propose a novel multi-hop calibration algorithm using geometric mean regression, which (i) highly reduces error propagation in the network, (ii) distinctly outperforms ordinary least squares in the multi-hop scenario, and (iii) requires considerably fewer ground truth measurements compared to existing network calibration algorithms. the proposed algorithm is especially valuable when calibrating large networks of heterogeneous sensors with different noise characteristics. We provide theoretical justifications for our claims. then, we conduct a detailed analysis with artificial data to study calibration accuracy under various settings and to identify different error sources. Finally, we use our algorithm to accurately calibrate 13 million temperature, ground ozone (O-3), and carbon monoxide (CO) measurements gathered by our mobile air pollution monitoring network.

关键词： Calibration

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analysis of data Preprocessing Increasing the Oversampling Ratio for Extremely Imbalanced Big data Classification 14

Analysis of Data Preprocessing Increasing the Oversampling R...

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作者： del Rio, Sara Benitez, Jose M. Herrera, Francisco Univ Granada Dept Comp Sci & Artificial Intelligence CITIC UGR Res Ctr Informat & Commun Technol E-18071 Granada Spain

ISBN: (纸本)9781467379526

the "big data" term has caught the attention of experts in the context of learning from data. this term is used to describe the exponential growth and availability of data (structured and unstructured). the design of effective models that can process and extract useful knowledge from these data represents a immense challenge. Focusing on classification problems, many real-world applications present a class distribution where one or more classes are represented by a large number of examples with respect to the negligible number of examples of other classes, which are precisely those of primary interest. this circumstance is known as the problem of classification with imbalanced datasets. In this work, we analyze a hypothesis in order to increment the accuracy of the underrepresented class when dealing with extremely imbalanced big data problems under the MapReduce framework. the performance of our solution has been analyzed in an experimental study that is carried out over the extremely imbalanced big data problem that was used in the ECBDL'14 Big data Competition. the results obtained show that is necessary to find a balance between the classes in order to obtain the highest precision.

关键词： Big data Hadoop Imbalance classification MapReduce Preprocessing big data data preprocessing Pretreatment data classification Taxonomy

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Continuous and Discrete Deep Classifiers for data Integration 14th

Continuous and Discrete Deep Classifiers for Data Integratio...

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14th International symposium on Intelligent data analysis (IDA)

作者： Sokolovska, Nataliya Rizkalla, Salwa Clement, Karine Zucker, Jean-Daniel Hop La Pitie Salpetriere Assistance Publ Hop Paris ICAN Paris France UPMC Univ Paris 6 Sorbonne Univ UMR S 1166 ICANNutriOmics Team Paris France NutriOmics Team UMR S U1166 INSERM Paris France UMMISCO UMI 209 Inst Dev Res Bondy France

ISBN: (纸本)9783319244655

data representation in a lower dimension is needed in applications, where information comes from multiple high dimensional sources. A final compact model has to be interpreted by human experts, and interpretation of a classifier whose weights are discrete is much more straightforward. In this contribution, we propose a novel approach, called Deep Kernel Dimensionality Reduction which is designed for learning layers of new compact data representations simultaneously. We show by experiments on standard and on real large-scale biomedical data sets that the proposed method embeds data in a new compact meaningful representation, and leads to a lower classification error compared to the state-of-the-art methods. We also consider some state-of-the art deep learners and their corresponding discrete classifiers. We illustrate by our experiments that although purely discrete models do not always perform better than real-valued classifiers, the trade-off between the model accuracy and the interpretability is quite reasonable.

关键词： Dimensionality reduction Heterogeneous data integration Bioinformatics

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GameVis: Game data visualization for the Web

GameVis: Game Data Visualization for the Web

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Brazilian symposium on Games and Digital Entertainment (SBGAMES)

作者： Victor R. M. Feitosa José G. R. Maia Leonardo O. Moreira George A. M. Gomes Virtual University Institute Federal University of Cear Fortaleza Brazil

ISBN: (纸本)9781467388443

Gaming and eSports have become increasingly popular and complex. For that reason, it is necessary that tournament hosts and game developers provide a great amount of meaningful information for their audiences. data visualization tools and frameworks are of upmost importance since these provides means to accomplish this task. In this paper we propose GameVis, a framework designed to aid developers in building game data visualization components for the Web. GameVis allows multiple data formats to be translated into a single data structure that can be extended and customized in many ways, allowing visualizations to suit different screen sizes and platforms while showing a great amount of variation and meaning. visualizations built within the framework may feature animations, annotations and might even adapt to different visual themes in order to provide a richer, more interactive user experience. this is shown through experimental results.

关键词： data visualization Games data analysis Animation Real-time systems Semantics Valves

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Scalable Learning of k-dependence Bayesian Classifiers under MapReduce 14

Scalable Learning of <i>k</i>-dependence Bayesian Classifier...

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作者： Arias, Jacinto Gamez, Jose A. Puerta, Jose M. Univ Castilla La Mancha Dept Comp Syst Albacete Spain

ISBN: (纸本)9781467379526

In data Mining there is a constant need to provide more scalable tools in order to tackle new domains with an increased level of complexity. Over the last few years one of the main challenges in this field is the growing size of the available data;owing to the level of data generation and storage capacities provided by new emergent technology, a range of new computational paradigms and parallel architectures have been proposed. MapReduce got the leading role in the field of Big data applications since its appearance, and many popular data analysis tools and techniques have been successfully adapted to this paradigm. Supervised classification is one of the most common problems in data Mining, and Bayesian Networks Classifiers (BNC) have become one of the most extended and competitive techniques to approach them. In this paper we propose a parallel definition of the KDB (k-dependence Bayesian classifier) algorithm under the MapReduce framework. We focus on obtaining maximum scalability and flexibility by exploring the concepts of vertical and horizontal parallelism, thus addressing both Big data and High Dimensional problems simultaneously. We analyse its properties and the advantages of applying it to large datasets of different nature. Finally, an experimental evaluation is performed by testing a Hadoop implementation of our proposal on a high-end cluster of computers.

关键词： Bayesian classifier data mining Scalability big data data generation Parallel architectures Computers Fields

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Trusted Tamper-Evident data Provenance 14

Trusted Tamper-Evident Data Provenance

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作者： Taha, Mohammad M. Bany Chaisiri, Sivadon Ko, Ryan K. L. Univ Waikato Cyber Secur Lab Hamilton New Zealand

ISBN: (纸本)9781467379526

data provenance, the origin and derivation history of data, is commonly used for security auditing, forensics and data analysis. While provenance loggers provide evidence of data changes, the integrity of the provenance logs is also critical for the integrity of the forensics process. However, to our best knowledge, few solutions are able to fully satisfy this trust requirement. In this paper, we propose a framework to enable tamper-evidence and preserve the confidentiality and integrity of data provenance using the Trusted Platform Module (TPM). Our framework also stores provenance logs in trusted and backup servers to guarantee the availability of data provenance. Tampered provenance logs can be discovered and consequently recovered by retrieving the original logs from the servers. Leveraging on TPM's technical capability, our framework guarantees data provenance collected to be admissible, complete, and confidential. More importantly, this framework can be applied to capture tampering evidence in large-scale cloud environments at system, network, and application granularities. We applied our framework to provide tamper-evidence for Progger, a cloud-based, kernel-space logger. Our results demonstrate the ability to conduct remote attestation of Progger logs' integrity, and uphold the completeness, confidential and admissible requirements.

关键词： Trusted Computing Accountability in Cloud Computing data Security Trusted Platform Module data Provenance Remote Attestation Tamper Evidence Cloud Computing

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data analysis of Distributed Application Platform Based on the R which Apply to Digital Library

Data Analysis of Distributed Application Platform Based on t...

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the 14th International symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2015)(第十四届分布式计算及其应用国际学术研讨会)

作者： Ningbo Wu Fan Yang College of Information Guizhou University of Finance and Economics Guiyang China Library Guizhou University of Finance and Economics Guiyang China

Digital Library large data resource lack of analysis and use,in order to mining the value of big data resources,proposed platformization analysis and processing *** integrate R and Hadoop to construct distributed data analysis platform,many big data analytical can be decomposed into “large” and “small” data processing section,overcome before scheme puzzle on analytical of large dataset,improve the performance of data analysis,platform able to handle data analysis tasks.

关键词： component big data distributed data analysis

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Automated Collection and analysis of Malware Disseminated via Online Advertising 14

Automated Collection and Analysis of Malware Disseminated vi...

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作者： Sakib, Muhammad N. Huang, Chin-Tser Univ South Carolina Depattment Comp Sci & Engn Columbia SC 29208 USA

ISBN: (纸本)9781467379526

Online advertising system has become a convenient and efficient channel to disseminate Web-based malware to the Internet users. Most of the free online services exist in exchange of the revenues generated through advertisements. therefore, considerable efforts are made to deliver the ads to the appropriate audiences. Cyber criminals can easily exploit this online ad delivery system to deliver malware to a very large number of end-users and their vulnerable machines. We observe that this active approach by cyber criminals can be exploited to expedite the collection of malware. In this paper, we propose an automated system that mimics high-risk browsing activities such as clicking on suspicious online ads, and as a result collects malicious executable files for further analysis and diagnosis. Using our system we crawled over the Internet for a period of 7 days to collect a significant amount of ad frame URLs, which has been monitored for another period of 7 days to collect more than 800 malicious executables. the experimental results showed that our system is quite effective in collecting online malware samples using very limited resources compared to other malware collecting honeypot systems.

关键词： malvertising malware collection analysis automation

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A Comparative analysis of Chinese Simile and Metaphor Based on a large Scale Chinese Corpus 1

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14th China National Conference on Computational Linguistics (CCL) / 3rd International symposium on Natural Language Processing Based on Naturally Annotated Big data (NLP-NABD)

作者： Wang, Zhimin Jia, Yuxiang Lacasella, Pierangelo Beijing Language & Culture Univ Beijing 100083 Peoples R China Zhengzhou Univ Zhengzhou 450052 Peoples R China

ISBN: (数字)9783319258164

ISBN: (纸本)9783319258164;9783319258157

this paper puts forward the mapping inheritance hypothesis which states that the ultimate goal of mapping is to inherit the attributes of source domains by comparing structures 'as A as Y'(Y-A) and 'n n/n + n'. Furthermore, we have built a knowledge base for simile and explored the distribution of source domain and its attribute hierarchy. the study shows that the number of S domain words in Chinese simile is different from metaphor, they only have in common 155 S domain words. Although simile and metaphor both tend to choose the semantic category B_object as their source domain, simile expressions are more likely to choose plants and animals, metaphorical expressions are more likely to choose inanimate objects.

关键词： Metaphor mapping Attribute inheritance TongYiCi CiLin Semantic distribution

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