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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461-470 订阅

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Distributed Source Encoding for Enhancement of Embedding Payload in Reversible data Hiding

Distributed Source Encoding for Enhancement of Embedding Pay...

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3rd international conference on computing, Communication, Control and Automation (ICCUBEA)

作者： Shilimkar, Amruta R. Shah, S. K. Smt Kashibai Navale Coll Engn Elect & Telecommun Pune Maharashtra India

ISBN: (纸本)9781538640081

Due to fast development of communication network and multimedia technology, security becomes a critical issue in information trade process over the network. Reversible data hiding is the art and science of imperceptible *** main challenge in reversible data hiding lies in finding the best trade-off between the embedding capacity (in terms of bpp) and the reconstructed image quality (in terms of Peak Signal to Noise Ratio),In this work, different image scrambling algorithms are implemented to illustrate the security capability of image for medical and military of scientific use. Different performance parameters such as Similarity index, Mean Absolute Difference, Mean Square Error and Peak Signal to Noise Ratio are carried out for encrypted and reconstructed image. The proposed image scrambling algorithm gives less SSIM for image encryption and high PSNR for recovered image. rdH with slepian wolf coding gives highest embedding rate as compared to other.

关键词： Slepian-wolf encoding Pixel transposition Pixel substitution Logistic map

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An Electric Power Sensor data Oriented data Cleaning Solution 14

An Electric Power Sensor Data Oriented Data Cleaning Solutio...

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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： Liu, He Chen, Jiangqi Huang, Fupeng Li, Han Global Energy Interconnect Res Inst Adv Comp & Big Data Technol Lab SGCC Beijing Peoples R China North China Univ Technol Coll Comp Sci Beijing Key Lab Integrat & Anal Large Scale Strea Beijing Peoples R China

ISBN: (纸本)9781538608401

With the development of Smart Grid Technology, more and more electric power sensor data are utilized in various electric power systems. To guarantee the effectiveness of such systems, it is necessary to ensure the quality of electric power sensor data, especially when the scale of electric power sensor data is large. In the field of large-scale electric power sensor data cleaning, the computational efficiency and accuracy of data cleaning are two vital requirements. In order to satisfy these requirements, this paper presents an electric power sensor data oriented data cleaning solution, which is composed of a data cleaning framework and a data cleaning method. Based on Hadoop, the given framework is able to support large-scale electric power sensor data acquisition, storage and processing. Meanwhile, the proposed method which achieves outlier detection and reparation is implemented on the basis of a time-relevant k-means clustering algorithm in Spark. The feasibility and effectiveness of the proposed method is evaluated on a data set which originates from charging piles. Experimental results show that the proposed data cleaning method is able to improve the data quality of electric power sensor data by finding and repairing most outliers. For large-scale electric power sensor data, the proposed data cleaning method has high parallel performance and strong scalability.

关键词： electric power senser data data cleaning k-means clustering outlier Spark

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Achieving Personalized and Privacy-Preserving Range Queries over Outsourced Cloud data

Achieving Personalized and Privacy-Preserving Range Queries ...

引用

IEEE international conference on Communications (ICC) / Wireless Communications Symposium / 3rd Workshop on Optical Wireless Communications (OWC)

作者： Shen, Yao Huang, Liusheng Yang, Wei Univ Sci & Technol China Sch Comp Sci & Technol Hefei Anhui Peoples R China Univ Sci & Technol China Suzhou Inst Adv Study Suzhou Peoples R China

ISBN: (纸本)9781467389990

With the increasing prevalence of cloud computing, data owners prefer to outsource their databases to the cloud. For the protection of data privacy, sensitive data have to be encrypted before outsourcing, which introduces much difficulty into effective data utilization. Most previous studies either suffer from privacy disclosure and low efficiency, or do not support personalized multidimensional range queries. In this paper, we focus on personalized private range queries over outsourced data. We propose a personalized and privacy-preserving private range query protocol (PPP), which uses bounding-box PIR (bbPIR) to trade access pattern privacy for flexible privacy and high efficiency, and satisfies various quality of service (QoS) requirements. To our best knowledge, PPP is the first to achieve personalized search according to owner-specified privacy-cost tradeoff. Furthermore, PPP is secure against semi-honest adversaries under known ciphertext model. Experimental results on real-world datasets show that PPP is efficient and able to achieve diverse QoS requirements.

关键词： Servers Privacy Protocols Indexes Cloud computing data privacy

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Grid-based indexing and search algorithms for large-scale and high-dimensional data 14

Grid-based indexing and search algorithms for large-scale an...

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作者： Yang, Chuanfu Li, Zhiyang Qu, Wenyu Liu, Zhaobin Qi, Heng Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116026 Peoples R China Tianjin Univ Sch Comp Software Tianjin 300000 Peoples R China Nagoya Univ Grad Sch Informat Nagoya Aichi Japan

ISBN: (纸本)9781538608401

The rapid development of Internet has resulted in massive information overloading recently. These information is usually represented by high-dimensional feature vectors in many related applications such as recognition, classification and retrieval. These applications usually need efficient indexing and search methods for such large-scale and high-dimensional database, which typically is a challenging task. Some efforts have been made and solved this problem to some extent. However, most of them are implemented in a single machine, which is not suitable to handle large-scale database. In this paper, we present a novel data index structure and nearest neighbor search algorithm implemented on Apache Spark. We impose a grid on the database and index data by non-empty grid cells. This grid-based index structure is simple and easy to be implemented in parallel. Moreover, we propose to build a scalable KNN graph on the grids, which increase the efficiency of this index structure by a low cost in parallel implementation. Finally, experiments are conducted in both public databases and synthetic databases, showing that the proposed methods achieve overall high performance in both efficiency and accuracy.

关键词： Large-scale data High-dimensional data Approximate Nearest Neighbor Search

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A distributed multi-exemplar affinity propagation clustering algorithm based on mapreduce 3

A distributed multi-exemplar affinity propagation clustering...

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3rd IEEE international conference on Big data computing Service and Applications, BigdataService 2017

作者： Yang, Yu-Bo Wang, Chang-Dong Lai, Jian-Huang School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9781509063185

Clustering algorithm is one of the fundamental techniques in data mining, which plays a crucial role in various applications, such as pattern recognition, document retrieval, and computer vision. As so far, many effective algorithms have been proposed. Affinity Propagation is an algorithm requires no parameter indicating the number of clusters, which is the most distinguishing advantage compared to the k-means clustering algorithm. Multi-Exemplar Affinity Propagation (MEAP) extends the single-exemplar model to the multi-exemplar model, which could describe the dataset with more complex structure. With the amount of data increasing rapidly, the growing size of dataset makes the clustering problem become more and more challenging. To solve this problem, the parallel computing framework is widely used, such as MapReduce. However, for the MEAP algorithm, it is not a straightforward task to implement the updating of MEAP messages in MapReduce, which without proper design would be time-consuming. In this paper, we propose to utilize the stability of data distribution to apply the MEAP algorithm on the MapReduce platform and develop an efficient Distributed Multi-Exemplar Affinity Propagation (DisMEAP) clustering algorithm by using three MapReduce stages. The experiment results demonstrate that our algorithm can perform well in processing large-scale datasets and could achieve the same accuracy as the original MEAP algorithm. © 2017 IEEE.

关键词： MapReduce

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Unsupervised Anomaly Detection Algorithm of Graph data Based on Graph Kernel 4

Unsupervised Anomaly Detection Algorithm of Graph Data Based...

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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： Zhang, Lili Wang, Huibin Li, Chenming Shao, Yehong Ye, Qing College of Computer and Information Engineering Hohai University Nanjing China Arts and Science Ohio University Southern IrontonOH United States

ISBN: (纸本)9781509066438

Nowadays, there are a lot of graph data in many fields such as biology, medicine, social networks and so on. However, it is difficult to detect anomaly and get the useful information if we want to apply the traditional algorithms in graph data. Statistical pattern recognition and structural pattern recognition are two main methods in pattern recognition. The disadvantage of statistical pattern recognition is that it is difficult to represent the relationship. In the structural pattern recognition, the object is generally expressed as a graph, and the key point is the similarity or matching of the graphs. However, graph matching is complex and NP-hard. Recently, graph kernel is proposed to solve the graph matching problem, so we can map the graphs into vector space. As a result, the operations in the vector space are applicable to graph data. In this paper, we propose a new algorithm to detect anomaly for graph data. Firstly, we use graph kernel to define the similarity of the graphs, and then we convert graph data into vector data. After that, we use the Kernel Principal Component Analysis (KPCA) to reduce the dimension, and then train these data by one-class classifier to get the model for anomaly detection. The experiments on datasets MUTAG and ENZYMES at the end of the paper show the efficiency of proposed algorithm. © 2017 IEEE.

关键词： Vector spaces

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Improvised dedupication with keys and chunks in HDFS storage 3

Improvised dedupication with keys and chunks in HDFS storage

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3rd IEEE international conference on science Technology, Engineering and Management, ICONSTEM 2017

作者： Kumar, S. Pradeep Aswini, A. Kavithadevi, M. Ramya, S. Department of Computer Science and Engineering Rajalakshmi Institute of Technology India

ISBN: (纸本)9781509048557

Cloud computing has providing unlimited storage space to store data in cloud. Service providers can increase space in cloud by incorporating with de-duplication systems. In the previous de-duplication systems, they consider single server to store data in cloud. However, as lots of de-duplication system and storage are intend by users and applications for higher reliability and the data are critical. Hence in this system, the concept of de-duplication and security is achieved by means of Proof of Ownership of the File[POF]. We used multiple server to store the data in cloud in this system. We outsource the keys to slave machine securely in order to keep the data very secure. De-key supports both file level and block level de-duplications. The overhead of key management is avoided in this system and provides fault tolerance for key management. In this system, Triple DES technique is used, as the plain text is encrypted triple times with the convergent keys so that the data will be more secured. Scalability increases as de-key achieved efficiently. Cost efficiency is achieved as multiple users of same data is just referred and not newly added. Deleting content of shared file of different user will allow deleting only convergent key reference not co0ntent stored in Hadoop Distributed File system Storage[HDFS]. © 2017 IEEE.

关键词： Fault tolerance

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Robust Correspondence Detection by L2E Estimator for Retinal Image Registration 14

Robust Correspondence Detection by L2E Estimator for Retinal...

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作者： Wei, Lifang Yang, Changcai Zhou, Shucheng Chen, Riqing Pan, Lin Fujian Agr & Forestry Univ Coll Comp & Informat Sci Inst Smart Agr & Forestry & Big Data Fuzhou Fujian Peoples R China Fuzhou Univ Coll Phys & Informat Engn Fuzhou Fujian Peoples R China

ISBN: (纸本)9781538608401

Establishing reliable correspondences is often challenging for retinal image registration with poor quality and low overlap. The conventional local search methods usually find many incorrect correspondences by feature descriptor, which would degrade the accuracy of image registration. In this paper, we propose a robust correspondence detection framework for low overlap and poor quality retinal image registration. Specifically, coherent spatial criterion is utilized to remove the false correspondences based on the initial matches in the first hierarchical. And the L-2-minimizing estimator is used for further hierarchical discarding significant fraction of outliers and estimating the transformation parameters to align the retinal images by affine model and quadratic model. Since such fitting inliers can be used to preserve the significant correspondences between the fixed image and to-be-aligned image with low overlap, it becomes more efficient to obtain accurate transformation. Through quantitative measurements and visual inspect, our proposed method shows the superior robustness and accuracy to the state-of-the-art methods.

关键词： image registration retinal image correspondence detection L-2-minimizing estimate

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Distributed Resource Sharing in Fog-assisted Big data Streaming

Distributed Resource Sharing in Fog-assisted Big Data Stream...

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IEEE international conference on Communications (ICC) / Wireless Communications Symposium / 3rd Workshop on Optical Wireless Communications (OWC)

作者： Yin, Bo Shen, Wenlong Cheng, Yu Cai, Lin X. Li, Qing IIT Dept Elect & Comp Engn Chicago IL 60616 USA Symantec Corp Mountain View CA USA

ISBN: (纸本)9781467389990

Fog computing is a promising architectural pattern to reduce the amount of data that is transferred to the cloud for processing and analysis. In this paper, we study fog-assisted data streaming scenario in which fog nodes at the network edge share their spare resources to help pre-process raw data of applications hosted in the cloud. A distributed resource sharing scheme is presented where the software defined network (SDN) controller dynamically adjusts the volume of application data that will be directed to fog nodes for pre-processing. The SDN controller makes decisions by coordinating fog nodes and cloud platform to collaboratively solve a social welfare maximization problem. Based on a hybrid alternating direction method of multipliers (H-ADMM) algorithm, computation burden for solving the optimization problem is fully distributed to fog nodes, cloud platform and SDN controller, where local variables of fog nodes are updated in parallel. With proper design of message exchange pattern, the communication overhead of the coordination to SDN controller grows smoothly with increasing number of participating fog nodes.

关键词： Resource management Cloud computing Big data Convex functions Algorithm design and analysis Optimization Silicon

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data Engineering and Intelligent computing: Proceedings of IC3T 2016

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2017年

作者： Suresh Chandra Satapathy Vikrant Bhateja K Srujan Raju B Janakiramaiah

ISBN: (纸本)9789811032226

The book is a compilation of high-quality scientific papers presented at the 3rd international conference on Computer & Communication Technologies (IC3T 2016). The individual papers address cutting-edge technologies and applications of soft computing, artificial intelligence and communication. In addition, a variety of further topics are discussed, which include data mining, machine intelligence, fuzzy computing, sensor networks, signal and image processing, human-computer interaction, web intelligence, etc. As such, it offers readers a valuable and unique resource.

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