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Reversible Fragile Watermarking based on Difference Expansion Using Manhattan Distances for 2D Vector Map

Procedia Technology 2013年 11卷 614-620页

作者： Shelvie Nidya Neyman Benhard Sitohang Sobar Sutisna Department of Computer Science– Faculty of Mathematics and Natural Sciences (FMIPA) Institut Pertanian Bogor (IPB) Gedung FMIPA Kampus IPB Dramaga Jl. Meranti Dramaga Bogor 16680 Indonesia Software Engineering & Data – Research Group School of Electrical Engineering and Informatics (STEI) nstitut Teknologi Bandung(ITB) Achmad Bakrie Building 2nd Floor Jl. Ganesha No 10 Bandung 40132 Indonesia Peneliti Utama Badan Informasi Geospasial (BIG) Jl.Raya Jakarta-Bogor Km.46 Cibinong 16911 Indonesia

The need for publishing maps in secure digital format, especially guarantees data integrity which motivated us to propose a scheme that detects and locates modification data with high accuracy while ensuring exact recovery of the original content. In particular, using fragile watermarking algorithm based on reversible manner to embed hidden data in 2D vector map for each spatial features. In this paper, a reversible data-hiding scheme is explored based on the idea of difference expansion with Manhattan distances. A set of invertible integer mappings is defined to extract Manhattan distances from coordinates and the hidden data are embedded by modifying the differences between the adjacent distances. Experiments results show that the proposed scheme has good performance in term invisibility and tamper modification ability. The scheme could detect modification data such addition and deletion some features, and exactly recovery the original content of the 2D vector map.

关键词： Reversible Watermarking Fragile Watermarking Difference Expansion Manhattan Distances Vector Map

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MAMMOTH:A MASSIVE-SCALE EMULATION PLATFORM FOR INTERNET OF THINGS

MAMMOTH:A MASSIVE-SCALE EMULATION PLATFORM FOR INTERNET OF T...

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2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems

作者： Vilen Looga Zhonghong Ou Yang Deng Antti Yl-Jski Data Communications Software Lab Department of Computer Science and EngineeringSchool of ScienceAalto University

Internet of Things (IoT) is increasingly used in a plethora of fields to enable radically new ways for various purposes, ranging from monitoring the environment to enhancing the wellbeing of human life. With the ever-increasing size of such networks, it is fundamental to understand the issues that come with scaling on different networking layers. A cost-efficient approach to examine large-scale networks is to use simulators or emulators to test the infrastructure and its ability to support the desired applications. In this paper, we investigate and compare the currently available simulation/emulation software. We found out that the current solutions are mostly appropriate for small- and medium-scale emulation, however they are not suitable for large-scale testing that reaches millions of node running concurrently. We then propose a large-scale IoT emulator called MAMMotH and present a brief overview of its design. Finally we discuss some of the current issues and future directions, e.g. radio link simulation.

关键词： Internet of Things (IoT) Large-scale emulation Simulation

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Corrigendum: “Provably-secure electronic cash based on certicateless partially-blind signatures”

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Electronic Commerce Research and Applications 2013年第1期12卷 60-60页

作者： Lei Zhang Futai Zhang Bo Qin Shubo Liu East China Normal University Software Engineering Institute Shanghai Key Laboratory of Trustworthy Computing Shanghai China Universitat Rovira i Virgili Department of Computer Engineering and Mathematics UNESCO Chair in Data Privacy Tarragona Catalonia Spain Nanjing Normal University School of Computer Science and Technology China Xi’an University of Technology School of Science Department of Maths China Wuhan University School of Computer Science Wuhan China

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Error control strategies for transmit-only sensor networks: A case study

Error control strategies for transmit-only sensor networks: ...

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IEEE International Conference on Networks

作者： Rachel Cardell-Oliver Andreas Willig Christof Huebner Thomas Buehring Alvaro Monsalve School of Computer Science & Software Engineering The University of Western Australia Perth Australia Department of Computer Science University of Canterbury Christchurch New Zealand Institute for Industrial Data Processing and Communication University of Applied Sciences Mannheim Germany

ISBN: (纸本)9781467345217

Wireless sensor networks using transmit-only sensor nodes are a promising new approach for applications such as environmental monitoring because they can provide high network scalability and long life-time at low cost. But achieving reliable data delivery in a transmit-only network is a challenging problem because there is no feedback channel for reporting lost or damaged packets. Taking a systems approach, this paper identifies three specific error control strategies that are applicable in this design space. The strategies are drawn from the areas of temporal diversity, spatial diversity and coding-based methods. An empirical investigation of these strategies is performed using two case study deployments in indoor and outdoor settings, running from two weeks to several months. In both case studies data replication in successive packets and receiver diversity performed best. In most cases, but not all, coding-based methods offered a poor balance between overhead and efficiency.

关键词： Receivers Error correction Reliability Decoding Diversity methods Ground penetrating radar

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Practical privacy for value-added applications in vehicular ad hoc networks 5th

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5th International Conference on Internet and Distributed Computing Systems, IDCS 2012

作者： Zhang, Lei Wu, Qianhong Qin, Bo Domingo-Ferrer, Josep Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai China UNESCO Chair in Data Privacy Dept. of Comp. Eng. and Maths Universitat Rovira i Virgili TarragonaCatalonia Spain Key Lab. of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan University School of Computer China Department of Maths School of Science Xi’an University of Technology China

ISBN: (纸本)9783642348822

Advances in mobile networking and information processing technologies have triggered vehicular ad hoc networks (VANETs) for traffic safety and value-added applications. Most efforts have been made to address the security concerns while little work has been done to investigate security and privacy for value-added applications in VANETs. To fill this gap, we propose a value-added application, specifically, a security and privacy preserving location-based service (LBS) scheme for VANETs. For each LBS transaction, the scheme provides authentication, integrity and non-repudiation for both the service provider and the user. A user can obtain the service in an anonymous way and hence user privacy is well protected. However, a tracing procedure can be invoked to find malicious users, thereby efficiently preventing users from abusing the anonymity provided by the system. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Location based services

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Pattern mining, semantic label identification and movement prediction using mobile phone data

Pattern mining, semantic label identification and movement p...

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8th International Conference on Advanced data Mining and Applications, ADMA 2012

作者： Xie, Rong Luo, Jun Yue, Yang Li, Qingquan Zou, Xiaoqing International School of Software Wuhan University Wuhan 430079 China Shenzhen Institutes of Advanced Technology CAS Shenzhen 518055 China Shenzhen Key Laboratory of High Performance Data Mining Shenzhen 518055 China Shenzhen University Shenzhen China State Key Lab. of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430079 China Faculty of Land Resource Engineering Kunming University of Science and Technology Kunming China

ISBN: (纸本)9783642355264

data collected from mobile phones have potential knowledge to provide with important behavior patterns of individuals. In this paper, we present approaches to discovering personal mobility and characteristics based on mobile phone location information and semantic analysis. We discuss three aspects related to very common mobile phone-related applications such as pattern mining, semantic label identification and movement prediction. We use real mobile phone data to perform functions of discovering these behavior patterns and demonstrate effectiveness of our approaches. © Springer-Verlag 2012.

关键词： Cellular telephones

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Learning-based automatic breast tumor detection and segmentation in ultrasound images

Learning-based automatic breast tumor detection and segmenta...

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IEEE International Symposium on Biomedical Imaging

作者： Peng Jiang Jingliang Peng Guoquan Zhang Erkang Cheng Vasileios Megalooikonomou Haibin Ling School of Computer Science and Technology Shandong Provincial Key Laboratory of Software Engineering P.R.China Department of Ultrasound Shandong Provincial Hospital Affiliated to Shandong University P.R. China Center for Data Analytics & Biomedical Informatics Temple University Philadelphia PA USA

ISBN: (纸本)9781457718571

Ultrasound (US) images have been widely used in the diagnosis of breast cancer in particular. While experienced doctors may locate the tumor regions in a US image manually, it is highly desirable to develop algorithms that automatically detect the tumor regions in order to assist medical diagnosis. In this paper, we propose a novel algorithm for automatic detection of breast tumors in US images. We formulate the tumor detection as a two step learning problem: tumor localization by bounding box and exact boundary delineation. Specifically, the proposed method uses an AdaBoost classifier on Harr-like features to detect a preliminary set of tumor regions. The preliminarily detected tumor regions are further screened with a support vector machine using quantized intensity features. Finally, the random walk segmentation algorithm is performed on the US image to retrieve the boundary of each detected tumor region. The proposed method has been evaluated on a data set containing 112 breast US images, including histologically confirmed 80 diseased ones and 32 normal ones. The data set contains one image from each patient and the patients are from 31 to 75 years old. Experiments demonstrate that the proposed algorithm can automatically detect breast tumors, with their locations and boundary shapes retrieved with high accuracy.

关键词： Image segmentation Support vector machines Breast tumors Ultrasonic imaging Feature extraction Accuracy

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Large scale report generation in data consolidation environments of banks

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Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of science and Technology (Natural science Edition) 2012年第SUPPL.1期40卷 5-8页

作者： Qin, Xiongpai Zhou, Xiaoyun Wu, Zhongxin Yang, Hongzhi Wang, Wei Ministry of Education Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China Information School Renmin University of China Beijing 100872 China Computer Science Department Jiangsu Normal University Xuzhou 221116 Jiangsu China Beijing Nantian Software Co. Ltd. Beijing 100085 China

To generate large number of reports in a limited time window, four techniques were proposed, including ROLAP&SQL, Shared Scanning, Hadoop based Solution, and MOLAP&Cube Sharding, an algorithm that performs in memory aggregation was designed for the second solution. The experiment results show that all techniques except ROLAP&SQL can meet the time window constraint, the Hadoop based solution is a promising technique owe to its highly scalability. Considering maturity of the techniques and their performance, we put MOLAP&Cube Sharding into practice while keeping an eye on Hadoop for future adoption.

关键词： Custom report data consolidation Hadoop Large bank MOLAP ROLAP

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WebSense: A lightweight and configurable application for publishing sensor network data

WebSense: A lightweight and configurable application for pub...

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2011 7th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2011

作者： Cardell-Oliver, Rachel Huebner, Christof Foeller-Nord, Miriam School of Computer Science and Software Engineering University of Western Australia Perth Australia Institute for Industrial Data Processing and Communication University of Applied Sciences Mannheim Mannheim Germany Institute for Embedded and Mobile Computing University of Applied Sciences Mannheim Mannheim Germany

ISBN: (纸本)9781457706738

Wireless sensor networks generate volumes of scientific observations. However, gathered data is typically not published for use by other scientists. This paper analyses the reasons that so little sensor network data is published online in a usable form, and identifies gaps in currently available publication systems. To address these gaps we present WebSense, a lightweight and configurable web-based tool for sensor network researchers to publish their collected data. The design goals and implementation of WebSense are described and the system is evaluated against the goals. WebSense is available from http://***/ © 2011 IEEE.

关键词： Wireless sensor networks

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Automatic Dent-landmark Detection in 3-D CBCT Dental Volumes

Automatic Dent-landmark Detection in 3-D CBCT Dental Volumes

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Erkang Cheng Jinwu Chen Jie Yang Huiyang Deng Yi Wu Vasileios Megalooikonomou Bryce Gable Haibin Ling Center for Data Analytics & Biomedical Informatics Computer & Information Science Department Temple University Division of Oral and Maxillofacial Radiology School of Dentistry Temple University College of Computer and Software Nanjing University of Information Science & Technology Data Engineering Laboratory Temple University

ISBN: (纸本)9781424441211

Orthodontic craniometric landmarks provide critical information in oral and maxillofacial imaging diagnosis and treatment planning. The Dent-landmark, defined as the odontoid process of the epistropheus, is one of the key landmarks to construct the midsagittal reference plane. In this paper, we propose a learning-based approach to automatically detect the Dent-landmark in the 3D cone-beam computed tomography (CBCT) dental data. Specifically, a detector is learned using the random forest with sampled context features. Furthermore, we use spacial prior to build a constrained search space other than use the full three dimensional space. The proposed method has been evaluated on a dataset containing 73 CBCT dental volumes and yields promising results.

关键词： Three dimensional displays Training Context Feature extraction Vegetation Dentistry Testing

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