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An Integrated Taxonomic Model of Gastronomic Offers Using Web Services and Databases
Paul Dayang,
Donald Didier Sepele Hlamna
Issue:
Volume 6, Issue 3, September 2021
Pages:
55-61
Received:
7 July 2021
Accepted:
19 July 2021
Published:
27 July 2021
Abstract: Data integration is the process of combining data from several different sources into a unified view, making it more actionable and valuable to access by different users with diverse interests. Among data integration approaches, figure prominently databases, cloud computing and web services. With the progression of the technology, mostly in the internet domain, we are facing a proliferation of websites with a high level of data redundancy. Several of those websites evolve in the same domain of activity. To limit the proliferation of websites that content mostly the same information, we propose in this paper, a data integration model based on web services and databases. This double data integration based on a services registry that content main web services in relationship with information contained in a database, respect a specific taxonomy for a specific domain. In this paper, we focus our research on the domain of gastronomy. In order to construct our model, we revisited some main architectures and models that exist so that we can use them as building blocks in the new approach. As building blocks, we can name service-oriented architecture, Universal Description, Discovery and Integration and taxonomy. Revisiting those established models have help us to build the taxonomic data integration model.
Abstract: Data integration is the process of combining data from several different sources into a unified view, making it more actionable and valuable to access by different users with diverse interests. Among data integration approaches, figure prominently databases, cloud computing and web services. With the progression of the technology, mostly in the int...
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Data Sensitivity and Classification Management: A Declarative Approach
Yuejin Zhang,
Hong Liu,
Guowei Wang
Issue:
Volume 6, Issue 3, September 2021
Pages:
62-65
Received:
12 July 2021
Accepted:
30 July 2021
Published:
9 August 2021
Abstract: Data protection according to sensitivity and classification has become a mandatory security mechanism for safety- and security-critical organizations. There is however no consensus on how to implement data sensitivity and classification in existing big-data systems. An approach is proposed to express and compute data sensitivity and multidimensional data classification in fine granularity. The approach is based on a declarative logic programming language, which is able to separate security requirement definitions and deduction from implementation details. Expressing and validating the security rules can be done transparently, ignoring underlying technical migrations and infrastructure differences. It is therefore possible to use the same set of security rules among various big data systems. Compared to other logic-programming-based approach, the declarative nature also makes it preferable for modular development and system maintenance. Sensitivity specification is shown and security analysis including conflict detection and resolution is performed on the same platform. Several typical types of data classification have also been illustrated and analyzed. The approach is capable of expressing complex classification methods, including classification with multiple parameters, classification according to graph computation, and classification based on relations among multiple data objects. The logic programming-based method is shown to have more expressive power and better complexity performance than conventional methods.
Abstract: Data protection according to sensitivity and classification has become a mandatory security mechanism for safety- and security-critical organizations. There is however no consensus on how to implement data sensitivity and classification in existing big-data systems. An approach is proposed to express and compute data sensitivity and multidimensiona...
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An Improved Adaptive Time Difference Estimation Method Based on Median Filter in Impulse Environment
Issue:
Volume 6, Issue 3, September 2021
Pages:
66-74
Received:
16 August 2021
Accepted:
11 September 2021
Published:
14 September 2021
Abstract: Boiler tube burst is the main reason for the unplanned shutdown of thermal power plants. The time difference can be estimated and analyzed by the acoustic signal generated when the boiler tube leaks, and the time difference can be converted into a distance difference to locate the leakage point. The accuracy of positioning depends on the accuracy of the time difference estimation, and the various background noises produced by the complex production environment of the boiler and the impulse noise generated by the uncertain factors in the signal acquisition circuit have a non-negligible effect on the accuracy of the time difference estimation. Aiming at the problem that the signal does not have obvious second-order statistics in the environment of impulse noise, a Wiener weighted adaptive time difference estimation method based on median filtering is proposed. First, the median filter is used to remove the impulse points in the noise to make it obey the normal distribution and have second-order statistics; Next, select the generalized correlation method based on the Wiener weighting function of linear minimum mean square error to eliminate the influence of noise; Finally, according to the characteristics of the generalized correlation method that relies on the prior knowledge of the signal but the ability to suppress noise and the characteristics of the adaptive method that the ability to suppress noise is weak but does not rely on the prior knowledge of the signal, the two methods are combined to form a Wiener-weighted generalized correlation and its adaptive method. Simulation experiments prove that the Wiener weighted adaptive time difference estimation method based on median filtering has better estimation performance under impulsive noise than the adaptive minimum average p-norm method based on fractional low-order statistics.
Abstract: Boiler tube burst is the main reason for the unplanned shutdown of thermal power plants. The time difference can be estimated and analyzed by the acoustic signal generated when the boiler tube leaks, and the time difference can be converted into a distance difference to locate the leakage point. The accuracy of positioning depends on the accuracy o...
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Fast Dual Discrete and Complexwavelet Based Video Magnification for 3D Facial Video Identification
Gamal Fahmy,
Omar Fahmy,
Mamdouh Fahmy
Issue:
Volume 6, Issue 3, September 2021
Pages:
75-84
Received:
8 September 2021
Accepted:
23 September 2021
Published:
30 September 2021
Abstract: Magnifying micro motion of videos that are undetectable by humans has recently been popular in many applications. This is due to its impact in numerous applications. In this paper, we explore this technique in 3D facial video identification, where we try to distinguish between real 3D facial objects in videos and 2D images of faces in a video frame sequence, and utilize this in biometric identification. We present a fast 2D Dual Discrete Wavelet Transform 2D-DWT based video magnification technique that detects micro movements by magnifying the phase differences between subsequent video frame's wavelet coefficients, at different sub bands. Next, in order to overcome shortcoming of 2D-DWT systems, 2D Dual Complex Wavelet Transform 2D-CWT has also been employed to estimate phase changes between subsequent video frames at different spatial locations of Complex Wavelets sub-bands. This latter presented CWT Technique uses the Radon Transform to detect any periodic motion in the video frames. Several simulation results are given to show that our proposed hybrid technique achieves comparable and sometimes superior performance with far less complexity when compared with recent literature in micro motion magnification, such as steerable pyramids STR and Riesz Transform RT based steerable pyramids RT-STR. Both DWT and CWT techniques are combined for 3D facial video identification. The attached videos demonstrate the superior video quality obtained by the proposed technique.
Abstract: Magnifying micro motion of videos that are undetectable by humans has recently been popular in many applications. This is due to its impact in numerous applications. In this paper, we explore this technique in 3D facial video identification, where we try to distinguish between real 3D facial objects in videos and 2D images of faces in a video frame...
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